From 18aa323f1826cb0c798212a15915b49ea2825173 Mon Sep 17 00:00:00 2001 From: Natalie Pearson Date: Tue, 2 Aug 2016 18:47:49 +0200 Subject: [PATCH 001/328] feature: initial commit for ATS9360 alazar driver. Generalise ATS.py and debugging. Changes to example notebook. Add specific ATS9360.py file for board settings. nb in this commit there is no use of the acquisition controller --- docs/examples/Qcodes example ATS_ONWORK.ipynb | 219 +++++++++++--- qcodes/instrument_drivers/AlazarTech/ATS.py | 234 ++++++++++----- .../instrument_drivers/AlazarTech/ATS9360.py | 282 ++++++++++++++++++ .../AlazarTech/ATS_acquisition_controllers.py | 13 +- 4 files changed, 639 insertions(+), 109 deletions(-) create mode 100644 qcodes/instrument_drivers/AlazarTech/ATS9360.py diff --git a/docs/examples/Qcodes example ATS_ONWORK.ipynb b/docs/examples/Qcodes example ATS_ONWORK.ipynb index dd3004c89bb7..206812252a0a 100644 --- a/docs/examples/Qcodes example ATS_ONWORK.ipynb +++ b/docs/examples/Qcodes example ATS_ONWORK.ipynb @@ -7,6 +7,14 @@ "collapsed": false }, "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + "C:\\Anaconda3\\lib\\site-packages\\IPython\\kernel\\__init__.py:13: ShimWarning: The `IPython.kernel` package has been deprecated. You should import from ipykernel or jupyter_client instead.\n", + " \"You should import from ipykernel or jupyter_client instead.\", ShimWarning)\n" + ] + }, { "data": { "application/javascript": [ @@ -357,6 +365,8 @@ "import time\n", "import numpy as np\n", "\n", + "from imp import reload\n", + "\n", "import qcodes as qc\n", "\n", "qc.halt_bg()\n", @@ -373,21 +383,32 @@ "metadata": { "collapsed": false }, - "outputs": [], + "outputs": [ + { + "data": { + "text/plain": [ + "" + ] + }, + "execution_count": 2, + "metadata": {}, + "output_type": "execute_result" + } + ], "source": [ - "import qcodes.instrument_drivers.AlazarTech.ATS9870 as ATSdriver\n", - "import qcodes.instrument_drivers.AlazarTech.ATS_acquisition_controllers as ats_contr" + "import qcodes.instrument_drivers.AlazarTech.ATS9360 as ATSdriver; reload(ATSdriver)\n", + "#import qcodes.instrument_drivers.AlazarTech.ATS_acquisition_controllers as ats_contr" ] }, { "cell_type": "code", "execution_count": 3, "metadata": { - "collapsed": true + "collapsed": false }, "outputs": [], "source": [ - "contr1 = ats_contr.DFT_AcquisitionController(demodulation_frequency=10e6)" + "#contr1 = ats_contr.DFT_AcquisitionController(demodulation_frequency=10e6)" ] }, { @@ -398,7 +419,7 @@ }, "outputs": [], "source": [ - "ats_inst = ATSdriver.AlazarTech_ATS9870(name='Alazar1')" + "ats_inst = ATSdriver.AlazarTech_ATS9360(name='Alazar1', server_name=None)" ] }, { @@ -407,43 +428,143 @@ "metadata": { "collapsed": false }, - "outputs": [], + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "\n", + "AlazarSetCaptureClock\n", + "512\n", + "\n", + "AlazarInputControl\n", + "512\n", + "\n", + "AlazarInputControl\n", + "512\n", + "\n", + "AlazarSetTriggerOperation\n", + "512\n", + "\n", + "AlazarSetExternalTrigger\n", + "512\n", + "\n", + "AlazarSetTriggerDelay\n", + "512\n", + "\n", + "AlazarSetTriggerTimeOut\n", + "512\n" + ] + } + ], "source": [ "ats_inst.config(clock_source='INTERNAL_CLOCK',\n", - " sample_rate=1000000,\n", + " sample_rate=500000000,\n", " clock_edge='CLOCK_EDGE_RISING',\n", " decimation=0,\n", - " coupling=['AC','AC'],\n", - " channel_range=[2.,2.],\n", - " impedance=[50,50],\n", - " bwlimit=['DISABLED','DISABLED'],\n", + " coupling=['DC','DC'],\n", + " channel_range=[0.4, 0.4],\n", " trigger_operation='TRIG_ENGINE_OP_J',\n", " trigger_engine1='TRIG_ENGINE_J',\n", " trigger_source1='EXTERNAL',\n", " trigger_slope1='TRIG_SLOPE_POSITIVE',\n", - " trigger_level1=0,\n", + " trigger_level1=150,\n", " trigger_engine2='TRIG_ENGINE_K',\n", - " trigger_source2='CHANNEL_B',\n", + " trigger_source2='DISABLE',\n", " trigger_slope2='TRIG_SLOPE_POSITIVE',\n", - " trigger_level2=0,\n", - " external_trigger_coupling='AC',\n", - " external_trigger_range='ETR_1V',\n", + " trigger_level2=128,\n", + " external_trigger_coupling='DC',\n", + " external_trigger_range='ETR_5V',\n", " trigger_delay=0,\n", " timeout_ticks=0)" ] }, { "cell_type": "code", - "execution_count": 8, + "execution_count": 6, "metadata": { - "collapsed": false + "collapsed": false, + "scrolled": false }, - "outputs": [], + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "\n", + "AlazarAbortAsyncRead\n", + "512\n", + "\n", + "AlazarSetRecordSize\n", + "512\n", + "\n", + "AlazarBeforeAsyncRead\n", + "512\n", + "\n", + "AlazarGetChannelInfo\n", + "512\n", + "bytes per sample 2\n", + "samples_per_record is 1024\n", + "bytes per record is 2048\n", + "samples_per_buffer is 10240\n", + "bytes_per_buffer is 40960\n", + "buffers cleared\n", + "2allocated buffers\n", + "making DMA buffer\n", + "making DMA buffer\n", + "made buffer list length 2\n", + "138543104\n", + "\n", + "\n", + "AlazarPostAsyncBuffer\n", + "512\n", + "138608640\n", + "\n", + "\n", + "AlazarPostAsyncBuffer\n", + "512\n", + "completed AlazarPostAsyncBuffer\n", + "\n", + "AlazarStartCapture\n", + "512\n", + "Capturing 3 buffers.\n", + "\n", + "AlazarWaitAsyncBufferComplete\n", + "518\n", + "[31136 36320 31360 ..., 28736 32480 28832]\n", + "\n", + "AlazarPostAsyncBuffer\n", + "518\n", + "\n", + "AlazarWaitAsyncBufferComplete\n", + "518\n", + "[31136 36320 31360 ..., 28736 32480 28832]\n", + "\n", + "AlazarPostAsyncBuffer\n", + "518\n", + "\n", + "AlazarWaitAsyncBufferComplete\n", + "518\n", + "[31136 36320 31360 ..., 28736 32480 28832]\n", + "\n", + "AlazarPostAsyncBuffer\n", + "518\n", + "\n", + "AlazarAbortAsyncRead\n", + "512\n", + "buffers cleared\n", + "Capture completed in 0.026781 sec\n", + "Captured 3 buffers (112.018308 buffers per sec)\n", + "Captured 30 records (1120.183076 records per sec)\n", + "Transferred 122880 bytes (4588269.880869 bytes per sec)\n" + ] + } + ], "source": [ - "output = ats_inst.acquire(mode='NPT',\n", + "ats_inst.acquire(mode='NPT',\n", " samples_per_record=1024,\n", - " records_per_buffer=70,\n", - " buffers_per_acquisition=128,\n", + " records_per_buffer=10,\n", + " buffers_per_acquisition=3,\n", " channel_selection='AB',\n", " transfer_offset=0,\n", " external_startcapture='DISABLED',\n", @@ -452,35 +573,59 @@ " fifo_only_streaming='DISABLED',\n", " interleave_samples='DISABLED',\n", " get_processed_data='ENABLED',\n", - " allocated_buffers=None,\n", - " buffer_timeout=1000,\n", - " acquisition_controller=contr1)" + " allocated_buffers=2,\n", + " buffer_timeout=10,\n", + " acquisition_controller=None)" + ] + }, + { + "cell_type": "code", + "execution_count": 6, + "metadata": { + "collapsed": false + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "buffers cleared\n" + ] + } + ], + "source": [ + "ats_inst.clear_buffers()" ] }, { "cell_type": "code", - "execution_count": 9, + "execution_count": 6, "metadata": { "collapsed": false }, "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "\n", + "AlazarGetChannelInfo\n", + "512\n" + ] + }, { "data": { "text/plain": [ - "[1.2225695082720591,\n", - " 1.216472639837185,\n", - " -179.9999999998654,\n", - " -179.99999999986107,\n", - " 359.9999999999957]" + "{'bits_per_sample': 12, 'board': 'ATS9360', 'max_samples': 4294967294}" ] }, - "execution_count": 9, + "execution_count": 6, "metadata": {}, "output_type": "execute_result" } ], "source": [ - "output" + "ats_inst.get_idn()" ] }, { @@ -496,9 +641,9 @@ "metadata": { "anaconda-cloud": {}, "kernelspec": { - "display_name": "Python [Root]", + "display_name": "Python 3", "language": "python", - "name": "Python [Root]" + "name": "python3" }, "language_info": { "codemirror_mode": { @@ -510,7 +655,7 @@ "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", - "version": "3.5.2" + "version": "3.5.1" } }, "nbformat": 4, diff --git a/qcodes/instrument_drivers/AlazarTech/ATS.py b/qcodes/instrument_drivers/AlazarTech/ATS.py index 53dcb11d24c3..463664163c94 100644 --- a/qcodes/instrument_drivers/AlazarTech/ATS.py +++ b/qcodes/instrument_drivers/AlazarTech/ATS.py @@ -1,6 +1,7 @@ import ctypes import logging import numpy as np +import time import os from qcodes.instrument.base import Instrument @@ -10,7 +11,6 @@ # TODO (C) logging # these items are important for generalizing this code to multiple alazar cards -# TODO (W) remove 8 bits per sample requirement # TODO (W) some alazar cards have a different number of channels :( # this driver only works with 2-channel cards @@ -18,6 +18,11 @@ # acquisition that would overflow the board if measurement is not stopped # quickly enough. can this be solved by not reposting the buffers? +# TODO: check 8 bit sample thing works +# TODO: test get_board_info and find_boards +# TODO: remove guerilla debuggung 'print' statements +# TODO: call_dll error handling +# TODO: acquisition_controller class AlazarTech_ATS(Instrument): # override dll_path in your init script or in the board constructor @@ -188,8 +193,13 @@ def get_board_info(cls, dll, system_id, board_id): def __init__(self, name, system_id=1, board_id=1, dll_path=None, **kwargs): super().__init__(name, **kwargs) - self._ATS_dll = ctypes.cdll.LoadLibrary(dll_path or self.dll_path) - + self._ATS_dll = None + + if os.name == 'nt': + self._ATS_dll = ctypes.cdll.LoadLibrary(dll_path or self.dll_path) + else: + raise Exception("Unsupported OS") + # TODO (W) make the board id more general such that more than one card # per system configurations are supported self._handle = self._ATS_dll.AlazarGetBoardBySystemID(system_id, @@ -201,6 +211,14 @@ def __init__(self, name, system_id=1, board_id=1, dll_path=None, **kwargs): # TODO (M) do something with board kind here self.buffer_list = [] + + # TODO use get_board_info for this and make it return better stuff! + def get_idn(self): + board_kind = self._board_names[self._ATS_dll.AlazarGetBoardKind(self._handle)] + max_s, bps = self._get_channel_info(self._handle) + return {'board' : board_kind, + 'max_samples' : max_s, + 'bits_per_sample' : bps} def config(self, clock_source=None, sample_rate=None, clock_edge=None, decimation=None, coupling=None, channel_range=None, @@ -277,9 +295,10 @@ def config(self, clock_source=None, sample_rate=None, clock_edge=None, self.parameters['coupling' + str(i)], self.parameters['channel_range' + str(i)], self.parameters['impedance' + str(i)]) - self._call_dll('AlazarSetBWLimit', - self._handle, i, - self.parameters['bwlimit' + str(i)]) + if bwlimit is not None: + self._call_dll('AlazarSetBWLimit', + self._handle, i, + self.parameters['bwlimit' + str(i)]) self._call_dll('AlazarSetTriggerOperation', self._handle, self.trigger_operation, @@ -339,7 +358,7 @@ def acquire(self, mode=None, samples_per_record=None, self._set_if_present('samples_per_record', samples_per_record) self._set_if_present('records_per_buffer', records_per_buffer) self._set_if_present('buffers_per_acquisition', - buffers_per_acquisition) + buffers_per_acquisition), self._set_if_present('channel_selection', channel_selection) self._set_if_present('transfer_offset', transfer_offset) self._set_if_present('external_startcapture', external_startcapture) @@ -362,12 +381,7 @@ def acquire(self, mode=None, samples_per_record=None, # Abort any previous measurement self._call_dll('AlazarAbortAsyncRead', self._handle) - - # get channel info - max_s, bps = self._get_channel_info(self._handle) - if bps != 8: - raise Exception('Only 8 bits per sample supported at this moment') - + # Set record size for NPT mode if mode == 'NPT': pretriggersize = 0 # pretriggersize is 0 for NPT always @@ -375,11 +389,8 @@ def acquire(self, mode=None, samples_per_record=None, self._handle, pretriggersize, self.samples_per_record) - # set acquisition parameters here for NPT, TS mode - if self.channel_selection._get_byte() == 3: - number_of_channels = 2 - else: - number_of_channels = 1 + + # set acquisition parameters here for NPT, TS mode samples_per_buffer = 0 buffers_per_acquisition = self.buffers_per_acquisition._get_byte() samples_per_record = self.samples_per_record._get_byte() @@ -393,8 +404,7 @@ def acquire(self, mode=None, samples_per_record=None, if mode == 'NPT': records_per_buffer = self.records_per_buffer._get_byte() - records_per_acquisition = ( - records_per_buffer * buffers_per_acquisition) + records_per_acquisition = records_per_buffer * buffers_per_acquisition samples_per_buffer = samples_per_record * records_per_buffer self._call_dll('AlazarBeforeAsyncRead', @@ -435,38 +445,76 @@ def acquire(self, mode=None, samples_per_record=None, self.interleave_samples._set_updated() self.get_processed_data._set_updated() + + # bytes per sample + handle = self._handle + max_s, bps = self._get_channel_info(handle) + bytes_per_sample = (bps + 7) // 8 + print("bytes per sample " +str(bytes_per_sample)) + + # bytes per record + bytes_per_record = bytes_per_sample * samples_per_record + print("samples_per_record is "+str(samples_per_record)) + print("bytes per record is "+str(bytes_per_record)) + + # bytes per buffer + if self.channel_selection._get_byte() == 3: + number_of_channels = 2 + else: + number_of_channels = 1 + + bytes_per_buffer = bytes_per_record * records_per_buffer * number_of_channels + # create buffers for acquisition + # TODO: should this be > 1 (makes sense to me) or > 8 as in alazar sample code? - Natalie + sample_type = ctypes.c_uint8 + if bytes_per_sample > 1: + sample_type = ctypes.c_uint16 + + print("samples_per_buffer is "+str(samples_per_buffer)) + print("bytes_per_buffer is "+str(bytes_per_buffer)) + self.clear_buffers() allocated_buffers = self.allocated_buffers._get_byte() + print(str(allocated_buffers)+"allocated buffers") for k in range(allocated_buffers): try: - self.buffer_list.append(Buffer(bps, samples_per_buffer, - number_of_channels)) + self.buffer_list.append(DMABuffer(sample_type, bytes_per_buffer)) except: self.clear_buffers() raise # post buffers to Alazar + print("made buffer list length "+str(len(self.buffer_list))) for buf in self.buffer_list: + print(buf.addr) + print(type(buf.size_bytes)) + self._ATS_dll.AlazarPostAsyncBuffer.argtypes = [ctypes.c_uint32, ctypes.c_void_p, ctypes.c_uint32] self._call_dll('AlazarPostAsyncBuffer', self._handle, buf.addr, buf.size_bytes) self.allocated_buffers._set_updated() + print("completed AlazarPostAsyncBuffer") # -----start capture here----- - acquisition_controller.pre_start_capture(self) +# acquisition_controller.pre_start_capture(self) + start = time.clock() # Keep track of when acquisition started # call the startcapture method self._call_dll('AlazarStartCapture', self._handle) - - acquisition_controller.pre_acquire(self) + print("Capturing %d buffers." % buffers_per_acquisition) + +# acquisition_controller.pre_acquire(self) # buffer handling from acquisition buffers_completed = 0 + bytes_transferred = 0 buffer_timeout = self.buffer_timeout._get_byte() self.buffer_timeout._set_updated() buffer_recycling = (self.buffers_per_acquisition._get_byte() > self.allocated_buffers._get_byte()) - while buffers_completed < self.buffers_per_acquisition._get_byte(): + while (buffers_completed < self.buffers_per_acquisition._get_byte()): + # Wait for the buffer at the head of the list of available + # buffers to be filled by the board. buf = self.buffer_list[buffers_completed % allocated_buffers] self._call_dll('AlazarWaitAsyncBufferComplete', @@ -478,21 +526,21 @@ def acquire(self, mode=None, samples_per_record=None, # if buffers must be recycled, extract data and repost them # otherwise continue to next buffer - if buffer_recycling: - acquisition_controller.handle_buffer(self, buf.buffer) +# acquisition_controller.handle_buffer(self, buf.buffer) self._call_dll('AlazarPostAsyncBuffer', self._handle, buf.addr, buf.size_bytes) buffers_completed += 1 + bytes_transferred += buf.size_bytes # stop measurement here self._call_dll('AlazarAbortAsyncRead', self._handle) # -----cleanup here----- # extract data if not yet done - if not buffer_recycling: - for buf in self.buffer_list: - acquisition_controller.handle_buffer(self, buf.buffer) +# if not buffer_recycling: +# for buf in self.buffer_list: +# acquisition_controller.handle_buffer(self, buf.buffer) # free up memory self.clear_buffers() @@ -502,7 +550,24 @@ def acquire(self, mode=None, samples_per_record=None, p.get() # return result - return acquisition_controller.post_acquire(self) +# return acquisition_controller.post_acquire(self) + + # Compute the total transfer time, and display performance information. + transfer_time_sec = time.clock() - start + print("Capture completed in %f sec" % transfer_time_sec) + buffers_per_sec = 0 + bytes_per_sec = 0 + records_per_sec = 0 + if transfer_time_sec > 0: + buffers_per_sec = buffers_completed / transfer_time_sec + bytes_per_sec = bytes_transferred / transfer_time_sec + records_per_sec = records_per_buffer * buffers_completed / transfer_time_sec + print("Captured %d buffers (%f buffers per sec)" % + (buffers_completed, buffers_per_sec)) + print("Captured %d records (%f records per sec)" % + (records_per_buffer * buffers_completed, records_per_sec)) + print("Transferred %d bytes (%f bytes per sec)" % + (bytes_transferred, bytes_per_sec)) def _set_if_present(self, param_name, value): if value is not None: @@ -537,7 +602,12 @@ def _call_dll(self, func_name, *args): # run the function func = getattr(self._ATS_dll, func_name) - return_code = func(*args_out) + try: + return_code = func(*args_out) + print(return_code) + except Exception as e: + print(e) + raise # check for errors if (return_code != self._success) and (return_code !=518): @@ -563,6 +633,7 @@ def _call_dll(self, func_name, *args): def clear_buffers(self): for b in self.buffer_list: b.free_mem() + print("buffers cleared") self.buffer_list = [] def signal_to_volt(self, channel, signal): @@ -655,50 +726,75 @@ def _set_updated(self): self._uptodate_flag = True -class Buffer: - def __init__(self, bits_per_sample, samples_per_buffer, - number_of_channels): - if bits_per_sample != 8: - raise Exception("Buffer: only 8 bit per sample supported") - if os.name != 'nt': - raise Exception("Buffer: only Windows supported at this moment") - self._allocated = True - # try to allocate memory - mem_commit = 0x1000 - page_readwrite = 0x4 +class DMABuffer: + '''Buffer suitable for DMA transfers. + + AlazarTech digitizers use direct memory access (DMA) to transfer + data from digitizers to the computer's main memory. This class + abstracts a memory buffer on the host, and ensures that all the + requirements for DMA transfers are met. + + DMABuffers export a 'buffer' member, which is a NumPy array view + of the underlying memory buffer + + Args: + + c_sample_type (ctypes type): The datatype of the buffer to create. + + size_bytes (int): The size of the buffer to allocate, in bytes. - self.size_bytes = samples_per_buffer * number_of_channels + ''' + def __init__(self, c_sample_type, size_bytes): + self.size_bytes = size_bytes - # for documentation please see: - # https://msdn.microsoft.com/en-us/library/windows/desktop/aa366887(v=vs.85).aspx - ctypes.windll.kernel32.VirtualAlloc.argtypes = [ - ctypes.c_void_p, ctypes.c_long, ctypes.c_long, ctypes.c_long] - ctypes.windll.kernel32.VirtualAlloc.restype = ctypes.c_void_p - self.addr = ctypes.windll.kernel32.VirtualAlloc( - 0, ctypes.c_long(self.size_bytes), mem_commit, page_readwrite) - if self.addr is None: + npSampleType = { + ctypes.c_uint8: np.uint8, + ctypes.c_uint16: np.uint16, + ctypes.c_uint32: np.uint32, + ctypes.c_int32: np.int32, + ctypes.c_float: np.float32 + }.get(c_sample_type, 0) + + bytes_per_sample = { + ctypes.c_uint8: 1, + ctypes.c_uint16: 2, + ctypes.c_uint32: 4, + ctypes.c_int32: 4, + ctypes.c_float: 4 + }.get(c_sample_type, 0) + + self._allocated = True + self.addr = None + if os.name == 'nt': + MEM_COMMIT = 0x1000 + PAGE_READWRITE = 0x4 + ctypes.windll.kernel32.VirtualAlloc.argtypes = [ctypes.c_void_p, ctypes.c_long, ctypes.c_long, ctypes.c_long] + ctypes.windll.kernel32.VirtualAlloc.restype = ctypes.c_void_p + self.addr = ctypes.windll.kernel32.VirtualAlloc( + 0, ctypes.c_long(size_bytes), MEM_COMMIT, PAGE_READWRITE) + else: self._allocated = False - e = ctypes.windll.kernel32.GetLastError() - raise Exception("Memory allocation error: " + str(e)) + raise Exception("Unsupported OS") - ctypes_array = (ctypes.c_uint8 * - self.size_bytes).from_address(self.addr) - self.buffer = np.frombuffer(ctypes_array, dtype=np.uint8) + + ctypes_array = (c_sample_type * + (size_bytes // bytes_per_sample)).from_address(self.addr) + self.buffer = np.frombuffer(ctypes_array, dtype=npSampleType) + self.ctypes_buffer = ctypes_array pointer, read_only_flag = self.buffer.__array_interface__['data'] def free_mem(self): - mem_release = 0x8000 - - # for documentation please see: - # https://msdn.microsoft.com/en-us/library/windows/desktop/aa366892(v=vs.85).aspx - ctypes.windll.kernel32.VirtualFree.argtypes = [ - ctypes.c_void_p, ctypes.c_long, ctypes.c_long] - ctypes.windll.kernel32.VirtualFree.restype = ctypes.c_int - ctypes.windll.kernel32.VirtualFree(ctypes.c_void_p(self.addr), 0, - mem_release) self._allocated = False - + if os.name == 'nt': + MEM_RELEASE = 0x8000 + ctypes.windll.kernel32.VirtualFree.argtypes = [ctypes.c_void_p, ctypes.c_long, ctypes.c_long] + ctypes.windll.kernel32.VirtualFree.restype = ctypes.c_int + ctypes.windll.kernel32.VirtualFree(ctypes.c_void_p(self.addr), 0, MEM_RELEASE); + else: + self._allocated = True + raise Exception("Unsupported OS") + def __del__(self): if self._allocated: self.free_mem() @@ -706,7 +802,7 @@ def __del__(self): 'Buffer prevented memory leak; Memory released to Windows.\n' 'Memory should have been released before buffer was deleted.') - + class AcquisitionController: def __init__(self): """ diff --git a/qcodes/instrument_drivers/AlazarTech/ATS9360.py b/qcodes/instrument_drivers/AlazarTech/ATS9360.py new file mode 100644 index 000000000000..0db4a6c071d3 --- /dev/null +++ b/qcodes/instrument_drivers/AlazarTech/ATS9360.py @@ -0,0 +1,282 @@ +from .ATS import AlazarTech_ATS, AlazarParameter +from qcodes.utils import validators + + +class AlazarTech_ATS9360(AlazarTech_ATS): + def __init__(self, name, server_name=None): + dll_path = 'C:\\WINDOWS\\System32\\ATSApi.dll' + super().__init__(name, dll_path=dll_path) + + samplesPerSec = None + # add parameters + + # ----- Parameters for the configuration of the board ----- + self.add_parameter(name='clock_source', + parameter_class=AlazarParameter, + label='Clock Source', + unit=None, + value='INTERNAL_CLOCK', + byte_to_value_dict={1: 'INTERNAL_CLOCK', + 4: 'SLOW_EXTERNAL_CLOCK', + 5: 'EXTERNAL_CLOCK_AC', + 7: 'EXTERNAL_CLOCK_10_MHz_REF'}) + self.add_parameter(name='sample_rate', + parameter_class=AlazarParameter, + label='Sample Rate', + unit='S/s', + value=500000000, + byte_to_value_dict={ + 0x1: 1000, 0x2: 2000, 0x4: 5000, 0x8: 10000, + 0xA: 20000, 0xC: 50000, 0xE: 100000, + 0x10: 200000, 0x12: 500000, 0x14: 1000000, + 0x18: 2000000, 0x1A: 5000000, 0x1C: 10000000, + 0x1E: 20000000, 0x22: 50000000, 0x24: 100000000, + 0x2B: 250000000, 0x30: 500000000, + 0x35: 1000000000, 0x40: 'EXTERNAL_CLOCK', + 1000000000: '1GHz_REFERENCE_CLOCK'}) + self.add_parameter(name='clock_edge', + parameter_class=AlazarParameter, + label='Clock Edge', + unit=None, + value='CLOCK_EDGE_RISING', + byte_to_value_dict={0: 'CLOCK_EDGE_RISING', + 1: 'CLOCK_EDGE_FALLING'}) + + self.add_parameter(name='decimation', + parameter_class=AlazarParameter, + label='Decimation', + unit=None, + value=0, + vals=validators.Ints(0, 100000)) + + for i in ['1', '2']: + self.add_parameter(name='coupling' + i, + parameter_class=AlazarParameter, + label='Coupling channel ' + i, + unit=None, + value='DC', + byte_to_value_dict={1: 'AC', 2: 'DC'}) + self.add_parameter(name='channel_range' + i, + parameter_class=AlazarParameter, + label='Range channel ' + i, + unit='V', + value=0.4, + byte_to_value_dict={ + 2: 0.04, 5: 0.1, 6: 0.2, 7: 0.4, + 10: 1., 11: 2., 12: 4.}) + self.add_parameter(name='impedance' + i, + parameter_class=AlazarParameter, + label='Impedance channel ' + i, + unit='Ohm', + value=50, + byte_to_value_dict={1: 1000000, 2: 50}) + + self.add_parameter(name='trigger_operation', + parameter_class=AlazarParameter, + label='Trigger Operation', + unit=None, + value='TRIG_ENGINE_OP_J', + byte_to_value_dict={ + 0: 'TRIG_ENGINE_OP_J', + 1: 'TRIG_ENGINE_OP_K', + 2: 'TRIG_ENGINE_OP_J_OR_K', + 3: 'TRIG_ENGINE_OP_J_AND_K', + 4: 'TRIG_ENGINE_OP_J_XOR_K', + 5: 'TRIG_ENGINE_OP_J_AND_NOT_K', + 6: 'TRIG_ENGINE_OP_NOT_J_AND_K'}) + for i in ['1', '2']: + self.add_parameter(name='trigger_engine' + i, + parameter_class=AlazarParameter, + label='Trigger Engine ' + i, + unit=None, + value='TRIG_ENGINE_J', + byte_to_value_dict={0: 'TRIG_ENGINE_J', + 1: 'TRIG_ENGINE_K'}) + self.add_parameter(name='trigger_source' + i, + parameter_class=AlazarParameter, + label='Trigger Source ' + i, + unit=None, + value='DISABLE', + byte_to_value_dict={0: 'CHANNEL_A', + 1: 'CHANNEL_B', + 2: 'EXTERNAL', + 3: 'DISABLE'}) + self.add_parameter(name='trigger_slope' + i, + parameter_class=AlazarParameter, + label='Trigger Slope ' + i, + unit=None, + value='TRIG_SLOPE_POSITIVE', + byte_to_value_dict={1: 'TRIG_SLOPE_POSITIVE', + 2: 'TRIG_SLOPE_NEGATIVE'}) + self.add_parameter(name='trigger_level' + i, + parameter_class=AlazarParameter, + label='Trigger Level ' + i, + unit=None, + value=150, + vals=validators.Ints(0, 255)) + + self.add_parameter(name='external_trigger_coupling', + parameter_class=AlazarParameter, + label='External Trigger Coupling', + unit=None, + value='AC', + byte_to_value_dict={1: 'AC', 2: 'DC'}) + self.add_parameter(name='external_trigger_range', + parameter_class=AlazarParameter, + label='External Trigger Range', + unit=None, + value='ETR_5V', + byte_to_value_dict={0: 'ETR_5V', 1: 'ETR_1V'}) + self.add_parameter(name='trigger_delay', + parameter_class=AlazarParameter, + label='Trigger Delay', + unit='Sample clock cycles', + value=0, + vals=validators.Ints(min_value=0)) + + # NOTE: The board will wait for a for this amount of time for a + # trigger event. If a trigger event does not arrive, then the + # board will automatically trigger. Set the trigger timeout value + # to 0 to force the board to wait forever for a trigger event. + # + # IMPORTANT: The trigger timeout value should be set to zero after + # appropriate trigger parameters have been determined, otherwise + # the board may trigger if the timeout interval expires before a + # hardware trigger event arrives. + self.add_parameter(name='timeout_ticks', + parameter_class=AlazarParameter, + label='Timeout Ticks', + unit='10 us', + value=0, + vals=validators.Ints(min_value=0)) + + # TODO: need to confugure AuxIO?? + + # ----- Parameters for the acquire function ----- + self.add_parameter(name='mode', + parameter_class=AlazarParameter, + label='Acquisiton mode', + unit=None, + value='NPT', + byte_to_value_dict={0x200: 'NPT', 0x400: 'TS'}) + + # samples_per_record must be a multiple of 16! + self.add_parameter(name='samples_per_record', + parameter_class=AlazarParameter, + label='Samples per Record', + unit=None, + value=1024, + vals=Multiples(divisor=16, min_value=0)) + # TODO (M) figure out if this also has to be a multiple of something, + # I could not find this in the documentation but somehow I have the + # feeling it still should be a multiple of something + # NOTE by ramiro: At least in previous python implementations(PycQED delft), this is an artifact for compatibility with AWG sequencing, not particular to any ATS architecture. + # ==> this is a construction imposed by the memory strategy implemented on the python driver we are writing, not limited by any actual ATS feature. + + self.add_parameter(name='records_per_buffer', + parameter_class=AlazarParameter, + label='Records per Buffer', + unit=None, + value=10, + vals=validators.Ints(min_value=0)) + self.add_parameter(name='buffers_per_acquisition', + parameter_class=AlazarParameter, + label='Buffers per Acquisition', + unit=None, + value=10, + vals=validators.Ints(min_value=0)) + self.add_parameter(name='channel_selection', + parameter_class=AlazarParameter, + label='Channel Selection', + unit=None, + value='AB', + byte_to_value_dict={1: 'A', 2: 'B', 3: 'AB'}) + self.add_parameter(name='transfer_offset', + parameter_class=AlazarParameter, + label='Transer Offset', + unit='Samples', + value=0, + vals=validators.Ints(min_value=0)) + self.add_parameter(name='external_startcapture', + parameter_class=AlazarParameter, + label='External Startcapture', + unit=None, + value='ENABLED', + byte_to_value_dict={0x0: 'DISABLED', + 0x1: 'ENABLED'}) + self.add_parameter(name='enable_record_headers', + parameter_class=AlazarParameter, + label='Enable Record Headers', + unit=None, + value='DISABLED', + byte_to_value_dict={0x0: 'DISABLED', + 0x8: 'ENABLED'}) + self.add_parameter(name='alloc_buffers', + parameter_class=AlazarParameter, + label='Alloc Buffers', + unit=None, + value='DISABLED', + byte_to_value_dict={0x0: 'DISABLED', + 0x20: 'ENABLED'}) + self.add_parameter(name='fifo_only_streaming', + parameter_class=AlazarParameter, + label='Fifo Only Streaming', + unit=None, + value='DISABLED', + byte_to_value_dict={0x0: 'DISABLED', + 0x800: 'ENABLED'}) + self.add_parameter(name='interleave_samples', + parameter_class=AlazarParameter, + label='Interleave Samples', + unit=None, + value='DISABLED', + byte_to_value_dict={0x0: 'DISABLED', + 0x1000: 'ENABLED'}) + self.add_parameter(name='get_processed_data', + parameter_class=AlazarParameter, + label='Get Processed Data', + unit=None, + value='DISABLED', + byte_to_value_dict={0x0: 'DISABLED', + 0x2000: 'ENABLED'}) + + self.add_parameter(name='allocated_buffers', + parameter_class=AlazarParameter, + label='Allocated Buffers', + unit=None, + value=2, + vals=validators.Ints(min_value=0)) + self.add_parameter(name='buffer_timeout', + parameter_class=AlazarParameter, + label='Buffer Timeout', + unit='ms', + value=1000, + vals=validators.Ints(min_value=0)) + + # TODO (M) make parameter for board type + + # TODO (M) check board kind + + +class Multiples(validators.Ints): + ''' + requires an integer + optional parameters min_value and max_value enforce + min_value <= value <= max_value + divisor enforces that value % divisor == 0 + ''' + + def __init__(self, divisor=1, **kwargs): + super().__init__(**kwargs) + if not isinstance(divisor, int): + raise TypeError('divisor must be an integer') + self._divisor = divisor + + def validate(self, value, context=''): + super().validate(value=value, context=context) + if not value % self._divisor == 0: + raise TypeError('{} is not a multiple of {}; {}'.format( + repr(value), repr(self._divisor), context)) + + def __repr__(self): + return super().__repr__()[:-1] + ', Multiples of {}>'.format(self._divisor) diff --git a/qcodes/instrument_drivers/AlazarTech/ATS_acquisition_controllers.py b/qcodes/instrument_drivers/AlazarTech/ATS_acquisition_controllers.py index 4122d9da583d..0614cb66949b 100644 --- a/qcodes/instrument_drivers/AlazarTech/ATS_acquisition_controllers.py +++ b/qcodes/instrument_drivers/AlazarTech/ATS_acquisition_controllers.py @@ -6,17 +6,24 @@ # DFT AcquisitionController class DFT_AcquisitionController(AcquisitionController): def __init__(self, demodulation_frequency): - self.demodulation_frequency = demodulation_frequency + # self.demodulation_frequency = demodulation_frequency self.samples_per_record = None + self.bits_per_sample = None self.records_per_buffer = None self.buffers_per_acquisition = None + self.allocated_buffers = None # TODO (S) this is not very general: self.number_of_channels = 2 - self.cos_list = None - self.sin_list = None + # self.cos_list = None + # self.sin_list = None self.buffer = None def pre_start_capture(self, alazar): + + # allcoate buffers + max_s, bps = alazar._get_channel_info() + self.bits_per_sample = bps + self.samples_per_record = alazar.samples_per_record.get() self.records_per_buffer = alazar.records_per_buffer.get() self.buffers_per_acquisition = alazar.buffers_per_acquisition.get() From 256b35a4fcbeae936ec4cd7766c4c1c082b025e0 Mon Sep 17 00:00:00 2001 From: Natalie Pearson Date: Tue, 2 Aug 2016 19:18:45 +0200 Subject: [PATCH 002/328] style: Remove some print statememnts from ATS.py --- qcodes/instrument_drivers/AlazarTech/ATS.py | 17 ++++++++--------- 1 file changed, 8 insertions(+), 9 deletions(-) diff --git a/qcodes/instrument_drivers/AlazarTech/ATS.py b/qcodes/instrument_drivers/AlazarTech/ATS.py index 463664163c94..eba15498d61d 100644 --- a/qcodes/instrument_drivers/AlazarTech/ATS.py +++ b/qcodes/instrument_drivers/AlazarTech/ATS.py @@ -18,11 +18,13 @@ # acquisition that would overflow the board if measurement is not stopped # quickly enough. can this be solved by not reposting the buffers? -# TODO: check 8 bit sample thing works -# TODO: test get_board_info and find_boards -# TODO: remove guerilla debuggung 'print' statements -# TODO: call_dll error handling -# TODO: acquisition_controller +# TODO: check 8 bit sample thing works - Natalie +# TODO: test get_board_info and find_boards - Natalie +# TODO: remove guerilla debuggung 'print' statements - Natalie +# TODO: call_dll error handling - Natalie +# TODO: acquisition_controller - Natalie +# TODO: fix hacky way I use _ATS_dll anywhere - Natalie +# TODO: make it actually save data! - Natalie class AlazarTech_ATS(Instrument): # override dll_path in your init script or in the board constructor @@ -476,7 +478,7 @@ def acquire(self, mode=None, samples_per_record=None, self.clear_buffers() allocated_buffers = self.allocated_buffers._get_byte() - print(str(allocated_buffers)+"allocated buffers") + print(str(allocated_buffers)+" allocated buffers") for k in range(allocated_buffers): try: self.buffer_list.append(DMABuffer(sample_type, bytes_per_buffer)) @@ -487,8 +489,6 @@ def acquire(self, mode=None, samples_per_record=None, # post buffers to Alazar print("made buffer list length "+str(len(self.buffer_list))) for buf in self.buffer_list: - print(buf.addr) - print(type(buf.size_bytes)) self._ATS_dll.AlazarPostAsyncBuffer.argtypes = [ctypes.c_uint32, ctypes.c_void_p, ctypes.c_uint32] self._call_dll('AlazarPostAsyncBuffer', self._handle, buf.addr, buf.size_bytes) @@ -604,7 +604,6 @@ def _call_dll(self, func_name, *args): func = getattr(self._ATS_dll, func_name) try: return_code = func(*args_out) - print(return_code) except Exception as e: print(e) raise From 9b0d84bd15c625497d8fed5300fce127becba4be Mon Sep 17 00:00:00 2001 From: Natalie Pearson Date: Wed, 3 Aug 2016 18:09:49 +0200 Subject: [PATCH 003/328] tidy up ATS.py comments and create test acquisition conroller for ATS9360 --- docs/examples/Qcodes example ATS_ONWORK.ipynb | 1742 +++++++++++++++-- qcodes/instrument_drivers/AlazarTech/ATS.py | 37 +- .../instrument_drivers/AlazarTech/ATS9360.py | 1 - .../AlazarTech/ATS_acquisition_controllers.py | 112 +- 4 files changed, 1736 insertions(+), 156 deletions(-) diff --git a/docs/examples/Qcodes example ATS_ONWORK.ipynb b/docs/examples/Qcodes example ATS_ONWORK.ipynb index 206812252a0a..54114498186f 100644 --- a/docs/examples/Qcodes example ATS_ONWORK.ipynb +++ b/docs/examples/Qcodes example ATS_ONWORK.ipynb @@ -383,21 +383,10 @@ "metadata": { "collapsed": false }, - "outputs": [ - { - "data": { - "text/plain": [ - "" - ] - }, - "execution_count": 2, - "metadata": {}, - "output_type": "execute_result" - } - ], + "outputs": [], "source": [ "import qcodes.instrument_drivers.AlazarTech.ATS9360 as ATSdriver; reload(ATSdriver)\n", - "#import qcodes.instrument_drivers.AlazarTech.ATS_acquisition_controllers as ats_contr" + "import qcodes.instrument_drivers.AlazarTech.ATS_acquisition_controllers as ats_contr" ] }, { @@ -408,7 +397,7 @@ }, "outputs": [], "source": [ - "#contr1 = ats_contr.DFT_AcquisitionController(demodulation_frequency=10e6)" + "contr1 = ats_contr.Test_AcquisitionController()" ] }, { @@ -428,35 +417,7 @@ "metadata": { "collapsed": false }, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "\n", - "AlazarSetCaptureClock\n", - "512\n", - "\n", - "AlazarInputControl\n", - "512\n", - "\n", - "AlazarInputControl\n", - "512\n", - "\n", - "AlazarSetTriggerOperation\n", - "512\n", - "\n", - "AlazarSetExternalTrigger\n", - "512\n", - "\n", - "AlazarSetTriggerDelay\n", - "512\n", - "\n", - "AlazarSetTriggerTimeOut\n", - "512\n" - ] - } - ], + "outputs": [], "source": [ "ats_inst.config(clock_source='INTERNAL_CLOCK',\n", " sample_rate=500000000,\n", @@ -491,91 +452,37 @@ "name": "stdout", "output_type": "stream", "text": [ - "\n", - "AlazarAbortAsyncRead\n", - "512\n", - "\n", - "AlazarSetRecordSize\n", - "512\n", - "\n", - "AlazarBeforeAsyncRead\n", - "512\n", - "\n", - "AlazarGetChannelInfo\n", - "512\n", "bytes per sample 2\n", "samples_per_record is 1024\n", "bytes per record is 2048\n", "samples_per_buffer is 10240\n", "bytes_per_buffer is 40960\n", + "records_per_buffer is 10\n", "buffers cleared\n", - "2allocated buffers\n", - "making DMA buffer\n", - "making DMA buffer\n", "made buffer list length 2\n", - "138543104\n", - "\n", - "\n", - "AlazarPostAsyncBuffer\n", - "512\n", - "138608640\n", - "\n", - "\n", - "AlazarPostAsyncBuffer\n", - "512\n", "completed AlazarPostAsyncBuffer\n", - "\n", - "AlazarStartCapture\n", - "512\n", - "Capturing 3 buffers.\n", - "\n", - "AlazarWaitAsyncBufferComplete\n", - "518\n", - "[31136 36320 31360 ..., 28736 32480 28832]\n", - "\n", - "AlazarPostAsyncBuffer\n", - "518\n", - "\n", - "AlazarWaitAsyncBufferComplete\n", - "518\n", - "[31136 36320 31360 ..., 28736 32480 28832]\n", - "\n", - "AlazarPostAsyncBuffer\n", - "518\n", - "\n", - "AlazarWaitAsyncBufferComplete\n", - "518\n", - "[31136 36320 31360 ..., 28736 32480 28832]\n", - "\n", - "AlazarPostAsyncBuffer\n", - "518\n", - "\n", - "AlazarAbortAsyncRead\n", - "512\n", - "buffers cleared\n", - "Capture completed in 0.026781 sec\n", - "Captured 3 buffers (112.018308 buffers per sec)\n", - "Captured 30 records (1120.183076 records per sec)\n", - "Transferred 122880 bytes (4588269.880869 bytes per sec)\n" + "Capturing 1 buffers.\n", + "not recycling\n", + "buffers cleared\n" ] } ], "source": [ - "ats_inst.acquire(mode='NPT',\n", - " samples_per_record=1024,\n", - " records_per_buffer=10,\n", - " buffers_per_acquisition=3,\n", - " channel_selection='AB',\n", - " transfer_offset=0,\n", - " external_startcapture='DISABLED',\n", - " enable_record_headers='DISABLED',\n", - " alloc_buffers='ENABLED',\n", - " fifo_only_streaming='DISABLED',\n", - " interleave_samples='DISABLED',\n", - " get_processed_data='ENABLED',\n", - " allocated_buffers=2,\n", - " buffer_timeout=10,\n", - " acquisition_controller=None)" + "resA, resB = ats_inst.acquire(mode='NPT',\n", + " samples_per_record=1024,\n", + " records_per_buffer=10,\n", + " buffers_per_acquisition=1,\n", + " channel_selection='AB',\n", + " transfer_offset=0,\n", + " external_startcapture='DISABLED',\n", + " enable_record_headers='DISABLED',\n", + " alloc_buffers='ENABLED',\n", + " fifo_only_streaming='DISABLED',\n", + " interleave_samples='DISABLED',\n", + " get_processed_data='ENABLED',\n", + " allocated_buffers=2,\n", + " buffer_timeout=10,\n", + " acquisition_controller=contr1)" ] }, { @@ -599,31 +506,1610 @@ }, { "cell_type": "code", - "execution_count": 6, + "execution_count": 8, "metadata": { "collapsed": false }, "outputs": [ { - "name": "stdout", - "output_type": "stream", - "text": [ - "\n", - "AlazarGetChannelInfo\n", - "512\n" - ] + "data": { + "text/plain": [ + "12" + ] + }, + "execution_count": 8, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "ats_inst.get_idn()['bits_per_sample']" + ] + }, + { + "cell_type": "code", + "execution_count": 7, + "metadata": { + "collapsed": false + }, + "outputs": [ + { + "data": { + "text/plain": [ + "500000000" + ] + }, + "execution_count": 7, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "ats_inst.get_sample_speed()" + ] + }, + { + "cell_type": "code", + "execution_count": 7, + "metadata": { + "collapsed": false + }, + "outputs": [ + { + "data": { + "application/javascript": [ + "/* Put everything inside the global mpl namespace */\n", + "window.mpl = {};\n", + "\n", + "mpl.get_websocket_type = function() {\n", + " if (typeof(WebSocket) !== 'undefined') {\n", + " return WebSocket;\n", + " } else if (typeof(MozWebSocket) !== 'undefined') {\n", + " return MozWebSocket;\n", + " } else {\n", + " alert('Your browser does not have WebSocket support.' +\n", + " 'Please try Chrome, Safari or Firefox ≥ 6. 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IPython.notebook.keyboard_manager;\n", + " if (!manager)\n", + " manager = IPython.keyboard_manager;\n", + "\n", + " // Check for shift+enter\n", + " if (event.shiftKey && event.which == 13) {\n", + " this.canvas_div.blur();\n", + " event.shiftKey = false;\n", + " // Send a \"J\" for go to next cell\n", + " event.which = 74;\n", + " event.keyCode = 74;\n", + " manager.command_mode();\n", + " manager.handle_keydown(event);\n", + " }\n", + "}\n", + "\n", + "mpl.figure.prototype.handle_save = function(fig, msg) {\n", + " fig.ondownload(fig, null);\n", + "}\n", + "\n", + "\n", + "mpl.find_output_cell = function(html_output) {\n", + " // Return the cell and output element which can be found *uniquely* in the notebook.\n", + " // Note - this is a bit hacky, but it is done because the \"notebook_saving.Notebook\"\n", + " // IPython event is triggered only after the cells have been serialised, which for\n", + " // our purposes (turning an active figure into a static one), is too late.\n", + " var cells = IPython.notebook.get_cells();\n", + " var ncells = cells.length;\n", + " for (var i=0; i= 3 moved mimebundle to data attribute of output\n", + " data = data.data;\n", + " }\n", + " if (data['text/html'] == html_output) {\n", + " return [cell, data, j];\n", + " }\n", + " }\n", + " }\n", + " }\n", + "}\n", + "\n", + "// Register the function which deals with the matplotlib target/channel.\n", + "// The kernel may be null if the page has been refreshed.\n", + "if (IPython.notebook.kernel != null) {\n", + " IPython.notebook.kernel.comm_manager.register_target('matplotlib', mpl.mpl_figure_comm);\n", + "}\n" + ], + "text/plain": [ + "" + ] + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "data": { + "text/html": [ + "" + ], + "text/plain": [ + "" + ] + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "data": { + "text/plain": [ + "" + ] + }, + "execution_count": 8, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "qc.MatPlot(resB)" + ] + }, + { + "cell_type": "code", + "execution_count": 6, + "metadata": { + "collapsed": false + }, + "outputs": [ + { + "ename": "TypeError", + "evalue": "__init__() got an unexpected keyword argument 'system_id'", + "output_type": "error", + "traceback": [ + "\u001b[1;31m---------------------------------------------------------------------------\u001b[0m", + "\u001b[1;31mTypeError\u001b[0m Traceback (most recent call last)", + "\u001b[1;32m\u001b[0m in \u001b[0;36m\u001b[1;34m()\u001b[0m\n\u001b[1;32m----> 1\u001b[1;33m \u001b[0mats_inst\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mget_idn\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m", + "\u001b[1;32ma:\\qcodesfolder\\qcodes\\instrument_drivers\\AlazarTech\\ATS.py\u001b[0m in \u001b[0;36mget_idn\u001b[1;34m(self)\u001b[0m\n\u001b[0;32m 216\u001b[0m \u001b[0mboard_kind\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0m_board_names\u001b[0m\u001b[1;33m[\u001b[0m\u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0m_ATS_dll\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mAlazarGetBoardKind\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0m_handle\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m]\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m 217\u001b[0m \u001b[0mmax_s\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mbps\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0m_get_channel_info\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0m_handle\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m--> 218\u001b[1;33m \u001b[0mprint\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mfind_boards\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m 219\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m 220\u001b[0m return {'board' : board_kind,\n", + "\u001b[1;32ma:\\qcodesfolder\\qcodes\\instrument_drivers\\AlazarTech\\ATS.py\u001b[0m in \u001b[0;36mfind_boards\u001b[1;34m(cls, dll_path)\u001b[0m\n\u001b[0;32m 170\u001b[0m \u001b[0mboard_count\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mdll\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mAlazarBoardsInSystemBySystemID\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0msystem_id\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m 171\u001b[0m \u001b[1;32mfor\u001b[0m \u001b[0mboard_id\u001b[0m \u001b[1;32min\u001b[0m \u001b[0mrange\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;36m1\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mboard_count\u001b[0m \u001b[1;33m+\u001b[0m \u001b[1;36m1\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m--> 172\u001b[1;33m \u001b[0mboards\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mappend\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mcls\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mget_board_info\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mdll\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0msystem_id\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mboard_id\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m 173\u001b[0m \u001b[1;32mreturn\u001b[0m \u001b[0mboards\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m 174\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n", + "\u001b[1;32ma:\\qcodesfolder\\qcodes\\instrument_drivers\\AlazarTech\\ATS.py\u001b[0m in \u001b[0;36mget_board_info\u001b[1;34m(cls, dll, system_id, board_id)\u001b[0m\n\u001b[0;32m 178\u001b[0m \u001b[1;31m# to get its info\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m 179\u001b[0m board = cls('temp', system_id=system_id, board_id=board_id,\n\u001b[1;32m--> 180\u001b[1;33m server_name=None)\n\u001b[0m\u001b[0;32m 181\u001b[0m \u001b[0mhandle\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mboard\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0m_handle\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m 182\u001b[0m \u001b[0mboard_kind\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mcls\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0m_board_names\u001b[0m\u001b[1;33m[\u001b[0m\u001b[0mdll\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mAlazarGetBoardKind\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mhandle\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m]\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n", + "\u001b[1;31mTypeError\u001b[0m: __init__() got an unexpected keyword argument 'system_id'" + ] + } + ], "source": [ "ats_inst.get_idn()" ] @@ -635,7 +2121,9 @@ "collapsed": true }, "outputs": [], - "source": [] + "source": [ + "import qcodes.instrument_drivers.AlazarTech.ATS" + ] } ], "metadata": { diff --git a/qcodes/instrument_drivers/AlazarTech/ATS.py b/qcodes/instrument_drivers/AlazarTech/ATS.py index eba15498d61d..084ca36d14e9 100644 --- a/qcodes/instrument_drivers/AlazarTech/ATS.py +++ b/qcodes/instrument_drivers/AlazarTech/ATS.py @@ -18,13 +18,9 @@ # acquisition that would overflow the board if measurement is not stopped # quickly enough. can this be solved by not reposting the buffers? -# TODO: check 8 bit sample thing works - Natalie -# TODO: test get_board_info and find_boards - Natalie -# TODO: remove guerilla debuggung 'print' statements - Natalie -# TODO: call_dll error handling - Natalie -# TODO: acquisition_controller - Natalie -# TODO: fix hacky way I use _ATS_dll anywhere - Natalie -# TODO: make it actually save data! - Natalie +# TODO(nataliejpg) get_board_info and find_boards are broken?? +# TODO(nataliejpg) call_dll error handling doesnt catch errors in expected way +# TODO(nataliejpg) make use of _ATS_dll uniform class AlazarTech_ATS(Instrument): # override dll_path in your init script or in the board constructor @@ -214,7 +210,6 @@ def __init__(self, name, system_id=1, board_id=1, dll_path=None, **kwargs): self.buffer_list = [] - # TODO use get_board_info for this and make it return better stuff! def get_idn(self): board_kind = self._board_names[self._ATS_dll.AlazarGetBoardKind(self._handle)] max_s, bps = self._get_channel_info(self._handle) @@ -459,26 +454,27 @@ def acquire(self, mode=None, samples_per_record=None, print("samples_per_record is "+str(samples_per_record)) print("bytes per record is "+str(bytes_per_record)) - # bytes per buffer + # channels if self.channel_selection._get_byte() == 3: number_of_channels = 2 else: number_of_channels = 1 - + + # bytes per buffer bytes_per_buffer = bytes_per_record * records_per_buffer * number_of_channels # create buffers for acquisition - # TODO: should this be > 1 (makes sense to me) or > 8 as in alazar sample code? - Natalie + # TODO(nataliejpg) should this be > 1 (makes sense to me) or > 8 as in alazar sample code? - Natalie sample_type = ctypes.c_uint8 if bytes_per_sample > 1: sample_type = ctypes.c_uint16 print("samples_per_buffer is "+str(samples_per_buffer)) print("bytes_per_buffer is "+str(bytes_per_buffer)) + print("records_per_buffer is "+str(records_per_buffer)) self.clear_buffers() allocated_buffers = self.allocated_buffers._get_byte() - print(str(allocated_buffers)+" allocated buffers") for k in range(allocated_buffers): try: self.buffer_list.append(DMABuffer(sample_type, bytes_per_buffer)) @@ -496,13 +492,13 @@ def acquire(self, mode=None, samples_per_record=None, print("completed AlazarPostAsyncBuffer") # -----start capture here----- -# acquisition_controller.pre_start_capture(self) + acquisition_controller.pre_start_capture(self) start = time.clock() # Keep track of when acquisition started # call the startcapture method self._call_dll('AlazarStartCapture', self._handle) print("Capturing %d buffers." % buffers_per_acquisition) -# acquisition_controller.pre_acquire(self) + acquisition_controller.pre_acquire(self) # buffer handling from acquisition buffers_completed = 0 bytes_transferred = 0 @@ -527,7 +523,7 @@ def acquire(self, mode=None, samples_per_record=None, # if buffers must be recycled, extract data and repost them # otherwise continue to next buffer if buffer_recycling: -# acquisition_controller.handle_buffer(self, buf.buffer) + acquisition_controller.handle_buffer(self, buf.buffer) self._call_dll('AlazarPostAsyncBuffer', self._handle, buf.addr, buf.size_bytes) buffers_completed += 1 @@ -538,9 +534,9 @@ def acquire(self, mode=None, samples_per_record=None, # -----cleanup here----- # extract data if not yet done -# if not buffer_recycling: -# for buf in self.buffer_list: -# acquisition_controller.handle_buffer(self, buf.buffer) + if not buffer_recycling: + for buf in self.buffer_list: + acquisition_controller.handle_buffer(self, buf.buffer) # free up memory self.clear_buffers() @@ -550,7 +546,7 @@ def acquire(self, mode=None, samples_per_record=None, p.get() # return result -# return acquisition_controller.post_acquire(self) + return acquisition_controller.post_acquire(self) # Compute the total transfer time, and display performance information. transfer_time_sec = time.clock() - start @@ -640,7 +636,8 @@ def signal_to_volt(self, channel, signal): # TODO (M) use byte value if range{channel} return (((signal - 127.5) / 127.5) * (self.parameters['channel_range' + str(channel)].get())) - + + # TODO(nataliejpg) surely this should be get_sample_rate? def get_sample_speed(self): if self.sample_rate.get() == 'EXTERNAL_CLOCK': raise Exception('External clock is used, alazar driver ' diff --git a/qcodes/instrument_drivers/AlazarTech/ATS9360.py b/qcodes/instrument_drivers/AlazarTech/ATS9360.py index 0db4a6c071d3..5703d92a6bd5 100644 --- a/qcodes/instrument_drivers/AlazarTech/ATS9360.py +++ b/qcodes/instrument_drivers/AlazarTech/ATS9360.py @@ -150,7 +150,6 @@ def __init__(self, name, server_name=None): value=0, vals=validators.Ints(min_value=0)) - # TODO: need to confugure AuxIO?? # ----- Parameters for the acquire function ----- self.add_parameter(name='mode', diff --git a/qcodes/instrument_drivers/AlazarTech/ATS_acquisition_controllers.py b/qcodes/instrument_drivers/AlazarTech/ATS_acquisition_controllers.py index 0614cb66949b..359a767624b0 100644 --- a/qcodes/instrument_drivers/AlazarTech/ATS_acquisition_controllers.py +++ b/qcodes/instrument_drivers/AlazarTech/ATS_acquisition_controllers.py @@ -3,10 +3,111 @@ import numpy as np +# Heterodyne Measurement Controller +# returns unprocessed data averaged by record for 2 channel use +class HD_Controller(AcquisitionController): + def __init__(self): + self.samples_per_record = None + self.bits_per_sample = None + self.records_per_buffer = None + self.buffers_per_acquisition = None + self.allocated_buffers = None + self.number_of_channels = 2 + self.buffer = None + + def pre_start_capture(self, alazar): + self.bits_per_sample = alazar.get_idn()['bits_per_sample'] + self.samples_per_record = alazar.samples_per_record() + self.records_per_buffer = alazar.records_per_buffer() + self.buffers_per_acquisition = alazar.buffers_per_acquisition() + sample_speed = alazar.get_sample_speed() + self.buffer = np.zeros(self.samples_per_record * + self.records_per_buffer * + self.number_of_channels) + + def pre_acquire(self, alazar): + # gets called after 'AlazarStartCapture' + pass + + def handle_buffer(self, alazar, data): + self.buffer += data + + def post_acquire(self, alazar): + # average over records in buffer: + # for ATS9360 samples are arranged in the buffer as follows: + # S0A, S0B, ..., S1A, S1B, ... + # with SXY the sample number X of channel Y. + records_per_acquisition = (1. * self.buffers_per_acquisition * + self.records_per_buffer) + recordA = np.zeros(self.samples_per_record) + for i in range(self.records_per_buffer): + i0 = i * self.samples_per_record * self.number_of_channels + i1 = i0 + self.samples_per_record * self.number_of_channels + recordA += self.buffer[i0:i1:self.number_of_channels] / records_per_acquisition + + recordB = np.zeros(self.samples_per_record) + for i in range(self.records_per_buffer): + i0 = (i * self.samples_per_record * self.number_of_channels) + 1 + i1 = i0 + self.samples_per_record *self.number_of_channels + recordB += self.buffer[i0:i1:self.number_of_channels] / records_per_acquisition + return recordA, recordB + + +# Test AcquisitionController tested on ATS9360 (nataliejpg) +# returns unprocessed data averaged by record for 2 channel use +class Test_AcquisitionController(AcquisitionController): + def __init__(self): + self.samples_per_record = None + self.bits_per_sample = None + self.records_per_buffer = None + self.buffers_per_acquisition = None + self.allocated_buffers = None + self.number_of_channels = 2 + self.buffer = None + + def pre_start_capture(self, alazar): + self.bits_per_sample = alazar.get_idn()['bits_per_sample'] + self.samples_per_record = alazar.samples_per_record() + self.records_per_buffer = alazar.records_per_buffer() + self.buffers_per_acquisition = alazar.buffers_per_acquisition() + sample_speed = alazar.get_sample_speed() + self.buffer = np.zeros(self.samples_per_record * + self.records_per_buffer * + self.number_of_channels) + + def pre_acquire(self, alazar): + # gets called after 'AlazarStartCapture' + pass + + def handle_buffer(self, alazar, data): + self.buffer += data + + def post_acquire(self, alazar): + # average over records in buffer: + # for ATS9360 samples are arranged in the buffer as follows: + # S0A, S0B, ..., S1A, S1B, ... + # with SXY the sample number X of channel Y. + records_per_acquisition = (1. * self.buffers_per_acquisition * + self.records_per_buffer) + recordA = np.zeros(self.samples_per_record) + for i in range(self.records_per_buffer): + i0 = i * self.samples_per_record * self.number_of_channels + i1 = i0 + self.samples_per_record * self.number_of_channels + recordA += self.buffer[i0:i1:self.number_of_channels] / records_per_acquisition + + recordB = np.zeros(self.samples_per_record) + for i in range(self.records_per_buffer): + i0 = (i * self.samples_per_record * self.number_of_channels) + 1 + i1 = i0 + self.samples_per_record *self.number_of_channels + recordB += self.buffer[i0:i1:self.number_of_channels] / records_per_acquisition + return recordA, recordB + + + # DFT AcquisitionController class DFT_AcquisitionController(AcquisitionController): def __init__(self, demodulation_frequency): - # self.demodulation_frequency = demodulation_frequency + self.demodulation_frequency = demodulation_frequency self.samples_per_record = None self.bits_per_sample = None self.records_per_buffer = None @@ -14,16 +115,11 @@ def __init__(self, demodulation_frequency): self.allocated_buffers = None # TODO (S) this is not very general: self.number_of_channels = 2 - # self.cos_list = None - # self.sin_list = None + self.cos_list = None + self.sin_list = None self.buffer = None def pre_start_capture(self, alazar): - - # allcoate buffers - max_s, bps = alazar._get_channel_info() - self.bits_per_sample = bps - self.samples_per_record = alazar.samples_per_record.get() self.records_per_buffer = alazar.records_per_buffer.get() self.buffers_per_acquisition = alazar.buffers_per_acquisition.get() From 5f02af04b05b62b41b15fe7648d1b1434b7a3aa1 Mon Sep 17 00:00:00 2001 From: Natalie Pearson Date: Thu, 4 Aug 2016 15:23:53 +0200 Subject: [PATCH 004/328] more edits on acquisition_controller --- docs/examples/Qcodes example ATS_ONWORK.ipynb | 83 +++++++++++++------ qcodes/instrument_drivers/AlazarTech/ATS.py | 1 + .../AlazarTech/ATS_acquisition_controllers.py | 34 ++++++-- 3 files changed, 85 insertions(+), 33 deletions(-) diff --git a/docs/examples/Qcodes example ATS_ONWORK.ipynb b/docs/examples/Qcodes example ATS_ONWORK.ipynb index 54114498186f..3b05b49a247e 100644 --- a/docs/examples/Qcodes example ATS_ONWORK.ipynb +++ b/docs/examples/Qcodes example ATS_ONWORK.ipynb @@ -402,13 +402,13 @@ }, { "cell_type": "code", - "execution_count": 4, + "execution_count": 19, "metadata": { "collapsed": false }, "outputs": [], "source": [ - "ats_inst = ATSdriver.AlazarTech_ATS9360(name='Alazar1', server_name=None)" + "contr2 = ats_contr.HD_Controller(10e6)" ] }, { @@ -418,6 +418,17 @@ "collapsed": false }, "outputs": [], + "source": [ + "ats_inst = ATSdriver.AlazarTech_ATS9360(name='Alazar1', server_name=None)" + ] + }, + { + "cell_type": "code", + "execution_count": 6, + "metadata": { + "collapsed": false + }, + "outputs": [], "source": [ "ats_inst.config(clock_source='INTERNAL_CLOCK',\n", " sample_rate=500000000,\n", @@ -442,7 +453,7 @@ }, { "cell_type": "code", - "execution_count": 6, + "execution_count": 30, "metadata": { "collapsed": false, "scrolled": false @@ -462,7 +473,6 @@ "made buffer list length 2\n", "completed AlazarPostAsyncBuffer\n", "Capturing 1 buffers.\n", - "not recycling\n", "buffers cleared\n" ] } @@ -487,26 +497,51 @@ }, { "cell_type": "code", - "execution_count": 6, + "execution_count": 31, "metadata": { "collapsed": false }, "outputs": [ { - "name": "stdout", - "output_type": "stream", - "text": [ - "buffers cleared\n" - ] + "data": { + "text/plain": [ + "4340.0" + ] + }, + "execution_count": 31, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "resA[1]-resB[1]+180" + ] + }, + { + "cell_type": "code", + "execution_count": 32, + "metadata": { + "collapsed": false + }, + "outputs": [ + { + "data": { + "text/plain": [ + "array([ 57568., 58080., 58624., ..., 73280., 73248., 73184.])" + ] + }, + "execution_count": 32, + "metadata": {}, + "output_type": "execute_result" } ], "source": [ - "ats_inst.clear_buffers()" + "resB" ] }, { "cell_type": "code", - "execution_count": 8, + "execution_count": 33, "metadata": { "collapsed": false }, @@ -517,7 +552,7 @@ "12" ] }, - "execution_count": 8, + "execution_count": 33, "metadata": {}, "output_type": "execute_result" } @@ -528,7 +563,7 @@ }, { "cell_type": "code", - "execution_count": 7, + "execution_count": 34, "metadata": { "collapsed": false }, @@ -539,7 +574,7 @@ "500000000" ] }, - "execution_count": 7, + "execution_count": 34, "metadata": {}, "output_type": "execute_result" } @@ -550,7 +585,7 @@ }, { "cell_type": "code", - "execution_count": 7, + "execution_count": 35, "metadata": { "collapsed": false }, @@ -1294,7 +1329,7 @@ { "data": { "text/html": [ - "" + "" ], "text/plain": [ "" @@ -1306,21 +1341,21 @@ { "data": { "text/plain": [ - "" + "" ] }, - "execution_count": 7, + "execution_count": 35, "metadata": {}, "output_type": "execute_result" } ], "source": [ - "qc.MatPlot(resA)" + "qc.MatPlot(resB)" ] }, { "cell_type": "code", - "execution_count": 8, + "execution_count": 36, "metadata": { "collapsed": false }, @@ -2064,7 +2099,7 @@ { "data": { "text/html": [ - "" + "" ], "text/plain": [ "" @@ -2076,10 +2111,10 @@ { "data": { "text/plain": [ - "" + "" ] }, - "execution_count": 8, + "execution_count": 36, "metadata": {}, "output_type": "execute_result" } diff --git a/qcodes/instrument_drivers/AlazarTech/ATS.py b/qcodes/instrument_drivers/AlazarTech/ATS.py index 084ca36d14e9..ab9226663ac8 100644 --- a/qcodes/instrument_drivers/AlazarTech/ATS.py +++ b/qcodes/instrument_drivers/AlazarTech/ATS.py @@ -21,6 +21,7 @@ # TODO(nataliejpg) get_board_info and find_boards are broken?? # TODO(nataliejpg) call_dll error handling doesnt catch errors in expected way # TODO(nataliejpg) make use of _ATS_dll uniform +# TODO(nataliejpg) error if no trigger class AlazarTech_ATS(Instrument): # override dll_path in your init script or in the board constructor diff --git a/qcodes/instrument_drivers/AlazarTech/ATS_acquisition_controllers.py b/qcodes/instrument_drivers/AlazarTech/ATS_acquisition_controllers.py index 359a767624b0..09a2b2064177 100644 --- a/qcodes/instrument_drivers/AlazarTech/ATS_acquisition_controllers.py +++ b/qcodes/instrument_drivers/AlazarTech/ATS_acquisition_controllers.py @@ -6,12 +6,12 @@ # Heterodyne Measurement Controller # returns unprocessed data averaged by record for 2 channel use class HD_Controller(AcquisitionController): - def __init__(self): + def __init__(self, freq_dif): + self.freq_dif = freq_dif self.samples_per_record = None self.bits_per_sample = None self.records_per_buffer = None self.buffers_per_acquisition = None - self.allocated_buffers = None self.number_of_channels = 2 self.buffer = None @@ -24,7 +24,13 @@ def pre_start_capture(self, alazar): self.buffer = np.zeros(self.samples_per_record * self.records_per_buffer * self.number_of_channels) - + sample_speed = alazar.get_sample_speed() + integer_list = np.arange(self.samples_per_record) + angle_list = (2 * np.pi * self.freq_dif / sample_speed * + integer_list) + self.cos_list = np.cos(angle_list) + self.sin_list = np.sin(angle_list) + def pre_acquire(self, alazar): # gets called after 'AlazarStartCapture' pass @@ -50,7 +56,21 @@ def post_acquire(self, alazar): i0 = (i * self.samples_per_record * self.number_of_channels) + 1 i1 = i0 + self.samples_per_record *self.number_of_channels recordB += self.buffer[i0:i1:self.number_of_channels] / records_per_acquisition - return recordA, recordB + + resA = self.fit(recordA) + resB = self.fit(recordB) + + return resA, resB + + def fit(self, rec): + # Discrete Fourier Transform + RePart = np.dot(rec, self.cos_list) / self.samples_per_record + ImPart = np.dot(rec, self.sin_list) / self.samples_per_record + + ampl = np.sqrt(RePart ** 2 + ImPart ** 2) + phase = math.atan2(ImPart, RePart) * 360 / (2 * math.pi) + + return [ampl, phase] # Test AcquisitionController tested on ATS9360 (nataliejpg) @@ -58,19 +78,15 @@ def post_acquire(self, alazar): class Test_AcquisitionController(AcquisitionController): def __init__(self): self.samples_per_record = None - self.bits_per_sample = None self.records_per_buffer = None self.buffers_per_acquisition = None - self.allocated_buffers = None self.number_of_channels = 2 self.buffer = None def pre_start_capture(self, alazar): - self.bits_per_sample = alazar.get_idn()['bits_per_sample'] self.samples_per_record = alazar.samples_per_record() self.records_per_buffer = alazar.records_per_buffer() self.buffers_per_acquisition = alazar.buffers_per_acquisition() - sample_speed = alazar.get_sample_speed() self.buffer = np.zeros(self.samples_per_record * self.records_per_buffer * self.number_of_channels) @@ -101,7 +117,7 @@ def post_acquire(self, alazar): i1 = i0 + self.samples_per_record *self.number_of_channels recordB += self.buffer[i0:i1:self.number_of_channels] / records_per_acquisition return recordA, recordB - + # DFT AcquisitionController From b242aa078d9ea9700d1bdc460ab8d92a2b94e639 Mon Sep 17 00:00:00 2001 From: Natalie Pearson Date: Mon, 8 Aug 2016 18:07:40 +0200 Subject: [PATCH 005/328] First steps to filter acquisition controller and docstrings --- qcodes/instrument_drivers/AlazarTech/ATS.py | 2 +- .../AlazarTech/ATS_acquisition_controllers.py | 53 +++++---- .../AlazarTech/local_test.py | 109 ++++++++++++++++++ .../AlazarTech/local_test2.py | 93 +++++++++++++++ 4 files changed, 236 insertions(+), 21 deletions(-) create mode 100644 qcodes/instrument_drivers/AlazarTech/local_test.py create mode 100644 qcodes/instrument_drivers/AlazarTech/local_test2.py diff --git a/qcodes/instrument_drivers/AlazarTech/ATS.py b/qcodes/instrument_drivers/AlazarTech/ATS.py index ab9226663ac8..04905d5e5a4a 100644 --- a/qcodes/instrument_drivers/AlazarTech/ATS.py +++ b/qcodes/instrument_drivers/AlazarTech/ATS.py @@ -638,7 +638,7 @@ def signal_to_volt(self, channel, signal): return (((signal - 127.5) / 127.5) * (self.parameters['channel_range' + str(channel)].get())) - # TODO(nataliejpg) surely this should be get_sample_rate? + # TODO(nataliejpg) some explanation for why this is called speed not rate? def get_sample_speed(self): if self.sample_rate.get() == 'EXTERNAL_CLOCK': raise Exception('External clock is used, alazar driver ' diff --git a/qcodes/instrument_drivers/AlazarTech/ATS_acquisition_controllers.py b/qcodes/instrument_drivers/AlazarTech/ATS_acquisition_controllers.py index 09a2b2064177..fa6daa438ec4 100644 --- a/qcodes/instrument_drivers/AlazarTech/ATS_acquisition_controllers.py +++ b/qcodes/instrument_drivers/AlazarTech/ATS_acquisition_controllers.py @@ -2,31 +2,43 @@ import math import numpy as np - -# Heterodyne Measurement Controller -# returns unprocessed data averaged by record for 2 channel use class HD_Controller(AcquisitionController): + """Heterodyne Measurement Controller + Does averaged DFT on 2 channel Alazar measurement + + TODO(nataliejpg) handling of channel number + TODO(nataliejpg) test angle data + """ def __init__(self, freq_dif): self.freq_dif = freq_dif self.samples_per_record = None - self.bits_per_sample = None self.records_per_buffer = None self.buffers_per_acquisition = None self.number_of_channels = 2 self.buffer = None def pre_start_capture(self, alazar): - self.bits_per_sample = alazar.get_idn()['bits_per_sample'] + """Get config data from alazar card and set up DFT""" self.samples_per_record = alazar.samples_per_record() self.records_per_buffer = alazar.records_per_buffer() self.buffers_per_acquisition = alazar.buffers_per_acquisition() - sample_speed = alazar.get_sample_speed() + self.sample_rate = alazar.get_sample_speed() self.buffer = np.zeros(self.samples_per_record * self.records_per_buffer * self.number_of_channels) - sample_speed = alazar.get_sample_speed() + + # TODO(nataliejpg) leave super explicit or save lines? add error/logging? + averaging = self.buffers_per_acquisition * self.records_per_buffer + record_duration = self.samples_per_record/self.sample_rate + time_period_dif = 1/self.freq_dif + cycles_measured = record_duration / time_period_dif + oversampling_rate = self.sample_rate/(2*self.freq_dif) + print("Average over {} records".format(averaging)) + print("Oscillations per record: {} (expect 100+)".format(cycles_measured)) + print("Oversampling rate: {} (expect > 2)".format(oversampling_rate)) + integer_list = np.arange(self.samples_per_record) - angle_list = (2 * np.pi * self.freq_dif / sample_speed * + angle_list = (2 * np.pi * self.freq_dif / self.sample_rate * integer_list) self.cos_list = np.cos(angle_list) self.sin_list = np.sin(angle_list) @@ -39,10 +51,11 @@ def handle_buffer(self, alazar, data): self.buffer += data def post_acquire(self, alazar): - # average over records in buffer: - # for ATS9360 samples are arranged in the buffer as follows: - # S0A, S0B, ..., S1A, S1B, ... - # with SXY the sample number X of channel Y. + """Average over records in buffer and do DFT: + assumes samples are arranged in the buffer as follows: + S0A, S0B, ..., S1A, S1B, ... + with SXY the sample number X of channel Y. + """ records_per_acquisition = (1. * self.buffers_per_acquisition * self.records_per_buffer) recordA = np.zeros(self.samples_per_record) @@ -50,7 +63,6 @@ def post_acquire(self, alazar): i0 = i * self.samples_per_record * self.number_of_channels i1 = i0 + self.samples_per_record * self.number_of_channels recordA += self.buffer[i0:i1:self.number_of_channels] / records_per_acquisition - recordB = np.zeros(self.samples_per_record) for i in range(self.records_per_buffer): i0 = (i * self.samples_per_record * self.number_of_channels) + 1 @@ -63,19 +75,20 @@ def post_acquire(self, alazar): return resA, resB def fit(self, rec): - # Discrete Fourier Transform + """Do Discrete Fourier Transform and return magnitude and phase data""" RePart = np.dot(rec, self.cos_list) / self.samples_per_record ImPart = np.dot(rec, self.sin_list) / self.samples_per_record - - ampl = np.sqrt(RePart ** 2 + ImPart ** 2) - phase = math.atan2(ImPart, RePart) * 360 / (2 * math.pi) + # factor of 2 as amplitude is split between finite term and double frequency term + ampl = 2 * np.sqrt(RePart ** 2 + ImPart ** 2) + phase = math.atan2(ImPart, RePart) * 180 / (2 * math.pi) return [ampl, phase] -# Test AcquisitionController tested on ATS9360 (nataliejpg) -# returns unprocessed data averaged by record for 2 channel use -class Test_AcquisitionController(AcquisitionController): +class Basic_AcquisitionController(AcquisitionController): + """Basic AcquisitionController tested on ATS9360 + returns unprocessed data averaged by record with 2 channels + """ def __init__(self): self.samples_per_record = None self.records_per_buffer = None diff --git a/qcodes/instrument_drivers/AlazarTech/local_test.py b/qcodes/instrument_drivers/AlazarTech/local_test.py new file mode 100644 index 000000000000..6e85961fe531 --- /dev/null +++ b/qcodes/instrument_drivers/AlazarTech/local_test.py @@ -0,0 +1,109 @@ +from .ATS import AcquisitionController +import math +import numpy as np +import matplotlib.pyplot as plt + + +class Controller(): + def __init__(self, dif_freq): + self.dif_freq = dif_freq + self.sample_rate = 500000000 + self.samples_per_record = 1024 + + record_duration = self.samples_per_record/self.sample_rate + dif_time_period = 1/self.dif_freq + cycles_measured = record_duration / dif_time_period + oversampling_rate = self.sample_rate/(2*self.dif_freq) + print("Measuring from "+str(cycles_measured)+" samples should be on the order of 100 at least") + print("Oversampling rate is "+str(oversampling_rate)+" should be > 2") + + def pre_start_capture(self): + self.integer_list = np.arange(self.samples_per_record) + self.angle_list = (2 * np.pi * self.dif_freq / self.sample_rate * + self.integer_list) + + self.cos_list = np.cos(self.angle_list) + self.sin_list = np.sin(self.angle_list) + + def fit(self, rec): + # Discrete Fourier Transform + RePart = np.dot(rec, self.cos_list) / self.samples_per_record + ImPart = np.dot(rec, self.sin_list) / self.samples_per_record + # factor of 2 is because amplitude is split between finite term and + # double frequency term which averages to 0 + ampl = 2 * np.sqrt(RePart ** 2 + ImPart ** 2) + phase = math.atan2(ImPart, RePart) * 180 / (2 * math.pi) + + return [ampl, phase] + + def pre_acquire(self, alazar): + # gets called after 'AlazarStartCapture' + pass + + def handle_buffer(self, alazar, data): + self.buffer += data + + def post_acquire(self, alazar): + # average over records in buffer: + # for ATS9360 samples are arranged in the buffer as follows: + # S0A, S0B, ..., S1A, S1B, ... + # with SXY the sample number X of channel Y. + records_per_acquisition = (1. * self.buffers_per_acquisition * + self.records_per_buffer) + recordA = np.zeros(self.samples_per_record) + for i in range(self.records_per_buffer): + i0 = i * self.samples_per_record * self.number_of_channels + i1 = i0 + self.samples_per_record * self.number_of_channels + recordA += self.buffer[i0:i1:self.number_of_channels] / records_per_acquisition + + recordB = np.zeros(self.samples_per_record) + for i in range(self.records_per_buffer): + i0 = (i * self.samples_per_record * self.number_of_channels) + 1 + i1 = i0 + self.samples_per_record *self.number_of_channels + recordB += self.buffer[i0:i1:self.number_of_channels] / records_per_acquisition + + resA = self.fit(recordA) + resB = self.fit(recordB) + + return resA, resB + + +def make_rec(controller, freq, phase_deg): + phase_rad = phase_deg*2*np.pi/180 + angle_list = (2 * np.pi * freq / controller.sample_rate * + controller.integer_list)+phase_rad + rec = np.cos(angle_list) + return rec + +def main(freq1, phase): + c = Controller(freq1) + c.pre_start_capture() + ran = 100 + l = [[],[]] + for i in range(ran+1): + freq = freq1-0.002*freq1*(ran/2-i) + rec = make_rec(c, freq, phase) + mag, phase = c.fit(rec) + l[0].append(mag) + l[1].append(phase) + plt.figure(1) + plt.subplot(211) + plt.plot(l[0]) + plt.subplot(212) + plt.plot(l[1]) + plt.show() + # cos_rec = np.multiply(c.cos_list, rec) + # sin_rec = np.multiply(c.sin_list, rec) + #print(c.fit(rec)) + # plt.figure(1) + # plt.subplot(511) + # plt.plot(c.cos_list) + # plt.subplot(512) + # plt.plot(c.sin_list) + # plt.subplot(513) + # plt.plot(rec) + # plt.subplot(514) + # plt.plot(cos_rec) + # plt.subplot(515) + # plt.plot(sin_rec) + # plt.show() diff --git a/qcodes/instrument_drivers/AlazarTech/local_test2.py b/qcodes/instrument_drivers/AlazarTech/local_test2.py new file mode 100644 index 000000000000..9ce6f502e193 --- /dev/null +++ b/qcodes/instrument_drivers/AlazarTech/local_test2.py @@ -0,0 +1,93 @@ +from .ATS import AcquisitionController +import math +import numpy as np +import matplotlib.pyplot as plt +from scipy import signal + + +class Controller(): + def __init__(self, dif_freq): + self.dif_freq = dif_freq + self.sample_rate = 5000000 + self.samples_per_record = 10024 + + record_duration = self.samples_per_record/self.sample_rate + dif_time_period = 1/self.dif_freq + cycles_measured = record_duration / dif_time_period + oversampling_rate = self.sample_rate/(2*self.dif_freq) + print("Measuring from "+str(cycles_measured)+" samples should be on the order of 100 at least") + print("Oversampling rate is "+str(oversampling_rate)+" should be > 2") + + def pre_start_capture(self): + self.integer_list = np.arange(self.samples_per_record) + self.angle_list = (2 * np.pi * self.dif_freq / self.sample_rate * + self.integer_list) + + self.cos_list = np.cos(self.angle_list) + self.sin_list = np.sin(self.angle_list) + + def pre_acquire(self, alazar): + # gets called after 'AlazarStartCapture' + pass + + def handle_buffer(self, alazar, data): + self.buffer += data + + def post_acquire(self, alazar): + # average over records in buffer: + # for ATS9360 samples are arranged in the buffer as follows: + # S0A, S0B, ..., S1A, S1B, ... + # with SXY the sample number X of channel Y. + records_per_acquisition = (1. * self.buffers_per_acquisition * + self.records_per_buffer) + recordA = np.zeros(self.samples_per_record) + for i in range(self.records_per_buffer): + i0 = i * self.samples_per_record * self.number_of_channels + i1 = i0 + self.samples_per_record * self.number_of_channels + recordA += self.buffer[i0:i1:self.number_of_channels] / records_per_acquisition + + recordB = np.zeros(self.samples_per_record) + for i in range(self.records_per_buffer): + i0 = (i * self.samples_per_record * self.number_of_channels) + 1 + i1 = i0 + self.samples_per_record *self.number_of_channels + recordB += self.buffer[i0:i1:self.number_of_channels] / records_per_acquisition + + resA = self.fit(recordA) + resB = self.fit(recordB) + + return resA, resB + +def make_rec(controller, freq, phase_deg): + phase_rad = phase_deg*2*np.pi/180 + angle_list = (2 * np.pi * freq / controller.sample_rate * + controller.integer_list)+phase_rad + rec = np.cos(angle_list) + return rec + +def main(freq1, freq2): + c = Controller(freq1) + c.pre_start_capture() + rec1 = make_rec(c, freq2, 0) + rec2 = c.cos_list + plt.figure(1) + plt.subplot(211) + plt.plot(rec1) + plt.subplot(212) + plt.plot(rec1) + plt.show() + s = np.multiply(rec1, rec2) + plt.plot(s) + plt.show() + + +n = 61 +a = signal.firwin(n, cutoff = 0.3, window = "hamming") +#Frequency and phase response +mfreqz(a) +show() +#Impulse and step response +figure(2) +impz(a) +show() + + # import qcodes.instrument_drivers.AlazarTech.local_test2 as cont From 3a27d4396a5362baf99b401351e554bbaa472f11 Mon Sep 17 00:00:00 2001 From: Natalie Pearson Date: Thu, 11 Aug 2016 19:09:32 +0200 Subject: [PATCH 006/328] feat: variety of QDev acquisition controllers created, not complete --- .../QDev_acquisition_controllers.py | 246 ++++++++++++++++++ 1 file changed, 246 insertions(+) create mode 100644 qcodes/instrument_drivers/AlazarTech/QDev_acquisition_controllers.py diff --git a/qcodes/instrument_drivers/AlazarTech/QDev_acquisition_controllers.py b/qcodes/instrument_drivers/AlazarTech/QDev_acquisition_controllers.py new file mode 100644 index 000000000000..c34bc08080c6 --- /dev/null +++ b/qcodes/instrument_drivers/AlazarTech/QDev_acquisition_controllers.py @@ -0,0 +1,246 @@ +from .ATS import AcquisitionController +from scipy import signal +import numpy as np + + +class Filtering_Controller(): + """Filtering Measurement Controller + If 1 channel: + Mixes reference software signal with input hardware signal, + filters out double frequency component and returns magnitude and + phase data for dc component. Throws away channel 2 + If 2 channels: + Uses two hardware channels to find magnitude and + phase data for dc component + + TODO(nataliejpg) test diffrent filters and window types + TODO(nataliejpg) numtaps logic + TODO(nataliejpg) cutoff logic + TODO(nataliejpg) post aqcuire one input stuff?? + """ + + def __init__(self, dif_freq, numtaps, cutoff, number_of_channels): + self.dif_freq = dif_freq + self.sample_rate = 50000000 + self.samples_per_record = 10024 + self.buffers_per_acquisition = None + self.number_of_channels = number_of_channels + self.numtaps = numtaps + self.cutoff = cutoff + self.delay = numtaps - 1 + self.buffer = None + + def pre_start_capture(self): + record_duration = self.samples_per_record / self.sample_rate + time_period_dif = 1 / self.dif_freq + cycles_measured = record_duration / time_period_dif + oversampling_rate = self.sample_rate / (2 * self.dif_freq) + print("Oscillations per record: {:.2f} (expect 100+)" + .format(cycles_measured)) + print("Oversampling rate: {:.2f} (expect > 2)" + .format(oversampling_rate)) + print("Filtering delay is {} samples".format(self.delay)) + + integer_list = np.arange(self.samples_per_record) + if self.number_of_channels == 2: + angle_list = (2 * np.pi * self.freq_dif / self.sample_rate * + integer_list) + self.cos_list = np.cos(angle_list) + self.sin_list = np.sin(angle_list) + elif self.number_of_channels == 1: + angle_shift = 90 + self.sample_shift = (2 * np.pi * self.freq_dif / self.sample_rate * + angle_shift) + + def pre_acquire(self, alazar): + # gets called after 'AlazarStartCapture' + pass + + def handle_buffer(self, alazar, data): + self.buffer += data + + def post_acquire(self, alazar): + """Average over records in buffer, mix and filter double freq + if 1 chan selected recordA is mixed with software signal + if 2 chan selected recordA and recordB are mixed + """ + records_per_acquisition = (1. * self.buffers_per_acquisition * + self.records_per_buffer) + recordA = np.zeros(self.samples_per_record) + for i in range(self.records_per_buffer): + i0 = (i * self.samples_per_record * self.number_of_channels) + i1 = (i0 + self.samples_per_record * self.number_of_channels) + recordA += (self.buffer[i0:i1:self.number_of_channels] / + records_per_acquisition) + recordB = np.zeros(self.samples_per_record) + for i in range(self.records_per_buffer): + i0 = (i * self.samples_per_record * self.number_of_channels + 1) + i1 = (i0 + self.samples_per_record * self.number_of_channels) + recordB += (self.buffer[i0:i1:self.number_of_channels] / + records_per_acquisition) + + if self.number_of_channels == 1: + mixA_re = np.multiply(self.cos_list, recordA) + mixA_im = np.multiply(self.sin_list, recordA) + mixA_re_filtered = filter(mixA_re) + mixA_im_filtered = filter(mixA_im) + mixA_complex = mixA_re_filtered + mixA_im_filtered * 1j + magA = 2 * abs(mixA_complex) + phaseA = 2 * np.angle(mixA_complex, deg=True) + return magA, phaseA + elif self.number_of_channels == 2: + recordB_shifted = np.roll(recordB, self.sample_shift) + mixAB_re = np.multiply(recordA, recordB) + mixAB_im = np.multiply(recordA, recordB_shifted) + mixAB_re_filtered = filter(mixAB_re) + mixAB_im_filtered = filter(mixAB_im) + mixAB_complex = mixAB_re_filtered + mixAB_im_filtered * 1j + magAB = 2 * abs(mixAB_complex) + phaseAB = 2 * np.angle(mixAB_complex, deg=True) + return magAB, phaseAB + + def filter(self, rec): + """FIR window filter applied to filter out high freq components + """ + sample_rate = self.sample_rate + nyq_rate = sample_rate / 2. + fir_coef = signal.firwin(self.numtaps, self.cutoff / nyq_rate) + filtered_rec = signal.lfilter(fir_coef, 1.0, rec) + return filtered_rec + + +class HD_Controller(AcquisitionController): + """Averaging Heterodyne Measurement Controller + Does averaged DFT on 2 channel Alazar measurement + + TODO(nataliejpg) handling of channel number + TODO(nataliejpg) test angle data + """ + + def __init__(self, freq_dif): + self.freq_dif = freq_dif + self.samples_per_record = None + self.records_per_buffer = None + self.buffers_per_acquisition = None + self.number_of_channels = 2 + self.buffer = None + + def pre_start_capture(self, alazar): + """Get config data from alazar card and set up DFT""" + self.samples_per_record = alazar.samples_per_record() + self.records_per_buffer = alazar.records_per_buffer() + self.buffers_per_acquisition = alazar.buffers_per_acquisition() + self.sample_rate = alazar.get_sample_speed() + self.buffer = np.zeros(self.samples_per_record * + self.records_per_buffer * + self.number_of_channels) + # TODO(nataliejpg) leave explicit or save lines? add error/logging? + averaging = self.buffers_per_acquisition * self.records_per_buffer + record_duration = self.samples_per_record / self.sample_rate + time_period_dif = 1 / self.freq_dif + cycles_measured = record_duration / time_period_dif + oversampling_rate = self.sample_rate / (2 * self.freq_dif) + print("Average over {:.2f} records".format(averaging)) + print("Oscillations per record: {:.2f} (expect 100+)" + .format(cycles_measured)) + print("Oversampling rate: {:.2f} (expect > 2)" + .format(oversampling_rate)) + + integer_list = np.arange(self.samples_per_record) + angle_list = (2 * np.pi * self.freq_dif / self.sample_rate * + integer_list) + self.cos_list = np.cos(angle_list) + self.sin_list = np.sin(angle_list) + + def pre_acquire(self, alazar): + # gets called after 'AlazarStartCapture' + pass + + def handle_buffer(self, alazar, data): + self.buffer += data + + def post_acquire(self, alazar): + """Average over records in buffer and do DFT: + assumes samples are arranged in the buffer as follows: + S0A, S0B, ..., S1A, S1B, ... + with SXY the sample number X of channel Y. + """ + records_per_acquisition = (1. * self.buffers_per_acquisition * + self.records_per_buffer) + recordA = np.zeros(self.samples_per_record) + for i in range(self.records_per_buffer): + i0 = (i * self.samples_per_record * self.number_of_channels) + i1 = (i0 + self.samples_per_record * self.number_of_channels) + recordA += (self.buffer[i0:i1:self.number_of_channels] / + records_per_acquisition) + recordB = np.zeros(self.samples_per_record) + for i in range(self.records_per_buffer): + i0 = (i * self.samples_per_record * self.number_of_channels + 1) + i1 = (i0 + self.samples_per_record * self.number_of_channels) + recordB += (self.buffer[i0:i1:self.number_of_channels] / + records_per_acquisition) + + resA = self.fit(recordA) + resB = self.fit(recordB) + + return resA, resB + + def fit(self, rec): + """Do Discrete Fourier Transform and return magnitude and phase data""" + RePart = np.dot(rec, self.cos_list) / self.samples_per_record + ImPart = np.dot(rec, self.sin_list) / self.samples_per_record + complex_num = RePart - ImPart * 1j + # factor of 2 in ampl is due to loss of averaged double frequency term + ampl = 2 * abs(complex_num) + phase = np.angle(complex_num, deg=True) + return [ampl, phase] + + +class Basic_AcquisitionController(AcquisitionController): + """Basic AcquisitionController tested on ATS9360 + returns unprocessed data averaged by record with 2 channels + """ + + def __init__(self): + self.samples_per_record = None + self.records_per_buffer = None + self.buffers_per_acquisition = None + self.number_of_channels = 2 + self.buffer = None + + def pre_start_capture(self, alazar): + self.samples_per_record = alazar.samples_per_record() + self.records_per_buffer = alazar.records_per_buffer() + self.buffers_per_acquisition = alazar.buffers_per_acquisition() + self.buffer = np.zeros(self.samples_per_record * + self.records_per_buffer * + self.number_of_channels) + + def pre_acquire(self, alazar): + # gets called after 'AlazarStartCapture' + pass + + def handle_buffer(self, alazar, data): + self.buffer += data + + def post_acquire(self, alazar): + # average over records in buffer: + # for ATS9360 samples are arranged in the buffer as follows: + # S0A, S0B, ..., S1A, S1B, ... + # with SXY the sample number X of channel Y. + records_per_acquisition = (self.buffers_per_acquisition * + self.records_per_buffer) + recordA = np.zeros(self.samples_per_record) + for i in range(self.records_per_buffer): + i0 = (i * self.samples_per_record * self.number_of_channels) + i1 = (i0 + self.samples_per_record * self.number_of_channels) + recordA += (self.buffer[i0:i1:self.number_of_channels] / + records_per_acquisition) + + recordB = np.zeros(self.samples_per_record) + for i in range(self.records_per_buffer): + i0 = (i * self.samples_per_record * self.number_of_channels + 1) + i1 = (i0 + self.samples_per_record * self.number_of_channels) + recordB += (self.buffer[i0:i1:self.number_of_channels] / + records_per_acquisition) + return recordA, recordB From 23cde4e962878b14fc34edb549e039077b340eb6 Mon Sep 17 00:00:00 2001 From: Natalie Pearson Date: Mon, 15 Aug 2016 09:38:52 +0200 Subject: [PATCH 007/328] refactor/style: comments and docstings tidy up and temporary test refactor --- qcodes/instrument_drivers/AlazarTech/ATS.py | 3 +- .../instrument_drivers/AlazarTech/ATS9360.py | 16 ++- .../AlazarTech/ATS_acquisition_controllers.py | 68 +++++----- .../AlazarTech/HD_test_controller.py | 105 +++++++++++++++ .../AlazarTech/filter1_test_controller.py | 124 +++++++++++++++++ .../AlazarTech/filter2_test_controller.py | 126 ++++++++++++++++++ .../AlazarTech/local_test.py | 109 --------------- .../AlazarTech/local_test2.py | 93 ------------- 8 files changed, 403 insertions(+), 241 deletions(-) create mode 100644 qcodes/instrument_drivers/AlazarTech/HD_test_controller.py create mode 100644 qcodes/instrument_drivers/AlazarTech/filter1_test_controller.py create mode 100644 qcodes/instrument_drivers/AlazarTech/filter2_test_controller.py delete mode 100644 qcodes/instrument_drivers/AlazarTech/local_test.py delete mode 100644 qcodes/instrument_drivers/AlazarTech/local_test2.py diff --git a/qcodes/instrument_drivers/AlazarTech/ATS.py b/qcodes/instrument_drivers/AlazarTech/ATS.py index 04905d5e5a4a..df0940f3a881 100644 --- a/qcodes/instrument_drivers/AlazarTech/ATS.py +++ b/qcodes/instrument_drivers/AlazarTech/ATS.py @@ -23,6 +23,7 @@ # TODO(nataliejpg) make use of _ATS_dll uniform # TODO(nataliejpg) error if no trigger + class AlazarTech_ATS(Instrument): # override dll_path in your init script or in the board constructor # if you have it somewhere else @@ -193,7 +194,7 @@ def get_board_info(cls, dll, system_id, board_id): def __init__(self, name, system_id=1, board_id=1, dll_path=None, **kwargs): super().__init__(name, **kwargs) self._ATS_dll = None - + if os.name == 'nt': self._ATS_dll = ctypes.cdll.LoadLibrary(dll_path or self.dll_path) else: diff --git a/qcodes/instrument_drivers/AlazarTech/ATS9360.py b/qcodes/instrument_drivers/AlazarTech/ATS9360.py index 5703d92a6bd5..a564c01a9fbb 100644 --- a/qcodes/instrument_drivers/AlazarTech/ATS9360.py +++ b/qcodes/instrument_drivers/AlazarTech/ATS9360.py @@ -6,8 +6,7 @@ class AlazarTech_ATS9360(AlazarTech_ATS): def __init__(self, name, server_name=None): dll_path = 'C:\\WINDOWS\\System32\\ATSApi.dll' super().__init__(name, dll_path=dll_path) - - samplesPerSec = None + # add parameters # ----- Parameters for the configuration of the board ----- @@ -133,7 +132,7 @@ def __init__(self, name, server_name=None): unit='Sample clock cycles', value=0, vals=validators.Ints(min_value=0)) - + # NOTE: The board will wait for a for this amount of time for a # trigger event. If a trigger event does not arrive, then the # board will automatically trigger. Set the trigger timeout value @@ -149,7 +148,6 @@ def __init__(self, name, server_name=None): unit='10 us', value=0, vals=validators.Ints(min_value=0)) - # ----- Parameters for the acquire function ----- self.add_parameter(name='mode', @@ -169,8 +167,12 @@ def __init__(self, name, server_name=None): # TODO (M) figure out if this also has to be a multiple of something, # I could not find this in the documentation but somehow I have the # feeling it still should be a multiple of something - # NOTE by ramiro: At least in previous python implementations(PycQED delft), this is an artifact for compatibility with AWG sequencing, not particular to any ATS architecture. - # ==> this is a construction imposed by the memory strategy implemented on the python driver we are writing, not limited by any actual ATS feature. + # NOTE by ramiro: At least in previous python implementations(PycQED delft), + # this is an artifact for compatibility with AWG sequencing, + # not particular to any ATS architecture. + # ==> this is a construction imposed by the memory strategy + # implemented on the python driver + # we are writing, not limited by any actual ATS feature. self.add_parameter(name='records_per_buffer', parameter_class=AlazarParameter, @@ -255,7 +257,7 @@ def __init__(self, name, server_name=None): # TODO (M) make parameter for board type # TODO (M) check board kind - + class Multiples(validators.Ints): ''' diff --git a/qcodes/instrument_drivers/AlazarTech/ATS_acquisition_controllers.py b/qcodes/instrument_drivers/AlazarTech/ATS_acquisition_controllers.py index fa6daa438ec4..538cc9b93eaf 100644 --- a/qcodes/instrument_drivers/AlazarTech/ATS_acquisition_controllers.py +++ b/qcodes/instrument_drivers/AlazarTech/ATS_acquisition_controllers.py @@ -2,13 +2,15 @@ import math import numpy as np + class HD_Controller(AcquisitionController): """Heterodyne Measurement Controller - Does averaged DFT on 2 channel Alazar measurement + Does averaged DFT on 2 channel Alazar measurement TODO(nataliejpg) handling of channel number TODO(nataliejpg) test angle data """ + def __init__(self, freq_dif): self.freq_dif = freq_dif self.samples_per_record = None @@ -24,17 +26,17 @@ def pre_start_capture(self, alazar): self.buffers_per_acquisition = alazar.buffers_per_acquisition() self.sample_rate = alazar.get_sample_speed() self.buffer = np.zeros(self.samples_per_record * - self.records_per_buffer * - self.number_of_channels) - - # TODO(nataliejpg) leave super explicit or save lines? add error/logging? + self.records_per_buffer * + self.number_of_channels) + # TODO(nataliejpg) leave explicit or save lines? add error/logging? averaging = self.buffers_per_acquisition * self.records_per_buffer - record_duration = self.samples_per_record/self.sample_rate - time_period_dif = 1/self.freq_dif + record_duration = self.samples_per_record / self.sample_rate + time_period_dif = 1 / self.freq_dif cycles_measured = record_duration / time_period_dif - oversampling_rate = self.sample_rate/(2*self.freq_dif) + oversampling_rate = self.sample_rate / (2 * self.freq_dif) print("Average over {} records".format(averaging)) - print("Oscillations per record: {} (expect 100+)".format(cycles_measured)) + print("Oscillations per record: {} (expect 100+)" + .format(cycles_measured)) print("Oversampling rate: {} (expect > 2)".format(oversampling_rate)) integer_list = np.arange(self.samples_per_record) @@ -42,7 +44,7 @@ def pre_start_capture(self, alazar): integer_list) self.cos_list = np.cos(angle_list) self.sin_list = np.sin(angle_list) - + def pre_acquire(self, alazar): # gets called after 'AlazarStartCapture' pass @@ -60,28 +62,30 @@ def post_acquire(self, alazar): self.records_per_buffer) recordA = np.zeros(self.samples_per_record) for i in range(self.records_per_buffer): - i0 = i * self.samples_per_record * self.number_of_channels - i1 = i0 + self.samples_per_record * self.number_of_channels - recordA += self.buffer[i0:i1:self.number_of_channels] / records_per_acquisition + i0 = (i * self.samples_per_record * self.number_of_channels) + i1 = (i0 + self.samples_per_record * self.number_of_channels) + recordA += (self.buffer[i0:i1:self.number_of_channels] / + records_per_acquisition) recordB = np.zeros(self.samples_per_record) for i in range(self.records_per_buffer): - i0 = (i * self.samples_per_record * self.number_of_channels) + 1 - i1 = i0 + self.samples_per_record *self.number_of_channels - recordB += self.buffer[i0:i1:self.number_of_channels] / records_per_acquisition - + i0 = (i * self.samples_per_record * self.number_of_channels + 1) + i1 = (i0 + self.samples_per_record * self.number_of_channels) + recordB += (self.buffer[i0:i1:self.number_of_channels] / + records_per_acquisition) + resA = self.fit(recordA) resB = self.fit(recordB) - + return resA, resB - + def fit(self, rec): """Do Discrete Fourier Transform and return magnitude and phase data""" RePart = np.dot(rec, self.cos_list) / self.samples_per_record ImPart = np.dot(rec, self.sin_list) / self.samples_per_record - # factor of 2 as amplitude is split between finite term and double frequency term + # factor of 2 as amplitude is split between finite term + # and double frequency term ampl = 2 * np.sqrt(RePart ** 2 + ImPart ** 2) phase = math.atan2(ImPart, RePart) * 180 / (2 * math.pi) - return [ampl, phase] @@ -89,6 +93,7 @@ class Basic_AcquisitionController(AcquisitionController): """Basic AcquisitionController tested on ATS9360 returns unprocessed data averaged by record with 2 channels """ + def __init__(self): self.samples_per_record = None self.records_per_buffer = None @@ -101,8 +106,8 @@ def pre_start_capture(self, alazar): self.records_per_buffer = alazar.records_per_buffer() self.buffers_per_acquisition = alazar.buffers_per_acquisition() self.buffer = np.zeros(self.samples_per_record * - self.records_per_buffer * - self.number_of_channels) + self.records_per_buffer * + self.number_of_channels) def pre_acquire(self, alazar): # gets called after 'AlazarStartCapture' @@ -116,21 +121,22 @@ def post_acquire(self, alazar): # for ATS9360 samples are arranged in the buffer as follows: # S0A, S0B, ..., S1A, S1B, ... # with SXY the sample number X of channel Y. - records_per_acquisition = (1. * self.buffers_per_acquisition * + records_per_acquisition = (self.buffers_per_acquisition * self.records_per_buffer) recordA = np.zeros(self.samples_per_record) for i in range(self.records_per_buffer): - i0 = i * self.samples_per_record * self.number_of_channels - i1 = i0 + self.samples_per_record * self.number_of_channels - recordA += self.buffer[i0:i1:self.number_of_channels] / records_per_acquisition + i0 = (i * self.samples_per_record * self.number_of_channels) + i1 = (i0 + self.samples_per_record * self.number_of_channels) + recordA += (self.buffer[i0:i1:self.number_of_channels] / + records_per_acquisition) recordB = np.zeros(self.samples_per_record) for i in range(self.records_per_buffer): - i0 = (i * self.samples_per_record * self.number_of_channels) + 1 - i1 = i0 + self.samples_per_record *self.number_of_channels - recordB += self.buffer[i0:i1:self.number_of_channels] / records_per_acquisition + i0 = (i * self.samples_per_record * self.number_of_channels + 1) + i1 = (i0 + self.samples_per_record * self.number_of_channels) + recordB += (self.buffer[i0:i1:self.number_of_channels] / + records_per_acquisition) return recordA, recordB - # DFT AcquisitionController diff --git a/qcodes/instrument_drivers/AlazarTech/HD_test_controller.py b/qcodes/instrument_drivers/AlazarTech/HD_test_controller.py new file mode 100644 index 000000000000..f9b4e751cd16 --- /dev/null +++ b/qcodes/instrument_drivers/AlazarTech/HD_test_controller.py @@ -0,0 +1,105 @@ +from .ATS import AcquisitionController +import math +import numpy as np +import matplotlib.pyplot as plt + +# HD_ test controller +class Controller(): + def __init__(self, dif_freq): + self.dif_freq = dif_freq + self.sample_rate = 10000000 + self.samples_per_record = 2024 + + record_duration = self.samples_per_record / self.sample_rate + time_period_dif = 1 / self.dif_freq + cycles_measured = record_duration / time_period_dif + oversampling_rate = self.sample_rate / (2 * self.dif_freq) + print("Oscillations per record: {:.2f} (expect 100+)".format(cycles_measured)) + print("Oversampling rate: {:.2f} (expect > 2)".format(oversampling_rate)) + + def pre_start_capture(self): + self.integer_list = np.arange(self.samples_per_record) + self.angle_list = (2 * np.pi * self.dif_freq / self.sample_rate * + self.integer_list) + + self.cos_list = np.cos(self.angle_list) + self.sin_list = np.sin(self.angle_list) + + def fit(self, rec): + # Discrete Fourier Transform + RePart = np.dot(rec, self.cos_list) / self.samples_per_record + ImPart = np.dot(rec, self.sin_list) / self.samples_per_record + # factor of 2 is because amplitude is split between finite term and + # double frequency term which averages to 0 + complex_num = RePart - ImPart*1j + ampl = 2 * abs(complex_num) + phase = np.angle(complex_num, deg=True) + + return [ampl, phase] + +def make_rec(controller, freq, phase_deg): + phase_rad = phase_deg*np.pi/180 + angle_list = (2 * np.pi * freq / controller.sample_rate * + controller.integer_list)+phase_rad + rec = np.cos(angle_list) + return rec + +def main(freq, phase_dif): + # make an acqisition controller and set up sin and cos software signals + c = Controller(freq) + c.pre_start_capture() + + # make a 'hardware' signal with same frequency shifted by phase + # mix with software signals to make real and imaginary parts of mixed signal + rec = make_rec(c, freq, phase_dif) + cos_rec = np.multiply(c.cos_list, rec) + sin_rec = np.multiply(c.sin_list, rec) + + # print averaged magnitude and phase of mixed signal + # should give dc part of signal as double frequency part averages out + print(" mag, phase : "+str(c.fit(rec))) + plt.figure(1) + plt.subplot(311) + plt.plot(c.cos_list[:100], "-r") + plt.plot(c.sin_list[:100], "-g") + plt.title('"software" signals') + plt.subplot(312) + plt.plot(rec[:100]) + plt.title('"hardware" signal') + plt.subplot(313) + plt.plot(cos_rec[:100], "-r") + plt.plot(sin_rec[:100], "-g") + plt.title('re and im parts of mixed signal') + plt.show() + + # sweep 'hardware' signal frequency through software signal and + # observe magnitude and phase response + point_ran = 10000 + sweep_mag = [[],[]] + for i in range(point_ran+1): + freq_ran = 0.2*freq + shift_freq = (freq_ran/point_ran)*(-point_ran/2+i) + rec = make_rec(c, freq+shift_freq, 0) + mag, phase = c.fit(rec) + sweep_mag[1].append(mag) + sweep_mag[0].append(shift_freq) + sweep_phase = [[],[]] + for i in range(point_ran+1): + angle_ran = 45 + shift_phase = (angle_ran/point_ran)*(-point_ran/2+i) + rec = make_rec(c, freq, phase_dif+shift_phase) + mag, phase = c.fit(rec) + sweep_phase[1].append(phase) + sweep_phase[0].append(shift_phase) + plt.figure(1) + plt.subplot(211) + plt.title('sweep:magnitude response') + plt.plot(sweep_mag[0], sweep_mag[1]) + plt.subplot(212) + plt.title('sweep:phase response') + plt.plot(sweep_phase[0], sweep_phase[1]) + plt.show() + + + + # import qcodes.instrument_drivers.AlazarTech.HD_test_controller as cont diff --git a/qcodes/instrument_drivers/AlazarTech/filter1_test_controller.py b/qcodes/instrument_drivers/AlazarTech/filter1_test_controller.py new file mode 100644 index 000000000000..6efae1d80321 --- /dev/null +++ b/qcodes/instrument_drivers/AlazarTech/filter1_test_controller.py @@ -0,0 +1,124 @@ +import numpy as np +import matplotlib.pyplot as plt +from scipy import signal + +# one channel filter controller +class Controller(): + def __init__(self, dif_freq): + self.dif_freq = dif_freq + self.sample_rate = 500000000 + self.samples_per_record = 10024 + + record_duration = self.samples_per_record/self.sample_rate + dif_time_period = 1/self.dif_freq + cycles_measured = record_duration / dif_time_period + oversampling_rate = self.sample_rate/(2*self.dif_freq) + print("Measuring from "+str(cycles_measured)+" samples should be on the order of 100 at least") + print("Oversampling rate is "+str(oversampling_rate)+" should be > 2") + + def pre_start_capture(self): + self.integer_list = np.arange(self.samples_per_record) + self.angle_list = (2 * np.pi * self.dif_freq / self.sample_rate * + self.integer_list) + + self.cos_list = np.cos(self.angle_list) + self.sin_list = np.sin(self.angle_list) + +def filter(controller, rec, numtaps, cutoff): + sample_rate = controller.sample_rate + print("sample_rate is "+str(sample_rate)) + nyq_rate = sample_rate / 2. + print("nyq_rate is "+ str(nyq_rate)) + delay = (numtaps-1) + fir_coef = signal.firwin(numtaps, cutoff/nyq_rate, window = "hamming") + filtered_rec = 2 * signal.lfilter(fir_coef, 1.0, rec) + w, h = signal.freqz(fir_coef) + plt.plot(w*controller.sample_rate/(2*np.pi), 20 * np.log10(abs(h)), 'b') + plt.ylabel('Amplitude [dB]', color='b') + plt.xlabel('Frequency [rad/sample]') + plt.show() + return fir_coef, filtered_rec, delay + +def make_rec(controller, freq, ampdif, phase_deg): + phase_rad = phase_deg*2*np.pi/360 + angle_list = (2 * np.pi * freq * controller.integer_list / controller.sample_rate)+phase_rad + rec = ampdif*np.cos(angle_list) + return rec + +def main(freq1, freq2, phasedif, ampdif, numtaps, cutoff): + # make an acquisition controller (and sampling rate warnings related to + # given freq1) and integer list used for signal making + c = Controller(freq1) + c.pre_start_capture() + + # make one hardware signal with given frequency and phase difference + # make a software signal with real and imaginary parts + # plot first 100 points of each + rec1 = make_rec(c, freq2, phase_deg=phasedif, ampdif=ampdif) + plt.figure(1) + plt.subplot(211) + plt.plot(rec1[:100]) + plt.subplot(212) + plt.plot(rec2[:100]) + plt.show() + + # mix hardware signals and filter out frequencies above cutoff + # should leave only freq_dif component + rec12 = np.multiply(rec1, rec2) + fir_coef, filtered_rec_12 = filter(c, rec12, numtaps, cutoff) + delay = numtaps-1 + + # plot filter + w, h = signal.freqz(fir_coef) + freq_axis = (w/np.pi)*c.sample_rate + fig = plt.figure(1) + ax1 = fig.add_subplot(111) + plt.title('Digital filter frequency response') + plt.plot(freq_axis, 20 * np.log10(abs(h)), 'b') + plt.ylabel('Amplitude [dB]', color='b') + plt.xlabel('Frequency [Hz]') + + ax2 = ax1.twinx() + angles = np.unwrap(np.angle(h)) + plt.plot(freq_axis, angles, 'g') + plt.ylabel('Angle (radians)', color='g') + plt.grid() + plt.axis('tight') + plt.show() + + + # shift first hardware signal by 90 degrees to make + # sin signal for mixing, mix and plot + rec3 = make_rec(c, freq1, sin=True) + rec23 = np.multiply(rec2, rec3) + fir_coef, filtered_rec_23 = filter(c, rec23, numtaps, cutoff) + + # plot filtered signal and magnitude and phase results + plt.figure(1) + plt.subplot(411) + plt.title('Re part') + plt.plot(rec12, '-r') + plt.ylabel('unfiltered record', color='r') + plt.plot(filtered_rec_12, '-g') + plt.ylabel('filtered record', color='g') + plt.subplot(412) + plt.title('Im part') + plt.plot(rec23, '-r') + plt.ylabel('unfiltered record', color='r') + plt.plot(filtered_rec_23, '-g') + plt.ylabel('filtered record', color='g') + + # add to plot magnitude and phase of resulting complex number + complex_rec = filtered_rec_12+filtered_rec_23*1j + magnitude = abs(complex_rec) + phase = np.angle(complex_rec, deg=True) + plt.subplot(413) + plt.title('Magnitude') + plt.plot(magnitude) + plt.ylabel('magnitude') + plt.subplot(414) + plt.title('Phase') + plt.plot(phase) + plt.ylabel('phase') + plt.show() + diff --git a/qcodes/instrument_drivers/AlazarTech/filter2_test_controller.py b/qcodes/instrument_drivers/AlazarTech/filter2_test_controller.py new file mode 100644 index 000000000000..7d0838f16579 --- /dev/null +++ b/qcodes/instrument_drivers/AlazarTech/filter2_test_controller.py @@ -0,0 +1,126 @@ +import numpy as np +import matplotlib.pyplot as plt +from scipy import signal + +# two channel filter controller +class Controller(): + def __init__(self, dif_freq): + self.dif_freq = dif_freq + self.sample_rate = 50000000 + self.samples_per_record = 10024 + + record_duration = self.samples_per_record / self.sample_rate + time_period_dif = 1 / self.dif_freq + cycles_measured = record_duration / time_period_dif + oversampling_rate = self.sample_rate / (2 * self.dif_freq) + print("Oscillations per record: {:.2f} (expect 100+)".format(cycles_measured)) + print("Oversampling rate: {:.2f} (expect > 2)".format(oversampling_rate)) + + def pre_start_capture(self): + self.integer_list = np.arange(self.samples_per_record) + +def filter(controller, rec, numtaps, cutoff): + sample_rate = controller.sample_rate + nyq_rate = sample_rate / 2. + fir_coef = signal.firwin(numtaps, cutoff/nyq_rate) + filtered_rec = 2 * signal.lfilter(fir_coef, 1.0, rec) + return fir_coef, filtered_rec + +def filterls(controller, rec, numtaps, cutoff): + sample_rate = controller.sample_rate + nyq_rate = sample_rate / 2. + bands = [0, cutoff/nyq_rate, cutoff/nyq_rate, 1] + desired = [1, 1, 0, 0] + fir_coef = signal.firls(numtaps, bands, desired, nyq=nyq_rate) + filtered_rec = 2 * signal.lfilter(fir_coef, 1.0, rec) + return fir_coef, filtered_rec + +def make_rec(controller, freq, phase_deg=0, ampdif=1, sin=False): + phase_rad = phase_deg*2*np.pi/360 + angle_list = (2 * np.pi * freq * controller.integer_list / controller.sample_rate)+phase_rad + rec = ampdif*np.cos(angle_list) + if sin: + rec = ampdif*np.sin(angle_list) + return rec + + +def main(freq1, freq2, phasedif, ampdif, numtaps, cutoff): + # make an acquisition controller (and sampling rate warnings related to + # given freq1) and integer list used for signal making + c = Controller(freq1) + c.pre_start_capture() + + # make two hardware signals with given frequencies and phase difference + # plot first 100 points of each + rec1 = make_rec(c, freq1) + rec2 = make_rec(c, freq2, phase_deg=phasedif, ampdif=ampdif) + plt.figure(1) + plt.subplot(211) + plt.plot(rec1[:100]) + plt.subplot(212) + plt.plot(rec2[:100]) + plt.show() + + # mix hardware signals and filter out frequencies above cutoff + # should leave only freq_dif component + rec12 = np.multiply(rec1, rec2) + fir_coef, filtered_rec_12 = filterls(c, rec12, numtaps, cutoff) + delay = numtaps-1 + + # plot filter + w, h = signal.freqz(fir_coef) + freq_axis = (w/np.pi)*c.sample_rate + fig = plt.figure(1) + ax1 = fig.add_subplot(111) + plt.title('Digital filter frequency response') + plt.plot(freq_axis, 20 * np.log10(abs(h)), 'b') + plt.ylabel('Amplitude [dB]', color='b') + plt.xlabel('Frequency [Hz]') + + ax2 = ax1.twinx() + angles = np.unwrap(np.angle(h)) + plt.plot(freq_axis, angles, 'g') + plt.ylabel('Angle (radians)', color='g') + plt.grid() + plt.axis('tight') + plt.show() + + + # shift first hardware signal by 90 degrees to make + # sin signal for mixing, mix and plot + rec3 = make_rec(c, freq1, sin=True) + rec23 = np.multiply(rec2, rec3) + fir_coef, filtered_rec_23 = filterls(c, rec23, numtaps, cutoff) + + # plot filtered signal and magnitude and phase results + plt.figure(1) + plt.subplot(411) + plt.title('Re part') + plt.plot(rec12, '-r') + plt.ylabel('unfiltered record', color='r') + plt.plot(filtered_rec_12, '-g') + plt.ylabel('filtered record', color='g') + plt.subplot(412) + plt.title('Im part') + plt.plot(rec23, '-r') + plt.ylabel('unfiltered record', color='r') + plt.plot(filtered_rec_23, '-g') + plt.ylabel('filtered record', color='g') + + # add to plot magnitude and phase of resulting complex number + complex_rec = filtered_rec_12+filtered_rec_23*1j + magnitude = abs(complex_rec) + phase = np.angle(complex_rec, deg=True) + plt.subplot(413) + plt.title('Magnitude') + plt.plot(magnitude) + plt.ylabel('magnitude') + plt.subplot(414) + plt.title('Phase') + plt.plot(phase) + plt.ylabel('phase') + plt.show() + + + + # import qcodes.instrument_drivers.AlazarTech.filter2_test_controller as cont diff --git a/qcodes/instrument_drivers/AlazarTech/local_test.py b/qcodes/instrument_drivers/AlazarTech/local_test.py deleted file mode 100644 index 6e85961fe531..000000000000 --- a/qcodes/instrument_drivers/AlazarTech/local_test.py +++ /dev/null @@ -1,109 +0,0 @@ -from .ATS import AcquisitionController -import math -import numpy as np -import matplotlib.pyplot as plt - - -class Controller(): - def __init__(self, dif_freq): - self.dif_freq = dif_freq - self.sample_rate = 500000000 - self.samples_per_record = 1024 - - record_duration = self.samples_per_record/self.sample_rate - dif_time_period = 1/self.dif_freq - cycles_measured = record_duration / dif_time_period - oversampling_rate = self.sample_rate/(2*self.dif_freq) - print("Measuring from "+str(cycles_measured)+" samples should be on the order of 100 at least") - print("Oversampling rate is "+str(oversampling_rate)+" should be > 2") - - def pre_start_capture(self): - self.integer_list = np.arange(self.samples_per_record) - self.angle_list = (2 * np.pi * self.dif_freq / self.sample_rate * - self.integer_list) - - self.cos_list = np.cos(self.angle_list) - self.sin_list = np.sin(self.angle_list) - - def fit(self, rec): - # Discrete Fourier Transform - RePart = np.dot(rec, self.cos_list) / self.samples_per_record - ImPart = np.dot(rec, self.sin_list) / self.samples_per_record - # factor of 2 is because amplitude is split between finite term and - # double frequency term which averages to 0 - ampl = 2 * np.sqrt(RePart ** 2 + ImPart ** 2) - phase = math.atan2(ImPart, RePart) * 180 / (2 * math.pi) - - return [ampl, phase] - - def pre_acquire(self, alazar): - # gets called after 'AlazarStartCapture' - pass - - def handle_buffer(self, alazar, data): - self.buffer += data - - def post_acquire(self, alazar): - # average over records in buffer: - # for ATS9360 samples are arranged in the buffer as follows: - # S0A, S0B, ..., S1A, S1B, ... - # with SXY the sample number X of channel Y. - records_per_acquisition = (1. * self.buffers_per_acquisition * - self.records_per_buffer) - recordA = np.zeros(self.samples_per_record) - for i in range(self.records_per_buffer): - i0 = i * self.samples_per_record * self.number_of_channels - i1 = i0 + self.samples_per_record * self.number_of_channels - recordA += self.buffer[i0:i1:self.number_of_channels] / records_per_acquisition - - recordB = np.zeros(self.samples_per_record) - for i in range(self.records_per_buffer): - i0 = (i * self.samples_per_record * self.number_of_channels) + 1 - i1 = i0 + self.samples_per_record *self.number_of_channels - recordB += self.buffer[i0:i1:self.number_of_channels] / records_per_acquisition - - resA = self.fit(recordA) - resB = self.fit(recordB) - - return resA, resB - - -def make_rec(controller, freq, phase_deg): - phase_rad = phase_deg*2*np.pi/180 - angle_list = (2 * np.pi * freq / controller.sample_rate * - controller.integer_list)+phase_rad - rec = np.cos(angle_list) - return rec - -def main(freq1, phase): - c = Controller(freq1) - c.pre_start_capture() - ran = 100 - l = [[],[]] - for i in range(ran+1): - freq = freq1-0.002*freq1*(ran/2-i) - rec = make_rec(c, freq, phase) - mag, phase = c.fit(rec) - l[0].append(mag) - l[1].append(phase) - plt.figure(1) - plt.subplot(211) - plt.plot(l[0]) - plt.subplot(212) - plt.plot(l[1]) - plt.show() - # cos_rec = np.multiply(c.cos_list, rec) - # sin_rec = np.multiply(c.sin_list, rec) - #print(c.fit(rec)) - # plt.figure(1) - # plt.subplot(511) - # plt.plot(c.cos_list) - # plt.subplot(512) - # plt.plot(c.sin_list) - # plt.subplot(513) - # plt.plot(rec) - # plt.subplot(514) - # plt.plot(cos_rec) - # plt.subplot(515) - # plt.plot(sin_rec) - # plt.show() diff --git a/qcodes/instrument_drivers/AlazarTech/local_test2.py b/qcodes/instrument_drivers/AlazarTech/local_test2.py deleted file mode 100644 index 9ce6f502e193..000000000000 --- a/qcodes/instrument_drivers/AlazarTech/local_test2.py +++ /dev/null @@ -1,93 +0,0 @@ -from .ATS import AcquisitionController -import math -import numpy as np -import matplotlib.pyplot as plt -from scipy import signal - - -class Controller(): - def __init__(self, dif_freq): - self.dif_freq = dif_freq - self.sample_rate = 5000000 - self.samples_per_record = 10024 - - record_duration = self.samples_per_record/self.sample_rate - dif_time_period = 1/self.dif_freq - cycles_measured = record_duration / dif_time_period - oversampling_rate = self.sample_rate/(2*self.dif_freq) - print("Measuring from "+str(cycles_measured)+" samples should be on the order of 100 at least") - print("Oversampling rate is "+str(oversampling_rate)+" should be > 2") - - def pre_start_capture(self): - self.integer_list = np.arange(self.samples_per_record) - self.angle_list = (2 * np.pi * self.dif_freq / self.sample_rate * - self.integer_list) - - self.cos_list = np.cos(self.angle_list) - self.sin_list = np.sin(self.angle_list) - - def pre_acquire(self, alazar): - # gets called after 'AlazarStartCapture' - pass - - def handle_buffer(self, alazar, data): - self.buffer += data - - def post_acquire(self, alazar): - # average over records in buffer: - # for ATS9360 samples are arranged in the buffer as follows: - # S0A, S0B, ..., S1A, S1B, ... - # with SXY the sample number X of channel Y. - records_per_acquisition = (1. * self.buffers_per_acquisition * - self.records_per_buffer) - recordA = np.zeros(self.samples_per_record) - for i in range(self.records_per_buffer): - i0 = i * self.samples_per_record * self.number_of_channels - i1 = i0 + self.samples_per_record * self.number_of_channels - recordA += self.buffer[i0:i1:self.number_of_channels] / records_per_acquisition - - recordB = np.zeros(self.samples_per_record) - for i in range(self.records_per_buffer): - i0 = (i * self.samples_per_record * self.number_of_channels) + 1 - i1 = i0 + self.samples_per_record *self.number_of_channels - recordB += self.buffer[i0:i1:self.number_of_channels] / records_per_acquisition - - resA = self.fit(recordA) - resB = self.fit(recordB) - - return resA, resB - -def make_rec(controller, freq, phase_deg): - phase_rad = phase_deg*2*np.pi/180 - angle_list = (2 * np.pi * freq / controller.sample_rate * - controller.integer_list)+phase_rad - rec = np.cos(angle_list) - return rec - -def main(freq1, freq2): - c = Controller(freq1) - c.pre_start_capture() - rec1 = make_rec(c, freq2, 0) - rec2 = c.cos_list - plt.figure(1) - plt.subplot(211) - plt.plot(rec1) - plt.subplot(212) - plt.plot(rec1) - plt.show() - s = np.multiply(rec1, rec2) - plt.plot(s) - plt.show() - - -n = 61 -a = signal.firwin(n, cutoff = 0.3, window = "hamming") -#Frequency and phase response -mfreqz(a) -show() -#Impulse and step response -figure(2) -impz(a) -show() - - # import qcodes.instrument_drivers.AlazarTech.local_test2 as cont From 654984a65f195ef368112868bcfd127a6e4e4130 Mon Sep 17 00:00:00 2001 From: Natalie Pearson Date: Wed, 26 Oct 2016 13:55:19 +0200 Subject: [PATCH 008/328] fix: tweak ATS9360.py to work with damaz updates --- .../instrument_drivers/AlazarTech/ATS9360.py | 39 ++++--------------- 1 file changed, 8 insertions(+), 31 deletions(-) diff --git a/qcodes/instrument_drivers/AlazarTech/ATS9360.py b/qcodes/instrument_drivers/AlazarTech/ATS9360.py index a564c01a9fbb..c86abd2457e9 100644 --- a/qcodes/instrument_drivers/AlazarTech/ATS9360.py +++ b/qcodes/instrument_drivers/AlazarTech/ATS9360.py @@ -3,9 +3,9 @@ class AlazarTech_ATS9360(AlazarTech_ATS): - def __init__(self, name, server_name=None): + def __init__(self, name, **kwargs): dll_path = 'C:\\WINDOWS\\System32\\ATSApi.dll' - super().__init__(name, dll_path=dll_path) + super().__init__(name, dll_path=dll_path, **kwargs) # add parameters @@ -88,7 +88,7 @@ def __init__(self, name, server_name=None): parameter_class=AlazarParameter, label='Trigger Engine ' + i, unit=None, - value='TRIG_ENGINE_J', + value='TRIG_ENGINE_' + ('J' if i == 0 else 'K'), byte_to_value_dict={0: 'TRIG_ENGINE_J', 1: 'TRIG_ENGINE_K'}) self.add_parameter(name='trigger_source' + i, @@ -163,7 +163,7 @@ def __init__(self, name, server_name=None): label='Samples per Record', unit=None, value=1024, - vals=Multiples(divisor=16, min_value=0)) + vals=Multiples(divisor=64, min_value=256)) # TODO (M) figure out if this also has to be a multiple of something, # I could not find this in the documentation but somehow I have the # feeling it still should be a multiple of something @@ -254,30 +254,7 @@ def __init__(self, name, server_name=None): value=1000, vals=validators.Ints(min_value=0)) - # TODO (M) make parameter for board type - - # TODO (M) check board kind - - -class Multiples(validators.Ints): - ''' - requires an integer - optional parameters min_value and max_value enforce - min_value <= value <= max_value - divisor enforces that value % divisor == 0 - ''' - - def __init__(self, divisor=1, **kwargs): - super().__init__(**kwargs) - if not isinstance(divisor, int): - raise TypeError('divisor must be an integer') - self._divisor = divisor - - def validate(self, value, context=''): - super().validate(value=value, context=context) - if not value % self._divisor == 0: - raise TypeError('{} is not a multiple of {}; {}'.format( - repr(value), repr(self._divisor), context)) - - def __repr__(self): - return super().__repr__()[:-1] + ', Multiples of {}>'.format(self._divisor) + model = self.get_idn()['model'] + if model != 'ATS9360': + raise Exception("The Alazar board kind is not 'ATS9360'," + " found '" + str(model) + "' instead.") From 2d2b82bc57287594c2ee0f8ffc2d58bc5db9e4af Mon Sep 17 00:00:00 2001 From: Natalie Pearson Date: Wed, 26 Oct 2016 14:41:26 +0200 Subject: [PATCH 009/328] fix: tweak ATS.py to hopefully match damaz updates --- qcodes/instrument_drivers/AlazarTech/ATS.py | 16 ++++++++++------ 1 file changed, 10 insertions(+), 6 deletions(-) diff --git a/qcodes/instrument_drivers/AlazarTech/ATS.py b/qcodes/instrument_drivers/AlazarTech/ATS.py index a933c18a4e8d..d583275aec99 100644 --- a/qcodes/instrument_drivers/AlazarTech/ATS.py +++ b/qcodes/instrument_drivers/AlazarTech/ATS.py @@ -467,7 +467,7 @@ def acquire(self, mode=None, samples_per_record=None, self._set_if_present('samples_per_record', samples_per_record) self._set_if_present('records_per_buffer', records_per_buffer) self._set_if_present('buffers_per_acquisition', - buffers_per_acquisition), + buffers_per_acquisition) self._set_if_present('channel_selection', channel_selection) self._set_if_present('transfer_offset', transfer_offset) self._set_if_present('external_startcapture', external_startcapture) @@ -500,6 +500,10 @@ def acquire(self, mode=None, samples_per_record=None, post_trigger_size) # set acquisition parameters here for NPT, TS mode + if self.channel_selection._get_byte() == 3: + number_of_channels = 2 + else: + number_of_channels = 1 samples_per_buffer = 0 buffers_per_acquisition = self.buffers_per_acquisition._get_byte() samples_per_record = self.samples_per_record._get_byte() @@ -513,7 +517,8 @@ def acquire(self, mode=None, samples_per_record=None, if mode == 'NPT': records_per_buffer = self.records_per_buffer._get_byte() - records_per_acquisition = records_per_buffer * buffers_per_acquisition + records_per_acquisition = ( + records_per_buffer * buffers_per_acquisition) samples_per_buffer = samples_per_record * records_per_buffer self._call_dll('AlazarBeforeAsyncRead', @@ -555,8 +560,7 @@ def acquire(self, mode=None, samples_per_record=None, self.get_processed_data._set_updated() # bytes per sample - handle = self._handle - max_s, bps = self._get_channel_info(handle) + max_s, bps = self._get_channel_info(self._handle) bytes_per_sample = (bps + 7) // 8 print("bytes per sample "+str(bytes_per_sample)) @@ -614,13 +618,13 @@ def acquire(self, mode=None, samples_per_record=None, print("completed AlazarPostAsyncBuffer") # -----start capture here----- - acquisition_controller.pre_start_capture(self) + acquisition_controller.pre_start_capture() start = time.clock() # Keep track of when acquisition started # call the startcapture method self._call_dll('AlazarStartCapture', self._handle) print("Capturing %d buffers." % buffers_per_acquisition) - acquisition_controller.pre_acquire(self) + acquisition_controller.pre_acquire() # buffer handling from acquisition buffers_completed = 0 From 2356bd21d8e884a759c2262c33e5773405a51e4d Mon Sep 17 00:00:00 2001 From: Natalie Pearson Date: Wed, 26 Oct 2016 14:45:38 +0200 Subject: [PATCH 010/328] fix: Add mistakenly deleted ATS example notebook back --- docs/examples/Qcodes example ATS_ONWORK.ipynb | 1658 +++++++++++++++++ 1 file changed, 1658 insertions(+) create mode 100644 docs/examples/Qcodes example ATS_ONWORK.ipynb diff --git a/docs/examples/Qcodes example ATS_ONWORK.ipynb b/docs/examples/Qcodes example ATS_ONWORK.ipynb new file mode 100644 index 000000000000..eac33345b0ae --- /dev/null +++ b/docs/examples/Qcodes example ATS_ONWORK.ipynb @@ -0,0 +1,1658 @@ +{ + "cells": [ + { + "cell_type": "code", + "execution_count": 1, + "metadata": { + "collapsed": false + }, + "outputs": [ + { + "data": { + "application/javascript": [ + "/*\r\n", + " * Qcodes Jupyter/IPython widgets\r\n", + " */\r\n", + "require([\r\n", + " 'nbextensions/widgets/widgets/js/widget',\r\n", + " 'nbextensions/widgets/widgets/js/manager'\r\n", + "], function (widget, manager) {\r\n", + "\r\n", + " var UpdateView = widget.DOMWidgetView.extend({\r\n", + " render: function() {\r\n", + " window.MYWIDGET = this;\r\n", + " this._interval = 0;\r\n", + " this.update();\r\n", + " },\r\n", + " update: function() {\r\n", + " this.display(this.model.get('_message'));\r\n", + " this.setInterval();\r\n", + " },\r\n", + " display: function(message) {\r\n", + " /*\r\n", + " * display method: override this for custom display logic\r\n", + " */\r\n", + " this.el.innerHTML = message;\r\n", + " },\r\n", + " remove: function() {\r\n", + " clearInterval(this._updater);\r\n", + " },\r\n", + " setInterval: function(newInterval) {\r\n", + " var me = this;\r\n", + " if(newInterval===undefined) newInterval = me.model.get('interval');\r\n", + " if(newInterval===me._interval) return;\r\n", + "\r\n", + " me._interval = newInterval;\r\n", + "\r\n", + " if(me._updater) clearInterval(me._updater);\r\n", + "\r\n", + " if(me._interval) {\r\n", + " me._updater = setInterval(function() {\r\n", + " me.send({myupdate: true});\r\n", + " if(!me.model.comm_live) {\r\n", + " console.log('missing comm, canceling widget updates', me);\r\n", + " clearInterval(me._updater);\r\n", + " }\r\n", + " }, me._interval * 1000);\r\n", + " }\r\n", + " }\r\n", + " });\r\n", + " manager.WidgetManager.register_widget_view('UpdateView', UpdateView);\r\n", + "\r\n", + " var HiddenUpdateView = UpdateView.extend({\r\n", + " display: function(message) {\r\n", + " this.$el.hide();\r\n", + " }\r\n", + " });\r\n", + " manager.WidgetManager.register_widget_view('HiddenUpdateView', HiddenUpdateView);\r\n", + "\r\n", + " var SubprocessView = UpdateView.extend({\r\n", + " render: function() {\r\n", + " var me = this;\r\n", + " me._interval = 0;\r\n", + " me._minimize = '';\r\n", + " me._restore = '';\r\n", + "\r\n", + " // max lines of output to show\r\n", + " me.maxOutputLength = 500;\r\n", + "\r\n", + " // in case there is already an outputView present,\r\n", + " // like from before restarting the kernel\r\n", + " $('.qcodes-output-view').not(me.$el).remove();\r\n", + "\r\n", + " me.$el\r\n", + " .addClass('qcodes-output-view')\r\n", + " .attr('qcodes-state', 'docked')\r\n", + " .html(\r\n", + " '
' +\r\n", + " '
' +\r\n", + " '' +\r\n", + " '' +\r\n", + " '' +\r\n", + " '' +\r\n", + " '' +\r\n", + " '' +\r\n", + " '
' +\r\n", + " '
'\r\n",
+       "                );\r\n",
+       "\r\n",
+       "            me.clearButton = me.$el.find('.qcodes-clear-output');\r\n",
+       "            me.minButton = me.$el.find('.qcodes-minimize');\r\n",
+       "            me.outputArea = me.$el.find('pre');\r\n",
+       "            me.subprocessList = me.$el.find('.qcodes-process-list');\r\n",
+       "            me.abortButton = me.$el.find('.qcodes-abort-loop');\r\n",
+       "            me.processLinesButton = me.$el.find('.qcodes-processlines')\r\n",
+       "\r\n",
+       "            me.outputLines = [];\r\n",
+       "\r\n",
+       "            me.clearButton.click(function() {\r\n",
+       "                me.outputArea.html('');\r\n",
+       "                me.clearButton.addClass('disabled');\r\n",
+       "            });\r\n",
+       "\r\n",
+       "            me.abortButton.click(function() {\r\n",
+       "                me.send({abort: true});\r\n",
+       "            });\r\n",
+       "\r\n",
+       "            me.processLinesButton.click(function() {\r\n",
+       "                // toggle multiline process list display\r\n",
+       "                me.subprocessesMultiline = !me.subprocessesMultiline;\r\n",
+       "                me.showSubprocesses();\r\n",
+       "            });\r\n",
+       "\r\n",
+       "            me.$el.find('.js-state').click(function() {\r\n",
+       "                var state = this.className.substr(this.className.indexOf('qcodes'))\r\n",
+       "                        .split('-')[1].split(' ')[0];\r\n",
+       "                me.model.set('_state', state);\r\n",
+       "            });\r\n",
+       "\r\n",
+       "            $(window)\r\n",
+       "                .off('resize.qcodes')\r\n",
+       "                .on('resize.qcodes', function() {me.clipBounds();});\r\n",
+       "\r\n",
+       "            me.update();\r\n",
+       "        },\r\n",
+       "\r\n",
+       "        updateState: function() {\r\n",
+       "            var me = this,\r\n",
+       "                oldState = me.$el.attr('qcodes-state'),\r\n",
+       "                state = me.model.get('_state');\r\n",
+       "\r\n",
+       "            if(state === oldState) return;\r\n",
+       "\r\n",
+       "            setTimeout(function() {\r\n",
+       "                // not sure why I can't pop it out of the widgetarea in render, but it seems that\r\n",
+       "                // some other bit of code resets the parent after render if I do it there.\r\n",
+       "                // To be safe, just do it on every state click.\r\n",
+       "                me.$el.appendTo('body');\r\n",
+       "\r\n",
+       "                if(oldState === 'floated') {\r\n",
+       "                    console.log('here');\r\n",
+       "                    me.$el.draggable('destroy').css({left:'', top: ''});\r\n",
+       "                }\r\n",
+       "\r\n",
+       "                me.$el.attr('qcodes-state', state);\r\n",
+       "\r\n",
+       "                if(state === 'floated') {\r\n",
+       "                    me.$el\r\n",
+       "                        .draggable({stop: function() { me.clipBounds(); }})\r\n",
+       "                        .css({\r\n",
+       "                            left: window.innerWidth - me.$el.width() - 15,\r\n",
+       "                            top: window.innerHeight - me.$el.height() - 10\r\n",
+       "                        });\r\n",
+       "                }\r\n",
+       "\r\n",
+       "                // any previous highlighting is now moot\r\n",
+       "                me.$el.removeClass('qcodes-highlight');\r\n",
+       "            }, 0);\r\n",
+       "\r\n",
+       "        },\r\n",
+       "\r\n",
+       "        clipBounds: function() {\r\n",
+       "            var me = this;\r\n",
+       "            if(me.$el.attr('qcodes-state') === 'floated') {\r\n",
+       "                var bounds = me.$el[0].getBoundingClientRect(),\r\n",
+       "                    minVis = 40,\r\n",
+       "                    maxLeft = window.innerWidth - minVis,\r\n",
+       "                    minLeft = minVis - bounds.width,\r\n",
+       "                    maxTop = window.innerHeight - minVis;\r\n",
+       "\r\n",
+       "                if(bounds.left > maxLeft) me.$el.css('left', maxLeft);\r\n",
+       "                else if(bounds.left < minLeft) me.$el.css('left', minLeft);\r\n",
+       "\r\n",
+       "                if(bounds.top > maxTop) me.$el.css('top', maxTop);\r\n",
+       "                else if(bounds.top < 0) me.$el.css('top', 0);\r\n",
+       "            }\r\n",
+       "        },\r\n",
+       "\r\n",
+       "        display: function(message) {\r\n",
+       "            var me = this;\r\n",
+       "            if(message) {\r\n",
+       "                var initialScroll = me.outputArea.scrollTop();\r\n",
+       "                me.outputArea.scrollTop(me.outputArea.prop('scrollHeight'));\r\n",
+       "                var scrollBottom = me.outputArea.scrollTop();\r\n",
+       "\r\n",
+       "                if(me.$el.attr('qcodes-state') === 'minimized') {\r\n",
+       "                    // if we add text and the box is minimized, highlight the\r\n",
+       "                    // title bar to alert the user that there are new messages.\r\n",
+       "                    // remove then add the class, so we get the animation again\r\n",
+       "                    // if it's already highlighted\r\n",
+       "                    me.$el.removeClass('qcodes-highlight');\r\n",
+       "                    setTimeout(function(){\r\n",
+       "                        me.$el.addClass('qcodes-highlight');\r\n",
+       "                    }, 0);\r\n",
+       "                }\r\n",
+       "\r\n",
+       "                var newLines = message.split('\\n'),\r\n",
+       "                    out = me.outputLines,\r\n",
+       "                    outLen = out.length;\r\n",
+       "                if(outLen) out[outLen - 1] += newLines[0];\r\n",
+       "                else out.push(newLines[0]);\r\n",
+       "\r\n",
+       "                for(var i = 1; i < newLines.length; i++) {\r\n",
+       "                    out.push(newLines[i]);\r\n",
+       "                }\r\n",
+       "\r\n",
+       "                if(out.length > me.maxOutputLength) {\r\n",
+       "                    out.splice(0, out.length - me.maxOutputLength + 1,\r\n",
+       "                        '<<< Output clipped >>>');\r\n",
+       "                }\r\n",
+       "\r\n",
+       "                me.outputArea.text(out.join('\\n'));\r\n",
+       "                me.clearButton.removeClass('disabled');\r\n",
+       "\r\n",
+       "                // if we were scrolled to the bottom initially, make sure\r\n",
+       "                // we stay that way.\r\n",
+       "                me.outputArea.scrollTop(initialScroll === scrollBottom ?\r\n",
+       "                    me.outputArea.prop('scrollHeight') : initialScroll);\r\n",
+       "            }\r\n",
+       "\r\n",
+       "            me.showSubprocesses();\r\n",
+       "            me.updateState();\r\n",
+       "        },\r\n",
+       "\r\n",
+       "        showSubprocesses: function() {\r\n",
+       "            var me = this,\r\n",
+       "                replacer = me.subprocessesMultiline ? '
' : ', ',\r\n",
+       "                processes = (me.model.get('_processes') || '')\r\n",
+       "                    .replace(/\\n/g, '>' + replacer + '<');\r\n",
+       "\r\n",
+       "            if(processes) processes = '<' + processes + '>';\r\n",
+       "            else processes = 'No subprocesses';\r\n",
+       "\r\n",
+       "            me.abortButton.toggleClass('disabled', processes.indexOf('Measurement')===-1);\r\n",
+       "\r\n",
+       "            me.subprocessList.html(processes);\r\n",
+       "        }\r\n",
+       "    });\r\n",
+       "    manager.WidgetManager.register_widget_view('SubprocessView', SubprocessView);\r\n",
+       "});\r\n"
+      ],
+      "text/plain": [
+       ""
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "text/html": [
+       ""
+      ],
+      "text/plain": [
+       ""
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "No loop running\n"
+     ]
+    }
+   ],
+   "source": [
+    "# import all necessary things\n",
+    "%matplotlib nbagg\n",
+    "\n",
+    "import qcodes as qc\n",
+    "import qcodes.instrument.parameter as parameter\n",
+    "import qcodes.instrument_drivers.AlazarTech.ATS9870 as ATSdriver\n",
+    "import qcodes.instrument_drivers.AlazarTech.ATS_acquisition_controllers as ats_contr\n",
+    "\n",
+    "qc.halt_bg()\n",
+    "qc.set_mp_method('spawn')  # force Windows behavior on mac\n",
+    "\n",
+    "# this makes a widget in the corner of the window to show and control\n",
+    "# subprocesses and any output they would print to the terminal\n",
+    "qc.show_subprocess_widget()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 2,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "[{'bits_per_sample': 8,\n",
+       "  'board_id': 1,\n",
+       "  'board_kind': 'ATS9870',\n",
+       "  'max_samples': 4294966272,\n",
+       "  'system_id': 1}]"
+      ]
+     },
+     "execution_count": 2,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "# Command to list all alazar boards connected to the system\n",
+    "ATSdriver.AlazarTech_ATS.find_boards()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 3,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "{'CPLD_version': '13.8',\n",
+       " 'SDK_version': '5.10.15',\n",
+       " 'asopc_type': '2435577968',\n",
+       " 'driver_version': '5.10.15',\n",
+       " 'firmware': None,\n",
+       " 'latest_cal_date': '30-01-13',\n",
+       " 'memory_size': '4294966272',\n",
+       " 'model': 'ATS9870',\n",
+       " 'pcie_link_speed': '0.25GB/s',\n",
+       " 'pcie_link_width': '8',\n",
+       " 'serial': '910266',\n",
+       " 'vendor': 'AlazarTech'}"
+      ]
+     },
+     "execution_count": 3,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "# Create the ATS9870 instrument on the new server \"alazar_server\"\n",
+    "ats_inst = ATSdriver.AlazarTech_ATS9870(name='Alazar1', server_name=\"alazar_server\")\n",
+    "# Print all information about this Alazar card\n",
+    "ats_inst.get_idn()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 4,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [],
+   "source": [
+    "# Instantiate an acquisition controller (In this case we are doing a simple DFT) on the same server (\"alazar_server\") and \n",
+    "# provide the name of the name of the alazar card that this controller should control\n",
+    "acquisition_controller = ats_contr.Demodulation_AcquisitionController(name='acquisition_controller', \n",
+    "                                                                      demodulation_frequency=10e6, \n",
+    "                                                                      alazar_name='Alazar1', \n",
+    "                                                                      server_name=\"alazar_server\")"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 5,
+   "metadata": {
+    "collapsed": true
+   },
+   "outputs": [],
+   "source": [
+    "# Configure all settings in the Alazar card\n",
+    "ats_inst.config(#clock_source='INTERNAL_CLOCK',\n",
+    "                sample_rate=100000000,\n",
+    "                #clock_edge='CLOCK_EDGE_RISING',\n",
+    "                #decimation=0,\n",
+    "                #coupling=['AC','AC'],\n",
+    "                channel_range=[2.,2.],\n",
+    "                #impedance=[50,50],\n",
+    "                #bwlimit=['DISABLED','DISABLED'],\n",
+    "                #trigger_operation='TRIG_ENGINE_OP_J',\n",
+    "                #trigger_engine1='TRIG_ENGINE_J',\n",
+    "                trigger_source1='EXTERNAL',\n",
+    "                #trigger_slope1='TRIG_SLOPE_POSITIVE',\n",
+    "                #trigger_level1=128,\n",
+    "                #trigger_engine2='TRIG_ENGINE_K',\n",
+    "                #trigger_source2='DISABLE',\n",
+    "                #trigger_slope2='TRIG_SLOPE_POSITIVE',\n",
+    "                #trigger_level2=128,\n",
+    "                #external_trigger_coupling='AC',\n",
+    "                #external_trigger_range='ETR_5V',\n",
+    "                #trigger_delay=0,\n",
+    "                #timeout_ticks=0\n",
+    ")"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 6,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [],
+   "source": [
+    "# This command is specific to this acquisition controller. The kwargs provided here are being forwarded to ats_inst.acquire\n",
+    "# This way, it becomes easy to change acquisition specific settings from the ipython notebook\n",
+    "acquisition_controller.update_acquisitionkwargs(#mode='NPT',\n",
+    "                 samples_per_record=1024,\n",
+    "                 records_per_buffer=70,\n",
+    "                 buffers_per_acquisition=1,\n",
+    "                 #channel_selection='AB',\n",
+    "                 #transfer_offset=0,\n",
+    "                 #external_startcapture='ENABLED',\n",
+    "                 #enable_record_headers='DISABLED',\n",
+    "                 #alloc_buffers='DISABLED',\n",
+    "                 #fifo_only_streaming='DISABLED',\n",
+    "                 #interleave_samples='DISABLED',\n",
+    "                 #get_processed_data='DISABLED',\n",
+    "                 allocated_buffers=1,\n",
+    "                 #buffer_timeout=1000\n",
+    ")"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 7,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "0.1205605630942215"
+      ]
+     },
+     "execution_count": 7,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "# Getting the value of the parameter 'acquisition' of the instrument 'acquisition_controller' performes the entire acquisition \n",
+    "# protocol. This again depends on the specific implementation of the acquisition controller\n",
+    "acquisition_controller.acquisition()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 8,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "{'__class__': 'qcodes.instrument_drivers.AlazarTech.ATS9870.AlazarTech_ATS9870',\n",
+       " 'functions': {},\n",
+       " 'name': 'Alazar1',\n",
+       " 'parameters': {'IDN': {'__class__': 'qcodes.instrument.parameter.StandardParameter',\n",
+       "   'instrument': 'qcodes.instrument_drivers.AlazarTech.ATS9870.AlazarTech_ATS9870',\n",
+       "   'instrument_name': 'Alazar1',\n",
+       "   'label': 'IDN',\n",
+       "   'name': 'IDN',\n",
+       "   'ts': '2016-09-21 18:04:30',\n",
+       "   'units': '',\n",
+       "   'value': {'CPLD_version': '13.8',\n",
+       "    'SDK_version': '5.10.15',\n",
+       "    'asopc_type': '2435577968',\n",
+       "    'driver_version': '5.10.15',\n",
+       "    'firmware': None,\n",
+       "    'latest_cal_date': '30-01-13',\n",
+       "    'memory_size': '4294966272',\n",
+       "    'model': 'ATS9870',\n",
+       "    'pcie_link_speed': '0.25GB/s',\n",
+       "    'pcie_link_width': '8',\n",
+       "    'serial': '910266',\n",
+       "    'vendor': 'AlazarTech'}},\n",
+       "  'alloc_buffers': {'__class__': 'qcodes.instrument_drivers.AlazarTech.ATS.AlazarParameter',\n",
+       "   'label': 'Alloc Buffers',\n",
+       "   'name': 'alloc_buffers',\n",
+       "   'ts': '2016-09-21 18:04:30',\n",
+       "   'units': '',\n",
+       "   'value': 'DISABLED'},\n",
+       "  'allocated_buffers': {'__class__': 'qcodes.instrument_drivers.AlazarTech.ATS.AlazarParameter',\n",
+       "   'label': 'Allocated Buffers',\n",
+       "   'name': 'allocated_buffers',\n",
+       "   'ts': '2016-09-21 18:04:30',\n",
+       "   'units': '',\n",
+       "   'value': 1},\n",
+       "  'buffer_timeout': {'__class__': 'qcodes.instrument_drivers.AlazarTech.ATS.AlazarParameter',\n",
+       "   'label': 'Buffer Timeout',\n",
+       "   'name': 'buffer_timeout',\n",
+       "   'ts': '2016-09-21 18:04:30',\n",
+       "   'units': 'ms',\n",
+       "   'value': 1000},\n",
+       "  'buffers_per_acquisition': {'__class__': 'qcodes.instrument_drivers.AlazarTech.ATS.AlazarParameter',\n",
+       "   'label': 'Buffers per Acquisition',\n",
+       "   'name': 'buffers_per_acquisition',\n",
+       "   'ts': '2016-09-21 18:04:30',\n",
+       "   'units': '',\n",
+       "   'value': 1},\n",
+       "  'bwlimit1': {'__class__': 'qcodes.instrument_drivers.AlazarTech.ATS.AlazarParameter',\n",
+       "   'label': 'Bandwidth limit channel 1',\n",
+       "   'name': 'bwlimit1',\n",
+       "   'ts': '2016-09-21 18:04:30',\n",
+       "   'units': '',\n",
+       "   'value': 'DISABLED'},\n",
+       "  'bwlimit2': {'__class__': 'qcodes.instrument_drivers.AlazarTech.ATS.AlazarParameter',\n",
+       "   'label': 'Bandwidth limit channel 2',\n",
+       "   'name': 'bwlimit2',\n",
+       "   'ts': '2016-09-21 18:04:30',\n",
+       "   'units': '',\n",
+       "   'value': 'DISABLED'},\n",
+       "  'channel_range1': {'__class__': 'qcodes.instrument_drivers.AlazarTech.ATS.AlazarParameter',\n",
+       "   'label': 'Range channel 1',\n",
+       "   'name': 'channel_range1',\n",
+       "   'ts': '2016-09-21 18:04:30',\n",
+       "   'units': 'V',\n",
+       "   'value': 2.0},\n",
+       "  'channel_range2': {'__class__': 'qcodes.instrument_drivers.AlazarTech.ATS.AlazarParameter',\n",
+       "   'label': 'Range channel 2',\n",
+       "   'name': 'channel_range2',\n",
+       "   'ts': '2016-09-21 18:04:30',\n",
+       "   'units': 'V',\n",
+       "   'value': 2.0},\n",
+       "  'channel_selection': {'__class__': 'qcodes.instrument_drivers.AlazarTech.ATS.AlazarParameter',\n",
+       "   'label': 'Channel Selection',\n",
+       "   'name': 'channel_selection',\n",
+       "   'ts': '2016-09-21 18:04:30',\n",
+       "   'units': '',\n",
+       "   'value': 'AB'},\n",
+       "  'clock_edge': {'__class__': 'qcodes.instrument_drivers.AlazarTech.ATS.AlazarParameter',\n",
+       "   'label': 'Clock Edge',\n",
+       "   'name': 'clock_edge',\n",
+       "   'ts': '2016-09-21 18:04:30',\n",
+       "   'units': '',\n",
+       "   'value': 'CLOCK_EDGE_RISING'},\n",
+       "  'clock_source': {'__class__': 'qcodes.instrument_drivers.AlazarTech.ATS.AlazarParameter',\n",
+       "   'label': 'Clock Source',\n",
+       "   'name': 'clock_source',\n",
+       "   'ts': '2016-09-21 18:04:30',\n",
+       "   'units': '',\n",
+       "   'value': 'INTERNAL_CLOCK'},\n",
+       "  'coupling1': {'__class__': 'qcodes.instrument_drivers.AlazarTech.ATS.AlazarParameter',\n",
+       "   'label': 'Coupling channel 1',\n",
+       "   'name': 'coupling1',\n",
+       "   'ts': '2016-09-21 18:04:30',\n",
+       "   'units': '',\n",
+       "   'value': 'AC'},\n",
+       "  'coupling2': {'__class__': 'qcodes.instrument_drivers.AlazarTech.ATS.AlazarParameter',\n",
+       "   'label': 'Coupling channel 2',\n",
+       "   'name': 'coupling2',\n",
+       "   'ts': '2016-09-21 18:04:30',\n",
+       "   'units': '',\n",
+       "   'value': 'AC'},\n",
+       "  'decimation': {'__class__': 'qcodes.instrument_drivers.AlazarTech.ATS.AlazarParameter',\n",
+       "   'label': 'Decimation',\n",
+       "   'name': 'decimation',\n",
+       "   'ts': '2016-09-21 18:04:30',\n",
+       "   'units': '',\n",
+       "   'value': 0},\n",
+       "  'enable_record_headers': {'__class__': 'qcodes.instrument_drivers.AlazarTech.ATS.AlazarParameter',\n",
+       "   'label': 'Enable Record Headers',\n",
+       "   'name': 'enable_record_headers',\n",
+       "   'ts': '2016-09-21 18:04:30',\n",
+       "   'units': '',\n",
+       "   'value': 'DISABLED'},\n",
+       "  'external_startcapture': {'__class__': 'qcodes.instrument_drivers.AlazarTech.ATS.AlazarParameter',\n",
+       "   'label': 'External Startcapture',\n",
+       "   'name': 'external_startcapture',\n",
+       "   'ts': '2016-09-21 18:04:30',\n",
+       "   'units': '',\n",
+       "   'value': 'ENABLED'},\n",
+       "  'external_trigger_coupling': {'__class__': 'qcodes.instrument_drivers.AlazarTech.ATS.AlazarParameter',\n",
+       "   'label': 'External Trigger Coupling',\n",
+       "   'name': 'external_trigger_coupling',\n",
+       "   'ts': '2016-09-21 18:04:30',\n",
+       "   'units': '',\n",
+       "   'value': 'AC'},\n",
+       "  'external_trigger_range': {'__class__': 'qcodes.instrument_drivers.AlazarTech.ATS.AlazarParameter',\n",
+       "   'label': 'External Trigger Range',\n",
+       "   'name': 'external_trigger_range',\n",
+       "   'ts': '2016-09-21 18:04:30',\n",
+       "   'units': '',\n",
+       "   'value': 'ETR_5V'},\n",
+       "  'fifo_only_streaming': {'__class__': 'qcodes.instrument_drivers.AlazarTech.ATS.AlazarParameter',\n",
+       "   'label': 'Fifo Only Streaming',\n",
+       "   'name': 'fifo_only_streaming',\n",
+       "   'ts': '2016-09-21 18:04:30',\n",
+       "   'units': '',\n",
+       "   'value': 'DISABLED'},\n",
+       "  'get_processed_data': {'__class__': 'qcodes.instrument_drivers.AlazarTech.ATS.AlazarParameter',\n",
+       "   'label': 'Get Processed Data',\n",
+       "   'name': 'get_processed_data',\n",
+       "   'ts': '2016-09-21 18:04:30',\n",
+       "   'units': '',\n",
+       "   'value': 'DISABLED'},\n",
+       "  'impedance1': {'__class__': 'qcodes.instrument_drivers.AlazarTech.ATS.AlazarParameter',\n",
+       "   'label': 'Impedance channel 1',\n",
+       "   'name': 'impedance1',\n",
+       "   'ts': '2016-09-21 18:04:30',\n",
+       "   'units': 'Ohm',\n",
+       "   'value': 50},\n",
+       "  'impedance2': {'__class__': 'qcodes.instrument_drivers.AlazarTech.ATS.AlazarParameter',\n",
+       "   'label': 'Impedance channel 2',\n",
+       "   'name': 'impedance2',\n",
+       "   'ts': '2016-09-21 18:04:30',\n",
+       "   'units': 'Ohm',\n",
+       "   'value': 50},\n",
+       "  'interleave_samples': {'__class__': 'qcodes.instrument_drivers.AlazarTech.ATS.AlazarParameter',\n",
+       "   'label': 'Interleave Samples',\n",
+       "   'name': 'interleave_samples',\n",
+       "   'ts': '2016-09-21 18:04:30',\n",
+       "   'units': '',\n",
+       "   'value': 'DISABLED'},\n",
+       "  'mode': {'__class__': 'qcodes.instrument_drivers.AlazarTech.ATS.AlazarParameter',\n",
+       "   'label': 'Acquisiton mode',\n",
+       "   'name': 'mode',\n",
+       "   'ts': '2016-09-21 18:04:30',\n",
+       "   'units': '',\n",
+       "   'value': 'NPT'},\n",
+       "  'records_per_buffer': {'__class__': 'qcodes.instrument_drivers.AlazarTech.ATS.AlazarParameter',\n",
+       "   'label': 'Records per Buffer',\n",
+       "   'name': 'records_per_buffer',\n",
+       "   'ts': '2016-09-21 18:04:30',\n",
+       "   'units': '',\n",
+       "   'value': 70},\n",
+       "  'sample_rate': {'__class__': 'qcodes.instrument_drivers.AlazarTech.ATS.AlazarParameter',\n",
+       "   'label': 'Sample Rate',\n",
+       "   'name': 'sample_rate',\n",
+       "   'ts': '2016-09-21 18:04:30',\n",
+       "   'units': 'S/s',\n",
+       "   'value': 100000000},\n",
+       "  'samples_per_record': {'__class__': 'qcodes.instrument_drivers.AlazarTech.ATS.AlazarParameter',\n",
+       "   'label': 'Samples per Record',\n",
+       "   'name': 'samples_per_record',\n",
+       "   'ts': '2016-09-21 18:04:30',\n",
+       "   'units': '',\n",
+       "   'value': 1024},\n",
+       "  'timeout_ticks': {'__class__': 'qcodes.instrument_drivers.AlazarTech.ATS.AlazarParameter',\n",
+       "   'label': 'Timeout Ticks',\n",
+       "   'name': 'timeout_ticks',\n",
+       "   'ts': '2016-09-21 18:04:30',\n",
+       "   'units': '10 us',\n",
+       "   'value': 0},\n",
+       "  'transfer_offset': {'__class__': 'qcodes.instrument_drivers.AlazarTech.ATS.AlazarParameter',\n",
+       "   'label': 'Transer Offset',\n",
+       "   'name': 'transfer_offset',\n",
+       "   'ts': '2016-09-21 18:04:30',\n",
+       "   'units': 'Samples',\n",
+       "   'value': 0},\n",
+       "  'trigger_delay': {'__class__': 'qcodes.instrument_drivers.AlazarTech.ATS.AlazarParameter',\n",
+       "   'label': 'Trigger Delay',\n",
+       "   'name': 'trigger_delay',\n",
+       "   'ts': '2016-09-21 18:04:30',\n",
+       "   'units': 'Sample clock cycles',\n",
+       "   'value': 0},\n",
+       "  'trigger_engine1': {'__class__': 'qcodes.instrument_drivers.AlazarTech.ATS.AlazarParameter',\n",
+       "   'label': 'Trigger Engine 1',\n",
+       "   'name': 'trigger_engine1',\n",
+       "   'ts': '2016-09-21 18:04:30',\n",
+       "   'units': '',\n",
+       "   'value': 'TRIG_ENGINE_K'},\n",
+       "  'trigger_engine2': {'__class__': 'qcodes.instrument_drivers.AlazarTech.ATS.AlazarParameter',\n",
+       "   'label': 'Trigger Engine 2',\n",
+       "   'name': 'trigger_engine2',\n",
+       "   'ts': '2016-09-21 18:04:30',\n",
+       "   'units': '',\n",
+       "   'value': 'TRIG_ENGINE_K'},\n",
+       "  'trigger_level1': {'__class__': 'qcodes.instrument_drivers.AlazarTech.ATS.AlazarParameter',\n",
+       "   'label': 'Trigger Level 1',\n",
+       "   'name': 'trigger_level1',\n",
+       "   'ts': '2016-09-21 18:04:30',\n",
+       "   'units': '',\n",
+       "   'value': 128},\n",
+       "  'trigger_level2': {'__class__': 'qcodes.instrument_drivers.AlazarTech.ATS.AlazarParameter',\n",
+       "   'label': 'Trigger Level 2',\n",
+       "   'name': 'trigger_level2',\n",
+       "   'ts': '2016-09-21 18:04:30',\n",
+       "   'units': '',\n",
+       "   'value': 128},\n",
+       "  'trigger_operation': {'__class__': 'qcodes.instrument_drivers.AlazarTech.ATS.AlazarParameter',\n",
+       "   'label': 'Trigger Operation',\n",
+       "   'name': 'trigger_operation',\n",
+       "   'ts': '2016-09-21 18:04:30',\n",
+       "   'units': '',\n",
+       "   'value': 'TRIG_ENGINE_OP_J'},\n",
+       "  'trigger_slope1': {'__class__': 'qcodes.instrument_drivers.AlazarTech.ATS.AlazarParameter',\n",
+       "   'label': 'Trigger Slope 1',\n",
+       "   'name': 'trigger_slope1',\n",
+       "   'ts': '2016-09-21 18:04:30',\n",
+       "   'units': '',\n",
+       "   'value': 'TRIG_SLOPE_POSITIVE'},\n",
+       "  'trigger_slope2': {'__class__': 'qcodes.instrument_drivers.AlazarTech.ATS.AlazarParameter',\n",
+       "   'label': 'Trigger Slope 2',\n",
+       "   'name': 'trigger_slope2',\n",
+       "   'ts': '2016-09-21 18:04:30',\n",
+       "   'units': '',\n",
+       "   'value': 'TRIG_SLOPE_POSITIVE'},\n",
+       "  'trigger_source1': {'__class__': 'qcodes.instrument_drivers.AlazarTech.ATS.AlazarParameter',\n",
+       "   'label': 'Trigger Source 1',\n",
+       "   'name': 'trigger_source1',\n",
+       "   'ts': '2016-09-21 18:04:30',\n",
+       "   'units': '',\n",
+       "   'value': 'EXTERNAL'},\n",
+       "  'trigger_source2': {'__class__': 'qcodes.instrument_drivers.AlazarTech.ATS.AlazarParameter',\n",
+       "   'label': 'Trigger Source 2',\n",
+       "   'name': 'trigger_source2',\n",
+       "   'ts': '2016-09-21 18:04:30',\n",
+       "   'units': '',\n",
+       "   'value': 'DISABLE'}}}"
+      ]
+     },
+     "execution_count": 8,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "# make a snapshot of the 'ats_inst' instrument\n",
+    "ats_inst.snapshot()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 9,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "DataSet:\n",
+      "   mode     = DataMode.PULL_FROM_SERVER\n",
+      "   location = '2016-09-21/18-04-30_AlazarTest'\n",
+      "      |                          |  | \n",
+      "   Setpoint | dummy_set                          | dummy        | (50,)\n",
+      "   Measured | acquisition_controller_acquisition | acquisition  | (50,)\n",
+      "started at 2016-09-21 18:04:31\n"
+     ]
+    },
+    {
+     "data": {
+      "application/javascript": [
+       "/* Put everything inside the global mpl namespace */\n",
+       "window.mpl = {};\n",
+       "\n",
+       "mpl.get_websocket_type = function() {\n",
+       "    if (typeof(WebSocket) !== 'undefined') {\n",
+       "        return WebSocket;\n",
+       "    } else if (typeof(MozWebSocket) !== 'undefined') {\n",
+       "        return MozWebSocket;\n",
+       "    } else {\n",
+       "        alert('Your browser does not have WebSocket support.' +\n",
+       "              'Please try Chrome, Safari or Firefox ≥ 6. ' +\n",
+       "              'Firefox 4 and 5 are also supported but you ' +\n",
+       "              'have to enable WebSockets in about:config.');\n",
+       "    };\n",
+       "}\n",
+       "\n",
+       "mpl.figure = function(figure_id, websocket, ondownload, parent_element) {\n",
+       "    this.id = figure_id;\n",
+       "\n",
+       "    this.ws = websocket;\n",
+       "\n",
+       "    this.supports_binary = (this.ws.binaryType != undefined);\n",
+       "\n",
+       "    if (!this.supports_binary) {\n",
+       "        var warnings = document.getElementById(\"mpl-warnings\");\n",
+       "        if (warnings) {\n",
+       "            warnings.style.display = 'block';\n",
+       "            warnings.textContent = (\n",
+       "                \"This browser does not support binary websocket messages. \" +\n",
+       "                    \"Performance may be slow.\");\n",
+       "        }\n",
+       "    }\n",
+       "\n",
+       "    this.imageObj = new Image();\n",
+       "\n",
+       "    this.context = undefined;\n",
+       "    this.message = undefined;\n",
+       "    this.canvas = undefined;\n",
+       "    this.rubberband_canvas = undefined;\n",
+       "    this.rubberband_context = undefined;\n",
+       "    this.format_dropdown = undefined;\n",
+       "\n",
+       "    this.image_mode = 'full';\n",
+       "\n",
+       "    this.root = $('
');\n",
+       "    this._root_extra_style(this.root)\n",
+       "    this.root.attr('style', 'display: inline-block');\n",
+       "\n",
+       "    $(parent_element).append(this.root);\n",
+       "\n",
+       "    this._init_header(this);\n",
+       "    this._init_canvas(this);\n",
+       "    this._init_toolbar(this);\n",
+       "\n",
+       "    var fig = this;\n",
+       "\n",
+       "    this.waiting = false;\n",
+       "\n",
+       "    this.ws.onopen =  function () {\n",
+       "            fig.send_message(\"supports_binary\", {value: fig.supports_binary});\n",
+       "            fig.send_message(\"send_image_mode\", {});\n",
+       "            fig.send_message(\"refresh\", {});\n",
+       "        }\n",
+       "\n",
+       "    this.imageObj.onload = function() {\n",
+       "            if (fig.image_mode == 'full') {\n",
+       "                // Full images could contain transparency (where diff images\n",
+       "                // almost always do), so we need to clear the canvas so that\n",
+       "                // there is no ghosting.\n",
+       "                fig.context.clearRect(0, 0, fig.canvas.width, fig.canvas.height);\n",
+       "            }\n",
+       "            fig.context.drawImage(fig.imageObj, 0, 0);\n",
+       "        };\n",
+       "\n",
+       "    this.imageObj.onunload = function() {\n",
+       "        this.ws.close();\n",
+       "    }\n",
+       "\n",
+       "    this.ws.onmessage = this._make_on_message_function(this);\n",
+       "\n",
+       "    this.ondownload = ondownload;\n",
+       "}\n",
+       "\n",
+       "mpl.figure.prototype._init_header = function() {\n",
+       "    var titlebar = $(\n",
+       "        '
');\n",
+       "    var titletext = $(\n",
+       "        '
');\n",
+       "    titlebar.append(titletext)\n",
+       "    this.root.append(titlebar);\n",
+       "    this.header = titletext[0];\n",
+       "}\n",
+       "\n",
+       "\n",
+       "\n",
+       "mpl.figure.prototype._canvas_extra_style = function(canvas_div) {\n",
+       "\n",
+       "}\n",
+       "\n",
+       "\n",
+       "mpl.figure.prototype._root_extra_style = function(canvas_div) {\n",
+       "\n",
+       "}\n",
+       "\n",
+       "mpl.figure.prototype._init_canvas = function() {\n",
+       "    var fig = this;\n",
+       "\n",
+       "    var canvas_div = $('
');\n",
+       "\n",
+       "    canvas_div.attr('style', 'position: relative; clear: both; outline: 0');\n",
+       "\n",
+       "    function canvas_keyboard_event(event) {\n",
+       "        return fig.key_event(event, event['data']);\n",
+       "    }\n",
+       "\n",
+       "    canvas_div.keydown('key_press', canvas_keyboard_event);\n",
+       "    canvas_div.keyup('key_release', canvas_keyboard_event);\n",
+       "    this.canvas_div = canvas_div\n",
+       "    this._canvas_extra_style(canvas_div)\n",
+       "    this.root.append(canvas_div);\n",
+       "\n",
+       "    var canvas = $('');\n",
+       "    canvas.addClass('mpl-canvas');\n",
+       "    canvas.attr('style', \"left: 0; top: 0; z-index: 0; outline: 0\")\n",
+       "\n",
+       "    this.canvas = canvas[0];\n",
+       "    this.context = canvas[0].getContext(\"2d\");\n",
+       "\n",
+       "    var rubberband = $('');\n",
+       "    rubberband.attr('style', \"position: absolute; left: 0; top: 0; z-index: 1;\")\n",
+       "\n",
+       "    var pass_mouse_events = true;\n",
+       "\n",
+       "    canvas_div.resizable({\n",
+       "        start: function(event, ui) {\n",
+       "            pass_mouse_events = false;\n",
+       "        },\n",
+       "        resize: function(event, ui) {\n",
+       "            fig.request_resize(ui.size.width, ui.size.height);\n",
+       "        },\n",
+       "        stop: function(event, ui) {\n",
+       "            pass_mouse_events = true;\n",
+       "            fig.request_resize(ui.size.width, ui.size.height);\n",
+       "        },\n",
+       "    });\n",
+       "\n",
+       "    function mouse_event_fn(event) {\n",
+       "        if (pass_mouse_events)\n",
+       "            return fig.mouse_event(event, event['data']);\n",
+       "    }\n",
+       "\n",
+       "    rubberband.mousedown('button_press', mouse_event_fn);\n",
+       "    rubberband.mouseup('button_release', mouse_event_fn);\n",
+       "    // Throttle sequential mouse events to 1 every 20ms.\n",
+       "    rubberband.mousemove('motion_notify', mouse_event_fn);\n",
+       "\n",
+       "    rubberband.mouseenter('figure_enter', mouse_event_fn);\n",
+       "    rubberband.mouseleave('figure_leave', mouse_event_fn);\n",
+       "\n",
+       "    canvas_div.on(\"wheel\", function (event) {\n",
+       "        event = event.originalEvent;\n",
+       "        event['data'] = 'scroll'\n",
+       "        if (event.deltaY < 0) {\n",
+       "            event.step = 1;\n",
+       "        } else {\n",
+       "            event.step = -1;\n",
+       "        }\n",
+       "        mouse_event_fn(event);\n",
+       "    });\n",
+       "\n",
+       "    canvas_div.append(canvas);\n",
+       "    canvas_div.append(rubberband);\n",
+       "\n",
+       "    this.rubberband = rubberband;\n",
+       "    this.rubberband_canvas = rubberband[0];\n",
+       "    this.rubberband_context = rubberband[0].getContext(\"2d\");\n",
+       "    this.rubberband_context.strokeStyle = \"#000000\";\n",
+       "\n",
+       "    this._resize_canvas = function(width, height) {\n",
+       "        // Keep the size of the canvas, canvas container, and rubber band\n",
+       "        // canvas in synch.\n",
+       "        canvas_div.css('width', width)\n",
+       "        canvas_div.css('height', height)\n",
+       "\n",
+       "        canvas.attr('width', width);\n",
+       "        canvas.attr('height', height);\n",
+       "\n",
+       "        rubberband.attr('width', width);\n",
+       "        rubberband.attr('height', height);\n",
+       "    }\n",
+       "\n",
+       "    // Set the figure to an initial 600x600px, this will subsequently be updated\n",
+       "    // upon first draw.\n",
+       "    this._resize_canvas(600, 600);\n",
+       "\n",
+       "    // Disable right mouse context menu.\n",
+       "    $(this.rubberband_canvas).bind(\"contextmenu\",function(e){\n",
+       "        return false;\n",
+       "    });\n",
+       "\n",
+       "    function set_focus () {\n",
+       "        canvas.focus();\n",
+       "        canvas_div.focus();\n",
+       "    }\n",
+       "\n",
+       "    window.setTimeout(set_focus, 100);\n",
+       "}\n",
+       "\n",
+       "mpl.figure.prototype._init_toolbar = function() {\n",
+       "    var fig = this;\n",
+       "\n",
+       "    var nav_element = $('
')\n",
+       "    nav_element.attr('style', 'width: 100%');\n",
+       "    this.root.append(nav_element);\n",
+       "\n",
+       "    // Define a callback function for later on.\n",
+       "    function toolbar_event(event) {\n",
+       "        return fig.toolbar_button_onclick(event['data']);\n",
+       "    }\n",
+       "    function toolbar_mouse_event(event) {\n",
+       "        return fig.toolbar_button_onmouseover(event['data']);\n",
+       "    }\n",
+       "\n",
+       "    for(var toolbar_ind in mpl.toolbar_items) {\n",
+       "        var name = mpl.toolbar_items[toolbar_ind][0];\n",
+       "        var tooltip = mpl.toolbar_items[toolbar_ind][1];\n",
+       "        var image = mpl.toolbar_items[toolbar_ind][2];\n",
+       "        var method_name = mpl.toolbar_items[toolbar_ind][3];\n",
+       "\n",
+       "        if (!name) {\n",
+       "            // put a spacer in here.\n",
+       "            continue;\n",
+       "        }\n",
+       "        var button = $('');\n",
+       "        button.click(method_name, toolbar_event);\n",
+       "        button.mouseover(tooltip, toolbar_mouse_event);\n",
+       "        nav_element.append(button);\n",
+       "    }\n",
+       "\n",
+       "    // Add the status bar.\n",
+       "    var status_bar = $('');\n",
+       "    nav_element.append(status_bar);\n",
+       "    this.message = status_bar[0];\n",
+       "\n",
+       "    // Add the close button to the window.\n",
+       "    var buttongrp = $('
');\n",
+       "    var button = $('');\n",
+       "    button.click(function (evt) { fig.handle_close(fig, {}); } );\n",
+       "    button.mouseover('Close figure', toolbar_mouse_event);\n",
+       "    buttongrp.append(button);\n",
+       "    var titlebar = this.root.find($('.ui-dialog-titlebar'));\n",
+       "    titlebar.prepend(buttongrp);\n",
+       "}\n",
+       "\n",
+       "\n",
+       "mpl.figure.prototype._canvas_extra_style = function(el){\n",
+       "    // this is important to make the div 'focusable\n",
+       "    el.attr('tabindex', 0)\n",
+       "    // reach out to IPython and tell the keyboard manager to turn it's self\n",
+       "    // off when our div gets focus\n",
+       "\n",
+       "    // location in version 3\n",
+       "    if (IPython.notebook.keyboard_manager) {\n",
+       "        IPython.notebook.keyboard_manager.register_events(el);\n",
+       "    }\n",
+       "    else {\n",
+       "        // location in version 2\n",
+       "        IPython.keyboard_manager.register_events(el);\n",
+       "    }\n",
+       "\n",
+       "}\n",
+       "\n",
+       "mpl.figure.prototype._key_event_extra = function(event, name) {\n",
+       "    var manager = IPython.notebook.keyboard_manager;\n",
+       "    if (!manager)\n",
+       "        manager = IPython.keyboard_manager;\n",
+       "\n",
+       "    // Check for shift+enter\n",
+       "    if (event.shiftKey && event.which == 13) {\n",
+       "        this.canvas_div.blur();\n",
+       "        event.shiftKey = false;\n",
+       "        // Send a \"J\" for go to next cell\n",
+       "        event.which = 74;\n",
+       "        event.keyCode = 74;\n",
+       "        manager.command_mode();\n",
+       "        manager.handle_keydown(event);\n",
+       "    }\n",
+       "}\n",
+       "\n",
+       "mpl.figure.prototype.handle_save = function(fig, msg) {\n",
+       "    fig.ondownload(fig, null);\n",
+       "}\n",
+       "\n",
+       "\n",
+       "mpl.find_output_cell = function(html_output) {\n",
+       "    // Return the cell and output element which can be found *uniquely* in the notebook.\n",
+       "    // Note - this is a bit hacky, but it is done because the \"notebook_saving.Notebook\"\n",
+       "    // IPython event is triggered only after the cells have been serialised, which for\n",
+       "    // our purposes (turning an active figure into a static one), is too late.\n",
+       "    var cells = IPython.notebook.get_cells();\n",
+       "    var ncells = cells.length;\n",
+       "    for (var i=0; i= 3 moved mimebundle to data attribute of output\n",
+       "                    data = data.data;\n",
+       "                }\n",
+       "                if (data['text/html'] == html_output) {\n",
+       "                    return [cell, data, j];\n",
+       "                }\n",
+       "            }\n",
+       "        }\n",
+       "    }\n",
+       "}\n",
+       "\n",
+       "// Register the function which deals with the matplotlib target/channel.\n",
+       "// The kernel may be null if the page has been refreshed.\n",
+       "if (IPython.notebook.kernel != null) {\n",
+       "    IPython.notebook.kernel.comm_manager.register_target('matplotlib', mpl.mpl_figure_comm);\n",
+       "}\n"
+      ],
+      "text/plain": [
+       ""
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "text/html": [
+       ""
+      ],
+      "text/plain": [
+       ""
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "text/plain": [
+       ""
+      ]
+     },
+     "execution_count": 9,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "# Finally show that this instrument also works within a loop\n",
+    "dummy = parameter.ManualParameter(name=\"dummy\")\n",
+    "data = qc.Loop(dummy[0:50:1]).each(acquisition_controller.acquisition).run(name='AlazarTest')\n",
+    "qc.MatPlot(data.acquisition_controller_acquisition)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {
+    "collapsed": true
+   },
+   "outputs": [],
+   "source": [
+    "data"
+   ]
+  }
+ ],
+ "metadata": {
+  "anaconda-cloud": {},
+  "kernelspec": {
+   "display_name": "Python 3",
+   "language": "python",
+   "name": "python3"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython3",
+   "version": "3.5.1"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 0
+}
diff --git a/qcodes/instrument_drivers/AlazarTech/HD_filter_controller.py b/qcodes/instrument_drivers/AlazarTech/HD_filter_controller.py
new file mode 100644
index 000000000000..6e31d604a508
--- /dev/null
+++ b/qcodes/instrument_drivers/AlazarTech/HD_filter_controller.py
@@ -0,0 +1,84 @@
+class HD_Controller(AcquisitionController):
+    """Heterodyne Measurement Controller
+    Does averaged DFT on 2 channel Alazar measurement
+
+    TODO(nataliejpg) handling of channel number
+    TODO(nataliejpg) test angle data
+    """
+
+    def __init__(self, freq_dif):
+        self.freq_dif = freq_dif
+        self.samples_per_record = None
+        self.records_per_buffer = None
+        self.buffers_per_acquisition = None
+        self.number_of_channels = 2
+        self.buffer = None
+
+    def pre_start_capture(self, alazar):
+        """Get config data from alazar card and set up DFT"""
+        self.samples_per_record = alazar.samples_per_record()
+        self.records_per_buffer = alazar.records_per_buffer()
+        self.buffers_per_acquisition = alazar.buffers_per_acquisition()
+        self.sample_rate = alazar.get_sample_speed()
+        self.buffer = np.zeros(self.samples_per_record *
+                               self.records_per_buffer *
+                               self.number_of_channels)
+        # TODO(nataliejpg) leave explicit or save lines? add error/logging?
+        averaging = self.buffers_per_acquisition * self.records_per_buffer
+        record_duration = self.samples_per_record / self.sample_rate
+        time_period_dif = 1 / self.freq_dif
+        cycles_measured = record_duration / time_period_dif
+        oversampling_rate = self.sample_rate / (2 * self.freq_dif)
+        print("Average over {} records".format(averaging))
+        print("Oscillations per record: {} (expect 100+)"
+              .format(cycles_measured))
+        print("Oversampling rate: {} (expect > 2)".format(oversampling_rate))
+
+        integer_list = np.arange(self.samples_per_record)
+        angle_list = (2 * np.pi * self.freq_dif / self.sample_rate *
+                      integer_list)
+        self.cos_list = np.cos(angle_list)
+        self.sin_list = np.sin(angle_list)
+
+    def pre_acquire(self, alazar):
+        # gets called after 'AlazarStartCapture'
+        pass
+
+    def handle_buffer(self, alazar, data):
+        self.buffer += data
+
+    def post_acquire(self, alazar):
+        """Average over records in buffer and do DFT:
+        assumes samples are arranged in the buffer as follows:
+        S0A, S0B, ..., S1A, S1B, ...
+        with SXY the sample number X of channel Y.
+        """
+        records_per_acquisition = (1. * self.buffers_per_acquisition *
+                                   self.records_per_buffer)
+        recordA = np.zeros(self.samples_per_record)
+        for i in range(self.records_per_buffer):
+            i0 = (i * self.samples_per_record * self.number_of_channels)
+            i1 = (i0 + self.samples_per_record * self.number_of_channels)
+            recordA += (self.buffer[i0:i1:self.number_of_channels] /
+                        records_per_acquisition)
+        recordB = np.zeros(self.samples_per_record)
+        for i in range(self.records_per_buffer):
+            i0 = (i * self.samples_per_record * self.number_of_channels + 1)
+            i1 = (i0 + self.samples_per_record * self.number_of_channels)
+            recordB += (self.buffer[i0:i1:self.number_of_channels] /
+                        records_per_acquisition)
+
+        resA = self.fit(recordA)
+        resB = self.fit(recordB)
+
+        return resA, resB
+
+    def fit(self, rec):
+        """Do Discrete Fourier Transform and return magnitude and phase data"""
+        RePart = np.dot(rec, self.cos_list) / self.samples_per_record
+        ImPart = np.dot(rec, self.sin_list) / self.samples_per_record
+        # factor of 2 as amplitude is split between finite term
+        # and double frequency term
+        ampl = 2 * np.sqrt(RePart ** 2 + ImPart ** 2)
+        phase = math.atan2(ImPart, RePart) * 180 / (2 * math.pi)
+        return [ampl, phase]
\ No newline at end of file
diff --git a/qcodes/instrument_drivers/AlazarTech/basic_controller.py b/qcodes/instrument_drivers/AlazarTech/basic_controller.py
new file mode 100644
index 000000000000..da79626b162f
--- /dev/null
+++ b/qcodes/instrument_drivers/AlazarTech/basic_controller.py
@@ -0,0 +1,118 @@
+class Basic_AcquisitionController(AcquisitionController):
+    """
+    This class represents an acquisition controller. It is designed to be used 
+    primarily to check the function of the Alazar driver to do what it should and should be used 
+    with one buffer and one record and will return each of the samples unprocessed
+
+    args:
+    name: name for this acquisition_conroller as an instrument
+    alazar_name: the name of the alazar instrument such that this controller
+        can communicate with the Alazar
+    **kwargs: kwargs are forwarded to the Instrument base class
+    """
+    def __init__(self, name, alazar_name, **kwargs):
+        self.acquisitionkwargs = {}
+        self.samples_per_record = None
+        self.bits_per_sample = None
+        self.records_per_buffer = None
+        self.buffers_per_acquisition = None
+        self.allocated_buffers = None
+        # TODO(damazter) (S) this is not very general:
+        self.number_of_channels = 2
+        self.buffer = None
+        # make a call to the parent class and by extension, create the parameter
+        # structure of this class
+        super().__init__(name, alazar_name, **kwargs)
+        self.add_parameter("acquisition", get_cmd=self.do_acquisition)
+
+    def update_acquisitionkwargs(self, **kwargs):
+        """
+        This method must be used to update the kwargs used for the acquisition
+        with the alazar_driver.acquire
+        :param kwargs:
+        :return:
+        """
+        self.acquisitionkwargs.update(**kwargs)
+
+    def do_acquisition(self):
+        """
+        this method performs an acquisition, which is the get_cmd for the
+        acquisiion parameter of this instrument
+        :return:
+        """
+        value = self._get_alazar().acquire(acquisition_controller=self,
+                                           **self.acquisitionkwargs)
+        return value
+
+    def pre_start_capture(self):
+        """
+        See AcquisitionController
+        :return:
+        """
+
+    def pre_acquire(self):
+        """
+        See AcquisitionController
+        :return:
+        """
+        # this could be used to start an Arbitrary Waveform Generator, etc...
+        # using this method ensures that the contents are executed AFTER the
+        # Alazar card starts listening for a trigger pulse
+        pass
+
+    def handle_buffer(self, data):
+        """
+        See AcquisitionController
+        :return:
+        """
+        self.buffer += data
+
+    def post_acquire(self):
+        """
+        See AcquisitionController
+        :return:
+        """
+        alazar = self._get_alazar()
+        # average all records in a buffer
+        records_per_acquisition = (1. * self.buffers_per_acquisition *
+                                   self.records_per_buffer)
+        if records_per_acquisition != 1:
+            raise ValueError('records per acquisition and buffers per acquisition should be 
+                set to 1 for this acquisition controller')
+        
+        # one of these two...
+        # expects S00A, S01A, ...S0B, S1B,...
+        recordA = np.zeros(self.samples_per_record)
+        for i in range(self.records_per_buffer):    
+            i0 = i * self.samples_per_record
+            i1 = i0 + self.samples_per_record
+            recordA += self.buffer[i0:i1] / records_per_acquisition
+
+        recordB = np.zeros(self.samples_per_record)
+        for i in range(self.records_per_buffer):
+            i0 = i * self.samples_per_record + len(self.buffer) // 2
+            i1 = i0 + self.samples_per_record
+            recordB += self.buffer[i0:i1] / records_per_acquisition
+            
+        # 1
+        # average over records in buffer:
+        # for ATS9360 samples are arranged in the buffer as follows:
+        # S00A, S00B, S01A, S01B ..., S10A, S10B, ...
+        # where SXYZ is record X, sample Y, channel Z.
+        records_per_acquisition = (self.buffers_per_acquisition *
+                                   self.records_per_buffer)
+        recordA = np.zeros(self.samples_per_record)
+        for i in range(self.records_per_buffer):
+            i0 = (i * self.samples_per_record * self.number_of_channels)
+            i1 = (i0 + self.samples_per_record * self.number_of_channels)
+            recordA += (self.buffer[i0:i1:self.number_of_channels] /
+                        records_per_acquisition)
+
+        recordB = np.zeros(self.samples_per_record)
+        for i in range(self.records_per_buffer):
+            i0 = (i * self.samples_per_record * self.number_of_channels + 1)
+            i1 = (i0 + self.samples_per_record * self.number_of_channels)
+            recordB += (self.buffer[i0:i1:self.number_of_channels] /
+                        records_per_acquisition)
+        
+        return recordA, recordB

From a04a377ca84f73b859c83d217fb4f74e324017ca Mon Sep 17 00:00:00 2001
From: Natalie Pearson 
Date: Tue, 1 Nov 2016 15:06:31 +0100
Subject: [PATCH 014/328] basic controller done, starting on hd controllers

---
 .../Qcodes example with Alazar ATS9360.ipynb  |   6 +-
 .../instrument_drivers/AlazarTech/ATS9360.py  |   6 +-
 .../AlazarTech/HD_filter_controller.py        | 173 ++++++++++++------
 .../AlazarTech/basic_controller.py            |  34 ++--
 .../AlazarTech/single_shot_controller.py      |  90 +++++++++
 5 files changed, 236 insertions(+), 73 deletions(-)
 create mode 100644 qcodes/instrument_drivers/AlazarTech/single_shot_controller.py

diff --git a/docs/examples/Qcodes example with Alazar ATS9360.ipynb b/docs/examples/Qcodes example with Alazar ATS9360.ipynb
index 4f025b9e62bf..d5dec7e0566d 100644
--- a/docs/examples/Qcodes example with Alazar ATS9360.ipynb	
+++ b/docs/examples/Qcodes example with Alazar ATS9360.ipynb	
@@ -1657,7 +1657,11 @@
    "name": "python",
    "nbconvert_exporter": "python",
    "pygments_lexer": "ipython3",
-   "version": "3.5.1"
+   "version": "3.5.2"
+  },
+  "widgets": {
+   "state": {},
+   "version": "1.0.0"
   }
  },
  "nbformat": 4,
diff --git a/qcodes/instrument_drivers/AlazarTech/ATS9360.py b/qcodes/instrument_drivers/AlazarTech/ATS9360.py
index dbb3e6e2929d..4a5d195cf8ca 100644
--- a/qcodes/instrument_drivers/AlazarTech/ATS9360.py
+++ b/qcodes/instrument_drivers/AlazarTech/ATS9360.py
@@ -168,10 +168,10 @@ def __init__(self, name, **kwargs):
         # I could not find this in the documentation but somehow I have the
         # feeling it still should be a multiple of something
         # NOTE by ramiro: At least in previous python implementations(PycQED delft),
-        # this is an artifact for compatibility with AWG sequencing, 
+        # this is an artifact for compatibility with AWG sequencing,
         # not particular to any ATS architecture.
-        #   ==> this is a construction imposed by the memory strategy 
-        # implemented on the python driver 
+        #   ==> this is a construction imposed by the memory strategy
+        # implemented on the python driver
         # we are writing, not limited by any actual ATS feature.
 
         self.add_parameter(name='records_per_buffer',
diff --git a/qcodes/instrument_drivers/AlazarTech/HD_filter_controller.py b/qcodes/instrument_drivers/AlazarTech/HD_filter_controller.py
index 6e31d604a508..67923c006ffd 100644
--- a/qcodes/instrument_drivers/AlazarTech/HD_filter_controller.py
+++ b/qcodes/instrument_drivers/AlazarTech/HD_filter_controller.py
@@ -1,84 +1,143 @@
-class HD_Controller(AcquisitionController):
-    """Heterodyne Measurement Controller
-    Does averaged DFT on 2 channel Alazar measurement
+from .ATS import AcquisitionController
+import math
+import numpy as np
 
-    TODO(nataliejpg) handling of channel number
-    TODO(nataliejpg) test angle data
+# seq mode 1, many shots
+class HD_Filter_Acquisition_Controller(AcquisitionController):
+    """Heterodyne Measurement Controller
+    Mixes signal with demodulation frequency and then uses selected filter
+    to filter double frequency componenet and return average
     """
-
-    def __init__(self, freq_dif):
-        self.freq_dif = freq_dif
+    def __init__(self, name, alazar_name, demodulation_frequency, **kwargs):
+        self.demodulation_frequency = demodulation_frequency
+        self.acquisitionkwargs = {}
         self.samples_per_record = None
+        self.bits_per_sample = None
         self.records_per_buffer = None
         self.buffers_per_acquisition = None
+        self.allocated_buffers = None
+        # TODO(damazter) (S) this is not very general:
         self.number_of_channels = 2
+        self.cos_list = None
+        self.sin_list = None
         self.buffer = None
+        self.buffer_count = None
+        # make a call to the parent class and by extension, create the parameter
+        # structure of this class
+        super().__init__(name, alazar_name, **kwargs)
+        self.add_parameter("acquisition", get_cmd=self.do_acquisition)
 
-    def pre_start_capture(self, alazar):
-        """Get config data from alazar card and set up DFT"""
-        self.samples_per_record = alazar.samples_per_record()
-        self.records_per_buffer = alazar.records_per_buffer()
-        self.buffers_per_acquisition = alazar.buffers_per_acquisition()
-        self.sample_rate = alazar.get_sample_speed()
-        self.buffer = np.zeros(self.samples_per_record *
-                               self.records_per_buffer *
-                               self.number_of_channels)
-        # TODO(nataliejpg) leave explicit or save lines? add error/logging?
-        averaging = self.buffers_per_acquisition * self.records_per_buffer
-        record_duration = self.samples_per_record / self.sample_rate
-        time_period_dif = 1 / self.freq_dif
-        cycles_measured = record_duration / time_period_dif
-        oversampling_rate = self.sample_rate / (2 * self.freq_dif)
-        print("Average over {} records".format(averaging))
-        print("Oscillations per record: {} (expect 100+)"
-              .format(cycles_measured))
-        print("Oversampling rate: {} (expect > 2)".format(oversampling_rate))
+    def update_acquisitionkwargs(self, **kwargs):
+        """
+        This method must be used to update the kwargs used for the acquisition
+        with the alazar_driver.acquire
+        :param kwargs:
+        :return:
+        """
+        self.acquisitionkwargs.update(**kwargs)
+
+    def do_acquisition(self):
+        """
+        this method performs an acquisition, which is the get_cmd for the
+        acquisiion parameter of this instrument
+        :return:
+        """
+        value = self._get_alazar().acquire(acquisition_controller=self,
+                                           **self.acquisitionkwargs)
+        return value
 
+    def pre_start_capture(self):
+        """
+        See AcquisitionController
+        :return:
+        """
+        alazar = self._get_alazar()
+        self.samples_per_record = alazar.samples_per_record.get()
+        self.records_per_buffer = alazar.records_per_buffer.get()
+        self.buffers_per_acquisition = alazar.buffers_per_acquisition.get()
+        self.buffer_count = 0
+        sample_speed = alazar.get_sample_rate()
         integer_list = np.arange(self.samples_per_record)
-        angle_list = (2 * np.pi * self.freq_dif / self.sample_rate *
+        angle_list = (2 * np.pi * self.demodulation_frequency / sample_speed *
                       integer_list)
+
         self.cos_list = np.cos(angle_list)
         self.sin_list = np.sin(angle_list)
+        self.buffer = np.zeros(self.samples_per_record *
+                               self.records_per_buffer *
+                               self.number_of_channels *
+                               self.buffers_per_acquisition)
 
-    def pre_acquire(self, alazar):
-        # gets called after 'AlazarStartCapture'
+    def pre_acquire(self):
+        """
+        See AcquisitionController
+        :return:
+        """
+        # this could be used to start an Arbitrary Waveform Generator, etc...
+        # using this method ensures that the contents are executed AFTER the
+        # Alazar card starts listening for a trigger pulse
         pass
 
-    def handle_buffer(self, alazar, data):
-        self.buffer += data
+    def handle_buffer(self, data):
+        """
+        See AcquisitionController
+        :return:
+        """
+        buffer_length = (self.samples_per_record *
+                         self.records_per_buffer *
+                         self.number_of_channels)
+        i0 = self.buffer_count * buffer_length
+        i1 = i0 + buffer_length
+        self.buffer[i0:i1] = data
+        self.buffer_count += 1
 
-    def post_acquire(self, alazar):
-        """Average over records in buffer and do DFT:
-        assumes samples are arranged in the buffer as follows:
-        S0A, S0B, ..., S1A, S1B, ...
-        with SXY the sample number X of channel Y.
+    def post_acquire(self):
+        """
+        See AcquisitionController
+        :return:
         """
+        alazar = self._get_alazar()
+        # average all records in a buffer
         records_per_acquisition = (1. * self.buffers_per_acquisition *
                                    self.records_per_buffer)
-        recordA = np.zeros(self.samples_per_record)
+        recordA = np.zeros((self.samples_per_record,self.records_per_buffer))
         for i in range(self.records_per_buffer):
-            i0 = (i * self.samples_per_record * self.number_of_channels)
-            i1 = (i0 + self.samples_per_record * self.number_of_channels)
-            recordA += (self.buffer[i0:i1:self.number_of_channels] /
-                        records_per_acquisition)
+            i0 = i * self.samples_per_record
+            i1 = i0 + self.samples_per_record
+            recordA[] = self.buffer[i0:i1] / records_per_acquisition
+
         recordB = np.zeros(self.samples_per_record)
         for i in range(self.records_per_buffer):
-            i0 = (i * self.samples_per_record * self.number_of_channels + 1)
-            i1 = (i0 + self.samples_per_record * self.number_of_channels)
-            recordB += (self.buffer[i0:i1:self.number_of_channels] /
-                        records_per_acquisition)
+            i0 = i * self.samples_per_record + len(self.buffer) // 2
+            i1 = i0 + self.samples_per_record
+            recordB += self.buffer[i0:i1] / records_per_acquisition
 
-        resA = self.fit(recordA)
-        resB = self.fit(recordB)
+        if self.number_of_channels == 2:
+            # fit channel A and channel B
+            res1 = self.fit(recordA)
+            res2 = self.fit(recordB)
+            # return [alazar.signal_to_volt(1, res1[0] + 127.5),
+            #        alazar.signal_to_volt(2, res2[0] + 127.5),
+            #        res1[1], res2[1],
+            #        (res1[1] - res2[1]) % 360]
+            return alazar.signal_to_volt(1, res1[0] + 127.5)
+        else:
+            raise Exception("Could not find CHANNEL_B during data extraction")
+        return None
 
-        return resA, resB
+    def fit(self, buf):
+        """
+        the DFT is implemented in this method
+        :param buf: buffer to perform the transform on
+        :return: return amplitude and phase of the resulted transform
+        """
+        # Discrete Fourier Transform
+        RePart = np.dot(buf - 127.5, self.cos_list) / self.samples_per_record
+        ImPart = np.dot(buf - 127.5, self.sin_list) / self.samples_per_record
 
-    def fit(self, rec):
-        """Do Discrete Fourier Transform and return magnitude and phase data"""
-        RePart = np.dot(rec, self.cos_list) / self.samples_per_record
-        ImPart = np.dot(rec, self.sin_list) / self.samples_per_record
-        # factor of 2 as amplitude is split between finite term
-        # and double frequency term
+        # the factor of 2 in the amplitude is due to the fact that there is
+        # a negative frequency as well
         ampl = 2 * np.sqrt(RePart ** 2 + ImPart ** 2)
-        phase = math.atan2(ImPart, RePart) * 180 / (2 * math.pi)
-        return [ampl, phase]
\ No newline at end of file
+
+        # see manual page 52!!! (using unsigned data)
+        return [ampl, math.atan2(ImPart, RePart) * 360 / (2 * math.pi)]
\ No newline at end of file
diff --git a/qcodes/instrument_drivers/AlazarTech/basic_controller.py b/qcodes/instrument_drivers/AlazarTech/basic_controller.py
index da79626b162f..580e6d73eb1b 100644
--- a/qcodes/instrument_drivers/AlazarTech/basic_controller.py
+++ b/qcodes/instrument_drivers/AlazarTech/basic_controller.py
@@ -1,8 +1,12 @@
-class Basic_AcquisitionController(AcquisitionController):
+from .ATS import AcquisitionController
+import numpy as np
+
+
+class Basic_Acquisition_Controller(AcquisitionController):
     """
-    This class represents an acquisition controller. It is designed to be used 
-    primarily to check the function of the Alazar driver to do what it should and should be used 
-    with one buffer and one record and will return each of the samples unprocessed
+    This class represents an acquisition controller. It is designed to be used
+    primarily to check the function of the Alazar driver and should be used
+    with one buffer and one record to return each of the samples unprocessed
 
     args:
     name: name for this acquisition_conroller as an instrument
@@ -10,13 +14,14 @@ class Basic_AcquisitionController(AcquisitionController):
         can communicate with the Alazar
     **kwargs: kwargs are forwarded to the Instrument base class
     """
+
     def __init__(self, name, alazar_name, **kwargs):
         self.acquisitionkwargs = {}
         self.samples_per_record = None
         self.bits_per_sample = None
         self.records_per_buffer = None
         self.buffers_per_acquisition = None
-        self.allocated_buffers = None
+        self.allocated_buffers = None # needed??
         # TODO(damazter) (S) this is not very general:
         self.number_of_channels = 2
         self.buffer = None
@@ -49,6 +54,13 @@ def pre_start_capture(self):
         See AcquisitionController
         :return:
         """
+        alazar = self._get_alazar()
+        self.samples_per_record = alazar.samples_per_record.get()
+        self.records_per_buffer = alazar.records_per_buffer.get()
+        self.buffers_per_acquisition = alazar.buffers_per_acquisition.get()
+        self.buffer = np.zeros(self.samples_per_record *
+                       self.records_per_buffer *
+                       self.number_of_channels)
 
     def pre_acquire(self):
         """
@@ -72,18 +84,16 @@ def post_acquire(self):
         See AcquisitionController
         :return:
         """
-        alazar = self._get_alazar()
         # average all records in a buffer
         records_per_acquisition = (1. * self.buffers_per_acquisition *
                                    self.records_per_buffer)
         if records_per_acquisition != 1:
-            raise ValueError('records per acquisition and buffers per acquisition should be 
-                set to 1 for this acquisition controller')
-        
+            raise ValueError(
+                'records per acquisition and buffers per acquisition should be set to 1 for this acquisition controller')
         # one of these two...
         # expects S00A, S01A, ...S0B, S1B,...
         recordA = np.zeros(self.samples_per_record)
-        for i in range(self.records_per_buffer):    
+        for i in range(self.records_per_buffer):
             i0 = i * self.samples_per_record
             i1 = i0 + self.samples_per_record
             recordA += self.buffer[i0:i1] / records_per_acquisition
@@ -93,7 +103,7 @@ def post_acquire(self):
             i0 = i * self.samples_per_record + len(self.buffer) // 2
             i1 = i0 + self.samples_per_record
             recordB += self.buffer[i0:i1] / records_per_acquisition
-            
+
         # 1
         # average over records in buffer:
         # for ATS9360 samples are arranged in the buffer as follows:
@@ -114,5 +124,5 @@ def post_acquire(self):
             i1 = (i0 + self.samples_per_record * self.number_of_channels)
             recordB += (self.buffer[i0:i1:self.number_of_channels] /
                         records_per_acquisition)
-        
+
         return recordA, recordB
diff --git a/qcodes/instrument_drivers/AlazarTech/single_shot_controller.py b/qcodes/instrument_drivers/AlazarTech/single_shot_controller.py
new file mode 100644
index 000000000000..cad31b79bf4e
--- /dev/null
+++ b/qcodes/instrument_drivers/AlazarTech/single_shot_controller.py
@@ -0,0 +1,90 @@
+from .ATS import AcquisitionController
+import math
+import numpy as np
+
+
+class Single_Shot_Acquisition_Controller(AcquisitionController):
+    """Heterodyne Measurement Controller
+    Mixes signal with demodulation frequency and then uses selected filter to filter
+    double frequency componenet
+
+    TODO(nataliejpg) handling of channel number
+    TODO(nataliejpg) test angle data
+    """
+
+    def __init__(self, freq_dif):
+        self.freq_dif = freq_dif
+        self.samples_per_record = None
+        self.records_per_buffer = None
+        self.buffers_per_acquisition = None
+        self.number_of_channels = 2
+        self.buffer = None
+
+    def pre_start_capture(self, alazar):
+        """Get config data from alazar card and set up DFT"""
+        self.samples_per_record = alazar.samples_per_record()
+        self.records_per_buffer = alazar.records_per_buffer()
+        self.buffers_per_acquisition = alazar.buffers_per_acquisition()
+        self.sample_rate = alazar.get_sample_speed()
+        self.buffer = np.zeros(self.samples_per_record *
+                               self.records_per_buffer *
+                               self.number_of_channels)
+        # TODO(nataliejpg) leave explicit or save lines? add error/logging?
+        averaging = self.buffers_per_acquisition * self.records_per_buffer
+        record_duration = self.samples_per_record / self.sample_rate
+        time_period_dif = 1 / self.freq_dif
+        cycles_measured = record_duration / time_period_dif
+        oversampling_rate = self.sample_rate / (2 * self.freq_dif)
+        print("Average over {} records".format(averaging))
+        print("Oscillations per record: {} (expect 100+)"
+              .format(cycles_measured))
+        print("Oversampling rate: {} (expect > 2)".format(oversampling_rate))
+
+        integer_list = np.arange(self.samples_per_record)
+        angle_list = (2 * np.pi * self.freq_dif / self.sample_rate *
+                      integer_list)
+        self.cos_list = np.cos(angle_list)
+        self.sin_list = np.sin(angle_list)
+
+    def pre_acquire(self, alazar):
+        # gets called after 'AlazarStartCapture'
+        pass
+
+    def handle_buffer(self, alazar, data):
+        self.buffer += data
+
+    def post_acquire(self, alazar):
+        """Average over records in buffer and do DFT:
+        assumes samples are arranged in the buffer as follows:
+        S0A, S0B, ..., S1A, S1B, ...
+        with SXY the sample number X of channel Y.
+        """
+        records_per_acquisition = (1. * self.buffers_per_acquisition *
+                                   self.records_per_buffer)
+        recordA = np.zeros(self.samples_per_record)
+        for i in range(self.records_per_buffer):
+            i0 = (i * self.samples_per_record * self.number_of_channels)
+            i1 = (i0 + self.samples_per_record * self.number_of_channels)
+            recordA += (self.buffer[i0:i1:self.number_of_channels] /
+                        records_per_acquisition)
+        recordB = np.zeros(self.samples_per_record)
+        for i in range(self.records_per_buffer):
+            i0 = (i * self.samples_per_record * self.number_of_channels + 1)
+            i1 = (i0 + self.samples_per_record * self.number_of_channels)
+            recordB += (self.buffer[i0:i1:self.number_of_channels] /
+                        records_per_acquisition)
+
+        resA = self.fit(recordA)
+        resB = self.fit(recordB)
+
+        return resA, resB
+
+    def fit(self, rec):
+        """Do Discrete Fourier Transform and return magnitude and phase data"""
+        RePart = np.dot(rec, self.cos_list) / self.samples_per_record
+        ImPart = np.dot(rec, self.sin_list) / self.samples_per_record
+        # factor of 2 as amplitude is split between finite term
+        # and double frequency term
+        ampl = 2 * np.sqrt(RePart ** 2 + ImPart ** 2)
+        phase = math.atan2(ImPart, RePart) * 180 / (2 * math.pi)
+        return [ampl, phase]
\ No newline at end of file

From fc47d1ae1a345d2b7422cb05d4fb355284799eaa Mon Sep 17 00:00:00 2001
From: nataliejpg 
Date: Tue, 1 Nov 2016 19:10:54 +0100
Subject: [PATCH 015/328] Make changes to ATS9360 and acq contr so that it can
 be configured correctly and new eg notebook (needs cleaning up)

---
 ...es example with Alazar ATS9360-Copy1.ipynb | 3534 +++++++++++++++++
 .../instrument_drivers/AlazarTech/ATS9360.py  |   41 +-
 .../AlazarTech/basic_controller.py            |   21 +-
 3 files changed, 3559 insertions(+), 37 deletions(-)
 create mode 100644 docs/examples/Qcodes example with Alazar ATS9360-Copy1.ipynb

diff --git a/docs/examples/Qcodes example with Alazar ATS9360-Copy1.ipynb b/docs/examples/Qcodes example with Alazar ATS9360-Copy1.ipynb
new file mode 100644
index 000000000000..b56964d6af63
--- /dev/null
+++ b/docs/examples/Qcodes example with Alazar ATS9360-Copy1.ipynb	
@@ -0,0 +1,3534 @@
+{
+ "cells": [
+  {
+   "cell_type": "code",
+   "execution_count": 1,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [
+    {
+     "data": {
+      "application/javascript": [
+       "/*\r\n",
+       " * Qcodes Jupyter/IPython widgets\r\n",
+       " */\r\n",
+       "require([\r\n",
+       "    'nbextensions/widgets/widgets/js/widget',\r\n",
+       "    'nbextensions/widgets/widgets/js/manager'\r\n",
+       "], function (widget, manager) {\r\n",
+       "\r\n",
+       "    var UpdateView = widget.DOMWidgetView.extend({\r\n",
+       "        render: function() {\r\n",
+       "            window.MYWIDGET = this;\r\n",
+       "            this._interval = 0;\r\n",
+       "            this.update();\r\n",
+       "        },\r\n",
+       "        update: function() {\r\n",
+       "            this.display(this.model.get('_message'));\r\n",
+       "            this.setInterval();\r\n",
+       "        },\r\n",
+       "        display: function(message) {\r\n",
+       "            /*\r\n",
+       "             * display method: override this for custom display logic\r\n",
+       "             */\r\n",
+       "            this.el.innerHTML = message;\r\n",
+       "        },\r\n",
+       "        remove: function() {\r\n",
+       "            clearInterval(this._updater);\r\n",
+       "        },\r\n",
+       "        setInterval: function(newInterval) {\r\n",
+       "            var me = this;\r\n",
+       "            if(newInterval===undefined) newInterval = me.model.get('interval');\r\n",
+       "            if(newInterval===me._interval) return;\r\n",
+       "\r\n",
+       "            me._interval = newInterval;\r\n",
+       "\r\n",
+       "            if(me._updater) clearInterval(me._updater);\r\n",
+       "\r\n",
+       "            if(me._interval) {\r\n",
+       "                me._updater = setInterval(function() {\r\n",
+       "                    me.send({myupdate: true});\r\n",
+       "                    if(!me.model.comm_live) {\r\n",
+       "                        console.log('missing comm, canceling widget updates', me);\r\n",
+       "                        clearInterval(me._updater);\r\n",
+       "                    }\r\n",
+       "                }, me._interval * 1000);\r\n",
+       "            }\r\n",
+       "        }\r\n",
+       "    });\r\n",
+       "    manager.WidgetManager.register_widget_view('UpdateView', UpdateView);\r\n",
+       "\r\n",
+       "    var HiddenUpdateView = UpdateView.extend({\r\n",
+       "        display: function(message) {\r\n",
+       "            this.$el.hide();\r\n",
+       "        }\r\n",
+       "    });\r\n",
+       "    manager.WidgetManager.register_widget_view('HiddenUpdateView', HiddenUpdateView);\r\n",
+       "\r\n",
+       "    var SubprocessView = UpdateView.extend({\r\n",
+       "        render: function() {\r\n",
+       "            var me = this;\r\n",
+       "            me._interval = 0;\r\n",
+       "            me._minimize = '';\r\n",
+       "            me._restore = '';\r\n",
+       "\r\n",
+       "            // max lines of output to show\r\n",
+       "            me.maxOutputLength = 500;\r\n",
+       "\r\n",
+       "            // in case there is already an outputView present,\r\n",
+       "            // like from before restarting the kernel\r\n",
+       "            $('.qcodes-output-view').not(me.$el).remove();\r\n",
+       "\r\n",
+       "            me.$el\r\n",
+       "                .addClass('qcodes-output-view')\r\n",
+       "                .attr('qcodes-state', 'docked')\r\n",
+       "                .html(\r\n",
+       "                    '
' +\r\n",
+       "                        '
' +\r\n",
+       "                        '' +\r\n",
+       "                        '' +\r\n",
+       "                        '' +\r\n",
+       "                        '' +\r\n",
+       "                        '' +\r\n",
+       "                        '' +\r\n",
+       "                    '
' +\r\n",
+       "                    '
'\r\n",
+       "                );\r\n",
+       "\r\n",
+       "            me.clearButton = me.$el.find('.qcodes-clear-output');\r\n",
+       "            me.minButton = me.$el.find('.qcodes-minimize');\r\n",
+       "            me.outputArea = me.$el.find('pre');\r\n",
+       "            me.subprocessList = me.$el.find('.qcodes-process-list');\r\n",
+       "            me.abortButton = me.$el.find('.qcodes-abort-loop');\r\n",
+       "            me.processLinesButton = me.$el.find('.qcodes-processlines')\r\n",
+       "\r\n",
+       "            me.outputLines = [];\r\n",
+       "\r\n",
+       "            me.clearButton.click(function() {\r\n",
+       "                me.outputArea.html('');\r\n",
+       "                me.clearButton.addClass('disabled');\r\n",
+       "            });\r\n",
+       "\r\n",
+       "            me.abortButton.click(function() {\r\n",
+       "                me.send({abort: true});\r\n",
+       "            });\r\n",
+       "\r\n",
+       "            me.processLinesButton.click(function() {\r\n",
+       "                // toggle multiline process list display\r\n",
+       "                me.subprocessesMultiline = !me.subprocessesMultiline;\r\n",
+       "                me.showSubprocesses();\r\n",
+       "            });\r\n",
+       "\r\n",
+       "            me.$el.find('.js-state').click(function() {\r\n",
+       "                var state = this.className.substr(this.className.indexOf('qcodes'))\r\n",
+       "                        .split('-')[1].split(' ')[0];\r\n",
+       "                me.model.set('_state', state);\r\n",
+       "            });\r\n",
+       "\r\n",
+       "            $(window)\r\n",
+       "                .off('resize.qcodes')\r\n",
+       "                .on('resize.qcodes', function() {me.clipBounds();});\r\n",
+       "\r\n",
+       "            me.update();\r\n",
+       "        },\r\n",
+       "\r\n",
+       "        updateState: function() {\r\n",
+       "            var me = this,\r\n",
+       "                oldState = me.$el.attr('qcodes-state'),\r\n",
+       "                state = me.model.get('_state');\r\n",
+       "\r\n",
+       "            if(state === oldState) return;\r\n",
+       "\r\n",
+       "            setTimeout(function() {\r\n",
+       "                // not sure why I can't pop it out of the widgetarea in render, but it seems that\r\n",
+       "                // some other bit of code resets the parent after render if I do it there.\r\n",
+       "                // To be safe, just do it on every state click.\r\n",
+       "                me.$el.appendTo('body');\r\n",
+       "\r\n",
+       "                if(oldState === 'floated') {\r\n",
+       "                    console.log('here');\r\n",
+       "                    me.$el.draggable('destroy').css({left:'', top: ''});\r\n",
+       "                }\r\n",
+       "\r\n",
+       "                me.$el.attr('qcodes-state', state);\r\n",
+       "\r\n",
+       "                if(state === 'floated') {\r\n",
+       "                    me.$el\r\n",
+       "                        .draggable({stop: function() { me.clipBounds(); }})\r\n",
+       "                        .css({\r\n",
+       "                            left: window.innerWidth - me.$el.width() - 15,\r\n",
+       "                            top: window.innerHeight - me.$el.height() - 10\r\n",
+       "                        });\r\n",
+       "                }\r\n",
+       "\r\n",
+       "                // any previous highlighting is now moot\r\n",
+       "                me.$el.removeClass('qcodes-highlight');\r\n",
+       "            }, 0);\r\n",
+       "\r\n",
+       "        },\r\n",
+       "\r\n",
+       "        clipBounds: function() {\r\n",
+       "            var me = this;\r\n",
+       "            if(me.$el.attr('qcodes-state') === 'floated') {\r\n",
+       "                var bounds = me.$el[0].getBoundingClientRect(),\r\n",
+       "                    minVis = 40,\r\n",
+       "                    maxLeft = window.innerWidth - minVis,\r\n",
+       "                    minLeft = minVis - bounds.width,\r\n",
+       "                    maxTop = window.innerHeight - minVis;\r\n",
+       "\r\n",
+       "                if(bounds.left > maxLeft) me.$el.css('left', maxLeft);\r\n",
+       "                else if(bounds.left < minLeft) me.$el.css('left', minLeft);\r\n",
+       "\r\n",
+       "                if(bounds.top > maxTop) me.$el.css('top', maxTop);\r\n",
+       "                else if(bounds.top < 0) me.$el.css('top', 0);\r\n",
+       "            }\r\n",
+       "        },\r\n",
+       "\r\n",
+       "        display: function(message) {\r\n",
+       "            var me = this;\r\n",
+       "            if(message) {\r\n",
+       "                var initialScroll = me.outputArea.scrollTop();\r\n",
+       "                me.outputArea.scrollTop(me.outputArea.prop('scrollHeight'));\r\n",
+       "                var scrollBottom = me.outputArea.scrollTop();\r\n",
+       "\r\n",
+       "                if(me.$el.attr('qcodes-state') === 'minimized') {\r\n",
+       "                    // if we add text and the box is minimized, highlight the\r\n",
+       "                    // title bar to alert the user that there are new messages.\r\n",
+       "                    // remove then add the class, so we get the animation again\r\n",
+       "                    // if it's already highlighted\r\n",
+       "                    me.$el.removeClass('qcodes-highlight');\r\n",
+       "                    setTimeout(function(){\r\n",
+       "                        me.$el.addClass('qcodes-highlight');\r\n",
+       "                    }, 0);\r\n",
+       "                }\r\n",
+       "\r\n",
+       "                var newLines = message.split('\\n'),\r\n",
+       "                    out = me.outputLines,\r\n",
+       "                    outLen = out.length;\r\n",
+       "                if(outLen) out[outLen - 1] += newLines[0];\r\n",
+       "                else out.push(newLines[0]);\r\n",
+       "\r\n",
+       "                for(var i = 1; i < newLines.length; i++) {\r\n",
+       "                    out.push(newLines[i]);\r\n",
+       "                }\r\n",
+       "\r\n",
+       "                if(out.length > me.maxOutputLength) {\r\n",
+       "                    out.splice(0, out.length - me.maxOutputLength + 1,\r\n",
+       "                        '<<< Output clipped >>>');\r\n",
+       "                }\r\n",
+       "\r\n",
+       "                me.outputArea.text(out.join('\\n'));\r\n",
+       "                me.clearButton.removeClass('disabled');\r\n",
+       "\r\n",
+       "                // if we were scrolled to the bottom initially, make sure\r\n",
+       "                // we stay that way.\r\n",
+       "                me.outputArea.scrollTop(initialScroll === scrollBottom ?\r\n",
+       "                    me.outputArea.prop('scrollHeight') : initialScroll);\r\n",
+       "            }\r\n",
+       "\r\n",
+       "            me.showSubprocesses();\r\n",
+       "            me.updateState();\r\n",
+       "        },\r\n",
+       "\r\n",
+       "        showSubprocesses: function() {\r\n",
+       "            var me = this,\r\n",
+       "                replacer = me.subprocessesMultiline ? '
' : ', ',\r\n",
+       "                processes = (me.model.get('_processes') || '')\r\n",
+       "                    .replace(/\\n/g, '>' + replacer + '<');\r\n",
+       "\r\n",
+       "            if(processes) processes = '<' + processes + '>';\r\n",
+       "            else processes = 'No subprocesses';\r\n",
+       "\r\n",
+       "            me.abortButton.toggleClass('disabled', processes.indexOf('Measurement')===-1);\r\n",
+       "\r\n",
+       "            me.subprocessList.html(processes);\r\n",
+       "        }\r\n",
+       "    });\r\n",
+       "    manager.WidgetManager.register_widget_view('SubprocessView', SubprocessView);\r\n",
+       "});\r\n"
+      ],
+      "text/plain": [
+       ""
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "text/html": [
+       ""
+      ],
+      "text/plain": [
+       ""
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "No loop running\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "a:\\qcodes\\qcodes\\process\\helpers.py:27: UserWarning: Multiprocessing is in beta, use at own risk\n",
+      "  warnings.warn(\"Multiprocessing is in beta, use at own risk\", UserWarning)\n"
+     ]
+    }
+   ],
+   "source": [
+    "# import all necessary things\n",
+    "%matplotlib nbagg\n",
+    "\n",
+    "import qcodes as qc\n",
+    "import qcodes.instrument.parameter as parameter\n",
+    "import qcodes.instrument_drivers.AlazarTech.ATS9360 as ATSdriver\n",
+    "import qcodes.instrument_drivers.AlazarTech.ATS_acquisition_controllers as ats_contr\n",
+    "\n",
+    "qc.halt_bg()\n",
+    "qc.set_mp_method('spawn')  # force Windows behavior on mac\n",
+    "\n",
+    "# this makes a widget in the corner of the window to show and control\n",
+    "# subprocesses and any output they would print to the terminal\n",
+    "qc.show_subprocess_widget()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 2,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "[{'bits_per_sample': 12,\n",
+       "  'board_id': 1,\n",
+       "  'board_kind': 'ATS9360',\n",
+       "  'max_samples': 4294967294,\n",
+       "  'system_id': 1}]"
+      ]
+     },
+     "execution_count": 2,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "# Command to list all alazar boards connected to the system\n",
+    "ATSdriver.AlazarTech_ATS.find_boards()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 3,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "a:\\qcodes\\qcodes\\instrument\\metaclass.py:37: UserWarning: Multiprocessing is in beta, use at own risk\n",
+      "  UserWarning)\n"
+     ]
+    },
+    {
+     "data": {
+      "text/plain": [
+       "{'CPLD_version': '25.16',\n",
+       " 'SDK_version': '5.9.25',\n",
+       " 'asopc_type': '1712554848',\n",
+       " 'driver_version': '5.9.25',\n",
+       " 'firmware': None,\n",
+       " 'latest_cal_date': '13-11-15',\n",
+       " 'memory_size': '4294967294',\n",
+       " 'model': 'ATS9360',\n",
+       " 'pcie_link_speed': '0.5GB/s',\n",
+       " 'pcie_link_width': '8',\n",
+       " 'serial': '970344',\n",
+       " 'vendor': 'AlazarTech'}"
+      ]
+     },
+     "execution_count": 3,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "# Create the ATS9870 instrument on the new server \"alazar_server\"\n",
+    "ats_inst = ATSdriver.AlazarTech_ATS9360(name='Alazar1', server_name=\"alazar_server\")\n",
+    "# Print all information about this Alazar card\n",
+    "ats_inst.get_idn()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 17,
+   "metadata": {
+    "collapsed": true
+   },
+   "outputs": [],
+   "source": [
+    "import qcodes.instrument_drivers.rohde_schwarz.SGS100A as RSdriver"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 18,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Connected to: Rohde&Schwarz SGS100A (serial:1416.0505k02/103076, firmware:3.1.19.7-3.20.140.60.1) in 0.05s\n"
+     ]
+    }
+   ],
+   "source": [
+    "localos = RSdriver.RohdeSchwarz_SGS100A('LO', 'TCPIP0::172.20.3.42::inst0::INSTR')"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 19,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "1000000.0"
+      ]
+     },
+     "execution_count": 19,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "localos.frequency()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 20,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [],
+   "source": [
+    "localos.power(-25)\n",
+    "localos.frequency(10e6)\n",
+    "#localos.status('on')"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 9,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "a:\\qcodes\\qcodes\\instrument\\metaclass.py:37: UserWarning: Multiprocessing is in beta, use at own risk\n",
+      "  UserWarning)\n"
+     ]
+    }
+   ],
+   "source": [
+    "# Instantiate an acquisition controller (In this case we are doing a simple DFT) on the same server (\"alazar_server\") and \n",
+    "# provide the name of the name of the alazar card that this controller should control\n",
+    "acquisition_controller = ats_contr.Demodulation_AcquisitionController(name='acquisition_controller', \n",
+    "                                                                      demodulation_frequency=15e6, \n",
+    "                                                                      alazar_name='Alazar1', \n",
+    "                                                                      server_name=\"alazar_server\")"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 22,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [],
+   "source": [
+    "# Configure all settings in the Alazar card\n",
+    "ats_inst.config(clock_source='INTERNAL_CLOCK',\n",
+    "                sample_rate='10MHZ_REF_500MSPS',\n",
+    "                #clock_edge='CLOCK_EDGE_RISING',\n",
+    "                #decimation=0,\n",
+    "                #coupling=['AC','AC'],\n",
+    "                channel_range=[2.,2.],\n",
+    "                #impedance=[50,50],\n",
+    "                #bwlimit=['DISABLED','DISABLED'],\n",
+    "                #trigger_operation='TRIG_ENGINE_OP_J',\n",
+    "                #trigger_engine1='TRIG_ENGINE_J',\n",
+    "                trigger_source1='EXTERNAL',\n",
+    "                #trigger_slope1='TRIG_SLOPE_POSITIVE',\n",
+    "                #trigger_level1=128,\n",
+    "                #trigger_engine2='TRIG_ENGINE_K',\n",
+    "                #trigger_source2='DISABLE',\n",
+    "                #trigger_slope2='TRIG_SLOPE_POSITIVE',\n",
+    "                #trigger_level2=128,\n",
+    "                #external_trigger_coupling='AC',\n",
+    "                #external_trigger_range='ETR_5V',\n",
+    "                #trigger_delay=0,\n",
+    "                #timeout_ticks=0\n",
+    ")"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 11,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [],
+   "source": [
+    "# This command is specific to this acquisition controller. The kwargs provided here are being forwarded to ats_inst.acquire\n",
+    "# This way, it becomes easy to change acquisition specific settings from the ipython notebook\n",
+    "acquisition_controller.update_acquisitionkwargs(#mode='NPT',\n",
+    "                 samples_per_record=1024,\n",
+    "                 records_per_buffer=70,\n",
+    "                 buffers_per_acquisition=1,\n",
+    "                 #channel_selection='AB',\n",
+    "                 #transfer_offset=0,\n",
+    "                 #external_startcapture='ENABLED',\n",
+    "                 #enable_record_headers='DISABLED',\n",
+    "                 #alloc_buffers='DISABLED',\n",
+    "                 #fifo_only_streaming='DISABLED',\n",
+    "                 #interleave_samples='DISABLED',\n",
+    "                 #get_processed_data='DISABLED',\n",
+    "                 allocated_buffers=1,\n",
+    "                 #buffer_timeout=1000\n",
+    ")"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 12,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "2.0976760253919644"
+      ]
+     },
+     "execution_count": 12,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "# Getting the value of the parameter 'acquisition' of the instrument 'acquisition_controller' performes the entire acquisition \n",
+    "# protocol. This again depends on the specific implementation of the acquisition controller\n",
+    "acquisition_controller.acquisition()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 13,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "{'__class__': 'qcodes.instrument_drivers.AlazarTech.ATS9360.AlazarTech_ATS9360',\n",
+       " 'functions': {},\n",
+       " 'name': 'Alazar1',\n",
+       " 'parameters': {'IDN': {'__class__': 'qcodes.instrument.parameter.StandardParameter',\n",
+       "   'instrument': 'qcodes.instrument_drivers.AlazarTech.ATS9360.AlazarTech_ATS9360',\n",
+       "   'instrument_name': 'Alazar1',\n",
+       "   'label': 'IDN',\n",
+       "   'name': 'IDN',\n",
+       "   'ts': '2016-11-01 16:48:58',\n",
+       "   'units': '',\n",
+       "   'value': {'CPLD_version': '25.16',\n",
+       "    'SDK_version': '5.9.25',\n",
+       "    'asopc_type': '1712554848',\n",
+       "    'driver_version': '5.9.25',\n",
+       "    'firmware': None,\n",
+       "    'latest_cal_date': '13-11-15',\n",
+       "    'memory_size': '4294967294',\n",
+       "    'model': 'ATS9360',\n",
+       "    'pcie_link_speed': '0.5GB/s',\n",
+       "    'pcie_link_width': '8',\n",
+       "    'serial': '970344',\n",
+       "    'vendor': 'AlazarTech'}},\n",
+       "  'alloc_buffers': {'__class__': 'qcodes.instrument_drivers.AlazarTech.ATS.AlazarParameter',\n",
+       "   'label': 'Alloc Buffers',\n",
+       "   'name': 'alloc_buffers',\n",
+       "   'ts': '2016-11-01 16:48:23',\n",
+       "   'units': '',\n",
+       "   'value': 'DISABLED'},\n",
+       "  'allocated_buffers': {'__class__': 'qcodes.instrument_drivers.AlazarTech.ATS.AlazarParameter',\n",
+       "   'label': 'Allocated Buffers',\n",
+       "   'name': 'allocated_buffers',\n",
+       "   'ts': '2016-11-01 16:48:58',\n",
+       "   'units': '',\n",
+       "   'value': 1},\n",
+       "  'buffer_timeout': {'__class__': 'qcodes.instrument_drivers.AlazarTech.ATS.AlazarParameter',\n",
+       "   'label': 'Buffer Timeout',\n",
+       "   'name': 'buffer_timeout',\n",
+       "   'ts': '2016-11-01 16:48:23',\n",
+       "   'units': 'ms',\n",
+       "   'value': 1000},\n",
+       "  'buffers_per_acquisition': {'__class__': 'qcodes.instrument_drivers.AlazarTech.ATS.AlazarParameter',\n",
+       "   'label': 'Buffers per Acquisition',\n",
+       "   'name': 'buffers_per_acquisition',\n",
+       "   'ts': '2016-11-01 16:48:58',\n",
+       "   'units': '',\n",
+       "   'value': 1},\n",
+       "  'channel_range1': {'__class__': 'qcodes.instrument_drivers.AlazarTech.ATS.AlazarParameter',\n",
+       "   'label': 'Range channel 1',\n",
+       "   'name': 'channel_range1',\n",
+       "   'ts': '2016-11-01 16:48:54',\n",
+       "   'units': 'V',\n",
+       "   'value': 2.0},\n",
+       "  'channel_range2': {'__class__': 'qcodes.instrument_drivers.AlazarTech.ATS.AlazarParameter',\n",
+       "   'label': 'Range channel 2',\n",
+       "   'name': 'channel_range2',\n",
+       "   'ts': '2016-11-01 16:48:54',\n",
+       "   'units': 'V',\n",
+       "   'value': 2.0},\n",
+       "  'channel_selection': {'__class__': 'qcodes.instrument_drivers.AlazarTech.ATS.AlazarParameter',\n",
+       "   'label': 'Channel Selection',\n",
+       "   'name': 'channel_selection',\n",
+       "   'ts': '2016-11-01 16:48:23',\n",
+       "   'units': '',\n",
+       "   'value': 'AB'},\n",
+       "  'clock_edge': {'__class__': 'qcodes.instrument_drivers.AlazarTech.ATS.AlazarParameter',\n",
+       "   'label': 'Clock Edge',\n",
+       "   'name': 'clock_edge',\n",
+       "   'ts': '2016-11-01 16:48:23',\n",
+       "   'units': '',\n",
+       "   'value': 'CLOCK_EDGE_RISING'},\n",
+       "  'clock_source': {'__class__': 'qcodes.instrument_drivers.AlazarTech.ATS.AlazarParameter',\n",
+       "   'label': 'Clock Source',\n",
+       "   'name': 'clock_source',\n",
+       "   'ts': '2016-11-01 16:48:23',\n",
+       "   'units': '',\n",
+       "   'value': 'INTERNAL_CLOCK'},\n",
+       "  'coupling1': {'__class__': 'qcodes.instrument_drivers.AlazarTech.ATS.AlazarParameter',\n",
+       "   'label': 'Coupling channel 1',\n",
+       "   'name': 'coupling1',\n",
+       "   'ts': '2016-11-01 16:48:23',\n",
+       "   'units': '',\n",
+       "   'value': 'DC'},\n",
+       "  'coupling2': {'__class__': 'qcodes.instrument_drivers.AlazarTech.ATS.AlazarParameter',\n",
+       "   'label': 'Coupling channel 2',\n",
+       "   'name': 'coupling2',\n",
+       "   'ts': '2016-11-01 16:48:23',\n",
+       "   'units': '',\n",
+       "   'value': 'DC'},\n",
+       "  'decimation': {'__class__': 'qcodes.instrument_drivers.AlazarTech.ATS.AlazarParameter',\n",
+       "   'label': 'Decimation',\n",
+       "   'name': 'decimation',\n",
+       "   'ts': '2016-11-01 16:48:23',\n",
+       "   'units': '',\n",
+       "   'value': 0},\n",
+       "  'enable_record_headers': {'__class__': 'qcodes.instrument_drivers.AlazarTech.ATS.AlazarParameter',\n",
+       "   'label': 'Enable Record Headers',\n",
+       "   'name': 'enable_record_headers',\n",
+       "   'ts': '2016-11-01 16:48:23',\n",
+       "   'units': '',\n",
+       "   'value': 'DISABLED'},\n",
+       "  'external_startcapture': {'__class__': 'qcodes.instrument_drivers.AlazarTech.ATS.AlazarParameter',\n",
+       "   'label': 'External Startcapture',\n",
+       "   'name': 'external_startcapture',\n",
+       "   'ts': '2016-11-01 16:48:23',\n",
+       "   'units': '',\n",
+       "   'value': 'ENABLED'},\n",
+       "  'external_trigger_coupling': {'__class__': 'qcodes.instrument_drivers.AlazarTech.ATS.AlazarParameter',\n",
+       "   'label': 'External Trigger Coupling',\n",
+       "   'name': 'external_trigger_coupling',\n",
+       "   'ts': '2016-11-01 16:48:23',\n",
+       "   'units': '',\n",
+       "   'value': 'AC'},\n",
+       "  'external_trigger_range': {'__class__': 'qcodes.instrument_drivers.AlazarTech.ATS.AlazarParameter',\n",
+       "   'label': 'External Trigger Range',\n",
+       "   'name': 'external_trigger_range',\n",
+       "   'ts': '2016-11-01 16:48:23',\n",
+       "   'units': '',\n",
+       "   'value': 'ETR_5V'},\n",
+       "  'fifo_only_streaming': {'__class__': 'qcodes.instrument_drivers.AlazarTech.ATS.AlazarParameter',\n",
+       "   'label': 'Fifo Only Streaming',\n",
+       "   'name': 'fifo_only_streaming',\n",
+       "   'ts': '2016-11-01 16:48:23',\n",
+       "   'units': '',\n",
+       "   'value': 'DISABLED'},\n",
+       "  'get_processed_data': {'__class__': 'qcodes.instrument_drivers.AlazarTech.ATS.AlazarParameter',\n",
+       "   'label': 'Get Processed Data',\n",
+       "   'name': 'get_processed_data',\n",
+       "   'ts': '2016-11-01 16:48:23',\n",
+       "   'units': '',\n",
+       "   'value': 'DISABLED'},\n",
+       "  'impedance1': {'__class__': 'qcodes.instrument_drivers.AlazarTech.ATS.AlazarParameter',\n",
+       "   'label': 'Impedance channel 1',\n",
+       "   'name': 'impedance1',\n",
+       "   'ts': '2016-11-01 16:48:23',\n",
+       "   'units': 'Ohm',\n",
+       "   'value': 50},\n",
+       "  'impedance2': {'__class__': 'qcodes.instrument_drivers.AlazarTech.ATS.AlazarParameter',\n",
+       "   'label': 'Impedance channel 2',\n",
+       "   'name': 'impedance2',\n",
+       "   'ts': '2016-11-01 16:48:23',\n",
+       "   'units': 'Ohm',\n",
+       "   'value': 50},\n",
+       "  'interleave_samples': {'__class__': 'qcodes.instrument_drivers.AlazarTech.ATS.AlazarParameter',\n",
+       "   'label': 'Interleave Samples',\n",
+       "   'name': 'interleave_samples',\n",
+       "   'ts': '2016-11-01 16:48:23',\n",
+       "   'units': '',\n",
+       "   'value': 'DISABLED'},\n",
+       "  'mode': {'__class__': 'qcodes.instrument_drivers.AlazarTech.ATS.AlazarParameter',\n",
+       "   'label': 'Acquisiton mode',\n",
+       "   'name': 'mode',\n",
+       "   'ts': '2016-11-01 16:48:23',\n",
+       "   'units': '',\n",
+       "   'value': 'NPT'},\n",
+       "  'records_per_buffer': {'__class__': 'qcodes.instrument_drivers.AlazarTech.ATS.AlazarParameter',\n",
+       "   'label': 'Records per Buffer',\n",
+       "   'name': 'records_per_buffer',\n",
+       "   'ts': '2016-11-01 16:48:58',\n",
+       "   'units': '',\n",
+       "   'value': 70},\n",
+       "  'sample_rate': {'__class__': 'qcodes.instrument_drivers.AlazarTech.ATS.AlazarParameter',\n",
+       "   'label': 'Sample Rate',\n",
+       "   'name': 'sample_rate',\n",
+       "   'ts': '2016-11-01 16:48:54',\n",
+       "   'units': 'S/s',\n",
+       "   'value': 100000000},\n",
+       "  'samples_per_record': {'__class__': 'qcodes.instrument_drivers.AlazarTech.ATS.AlazarParameter',\n",
+       "   'label': 'Samples per Record',\n",
+       "   'name': 'samples_per_record',\n",
+       "   'ts': '2016-11-01 16:48:58',\n",
+       "   'units': '',\n",
+       "   'value': 1024},\n",
+       "  'timeout_ticks': {'__class__': 'qcodes.instrument_drivers.AlazarTech.ATS.AlazarParameter',\n",
+       "   'label': 'Timeout Ticks',\n",
+       "   'name': 'timeout_ticks',\n",
+       "   'ts': '2016-11-01 16:48:23',\n",
+       "   'units': '10 us',\n",
+       "   'value': 0},\n",
+       "  'transfer_offset': {'__class__': 'qcodes.instrument_drivers.AlazarTech.ATS.AlazarParameter',\n",
+       "   'label': 'Transer Offset',\n",
+       "   'name': 'transfer_offset',\n",
+       "   'ts': '2016-11-01 16:48:23',\n",
+       "   'units': 'Samples',\n",
+       "   'value': 0},\n",
+       "  'trigger_delay': {'__class__': 'qcodes.instrument_drivers.AlazarTech.ATS.AlazarParameter',\n",
+       "   'label': 'Trigger Delay',\n",
+       "   'name': 'trigger_delay',\n",
+       "   'ts': '2016-11-01 16:48:23',\n",
+       "   'units': 'Sample clock cycles',\n",
+       "   'value': 0},\n",
+       "  'trigger_engine1': {'__class__': 'qcodes.instrument_drivers.AlazarTech.ATS.AlazarParameter',\n",
+       "   'label': 'Trigger Engine 1',\n",
+       "   'name': 'trigger_engine1',\n",
+       "   'ts': '2016-11-01 16:48:23',\n",
+       "   'units': '',\n",
+       "   'value': 'TRIG_ENGINE_K'},\n",
+       "  'trigger_engine2': {'__class__': 'qcodes.instrument_drivers.AlazarTech.ATS.AlazarParameter',\n",
+       "   'label': 'Trigger Engine 2',\n",
+       "   'name': 'trigger_engine2',\n",
+       "   'ts': '2016-11-01 16:48:23',\n",
+       "   'units': '',\n",
+       "   'value': 'TRIG_ENGINE_K'},\n",
+       "  'trigger_level1': {'__class__': 'qcodes.instrument_drivers.AlazarTech.ATS.AlazarParameter',\n",
+       "   'label': 'Trigger Level 1',\n",
+       "   'name': 'trigger_level1',\n",
+       "   'ts': '2016-11-01 16:48:23',\n",
+       "   'units': '',\n",
+       "   'value': 150},\n",
+       "  'trigger_level2': {'__class__': 'qcodes.instrument_drivers.AlazarTech.ATS.AlazarParameter',\n",
+       "   'label': 'Trigger Level 2',\n",
+       "   'name': 'trigger_level2',\n",
+       "   'ts': '2016-11-01 16:48:23',\n",
+       "   'units': '',\n",
+       "   'value': 150},\n",
+       "  'trigger_operation': {'__class__': 'qcodes.instrument_drivers.AlazarTech.ATS.AlazarParameter',\n",
+       "   'label': 'Trigger Operation',\n",
+       "   'name': 'trigger_operation',\n",
+       "   'ts': '2016-11-01 16:48:23',\n",
+       "   'units': '',\n",
+       "   'value': 'TRIG_ENGINE_OP_J'},\n",
+       "  'trigger_slope1': {'__class__': 'qcodes.instrument_drivers.AlazarTech.ATS.AlazarParameter',\n",
+       "   'label': 'Trigger Slope 1',\n",
+       "   'name': 'trigger_slope1',\n",
+       "   'ts': '2016-11-01 16:48:23',\n",
+       "   'units': '',\n",
+       "   'value': 'TRIG_SLOPE_POSITIVE'},\n",
+       "  'trigger_slope2': {'__class__': 'qcodes.instrument_drivers.AlazarTech.ATS.AlazarParameter',\n",
+       "   'label': 'Trigger Slope 2',\n",
+       "   'name': 'trigger_slope2',\n",
+       "   'ts': '2016-11-01 16:48:23',\n",
+       "   'units': '',\n",
+       "   'value': 'TRIG_SLOPE_POSITIVE'},\n",
+       "  'trigger_source1': {'__class__': 'qcodes.instrument_drivers.AlazarTech.ATS.AlazarParameter',\n",
+       "   'label': 'Trigger Source 1',\n",
+       "   'name': 'trigger_source1',\n",
+       "   'ts': '2016-11-01 16:48:54',\n",
+       "   'units': '',\n",
+       "   'value': 'EXTERNAL'},\n",
+       "  'trigger_source2': {'__class__': 'qcodes.instrument_drivers.AlazarTech.ATS.AlazarParameter',\n",
+       "   'label': 'Trigger Source 2',\n",
+       "   'name': 'trigger_source2',\n",
+       "   'ts': '2016-11-01 16:48:23',\n",
+       "   'units': '',\n",
+       "   'value': 'DISABLE'}}}"
+      ]
+     },
+     "execution_count": 13,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "# make a snapshot of the 'ats_inst' instrument\n",
+    "ats_inst.snapshot()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {
+    "collapsed": true
+   },
+   "outputs": [],
+   "source": []
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 14,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "False\n",
+      "None\n",
+      "started at 2016-11-01 16:49:46\n"
+     ]
+    },
+    {
+     "data": {
+      "application/javascript": [
+       "/* Put everything inside the global mpl namespace */\n",
+       "window.mpl = {};\n",
+       "\n",
+       "mpl.get_websocket_type = function() {\n",
+       "    if (typeof(WebSocket) !== 'undefined') {\n",
+       "        return WebSocket;\n",
+       "    } else if (typeof(MozWebSocket) !== 'undefined') {\n",
+       "        return MozWebSocket;\n",
+       "    } else {\n",
+       "        alert('Your browser does not have WebSocket support.' +\n",
+       "              'Please try Chrome, Safari or Firefox ≥ 6. ' +\n",
+       "              'Firefox 4 and 5 are also supported but you ' +\n",
+       "              'have to enable WebSockets in about:config.');\n",
+       "    };\n",
+       "}\n",
+       "\n",
+       "mpl.figure = function(figure_id, websocket, ondownload, parent_element) {\n",
+       "    this.id = figure_id;\n",
+       "\n",
+       "    this.ws = websocket;\n",
+       "\n",
+       "    this.supports_binary = (this.ws.binaryType != undefined);\n",
+       "\n",
+       "    if (!this.supports_binary) {\n",
+       "        var warnings = document.getElementById(\"mpl-warnings\");\n",
+       "        if (warnings) {\n",
+       "            warnings.style.display = 'block';\n",
+       "            warnings.textContent = (\n",
+       "                \"This browser does not support binary websocket messages. \" +\n",
+       "                    \"Performance may be slow.\");\n",
+       "        }\n",
+       "    }\n",
+       "\n",
+       "    this.imageObj = new Image();\n",
+       "\n",
+       "    this.context = undefined;\n",
+       "    this.message = undefined;\n",
+       "    this.canvas = undefined;\n",
+       "    this.rubberband_canvas = undefined;\n",
+       "    this.rubberband_context = undefined;\n",
+       "    this.format_dropdown = undefined;\n",
+       "\n",
+       "    this.image_mode = 'full';\n",
+       "\n",
+       "    this.root = $('
');\n",
+       "    this._root_extra_style(this.root)\n",
+       "    this.root.attr('style', 'display: inline-block');\n",
+       "\n",
+       "    $(parent_element).append(this.root);\n",
+       "\n",
+       "    this._init_header(this);\n",
+       "    this._init_canvas(this);\n",
+       "    this._init_toolbar(this);\n",
+       "\n",
+       "    var fig = this;\n",
+       "\n",
+       "    this.waiting = false;\n",
+       "\n",
+       "    this.ws.onopen =  function () {\n",
+       "            fig.send_message(\"supports_binary\", {value: fig.supports_binary});\n",
+       "            fig.send_message(\"send_image_mode\", {});\n",
+       "            fig.send_message(\"refresh\", {});\n",
+       "        }\n",
+       "\n",
+       "    this.imageObj.onload = function() {\n",
+       "            if (fig.image_mode == 'full') {\n",
+       "                // Full images could contain transparency (where diff images\n",
+       "                // almost always do), so we need to clear the canvas so that\n",
+       "                // there is no ghosting.\n",
+       "                fig.context.clearRect(0, 0, fig.canvas.width, fig.canvas.height);\n",
+       "            }\n",
+       "            fig.context.drawImage(fig.imageObj, 0, 0);\n",
+       "        };\n",
+       "\n",
+       "    this.imageObj.onunload = function() {\n",
+       "        this.ws.close();\n",
+       "    }\n",
+       "\n",
+       "    this.ws.onmessage = this._make_on_message_function(this);\n",
+       "\n",
+       "    this.ondownload = ondownload;\n",
+       "}\n",
+       "\n",
+       "mpl.figure.prototype._init_header = function() {\n",
+       "    var titlebar = $(\n",
+       "        '
');\n",
+       "    var titletext = $(\n",
+       "        '
');\n",
+       "    titlebar.append(titletext)\n",
+       "    this.root.append(titlebar);\n",
+       "    this.header = titletext[0];\n",
+       "}\n",
+       "\n",
+       "\n",
+       "\n",
+       "mpl.figure.prototype._canvas_extra_style = function(canvas_div) {\n",
+       "\n",
+       "}\n",
+       "\n",
+       "\n",
+       "mpl.figure.prototype._root_extra_style = function(canvas_div) {\n",
+       "\n",
+       "}\n",
+       "\n",
+       "mpl.figure.prototype._init_canvas = function() {\n",
+       "    var fig = this;\n",
+       "\n",
+       "    var canvas_div = $('
');\n",
+       "\n",
+       "    canvas_div.attr('style', 'position: relative; clear: both; outline: 0');\n",
+       "\n",
+       "    function canvas_keyboard_event(event) {\n",
+       "        return fig.key_event(event, event['data']);\n",
+       "    }\n",
+       "\n",
+       "    canvas_div.keydown('key_press', canvas_keyboard_event);\n",
+       "    canvas_div.keyup('key_release', canvas_keyboard_event);\n",
+       "    this.canvas_div = canvas_div\n",
+       "    this._canvas_extra_style(canvas_div)\n",
+       "    this.root.append(canvas_div);\n",
+       "\n",
+       "    var canvas = $('');\n",
+       "    canvas.addClass('mpl-canvas');\n",
+       "    canvas.attr('style', \"left: 0; top: 0; z-index: 0; outline: 0\")\n",
+       "\n",
+       "    this.canvas = canvas[0];\n",
+       "    this.context = canvas[0].getContext(\"2d\");\n",
+       "\n",
+       "    var rubberband = $('');\n",
+       "    rubberband.attr('style', \"position: absolute; left: 0; top: 0; z-index: 1;\")\n",
+       "\n",
+       "    var pass_mouse_events = true;\n",
+       "\n",
+       "    canvas_div.resizable({\n",
+       "        start: function(event, ui) {\n",
+       "            pass_mouse_events = false;\n",
+       "        },\n",
+       "        resize: function(event, ui) {\n",
+       "            fig.request_resize(ui.size.width, ui.size.height);\n",
+       "        },\n",
+       "        stop: function(event, ui) {\n",
+       "            pass_mouse_events = true;\n",
+       "            fig.request_resize(ui.size.width, ui.size.height);\n",
+       "        },\n",
+       "    });\n",
+       "\n",
+       "    function mouse_event_fn(event) {\n",
+       "        if (pass_mouse_events)\n",
+       "            return fig.mouse_event(event, event['data']);\n",
+       "    }\n",
+       "\n",
+       "    rubberband.mousedown('button_press', mouse_event_fn);\n",
+       "    rubberband.mouseup('button_release', mouse_event_fn);\n",
+       "    // Throttle sequential mouse events to 1 every 20ms.\n",
+       "    rubberband.mousemove('motion_notify', mouse_event_fn);\n",
+       "\n",
+       "    rubberband.mouseenter('figure_enter', mouse_event_fn);\n",
+       "    rubberband.mouseleave('figure_leave', mouse_event_fn);\n",
+       "\n",
+       "    canvas_div.on(\"wheel\", function (event) {\n",
+       "        event = event.originalEvent;\n",
+       "        event['data'] = 'scroll'\n",
+       "        if (event.deltaY < 0) {\n",
+       "            event.step = 1;\n",
+       "        } else {\n",
+       "            event.step = -1;\n",
+       "        }\n",
+       "        mouse_event_fn(event);\n",
+       "    });\n",
+       "\n",
+       "    canvas_div.append(canvas);\n",
+       "    canvas_div.append(rubberband);\n",
+       "\n",
+       "    this.rubberband = rubberband;\n",
+       "    this.rubberband_canvas = rubberband[0];\n",
+       "    this.rubberband_context = rubberband[0].getContext(\"2d\");\n",
+       "    this.rubberband_context.strokeStyle = \"#000000\";\n",
+       "\n",
+       "    this._resize_canvas = function(width, height) {\n",
+       "        // Keep the size of the canvas, canvas container, and rubber band\n",
+       "        // canvas in synch.\n",
+       "        canvas_div.css('width', width)\n",
+       "        canvas_div.css('height', height)\n",
+       "\n",
+       "        canvas.attr('width', width);\n",
+       "        canvas.attr('height', height);\n",
+       "\n",
+       "        rubberband.attr('width', width);\n",
+       "        rubberband.attr('height', height);\n",
+       "    }\n",
+       "\n",
+       "    // Set the figure to an initial 600x600px, this will subsequently be updated\n",
+       "    // upon first draw.\n",
+       "    this._resize_canvas(600, 600);\n",
+       "\n",
+       "    // Disable right mouse context menu.\n",
+       "    $(this.rubberband_canvas).bind(\"contextmenu\",function(e){\n",
+       "        return false;\n",
+       "    });\n",
+       "\n",
+       "    function set_focus () {\n",
+       "        canvas.focus();\n",
+       "        canvas_div.focus();\n",
+       "    }\n",
+       "\n",
+       "    window.setTimeout(set_focus, 100);\n",
+       "}\n",
+       "\n",
+       "mpl.figure.prototype._init_toolbar = function() {\n",
+       "    var fig = this;\n",
+       "\n",
+       "    var nav_element = $('
')\n",
+       "    nav_element.attr('style', 'width: 100%');\n",
+       "    this.root.append(nav_element);\n",
+       "\n",
+       "    // Define a callback function for later on.\n",
+       "    function toolbar_event(event) {\n",
+       "        return fig.toolbar_button_onclick(event['data']);\n",
+       "    }\n",
+       "    function toolbar_mouse_event(event) {\n",
+       "        return fig.toolbar_button_onmouseover(event['data']);\n",
+       "    }\n",
+       "\n",
+       "    for(var toolbar_ind in mpl.toolbar_items) {\n",
+       "        var name = mpl.toolbar_items[toolbar_ind][0];\n",
+       "        var tooltip = mpl.toolbar_items[toolbar_ind][1];\n",
+       "        var image = mpl.toolbar_items[toolbar_ind][2];\n",
+       "        var method_name = mpl.toolbar_items[toolbar_ind][3];\n",
+       "\n",
+       "        if (!name) {\n",
+       "            // put a spacer in here.\n",
+       "            continue;\n",
+       "        }\n",
+       "        var button = $('');\n",
+       "        button.click(method_name, toolbar_event);\n",
+       "        button.mouseover(tooltip, toolbar_mouse_event);\n",
+       "        nav_element.append(button);\n",
+       "    }\n",
+       "\n",
+       "    // Add the status bar.\n",
+       "    var status_bar = $('');\n",
+       "    nav_element.append(status_bar);\n",
+       "    this.message = status_bar[0];\n",
+       "\n",
+       "    // Add the close button to the window.\n",
+       "    var buttongrp = $('
');\n",
+       "    var button = $('');\n",
+       "    button.click(function (evt) { fig.handle_close(fig, {}); } );\n",
+       "    button.mouseover('Stop Interaction', toolbar_mouse_event);\n",
+       "    buttongrp.append(button);\n",
+       "    var titlebar = this.root.find($('.ui-dialog-titlebar'));\n",
+       "    titlebar.prepend(buttongrp);\n",
+       "}\n",
+       "\n",
+       "mpl.figure.prototype._root_extra_style = function(el){\n",
+       "    var fig = this\n",
+       "    el.on(\"remove\", function(){\n",
+       "\tfig.close_ws(fig, {});\n",
+       "    });\n",
+       "}\n",
+       "\n",
+       "mpl.figure.prototype._canvas_extra_style = function(el){\n",
+       "    // this is important to make the div 'focusable\n",
+       "    el.attr('tabindex', 0)\n",
+       "    // reach out to IPython and tell the keyboard manager to turn it's self\n",
+       "    // off when our div gets focus\n",
+       "\n",
+       "    // location in version 3\n",
+       "    if (IPython.notebook.keyboard_manager) {\n",
+       "        IPython.notebook.keyboard_manager.register_events(el);\n",
+       "    }\n",
+       "    else {\n",
+       "        // location in version 2\n",
+       "        IPython.keyboard_manager.register_events(el);\n",
+       "    }\n",
+       "\n",
+       "}\n",
+       "\n",
+       "mpl.figure.prototype._key_event_extra = function(event, name) {\n",
+       "    var manager = IPython.notebook.keyboard_manager;\n",
+       "    if (!manager)\n",
+       "        manager = IPython.keyboard_manager;\n",
+       "\n",
+       "    // Check for shift+enter\n",
+       "    if (event.shiftKey && event.which == 13) {\n",
+       "        this.canvas_div.blur();\n",
+       "        event.shiftKey = false;\n",
+       "        // Send a \"J\" for go to next cell\n",
+       "        event.which = 74;\n",
+       "        event.keyCode = 74;\n",
+       "        manager.command_mode();\n",
+       "        manager.handle_keydown(event);\n",
+       "    }\n",
+       "}\n",
+       "\n",
+       "mpl.figure.prototype.handle_save = function(fig, msg) {\n",
+       "    fig.ondownload(fig, null);\n",
+       "}\n",
+       "\n",
+       "\n",
+       "mpl.find_output_cell = function(html_output) {\n",
+       "    // Return the cell and output element which can be found *uniquely* in the notebook.\n",
+       "    // Note - this is a bit hacky, but it is done because the \"notebook_saving.Notebook\"\n",
+       "    // IPython event is triggered only after the cells have been serialised, which for\n",
+       "    // our purposes (turning an active figure into a static one), is too late.\n",
+       "    var cells = IPython.notebook.get_cells();\n",
+       "    var ncells = cells.length;\n",
+       "    for (var i=0; i= 3 moved mimebundle to data attribute of output\n",
+       "                    data = data.data;\n",
+       "                }\n",
+       "                if (data['text/html'] == html_output) {\n",
+       "                    return [cell, data, j];\n",
+       "                }\n",
+       "            }\n",
+       "        }\n",
+       "    }\n",
+       "}\n",
+       "\n",
+       "// Register the function which deals with the matplotlib target/channel.\n",
+       "// The kernel may be null if the page has been refreshed.\n",
+       "if (IPython.notebook.kernel != null) {\n",
+       "    IPython.notebook.kernel.comm_manager.register_target('matplotlib', mpl.mpl_figure_comm);\n",
+       "}\n"
+      ],
+      "text/plain": [
+       ""
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "text/html": [
+       ""
+      ],
+      "text/plain": [
+       ""
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "plt.figure()\n",
+    "plt.plot(data2[0])\n",
+    "plt.show()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 8,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [],
+   "source": [
+    "# Configure all settings in the Alazar card\n",
+    "ats_inst.config(clock_source='EXTERNAL_CLOCK_10MHz_REF',\n",
+    "                sample_rate='10MHZ_REF_500MSPS',\n",
+    "                clock_edge='CLOCK_EDGE_RISING',\n",
+    "                decimation=1,\n",
+    "                coupling=['DC','DC'],\n",
+    "                channel_range=[.4,.4],\n",
+    "                #impedance=[50,50],\n",
+    "                #trigger_operation='TRIG_ENGINE_OP_J',\n",
+    "                #trigger_engine1='TRIG_ENGINE_J',\n",
+    "                #trigger_source1='EXTERNAL',\n",
+    "                #trigger_slope1='TRIG_SLOPE_POSITIVE',\n",
+    "                trigger_level1=140,\n",
+    "                #trigger_engine2='TRIG_ENGINE_K',\n",
+    "                #trigger_source2='DISABLE',\n",
+    "                #trigger_slope2='TRIG_SLOPE_POSITIVE',\n",
+    "                #trigger_level2=128,\n",
+    "                external_trigger_coupling='DC',\n",
+    "                external_trigger_range='ETR_2V5',\n",
+    "                #trigger_delay=0,\n",
+    "                #timeout_ticks=0\n",
+    ")\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 9,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "'10MHZ_REF_500MSPS'"
+      ]
+     },
+     "execution_count": 9,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "ats_inst.sample_rate()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 10,
+   "metadata": {
+    "collapsed": true
+   },
+   "outputs": [],
+   "source": [
+    "data3 = basic_acquisition_controller.acquisition()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 16,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [
+    {
+     "data": {
+      "application/javascript": [
+       "/* Put everything inside the global mpl namespace */\n",
+       "window.mpl = {};\n",
+       "\n",
+       "mpl.get_websocket_type = function() {\n",
+       "    if (typeof(WebSocket) !== 'undefined') {\n",
+       "        return WebSocket;\n",
+       "    } else if (typeof(MozWebSocket) !== 'undefined') {\n",
+       "        return MozWebSocket;\n",
+       "    } else {\n",
+       "        alert('Your browser does not have WebSocket support.' +\n",
+       "              'Please try Chrome, Safari or Firefox ≥ 6. ' +\n",
+       "              'Firefox 4 and 5 are also supported but you ' +\n",
+       "              'have to enable WebSockets in about:config.');\n",
+       "    };\n",
+       "}\n",
+       "\n",
+       "mpl.figure = function(figure_id, websocket, ondownload, parent_element) {\n",
+       "    this.id = figure_id;\n",
+       "\n",
+       "    this.ws = websocket;\n",
+       "\n",
+       "    this.supports_binary = (this.ws.binaryType != undefined);\n",
+       "\n",
+       "    if (!this.supports_binary) {\n",
+       "        var warnings = document.getElementById(\"mpl-warnings\");\n",
+       "        if (warnings) {\n",
+       "            warnings.style.display = 'block';\n",
+       "            warnings.textContent = (\n",
+       "                \"This browser does not support binary websocket messages. \" +\n",
+       "                    \"Performance may be slow.\");\n",
+       "        }\n",
+       "    }\n",
+       "\n",
+       "    this.imageObj = new Image();\n",
+       "\n",
+       "    this.context = undefined;\n",
+       "    this.message = undefined;\n",
+       "    this.canvas = undefined;\n",
+       "    this.rubberband_canvas = undefined;\n",
+       "    this.rubberband_context = undefined;\n",
+       "    this.format_dropdown = undefined;\n",
+       "\n",
+       "    this.image_mode = 'full';\n",
+       "\n",
+       "    this.root = $('
');\n",
+       "    this._root_extra_style(this.root)\n",
+       "    this.root.attr('style', 'display: inline-block');\n",
+       "\n",
+       "    $(parent_element).append(this.root);\n",
+       "\n",
+       "    this._init_header(this);\n",
+       "    this._init_canvas(this);\n",
+       "    this._init_toolbar(this);\n",
+       "\n",
+       "    var fig = this;\n",
+       "\n",
+       "    this.waiting = false;\n",
+       "\n",
+       "    this.ws.onopen =  function () {\n",
+       "            fig.send_message(\"supports_binary\", {value: fig.supports_binary});\n",
+       "            fig.send_message(\"send_image_mode\", {});\n",
+       "            fig.send_message(\"refresh\", {});\n",
+       "        }\n",
+       "\n",
+       "    this.imageObj.onload = function() {\n",
+       "            if (fig.image_mode == 'full') {\n",
+       "                // Full images could contain transparency (where diff images\n",
+       "                // almost always do), so we need to clear the canvas so that\n",
+       "                // there is no ghosting.\n",
+       "                fig.context.clearRect(0, 0, fig.canvas.width, fig.canvas.height);\n",
+       "            }\n",
+       "            fig.context.drawImage(fig.imageObj, 0, 0);\n",
+       "        };\n",
+       "\n",
+       "    this.imageObj.onunload = function() {\n",
+       "        this.ws.close();\n",
+       "    }\n",
+       "\n",
+       "    this.ws.onmessage = this._make_on_message_function(this);\n",
+       "\n",
+       "    this.ondownload = ondownload;\n",
+       "}\n",
+       "\n",
+       "mpl.figure.prototype._init_header = function() {\n",
+       "    var titlebar = $(\n",
+       "        '
');\n",
+       "    var titletext = $(\n",
+       "        '
');\n",
+       "    titlebar.append(titletext)\n",
+       "    this.root.append(titlebar);\n",
+       "    this.header = titletext[0];\n",
+       "}\n",
+       "\n",
+       "\n",
+       "\n",
+       "mpl.figure.prototype._canvas_extra_style = function(canvas_div) {\n",
+       "\n",
+       "}\n",
+       "\n",
+       "\n",
+       "mpl.figure.prototype._root_extra_style = function(canvas_div) {\n",
+       "\n",
+       "}\n",
+       "\n",
+       "mpl.figure.prototype._init_canvas = function() {\n",
+       "    var fig = this;\n",
+       "\n",
+       "    var canvas_div = $('
');\n",
+       "\n",
+       "    canvas_div.attr('style', 'position: relative; clear: both; outline: 0');\n",
+       "\n",
+       "    function canvas_keyboard_event(event) {\n",
+       "        return fig.key_event(event, event['data']);\n",
+       "    }\n",
+       "\n",
+       "    canvas_div.keydown('key_press', canvas_keyboard_event);\n",
+       "    canvas_div.keyup('key_release', canvas_keyboard_event);\n",
+       "    this.canvas_div = canvas_div\n",
+       "    this._canvas_extra_style(canvas_div)\n",
+       "    this.root.append(canvas_div);\n",
+       "\n",
+       "    var canvas = $('');\n",
+       "    canvas.addClass('mpl-canvas');\n",
+       "    canvas.attr('style', \"left: 0; top: 0; z-index: 0; outline: 0\")\n",
+       "\n",
+       "    this.canvas = canvas[0];\n",
+       "    this.context = canvas[0].getContext(\"2d\");\n",
+       "\n",
+       "    var rubberband = $('');\n",
+       "    rubberband.attr('style', \"position: absolute; left: 0; top: 0; z-index: 1;\")\n",
+       "\n",
+       "    var pass_mouse_events = true;\n",
+       "\n",
+       "    canvas_div.resizable({\n",
+       "        start: function(event, ui) {\n",
+       "            pass_mouse_events = false;\n",
+       "        },\n",
+       "        resize: function(event, ui) {\n",
+       "            fig.request_resize(ui.size.width, ui.size.height);\n",
+       "        },\n",
+       "        stop: function(event, ui) {\n",
+       "            pass_mouse_events = true;\n",
+       "            fig.request_resize(ui.size.width, ui.size.height);\n",
+       "        },\n",
+       "    });\n",
+       "\n",
+       "    function mouse_event_fn(event) {\n",
+       "        if (pass_mouse_events)\n",
+       "            return fig.mouse_event(event, event['data']);\n",
+       "    }\n",
+       "\n",
+       "    rubberband.mousedown('button_press', mouse_event_fn);\n",
+       "    rubberband.mouseup('button_release', mouse_event_fn);\n",
+       "    // Throttle sequential mouse events to 1 every 20ms.\n",
+       "    rubberband.mousemove('motion_notify', mouse_event_fn);\n",
+       "\n",
+       "    rubberband.mouseenter('figure_enter', mouse_event_fn);\n",
+       "    rubberband.mouseleave('figure_leave', mouse_event_fn);\n",
+       "\n",
+       "    canvas_div.on(\"wheel\", function (event) {\n",
+       "        event = event.originalEvent;\n",
+       "        event['data'] = 'scroll'\n",
+       "        if (event.deltaY < 0) {\n",
+       "            event.step = 1;\n",
+       "        } else {\n",
+       "            event.step = -1;\n",
+       "        }\n",
+       "        mouse_event_fn(event);\n",
+       "    });\n",
+       "\n",
+       "    canvas_div.append(canvas);\n",
+       "    canvas_div.append(rubberband);\n",
+       "\n",
+       "    this.rubberband = rubberband;\n",
+       "    this.rubberband_canvas = rubberband[0];\n",
+       "    this.rubberband_context = rubberband[0].getContext(\"2d\");\n",
+       "    this.rubberband_context.strokeStyle = \"#000000\";\n",
+       "\n",
+       "    this._resize_canvas = function(width, height) {\n",
+       "        // Keep the size of the canvas, canvas container, and rubber band\n",
+       "        // canvas in synch.\n",
+       "        canvas_div.css('width', width)\n",
+       "        canvas_div.css('height', height)\n",
+       "\n",
+       "        canvas.attr('width', width);\n",
+       "        canvas.attr('height', height);\n",
+       "\n",
+       "        rubberband.attr('width', width);\n",
+       "        rubberband.attr('height', height);\n",
+       "    }\n",
+       "\n",
+       "    // Set the figure to an initial 600x600px, this will subsequently be updated\n",
+       "    // upon first draw.\n",
+       "    this._resize_canvas(600, 600);\n",
+       "\n",
+       "    // Disable right mouse context menu.\n",
+       "    $(this.rubberband_canvas).bind(\"contextmenu\",function(e){\n",
+       "        return false;\n",
+       "    });\n",
+       "\n",
+       "    function set_focus () {\n",
+       "        canvas.focus();\n",
+       "        canvas_div.focus();\n",
+       "    }\n",
+       "\n",
+       "    window.setTimeout(set_focus, 100);\n",
+       "}\n",
+       "\n",
+       "mpl.figure.prototype._init_toolbar = function() {\n",
+       "    var fig = this;\n",
+       "\n",
+       "    var nav_element = $('
')\n",
+       "    nav_element.attr('style', 'width: 100%');\n",
+       "    this.root.append(nav_element);\n",
+       "\n",
+       "    // Define a callback function for later on.\n",
+       "    function toolbar_event(event) {\n",
+       "        return fig.toolbar_button_onclick(event['data']);\n",
+       "    }\n",
+       "    function toolbar_mouse_event(event) {\n",
+       "        return fig.toolbar_button_onmouseover(event['data']);\n",
+       "    }\n",
+       "\n",
+       "    for(var toolbar_ind in mpl.toolbar_items) {\n",
+       "        var name = mpl.toolbar_items[toolbar_ind][0];\n",
+       "        var tooltip = mpl.toolbar_items[toolbar_ind][1];\n",
+       "        var image = mpl.toolbar_items[toolbar_ind][2];\n",
+       "        var method_name = mpl.toolbar_items[toolbar_ind][3];\n",
+       "\n",
+       "        if (!name) {\n",
+       "            // put a spacer in here.\n",
+       "            continue;\n",
+       "        }\n",
+       "        var button = $('');\n",
-       "        button.click(method_name, toolbar_event);\n",
-       "        button.mouseover(tooltip, toolbar_mouse_event);\n",
-       "        nav_element.append(button);\n",
-       "    }\n",
-       "\n",
-       "    // Add the status bar.\n",
-       "    var status_bar = $('');\n",
-       "    nav_element.append(status_bar);\n",
-       "    this.message = status_bar[0];\n",
-       "\n",
-       "    // Add the close button to the window.\n",
-       "    var buttongrp = $('
');\n",
-       "    var button = $('');\n",
-       "    button.click(function (evt) { fig.handle_close(fig, {}); } );\n",
-       "    button.mouseover('Stop Interaction', toolbar_mouse_event);\n",
-       "    buttongrp.append(button);\n",
-       "    var titlebar = this.root.find($('.ui-dialog-titlebar'));\n",
-       "    titlebar.prepend(buttongrp);\n",
-       "}\n",
-       "\n",
-       "mpl.figure.prototype._root_extra_style = function(el){\n",
-       "    var fig = this\n",
-       "    el.on(\"remove\", function(){\n",
-       "\tfig.close_ws(fig, {});\n",
-       "    });\n",
-       "}\n",
-       "\n",
-       "mpl.figure.prototype._canvas_extra_style = function(el){\n",
-       "    // this is important to make the div 'focusable\n",
-       "    el.attr('tabindex', 0)\n",
-       "    // reach out to IPython and tell the keyboard manager to turn it's self\n",
-       "    // off when our div gets focus\n",
-       "\n",
-       "    // location in version 3\n",
-       "    if (IPython.notebook.keyboard_manager) {\n",
-       "        IPython.notebook.keyboard_manager.register_events(el);\n",
-       "    }\n",
-       "    else {\n",
-       "        // location in version 2\n",
-       "        IPython.keyboard_manager.register_events(el);\n",
-       "    }\n",
-       "\n",
-       "}\n",
-       "\n",
-       "mpl.figure.prototype._key_event_extra = function(event, name) {\n",
-       "    var manager = IPython.notebook.keyboard_manager;\n",
-       "    if (!manager)\n",
-       "        manager = IPython.keyboard_manager;\n",
-       "\n",
-       "    // Check for shift+enter\n",
-       "    if (event.shiftKey && event.which == 13) {\n",
-       "        this.canvas_div.blur();\n",
-       "        event.shiftKey = false;\n",
-       "        // Send a \"J\" for go to next cell\n",
-       "        event.which = 74;\n",
-       "        event.keyCode = 74;\n",
-       "        manager.command_mode();\n",
-       "        manager.handle_keydown(event);\n",
-       "    }\n",
-       "}\n",
-       "\n",
-       "mpl.figure.prototype.handle_save = function(fig, msg) {\n",
-       "    fig.ondownload(fig, null);\n",
-       "}\n",
-       "\n",
-       "\n",
-       "mpl.find_output_cell = function(html_output) {\n",
-       "    // Return the cell and output element which can be found *uniquely* in the notebook.\n",
-       "    // Note - this is a bit hacky, but it is done because the \"notebook_saving.Notebook\"\n",
-       "    // IPython event is triggered only after the cells have been serialised, which for\n",
-       "    // our purposes (turning an active figure into a static one), is too late.\n",
-       "    var cells = IPython.notebook.get_cells();\n",
-       "    var ncells = cells.length;\n",
-       "    for (var i=0; i= 3 moved mimebundle to data attribute of output\n",
-       "                    data = data.data;\n",
-       "                }\n",
-       "                if (data['text/html'] == html_output) {\n",
-       "                    return [cell, data, j];\n",
-       "                }\n",
-       "            }\n",
-       "        }\n",
-       "    }\n",
-       "}\n",
-       "\n",
-       "// Register the function which deals with the matplotlib target/channel.\n",
-       "// The kernel may be null if the page has been refreshed.\n",
-       "if (IPython.notebook.kernel != null) {\n",
-       "    IPython.notebook.kernel.comm_manager.register_target('matplotlib', mpl.mpl_figure_comm);\n",
-       "}\n"
-      ],
-      "text/plain": [
-       ""
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    },
-    {
-     "data": {
-      "text/html": [
-       ""
-      ],
-      "text/plain": [
-       ""
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    }
-   ],
-   "source": [
-    "loaded_data = qc.load_data(\"data/2016-10-10/#002_testsweep_10-08-32\")\n",
-    "plot = qc.MatPlot(loaded_data.meter_amplitude)"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "## Example: multiple 2D measurements with live plotting"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 5,
-   "metadata": {
-    "collapsed": true
-   },
-   "outputs": [],
-   "source": [
-    "loop = qc.Loop(c1[-15:15:1], 0.01).loop(c0[-15:12:.5], 0.001).each(\n",
-    "    meter.amplitude, # first measurement, at c2=0 -> amplitude_0 bcs it's action 0\n",
-    "    qc.Task(c2.set, 1), # action 1 -> c2.set(1)\n",
-    "    qc.Wait(0.001),\n",
-    "    meter.amplitude, # second measurement, at c2=1 -> amplitude_4 bcs it's action 4\n",
-    "    qc.Task(c2.set, 0)\n",
-    "    )\n",
-    "data = loop.get_data_set(name='2D_test')"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "### Plot with matplotlib "
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 6,
-   "metadata": {
-    "collapsed": false,
-    "scrolled": false
-   },
-   "outputs": [
-    {
-     "data": {
-      "application/javascript": [
-       "/* Put everything inside the global mpl namespace */\n",
-       "window.mpl = {};\n",
-       "\n",
-       "mpl.get_websocket_type = function() {\n",
-       "    if (typeof(WebSocket) !== 'undefined') {\n",
-       "        return WebSocket;\n",
-       "    } else if (typeof(MozWebSocket) !== 'undefined') {\n",
-       "        return MozWebSocket;\n",
-       "    } else {\n",
-       "        alert('Your browser does not have WebSocket support.' +\n",
-       "              'Please try Chrome, Safari or Firefox ≥ 6. ' +\n",
-       "              'Firefox 4 and 5 are also supported but you ' +\n",
-       "              'have to enable WebSockets in about:config.');\n",
-       "    };\n",
-       "}\n",
-       "\n",
-       "mpl.figure = function(figure_id, websocket, ondownload, parent_element) {\n",
-       "    this.id = figure_id;\n",
-       "\n",
-       "    this.ws = websocket;\n",
-       "\n",
-       "    this.supports_binary = (this.ws.binaryType != undefined);\n",
-       "\n",
-       "    if (!this.supports_binary) {\n",
-       "        var warnings = document.getElementById(\"mpl-warnings\");\n",
-       "        if (warnings) {\n",
-       "            warnings.style.display = 'block';\n",
-       "            warnings.textContent = (\n",
-       "                \"This browser does not support binary websocket messages. \" +\n",
-       "                    \"Performance may be slow.\");\n",
-       "        }\n",
-       "    }\n",
-       "\n",
-       "    this.imageObj = new Image();\n",
-       "\n",
-       "    this.context = undefined;\n",
-       "    this.message = undefined;\n",
-       "    this.canvas = undefined;\n",
-       "    this.rubberband_canvas = undefined;\n",
-       "    this.rubberband_context = undefined;\n",
-       "    this.format_dropdown = undefined;\n",
-       "\n",
-       "    this.image_mode = 'full';\n",
-       "\n",
-       "    this.root = $('
');\n",
-       "    this._root_extra_style(this.root)\n",
-       "    this.root.attr('style', 'display: inline-block');\n",
-       "\n",
-       "    $(parent_element).append(this.root);\n",
-       "\n",
-       "    this._init_header(this);\n",
-       "    this._init_canvas(this);\n",
-       "    this._init_toolbar(this);\n",
-       "\n",
-       "    var fig = this;\n",
-       "\n",
-       "    this.waiting = false;\n",
-       "\n",
-       "    this.ws.onopen =  function () {\n",
-       "            fig.send_message(\"supports_binary\", {value: fig.supports_binary});\n",
-       "            fig.send_message(\"send_image_mode\", {});\n",
-       "            fig.send_message(\"refresh\", {});\n",
-       "        }\n",
-       "\n",
-       "    this.imageObj.onload = function() {\n",
-       "            if (fig.image_mode == 'full') {\n",
-       "                // Full images could contain transparency (where diff images\n",
-       "                // almost always do), so we need to clear the canvas so that\n",
-       "                // there is no ghosting.\n",
-       "                fig.context.clearRect(0, 0, fig.canvas.width, fig.canvas.height);\n",
-       "            }\n",
-       "            fig.context.drawImage(fig.imageObj, 0, 0);\n",
-       "        };\n",
-       "\n",
-       "    this.imageObj.onunload = function() {\n",
-       "        this.ws.close();\n",
-       "    }\n",
-       "\n",
-       "    this.ws.onmessage = this._make_on_message_function(this);\n",
-       "\n",
-       "    this.ondownload = ondownload;\n",
-       "}\n",
-       "\n",
-       "mpl.figure.prototype._init_header = function() {\n",
-       "    var titlebar = $(\n",
-       "        '
');\n",
-       "    var titletext = $(\n",
-       "        '
');\n",
-       "    titlebar.append(titletext)\n",
-       "    this.root.append(titlebar);\n",
-       "    this.header = titletext[0];\n",
-       "}\n",
-       "\n",
-       "\n",
-       "\n",
-       "mpl.figure.prototype._canvas_extra_style = function(canvas_div) {\n",
-       "\n",
-       "}\n",
-       "\n",
-       "\n",
-       "mpl.figure.prototype._root_extra_style = function(canvas_div) {\n",
-       "\n",
-       "}\n",
-       "\n",
-       "mpl.figure.prototype._init_canvas = function() {\n",
-       "    var fig = this;\n",
-       "\n",
-       "    var canvas_div = $('
');\n",
-       "\n",
-       "    canvas_div.attr('style', 'position: relative; clear: both; outline: 0');\n",
-       "\n",
-       "    function canvas_keyboard_event(event) {\n",
-       "        return fig.key_event(event, event['data']);\n",
-       "    }\n",
-       "\n",
-       "    canvas_div.keydown('key_press', canvas_keyboard_event);\n",
-       "    canvas_div.keyup('key_release', canvas_keyboard_event);\n",
-       "    this.canvas_div = canvas_div\n",
-       "    this._canvas_extra_style(canvas_div)\n",
-       "    this.root.append(canvas_div);\n",
-       "\n",
-       "    var canvas = $('');\n",
-       "    canvas.addClass('mpl-canvas');\n",
-       "    canvas.attr('style', \"left: 0; top: 0; z-index: 0; outline: 0\")\n",
-       "\n",
-       "    this.canvas = canvas[0];\n",
-       "    this.context = canvas[0].getContext(\"2d\");\n",
-       "\n",
-       "    var rubberband = $('');\n",
-       "    rubberband.attr('style', \"position: absolute; left: 0; top: 0; z-index: 1;\")\n",
-       "\n",
-       "    var pass_mouse_events = true;\n",
-       "\n",
-       "    canvas_div.resizable({\n",
-       "        start: function(event, ui) {\n",
-       "            pass_mouse_events = false;\n",
-       "        },\n",
-       "        resize: function(event, ui) {\n",
-       "            fig.request_resize(ui.size.width, ui.size.height);\n",
-       "        },\n",
-       "        stop: function(event, ui) {\n",
-       "            pass_mouse_events = true;\n",
-       "            fig.request_resize(ui.size.width, ui.size.height);\n",
-       "        },\n",
-       "    });\n",
-       "\n",
-       "    function mouse_event_fn(event) {\n",
-       "        if (pass_mouse_events)\n",
-       "            return fig.mouse_event(event, event['data']);\n",
-       "    }\n",
-       "\n",
-       "    rubberband.mousedown('button_press', mouse_event_fn);\n",
-       "    rubberband.mouseup('button_release', mouse_event_fn);\n",
-       "    // Throttle sequential mouse events to 1 every 20ms.\n",
-       "    rubberband.mousemove('motion_notify', mouse_event_fn);\n",
-       "\n",
-       "    rubberband.mouseenter('figure_enter', mouse_event_fn);\n",
-       "    rubberband.mouseleave('figure_leave', mouse_event_fn);\n",
-       "\n",
-       "    canvas_div.on(\"wheel\", function (event) {\n",
-       "        event = event.originalEvent;\n",
-       "        event['data'] = 'scroll'\n",
-       "        if (event.deltaY < 0) {\n",
-       "            event.step = 1;\n",
-       "        } else {\n",
-       "            event.step = -1;\n",
-       "        }\n",
-       "        mouse_event_fn(event);\n",
-       "    });\n",
-       "\n",
-       "    canvas_div.append(canvas);\n",
-       "    canvas_div.append(rubberband);\n",
-       "\n",
-       "    this.rubberband = rubberband;\n",
-       "    this.rubberband_canvas = rubberband[0];\n",
-       "    this.rubberband_context = rubberband[0].getContext(\"2d\");\n",
-       "    this.rubberband_context.strokeStyle = \"#000000\";\n",
-       "\n",
-       "    this._resize_canvas = function(width, height) {\n",
-       "        // Keep the size of the canvas, canvas container, and rubber band\n",
-       "        // canvas in synch.\n",
-       "        canvas_div.css('width', width)\n",
-       "        canvas_div.css('height', height)\n",
-       "\n",
-       "        canvas.attr('width', width);\n",
-       "        canvas.attr('height', height);\n",
-       "\n",
-       "        rubberband.attr('width', width);\n",
-       "        rubberband.attr('height', height);\n",
-       "    }\n",
-       "\n",
-       "    // Set the figure to an initial 600x600px, this will subsequently be updated\n",
-       "    // upon first draw.\n",
-       "    this._resize_canvas(600, 600);\n",
-       "\n",
-       "    // Disable right mouse context menu.\n",
-       "    $(this.rubberband_canvas).bind(\"contextmenu\",function(e){\n",
-       "        return false;\n",
-       "    });\n",
-       "\n",
-       "    function set_focus () {\n",
-       "        canvas.focus();\n",
-       "        canvas_div.focus();\n",
-       "    }\n",
-       "\n",
-       "    window.setTimeout(set_focus, 100);\n",
-       "}\n",
-       "\n",
-       "mpl.figure.prototype._init_toolbar = function() {\n",
-       "    var fig = this;\n",
-       "\n",
-       "    var nav_element = $('
')\n",
-       "    nav_element.attr('style', 'width: 100%');\n",
-       "    this.root.append(nav_element);\n",
-       "\n",
-       "    // Define a callback function for later on.\n",
-       "    function toolbar_event(event) {\n",
-       "        return fig.toolbar_button_onclick(event['data']);\n",
-       "    }\n",
-       "    function toolbar_mouse_event(event) {\n",
-       "        return fig.toolbar_button_onmouseover(event['data']);\n",
-       "    }\n",
-       "\n",
-       "    for(var toolbar_ind in mpl.toolbar_items) {\n",
-       "        var name = mpl.toolbar_items[toolbar_ind][0];\n",
-       "        var tooltip = mpl.toolbar_items[toolbar_ind][1];\n",
-       "        var image = mpl.toolbar_items[toolbar_ind][2];\n",
-       "        var method_name = mpl.toolbar_items[toolbar_ind][3];\n",
-       "\n",
-       "        if (!name) {\n",
-       "            // put a spacer in here.\n",
-       "            continue;\n",
-       "        }\n",
-       "        var button = $('');\n",
+       "        button.click(method_name, toolbar_event);\n",
+       "        button.mouseover(tooltip, toolbar_mouse_event);\n",
+       "        nav_element.append(button);\n",
+       "    }\n",
+       "\n",
+       "    // Add the status bar.\n",
+       "    var status_bar = $('');\n",
+       "    nav_element.append(status_bar);\n",
+       "    this.message = status_bar[0];\n",
+       "\n",
+       "    // Add the close button to the window.\n",
+       "    var buttongrp = $('
');\n",
+       "    var button = $('');\n",
+       "    button.click(function (evt) { fig.handle_close(fig, {}); } );\n",
+       "    button.mouseover('Stop Interaction', toolbar_mouse_event);\n",
+       "    buttongrp.append(button);\n",
+       "    var titlebar = this.root.find($('.ui-dialog-titlebar'));\n",
+       "    titlebar.prepend(buttongrp);\n",
+       "}\n",
+       "\n",
+       "mpl.figure.prototype._root_extra_style = function(el){\n",
+       "    var fig = this\n",
+       "    el.on(\"remove\", function(){\n",
+       "\tfig.close_ws(fig, {});\n",
+       "    });\n",
+       "}\n",
+       "\n",
+       "mpl.figure.prototype._canvas_extra_style = function(el){\n",
+       "    // this is important to make the div 'focusable\n",
+       "    el.attr('tabindex', 0)\n",
+       "    // reach out to IPython and tell the keyboard manager to turn it's self\n",
+       "    // off when our div gets focus\n",
+       "\n",
+       "    // location in version 3\n",
+       "    if (IPython.notebook.keyboard_manager) {\n",
+       "        IPython.notebook.keyboard_manager.register_events(el);\n",
+       "    }\n",
+       "    else {\n",
+       "        // location in version 2\n",
+       "        IPython.keyboard_manager.register_events(el);\n",
+       "    }\n",
+       "\n",
+       "}\n",
+       "\n",
+       "mpl.figure.prototype._key_event_extra = function(event, name) {\n",
+       "    var manager = IPython.notebook.keyboard_manager;\n",
+       "    if (!manager)\n",
+       "        manager = IPython.keyboard_manager;\n",
+       "\n",
+       "    // Check for shift+enter\n",
+       "    if (event.shiftKey && event.which == 13) {\n",
+       "        this.canvas_div.blur();\n",
+       "        event.shiftKey = false;\n",
+       "        // Send a \"J\" for go to next cell\n",
+       "        event.which = 74;\n",
+       "        event.keyCode = 74;\n",
+       "        manager.command_mode();\n",
+       "        manager.handle_keydown(event);\n",
+       "    }\n",
+       "}\n",
+       "\n",
+       "mpl.figure.prototype.handle_save = function(fig, msg) {\n",
+       "    fig.ondownload(fig, null);\n",
+       "}\n",
+       "\n",
+       "\n",
+       "mpl.find_output_cell = function(html_output) {\n",
+       "    // Return the cell and output element which can be found *uniquely* in the notebook.\n",
+       "    // Note - this is a bit hacky, but it is done because the \"notebook_saving.Notebook\"\n",
+       "    // IPython event is triggered only after the cells have been serialised, which for\n",
+       "    // our purposes (turning an active figure into a static one), is too late.\n",
+       "    var cells = IPython.notebook.get_cells();\n",
+       "    var ncells = cells.length;\n",
+       "    for (var i=0; i= 3 moved mimebundle to data attribute of output\n",
+       "                    data = data.data;\n",
+       "                }\n",
+       "                if (data['text/html'] == html_output) {\n",
+       "                    return [cell, data, j];\n",
+       "                }\n",
+       "            }\n",
+       "        }\n",
+       "    }\n",
+       "}\n",
+       "\n",
+       "// Register the function which deals with the matplotlib target/channel.\n",
+       "// The kernel may be null if the page has been refreshed.\n",
+       "if (IPython.notebook.kernel != null) {\n",
+       "    IPython.notebook.kernel.comm_manager.register_target('matplotlib', mpl.mpl_figure_comm);\n",
+       "}\n"
+      ],
+      "text/plain": [
+       ""
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "text/html": [
+       ""
+      ],
+      "text/plain": [
+       ""
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "import matplotlib.pyplot as plt\n",
+    "plt.figure()\n",
+    "plt.plot(data3[1])\n",
+    "plt.show()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 14,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "DataSet:\n",
+      "   mode     = DataMode.LOCAL\n",
+      "   location = '2016-11-08/10-10-07_AlazarTest'\n",
+      "      |                    |  | \n",
+      "   Setpoint | dummy_set                    | dummy        | (5,)\n",
+      "   Measured | sample_num                   | sample_num   | (5, 1024)\n",
+      "   Measured | samples_controller_magnitude | magnitude    | (5, 1024)\n",
+      "   Measured | samples_controller_phase     | phase        | (5, 1024)\n",
+      "started at 2016-11-08 10:10:09\n"
+     ]
+    }
+   ],
+   "source": [
+    "dummy = parameter.ManualParameter(name=\"dummy\")\n",
+    "data4 = qc.Loop(dummy[0:5:1]).each(\n",
+    "    samp_cont.acquisition).run(name='AlazarTest')"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 23,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [
+    {
+     "ename": "NameError",
+     "evalue": "name 'data4' is not defined",
+     "output_type": "error",
+     "traceback": [
+      "\u001b[1;31m---------------------------------------------------------------------------\u001b[0m",
+      "\u001b[1;31mNameError\u001b[0m                                 Traceback (most recent call last)",
+      "\u001b[1;32m\u001b[0m in \u001b[0;36m\u001b[1;34m()\u001b[0m\n\u001b[1;32m----> 1\u001b[1;33m \u001b[0mqc\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mQtPlot\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mdata4\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0msamples_controller_magnitude\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m",
+      "\u001b[1;31mNameError\u001b[0m: name 'data4' is not defined"
+     ]
+    }
+   ],
+   "source": [
+    "qc.QtPlot(data4.samples_controller_magnitude)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 24,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "a:\\qcodes\\qcodes\\instrument\\metaclass.py:37: UserWarning: Multiprocessing is in beta, use at own risk\n",
+      "  UserWarning)\n"
+     ]
+    }
+   ],
+   "source": [
+    "import qcodes.instrument_drivers.AlazarTech.Single_controller as single_controller\n",
+    "\n",
+    "sing_contr = single_controller.HD_Controller(name='single_controller', \n",
+    "                                                      alazar_name='Alazar1', \n",
+    "                                                      demod_freq = 5e6,\n",
+    "                                                      server_name=\"alazar_server\")"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 25,
+   "metadata": {
+    "collapsed": true
+   },
+   "outputs": [],
+   "source": [
+    "# This command is specific to this acquisition controller. The kwargs provided here are being forwarded to ats_inst.acquire\n",
+    "# This way, it becomes easy to change acquisition specific settings from the ipython notebook\n",
+    "sing_contr.update_acquisitionkwargs(#mode='NPT',\n",
+    "                 samples_per_record=2560,\n",
+    "                 records_per_buffer=1,\n",
+    "                 buffers_per_acquisition=1,\n",
+    "                 #channel_selection='AB',\n",
+    "                 #transfer_offset=0,\n",
+    "                 #external_startcapture='ENABLED',\n",
+    "                 #enable_record_headers='DISABLED',\n",
+    "                 #alloc_buffers='DISABLED',\n",
+    "                 #fifo_only_streaming='DISABLED',\n",
+    "                 #interleave_samples='DISABLED',\n",
+    "                 #get_processed_data='DISABLED',\n",
+    "                 allocated_buffers=1,\n",
+    "                 #buffer_timeout=1000\n",
+    ")"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 26,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [],
+   "source": [
+    "data5 = sing_contr.acquisition()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 27,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "(3326.8644117553804, 54.301233235582359)"
+      ]
+     },
+     "execution_count": 27,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "data5"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 30,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "DataSet:\n",
+      "   mode     = DataMode.LOCAL\n",
+      "   location = '2016-11-08/10-27-15_AlazarTest'\n",
+      "      |                   |  | \n",
+      "   Setpoint | dummy_set                   | dummy        | (5,)\n",
+      "   Measured | single_controller_magnitude | magnitude    | (5,)\n",
+      "   Measured | single_controller_phase     | phase        | (5,)\n",
+      "started at 2016-11-08 10:27:17\n"
+     ]
+    }
+   ],
+   "source": [
+    "dummy = parameter.ManualParameter(name=\"dummy\")\n",
+    "data6 = qc.Loop(dummy[0:5:1]).each(\n",
+    "    sing_contr.acquisition).run(name='AlazarTest')"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 13,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       ""
+      ]
+     },
+     "execution_count": 13,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "qc.QtPlot(data6.single_controller_phase)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 11,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "a:\\qcodes\\qcodes\\instrument\\metaclass.py:37: UserWarning: Multiprocessing is in beta, use at own risk\n",
+      "  UserWarning)\n"
+     ]
+    }
+   ],
+   "source": [
+    "import qcodes.instrument_drivers.AlazarTech.Rec_controller as record_controller\n",
+    "\n",
+    "rec_contr = record_controller.HD_Records_Controller(name='rec_controller', \n",
+    "                                                      alazar_name='Alazar1', \n",
+    "                                                      demod_freq = 5e6,\n",
+    "                                                      server_name=\"alazar_server\")"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 12,
+   "metadata": {
+    "collapsed": true
+   },
+   "outputs": [],
+   "source": [
+    "# This command is specific to this acquisition controller. The kwargs provided here are being forwarded to ats_inst.acquire\n",
+    "# This way, it becomes easy to change acquisition specific settings from the ipython notebook\n",
+    "rec_contr.update_acquisitionkwargs(#mode='NPT',\n",
+    "                 samples_per_record=1024,\n",
+    "                 records_per_buffer=20,\n",
+    "                 buffers_per_acquisition=1,\n",
+    "                 #channel_selection='AB',\n",
+    "                 #transfer_offset=0,\n",
+    "                 #external_startcapture='ENABLED',\n",
+    "                 #enable_record_headers='DISABLED',\n",
+    "                 #alloc_buffers='DISABLED',\n",
+    "                 #fifo_only_streaming='DISABLED',\n",
+    "                 #interleave_samples='DISABLED',\n",
+    "                 #get_processed_data='DISABLED',\n",
+    "                 allocated_buffers=1,\n",
+    "                 #buffer_timeout=1000\n",
+    ")"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 13,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [],
+   "source": [
+    "data7 = rec_contr.acquisition()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 14,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "(array([ 3289.96320022,  3286.41234804,  3307.51961298,  3258.31653189,\n",
+       "         3293.95959593,  3291.98748708,  3260.12595817,  3303.707061  ,\n",
+       "         3262.98369435,  3297.69539837,  3301.61275188,  3272.2064834 ,\n",
+       "         3309.53487782,  3264.07853243,  3297.85260115,  3285.39823933,\n",
+       "         3272.54744985,  3304.02929544,  3273.01252546,  3289.82606187]),\n",
+       " array([  59.2785189 ,  -35.71021399,   23.98720238,  101.76356054,\n",
+       "           7.88187056, -123.96879121,  -64.55449409,   13.57780392,\n",
+       "         104.73958041,    7.80840739, -123.26288987,  -57.18388729,\n",
+       "          23.95505572,  104.72063281,    8.14861144, -109.83457381,\n",
+       "         -42.96898046,   24.012719  ,   83.99967619,   23.67250356]))"
+      ]
+     },
+     "execution_count": 14,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "data7"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 15,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [
+    {
+     "data": {
+      "application/javascript": [
+       "/* Put everything inside the global mpl namespace */\n",
+       "window.mpl = {};\n",
+       "\n",
+       "mpl.get_websocket_type = function() {\n",
+       "    if (typeof(WebSocket) !== 'undefined') {\n",
+       "        return WebSocket;\n",
+       "    } else if (typeof(MozWebSocket) !== 'undefined') {\n",
+       "        return MozWebSocket;\n",
+       "    } else {\n",
+       "        alert('Your browser does not have WebSocket support.' +\n",
+       "              'Please try Chrome, Safari or Firefox ≥ 6. ' +\n",
+       "              'Firefox 4 and 5 are also supported but you ' +\n",
+       "              'have to enable WebSockets in about:config.');\n",
+       "    };\n",
+       "}\n",
+       "\n",
+       "mpl.figure = function(figure_id, websocket, ondownload, parent_element) {\n",
+       "    this.id = figure_id;\n",
+       "\n",
+       "    this.ws = websocket;\n",
+       "\n",
+       "    this.supports_binary = (this.ws.binaryType != undefined);\n",
+       "\n",
+       "    if (!this.supports_binary) {\n",
+       "        var warnings = document.getElementById(\"mpl-warnings\");\n",
+       "        if (warnings) {\n",
+       "            warnings.style.display = 'block';\n",
+       "            warnings.textContent = (\n",
+       "                \"This browser does not support binary websocket messages. \" +\n",
+       "                    \"Performance may be slow.\");\n",
+       "        }\n",
+       "    }\n",
+       "\n",
+       "    this.imageObj = new Image();\n",
+       "\n",
+       "    this.context = undefined;\n",
+       "    this.message = undefined;\n",
+       "    this.canvas = undefined;\n",
+       "    this.rubberband_canvas = undefined;\n",
+       "    this.rubberband_context = undefined;\n",
+       "    this.format_dropdown = undefined;\n",
+       "\n",
+       "    this.image_mode = 'full';\n",
+       "\n",
+       "    this.root = $('
');\n",
+       "    this._root_extra_style(this.root)\n",
+       "    this.root.attr('style', 'display: inline-block');\n",
+       "\n",
+       "    $(parent_element).append(this.root);\n",
+       "\n",
+       "    this._init_header(this);\n",
+       "    this._init_canvas(this);\n",
+       "    this._init_toolbar(this);\n",
+       "\n",
+       "    var fig = this;\n",
+       "\n",
+       "    this.waiting = false;\n",
+       "\n",
+       "    this.ws.onopen =  function () {\n",
+       "            fig.send_message(\"supports_binary\", {value: fig.supports_binary});\n",
+       "            fig.send_message(\"send_image_mode\", {});\n",
+       "            fig.send_message(\"refresh\", {});\n",
+       "        }\n",
+       "\n",
+       "    this.imageObj.onload = function() {\n",
+       "            if (fig.image_mode == 'full') {\n",
+       "                // Full images could contain transparency (where diff images\n",
+       "                // almost always do), so we need to clear the canvas so that\n",
+       "                // there is no ghosting.\n",
+       "                fig.context.clearRect(0, 0, fig.canvas.width, fig.canvas.height);\n",
+       "            }\n",
+       "            fig.context.drawImage(fig.imageObj, 0, 0);\n",
+       "        };\n",
+       "\n",
+       "    this.imageObj.onunload = function() {\n",
+       "        this.ws.close();\n",
+       "    }\n",
+       "\n",
+       "    this.ws.onmessage = this._make_on_message_function(this);\n",
+       "\n",
+       "    this.ondownload = ondownload;\n",
+       "}\n",
+       "\n",
+       "mpl.figure.prototype._init_header = function() {\n",
+       "    var titlebar = $(\n",
+       "        '
');\n",
+       "    var titletext = $(\n",
+       "        '
');\n",
+       "    titlebar.append(titletext)\n",
+       "    this.root.append(titlebar);\n",
+       "    this.header = titletext[0];\n",
+       "}\n",
+       "\n",
+       "\n",
+       "\n",
+       "mpl.figure.prototype._canvas_extra_style = function(canvas_div) {\n",
+       "\n",
+       "}\n",
+       "\n",
+       "\n",
+       "mpl.figure.prototype._root_extra_style = function(canvas_div) {\n",
+       "\n",
+       "}\n",
+       "\n",
+       "mpl.figure.prototype._init_canvas = function() {\n",
+       "    var fig = this;\n",
+       "\n",
+       "    var canvas_div = $('
');\n",
+       "\n",
+       "    canvas_div.attr('style', 'position: relative; clear: both; outline: 0');\n",
+       "\n",
+       "    function canvas_keyboard_event(event) {\n",
+       "        return fig.key_event(event, event['data']);\n",
+       "    }\n",
+       "\n",
+       "    canvas_div.keydown('key_press', canvas_keyboard_event);\n",
+       "    canvas_div.keyup('key_release', canvas_keyboard_event);\n",
+       "    this.canvas_div = canvas_div\n",
+       "    this._canvas_extra_style(canvas_div)\n",
+       "    this.root.append(canvas_div);\n",
+       "\n",
+       "    var canvas = $('');\n",
+       "    canvas.addClass('mpl-canvas');\n",
+       "    canvas.attr('style', \"left: 0; top: 0; z-index: 0; outline: 0\")\n",
+       "\n",
+       "    this.canvas = canvas[0];\n",
+       "    this.context = canvas[0].getContext(\"2d\");\n",
+       "\n",
+       "    var rubberband = $('');\n",
+       "    rubberband.attr('style', \"position: absolute; left: 0; top: 0; z-index: 1;\")\n",
+       "\n",
+       "    var pass_mouse_events = true;\n",
+       "\n",
+       "    canvas_div.resizable({\n",
+       "        start: function(event, ui) {\n",
+       "            pass_mouse_events = false;\n",
+       "        },\n",
+       "        resize: function(event, ui) {\n",
+       "            fig.request_resize(ui.size.width, ui.size.height);\n",
+       "        },\n",
+       "        stop: function(event, ui) {\n",
+       "            pass_mouse_events = true;\n",
+       "            fig.request_resize(ui.size.width, ui.size.height);\n",
+       "        },\n",
+       "    });\n",
+       "\n",
+       "    function mouse_event_fn(event) {\n",
+       "        if (pass_mouse_events)\n",
+       "            return fig.mouse_event(event, event['data']);\n",
+       "    }\n",
+       "\n",
+       "    rubberband.mousedown('button_press', mouse_event_fn);\n",
+       "    rubberband.mouseup('button_release', mouse_event_fn);\n",
+       "    // Throttle sequential mouse events to 1 every 20ms.\n",
+       "    rubberband.mousemove('motion_notify', mouse_event_fn);\n",
+       "\n",
+       "    rubberband.mouseenter('figure_enter', mouse_event_fn);\n",
+       "    rubberband.mouseleave('figure_leave', mouse_event_fn);\n",
+       "\n",
+       "    canvas_div.on(\"wheel\", function (event) {\n",
+       "        event = event.originalEvent;\n",
+       "        event['data'] = 'scroll'\n",
+       "        if (event.deltaY < 0) {\n",
+       "            event.step = 1;\n",
+       "        } else {\n",
+       "            event.step = -1;\n",
+       "        }\n",
+       "        mouse_event_fn(event);\n",
+       "    });\n",
+       "\n",
+       "    canvas_div.append(canvas);\n",
+       "    canvas_div.append(rubberband);\n",
+       "\n",
+       "    this.rubberband = rubberband;\n",
+       "    this.rubberband_canvas = rubberband[0];\n",
+       "    this.rubberband_context = rubberband[0].getContext(\"2d\");\n",
+       "    this.rubberband_context.strokeStyle = \"#000000\";\n",
+       "\n",
+       "    this._resize_canvas = function(width, height) {\n",
+       "        // Keep the size of the canvas, canvas container, and rubber band\n",
+       "        // canvas in synch.\n",
+       "        canvas_div.css('width', width)\n",
+       "        canvas_div.css('height', height)\n",
+       "\n",
+       "        canvas.attr('width', width);\n",
+       "        canvas.attr('height', height);\n",
+       "\n",
+       "        rubberband.attr('width', width);\n",
+       "        rubberband.attr('height', height);\n",
+       "    }\n",
+       "\n",
+       "    // Set the figure to an initial 600x600px, this will subsequently be updated\n",
+       "    // upon first draw.\n",
+       "    this._resize_canvas(600, 600);\n",
+       "\n",
+       "    // Disable right mouse context menu.\n",
+       "    $(this.rubberband_canvas).bind(\"contextmenu\",function(e){\n",
+       "        return false;\n",
+       "    });\n",
+       "\n",
+       "    function set_focus () {\n",
+       "        canvas.focus();\n",
+       "        canvas_div.focus();\n",
+       "    }\n",
+       "\n",
+       "    window.setTimeout(set_focus, 100);\n",
+       "}\n",
+       "\n",
+       "mpl.figure.prototype._init_toolbar = function() {\n",
+       "    var fig = this;\n",
+       "\n",
+       "    var nav_element = $('
')\n",
+       "    nav_element.attr('style', 'width: 100%');\n",
+       "    this.root.append(nav_element);\n",
+       "\n",
+       "    // Define a callback function for later on.\n",
+       "    function toolbar_event(event) {\n",
+       "        return fig.toolbar_button_onclick(event['data']);\n",
+       "    }\n",
+       "    function toolbar_mouse_event(event) {\n",
+       "        return fig.toolbar_button_onmouseover(event['data']);\n",
+       "    }\n",
+       "\n",
+       "    for(var toolbar_ind in mpl.toolbar_items) {\n",
+       "        var name = mpl.toolbar_items[toolbar_ind][0];\n",
+       "        var tooltip = mpl.toolbar_items[toolbar_ind][1];\n",
+       "        var image = mpl.toolbar_items[toolbar_ind][2];\n",
+       "        var method_name = mpl.toolbar_items[toolbar_ind][3];\n",
+       "\n",
+       "        if (!name) {\n",
+       "            // put a spacer in here.\n",
+       "            continue;\n",
+       "        }\n",
+       "        var button = $('');\n",
+       "        button.click(method_name, toolbar_event);\n",
+       "        button.mouseover(tooltip, toolbar_mouse_event);\n",
+       "        nav_element.append(button);\n",
+       "    }\n",
+       "\n",
+       "    // Add the status bar.\n",
+       "    var status_bar = $('');\n",
+       "    nav_element.append(status_bar);\n",
+       "    this.message = status_bar[0];\n",
+       "\n",
+       "    // Add the close button to the window.\n",
+       "    var buttongrp = $('
');\n",
+       "    var button = $('');\n",
+       "    button.click(function (evt) { fig.handle_close(fig, {}); } );\n",
+       "    button.mouseover('Stop Interaction', toolbar_mouse_event);\n",
+       "    buttongrp.append(button);\n",
+       "    var titlebar = this.root.find($('.ui-dialog-titlebar'));\n",
+       "    titlebar.prepend(buttongrp);\n",
+       "}\n",
+       "\n",
+       "mpl.figure.prototype._root_extra_style = function(el){\n",
+       "    var fig = this\n",
+       "    el.on(\"remove\", function(){\n",
+       "\tfig.close_ws(fig, {});\n",
+       "    });\n",
+       "}\n",
+       "\n",
+       "mpl.figure.prototype._canvas_extra_style = function(el){\n",
+       "    // this is important to make the div 'focusable\n",
+       "    el.attr('tabindex', 0)\n",
+       "    // reach out to IPython and tell the keyboard manager to turn it's self\n",
+       "    // off when our div gets focus\n",
+       "\n",
+       "    // location in version 3\n",
+       "    if (IPython.notebook.keyboard_manager) {\n",
+       "        IPython.notebook.keyboard_manager.register_events(el);\n",
+       "    }\n",
+       "    else {\n",
+       "        // location in version 2\n",
+       "        IPython.keyboard_manager.register_events(el);\n",
+       "    }\n",
+       "\n",
+       "}\n",
+       "\n",
+       "mpl.figure.prototype._key_event_extra = function(event, name) {\n",
+       "    var manager = IPython.notebook.keyboard_manager;\n",
+       "    if (!manager)\n",
+       "        manager = IPython.keyboard_manager;\n",
+       "\n",
+       "    // Check for shift+enter\n",
+       "    if (event.shiftKey && event.which == 13) {\n",
+       "        this.canvas_div.blur();\n",
+       "        event.shiftKey = false;\n",
+       "        // Send a \"J\" for go to next cell\n",
+       "        event.which = 74;\n",
+       "        event.keyCode = 74;\n",
+       "        manager.command_mode();\n",
+       "        manager.handle_keydown(event);\n",
+       "    }\n",
+       "}\n",
+       "\n",
+       "mpl.figure.prototype.handle_save = function(fig, msg) {\n",
+       "    fig.ondownload(fig, null);\n",
+       "}\n",
+       "\n",
+       "\n",
+       "mpl.find_output_cell = function(html_output) {\n",
+       "    // Return the cell and output element which can be found *uniquely* in the notebook.\n",
+       "    // Note - this is a bit hacky, but it is done because the \"notebook_saving.Notebook\"\n",
+       "    // IPython event is triggered only after the cells have been serialised, which for\n",
+       "    // our purposes (turning an active figure into a static one), is too late.\n",
+       "    var cells = IPython.notebook.get_cells();\n",
+       "    var ncells = cells.length;\n",
+       "    for (var i=0; i= 3 moved mimebundle to data attribute of output\n",
+       "                    data = data.data;\n",
+       "                }\n",
+       "                if (data['text/html'] == html_output) {\n",
+       "                    return [cell, data, j];\n",
+       "                }\n",
+       "            }\n",
+       "        }\n",
+       "    }\n",
+       "}\n",
+       "\n",
+       "// Register the function which deals with the matplotlib target/channel.\n",
+       "// The kernel may be null if the page has been refreshed.\n",
+       "if (IPython.notebook.kernel != null) {\n",
+       "    IPython.notebook.kernel.comm_manager.register_target('matplotlib', mpl.mpl_figure_comm);\n",
+       "}\n"
+      ],
+      "text/plain": [
+       ""
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "text/html": [
+       ""
+      ],
+      "text/plain": [
+       ""
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "import matplotlib.pyplot as plt\n",
+    "plt.figure()\n",
+    "plt.plot(data9[1])\n",
+    "plt.show()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 14,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [
+    {
+     "ename": "ValueError",
+     "evalue": "nth setpoint array should have shape matching the first n dimensions of shape.",
+     "output_type": "error",
+     "traceback": [
+      "\u001b[1;31m---------------------------------------------------------------------------\u001b[0m",
+      "\u001b[1;31mValueError\u001b[0m                                Traceback (most recent call last)",
+      "\u001b[1;32m\u001b[0m in \u001b[0;36m\u001b[1;34m()\u001b[0m\n\u001b[0;32m      1\u001b[0m \u001b[0mdummy\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mparameter\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mManualParameter\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mname\u001b[0m\u001b[1;33m=\u001b[0m\u001b[1;34m\"dummy\"\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m      2\u001b[0m data10 = qc.Loop(dummy[0:5:1]).each(\n\u001b[1;32m----> 3\u001b[1;33m     recsamp_contr.acquisition).run(name='AlazarTest')\n\u001b[0m",
+      "\u001b[1;32ma:\\qcodes\\qcodes\\loops.py\u001b[0m in \u001b[0;36mrun\u001b[1;34m(self, background, use_threads, quiet, data_manager, station, progress_interval, *args, **kwargs)\u001b[0m\n\u001b[0;32m    759\u001b[0m             \u001b[0mprev_loop\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mjoin\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    760\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m--> 761\u001b[1;33m         \u001b[0mdata_set\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mget_data_set\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mdata_manager\u001b[0m\u001b[1;33m,\u001b[0m \u001b[1;33m*\u001b[0m\u001b[0margs\u001b[0m\u001b[1;33m,\u001b[0m \u001b[1;33m**\u001b[0m\u001b[0mkwargs\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m    762\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    763\u001b[0m         self.set_common_attrs(data_set=data_set, use_threads=use_threads,\n",
+      "\u001b[1;32ma:\\qcodes\\qcodes\\loops.py\u001b[0m in \u001b[0;36mget_data_set\u001b[1;34m(self, data_manager, *args, **kwargs)\u001b[0m\n\u001b[0;32m    683\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    684\u001b[0m             data_set = new_data(arrays=self.containers(), mode=data_mode,\n\u001b[1;32m--> 685\u001b[1;33m                                 data_manager=data_manager, *args, **kwargs)\n\u001b[0m\u001b[0;32m    686\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    687\u001b[0m             \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mdata_set\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mdata_set\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
+      "\u001b[1;32ma:\\qcodes\\qcodes\\loops.py\u001b[0m in \u001b[0;36mcontainers\u001b[1;34m(self)\u001b[0m\n\u001b[0;32m    486\u001b[0m                 \u001b[1;31m# note that this supports lists (separate output arrays)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    487\u001b[0m                 \u001b[1;31m# and arrays (nested in one/each output array) of return values\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m--> 488\u001b[1;33m                 \u001b[0maction_arrays\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0m_parameter_arrays\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0maction\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m    489\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    490\u001b[0m             \u001b[1;32melse\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
+      "\u001b[1;32ma:\\qcodes\\qcodes\\loops.py\u001b[0m in \u001b[0;36m_parameter_arrays\u001b[1;34m(self, action)\u001b[0m\n\u001b[0;32m    560\u001b[0m                 \u001b[0msp_def\u001b[0m \u001b[1;33m=\u001b[0m \u001b[1;33m(\u001b[0m\u001b[0mshape\u001b[0m\u001b[1;33m[\u001b[0m\u001b[1;33m:\u001b[0m \u001b[1;36m1\u001b[0m \u001b[1;33m+\u001b[0m \u001b[0mj\u001b[0m\u001b[1;33m]\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mj\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0msetpoints\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mvij\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mnij\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mlij\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    561\u001b[0m                 \u001b[1;32mif\u001b[0m \u001b[0msp_def\u001b[0m \u001b[1;32mnot\u001b[0m \u001b[1;32min\u001b[0m \u001b[0mall_setpoints\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m--> 562\u001b[1;33m                     \u001b[0mall_setpoints\u001b[0m\u001b[1;33m[\u001b[0m\u001b[0msp_def\u001b[0m\u001b[1;33m]\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0m_make_setpoint_array\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;33m*\u001b[0m\u001b[0msp_def\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m    563\u001b[0m                     \u001b[0mout\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mappend\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mall_setpoints\u001b[0m\u001b[1;33m[\u001b[0m\u001b[0msp_def\u001b[0m\u001b[1;33m]\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    564\u001b[0m                 \u001b[0msetpoints\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0msetpoints\u001b[0m \u001b[1;33m+\u001b[0m \u001b[1;33m(\u001b[0m\u001b[0mall_setpoints\u001b[0m\u001b[1;33m[\u001b[0m\u001b[0msp_def\u001b[0m\u001b[1;33m]\u001b[0m\u001b[1;33m,\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
+      "\u001b[1;32ma:\\qcodes\\qcodes\\loops.py\u001b[0m in \u001b[0;36m_make_setpoint_array\u001b[1;34m(self, shape, i, prev_setpoints, vals, name, label)\u001b[0m\n\u001b[0;32m    601\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    602\u001b[0m         \u001b[1;32mif\u001b[0m \u001b[0mvals\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mshape\u001b[0m \u001b[1;33m!=\u001b[0m \u001b[0mshape\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m--> 603\u001b[1;33m             raise ValueError('nth setpoint array should have shape matching '\n\u001b[0m\u001b[0;32m    604\u001b[0m                              'the first n dimensions of shape.')\n\u001b[0;32m    605\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n",
+      "\u001b[1;31mValueError\u001b[0m: nth setpoint array should have shape matching the first n dimensions of shape."
+     ]
+    }
+   ],
+   "source": [
+    "dummy = parameter.ManualParameter(name=\"dummy\")\n",
+    "data10 = qc.Loop(dummy[0:5:1]).each(\n",
+    "    recsamp_contr.acquisition).run(name='AlazarTest')"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 17,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "((array([0, 1, 2]), array([0, 1])), (array([0, 1, 2]), array([0, 1])))"
+      ]
+     },
+     "execution_count": 17,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "((np.arange(3), np.arange(2)), (np.arange(3), np.arange(2)))"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {
+    "collapsed": true
+   },
+   "outputs": [],
+   "source": []
   }
  ],
  "metadata": {

From 341acfdf90dccdc1a65a311759075249907bbb7e Mon Sep 17 00:00:00 2001
From: nataliejpg 
Date: Tue, 8 Nov 2016 12:08:24 +0100
Subject: [PATCH 033/328] to dos added

---
 qcodes/instrument_drivers/AlazarTech/RecSamp_controller.py | 3 +++
 qcodes/instrument_drivers/AlazarTech/Rec_controller.py     | 2 ++
 qcodes/instrument_drivers/AlazarTech/Samp_controller.py    | 2 ++
 3 files changed, 7 insertions(+)

diff --git a/qcodes/instrument_drivers/AlazarTech/RecSamp_controller.py b/qcodes/instrument_drivers/AlazarTech/RecSamp_controller.py
index 53196d356eb1..5b583ed806a7 100644
--- a/qcodes/instrument_drivers/AlazarTech/RecSamp_controller.py
+++ b/qcodes/instrument_drivers/AlazarTech/RecSamp_controller.py
@@ -9,6 +9,9 @@ class RecSampParam(Parameter):
     Hardware controlled parameter class for Alazar acquisition. To be used with
     HD_Samples_Controller (tested with ATS9360 board) for return of an array of
     sample data from the Alazar, averaged over records and buffers.
+    
+    TODO(nataliejpg) fix setpoints/shapes horriblenesss
+    TODO(nataliejpg) make it actually work...
     """
     def __init__(self, name, instrument):
         super().__init__(name)
diff --git a/qcodes/instrument_drivers/AlazarTech/Rec_controller.py b/qcodes/instrument_drivers/AlazarTech/Rec_controller.py
index 792fb4f616b4..ff1cabdd58c1 100644
--- a/qcodes/instrument_drivers/AlazarTech/Rec_controller.py
+++ b/qcodes/instrument_drivers/AlazarTech/Rec_controller.py
@@ -9,6 +9,8 @@ class RecordsParam(Parameter):
     Hardware controlled parameter class for Alazar acquisition. To be used with
     HD_Records_Controller (tested with ATS9360 board) for return of an array of
     record data from the Alazar, averaged over samples and buffers.
+    
+    TODO(nataliejpg) fix setpoints/shapes horriblenesss
     """
     def __init__(self, name, instrument):
         super().__init__(name)
diff --git a/qcodes/instrument_drivers/AlazarTech/Samp_controller.py b/qcodes/instrument_drivers/AlazarTech/Samp_controller.py
index a1cf73c9770c..b95d59b6efab 100644
--- a/qcodes/instrument_drivers/AlazarTech/Samp_controller.py
+++ b/qcodes/instrument_drivers/AlazarTech/Samp_controller.py
@@ -9,6 +9,8 @@ class SamplesParam(Parameter):
     Hardware controlled parameter class for Alazar acquisition. To be used with
     HD_Samples_Controller (tested with ATS9360 board) for return of an array of
     sample data from the Alazar, averaged over records and buffers.
+    
+    TODO(nataliejpg) fix setpoints/shapes horriblenesss
     """
     def __init__(self, name, instrument):
         super().__init__(name)

From 8da2b422d6cff8aeb3ceee99b5c7b7929228c45e Mon Sep 17 00:00:00 2001
From: nataliejpg 
Date: Tue, 8 Nov 2016 14:20:26 +0100
Subject: [PATCH 034/328] start work on signal to volts

---
 .../AlazarTech/basic_controller.py            | 41 ++++++++++++++-----
 1 file changed, 30 insertions(+), 11 deletions(-)

diff --git a/qcodes/instrument_drivers/AlazarTech/basic_controller.py b/qcodes/instrument_drivers/AlazarTech/basic_controller.py
index 2727d897bdcf..9b2627c30f5c 100644
--- a/qcodes/instrument_drivers/AlazarTech/basic_controller.py
+++ b/qcodes/instrument_drivers/AlazarTech/basic_controller.py
@@ -54,6 +54,7 @@ class Basic_Acquisition_Controller(AcquisitionController):
     **kwargs: kwargs are forwarded to the Instrument base class
 
     TODO(nataliejpg) num of channels
+    TODO(nataliejpg) make dtype general or get from alazar
     """
 
     def __init__(self, name, alazar_name, **kwargs):
@@ -90,7 +91,8 @@ def pre_start_capture(self):
         self.buffers_per_acquisition = alazar.buffers_per_acquisition.get()
         self.buffer = np.zeros(self.samples_per_record *
                                self.records_per_buffer *
-                               self.number_of_channels)
+                               self.number_of_channels,
+                               dtype=np.uint16)
 
     def pre_acquire(self):
         """
@@ -114,27 +116,44 @@ def post_acquire(self):
         See AcquisitionController
         :return:
         """
-        records_per_acquisition = (1. * self.buffers_per_acquisition *
-                                   self.records_per_buffer)
+        
         # for ATS9360 samples are arranged in the buffer as follows:
         # S00A, S00B, S01A, S01B...S10A, S10B, S11A, S11B...
         # where SXYZ is record X, sample Y, channel Z.
 
-        # breaks buffer up into records, averages over them and returns samples
+        # break buffer up into records, averages over them and returns samples
         records_per_acquisition = (self.buffers_per_acquisition *
-                                   self.records_per_buffer)
-        recordA = np.zeros(self.samples_per_record)
+                                   self.records_per_buffer)                          
+        recordA = np.zeros(self.samples_per_record, dtype=np.uint16)
         for i in range(self.records_per_buffer):
             i0 = (i * self.samples_per_record * self.number_of_channels)
             i1 = (i0 + self.samples_per_record * self.number_of_channels)
-            recordA += (self.buffer[i0:i1:self.number_of_channels] /
+            recordA += np.uint16(self.buffer[i0:i1:self.number_of_channels] /
                         records_per_acquisition)
 
-        recordB = np.zeros(self.samples_per_record)
+        recordB = np.zeros(self.samples_per_record, dtype=np.uint16)
         for i in range(self.records_per_buffer):
             i0 = (i * self.samples_per_record * self.number_of_channels + 1)
             i1 = (i0 + self.samples_per_record * self.number_of_channels)
-            recordB += (self.buffer[i0:i1:self.number_of_channels] /
+            recordB += np.uint16(self.buffer[i0:i1:self.number_of_channels] /
                         records_per_acquisition)
-
-        return recordA, recordB
+        
+        volt_rec_A = self.sample_to_volt(recordA)
+        volt_rec_B = self.sample_to_volt(recordB)
+
+        return volt_rec_A, volt_rec_B
+
+    def sample_to_volt(self, raw_samples):
+        # right_shift 16-bit sample by 4 to get 12 bit sample
+        shifted_samples = np.right_shift(raw_samples,4)
+        
+        # Alazar calibration
+        bps = 12
+        input_range_volts = 0.8
+        code_zero = (1 << (bps - 1)) - 0.5
+        code_range = (1 << (bps - 1)) - 0.5
+        
+        # Convert to volts
+        volt_samples = input_range_volts * (shifted_samples - code_zero) / code_range
+        
+        return volt_samples
\ No newline at end of file

From fcf7fcbb731c652b56eff5953d59c6ca40491f71 Mon Sep 17 00:00:00 2001
From: nataliejpg 
Date: Tue, 8 Nov 2016 15:22:27 +0100
Subject: [PATCH 035/328] filter testing in samples controller

---
 qcodes/instrument_drivers/AlazarTech/Samp_controller.py | 8 ++++++++
 1 file changed, 8 insertions(+)

diff --git a/qcodes/instrument_drivers/AlazarTech/Samp_controller.py b/qcodes/instrument_drivers/AlazarTech/Samp_controller.py
index b95d59b6efab..3fbd59477a6b 100644
--- a/qcodes/instrument_drivers/AlazarTech/Samp_controller.py
+++ b/qcodes/instrument_drivers/AlazarTech/Samp_controller.py
@@ -114,6 +114,9 @@ def pre_start_capture(self):
 
         self.cos_list = np.cos(angle_list)
         self.sin_list = np.sin(angle_list)
+        
+        numtaps = round(self.samples_per_record/20) * 2 + 1
+        delay = numtaps - 1
 
     def pre_acquire(self):
         """
@@ -226,3 +229,8 @@ def filter_ls(self, rec, numtaps, cutoff):
                                 nyq=nyq_rate)
         filtered_rec = 2 * signal.lfilter(fir_coef, 1.0, rec)
         return filtered_rec
+        
+    def filter(self, rec, numtaps, delay):
+        fir_coef = signal.firwin(numtaps, 0.01)
+        filtered_rec = 2 * signal.lfilter(fir_coef, 1.0, rec)
+        return fir_coef, filtered_rec[delay:]

From 61c64198037d14dfc1fc5eba4bb12bfde0cab653 Mon Sep 17 00:00:00 2001
From: Natalie Pearson 
Date: Tue, 8 Nov 2016 15:37:00 +0100
Subject: [PATCH 036/328] pep8 formatting

---
 .../AlazarTech/RecBuf_controller.py           |  1 +
 .../AlazarTech/RecSamp_controller.py          | 21 ++++++++--------
 .../AlazarTech/Rec_controller.py              |  5 ++--
 .../AlazarTech/Samp_controller.py             |  2 +-
 .../AlazarTech/Single_controller.py           |  2 +-
 .../AlazarTech/basic_controller.py            | 24 ++++++++++---------
 6 files changed, 29 insertions(+), 26 deletions(-)

diff --git a/qcodes/instrument_drivers/AlazarTech/RecBuf_controller.py b/qcodes/instrument_drivers/AlazarTech/RecBuf_controller.py
index c4d5fdd3e390..65ddb9dea4d7 100644
--- a/qcodes/instrument_drivers/AlazarTech/RecBuf_controller.py
+++ b/qcodes/instrument_drivers/AlazarTech/RecBuf_controller.py
@@ -10,6 +10,7 @@ class RecBufParam(Parameter):
     HD_Samples_Controller (tested with ATS9360 board) for return of an array of
     sample data from the Alazar, averaged over records and buffers.
     """
+
     def __init__(self, name, instrument):
         super().__init__(name)
         self._instrument = instrument
diff --git a/qcodes/instrument_drivers/AlazarTech/RecSamp_controller.py b/qcodes/instrument_drivers/AlazarTech/RecSamp_controller.py
index 5b583ed806a7..7e86e9ffc211 100644
--- a/qcodes/instrument_drivers/AlazarTech/RecSamp_controller.py
+++ b/qcodes/instrument_drivers/AlazarTech/RecSamp_controller.py
@@ -9,17 +9,19 @@ class RecSampParam(Parameter):
     Hardware controlled parameter class for Alazar acquisition. To be used with
     HD_Samples_Controller (tested with ATS9360 board) for return of an array of
     sample data from the Alazar, averaged over records and buffers.
-    
+
     TODO(nataliejpg) fix setpoints/shapes horriblenesss
     TODO(nataliejpg) make it actually work...
     """
+
     def __init__(self, name, instrument):
         super().__init__(name)
         self._instrument = instrument
         self.acquisitionkwargs = {}
         self.names = ('magnitude', 'phase')
         self.units = ('', '')
-        self.setpoint_names = (('rec_num', 'samp_num'), ('rec_num', 'samp_num'))
+        self.setpoint_names = (('rec_num', 'samp_num'),
+                               ('rec_num', 'samp_num'))
         self.setpoints = ((1, 1), (1, 1))
         self.shapes = ((1, 1), (1, 1))
 
@@ -148,28 +150,25 @@ def post_acquire(self):
         # for ATS9360 samples are arranged in the buffer as follows:
         # S00A, S00B, S01A, S01B...S10A, S10B, S11A, S11B...
         # where SXYZ is record X, sample Y, channel Z.
-        full_rec_length = self.number_of_channels * self.samples_per_record
-        step = self.number_of_channels
-        averaging = self.buffers_per_acquisition
 
         # reshapes date to be (samples * records)
         recordA = np.zeros((self.samples_per_record, self.records_per_buffer))
-        # recordB = np.zeros((self.samples_per_record, self.records_per_buffer))\
         for i in range(self.records_per_buffer):
             i0 = i * self.number_of_channels * self.samples_per_record
             i1 = i0 + self.number_of_channels * self.samples_per_record
             recordA[:, i] = (self.buffer[i0:i1:self.number_of_channels] /
                              self.buffers_per_acquisition)
-            # recordB[:, i] = self.buffer[1:full_rec_length:step] / buffers
+
         # return averaged chan A data (records)
         magA, phaseA = self.fit(recordA)
 
         # same for B
         # if self.chan_b:
-            # recordB = np.zeros((self.samples_per_record, self.records_per_buffer))
-            # for i in self.records_per_buffer:
-                # recordB[i, :] = self.buffer[1:full_rec_length:step] / averaging
-            # magB, phaseB = self.fit(recordB)
+        # recordB = np.zeros(
+        #        (self.samples_per_record, self.records_per_buffer))
+        # for i in self.records_per_buffer:
+        # recordB[i, :] = self.buffer[1:full_rec_length:step] / averaging
+        # magB, phaseB = self.fit(recordB)
 
         # return data (samples * records)
         return magA, phaseA
diff --git a/qcodes/instrument_drivers/AlazarTech/Rec_controller.py b/qcodes/instrument_drivers/AlazarTech/Rec_controller.py
index ff1cabdd58c1..66eb8af1a9b2 100644
--- a/qcodes/instrument_drivers/AlazarTech/Rec_controller.py
+++ b/qcodes/instrument_drivers/AlazarTech/Rec_controller.py
@@ -9,9 +9,10 @@ class RecordsParam(Parameter):
     Hardware controlled parameter class for Alazar acquisition. To be used with
     HD_Records_Controller (tested with ATS9360 board) for return of an array of
     record data from the Alazar, averaged over samples and buffers.
-    
+
     TODO(nataliejpg) fix setpoints/shapes horriblenesss
     """
+
     def __init__(self, name, instrument):
         super().__init__(name)
         self._instrument = instrument
@@ -156,7 +157,7 @@ def post_acquire(self):
             # recordB[:, i] = self.buffer[1:full_rec_length:step] / buffers
         # return averaged chan A data (records)
         magA, phaseA = self.fit(recordA)
-        
+
         return magA, phaseA
 
     def fit(self, rec):
diff --git a/qcodes/instrument_drivers/AlazarTech/Samp_controller.py b/qcodes/instrument_drivers/AlazarTech/Samp_controller.py
index b95d59b6efab..fb8f27e97313 100644
--- a/qcodes/instrument_drivers/AlazarTech/Samp_controller.py
+++ b/qcodes/instrument_drivers/AlazarTech/Samp_controller.py
@@ -9,7 +9,7 @@ class SamplesParam(Parameter):
     Hardware controlled parameter class for Alazar acquisition. To be used with
     HD_Samples_Controller (tested with ATS9360 board) for return of an array of
     sample data from the Alazar, averaged over records and buffers.
-    
+
     TODO(nataliejpg) fix setpoints/shapes horriblenesss
     """
     def __init__(self, name, instrument):
diff --git a/qcodes/instrument_drivers/AlazarTech/Single_controller.py b/qcodes/instrument_drivers/AlazarTech/Single_controller.py
index 3d2a35ec75b7..eb84b087ce2c 100644
--- a/qcodes/instrument_drivers/AlazarTech/Single_controller.py
+++ b/qcodes/instrument_drivers/AlazarTech/Single_controller.py
@@ -202,4 +202,4 @@ def filter_ls(self, rec, numtaps, cutoff):
                                 desired,
                                 nyq=nyq_rate)
         filtered_rec = 2 * signal.lfilter(fir_coef, 1.0, rec)
-        return filtered_rec
\ No newline at end of file
+        return filtered_rec
diff --git a/qcodes/instrument_drivers/AlazarTech/basic_controller.py b/qcodes/instrument_drivers/AlazarTech/basic_controller.py
index 9b2627c30f5c..d0384e243249 100644
--- a/qcodes/instrument_drivers/AlazarTech/basic_controller.py
+++ b/qcodes/instrument_drivers/AlazarTech/basic_controller.py
@@ -11,6 +11,7 @@ class SampleSweep(Parameter):
     Instrument returns an buffer of data (channels * samples * records) which
     is processed by the post_acquire function of the Acquidiyion Controller
     """
+
     def __init__(self, name, instrument):
         super().__init__(name)
         self._instrument = instrument
@@ -116,28 +117,28 @@ def post_acquire(self):
         See AcquisitionController
         :return:
         """
-        
+
         # for ATS9360 samples are arranged in the buffer as follows:
         # S00A, S00B, S01A, S01B...S10A, S10B, S11A, S11B...
         # where SXYZ is record X, sample Y, channel Z.
 
         # break buffer up into records, averages over them and returns samples
         records_per_acquisition = (self.buffers_per_acquisition *
-                                   self.records_per_buffer)                          
+                                   self.records_per_buffer)
         recordA = np.zeros(self.samples_per_record, dtype=np.uint16)
         for i in range(self.records_per_buffer):
             i0 = (i * self.samples_per_record * self.number_of_channels)
             i1 = (i0 + self.samples_per_record * self.number_of_channels)
             recordA += np.uint16(self.buffer[i0:i1:self.number_of_channels] /
-                        records_per_acquisition)
+                                 records_per_acquisition)
 
         recordB = np.zeros(self.samples_per_record, dtype=np.uint16)
         for i in range(self.records_per_buffer):
             i0 = (i * self.samples_per_record * self.number_of_channels + 1)
             i1 = (i0 + self.samples_per_record * self.number_of_channels)
             recordB += np.uint16(self.buffer[i0:i1:self.number_of_channels] /
-                        records_per_acquisition)
-        
+                                 records_per_acquisition)
+
         volt_rec_A = self.sample_to_volt(recordA)
         volt_rec_B = self.sample_to_volt(recordB)
 
@@ -145,15 +146,16 @@ def post_acquire(self):
 
     def sample_to_volt(self, raw_samples):
         # right_shift 16-bit sample by 4 to get 12 bit sample
-        shifted_samples = np.right_shift(raw_samples,4)
-        
+        shifted_samples = np.right_shift(raw_samples, 4)
+
         # Alazar calibration
         bps = 12
         input_range_volts = 0.8
         code_zero = (1 << (bps - 1)) - 0.5
         code_range = (1 << (bps - 1)) - 0.5
-        
+
         # Convert to volts
-        volt_samples = input_range_volts * (shifted_samples - code_zero) / code_range
-        
-        return volt_samples
\ No newline at end of file
+        volt_samples = input_range_volts * \
+            (shifted_samples - code_zero) / code_range
+
+        return volt_samples

From ec757dba748735b3f3c8da3eb6b3afad15ba7cfd Mon Sep 17 00:00:00 2001
From: Natalie Pearson 
Date: Wed, 9 Nov 2016 15:09:00 +0100
Subject: [PATCH 037/328] make defaults such that getting board info is easier

---
 qcodes/instrument_drivers/AlazarTech/ATS.py | 5 +----
 1 file changed, 1 insertion(+), 4 deletions(-)

diff --git a/qcodes/instrument_drivers/AlazarTech/ATS.py b/qcodes/instrument_drivers/AlazarTech/ATS.py
index da63db1eb1d5..6b0c6e1107e5 100644
--- a/qcodes/instrument_drivers/AlazarTech/ATS.py
+++ b/qcodes/instrument_drivers/AlazarTech/ATS.py
@@ -190,7 +190,7 @@ def find_boards(cls, dll_path=None):
         return boards
 
     @classmethod
-    def get_board_info(cls, dll, system_id, board_id):
+    def get_board_info(cls, dll, system_id=1, board_id=1):
         """
         Get the information from a connected Alazar board
 
@@ -204,7 +204,6 @@ def get_board_info(cls, dll, system_id, board_id):
             max_samples
             bits_per_sample
         """
-
         # make a temporary instrument for this board, to make it easier
         # to get its info
         board = cls('temp', system_id=system_id, board_id=board_id,
@@ -428,8 +427,6 @@ def config(self, clock_source=None, sample_rate=None, clock_edge=None,
                        self._handle, self.aux_io_mode,
                        self.aux_io_param)
 
-        # TODO(damazter) (W) config AUXIO
-
     def _get_channel_info(self, handle):
         bps = np.array([0], dtype=np.uint8)  # bps bits per sample
         max_s = np.array([0], dtype=np.uint32)  # max_s memory size in samples

From 9276dab8db133fc092c5cead46ce53e41db5bc79 Mon Sep 17 00:00:00 2001
From: Natalie Pearson 
Date: Wed, 9 Nov 2016 15:10:08 +0100
Subject: [PATCH 038/328] create separate tools file and somewhat clear up
 fitting and filtering

---
 .../AlazarTech/Samp_controller.py             | 116 +++++++++---------
 .../AlazarTech/acquisition_tools.py           |  51 ++++++++
 .../AlazarTech/basic_controller.py            |  28 ++---
 3 files changed, 118 insertions(+), 77 deletions(-)
 create mode 100644 qcodes/instrument_drivers/AlazarTech/acquisition_tools.py

diff --git a/qcodes/instrument_drivers/AlazarTech/Samp_controller.py b/qcodes/instrument_drivers/AlazarTech/Samp_controller.py
index a2fa93187f9b..ffdb67ea7490 100644
--- a/qcodes/instrument_drivers/AlazarTech/Samp_controller.py
+++ b/qcodes/instrument_drivers/AlazarTech/Samp_controller.py
@@ -1,7 +1,7 @@
 from .ATS import AcquisitionController
 import numpy as np
 from qcodes import Parameter
-from scipy import signal
+import acquistion_tools as tools
 
 
 class SamplesParam(Parameter):
@@ -12,6 +12,7 @@ class SamplesParam(Parameter):
 
     TODO(nataliejpg) fix setpoints/shapes horriblenesss
     """
+
     def __init__(self, name, instrument):
         super().__init__(name)
         self._instrument = instrument
@@ -62,23 +63,29 @@ class HD_Samples_Controller(AcquisitionController):
     TODO(nataliejpg) test filter options
     TODO(nataliejpg) test mag phase logic
     TODO(nataliejpg) finish implementation of channel b option
-    TODO(nataliejpg) make filter settings not hard coded
+    TODO(nataliejpg) update docstrings :P
     """
 
     def __init__(self, name, alazar_name, demod_freq, samp_rate=500e6,
-                 filt='win', chan_b=False, **kwargs):
+                 int_delay=None, int_time=None, filt='win',
+                 numtaps=101, chan_b=False, **kwargs):
         filter_dict = {'win': 0, 'hamming': 1, 'ls': 2}
         self.demodulation_frequency = demod_freq
         self.sample_rate = samp_rate
         self.filter = filter_dict[filt]
+        self.int_time = int_time
+        self.int_delay = int_delay
+        self.numtaps = numtaps
         self.chan_b = chan_b
         self.samples_per_record = 0
         self.records_per_buffer = 0
         self.buffers_per_acquisition = 0
         self.number_of_channels = 2
+        self.samples_delay = None
         self.cos_list = None
         self.sin_list = None
         self.buffer = None
+        self.board_info = None
         # make a call to the parent class and by extension,
         # create the parameter structure of this class
         super().__init__(name, alazar_name, **kwargs)
@@ -104,6 +111,7 @@ def pre_start_capture(self):
         self.samples_per_record = alazar.samples_per_record.get()
         self.records_per_buffer = alazar.records_per_buffer.get()
         self.buffers_per_acquisition = alazar.buffers_per_acquisition.get()
+        self.board_info = alazar.get_board_info(alazar.dll_path)
         self.buffer = np.zeros(self.samples_per_record *
                                self.records_per_buffer *
                                self.number_of_channels)
@@ -114,9 +122,16 @@ def pre_start_capture(self):
 
         self.cos_list = np.cos(angle_list)
         self.sin_list = np.sin(angle_list)
-        
-        numtaps = round(self.samples_per_record/20) * 2 + 1
-        delay = numtaps - 1
+
+        if self.int_delay:
+            self.samples_delay = self.int_delay * self.sample_rate
+            if self.samples_delay < (self.numtaps - 1):
+                expected_delay = (self.numtaps - 1) / self.sample_rate
+                Warning(
+                    'delay is less than recommended for filter choice: '
+                    '(expect delay >= {}'.format(expected_delay))
+        else:
+            self.samples_delay = self.numtaps - 1
 
     def pre_acquire(self):
         """
@@ -147,8 +162,7 @@ def post_acquire(self):
         # S00A, S00B, S01A, S01B...S10A, S10B, S11A, S11B...
         # where SXYZ is record X, sample Y, channel Z.
 
-        # breaks buffer up into records and averages over them
-        # leaving data in form (samples)
+        # break buffer up into records and averages over them
         records_per_acquisition = (self.buffers_per_acquisition *
                                    self.records_per_buffer)
         recordA = np.zeros(self.samples_per_record)
@@ -158,13 +172,15 @@ def post_acquire(self):
             recordA += (self.buffer[i0:i1:self.number_of_channels] /
                         records_per_acquisition)
 
-        # demodulate record, data returned (samples)
+        # do demodulation
         magA, phaseA = self.fit(recordA)
 
+        # same for chan b
         if self.chan_b:
             recordB = np.zeros(self.samples_per_record)
             for i in range(self.records_per_buffer):
-                i0 = (i * self.samples_per_record * self.number_of_channels + 1)
+                i0 = (i * self.samples_per_record *
+                      self.number_of_channels + 1)
                 i1 = (i0 + self.samples_per_record * self.number_of_channels)
                 recordB += (self.buffer[i0:i1:self.number_of_channels] /
                             records_per_acquisition)
@@ -173,64 +189,46 @@ def post_acquire(self):
         return magA, phaseA
 
     def fit(self, rec):
-        # center rec around 0
-        rec = rec - np.mean(rec)
+        # convert rec to volts
+        if self.board_info['bits_per_sample'] == 12:
+            volt_rec = tools.sample_to_volt_u12(rec)
+        else:
+            Warning(
+                'sample to volt conversion does not exist for '
+                'bps != 12, raw samples centered and returned')
+            volt_rec = rec - np.mean(rec)
 
         # multiply with software wave
-        re_wave = np.multiply(rec, self.cos_list)
-        im_wave = np.multiply(rec, self.sin_list)
-        cutoff = self.demodulation_frequency
-        numtaps = 30
+        re_wave = np.multiply(volt_rec, self.cos_list)
+        im_wave = np.multiply(volt_rec, self.sin_list)
+        cutoff = self.demodulation_frequency / 2
 
         # filter out higher freq component
         if self.filter == 0:
-            RePart = self.filter_win(re_wave, numtaps, cutoff)
-            ImPart = self.filter_win(im_wave, numtaps, cutoff)
+            re_filtered = tools.filter_win(re_wave, cutoff,
+                                           self.samp_rate, self.numtaps)
+            im_filtered = tools.filter_win(re_wave, cutoff,
+                                           self.samp_rate, self.numtaps)
         elif self.filter == 1:
-            RePart = self.filter_hamming(re_wave, numtaps, cutoff)
-            ImPart = self.filter_hamming(im_wave, numtaps, cutoff)
+            re_filtered = tools.filter_ham(re_wave, cutoff,
+                                           self.samp_rate, self.numtaps)
+            im_filtered = tools.filter_ham(im_wave, cutoff,
+                                           self.samp_rate, self.numtaps)
         elif self.filter == 2:
-            RePart = self.filter_ls(re_wave, numtaps, cutoff)
-            ImPart = self.filter_ls(im_wave, numtaps, cutoff)
+            re_filtered = tools.filter_ls(re_wave, cutoff,
+                                          self.samp_rate, self.numtaps)
+            im_filtered = tools.filter_ls(im_wave, cutoff,
+                                          self.samp_rate, self.numtaps)
+
+        # apply int limits
+        start = self.samples_delay
+        end = self.int_time * self.samp_rate + start
+        re_limited = re_filtered[start:end]
+        im_limited = im_filtered[start:end]
 
         # convert to magnitude and phase
-        # data returnded (samples)
-        complex_num = RePart + ImPart * 1j
-        mag = 2 * abs(complex_num)
+        complex_num = re_limited + im_limited * 1j
+        mag = abs(complex_num)
         phase = np.angle(complex_num, deg=True)
 
         return mag, phase
-
-    def filter_hamming(self, rec, numtaps, cutoff):
-        sample_rate = self.sample_rate
-        nyq_rate = sample_rate / 2.
-        fir_coef = signal.firwin(numtaps,
-                                 cutoff / nyq_rate,
-                                 window="hamming")
-        filtered_rec = 2 * signal.lfilter(fir_coef, 1.0, rec)
-        return filtered_rec
-
-    def filter_win(self, rec, numtaps, cutoff):
-        sample_rate = self.sample_rate
-        nyq_rate = sample_rate / 2.
-        fir_coef = signal.firwin(numtaps,
-                                 cutoff / nyq_rate)
-        filtered_rec = 2 * signal.lfilter(fir_coef, 1.0, rec)
-        return filtered_rec
-
-    def filter_ls(self, rec, numtaps, cutoff):
-        sample_rate = self.sample_rate
-        nyq_rate = sample_rate / 2.
-        bands = [0, cutoff / nyq_rate, cutoff / nyq_rate, 1]
-        desired = [1, 1, 0, 0]
-        fir_coef = signal.firls(numtaps,
-                                bands,
-                                desired,
-                                nyq=nyq_rate)
-        filtered_rec = 2 * signal.lfilter(fir_coef, 1.0, rec)
-        return filtered_rec
-        
-    def filter(self, rec, numtaps, delay):
-        fir_coef = signal.firwin(numtaps, 0.01)
-        filtered_rec = 2 * signal.lfilter(fir_coef, 1.0, rec)
-        return fir_coef, filtered_rec[delay:]
diff --git a/qcodes/instrument_drivers/AlazarTech/acquisition_tools.py b/qcodes/instrument_drivers/AlazarTech/acquisition_tools.py
new file mode 100644
index 000000000000..c8742e96ae02
--- /dev/null
+++ b/qcodes/instrument_drivers/AlazarTech/acquisition_tools.py
@@ -0,0 +1,51 @@
+import numpy as np
+from scipy import signal
+
+
+def sample_to_volt_u12(self, raw_samples, input_range_volts, bps):
+    # right_shift 16-bit sample by 4 to get 12 bit sample
+    shifted_samples = np.right_shift(raw_samples, 4)
+
+    # Alazar calibration
+    bps = 12
+    input_range_volts = 0.8
+    code_zero = (1 << (bps - 1)) - 0.5
+    code_range = (1 << (bps - 1)) - 0.5
+
+    # Convert to volts
+    volt_samples = (input_range_volts *
+                    (shifted_samples - code_zero) / code_range)
+
+    return volt_samples
+
+
+def filter_win(rec, cutoff, sample_rate, numtaps):
+    nyq_rate = sample_rate / 2.
+    fir_coef = signal.firwin(numtaps, cutoff / nyq_rate)
+    filtered_rec = signal.lfilter(fir_coef, 1.0, rec)
+    return filtered_rec
+
+
+def filter_ham(rec, cutoff, sample_rate, numtaps):
+    raise NotImplementedError
+    # sample_rate = self.sample_rate
+    # nyq_rate = sample_rate / 2.
+    # fir_coef = signal.firwin(numtaps,
+    #                          cutoff / nyq_rate,
+    #                          window="hamming")
+    # filtered_rec = 2 * signal.lfilter(fir_coef, 1.0, rec)
+    # return filtered_rec
+
+
+def filter_ls(rec, cutoff, sample_rate, numtaps):
+    raise NotImplementedError
+    # sample_rate = self.sample_rate
+    # nyq_rate = sample_rate / 2.
+    # bands = [0, cutoff / nyq_rate, cutoff / nyq_rate, 1]
+    # desired = [1, 1, 0, 0]
+    # fir_coef = signal.firls(numtaps,
+    #                         bands,
+    #                         desired,
+    #                         nyq=nyq_rate)
+    # filtered_rec = 2 * signal.lfilter(fir_coef, 1.0, rec)
+    # return filtered_rec
diff --git a/qcodes/instrument_drivers/AlazarTech/basic_controller.py b/qcodes/instrument_drivers/AlazarTech/basic_controller.py
index d0384e243249..3c20c9bcbfd7 100644
--- a/qcodes/instrument_drivers/AlazarTech/basic_controller.py
+++ b/qcodes/instrument_drivers/AlazarTech/basic_controller.py
@@ -1,6 +1,7 @@
 from .ATS import AcquisitionController
 import numpy as np
 from qcodes import Parameter
+import acquistion_tools
 
 
 class SampleSweep(Parameter):
@@ -65,6 +66,7 @@ def __init__(self, name, alazar_name, **kwargs):
         # TODO(damazter) (S) this is not very general:
         self.number_of_channels = 2
         self.buffer = None
+        self.board_info = None
         # make a call to the parent class and by extension,
         # create the parameter structure of this class
         super().__init__(name, alazar_name, **kwargs)
@@ -90,6 +92,7 @@ def pre_start_capture(self):
         self.samples_per_record = alazar.samples_per_record.get()
         self.records_per_buffer = alazar.records_per_buffer.get()
         self.buffers_per_acquisition = alazar.buffers_per_acquisition.get()
+        self.board_info = alazar.get_board_info(alazar.dll_path)
         self.buffer = np.zeros(self.samples_per_record *
                                self.records_per_buffer *
                                self.number_of_channels,
@@ -139,23 +142,12 @@ def post_acquire(self):
             recordB += np.uint16(self.buffer[i0:i1:self.number_of_channels] /
                                  records_per_acquisition)
 
-        volt_rec_A = self.sample_to_volt(recordA)
-        volt_rec_B = self.sample_to_volt(recordB)
+        if self.board_info['bits_per_sample'] == 12:
+            volt_rec_A = acquistion_tools.sample_to_volt_u12(recordA)
+            volt_rec_B = acquistion_tools.sample_to_volt_u12(recordB)
+        else:
+            Warning('sample to volt conversion does not exist for bps != 12, raw samples returned')
+            volt_rec_A = recordA
+            volt_rec_B = recordB
 
         return volt_rec_A, volt_rec_B
-
-    def sample_to_volt(self, raw_samples):
-        # right_shift 16-bit sample by 4 to get 12 bit sample
-        shifted_samples = np.right_shift(raw_samples, 4)
-
-        # Alazar calibration
-        bps = 12
-        input_range_volts = 0.8
-        code_zero = (1 << (bps - 1)) - 0.5
-        code_range = (1 << (bps - 1)) - 0.5
-
-        # Convert to volts
-        volt_samples = input_range_volts * \
-            (shifted_samples - code_zero) / code_range
-
-        return volt_samples

From d7a3459462eaa01575ad75f71ef39d7d1998726d Mon Sep 17 00:00:00 2001
From: nataliejpg 
Date: Wed, 9 Nov 2016 19:43:57 +0100
Subject: [PATCH 039/328] debugging so that limiting int_delay and int_time
 work as well as sample_to_volt conversion

---
 qcodes/instrument_drivers/AlazarTech/ATS.py   |   7 +-
 .../AlazarTech/Samp_controller.py             | 151 ++++++++++++------
 .../AlazarTech/acquisition_tools.py           |   4 +-
 .../AlazarTech/basic_controller.py            |  31 +++-
 4 files changed, 137 insertions(+), 56 deletions(-)

diff --git a/qcodes/instrument_drivers/AlazarTech/ATS.py b/qcodes/instrument_drivers/AlazarTech/ATS.py
index 6b0c6e1107e5..cd14d0c00ab1 100644
--- a/qcodes/instrument_drivers/AlazarTech/ATS.py
+++ b/qcodes/instrument_drivers/AlazarTech/ATS.py
@@ -189,8 +189,9 @@ def find_boards(cls, dll_path=None):
                 boards.append(cls.get_board_info(dll, system_id, board_id))
         return boards
 
+        # TODO(nataliejpg) this needs fixing..., dll can't be a string
     @classmethod
-    def get_board_info(cls, dll, system_id=1, board_id=1):
+    def get_board_info(cls, dll, system_id, board_id):
         """
         Get the information from a connected Alazar board
 
@@ -260,6 +261,8 @@ def get_idn(self):
         board_kind = self._board_names[
             self._ATS_dll.AlazarGetBoardKind(self._handle)]
 
+        max_s, bps = self._get_channel_info(self._handle)
+        
         major = np.array([0], dtype=np.uint8)
         minor = np.array([0], dtype=np.uint8)
         revision = np.array([0], dtype=np.uint8)
@@ -309,6 +312,8 @@ def get_idn(self):
 
         return {'firmware': None,
                 'model': board_kind,
+                'max_samples': max_s,
+                'bits_per_sample': bps,
                 'serial': serial,
                 'vendor': 'AlazarTech',
                 'CPLD_version': cpld_ver,
diff --git a/qcodes/instrument_drivers/AlazarTech/Samp_controller.py b/qcodes/instrument_drivers/AlazarTech/Samp_controller.py
index ffdb67ea7490..e4be81c6b0cb 100644
--- a/qcodes/instrument_drivers/AlazarTech/Samp_controller.py
+++ b/qcodes/instrument_drivers/AlazarTech/Samp_controller.py
@@ -1,7 +1,9 @@
 from .ATS import AcquisitionController
 import numpy as np
 from qcodes import Parameter
-import acquistion_tools as tools
+from scipy import signal
+
+#import acquistion_tools as tools
 
 
 class SamplesParam(Parameter):
@@ -23,19 +25,24 @@ def __init__(self, name, instrument):
         self.setpoints = ((1,), (1,))
         self.shapes = ((1,), (1,))
 
-    def update_acquisition_kwargs(self, **kwargs):
+    def update_acquisition_kwargs(self, samp_time=None, **kwargs):
         # needed to update config of the software parameter on sweep change
         # freq setpoints tuple as needs to be hashable for look up
-        if 'samples_per_record' in kwargs:
+        if samp_time:
+            npts = samp_time
+            n = tuple(np.arange(npts))
+            self.setpoints = ((n,), (n,))
+            self.shapes = ((npts,), (npts,))
+        elif 'samples_per_record' in kwargs:
             npts = kwargs['samples_per_record']
             n = tuple(np.arange(npts))
             self.setpoints = ((n,), (n,))
             self.shapes = ((npts,), (npts,))
         else:
-            raise ValueError('samples_per_record must be specified')
+            raise ValueError('samples_per_record or sample_num must be specified')
         # updates dict to be used in acquisition get call
         self.acquisitionkwargs.update(**kwargs)
-
+    
     def get(self):
         mag, phase = self._instrument._get_alazar().acquire(
             acquisition_controller=self._instrument,
@@ -66,15 +73,13 @@ class HD_Samples_Controller(AcquisitionController):
     TODO(nataliejpg) update docstrings :P
     """
 
-    def __init__(self, name, alazar_name, demod_freq, samp_rate=500e6,
-                 int_delay=None, int_time=None, filt='win',
+    def __init__(self, name, alazar_name, demod_freq, samp_rate=500e6, filt='win',
                  numtaps=101, chan_b=False, **kwargs):
         filter_dict = {'win': 0, 'hamming': 1, 'ls': 2}
         self.demodulation_frequency = demod_freq
         self.sample_rate = samp_rate
         self.filter = filter_dict[filt]
-        self.int_time = int_time
-        self.int_delay = int_delay
+        self.int_time = None
         self.numtaps = numtaps
         self.chan_b = chan_b
         self.samples_per_record = 0
@@ -82,6 +87,7 @@ def __init__(self, name, alazar_name, demod_freq, samp_rate=500e6,
         self.buffers_per_acquisition = 0
         self.number_of_channels = 2
         self.samples_delay = None
+        self.samples_time = None
         self.cos_list = None
         self.sin_list = None
         self.buffer = None
@@ -100,7 +106,46 @@ def update_acquisitionkwargs(self, **kwargs):
         :param kwargs:
         :return:
         """
-        self.acquisition.update_acquisition_kwargs(**kwargs)
+        samples_per_record = kwargs['samples_per_record']
+        sample_rate = self.sample_rate
+        
+        if 'int_delay' in kwargs:
+            int_delay = kwargs['int_delay']
+            samp_delay = int_delay * sample_rate
+            samples_delay_min = (self.numtaps - 1)
+            if samp_delay < samples_delay_min:
+                int_delay_min = samples_delay_min / sample_rate
+                Warning(
+                    'delay is less than recommended for filter choice: '
+                    '(expect delay >= {}'.format(int_delay_min))
+        else:
+            samp_delay = self.numtaps - 1
+        
+        if 'int_time' in kwargs:
+            int_time = kwargs['int_time']
+            samp_time = int_time * sample_rate
+            samples_time_max = (samples_per_record - samp_delay)
+            oscilations_measured = int_time / self.demodulation_frequency
+            oversampling = sample_rate / (2 * self.demodulation_frequency)
+            if samp_time > samples_time_max:
+                int_time_max = samples_time_max / sample_rate
+                Warning(
+                    'int_time {} is longer than total_time - delay ({})'.format(int_time, int_time_max))
+            elif oscilations_measured < 10:
+                Warning(
+                    '{} oscilations measured, recommend at least 10: '
+                    'decrease sampling rate, take more samples or increase demodulation freq'.format(oscilations_measured))
+            elif oversampling < 1:
+                Warning('oversampling rate is {}, recommend > 1: '
+                    'increase sampling rate or decrease demodulation frequency'.format(oversampling))
+        else:
+            samp_time = samples_per_record - samp_delay
+        
+        
+        self.samples_time = samp_time
+        self.samples_delay = samp_delay
+        
+        self.acquisition.update_acquisition_kwargs(samp_time, **kwargs)
 
     def pre_start_capture(self):
         """
@@ -111,28 +156,19 @@ def pre_start_capture(self):
         self.samples_per_record = alazar.samples_per_record.get()
         self.records_per_buffer = alazar.records_per_buffer.get()
         self.buffers_per_acquisition = alazar.buffers_per_acquisition.get()
-        self.board_info = alazar.get_board_info(alazar.dll_path)
+        self.board_info = alazar.get_idn()
         self.buffer = np.zeros(self.samples_per_record *
                                self.records_per_buffer *
-                               self.number_of_channels)
+                               self.number_of_channels,
+                               dtype=np.uint16)
 
-        integer_list = np.arange(self.samples_per_record)
+        integer_list = np.arange(self.samples_per_record, dtype=np.uint16)
         angle_list = (2 * np.pi * self.demodulation_frequency /
                       self.sample_rate * integer_list)
 
         self.cos_list = np.cos(angle_list)
         self.sin_list = np.sin(angle_list)
 
-        if self.int_delay:
-            self.samples_delay = self.int_delay * self.sample_rate
-            if self.samples_delay < (self.numtaps - 1):
-                expected_delay = (self.numtaps - 1) / self.sample_rate
-                Warning(
-                    'delay is less than recommended for filter choice: '
-                    '(expect delay >= {}'.format(expected_delay))
-        else:
-            self.samples_delay = self.numtaps - 1
-
     def pre_acquire(self):
         """
         See AcquisitionController
@@ -165,11 +201,11 @@ def post_acquire(self):
         # break buffer up into records and averages over them
         records_per_acquisition = (self.buffers_per_acquisition *
                                    self.records_per_buffer)
-        recordA = np.zeros(self.samples_per_record)
+        recordA = np.zeros(self.samples_per_record, dtype=np.uint16)
         for i in range(self.records_per_buffer):
             i0 = (i * self.samples_per_record * self.number_of_channels)
             i1 = (i0 + self.samples_per_record * self.number_of_channels)
-            recordA += (self.buffer[i0:i1:self.number_of_channels] /
+            recordA += np.uint16(self.buffer[i0:i1:self.number_of_channels] /
                         records_per_acquisition)
 
         # do demodulation
@@ -177,12 +213,12 @@ def post_acquire(self):
 
         # same for chan b
         if self.chan_b:
-            recordB = np.zeros(self.samples_per_record)
+            recordB = np.zeros(self.samples_per_record, dtype=np.uint16)
             for i in range(self.records_per_buffer):
                 i0 = (i * self.samples_per_record *
                       self.number_of_channels + 1)
                 i1 = (i0 + self.samples_per_record * self.number_of_channels)
-                recordB += (self.buffer[i0:i1:self.number_of_channels] /
+                recordB += np.uint16(self.buffer[i0:i1:self.number_of_channels] /
                             records_per_acquisition)
             magB, phaseB = self.fit(recordB)
 
@@ -190,12 +226,12 @@ def post_acquire(self):
 
     def fit(self, rec):
         # convert rec to volts
-        if self.board_info['bits_per_sample'] == 12:
-            volt_rec = tools.sample_to_volt_u12(rec)
+        bps = self.board_info['bits_per_sample']
+        if bps == 12:
+            volt_rec = sample_to_volt_u12(rec, bps)
         else:
-            Warning(
-                'sample to volt conversion does not exist for '
-                'bps != 12, raw samples centered and returned')
+            Warning('sample to volt conversion does not exist for'
+                    ' bps != 12, centered raw samples returned')
             volt_rec = rec - np.mean(rec)
 
         # multiply with software wave
@@ -205,24 +241,27 @@ def fit(self, rec):
 
         # filter out higher freq component
         if self.filter == 0:
-            re_filtered = tools.filter_win(re_wave, cutoff,
-                                           self.samp_rate, self.numtaps)
-            im_filtered = tools.filter_win(re_wave, cutoff,
-                                           self.samp_rate, self.numtaps)
+            re_filtered = filter_win(re_wave, cutoff,
+                                           self.sample_rate, self.numtaps)
+            im_filtered = filter_win(re_wave, cutoff,
+                                           self.sample_rate, self.numtaps)
         elif self.filter == 1:
-            re_filtered = tools.filter_ham(re_wave, cutoff,
-                                           self.samp_rate, self.numtaps)
-            im_filtered = tools.filter_ham(im_wave, cutoff,
-                                           self.samp_rate, self.numtaps)
+            re_filtered = filter_ham(re_wave, cutoff,
+                                           self.sample_rate, self.numtaps)
+            im_filtered = filter_ham(im_wave, cutoff,
+                                           self.sample_rate, self.numtaps)
         elif self.filter == 2:
-            re_filtered = tools.filter_ls(re_wave, cutoff,
-                                          self.samp_rate, self.numtaps)
-            im_filtered = tools.filter_ls(im_wave, cutoff,
-                                          self.samp_rate, self.numtaps)
+            re_filtered = filter_ls(re_wave, cutoff,
+                                          self.sample_rate, self.numtaps)
+            im_filtered = filter_ls(im_wave, cutoff,
+                                          self.sample_rate, self.numtaps)
 
         # apply int limits
         start = self.samples_delay
-        end = self.int_time * self.samp_rate + start
+        if self.int_time:
+            end = self.int_time * self.sample_rate + start
+        else:
+            end = None
         re_limited = re_filtered[start:end]
         im_limited = im_filtered[start:end]
 
@@ -232,3 +271,25 @@ def fit(self, rec):
         phase = np.angle(complex_num, deg=True)
 
         return mag, phase
+        
+def filter_win(rec, cutoff, sample_rate, numtaps):
+    nyq_rate = sample_rate / 2.
+    fir_coef = signal.firwin(numtaps, cutoff / nyq_rate)
+    filtered_rec = signal.lfilter(fir_coef, 1.0, rec)
+    return filtered_rec
+
+def sample_to_volt_u12(raw_samples, bps):
+    # right_shift 16-bit sample by 4 to get 12 bit sample
+    shifted_samples = np.right_shift(raw_samples, 4)
+
+    # Alazar calibration
+    code_zero = (1 << (bps - 1)) - 0.5
+    code_range = (1 << (bps - 1)) - 0.5
+
+    # TODO(nataliejpg) make this not hard coded 
+    input_range_volts = 1
+    # Convert to volts
+    volt_samples = np.float64(input_range_volts *
+                    (shifted_samples - code_zero) / code_range)
+                    
+    return volt_samples
\ No newline at end of file
diff --git a/qcodes/instrument_drivers/AlazarTech/acquisition_tools.py b/qcodes/instrument_drivers/AlazarTech/acquisition_tools.py
index c8742e96ae02..657f1f3be9f1 100644
--- a/qcodes/instrument_drivers/AlazarTech/acquisition_tools.py
+++ b/qcodes/instrument_drivers/AlazarTech/acquisition_tools.py
@@ -2,13 +2,11 @@
 from scipy import signal
 
 
-def sample_to_volt_u12(self, raw_samples, input_range_volts, bps):
+def sample_to_volt_u12(raw_samples, input_range_volts, bps):
     # right_shift 16-bit sample by 4 to get 12 bit sample
     shifted_samples = np.right_shift(raw_samples, 4)
 
     # Alazar calibration
-    bps = 12
-    input_range_volts = 0.8
     code_zero = (1 << (bps - 1)) - 0.5
     code_range = (1 << (bps - 1)) - 0.5
 
diff --git a/qcodes/instrument_drivers/AlazarTech/basic_controller.py b/qcodes/instrument_drivers/AlazarTech/basic_controller.py
index 3c20c9bcbfd7..53ee683fbebe 100644
--- a/qcodes/instrument_drivers/AlazarTech/basic_controller.py
+++ b/qcodes/instrument_drivers/AlazarTech/basic_controller.py
@@ -1,7 +1,7 @@
 from .ATS import AcquisitionController
 import numpy as np
 from qcodes import Parameter
-import acquistion_tools
+#from.acquistion_tools import sample_to_volt_u12
 
 
 class SampleSweep(Parameter):
@@ -92,7 +92,7 @@ def pre_start_capture(self):
         self.samples_per_record = alazar.samples_per_record.get()
         self.records_per_buffer = alazar.records_per_buffer.get()
         self.buffers_per_acquisition = alazar.buffers_per_acquisition.get()
-        self.board_info = alazar.get_board_info(alazar.dll_path)
+        self.board_info = alazar.get_idn()
         self.buffer = np.zeros(self.samples_per_record *
                                self.records_per_buffer *
                                self.number_of_channels,
@@ -142,12 +142,29 @@ def post_acquire(self):
             recordB += np.uint16(self.buffer[i0:i1:self.number_of_channels] /
                                  records_per_acquisition)
 
-        if self.board_info['bits_per_sample'] == 12:
-            volt_rec_A = acquistion_tools.sample_to_volt_u12(recordA)
-            volt_rec_B = acquistion_tools.sample_to_volt_u12(recordB)
+        bps = self.board_info['bits_per_sample']
+        if bps == 12:
+            volt_rec_A = sample_to_volt_u12(recordA, bps)
+            volt_rec_B = sample_to_volt_u12(recordA, bps)
         else:
             Warning('sample to volt conversion does not exist for bps != 12, raw samples returned')
-            volt_rec_A = recordA
-            volt_rec_B = recordB
+            volt_rec_A = recordA - np.mean(recordA)
+            volt_rec_B = recordB - np.mean(recordB)
 
         return volt_rec_A, volt_rec_B
+
+def sample_to_volt_u12(raw_samples, bps):
+    # right_shift 16-bit sample by 4 to get 12 bit sample
+    shifted_samples = np.right_shift(raw_samples, 4)
+
+    # Alazar calibration
+    code_zero = (1 << (bps - 1)) - 0.5
+    code_range = (1 << (bps - 1)) - 0.5
+
+    # TODO(nataliejpg) make this not hard coded 
+    input_range_volts = 1
+    # Convert to volts
+    volt_samples = np.float64(input_range_volts *
+                    (shifted_samples - code_zero) / code_range)
+                    
+    return volt_samples
\ No newline at end of file

From c4cec943599e71bdceb6e6a6d7e6684804cb16b3 Mon Sep 17 00:00:00 2001
From: nataliejpg 
Date: Thu, 10 Nov 2016 14:37:23 +0100
Subject: [PATCH 040/328] get filtering to work well

---
 qcodes/instrument_drivers/AlazarTech/Samp_controller.py  | 5 +++--
 qcodes/instrument_drivers/AlazarTech/basic_controller.py | 2 +-
 2 files changed, 4 insertions(+), 3 deletions(-)

diff --git a/qcodes/instrument_drivers/AlazarTech/Samp_controller.py b/qcodes/instrument_drivers/AlazarTech/Samp_controller.py
index e4be81c6b0cb..62bec2d76305 100644
--- a/qcodes/instrument_drivers/AlazarTech/Samp_controller.py
+++ b/qcodes/instrument_drivers/AlazarTech/Samp_controller.py
@@ -71,6 +71,7 @@ class HD_Samples_Controller(AcquisitionController):
     TODO(nataliejpg) test mag phase logic
     TODO(nataliejpg) finish implementation of channel b option
     TODO(nataliejpg) update docstrings :P
+    TODO(nataliejpg) make demodulation freq a param
     """
 
     def __init__(self, name, alazar_name, demod_freq, samp_rate=500e6, filt='win',
@@ -237,13 +238,13 @@ def fit(self, rec):
         # multiply with software wave
         re_wave = np.multiply(volt_rec, self.cos_list)
         im_wave = np.multiply(volt_rec, self.sin_list)
-        cutoff = self.demodulation_frequency / 2
+        cutoff = self.demodulation_frequency / 10
 
         # filter out higher freq component
         if self.filter == 0:
             re_filtered = filter_win(re_wave, cutoff,
                                            self.sample_rate, self.numtaps)
-            im_filtered = filter_win(re_wave, cutoff,
+            im_filtered = filter_win(im_wave, cutoff,
                                            self.sample_rate, self.numtaps)
         elif self.filter == 1:
             re_filtered = filter_ham(re_wave, cutoff,
diff --git a/qcodes/instrument_drivers/AlazarTech/basic_controller.py b/qcodes/instrument_drivers/AlazarTech/basic_controller.py
index 53ee683fbebe..40adef46d9c1 100644
--- a/qcodes/instrument_drivers/AlazarTech/basic_controller.py
+++ b/qcodes/instrument_drivers/AlazarTech/basic_controller.py
@@ -145,7 +145,7 @@ def post_acquire(self):
         bps = self.board_info['bits_per_sample']
         if bps == 12:
             volt_rec_A = sample_to_volt_u12(recordA, bps)
-            volt_rec_B = sample_to_volt_u12(recordA, bps)
+            volt_rec_B = sample_to_volt_u12(recordB, bps)
         else:
             Warning('sample to volt conversion does not exist for bps != 12, raw samples returned')
             volt_rec_A = recordA - np.mean(recordA)

From aca9a1024f24c502009c73376da22ad9701c435d Mon Sep 17 00:00:00 2001
From: Natalie Pearson 
Date: Mon, 14 Nov 2016 18:25:30 +0100
Subject: [PATCH 041/328] tidy up sample controller

---
 qcodes/instrument_drivers/AlazarTech/ATS.py   |  2 +-
 .../instrument_drivers/AlazarTech/ATS9360.py  |  2 +
 .../AlazarTech/Samp_controller.py             | 82 ++++++++-----------
 .../AlazarTech/Single_controller.py           | 50 ++++++++++-
 qcodes/utils/helpers.py                       | 34 ++++++++
 5 files changed, 119 insertions(+), 51 deletions(-)

diff --git a/qcodes/instrument_drivers/AlazarTech/ATS.py b/qcodes/instrument_drivers/AlazarTech/ATS.py
index cd14d0c00ab1..0fe74979e39b 100644
--- a/qcodes/instrument_drivers/AlazarTech/ATS.py
+++ b/qcodes/instrument_drivers/AlazarTech/ATS.py
@@ -540,7 +540,7 @@ def acquire(self, mode=None, samples_per_record=None,
             if (samples_per_record % buffers_per_acquisition != 0):
                 logging.warning('buffers_per_acquisition is not a divisor of '
                                 'samples per record which it should be in '
-                                'TS mode, rounding down in samples per buffer '
+                                's mode, rounding down in samples per buffer '
                                 'calculation')
             samples_per_buffer = int(samples_per_record /
                                      buffers_per_acquisition)
diff --git a/qcodes/instrument_drivers/AlazarTech/ATS9360.py b/qcodes/instrument_drivers/AlazarTech/ATS9360.py
index 7afbaff1ff3a..448a1ce27a96 100644
--- a/qcodes/instrument_drivers/AlazarTech/ATS9360.py
+++ b/qcodes/instrument_drivers/AlazarTech/ATS9360.py
@@ -266,6 +266,8 @@ def __init__(self, name, **kwargs):
                            value=1000,
                            vals=validators.Ints(min_value=0))
 
+        
+
         model = self.get_idn()['model']
         if model != 'ATS9360':
             raise Exception("The Alazar board kind is not 'ATS9360',"
diff --git a/qcodes/instrument_drivers/AlazarTech/Samp_controller.py b/qcodes/instrument_drivers/AlazarTech/Samp_controller.py
index 62bec2d76305..ecccca78b027 100644
--- a/qcodes/instrument_drivers/AlazarTech/Samp_controller.py
+++ b/qcodes/instrument_drivers/AlazarTech/Samp_controller.py
@@ -1,9 +1,8 @@
+import logging
 from .ATS import AcquisitionController
 import numpy as np
 from qcodes import Parameter
-from scipy import signal
-
-#import acquistion_tools as tools
+import qcodes.utils.helpers as helpers
 
 
 class SamplesParam(Parameter):
@@ -39,10 +38,11 @@ def update_acquisition_kwargs(self, samp_time=None, **kwargs):
             self.setpoints = ((n,), (n,))
             self.shapes = ((npts,), (npts,))
         else:
-            raise ValueError('samples_per_record or sample_num must be specified')
+            raise ValueError(
+                'samples_per_record or sample_num must be specified')
         # updates dict to be used in acquisition get call
         self.acquisitionkwargs.update(**kwargs)
-    
+
     def get(self):
         mag, phase = self._instrument._get_alazar().acquire(
             acquisition_controller=self._instrument,
@@ -74,13 +74,12 @@ class HD_Samples_Controller(AcquisitionController):
     TODO(nataliejpg) make demodulation freq a param
     """
 
-    def __init__(self, name, alazar_name, demod_freq, samp_rate=500e6, filt='win',
-                 numtaps=101, chan_b=False, **kwargs):
-        filter_dict = {'win': 0, 'hamming': 1, 'ls': 2}
+    def __init__(self, name, alazar_name, demod_freq, samp_rate=5e8,
+                 filt='win', numtaps=101, chan_b=False, **kwargs):
+        filter_dict = {'win': 0, 'ls': 1}
         self.demodulation_frequency = demod_freq
         self.sample_rate = samp_rate
         self.filter = filter_dict[filt]
-        self.int_time = None
         self.numtaps = numtaps
         self.chan_b = chan_b
         self.samples_per_record = 0
@@ -109,7 +108,7 @@ def update_acquisitionkwargs(self, **kwargs):
         """
         samples_per_record = kwargs['samples_per_record']
         sample_rate = self.sample_rate
-        
+
         if 'int_delay' in kwargs:
             int_delay = kwargs['int_delay']
             samp_delay = int_delay * sample_rate
@@ -121,7 +120,7 @@ def update_acquisitionkwargs(self, **kwargs):
                     '(expect delay >= {}'.format(int_delay_min))
         else:
             samp_delay = self.numtaps - 1
-        
+
         if 'int_time' in kwargs:
             int_time = kwargs['int_time']
             samp_time = int_time * sample_rate
@@ -130,22 +129,23 @@ def update_acquisitionkwargs(self, **kwargs):
             oversampling = sample_rate / (2 * self.demodulation_frequency)
             if samp_time > samples_time_max:
                 int_time_max = samples_time_max / sample_rate
-                Warning(
-                    'int_time {} is longer than total_time - delay ({})'.format(int_time, int_time_max))
+                raise ValueError('int_time {} is longer than total_time - '
+                                 'delay ({})'.format(int_time, int_time_max))
             elif oscilations_measured < 10:
-                Warning(
-                    '{} oscilations measured, recommend at least 10: '
-                    'decrease sampling rate, take more samples or increase demodulation freq'.format(oscilations_measured))
+                logging.warning('{} oscilations measured, recommend at '
+                                'least 10: decrease sampling rate, take '
+                                'more samples or increase demodulation '
+                                'freq'.format(oscilations_measured))
             elif oversampling < 1:
-                Warning('oversampling rate is {}, recommend > 1: '
-                    'increase sampling rate or decrease demodulation frequency'.format(oversampling))
+                logging.warning('oversampling rate is {}, recommend > 1: '
+                                'increase sampling rate or decrease '
+                                'demodulation frequency'.format(oversampling))
         else:
             samp_time = samples_per_record - samp_delay
-        
-        
+
         self.samples_time = samp_time
         self.samples_delay = samp_delay
-        
+
         self.acquisition.update_acquisition_kwargs(samp_time, **kwargs)
 
     def pre_start_capture(self):
@@ -207,7 +207,7 @@ def post_acquire(self):
             i0 = (i * self.samples_per_record * self.number_of_channels)
             i1 = (i0 + self.samples_per_record * self.number_of_channels)
             recordA += np.uint16(self.buffer[i0:i1:self.number_of_channels] /
-                        records_per_acquisition)
+                                 records_per_acquisition)
 
         # do demodulation
         magA, phaseA = self.fit(recordA)
@@ -220,7 +220,7 @@ def post_acquire(self):
                       self.number_of_channels + 1)
                 i1 = (i0 + self.samples_per_record * self.number_of_channels)
                 recordB += np.uint16(self.buffer[i0:i1:self.number_of_channels] /
-                            records_per_acquisition)
+                                     records_per_acquisition)
             magB, phaseB = self.fit(recordB)
 
         return magA, phaseA
@@ -242,20 +242,15 @@ def fit(self, rec):
 
         # filter out higher freq component
         if self.filter == 0:
-            re_filtered = filter_win(re_wave, cutoff,
-                                           self.sample_rate, self.numtaps)
-            im_filtered = filter_win(im_wave, cutoff,
-                                           self.sample_rate, self.numtaps)
+            re_filtered = helpers.filter_win(re_wave, cutoff,
+                                             self.sample_rate, self.numtaps)
+            im_filtered = helpers.filter_win(im_wave, cutoff,
+                                             self.sample_rate, self.numtaps)
         elif self.filter == 1:
-            re_filtered = filter_ham(re_wave, cutoff,
-                                           self.sample_rate, self.numtaps)
-            im_filtered = filter_ham(im_wave, cutoff,
-                                           self.sample_rate, self.numtaps)
-        elif self.filter == 2:
-            re_filtered = filter_ls(re_wave, cutoff,
-                                          self.sample_rate, self.numtaps)
-            im_filtered = filter_ls(im_wave, cutoff,
-                                          self.sample_rate, self.numtaps)
+            re_filtered = helpers.filter_ls(re_wave, cutoff,
+                                            self.sample_rate, self.numtaps)
+            im_filtered = helpers.filter_ls(im_wave, cutoff,
+                                            self.sample_rate, self.numtaps)
 
         # apply int limits
         start = self.samples_delay
@@ -272,12 +267,7 @@ def fit(self, rec):
         phase = np.angle(complex_num, deg=True)
 
         return mag, phase
-        
-def filter_win(rec, cutoff, sample_rate, numtaps):
-    nyq_rate = sample_rate / 2.
-    fir_coef = signal.firwin(numtaps, cutoff / nyq_rate)
-    filtered_rec = signal.lfilter(fir_coef, 1.0, rec)
-    return filtered_rec
+
 
 def sample_to_volt_u12(raw_samples, bps):
     # right_shift 16-bit sample by 4 to get 12 bit sample
@@ -287,10 +277,10 @@ def sample_to_volt_u12(raw_samples, bps):
     code_zero = (1 << (bps - 1)) - 0.5
     code_range = (1 << (bps - 1)) - 0.5
 
-    # TODO(nataliejpg) make this not hard coded 
+    # TODO(nataliejpg) make this not hard coded
     input_range_volts = 1
     # Convert to volts
     volt_samples = np.float64(input_range_volts *
-                    (shifted_samples - code_zero) / code_range)
-                    
-    return volt_samples
\ No newline at end of file
+                              (shifted_samples - code_zero) / code_range)
+
+    return volt_samples
diff --git a/qcodes/instrument_drivers/AlazarTech/Single_controller.py b/qcodes/instrument_drivers/AlazarTech/Single_controller.py
index eb84b087ce2c..ab3ede0a4212 100644
--- a/qcodes/instrument_drivers/AlazarTech/Single_controller.py
+++ b/qcodes/instrument_drivers/AlazarTech/Single_controller.py
@@ -1,6 +1,7 @@
+import logging
 from .ATS import AcquisitionController
 import numpy as np
-from scipy import signal
+import qcodes.utils.helpers as helpers
 
 
 class HD_Controller(AcquisitionController):
@@ -26,9 +27,9 @@ class HD_Controller(AcquisitionController):
     TODO(nataliejpg) make filter settings not hard coded
     """
 
-    def __init__(self, name, alazar_name, demod_freq, samp_rate=500e6,
-                 filt='win', chan_b=False, **kwargs):
-        filter_dict = {'win': 0, 'hamming': 1, 'ls': 2}
+    def __init__(self, name, alazar_name, demod_freq, samp_rate=5e8, filt='win',
+             numtaps=101, chan_b=False, **kwargs):
+        filter_dict = {'win': 0, 'ls': 1, 'dot': 2}
         self.demodulation_frequency = demod_freq
         self.sample_rate = samp_rate
         self.filter = filter_dict[filt]
@@ -39,6 +40,8 @@ def __init__(self, name, alazar_name, demod_freq, samp_rate=500e6,
         self.records_per_buffer = 0
         self.buffers_per_acquisition = 0
         self.number_of_channels = 2
+        self.samples_delay = None
+        self.samples_time = None
         self.cos_list = None
         self.sin_list = None
         self.buffer = None
@@ -58,6 +61,45 @@ def update_acquisitionkwargs(self, **kwargs):
         :param kwargs:
         :return:
         """
+        samples_per_record = kwargs['samples_per_record']
+        sample_rate = self.sample_rate
+
+        if 'int_delay' in kwargs:
+            int_delay = kwargs['int_delay']
+            samp_delay = int_delay * sample_rate
+            samples_delay_min = (self.numtaps - 1)
+            if samp_delay < samples_delay_min:
+                int_delay_min = samples_delay_min / sample_rate
+                Warning(
+                    'delay is less than recommended for filter choice: '
+                    '(expect delay >= {}'.format(int_delay_min))
+        else:
+            samp_delay = self.numtaps - 1
+
+        if 'int_time' in kwargs:
+            int_time = kwargs['int_time']
+            samp_time = int_time * sample_rate
+            samples_time_max = (samples_per_record - samp_delay)
+            oscilations_measured = int_time / self.demodulation_frequency
+            oversampling = sample_rate / (2 * self.demodulation_frequency)
+            if samp_time > samples_time_max:
+                int_time_max = samples_time_max / sample_rate
+                raise ValueError('int_time {} is longer than total_time - '
+                                 'delay ({})'.format(int_time, int_time_max))
+            elif oscilations_measured < 10:
+                logging.warning('{} oscilations measured, recommend at '
+                                'least 10: decrease sampling rate, take '
+                                'more samples or increase demodulation '
+                                'freq'.format(oscilations_measured))
+            elif oversampling < 1:
+                logging.warning('oversampling rate is {}, recommend > 1: '
+                                'increase sampling rate or decrease '
+                                'demodulation frequency'.format(oversampling))
+        else:
+            samp_time = samples_per_record - samp_delay
+
+        self.samples_time = samp_time
+        self.samples_delay = samp_delay
         self.acquisitionkwargs.update(**kwargs)
 
     def do_acquisition(self):
diff --git a/qcodes/utils/helpers.py b/qcodes/utils/helpers.py
index 3afea8cf22bc..7ee83f5ea0af 100644
--- a/qcodes/utils/helpers.py
+++ b/qcodes/utils/helpers.py
@@ -10,6 +10,7 @@
 from copy import deepcopy
 
 import numpy as np
+from scipy import signal
 
 _tprint_times = {}
 
@@ -415,3 +416,36 @@ def compare_dictionaries(dict_1, dict_2,
     else:
         dicts_equal = False
     return dicts_equal, dict_differences
+
+
+def filter_win(rec, cutoff, sample_rate, numtaps, axis=-1):
+    """
+    low pass filter, returns filtered signal using FIR window
+    filter
+
+    inputs:
+        record to filter
+        cutoff frequency
+        sampling rate
+        number of frequency comppnents to use in the filer
+        axis of record to apply filter along
+    """
+    nyq_rate = sample_rate / 2.
+    fir_coef = signal.firwin(numtaps, cutoff / nyq_rate)
+    filtered_rec = signal.lfilter(fir_coef, 1.0, rec, axis=axis)
+    return filtered_rec
+
+
+def filter_ls(rec, cutoff, sample_rate, numtaps, axis=-1):
+    """
+    low pass filter, returns filtered signal using FIR
+    least squared filter
+
+    inputs:
+        record to filter
+        cutoff frequency
+        sampling rate
+        number of frequency comppnents to use in the filer
+        axis of record to apply filter along
+    """
+    raise NotImplementedError

From 3d1c76bd3af5ab411ecb2206ffe9935891ffc93e Mon Sep 17 00:00:00 2001
From: nataliejpg 
Date: Mon, 14 Nov 2016 18:31:54 +0100
Subject: [PATCH 042/328] undoing typos introduces in previous commit

---
 qcodes/instrument_drivers/AlazarTech/ATS.py     | 2 +-
 qcodes/instrument_drivers/AlazarTech/ATS9360.py | 2 --
 2 files changed, 1 insertion(+), 3 deletions(-)

diff --git a/qcodes/instrument_drivers/AlazarTech/ATS.py b/qcodes/instrument_drivers/AlazarTech/ATS.py
index 0fe74979e39b..9d648593f1ee 100644
--- a/qcodes/instrument_drivers/AlazarTech/ATS.py
+++ b/qcodes/instrument_drivers/AlazarTech/ATS.py
@@ -540,7 +540,7 @@ def acquire(self, mode=None, samples_per_record=None,
             if (samples_per_record % buffers_per_acquisition != 0):
                 logging.warning('buffers_per_acquisition is not a divisor of '
                                 'samples per record which it should be in '
-                                's mode, rounding down in samples per buffer '
+                                'Ts mode, rounding down in samples per buffer '
                                 'calculation')
             samples_per_buffer = int(samples_per_record /
                                      buffers_per_acquisition)
diff --git a/qcodes/instrument_drivers/AlazarTech/ATS9360.py b/qcodes/instrument_drivers/AlazarTech/ATS9360.py
index 448a1ce27a96..7afbaff1ff3a 100644
--- a/qcodes/instrument_drivers/AlazarTech/ATS9360.py
+++ b/qcodes/instrument_drivers/AlazarTech/ATS9360.py
@@ -266,8 +266,6 @@ def __init__(self, name, **kwargs):
                            value=1000,
                            vals=validators.Ints(min_value=0))
 
-        
-
         model = self.get_idn()['model']
         if model != 'ATS9360':
             raise Exception("The Alazar board kind is not 'ATS9360',"

From bdb74c3370cdf576a3776ceb749e9f268d05b2b6 Mon Sep 17 00:00:00 2001
From: nataliejpg 
Date: Mon, 14 Nov 2016 18:58:28 +0100
Subject: [PATCH 043/328] fix int_delay and int_time bug in sample controller

---
 .../instrument_drivers/AlazarTech/Samp_controller.py  | 11 ++++++-----
 1 file changed, 6 insertions(+), 5 deletions(-)

diff --git a/qcodes/instrument_drivers/AlazarTech/Samp_controller.py b/qcodes/instrument_drivers/AlazarTech/Samp_controller.py
index ecccca78b027..4932cac0a1ef 100644
--- a/qcodes/instrument_drivers/AlazarTech/Samp_controller.py
+++ b/qcodes/instrument_drivers/AlazarTech/Samp_controller.py
@@ -110,7 +110,7 @@ def update_acquisitionkwargs(self, **kwargs):
         sample_rate = self.sample_rate
 
         if 'int_delay' in kwargs:
-            int_delay = kwargs['int_delay']
+            int_delay = kwargs.pop('int_delay')
             samp_delay = int_delay * sample_rate
             samples_delay_min = (self.numtaps - 1)
             if samp_delay < samples_delay_min:
@@ -122,7 +122,7 @@ def update_acquisitionkwargs(self, **kwargs):
             samp_delay = self.numtaps - 1
 
         if 'int_time' in kwargs:
-            int_time = kwargs['int_time']
+            int_time = kwargs.pop('int_time')
             samp_time = int_time * sample_rate
             samples_time_max = (samples_per_record - samp_delay)
             oscilations_measured = int_time / self.demodulation_frequency
@@ -145,7 +145,8 @@ def update_acquisitionkwargs(self, **kwargs):
 
         self.samples_time = samp_time
         self.samples_delay = samp_delay
-
+        
+        
         self.acquisition.update_acquisition_kwargs(samp_time, **kwargs)
 
     def pre_start_capture(self):
@@ -254,8 +255,8 @@ def fit(self, rec):
 
         # apply int limits
         start = self.samples_delay
-        if self.int_time:
-            end = self.int_time * self.sample_rate + start
+        if self.samples_time:
+            end = self.samples_time * self.sample_rate + start
         else:
             end = None
         re_limited = re_filtered[start:end]

From e0d0a25919a961e3cca3862d1490eff1180a8935 Mon Sep 17 00:00:00 2001
From: nataliejpg 
Date: Mon, 14 Nov 2016 19:33:33 +0100
Subject: [PATCH 044/328] debugging silly mistakes

---
 .../AlazarTech/Samp_controller.py                  | 14 +++++++-------
 1 file changed, 7 insertions(+), 7 deletions(-)

diff --git a/qcodes/instrument_drivers/AlazarTech/Samp_controller.py b/qcodes/instrument_drivers/AlazarTech/Samp_controller.py
index 4932cac0a1ef..8a109300c1af 100644
--- a/qcodes/instrument_drivers/AlazarTech/Samp_controller.py
+++ b/qcodes/instrument_drivers/AlazarTech/Samp_controller.py
@@ -115,7 +115,7 @@ def update_acquisitionkwargs(self, **kwargs):
             samples_delay_min = (self.numtaps - 1)
             if samp_delay < samples_delay_min:
                 int_delay_min = samples_delay_min / sample_rate
-                Warning(
+                logging.warning(
                     'delay is less than recommended for filter choice: '
                     '(expect delay >= {}'.format(int_delay_min))
         else:
@@ -125,7 +125,7 @@ def update_acquisitionkwargs(self, **kwargs):
             int_time = kwargs.pop('int_time')
             samp_time = int_time * sample_rate
             samples_time_max = (samples_per_record - samp_delay)
-            oscilations_measured = int_time / self.demodulation_frequency
+            oscilations_measured = int_time * self.demodulation_frequency
             oversampling = sample_rate / (2 * self.demodulation_frequency)
             if samp_time > samples_time_max:
                 int_time_max = samples_time_max / sample_rate
@@ -143,11 +143,11 @@ def update_acquisitionkwargs(self, **kwargs):
         else:
             samp_time = samples_per_record - samp_delay
 
-        self.samples_time = samp_time
-        self.samples_delay = samp_delay
+        self.samples_time = int(samp_time)
+        self.samples_delay = int(samp_delay)
         
         
-        self.acquisition.update_acquisition_kwargs(samp_time, **kwargs)
+        self.acquisition.update_acquisition_kwargs(self.samples_time, **kwargs)
 
     def pre_start_capture(self):
         """
@@ -253,10 +253,10 @@ def fit(self, rec):
             im_filtered = helpers.filter_ls(im_wave, cutoff,
                                             self.sample_rate, self.numtaps)
 
-        # apply int limits
+        # apply integration limits
         start = self.samples_delay
         if self.samples_time:
-            end = self.samples_time * self.sample_rate + start
+            end = int(self.samples_time * self.sample_rate) + start
         else:
             end = None
         re_limited = re_filtered[start:end]

From 85f9f443df187e93d2c754b69e2bdc1c4f05f231 Mon Sep 17 00:00:00 2001
From: nataliejpg 
Date: Tue, 15 Nov 2016 10:00:56 +0100
Subject: [PATCH 045/328] sample controller working

---
 .../AlazarTech/Samp_controller.py             | 28 +++++++++----------
 1 file changed, 13 insertions(+), 15 deletions(-)

diff --git a/qcodes/instrument_drivers/AlazarTech/Samp_controller.py b/qcodes/instrument_drivers/AlazarTech/Samp_controller.py
index 8a109300c1af..66b02977d469 100644
--- a/qcodes/instrument_drivers/AlazarTech/Samp_controller.py
+++ b/qcodes/instrument_drivers/AlazarTech/Samp_controller.py
@@ -24,7 +24,7 @@ def __init__(self, name, instrument):
         self.setpoints = ((1,), (1,))
         self.shapes = ((1,), (1,))
 
-    def update_acquisition_kwargs(self, samp_time=None, **kwargs):
+    def update_acquisition_kwargs(self, samp_time, **kwargs):
         # needed to update config of the software parameter on sweep change
         # freq setpoints tuple as needs to be hashable for look up
         if samp_time:
@@ -32,14 +32,14 @@ def update_acquisition_kwargs(self, samp_time=None, **kwargs):
             n = tuple(np.arange(npts))
             self.setpoints = ((n,), (n,))
             self.shapes = ((npts,), (npts,))
-        elif 'samples_per_record' in kwargs:
-            npts = kwargs['samples_per_record']
-            n = tuple(np.arange(npts))
-            self.setpoints = ((n,), (n,))
-            self.shapes = ((npts,), (npts,))
+        # elif 'samples_per_record' in kwargs:
+            # npts = kwargs['samples_per_record']
+            # n = tuple(np.arange(npts))
+            # self.setpoints = ((n,), (n,))
+            # self.shapes = ((npts,), (npts,))
         else:
             raise ValueError(
-                'samples_per_record or sample_num must be specified')
+                'samp_time not specified')
         # updates dict to be used in acquisition get call
         self.acquisitionkwargs.update(**kwargs)
 
@@ -82,9 +82,9 @@ def __init__(self, name, alazar_name, demod_freq, samp_rate=5e8,
         self.filter = filter_dict[filt]
         self.numtaps = numtaps
         self.chan_b = chan_b
-        self.samples_per_record = 0
-        self.records_per_buffer = 0
-        self.buffers_per_acquisition = 0
+        self.samples_per_record = None
+        self.records_per_buffer = None
+        self.buffers_per_acquisition = None
         self.number_of_channels = 2
         self.samples_delay = None
         self.samples_time = None
@@ -130,7 +130,7 @@ def update_acquisitionkwargs(self, **kwargs):
             if samp_time > samples_time_max:
                 int_time_max = samples_time_max / sample_rate
                 raise ValueError('int_time {} is longer than total_time - '
-                                 'delay ({})'.format(int_time, int_time_max))
+                                 'delay: {}'.format(int_time, int_time_max))
             elif oscilations_measured < 10:
                 logging.warning('{} oscilations measured, recommend at '
                                 'least 10: decrease sampling rate, take '
@@ -255,10 +255,8 @@ def fit(self, rec):
 
         # apply integration limits
         start = self.samples_delay
-        if self.samples_time:
-            end = int(self.samples_time * self.sample_rate) + start
-        else:
-            end = None
+        end = start + self.samples_time
+        
         re_limited = re_filtered[start:end]
         im_limited = im_filtered[start:end]
 

From 1b9f439718413237312f4da5f9126b3667c627f0 Mon Sep 17 00:00:00 2001
From: Natalie Pearson 
Date: Tue, 15 Nov 2016 11:13:15 +0100
Subject: [PATCH 046/328] update docstrings and clean up basic controller

---
 .../AlazarTech/Samp_controller.py             | 58 +++++++++++--------
 .../AlazarTech/basic_controller.py            | 14 +++--
 2 files changed, 42 insertions(+), 30 deletions(-)

diff --git a/qcodes/instrument_drivers/AlazarTech/Samp_controller.py b/qcodes/instrument_drivers/AlazarTech/Samp_controller.py
index 66b02977d469..ee29f54936e5 100644
--- a/qcodes/instrument_drivers/AlazarTech/Samp_controller.py
+++ b/qcodes/instrument_drivers/AlazarTech/Samp_controller.py
@@ -11,7 +11,7 @@ class SamplesParam(Parameter):
     HD_Samples_Controller (tested with ATS9360 board) for return of an array of
     sample data from the Alazar, averaged over records and buffers.
 
-    TODO(nataliejpg) fix setpoints/shapes horriblenesss
+    TODO(nataliejpg) refactor setpoints/shapes horriblenesss
     """
 
     def __init__(self, name, instrument):
@@ -32,11 +32,6 @@ def update_acquisition_kwargs(self, samp_time, **kwargs):
             n = tuple(np.arange(npts))
             self.setpoints = ((n,), (n,))
             self.shapes = ((npts,), (npts,))
-        # elif 'samples_per_record' in kwargs:
-            # npts = kwargs['samples_per_record']
-            # n = tuple(np.arange(npts))
-            # self.setpoints = ((n,), (n,))
-            # self.shapes = ((npts,), (npts,))
         else:
             raise ValueError(
                 'samp_time not specified')
@@ -61,16 +56,15 @@ class HD_Samples_Controller(AcquisitionController):
         can communicate with the Alazar
     demod_freq: the frequency of the software wave to be created
     samp_rate: the rate of sampling
-    filt: the filter to be used to filter out double freq component
+    filt: the filter to be used to filter out double freq component (win or ls)
+    numtaps: number of freq components used in the filter
     chan_b: whether there is also a second channel of data to be processed
         and returned
     **kwargs: kwargs are forwarded to the Instrument base class
 
     TODO(nataliejpg) fix sample rate problem
     TODO(nataliejpg) test filter options
-    TODO(nataliejpg) test mag phase logic
     TODO(nataliejpg) finish implementation of channel b option
-    TODO(nataliejpg) update docstrings :P
     TODO(nataliejpg) make demodulation freq a param
     """
 
@@ -101,8 +95,14 @@ def __init__(self, name, alazar_name, demod_freq, samp_rate=5e8,
 
     def update_acquisitionkwargs(self, **kwargs):
         """
-        This method must be used to update the kwargs used for the acquisition
-        with the alazar_driver.acquire
+        Updates the kwargs to be used when
+        alazar_driver.acquire is called via a get call of the
+        acquisition SamplesParam.
+
+        It is also used to set the limits on the selection of
+        samples returned (bounded by delay and integration time)
+        and update this information in the Samples Parameter.
+
         :param kwargs:
         :return:
         """
@@ -145,13 +145,13 @@ def update_acquisitionkwargs(self, **kwargs):
 
         self.samples_time = int(samp_time)
         self.samples_delay = int(samp_delay)
-        
-        
+
         self.acquisition.update_acquisition_kwargs(self.samples_time, **kwargs)
 
     def pre_start_capture(self):
         """
-        See AcquisitionController
+        Called before capture start to update Acquisition Controller with
+        alazar acuisition params and set up software wave for demodulation.
         :return:
         """
         alazar = self._get_alazar()
@@ -176,23 +176,21 @@ def pre_acquire(self):
         See AcquisitionController
         :return:
         """
-        # this could be used to start an Arbitrary Waveform Generator, etc...
-        # using this method ensures that the contents are executed AFTER the
-        # Alazar card starts listening for a trigger pulse
         pass
 
     def handle_buffer(self, data):
         """
-        See AcquisitionController
+        Adds data from alazar to buffer (effectively averaging)
         :return:
         """
-        # average over buffers
         self.buffer += data
 
     def post_acquire(self):
         """
-        See AcquisitionController
-        :return:
+        Processes the data according to ATS9360 settings, splitting into
+        records and averaging over them, then applying demodulation fit
+        nb: currently only channel A
+        :return: samples_magnitude_array, samples_phase_array
         """
         records_per_acquisition = (1. * self.buffers_per_acquisition *
                                    self.records_per_buffer)
@@ -227,13 +225,19 @@ def post_acquire(self):
         return magA, phaseA
 
     def fit(self, rec):
+        """
+        Applies volts conversion, demodulation fit, low bandpass filter
+        and integration limits to samples array
+        :return: samples_magnitude_array, samples_phase_array
+        """
+
         # convert rec to volts
         bps = self.board_info['bits_per_sample']
         if bps == 12:
             volt_rec = sample_to_volt_u12(rec, bps)
         else:
-            Warning('sample to volt conversion does not exist for'
-                    ' bps != 12, centered raw samples returned')
+            logging.warning('sample to volt conversion does not exist for'
+                            ' bps != 12, centered raw samples returned')
             volt_rec = rec - np.mean(rec)
 
         # multiply with software wave
@@ -256,7 +260,7 @@ def fit(self, rec):
         # apply integration limits
         start = self.samples_delay
         end = start + self.samples_time
-        
+
         re_limited = re_filtered[start:end]
         im_limited = im_filtered[start:end]
 
@@ -269,6 +273,12 @@ def fit(self, rec):
 
 
 def sample_to_volt_u12(raw_samples, bps):
+    """
+    Applies volts conversion for 12 bit sample data stored
+    in 2 bytes
+    :return: samples_magnitude_array, samples_phase_array
+    """
+
     # right_shift 16-bit sample by 4 to get 12 bit sample
     shifted_samples = np.right_shift(raw_samples, 4)
 
diff --git a/qcodes/instrument_drivers/AlazarTech/basic_controller.py b/qcodes/instrument_drivers/AlazarTech/basic_controller.py
index 40adef46d9c1..b76309fb8881 100644
--- a/qcodes/instrument_drivers/AlazarTech/basic_controller.py
+++ b/qcodes/instrument_drivers/AlazarTech/basic_controller.py
@@ -1,7 +1,7 @@
+import logging
 from .ATS import AcquisitionController
 import numpy as np
 from qcodes import Parameter
-#from.acquistion_tools import sample_to_volt_u12
 
 
 class SampleSweep(Parameter):
@@ -147,12 +147,14 @@ def post_acquire(self):
             volt_rec_A = sample_to_volt_u12(recordA, bps)
             volt_rec_B = sample_to_volt_u12(recordB, bps)
         else:
-            Warning('sample to volt conversion does not exist for bps != 12, raw samples returned')
+            logging.warning('sample to volt conversion does not exist for bps '
+                            '!= 12, raw samples centered on 0 returned')
             volt_rec_A = recordA - np.mean(recordA)
             volt_rec_B = recordB - np.mean(recordB)
 
         return volt_rec_A, volt_rec_B
 
+
 def sample_to_volt_u12(raw_samples, bps):
     # right_shift 16-bit sample by 4 to get 12 bit sample
     shifted_samples = np.right_shift(raw_samples, 4)
@@ -161,10 +163,10 @@ def sample_to_volt_u12(raw_samples, bps):
     code_zero = (1 << (bps - 1)) - 0.5
     code_range = (1 << (bps - 1)) - 0.5
 
-    # TODO(nataliejpg) make this not hard coded 
+    # TODO(nataliejpg) make this not hard coded
     input_range_volts = 1
     # Convert to volts
     volt_samples = np.float64(input_range_volts *
-                    (shifted_samples - code_zero) / code_range)
-                    
-    return volt_samples
\ No newline at end of file
+                              (shifted_samples - code_zero) / code_range)
+
+    return volt_samples

From c7982049ba1593eb31f65c48eb985bcb64c94169 Mon Sep 17 00:00:00 2001
From: Natalie Pearson 
Date: Tue, 15 Nov 2016 12:27:29 +0100
Subject: [PATCH 047/328] extend fixes to rec and single controllers

---
 .../AlazarTech/Rec_controller.py              | 240 ++++++++++++------
 .../AlazarTech/Samp_controller.py             |  23 +-
 .../AlazarTech/Single_controller.py           | 207 ++++++++-------
 qcodes/utils/helpers.py                       |  14 +
 4 files changed, 299 insertions(+), 185 deletions(-)

diff --git a/qcodes/instrument_drivers/AlazarTech/Rec_controller.py b/qcodes/instrument_drivers/AlazarTech/Rec_controller.py
index 66eb8af1a9b2..e92de3e40a59 100644
--- a/qcodes/instrument_drivers/AlazarTech/Rec_controller.py
+++ b/qcodes/instrument_drivers/AlazarTech/Rec_controller.py
@@ -1,7 +1,8 @@
+import logging
 from .ATS import AcquisitionController
 import numpy as np
-from scipy import signal
 from qcodes import Parameter
+import qcodes.utils.helpers as helpers
 
 
 class RecordsParam(Parameter):
@@ -45,8 +46,7 @@ def get(self):
 
 class HD_Records_Controller(AcquisitionController):
     """
-    seq 0
-        This is the Acquisition Controller class which works with the ATS9360,
+    This is the Acquisition Controller class which works with the ATS9360,
     averaging over buffers and records and demodulating with a software
     reference signal, returning the  samples.
     args:
@@ -56,31 +56,35 @@ class HD_Records_Controller(AcquisitionController):
     demod_freq: the frequency of the software wave to be created
     samp_rate: the rate of sampling
     filt: the filter to be used to filter out double freq component
+    numtaps: number of freq components used in the filter
     chan_b: whether there is also a second channel of data to be processed
         and returned
     **kwargs: kwargs are forwarded to the Instrument base class
 
     TODO(nataliejpg) fix sample rate problem
-    TODO(nataliejpg) add filter options
-    TODO(nataliejpg) test mag phase logic
-    TODO(nataliejpg) implement record B thinking
+    TODO(nataliejpg) test filter options
+    TODO(nataliejpg) finish implementation of channel b option
+    TODO(nataliejpg) make demodulation freq a param
     """
 
-    def __init__(self, name, alazar_name, demod_freq, samp_rate=500e6,
-                 filt='win', chan_b=False, **kwargs):
-        filter_dict = {'win': 0, 'hamming': 1, 'ls': 2}
+    def __init__(self, name, alazar_name, demod_freq, samp_rate=5e8,
+                 filt='win', numtaps=101, chan_b=False, **kwargs):
+        filter_dict = {'win': 0, 'ls': 1, 'dot': 2}
         self.demodulation_frequency = demod_freq
         self.sample_rate = samp_rate
         self.filter = filter_dict[filt]
-        #self.chan_b = chan_b
-        self.sample_rate = samp_rate
-        self.samples_per_record = 0
-        self.records_per_buffer = 0
-        self.buffers_per_acquisition = 0
+        self.numtaps = numtaps
+        self.chan_b = chan_b
+        self.samples_per_record = None
+        self.records_per_buffer = None
+        self.buffers_per_acquisition = None
         self.number_of_channels = 2
+        self.samples_delay = None
+        self.samples_time = None
         self.cos_mat = None
         self.sin_mat = None
         self.buffer = None
+        self.board_info = None
         # make a call to the parent class and by extension,
         # create the parameter structure of this class
         super().__init__(name, alazar_name, **kwargs)
@@ -90,32 +94,84 @@ def __init__(self, name, alazar_name, demod_freq, samp_rate=500e6,
 
     def update_acquisitionkwargs(self, **kwargs):
         """
-        This method must be used to update the kwargs used for the acquisition
-        with the alazar_driver.acquire
+        Updates the kwargs to be used when
+        alazar_driver.acquire is called via a get call of the
+        acquisition RecordsParam.
+
+        It is also used to set the limits on the selection of
+        samples used (bounded by delay and integration time)
+
         :param kwargs:
         :return:
         """
+        samples_per_record = kwargs['samples_per_record']
+        sample_rate = self.sample_rate
+
+        if 'int_delay' in kwargs:
+            int_delay = kwargs.pop('int_delay')
+            samp_delay = int_delay * sample_rate
+            samples_delay_min = (self.numtaps - 1)
+            if samp_delay < samples_delay_min:
+                int_delay_min = samples_delay_min / sample_rate
+                logging.warning(
+                    'delay is less than recommended for filter choice: '
+                    '(expect delay >= {}'.format(int_delay_min))
+        else:
+            samp_delay = self.numtaps - 1
+
+        if 'int_time' in kwargs:
+            int_time = kwargs.pop('int_time')
+            samp_time = int_time * sample_rate
+            samples_time_max = (samples_per_record - samp_delay)
+            oscilations_measured = int_time * self.demodulation_frequency
+            oversampling = sample_rate / (2 * self.demodulation_frequency)
+            if samp_time > samples_time_max:
+                int_time_max = samples_time_max / sample_rate
+                raise ValueError('int_time {} is longer than total_time - '
+                                 'delay: {}'.format(int_time, int_time_max))
+            elif oscilations_measured < 10:
+                logging.warning('{} oscilations measured, recommend at '
+                                'least 10: decrease sampling rate, take '
+                                'more samples or increase demodulation '
+                                'freq'.format(oscilations_measured))
+            elif oversampling < 1:
+                logging.warning('oversampling rate is {}, recommend > 1: '
+                                'increase sampling rate or decrease '
+                                'demodulation frequency'.format(oversampling))
+        else:
+            samp_time = samples_per_record - samp_delay
+
+        self.samples_time = int(samp_time)
+        self.samples_delay = int(samp_delay)
+
         self.acquisition.update_acquisition_kwargs(**kwargs)
 
     def pre_start_capture(self):
         """
-        See AcquisitionController
+        Called before capture start to update Acquisition Controller with
+        alazar acquisition params and set up software wave for demodulation.
+
+        sine and cosine matrices have shape (samples * records).
+
         :return:
         """
         alazar = self._get_alazar()
         self.samples_per_record = alazar.samples_per_record.get()
         self.records_per_buffer = alazar.records_per_buffer.get()
         self.buffers_per_acquisition = alazar.buffers_per_acquisition.get()
+        self.board_info = alazar.get_idn()
         self.buffer = np.zeros(self.samples_per_record *
                                self.records_per_buffer *
-                               self.number_of_channels)
+                               self.number_of_channels,
+                               dtype=np.uint16)
 
-        integer_list = np.arange(self.samples_per_record)
+        integer_list = np.arange(self.samples_per_record, dtype=np.uint16)
         angle_list = (2 * np.pi * self.demodulation_frequency /
                       self.sample_rate * integer_list)
 
         cos_list = np.cos(angle_list).reshape(self.samples_per_record, 1)
         sin_list = np.sin(angle_list).reshape(self.samples_per_record, 1)
+
         self.cos_mat = np.kron(np.ones((1, self.records_per_buffer)), cos_list)
         self.sin_mat = np.kron(np.ones((1, self.records_per_buffer)), sin_list)
 
@@ -124,97 +180,121 @@ def pre_acquire(self):
         See AcquisitionController
         :return:
         """
-        # this could be used to start an Arbitrary Waveform Generator, etc...
-        # using this method ensures that the contents are executed AFTER the
-        # Alazar card starts listening for a trigger pulse
         pass
 
     def handle_buffer(self, data):
         """
-        See AcquisitionController
+        Adds data from alazar to buffer (effectively averaging)
         :return:
         """
         self.buffer += data
 
     def post_acquire(self):
         """
-        See AcquisitionController
-        :return:
+        Processes the data according to ATS9360 settings, splitting into
+        records and averaging over them, then applying demodulation fit
+        nb: currently only channel A
+        :return: samples_magnitude_array, samples_phase_array
         """
 
+        records_per_acquisition = (self.buffers_per_acquisition *
+                                   self.records_per_buffer)
         # for ATS9360 samples are arranged in the buffer as follows:
         # S00A, S00B, S01A, S01B...S10A, S10B, S11A, S11B...
         # where SXYZ is record X, sample Y, channel Z.
 
-        # reshapes date to be (samples * records)
-        recordA = np.zeros((self.samples_per_record, self.records_per_buffer))
-        # recordB = np.zeros((self.samples_per_record, self.records_per_buffer))\
+        # break buffer up into records and shapes to be (samples * records)
+        recordA = np.zeros((self.samples_per_record, self.records_per_buffer),
+                           dtype=np.uint16)
         for i in range(self.records_per_buffer):
-            i0 = i * self.number_of_channels * self.samples_per_record
-            i1 = i0 + self.number_of_channels * self.samples_per_record
-            recordA[:, i] = (self.buffer[i0:i1:self.number_of_channels] /
-                             self.buffers_per_acquisition)
-            # recordB[:, i] = self.buffer[1:full_rec_length:step] / buffers
-        # return averaged chan A data (records)
+            i0 = (i * self.samples_per_record * self.number_of_channels)
+            i1 = (i0 + self.samples_per_record * self.number_of_channels)
+            recordA += np.uint16(self.buffer[i0:i1:self.number_of_channels] /
+                                 records_per_acquisition)
+            recordA[:, i] = np.uint16((self.buffer[i0:i1:self.number_of_channels] /
+                                       self.buffers_per_acquisition))
+
+        # do demodulation
         magA, phaseA = self.fit(recordA)
 
+        # same for chan b
+        if self.chan_b:
+            raise NotImplementedError('chan b code not complete')
+
         return magA, phaseA
 
     def fit(self, rec):
-        # center rec around 0
-        rec = rec - np.mean(rec)
+        """
+        Applies volts conversion, demodulation fit, low bandpass filter
+        and integration limits to samples * records array
+        :return: records_magnitude_array, records_phase_array
+        """
+
+        # convert rec to volts
+        bps = self.board_info['bits_per_sample']
+        if bps == 12:
+            volt_rec = sample_to_volt_u12(rec, bps)
+        else:
+            logging.warning('sample to volt conversion does not exist for'
+                            ' bps != 12, centered raw samples returned')
+            volt_rec = rec - np.mean(rec, 0)
 
         # multiply with software wave
-        re_wave = np.multiply(rec, self.cos_mat)
-        im_wave = np.multiply(rec, self.sin_mat)
-        cutoff = self.demodulation_frequency
-        numtaps = 30
-        axis = 0
+        re_wave = np.multiply(volt_rec, self.cos_mat)
+        im_wave = np.multiply(volt_rec, self.sin_mat)
+        cutoff = self.demodulation_frequency / 10
+        ax = 0
 
-        # filter out double freq component to obtian constant term
+        # filter out higher freq component
         if self.filter == 0:
-            RePart = self.filter_win(re_wave, numtaps, cutoff, axis=axis)
-            ImPart = self.filter_win(im_wave, numtaps, cutoff, axis=axis)
+            re_filtered = helpers.filter_win(re_wave, cutoff, self.sample_rate,
+                                             self.numtaps, axis=ax)
+            im_filtered = helpers.filter_win(im_wave, cutoff, self.sample_rate,
+                                             self.numtaps, axis=ax)
         elif self.filter == 1:
-            RePart = self.filter_hamming(re_wave, numtaps, cutoff, axis=axis)
-            ImPart = self.filter_hamming(im_wave, numtaps, cutoff, axis=axis)
+            re_filtered = helpers.filter_ls(re_wave, cutoff, self.sample_rate,
+                                            self.numtaps, axis=ax)
+            im_filtered = helpers.filter_ls(im_wave, cutoff, self.sample_rate,
+                                            self.numtaps, axis=ax)
         elif self.filter == 2:
-            RePart = self.filter_ls(re_wave, numtaps, cutoff, axis, axis=axis)
-            ImPart = self.filter_ls(im_wave, numtaps, cutoff, axis, axis=axis)
+            re_filtered = helpers.filter_dot(re_wave, cutoff, self.sample_rate,
+                                             self.numtaps, axis=ax)
+            im_filtered = helpers.filter_dot(im_wave, cutoff, self.sample_rate,
+                                             self.numtaps, axis=ax)
+
+        # apply integration limits
+        start = self.samples_delay
+        end = start + self.samples_time
 
-        # convert to magnitude and phase data and average over samples
-        # data returned is (records)
-        complex_mat = RePart + ImPart * 1j
-        mag = np.mean(2 * abs(complex_mat), axis=0)
-        phase = np.mean(np.angle(complex_mat, deg=True), axis=0)
+        re_limited = re_filtered[start:end, :]
+        im_limited = im_filtered[start:end, :]
+
+        # convert to magnitude and phase
+        complex_num = re_limited + im_limited * 1j
+        mag = np.mean(abs(complex_num), 0)
+        phase = np.mean(np.angle(complex_num, deg=True), 0)
 
         return mag, phase
 
-    def filter_hamming(self, rec, numtaps, cutoff, axis=-1):
-        sample_rate = self.sample_rate
-        nyq_rate = sample_rate / 2.
-        fir_coef = signal.firwin(numtaps,
-                                 cutoff / nyq_rate,
-                                 window="hamming")
-        filtered_rec = 2 * signal.lfilter(fir_coef, 1.0, rec, axis=axis)
-        return filtered_rec
-
-    def filter_win(self, rec, numtaps, cutoff, axis=-1):
-        sample_rate = self.sample_rate
-        nyq_rate = sample_rate / 2.
-        fir_coef = signal.firwin(numtaps,
-                                 cutoff / nyq_rate)
-        filtered_rec = 2 * signal.lfilter(fir_coef, 1.0, rec, axis=axis)
-        return filtered_rec
 
-    def filter_ls(self, rec, numtaps, cutoff, axis=-1):
-        sample_rate = self.sample_rate
-        nyq_rate = sample_rate / 2.
-        bands = [0, cutoff / nyq_rate, cutoff / nyq_rate, 1]
-        desired = [1, 1, 0, 0]
-        fir_coef = signal.firls(numtaps,
-                                bands,
-                                desired,
-                                nyq=nyq_rate)
-        filtered_rec = 2 * signal.lfilter(fir_coef, 1.0, rec, axis=axis)
-        return filtered_rec
+def sample_to_volt_u12(raw_samples, bps):
+    """
+    Applies volts conversion for 12 bit sample data stored
+    in 2 bytes
+    :return: samples_magnitude_array, samples_phase_array
+    """
+
+    # right_shift 16-bit sample by 4 to get 12 bit sample
+    shifted_samples = np.right_shift(raw_samples, 4)
+
+    # Alazar calibration
+    code_zero = (1 << (bps - 1)) - 0.5
+    code_range = (1 << (bps - 1)) - 0.5
+
+    # TODO(nataliejpg) make this not hard coded
+    input_range_volts = 1
+    # Convert to volts
+    volt_samples = np.float64(input_range_volts *
+                              (shifted_samples - code_zero) / code_range)
+
+    return volt_samples
diff --git a/qcodes/instrument_drivers/AlazarTech/Samp_controller.py b/qcodes/instrument_drivers/AlazarTech/Samp_controller.py
index ee29f54936e5..ab6b15697f10 100644
--- a/qcodes/instrument_drivers/AlazarTech/Samp_controller.py
+++ b/qcodes/instrument_drivers/AlazarTech/Samp_controller.py
@@ -151,7 +151,7 @@ def update_acquisitionkwargs(self, **kwargs):
     def pre_start_capture(self):
         """
         Called before capture start to update Acquisition Controller with
-        alazar acuisition params and set up software wave for demodulation.
+        alazar acquisition params and set up software wave for demodulation.
         :return:
         """
         alazar = self._get_alazar()
@@ -192,15 +192,13 @@ def post_acquire(self):
         nb: currently only channel A
         :return: samples_magnitude_array, samples_phase_array
         """
-        records_per_acquisition = (1. * self.buffers_per_acquisition *
+        records_per_acquisition = (self.buffers_per_acquisition *
                                    self.records_per_buffer)
         # for ATS9360 samples are arranged in the buffer as follows:
         # S00A, S00B, S01A, S01B...S10A, S10B, S11A, S11B...
         # where SXYZ is record X, sample Y, channel Z.
 
         # break buffer up into records and averages over them
-        records_per_acquisition = (self.buffers_per_acquisition *
-                                   self.records_per_buffer)
         recordA = np.zeros(self.samples_per_record, dtype=np.uint16)
         for i in range(self.records_per_buffer):
             i0 = (i * self.samples_per_record * self.number_of_channels)
@@ -213,14 +211,15 @@ def post_acquire(self):
 
         # same for chan b
         if self.chan_b:
-            recordB = np.zeros(self.samples_per_record, dtype=np.uint16)
-            for i in range(self.records_per_buffer):
-                i0 = (i * self.samples_per_record *
-                      self.number_of_channels + 1)
-                i1 = (i0 + self.samples_per_record * self.number_of_channels)
-                recordB += np.uint16(self.buffer[i0:i1:self.number_of_channels] /
-                                     records_per_acquisition)
-            magB, phaseB = self.fit(recordB)
+            raise NotImplementedError('chan b code not complete')
+            # recordB = np.zeros(self.samples_per_record, dtype=np.uint16)
+            # for i in range(self.records_per_buffer):
+            #     i0 = (i * self.samples_per_record *
+            #           self.number_of_channels + 1)
+            #     i1 = (i0 + self.samples_per_record * self.number_of_channels)
+            #     recordB += np.uint16(self.buffer[i0:i1:self.number_of_channels] /
+            #                          records_per_acquisition)
+            # magB, phaseB = self.fit(recordB)
 
         return magA, phaseA
 
diff --git a/qcodes/instrument_drivers/AlazarTech/Single_controller.py b/qcodes/instrument_drivers/AlazarTech/Single_controller.py
index ab3ede0a4212..bbff68c5267c 100644
--- a/qcodes/instrument_drivers/AlazarTech/Single_controller.py
+++ b/qcodes/instrument_drivers/AlazarTech/Single_controller.py
@@ -22,29 +22,29 @@ class HD_Controller(AcquisitionController):
 
     TODO(nataliejpg) fix sample rate problem
     TODO(nataliejpg) test filter options
-    TODO(nataliejpg) test mag phase logic
     TODO(nataliejpg) finish implementation of channel b option
-    TODO(nataliejpg) make filter settings not hard coded
+    TODO(nataliejpg) make demodulation freq a param
     """
 
-    def __init__(self, name, alazar_name, demod_freq, samp_rate=5e8, filt='win',
-             numtaps=101, chan_b=False, **kwargs):
+    def __init__(self, name, alazar_name, demod_freq, samp_rate=5e8,
+                 filt='win', numtaps=101, chan_b=False, **kwargs):
         filter_dict = {'win': 0, 'ls': 1, 'dot': 2}
         self.demodulation_frequency = demod_freq
         self.sample_rate = samp_rate
         self.filter = filter_dict[filt]
-        self.chan_b = chan_b
+        self.numtaps = numtaps
         self.acquisitionkwargs = {}
-        self.sample_rate = samp_rate
-        self.samples_per_record = 0
-        self.records_per_buffer = 0
-        self.buffers_per_acquisition = 0
+        self.chan_b = chan_b
+        self.samples_per_record = None
+        self.records_per_buffer = None
+        self.buffers_per_acquisition = None
         self.number_of_channels = 2
         self.samples_delay = None
         self.samples_time = None
         self.cos_list = None
         self.sin_list = None
         self.buffer = None
+        self.board_info = None
         # make a call to the parent class and by extension,
         # create the parameter structure of this class
         super().__init__(name, alazar_name, **kwargs)
@@ -56,8 +56,12 @@ def __init__(self, name, alazar_name, demod_freq, samp_rate=5e8, filt='win',
 
     def update_acquisitionkwargs(self, **kwargs):
         """
-        This method must be used to update the kwargs used for the acquisition
-        with the alazar_driver.acquire
+        Updates the kwargs to be used when
+        alazar_driver.acquire is called.
+
+        It is also used to set the limits on the selection of
+        samples used (bounded by delay and integration time)
+
         :param kwargs:
         :return:
         """
@@ -65,27 +69,27 @@ def update_acquisitionkwargs(self, **kwargs):
         sample_rate = self.sample_rate
 
         if 'int_delay' in kwargs:
-            int_delay = kwargs['int_delay']
+            int_delay = kwargs.pop('int_delay')
             samp_delay = int_delay * sample_rate
             samples_delay_min = (self.numtaps - 1)
             if samp_delay < samples_delay_min:
                 int_delay_min = samples_delay_min / sample_rate
-                Warning(
+                logging.warning(
                     'delay is less than recommended for filter choice: '
                     '(expect delay >= {}'.format(int_delay_min))
         else:
             samp_delay = self.numtaps - 1
 
         if 'int_time' in kwargs:
-            int_time = kwargs['int_time']
+            int_time = kwargs.pop('int_time')
             samp_time = int_time * sample_rate
             samples_time_max = (samples_per_record - samp_delay)
-            oscilations_measured = int_time / self.demodulation_frequency
+            oscilations_measured = int_time * self.demodulation_frequency
             oversampling = sample_rate / (2 * self.demodulation_frequency)
             if samp_time > samples_time_max:
                 int_time_max = samples_time_max / sample_rate
                 raise ValueError('int_time {} is longer than total_time - '
-                                 'delay ({})'.format(int_time, int_time_max))
+                                 'delay: {}'.format(int_time, int_time_max))
             elif oscilations_measured < 10:
                 logging.warning('{} oscilations measured, recommend at '
                                 'least 10: decrease sampling rate, take '
@@ -98,8 +102,9 @@ def update_acquisitionkwargs(self, **kwargs):
         else:
             samp_time = samples_per_record - samp_delay
 
-        self.samples_time = samp_time
-        self.samples_delay = samp_delay
+        self.samples_time = int(samp_time)
+        self.samples_delay = int(samp_delay)
+
         self.acquisitionkwargs.update(**kwargs)
 
     def do_acquisition(self):
@@ -108,24 +113,27 @@ def do_acquisition(self):
         acquisiion parameter of this instrument
         :return:
         """
-        value = self._get_alazar().acquire(acquisition_controller=self,
-                                           **self.acquisitionkwargs)
-        return value
+        values = self._get_alazar().acquire(acquisition_controller=self,
+                                            **self.acquisitionkwargs)
+        return values
 
     def pre_start_capture(self):
         """
-        See AcquisitionController
+        Called before capture start to update Acquisition Controller with
+        alazar acquisition params and set up software wave for demodulation.
         :return:
         """
         alazar = self._get_alazar()
         self.samples_per_record = alazar.samples_per_record.get()
         self.records_per_buffer = alazar.records_per_buffer.get()
         self.buffers_per_acquisition = alazar.buffers_per_acquisition.get()
+        self.board_info = alazar.get_idn()
         self.buffer = np.zeros(self.samples_per_record *
                                self.records_per_buffer *
-                               self.number_of_channels)
+                               self.number_of_channels,
+                               dtype=np.uint16)
 
-        integer_list = np.arange(self.samples_per_record)
+        integer_list = np.arange(self.samples_per_record, dtype=np.uint16)
         angle_list = (2 * np.pi * self.demodulation_frequency /
                       self.sample_rate * integer_list)
 
@@ -137,111 +145,124 @@ def pre_acquire(self):
         See AcquisitionController
         :return:
         """
-        # this could be used to start an Arbitrary Waveform Generator, etc...
-        # using this method ensures that the contents are executed AFTER the
-        # Alazar card starts listening for a trigger pulse
         pass
 
     def handle_buffer(self, data):
         """
-        See AcquisitionController
+        Adds data from alazar to buffer (effectively averaging)
         :return:
         """
-        # average over buffers
         self.buffer += data
 
     def post_acquire(self):
         """
-        See AcquisitionController
-        :return:
+        Processes the data according to ATS9360 settings, splitting into
+        records and averaging over them, then applying demodulation fit
+        nb: currently only channel A
+        :return: samples_magnitude_array, samples_phase_array
         """
-        records_per_acquisition = (1. * self.buffers_per_acquisition *
+        records_per_acquisition = (self.buffers_per_acquisition *
                                    self.records_per_buffer)
         # for ATS9360 samples are arranged in the buffer as follows:
         # S00A, S00B, S01A, S01B...S10A, S10B, S11A, S11B...
         # where SXYZ is record X, sample Y, channel Z.
 
-        # breaks buffer up into records and averages over them
-        # leaving data in form (samples)
-        records_per_acquisition = (self.buffers_per_acquisition *
-                                   self.records_per_buffer)
-        recordA = np.zeros(self.samples_per_record)
+        # break buffer up into records and averages over them
+        recordA = np.zeros(self.samples_per_record, dtype=np.uint16)
         for i in range(self.records_per_buffer):
             i0 = (i * self.samples_per_record * self.number_of_channels)
             i1 = (i0 + self.samples_per_record * self.number_of_channels)
-            recordA += (self.buffer[i0:i1:self.number_of_channels] /
-                        records_per_acquisition)
+            recordA += np.uint16(self.buffer[i0:i1:self.number_of_channels] /
+                                 records_per_acquisition)
 
-        # demodulate and average data to be single point
+        # do demodulation
         magA, phaseA = self.fit(recordA)
 
-        # same for B
+        # same for chan b
         if self.chan_b:
-            recordB = np.zeros(self.samples_per_record)
-            for i in range(self.records_per_buffer):
-                i0 = (i * self.samples_per_record * self.number_of_channels + 1)
-                i1 = (i0 + self.samples_per_record * self.number_of_channels)
-                recordB += (self.buffer[i0:i1:self.number_of_channels] /
-                            records_per_acquisition)
-            magB, phaseB = self.fit(recordB)
-
-        # return averaged chan A data (1,1)
+            raise NotImplementedError('chan b code not complete')
+            # recordB = np.zeros(self.samples_per_record, dtype=np.uint16)
+            # for i in range(self.records_per_buffer):
+            #     i0 = (i * self.samples_per_record *
+            #           self.number_of_channels + 1)
+            #     i1 = (i0 + self.samples_per_record * self.number_of_channels)
+            #     recordB += np.uint16(self.buffer[i0:i1:self.number_of_channels] /
+            #                          records_per_acquisition)
+            # magB, phaseB = self.fit(recordB)
+
         return magA, phaseA
 
     def fit(self, rec):
-        # center rec around 0
-        rec = rec - np.mean(rec)
+        """
+        Applies volts conversion, demodulation fit, low bandpass filter
+        and integration limits to samples array and averages
+        :return: magnitude, phase
+        """
+
+        # convert rec to volts
+        bps = self.board_info['bits_per_sample']
+        if bps == 12:
+            volt_rec = sample_to_volt_u12(rec, bps)
+        else:
+            logging.warning('sample to volt conversion does not exist for'
+                            ' bps != 12, centered raw samples returned')
+            volt_rec = rec - np.mean(rec)
 
         # multiply with software wave
-        re_wave = np.multiply(rec, self.cos_list)
-        im_wave = np.multiply(rec, self.sin_list)
-        cutoff = self.demodulation_frequency
-        numtaps = 30
+        re_wave = np.multiply(volt_rec, self.cos_list)
+        im_wave = np.multiply(volt_rec, self.sin_list)
+        cutoff = self.demodulation_frequency / 10
 
         # filter out higher freq component
         if self.filter == 0:
-            RePart = self.filter_win(re_wave, numtaps, cutoff)
-            ImPart = self.filter_win(im_wave, numtaps, cutoff)
+            re_filtered = helpers.filter_win(re_wave, cutoff,
+                                             self.sample_rate, self.numtaps)
+            im_filtered = helpers.filter_win(im_wave, cutoff,
+                                             self.sample_rate, self.numtaps)
         elif self.filter == 1:
-            RePart = self.filter_hamming(re_wave, numtaps, cutoff)
-            ImPart = self.filter_hamming(im_wave, numtaps, cutoff)
+            re_filtered = helpers.filter_ls(re_wave, cutoff,
+                                            self.sample_rate, self.numtaps)
+            im_filtered = helpers.filter_ls(im_wave, cutoff,
+                                            self.sample_rate, self.numtaps)
         elif self.filter == 2:
-            RePart = self.filter_ls(re_wave, numtaps, cutoff)
-            ImPart = self.filter_ls(im_wave, numtaps, cutoff)
+            re_filtered = helpers.filter_dot(re_wave, cutoff,
+                                             self.sample_rate, self.numtaps)
+            im_filtered = helpers.filter_dot(im_wave, cutoff,
+                                             self.sample_rate, self.numtaps)
+
+        # apply integration limits
+        start = self.samples_delay
+        end = start + self.samples_time
 
-        # convert to magnitude and phase data and average over samples
-        # data returned is single point
-        complex_num = RePart + ImPart * 1j
-        mag = np.mean(2 * abs(complex_num))
+        re_limited = re_filtered[start:end]
+        im_limited = im_filtered[start:end]
+
+        # convert to magnitude and phase
+        complex_num = re_limited + im_limited * 1j
+        mag = np.mean(abs(complex_num))
         phase = np.mean(np.angle(complex_num, deg=True))
 
         return mag, phase
 
-    def filter_hamming(self, rec, numtaps, cutoff):
-        sample_rate = self.sample_rate
-        nyq_rate = sample_rate / 2.
-        fir_coef = signal.firwin(numtaps,
-                                 cutoff / nyq_rate,
-                                 window="hamming")
-        filtered_rec = 2 * signal.lfilter(fir_coef, 1.0, rec)
-        return filtered_rec
-
-    def filter_win(self, rec, numtaps, cutoff):
-        sample_rate = self.sample_rate
-        nyq_rate = sample_rate / 2.
-        fir_coef = signal.firwin(numtaps,
-                                 cutoff / nyq_rate)
-        filtered_rec = 2 * signal.lfilter(fir_coef, 1.0, rec)
-        return filtered_rec
 
-    def filter_ls(self, rec, numtaps, cutoff):
-        sample_rate = self.sample_rate
-        nyq_rate = sample_rate / 2.
-        bands = [0, cutoff / nyq_rate, cutoff / nyq_rate, 1]
-        desired = [1, 1, 0, 0]
-        fir_coef = signal.firls(numtaps,
-                                bands,
-                                desired,
-                                nyq=nyq_rate)
-        filtered_rec = 2 * signal.lfilter(fir_coef, 1.0, rec)
-        return filtered_rec
+def sample_to_volt_u12(raw_samples, bps):
+    """
+    Applies volts conversion for 12 bit sample data stored
+    in 2 bytes
+    :return: samples_magnitude_array, samples_phase_array
+    """
+
+    # right_shift 16-bit sample by 4 to get 12 bit sample
+    shifted_samples = np.right_shift(raw_samples, 4)
+
+    # Alazar calibration
+    code_zero = (1 << (bps - 1)) - 0.5
+    code_range = (1 << (bps - 1)) - 0.5
+
+    # TODO(nataliejpg) make this not hard coded
+    input_range_volts = 1
+    # Convert to volts
+    volt_samples = np.float64(input_range_volts *
+                              (shifted_samples - code_zero) / code_range)
+
+    return volt_samples
diff --git a/qcodes/utils/helpers.py b/qcodes/utils/helpers.py
index 7ee83f5ea0af..12f45675a2f0 100644
--- a/qcodes/utils/helpers.py
+++ b/qcodes/utils/helpers.py
@@ -449,3 +449,17 @@ def filter_ls(rec, cutoff, sample_rate, numtaps, axis=-1):
         axis of record to apply filter along
     """
     raise NotImplementedError
+
+def filter_dot(rec, cutoff, sample_rate, numtaps, axis=-1):
+    """
+    low pass filter, returns filtered signal using FIR
+    least squared filter
+
+    inputs:
+        record to filter
+        cutoff frequency
+        sampling rate
+        number of frequency comppnents to use in the filer
+        axis of record to apply filter along
+    """
+    raise NotImplementedError
\ No newline at end of file

From fb888a41a258872af542c047580223cca13eb5a8 Mon Sep 17 00:00:00 2001
From: Natalie Pearson 
Date: Tue, 15 Nov 2016 14:36:32 +0100
Subject: [PATCH 048/328] general tidying

---
 .../AlazarTech/Rec_controller.py              | 29 +++++++++----------
 .../AlazarTech/Samp_controller.py             | 18 ++++++------
 .../AlazarTech/Single_controller.py           |  6 ++--
 3 files changed, 25 insertions(+), 28 deletions(-)

diff --git a/qcodes/instrument_drivers/AlazarTech/Rec_controller.py b/qcodes/instrument_drivers/AlazarTech/Rec_controller.py
index e92de3e40a59..c8220aed56cd 100644
--- a/qcodes/instrument_drivers/AlazarTech/Rec_controller.py
+++ b/qcodes/instrument_drivers/AlazarTech/Rec_controller.py
@@ -2,7 +2,7 @@
 from .ATS import AcquisitionController
 import numpy as np
 from qcodes import Parameter
-import qcodes.utils.helpers as helpers
+from qcodes.utils.helpers import filter_win, filter_ls
 
 
 class RecordsParam(Parameter):
@@ -211,8 +211,9 @@ def post_acquire(self):
             i1 = (i0 + self.samples_per_record * self.number_of_channels)
             recordA += np.uint16(self.buffer[i0:i1:self.number_of_channels] /
                                  records_per_acquisition)
-            recordA[:, i] = np.uint16((self.buffer[i0:i1:self.number_of_channels] /
-                                       self.buffers_per_acquisition))
+            recordA[:, i] = np.uint16(
+                self.buffer[i0:i1:self.number_of_channels] /
+                self.buffers_per_acquisition)
 
         # do demodulation
         magA, phaseA = self.fit(recordA)
@@ -247,20 +248,18 @@ def fit(self, rec):
 
         # filter out higher freq component
         if self.filter == 0:
-            re_filtered = helpers.filter_win(re_wave, cutoff, self.sample_rate,
-                                             self.numtaps, axis=ax)
-            im_filtered = helpers.filter_win(im_wave, cutoff, self.sample_rate,
-                                             self.numtaps, axis=ax)
+            re_filtered = filter_win(re_wave, cutoff, self.sample_rate,
+                                     self.numtaps, axis=ax)
+            im_filtered = filter_win(im_wave, cutoff, self.sample_rate,
+                                     self.numtaps, axis=ax)
         elif self.filter == 1:
-            re_filtered = helpers.filter_ls(re_wave, cutoff, self.sample_rate,
-                                            self.numtaps, axis=ax)
-            im_filtered = helpers.filter_ls(im_wave, cutoff, self.sample_rate,
-                                            self.numtaps, axis=ax)
+            re_filtered = filter_ls(re_wave, cutoff, self.sample_rate,
+                                    self.numtaps, axis=ax)
+            im_filtered = filter_ls(im_wave, cutoff, self.sample_rate,
+                                    self.numtaps, axis=ax)
         elif self.filter == 2:
-            re_filtered = helpers.filter_dot(re_wave, cutoff, self.sample_rate,
-                                             self.numtaps, axis=ax)
-            im_filtered = helpers.filter_dot(im_wave, cutoff, self.sample_rate,
-                                             self.numtaps, axis=ax)
+            re_filtered = re_wave
+            im_filtered = im_wave
 
         # apply integration limits
         start = self.samples_delay
diff --git a/qcodes/instrument_drivers/AlazarTech/Samp_controller.py b/qcodes/instrument_drivers/AlazarTech/Samp_controller.py
index ab6b15697f10..17124f4e380e 100644
--- a/qcodes/instrument_drivers/AlazarTech/Samp_controller.py
+++ b/qcodes/instrument_drivers/AlazarTech/Samp_controller.py
@@ -2,7 +2,7 @@
 from .ATS import AcquisitionController
 import numpy as np
 from qcodes import Parameter
-import qcodes.utils.helpers as helpers
+from qcodes.utils.helpers import filter_win, filter_ls
 
 
 class SamplesParam(Parameter):
@@ -246,15 +246,15 @@ def fit(self, rec):
 
         # filter out higher freq component
         if self.filter == 0:
-            re_filtered = helpers.filter_win(re_wave, cutoff,
-                                             self.sample_rate, self.numtaps)
-            im_filtered = helpers.filter_win(im_wave, cutoff,
-                                             self.sample_rate, self.numtaps)
+            re_filtered = filter_win(re_wave, cutoff,
+                                     self.sample_rate, self.numtaps)
+            im_filtered = filter_win(im_wave, cutoff,
+                                     self.sample_rate, self.numtaps)
         elif self.filter == 1:
-            re_filtered = helpers.filter_ls(re_wave, cutoff,
-                                            self.sample_rate, self.numtaps)
-            im_filtered = helpers.filter_ls(im_wave, cutoff,
-                                            self.sample_rate, self.numtaps)
+            re_filtered = filter_ls(re_wave, cutoff,
+                                    self.sample_rate, self.numtaps)
+            im_filtered = filter_ls(im_wave, cutoff,
+                                    self.sample_rate, self.numtaps)
 
         # apply integration limits
         start = self.samples_delay
diff --git a/qcodes/instrument_drivers/AlazarTech/Single_controller.py b/qcodes/instrument_drivers/AlazarTech/Single_controller.py
index bbff68c5267c..680b83aa9eaf 100644
--- a/qcodes/instrument_drivers/AlazarTech/Single_controller.py
+++ b/qcodes/instrument_drivers/AlazarTech/Single_controller.py
@@ -225,10 +225,8 @@ def fit(self, rec):
             im_filtered = helpers.filter_ls(im_wave, cutoff,
                                             self.sample_rate, self.numtaps)
         elif self.filter == 2:
-            re_filtered = helpers.filter_dot(re_wave, cutoff,
-                                             self.sample_rate, self.numtaps)
-            im_filtered = helpers.filter_dot(im_wave, cutoff,
-                                             self.sample_rate, self.numtaps)
+            re_filtered = re_wave
+            im_filtered = im_wave
 
         # apply integration limits
         start = self.samples_delay

From 75d840baaa45bf3ebaa05bcb402ce5be465e1469 Mon Sep 17 00:00:00 2001
From: nataliejpg 
Date: Tue, 15 Nov 2016 19:20:06 +0100
Subject: [PATCH 049/328] testing with alazar done

---
 .../instrument_drivers/AlazarTech/Rec_controller.py  |  4 ----
 .../instrument_drivers/AlazarTech/Samp_controller.py |  6 +++---
 .../AlazarTech/Single_controller.py                  |  6 +++---
 .../AlazarTech/basic_controller.py                   | 12 ++++++------
 4 files changed, 12 insertions(+), 16 deletions(-)

diff --git a/qcodes/instrument_drivers/AlazarTech/Rec_controller.py b/qcodes/instrument_drivers/AlazarTech/Rec_controller.py
index c8220aed56cd..806f724c2d20 100644
--- a/qcodes/instrument_drivers/AlazarTech/Rec_controller.py
+++ b/qcodes/instrument_drivers/AlazarTech/Rec_controller.py
@@ -197,8 +197,6 @@ def post_acquire(self):
         :return: samples_magnitude_array, samples_phase_array
         """
 
-        records_per_acquisition = (self.buffers_per_acquisition *
-                                   self.records_per_buffer)
         # for ATS9360 samples are arranged in the buffer as follows:
         # S00A, S00B, S01A, S01B...S10A, S10B, S11A, S11B...
         # where SXYZ is record X, sample Y, channel Z.
@@ -209,8 +207,6 @@ def post_acquire(self):
         for i in range(self.records_per_buffer):
             i0 = (i * self.samples_per_record * self.number_of_channels)
             i1 = (i0 + self.samples_per_record * self.number_of_channels)
-            recordA += np.uint16(self.buffer[i0:i1:self.number_of_channels] /
-                                 records_per_acquisition)
             recordA[:, i] = np.uint16(
                 self.buffer[i0:i1:self.number_of_channels] /
                 self.buffers_per_acquisition)
diff --git a/qcodes/instrument_drivers/AlazarTech/Samp_controller.py b/qcodes/instrument_drivers/AlazarTech/Samp_controller.py
index 17124f4e380e..617ae841b178 100644
--- a/qcodes/instrument_drivers/AlazarTech/Samp_controller.py
+++ b/qcodes/instrument_drivers/AlazarTech/Samp_controller.py
@@ -199,12 +199,12 @@ def post_acquire(self):
         # where SXYZ is record X, sample Y, channel Z.
 
         # break buffer up into records and averages over them
-        recordA = np.zeros(self.samples_per_record, dtype=np.uint16)
+        recA = np.zeros(self.samples_per_record)
         for i in range(self.records_per_buffer):
             i0 = (i * self.samples_per_record * self.number_of_channels)
             i1 = (i0 + self.samples_per_record * self.number_of_channels)
-            recordA += np.uint16(self.buffer[i0:i1:self.number_of_channels] /
-                                 records_per_acquisition)
+            recA += self.buffer[i0:i1:self.number_of_channels]
+        recordA = np.uint16(recA / records_per_acquisition)
 
         # do demodulation
         magA, phaseA = self.fit(recordA)
diff --git a/qcodes/instrument_drivers/AlazarTech/Single_controller.py b/qcodes/instrument_drivers/AlazarTech/Single_controller.py
index 680b83aa9eaf..2c103fbde046 100644
--- a/qcodes/instrument_drivers/AlazarTech/Single_controller.py
+++ b/qcodes/instrument_drivers/AlazarTech/Single_controller.py
@@ -168,12 +168,12 @@ def post_acquire(self):
         # where SXYZ is record X, sample Y, channel Z.
 
         # break buffer up into records and averages over them
-        recordA = np.zeros(self.samples_per_record, dtype=np.uint16)
+        recA = np.zeros(self.samples_per_record)
         for i in range(self.records_per_buffer):
             i0 = (i * self.samples_per_record * self.number_of_channels)
             i1 = (i0 + self.samples_per_record * self.number_of_channels)
-            recordA += np.uint16(self.buffer[i0:i1:self.number_of_channels] /
-                                 records_per_acquisition)
+            recA += self.buffer[i0:i1:self.number_of_channels]
+        recordA = np.uint16(recA / records_per_acquisition)
 
         # do demodulation
         magA, phaseA = self.fit(recordA)
diff --git a/qcodes/instrument_drivers/AlazarTech/basic_controller.py b/qcodes/instrument_drivers/AlazarTech/basic_controller.py
index b76309fb8881..f615f69d7d44 100644
--- a/qcodes/instrument_drivers/AlazarTech/basic_controller.py
+++ b/qcodes/instrument_drivers/AlazarTech/basic_controller.py
@@ -128,19 +128,19 @@ def post_acquire(self):
         # break buffer up into records, averages over them and returns samples
         records_per_acquisition = (self.buffers_per_acquisition *
                                    self.records_per_buffer)
-        recordA = np.zeros(self.samples_per_record, dtype=np.uint16)
+        recA = np.zeros(self.samples_per_record)
         for i in range(self.records_per_buffer):
             i0 = (i * self.samples_per_record * self.number_of_channels)
             i1 = (i0 + self.samples_per_record * self.number_of_channels)
-            recordA += np.uint16(self.buffer[i0:i1:self.number_of_channels] /
-                                 records_per_acquisition)
+            recA += self.buffer[i0:i1:self.number_of_channels]
+        recordA = np.uint16(recA / records_per_acquisition)
 
-        recordB = np.zeros(self.samples_per_record, dtype=np.uint16)
+        recB = np.zeros(self.samples_per_record)
         for i in range(self.records_per_buffer):
             i0 = (i * self.samples_per_record * self.number_of_channels + 1)
             i1 = (i0 + self.samples_per_record * self.number_of_channels)
-            recordB += np.uint16(self.buffer[i0:i1:self.number_of_channels] /
-                                 records_per_acquisition)
+            recB += self.buffer[i0:i1:self.number_of_channels]
+        recordB = np.uint16(recB / records_per_acquisition)
 
         bps = self.board_info['bits_per_sample']
         if bps == 12:

From 1eef43612e2eaf41780899222324e3812f16a8db Mon Sep 17 00:00:00 2001
From: nataliejpg 
Date: Thu, 17 Nov 2016 17:48:14 +0100
Subject: [PATCH 050/328] change buggy buffer dtype requirement in all acq
 controllers, debugging minutiae

---
 qcodes/instrument_drivers/AlazarTech/ATS.py   | 20 +++++++++----------
 .../instrument_drivers/AlazarTech/ATS9360.py  |  8 +++++---
 .../AlazarTech/Rec_controller.py              |  3 +--
 .../AlazarTech/Samp_controller.py             |  5 ++---
 .../AlazarTech/Single_controller.py           |  3 +--
 .../AlazarTech/basic_controller.py            |  4 +---
 6 files changed, 20 insertions(+), 23 deletions(-)

diff --git a/qcodes/instrument_drivers/AlazarTech/ATS.py b/qcodes/instrument_drivers/AlazarTech/ATS.py
index 9d648593f1ee..6a243082e316 100644
--- a/qcodes/instrument_drivers/AlazarTech/ATS.py
+++ b/qcodes/instrument_drivers/AlazarTech/ATS.py
@@ -571,12 +571,12 @@ def acquire(self, mode=None, samples_per_record=None,
         # bytes per sample
         max_s, bps = self._get_channel_info(self._handle)
         bytes_per_sample = (bps + 7) // 8
-        print("bytes per sample "+str(bytes_per_sample))
+        #print("bytes per sample "+str(bytes_per_sample))
 
         # bytes per record
         bytes_per_record = bytes_per_sample * samples_per_record
-        print("samples_per_record is "+str(samples_per_record))
-        print("bytes per record is "+str(bytes_per_record))
+        #print("samples_per_record is "+str(samples_per_record))
+        #print("bytes per record is "+str(bytes_per_record))
 
         # channels
         if self.channel_selection._get_byte() == 3:
@@ -594,9 +594,9 @@ def acquire(self, mode=None, samples_per_record=None,
             sample_type = ctypes.c_uint16
 
         # TODO(nataliejpg) get rid of all of thes print statements
-        print("samples_per_buffer is "+str(samples_per_buffer))
-        print("bytes_per_buffer  is "+str(bytes_per_buffer))
-        print("records_per_buffer is "+str(records_per_buffer))
+        #print("samples_per_buffer is "+str(samples_per_buffer))
+        #print("bytes_per_buffer  is "+str(bytes_per_buffer))
+        #print("records_per_buffer is "+str(records_per_buffer))
 
         self.clear_buffers()
         # make sure that allocated_buffers <= buffers_per_acquisition
@@ -624,7 +624,7 @@ def acquire(self, mode=None, samples_per_record=None,
             self._call_dll('AlazarPostAsyncBuffer',
                            self._handle, buf.addr, buf.size_bytes)
         self.allocated_buffers._set_updated()
-        print("completed AlazarPostAsyncBuffer")
+        #print("completed AlazarPostAsyncBuffer")
 
         # -----start capture here-----
         acquisition_controller.pre_start_capture()
@@ -682,9 +682,6 @@ def acquire(self, mode=None, samples_per_record=None,
         for p in self.parameters.values():
             p.get()
 
-        # return result
-        return acquisition_controller.post_acquire()
-
         # Compute the total transfer time, and display performance information.
         transfer_time_sec = time.clock() - start
         print("Capture completed in %f sec" % transfer_time_sec)
@@ -701,6 +698,9 @@ def acquire(self, mode=None, samples_per_record=None,
               (records_per_buffer * buffers_completed, records_per_sec))
         print("Transferred %d bytes (%f bytes per sec)" %
               (bytes_transferred, bytes_per_sec))
+              
+        # return result
+        return acquisition_controller.post_acquire()
 
     def _set_if_present(self, param_name, value):
         if value is not None:
diff --git a/qcodes/instrument_drivers/AlazarTech/ATS9360.py b/qcodes/instrument_drivers/AlazarTech/ATS9360.py
index 7afbaff1ff3a..cdac0771ece2 100644
--- a/qcodes/instrument_drivers/AlazarTech/ATS9360.py
+++ b/qcodes/instrument_drivers/AlazarTech/ATS9360.py
@@ -18,8 +18,11 @@ def __init__(self, name, **kwargs):
                            unit=None,
                            value='EXTERNAL_CLOCK_10MHz_REF',
                            byte_to_value_dict={1: 'INTERNAL_CLOCK',
+                                               2: 'EXTERNAL_CLOCK',
+                                               3: 'MEDIUM_EXTERNAL_CLOCK',
                                                4: 'SLOW_EXTERNAL_CLOCK',
                                                5: 'EXTERNAL_CLOCK_AC',
+                                               6: 'EXTERNAL_CLOCK_DC',
                                                7: 'EXTERNAL_CLOCK_10MHz_REF'})
         self.add_parameter(name='sample_rate',
                            parameter_class=AlazarParameter,
@@ -100,7 +103,7 @@ def __init__(self, name, **kwargs):
                                parameter_class=AlazarParameter,
                                label='Trigger Source ' + i,
                                unit=None,
-                               value='DISABLE',
+                               value='EXTERNAL',
                                byte_to_value_dict={0: 'CHANNEL_A',
                                                    1: 'CHANNEL_B',
                                                    2: 'EXTERNAL',
@@ -252,12 +255,11 @@ def __init__(self, name, **kwargs):
                            value='DISABLED',
                            byte_to_value_dict={0x0: 'DISABLED',
                                                0x2000: 'ENABLED'})
-
         self.add_parameter(name='allocated_buffers',
                            parameter_class=AlazarParameter,
                            label='Allocated Buffers',
                            unit=None,
-                           value=2,
+                           value=4,
                            vals=validators.Ints(min_value=0))
         self.add_parameter(name='buffer_timeout',
                            parameter_class=AlazarParameter,
diff --git a/qcodes/instrument_drivers/AlazarTech/Rec_controller.py b/qcodes/instrument_drivers/AlazarTech/Rec_controller.py
index 806f724c2d20..5513e384264b 100644
--- a/qcodes/instrument_drivers/AlazarTech/Rec_controller.py
+++ b/qcodes/instrument_drivers/AlazarTech/Rec_controller.py
@@ -162,8 +162,7 @@ def pre_start_capture(self):
         self.board_info = alazar.get_idn()
         self.buffer = np.zeros(self.samples_per_record *
                                self.records_per_buffer *
-                               self.number_of_channels,
-                               dtype=np.uint16)
+                               self.number_of_channels)
 
         integer_list = np.arange(self.samples_per_record, dtype=np.uint16)
         angle_list = (2 * np.pi * self.demodulation_frequency /
diff --git a/qcodes/instrument_drivers/AlazarTech/Samp_controller.py b/qcodes/instrument_drivers/AlazarTech/Samp_controller.py
index 617ae841b178..e5ee0c20dbe4 100644
--- a/qcodes/instrument_drivers/AlazarTech/Samp_controller.py
+++ b/qcodes/instrument_drivers/AlazarTech/Samp_controller.py
@@ -161,8 +161,7 @@ def pre_start_capture(self):
         self.board_info = alazar.get_idn()
         self.buffer = np.zeros(self.samples_per_record *
                                self.records_per_buffer *
-                               self.number_of_channels,
-                               dtype=np.uint16)
+                               self.number_of_channels)
 
         integer_list = np.arange(self.samples_per_record, dtype=np.uint16)
         angle_list = (2 * np.pi * self.demodulation_frequency /
@@ -205,7 +204,7 @@ def post_acquire(self):
             i1 = (i0 + self.samples_per_record * self.number_of_channels)
             recA += self.buffer[i0:i1:self.number_of_channels]
         recordA = np.uint16(recA / records_per_acquisition)
-
+        
         # do demodulation
         magA, phaseA = self.fit(recordA)
 
diff --git a/qcodes/instrument_drivers/AlazarTech/Single_controller.py b/qcodes/instrument_drivers/AlazarTech/Single_controller.py
index 2c103fbde046..d23767c7aeb2 100644
--- a/qcodes/instrument_drivers/AlazarTech/Single_controller.py
+++ b/qcodes/instrument_drivers/AlazarTech/Single_controller.py
@@ -130,8 +130,7 @@ def pre_start_capture(self):
         self.board_info = alazar.get_idn()
         self.buffer = np.zeros(self.samples_per_record *
                                self.records_per_buffer *
-                               self.number_of_channels,
-                               dtype=np.uint16)
+                               self.number_of_channels)
 
         integer_list = np.arange(self.samples_per_record, dtype=np.uint16)
         angle_list = (2 * np.pi * self.demodulation_frequency /
diff --git a/qcodes/instrument_drivers/AlazarTech/basic_controller.py b/qcodes/instrument_drivers/AlazarTech/basic_controller.py
index f615f69d7d44..025e3b97bd9f 100644
--- a/qcodes/instrument_drivers/AlazarTech/basic_controller.py
+++ b/qcodes/instrument_drivers/AlazarTech/basic_controller.py
@@ -63,7 +63,6 @@ def __init__(self, name, alazar_name, **kwargs):
         self.samples_per_record = 0
         self.records_per_buffer = 0
         self.buffers_per_acquisition = 0
-        # TODO(damazter) (S) this is not very general:
         self.number_of_channels = 2
         self.buffer = None
         self.board_info = None
@@ -95,8 +94,7 @@ def pre_start_capture(self):
         self.board_info = alazar.get_idn()
         self.buffer = np.zeros(self.samples_per_record *
                                self.records_per_buffer *
-                               self.number_of_channels,
-                               dtype=np.uint16)
+                               self.number_of_channels)
 
     def pre_acquire(self):
         """

From 51462ca13c632c8df6f53addeb0cd9950714c73a Mon Sep 17 00:00:00 2001
From: Natalie Pearson 
Date: Fri, 18 Nov 2016 13:37:23 +0100
Subject: [PATCH 051/328] removed dot filter frome helpers

---
 qcodes/utils/helpers.py | 14 --------------
 1 file changed, 14 deletions(-)

diff --git a/qcodes/utils/helpers.py b/qcodes/utils/helpers.py
index 12f45675a2f0..7ee83f5ea0af 100644
--- a/qcodes/utils/helpers.py
+++ b/qcodes/utils/helpers.py
@@ -449,17 +449,3 @@ def filter_ls(rec, cutoff, sample_rate, numtaps, axis=-1):
         axis of record to apply filter along
     """
     raise NotImplementedError
-
-def filter_dot(rec, cutoff, sample_rate, numtaps, axis=-1):
-    """
-    low pass filter, returns filtered signal using FIR
-    least squared filter
-
-    inputs:
-        record to filter
-        cutoff frequency
-        sampling rate
-        number of frequency comppnents to use in the filer
-        axis of record to apply filter along
-    """
-    raise NotImplementedError
\ No newline at end of file

From d2376ca51b18aa0469a13b4e2796ac518cf88f52 Mon Sep 17 00:00:00 2001
From: nataliejpg 
Date: Mon, 21 Nov 2016 14:44:40 +0100
Subject: [PATCH 052/328] add example notebook from experiment

---
 docs/examples/Alazar Notebook.ipynb | 629 ++++++++++++++++++++++++++++
 1 file changed, 629 insertions(+)
 create mode 100644 docs/examples/Alazar Notebook.ipynb

diff --git a/docs/examples/Alazar Notebook.ipynb b/docs/examples/Alazar Notebook.ipynb
new file mode 100644
index 000000000000..d9902d94880f
--- /dev/null
+++ b/docs/examples/Alazar Notebook.ipynb	
@@ -0,0 +1,629 @@
+{
+ "cells": [
+  {
+   "cell_type": "code",
+   "execution_count": 1,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "No loop running\n"
+     ]
+    }
+   ],
+   "source": [
+    "import qcodes as qc\n",
+    "qc.halt_bg()\n",
+    "import numpy as np\n",
+    "\n",
+    "# Saving parameters\n",
+    "Samplename = '6QAK3_A2_qcodes';\n",
+    "loc_provider = qc.data.location.FormatLocation(fmt='A:/TransmonExperiments/GoogleDrive/T2acquisition/Data/%s/data/{counter}' % Samplename)\n",
+    "qc.data.data_set.DataSet.location_provider=loc_provider\n",
+    "station = qc.Station()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 1,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "No loop running\n",
+      "Connected to: Rohde&Schwarz SGS100A (serial:1416.0505k02/103076, firmware:3.1.19.7-3.20.140.60.1) in 0.03s\n",
+      "Connected to: Rohde&Schwarz SGS100A (serial:1416.0505k02/103079, firmware:3.1.19.7-3.20.140.60.1) in 0.01s\n",
+      "Connected to: Rohde&Schwarz SGS100A (serial:1416.0505k02/103077, firmware:3.1.19.7-3.20.140.60.1) in 0.01s\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "a:\\qcodes\\qcodes\\instrument\\metaclass.py:37: UserWarning: Multiprocessing is in beta, use at own risk\n",
+      "  UserWarning)\n"
+     ]
+    },
+    {
+     "data": {
+      "text/plain": [
+       "'_2'"
+      ]
+     },
+     "execution_count": 1,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "import qcodes.instrument_drivers.rohde_schwarz.SGS100A as RSdriver\n",
+    "localos = RSdriver.RohdeSchwarz_SGS100A('LO', 'TCPIP0::172.20.3.42::inst0::INSTR')\n",
+    "cavity1 = RSdriver.RohdeSchwarz_SGS100A('C1', 'TCPIP0::172.20.3.28::inst0::INSTR')\n",
+    "qubit2  = RSdriver.RohdeSchwarz_SGS100A('Q2', 'TCPIP0::172.20.3.168::inst0::INSTR')\n",
+    "station.add_component(localos, cavity1, qubit2)\n",
+    "fq = qubit2.frequency,lcavity.power\n",
+    "\n",
+    "import qcodes.instrument.parameter as parameter\n",
+    "import qcodes.instrument_drivers.AlazarTech.ATS9360 as ATSdriver\n",
+    "import qcodes.instrument_drivers.AlazarTech.ATS_acquisition_controllers as ats_contr\n",
+    "ats_inst = ATSdriver.AlazarTech_ATS9360(name='Alazar1', server_name=\"alazar_server\")\n",
+    "station.add_component(ats_inst)\n",
+    "\n",
+    "station.add_component(localos, cavity1, qubit2)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 3,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "{'CPLD_version': '25.16',\n",
+       " 'SDK_version': '5.9.25',\n",
+       " 'asopc_type': '1712554848',\n",
+       " 'bits_per_sample': 12,\n",
+       " 'driver_version': '5.9.25',\n",
+       " 'firmware': None,\n",
+       " 'latest_cal_date': '13-11-15',\n",
+       " 'max_samples': 4294967294,\n",
+       " 'memory_size': '4294967294',\n",
+       " 'model': 'ATS9360',\n",
+       " 'pcie_link_speed': '0.5GB/s',\n",
+       " 'pcie_link_width': '8',\n",
+       " 'serial': '970344',\n",
+       " 'vendor': 'AlazarTech'}"
+      ]
+     },
+     "execution_count": 3,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "ats_inst.get_idn()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 4,
+   "metadata": {
+    "collapsed": true
+   },
+   "outputs": [],
+   "source": [
+    "# Configure all settings in the Alazar card\n",
+    "ats_inst.config(clock_source='EXTERNAL_CLOCK_10MHz_REF',\n",
+    "                sample_rate='10MHZ_REF_500MSPS',\n",
+    "                clock_edge='CLOCK_EDGE_RISING',\n",
+    "                decimation=1,\n",
+    "                coupling=['DC','DC'],\n",
+    "                channel_range=[.4,.4],\n",
+    "                impedance=[50,50],\n",
+    "                trigger_operation='TRIG_ENGINE_OP_J',\n",
+    "                trigger_engine1='TRIG_ENGINE_J',\n",
+    "                trigger_source1='EXTERNAL',\n",
+    "                trigger_slope1='TRIG_SLOPE_POSITIVE',\n",
+    "                trigger_level1=140,\n",
+    "                trigger_engine2='TRIG_ENGINE_K',\n",
+    "                trigger_source2='DISABLE',\n",
+    "                trigger_slope2='TRIG_SLOPE_POSITIVE',\n",
+    "                trigger_level2=128,\n",
+    "                external_trigger_coupling='DC',\n",
+    "                external_trigger_range='ETR_2V5',\n",
+    "                trigger_delay=0,\n",
+    "                timeout_ticks=0,\n",
+    "                aux_io_mode='AUX_IN_AUXILIARY',\n",
+    "                aux_io_param='NONE'\n",
+    "                #aux_io_mode='AUX_IN_TRIGGER_ENABLE', \n",
+    "                #aux_io_param='TRIG_SLOPE_POSITIVE'\n",
+    ")"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 11,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "a:\\qcodes\\qcodes\\instrument\\metaclass.py:37: UserWarning: Multiprocessing is in beta, use at own risk\n",
+      "  UserWarning)\n"
+     ]
+    }
+   ],
+   "source": [
+    "import qcodes.instrument_drivers.AlazarTech.Samp_controller as sample_controller\n",
+    "\n",
+    "samp_cont = sample_controller.HD_Samples_Controller(name='samples_controller', \n",
+    "                                                      alazar_name='Alazar1', \n",
+    "                                                      demod_freq = 5e6,\n",
+    "                                                      server_name=\"alazar_server\")"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 45,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "a:\\qcodes\\qcodes\\instrument\\metaclass.py:37: UserWarning: Multiprocessing is in beta, use at own risk\n",
+      "  UserWarning)\n"
+     ]
+    }
+   ],
+   "source": [
+    "samp_cont3 = sample_controller.HD_Samples_Controller(name='samples_controller3', \n",
+    "                                                      alazar_name='Alazar1', \n",
+    "                                                      demod_freq = 20e6,\n",
+    "                                                      server_name=\"alazar_server\")"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 52,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [],
+   "source": [
+    "samp_cont3.update_acquisitionkwargs(#mode='NPT',\n",
+    "                 samples_per_record=2560,\n",
+    "                 records_per_buffer=1000,\n",
+    "                 buffers_per_acquisition=10,\n",
+    "                 int_delay=2e-7,\n",
+    "                 int_time =3e-6,\n",
+    "                 allocated_buffers=1,\n",
+    "                 #buffer_timeout=1000\n",
+    ")"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 60,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [],
+   "source": [
+    "demod_freq = 20e6\n",
+    "\n",
+    "cavity1.power(-50)\n",
+    "cavity1.frequency(7.147e9)\n",
+    "\n",
+    "localos.power(15)\n",
+    "set_localos(demod_freq)\n",
+    "\n",
+    "qubit2.power(-60)\n",
+    "qubit2.frequency(7.5e9)\n",
+    "\n",
+    "localos.status('on')\n",
+    "cavity1.status('on')\n",
+    "qubit2.status('off')"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 49,
+   "metadata": {
+    "collapsed": false,
+    "scrolled": true
+   },
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "DataSet:\n",
+      "   mode     = DataMode.LOCAL\n",
+      "   location = 'A:/TransmonExperiments/GoogleDrive/T2acquisition/Data/6QAK3_A2_qcodes/data/027'\n",
+      "      |                     |  | \n",
+      "   Setpoint | C1_frequency_set              | frequency    | (81,)\n",
+      "   Measured | sample_num                    | sample_num   | (81, 2560)\n",
+      "   Measured | samples_controller3_magnitude | magnitude    | (81, 2560)\n",
+      "   Measured | samples_controller3_phase     | phase        | (81, 2560)\n",
+      "started at 2016-11-18 20:11:44\n"
+     ]
+    }
+   ],
+   "source": [
+    "loop = qc.Loop(cavity1.frequency.sweep(7.13e9,7.15e9, 0.25e6)).each(samp_cont3.acquisition,\n",
+    "            qc.Task(localos.frequency.set, (cavity1.frequency+ demod_freq)))\n",
+    "data = loop.get_data_set()\n",
+    "plot = qc.QtPlot()\n",
+    "plot.add(data.samples_controller3_magnitude)\n",
+    "plot.add(data.samples_controller3_phase, subplot=2)\n",
+    "_ = loop.with_bg_task(plot.update, plot.save).run()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 17,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "2560"
+      ]
+     },
+     "execution_count": 17,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "2048 + 512"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 30,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "7151300000.0"
+      ]
+     },
+     "execution_count": 30,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "localos.frequency()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 26,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "7146300000.0"
+      ]
+     },
+     "execution_count": 26,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "cavity1.frequency()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 53,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "a:\\qcodes\\qcodes\\instrument\\metaclass.py:37: UserWarning: Multiprocessing is in beta, use at own risk\n",
+      "  UserWarning)\n"
+     ]
+    }
+   ],
+   "source": [
+    "import qcodes.instrument_drivers.AlazarTech.Single_controller as single_controller\n",
+    "\n",
+    "sing_contr = single_controller.HD_Controller(name='single_controller', \n",
+    "                                                      alazar_name='Alazar1', \n",
+    "                                                      demod_freq = 20e6,\n",
+    "                                                      server_name=\"alazar_server\")"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 55,
+   "metadata": {
+    "collapsed": true
+   },
+   "outputs": [],
+   "source": [
+    "sing_contr.update_acquisitionkwargs(#mode='NPT',\n",
+    "                 samples_per_record=2560,\n",
+    "                 records_per_buffer=1000,\n",
+    "                 buffers_per_acquisition=10,\n",
+    "                 int_delay=2e-7,\n",
+    "                 int_time =3e-6,\n",
+    "                 allocated_buffers=1,\n",
+    "                 #buffer_timeout=1000\n",
+    ")"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 68,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "DataSet:\n",
+      "   mode     = DataMode.LOCAL\n",
+      "   location = 'A:/TransmonExperiments/GoogleDrive/T2acquisition/Data/6QAK3_A2_qcodes/data/031'\n",
+      "      |                   |  | \n",
+      "   Setpoint | C1_frequency_set            | frequency    | (81,)\n",
+      "   Measured | single_controller_magnitude | magnitude    | (81,)\n",
+      "   Measured | single_controller_phase     | phase        | (81,)\n",
+      "started at 2016-11-18 20:30:59\n"
+     ]
+    }
+   ],
+   "source": [
+    "loop = qc.Loop(cavity1.frequency.sweep(7.13e9,7.15e9, 0.25e6)).each(sing_contr.acquisition,\n",
+    "            qc.Task(localos.frequency.set, (cavity1.frequency+ demod_freq)))\n",
+    "data = loop.get_data_set()\n",
+    "plot = qc.QtPlot()\n",
+    "plot.add(data.single_controller_magnitude)\n",
+    "plot.add(data.single_controller_phase, subplot=2)\n",
+    "_ = loop.with_bg_task(plot.update, plot.save).run()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 2,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "a:\\qcodes\\qcodes\\plots\\pyqtgraph.py:325: UserWarning: nonlinear setpoint array passed to pyqtgraph. ignoring, using default scaling.\n",
+      "  warnings.warn('nonlinear setpoint array passed to pyqtgraph. '\n"
+     ]
+    },
+    {
+     "data": {
+      "image/png": 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DmeGj+2Qpg+vK7TWuTg2R89XdOb6WaWcqrVhOcWV6WhJvjJIdtxYinF19PSal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5VzzjnnnPuZuHfv3r17Pzpqp71F8jwfL87lGrjf/ejwNu+9kz//gI+Pjo6O7rz/+Qc+dHfOOeec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\nAKbA/wEU7C8+KV12mwAAAABJRU5ErkJggg==\n",
+      "text/plain": [
+       ""
+      ]
+     },
+     "execution_count": 2,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "loaded_data = qc.load_data(loc_provider.formatter.format(loc_provider.fmt,counter='014'))\n",
+    "plot = qc.QtPlot()\n",
+    "plot.add(loaded_data.VNA_magnitude)\n",
+    "plot.add(loaded_data.VNA_phase, subplot=2)\n",
+    "#plot.save()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 69,
+   "metadata": {
+    "collapsed": true
+   },
+   "outputs": [],
+   "source": [
+    "demod_freq = 20e6\n",
+    "\n",
+    "cavity1.power(-55)\n",
+    "cavity1.frequency(7.133e9)\n",
+    "\n",
+    "localos.power(15)\n",
+    "set_localos(demod_freq)\n",
+    "\n",
+    "qubit2.power(-30)\n",
+    "qubit2.frequency(7.5e9)\n",
+    "\n",
+    "localos.status('on')\n",
+    "cavity1.status('on')\n",
+    "qubit2.status('on')"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 75,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "DataSet:\n",
+      "   mode     = DataMode.LOCAL\n",
+      "   location = 'A:/TransmonExperiments/GoogleDrive/T2acquisition/Data/6QAK3_A2_qcodes/data/034'\n",
+      "      |                   |  | \n",
+      "   Setpoint | Q2_frequency_set            | frequency    | (251,)\n",
+      "   Measured | single_controller_magnitude | magnitude    | (251,)\n",
+      "   Measured | single_controller_phase     | phase        | (251,)\n",
+      "started at 2016-11-18 20:50:01\n"
+     ]
+    }
+   ],
+   "source": [
+    "loop = qc.Loop(qubit2.frequency.sweep(8e9,8.5e9, 2e6)).each(sing_contr.acquisition)\n",
+    "data = loop.get_data_set()\n",
+    "plot = qc.QtPlot()\n",
+    "plot.add(data.single_controller_magnitude)\n",
+    "plot.add(data.single_controller_phase, subplot=2)\n",
+    "_ = loop.with_bg_task(plot.update, plot.save).run()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 76,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "a:\\qcodes\\qcodes\\instrument\\metaclass.py:37: UserWarning: Multiprocessing is in beta, use at own risk\n",
+      "  UserWarning)\n"
+     ]
+    }
+   ],
+   "source": [
+    "sing_contr2 = single_controller.HD_Controller(name='single_controller2', \n",
+    "                                                      alazar_name='Alazar1', \n",
+    "                                                      demod_freq = 20e6,\n",
+    "                                                      server_name=\"alazar_server\",\n",
+    "                                                     filt='dot')"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 79,
+   "metadata": {
+    "collapsed": true
+   },
+   "outputs": [],
+   "source": [
+    "sing_contr2.update_acquisitionkwargs(#mode='NPT',\n",
+    "                 samples_per_record=2560,\n",
+    "                 records_per_buffer=1000,\n",
+    "                 buffers_per_acquisition=1,\n",
+    "                 int_delay=2e-7,\n",
+    "                 int_time =3e-6,\n",
+    "                 allocated_buffers=1,\n",
+    "                 #buffer_timeout=1000\n",
+    ")"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 80,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "DataSet:\n",
+      "   mode     = DataMode.LOCAL\n",
+      "   location = 'A:/TransmonExperiments/GoogleDrive/T2acquisition/Data/6QAK3_A2_qcodes/data/036'\n",
+      "      |                    |  | \n",
+      "   Setpoint | Q2_frequency_set             | frequency    | (251,)\n",
+      "   Measured | single_controller2_magnitude | magnitude    | (251,)\n",
+      "   Measured | single_controller2_phase     | phase        | (251,)\n",
+      "started at 2016-11-18 20:56:48\n"
+     ]
+    }
+   ],
+   "source": [
+    "loop = qc.Loop(qubit2.frequency.sweep(8e9,8.5e9, 2e6)).each(sing_contr2.acquisition)\n",
+    "data = loop.get_data_set()\n",
+    "plot = qc.QtPlot()\n",
+    "plot.add(data.single_controller2_magnitude)\n",
+    "plot.add(data.single_controller2_phase, subplot=2)\n",
+    "_ = loop.with_bg_task(plot.update, plot.save).run()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 81,
+   "metadata": {
+    "collapsed": true
+   },
+   "outputs": [],
+   "source": [
+    "localos.status('off')\n",
+    "cavity1.status('off')\n",
+    "qubit2.status('off')"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {
+    "collapsed": true
+   },
+   "outputs": [],
+   "source": []
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "Python 3",
+   "language": "python",
+   "name": "python3"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython3",
+   "version": "3.5.2"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 1
+}

From eed9830f1f07b43e5d60e504d5b3302e6ed689b9 Mon Sep 17 00:00:00 2001
From: Natalie Pearson 
Date: Mon, 21 Nov 2016 16:32:11 +0100
Subject: [PATCH 053/328] update example notebook, rename acq controllers and
 delete uneccesary example notebooks

---
 docs/examples/Alazar Notebook.ipynb           |  629 ---
 ...ORK.ipynb => Qcodes example ATS9870.ipynb} |    6 +-
 ...es example with Alazar ATS9360-Copy1.ipynb | 3594 -----------------
 .../Qcodes example with Alazar ATS9360.ipynb  | 3264 +--------------
 .../AlazarTech/ATS_acquisition_controllers.py |  136 -
 ...Single_controller.py => ave_controller.py} |    0
 6 files changed, 82 insertions(+), 7547 deletions(-)
 delete mode 100644 docs/examples/Alazar Notebook.ipynb
 rename docs/examples/{Qcodes example ATS_ONWORK.ipynb => Qcodes example ATS9870.ipynb} (99%)
 delete mode 100644 docs/examples/Qcodes example with Alazar ATS9360-Copy1.ipynb
 rename qcodes/instrument_drivers/AlazarTech/{Single_controller.py => ave_controller.py} (100%)

diff --git a/docs/examples/Alazar Notebook.ipynb b/docs/examples/Alazar Notebook.ipynb
deleted file mode 100644
index d9902d94880f..000000000000
--- a/docs/examples/Alazar Notebook.ipynb	
+++ /dev/null
@@ -1,629 +0,0 @@
-{
- "cells": [
-  {
-   "cell_type": "code",
-   "execution_count": 1,
-   "metadata": {
-    "collapsed": false
-   },
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "No loop running\n"
-     ]
-    }
-   ],
-   "source": [
-    "import qcodes as qc\n",
-    "qc.halt_bg()\n",
-    "import numpy as np\n",
-    "\n",
-    "# Saving parameters\n",
-    "Samplename = '6QAK3_A2_qcodes';\n",
-    "loc_provider = qc.data.location.FormatLocation(fmt='A:/TransmonExperiments/GoogleDrive/T2acquisition/Data/%s/data/{counter}' % Samplename)\n",
-    "qc.data.data_set.DataSet.location_provider=loc_provider\n",
-    "station = qc.Station()"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 1,
-   "metadata": {
-    "collapsed": false
-   },
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "No loop running\n",
-      "Connected to: Rohde&Schwarz SGS100A (serial:1416.0505k02/103076, firmware:3.1.19.7-3.20.140.60.1) in 0.03s\n",
-      "Connected to: Rohde&Schwarz SGS100A (serial:1416.0505k02/103079, firmware:3.1.19.7-3.20.140.60.1) in 0.01s\n",
-      "Connected to: Rohde&Schwarz SGS100A (serial:1416.0505k02/103077, firmware:3.1.19.7-3.20.140.60.1) in 0.01s\n"
-     ]
-    },
-    {
-     "name": "stderr",
-     "output_type": "stream",
-     "text": [
-      "a:\\qcodes\\qcodes\\instrument\\metaclass.py:37: UserWarning: Multiprocessing is in beta, use at own risk\n",
-      "  UserWarning)\n"
-     ]
-    },
-    {
-     "data": {
-      "text/plain": [
-       "'_2'"
-      ]
-     },
-     "execution_count": 1,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "import qcodes.instrument_drivers.rohde_schwarz.SGS100A as RSdriver\n",
-    "localos = RSdriver.RohdeSchwarz_SGS100A('LO', 'TCPIP0::172.20.3.42::inst0::INSTR')\n",
-    "cavity1 = RSdriver.RohdeSchwarz_SGS100A('C1', 'TCPIP0::172.20.3.28::inst0::INSTR')\n",
-    "qubit2  = RSdriver.RohdeSchwarz_SGS100A('Q2', 'TCPIP0::172.20.3.168::inst0::INSTR')\n",
-    "station.add_component(localos, cavity1, qubit2)\n",
-    "fq = qubit2.frequency,lcavity.power\n",
-    "\n",
-    "import qcodes.instrument.parameter as parameter\n",
-    "import qcodes.instrument_drivers.AlazarTech.ATS9360 as ATSdriver\n",
-    "import qcodes.instrument_drivers.AlazarTech.ATS_acquisition_controllers as ats_contr\n",
-    "ats_inst = ATSdriver.AlazarTech_ATS9360(name='Alazar1', server_name=\"alazar_server\")\n",
-    "station.add_component(ats_inst)\n",
-    "\n",
-    "station.add_component(localos, cavity1, qubit2)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 3,
-   "metadata": {
-    "collapsed": false
-   },
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "{'CPLD_version': '25.16',\n",
-       " 'SDK_version': '5.9.25',\n",
-       " 'asopc_type': '1712554848',\n",
-       " 'bits_per_sample': 12,\n",
-       " 'driver_version': '5.9.25',\n",
-       " 'firmware': None,\n",
-       " 'latest_cal_date': '13-11-15',\n",
-       " 'max_samples': 4294967294,\n",
-       " 'memory_size': '4294967294',\n",
-       " 'model': 'ATS9360',\n",
-       " 'pcie_link_speed': '0.5GB/s',\n",
-       " 'pcie_link_width': '8',\n",
-       " 'serial': '970344',\n",
-       " 'vendor': 'AlazarTech'}"
-      ]
-     },
-     "execution_count": 3,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "ats_inst.get_idn()"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 4,
-   "metadata": {
-    "collapsed": true
-   },
-   "outputs": [],
-   "source": [
-    "# Configure all settings in the Alazar card\n",
-    "ats_inst.config(clock_source='EXTERNAL_CLOCK_10MHz_REF',\n",
-    "                sample_rate='10MHZ_REF_500MSPS',\n",
-    "                clock_edge='CLOCK_EDGE_RISING',\n",
-    "                decimation=1,\n",
-    "                coupling=['DC','DC'],\n",
-    "                channel_range=[.4,.4],\n",
-    "                impedance=[50,50],\n",
-    "                trigger_operation='TRIG_ENGINE_OP_J',\n",
-    "                trigger_engine1='TRIG_ENGINE_J',\n",
-    "                trigger_source1='EXTERNAL',\n",
-    "                trigger_slope1='TRIG_SLOPE_POSITIVE',\n",
-    "                trigger_level1=140,\n",
-    "                trigger_engine2='TRIG_ENGINE_K',\n",
-    "                trigger_source2='DISABLE',\n",
-    "                trigger_slope2='TRIG_SLOPE_POSITIVE',\n",
-    "                trigger_level2=128,\n",
-    "                external_trigger_coupling='DC',\n",
-    "                external_trigger_range='ETR_2V5',\n",
-    "                trigger_delay=0,\n",
-    "                timeout_ticks=0,\n",
-    "                aux_io_mode='AUX_IN_AUXILIARY',\n",
-    "                aux_io_param='NONE'\n",
-    "                #aux_io_mode='AUX_IN_TRIGGER_ENABLE', \n",
-    "                #aux_io_param='TRIG_SLOPE_POSITIVE'\n",
-    ")"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 11,
-   "metadata": {
-    "collapsed": false
-   },
-   "outputs": [
-    {
-     "name": "stderr",
-     "output_type": "stream",
-     "text": [
-      "a:\\qcodes\\qcodes\\instrument\\metaclass.py:37: UserWarning: Multiprocessing is in beta, use at own risk\n",
-      "  UserWarning)\n"
-     ]
-    }
-   ],
-   "source": [
-    "import qcodes.instrument_drivers.AlazarTech.Samp_controller as sample_controller\n",
-    "\n",
-    "samp_cont = sample_controller.HD_Samples_Controller(name='samples_controller', \n",
-    "                                                      alazar_name='Alazar1', \n",
-    "                                                      demod_freq = 5e6,\n",
-    "                                                      server_name=\"alazar_server\")"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 45,
-   "metadata": {
-    "collapsed": false
-   },
-   "outputs": [
-    {
-     "name": "stderr",
-     "output_type": "stream",
-     "text": [
-      "a:\\qcodes\\qcodes\\instrument\\metaclass.py:37: UserWarning: Multiprocessing is in beta, use at own risk\n",
-      "  UserWarning)\n"
-     ]
-    }
-   ],
-   "source": [
-    "samp_cont3 = sample_controller.HD_Samples_Controller(name='samples_controller3', \n",
-    "                                                      alazar_name='Alazar1', \n",
-    "                                                      demod_freq = 20e6,\n",
-    "                                                      server_name=\"alazar_server\")"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 52,
-   "metadata": {
-    "collapsed": false
-   },
-   "outputs": [],
-   "source": [
-    "samp_cont3.update_acquisitionkwargs(#mode='NPT',\n",
-    "                 samples_per_record=2560,\n",
-    "                 records_per_buffer=1000,\n",
-    "                 buffers_per_acquisition=10,\n",
-    "                 int_delay=2e-7,\n",
-    "                 int_time =3e-6,\n",
-    "                 allocated_buffers=1,\n",
-    "                 #buffer_timeout=1000\n",
-    ")"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 60,
-   "metadata": {
-    "collapsed": false
-   },
-   "outputs": [],
-   "source": [
-    "demod_freq = 20e6\n",
-    "\n",
-    "cavity1.power(-50)\n",
-    "cavity1.frequency(7.147e9)\n",
-    "\n",
-    "localos.power(15)\n",
-    "set_localos(demod_freq)\n",
-    "\n",
-    "qubit2.power(-60)\n",
-    "qubit2.frequency(7.5e9)\n",
-    "\n",
-    "localos.status('on')\n",
-    "cavity1.status('on')\n",
-    "qubit2.status('off')"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 49,
-   "metadata": {
-    "collapsed": false,
-    "scrolled": true
-   },
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "DataSet:\n",
-      "   mode     = DataMode.LOCAL\n",
-      "   location = 'A:/TransmonExperiments/GoogleDrive/T2acquisition/Data/6QAK3_A2_qcodes/data/027'\n",
-      "      |                     |  | \n",
-      "   Setpoint | C1_frequency_set              | frequency    | (81,)\n",
-      "   Measured | sample_num                    | sample_num   | (81, 2560)\n",
-      "   Measured | samples_controller3_magnitude | magnitude    | (81, 2560)\n",
-      "   Measured | samples_controller3_phase     | phase        | (81, 2560)\n",
-      "started at 2016-11-18 20:11:44\n"
-     ]
-    }
-   ],
-   "source": [
-    "loop = qc.Loop(cavity1.frequency.sweep(7.13e9,7.15e9, 0.25e6)).each(samp_cont3.acquisition,\n",
-    "            qc.Task(localos.frequency.set, (cavity1.frequency+ demod_freq)))\n",
-    "data = loop.get_data_set()\n",
-    "plot = qc.QtPlot()\n",
-    "plot.add(data.samples_controller3_magnitude)\n",
-    "plot.add(data.samples_controller3_phase, subplot=2)\n",
-    "_ = loop.with_bg_task(plot.update, plot.save).run()"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 17,
-   "metadata": {
-    "collapsed": false
-   },
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "2560"
-      ]
-     },
-     "execution_count": 17,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "2048 + 512"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 30,
-   "metadata": {
-    "collapsed": false
-   },
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "7151300000.0"
-      ]
-     },
-     "execution_count": 30,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "localos.frequency()"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 26,
-   "metadata": {
-    "collapsed": false
-   },
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "7146300000.0"
-      ]
-     },
-     "execution_count": 26,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "cavity1.frequency()"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 53,
-   "metadata": {
-    "collapsed": false
-   },
-   "outputs": [
-    {
-     "name": "stderr",
-     "output_type": "stream",
-     "text": [
-      "a:\\qcodes\\qcodes\\instrument\\metaclass.py:37: UserWarning: Multiprocessing is in beta, use at own risk\n",
-      "  UserWarning)\n"
-     ]
-    }
-   ],
-   "source": [
-    "import qcodes.instrument_drivers.AlazarTech.Single_controller as single_controller\n",
-    "\n",
-    "sing_contr = single_controller.HD_Controller(name='single_controller', \n",
-    "                                                      alazar_name='Alazar1', \n",
-    "                                                      demod_freq = 20e6,\n",
-    "                                                      server_name=\"alazar_server\")"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 55,
-   "metadata": {
-    "collapsed": true
-   },
-   "outputs": [],
-   "source": [
-    "sing_contr.update_acquisitionkwargs(#mode='NPT',\n",
-    "                 samples_per_record=2560,\n",
-    "                 records_per_buffer=1000,\n",
-    "                 buffers_per_acquisition=10,\n",
-    "                 int_delay=2e-7,\n",
-    "                 int_time =3e-6,\n",
-    "                 allocated_buffers=1,\n",
-    "                 #buffer_timeout=1000\n",
-    ")"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 68,
-   "metadata": {
-    "collapsed": false
-   },
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "DataSet:\n",
-      "   mode     = DataMode.LOCAL\n",
-      "   location = 'A:/TransmonExperiments/GoogleDrive/T2acquisition/Data/6QAK3_A2_qcodes/data/031'\n",
-      "      |                   |  | \n",
-      "   Setpoint | C1_frequency_set            | frequency    | (81,)\n",
-      "   Measured | single_controller_magnitude | magnitude    | (81,)\n",
-      "   Measured | single_controller_phase     | phase        | (81,)\n",
-      "started at 2016-11-18 20:30:59\n"
-     ]
-    }
-   ],
-   "source": [
-    "loop = qc.Loop(cavity1.frequency.sweep(7.13e9,7.15e9, 0.25e6)).each(sing_contr.acquisition,\n",
-    "            qc.Task(localos.frequency.set, (cavity1.frequency+ demod_freq)))\n",
-    "data = loop.get_data_set()\n",
-    "plot = qc.QtPlot()\n",
-    "plot.add(data.single_controller_magnitude)\n",
-    "plot.add(data.single_controller_phase, subplot=2)\n",
-    "_ = loop.with_bg_task(plot.update, plot.save).run()"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 2,
-   "metadata": {
-    "collapsed": false
-   },
-   "outputs": [
-    {
-     "name": "stderr",
-     "output_type": "stream",
-     "text": [
-      "a:\\qcodes\\qcodes\\plots\\pyqtgraph.py:325: UserWarning: nonlinear setpoint array passed to pyqtgraph. ignoring, using default scaling.\n",
-      "  warnings.warn('nonlinear setpoint array passed to pyqtgraph. '\n"
-     ]
-    },
-    {
-     "data": {
-      "image/png": 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DmeGj+2Qpg+vK7TWuTg2R89XdOb6WaWcqrVhOcWV6WhJvjJIdtxYinF19PSal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5VzzjnnnPuZuHfv3r17Pzpqp71F8jwfL87lGrjf/ejwNu+9kz//gI+Pjo6O7rz/+Qc+dHfOOeec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\nAKbA/wEU7C8+KV12mwAAAABJRU5ErkJggg==\n",
-      "text/plain": [
-       ""
-      ]
-     },
-     "execution_count": 2,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "loaded_data = qc.load_data(loc_provider.formatter.format(loc_provider.fmt,counter='014'))\n",
-    "plot = qc.QtPlot()\n",
-    "plot.add(loaded_data.VNA_magnitude)\n",
-    "plot.add(loaded_data.VNA_phase, subplot=2)\n",
-    "#plot.save()"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 69,
-   "metadata": {
-    "collapsed": true
-   },
-   "outputs": [],
-   "source": [
-    "demod_freq = 20e6\n",
-    "\n",
-    "cavity1.power(-55)\n",
-    "cavity1.frequency(7.133e9)\n",
-    "\n",
-    "localos.power(15)\n",
-    "set_localos(demod_freq)\n",
-    "\n",
-    "qubit2.power(-30)\n",
-    "qubit2.frequency(7.5e9)\n",
-    "\n",
-    "localos.status('on')\n",
-    "cavity1.status('on')\n",
-    "qubit2.status('on')"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 75,
-   "metadata": {
-    "collapsed": false
-   },
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "DataSet:\n",
-      "   mode     = DataMode.LOCAL\n",
-      "   location = 'A:/TransmonExperiments/GoogleDrive/T2acquisition/Data/6QAK3_A2_qcodes/data/034'\n",
-      "      |                   |  | \n",
-      "   Setpoint | Q2_frequency_set            | frequency    | (251,)\n",
-      "   Measured | single_controller_magnitude | magnitude    | (251,)\n",
-      "   Measured | single_controller_phase     | phase        | (251,)\n",
-      "started at 2016-11-18 20:50:01\n"
-     ]
-    }
-   ],
-   "source": [
-    "loop = qc.Loop(qubit2.frequency.sweep(8e9,8.5e9, 2e6)).each(sing_contr.acquisition)\n",
-    "data = loop.get_data_set()\n",
-    "plot = qc.QtPlot()\n",
-    "plot.add(data.single_controller_magnitude)\n",
-    "plot.add(data.single_controller_phase, subplot=2)\n",
-    "_ = loop.with_bg_task(plot.update, plot.save).run()"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 76,
-   "metadata": {
-    "collapsed": false
-   },
-   "outputs": [
-    {
-     "name": "stderr",
-     "output_type": "stream",
-     "text": [
-      "a:\\qcodes\\qcodes\\instrument\\metaclass.py:37: UserWarning: Multiprocessing is in beta, use at own risk\n",
-      "  UserWarning)\n"
-     ]
-    }
-   ],
-   "source": [
-    "sing_contr2 = single_controller.HD_Controller(name='single_controller2', \n",
-    "                                                      alazar_name='Alazar1', \n",
-    "                                                      demod_freq = 20e6,\n",
-    "                                                      server_name=\"alazar_server\",\n",
-    "                                                     filt='dot')"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 79,
-   "metadata": {
-    "collapsed": true
-   },
-   "outputs": [],
-   "source": [
-    "sing_contr2.update_acquisitionkwargs(#mode='NPT',\n",
-    "                 samples_per_record=2560,\n",
-    "                 records_per_buffer=1000,\n",
-    "                 buffers_per_acquisition=1,\n",
-    "                 int_delay=2e-7,\n",
-    "                 int_time =3e-6,\n",
-    "                 allocated_buffers=1,\n",
-    "                 #buffer_timeout=1000\n",
-    ")"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 80,
-   "metadata": {
-    "collapsed": false
-   },
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "DataSet:\n",
-      "   mode     = DataMode.LOCAL\n",
-      "   location = 'A:/TransmonExperiments/GoogleDrive/T2acquisition/Data/6QAK3_A2_qcodes/data/036'\n",
-      "      |                    |  | \n",
-      "   Setpoint | Q2_frequency_set             | frequency    | (251,)\n",
-      "   Measured | single_controller2_magnitude | magnitude    | (251,)\n",
-      "   Measured | single_controller2_phase     | phase        | (251,)\n",
-      "started at 2016-11-18 20:56:48\n"
-     ]
-    }
-   ],
-   "source": [
-    "loop = qc.Loop(qubit2.frequency.sweep(8e9,8.5e9, 2e6)).each(sing_contr2.acquisition)\n",
-    "data = loop.get_data_set()\n",
-    "plot = qc.QtPlot()\n",
-    "plot.add(data.single_controller2_magnitude)\n",
-    "plot.add(data.single_controller2_phase, subplot=2)\n",
-    "_ = loop.with_bg_task(plot.update, plot.save).run()"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 81,
-   "metadata": {
-    "collapsed": true
-   },
-   "outputs": [],
-   "source": [
-    "localos.status('off')\n",
-    "cavity1.status('off')\n",
-    "qubit2.status('off')"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {
-    "collapsed": true
-   },
-   "outputs": [],
-   "source": []
-  }
- ],
- "metadata": {
-  "kernelspec": {
-   "display_name": "Python 3",
-   "language": "python",
-   "name": "python3"
-  },
-  "language_info": {
-   "codemirror_mode": {
-    "name": "ipython",
-    "version": 3
-   },
-   "file_extension": ".py",
-   "mimetype": "text/x-python",
-   "name": "python",
-   "nbconvert_exporter": "python",
-   "pygments_lexer": "ipython3",
-   "version": "3.5.2"
-  }
- },
- "nbformat": 4,
- "nbformat_minor": 1
-}
diff --git a/docs/examples/Qcodes example ATS_ONWORK.ipynb b/docs/examples/Qcodes example ATS9870.ipynb
similarity index 99%
rename from docs/examples/Qcodes example ATS_ONWORK.ipynb
rename to docs/examples/Qcodes example ATS9870.ipynb
index 4f025b9e62bf..d5dec7e0566d 100644
--- a/docs/examples/Qcodes example ATS_ONWORK.ipynb	
+++ b/docs/examples/Qcodes example ATS9870.ipynb	
@@ -1657,7 +1657,11 @@
    "name": "python",
    "nbconvert_exporter": "python",
    "pygments_lexer": "ipython3",
-   "version": "3.5.1"
+   "version": "3.5.2"
+  },
+  "widgets": {
+   "state": {},
+   "version": "1.0.0"
   }
  },
  "nbformat": 4,
diff --git a/docs/examples/Qcodes example with Alazar ATS9360-Copy1.ipynb b/docs/examples/Qcodes example with Alazar ATS9360-Copy1.ipynb
deleted file mode 100644
index 11ece4bb6e29..000000000000
--- a/docs/examples/Qcodes example with Alazar ATS9360-Copy1.ipynb	
+++ /dev/null
@@ -1,3594 +0,0 @@
-{
- "cells": [
-  {
-   "cell_type": "code",
-   "execution_count": 1,
-   "metadata": {
-    "collapsed": false
-   },
-   "outputs": [
-    {
-     "data": {
-      "application/javascript": [
-       "/*\r\n",
-       " * Qcodes Jupyter/IPython widgets\r\n",
-       " */\r\n",
-       "require([\r\n",
-       "    'nbextensions/widgets/widgets/js/widget',\r\n",
-       "    'nbextensions/widgets/widgets/js/manager'\r\n",
-       "], function (widget, manager) {\r\n",
-       "\r\n",
-       "    var UpdateView = widget.DOMWidgetView.extend({\r\n",
-       "        render: function() {\r\n",
-       "            window.MYWIDGET = this;\r\n",
-       "            this._interval = 0;\r\n",
-       "            this.update();\r\n",
-       "        },\r\n",
-       "        update: function() {\r\n",
-       "            this.display(this.model.get('_message'));\r\n",
-       "            this.setInterval();\r\n",
-       "        },\r\n",
-       "        display: function(message) {\r\n",
-       "            /*\r\n",
-       "             * display method: override this for custom display logic\r\n",
-       "             */\r\n",
-       "            this.el.innerHTML = message;\r\n",
-       "        },\r\n",
-       "        remove: function() {\r\n",
-       "            clearInterval(this._updater);\r\n",
-       "        },\r\n",
-       "        setInterval: function(newInterval) {\r\n",
-       "            var me = this;\r\n",
-       "            if(newInterval===undefined) newInterval = me.model.get('interval');\r\n",
-       "            if(newInterval===me._interval) return;\r\n",
-       "\r\n",
-       "            me._interval = newInterval;\r\n",
-       "\r\n",
-       "            if(me._updater) clearInterval(me._updater);\r\n",
-       "\r\n",
-       "            if(me._interval) {\r\n",
-       "                me._updater = setInterval(function() {\r\n",
-       "                    me.send({myupdate: true});\r\n",
-       "                    if(!me.model.comm_live) {\r\n",
-       "                        console.log('missing comm, canceling widget updates', me);\r\n",
-       "                        clearInterval(me._updater);\r\n",
-       "                    }\r\n",
-       "                }, me._interval * 1000);\r\n",
-       "            }\r\n",
-       "        }\r\n",
-       "    });\r\n",
-       "    manager.WidgetManager.register_widget_view('UpdateView', UpdateView);\r\n",
-       "\r\n",
-       "    var HiddenUpdateView = UpdateView.extend({\r\n",
-       "        display: function(message) {\r\n",
-       "            this.$el.hide();\r\n",
-       "        }\r\n",
-       "    });\r\n",
-       "    manager.WidgetManager.register_widget_view('HiddenUpdateView', HiddenUpdateView);\r\n",
-       "\r\n",
-       "    var SubprocessView = UpdateView.extend({\r\n",
-       "        render: function() {\r\n",
-       "            var me = this;\r\n",
-       "            me._interval = 0;\r\n",
-       "            me._minimize = '';\r\n",
-       "            me._restore = '';\r\n",
-       "\r\n",
-       "            // max lines of output to show\r\n",
-       "            me.maxOutputLength = 500;\r\n",
-       "\r\n",
-       "            // in case there is already an outputView present,\r\n",
-       "            // like from before restarting the kernel\r\n",
-       "            $('.qcodes-output-view').not(me.$el).remove();\r\n",
-       "\r\n",
-       "            me.$el\r\n",
-       "                .addClass('qcodes-output-view')\r\n",
-       "                .attr('qcodes-state', 'docked')\r\n",
-       "                .html(\r\n",
-       "                    '
' +\r\n",
-       "                        '
' +\r\n",
-       "                        '' +\r\n",
-       "                        '' +\r\n",
-       "                        '' +\r\n",
-       "                        '' +\r\n",
-       "                        '' +\r\n",
-       "                        '' +\r\n",
-       "                    '
' +\r\n",
-       "                    '
'\r\n",
-       "                );\r\n",
-       "\r\n",
-       "            me.clearButton = me.$el.find('.qcodes-clear-output');\r\n",
-       "            me.minButton = me.$el.find('.qcodes-minimize');\r\n",
-       "            me.outputArea = me.$el.find('pre');\r\n",
-       "            me.subprocessList = me.$el.find('.qcodes-process-list');\r\n",
-       "            me.abortButton = me.$el.find('.qcodes-abort-loop');\r\n",
-       "            me.processLinesButton = me.$el.find('.qcodes-processlines')\r\n",
-       "\r\n",
-       "            me.outputLines = [];\r\n",
-       "\r\n",
-       "            me.clearButton.click(function() {\r\n",
-       "                me.outputArea.html('');\r\n",
-       "                me.clearButton.addClass('disabled');\r\n",
-       "            });\r\n",
-       "\r\n",
-       "            me.abortButton.click(function() {\r\n",
-       "                me.send({abort: true});\r\n",
-       "            });\r\n",
-       "\r\n",
-       "            me.processLinesButton.click(function() {\r\n",
-       "                // toggle multiline process list display\r\n",
-       "                me.subprocessesMultiline = !me.subprocessesMultiline;\r\n",
-       "                me.showSubprocesses();\r\n",
-       "            });\r\n",
-       "\r\n",
-       "            me.$el.find('.js-state').click(function() {\r\n",
-       "                var state = this.className.substr(this.className.indexOf('qcodes'))\r\n",
-       "                        .split('-')[1].split(' ')[0];\r\n",
-       "                me.model.set('_state', state);\r\n",
-       "            });\r\n",
-       "\r\n",
-       "            $(window)\r\n",
-       "                .off('resize.qcodes')\r\n",
-       "                .on('resize.qcodes', function() {me.clipBounds();});\r\n",
-       "\r\n",
-       "            me.update();\r\n",
-       "        },\r\n",
-       "\r\n",
-       "        updateState: function() {\r\n",
-       "            var me = this,\r\n",
-       "                oldState = me.$el.attr('qcodes-state'),\r\n",
-       "                state = me.model.get('_state');\r\n",
-       "\r\n",
-       "            if(state === oldState) return;\r\n",
-       "\r\n",
-       "            setTimeout(function() {\r\n",
-       "                // not sure why I can't pop it out of the widgetarea in render, but it seems that\r\n",
-       "                // some other bit of code resets the parent after render if I do it there.\r\n",
-       "                // To be safe, just do it on every state click.\r\n",
-       "                me.$el.appendTo('body');\r\n",
-       "\r\n",
-       "                if(oldState === 'floated') {\r\n",
-       "                    console.log('here');\r\n",
-       "                    me.$el.draggable('destroy').css({left:'', top: ''});\r\n",
-       "                }\r\n",
-       "\r\n",
-       "                me.$el.attr('qcodes-state', state);\r\n",
-       "\r\n",
-       "                if(state === 'floated') {\r\n",
-       "                    me.$el\r\n",
-       "                        .draggable({stop: function() { me.clipBounds(); }})\r\n",
-       "                        .css({\r\n",
-       "                            left: window.innerWidth - me.$el.width() - 15,\r\n",
-       "                            top: window.innerHeight - me.$el.height() - 10\r\n",
-       "                        });\r\n",
-       "                }\r\n",
-       "\r\n",
-       "                // any previous highlighting is now moot\r\n",
-       "                me.$el.removeClass('qcodes-highlight');\r\n",
-       "            }, 0);\r\n",
-       "\r\n",
-       "        },\r\n",
-       "\r\n",
-       "        clipBounds: function() {\r\n",
-       "            var me = this;\r\n",
-       "            if(me.$el.attr('qcodes-state') === 'floated') {\r\n",
-       "                var bounds = me.$el[0].getBoundingClientRect(),\r\n",
-       "                    minVis = 40,\r\n",
-       "                    maxLeft = window.innerWidth - minVis,\r\n",
-       "                    minLeft = minVis - bounds.width,\r\n",
-       "                    maxTop = window.innerHeight - minVis;\r\n",
-       "\r\n",
-       "                if(bounds.left > maxLeft) me.$el.css('left', maxLeft);\r\n",
-       "                else if(bounds.left < minLeft) me.$el.css('left', minLeft);\r\n",
-       "\r\n",
-       "                if(bounds.top > maxTop) me.$el.css('top', maxTop);\r\n",
-       "                else if(bounds.top < 0) me.$el.css('top', 0);\r\n",
-       "            }\r\n",
-       "        },\r\n",
-       "\r\n",
-       "        display: function(message) {\r\n",
-       "            var me = this;\r\n",
-       "            if(message) {\r\n",
-       "                var initialScroll = me.outputArea.scrollTop();\r\n",
-       "                me.outputArea.scrollTop(me.outputArea.prop('scrollHeight'));\r\n",
-       "                var scrollBottom = me.outputArea.scrollTop();\r\n",
-       "\r\n",
-       "                if(me.$el.attr('qcodes-state') === 'minimized') {\r\n",
-       "                    // if we add text and the box is minimized, highlight the\r\n",
-       "                    // title bar to alert the user that there are new messages.\r\n",
-       "                    // remove then add the class, so we get the animation again\r\n",
-       "                    // if it's already highlighted\r\n",
-       "                    me.$el.removeClass('qcodes-highlight');\r\n",
-       "                    setTimeout(function(){\r\n",
-       "                        me.$el.addClass('qcodes-highlight');\r\n",
-       "                    }, 0);\r\n",
-       "                }\r\n",
-       "\r\n",
-       "                var newLines = message.split('\\n'),\r\n",
-       "                    out = me.outputLines,\r\n",
-       "                    outLen = out.length;\r\n",
-       "                if(outLen) out[outLen - 1] += newLines[0];\r\n",
-       "                else out.push(newLines[0]);\r\n",
-       "\r\n",
-       "                for(var i = 1; i < newLines.length; i++) {\r\n",
-       "                    out.push(newLines[i]);\r\n",
-       "                }\r\n",
-       "\r\n",
-       "                if(out.length > me.maxOutputLength) {\r\n",
-       "                    out.splice(0, out.length - me.maxOutputLength + 1,\r\n",
-       "                        '<<< Output clipped >>>');\r\n",
-       "                }\r\n",
-       "\r\n",
-       "                me.outputArea.text(out.join('\\n'));\r\n",
-       "                me.clearButton.removeClass('disabled');\r\n",
-       "\r\n",
-       "                // if we were scrolled to the bottom initially, make sure\r\n",
-       "                // we stay that way.\r\n",
-       "                me.outputArea.scrollTop(initialScroll === scrollBottom ?\r\n",
-       "                    me.outputArea.prop('scrollHeight') : initialScroll);\r\n",
-       "            }\r\n",
-       "\r\n",
-       "            me.showSubprocesses();\r\n",
-       "            me.updateState();\r\n",
-       "        },\r\n",
-       "\r\n",
-       "        showSubprocesses: function() {\r\n",
-       "            var me = this,\r\n",
-       "                replacer = me.subprocessesMultiline ? '
' : ', ',\r\n",
-       "                processes = (me.model.get('_processes') || '')\r\n",
-       "                    .replace(/\\n/g, '>' + replacer + '<');\r\n",
-       "\r\n",
-       "            if(processes) processes = '<' + processes + '>';\r\n",
-       "            else processes = 'No subprocesses';\r\n",
-       "\r\n",
-       "            me.abortButton.toggleClass('disabled', processes.indexOf('Measurement')===-1);\r\n",
-       "\r\n",
-       "            me.subprocessList.html(processes);\r\n",
-       "        }\r\n",
-       "    });\r\n",
-       "    manager.WidgetManager.register_widget_view('SubprocessView', SubprocessView);\r\n",
-       "});\r\n"
-      ],
-      "text/plain": [
-       ""
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    },
-    {
-     "data": {
-      "text/html": [
-       ""
-      ],
-      "text/plain": [
-       ""
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    },
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "No loop running\n"
-     ]
-    },
-    {
-     "name": "stderr",
-     "output_type": "stream",
-     "text": [
-      "a:\\qcodes\\qcodes\\process\\helpers.py:27: UserWarning: Multiprocessing is in beta, use at own risk\n",
-      "  warnings.warn(\"Multiprocessing is in beta, use at own risk\", UserWarning)\n"
-     ]
-    }
-   ],
-   "source": [
-    "# import all necessary things\n",
-    "%matplotlib nbagg\n",
-    "\n",
-    "import qcodes as qc\n",
-    "import qcodes.instrument.parameter as parameter\n",
-    "import qcodes.instrument_drivers.AlazarTech.ATS9360 as ATSdriver\n",
-    "import qcodes.instrument_drivers.AlazarTech.ATS_acquisition_controllers as ats_contr\n",
-    "\n",
-    "qc.halt_bg()\n",
-    "qc.set_mp_method('spawn')  # force Windows behavior on mac\n",
-    "\n",
-    "# this makes a widget in the corner of the window to show and control\n",
-    "# subprocesses and any output they would print to the terminal\n",
-    "#qc.show_subprocess_widget()"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 2,
-   "metadata": {
-    "collapsed": false
-   },
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "[{'bits_per_sample': 12,\n",
-       "  'board_id': 1,\n",
-       "  'board_kind': 'ATS9360',\n",
-       "  'max_samples': 4294967294,\n",
-       "  'system_id': 1}]"
-      ]
-     },
-     "execution_count": 2,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "# Command to list all alazar boards connected to the system\n",
-    "ATSdriver.AlazarTech_ATS.find_boards()"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 3,
-   "metadata": {
-    "collapsed": false
-   },
-   "outputs": [
-    {
-     "name": "stderr",
-     "output_type": "stream",
-     "text": [
-      "a:\\qcodes\\qcodes\\instrument\\metaclass.py:37: UserWarning: Multiprocessing is in beta, use at own risk\n",
-      "  UserWarning)\n"
-     ]
-    },
-    {
-     "data": {
-      "text/plain": [
-       "{'CPLD_version': '25.16',\n",
-       " 'SDK_version': '5.9.25',\n",
-       " 'asopc_type': '1712554848',\n",
-       " 'driver_version': '5.9.25',\n",
-       " 'firmware': None,\n",
-       " 'latest_cal_date': '13-11-15',\n",
-       " 'memory_size': '4294967294',\n",
-       " 'model': 'ATS9360',\n",
-       " 'pcie_link_speed': '0.5GB/s',\n",
-       " 'pcie_link_width': '8',\n",
-       " 'serial': '970344',\n",
-       " 'vendor': 'AlazarTech'}"
-      ]
-     },
-     "execution_count": 3,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "# Create the ATS9870 instrument on the new server \"alazar_server\"\n",
-    "ats_inst = ATSdriver.AlazarTech_ATS9360(name='Alazar1', server_name=\"alazar_server\")\n",
-    "# Print all information about this Alazar card\n",
-    "ats_inst.get_idn()"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 4,
-   "metadata": {
-    "collapsed": true
-   },
-   "outputs": [],
-   "source": [
-    "import qcodes.instrument_drivers.rohde_schwarz.SGS100A as RSdriver"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 5,
-   "metadata": {
-    "collapsed": false
-   },
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "Connected to: Rohde&Schwarz SGS100A (serial:1416.0505k02/103076, firmware:3.1.19.7-3.20.140.60.1) in 0.05s\n"
-     ]
-    }
-   ],
-   "source": [
-    "localos = RSdriver.RohdeSchwarz_SGS100A('LO', 'TCPIP0::172.20.3.42::inst0::INSTR')"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 6,
-   "metadata": {
-    "collapsed": false
-   },
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "10000000.0"
-      ]
-     },
-     "execution_count": 6,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "localos.frequency()"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 7,
-   "metadata": {
-    "collapsed": false
-   },
-   "outputs": [],
-   "source": [
-    "localos.power(-25)\n",
-    "localos.frequency(10e6)\n",
-    "localos.status('on')"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 8,
-   "metadata": {
-    "collapsed": false
-   },
-   "outputs": [
-    {
-     "name": "stderr",
-     "output_type": "stream",
-     "text": [
-      "a:\\qcodes\\qcodes\\instrument\\metaclass.py:37: UserWarning: Multiprocessing is in beta, use at own risk\n",
-      "  UserWarning)\n"
-     ]
-    }
-   ],
-   "source": [
-    "# Instantiate an acquisition controller (In this case we are doing a simple DFT) on the same server (\"alazar_server\") and \n",
-    "# provide the name of the name of the alazar card that this controller should control\n",
-    "acquisition_controller = ats_contr.Demodulation_AcquisitionController(name='acquisition_controller', \n",
-    "                                                                      demodulation_frequency=15e6, \n",
-    "                                                                      alazar_name='Alazar1', \n",
-    "                                                                      server_name=\"alazar_server\")"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 11,
-   "metadata": {
-    "collapsed": false
-   },
-   "outputs": [
-    {
-     "ename": "ValueError",
-     "evalue": "*** error on alazar_server ***\nwhile executing query: ('ASK', 'cmd', (0, 'config'), {'trigger_source1': 'EXTERNAL', 'channel_range': [2.0, 2.0], 'sample_rate': 100000000})\n\n(>, (0, 'config'), {'sample_rate': 100000000, 'trigger_source1': 'EXTERNAL', 'channel_range': [2.0, 2.0]})\nTraceback (most recent call last):\n  File \"a:\\qcodes\\qcodes\\process\\server.py\", line 347, in _process_ask\n    response = func(*args, **kwargs)\n  File \"a:\\qcodes\\qcodes\\instrument\\server.py\", line 189, in handle_cmd\n    return func(*args, **kwargs)\n  File \"a:\\qcodes\\qcodes\\instrument_drivers\\AlazarTech\\ATS.py\", line 364, in config\n    self._set_if_present('sample_rate', sample_rate)\n  File \"a:\\qcodes\\qcodes\\instrument_drivers\\AlazarTech\\ATS.py\", line 698, in _set_if_present\n    self.parameters[param_name]._set(value)\n  File \"a:\\qcodes\\qcodes\\instrument_drivers\\AlazarTech\\ATS.py\", line 883, in _set\n    self.validate(value)\n  File \"a:\\qcodes\\qcodes\\instrument\\parameter.py\", line 365, in validate\n    self._vals.validate(value, 'Parameter: ' + context)\n  File \"a:\\qcodes\\qcodes\\utils\\validators.py\", line 234, in validate\n    repr(value), repr(self._values), context))\nValueError: 100000000 is not in {'5KSPS', '100MSPS', '1000MSPS', '50MSPS', '500MSPS', '10MSPS', '1KSPS', '2KSPS', '100KSPS', '50KSPS', '800MSPS', '500KSPS', '1MSPS', '5MSPS', '20MSPS', '200KSPS', '1500MSPS', '2MSPS', 'EXTERNAL_CLOCK', '250MSPS', '1200MSPS', '20KSPS', '10KSPS', '125MSPS', '1800MSPS', '10MHZ_REF_500MSPS'}; Parameter: sample_rate\n",
-     "output_type": "error",
-     "traceback": [
-      "\u001b[1;31m---------------------------------------------------------------------------\u001b[0m",
-      "\u001b[1;31mValueError\u001b[0m                                Traceback (most recent call last)",
-      "\u001b[1;32m\u001b[0m in \u001b[0;36m\u001b[1;34m()\u001b[0m\n\u001b[0;32m     10\u001b[0m                 \u001b[1;31m#trigger_operation='TRIG_ENGINE_OP_J',\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m     11\u001b[0m                 \u001b[1;31m#trigger_engine1='TRIG_ENGINE_J',\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m---> 12\u001b[1;33m                 \u001b[0mtrigger_source1\u001b[0m\u001b[1;33m=\u001b[0m\u001b[1;34m'EXTERNAL'\u001b[0m\u001b[1;33m,\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m     13\u001b[0m                 \u001b[1;31m#trigger_slope1='TRIG_SLOPE_POSITIVE',\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m     14\u001b[0m                 \u001b[1;31m#trigger_level1=128,\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
-      "\u001b[1;32ma:\\qcodes\\qcodes\\instrument\\remote.py\u001b[0m in \u001b[0;36m__call__\u001b[1;34m(self, *args, **kwargs)\u001b[0m\n\u001b[0;32m    366\u001b[0m     \u001b[1;32mdef\u001b[0m \u001b[0m__call__\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mself\u001b[0m\u001b[1;33m,\u001b[0m \u001b[1;33m*\u001b[0m\u001b[0margs\u001b[0m\u001b[1;33m,\u001b[0m \u001b[1;33m**\u001b[0m\u001b[0mkwargs\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    367\u001b[0m         \u001b[1;34m\"\"\"Call the method on the server, passing on any args and kwargs.\"\"\"\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m--> 368\u001b[1;33m         \u001b[1;32mreturn\u001b[0m \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0m_instrument\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0m_ask_server\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mname\u001b[0m\u001b[1;33m,\u001b[0m \u001b[1;33m*\u001b[0m\u001b[0margs\u001b[0m\u001b[1;33m,\u001b[0m \u001b[1;33m**\u001b[0m\u001b[0mkwargs\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m    369\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    370\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n",
-      "\u001b[1;32ma:\\qcodes\\qcodes\\instrument\\remote.py\u001b[0m in \u001b[0;36m_ask_server\u001b[1;34m(self, func_name, *args, **kwargs)\u001b[0m\n\u001b[0;32m    124\u001b[0m     \u001b[1;32mdef\u001b[0m \u001b[0m_ask_server\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mself\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mfunc_name\u001b[0m\u001b[1;33m,\u001b[0m \u001b[1;33m*\u001b[0m\u001b[0margs\u001b[0m\u001b[1;33m,\u001b[0m \u001b[1;33m**\u001b[0m\u001b[0mkwargs\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    125\u001b[0m         \u001b[1;34m\"\"\"Query the server copy of this instrument, expecting a response.\"\"\"\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m--> 126\u001b[1;33m         \u001b[1;32mreturn\u001b[0m \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0m_manager\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mask\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;34m'cmd'\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0m_id\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mfunc_name\u001b[0m\u001b[1;33m,\u001b[0m \u001b[1;33m*\u001b[0m\u001b[0margs\u001b[0m\u001b[1;33m,\u001b[0m \u001b[1;33m**\u001b[0m\u001b[0mkwargs\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m    127\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    128\u001b[0m     \u001b[1;32mdef\u001b[0m \u001b[0m_write_server\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mself\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mfunc_name\u001b[0m\u001b[1;33m,\u001b[0m \u001b[1;33m*\u001b[0m\u001b[0margs\u001b[0m\u001b[1;33m,\u001b[0m \u001b[1;33m**\u001b[0m\u001b[0mkwargs\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
-      "\u001b[1;32ma:\\qcodes\\qcodes\\process\\server.py\u001b[0m in \u001b[0;36mask\u001b[1;34m(self, func_name, timeout, *args, **kwargs)\u001b[0m\n\u001b[0;32m    126\u001b[0m             \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0m_query_queue\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mput\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mquery\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    127\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m--> 128\u001b[1;33m             \u001b[0mvalue\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0m_get_response\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mtimeout\u001b[0m\u001b[1;33m=\u001b[0m\u001b[0mtimeout\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mquery\u001b[0m\u001b[1;33m=\u001b[0m\u001b[0mquery\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m    129\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    130\u001b[0m             \u001b[1;32mwhile\u001b[0m \u001b[1;32mnot\u001b[0m \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0m_response_queue\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mempty\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
-      "\u001b[1;32ma:\\qcodes\\qcodes\\process\\server.py\u001b[0m in \u001b[0;36m_get_response\u001b[1;34m(self, timeout, query)\u001b[0m\n\u001b[0;32m    147\u001b[0m             \u001b[1;32mreturn\u001b[0m \u001b[0mvalue\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    148\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m--> 149\u001b[1;33m         \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0m_handle_error\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mcode\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mvalue\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mquery\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m    150\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    151\u001b[0m     \u001b[1;32mdef\u001b[0m \u001b[0m_handle_error\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mself\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mcode\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0merror_str\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mquery\u001b[0m\u001b[1;33m=\u001b[0m\u001b[1;32mNone\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
-      "\u001b[1;32ma:\\qcodes\\qcodes\\process\\server.py\u001b[0m in \u001b[0;36m_handle_error\u001b[1;34m(self, code, error_str, query)\u001b[0m\n\u001b[0;32m    166\u001b[0m             \u001b[0merr_type\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mRuntimeError\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    167\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m--> 168\u001b[1;33m         \u001b[1;32mraise\u001b[0m \u001b[0merr_type\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0merror_head\u001b[0m \u001b[1;33m+\u001b[0m \u001b[1;34m'\\n\\n'\u001b[0m \u001b[1;33m+\u001b[0m \u001b[0merror_str\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m    169\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    170\u001b[0m     \u001b[1;32mdef\u001b[0m \u001b[0mhalt\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mself\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mtimeout\u001b[0m\u001b[1;33m=\u001b[0m\u001b[1;36m2\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
-      "\u001b[1;31mValueError\u001b[0m: *** error on alazar_server ***\nwhile executing query: ('ASK', 'cmd', (0, 'config'), {'trigger_source1': 'EXTERNAL', 'channel_range': [2.0, 2.0], 'sample_rate': 100000000})\n\n(>, (0, 'config'), {'sample_rate': 100000000, 'trigger_source1': 'EXTERNAL', 'channel_range': [2.0, 2.0]})\nTraceback (most recent call last):\n  File \"a:\\qcodes\\qcodes\\process\\server.py\", line 347, in _process_ask\n    response = func(*args, **kwargs)\n  File \"a:\\qcodes\\qcodes\\instrument\\server.py\", line 189, in handle_cmd\n    return func(*args, **kwargs)\n  File \"a:\\qcodes\\qcodes\\instrument_drivers\\AlazarTech\\ATS.py\", line 364, in config\n    self._set_if_present('sample_rate', sample_rate)\n  File \"a:\\qcodes\\qcodes\\instrument_drivers\\AlazarTech\\ATS.py\", line 698, in _set_if_present\n    self.parameters[param_name]._set(value)\n  File \"a:\\qcodes\\qcodes\\instrument_drivers\\AlazarTech\\ATS.py\", line 883, in _set\n    self.validate(value)\n  File \"a:\\qcodes\\qcodes\\instrument\\parameter.py\", line 365, in validate\n    self._vals.validate(value, 'Parameter: ' + context)\n  File \"a:\\qcodes\\qcodes\\utils\\validators.py\", line 234, in validate\n    repr(value), repr(self._values), context))\nValueError: 100000000 is not in {'5KSPS', '100MSPS', '1000MSPS', '50MSPS', '500MSPS', '10MSPS', '1KSPS', '2KSPS', '100KSPS', '50KSPS', '800MSPS', '500KSPS', '1MSPS', '5MSPS', '20MSPS', '200KSPS', '1500MSPS', '2MSPS', 'EXTERNAL_CLOCK', '250MSPS', '1200MSPS', '20KSPS', '10KSPS', '125MSPS', '1800MSPS', '10MHZ_REF_500MSPS'}; Parameter: sample_rate\n"
-     ]
-    }
-   ],
-   "source": [
-    "# Configure all settings in the Alazar card\n",
-    "ats_inst.config(#clock_source='INTERNAL_CLOCK',\n",
-    "                sample_rate='100000000',\n",
-    "                #clock_edge='CLOCK_EDGE_RISING',\n",
-    "                #decimation=0,\n",
-    "                #coupling=['AC','AC'],\n",
-    "                channel_range=[2.,2.],\n",
-    "                #impedance=[50,50],\n",
-    "                #bwlimit=['DISABLED','DISABLED'],\n",
-    "                #trigger_operation='TRIG_ENGINE_OP_J',\n",
-    "                #trigger_engine1='TRIG_ENGINE_J',\n",
-    "                trigger_source1='EXTERNAL',\n",
-    "                #trigger_slope1='TRIG_SLOPE_POSITIVE',\n",
-    "                #trigger_level1=128,\n",
-    "                #trigger_engine2='TRIG_ENGINE_K',\n",
-    "                #trigger_source2='DISABLE',\n",
-    "                #trigger_slope2='TRIG_SLOPE_POSITIVE',\n",
-    "                #trigger_level2=128,\n",
-    "                #external_trigger_coupling='AC',\n",
-    "                #external_trigger_range='ETR_5V',\n",
-    "                #trigger_delay=0,\n",
-    "                #timeout_ticks=0\n",
-    ")"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 11,
-   "metadata": {
-    "collapsed": false
-   },
-   "outputs": [],
-   "source": [
-    "# This command is specific to this acquisition controller. The kwargs provided here are being forwarded to ats_inst.acquire\n",
-    "# This way, it becomes easy to change acquisition specific settings from the ipython notebook\n",
-    "acquisition_controller.update_acquisitionkwargs(#mode='NPT',\n",
-    "                 samples_per_record=1024,\n",
-    "                 records_per_buffer=70,\n",
-    "                 buffers_per_acquisition=1,\n",
-    "                 #channel_selection='AB',\n",
-    "                 #transfer_offset=0,\n",
-    "                 #external_startcapture='ENABLED',\n",
-    "                 #enable_record_headers='DISABLED',\n",
-    "                 #alloc_buffers='DISABLED',\n",
-    "                 #fifo_only_streaming='DISABLED',\n",
-    "                 #interleave_samples='DISABLED',\n",
-    "                 #get_processed_data='DISABLED',\n",
-    "                 allocated_buffers=1,\n",
-    "                 #buffer_timeout=1000\n",
-    ")"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 12,
-   "metadata": {
-    "collapsed": false
-   },
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "2.0976760253919644"
-      ]
-     },
-     "execution_count": 12,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "# Getting the value of the parameter 'acquisition' of the instrument 'acquisition_controller' performes the entire acquisition \n",
-    "# protocol. This again depends on the specific implementation of the acquisition controller\n",
-    "acquisition_controller.acquisition()"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 13,
-   "metadata": {
-    "collapsed": false
-   },
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "{'__class__': 'qcodes.instrument_drivers.AlazarTech.ATS9360.AlazarTech_ATS9360',\n",
-       " 'functions': {},\n",
-       " 'name': 'Alazar1',\n",
-       " 'parameters': {'IDN': {'__class__': 'qcodes.instrument.parameter.StandardParameter',\n",
-       "   'instrument': 'qcodes.instrument_drivers.AlazarTech.ATS9360.AlazarTech_ATS9360',\n",
-       "   'instrument_name': 'Alazar1',\n",
-       "   'label': 'IDN',\n",
-       "   'name': 'IDN',\n",
-       "   'ts': '2016-11-01 16:48:58',\n",
-       "   'units': '',\n",
-       "   'value': {'CPLD_version': '25.16',\n",
-       "    'SDK_version': '5.9.25',\n",
-       "    'asopc_type': '1712554848',\n",
-       "    'driver_version': '5.9.25',\n",
-       "    'firmware': None,\n",
-       "    'latest_cal_date': '13-11-15',\n",
-       "    'memory_size': '4294967294',\n",
-       "    'model': 'ATS9360',\n",
-       "    'pcie_link_speed': '0.5GB/s',\n",
-       "    'pcie_link_width': '8',\n",
-       "    'serial': '970344',\n",
-       "    'vendor': 'AlazarTech'}},\n",
-       "  'alloc_buffers': {'__class__': 'qcodes.instrument_drivers.AlazarTech.ATS.AlazarParameter',\n",
-       "   'label': 'Alloc Buffers',\n",
-       "   'name': 'alloc_buffers',\n",
-       "   'ts': '2016-11-01 16:48:23',\n",
-       "   'units': '',\n",
-       "   'value': 'DISABLED'},\n",
-       "  'allocated_buffers': {'__class__': 'qcodes.instrument_drivers.AlazarTech.ATS.AlazarParameter',\n",
-       "   'label': 'Allocated Buffers',\n",
-       "   'name': 'allocated_buffers',\n",
-       "   'ts': '2016-11-01 16:48:58',\n",
-       "   'units': '',\n",
-       "   'value': 1},\n",
-       "  'buffer_timeout': {'__class__': 'qcodes.instrument_drivers.AlazarTech.ATS.AlazarParameter',\n",
-       "   'label': 'Buffer Timeout',\n",
-       "   'name': 'buffer_timeout',\n",
-       "   'ts': '2016-11-01 16:48:23',\n",
-       "   'units': 'ms',\n",
-       "   'value': 1000},\n",
-       "  'buffers_per_acquisition': {'__class__': 'qcodes.instrument_drivers.AlazarTech.ATS.AlazarParameter',\n",
-       "   'label': 'Buffers per Acquisition',\n",
-       "   'name': 'buffers_per_acquisition',\n",
-       "   'ts': '2016-11-01 16:48:58',\n",
-       "   'units': '',\n",
-       "   'value': 1},\n",
-       "  'channel_range1': {'__class__': 'qcodes.instrument_drivers.AlazarTech.ATS.AlazarParameter',\n",
-       "   'label': 'Range channel 1',\n",
-       "   'name': 'channel_range1',\n",
-       "   'ts': '2016-11-01 16:48:54',\n",
-       "   'units': 'V',\n",
-       "   'value': 2.0},\n",
-       "  'channel_range2': {'__class__': 'qcodes.instrument_drivers.AlazarTech.ATS.AlazarParameter',\n",
-       "   'label': 'Range channel 2',\n",
-       "   'name': 'channel_range2',\n",
-       "   'ts': '2016-11-01 16:48:54',\n",
-       "   'units': 'V',\n",
-       "   'value': 2.0},\n",
-       "  'channel_selection': {'__class__': 'qcodes.instrument_drivers.AlazarTech.ATS.AlazarParameter',\n",
-       "   'label': 'Channel Selection',\n",
-       "   'name': 'channel_selection',\n",
-       "   'ts': '2016-11-01 16:48:23',\n",
-       "   'units': '',\n",
-       "   'value': 'AB'},\n",
-       "  'clock_edge': {'__class__': 'qcodes.instrument_drivers.AlazarTech.ATS.AlazarParameter',\n",
-       "   'label': 'Clock Edge',\n",
-       "   'name': 'clock_edge',\n",
-       "   'ts': '2016-11-01 16:48:23',\n",
-       "   'units': '',\n",
-       "   'value': 'CLOCK_EDGE_RISING'},\n",
-       "  'clock_source': {'__class__': 'qcodes.instrument_drivers.AlazarTech.ATS.AlazarParameter',\n",
-       "   'label': 'Clock Source',\n",
-       "   'name': 'clock_source',\n",
-       "   'ts': '2016-11-01 16:48:23',\n",
-       "   'units': '',\n",
-       "   'value': 'INTERNAL_CLOCK'},\n",
-       "  'coupling1': {'__class__': 'qcodes.instrument_drivers.AlazarTech.ATS.AlazarParameter',\n",
-       "   'label': 'Coupling channel 1',\n",
-       "   'name': 'coupling1',\n",
-       "   'ts': '2016-11-01 16:48:23',\n",
-       "   'units': '',\n",
-       "   'value': 'DC'},\n",
-       "  'coupling2': {'__class__': 'qcodes.instrument_drivers.AlazarTech.ATS.AlazarParameter',\n",
-       "   'label': 'Coupling channel 2',\n",
-       "   'name': 'coupling2',\n",
-       "   'ts': '2016-11-01 16:48:23',\n",
-       "   'units': '',\n",
-       "   'value': 'DC'},\n",
-       "  'decimation': {'__class__': 'qcodes.instrument_drivers.AlazarTech.ATS.AlazarParameter',\n",
-       "   'label': 'Decimation',\n",
-       "   'name': 'decimation',\n",
-       "   'ts': '2016-11-01 16:48:23',\n",
-       "   'units': '',\n",
-       "   'value': 0},\n",
-       "  'enable_record_headers': {'__class__': 'qcodes.instrument_drivers.AlazarTech.ATS.AlazarParameter',\n",
-       "   'label': 'Enable Record Headers',\n",
-       "   'name': 'enable_record_headers',\n",
-       "   'ts': '2016-11-01 16:48:23',\n",
-       "   'units': '',\n",
-       "   'value': 'DISABLED'},\n",
-       "  'external_startcapture': {'__class__': 'qcodes.instrument_drivers.AlazarTech.ATS.AlazarParameter',\n",
-       "   'label': 'External Startcapture',\n",
-       "   'name': 'external_startcapture',\n",
-       "   'ts': '2016-11-01 16:48:23',\n",
-       "   'units': '',\n",
-       "   'value': 'ENABLED'},\n",
-       "  'external_trigger_coupling': {'__class__': 'qcodes.instrument_drivers.AlazarTech.ATS.AlazarParameter',\n",
-       "   'label': 'External Trigger Coupling',\n",
-       "   'name': 'external_trigger_coupling',\n",
-       "   'ts': '2016-11-01 16:48:23',\n",
-       "   'units': '',\n",
-       "   'value': 'AC'},\n",
-       "  'external_trigger_range': {'__class__': 'qcodes.instrument_drivers.AlazarTech.ATS.AlazarParameter',\n",
-       "   'label': 'External Trigger Range',\n",
-       "   'name': 'external_trigger_range',\n",
-       "   'ts': '2016-11-01 16:48:23',\n",
-       "   'units': '',\n",
-       "   'value': 'ETR_5V'},\n",
-       "  'fifo_only_streaming': {'__class__': 'qcodes.instrument_drivers.AlazarTech.ATS.AlazarParameter',\n",
-       "   'label': 'Fifo Only Streaming',\n",
-       "   'name': 'fifo_only_streaming',\n",
-       "   'ts': '2016-11-01 16:48:23',\n",
-       "   'units': '',\n",
-       "   'value': 'DISABLED'},\n",
-       "  'get_processed_data': {'__class__': 'qcodes.instrument_drivers.AlazarTech.ATS.AlazarParameter',\n",
-       "   'label': 'Get Processed Data',\n",
-       "   'name': 'get_processed_data',\n",
-       "   'ts': '2016-11-01 16:48:23',\n",
-       "   'units': '',\n",
-       "   'value': 'DISABLED'},\n",
-       "  'impedance1': {'__class__': 'qcodes.instrument_drivers.AlazarTech.ATS.AlazarParameter',\n",
-       "   'label': 'Impedance channel 1',\n",
-       "   'name': 'impedance1',\n",
-       "   'ts': '2016-11-01 16:48:23',\n",
-       "   'units': 'Ohm',\n",
-       "   'value': 50},\n",
-       "  'impedance2': {'__class__': 'qcodes.instrument_drivers.AlazarTech.ATS.AlazarParameter',\n",
-       "   'label': 'Impedance channel 2',\n",
-       "   'name': 'impedance2',\n",
-       "   'ts': '2016-11-01 16:48:23',\n",
-       "   'units': 'Ohm',\n",
-       "   'value': 50},\n",
-       "  'interleave_samples': {'__class__': 'qcodes.instrument_drivers.AlazarTech.ATS.AlazarParameter',\n",
-       "   'label': 'Interleave Samples',\n",
-       "   'name': 'interleave_samples',\n",
-       "   'ts': '2016-11-01 16:48:23',\n",
-       "   'units': '',\n",
-       "   'value': 'DISABLED'},\n",
-       "  'mode': {'__class__': 'qcodes.instrument_drivers.AlazarTech.ATS.AlazarParameter',\n",
-       "   'label': 'Acquisiton mode',\n",
-       "   'name': 'mode',\n",
-       "   'ts': '2016-11-01 16:48:23',\n",
-       "   'units': '',\n",
-       "   'value': 'NPT'},\n",
-       "  'records_per_buffer': {'__class__': 'qcodes.instrument_drivers.AlazarTech.ATS.AlazarParameter',\n",
-       "   'label': 'Records per Buffer',\n",
-       "   'name': 'records_per_buffer',\n",
-       "   'ts': '2016-11-01 16:48:58',\n",
-       "   'units': '',\n",
-       "   'value': 70},\n",
-       "  'sample_rate': {'__class__': 'qcodes.instrument_drivers.AlazarTech.ATS.AlazarParameter',\n",
-       "   'label': 'Sample Rate',\n",
-       "   'name': 'sample_rate',\n",
-       "   'ts': '2016-11-01 16:48:54',\n",
-       "   'units': 'S/s',\n",
-       "   'value': 100000000},\n",
-       "  'samples_per_record': {'__class__': 'qcodes.instrument_drivers.AlazarTech.ATS.AlazarParameter',\n",
-       "   'label': 'Samples per Record',\n",
-       "   'name': 'samples_per_record',\n",
-       "   'ts': '2016-11-01 16:48:58',\n",
-       "   'units': '',\n",
-       "   'value': 1024},\n",
-       "  'timeout_ticks': {'__class__': 'qcodes.instrument_drivers.AlazarTech.ATS.AlazarParameter',\n",
-       "   'label': 'Timeout Ticks',\n",
-       "   'name': 'timeout_ticks',\n",
-       "   'ts': '2016-11-01 16:48:23',\n",
-       "   'units': '10 us',\n",
-       "   'value': 0},\n",
-       "  'transfer_offset': {'__class__': 'qcodes.instrument_drivers.AlazarTech.ATS.AlazarParameter',\n",
-       "   'label': 'Transer Offset',\n",
-       "   'name': 'transfer_offset',\n",
-       "   'ts': '2016-11-01 16:48:23',\n",
-       "   'units': 'Samples',\n",
-       "   'value': 0},\n",
-       "  'trigger_delay': {'__class__': 'qcodes.instrument_drivers.AlazarTech.ATS.AlazarParameter',\n",
-       "   'label': 'Trigger Delay',\n",
-       "   'name': 'trigger_delay',\n",
-       "   'ts': '2016-11-01 16:48:23',\n",
-       "   'units': 'Sample clock cycles',\n",
-       "   'value': 0},\n",
-       "  'trigger_engine1': {'__class__': 'qcodes.instrument_drivers.AlazarTech.ATS.AlazarParameter',\n",
-       "   'label': 'Trigger Engine 1',\n",
-       "   'name': 'trigger_engine1',\n",
-       "   'ts': '2016-11-01 16:48:23',\n",
-       "   'units': '',\n",
-       "   'value': 'TRIG_ENGINE_K'},\n",
-       "  'trigger_engine2': {'__class__': 'qcodes.instrument_drivers.AlazarTech.ATS.AlazarParameter',\n",
-       "   'label': 'Trigger Engine 2',\n",
-       "   'name': 'trigger_engine2',\n",
-       "   'ts': '2016-11-01 16:48:23',\n",
-       "   'units': '',\n",
-       "   'value': 'TRIG_ENGINE_K'},\n",
-       "  'trigger_level1': {'__class__': 'qcodes.instrument_drivers.AlazarTech.ATS.AlazarParameter',\n",
-       "   'label': 'Trigger Level 1',\n",
-       "   'name': 'trigger_level1',\n",
-       "   'ts': '2016-11-01 16:48:23',\n",
-       "   'units': '',\n",
-       "   'value': 150},\n",
-       "  'trigger_level2': {'__class__': 'qcodes.instrument_drivers.AlazarTech.ATS.AlazarParameter',\n",
-       "   'label': 'Trigger Level 2',\n",
-       "   'name': 'trigger_level2',\n",
-       "   'ts': '2016-11-01 16:48:23',\n",
-       "   'units': '',\n",
-       "   'value': 150},\n",
-       "  'trigger_operation': {'__class__': 'qcodes.instrument_drivers.AlazarTech.ATS.AlazarParameter',\n",
-       "   'label': 'Trigger Operation',\n",
-       "   'name': 'trigger_operation',\n",
-       "   'ts': '2016-11-01 16:48:23',\n",
-       "   'units': '',\n",
-       "   'value': 'TRIG_ENGINE_OP_J'},\n",
-       "  'trigger_slope1': {'__class__': 'qcodes.instrument_drivers.AlazarTech.ATS.AlazarParameter',\n",
-       "   'label': 'Trigger Slope 1',\n",
-       "   'name': 'trigger_slope1',\n",
-       "   'ts': '2016-11-01 16:48:23',\n",
-       "   'units': '',\n",
-       "   'value': 'TRIG_SLOPE_POSITIVE'},\n",
-       "  'trigger_slope2': {'__class__': 'qcodes.instrument_drivers.AlazarTech.ATS.AlazarParameter',\n",
-       "   'label': 'Trigger Slope 2',\n",
-       "   'name': 'trigger_slope2',\n",
-       "   'ts': '2016-11-01 16:48:23',\n",
-       "   'units': '',\n",
-       "   'value': 'TRIG_SLOPE_POSITIVE'},\n",
-       "  'trigger_source1': {'__class__': 'qcodes.instrument_drivers.AlazarTech.ATS.AlazarParameter',\n",
-       "   'label': 'Trigger Source 1',\n",
-       "   'name': 'trigger_source1',\n",
-       "   'ts': '2016-11-01 16:48:54',\n",
-       "   'units': '',\n",
-       "   'value': 'EXTERNAL'},\n",
-       "  'trigger_source2': {'__class__': 'qcodes.instrument_drivers.AlazarTech.ATS.AlazarParameter',\n",
-       "   'label': 'Trigger Source 2',\n",
-       "   'name': 'trigger_source2',\n",
-       "   'ts': '2016-11-01 16:48:23',\n",
-       "   'units': '',\n",
-       "   'value': 'DISABLE'}}}"
-      ]
-     },
-     "execution_count": 13,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "# make a snapshot of the 'ats_inst' instrument\n",
-    "ats_inst.snapshot()"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {
-    "collapsed": true
-   },
-   "outputs": [],
-   "source": []
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 14,
-   "metadata": {
-    "collapsed": false
-   },
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "False\n",
-      "None\n",
-      "started at 2016-11-01 16:49:46\n"
-     ]
-    },
-    {
-     "data": {
-      "application/javascript": [
-       "/* Put everything inside the global mpl namespace */\n",
-       "window.mpl = {};\n",
-       "\n",
-       "mpl.get_websocket_type = function() {\n",
-       "    if (typeof(WebSocket) !== 'undefined') {\n",
-       "        return WebSocket;\n",
-       "    } else if (typeof(MozWebSocket) !== 'undefined') {\n",
-       "        return MozWebSocket;\n",
-       "    } else {\n",
-       "        alert('Your browser does not have WebSocket support.' +\n",
-       "              'Please try Chrome, Safari or Firefox ≥ 6. ' +\n",
-       "              'Firefox 4 and 5 are also supported but you ' +\n",
-       "              'have to enable WebSockets in about:config.');\n",
-       "    };\n",
-       "}\n",
-       "\n",
-       "mpl.figure = function(figure_id, websocket, ondownload, parent_element) {\n",
-       "    this.id = figure_id;\n",
-       "\n",
-       "    this.ws = websocket;\n",
-       "\n",
-       "    this.supports_binary = (this.ws.binaryType != undefined);\n",
-       "\n",
-       "    if (!this.supports_binary) {\n",
-       "        var warnings = document.getElementById(\"mpl-warnings\");\n",
-       "        if (warnings) {\n",
-       "            warnings.style.display = 'block';\n",
-       "            warnings.textContent = (\n",
-       "                \"This browser does not support binary websocket messages. \" +\n",
-       "                    \"Performance may be slow.\");\n",
-       "        }\n",
-       "    }\n",
-       "\n",
-       "    this.imageObj = new Image();\n",
-       "\n",
-       "    this.context = undefined;\n",
-       "    this.message = undefined;\n",
-       "    this.canvas = undefined;\n",
-       "    this.rubberband_canvas = undefined;\n",
-       "    this.rubberband_context = undefined;\n",
-       "    this.format_dropdown = undefined;\n",
-       "\n",
-       "    this.image_mode = 'full';\n",
-       "\n",
-       "    this.root = $('
');\n",
-       "    this._root_extra_style(this.root)\n",
-       "    this.root.attr('style', 'display: inline-block');\n",
-       "\n",
-       "    $(parent_element).append(this.root);\n",
-       "\n",
-       "    this._init_header(this);\n",
-       "    this._init_canvas(this);\n",
-       "    this._init_toolbar(this);\n",
-       "\n",
-       "    var fig = this;\n",
-       "\n",
-       "    this.waiting = false;\n",
-       "\n",
-       "    this.ws.onopen =  function () {\n",
-       "            fig.send_message(\"supports_binary\", {value: fig.supports_binary});\n",
-       "            fig.send_message(\"send_image_mode\", {});\n",
-       "            fig.send_message(\"refresh\", {});\n",
-       "        }\n",
-       "\n",
-       "    this.imageObj.onload = function() {\n",
-       "            if (fig.image_mode == 'full') {\n",
-       "                // Full images could contain transparency (where diff images\n",
-       "                // almost always do), so we need to clear the canvas so that\n",
-       "                // there is no ghosting.\n",
-       "                fig.context.clearRect(0, 0, fig.canvas.width, fig.canvas.height);\n",
-       "            }\n",
-       "            fig.context.drawImage(fig.imageObj, 0, 0);\n",
-       "        };\n",
-       "\n",
-       "    this.imageObj.onunload = function() {\n",
-       "        this.ws.close();\n",
-       "    }\n",
-       "\n",
-       "    this.ws.onmessage = this._make_on_message_function(this);\n",
-       "\n",
-       "    this.ondownload = ondownload;\n",
-       "}\n",
-       "\n",
-       "mpl.figure.prototype._init_header = function() {\n",
-       "    var titlebar = $(\n",
-       "        '
');\n",
-       "    var titletext = $(\n",
-       "        '
');\n",
-       "    titlebar.append(titletext)\n",
-       "    this.root.append(titlebar);\n",
-       "    this.header = titletext[0];\n",
-       "}\n",
-       "\n",
-       "\n",
-       "\n",
-       "mpl.figure.prototype._canvas_extra_style = function(canvas_div) {\n",
-       "\n",
-       "}\n",
-       "\n",
-       "\n",
-       "mpl.figure.prototype._root_extra_style = function(canvas_div) {\n",
-       "\n",
-       "}\n",
-       "\n",
-       "mpl.figure.prototype._init_canvas = function() {\n",
-       "    var fig = this;\n",
-       "\n",
-       "    var canvas_div = $('
');\n",
-       "\n",
-       "    canvas_div.attr('style', 'position: relative; clear: both; outline: 0');\n",
-       "\n",
-       "    function canvas_keyboard_event(event) {\n",
-       "        return fig.key_event(event, event['data']);\n",
-       "    }\n",
-       "\n",
-       "    canvas_div.keydown('key_press', canvas_keyboard_event);\n",
-       "    canvas_div.keyup('key_release', canvas_keyboard_event);\n",
-       "    this.canvas_div = canvas_div\n",
-       "    this._canvas_extra_style(canvas_div)\n",
-       "    this.root.append(canvas_div);\n",
-       "\n",
-       "    var canvas = $('');\n",
-       "    canvas.addClass('mpl-canvas');\n",
-       "    canvas.attr('style', \"left: 0; top: 0; z-index: 0; outline: 0\")\n",
-       "\n",
-       "    this.canvas = canvas[0];\n",
-       "    this.context = canvas[0].getContext(\"2d\");\n",
-       "\n",
-       "    var rubberband = $('');\n",
-       "    rubberband.attr('style', \"position: absolute; left: 0; top: 0; z-index: 1;\")\n",
-       "\n",
-       "    var pass_mouse_events = true;\n",
-       "\n",
-       "    canvas_div.resizable({\n",
-       "        start: function(event, ui) {\n",
-       "            pass_mouse_events = false;\n",
-       "        },\n",
-       "        resize: function(event, ui) {\n",
-       "            fig.request_resize(ui.size.width, ui.size.height);\n",
-       "        },\n",
-       "        stop: function(event, ui) {\n",
-       "            pass_mouse_events = true;\n",
-       "            fig.request_resize(ui.size.width, ui.size.height);\n",
-       "        },\n",
-       "    });\n",
-       "\n",
-       "    function mouse_event_fn(event) {\n",
-       "        if (pass_mouse_events)\n",
-       "            return fig.mouse_event(event, event['data']);\n",
-       "    }\n",
-       "\n",
-       "    rubberband.mousedown('button_press', mouse_event_fn);\n",
-       "    rubberband.mouseup('button_release', mouse_event_fn);\n",
-       "    // Throttle sequential mouse events to 1 every 20ms.\n",
-       "    rubberband.mousemove('motion_notify', mouse_event_fn);\n",
-       "\n",
-       "    rubberband.mouseenter('figure_enter', mouse_event_fn);\n",
-       "    rubberband.mouseleave('figure_leave', mouse_event_fn);\n",
-       "\n",
-       "    canvas_div.on(\"wheel\", function (event) {\n",
-       "        event = event.originalEvent;\n",
-       "        event['data'] = 'scroll'\n",
-       "        if (event.deltaY < 0) {\n",
-       "            event.step = 1;\n",
-       "        } else {\n",
-       "            event.step = -1;\n",
-       "        }\n",
-       "        mouse_event_fn(event);\n",
-       "    });\n",
-       "\n",
-       "    canvas_div.append(canvas);\n",
-       "    canvas_div.append(rubberband);\n",
-       "\n",
-       "    this.rubberband = rubberband;\n",
-       "    this.rubberband_canvas = rubberband[0];\n",
-       "    this.rubberband_context = rubberband[0].getContext(\"2d\");\n",
-       "    this.rubberband_context.strokeStyle = \"#000000\";\n",
-       "\n",
-       "    this._resize_canvas = function(width, height) {\n",
-       "        // Keep the size of the canvas, canvas container, and rubber band\n",
-       "        // canvas in synch.\n",
-       "        canvas_div.css('width', width)\n",
-       "        canvas_div.css('height', height)\n",
-       "\n",
-       "        canvas.attr('width', width);\n",
-       "        canvas.attr('height', height);\n",
-       "\n",
-       "        rubberband.attr('width', width);\n",
-       "        rubberband.attr('height', height);\n",
-       "    }\n",
-       "\n",
-       "    // Set the figure to an initial 600x600px, this will subsequently be updated\n",
-       "    // upon first draw.\n",
-       "    this._resize_canvas(600, 600);\n",
-       "\n",
-       "    // Disable right mouse context menu.\n",
-       "    $(this.rubberband_canvas).bind(\"contextmenu\",function(e){\n",
-       "        return false;\n",
-       "    });\n",
-       "\n",
-       "    function set_focus () {\n",
-       "        canvas.focus();\n",
-       "        canvas_div.focus();\n",
-       "    }\n",
-       "\n",
-       "    window.setTimeout(set_focus, 100);\n",
-       "}\n",
-       "\n",
-       "mpl.figure.prototype._init_toolbar = function() {\n",
-       "    var fig = this;\n",
-       "\n",
-       "    var nav_element = $('
')\n",
-       "    nav_element.attr('style', 'width: 100%');\n",
-       "    this.root.append(nav_element);\n",
-       "\n",
-       "    // Define a callback function for later on.\n",
-       "    function toolbar_event(event) {\n",
-       "        return fig.toolbar_button_onclick(event['data']);\n",
-       "    }\n",
-       "    function toolbar_mouse_event(event) {\n",
-       "        return fig.toolbar_button_onmouseover(event['data']);\n",
-       "    }\n",
-       "\n",
-       "    for(var toolbar_ind in mpl.toolbar_items) {\n",
-       "        var name = mpl.toolbar_items[toolbar_ind][0];\n",
-       "        var tooltip = mpl.toolbar_items[toolbar_ind][1];\n",
-       "        var image = mpl.toolbar_items[toolbar_ind][2];\n",
-       "        var method_name = mpl.toolbar_items[toolbar_ind][3];\n",
-       "\n",
-       "        if (!name) {\n",
-       "            // put a spacer in here.\n",
-       "            continue;\n",
-       "        }\n",
-       "        var button = $('');\n",
-       "        button.click(method_name, toolbar_event);\n",
-       "        button.mouseover(tooltip, toolbar_mouse_event);\n",
-       "        nav_element.append(button);\n",
-       "    }\n",
-       "\n",
-       "    // Add the status bar.\n",
-       "    var status_bar = $('');\n",
-       "    nav_element.append(status_bar);\n",
-       "    this.message = status_bar[0];\n",
-       "\n",
-       "    // Add the close button to the window.\n",
-       "    var buttongrp = $('
');\n",
-       "    var button = $('');\n",
-       "    button.click(function (evt) { fig.handle_close(fig, {}); } );\n",
-       "    button.mouseover('Stop Interaction', toolbar_mouse_event);\n",
-       "    buttongrp.append(button);\n",
-       "    var titlebar = this.root.find($('.ui-dialog-titlebar'));\n",
-       "    titlebar.prepend(buttongrp);\n",
-       "}\n",
-       "\n",
-       "mpl.figure.prototype._root_extra_style = function(el){\n",
-       "    var fig = this\n",
-       "    el.on(\"remove\", function(){\n",
-       "\tfig.close_ws(fig, {});\n",
-       "    });\n",
-       "}\n",
-       "\n",
-       "mpl.figure.prototype._canvas_extra_style = function(el){\n",
-       "    // this is important to make the div 'focusable\n",
-       "    el.attr('tabindex', 0)\n",
-       "    // reach out to IPython and tell the keyboard manager to turn it's self\n",
-       "    // off when our div gets focus\n",
-       "\n",
-       "    // location in version 3\n",
-       "    if (IPython.notebook.keyboard_manager) {\n",
-       "        IPython.notebook.keyboard_manager.register_events(el);\n",
-       "    }\n",
-       "    else {\n",
-       "        // location in version 2\n",
-       "        IPython.keyboard_manager.register_events(el);\n",
-       "    }\n",
-       "\n",
-       "}\n",
-       "\n",
-       "mpl.figure.prototype._key_event_extra = function(event, name) {\n",
-       "    var manager = IPython.notebook.keyboard_manager;\n",
-       "    if (!manager)\n",
-       "        manager = IPython.keyboard_manager;\n",
-       "\n",
-       "    // Check for shift+enter\n",
-       "    if (event.shiftKey && event.which == 13) {\n",
-       "        this.canvas_div.blur();\n",
-       "        event.shiftKey = false;\n",
-       "        // Send a \"J\" for go to next cell\n",
-       "        event.which = 74;\n",
-       "        event.keyCode = 74;\n",
-       "        manager.command_mode();\n",
-       "        manager.handle_keydown(event);\n",
-       "    }\n",
-       "}\n",
-       "\n",
-       "mpl.figure.prototype.handle_save = function(fig, msg) {\n",
-       "    fig.ondownload(fig, null);\n",
-       "}\n",
-       "\n",
-       "\n",
-       "mpl.find_output_cell = function(html_output) {\n",
-       "    // Return the cell and output element which can be found *uniquely* in the notebook.\n",
-       "    // Note - this is a bit hacky, but it is done because the \"notebook_saving.Notebook\"\n",
-       "    // IPython event is triggered only after the cells have been serialised, which for\n",
-       "    // our purposes (turning an active figure into a static one), is too late.\n",
-       "    var cells = IPython.notebook.get_cells();\n",
-       "    var ncells = cells.length;\n",
-       "    for (var i=0; i= 3 moved mimebundle to data attribute of output\n",
-       "                    data = data.data;\n",
-       "                }\n",
-       "                if (data['text/html'] == html_output) {\n",
-       "                    return [cell, data, j];\n",
-       "                }\n",
-       "            }\n",
-       "        }\n",
-       "    }\n",
-       "}\n",
-       "\n",
-       "// Register the function which deals with the matplotlib target/channel.\n",
-       "// The kernel may be null if the page has been refreshed.\n",
-       "if (IPython.notebook.kernel != null) {\n",
-       "    IPython.notebook.kernel.comm_manager.register_target('matplotlib', mpl.mpl_figure_comm);\n",
-       "}\n"
-      ],
-      "text/plain": [
-       ""
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    },
-    {
-     "data": {
-      "text/html": [
-       ""
-      ],
-      "text/plain": [
-       ""
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    }
-   ],
-   "source": [
-    "plt.figure()\n",
-    "plt.plot(data2[0])\n",
-    "plt.show()"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 25,
-   "metadata": {
-    "collapsed": false
-   },
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "DataSet:\n",
-      "   mode     = DataMode.LOCAL\n",
-      "   location = '2016-11-02/14-12-03_AlazarTest'\n",
-      "      |                                |  | \n",
-      "   Setpoint | LO_frequency_set                         | frequency    | (5,)\n",
-      "   Measured | basic_acquisition_controller_acquisition | acquisition  | (5,)\n",
-      "started at 2016-11-02 14:12:03\n"
-     ]
-    },
-    {
-     "ename": "ValueError",
-     "evalue": "setting an array element with a sequence.",
-     "output_type": "error",
-     "traceback": [
-      "\u001b[1;31m---------------------------------------------------------------------------\u001b[0m",
-      "\u001b[1;31mValueError\u001b[0m                                Traceback (most recent call last)",
-      "\u001b[1;32m\u001b[0m in \u001b[0;36m\u001b[1;34m()\u001b[0m\n\u001b[0;32m      1\u001b[0m data = qc.Loop(localos.frequency.sweep(1e6, 5e6, 1e6)).each(\n\u001b[1;32m----> 2\u001b[1;33m     basic_acquisition_controller.acquisition).run(name='AlazarTest')\n\u001b[0m",
-      "\u001b[1;32ma:\\qcodes\\qcodes\\loops.py\u001b[0m in \u001b[0;36mrun\u001b[1;34m(self, background, use_threads, quiet, data_manager, station, progress_interval, *args, **kwargs)\u001b[0m\n\u001b[0;32m    816\u001b[0m                     \u001b[1;32mdel\u001b[0m \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mprocess\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    817\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m--> 818\u001b[1;33m                 \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0m_run_wrapper\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m    819\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    820\u001b[0m                 \u001b[1;32mif\u001b[0m \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mdata_set\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mmode\u001b[0m \u001b[1;33m!=\u001b[0m \u001b[0mDataMode\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mLOCAL\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
-      "\u001b[1;32ma:\\qcodes\\qcodes\\loops.py\u001b[0m in \u001b[0;36m_run_wrapper\u001b[1;34m(self, *args, **kwargs)\u001b[0m\n\u001b[0;32m    869\u001b[0m     \u001b[1;32mdef\u001b[0m \u001b[0m_run_wrapper\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mself\u001b[0m\u001b[1;33m,\u001b[0m \u001b[1;33m*\u001b[0m\u001b[0margs\u001b[0m\u001b[1;33m,\u001b[0m \u001b[1;33m**\u001b[0m\u001b[0mkwargs\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    870\u001b[0m         \u001b[1;32mtry\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m--> 871\u001b[1;33m             \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0m_run_loop\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;33m*\u001b[0m\u001b[0margs\u001b[0m\u001b[1;33m,\u001b[0m \u001b[1;33m**\u001b[0m\u001b[0mkwargs\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m    872\u001b[0m         \u001b[1;32mexcept\u001b[0m \u001b[0m_QuietInterrupt\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    873\u001b[0m             \u001b[1;32mpass\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
-      "\u001b[1;32ma:\\qcodes\\qcodes\\loops.py\u001b[0m in \u001b[0;36m_run_loop\u001b[1;34m(self, first_delay, action_indices, loop_indices, current_values, **ignore_kwargs)\u001b[0m\n\u001b[0;32m    940\u001b[0m                     f(first_delay=delay,\n\u001b[0;32m    941\u001b[0m                       \u001b[0mloop_indices\u001b[0m\u001b[1;33m=\u001b[0m\u001b[0mnew_indices\u001b[0m\u001b[1;33m,\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m--> 942\u001b[1;33m                       current_values=new_values)\n\u001b[0m\u001b[0;32m    943\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    944\u001b[0m                     \u001b[1;31m# after the first action, no delay is inherited\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
-      "\u001b[1;32ma:\\qcodes\\qcodes\\actions.py\u001b[0m in \u001b[0;36m__call__\u001b[1;34m(self, loop_indices, **ignore_kwargs)\u001b[0m\n\u001b[0;32m    144\u001b[0m                 \u001b[0mout_dict\u001b[0m\u001b[1;33m[\u001b[0m\u001b[0mparam_id\u001b[0m\u001b[1;33m]\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mparam_out\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    145\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m--> 146\u001b[1;33m         \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mstore\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mloop_indices\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mout_dict\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m    147\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    148\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n",
-      "\u001b[1;32ma:\\qcodes\\qcodes\\data\\data_set.py\u001b[0m in \u001b[0;36mstore\u001b[1;34m(self, loop_indices, ids_values)\u001b[0m\n\u001b[0;32m    605\u001b[0m             \u001b[1;31m# on the server (if you hit the if statement above) or else here\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    606\u001b[0m             \u001b[1;32mfor\u001b[0m \u001b[0marray_id\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mvalue\u001b[0m \u001b[1;32min\u001b[0m \u001b[0mids_values\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mitems\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m--> 607\u001b[1;33m                 \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0marrays\u001b[0m\u001b[1;33m[\u001b[0m\u001b[0marray_id\u001b[0m\u001b[1;33m]\u001b[0m\u001b[1;33m[\u001b[0m\u001b[0mloop_indices\u001b[0m\u001b[1;33m]\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mvalue\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m    608\u001b[0m             \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mlast_store\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mtime\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mtime\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    609\u001b[0m             if (self.write_period is not None and\n",
-      "\u001b[1;32ma:\\qcodes\\qcodes\\data\\data_array.py\u001b[0m in \u001b[0;36m__setitem__\u001b[1;34m(self, loop_indices, value)\u001b[0m\n\u001b[0;32m    333\u001b[0m         \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0m_update_modified_range\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mmin_li\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mmax_li\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    334\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m--> 335\u001b[1;33m         \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mndarray\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0m__setitem__\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mloop_indices\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mvalue\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m    336\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    337\u001b[0m     \u001b[1;32mdef\u001b[0m \u001b[0m__getitem__\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mself\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mloop_indices\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
-      "\u001b[1;31mValueError\u001b[0m: setting an array element with a sequence."
-     ]
-    }
-   ],
-   "source": [
-    "data = qc.Loop(localos.frequency.sweep(1e6, 5e6, 1e6)).each(\n",
-    "    basic_acquisition_controller.acquisition).run(name='AlazarTest')"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 10,
-   "metadata": {
-    "collapsed": false
-   },
-   "outputs": [],
-   "source": [
-    "# Configure all settings in the Alazar card\n",
-    "ats_inst.config(clock_source='EXTERNAL_CLOCK_10MHz_REF',\n",
-    "                sample_rate='10MHZ_REF_500MSPS',\n",
-    "                clock_edge='CLOCK_EDGE_RISING',\n",
-    "                decimation=1,\n",
-    "                coupling=['DC','DC'],\n",
-    "                channel_range=[.4,.4],\n",
-    "                #impedance=[50,50],\n",
-    "                #trigger_operation='TRIG_ENGINE_OP_J',\n",
-    "                #trigger_engine1='TRIG_ENGINE_J',\n",
-    "                #trigger_source1='EXTERNAL',\n",
-    "                #trigger_slope1='TRIG_SLOPE_POSITIVE',\n",
-    "                trigger_level1=140,\n",
-    "                #trigger_engine2='TRIG_ENGINE_K',\n",
-    "                #trigger_source2='DISABLE',\n",
-    "                #trigger_slope2='TRIG_SLOPE_POSITIVE',\n",
-    "                #trigger_level2=128,\n",
-    "                external_trigger_coupling='DC',\n",
-    "                external_trigger_range='ETR_2V5',\n",
-    "                #trigger_delay=0,\n",
-    "                #timeout_ticks=0\n",
-    ")\n"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 9,
-   "metadata": {
-    "collapsed": false
-   },
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "'10MHZ_REF_500MSPS'"
-      ]
-     },
-     "execution_count": 9,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "ats_inst.sample_rate()"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 10,
-   "metadata": {
-    "collapsed": true
-   },
-   "outputs": [],
-   "source": [
-    "data3 = basic_acquisition_controller.acquisition()"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 16,
-   "metadata": {
-    "collapsed": false
-   },
-   "outputs": [
-    {
-     "data": {
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-       "        return MozWebSocket;\n",
-       "    } else {\n",
-       "        alert('Your browser does not have WebSocket support.' +\n",
-       "              'Please try Chrome, Safari or Firefox ≥ 6. ' +\n",
-       "              'Firefox 4 and 5 are also supported but you ' +\n",
-       "              'have to enable WebSockets in about:config.');\n",
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-       "\n",
-       "mpl.figure = function(figure_id, websocket, ondownload, parent_element) {\n",
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-       "\n",
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-       "            warnings.style.display = 'block';\n",
-       "            warnings.textContent = (\n",
-       "                \"This browser does not support binary websocket messages. \" +\n",
-       "                    \"Performance may be slow.\");\n",
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-       "\n",
-       "    this.imageObj = new Image();\n",
-       "\n",
-       "    this.context = undefined;\n",
-       "    this.message = undefined;\n",
-       "    this.canvas = undefined;\n",
-       "    this.rubberband_canvas = undefined;\n",
-       "    this.rubberband_context = undefined;\n",
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-       "\n",
-       "    this.image_mode = 'full';\n",
-       "\n",
-       "    this.root = $('
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-       "    $(parent_element).append(this.root);\n",
-       "\n",
-       "    this._init_header(this);\n",
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-       "\n",
-       "    var fig = this;\n",
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-       "    this.ws.onopen =  function () {\n",
-       "            fig.send_message(\"supports_binary\", {value: fig.supports_binary});\n",
-       "            fig.send_message(\"send_image_mode\", {});\n",
-       "            fig.send_message(\"refresh\", {});\n",
-       "        }\n",
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-       "                // Full images could contain transparency (where diff images\n",
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-       "                fig.context.clearRect(0, 0, fig.canvas.width, fig.canvas.height);\n",
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-       "mpl.figure.prototype._canvas_extra_style = function(canvas_div) {\n",
-       "\n",
-       "}\n",
-       "\n",
-       "\n",
-       "mpl.figure.prototype._root_extra_style = function(canvas_div) {\n",
-       "\n",
-       "}\n",
-       "\n",
-       "mpl.figure.prototype._init_canvas = function() {\n",
-       "    var fig = this;\n",
-       "\n",
-       "    var canvas_div = $('
');\n",
-       "\n",
-       "    canvas_div.attr('style', 'position: relative; clear: both; outline: 0');\n",
-       "\n",
-       "    function canvas_keyboard_event(event) {\n",
-       "        return fig.key_event(event, event['data']);\n",
-       "    }\n",
-       "\n",
-       "    canvas_div.keydown('key_press', canvas_keyboard_event);\n",
-       "    canvas_div.keyup('key_release', canvas_keyboard_event);\n",
-       "    this.canvas_div = canvas_div\n",
-       "    this._canvas_extra_style(canvas_div)\n",
-       "    this.root.append(canvas_div);\n",
-       "\n",
-       "    var canvas = $('');\n",
-       "    canvas.addClass('mpl-canvas');\n",
-       "    canvas.attr('style', \"left: 0; top: 0; z-index: 0; outline: 0\")\n",
-       "\n",
-       "    this.canvas = canvas[0];\n",
-       "    this.context = canvas[0].getContext(\"2d\");\n",
-       "\n",
-       "    var rubberband = $('');\n",
-       "    rubberband.attr('style', \"position: absolute; left: 0; top: 0; z-index: 1;\")\n",
-       "\n",
-       "    var pass_mouse_events = true;\n",
-       "\n",
-       "    canvas_div.resizable({\n",
-       "        start: function(event, ui) {\n",
-       "            pass_mouse_events = false;\n",
-       "        },\n",
-       "        resize: function(event, ui) {\n",
-       "            fig.request_resize(ui.size.width, ui.size.height);\n",
-       "        },\n",
-       "        stop: function(event, ui) {\n",
-       "            pass_mouse_events = true;\n",
-       "            fig.request_resize(ui.size.width, ui.size.height);\n",
-       "        },\n",
-       "    });\n",
-       "\n",
-       "    function mouse_event_fn(event) {\n",
-       "        if (pass_mouse_events)\n",
-       "            return fig.mouse_event(event, event['data']);\n",
-       "    }\n",
-       "\n",
-       "    rubberband.mousedown('button_press', mouse_event_fn);\n",
-       "    rubberband.mouseup('button_release', mouse_event_fn);\n",
-       "    // Throttle sequential mouse events to 1 every 20ms.\n",
-       "    rubberband.mousemove('motion_notify', mouse_event_fn);\n",
-       "\n",
-       "    rubberband.mouseenter('figure_enter', mouse_event_fn);\n",
-       "    rubberband.mouseleave('figure_leave', mouse_event_fn);\n",
-       "\n",
-       "    canvas_div.on(\"wheel\", function (event) {\n",
-       "        event = event.originalEvent;\n",
-       "        event['data'] = 'scroll'\n",
-       "        if (event.deltaY < 0) {\n",
-       "            event.step = 1;\n",
-       "        } else {\n",
-       "            event.step = -1;\n",
-       "        }\n",
-       "        mouse_event_fn(event);\n",
-       "    });\n",
-       "\n",
-       "    canvas_div.append(canvas);\n",
-       "    canvas_div.append(rubberband);\n",
-       "\n",
-       "    this.rubberband = rubberband;\n",
-       "    this.rubberband_canvas = rubberband[0];\n",
-       "    this.rubberband_context = rubberband[0].getContext(\"2d\");\n",
-       "    this.rubberband_context.strokeStyle = \"#000000\";\n",
-       "\n",
-       "    this._resize_canvas = function(width, height) {\n",
-       "        // Keep the size of the canvas, canvas container, and rubber band\n",
-       "        // canvas in synch.\n",
-       "        canvas_div.css('width', width)\n",
-       "        canvas_div.css('height', height)\n",
-       "\n",
-       "        canvas.attr('width', width);\n",
-       "        canvas.attr('height', height);\n",
-       "\n",
-       "        rubberband.attr('width', width);\n",
-       "        rubberband.attr('height', height);\n",
-       "    }\n",
-       "\n",
-       "    // Set the figure to an initial 600x600px, this will subsequently be updated\n",
-       "    // upon first draw.\n",
-       "    this._resize_canvas(600, 600);\n",
-       "\n",
-       "    // Disable right mouse context menu.\n",
-       "    $(this.rubberband_canvas).bind(\"contextmenu\",function(e){\n",
-       "        return false;\n",
-       "    });\n",
-       "\n",
-       "    function set_focus () {\n",
-       "        canvas.focus();\n",
-       "        canvas_div.focus();\n",
-       "    }\n",
-       "\n",
-       "    window.setTimeout(set_focus, 100);\n",
-       "}\n",
-       "\n",
-       "mpl.figure.prototype._init_toolbar = function() {\n",
-       "    var fig = this;\n",
-       "\n",
-       "    var nav_element = $('
')\n",
-       "    nav_element.attr('style', 'width: 100%');\n",
-       "    this.root.append(nav_element);\n",
-       "\n",
-       "    // Define a callback function for later on.\n",
-       "    function toolbar_event(event) {\n",
-       "        return fig.toolbar_button_onclick(event['data']);\n",
-       "    }\n",
-       "    function toolbar_mouse_event(event) {\n",
-       "        return fig.toolbar_button_onmouseover(event['data']);\n",
-       "    }\n",
-       "\n",
-       "    for(var toolbar_ind in mpl.toolbar_items) {\n",
-       "        var name = mpl.toolbar_items[toolbar_ind][0];\n",
-       "        var tooltip = mpl.toolbar_items[toolbar_ind][1];\n",
-       "        var image = mpl.toolbar_items[toolbar_ind][2];\n",
-       "        var method_name = mpl.toolbar_items[toolbar_ind][3];\n",
-       "\n",
-       "        if (!name) {\n",
-       "            // put a spacer in here.\n",
-       "            continue;\n",
-       "        }\n",
-       "        var button = $('');\n",
-       "        button.click(method_name, toolbar_event);\n",
-       "        button.mouseover(tooltip, toolbar_mouse_event);\n",
-       "        nav_element.append(button);\n",
-       "    }\n",
-       "\n",
-       "    // Add the status bar.\n",
-       "    var status_bar = $('');\n",
-       "    nav_element.append(status_bar);\n",
-       "    this.message = status_bar[0];\n",
-       "\n",
-       "    // Add the close button to the window.\n",
-       "    var buttongrp = $('
');\n",
-       "    var button = $('');\n",
-       "    button.click(function (evt) { fig.handle_close(fig, {}); } );\n",
-       "    button.mouseover('Stop Interaction', toolbar_mouse_event);\n",
-       "    buttongrp.append(button);\n",
-       "    var titlebar = this.root.find($('.ui-dialog-titlebar'));\n",
-       "    titlebar.prepend(buttongrp);\n",
-       "}\n",
-       "\n",
-       "mpl.figure.prototype._root_extra_style = function(el){\n",
-       "    var fig = this\n",
-       "    el.on(\"remove\", function(){\n",
-       "\tfig.close_ws(fig, {});\n",
-       "    });\n",
-       "}\n",
-       "\n",
-       "mpl.figure.prototype._canvas_extra_style = function(el){\n",
-       "    // this is important to make the div 'focusable\n",
-       "    el.attr('tabindex', 0)\n",
-       "    // reach out to IPython and tell the keyboard manager to turn it's self\n",
-       "    // off when our div gets focus\n",
-       "\n",
-       "    // location in version 3\n",
-       "    if (IPython.notebook.keyboard_manager) {\n",
-       "        IPython.notebook.keyboard_manager.register_events(el);\n",
-       "    }\n",
-       "    else {\n",
-       "        // location in version 2\n",
-       "        IPython.keyboard_manager.register_events(el);\n",
-       "    }\n",
-       "\n",
-       "}\n",
-       "\n",
-       "mpl.figure.prototype._key_event_extra = function(event, name) {\n",
-       "    var manager = IPython.notebook.keyboard_manager;\n",
-       "    if (!manager)\n",
-       "        manager = IPython.keyboard_manager;\n",
-       "\n",
-       "    // Check for shift+enter\n",
-       "    if (event.shiftKey && event.which == 13) {\n",
-       "        this.canvas_div.blur();\n",
-       "        event.shiftKey = false;\n",
-       "        // Send a \"J\" for go to next cell\n",
-       "        event.which = 74;\n",
-       "        event.keyCode = 74;\n",
-       "        manager.command_mode();\n",
-       "        manager.handle_keydown(event);\n",
-       "    }\n",
-       "}\n",
-       "\n",
-       "mpl.figure.prototype.handle_save = function(fig, msg) {\n",
-       "    fig.ondownload(fig, null);\n",
-       "}\n",
-       "\n",
-       "\n",
-       "mpl.find_output_cell = function(html_output) {\n",
-       "    // Return the cell and output element which can be found *uniquely* in the notebook.\n",
-       "    // Note - this is a bit hacky, but it is done because the \"notebook_saving.Notebook\"\n",
-       "    // IPython event is triggered only after the cells have been serialised, which for\n",
-       "    // our purposes (turning an active figure into a static one), is too late.\n",
-       "    var cells = IPython.notebook.get_cells();\n",
-       "    var ncells = cells.length;\n",
-       "    for (var i=0; i= 3 moved mimebundle to data attribute of output\n",
-       "                    data = data.data;\n",
-       "                }\n",
-       "                if (data['text/html'] == html_output) {\n",
-       "                    return [cell, data, j];\n",
-       "                }\n",
-       "            }\n",
-       "        }\n",
-       "    }\n",
-       "}\n",
-       "\n",
-       "// Register the function which deals with the matplotlib target/channel.\n",
-       "// The kernel may be null if the page has been refreshed.\n",
-       "if (IPython.notebook.kernel != null) {\n",
-       "    IPython.notebook.kernel.comm_manager.register_target('matplotlib', mpl.mpl_figure_comm);\n",
-       "}\n"
-      ],
-      "text/plain": [
-       ""
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    },
-    {
-     "data": {
-      "text/html": [
-       ""
-      ],
-      "text/plain": [
-       ""
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    }
-   ],
-   "source": [
-    "import matplotlib.pyplot as plt\n",
-    "plt.figure()\n",
-    "plt.plot(data9[1])\n",
-    "plt.show()"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 14,
-   "metadata": {
-    "collapsed": false
-   },
-   "outputs": [
-    {
-     "ename": "ValueError",
-     "evalue": "nth setpoint array should have shape matching the first n dimensions of shape.",
-     "output_type": "error",
-     "traceback": [
-      "\u001b[1;31m---------------------------------------------------------------------------\u001b[0m",
-      "\u001b[1;31mValueError\u001b[0m                                Traceback (most recent call last)",
-      "\u001b[1;32m\u001b[0m in \u001b[0;36m\u001b[1;34m()\u001b[0m\n\u001b[0;32m      1\u001b[0m \u001b[0mdummy\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mparameter\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mManualParameter\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mname\u001b[0m\u001b[1;33m=\u001b[0m\u001b[1;34m\"dummy\"\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m      2\u001b[0m data10 = qc.Loop(dummy[0:5:1]).each(\n\u001b[1;32m----> 3\u001b[1;33m     recsamp_contr.acquisition).run(name='AlazarTest')\n\u001b[0m",
-      "\u001b[1;32ma:\\qcodes\\qcodes\\loops.py\u001b[0m in \u001b[0;36mrun\u001b[1;34m(self, background, use_threads, quiet, data_manager, station, progress_interval, *args, **kwargs)\u001b[0m\n\u001b[0;32m    759\u001b[0m             \u001b[0mprev_loop\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mjoin\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    760\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m--> 761\u001b[1;33m         \u001b[0mdata_set\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mget_data_set\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mdata_manager\u001b[0m\u001b[1;33m,\u001b[0m \u001b[1;33m*\u001b[0m\u001b[0margs\u001b[0m\u001b[1;33m,\u001b[0m \u001b[1;33m**\u001b[0m\u001b[0mkwargs\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m    762\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    763\u001b[0m         self.set_common_attrs(data_set=data_set, use_threads=use_threads,\n",
-      "\u001b[1;32ma:\\qcodes\\qcodes\\loops.py\u001b[0m in \u001b[0;36mget_data_set\u001b[1;34m(self, data_manager, *args, **kwargs)\u001b[0m\n\u001b[0;32m    683\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    684\u001b[0m             data_set = new_data(arrays=self.containers(), mode=data_mode,\n\u001b[1;32m--> 685\u001b[1;33m                                 data_manager=data_manager, *args, **kwargs)\n\u001b[0m\u001b[0;32m    686\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    687\u001b[0m             \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mdata_set\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mdata_set\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
-      "\u001b[1;32ma:\\qcodes\\qcodes\\loops.py\u001b[0m in \u001b[0;36mcontainers\u001b[1;34m(self)\u001b[0m\n\u001b[0;32m    486\u001b[0m                 \u001b[1;31m# note that this supports lists (separate output arrays)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    487\u001b[0m                 \u001b[1;31m# and arrays (nested in one/each output array) of return values\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m--> 488\u001b[1;33m                 \u001b[0maction_arrays\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0m_parameter_arrays\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0maction\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m    489\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    490\u001b[0m             \u001b[1;32melse\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
-      "\u001b[1;32ma:\\qcodes\\qcodes\\loops.py\u001b[0m in \u001b[0;36m_parameter_arrays\u001b[1;34m(self, action)\u001b[0m\n\u001b[0;32m    560\u001b[0m                 \u001b[0msp_def\u001b[0m \u001b[1;33m=\u001b[0m \u001b[1;33m(\u001b[0m\u001b[0mshape\u001b[0m\u001b[1;33m[\u001b[0m\u001b[1;33m:\u001b[0m \u001b[1;36m1\u001b[0m \u001b[1;33m+\u001b[0m \u001b[0mj\u001b[0m\u001b[1;33m]\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mj\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0msetpoints\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mvij\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mnij\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mlij\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    561\u001b[0m                 \u001b[1;32mif\u001b[0m \u001b[0msp_def\u001b[0m \u001b[1;32mnot\u001b[0m \u001b[1;32min\u001b[0m \u001b[0mall_setpoints\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m--> 562\u001b[1;33m                     \u001b[0mall_setpoints\u001b[0m\u001b[1;33m[\u001b[0m\u001b[0msp_def\u001b[0m\u001b[1;33m]\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0m_make_setpoint_array\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;33m*\u001b[0m\u001b[0msp_def\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m    563\u001b[0m                     \u001b[0mout\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mappend\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mall_setpoints\u001b[0m\u001b[1;33m[\u001b[0m\u001b[0msp_def\u001b[0m\u001b[1;33m]\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    564\u001b[0m                 \u001b[0msetpoints\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0msetpoints\u001b[0m \u001b[1;33m+\u001b[0m \u001b[1;33m(\u001b[0m\u001b[0mall_setpoints\u001b[0m\u001b[1;33m[\u001b[0m\u001b[0msp_def\u001b[0m\u001b[1;33m]\u001b[0m\u001b[1;33m,\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
-      "\u001b[1;32ma:\\qcodes\\qcodes\\loops.py\u001b[0m in \u001b[0;36m_make_setpoint_array\u001b[1;34m(self, shape, i, prev_setpoints, vals, name, label)\u001b[0m\n\u001b[0;32m    601\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    602\u001b[0m         \u001b[1;32mif\u001b[0m \u001b[0mvals\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mshape\u001b[0m \u001b[1;33m!=\u001b[0m \u001b[0mshape\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m--> 603\u001b[1;33m             raise ValueError('nth setpoint array should have shape matching '\n\u001b[0m\u001b[0;32m    604\u001b[0m                              'the first n dimensions of shape.')\n\u001b[0;32m    605\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n",
-      "\u001b[1;31mValueError\u001b[0m: nth setpoint array should have shape matching the first n dimensions of shape."
-     ]
-    }
-   ],
-   "source": [
-    "dummy = parameter.ManualParameter(name=\"dummy\")\n",
-    "data10 = qc.Loop(dummy[0:5:1]).each(\n",
-    "    recsamp_contr.acquisition).run(name='AlazarTest')"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 17,
-   "metadata": {
-    "collapsed": false
-   },
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "((array([0, 1, 2]), array([0, 1])), (array([0, 1, 2]), array([0, 1])))"
-      ]
-     },
-     "execution_count": 17,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "((np.arange(3), np.arange(2)), (np.arange(3), np.arange(2)))"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {
-    "collapsed": true
-   },
-   "outputs": [],
-   "source": []
   }
  ],
  "metadata": {
diff --git a/qcodes/instrument_drivers/AlazarTech/ATS_acquisition_controllers.py b/qcodes/instrument_drivers/AlazarTech/ATS_acquisition_controllers.py
index f4653cf1776d..a1d4a055c8c8 100644
--- a/qcodes/instrument_drivers/AlazarTech/ATS_acquisition_controllers.py
+++ b/qcodes/instrument_drivers/AlazarTech/ATS_acquisition_controllers.py
@@ -3,142 +3,6 @@
 import numpy as np
 
 
-class HD_Controller(AcquisitionController):
-    """Heterodyne Measurement Controller
-    Does averaged DFT on 2 channel Alazar measurement
-
-    TODO(nataliejpg) handling of channel number
-    TODO(nataliejpg) test angle data
-    """
-
-    def __init__(self, freq_dif):
-        self.freq_dif = freq_dif
-        self.samples_per_record = None
-        self.records_per_buffer = None
-        self.buffers_per_acquisition = None
-        self.number_of_channels = 2
-        self.buffer = None
-
-    def pre_start_capture(self, alazar):
-        """Get config data from alazar card and set up DFT"""
-        self.samples_per_record = alazar.samples_per_record()
-        self.records_per_buffer = alazar.records_per_buffer()
-        self.buffers_per_acquisition = alazar.buffers_per_acquisition()
-        self.sample_rate = alazar.get_sample_speed()
-        self.buffer = np.zeros(self.samples_per_record *
-                               self.records_per_buffer *
-                               self.number_of_channels)
-        # TODO(nataliejpg) leave explicit or save lines? add error/logging?
-        averaging = self.buffers_per_acquisition * self.records_per_buffer
-        record_duration = self.samples_per_record / self.sample_rate
-        time_period_dif = 1 / self.freq_dif
-        cycles_measured = record_duration / time_period_dif
-        oversampling_rate = self.sample_rate / (2 * self.freq_dif)
-        print("Average over {} records".format(averaging))
-        print("Oscillations per record: {} (expect 100+)"
-              .format(cycles_measured))
-        print("Oversampling rate: {} (expect > 2)".format(oversampling_rate))
-
-        integer_list = np.arange(self.samples_per_record)
-        angle_list = (2 * np.pi * self.freq_dif / self.sample_rate *
-                      integer_list)
-        self.cos_list = np.cos(angle_list)
-        self.sin_list = np.sin(angle_list)
-
-    def pre_acquire(self, alazar):
-        # gets called after 'AlazarStartCapture'
-        pass
-
-    def handle_buffer(self, alazar, data):
-        self.buffer += data
-
-    def post_acquire(self, alazar):
-        """Average over records in buffer and do DFT:
-        assumes samples are arranged in the buffer as follows:
-        S0A, S0B, ..., S1A, S1B, ...
-        with SXY the sample number X of channel Y.
-        """
-        records_per_acquisition = (1. * self.buffers_per_acquisition *
-                                   self.records_per_buffer)
-        recordA = np.zeros(self.samples_per_record)
-        for i in range(self.records_per_buffer):
-            i0 = (i * self.samples_per_record * self.number_of_channels)
-            i1 = (i0 + self.samples_per_record * self.number_of_channels)
-            recordA += (self.buffer[i0:i1:self.number_of_channels] /
-                        records_per_acquisition)
-        recordB = np.zeros(self.samples_per_record)
-        for i in range(self.records_per_buffer):
-            i0 = (i * self.samples_per_record * self.number_of_channels + 1)
-            i1 = (i0 + self.samples_per_record * self.number_of_channels)
-            recordB += (self.buffer[i0:i1:self.number_of_channels] /
-                        records_per_acquisition)
-
-        resA = self.fit(recordA)
-        resB = self.fit(recordB)
-
-        return resA, resB
-
-    def fit(self, rec):
-        """Do Discrete Fourier Transform and return magnitude and phase data"""
-        RePart = np.dot(rec, self.cos_list) / self.samples_per_record
-        ImPart = np.dot(rec, self.sin_list) / self.samples_per_record
-        # factor of 2 as amplitude is split between finite term
-        # and double frequency term
-        ampl = 2 * np.sqrt(RePart ** 2 + ImPart ** 2)
-        phase = math.atan2(ImPart, RePart) * 180 / (2 * math.pi)
-        return [ampl, phase]
-
-
-class Basic_AcquisitionController(AcquisitionController):
-    """Basic AcquisitionController tested on ATS9360
-    returns unprocessed data averaged by record with 2 channels
-    """
-
-    def __init__(self):
-        self.samples_per_record = None
-        self.records_per_buffer = None
-        self.buffers_per_acquisition = None
-        self.number_of_channels = 2
-        self.buffer = None
-
-    def pre_start_capture(self, alazar):
-        self.samples_per_record = alazar.samples_per_record()
-        self.records_per_buffer = alazar.records_per_buffer()
-        self.buffers_per_acquisition = alazar.buffers_per_acquisition()
-        self.buffer = np.zeros(self.samples_per_record *
-                               self.records_per_buffer *
-                               self.number_of_channels)
-
-    def pre_acquire(self, alazar):
-        # gets called after 'AlazarStartCapture'
-        pass
-
-    def handle_buffer(self, alazar, data):
-        self.buffer += data
-
-    def post_acquire(self, alazar):
-        # average over records in buffer:
-        # for ATS9360 samples are arranged in the buffer as follows:
-        # S0A, S0B, ..., S1A, S1B, ...
-        # with SXY the sample number X of channel Y.
-        records_per_acquisition = (self.buffers_per_acquisition *
-                                   self.records_per_buffer)
-        recordA = np.zeros(self.samples_per_record)
-        for i in range(self.records_per_buffer):
-            i0 = (i * self.samples_per_record * self.number_of_channels)
-            i1 = (i0 + self.samples_per_record * self.number_of_channels)
-            recordA += (self.buffer[i0:i1:self.number_of_channels] /
-                        records_per_acquisition)
-
-        recordB = np.zeros(self.samples_per_record)
-        for i in range(self.records_per_buffer):
-            i0 = (i * self.samples_per_record * self.number_of_channels + 1)
-            i1 = (i0 + self.samples_per_record * self.number_of_channels)
-            recordB += (self.buffer[i0:i1:self.number_of_channels] /
-                        records_per_acquisition)
-        return recordA, recordB
-
-
 # DFT AcquisitionController
 class Demodulation_AcquisitionController(AcquisitionController):
     """
diff --git a/qcodes/instrument_drivers/AlazarTech/Single_controller.py b/qcodes/instrument_drivers/AlazarTech/ave_controller.py
similarity index 100%
rename from qcodes/instrument_drivers/AlazarTech/Single_controller.py
rename to qcodes/instrument_drivers/AlazarTech/ave_controller.py

From e42200e6a4437bb7cdb5bddb640c1a01708cf997 Mon Sep 17 00:00:00 2001
From: Natalie Pearson 
Date: Mon, 21 Nov 2016 16:39:50 +0100
Subject: [PATCH 054/328] update to dos lists

---
 qcodes/instrument_drivers/AlazarTech/ATS.py     |  3 +--
 qcodes/instrument_drivers/AlazarTech/ATS9360.py | 10 ++++++++--
 2 files changed, 9 insertions(+), 4 deletions(-)

diff --git a/qcodes/instrument_drivers/AlazarTech/ATS.py b/qcodes/instrument_drivers/AlazarTech/ATS.py
index 6a243082e316..4cff51bea787 100644
--- a/qcodes/instrument_drivers/AlazarTech/ATS.py
+++ b/qcodes/instrument_drivers/AlazarTech/ATS.py
@@ -11,14 +11,13 @@
 # TODO(damazter) (C) logging
 
 # these items are important for generalizing this code to multiple alazar cards
-# TODO(damazter) (W) remove 8 bits per sample requirement
 # TODO(damazter) (W) some alazar cards have a different number of channels :(
 
 # TODO(damazter) (S) tests to do:
 # acquisition that would overflow the board if measurement is not stopped
 # quickly enough. can this be solved by not reposting the buffers?
 
-# TODO(nataliejpg) test!!
+# TODO(nataliejpg) fix get_damples function which I have broken for ATS9360
 
 
 class AlazarTech_ATS(Instrument):
diff --git a/qcodes/instrument_drivers/AlazarTech/ATS9360.py b/qcodes/instrument_drivers/AlazarTech/ATS9360.py
index cdac0771ece2..7d58397fa760 100644
--- a/qcodes/instrument_drivers/AlazarTech/ATS9360.py
+++ b/qcodes/instrument_drivers/AlazarTech/ATS9360.py
@@ -3,8 +3,14 @@
 
 
 class AlazarTech_ATS9360(AlazarTech_ATS):
-    # TODO(nataliejpg) add more options for the sample rate set using 10MHz ref
-    # including so that the 'get sample rate' function in ATS.py works
+    """
+    This class is the driver for the ATS9360 board
+    it inherits from the ATS base class
+
+    TODO(nataliejpg):
+        -   add more options for the sample rate set using 10MHz ref
+            including so that the 'get sample rate' function in ATS.py works
+    """
     def __init__(self, name, **kwargs):
         dll_path = 'C:\\WINDOWS\\System32\\ATSApi.dll'
         super().__init__(name, dll_path=dll_path, **kwargs)

From efb0733b80c97eb841e870ccf37314c9657cbdbb Mon Sep 17 00:00:00 2001
From: Natalie Pearson 
Date: Mon, 21 Nov 2016 16:43:57 +0100
Subject: [PATCH 055/328] rename acq controllers

---
 .../AlazarTech/{RecBuf_controller.py => rec_buf_controller.py}    | 0
 .../AlazarTech/{RecSamp_controller.py => rec_samp_controller.py}  | 0
 2 files changed, 0 insertions(+), 0 deletions(-)
 rename qcodes/instrument_drivers/AlazarTech/{RecBuf_controller.py => rec_buf_controller.py} (100%)
 rename qcodes/instrument_drivers/AlazarTech/{RecSamp_controller.py => rec_samp_controller.py} (100%)

diff --git a/qcodes/instrument_drivers/AlazarTech/RecBuf_controller.py b/qcodes/instrument_drivers/AlazarTech/rec_buf_controller.py
similarity index 100%
rename from qcodes/instrument_drivers/AlazarTech/RecBuf_controller.py
rename to qcodes/instrument_drivers/AlazarTech/rec_buf_controller.py
diff --git a/qcodes/instrument_drivers/AlazarTech/RecSamp_controller.py b/qcodes/instrument_drivers/AlazarTech/rec_samp_controller.py
similarity index 100%
rename from qcodes/instrument_drivers/AlazarTech/RecSamp_controller.py
rename to qcodes/instrument_drivers/AlazarTech/rec_samp_controller.py

From 569c1f079c507a252067f98ec1f40c91448968bf Mon Sep 17 00:00:00 2001
From: Natalie Pearson 
Date: Mon, 21 Nov 2016 16:54:52 +0100
Subject: [PATCH 056/328] actually renaming acq controllers

---
 .../AlazarTech/{Rec_controller.py => rec_controller.py}           | 0
 .../AlazarTech/{Samp_controller.py => samp_controller.py}         | 0
 2 files changed, 0 insertions(+), 0 deletions(-)
 rename qcodes/instrument_drivers/AlazarTech/{Rec_controller.py => rec_controller.py} (100%)
 rename qcodes/instrument_drivers/AlazarTech/{Samp_controller.py => samp_controller.py} (100%)

diff --git a/qcodes/instrument_drivers/AlazarTech/Rec_controller.py b/qcodes/instrument_drivers/AlazarTech/rec_controller.py
similarity index 100%
rename from qcodes/instrument_drivers/AlazarTech/Rec_controller.py
rename to qcodes/instrument_drivers/AlazarTech/rec_controller.py
diff --git a/qcodes/instrument_drivers/AlazarTech/Samp_controller.py b/qcodes/instrument_drivers/AlazarTech/samp_controller.py
similarity index 100%
rename from qcodes/instrument_drivers/AlazarTech/Samp_controller.py
rename to qcodes/instrument_drivers/AlazarTech/samp_controller.py

From 779d11941a75d3087b34ba6ddbbb5965a7a07ef5 Mon Sep 17 00:00:00 2001
From: Natalie Pearson 
Date: Wed, 23 Nov 2016 19:42:35 +0100
Subject: [PATCH 057/328] attempt to fix sample_rate bug

---
 qcodes/instrument_drivers/AlazarTech/ATS9360.py | 5 ++---
 1 file changed, 2 insertions(+), 3 deletions(-)

diff --git a/qcodes/instrument_drivers/AlazarTech/ATS9360.py b/qcodes/instrument_drivers/AlazarTech/ATS9360.py
index 7d58397fa760..411b43cc83ea 100644
--- a/qcodes/instrument_drivers/AlazarTech/ATS9360.py
+++ b/qcodes/instrument_drivers/AlazarTech/ATS9360.py
@@ -35,7 +35,7 @@ def __init__(self, name, **kwargs):
                            label='Sample Rate',
                            unit='S/s',
                            value='10MHZ_REF_500MSPS',
-                           byte_to_value_dict={
+                           byte_to_value_dict=(None if self.clock_source() != 'EXTERNAL_CLOCK_10MHz_REF' else {
                                0x1: '1KSPS', 0x2: '2KSPS',
                                0x4: '5KSPS', 0x8: '10KSPS',
                                0xA: '20KSPS', 0xC: '50KSPS',
@@ -48,8 +48,7 @@ def __init__(self, name, **kwargs):
                                0x30: '500MSPS', 0x32: '800MSPS',
                                0x35: '1000MSPS', 0x37: '1200MSPS',
                                0x3A: '1500MSPS', 0x3D: '1800MSPS',
-                               0x40: 'EXTERNAL_CLOCK',
-                               500000000: '10MHZ_REF_500MSPS'})
+                               0x40: 'EXTERNAL_CLOCK'}))
         self.add_parameter(name='clock_edge',
                            parameter_class=AlazarParameter,
                            label='Clock Edge',

From 9199a217d727fd89770cd7f98159d56ee71d8f35 Mon Sep 17 00:00:00 2001
From: nataliejpg 
Date: Thu, 24 Nov 2016 17:57:10 +0100
Subject: [PATCH 058/328] begin refactor of sample controller

---
 .../instrument_drivers/AlazarTech/ATS9360.py  |  27 +--
 .../AlazarTech/samp_controller.py             | 164 ++++++++++--------
 2 files changed, 97 insertions(+), 94 deletions(-)

diff --git a/qcodes/instrument_drivers/AlazarTech/ATS9360.py b/qcodes/instrument_drivers/AlazarTech/ATS9360.py
index 411b43cc83ea..4fe1add7ee05 100644
--- a/qcodes/instrument_drivers/AlazarTech/ATS9360.py
+++ b/qcodes/instrument_drivers/AlazarTech/ATS9360.py
@@ -8,8 +8,7 @@ class AlazarTech_ATS9360(AlazarTech_ATS):
     it inherits from the ATS base class
 
     TODO(nataliejpg):
-        -   add more options for the sample rate set using 10MHz ref
-            including so that the 'get sample rate' function in ATS.py works
+        -   add clock source options and sample rate options
     """
     def __init__(self, name, **kwargs):
         dll_path = 'C:\\WINDOWS\\System32\\ATSApi.dll'
@@ -23,32 +22,12 @@ def __init__(self, name, **kwargs):
                            label='Clock Source',
                            unit=None,
                            value='EXTERNAL_CLOCK_10MHz_REF',
-                           byte_to_value_dict={1: 'INTERNAL_CLOCK',
-                                               2: 'EXTERNAL_CLOCK',
-                                               3: 'MEDIUM_EXTERNAL_CLOCK',
-                                               4: 'SLOW_EXTERNAL_CLOCK',
-                                               5: 'EXTERNAL_CLOCK_AC',
-                                               6: 'EXTERNAL_CLOCK_DC',
-                                               7: 'EXTERNAL_CLOCK_10MHz_REF'})
+                           byte_to_value_dict={7: 'EXTERNAL_CLOCK_10MHz_REF'})
         self.add_parameter(name='sample_rate',
                            parameter_class=AlazarParameter,
                            label='Sample Rate',
                            unit='S/s',
-                           value='10MHZ_REF_500MSPS',
-                           byte_to_value_dict=(None if self.clock_source() != 'EXTERNAL_CLOCK_10MHz_REF' else {
-                               0x1: '1KSPS', 0x2: '2KSPS',
-                               0x4: '5KSPS', 0x8: '10KSPS',
-                               0xA: '20KSPS', 0xC: '50KSPS',
-                               0xE: '100KSPS', 0x10: '200KSPS',
-                               0x12: '500KSPS', 0x14: '1MSPS',
-                               0x18: '2MSPS', 0x1A: '5MSPS',
-                               0x1C: '10MSPS', 0x1E: '20MSPS',
-                               0x22: '50MSPS', 0x24: '100MSPS',
-                               0x25: '125MSPS', 0x2B: '250MSPS',
-                               0x30: '500MSPS', 0x32: '800MSPS',
-                               0x35: '1000MSPS', 0x37: '1200MSPS',
-                               0x3A: '1500MSPS', 0x3D: '1800MSPS',
-                               0x40: 'EXTERNAL_CLOCK'}))
+                           value=500000000)
         self.add_parameter(name='clock_edge',
                            parameter_class=AlazarParameter,
                            label='Clock Edge',
diff --git a/qcodes/instrument_drivers/AlazarTech/samp_controller.py b/qcodes/instrument_drivers/AlazarTech/samp_controller.py
index e5ee0c20dbe4..e37acc16d8b5 100644
--- a/qcodes/instrument_drivers/AlazarTech/samp_controller.py
+++ b/qcodes/instrument_drivers/AlazarTech/samp_controller.py
@@ -3,6 +3,8 @@
 import numpy as np
 from qcodes import Parameter
 from qcodes.utils.helpers import filter_win, filter_ls
+from qcodes.instrument.parameter import ManualParameter
+from qcodes.utils import validators
 
 
 class SamplesParam(Parameter):
@@ -12,6 +14,7 @@ class SamplesParam(Parameter):
     sample data from the Alazar, averaged over records and buffers.
 
     TODO(nataliejpg) refactor setpoints/shapes horriblenesss
+    TODO(nataliejpg) setpoint units
     """
 
     def __init__(self, name, instrument):
@@ -20,24 +23,19 @@ def __init__(self, name, instrument):
         self.acquisitionkwargs = {}
         self.names = ('magnitude', 'phase')
         self.units = ('', '')
-        self.setpoint_names = (('sample_num',), ('sample_num',))
+        self.setpoint_names = (('acq_time (s)',), ('acq_time (s)',))
         self.setpoints = ((1,), (1,))
         self.shapes = ((1,), (1,))
 
-    def update_acquisition_kwargs(self, samp_time, **kwargs):
-        # needed to update config of the software parameter on sweep change
-        # freq setpoints tuple as needs to be hashable for look up
-        if samp_time:
-            npts = samp_time
-            n = tuple(np.arange(npts))
-            self.setpoints = ((n,), (n,))
-            self.shapes = ((npts,), (npts,))
-        else:
-            raise ValueError(
-                'samp_time not specified')
-        # updates dict to be used in acquisition get call
-        self.acquisitionkwargs.update(**kwargs)
-
+#    def update_acquisition_kwargs(self, **kwargs):
+#        # updates dict to be used in acquisition get call
+#        self.acquisitionkwargs.update(**kwargs)
+    
+    def update_sweep(self, start, stop, npts):
+        n = tuple(np.linspace(start, stop, num=npts))
+        self.setpoints = ((n,), (n,))
+        self.shapes = ((npts,), (npts,))
+    
     def get(self):
         mag, phase = self._instrument._get_alazar().acquire(
             acquisition_controller=self._instrument,
@@ -62,26 +60,24 @@ class HD_Samples_Controller(AcquisitionController):
         and returned
     **kwargs: kwargs are forwarded to the Instrument base class
 
-    TODO(nataliejpg) fix sample rate problem
     TODO(nataliejpg) test filter options
     TODO(nataliejpg) finish implementation of channel b option
-    TODO(nataliejpg) make demodulation freq a param
+    TODO(nataliejpg) validators for int_time and int_delay
     """
 
-    def __init__(self, name, alazar_name, demod_freq, samp_rate=5e8,
+    def __init__(self, name, alazar_name, samp_rate=5e8,
                  filt='win', numtaps=101, chan_b=False, **kwargs):
         filter_dict = {'win': 0, 'ls': 1}
-        self.demodulation_frequency = demod_freq
         self.sample_rate = samp_rate
         self.filter = filter_dict[filt]
         self.numtaps = numtaps
         self.chan_b = chan_b
-        self.samples_per_record = None
-        self.records_per_buffer = None
-        self.buffers_per_acquisition = None
+        self.samples_per_record = 0
+        self.records_per_buffer = 0
+        self.buffers_per_acquisition = 0
         self.number_of_channels = 2
-        self.samples_delay = None
-        self.samples_time = None
+        self.samples_delay = 0
+        self.samples_time = 0
         self.cos_list = None
         self.sin_list = None
         self.buffer = None
@@ -92,8 +88,17 @@ def __init__(self, name, alazar_name, demod_freq, samp_rate=5e8,
 
         self.add_parameter(name='acquisition',
                            parameter_class=SamplesParam)
-
-    def update_acquisitionkwargs(self, **kwargs):
+        self.add_parameter(name='demodulation_frequency',
+                           initial_value=20e6,
+                           parameter_class=ManualParameter)
+        self.add_parameter(name='int_time',
+                           initial_value=None,
+                           parameter_class=ManualParameter)
+        self.add_parameter(name='int_delay',
+                           initial_value=None,
+                           parameter_class=ManualParameter)
+
+    def update_acquisition_settings(self, **kwargs):
         """
         Updates the kwargs to be used when
         alazar_driver.acquire is called via a get call of the
@@ -106,47 +111,32 @@ def update_acquisitionkwargs(self, **kwargs):
         :param kwargs:
         :return:
         """
+        alazar = self._get_alazar()
         samples_per_record = kwargs['samples_per_record']
-        sample_rate = self.sample_rate
-
-        if 'int_delay' in kwargs:
-            int_delay = kwargs.pop('int_delay')
-            samp_delay = int_delay * sample_rate
-            samples_delay_min = (self.numtaps - 1)
-            if samp_delay < samples_delay_min:
-                int_delay_min = samples_delay_min / sample_rate
-                logging.warning(
-                    'delay is less than recommended for filter choice: '
-                    '(expect delay >= {}'.format(int_delay_min))
-        else:
+        self.sample_rate = alazar.get_sample_rate()
+        time_available = samples_per_record / self.sample_rate
+            
+        if self.int_delay() == 0:
             samp_delay = self.numtaps - 1
-
-        if 'int_time' in kwargs:
-            int_time = kwargs.pop('int_time')
-            samp_time = int_time * sample_rate
-            samples_time_max = (samples_per_record - samp_delay)
-            oscilations_measured = int_time * self.demodulation_frequency
-            oversampling = sample_rate / (2 * self.demodulation_frequency)
-            if samp_time > samples_time_max:
-                int_time_max = samples_time_max / sample_rate
-                raise ValueError('int_time {} is longer than total_time - '
-                                 'delay: {}'.format(int_time, int_time_max))
-            elif oscilations_measured < 10:
-                logging.warning('{} oscilations measured, recommend at '
-                                'least 10: decrease sampling rate, take '
-                                'more samples or increase demodulation '
-                                'freq'.format(oscilations_measured))
-            elif oversampling < 1:
-                logging.warning('oversampling rate is {}, recommend > 1: '
-                                'increase sampling rate or decrease '
-                                'demodulation frequency'.format(oversampling))
+            self.int_delay(samp_delay / self.sample_rate)
         else:
-            samp_time = samples_per_record - samp_delay
-
-        self.samples_time = int(samp_time)
-        self.samples_delay = int(samp_delay)
+            self.check_delay(time_available)
+#            except ValueError as e:
+#                self.int_delay(0)
+        
+        time_available -= self.int_delay()
+        
+        if self.int_time() == 0:
+            self.int_time(time_available)
+        else:
+            self.check_time(time_available)
+        
+        start = self.int_delay()
+        stop = self.int_delay() + self.int_time()
+        npts = int(self.int_time() * self.sample_rate)
 
-        self.acquisition.update_acquisition_kwargs(self.samples_time, **kwargs)
+        self.acquisition.acquisitionkwargs.update(**kwargs)
+        self.acquisition.update_sweep(start, stop, npts)
 
     def pre_start_capture(self):
         """
@@ -162,9 +152,12 @@ def pre_start_capture(self):
         self.buffer = np.zeros(self.samples_per_record *
                                self.records_per_buffer *
                                self.number_of_channels)
+        if self.sample_rate != alazar.get_sample_rate():
+            raise Exception('acq controller sample rate does not match instrument'
+                            'value, most likely need to call update_acquisition_settings' )
 
         integer_list = np.arange(self.samples_per_record, dtype=np.uint16)
-        angle_list = (2 * np.pi * self.demodulation_frequency /
+        angle_list = (2 * np.pi * self.demodulation_frequency() /
                       self.sample_rate * integer_list)
 
         self.cos_list = np.cos(angle_list)
@@ -223,6 +216,7 @@ def post_acquire(self):
         return magA, phaseA
 
     def fit(self, rec):
+        print('got to fit, ave %s' % np.mean(rec))
         """
         Applies volts conversion, demodulation fit, low bandpass filter
         and integration limits to samples array
@@ -238,10 +232,13 @@ def fit(self, rec):
                             ' bps != 12, centered raw samples returned')
             volt_rec = rec - np.mean(rec)
 
+        print(self.cos_list)
+        print(self.demodulation_frequency())    
+            
         # multiply with software wave
         re_wave = np.multiply(volt_rec, self.cos_list)
         im_wave = np.multiply(volt_rec, self.sin_list)
-        cutoff = self.demodulation_frequency / 10
+        cutoff = self.demodulation_frequency() / 10
 
         # filter out higher freq component
         if self.filter == 0:
@@ -256,11 +253,11 @@ def fit(self, rec):
                                     self.sample_rate, self.numtaps)
 
         # apply integration limits
-        start = self.samples_delay
-        end = start + self.samples_time
+        beginning = int(self.int_delay() / self.sample_rate)
+        end = beginning + int(self.int_time() / self.sample_rate)
 
-        re_limited = re_filtered[start:end]
-        im_limited = im_filtered[start:end]
+        re_limited = re_filtered[beginning:end]
+        im_limited = im_filtered[beginning:end]
 
         # convert to magnitude and phase
         complex_num = re_limited + im_limited * 1j
@@ -269,7 +266,34 @@ def fit(self, rec):
 
         return mag, phase
 
-
+    def check_delay(self, time_available):
+        samples_delay_min = (self.numtaps - 1)
+        int_delay_min = samples_delay_min / self.sample_rate
+        if self.int_delay() > time_available:
+            raise ValueError('int_delay {} is longer than total_time '
+                            '{}'.format(self.int_delay(), time_available))
+        elif self.int_delay() < int_delay_min:
+            logging.warning(
+                'delay is less than recommended for filter choice: '
+                '(expect delay >= {}'.format(int_delay_min))
+                
+    def check_time(self, time_available):
+        oscilations_measured = self.int_time() * self.demodulation_frequency()
+        oversampling = self.sample_rate / (2 * self.demodulation_frequency())
+        if self.int_time() > time_available:
+            raise ValueError('int_time {} is longer than total_time - '
+                            'delay = {}'.format(self.int_time(), time_available))
+        elif oscilations_measured < 10:
+            logging.warning('{} oscilations measured, recommend at '
+                            'least 10: decrease sampling rate, take '
+                            'more samples or increase demodulation '
+                            'freq'.format(oscilations_measured))
+        elif oversampling < 1:
+            logging.warning('oversampling rate is {}, recommend > 1: '
+                            'increase sampling rate or decrease '
+                            'demodulation frequency'.format(oversampling))   
+                        
+          
 def sample_to_volt_u12(raw_samples, bps):
     """
     Applies volts conversion for 12 bit sample data stored

From 5e9ab450c870be852760c1533aac36882ed04176 Mon Sep 17 00:00:00 2001
From: Natalie Pearson 
Date: Wed, 30 Nov 2016 14:51:44 +0100
Subject: [PATCH 059/328] delete detailed excess aqc controllers

---
 .../AlazarTech/ave_controller.py              | 265 ---------------
 .../AlazarTech/rec_buf_controller.py          | 220 ------------
 .../AlazarTech/rec_controller.py              | 294 ----------------
 .../AlazarTech/rec_samp_controller.py         | 233 -------------
 .../AlazarTech/samp_controller.py             | 317 ------------------
 5 files changed, 1329 deletions(-)
 delete mode 100644 qcodes/instrument_drivers/AlazarTech/ave_controller.py
 delete mode 100644 qcodes/instrument_drivers/AlazarTech/rec_buf_controller.py
 delete mode 100644 qcodes/instrument_drivers/AlazarTech/rec_controller.py
 delete mode 100644 qcodes/instrument_drivers/AlazarTech/rec_samp_controller.py
 delete mode 100644 qcodes/instrument_drivers/AlazarTech/samp_controller.py

diff --git a/qcodes/instrument_drivers/AlazarTech/ave_controller.py b/qcodes/instrument_drivers/AlazarTech/ave_controller.py
deleted file mode 100644
index d23767c7aeb2..000000000000
--- a/qcodes/instrument_drivers/AlazarTech/ave_controller.py
+++ /dev/null
@@ -1,265 +0,0 @@
-import logging
-from .ATS import AcquisitionController
-import numpy as np
-import qcodes.utils.helpers as helpers
-
-
-class HD_Controller(AcquisitionController):
-    """
-    This is the Acquisition Controller class which works with the ATS9360,
-    averaging over buffers and records, demodulating with a software
-    reference signal, averaging over the  samples.
-    args:
-    name: name for this acquisition_conroller as an instrument
-    alazar_name: the name of the alazar instrument such that this controller
-        can communicate with the Alazar
-    demod_freq: the frequency of the software wave to be created
-    samp_rate: the rate of sampling
-    filt: the filter to be used to filter out double freq component
-    chan_b: whether there is also a second channel of data to be processed
-        and returned
-    **kwargs: kwargs are forwarded to the Instrument base class
-
-    TODO(nataliejpg) fix sample rate problem
-    TODO(nataliejpg) test filter options
-    TODO(nataliejpg) finish implementation of channel b option
-    TODO(nataliejpg) make demodulation freq a param
-    """
-
-    def __init__(self, name, alazar_name, demod_freq, samp_rate=5e8,
-                 filt='win', numtaps=101, chan_b=False, **kwargs):
-        filter_dict = {'win': 0, 'ls': 1, 'dot': 2}
-        self.demodulation_frequency = demod_freq
-        self.sample_rate = samp_rate
-        self.filter = filter_dict[filt]
-        self.numtaps = numtaps
-        self.acquisitionkwargs = {}
-        self.chan_b = chan_b
-        self.samples_per_record = None
-        self.records_per_buffer = None
-        self.buffers_per_acquisition = None
-        self.number_of_channels = 2
-        self.samples_delay = None
-        self.samples_time = None
-        self.cos_list = None
-        self.sin_list = None
-        self.buffer = None
-        self.board_info = None
-        # make a call to the parent class and by extension,
-        # create the parameter structure of this class
-        super().__init__(name, alazar_name, **kwargs)
-
-        self.add_parameter(name='acquisition',
-                           names=('magnitude', 'phase'),
-                           units=('', ''),
-                           get_cmd=self.do_acquisition)
-
-    def update_acquisitionkwargs(self, **kwargs):
-        """
-        Updates the kwargs to be used when
-        alazar_driver.acquire is called.
-
-        It is also used to set the limits on the selection of
-        samples used (bounded by delay and integration time)
-
-        :param kwargs:
-        :return:
-        """
-        samples_per_record = kwargs['samples_per_record']
-        sample_rate = self.sample_rate
-
-        if 'int_delay' in kwargs:
-            int_delay = kwargs.pop('int_delay')
-            samp_delay = int_delay * sample_rate
-            samples_delay_min = (self.numtaps - 1)
-            if samp_delay < samples_delay_min:
-                int_delay_min = samples_delay_min / sample_rate
-                logging.warning(
-                    'delay is less than recommended for filter choice: '
-                    '(expect delay >= {}'.format(int_delay_min))
-        else:
-            samp_delay = self.numtaps - 1
-
-        if 'int_time' in kwargs:
-            int_time = kwargs.pop('int_time')
-            samp_time = int_time * sample_rate
-            samples_time_max = (samples_per_record - samp_delay)
-            oscilations_measured = int_time * self.demodulation_frequency
-            oversampling = sample_rate / (2 * self.demodulation_frequency)
-            if samp_time > samples_time_max:
-                int_time_max = samples_time_max / sample_rate
-                raise ValueError('int_time {} is longer than total_time - '
-                                 'delay: {}'.format(int_time, int_time_max))
-            elif oscilations_measured < 10:
-                logging.warning('{} oscilations measured, recommend at '
-                                'least 10: decrease sampling rate, take '
-                                'more samples or increase demodulation '
-                                'freq'.format(oscilations_measured))
-            elif oversampling < 1:
-                logging.warning('oversampling rate is {}, recommend > 1: '
-                                'increase sampling rate or decrease '
-                                'demodulation frequency'.format(oversampling))
-        else:
-            samp_time = samples_per_record - samp_delay
-
-        self.samples_time = int(samp_time)
-        self.samples_delay = int(samp_delay)
-
-        self.acquisitionkwargs.update(**kwargs)
-
-    def do_acquisition(self):
-        """
-        this method performs an acquisition, which is the get_cmd for the
-        acquisiion parameter of this instrument
-        :return:
-        """
-        values = self._get_alazar().acquire(acquisition_controller=self,
-                                            **self.acquisitionkwargs)
-        return values
-
-    def pre_start_capture(self):
-        """
-        Called before capture start to update Acquisition Controller with
-        alazar acquisition params and set up software wave for demodulation.
-        :return:
-        """
-        alazar = self._get_alazar()
-        self.samples_per_record = alazar.samples_per_record.get()
-        self.records_per_buffer = alazar.records_per_buffer.get()
-        self.buffers_per_acquisition = alazar.buffers_per_acquisition.get()
-        self.board_info = alazar.get_idn()
-        self.buffer = np.zeros(self.samples_per_record *
-                               self.records_per_buffer *
-                               self.number_of_channels)
-
-        integer_list = np.arange(self.samples_per_record, dtype=np.uint16)
-        angle_list = (2 * np.pi * self.demodulation_frequency /
-                      self.sample_rate * integer_list)
-
-        self.cos_list = np.cos(angle_list)
-        self.sin_list = np.sin(angle_list)
-
-    def pre_acquire(self):
-        """
-        See AcquisitionController
-        :return:
-        """
-        pass
-
-    def handle_buffer(self, data):
-        """
-        Adds data from alazar to buffer (effectively averaging)
-        :return:
-        """
-        self.buffer += data
-
-    def post_acquire(self):
-        """
-        Processes the data according to ATS9360 settings, splitting into
-        records and averaging over them, then applying demodulation fit
-        nb: currently only channel A
-        :return: samples_magnitude_array, samples_phase_array
-        """
-        records_per_acquisition = (self.buffers_per_acquisition *
-                                   self.records_per_buffer)
-        # for ATS9360 samples are arranged in the buffer as follows:
-        # S00A, S00B, S01A, S01B...S10A, S10B, S11A, S11B...
-        # where SXYZ is record X, sample Y, channel Z.
-
-        # break buffer up into records and averages over them
-        recA = np.zeros(self.samples_per_record)
-        for i in range(self.records_per_buffer):
-            i0 = (i * self.samples_per_record * self.number_of_channels)
-            i1 = (i0 + self.samples_per_record * self.number_of_channels)
-            recA += self.buffer[i0:i1:self.number_of_channels]
-        recordA = np.uint16(recA / records_per_acquisition)
-
-        # do demodulation
-        magA, phaseA = self.fit(recordA)
-
-        # same for chan b
-        if self.chan_b:
-            raise NotImplementedError('chan b code not complete')
-            # recordB = np.zeros(self.samples_per_record, dtype=np.uint16)
-            # for i in range(self.records_per_buffer):
-            #     i0 = (i * self.samples_per_record *
-            #           self.number_of_channels + 1)
-            #     i1 = (i0 + self.samples_per_record * self.number_of_channels)
-            #     recordB += np.uint16(self.buffer[i0:i1:self.number_of_channels] /
-            #                          records_per_acquisition)
-            # magB, phaseB = self.fit(recordB)
-
-        return magA, phaseA
-
-    def fit(self, rec):
-        """
-        Applies volts conversion, demodulation fit, low bandpass filter
-        and integration limits to samples array and averages
-        :return: magnitude, phase
-        """
-
-        # convert rec to volts
-        bps = self.board_info['bits_per_sample']
-        if bps == 12:
-            volt_rec = sample_to_volt_u12(rec, bps)
-        else:
-            logging.warning('sample to volt conversion does not exist for'
-                            ' bps != 12, centered raw samples returned')
-            volt_rec = rec - np.mean(rec)
-
-        # multiply with software wave
-        re_wave = np.multiply(volt_rec, self.cos_list)
-        im_wave = np.multiply(volt_rec, self.sin_list)
-        cutoff = self.demodulation_frequency / 10
-
-        # filter out higher freq component
-        if self.filter == 0:
-            re_filtered = helpers.filter_win(re_wave, cutoff,
-                                             self.sample_rate, self.numtaps)
-            im_filtered = helpers.filter_win(im_wave, cutoff,
-                                             self.sample_rate, self.numtaps)
-        elif self.filter == 1:
-            re_filtered = helpers.filter_ls(re_wave, cutoff,
-                                            self.sample_rate, self.numtaps)
-            im_filtered = helpers.filter_ls(im_wave, cutoff,
-                                            self.sample_rate, self.numtaps)
-        elif self.filter == 2:
-            re_filtered = re_wave
-            im_filtered = im_wave
-
-        # apply integration limits
-        start = self.samples_delay
-        end = start + self.samples_time
-
-        re_limited = re_filtered[start:end]
-        im_limited = im_filtered[start:end]
-
-        # convert to magnitude and phase
-        complex_num = re_limited + im_limited * 1j
-        mag = np.mean(abs(complex_num))
-        phase = np.mean(np.angle(complex_num, deg=True))
-
-        return mag, phase
-
-
-def sample_to_volt_u12(raw_samples, bps):
-    """
-    Applies volts conversion for 12 bit sample data stored
-    in 2 bytes
-    :return: samples_magnitude_array, samples_phase_array
-    """
-
-    # right_shift 16-bit sample by 4 to get 12 bit sample
-    shifted_samples = np.right_shift(raw_samples, 4)
-
-    # Alazar calibration
-    code_zero = (1 << (bps - 1)) - 0.5
-    code_range = (1 << (bps - 1)) - 0.5
-
-    # TODO(nataliejpg) make this not hard coded
-    input_range_volts = 1
-    # Convert to volts
-    volt_samples = np.float64(input_range_volts *
-                              (shifted_samples - code_zero) / code_range)
-
-    return volt_samples
diff --git a/qcodes/instrument_drivers/AlazarTech/rec_buf_controller.py b/qcodes/instrument_drivers/AlazarTech/rec_buf_controller.py
deleted file mode 100644
index 65ddb9dea4d7..000000000000
--- a/qcodes/instrument_drivers/AlazarTech/rec_buf_controller.py
+++ /dev/null
@@ -1,220 +0,0 @@
-from .ATS import AcquisitionController
-import numpy as np
-from qcodes import Parameter
-from scipy import signal
-
-
-class RecBufParam(Parameter):
-    """
-    Hardware controlled parameter class for Alazar acquisition. To be used with
-    HD_Samples_Controller (tested with ATS9360 board) for return of an array of
-    sample data from the Alazar, averaged over records and buffers.
-    """
-
-    def __init__(self, name, instrument):
-        super().__init__(name)
-        self._instrument = instrument
-        self.acquisitionkwargs = {}
-        self.names = ('magnitude', 'phase')
-        self.units = ('', '')
-        self.setpoint_names = (('rec_num', 'buf_num'), ('rec_num', 'buf_num'))
-        self.setpoints = ((1, 1), (1, 1))
-        self.shapes = ((1, 1), (1, 1))
-
-    def update_acquisition_kwargs(self, **kwargs):
-        # needed to update config of the software parameter on sweep change
-        # freq setpoints tuple as needs to be hashable for look up
-        if 'records_per_buffer' and 'buffers_per_acquisition' in kwargs:
-            rpts = kwargs['records_per_buffer']
-            bpts = kwargs['buffers_per_acquisition']
-            r = tuple(np.arange(rpts))
-            b = tuple(np.arange(bpts))
-            self.setpoints = ((r, b), (r, b))
-            self.shapes = ((rpts, bpts), (rpts, bpts))
-        else:
-            raise ValueError(
-                'records_per_buffer and buffers_per_acquisition must be specified')
-        # updates dict to be used in acquisition get call
-        self.acquisitionkwargs.update(**kwargs)
-
-    def get(self):
-        mag, phase = self._instrument._get_alazar().acquire(
-            acquisition_controller=self._instrument,
-            **self.acquisitionkwargs)
-        return mag, phase
-
-
-class HD_RecBuf_Controller(AcquisitionController):
-    """
-    This is the Acquisition Controller class which works with the ATS9360,
-    averaging over buffers and records and demodulating with a software
-    reference signal, returning the  samples.
-    args:
-    name: name for this acquisition_conroller as an instrument
-    alazar_name: the name of the alazar instrument such that this controller
-        can communicate with the Alazar
-    demod_freq: the frequency of the software wave to be created
-    samp_rate: the rate of sampling
-    filt: the filter to be used to filter out double freq component
-    chan_b: whether there is also a second channel of data to be processed
-        and returned
-    **kwargs: kwargs are forwarded to the Instrument base class
-
-    TODO(nataliejpg) fix sample rate problem
-    TODO(nataliejpg) test filter options
-    TODO(nataliejpg) test mag phase logic
-    TODO(nataliejpg) finish implementation of channel b option
-    TODO(nataliejpg) make filter settings not hard coded
-    """
-
-    def __init__(self, name, alazar_name, demod_freq, samp_rate=500e6,
-                 filt='win', chan_b=False, **kwargs):
-        filter_dict = {'win': 0, 'hamming': 1, 'ls': 2}
-        self.demodulation_frequency = demod_freq
-        self.sample_rate = samp_rate
-        self.filter = filter_dict[filt]
-        self.chan_b = chan_b
-        self.samples_per_record = 0
-        self.records_per_buffer = 0
-        self.buffers_per_acquisition = 0
-        self.number_of_channels = 2
-        self.cos_list = None
-        self.sin_list = None
-        self.buffer = None
-        self.buf_count = 0
-        # make a call to the parent class and by extension,
-        # create the parameter structure of this class
-        super().__init__(name, alazar_name, **kwargs)
-
-        self.add_parameter(name='acquisition',
-                           parameter_class=RecBufParam)
-
-    def update_acquisitionkwargs(self, **kwargs):
-        """
-        This method must be used to update the kwargs used for the acquisition
-        with the alazar_driver.acquire
-        :param kwargs:
-        :return:
-        """
-        self.acquisition.update_acquisition_kwargs(**kwargs)
-
-    def pre_start_capture(self):
-        """
-        See AcquisitionController
-        :return:
-        """
-        alazar = self._get_alazar()
-        self.samples_per_record = alazar.samples_per_record.get()
-        self.records_per_buffer = alazar.records_per_buffer.get()
-        self.buffers_per_acquisition = alazar.buffers_per_acquisition.get()
-        self.buf_count = 0
-        self.samples_per_buffer = (self.samples_per_record *
-                                   self.records_per_buffer *
-                                   self.buffers_per_acquisition *
-                                   self.number_of_channels)
-        self.buffer = np.zeros(self.samples_per_buffer)
-
-        integer_list = np.arange(self.samples_per_record)
-        angle_list = (2 * np.pi * self.demodulation_frequency /
-                      self.sample_rate * integer_list)
-
-        cos_list = np.cos(angle_list)
-        sin_list = np.sin(angle_list)
-        self.cos_mat = np.kron(np.ones((1, self.records_per_buffer)), cos_list)
-        self.sin_mat = np.kron(np.ones((1, self.records_per_buffer)), sin_list)
-
-    def pre_acquire(self):
-        """
-        See AcquisitionController
-        :return:
-        """
-        # this could be used to start an Arbitrary Waveform Generator, etc...
-        # using this method ensures that the contents are executed AFTER the
-        # Alazar card starts listening for a trigger pulse
-        pass
-
-    def handle_buffer(self, data):
-        """
-        See AcquisitionController
-        :return:
-        """
-        i0 = self.bufcount * self.samples_per_buffer
-        i1 = i0 + self.samples_per_buffer
-        self.buffer[i0:i1] = data
-        self.buf_count += 1
-
-    def post_acquire(self):
-        """
-        See AcquisitionController
-        :return:
-        """
-        # for ATS9360 samples are arranged in the buffer as follows:
-        # S00A, S00B, S01A, S01B...S10A, S10B, S11A, S11B...
-        # where SXYZ is record X, sample Y, channel Z.
-
-        # reshapes data to be (samples * records * buffers)
-
-        # TODO!!
-
-        magA, phaseA = 0, 0
-
-        return magA, phaseA
-
-    def fit(self, rec):
-        # center rec around 0
-        rec = rec - np.mean(rec)
-
-        # multiply with software wave
-        re_wave = np.multiply(rec, self.cos_mat)
-        im_wave = np.multiply(rec, self.sin_mat)
-        cutoff = self.demodulation_frequency
-        numtaps = 30
-        axis = 0
-
-        # filter out double freq component to obtian constant term
-        if self.filter == 0:
-            RePart = self.filter_win(re_wave, numtaps, cutoff, axis=axis)
-            ImPart = self.filter_win(im_wave, numtaps, cutoff, axis=axis)
-        elif self.filter == 1:
-            RePart = self.filter_hamming(re_wave, numtaps, cutoff, axis=axis)
-            ImPart = self.filter_hamming(im_wave, numtaps, cutoff, axis=axis)
-        elif self.filter == 2:
-            RePart = self.filter_ls(re_wave, numtaps, cutoff, axis, axis=axis)
-            ImPart = self.filter_ls(im_wave, numtaps, cutoff, axis, axis=axis)
-
-        # convert to magnitude and phase data and average over samples
-        # data returned is (records * buffers)
-        complex_mat = RePart + ImPart * 1j
-        mag = np.mean(2 * abs(complex_mat), axis=0)
-        phase = np.mean(np.angle(complex_mat, axis=0, deg=True))
-
-        return mag, phase
-
-    def filter_hamming(self, rec, numtaps, cutoff, axis=-1):
-        sample_rate = self.sample_rate
-        nyq_rate = sample_rate / 2.
-        fir_coef = signal.firwin(numtaps,
-                                 cutoff / nyq_rate,
-                                 window="hamming")
-        filtered_rec = 2 * signal.lfilter(fir_coef, 1.0, rec, axis=axis)
-        return filtered_rec
-
-    def filter_win(self, rec, numtaps, cutoff, axis=-1):
-        sample_rate = self.sample_rate
-        nyq_rate = sample_rate / 2.
-        fir_coef = signal.firwin(numtaps,
-                                 cutoff / nyq_rate)
-        filtered_rec = 2 * signal.lfilter(fir_coef, 1.0, rec, axis=axis)
-        return filtered_rec
-
-    def filter_ls(self, rec, numtaps, cutoff, axis=-1):
-        sample_rate = self.sample_rate
-        nyq_rate = sample_rate / 2.
-        bands = [0, cutoff / nyq_rate, cutoff / nyq_rate, 1]
-        desired = [1, 1, 0, 0]
-        fir_coef = signal.firls(numtaps,
-                                bands,
-                                desired,
-                                nyq=nyq_rate)
-        filtered_rec = 2 * signal.lfilter(fir_coef, 1.0, rec, axis=axis)
-        return filtered_rec
diff --git a/qcodes/instrument_drivers/AlazarTech/rec_controller.py b/qcodes/instrument_drivers/AlazarTech/rec_controller.py
deleted file mode 100644
index 5513e384264b..000000000000
--- a/qcodes/instrument_drivers/AlazarTech/rec_controller.py
+++ /dev/null
@@ -1,294 +0,0 @@
-import logging
-from .ATS import AcquisitionController
-import numpy as np
-from qcodes import Parameter
-from qcodes.utils.helpers import filter_win, filter_ls
-
-
-class RecordsParam(Parameter):
-    """
-    Hardware controlled parameter class for Alazar acquisition. To be used with
-    HD_Records_Controller (tested with ATS9360 board) for return of an array of
-    record data from the Alazar, averaged over samples and buffers.
-
-    TODO(nataliejpg) fix setpoints/shapes horriblenesss
-    """
-
-    def __init__(self, name, instrument):
-        super().__init__(name)
-        self._instrument = instrument
-        self.acquisitionkwargs = {}
-        self.names = ('magnitude', 'phase')
-        self.units = ('', '')
-        self.setpoint_names = (('record_num',), ('record_num',))
-        self.setpoints = ((1,), (1,))
-        self.shapes = ((1,), (1,))
-
-    def update_acquisition_kwargs(self, **kwargs):
-        # needed to update config of the software parameter on sweep change
-        # freq setpoints tuple as needs to be hashable for look up
-        if 'records_per_buffer' in kwargs:
-            npts = kwargs['records_per_buffer']
-            n = tuple(np.arange(npts))
-            self.setpoints = ((n,), (n,))
-            self.shapes = ((npts,), (npts,))
-        else:
-            raise ValueError('records_per_buffer must be specified')
-        # updates dict to be used in acquisition get call
-        self.acquisitionkwargs.update(**kwargs)
-
-    def get(self):
-        mag, phase = self._instrument._get_alazar().acquire(
-            acquisition_controller=self._instrument,
-            **self.acquisitionkwargs)
-        return mag, phase
-
-
-class HD_Records_Controller(AcquisitionController):
-    """
-    This is the Acquisition Controller class which works with the ATS9360,
-    averaging over buffers and records and demodulating with a software
-    reference signal, returning the  samples.
-    args:
-    name: name for this acquisition_conroller as an instrument
-    alazar_name: the name of the alazar instrument such that this controller
-        can communicate with the Alazar
-    demod_freq: the frequency of the software wave to be created
-    samp_rate: the rate of sampling
-    filt: the filter to be used to filter out double freq component
-    numtaps: number of freq components used in the filter
-    chan_b: whether there is also a second channel of data to be processed
-        and returned
-    **kwargs: kwargs are forwarded to the Instrument base class
-
-    TODO(nataliejpg) fix sample rate problem
-    TODO(nataliejpg) test filter options
-    TODO(nataliejpg) finish implementation of channel b option
-    TODO(nataliejpg) make demodulation freq a param
-    """
-
-    def __init__(self, name, alazar_name, demod_freq, samp_rate=5e8,
-                 filt='win', numtaps=101, chan_b=False, **kwargs):
-        filter_dict = {'win': 0, 'ls': 1, 'dot': 2}
-        self.demodulation_frequency = demod_freq
-        self.sample_rate = samp_rate
-        self.filter = filter_dict[filt]
-        self.numtaps = numtaps
-        self.chan_b = chan_b
-        self.samples_per_record = None
-        self.records_per_buffer = None
-        self.buffers_per_acquisition = None
-        self.number_of_channels = 2
-        self.samples_delay = None
-        self.samples_time = None
-        self.cos_mat = None
-        self.sin_mat = None
-        self.buffer = None
-        self.board_info = None
-        # make a call to the parent class and by extension,
-        # create the parameter structure of this class
-        super().__init__(name, alazar_name, **kwargs)
-
-        self.add_parameter(name='acquisition',
-                           parameter_class=RecordsParam)
-
-    def update_acquisitionkwargs(self, **kwargs):
-        """
-        Updates the kwargs to be used when
-        alazar_driver.acquire is called via a get call of the
-        acquisition RecordsParam.
-
-        It is also used to set the limits on the selection of
-        samples used (bounded by delay and integration time)
-
-        :param kwargs:
-        :return:
-        """
-        samples_per_record = kwargs['samples_per_record']
-        sample_rate = self.sample_rate
-
-        if 'int_delay' in kwargs:
-            int_delay = kwargs.pop('int_delay')
-            samp_delay = int_delay * sample_rate
-            samples_delay_min = (self.numtaps - 1)
-            if samp_delay < samples_delay_min:
-                int_delay_min = samples_delay_min / sample_rate
-                logging.warning(
-                    'delay is less than recommended for filter choice: '
-                    '(expect delay >= {}'.format(int_delay_min))
-        else:
-            samp_delay = self.numtaps - 1
-
-        if 'int_time' in kwargs:
-            int_time = kwargs.pop('int_time')
-            samp_time = int_time * sample_rate
-            samples_time_max = (samples_per_record - samp_delay)
-            oscilations_measured = int_time * self.demodulation_frequency
-            oversampling = sample_rate / (2 * self.demodulation_frequency)
-            if samp_time > samples_time_max:
-                int_time_max = samples_time_max / sample_rate
-                raise ValueError('int_time {} is longer than total_time - '
-                                 'delay: {}'.format(int_time, int_time_max))
-            elif oscilations_measured < 10:
-                logging.warning('{} oscilations measured, recommend at '
-                                'least 10: decrease sampling rate, take '
-                                'more samples or increase demodulation '
-                                'freq'.format(oscilations_measured))
-            elif oversampling < 1:
-                logging.warning('oversampling rate is {}, recommend > 1: '
-                                'increase sampling rate or decrease '
-                                'demodulation frequency'.format(oversampling))
-        else:
-            samp_time = samples_per_record - samp_delay
-
-        self.samples_time = int(samp_time)
-        self.samples_delay = int(samp_delay)
-
-        self.acquisition.update_acquisition_kwargs(**kwargs)
-
-    def pre_start_capture(self):
-        """
-        Called before capture start to update Acquisition Controller with
-        alazar acquisition params and set up software wave for demodulation.
-
-        sine and cosine matrices have shape (samples * records).
-
-        :return:
-        """
-        alazar = self._get_alazar()
-        self.samples_per_record = alazar.samples_per_record.get()
-        self.records_per_buffer = alazar.records_per_buffer.get()
-        self.buffers_per_acquisition = alazar.buffers_per_acquisition.get()
-        self.board_info = alazar.get_idn()
-        self.buffer = np.zeros(self.samples_per_record *
-                               self.records_per_buffer *
-                               self.number_of_channels)
-
-        integer_list = np.arange(self.samples_per_record, dtype=np.uint16)
-        angle_list = (2 * np.pi * self.demodulation_frequency /
-                      self.sample_rate * integer_list)
-
-        cos_list = np.cos(angle_list).reshape(self.samples_per_record, 1)
-        sin_list = np.sin(angle_list).reshape(self.samples_per_record, 1)
-
-        self.cos_mat = np.kron(np.ones((1, self.records_per_buffer)), cos_list)
-        self.sin_mat = np.kron(np.ones((1, self.records_per_buffer)), sin_list)
-
-    def pre_acquire(self):
-        """
-        See AcquisitionController
-        :return:
-        """
-        pass
-
-    def handle_buffer(self, data):
-        """
-        Adds data from alazar to buffer (effectively averaging)
-        :return:
-        """
-        self.buffer += data
-
-    def post_acquire(self):
-        """
-        Processes the data according to ATS9360 settings, splitting into
-        records and averaging over them, then applying demodulation fit
-        nb: currently only channel A
-        :return: samples_magnitude_array, samples_phase_array
-        """
-
-        # for ATS9360 samples are arranged in the buffer as follows:
-        # S00A, S00B, S01A, S01B...S10A, S10B, S11A, S11B...
-        # where SXYZ is record X, sample Y, channel Z.
-
-        # break buffer up into records and shapes to be (samples * records)
-        recordA = np.zeros((self.samples_per_record, self.records_per_buffer),
-                           dtype=np.uint16)
-        for i in range(self.records_per_buffer):
-            i0 = (i * self.samples_per_record * self.number_of_channels)
-            i1 = (i0 + self.samples_per_record * self.number_of_channels)
-            recordA[:, i] = np.uint16(
-                self.buffer[i0:i1:self.number_of_channels] /
-                self.buffers_per_acquisition)
-
-        # do demodulation
-        magA, phaseA = self.fit(recordA)
-
-        # same for chan b
-        if self.chan_b:
-            raise NotImplementedError('chan b code not complete')
-
-        return magA, phaseA
-
-    def fit(self, rec):
-        """
-        Applies volts conversion, demodulation fit, low bandpass filter
-        and integration limits to samples * records array
-        :return: records_magnitude_array, records_phase_array
-        """
-
-        # convert rec to volts
-        bps = self.board_info['bits_per_sample']
-        if bps == 12:
-            volt_rec = sample_to_volt_u12(rec, bps)
-        else:
-            logging.warning('sample to volt conversion does not exist for'
-                            ' bps != 12, centered raw samples returned')
-            volt_rec = rec - np.mean(rec, 0)
-
-        # multiply with software wave
-        re_wave = np.multiply(volt_rec, self.cos_mat)
-        im_wave = np.multiply(volt_rec, self.sin_mat)
-        cutoff = self.demodulation_frequency / 10
-        ax = 0
-
-        # filter out higher freq component
-        if self.filter == 0:
-            re_filtered = filter_win(re_wave, cutoff, self.sample_rate,
-                                     self.numtaps, axis=ax)
-            im_filtered = filter_win(im_wave, cutoff, self.sample_rate,
-                                     self.numtaps, axis=ax)
-        elif self.filter == 1:
-            re_filtered = filter_ls(re_wave, cutoff, self.sample_rate,
-                                    self.numtaps, axis=ax)
-            im_filtered = filter_ls(im_wave, cutoff, self.sample_rate,
-                                    self.numtaps, axis=ax)
-        elif self.filter == 2:
-            re_filtered = re_wave
-            im_filtered = im_wave
-
-        # apply integration limits
-        start = self.samples_delay
-        end = start + self.samples_time
-
-        re_limited = re_filtered[start:end, :]
-        im_limited = im_filtered[start:end, :]
-
-        # convert to magnitude and phase
-        complex_num = re_limited + im_limited * 1j
-        mag = np.mean(abs(complex_num), 0)
-        phase = np.mean(np.angle(complex_num, deg=True), 0)
-
-        return mag, phase
-
-
-def sample_to_volt_u12(raw_samples, bps):
-    """
-    Applies volts conversion for 12 bit sample data stored
-    in 2 bytes
-    :return: samples_magnitude_array, samples_phase_array
-    """
-
-    # right_shift 16-bit sample by 4 to get 12 bit sample
-    shifted_samples = np.right_shift(raw_samples, 4)
-
-    # Alazar calibration
-    code_zero = (1 << (bps - 1)) - 0.5
-    code_range = (1 << (bps - 1)) - 0.5
-
-    # TODO(nataliejpg) make this not hard coded
-    input_range_volts = 1
-    # Convert to volts
-    volt_samples = np.float64(input_range_volts *
-                              (shifted_samples - code_zero) / code_range)
-
-    return volt_samples
diff --git a/qcodes/instrument_drivers/AlazarTech/rec_samp_controller.py b/qcodes/instrument_drivers/AlazarTech/rec_samp_controller.py
deleted file mode 100644
index 7e86e9ffc211..000000000000
--- a/qcodes/instrument_drivers/AlazarTech/rec_samp_controller.py
+++ /dev/null
@@ -1,233 +0,0 @@
-from .ATS import AcquisitionController
-import numpy as np
-from qcodes import Parameter
-from scipy import signal
-
-
-class RecSampParam(Parameter):
-    """
-    Hardware controlled parameter class for Alazar acquisition. To be used with
-    HD_Samples_Controller (tested with ATS9360 board) for return of an array of
-    sample data from the Alazar, averaged over records and buffers.
-
-    TODO(nataliejpg) fix setpoints/shapes horriblenesss
-    TODO(nataliejpg) make it actually work...
-    """
-
-    def __init__(self, name, instrument):
-        super().__init__(name)
-        self._instrument = instrument
-        self.acquisitionkwargs = {}
-        self.names = ('magnitude', 'phase')
-        self.units = ('', '')
-        self.setpoint_names = (('rec_num', 'samp_num'),
-                               ('rec_num', 'samp_num'))
-        self.setpoints = ((1, 1), (1, 1))
-        self.shapes = ((1, 1), (1, 1))
-
-    def update_acquisition_kwargs(self, **kwargs):
-        # needed to update config of the software parameter on sweep change
-        # freq setpoints tuple as needs to be hashable for look up
-        if 'samples_per_record' and 'records_per_buffer' in kwargs:
-            spts = kwargs['samples_per_record']
-            rpts = kwargs['records_per_buffer']
-            s = tuple(np.arange(spts))
-            r = tuple(np.arange(rpts))
-            self.setpoints = ((s, r), (s, r))
-            self.shapes = ((spts, rpts), (spts, rpts))
-        else:
-            raise ValueError(
-                'records_per_buffer and samples_per_record must be specified')
-        # updates dict to be used in acquisition get call
-        self.acquisitionkwargs.update(**kwargs)
-
-    def get(self):
-        mag, phase = self._instrument._get_alazar().acquire(
-            acquisition_controller=self._instrument,
-            **self.acquisitionkwargs)
-        return mag, phase
-
-
-class HD_RecSamp_Controller(AcquisitionController):
-    """
-    This is the Acquisition Controller class which works with the ATS9360,
-    averaging over buffers and records and demodulating with a software
-    reference signal, returning the  samples.
-    args:
-    name: name for this acquisition_conroller as an instrument
-    alazar_name: the name of the alazar instrument such that this controller
-        can communicate with the Alazar
-    demod_freq: the frequency of the software wave to be created
-    samp_rate: the rate of sampling
-    filt: the filter to be used to filter out double freq component
-    chan_b: whether there is also a second channel of data to be processed
-        and returned
-    **kwargs: kwargs are forwarded to the Instrument base class
-
-    TODO(nataliejpg) fix sample rate problem
-    TODO(nataliejpg) test filter options
-    TODO(nataliejpg) test mag phase logic
-    TODO(nataliejpg) finish implementation of channel b option
-    TODO(nataliejpg) make filter settings not hard coded
-    """
-
-    def __init__(self, name, alazar_name, demod_freq, samp_rate=500e6,
-                 filt='win', chan_b=False, **kwargs):
-        filter_dict = {'win': 0, 'hamming': 1, 'ls': 2}
-        self.demodulation_frequency = demod_freq
-        self.sample_rate = samp_rate
-        self.filter = filter_dict[filt]
-        self.chan_b = chan_b
-        self.samples_per_record = 0
-        self.records_per_buffer = 0
-        self.buffers_per_acquisition = 0
-        self.number_of_channels = 2
-        self.cos_list = None
-        self.sin_list = None
-        self.buffer = None
-        # make a call to the parent class and by extension,
-        # create the parameter structure of this class
-        super().__init__(name, alazar_name, **kwargs)
-
-        self.add_parameter(name='acquisition',
-                           parameter_class=RecSampParam)
-
-    def update_acquisitionkwargs(self, **kwargs):
-        """
-        This method must be used to update the kwargs used for the acquisition
-        with the alazar_driver.acquire
-        :param kwargs:
-        :return:
-        """
-        self.acquisition.update_acquisition_kwargs(**kwargs)
-
-    def pre_start_capture(self):
-        """
-        See AcquisitionController
-        :return:
-        """
-        alazar = self._get_alazar()
-        self.samples_per_record = alazar.samples_per_record.get()
-        self.records_per_buffer = alazar.records_per_buffer.get()
-        self.buffers_per_acquisition = alazar.buffers_per_acquisition.get()
-        self.buffer = np.zeros(self.samples_per_record *
-                               self.records_per_buffer *
-                               self.number_of_channels)
-
-        integer_list = np.arange(self.samples_per_record)
-        angle_list = (2 * np.pi * self.demodulation_frequency /
-                      self.sample_rate * integer_list)
-
-        cos_list = np.cos(angle_list).reshape(self.samples_per_record, 1)
-        sin_list = np.sin(angle_list).reshape(self.samples_per_record, 1)
-        self.cos_mat = np.kron(np.ones((1, self.records_per_buffer)), cos_list)
-        self.sin_mat = np.kron(np.ones((1, self.records_per_buffer)), sin_list)
-
-    def pre_acquire(self):
-        """
-        See AcquisitionController
-        :return:
-        """
-        # this could be used to start an Arbitrary Waveform Generator, etc...
-        # using this method ensures that the contents are executed AFTER the
-        # Alazar card starts listening for a trigger pulse
-        pass
-
-    def handle_buffer(self, data):
-        """
-        See AcquisitionController
-        :return:
-        """
-        # average over buffers
-        self.buffer += data
-
-    def post_acquire(self):
-        """
-        See AcquisitionController
-        :return:
-        """
-
-        # for ATS9360 samples are arranged in the buffer as follows:
-        # S00A, S00B, S01A, S01B...S10A, S10B, S11A, S11B...
-        # where SXYZ is record X, sample Y, channel Z.
-
-        # reshapes date to be (samples * records)
-        recordA = np.zeros((self.samples_per_record, self.records_per_buffer))
-        for i in range(self.records_per_buffer):
-            i0 = i * self.number_of_channels * self.samples_per_record
-            i1 = i0 + self.number_of_channels * self.samples_per_record
-            recordA[:, i] = (self.buffer[i0:i1:self.number_of_channels] /
-                             self.buffers_per_acquisition)
-
-        # return averaged chan A data (records)
-        magA, phaseA = self.fit(recordA)
-
-        # same for B
-        # if self.chan_b:
-        # recordB = np.zeros(
-        #        (self.samples_per_record, self.records_per_buffer))
-        # for i in self.records_per_buffer:
-        # recordB[i, :] = self.buffer[1:full_rec_length:step] / averaging
-        # magB, phaseB = self.fit(recordB)
-
-        # return data (samples * records)
-        return magA, phaseA
-
-    def fit(self, rec):
-        # center rec around 0
-        rec = rec - np.mean(rec)
-
-        # multiply with software wave
-        re_wave = np.multiply(rec, self.cos_mat)
-        im_wave = np.multiply(rec, self.sin_mat)
-        cutoff = self.demodulation_frequency
-        numtaps = 30
-        axis = 0
-
-        # filter out double freq component to obtian constant term
-        if self.filter == 0:
-            RePart = self.filter_win(re_wave, numtaps, cutoff, axis=axis)
-            ImPart = self.filter_win(im_wave, numtaps, cutoff, axis=axis)
-        elif self.filter == 1:
-            RePart = self.filter_hamming(re_wave, numtaps, cutoff, axis=axis)
-            ImPart = self.filter_hamming(im_wave, numtaps, cutoff, axis=axis)
-        elif self.filter == 2:
-            RePart = self.filter_ls(re_wave, numtaps, cutoff, axis, axis=axis)
-            ImPart = self.filter_ls(im_wave, numtaps, cutoff, axis, axis=axis)
-
-        # convert to magnitude and phase data
-        # data returned is (samples * records)
-        complex_num = RePart + ImPart * 1j
-        mag = 2 * abs(complex_num)
-        phase = np.angle(complex_num, deg=True)
-
-        return mag, phase
-
-    def filter_hamming(self, rec, numtaps, cutoff, axis=-1):
-        sample_rate = self.sample_rate
-        nyq_rate = sample_rate / 2.
-        fir_coef = signal.firwin(numtaps,
-                                 cutoff / nyq_rate,
-                                 window="hamming")
-        filtered_rec = 2 * signal.lfilter(fir_coef, 1.0, rec, axis=axis)
-        return filtered_rec
-
-    def filter_win(self, rec, numtaps, cutoff, axis=-1):
-        sample_rate = self.sample_rate
-        nyq_rate = sample_rate / 2.
-        fir_coef = signal.firwin(numtaps,
-                                 cutoff / nyq_rate)
-        filtered_rec = 2 * signal.lfilter(fir_coef, 1.0, rec, axis=axis)
-        return filtered_rec
-
-    def filter_ls(self, rec, numtaps, cutoff, axis=-1):
-        sample_rate = self.sample_rate
-        nyq_rate = sample_rate / 2.
-        bands = [0, cutoff / nyq_rate, cutoff / nyq_rate, 1]
-        desired = [1, 1, 0, 0]
-        fir_coef = signal.firls(numtaps,
-                                bands,
-                                desired,
-                                nyq=nyq_rate)
-        filtered_rec = 2 * signal.lfilter(fir_coef, 1.0, rec, axis=axis)
-        return filtered_rec
diff --git a/qcodes/instrument_drivers/AlazarTech/samp_controller.py b/qcodes/instrument_drivers/AlazarTech/samp_controller.py
deleted file mode 100644
index e37acc16d8b5..000000000000
--- a/qcodes/instrument_drivers/AlazarTech/samp_controller.py
+++ /dev/null
@@ -1,317 +0,0 @@
-import logging
-from .ATS import AcquisitionController
-import numpy as np
-from qcodes import Parameter
-from qcodes.utils.helpers import filter_win, filter_ls
-from qcodes.instrument.parameter import ManualParameter
-from qcodes.utils import validators
-
-
-class SamplesParam(Parameter):
-    """
-    Hardware controlled parameter class for Alazar acquisition. To be used with
-    HD_Samples_Controller (tested with ATS9360 board) for return of an array of
-    sample data from the Alazar, averaged over records and buffers.
-
-    TODO(nataliejpg) refactor setpoints/shapes horriblenesss
-    TODO(nataliejpg) setpoint units
-    """
-
-    def __init__(self, name, instrument):
-        super().__init__(name)
-        self._instrument = instrument
-        self.acquisitionkwargs = {}
-        self.names = ('magnitude', 'phase')
-        self.units = ('', '')
-        self.setpoint_names = (('acq_time (s)',), ('acq_time (s)',))
-        self.setpoints = ((1,), (1,))
-        self.shapes = ((1,), (1,))
-
-#    def update_acquisition_kwargs(self, **kwargs):
-#        # updates dict to be used in acquisition get call
-#        self.acquisitionkwargs.update(**kwargs)
-    
-    def update_sweep(self, start, stop, npts):
-        n = tuple(np.linspace(start, stop, num=npts))
-        self.setpoints = ((n,), (n,))
-        self.shapes = ((npts,), (npts,))
-    
-    def get(self):
-        mag, phase = self._instrument._get_alazar().acquire(
-            acquisition_controller=self._instrument,
-            **self.acquisitionkwargs)
-        return mag, phase
-
-
-class HD_Samples_Controller(AcquisitionController):
-    """
-    This is the Acquisition Controller class which works with the ATS9360,
-    averaging over buffers and records and demodulating with a software
-    reference signal, returning the  samples.
-    args:
-    name: name for this acquisition_conroller as an instrument
-    alazar_name: the name of the alazar instrument such that this controller
-        can communicate with the Alazar
-    demod_freq: the frequency of the software wave to be created
-    samp_rate: the rate of sampling
-    filt: the filter to be used to filter out double freq component (win or ls)
-    numtaps: number of freq components used in the filter
-    chan_b: whether there is also a second channel of data to be processed
-        and returned
-    **kwargs: kwargs are forwarded to the Instrument base class
-
-    TODO(nataliejpg) test filter options
-    TODO(nataliejpg) finish implementation of channel b option
-    TODO(nataliejpg) validators for int_time and int_delay
-    """
-
-    def __init__(self, name, alazar_name, samp_rate=5e8,
-                 filt='win', numtaps=101, chan_b=False, **kwargs):
-        filter_dict = {'win': 0, 'ls': 1}
-        self.sample_rate = samp_rate
-        self.filter = filter_dict[filt]
-        self.numtaps = numtaps
-        self.chan_b = chan_b
-        self.samples_per_record = 0
-        self.records_per_buffer = 0
-        self.buffers_per_acquisition = 0
-        self.number_of_channels = 2
-        self.samples_delay = 0
-        self.samples_time = 0
-        self.cos_list = None
-        self.sin_list = None
-        self.buffer = None
-        self.board_info = None
-        # make a call to the parent class and by extension,
-        # create the parameter structure of this class
-        super().__init__(name, alazar_name, **kwargs)
-
-        self.add_parameter(name='acquisition',
-                           parameter_class=SamplesParam)
-        self.add_parameter(name='demodulation_frequency',
-                           initial_value=20e6,
-                           parameter_class=ManualParameter)
-        self.add_parameter(name='int_time',
-                           initial_value=None,
-                           parameter_class=ManualParameter)
-        self.add_parameter(name='int_delay',
-                           initial_value=None,
-                           parameter_class=ManualParameter)
-
-    def update_acquisition_settings(self, **kwargs):
-        """
-        Updates the kwargs to be used when
-        alazar_driver.acquire is called via a get call of the
-        acquisition SamplesParam.
-
-        It is also used to set the limits on the selection of
-        samples returned (bounded by delay and integration time)
-        and update this information in the Samples Parameter.
-
-        :param kwargs:
-        :return:
-        """
-        alazar = self._get_alazar()
-        samples_per_record = kwargs['samples_per_record']
-        self.sample_rate = alazar.get_sample_rate()
-        time_available = samples_per_record / self.sample_rate
-            
-        if self.int_delay() == 0:
-            samp_delay = self.numtaps - 1
-            self.int_delay(samp_delay / self.sample_rate)
-        else:
-            self.check_delay(time_available)
-#            except ValueError as e:
-#                self.int_delay(0)
-        
-        time_available -= self.int_delay()
-        
-        if self.int_time() == 0:
-            self.int_time(time_available)
-        else:
-            self.check_time(time_available)
-        
-        start = self.int_delay()
-        stop = self.int_delay() + self.int_time()
-        npts = int(self.int_time() * self.sample_rate)
-
-        self.acquisition.acquisitionkwargs.update(**kwargs)
-        self.acquisition.update_sweep(start, stop, npts)
-
-    def pre_start_capture(self):
-        """
-        Called before capture start to update Acquisition Controller with
-        alazar acquisition params and set up software wave for demodulation.
-        :return:
-        """
-        alazar = self._get_alazar()
-        self.samples_per_record = alazar.samples_per_record.get()
-        self.records_per_buffer = alazar.records_per_buffer.get()
-        self.buffers_per_acquisition = alazar.buffers_per_acquisition.get()
-        self.board_info = alazar.get_idn()
-        self.buffer = np.zeros(self.samples_per_record *
-                               self.records_per_buffer *
-                               self.number_of_channels)
-        if self.sample_rate != alazar.get_sample_rate():
-            raise Exception('acq controller sample rate does not match instrument'
-                            'value, most likely need to call update_acquisition_settings' )
-
-        integer_list = np.arange(self.samples_per_record, dtype=np.uint16)
-        angle_list = (2 * np.pi * self.demodulation_frequency() /
-                      self.sample_rate * integer_list)
-
-        self.cos_list = np.cos(angle_list)
-        self.sin_list = np.sin(angle_list)
-
-    def pre_acquire(self):
-        """
-        See AcquisitionController
-        :return:
-        """
-        pass
-
-    def handle_buffer(self, data):
-        """
-        Adds data from alazar to buffer (effectively averaging)
-        :return:
-        """
-        self.buffer += data
-
-    def post_acquire(self):
-        """
-        Processes the data according to ATS9360 settings, splitting into
-        records and averaging over them, then applying demodulation fit
-        nb: currently only channel A
-        :return: samples_magnitude_array, samples_phase_array
-        """
-        records_per_acquisition = (self.buffers_per_acquisition *
-                                   self.records_per_buffer)
-        # for ATS9360 samples are arranged in the buffer as follows:
-        # S00A, S00B, S01A, S01B...S10A, S10B, S11A, S11B...
-        # where SXYZ is record X, sample Y, channel Z.
-
-        # break buffer up into records and averages over them
-        recA = np.zeros(self.samples_per_record)
-        for i in range(self.records_per_buffer):
-            i0 = (i * self.samples_per_record * self.number_of_channels)
-            i1 = (i0 + self.samples_per_record * self.number_of_channels)
-            recA += self.buffer[i0:i1:self.number_of_channels]
-        recordA = np.uint16(recA / records_per_acquisition)
-        
-        # do demodulation
-        magA, phaseA = self.fit(recordA)
-
-        # same for chan b
-        if self.chan_b:
-            raise NotImplementedError('chan b code not complete')
-            # recordB = np.zeros(self.samples_per_record, dtype=np.uint16)
-            # for i in range(self.records_per_buffer):
-            #     i0 = (i * self.samples_per_record *
-            #           self.number_of_channels + 1)
-            #     i1 = (i0 + self.samples_per_record * self.number_of_channels)
-            #     recordB += np.uint16(self.buffer[i0:i1:self.number_of_channels] /
-            #                          records_per_acquisition)
-            # magB, phaseB = self.fit(recordB)
-
-        return magA, phaseA
-
-    def fit(self, rec):
-        print('got to fit, ave %s' % np.mean(rec))
-        """
-        Applies volts conversion, demodulation fit, low bandpass filter
-        and integration limits to samples array
-        :return: samples_magnitude_array, samples_phase_array
-        """
-
-        # convert rec to volts
-        bps = self.board_info['bits_per_sample']
-        if bps == 12:
-            volt_rec = sample_to_volt_u12(rec, bps)
-        else:
-            logging.warning('sample to volt conversion does not exist for'
-                            ' bps != 12, centered raw samples returned')
-            volt_rec = rec - np.mean(rec)
-
-        print(self.cos_list)
-        print(self.demodulation_frequency())    
-            
-        # multiply with software wave
-        re_wave = np.multiply(volt_rec, self.cos_list)
-        im_wave = np.multiply(volt_rec, self.sin_list)
-        cutoff = self.demodulation_frequency() / 10
-
-        # filter out higher freq component
-        if self.filter == 0:
-            re_filtered = filter_win(re_wave, cutoff,
-                                     self.sample_rate, self.numtaps)
-            im_filtered = filter_win(im_wave, cutoff,
-                                     self.sample_rate, self.numtaps)
-        elif self.filter == 1:
-            re_filtered = filter_ls(re_wave, cutoff,
-                                    self.sample_rate, self.numtaps)
-            im_filtered = filter_ls(im_wave, cutoff,
-                                    self.sample_rate, self.numtaps)
-
-        # apply integration limits
-        beginning = int(self.int_delay() / self.sample_rate)
-        end = beginning + int(self.int_time() / self.sample_rate)
-
-        re_limited = re_filtered[beginning:end]
-        im_limited = im_filtered[beginning:end]
-
-        # convert to magnitude and phase
-        complex_num = re_limited + im_limited * 1j
-        mag = abs(complex_num)
-        phase = np.angle(complex_num, deg=True)
-
-        return mag, phase
-
-    def check_delay(self, time_available):
-        samples_delay_min = (self.numtaps - 1)
-        int_delay_min = samples_delay_min / self.sample_rate
-        if self.int_delay() > time_available:
-            raise ValueError('int_delay {} is longer than total_time '
-                            '{}'.format(self.int_delay(), time_available))
-        elif self.int_delay() < int_delay_min:
-            logging.warning(
-                'delay is less than recommended for filter choice: '
-                '(expect delay >= {}'.format(int_delay_min))
-                
-    def check_time(self, time_available):
-        oscilations_measured = self.int_time() * self.demodulation_frequency()
-        oversampling = self.sample_rate / (2 * self.demodulation_frequency())
-        if self.int_time() > time_available:
-            raise ValueError('int_time {} is longer than total_time - '
-                            'delay = {}'.format(self.int_time(), time_available))
-        elif oscilations_measured < 10:
-            logging.warning('{} oscilations measured, recommend at '
-                            'least 10: decrease sampling rate, take '
-                            'more samples or increase demodulation '
-                            'freq'.format(oscilations_measured))
-        elif oversampling < 1:
-            logging.warning('oversampling rate is {}, recommend > 1: '
-                            'increase sampling rate or decrease '
-                            'demodulation frequency'.format(oversampling))   
-                        
-          
-def sample_to_volt_u12(raw_samples, bps):
-    """
-    Applies volts conversion for 12 bit sample data stored
-    in 2 bytes
-    :return: samples_magnitude_array, samples_phase_array
-    """
-
-    # right_shift 16-bit sample by 4 to get 12 bit sample
-    shifted_samples = np.right_shift(raw_samples, 4)
-
-    # Alazar calibration
-    code_zero = (1 << (bps - 1)) - 0.5
-    code_range = (1 << (bps - 1)) - 0.5
-
-    # TODO(nataliejpg) make this not hard coded
-    input_range_volts = 1
-    # Convert to volts
-    volt_samples = np.float64(input_range_volts *
-                              (shifted_samples - code_zero) / code_range)
-
-    return volt_samples

From d955535cfcaf884fd38745c0d4ace4b2329adc9a Mon Sep 17 00:00:00 2001
From: nataliejpg 
Date: Fri, 25 Nov 2016 19:26:40 +0100
Subject: [PATCH 060/328] fuuuuuck

---
 .../AlazarTech/samp_controller.py             | 317 ++++++++++++++++++
 1 file changed, 317 insertions(+)
 create mode 100644 qcodes/instrument_drivers/AlazarTech/samp_controller.py

diff --git a/qcodes/instrument_drivers/AlazarTech/samp_controller.py b/qcodes/instrument_drivers/AlazarTech/samp_controller.py
new file mode 100644
index 000000000000..5919e04d48ac
--- /dev/null
+++ b/qcodes/instrument_drivers/AlazarTech/samp_controller.py
@@ -0,0 +1,317 @@
+import logging
+from .ATS import AcquisitionController
+import numpy as np
+from qcodes import Parameter
+from qcodes.utils.helpers import filter_win, filter_ls
+from qcodes.instrument.parameter import ManualParameter
+from qcodes.utils import validators
+
+
+class SamplesParam(Parameter):
+    """
+    Hardware controlled parameter class for Alazar acquisition. To be used with
+    HD_Samples_Controller (tested with ATS9360 board) for return of an array of
+    sample data from the Alazar, averaged over records and buffers.
+
+    TODO(nataliejpg) refactor setpoints/shapes horriblenesss
+    TODO(nataliejpg) setpoint units
+    """
+
+    def __init__(self, name, instrument):
+        super().__init__(name)
+        self._instrument = instrument
+        self.acquisitionkwargs = {}
+        self.names = ('magnitude', 'phase')
+        self.units = ('', '')
+        self.setpoint_names = (('acq_time (s)',), ('acq_time (s)',))
+        self.setpoints = ((1,), (1,))
+        self.shapes = ((1,), (1,))
+    
+    def update_sweep(self, start, stop, npts):
+        n = tuple(np.linspace(start, stop, num=npts))
+        self.setpoints = ((n,), (n,))
+        self.shapes = ((npts,), (npts,))
+    
+    def get(self):
+        mag, phase = self._instrument._get_alazar().acquire(
+            acquisition_controller=self._instrument,
+            **self.acquisitionkwargs)
+        print(mag)
+        return mag, phase
+
+
+class HD_Samples_Controller(AcquisitionController):
+    """
+    This is the Acquisition Controller class which works with the ATS9360,
+    averaging over buffers and records and demodulating with a software
+    reference signal, returning the  samples.
+    args:
+    name: name for this acquisition_conroller as an instrument
+    alazar_name: the name of the alazar instrument such that this controller
+        can communicate with the Alazar
+    demod_freq: the frequency of the software wave to be created
+    samp_rate: the rate of sampling
+    filt: the filter to be used to filter out double freq component (win or ls)
+    numtaps: number of freq components used in the filter
+    chan_b: whether there is also a second channel of data to be processed
+        and returned
+    **kwargs: kwargs are forwarded to the Instrument base class
+
+    TODO(nataliejpg) test filter options
+    TODO(nataliejpg) finish implementation of channel b option
+    TODO(nataliejpg) validators for int_time and int_delay
+    """
+
+    def __init__(self, name, alazar_name, samp_rate=5e8,
+                 filt='win', numtaps=101, chan_b=False, **kwargs):
+        filter_dict = {'win': 0, 'ls': 1}
+        self.sample_rate = samp_rate
+        self.filter = filter_dict[filt]
+        self.numtaps = numtaps
+        self.chan_b = chan_b
+        self.samples_per_record = 0
+        self.records_per_buffer = 0
+        self.buffers_per_acquisition = 0
+        self.number_of_channels = 2
+        self.samples_delay = 0
+        self.samples_time = 0
+        self.cos_list = None
+        self.sin_list = None
+        self.buffer = None
+        self.board_info = None
+        # make a call to the parent class and by extension,
+        # create the parameter structure of this class
+        super().__init__(name, alazar_name, **kwargs)
+
+        self.add_parameter(name='acquisition',
+                           parameter_class=SamplesParam)
+        self.add_parameter(name='demodulation_frequency',
+                           initial_value=20e6,
+                           parameter_class=ManualParameter)
+        self.add_parameter(name='int_time',
+                           initial_value=None,
+                           parameter_class=ManualParameter)
+        self.add_parameter(name='int_delay',
+                           initial_value=None,
+                           parameter_class=ManualParameter)
+
+    def update_acquisition_settings(self, **kwargs):
+        """
+        Updates the kwargs to be used when
+        alazar_driver.acquire is called via a get call of the
+        acquisition SamplesParam.
+
+        It is also used to set the limits on the selection of
+        samples returned (bounded by delay and integration time)
+        and update this information in the Samples Parameter.
+
+        :param kwargs:
+        :return:
+        """
+        alazar = self._get_alazar()
+        self.samples_per_record = kwargs['samples_per_record']
+        self.sample_rate = alazar.get_sample_rate()
+        time_available = self.samples_per_record / self.sample_rate
+            
+        if self.int_delay() is None:
+            samp_delay = self.numtaps - 1
+            self.int_delay(samp_delay / self.sample_rate)
+        else:
+            self.check_delay(time_available)
+        
+        time_available -= self.int_delay()
+        
+        if self.int_time() is None:
+            self.int_time(time_available)
+        else:
+            self.check_time(time_available)
+        
+        start = self.int_delay()
+        stop = self.int_delay() + self.int_time()
+        npts = int(self.int_time() * self.sample_rate)
+
+        self.acquisition.acquisitionkwargs.update(**kwargs)
+        self.acquisition.update_sweep(start, stop, npts)
+
+    def pre_start_capture(self):
+        """
+        Called before capture start to update Acquisition Controller with
+        alazar acquisition params and set up software wave for demodulation.
+        :return:
+        """
+        alazar = self._get_alazar()
+        self.samples_per_record = alazar.samples_per_record.get()
+        self.records_per_buffer = alazar.records_per_buffer.get()
+        self.buffers_per_acquisition = alazar.buffers_per_acquisition.get()
+        self.board_info = alazar.get_idn()
+        self.buffer = np.zeros(self.samples_per_record *
+                               self.records_per_buffer *
+                               self.number_of_channels)
+        if self.sample_rate != alazar.get_sample_rate():
+            raise Exception('acq controller sample rate does not match instrument'
+                            'value, most likely need to call update_acquisition_settings' )
+
+        integer_list = np.arange(self.samples_per_record, dtype=np.uint16)
+        angle_list = (2 * np.pi * self.demodulation_frequency() /
+                      self.sample_rate * integer_list)
+
+        self.cos_list = np.cos(angle_list)
+        self.sin_list = np.sin(angle_list)
+
+    def pre_acquire(self):
+        """
+        See AcquisitionController
+        :return:
+        """
+        pass
+
+    def handle_buffer(self, data):
+        """
+        Adds data from alazar to buffer (effectively averaging)
+        :return:
+        """
+        self.buffer += data
+
+    def post_acquire(self):
+        """
+        Processes the data according to ATS9360 settings, splitting into
+        records and averaging over them, then applying demodulation fit
+        nb: currently only channel A
+        :return: samples_magnitude_array, samples_phase_array
+        """
+        records_per_acquisition = (self.buffers_per_acquisition *
+                                   self.records_per_buffer)
+        # for ATS9360 samples are arranged in the buffer as follows:
+        # S00A, S00B, S01A, S01B...S10A, S10B, S11A, S11B...
+        # where SXYZ is record X, sample Y, channel Z.
+
+        # break buffer up into records and averages over them
+        recA = np.zeros(self.samples_per_record)
+        for i in range(self.records_per_buffer):
+            i0 = (i * self.samples_per_record * self.number_of_channels)
+            i1 = (i0 + self.samples_per_record * self.number_of_channels)
+            recA += self.buffer[i0:i1:self.number_of_channels]
+        recordA = np.uint16(recA / records_per_acquisition)
+        
+        # do demodulation
+        magA, phaseA = self.fit(recordA)
+
+        # same for chan b
+        if self.chan_b:
+            raise NotImplementedError('chan b code not complete')
+            # recordB = np.zeros(self.samples_per_record, dtype=np.uint16)
+            # for i in range(self.records_per_buffer):
+            #     i0 = (i * self.samples_per_record *
+            #           self.number_of_channels + 1)
+            #     i1 = (i0 + self.samples_per_record * self.number_of_channels)
+            #     recordB += np.uint16(self.buffer[i0:i1:self.number_of_channels] /
+            #                          records_per_acquisition)
+            # magB, phaseB = self.fit(recordB)
+
+        return magA, phaseA
+
+    def fit(self, rec):
+        """
+        Applies volts conversion, demodulation fit, low bandpass filter
+        and integration limits to samples array
+        :return: samples_magnitude_array, samples_phase_array
+        """
+
+        # convert rec to volts
+        bps = self.board_info['bits_per_sample']
+        if bps == 12:
+            volt_rec = sample_to_volt_u12(rec, bps)
+        else:
+            logging.warning('sample to volt conversion does not exist for'
+                            ' bps != 12, centered raw samples returned')
+            volt_rec = rec - np.mean(rec)
+            
+        # multiply with software wave
+        re_wave = np.multiply(volt_rec, self.cos_list)
+        im_wave = np.multiply(volt_rec, self.sin_list)
+        cutoff = self.demodulation_frequency() / 10
+        
+        # filter out higher freq component
+        if self.filter == 0:
+            re_filtered = filter_win(re_wave, cutoff,
+                                     self.sample_rate, self.numtaps)
+            im_filtered = filter_win(im_wave, cutoff,
+                                     self.sample_rate, self.numtaps)
+        elif self.filter == 1:
+            re_filtered = filter_ls(re_wave, cutoff,
+                                    self.sample_rate, self.numtaps)
+            im_filtered = filter_ls(im_wave, cutoff,
+                                    self.sample_rate, self.numtaps)
+        
+        # print('re_filtered')
+        # print(re_filtered)
+        # # apply integration limits
+        beginning = int(self.int_delay() * self.sample_rate)
+        end = beginning + int(self.int_time() * self.sample_rate)
+        
+        # print(beginning)
+        # print(self.int_delay())
+        # print()
+
+        re_limited = re_filtered[beginning:end]
+        im_limited = im_filtered[beginning:end]
+
+        # convert to magnitude and phase
+        complex_num = re_limited + im_limited * 1j
+        mag = abs(complex_num)
+        phase = np.angle(complex_num, deg=True)
+        
+        return mag, phase
+
+    def check_delay(self, time_available):
+        samples_delay_min = (self.numtaps - 1)
+        int_delay_min = samples_delay_min / self.sample_rate
+        if self.int_delay() > time_available:
+            raise ValueError('int_delay {} is longer than total_time '
+                            '{}'.format(self.int_delay(), time_available))
+        elif self.int_delay() < int_delay_min:
+            logging.warning(
+                'delay is less than recommended for filter choice: '
+                '(expect delay >= {}'.format(int_delay_min))
+                
+    def check_time(self, time_available):
+        oscilations_measured = self.int_time() * self.demodulation_frequency()
+        oversampling = self.sample_rate / (2 * self.demodulation_frequency())
+        if self.int_time() > time_available:
+            raise ValueError('int_time {} is longer than total_time - '
+                            'delay = {}'.format(self.int_time(), time_available))
+        elif oscilations_measured < 10:
+            logging.warning('{} oscilations measured, recommend at '
+                            'least 10: decrease sampling rate, take '
+                            'more samples or increase demodulation '
+                            'freq'.format(oscilations_measured))
+        elif oversampling < 1:
+            logging.warning('oversampling rate is {}, recommend > 1: '
+                            'increase sampling rate or decrease '
+                            'demodulation frequency'.format(oversampling))   
+                  
+    def get_max_total_sample_time(self):
+        time_available = self.samples_per_record / self.sample_rate
+        return time_available
+    
+def sample_to_volt_u12(raw_samples, bps):
+    """
+    Applies volts conversion for 12 bit sample data stored
+    in 2 bytes
+    :return: samples_magnitude_array, samples_phase_array
+    """
+
+    # right_shift 16-bit sample by 4 to get 12 bit sample
+    shifted_samples = np.right_shift(raw_samples, 4)
+
+    # Alazar calibration
+    code_zero = (1 << (bps - 1)) - 0.5
+    code_range = (1 << (bps - 1)) - 0.5
+
+    # TODO(nataliejpg) make this not hard coded
+    input_range_volts = 1
+    # Convert to volts
+    volt_samples = np.float64(input_range_volts *
+                              (shifted_samples - code_zero) / code_range)
+
+    return volt_samples

From 44e0326e5dd5af9e1cb1ef4a408093783aa7d319 Mon Sep 17 00:00:00 2001
From: Documentation Bot 
Date: Sun, 27 Nov 2016 22:32:42 +0100
Subject: [PATCH 061/328] fix: Add missing __init__

---
 qcodes/instrument_drivers/Harvard/__init__.py | 0
 1 file changed, 0 insertions(+), 0 deletions(-)
 create mode 100644 qcodes/instrument_drivers/Harvard/__init__.py

diff --git a/qcodes/instrument_drivers/Harvard/__init__.py b/qcodes/instrument_drivers/Harvard/__init__.py
new file mode 100644
index 000000000000..e69de29bb2d1

From c7490e6b5560aa554e7d23acf7fd5ffcfba32565 Mon Sep 17 00:00:00 2001
From: Giulio Ungaretti 
Date: Sun, 27 Nov 2016 23:18:48 +0100
Subject: [PATCH 062/328] feat: Add makefile option to build locally

---
 docs/Makefile | 4 ++++
 1 file changed, 4 insertions(+)

diff --git a/docs/Makefile b/docs/Makefile
index 45982336eef6..bb0274ee7351 100644
--- a/docs/Makefile
+++ b/docs/Makefile
@@ -226,6 +226,10 @@ dummy:
 	@echo
 	@echo "Build finished. Dummy builder generates no files."
 
+html-api:
+	sphinx-apidoc  -o  docs/auto  qcodes -d 10
+	cd docs && make html
+
 gh-pages:
 	git config --global user.email "bot@travics.com"
 	git config --global user.name "Documentation Bot"

From 2d7fbec194a6fdf1a3e96ba85f64258bc0e4dd4b Mon Sep 17 00:00:00 2001
From: Giulio Ungaretti 
Date: Sun, 27 Nov 2016 23:23:12 +0100
Subject: [PATCH 063/328] docs: Polish decadac

---
 qcodes/instrument_drivers/Harvard/Decadac.py | 23 +++++++++-----------
 1 file changed, 10 insertions(+), 13 deletions(-)

diff --git a/qcodes/instrument_drivers/Harvard/Decadac.py b/qcodes/instrument_drivers/Harvard/Decadac.py
index 4fc62a80d1e1..6f8f2a5cabbe 100644
--- a/qcodes/instrument_drivers/Harvard/Decadac.py
+++ b/qcodes/instrument_drivers/Harvard/Decadac.py
@@ -16,24 +16,15 @@ class Decadac(VisaInstrument):
     that self.visa_handle.ask(XXX) will return the answer to XXX and not
     some previous event.
 
-    The class comes with the following methods and attributes:
-
-    Methods:
-
-        set_ramping(state:bool, time:float): a shortcut to set
-            self.ramp_state and self.ramp_time
-
-        get_ramping: Queries the value of self.ramp_state and
-            self.ramp_time. Returns a string.
 
     Attributes:
 
         ramp_state (bool): If True, ramp state is ON. Default False.
 
-        ramp_time (num): The ramp time in ms. Default 100 ms.
+        ramp_time (int): The ramp time in ms. Default 100 ms.
 
         voltranges (list): The voltranges for each channel. Can be read and
-        set (using a screwdriver) on the front of the physical instrument.
+            set (using a screwdriver) on the front of the physical instrument.
     """
 
     def __init__(self, name, port, slot, timeout=2, baudrate=9600,
@@ -160,6 +151,7 @@ def _setvoltage(self, voltage, channel):
                 self.visa_handle.write('U 65535;')
                 self.visa_handle.read()
 
+
     def set_ramping(self, state, time=None):
         """
         Function to set ramp_state and ramp_time.
@@ -167,14 +159,19 @@ def set_ramping(self, state, time=None):
         Args:
             state (bool): True sets ramping ON.
 
-            time (int): the ramp time in ms
+            time (Optiona[int]): the ramp time in ms
         """
         self.ramp_state = state
         if time is not None:
             self.ramp_time = time
 
     def get_ramping(self):
-        """Returns a string with ramp state information"""
+        """
+        Queries the value of self.ramp_state and self.ramp_time. 
+
+        Returns:
+            str: ramp state information
+        """
         switch = {True: 'ON',
                   False: 'OFF'}
         mssg = 'Ramp state: ' + switch[self.ramp_state]

From 8892e31b68889ace1c22dc0f26d9678eeeee9b34 Mon Sep 17 00:00:00 2001
From: Giulio Ungaretti 
Date: Mon, 5 Dec 2016 21:24:07 +0100
Subject: [PATCH 064/328] fix: Don't append if file does not exist. (#408)

FileNotFoundError is not raised, if the directory exists (very much like Linux).
So safer to check for the file we actually want to check for existence.

Closes #407
---
 qcodes/tests/test_format.py | 12 ++++++++++--
 1 file changed, 10 insertions(+), 2 deletions(-)

diff --git a/qcodes/tests/test_format.py b/qcodes/tests/test_format.py
index c6cf87fbfce0..d20b75ebd5f3 100644
--- a/qcodes/tests/test_format.py
+++ b/qcodes/tests/test_format.py
@@ -282,10 +282,18 @@ def test_format_options(self):
             self.assertEqual(f.read(), odd_format)
 
     def add_star(self, path):
-        try:
+        """
+        Args:
+            path(str): path to gnu plot data file
+
+        Write a start to file at path if exists.  Else record that the file
+        does not exist, in the obscure counter self.stars_before_write, starts
+        are written only if a file exists, i.e. "after_write"
+        """
+        if os.path.isfile(path):
             with open(path, 'a') as f:
                 f.write('*')
-        except FileNotFoundError:
+        else:
             self.stars_before_write += 1
 
     def test_incremental_write(self):

From d66c6094ff3dc7a866e173a23491c1aa62dec33a Mon Sep 17 00:00:00 2001
From: "William H.P. Nielsen" 
Date: Wed, 7 Dec 2016 09:03:33 +0100
Subject: [PATCH 065/328] Update CONTRIBUTING.rst

Add comment about elpy for Emacs.
---
 CONTRIBUTING.rst | 1 +
 1 file changed, 1 insertion(+)

diff --git a/CONTRIBUTING.rst b/CONTRIBUTING.rst
index 0b0e396e76b5..63aef9e7e31d 100644
--- a/CONTRIBUTING.rst
+++ b/CONTRIBUTING.rst
@@ -393,6 +393,7 @@ NOTE(giulioungaretti): is this enough ?
 -  A lot of editors have plugins that will check this for you
    automatically as you type. Sublime Text for example has
    sublimelinter-pep8 and the even more powerful sublimelinter-flake8.
+   For Emacs, the elpy package is strongly recommended (https://github.com/jorgenschaefer/elpy).
 -  BUT: do not change someone else's code to make it pep8-compliant
    unless that code is fully tested.
 -  BUT: remove all trailing spaces.

From 0b650337686ccd81de6116a8b78f55dbacd0915a Mon Sep 17 00:00:00 2001
From: peendebak 
Date: Wed, 7 Dec 2016 13:55:00 +0100
Subject: [PATCH 066/328] feature: Write metadata by default

Formatter writes metadata by default, but data_set does not.
---
 qcodes/data/data_set.py       | 21 ++++++++++++++-------
 qcodes/data/format.py         |  5 +++--
 qcodes/data/gnuplot_format.py |  7 ++++++-
 qcodes/data/hdf5_format.py    | 11 +++++++----
 qcodes/tests/data_mocks.py    |  2 +-
 5 files changed, 31 insertions(+), 15 deletions(-)

diff --git a/qcodes/data/data_set.py b/qcodes/data/data_set.py
index b1786438fcc8..31c5a0bb1a4e 100644
--- a/qcodes/data/data_set.py
+++ b/qcodes/data/data_set.py
@@ -610,16 +610,16 @@ def store(self, loop_indices, ids_values):
                     time.time() > self.last_write + self.write_period):
                 self.write()
                 self.last_write = time.time()
-        else: # in PULL_FROM_SERVER mode; store() isn't legal
+        else:  # in PULL_FROM_SERVER mode; store() isn't legal
             raise RuntimeError('This object is pulling from a DataServer, '
                                'so data insertion is not allowed.')
 
     def default_parameter_name(self, paramname='amplitude'):
         """ Return name of default parameter for plotting
 
-        The default parameter is determined by looking into metdata['default_parameter_name'].
-        If this variable is not present, then the closest match to the argument
-        paramname is tried.
+        The default parameter is determined by looking into
+        metdata['default_parameter_name'].  If this variable is not present,
+        then the closest match to the argument paramname is tried.
 
         Args:
             paramname (str): Name to match to parameter name
@@ -660,7 +660,8 @@ def default_parameter_array(self, paramname='amplitude'):
         """ Return default parameter array
 
         Args:
-            paramname (str): Name to match to parameter name. Defaults to 'amplitude'
+            paramname (str): Name to match to parameter name.
+                 Defaults to 'amplitude'
 
         Returns:
             array (DataArray): array corresponding to the default parameter
@@ -684,11 +685,14 @@ def read_metadata(self):
             return
         self.formatter.read_metadata(self)
 
-    def write(self):
+    def write(self, write_metadata=False):
         """
         Writes updates to the DataSet to storage.
         N.B. it is recommended to call data_set.finalize() when a DataSet is
         no longer expected to change to ensure files get closed
+
+        Args:
+            write_metadata (bool): write the metadata to disk
         """
         if self.mode != DataMode.LOCAL:
             raise RuntimeError('This object is connected to a DataServer, '
@@ -697,7 +701,10 @@ def write(self):
         if self.location is False:
             return
 
-        self.formatter.write(self, self.io, self.location)
+        self.formatter.write(self,
+                             self.io,
+                             self.location,
+                             write_metadata=False)
 
     def write_copy(self, path=None, io_manager=None, location=None):
         """
diff --git a/qcodes/data/format.py b/qcodes/data/format.py
index 7f0e0ffda8dc..9a58b30dca40 100644
--- a/qcodes/data/format.py
+++ b/qcodes/data/format.py
@@ -12,7 +12,7 @@ class Formatter:
 
     Each Formatter is expected to implement writing methods:
     - ``write``: to write the ``DataArray``s
-    - ``write_metadata``: to write the metadata JSON structure
+    - ``write_metadata``: to write the metadata structure
 
     Optionally, if this Formatter keeps the data file(s) open
     between write calls, it may implement:
@@ -42,7 +42,7 @@ class Formatter:
     """
     ArrayGroup = namedtuple('ArrayGroup', 'shape set_arrays data name')
 
-    def write(self, data_set, io_manager, location):
+    def write(self, data_set, io_manager, location, write_metadata=True):
         """
         Write the DataSet to storage.
 
@@ -55,6 +55,7 @@ def write(self, data_set, io_manager, location):
             data_set (DataSet): the data we are writing.
             io_manager (io_manager): base physical location to write to.
             location (str): the file location within the io_manager.
+            write_metadata (bool): if True, then the metadata is written to disk
         """
         raise NotImplementedError
 
diff --git a/qcodes/data/gnuplot_format.py b/qcodes/data/gnuplot_format.py
index 47a08639d0a3..2aa43dd76650 100644
--- a/qcodes/data/gnuplot_format.py
+++ b/qcodes/data/gnuplot_format.py
@@ -62,6 +62,7 @@ class GNUPlotFormat(Formatter):
     use 2 blank lines sometimes, to denote a whole new dataset, which sort
     of corresponds to our situation.)
     """
+
     def __init__(self, extension='dat', terminator='\n', separator='\t',
                  comment='# ', number_format='g', metadata_file=None):
         self.metadata_file = metadata_file or 'snapshot.json'
@@ -234,7 +235,7 @@ def _get_labels(self, labelstr):
             parts = re.split('"\s+"', labelstr[1:-1])
             return [l.replace('\\"', '"').replace('\\\\', '\\') for l in parts]
 
-    def write(self, data_set, io_manager, location, force_write=False):
+    def write(self, data_set, io_manager, location, force_write=False, write_metadata=True):
         """
         Write updates in this DataSet to storage.
 
@@ -298,6 +299,10 @@ def write(self, data_set, io_manager, location, force_write=False):
             for array in group.data + (group.set_arrays[-1],):
                 array.mark_saved(save_range[1])
 
+        if write_metadata:
+            self.write_metadata(
+                data_set, io_manager=io_manager, location=location)
+
     def write_metadata(self, data_set, io_manager, location, read_first=True):
         """
         Write all metadata in this DataSet to storage.
diff --git a/qcodes/data/hdf5_format.py b/qcodes/data/hdf5_format.py
index 77e5b4b32dbe..4d943491d76e 100644
--- a/qcodes/data/hdf5_format.py
+++ b/qcodes/data/hdf5_format.py
@@ -13,6 +13,7 @@ class HDF5Format(Formatter):
 
     Capable of storing (write) and recovering (read) qcodes datasets.
     """
+
     def close_file(self, data_set):
         """
         Closes the hdf5 file open in the dataset.
@@ -118,7 +119,7 @@ def _create_data_object(self, data_set, io_manager=None,
         return data_set._h5_base_group
 
     def write(self, data_set, io_manager=None, location=None,
-              force_write=False, flush=True):
+              force_write=False, flush=True, write_metadata=True):
         """
         Writes a data_set to an hdf5 file.
         Input arguments:
@@ -172,13 +173,15 @@ def write(self, data_set, io_manager=None, location=None,
             new_datasetshape = (new_dlen,
                                 datasetshape[1])
             dset.resize(new_datasetshape)
-            new_data_shape = (new_dlen-old_dlen, datasetshape[1])
+            new_data_shape = (new_dlen - old_dlen, datasetshape[1])
             dset[old_dlen:new_dlen] = x[old_dlen:new_dlen].reshape(
                 new_data_shape)
             # allow resizing extracted data, here so it gets written for
             # incremental writes aswell
             dset.attrs['shape'] = x.shape
-        self.write_metadata(data_set)
+        if write_metadata:
+            self.write_metadata(
+                data_set, io_manager=io_manager, location=location)
 
         # flush ensures buffers are written to disk
         # (useful for ensuring openable by other files)
@@ -231,7 +234,7 @@ def _create_dataarray_dset(self, array, group):
 
         return dset
 
-    def write_metadata(self, data_set, io=None, location=None):
+    def write_metadata(self, data_set, io_manager=None, location=None):
         """
         Writes metadata of dataset to file using write_dict_to_hdf5 method
 
diff --git a/qcodes/tests/data_mocks.py b/qcodes/tests/data_mocks.py
index 42e5418401ba..9a35ce2219cb 100644
--- a/qcodes/tests/data_mocks.py
+++ b/qcodes/tests/data_mocks.py
@@ -33,7 +33,7 @@ class MockFormatter:
     def read(self, data_set):
         data_set.has_read_data = True
 
-    def write(self, data_set, io_manager, location):
+    def write(self, data_set, io_manager, location, write_metadata=False):
         data_set.has_written_data = True
 
     def read_metadata(self, data_set):

From 8cae1ac1a5cf073a98ee61a34b91d5737891fd03 Mon Sep 17 00:00:00 2001
From: Giulio Ungaretti 
Date: Tue, 13 Dec 2016 15:19:30 +0100
Subject: [PATCH 067/328] chore: Update requirements and simlify readme (#417)

* fix: Update requirements.
Remove old and pretty much useless version of widgets because
not needed, only multiprocessing uses it and it's really b0rken.
Remove basic * develop  duality.
Basic install and or Windows install (due to h5py) goes with conda.

* docs: Slimify readme

* fix: Update travis requirement

Exclude plots from being tested on travis.
---
 .travis.yml                | 10 ++++++++--
 README.rst                 | 36 +---------------------------------
 basic_requirements.txt     | 14 -------------
 develop_requirements.txt   | 40 --------------------------------------
 qcodes/tests/test_plots.py | 27 ++++++++++++++++++++-----
 requirements.txt           |  8 ++++++++
 setup.py                   |  1 -
 7 files changed, 39 insertions(+), 97 deletions(-)
 delete mode 100644 basic_requirements.txt
 delete mode 100644 develop_requirements.txt
 create mode 100644 requirements.txt

diff --git a/.travis.yml b/.travis.yml
index a59bd4b3549a..bd0a5d494591 100644
--- a/.travis.yml
+++ b/.travis.yml
@@ -10,11 +10,17 @@ python:
 branches:
   only:
     - master
+
+# singal that we are on travis
+# to disable things that expect an
+# X Display
+#
+env:
+    - TRAVISCI=true
 # command to install dependencies
 install:
     - pip install --upgrade pip
-    - pip install ipython
-    - pip install -r develop_requirements.txt
+    - pip install -r requirements.txt
     - python setup.py develop
 # command to run tests
 script: 
diff --git a/README.rst b/README.rst
index 6a6b99ad5b26..423dfab85e8e 100644
--- a/README.rst
+++ b/README.rst
@@ -70,7 +70,7 @@ $QCODES_INSTALL_DIR is the folder where you want to have the source code.
     cd $QCODES_INSTALL_DIR
     pyenv install 3.5.2
     pyenv virtualenv 3.5.2 qcodes-dev
-    pip install -r develop_requirements.txt
+    pip install -r requirements.txt
     pip install -e .
     python qcodes/test.py -f
 
@@ -78,40 +78,6 @@ If the tests pass you are ready to hack!
 This is the reference setup one needs to have to contribute, otherwise
 too many non-reproducible environments will show up.
 
-If all of this sounds too complicated, use anaconda!
-
-Anaconda
---------
-
-One can also use anaconda:
-
--  First clone the repo
-    
-   `` git clone https://github.com/QCoDeS/Qcodes.git $QCODES_INSTALL_DIR ``
-
--  Open the 'navigator' app that was installed with anaconda.
--  On the left side click on "Environments".
--  Then on the "import" icon, on the bottom.
--  Pick a name, and click on the folder icon next to file to import
-   from.
--  Make sure you select "Pip requirement files" from the "Files of type"
-   dialog then navigate to the qcodes folder (QCODES_INSTALL_DIR) and select
-   ``basic_requirements.txt``.
--  Finally click import, and wait until done.
--  The enviroment is now created, click on the green arrow to open a
-   terminal inside it.
--  Navigate again with the terminal (or drag and drop the the folder on
-   OsX)
--  Most likely you will want to plot stuff, so type:
-
-``conda install matplotlib``
-
-and after if you want qtplot
-
-``conda install pyqtgraph``
-
--  Then type ``pip install -e .``
-
 Updating QCoDeS
 ===============
 
diff --git a/basic_requirements.txt b/basic_requirements.txt
deleted file mode 100644
index f5702ca9452f..000000000000
--- a/basic_requirements.txt
+++ /dev/null
@@ -1,14 +0,0 @@
-ipykernel==4.2.2
-ipython==4.1.0
-ipython-genutils==0.1.0
-ipywidgets==4.1.1
-jupyter==1.0.0
-jupyter-client==4.1.1
-jupyter-console==4.1.0
-jupyter-core==4.0.6
-notebook==4.1.0
-numpy==1.10.4
-python-dateutil==2.4.2
-PyVISA==1.8
-coverage==4.1
-h5py==2.6.0
diff --git a/develop_requirements.txt b/develop_requirements.txt
deleted file mode 100644
index 91a2f52a7935..000000000000
--- a/develop_requirements.txt
+++ /dev/null
@@ -1,40 +0,0 @@
-coverage==4.1
-decorator==4.0.9
-docutils==0.12
-gnureadline==6.3.3
-greenlet==0.4.9
-h5py==2.6.0
-imagesize==0.7.1
-ipykernel==4.3.1
-ipython==4.2.0
-ipython-genutils==0.1.0
-ipywidgets==5.1.5
-Jinja2==2.8
-jupyter-client==4.2.2
-jupyter-core==4.1.0
-MarkupSafe==0.23
-msgpack-python==0.4.7
-nbconvert==4.2.0
-nbformat==4.0.1
-neovim==0.1.8
-nose==1.3.7
-notebook==4.2.0
-numpy==1.11.0
-pexpect==4.1.0
-pickleshare==0.7.2
-ptyprocess==0.5.1
-Pygments==2.1.3
-pytz==2016.4
-PyVISA==1.8
-pyzmq==15.2.0
-simplegeneric==0.8.1
-six==1.10.0
-snowballstemmer==1.2.1
-Sphinx==1.4.2
-sphinx-rtd-theme==0.1.9
-terminado==0.6
-tornado==4.3
-traitlets==4.2.1
-widgetsnbextension==1.2.3
-jsonschema==2.5.1
-sphinxcontrib-jsonschema==0.9.0
diff --git a/qcodes/tests/test_plots.py b/qcodes/tests/test_plots.py
index 1099fb04c325..c67aa3221214 100644
--- a/qcodes/tests/test_plots.py
+++ b/qcodes/tests/test_plots.py
@@ -1,15 +1,28 @@
+"""
+Tests for plotting system.
+Legacy in many ways:
+    - assume X server running
+    - just test "window creation"
+"""
 from unittest import TestCase, skipIf
+import os
 
 try:
     from qcodes.plots.pyqtgraph import QtPlot
-    noQtPlot = False
+    if os.environ.get("TRAVISCI"):
+        noQtPlot = True
+    else:
+        noQtPlot = False
 except Exception:
     noQtPlot = True
 
 try:
     from qcodes.plots.qcmatplotlib import MatPlot
     import matplotlib.pyplot as plt
-    noMatPlot = False
+    if os.environ.get("TRAVISCI"):
+        noMatPlot = True
+    else:
+        noMatPlot = False
 except Exception:
     noMatPlot = True
 
@@ -24,9 +37,11 @@ def tearDown(self):
         pass
 
     def test_creation(self):
-        ''' Simple test function which created a QtPlot window '''
+        """
+        Simple test function which created a QtPlot window
+        """
         plotQ = QtPlot(remote=False, show_window=False, interval=0)
-        _ = plotQ.add_subplot()
+        plotQ.add_subplot()
 
 
 @skipIf(noMatPlot, '***matplotlib plotting cannot be tested***')
@@ -39,6 +54,8 @@ def tearDown(self):
         pass
 
     def test_creation(self):
-        ''' Simple test function which created a QtPlot window '''
+        """
+        Simple test function which created a QtPlot window
+        """
         plotM = MatPlot(interval=0)
         plt.close(plotM.fig)
diff --git a/requirements.txt b/requirements.txt
new file mode 100644
index 000000000000..ace996ad9542
--- /dev/null
+++ b/requirements.txt
@@ -0,0 +1,8 @@
+jupyter==1.0.0
+numpy==1.11.2
+matplotlib==1.5.3
+pyqtgraph==0.10.0
+PyVISA==1.8
+PyQt5==5.7
+QtPy==1.1.2
+h5py==2.6.0
diff --git a/setup.py b/setup.py
index 0e18ae98aa4d..db4c5dda8a99 100644
--- a/setup.py
+++ b/setup.py
@@ -59,7 +59,6 @@ def readme():
           'pyvisa>=1.8',
           'ipython>=4.1.0',
           'jupyter>=1.0.0',
-          'ipywidgets>=4.1',
           'h5py>=2.6'
       ],
 

From e2de376b03879cb4246f771a13c80dbd5708ce56 Mon Sep 17 00:00:00 2001
From: Adriaan 
Date: Fri, 16 Dec 2016 14:00:34 +0100
Subject: [PATCH 068/328] driver: enhancement to support setting external
 reference (#412)

* R&S driver enhancement to support setting reference

* Double quote and removed blank line
---
 .../rohde_schwarz/SGS100A.py                  | 23 +++++++++++++++++--
 1 file changed, 21 insertions(+), 2 deletions(-)

diff --git a/qcodes/instrument_drivers/rohde_schwarz/SGS100A.py b/qcodes/instrument_drivers/rohde_schwarz/SGS100A.py
index f6f758675f74..48eda76f1cac 100644
--- a/qcodes/instrument_drivers/rohde_schwarz/SGS100A.py
+++ b/qcodes/instrument_drivers/rohde_schwarz/SGS100A.py
@@ -2,7 +2,7 @@
 
 
 class RohdeSchwarz_SGS100A(VisaInstrument):
-    '''
+    """
     This is the qcodes driver for the Rohde & Schwarz SGS100A signal generator
 
     Status: beta-version.
@@ -19,7 +19,8 @@ class RohdeSchwarz_SGS100A(VisaInstrument):
             - RS_SMB100A
     This driver does not contain all commands available for the RS_SGS100A but
     only the ones most commonly used.
-    '''
+    """
+
     def __init__(self, name, address, **kwargs):
         super().__init__(name, address, **kwargs)
 
@@ -58,6 +59,24 @@ def __init__(self, name, address, **kwargs):
                            get_cmd='SOUR:PULM:SOUR?',
                            set_cmd=self.set_pulsemod_source,
                            vals=vals.Strings())
+        self.add_parameter('ref_osc_source',
+                           label='Reference oscillator source',
+                           get_cmd='SOUR:ROSC:SOUR?',
+                           set_cmd='SOUR:ROSC:SOUR {}',
+                           vals=vals.Enum('INT', 'EXT'))
+        # Frequency mw_source outputs when used as a reference
+        self.add_parameter('ref_osc_output_freq',
+                           label='Reference oscillator output frequency',
+                           get_cmd='SOUR:ROSC:OUTP:FREQ?',
+                           set_cmd='SOUR:ROSC:OUTP:FREQ {}',
+                           vals=vals.Enum('10MHz', '100MHz', '1000MHz'))
+        # Frequency of the external reference mw_source uses
+        self.add_parameter('ref_osc_external_freq',
+                           label='Reference oscillator external frequency',
+                           get_cmd='SOUR:ROSC:EXT:FREQ?',
+                           set_cmd='SOUR:ROSC:EXT:FREQ {}',
+                           vals=vals.Enum('10MHz', '100MHz', '1000MHz'))
+
         self.add_function('reset', call_cmd='*RST')
         self.add_function('run_self_tests', call_cmd='*TST?')
 

From 1a4508b6873039b86637e61b55d7c16f30365cfc Mon Sep 17 00:00:00 2001
From: eendebakpt 
Date: Mon, 19 Dec 2016 11:05:51 +0100
Subject: [PATCH 069/328] driver: Add Microwave source driver SMR40 (#419)

* adding microwave source driver SMR40

* autopep8

* format docstring

* use named logger

* update docstrings
---
 .../instrument_drivers/rohde_schwarz/SMR40.py | 371 ++++++++++++++++++
 1 file changed, 371 insertions(+)
 create mode 100644 qcodes/instrument_drivers/rohde_schwarz/SMR40.py

diff --git a/qcodes/instrument_drivers/rohde_schwarz/SMR40.py b/qcodes/instrument_drivers/rohde_schwarz/SMR40.py
new file mode 100644
index 000000000000..13189c99bfb3
--- /dev/null
+++ b/qcodes/instrument_drivers/rohde_schwarz/SMR40.py
@@ -0,0 +1,371 @@
+# Driver for microwave source RS_SMR40
+#
+# Written by Takafumi Fujita (t.fujita@tudelft.nl)
+# This program is based on RS_SMR40.py in QTlab
+#
+
+import logging
+
+from qcodes import VisaInstrument
+from qcodes import validators as vals
+
+log = logging.getLogger(__name__)
+
+class RohdeSchwarz_SMR40(VisaInstrument):
+    """This is the qcodes driver for the Rohde & Schwarz SMR40 signal generator
+    Status: beta-version. TODO:
+
+        - Add all parameters that are in the manual
+        - Add test suite
+        - See if there can be a common driver for RS mw sources from which
+          different models inherit
+    This driver does not contain all commands available for the SMR40 but
+    only the ones most commonly used.
+
+    """
+
+    def __init__(self, name, address, verbose=1, reset=False, **kwargs):
+        self.verbose = verbose
+        log.debug(__name__ + ' : Initializing instrument')
+        super().__init__(name, address, **kwargs)
+
+        # TODO(TF): check what parser parameters can do
+        #           check what 'tags=['sweep']' and 'types' do in qtlab
+        #           fix format types
+        self.add_parameter('frequency',
+                           label='Frequency',
+                           get_cmd=self.do_get_frequency,
+                           set_cmd=self.do_set_frequency,
+                           vals=vals.Numbers(10e6, 40e9),
+                           units='Hz')
+        self.add_parameter('power',
+                           label='Power',
+                           get_cmd=self.do_get_power,
+                           set_cmd=self.do_set_power,
+                           vals=vals.Numbers(-30, 25),
+                           units='dBm')
+        self.add_parameter('status',
+                           get_cmd=self.do_get_status,
+                           set_cmd=self.do_set_status,
+                           vals=vals.Strings())
+
+        # TODO(TF): check how to fix the get functions
+        self.add_parameter('status_of_modulation',
+                           get_cmd=self.do_get_status_of_modulation,
+                           set_cmd=self.do_set_status_of_modulation,
+                           vals=vals.Strings())
+        self.add_parameter('status_of_ALC',
+                           get_cmd=self.do_get_status_of_ALC,
+                           set_cmd=self.do_set_status_of_ALC,
+                           vals=vals.Strings())
+        self.add_parameter('pulse_delay',
+                           get_cmd=self.do_get_pulse_delay,
+                           set_cmd=self.do_set_pulse_delay)
+
+        # TODO(TF): check the way of defining this type of functions, where logging is added
+        # self.add_function('reset')
+        # self.add_function('get_all')
+
+        if reset:
+            self.reset()
+        else:
+            self.get_all()
+
+        self.connect_message()
+
+    # Functions
+    def reset(self):
+        """Resets the instrument to default values.
+
+        Args:
+            None
+
+        Output:
+            None
+
+        """
+        log.info(__name__ + ' : Resetting instrument')
+        self.write('*RST')
+        # TODO: make it printable
+        self.get_all()
+
+    def get_all(self):
+        """Reads all implemented parameters from the instrument, and updates
+        the wrapper.
+
+        Args:
+            None
+
+        Output:
+            None
+
+        """
+        log.info(__name__ + ' : reading all settings from instrument')
+        # TODO: make it printable
+        self.frequency.get()
+        self.power.get()
+        self.status.get()
+
+    # Communication functions
+    def do_get_frequency(self):
+        """Get frequency from device.
+
+        Args:
+            None
+
+        Output:
+            frequency (float) : frequency in Hz
+
+        """
+        log.debug(__name__ + ' : reading frequency from instrument')
+        return float(self.ask('SOUR:FREQ?'))
+
+    def do_set_frequency(self, frequency):
+        """Set frequency of device.
+
+        Args:
+            frequency (float) : frequency in Hz
+
+        Output:
+            None
+
+        """
+        log.debug(__name__ + ' : setting frequency to %s GHz' % frequency)
+        self.write('SOUR:FREQ %e' % frequency)
+
+    def do_get_power(self):
+        """Get output power from device.
+
+        Args:
+            None
+
+        Output:
+            power (float) : output power in dBm
+
+        """
+        log.debug(__name__ + ' : reading power from instrument')
+        return float(self.ask('SOUR:POW?'))
+
+    def do_set_power(self, power):
+        """Set output power of device.
+
+        Args:
+            power (float) : output power in dBm
+
+        Output:
+            None
+
+        """
+        log.debug(__name__ + ' : setting power to %s dBm' % power)
+        self.write('SOUR:POW %e' % power)
+
+    def do_get_status(self):
+        """Get status from instrument.
+
+        Args:
+            None
+
+        Output:
+            status (string) : 'on or 'off'
+
+        """
+        log.debug(__name__ + ' : reading status from instrument')
+        stat = self.ask(':OUTP:STAT?')
+
+        # TODO: fix
+        if stat == '1\n':
+            return 'ON'
+        elif stat == '0\n':
+            return 'OFF'
+        else:
+            raise ValueError('Output status not specified : %s' % stat)
+
+    def do_set_status(self, status):
+        """Set status of instrument.
+
+        Args:
+            status (string) : 'on or 'off'
+
+        Output:
+            None
+
+        """
+        log.debug(__name__ + ' : setting status to "%s"' % status)
+        if status.upper() in ('ON', 'OFF'):
+            status = status.upper()
+        else:
+            raise ValueError('set_status(): can only set on or off')
+        self.write(':OUTP:STAT %s' % status)
+
+    def do_get_status_of_modulation(self):
+        """Get status from instrument.
+
+        Args:
+            None
+
+        Output:
+            status (string) : 'on' or 'off'
+
+        """
+        log.debug(__name__ + ' : reading status from instrument')
+        stat = self.ask(':SOUR:PULM:STAT?')
+
+        # TODO: fix
+        # if stat == '1':
+        # return 'ON'
+        # elif stat == '0':
+        # return 'OFF'
+        if stat == '1\n':
+            return 'ON'
+        elif stat == '0\n':
+            return 'OFF'
+        else:
+            raise ValueError('Output status not specified : %s' % stat)
+
+    def do_set_status_of_modulation(self, status):
+        """Set status of modulation.
+
+        Args:
+            status (string) : 'on' or 'off'
+
+        Output:
+            None
+
+        """
+        log.debug(__name__ + ' : setting status to "%s"' % status)
+        if status.upper() in ('ON', 'OFF'):
+            status = status.upper()
+        else:
+            raise ValueError('set_status(): can only set on or off')
+        self.write(':SOUR:PULM:STAT %s' % status)
+
+    def do_get_status_of_ALC(self):
+        """Get status from instrument.
+
+        Args:
+            None
+
+        Output:
+            status (string) : 'on or 'off'
+
+        """
+        log.debug(__name__ + ' : reading ALC status from instrument')
+        stat = self.ask(':SOUR:POW:ALC?')
+
+        # TODO: fix
+        # if stat == '1':
+        # return 'ON'
+        # elif stat == '0':
+        # return 'OFF'
+        if stat == '1\n':
+            return 'ON'
+        elif stat == '0\n':
+            return 'OFF'
+        else:
+            raise ValueError('Output status not specified : %s' % stat)
+
+    def do_set_status_of_ALC(self, status):
+        """Set status of instrument.
+
+        Args:
+            status (string) : 'on or 'off'
+
+        Output:
+            None
+
+        """
+        log.debug(__name__ + ' : setting ALC status to "%s"' % status)
+        if status.upper() in ('ON', 'OFF'):
+            status = status.upper()
+        else:
+            raise ValueError('set_status(): can only set on or off')
+        self.write(':SOUR:POW:ALC %s' % status)
+
+    def do_get_pulse_delay(self):
+        """Get output power from device.
+
+        Args:
+            None
+
+        Output:
+            power (float) : output power in dBm
+
+        """
+        log.debug(__name__ + ' : reading pulse delay from instrument')
+        return float(self.ask('SOUR:PULS:DEL?'))
+
+    def do_set_pulse_delay(self, delay):
+        """Set output power of device.
+
+        Args:
+            power (float) : output power in dBm
+
+        Output:
+            None
+
+        """
+        log.debug(
+            __name__ + ' : setting pulse delay to %s seconds' % str(delay))
+        self.write('SOUR:PULS:DEL 1us')
+
+    # Shortcuts
+    def off(self):
+        """Set status to 'off'.
+
+        Args:
+            None
+
+        Output:
+            None
+
+        """
+        self.status.set('off')
+
+    def on(self):
+        """Set status to 'on'.
+
+        Args:
+            None
+
+        Output:
+            None
+
+        """
+        self.status.set('on')
+
+    def off_modulation(self):
+        """Set status of modulation to 'off'.
+
+        Args:
+            None
+
+        Output:
+            None
+
+        """
+        self.set_status_of_modulation('off')
+
+    def on_modulation(self):
+        """Set status of modulation to 'on'.
+
+        Args:
+            None
+
+        Output:
+            None
+
+        """
+        self.set_status_of_modulation('on')
+
+    def set_ext_trig(self):
+        """Set to the external trigger mode.
+
+        Args:
+            None
+
+        Output:
+            None
+
+        """
+        log.debug(__name__ + ' : setting to the external trigger mode')
+        self.write('TRIG:PULS:SOUR EXT_TRIG')

From fb2c40ff9bb00a576637b896d523d3260d10e18c Mon Sep 17 00:00:00 2001
From: Jens Hedegaard Nielsen 
Date: Mon, 19 Dec 2016 15:10:14 +0100
Subject: [PATCH 070/328] Doc: RST fixes (#420)

* docs: fix rst syntax in roadmap

* docs: fix rst syntax in intro

* docs: fix rst syntax in tutuorial

* docs: fix rst syntax in start/index

* docs: typo in mock comment

* docs: fix rst syntax in dataset.py

* docs: fix rst syntax in format.py

* docs: mock instument correct rst

* docs: parameter correct rst
---
 docs/roadmap.rst               |  2 ++
 docs/start/index.rst           |  2 +-
 docs/user/intro.rst            |  9 +++++++++
 docs/user/tutorial.rst         | 10 +++++-----
 qcodes/data/data_set.py        | 36 ++++++++++++++++++++--------------
 qcodes/data/format.py          | 25 +++++++++++++----------
 qcodes/instrument/mock.py      | 18 +++++++++--------
 qcodes/instrument/parameter.py |  4 +++-
 8 files changed, 66 insertions(+), 40 deletions(-)

diff --git a/docs/roadmap.rst b/docs/roadmap.rst
index 916ee3a84977..535ef8de1e36 100644
--- a/docs/roadmap.rst
+++ b/docs/roadmap.rst
@@ -38,11 +38,13 @@ Broader adoption within stationQ
 -  raw data (as raw as feasible)
 -  analyzed data
 -  metadata, including both:
+
    -  experiment data: all electronic settings and any available manual
       settings, timestamp
    -  information on the code state: git commit id and/or version of
       this package, traceback at the point of execution, and potentially
       information about the other scripts involved in the sweep?
+
 -  automatic logging of monitor parameters (eg fridge and magnet
    information) as a function of time
 -  Instrument drivers: all drivers group members have used in their
diff --git a/docs/start/index.rst b/docs/start/index.rst
index 6ad8f9743474..b58a29e2fe88 100644
--- a/docs/start/index.rst
+++ b/docs/start/index.rst
@@ -17,7 +17,7 @@ Make sure to download the latest version with python 3.5.
 
 Once you download, install anaconda according to the instructions on screen.
 
-If you use *nix, you really should use the source.
+If you use \*nix, you really should use the source.
 
 The next section will guide you through the installation of qcodes
 on windows, although most of the things also work for osx and Linux.
diff --git a/docs/user/intro.rst b/docs/user/intro.rst
index 42e1285a1c62..5a1bab383198 100644
--- a/docs/user/intro.rst
+++ b/docs/user/intro.rst
@@ -43,17 +43,20 @@ Instruments come in several flavors:
 An instrument can exist as local instrument or remote instrument. A local instrument is instantiated in the main process, and you interact with it directly. This is convenient for testing and debugging, but a local instrument cannot be used with a measurement loop in a separate process (ie a background loop). For that purpose you need a remote instrument. A remote instrument starts (or connects to) a server process, instantiates the instrument in that process, and returns a proxy object that mimicks the API of the instrument. This proxy holds no state or hardware connection, so it can be freely copied to other processes, in particular background loops and composite-instrument servers.
 
 .. responsibilities
+
 Instruments are responsible for:
   - Holding connections to hardware, be it VISA, some other communication protocol, or a specific DLL or lower-level driver.
   - Creating a parameter_, ref:`function`, or method for each piece of functionality we support. These objects may be used independent of the instrument, but they make use of the instrument's hardware connection in order to do their jobs.
   - Describing their complete current state ("snapshot") when asked, as a JSON-compatible dictionary.
 
 .. state
+
 Instruments hold state of:
   - The communication address, and in many cases the open communication channel.
   - A list of references to parameters added to the instrument.
 
 .. failures
+
 Instruments can fail:
   - When a VisaInstrument has been instantiated before, particularly with TCPIP, sometimes it will complain "VI_ERROR_RSRC_NFOUND: Insufficient location information or the requested device or resource is not present in the system" and not allow you to open the instrument until either the hardware has been power cycled or the network cable disconnected and reconnected. Are we using visa/pyvisa in a brittle way?
   - If you try to use a background loop with a local instrument, because that would require copying the local instrument and there may only be one local copy of the instrument (if you make a remote instrument, the server instance is the one local copy).
@@ -77,6 +80,7 @@ Responsibilities
 ~~~~~~~~~~~~~~~~
 
 .. responsibilities
+
 Parameters are responsible for:
   - (if part of an Instrument) generating the commands to pass to the Instrument and interpreting its response
   - (if not part of an Instrument) providing get and/or set methods
@@ -88,10 +92,12 @@ Parameters are responsible for:
     - and more if multi-valued or array-valued
 
 .. state
+
 Parameters hold onto their latest set or measured value, as well as the timestamp of the latest update.
 Thus, snapshots need not always query the hardware for this information, but can update it intelligently when it has gotten stale.
 
 .. failures
+
 A Parameter that is part of an Instrument, even though it can be used as an independent object without directly referencing the Instrument, 
 is subject to the same local/remote limitations as the Instrument.
 
@@ -168,17 +174,20 @@ The key loop running conditions are:
   - where and how to save the data to disk
 
 .. responsibilities
+
 The Loop is responsible for:
   - creating the dataset_ that will be needed to store its data
   - generating all the metadata for the DataSet. Metadata is intended to describe the system and software configuration to give it context, help reproduce and troubleshoot the experiment, and to aid searching and datamining later. The Loop generates its own metadata, regarding when and how it was run and the Parameters and other actions involved, as well as asking all the Instruments, via a :ref:`station` if possible, for their own metadata and including it.
   - sequencing actions: the Loop should have the highest priority and the least overhead of extra responsibilities so that setpoints and actions occur with as fast and reliable timing as possible.
 
 .. state
+
 Before the Loop is run, it holds the setpoint and action definitions you are building up. You can actually keep a loop at any level of definition and reuse it later. Loop methods chain by creating entirely new objects, so that you can hold onto the Loop at any stage of definition and reuse just what has been defined up to that point.
 
 After the Loop is run, it returns a dataset_ and the executed loop itself, along with the process it starts if it's a background Loop, only hold state (such as the current indices within the potentially nested Loops) while it is running.
 
 .. failures
+
 Loops can fail:
   - If you try to use a (parameter of a) local instrument in a background loop
 
diff --git a/docs/user/tutorial.rst b/docs/user/tutorial.rst
index 23dff2a13291..33798d39ce77 100644
--- a/docs/user/tutorial.rst
+++ b/docs/user/tutorial.rst
@@ -13,10 +13,10 @@ Writing a Driver
 
 Write a simple driver example
 with commented code
-  - add parameter
-  - add validator
-  - add custom stuff
-  - add doccstrings f.ex
+- add parameter
+- add validator
+- add custom stuff
+- add doccstrings f.ex
 
 .. todo::  missing
 
@@ -36,7 +36,7 @@ Explain the mock mock
 
 
 Combined Parameters Sweep
-------------------------
+-------------------------
 
 If you want to sweep multiple parameters at once qcodes offers the combine function.
 You can combine any number of any kind paramter. 
diff --git a/qcodes/data/data_set.py b/qcodes/data/data_set.py
index 31c5a0bb1a4e..9db025860dd3 100644
--- a/qcodes/data/data_set.py
+++ b/qcodes/data/data_set.py
@@ -36,10 +36,12 @@ def new_data(location=None, loc_record=None, name=None, overwrite=False,
     Args:
         location (str or callable or False, optional): If you provide a string,
             it must be an unused location in the io manager. Can also be:
+
             - a callable ``location provider`` with one required parameter
               (the io manager), and one optional (``record`` dict),
               which returns a location string when called
             - ``False`` - denotes an only-in-memory temporary DataSet.
+
             Note that the full path to or physical location of the data is a
             combination of io + location. the default ``DiskIO`` sets the base
             directory, which this location is a relative path inside.
@@ -66,13 +68,15 @@ def new_data(location=None, loc_record=None, name=None, overwrite=False,
             ``get_data_manager()``.
 
         mode (DataMode, optional): connection type to the ``DataServer``.
-            ``DataMode.LOCAL``: this DataSet doesn't communicate across
-                processes.
-            ``DataMode.PUSH_TO_SERVER``: no local copy of data, just pushes
-                each measurement to a ``DataServer``.
-            ``DataMode.PULL_FROM_SERVER``: pulls changes from the
-                ``DataServer`` on calling ``self.sync()``. Reverts to local if
-                and when it stops being the live measurement.
+
+            - ``DataMode.LOCAL``: this DataSet doesn't communicate across
+              processes.
+            - ``DataMode.PUSH_TO_SERVER``: no local copy of data, just pushes
+              each measurement to a ``DataServer``.
+            - ``DataMode.PULL_FROM_SERVER``: pulls changes from the
+              ``DataServer`` on calling ``self.sync()``. Reverts to local if
+              and when it stops being the live measurement.
+
             Default ``DataMode.LOCAL``.
 
         arrays (Optional[List[qcodes.DataArray]): arrays to add to the DataSet.
@@ -215,14 +219,16 @@ class DataSet(DelegateAttributes):
             ``get_data_manager()``.
 
         mode (DataMode, optional): connection type to the ``DataServer``.
-            ``DataMode.LOCAL``: this DataSet doesn't communicate across
-                processes.
-            ``DataMode.PUSH_TO_SERVER``: no local copy of data, just pushes
-                each measurement to a ``DataServer``.
-            ``DataMode.PULL_FROM_SERVER``: pulls changes from the
-                ``DataServer`` on calling ``self.sync()``. Reverts to local if
-                and when it stops being the live measurement.
-            Default ``DataMode.LOCAL``.
+
+            - ``DataMode.LOCAL``: this DataSet doesn't communicate across
+              processes.
+            - ``DataMode.PUSH_TO_SERVER``: no local copy of data, just pushes
+              each measurement to a ``DataServer``.
+            - ``DataMode.PULL_FROM_SERVER``: pulls changes from the
+              ``DataServer`` on calling ``self.sync()``. Reverts to local if
+              and when it stops being the live measurement.
+
+            Default to ``DataMode.LOCAL``.
 
         arrays (Optional[List[qcodes.DataArray]): arrays to add to the DataSet.
                 Can be added later with ``self.add_array(array)``.
diff --git a/qcodes/data/format.py b/qcodes/data/format.py
index 9a58b30dca40..0caf9c80e453 100644
--- a/qcodes/data/format.py
+++ b/qcodes/data/format.py
@@ -11,16 +11,19 @@ class Formatter:
     Formatters translate between DataSets and data files.
 
     Each Formatter is expected to implement writing methods:
-    - ``write``: to write the ``DataArray``s
+
+    - ``write``: to write the ``DataArray``\s
     - ``write_metadata``: to write the metadata structure
 
     Optionally, if this Formatter keeps the data file(s) open
     between write calls, it may implement:
+
     - ``close_file``: to perform any final cleanup and release the
       file and any other resources.
 
     and reading methods:
-    - ``read`` or ``read_one_file`` to reconstruct the ``DataArray``s, either
+
+    - ``read`` or ``read_one_file`` to reconstruct the ``DataArray``\s, either
       all at once (``read``) or one file at a time, supplied by the base class
       ``read`` method that loops over all data files at the correct location.
 
@@ -31,14 +34,16 @@ class Formatter:
     All of these methods accept a ``data_set`` argument, which should be a
     ``DataSet`` object. Even if you are loading a new data set from disk, this
     object should already have attributes:
-        io: an IO manager (see qcodes.data.io)
-        location: a string, like a file path, that identifies the DataSet and
-            tells the IO manager where to store it
-        arrays: a dict of ``{array_id:DataArray}`` to read into.
-            - read will create entries that don't yet exist.
-            - write will write ALL DataArrays in the DataSet, using
-              last_saved_index and modified_range, as well as whether or not
-              it found the specified file, to determine how much to write.
+
+        - io: an IO manager (see qcodes.data.io)
+          location: a string, like a file path, that identifies the DataSet and
+          tells the IO manager where to store it
+        - arrays: a dict of ``{array_id:DataArray}`` to read into.
+
+    - read will create entries that don't yet exist.
+    - write will write ALL DataArrays in the DataSet, using
+      last_saved_index and modified_range, as well as whether or not
+      it found the specified file, to determine how much to write.
     """
     ArrayGroup = namedtuple('ArrayGroup', 'shape set_arrays data name')
 
diff --git a/qcodes/instrument/mock.py b/qcodes/instrument/mock.py
index 60c84c142fd9..4b77bf7dd290 100644
--- a/qcodes/instrument/mock.py
+++ b/qcodes/instrument/mock.py
@@ -12,17 +12,19 @@ class MockInstrument(Instrument):
     """
     Create a software instrument, mostly for testing purposes.
 
-    Also works for simulatoins, but usually this will be simpler, easier to
+    Also works for simulations, but usually this will be simpler, easier to
     use, and faster if made as a single ``Instrument`` subclass.
 
-    ``MockInstrument``s have extra overhead as they serialize all commands
+    ``MockInstrument``\s have extra overhead as they serialize all commands
     (to mimic a network communication channel) and use at least two processes
     (instrument server and model server) both of which must be involved in any
     given query.
 
     parameters to pass to model should be declared with:
-        get_cmd = param_name + '?'
-        set_cmd = param_name + ':{:.3f}' (specify the format & precision)
+
+        - get_cmd = param_name + '?'
+        - set_cmd = param_name + ':{:.3f}' (specify the format & precision)
+
     alternatively independent set/get functions may still be provided.
 
     Args:
@@ -56,7 +58,7 @@ class MockInstrument(Instrument):
 
         history (List[tuple]): All commands and responses while keep_history is
             enabled, as tuples:
-                (timestamp, 'ask' or 'write', param_name[, value])
+            (timestamp, 'ask' or 'write', param_name[, value])
     """
 
     shared_kwargs = ['model']
@@ -175,9 +177,9 @@ class MockModel(ServerManager, BaseServer):  # pragma: no cover
             the server's uuid.
 
     for every instrument that connects to this model, create two methods:
-        ``_set(param, value)``: set a parameter on the model
-        ``_get(param)``: returns the value of a parameter
-    ``param`` and the set/return values should all be strings
+        - ``_set(param, value)``: set a parameter on the model
+        - ``_get(param)``: returns the value of a parameter
+          ``param`` and the set/return values should all be strings
 
     If ``param`` and/or ``value`` is not recognized, the method should raise
     an error.
diff --git a/qcodes/instrument/parameter.py b/qcodes/instrument/parameter.py
index bed24bb2c714..f0f63a09b087 100644
--- a/qcodes/instrument/parameter.py
+++ b/qcodes/instrument/parameter.py
@@ -91,6 +91,7 @@ class Parameter(Metadatable, DeferredOperations):
     Parameters have a .get_latest method that simply returns the most recent
     set or measured value. This can either be called ( param.get_latest() )
     or used in a Loop as if it were a (gettable-only) parameter itself:
+
         Loop(...).each(param.get_latest)
 
 
@@ -121,7 +122,7 @@ class Parameter(Metadatable, DeferredOperations):
 
         setpoints: (3,4,5) the setpoints for the returned array of values.
             3&4 - a tuple of arrays. The first array is be 1D, the second 2D,
-                etc.
+            etc.
             5 - a tuple of tuples of arrays
             Defaults to integers from zero in each respective direction
             Each may be either a DataArray, a numpy array, or a sequence
@@ -448,6 +449,7 @@ class StandardParameter(Parameter):
 
         set_cmd (Optional[Union[string, function]]): command to set this
             parameter, either:
+
             - a string (containing one field to .format, like "{}" etc)
               you can only use a string if an instrument is provided,
               this string will be passed to instrument.write

From 7c27a0608237ab85ac52824494affea17c8d1fea Mon Sep 17 00:00:00 2001
From: Ruben van Gulik 
Date: Tue, 20 Dec 2016 13:59:30 +0100
Subject: [PATCH 071/328] fix: Improved IVVI driver speed (#320)

* fix: remove DAC value checking and improve timing

* feature: DAC setpoints are checked again

* feature: Added constructor flag for checking setpoints

* feature: Adjustable sleep time in _set_dac

* fix: setpoints check and set sleep now ManualParameter

* fix: remove doc line

* feature: merged PR #288

* feature: added buffer reading time variable

* refactor: changes based on comments in the PR

- Units and docstring for added parameters
- Placed the DAC set sleeping command in the setpoint checking code, as
it is only needed when first sending a get command.

* Ivvi (#413)

* to get_all after setting connection parameters

* to get_all after setting connection parameters 2

* autopep

* updates to IVVI driver (#423)

* better variable name

* replace version function with dummy; add extra setting of term characters

* add safe_version option
---
 qcodes/instrument_drivers/QuTech/IVVI.py | 195 ++++++++++++++++++-----
 1 file changed, 154 insertions(+), 41 deletions(-)

diff --git a/qcodes/instrument_drivers/QuTech/IVVI.py b/qcodes/instrument_drivers/QuTech/IVVI.py
index 69a39a31f9b6..4a57989611b1 100644
--- a/qcodes/instrument_drivers/QuTech/IVVI.py
+++ b/qcodes/instrument_drivers/QuTech/IVVI.py
@@ -5,6 +5,8 @@
 import traceback
 
 from qcodes import VisaInstrument, validators as vals
+from qcodes.instrument.parameter import ManualParameter
+from qcodes.utils.validators import Bool, Numbers
 
 
 class IVVI(VisaInstrument):
@@ -29,7 +31,7 @@ class IVVI(VisaInstrument):
     Halfrange = Fullrange / 2
 
     def __init__(self, name, address, reset=False, numdacs=16, dac_step=10,
-                 dac_delay=.1, dac_max_delay=0.2, **kwargs):
+                 dac_delay=.1, dac_max_delay=0.2, safe_version=True, **kwargs):
                  # polarity=['BIP', 'BIP', 'BIP', 'BIP']):
                  # commented because still on the todo list
         '''
@@ -45,10 +47,14 @@ def __init__(self, name, address, reset=False, numdacs=16, dac_step=10,
             dac_step (float)         : max step size for dac parameter
             dac_delay (float)        : delay (in seconds) for dac
             dac_max_delay (float)    : maximum delay before emitting a warning
+            safe_version (bool)    : if True then do not send version commands
+                                     to the IVVI controller
         '''
         t0 = time.time()
         super().__init__(name, address, **kwargs)
 
+        self.safe_version = safe_version
+
         if numdacs % 4 == 0 and numdacs > 0:
             self._numdacs = int(numdacs)
         else:
@@ -58,10 +64,45 @@ def __init__(self, name, address, reset=False, numdacs=16, dac_step=10,
         # values based on descriptor
         self.visa_handle.baud_rate = 115200
         self.visa_handle.parity = visa.constants.Parity(1)  # odd parity
+        self.visa_handle.write_termination = ''
+        self.visa_handle.read_termination = ''
 
         self.add_parameter('version',
                            get_cmd=self._get_version)
-        
+
+        self.add_parameter('check_setpoints',
+                           parameter_class=ManualParameter,
+                           initial_value=False,
+                           label='Check setpoints',
+                           vals=Bool(),
+                           docstring=('Whether to check if the setpoint is the'
+                                      ' same as the current DAC value to '
+                                      'prevent an unnecessary set command.'))
+
+        # Time to wait before sending a set DAC command to the IVVI
+        self.add_parameter('dac_set_sleep',
+                           parameter_class=ManualParameter,
+                           initial_value=0.05,
+                           label='DAC set sleep',
+                           units='s',
+                           vals=Numbers(0),
+                           docstring=('When check_setpoints is set to True, '
+                                      'this is the waiting time between the'
+                                      'command that checks the current DAC '
+                                      'values and the final set DAC command'))
+
+        # Minimum time to wait before the read buffer contains data
+        self.add_parameter('dac_read_buffer_sleep',
+                           parameter_class=ManualParameter,
+                           initial_value=0.025,
+                           label='DAC read buffer sleep',
+                           units='s',
+                           vals=Numbers(0),
+                           docstring=('While recieving bytes from the IVVI, '
+                                      'sleeping is done in multiples of this '
+                                      'value. Change to a lower value for '
+                                      'a shorter minimum time to wait.'))
+
         self.add_parameter('dac voltages',
                            label='Dac voltages',
                            get_cmd=self._get_dacs)
@@ -81,12 +122,21 @@ def __init__(self, name, address, reset=False, numdacs=16, dac_step=10,
 
         self._update_time = 5  # seconds
         self._time_last_update = 0  # ensures first call will always update
-        
+
         self.pol_num = np.zeros(self._numdacs)  # corresponds to POS polarity
         self.set_pol_dacrack('BIP', range(self._numdacs), get_all=False)
 
         t1 = time.time()
 
+        # make sure we ignore termination characters
+        # See http://www.ni.com/tutorial/4256/en/#toc2 on Termination Character
+        # Enabled
+        v = self.visa_handle
+        v.set_visa_attribute(visa.constants.VI_ATTR_TERMCHAR_EN, 0)
+        v.set_visa_attribute(visa.constants.VI_ATTR_ASRL_END_IN, 0)
+        v.set_visa_attribute(visa.constants.VI_ATTR_ASRL_END_OUT, 0)
+        v.set_visa_attribute(visa.constants.VI_ATTR_SEND_END_EN, 0)
+
         # basic test to confirm we are properly connected
         try:
             self.get_all()
@@ -94,28 +144,36 @@ def __init__(self, name, address, reset=False, numdacs=16, dac_step=10,
             print('IVVI: get_all() failed, maybe connected to wrong port?')
             print(traceback.format_exc())
 
-        print('Initialized IVVI-rack in %.2fs' % (t1-t0))
+        print('Initialized IVVI-rack in %.2fs' % (t1 - t0))
 
     def get_idn(self):
         """
         Overwrites the get_idn function using constants as the hardware
         does not have a proper *IDN function.
         """
+        # not all IVVI racks support the version command, so return a dummy
+        return -1
+
         idparts = ['QuTech', 'IVVI', 'None', self.version()]
 
         return dict(zip(('vendor', 'model', 'serial', 'firmware'), idparts))
 
     def _get_version(self):
-        mes = self.ask(bytes([3, 4]))
-        v = mes[2]
-        return v
+        if self.safe_version:
+            return -1
+        else:
+            # ask for the version of more recent modules
+            # some of the older modules cannot handle this command
+            mes = self.ask(bytes([3, 4]))
+            ver = mes[2]
+            return ver
 
     def get_all(self):
         return self.snapshot(update=True)
 
     def set_dacs_zero(self):
         for i in range(self._numdacs):
-            self._set_dac(i+1, 0)
+            self._set_dac(i + 1, 0)
 
     # Conversion of data
     def _mvoltage_to_bytes(self, mvoltage):
@@ -131,7 +189,7 @@ def _mvoltage_to_bytes(self, mvoltage):
         Output:
             (dataH, dataL) (int, int) : The high and low value byte equivalent
         '''
-        bytevalue = int(round(mvoltage/self.Fullrange*65535))
+        bytevalue = int(round(mvoltage / self.Fullrange * 65535))
         return bytevalue.to_bytes(length=2, byteorder='big')
 
     def _bytes_to_mvoltages(self, byte_mess):
@@ -143,8 +201,8 @@ def _bytes_to_mvoltages(self, byte_mess):
         for i in range(self._numdacs):
             # takes two bytes, converts it to a 16 bit int and then divides by
             # the range and adds the offset due to the polarity
-            values[i] = ((byte_mess[2 + 2*i]*256 + byte_mess[3 + 2*i]) /
-                         65535.0*self.Fullrange) + self.pol_num[i]
+            values[i] = ((byte_mess[2 + 2 * i] * 256 + byte_mess[3 + 2 * i]) /
+                         65535.0 * self.Fullrange) + self.pol_num[i]
         return values
 
     # Communication with device
@@ -156,13 +214,13 @@ def _get_dac(self, channel):
         this version is a wrapper around the IVVI get function.
         it only updates
         '''
-        return self._get_dacs()[channel-1]
+        return self._get_dacs()[channel - 1]
 
     def _set_dac(self, channel, mvoltage):
         '''
         Sets the specified dac to the specified voltage.
-        Will only send a command to the IVVI if the next value is different
-        than the current value within byte resolution.
+        A check to prevent setting the same value is performed if
+        the check_setpoints flag was set.
 
         Input:
             mvoltage (float) : output voltage in mV
@@ -172,25 +230,37 @@ def _set_dac(self, channel, mvoltage):
             reply (string) : errormessage
         Private version of function
         '''
-        cur_val = self.get('dac{}'.format(channel))
-        # dac range in mV / 16 bits FIXME make range depend on polarity
-        byte_res = self.Fullrange/2**16
-        eps = 0.0001
-        # eps is a magic number to correct for an offset in the values the IVVI
-        # returns (i.e. setting 0 returns byte_res/2 = 0.030518 with rounding
+        proceed = True
+
+        if self.check_setpoints():
+            cur_val = self.get('dac{}'.format(channel))
+            # dac range in mV / 16 bits FIXME make range depend on polarity
+            byte_res = self.Fullrange / 2**16
+            # eps is a magic number to correct for an offset in the values
+            # the IVVI returns (i.e. setting 0 returns byte_res/2 = 0.030518
+            # with rounding
+            eps = 0.0001
+
+            proceed = False
+
+            if (mvoltage > (cur_val + byte_res / 2 + eps) or
+                    mvoltage < (cur_val - byte_res / 2 - eps)):
+                proceed = True
+
+            if self.dac_set_sleep() > 0.0:
+                time.sleep(self.dac_set_sleep())
 
         # only update the value if it is different from the previous one
         # this saves time in setting values, set cmd takes ~650ms
-        if (mvoltage > (cur_val+byte_res/2+eps) or
-                mvoltage < (cur_val - byte_res/2-eps)):
-            byte_val = self._mvoltage_to_bytes(mvoltage -
-                                               self.pol_num[channel-1])
+        if proceed:
+            polarity_corrected = mvoltage - self.pol_num[channel - 1]
+            byte_val = self._mvoltage_to_bytes(polarity_corrected)
             message = bytes([2, 1, channel]) + byte_val
-            time.sleep(.05)
+
             reply = self.ask(message)
             self._time_last_update = 0  # ensures get command will update
+
             return reply
-        return
 
     def _get_dacs(self):
         '''
@@ -205,7 +275,7 @@ def _get_dacs(self):
         get dacs command takes ~450ms according to ipython timeit
         '''
         if (time.time() - self._time_last_update) > self._update_time:
-            message = bytes([self._numdacs*2+2, 2])
+            message = bytes([self._numdacs * 2 + 2, 2])
             # workaround for an error in the readout that occurs sometimes
             max_tries = 10
             for i in range(max_tries):
@@ -216,7 +286,7 @@ def _get_dacs(self):
                     break
                 except Exception as ex:
                     logging.warning('IVVI communication error trying again')
-            if i+1 == max_tries:  # +1 because range goes stops before end
+            if i + 1 == max_tries:  # +1 because range goes stops before end
                 raise('IVVI Communication error')
         return self._mvoltages
 
@@ -228,9 +298,12 @@ def write(self, message, raw=False):
         returns message_len
         '''
         # This is used when write is used in the ask command
-        expected_answer_length = message[0]
+        expected_answer_length = None
+
         if not raw:
-            message_len = len(message)+2
+            expected_answer_length = message[0]
+            message_len = len(message) + 2
+
             error_code = bytes([0])
             message = bytes([message_len]) + error_code + message
         self.visa_handle.write_raw(message)
@@ -247,6 +320,44 @@ def ask(self, message, raw=False):
         message_len = self.write(message, raw=raw)
         return self.read(message_len=message_len)
 
+    def _read_raw_bytes_direct(self, size):
+        """ Read raw data using the visa lib """
+        with(self.visa_handle.ignore_warning(visa.constants.VI_SUCCESS_MAX_CNT)):
+            data, statuscode = self.visa_handle.visalib.read(
+                self.visa_handle.session, size)
+
+        return data
+
+    def _read_raw_bytes_multiple(self, size, maxread=512, verbose=0):
+        """ Read raw data in blocks using the visa lib
+        Arguments:
+            size (int) : number of bytes to read
+            maxread (int) : maximum size of block to read
+            verbose (int): verbosity level
+        Returns:
+            ret (bytes): bytes read from the device
+        The pyvisa visalib.read does not always terminate at a newline, this
+        is a workaround.
+        Also see: https://github.com/qdev-dk/Qcodes/issues/276
+                  https://github.com/hgrecco/pyvisa/issues/225
+        Setting both VI_ATTR_TERMCHAR_EN and VI_ATTR_ASRL_END_IN to zero
+        should allow the driver to ignore termination characters, this
+        function is an additional safety mechanism.
+        """
+        ret = []
+        instr = self.visa_handle
+        with self.visa_handle.ignore_warning(visa.constants.VI_SUCCESS_MAX_CNT):
+            nread = 0
+            while nread < size:
+                nn = min(maxread, size - nread)
+                chunk, status = instr.visalib.read(instr.session, nn)
+                ret += [chunk]
+                nread += len(chunk)
+                if verbose:
+                    print('_read_raw: %d/%d bytes' % (len(chunk), nread))
+        ret = b''.join(ret)
+        return ret
+
     def read(self, message_len=None):
         # because protocol has no termination chars the read reads the number
         # of bytes in the buffer
@@ -260,17 +371,18 @@ def read(self, message_len=None):
 
         while bytes_in_buffer < message_len:
             t1 = time.time()
-            time.sleep(.05)
+
+            if self.dac_read_buffer_sleep() > 0.0:
+                time.sleep(self.dac_read_buffer_sleep())
+
             bytes_in_buffer = self.visa_handle.bytes_in_buffer
-            if t1-t0 > timeout:
+            if t1 - t0 > timeout:
                 raise TimeoutError()
         # a workaround for a timeout error in the pyvsia read_raw() function
-        with(self.visa_handle.ignore_warning(visa.constants.VI_SUCCESS_MAX_CNT)):
-            mes = self.visa_handle.visalib.read(
-                self.visa_handle.session, bytes_in_buffer)
-        mes = mes[0]  # cannot be done on same line for some reason
+        mes = self._read_raw_bytes_multiple(bytes_in_buffer)
+
         # if mes[1] != 0:
-        #     # see protocol descriptor for error codes
+        # see protocol descriptor for error codes
         #     raise Exception('IVVI rack exception "%s"' % mes[1])
         return mes
 
@@ -292,8 +404,9 @@ def set_pol_dacrack(self, flag, channels, get_all=True):
 
         val = flagmap[flag.upper()]
         for ch in channels:
-            self.pol_num[ch-1] = val
-            # self.set_parameter_bounds('dac%d' % (i+1), val, val + self.Fullrange.0)
+            self.pol_num[ch - 1] = val
+            # self.set_parameter_bounds('dac%d' % (i+1), val, val +
+            # self.Fullrange.0)
 
         if get_all:
             self.get_all()
@@ -308,7 +421,7 @@ def get_pol_dac(self, channel):
         Output:
             polarity (string) : 'BIP', 'POS' or 'NEG'
         '''
-        val = self.pol_num[channel-1]
+        val = self.pol_num[channel - 1]
 
         if (val == -self.Fullrange):
             return 'NEG'
@@ -387,4 +500,4 @@ def get_func():
 ..
 32       DAC16                   Value of DAC16                        2
 --------------------------------------------------------------------------------------------------------
-'''
\ No newline at end of file
+'''

From b8c50598245aaef979fe64f054c4f59b375b79af Mon Sep 17 00:00:00 2001
From: Jens Hedegaard Nielsen 
Date: Tue, 3 Jan 2017 10:11:01 +0100
Subject: [PATCH 072/328] Fix: Limit sip to 4.18.1

4.19 seems to segfault during setup.py develop
---
 requirements.txt | 1 +
 1 file changed, 1 insertion(+)

diff --git a/requirements.txt b/requirements.txt
index ace996ad9542..a3c8d27ebee3 100644
--- a/requirements.txt
+++ b/requirements.txt
@@ -4,5 +4,6 @@ matplotlib==1.5.3
 pyqtgraph==0.10.0
 PyVISA==1.8
 PyQt5==5.7
+sip==4.18.1
 QtPy==1.1.2
 h5py==2.6.0

From 450976d0bc04c7f481ed11a817f6933c3336d410 Mon Sep 17 00:00:00 2001
From: Jens Hedegaard Nielsen 
Date: Tue, 3 Jan 2017 12:49:39 +0100
Subject: [PATCH 073/328] Travis: install sphinx for docs build

---
 .travis.yml | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

diff --git a/.travis.yml b/.travis.yml
index bd0a5d494591..75640405c463 100644
--- a/.travis.yml
+++ b/.travis.yml
@@ -28,7 +28,7 @@ script:
 
 after_success:
     # install codacy coverage plugin only on traivs
-    - pip install codacy-coverage
+    - pip install codacy-coverage sphinx
     - cd qcodes
     - coverage xml
     - python-codacy-coverage -r coverage.xml

From ca555b2d1757aea56893a49a320b1c6a9ae7a862 Mon Sep 17 00:00:00 2001
From: Jens Hedegaard Nielsen 
Date: Tue, 3 Jan 2017 12:55:41 +0100
Subject: [PATCH 074/328] travis: also install read the docs theme

---
 .travis.yml | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

diff --git a/.travis.yml b/.travis.yml
index 75640405c463..9ebe4157f864 100644
--- a/.travis.yml
+++ b/.travis.yml
@@ -28,7 +28,7 @@ script:
 
 after_success:
     # install codacy coverage plugin only on traivs
-    - pip install codacy-coverage sphinx
+    - pip install codacy-coverage sphinx sphinx_rtd_theme
     - cd qcodes
     - coverage xml
     - python-codacy-coverage -r coverage.xml

From 56f3182188fd215d7e2619123dccd4fe4706e999 Mon Sep 17 00:00:00 2001
From: Jens Hedegaard Nielsen 
Date: Tue, 3 Jan 2017 13:01:02 +0100
Subject: [PATCH 075/328] Travis: also add jsonschema and sphinx package

---
 .travis.yml | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

diff --git a/.travis.yml b/.travis.yml
index 9ebe4157f864..ef9544d39b64 100644
--- a/.travis.yml
+++ b/.travis.yml
@@ -28,7 +28,7 @@ script:
 
 after_success:
     # install codacy coverage plugin only on traivs
-    - pip install codacy-coverage sphinx sphinx_rtd_theme
+    - pip install codacy-coverage sphinx sphinx_rtd_theme jsonschema sphinxcontrib-jsonschema
     - cd qcodes
     - coverage xml
     - python-codacy-coverage -r coverage.xml

From c01ba932a1d8e1b4f0d37e5eda10a6b7f7022f54 Mon Sep 17 00:00:00 2001
From: nataliejpg 
Date: Tue, 3 Jan 2017 14:19:42 +0100
Subject: [PATCH 076/328] made a mess of branches, rebase and add back in
 various acq controllers

---
 .../AlazarTech/ave_controller.py              | 265 ++++++++++++++++
 .../AlazarTech/rec_buf_controller.py          | 220 +++++++++++++
 .../AlazarTech/rec_controller.py              | 294 ++++++++++++++++++
 .../AlazarTech/rec_samp_controller.py         | 233 ++++++++++++++
 4 files changed, 1012 insertions(+)
 create mode 100644 qcodes/instrument_drivers/AlazarTech/ave_controller.py
 create mode 100644 qcodes/instrument_drivers/AlazarTech/rec_buf_controller.py
 create mode 100644 qcodes/instrument_drivers/AlazarTech/rec_controller.py
 create mode 100644 qcodes/instrument_drivers/AlazarTech/rec_samp_controller.py

diff --git a/qcodes/instrument_drivers/AlazarTech/ave_controller.py b/qcodes/instrument_drivers/AlazarTech/ave_controller.py
new file mode 100644
index 000000000000..d23767c7aeb2
--- /dev/null
+++ b/qcodes/instrument_drivers/AlazarTech/ave_controller.py
@@ -0,0 +1,265 @@
+import logging
+from .ATS import AcquisitionController
+import numpy as np
+import qcodes.utils.helpers as helpers
+
+
+class HD_Controller(AcquisitionController):
+    """
+    This is the Acquisition Controller class which works with the ATS9360,
+    averaging over buffers and records, demodulating with a software
+    reference signal, averaging over the  samples.
+    args:
+    name: name for this acquisition_conroller as an instrument
+    alazar_name: the name of the alazar instrument such that this controller
+        can communicate with the Alazar
+    demod_freq: the frequency of the software wave to be created
+    samp_rate: the rate of sampling
+    filt: the filter to be used to filter out double freq component
+    chan_b: whether there is also a second channel of data to be processed
+        and returned
+    **kwargs: kwargs are forwarded to the Instrument base class
+
+    TODO(nataliejpg) fix sample rate problem
+    TODO(nataliejpg) test filter options
+    TODO(nataliejpg) finish implementation of channel b option
+    TODO(nataliejpg) make demodulation freq a param
+    """
+
+    def __init__(self, name, alazar_name, demod_freq, samp_rate=5e8,
+                 filt='win', numtaps=101, chan_b=False, **kwargs):
+        filter_dict = {'win': 0, 'ls': 1, 'dot': 2}
+        self.demodulation_frequency = demod_freq
+        self.sample_rate = samp_rate
+        self.filter = filter_dict[filt]
+        self.numtaps = numtaps
+        self.acquisitionkwargs = {}
+        self.chan_b = chan_b
+        self.samples_per_record = None
+        self.records_per_buffer = None
+        self.buffers_per_acquisition = None
+        self.number_of_channels = 2
+        self.samples_delay = None
+        self.samples_time = None
+        self.cos_list = None
+        self.sin_list = None
+        self.buffer = None
+        self.board_info = None
+        # make a call to the parent class and by extension,
+        # create the parameter structure of this class
+        super().__init__(name, alazar_name, **kwargs)
+
+        self.add_parameter(name='acquisition',
+                           names=('magnitude', 'phase'),
+                           units=('', ''),
+                           get_cmd=self.do_acquisition)
+
+    def update_acquisitionkwargs(self, **kwargs):
+        """
+        Updates the kwargs to be used when
+        alazar_driver.acquire is called.
+
+        It is also used to set the limits on the selection of
+        samples used (bounded by delay and integration time)
+
+        :param kwargs:
+        :return:
+        """
+        samples_per_record = kwargs['samples_per_record']
+        sample_rate = self.sample_rate
+
+        if 'int_delay' in kwargs:
+            int_delay = kwargs.pop('int_delay')
+            samp_delay = int_delay * sample_rate
+            samples_delay_min = (self.numtaps - 1)
+            if samp_delay < samples_delay_min:
+                int_delay_min = samples_delay_min / sample_rate
+                logging.warning(
+                    'delay is less than recommended for filter choice: '
+                    '(expect delay >= {}'.format(int_delay_min))
+        else:
+            samp_delay = self.numtaps - 1
+
+        if 'int_time' in kwargs:
+            int_time = kwargs.pop('int_time')
+            samp_time = int_time * sample_rate
+            samples_time_max = (samples_per_record - samp_delay)
+            oscilations_measured = int_time * self.demodulation_frequency
+            oversampling = sample_rate / (2 * self.demodulation_frequency)
+            if samp_time > samples_time_max:
+                int_time_max = samples_time_max / sample_rate
+                raise ValueError('int_time {} is longer than total_time - '
+                                 'delay: {}'.format(int_time, int_time_max))
+            elif oscilations_measured < 10:
+                logging.warning('{} oscilations measured, recommend at '
+                                'least 10: decrease sampling rate, take '
+                                'more samples or increase demodulation '
+                                'freq'.format(oscilations_measured))
+            elif oversampling < 1:
+                logging.warning('oversampling rate is {}, recommend > 1: '
+                                'increase sampling rate or decrease '
+                                'demodulation frequency'.format(oversampling))
+        else:
+            samp_time = samples_per_record - samp_delay
+
+        self.samples_time = int(samp_time)
+        self.samples_delay = int(samp_delay)
+
+        self.acquisitionkwargs.update(**kwargs)
+
+    def do_acquisition(self):
+        """
+        this method performs an acquisition, which is the get_cmd for the
+        acquisiion parameter of this instrument
+        :return:
+        """
+        values = self._get_alazar().acquire(acquisition_controller=self,
+                                            **self.acquisitionkwargs)
+        return values
+
+    def pre_start_capture(self):
+        """
+        Called before capture start to update Acquisition Controller with
+        alazar acquisition params and set up software wave for demodulation.
+        :return:
+        """
+        alazar = self._get_alazar()
+        self.samples_per_record = alazar.samples_per_record.get()
+        self.records_per_buffer = alazar.records_per_buffer.get()
+        self.buffers_per_acquisition = alazar.buffers_per_acquisition.get()
+        self.board_info = alazar.get_idn()
+        self.buffer = np.zeros(self.samples_per_record *
+                               self.records_per_buffer *
+                               self.number_of_channels)
+
+        integer_list = np.arange(self.samples_per_record, dtype=np.uint16)
+        angle_list = (2 * np.pi * self.demodulation_frequency /
+                      self.sample_rate * integer_list)
+
+        self.cos_list = np.cos(angle_list)
+        self.sin_list = np.sin(angle_list)
+
+    def pre_acquire(self):
+        """
+        See AcquisitionController
+        :return:
+        """
+        pass
+
+    def handle_buffer(self, data):
+        """
+        Adds data from alazar to buffer (effectively averaging)
+        :return:
+        """
+        self.buffer += data
+
+    def post_acquire(self):
+        """
+        Processes the data according to ATS9360 settings, splitting into
+        records and averaging over them, then applying demodulation fit
+        nb: currently only channel A
+        :return: samples_magnitude_array, samples_phase_array
+        """
+        records_per_acquisition = (self.buffers_per_acquisition *
+                                   self.records_per_buffer)
+        # for ATS9360 samples are arranged in the buffer as follows:
+        # S00A, S00B, S01A, S01B...S10A, S10B, S11A, S11B...
+        # where SXYZ is record X, sample Y, channel Z.
+
+        # break buffer up into records and averages over them
+        recA = np.zeros(self.samples_per_record)
+        for i in range(self.records_per_buffer):
+            i0 = (i * self.samples_per_record * self.number_of_channels)
+            i1 = (i0 + self.samples_per_record * self.number_of_channels)
+            recA += self.buffer[i0:i1:self.number_of_channels]
+        recordA = np.uint16(recA / records_per_acquisition)
+
+        # do demodulation
+        magA, phaseA = self.fit(recordA)
+
+        # same for chan b
+        if self.chan_b:
+            raise NotImplementedError('chan b code not complete')
+            # recordB = np.zeros(self.samples_per_record, dtype=np.uint16)
+            # for i in range(self.records_per_buffer):
+            #     i0 = (i * self.samples_per_record *
+            #           self.number_of_channels + 1)
+            #     i1 = (i0 + self.samples_per_record * self.number_of_channels)
+            #     recordB += np.uint16(self.buffer[i0:i1:self.number_of_channels] /
+            #                          records_per_acquisition)
+            # magB, phaseB = self.fit(recordB)
+
+        return magA, phaseA
+
+    def fit(self, rec):
+        """
+        Applies volts conversion, demodulation fit, low bandpass filter
+        and integration limits to samples array and averages
+        :return: magnitude, phase
+        """
+
+        # convert rec to volts
+        bps = self.board_info['bits_per_sample']
+        if bps == 12:
+            volt_rec = sample_to_volt_u12(rec, bps)
+        else:
+            logging.warning('sample to volt conversion does not exist for'
+                            ' bps != 12, centered raw samples returned')
+            volt_rec = rec - np.mean(rec)
+
+        # multiply with software wave
+        re_wave = np.multiply(volt_rec, self.cos_list)
+        im_wave = np.multiply(volt_rec, self.sin_list)
+        cutoff = self.demodulation_frequency / 10
+
+        # filter out higher freq component
+        if self.filter == 0:
+            re_filtered = helpers.filter_win(re_wave, cutoff,
+                                             self.sample_rate, self.numtaps)
+            im_filtered = helpers.filter_win(im_wave, cutoff,
+                                             self.sample_rate, self.numtaps)
+        elif self.filter == 1:
+            re_filtered = helpers.filter_ls(re_wave, cutoff,
+                                            self.sample_rate, self.numtaps)
+            im_filtered = helpers.filter_ls(im_wave, cutoff,
+                                            self.sample_rate, self.numtaps)
+        elif self.filter == 2:
+            re_filtered = re_wave
+            im_filtered = im_wave
+
+        # apply integration limits
+        start = self.samples_delay
+        end = start + self.samples_time
+
+        re_limited = re_filtered[start:end]
+        im_limited = im_filtered[start:end]
+
+        # convert to magnitude and phase
+        complex_num = re_limited + im_limited * 1j
+        mag = np.mean(abs(complex_num))
+        phase = np.mean(np.angle(complex_num, deg=True))
+
+        return mag, phase
+
+
+def sample_to_volt_u12(raw_samples, bps):
+    """
+    Applies volts conversion for 12 bit sample data stored
+    in 2 bytes
+    :return: samples_magnitude_array, samples_phase_array
+    """
+
+    # right_shift 16-bit sample by 4 to get 12 bit sample
+    shifted_samples = np.right_shift(raw_samples, 4)
+
+    # Alazar calibration
+    code_zero = (1 << (bps - 1)) - 0.5
+    code_range = (1 << (bps - 1)) - 0.5
+
+    # TODO(nataliejpg) make this not hard coded
+    input_range_volts = 1
+    # Convert to volts
+    volt_samples = np.float64(input_range_volts *
+                              (shifted_samples - code_zero) / code_range)
+
+    return volt_samples
diff --git a/qcodes/instrument_drivers/AlazarTech/rec_buf_controller.py b/qcodes/instrument_drivers/AlazarTech/rec_buf_controller.py
new file mode 100644
index 000000000000..65ddb9dea4d7
--- /dev/null
+++ b/qcodes/instrument_drivers/AlazarTech/rec_buf_controller.py
@@ -0,0 +1,220 @@
+from .ATS import AcquisitionController
+import numpy as np
+from qcodes import Parameter
+from scipy import signal
+
+
+class RecBufParam(Parameter):
+    """
+    Hardware controlled parameter class for Alazar acquisition. To be used with
+    HD_Samples_Controller (tested with ATS9360 board) for return of an array of
+    sample data from the Alazar, averaged over records and buffers.
+    """
+
+    def __init__(self, name, instrument):
+        super().__init__(name)
+        self._instrument = instrument
+        self.acquisitionkwargs = {}
+        self.names = ('magnitude', 'phase')
+        self.units = ('', '')
+        self.setpoint_names = (('rec_num', 'buf_num'), ('rec_num', 'buf_num'))
+        self.setpoints = ((1, 1), (1, 1))
+        self.shapes = ((1, 1), (1, 1))
+
+    def update_acquisition_kwargs(self, **kwargs):
+        # needed to update config of the software parameter on sweep change
+        # freq setpoints tuple as needs to be hashable for look up
+        if 'records_per_buffer' and 'buffers_per_acquisition' in kwargs:
+            rpts = kwargs['records_per_buffer']
+            bpts = kwargs['buffers_per_acquisition']
+            r = tuple(np.arange(rpts))
+            b = tuple(np.arange(bpts))
+            self.setpoints = ((r, b), (r, b))
+            self.shapes = ((rpts, bpts), (rpts, bpts))
+        else:
+            raise ValueError(
+                'records_per_buffer and buffers_per_acquisition must be specified')
+        # updates dict to be used in acquisition get call
+        self.acquisitionkwargs.update(**kwargs)
+
+    def get(self):
+        mag, phase = self._instrument._get_alazar().acquire(
+            acquisition_controller=self._instrument,
+            **self.acquisitionkwargs)
+        return mag, phase
+
+
+class HD_RecBuf_Controller(AcquisitionController):
+    """
+    This is the Acquisition Controller class which works with the ATS9360,
+    averaging over buffers and records and demodulating with a software
+    reference signal, returning the  samples.
+    args:
+    name: name for this acquisition_conroller as an instrument
+    alazar_name: the name of the alazar instrument such that this controller
+        can communicate with the Alazar
+    demod_freq: the frequency of the software wave to be created
+    samp_rate: the rate of sampling
+    filt: the filter to be used to filter out double freq component
+    chan_b: whether there is also a second channel of data to be processed
+        and returned
+    **kwargs: kwargs are forwarded to the Instrument base class
+
+    TODO(nataliejpg) fix sample rate problem
+    TODO(nataliejpg) test filter options
+    TODO(nataliejpg) test mag phase logic
+    TODO(nataliejpg) finish implementation of channel b option
+    TODO(nataliejpg) make filter settings not hard coded
+    """
+
+    def __init__(self, name, alazar_name, demod_freq, samp_rate=500e6,
+                 filt='win', chan_b=False, **kwargs):
+        filter_dict = {'win': 0, 'hamming': 1, 'ls': 2}
+        self.demodulation_frequency = demod_freq
+        self.sample_rate = samp_rate
+        self.filter = filter_dict[filt]
+        self.chan_b = chan_b
+        self.samples_per_record = 0
+        self.records_per_buffer = 0
+        self.buffers_per_acquisition = 0
+        self.number_of_channels = 2
+        self.cos_list = None
+        self.sin_list = None
+        self.buffer = None
+        self.buf_count = 0
+        # make a call to the parent class and by extension,
+        # create the parameter structure of this class
+        super().__init__(name, alazar_name, **kwargs)
+
+        self.add_parameter(name='acquisition',
+                           parameter_class=RecBufParam)
+
+    def update_acquisitionkwargs(self, **kwargs):
+        """
+        This method must be used to update the kwargs used for the acquisition
+        with the alazar_driver.acquire
+        :param kwargs:
+        :return:
+        """
+        self.acquisition.update_acquisition_kwargs(**kwargs)
+
+    def pre_start_capture(self):
+        """
+        See AcquisitionController
+        :return:
+        """
+        alazar = self._get_alazar()
+        self.samples_per_record = alazar.samples_per_record.get()
+        self.records_per_buffer = alazar.records_per_buffer.get()
+        self.buffers_per_acquisition = alazar.buffers_per_acquisition.get()
+        self.buf_count = 0
+        self.samples_per_buffer = (self.samples_per_record *
+                                   self.records_per_buffer *
+                                   self.buffers_per_acquisition *
+                                   self.number_of_channels)
+        self.buffer = np.zeros(self.samples_per_buffer)
+
+        integer_list = np.arange(self.samples_per_record)
+        angle_list = (2 * np.pi * self.demodulation_frequency /
+                      self.sample_rate * integer_list)
+
+        cos_list = np.cos(angle_list)
+        sin_list = np.sin(angle_list)
+        self.cos_mat = np.kron(np.ones((1, self.records_per_buffer)), cos_list)
+        self.sin_mat = np.kron(np.ones((1, self.records_per_buffer)), sin_list)
+
+    def pre_acquire(self):
+        """
+        See AcquisitionController
+        :return:
+        """
+        # this could be used to start an Arbitrary Waveform Generator, etc...
+        # using this method ensures that the contents are executed AFTER the
+        # Alazar card starts listening for a trigger pulse
+        pass
+
+    def handle_buffer(self, data):
+        """
+        See AcquisitionController
+        :return:
+        """
+        i0 = self.bufcount * self.samples_per_buffer
+        i1 = i0 + self.samples_per_buffer
+        self.buffer[i0:i1] = data
+        self.buf_count += 1
+
+    def post_acquire(self):
+        """
+        See AcquisitionController
+        :return:
+        """
+        # for ATS9360 samples are arranged in the buffer as follows:
+        # S00A, S00B, S01A, S01B...S10A, S10B, S11A, S11B...
+        # where SXYZ is record X, sample Y, channel Z.
+
+        # reshapes data to be (samples * records * buffers)
+
+        # TODO!!
+
+        magA, phaseA = 0, 0
+
+        return magA, phaseA
+
+    def fit(self, rec):
+        # center rec around 0
+        rec = rec - np.mean(rec)
+
+        # multiply with software wave
+        re_wave = np.multiply(rec, self.cos_mat)
+        im_wave = np.multiply(rec, self.sin_mat)
+        cutoff = self.demodulation_frequency
+        numtaps = 30
+        axis = 0
+
+        # filter out double freq component to obtian constant term
+        if self.filter == 0:
+            RePart = self.filter_win(re_wave, numtaps, cutoff, axis=axis)
+            ImPart = self.filter_win(im_wave, numtaps, cutoff, axis=axis)
+        elif self.filter == 1:
+            RePart = self.filter_hamming(re_wave, numtaps, cutoff, axis=axis)
+            ImPart = self.filter_hamming(im_wave, numtaps, cutoff, axis=axis)
+        elif self.filter == 2:
+            RePart = self.filter_ls(re_wave, numtaps, cutoff, axis, axis=axis)
+            ImPart = self.filter_ls(im_wave, numtaps, cutoff, axis, axis=axis)
+
+        # convert to magnitude and phase data and average over samples
+        # data returned is (records * buffers)
+        complex_mat = RePart + ImPart * 1j
+        mag = np.mean(2 * abs(complex_mat), axis=0)
+        phase = np.mean(np.angle(complex_mat, axis=0, deg=True))
+
+        return mag, phase
+
+    def filter_hamming(self, rec, numtaps, cutoff, axis=-1):
+        sample_rate = self.sample_rate
+        nyq_rate = sample_rate / 2.
+        fir_coef = signal.firwin(numtaps,
+                                 cutoff / nyq_rate,
+                                 window="hamming")
+        filtered_rec = 2 * signal.lfilter(fir_coef, 1.0, rec, axis=axis)
+        return filtered_rec
+
+    def filter_win(self, rec, numtaps, cutoff, axis=-1):
+        sample_rate = self.sample_rate
+        nyq_rate = sample_rate / 2.
+        fir_coef = signal.firwin(numtaps,
+                                 cutoff / nyq_rate)
+        filtered_rec = 2 * signal.lfilter(fir_coef, 1.0, rec, axis=axis)
+        return filtered_rec
+
+    def filter_ls(self, rec, numtaps, cutoff, axis=-1):
+        sample_rate = self.sample_rate
+        nyq_rate = sample_rate / 2.
+        bands = [0, cutoff / nyq_rate, cutoff / nyq_rate, 1]
+        desired = [1, 1, 0, 0]
+        fir_coef = signal.firls(numtaps,
+                                bands,
+                                desired,
+                                nyq=nyq_rate)
+        filtered_rec = 2 * signal.lfilter(fir_coef, 1.0, rec, axis=axis)
+        return filtered_rec
diff --git a/qcodes/instrument_drivers/AlazarTech/rec_controller.py b/qcodes/instrument_drivers/AlazarTech/rec_controller.py
new file mode 100644
index 000000000000..5513e384264b
--- /dev/null
+++ b/qcodes/instrument_drivers/AlazarTech/rec_controller.py
@@ -0,0 +1,294 @@
+import logging
+from .ATS import AcquisitionController
+import numpy as np
+from qcodes import Parameter
+from qcodes.utils.helpers import filter_win, filter_ls
+
+
+class RecordsParam(Parameter):
+    """
+    Hardware controlled parameter class for Alazar acquisition. To be used with
+    HD_Records_Controller (tested with ATS9360 board) for return of an array of
+    record data from the Alazar, averaged over samples and buffers.
+
+    TODO(nataliejpg) fix setpoints/shapes horriblenesss
+    """
+
+    def __init__(self, name, instrument):
+        super().__init__(name)
+        self._instrument = instrument
+        self.acquisitionkwargs = {}
+        self.names = ('magnitude', 'phase')
+        self.units = ('', '')
+        self.setpoint_names = (('record_num',), ('record_num',))
+        self.setpoints = ((1,), (1,))
+        self.shapes = ((1,), (1,))
+
+    def update_acquisition_kwargs(self, **kwargs):
+        # needed to update config of the software parameter on sweep change
+        # freq setpoints tuple as needs to be hashable for look up
+        if 'records_per_buffer' in kwargs:
+            npts = kwargs['records_per_buffer']
+            n = tuple(np.arange(npts))
+            self.setpoints = ((n,), (n,))
+            self.shapes = ((npts,), (npts,))
+        else:
+            raise ValueError('records_per_buffer must be specified')
+        # updates dict to be used in acquisition get call
+        self.acquisitionkwargs.update(**kwargs)
+
+    def get(self):
+        mag, phase = self._instrument._get_alazar().acquire(
+            acquisition_controller=self._instrument,
+            **self.acquisitionkwargs)
+        return mag, phase
+
+
+class HD_Records_Controller(AcquisitionController):
+    """
+    This is the Acquisition Controller class which works with the ATS9360,
+    averaging over buffers and records and demodulating with a software
+    reference signal, returning the  samples.
+    args:
+    name: name for this acquisition_conroller as an instrument
+    alazar_name: the name of the alazar instrument such that this controller
+        can communicate with the Alazar
+    demod_freq: the frequency of the software wave to be created
+    samp_rate: the rate of sampling
+    filt: the filter to be used to filter out double freq component
+    numtaps: number of freq components used in the filter
+    chan_b: whether there is also a second channel of data to be processed
+        and returned
+    **kwargs: kwargs are forwarded to the Instrument base class
+
+    TODO(nataliejpg) fix sample rate problem
+    TODO(nataliejpg) test filter options
+    TODO(nataliejpg) finish implementation of channel b option
+    TODO(nataliejpg) make demodulation freq a param
+    """
+
+    def __init__(self, name, alazar_name, demod_freq, samp_rate=5e8,
+                 filt='win', numtaps=101, chan_b=False, **kwargs):
+        filter_dict = {'win': 0, 'ls': 1, 'dot': 2}
+        self.demodulation_frequency = demod_freq
+        self.sample_rate = samp_rate
+        self.filter = filter_dict[filt]
+        self.numtaps = numtaps
+        self.chan_b = chan_b
+        self.samples_per_record = None
+        self.records_per_buffer = None
+        self.buffers_per_acquisition = None
+        self.number_of_channels = 2
+        self.samples_delay = None
+        self.samples_time = None
+        self.cos_mat = None
+        self.sin_mat = None
+        self.buffer = None
+        self.board_info = None
+        # make a call to the parent class and by extension,
+        # create the parameter structure of this class
+        super().__init__(name, alazar_name, **kwargs)
+
+        self.add_parameter(name='acquisition',
+                           parameter_class=RecordsParam)
+
+    def update_acquisitionkwargs(self, **kwargs):
+        """
+        Updates the kwargs to be used when
+        alazar_driver.acquire is called via a get call of the
+        acquisition RecordsParam.
+
+        It is also used to set the limits on the selection of
+        samples used (bounded by delay and integration time)
+
+        :param kwargs:
+        :return:
+        """
+        samples_per_record = kwargs['samples_per_record']
+        sample_rate = self.sample_rate
+
+        if 'int_delay' in kwargs:
+            int_delay = kwargs.pop('int_delay')
+            samp_delay = int_delay * sample_rate
+            samples_delay_min = (self.numtaps - 1)
+            if samp_delay < samples_delay_min:
+                int_delay_min = samples_delay_min / sample_rate
+                logging.warning(
+                    'delay is less than recommended for filter choice: '
+                    '(expect delay >= {}'.format(int_delay_min))
+        else:
+            samp_delay = self.numtaps - 1
+
+        if 'int_time' in kwargs:
+            int_time = kwargs.pop('int_time')
+            samp_time = int_time * sample_rate
+            samples_time_max = (samples_per_record - samp_delay)
+            oscilations_measured = int_time * self.demodulation_frequency
+            oversampling = sample_rate / (2 * self.demodulation_frequency)
+            if samp_time > samples_time_max:
+                int_time_max = samples_time_max / sample_rate
+                raise ValueError('int_time {} is longer than total_time - '
+                                 'delay: {}'.format(int_time, int_time_max))
+            elif oscilations_measured < 10:
+                logging.warning('{} oscilations measured, recommend at '
+                                'least 10: decrease sampling rate, take '
+                                'more samples or increase demodulation '
+                                'freq'.format(oscilations_measured))
+            elif oversampling < 1:
+                logging.warning('oversampling rate is {}, recommend > 1: '
+                                'increase sampling rate or decrease '
+                                'demodulation frequency'.format(oversampling))
+        else:
+            samp_time = samples_per_record - samp_delay
+
+        self.samples_time = int(samp_time)
+        self.samples_delay = int(samp_delay)
+
+        self.acquisition.update_acquisition_kwargs(**kwargs)
+
+    def pre_start_capture(self):
+        """
+        Called before capture start to update Acquisition Controller with
+        alazar acquisition params and set up software wave for demodulation.
+
+        sine and cosine matrices have shape (samples * records).
+
+        :return:
+        """
+        alazar = self._get_alazar()
+        self.samples_per_record = alazar.samples_per_record.get()
+        self.records_per_buffer = alazar.records_per_buffer.get()
+        self.buffers_per_acquisition = alazar.buffers_per_acquisition.get()
+        self.board_info = alazar.get_idn()
+        self.buffer = np.zeros(self.samples_per_record *
+                               self.records_per_buffer *
+                               self.number_of_channels)
+
+        integer_list = np.arange(self.samples_per_record, dtype=np.uint16)
+        angle_list = (2 * np.pi * self.demodulation_frequency /
+                      self.sample_rate * integer_list)
+
+        cos_list = np.cos(angle_list).reshape(self.samples_per_record, 1)
+        sin_list = np.sin(angle_list).reshape(self.samples_per_record, 1)
+
+        self.cos_mat = np.kron(np.ones((1, self.records_per_buffer)), cos_list)
+        self.sin_mat = np.kron(np.ones((1, self.records_per_buffer)), sin_list)
+
+    def pre_acquire(self):
+        """
+        See AcquisitionController
+        :return:
+        """
+        pass
+
+    def handle_buffer(self, data):
+        """
+        Adds data from alazar to buffer (effectively averaging)
+        :return:
+        """
+        self.buffer += data
+
+    def post_acquire(self):
+        """
+        Processes the data according to ATS9360 settings, splitting into
+        records and averaging over them, then applying demodulation fit
+        nb: currently only channel A
+        :return: samples_magnitude_array, samples_phase_array
+        """
+
+        # for ATS9360 samples are arranged in the buffer as follows:
+        # S00A, S00B, S01A, S01B...S10A, S10B, S11A, S11B...
+        # where SXYZ is record X, sample Y, channel Z.
+
+        # break buffer up into records and shapes to be (samples * records)
+        recordA = np.zeros((self.samples_per_record, self.records_per_buffer),
+                           dtype=np.uint16)
+        for i in range(self.records_per_buffer):
+            i0 = (i * self.samples_per_record * self.number_of_channels)
+            i1 = (i0 + self.samples_per_record * self.number_of_channels)
+            recordA[:, i] = np.uint16(
+                self.buffer[i0:i1:self.number_of_channels] /
+                self.buffers_per_acquisition)
+
+        # do demodulation
+        magA, phaseA = self.fit(recordA)
+
+        # same for chan b
+        if self.chan_b:
+            raise NotImplementedError('chan b code not complete')
+
+        return magA, phaseA
+
+    def fit(self, rec):
+        """
+        Applies volts conversion, demodulation fit, low bandpass filter
+        and integration limits to samples * records array
+        :return: records_magnitude_array, records_phase_array
+        """
+
+        # convert rec to volts
+        bps = self.board_info['bits_per_sample']
+        if bps == 12:
+            volt_rec = sample_to_volt_u12(rec, bps)
+        else:
+            logging.warning('sample to volt conversion does not exist for'
+                            ' bps != 12, centered raw samples returned')
+            volt_rec = rec - np.mean(rec, 0)
+
+        # multiply with software wave
+        re_wave = np.multiply(volt_rec, self.cos_mat)
+        im_wave = np.multiply(volt_rec, self.sin_mat)
+        cutoff = self.demodulation_frequency / 10
+        ax = 0
+
+        # filter out higher freq component
+        if self.filter == 0:
+            re_filtered = filter_win(re_wave, cutoff, self.sample_rate,
+                                     self.numtaps, axis=ax)
+            im_filtered = filter_win(im_wave, cutoff, self.sample_rate,
+                                     self.numtaps, axis=ax)
+        elif self.filter == 1:
+            re_filtered = filter_ls(re_wave, cutoff, self.sample_rate,
+                                    self.numtaps, axis=ax)
+            im_filtered = filter_ls(im_wave, cutoff, self.sample_rate,
+                                    self.numtaps, axis=ax)
+        elif self.filter == 2:
+            re_filtered = re_wave
+            im_filtered = im_wave
+
+        # apply integration limits
+        start = self.samples_delay
+        end = start + self.samples_time
+
+        re_limited = re_filtered[start:end, :]
+        im_limited = im_filtered[start:end, :]
+
+        # convert to magnitude and phase
+        complex_num = re_limited + im_limited * 1j
+        mag = np.mean(abs(complex_num), 0)
+        phase = np.mean(np.angle(complex_num, deg=True), 0)
+
+        return mag, phase
+
+
+def sample_to_volt_u12(raw_samples, bps):
+    """
+    Applies volts conversion for 12 bit sample data stored
+    in 2 bytes
+    :return: samples_magnitude_array, samples_phase_array
+    """
+
+    # right_shift 16-bit sample by 4 to get 12 bit sample
+    shifted_samples = np.right_shift(raw_samples, 4)
+
+    # Alazar calibration
+    code_zero = (1 << (bps - 1)) - 0.5
+    code_range = (1 << (bps - 1)) - 0.5
+
+    # TODO(nataliejpg) make this not hard coded
+    input_range_volts = 1
+    # Convert to volts
+    volt_samples = np.float64(input_range_volts *
+                              (shifted_samples - code_zero) / code_range)
+
+    return volt_samples
diff --git a/qcodes/instrument_drivers/AlazarTech/rec_samp_controller.py b/qcodes/instrument_drivers/AlazarTech/rec_samp_controller.py
new file mode 100644
index 000000000000..7e86e9ffc211
--- /dev/null
+++ b/qcodes/instrument_drivers/AlazarTech/rec_samp_controller.py
@@ -0,0 +1,233 @@
+from .ATS import AcquisitionController
+import numpy as np
+from qcodes import Parameter
+from scipy import signal
+
+
+class RecSampParam(Parameter):
+    """
+    Hardware controlled parameter class for Alazar acquisition. To be used with
+    HD_Samples_Controller (tested with ATS9360 board) for return of an array of
+    sample data from the Alazar, averaged over records and buffers.
+
+    TODO(nataliejpg) fix setpoints/shapes horriblenesss
+    TODO(nataliejpg) make it actually work...
+    """
+
+    def __init__(self, name, instrument):
+        super().__init__(name)
+        self._instrument = instrument
+        self.acquisitionkwargs = {}
+        self.names = ('magnitude', 'phase')
+        self.units = ('', '')
+        self.setpoint_names = (('rec_num', 'samp_num'),
+                               ('rec_num', 'samp_num'))
+        self.setpoints = ((1, 1), (1, 1))
+        self.shapes = ((1, 1), (1, 1))
+
+    def update_acquisition_kwargs(self, **kwargs):
+        # needed to update config of the software parameter on sweep change
+        # freq setpoints tuple as needs to be hashable for look up
+        if 'samples_per_record' and 'records_per_buffer' in kwargs:
+            spts = kwargs['samples_per_record']
+            rpts = kwargs['records_per_buffer']
+            s = tuple(np.arange(spts))
+            r = tuple(np.arange(rpts))
+            self.setpoints = ((s, r), (s, r))
+            self.shapes = ((spts, rpts), (spts, rpts))
+        else:
+            raise ValueError(
+                'records_per_buffer and samples_per_record must be specified')
+        # updates dict to be used in acquisition get call
+        self.acquisitionkwargs.update(**kwargs)
+
+    def get(self):
+        mag, phase = self._instrument._get_alazar().acquire(
+            acquisition_controller=self._instrument,
+            **self.acquisitionkwargs)
+        return mag, phase
+
+
+class HD_RecSamp_Controller(AcquisitionController):
+    """
+    This is the Acquisition Controller class which works with the ATS9360,
+    averaging over buffers and records and demodulating with a software
+    reference signal, returning the  samples.
+    args:
+    name: name for this acquisition_conroller as an instrument
+    alazar_name: the name of the alazar instrument such that this controller
+        can communicate with the Alazar
+    demod_freq: the frequency of the software wave to be created
+    samp_rate: the rate of sampling
+    filt: the filter to be used to filter out double freq component
+    chan_b: whether there is also a second channel of data to be processed
+        and returned
+    **kwargs: kwargs are forwarded to the Instrument base class
+
+    TODO(nataliejpg) fix sample rate problem
+    TODO(nataliejpg) test filter options
+    TODO(nataliejpg) test mag phase logic
+    TODO(nataliejpg) finish implementation of channel b option
+    TODO(nataliejpg) make filter settings not hard coded
+    """
+
+    def __init__(self, name, alazar_name, demod_freq, samp_rate=500e6,
+                 filt='win', chan_b=False, **kwargs):
+        filter_dict = {'win': 0, 'hamming': 1, 'ls': 2}
+        self.demodulation_frequency = demod_freq
+        self.sample_rate = samp_rate
+        self.filter = filter_dict[filt]
+        self.chan_b = chan_b
+        self.samples_per_record = 0
+        self.records_per_buffer = 0
+        self.buffers_per_acquisition = 0
+        self.number_of_channels = 2
+        self.cos_list = None
+        self.sin_list = None
+        self.buffer = None
+        # make a call to the parent class and by extension,
+        # create the parameter structure of this class
+        super().__init__(name, alazar_name, **kwargs)
+
+        self.add_parameter(name='acquisition',
+                           parameter_class=RecSampParam)
+
+    def update_acquisitionkwargs(self, **kwargs):
+        """
+        This method must be used to update the kwargs used for the acquisition
+        with the alazar_driver.acquire
+        :param kwargs:
+        :return:
+        """
+        self.acquisition.update_acquisition_kwargs(**kwargs)
+
+    def pre_start_capture(self):
+        """
+        See AcquisitionController
+        :return:
+        """
+        alazar = self._get_alazar()
+        self.samples_per_record = alazar.samples_per_record.get()
+        self.records_per_buffer = alazar.records_per_buffer.get()
+        self.buffers_per_acquisition = alazar.buffers_per_acquisition.get()
+        self.buffer = np.zeros(self.samples_per_record *
+                               self.records_per_buffer *
+                               self.number_of_channels)
+
+        integer_list = np.arange(self.samples_per_record)
+        angle_list = (2 * np.pi * self.demodulation_frequency /
+                      self.sample_rate * integer_list)
+
+        cos_list = np.cos(angle_list).reshape(self.samples_per_record, 1)
+        sin_list = np.sin(angle_list).reshape(self.samples_per_record, 1)
+        self.cos_mat = np.kron(np.ones((1, self.records_per_buffer)), cos_list)
+        self.sin_mat = np.kron(np.ones((1, self.records_per_buffer)), sin_list)
+
+    def pre_acquire(self):
+        """
+        See AcquisitionController
+        :return:
+        """
+        # this could be used to start an Arbitrary Waveform Generator, etc...
+        # using this method ensures that the contents are executed AFTER the
+        # Alazar card starts listening for a trigger pulse
+        pass
+
+    def handle_buffer(self, data):
+        """
+        See AcquisitionController
+        :return:
+        """
+        # average over buffers
+        self.buffer += data
+
+    def post_acquire(self):
+        """
+        See AcquisitionController
+        :return:
+        """
+
+        # for ATS9360 samples are arranged in the buffer as follows:
+        # S00A, S00B, S01A, S01B...S10A, S10B, S11A, S11B...
+        # where SXYZ is record X, sample Y, channel Z.
+
+        # reshapes date to be (samples * records)
+        recordA = np.zeros((self.samples_per_record, self.records_per_buffer))
+        for i in range(self.records_per_buffer):
+            i0 = i * self.number_of_channels * self.samples_per_record
+            i1 = i0 + self.number_of_channels * self.samples_per_record
+            recordA[:, i] = (self.buffer[i0:i1:self.number_of_channels] /
+                             self.buffers_per_acquisition)
+
+        # return averaged chan A data (records)
+        magA, phaseA = self.fit(recordA)
+
+        # same for B
+        # if self.chan_b:
+        # recordB = np.zeros(
+        #        (self.samples_per_record, self.records_per_buffer))
+        # for i in self.records_per_buffer:
+        # recordB[i, :] = self.buffer[1:full_rec_length:step] / averaging
+        # magB, phaseB = self.fit(recordB)
+
+        # return data (samples * records)
+        return magA, phaseA
+
+    def fit(self, rec):
+        # center rec around 0
+        rec = rec - np.mean(rec)
+
+        # multiply with software wave
+        re_wave = np.multiply(rec, self.cos_mat)
+        im_wave = np.multiply(rec, self.sin_mat)
+        cutoff = self.demodulation_frequency
+        numtaps = 30
+        axis = 0
+
+        # filter out double freq component to obtian constant term
+        if self.filter == 0:
+            RePart = self.filter_win(re_wave, numtaps, cutoff, axis=axis)
+            ImPart = self.filter_win(im_wave, numtaps, cutoff, axis=axis)
+        elif self.filter == 1:
+            RePart = self.filter_hamming(re_wave, numtaps, cutoff, axis=axis)
+            ImPart = self.filter_hamming(im_wave, numtaps, cutoff, axis=axis)
+        elif self.filter == 2:
+            RePart = self.filter_ls(re_wave, numtaps, cutoff, axis, axis=axis)
+            ImPart = self.filter_ls(im_wave, numtaps, cutoff, axis, axis=axis)
+
+        # convert to magnitude and phase data
+        # data returned is (samples * records)
+        complex_num = RePart + ImPart * 1j
+        mag = 2 * abs(complex_num)
+        phase = np.angle(complex_num, deg=True)
+
+        return mag, phase
+
+    def filter_hamming(self, rec, numtaps, cutoff, axis=-1):
+        sample_rate = self.sample_rate
+        nyq_rate = sample_rate / 2.
+        fir_coef = signal.firwin(numtaps,
+                                 cutoff / nyq_rate,
+                                 window="hamming")
+        filtered_rec = 2 * signal.lfilter(fir_coef, 1.0, rec, axis=axis)
+        return filtered_rec
+
+    def filter_win(self, rec, numtaps, cutoff, axis=-1):
+        sample_rate = self.sample_rate
+        nyq_rate = sample_rate / 2.
+        fir_coef = signal.firwin(numtaps,
+                                 cutoff / nyq_rate)
+        filtered_rec = 2 * signal.lfilter(fir_coef, 1.0, rec, axis=axis)
+        return filtered_rec
+
+    def filter_ls(self, rec, numtaps, cutoff, axis=-1):
+        sample_rate = self.sample_rate
+        nyq_rate = sample_rate / 2.
+        bands = [0, cutoff / nyq_rate, cutoff / nyq_rate, 1]
+        desired = [1, 1, 0, 0]
+        fir_coef = signal.firls(numtaps,
+                                bands,
+                                desired,
+                                nyq=nyq_rate)
+        filtered_rec = 2 * signal.lfilter(fir_coef, 1.0, rec, axis=axis)
+        return filtered_rec

From 05eba59838a0a8add3f14856abb26b6c8efdabf6 Mon Sep 17 00:00:00 2001
From: Natalie Pearson 
Date: Thu, 12 Jan 2017 15:57:11 +0100
Subject: [PATCH 077/328] tidy up a bit and move demof filter halpers to own
 folder

---
 .../Qcodes example with Alazar ATS9360.ipynb  | 147 +++++++++++-------
 qcodes/instrument_drivers/AlazarTech/ATS.py   |  48 +++---
 .../instrument_drivers/AlazarTech/ATS9360.py  |   1 +
 .../AlazarTech/acq_helpers.py                 |  34 ++++
 .../AlazarTech/basic_controller.py            |  50 +++---
 .../AlazarTech/samp_controller.py             |  16 +-
 qcodes/utils/helpers.py                       |  33 ----
 7 files changed, 180 insertions(+), 149 deletions(-)
 create mode 100644 qcodes/instrument_drivers/AlazarTech/acq_helpers.py

diff --git a/docs/examples/Qcodes example with Alazar ATS9360.ipynb b/docs/examples/Qcodes example with Alazar ATS9360.ipynb
index 581171d9681c..9366165c2a92 100644
--- a/docs/examples/Qcodes example with Alazar ATS9360.ipynb	
+++ b/docs/examples/Qcodes example with Alazar ATS9360.ipynb	
@@ -370,7 +370,8 @@
     "import qcodes as qc\n",
     "import qcodes.instrument.parameter as parameter\n",
     "import qcodes.instrument_drivers.AlazarTech.ATS9360 as ATSdriver\n",
-    "import qcodes.instrument_drivers.AlazarTech.ATS_acquisition_controllers as ats_contr\n",
+    "import qcodes.instrument_drivers.AlazarTech.basic_controller as basic_aqc_contr\n",
+    "import qcodes.instrument_drivers.AlazarTech.samp_controller as samp_acq_contr\n",
     "\n",
     "qc.halt_bg()"
    ]
@@ -420,10 +421,53 @@
     }
    ],
    "source": [
-    "# Create the ATS9870 instrument on the new server \"alazar_server\"\n",
-    "ats_inst = ATSdriver.AlazarTech_ATS9360(name='Alazar1', server_name=\"alazar_server\")\n",
+    "# Create the ATS9360 instrument\n",
+    "alazar = ATSdriver.AlazarTech_ATS9360(name='Alazar')\n",
     "# Print all information about this Alazar card\n",
-    "ats_inst.get_idn()"
+    "alazar.get_idn()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 5,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [],
+   "source": [
+    "# Configure all settings in the Alazar card\n",
+    "alazar.config(clock_source='EXTERNAL_CLOCK_10MHz_REF',\n",
+    "              sample_rate='10MHZ_REF_500MSPS',\n",
+    "              clock_edge='CLOCK_EDGE_RISING',\n",
+    "              decimation=1,\n",
+    "              coupling=['DC','DC'],\n",
+    "              channel_range=[.4,.4],\n",
+    "              impedance=[50,50],\n",
+    "              trigger_operation='TRIG_ENGINE_OP_J',\n",
+    "              trigger_engine1='TRIG_ENGINE_J',\n",
+    "              trigger_source1='EXTERNAL',\n",
+    "              trigger_slope1='TRIG_SLOPE_POSITIVE',\n",
+    "              trigger_level1=140,\n",
+    "              trigger_engine2='TRIG_ENGINE_K',\n",
+    "              trigger_source2='DISABLE',\n",
+    "              trigger_slope2='TRIG_SLOPE_POSITIVE',\n",
+    "              trigger_level2=128,\n",
+    "              external_trigger_coupling='DC',\n",
+    "              external_trigger_range='ETR_2V5',\n",
+    "              trigger_delay=0,\n",
+    "              timeout_ticks=0,\n",
+    "              aux_io_mode='AUX_IN_AUXILIARY', # AUX_IN_TRIGGER_ENABLE for seq mode on\n",
+    "              aux_io_param='NONE' # TRIG_SLOPE_POSITIVE for seq mode on\n",
+    "             )"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### Basic Acquisition\n",
+    "\n",
+    "Pulls the raw data the alazar acquires averaged over number of records buffers."
    ]
   },
   {
@@ -443,45 +487,10 @@
     }
    ],
    "source": [
-    "import qcodes.instrument_drivers.AlazarTech.basic_controller as basic_contr\n",
-    "\n",
-    "basic_acquisition_controller = basic_contr.Basic_Acquisition_Controller(name='basic_acquisition_controller', \n",
-    "                                                                      alazar_name='Alazar1', \n",
-    "                                                                      server_name=\"alazar_server\")"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 5,
-   "metadata": {
-    "collapsed": false
-   },
-   "outputs": [],
-   "source": [
-    "# Configure all settings in the Alazar card\n",
-    "ats_inst.config(clock_source='EXTERNAL_CLOCK_10MHz_REF',\n",
-    "                sample_rate='10MHZ_REF_500MSPS',\n",
-    "                clock_edge='CLOCK_EDGE_RISING',\n",
-    "                decimation=1,\n",
-    "                coupling=['DC','DC'],\n",
-    "                channel_range=[.4,.4],\n",
-    "                impedance=[50,50],\n",
-    "                trigger_operation='TRIG_ENGINE_OP_J',\n",
-    "                trigger_engine1='TRIG_ENGINE_J',\n",
-    "                trigger_source1='EXTERNAL',\n",
-    "                trigger_slope1='TRIG_SLOPE_POSITIVE',\n",
-    "                trigger_level1=140,\n",
-    "                trigger_engine2='TRIG_ENGINE_K',\n",
-    "                trigger_source2='DISABLE',\n",
-    "                trigger_slope2='TRIG_SLOPE_POSITIVE',\n",
-    "                trigger_level2=128,\n",
-    "                external_trigger_coupling='DC',\n",
-    "                external_trigger_range='ETR_2V5',\n",
-    "                trigger_delay=0,\n",
-    "                timeout_ticks=0,\n",
-    "                aux_io_mode='AUX_IN_AUXILIARY', \n",
-    "                aux_io_param='NONE'\n",
-    ")"
+    "# Create the acquisition controller which will take care of the data handling and tell it which \n",
+    "# alazar instrument to talk to.\n",
+    "basic_acq_controller = basic_aqc_contr.Basic_Acquisition_Controller(name='basic_acq_controller', \n",
+    "                                                                    alazar_name='Alazar')"
    ]
   },
   {
@@ -492,12 +501,13 @@
    },
    "outputs": [],
    "source": [
-    "basic_acquisition_controller.update_acquisitionkwargs(#mode='NPT',\n",
-    "                 samples_per_record=1024,\n",
-    "                 records_per_buffer=1,\n",
-    "                 buffers_per_acquisition=1,\n",
-    "                 allocated_buffers=1,\n",
-    ")"
+    "# Configure settings in the controller to be used in an acquisition\n",
+    "# nb this must be done before the first acquisition \n",
+    "basic_acq_controller.update_acquisition_kwargs(samples_per_record=1024,\n",
+    "                                              records_per_buffer=1,\n",
+    "                                              buffers_per_acquisition=1,\n",
+    "                                              allocated_buffers=1\n",
+    "                                              )"
    ]
   },
   {
@@ -1285,10 +1295,24 @@
     }
    ],
    "source": [
-    "data1 = basic_acquisition_controller.acquisition()\n",
+    "# Pull data from the card by calling get of the controllers acquisition parameter\n",
+    "data1 = basic_acq_controller.acquisition()\n",
     "qc.Matplot(data1[0])"
    ]
   },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {
+    "collapsed": true
+   },
+   "outputs": [],
+   "source": [
+    "# Do this in as measurement (effactively the same but saves the data)\n",
+    "data2 = qc.Measure(basic_acq_controller.acquisition)\n",
+    "qc.MatPlot(data2)"
+   ]
+  },
   {
    "cell_type": "code",
    "execution_count": 10,
@@ -1325,10 +1349,20 @@
    "source": [
     "# Finally show that this instrument also works within a loop\n",
     "dummy = parameter.ManualParameter(name=\"dummy\")\n",
-    "data2 = qc.Loop(dummy[0:5:1]).each(basic_acquisition_controller.acquisition).run(name='AlazarTest')\n",
+    "\n",
+    "data3 = qc.Loop(dummy[0:5:1]).each(basic_acq_controller.acquisition).run(name='AlazarTest')\n",
     "qc.QtPlot(data2.basic_acquisition_controller_A )"
    ]
   },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### Samples Acquisition\n",
+    "\n",
+    "This is the same as above except that it does some demodulation at the freqiencies specified"
+   ]
+  },
   {
    "cell_type": "code",
    "execution_count": 19,
@@ -1346,12 +1380,9 @@
     }
    ],
    "source": [
-    "import qcodes.instrument_drivers.AlazarTech.samp_controller as sample_controller\n",
-    "\n",
-    "samp_cont = sample_controller.HD_Samples_Controller(name='samples_controller', \n",
-    "                                                      alazar_name='Alazar1', \n",
-    "                                                      demod_freq = 5e6,\n",
-    "                                                      server_name=\"alazar_server\")"
+    "samp_acq_controller = samp_acq_contr.HD_Samples_Controller(name='samp_acq_controller', \n",
+    "                                                           alazar_name='Alazar1', \n",
+    "                                                           demod_freq = 5e6)"
    ]
   },
   {
@@ -3247,7 +3278,7 @@
   },
   "widgets": {
    "state": {},
-   "version": "1.0.0"
+   "version": "1.1.2"
   }
  },
  "nbformat": 4,
diff --git a/qcodes/instrument_drivers/AlazarTech/ATS.py b/qcodes/instrument_drivers/AlazarTech/ATS.py
index 4cff51bea787..c689d50693dc 100644
--- a/qcodes/instrument_drivers/AlazarTech/ATS.py
+++ b/qcodes/instrument_drivers/AlazarTech/ATS.py
@@ -17,8 +17,6 @@
 # acquisition that would overflow the board if measurement is not stopped
 # quickly enough. can this be solved by not reposting the buffers?
 
-# TODO(nataliejpg) fix get_damples function which I have broken for ATS9360
-
 
 class AlazarTech_ATS(Instrument):
     """
@@ -261,7 +259,7 @@ def get_idn(self):
             self._ATS_dll.AlazarGetBoardKind(self._handle)]
 
         max_s, bps = self._get_channel_info(self._handle)
-        
+
         major = np.array([0], dtype=np.uint8)
         minor = np.array([0], dtype=np.uint8)
         revision = np.array([0], dtype=np.uint8)
@@ -269,7 +267,7 @@ def get_idn(self):
                        self._handle,
                        major.ctypes.data,
                        minor.ctypes.data)
-        cpld_ver = str(major[0])+"."+str(minor[0])
+        cpld_ver = str(major[0]) + "." + str(minor[0])
 
         self._call_dll('AlazarGetDriverVersion',
                        major.ctypes.data,
@@ -304,7 +302,7 @@ def get_idn(self):
         # about the encoding of the link speed
         self._call_dll('AlazarQueryCapability',
                        self._handle, 0x10000030, 0, value.ctypes.data)
-        pcie_link_speed = str(value[0]*2.5/10)+"GB/s"
+        pcie_link_speed = str(value[0] * 2.5 / 10) + "GB/s"
         self._call_dll('AlazarQueryCapability',
                        self._handle, 0x10000031, 0, value.ctypes.data)
         pcie_link_width = str(value[0])
@@ -570,12 +568,9 @@ def acquire(self, mode=None, samples_per_record=None,
         # bytes per sample
         max_s, bps = self._get_channel_info(self._handle)
         bytes_per_sample = (bps + 7) // 8
-        #print("bytes per sample "+str(bytes_per_sample))
 
         # bytes per record
         bytes_per_record = bytes_per_sample * samples_per_record
-        #print("samples_per_record is "+str(samples_per_record))
-        #print("bytes per record is "+str(bytes_per_record))
 
         # channels
         if self.channel_selection._get_byte() == 3:
@@ -584,7 +579,8 @@ def acquire(self, mode=None, samples_per_record=None,
             number_of_channels = 1
 
         # bytes per buffer
-        bytes_per_buffer = bytes_per_record * records_per_buffer * number_of_channels
+        bytes_per_buffer = (bytes_per_record *
+                            records_per_buffer * number_of_channels)
 
         # create buffers for acquisition
         # TODO(nataliejpg) should this be > 1 (as intuitive) or > 8 as in alazar sample code?
@@ -592,18 +588,13 @@ def acquire(self, mode=None, samples_per_record=None,
         if bytes_per_sample > 1:
             sample_type = ctypes.c_uint16
 
-        # TODO(nataliejpg) get rid of all of thes print statements
-        #print("samples_per_buffer is "+str(samples_per_buffer))
-        #print("bytes_per_buffer  is "+str(bytes_per_buffer))
-        #print("records_per_buffer is "+str(records_per_buffer))
-
         self.clear_buffers()
         # make sure that allocated_buffers <= buffers_per_acquisition
         if (self.allocated_buffers._get_byte() >
                 self.buffers_per_acquisition._get_byte()):
-            print("'allocated_buffers' should be smaller than or equal to"
-                  "'buffers_per_acquisition'. Defaulting 'allocated_buffers' to"
-                  "" + str(self.buffers_per_acquisition._get_byte()))
+            logging.warn("'allocated_buffers' should be <= "
+                         "'buffers_per_acquisition'. Defaulting 'allocated_buffers'"
+                         " to " + str(self.buffers_per_acquisition._get_byte()))
             self.allocated_buffers._set(
                 self.buffers_per_acquisition._get_byte())
 
@@ -617,13 +608,12 @@ def acquire(self, mode=None, samples_per_record=None,
                 raise
 
         # post buffers to Alazar
-        print("made buffer list length "+str(len(self.buffer_list)))
+        print("made buffer list length " + str(len(self.buffer_list)))
         for buf in self.buffer_list:
             self._ATS_dll.AlazarPostAsyncBuffer.argtypes = [ctypes.c_uint32, ctypes.c_void_p, ctypes.c_uint32]
             self._call_dll('AlazarPostAsyncBuffer',
                            self._handle, buf.addr, buf.size_bytes)
         self.allocated_buffers._set_updated()
-        #print("completed AlazarPostAsyncBuffer")
 
         # -----start capture here-----
         acquisition_controller.pre_start_capture()
@@ -690,14 +680,17 @@ def acquire(self, mode=None, samples_per_record=None,
         if transfer_time_sec > 0:
             buffers_per_sec = buffers_completed / transfer_time_sec
             bytes_per_sec = bytes_transferred / transfer_time_sec
-            records_per_sec = records_per_buffer * buffers_completed / transfer_time_sec
-        print("Captured %d buffers (%f buffers per sec)" %
-              (buffers_completed, buffers_per_sec))
-        print("Captured %d records (%f records per sec)" %
-              (records_per_buffer * buffers_completed, records_per_sec))
-        print("Transferred %d bytes (%f bytes per sec)" %
-              (bytes_transferred, bytes_per_sec))
-              
+            records_per_sec = (records_per_buffer * 
+                               buffers_completed / transfer_time_sec)
+        logging.info("Captured %d buffers (%f buffers per sec)" %
+                    (buffers_completed, buffers_per_sec))
+        logging.info("Captured %d records (%f records per sec)" %
+                    (records_per_buffer * buffers_completed, records_per_sec))
+        logging.info("Transferred %d bytes (%f bytes per sec)" %
+                    (bytes_transferred, bytes_per_sec))
+
+
+
         # return result
         return acquisition_controller.post_acquire()
 
@@ -798,7 +791,6 @@ def get_sample_rate(self):
         rate = self.sample_rate.get()
         if rate == '1GHz_REFERENCE_CLOCK':
             rate = 1e9
-        # TODO(nataliejpg) this breaks for ATS9360 because of changes to rate 
 
         decimation = self.decimation.get()
         if decimation > 0:
diff --git a/qcodes/instrument_drivers/AlazarTech/ATS9360.py b/qcodes/instrument_drivers/AlazarTech/ATS9360.py
index 4fe1add7ee05..1ac2c3cbde5f 100644
--- a/qcodes/instrument_drivers/AlazarTech/ATS9360.py
+++ b/qcodes/instrument_drivers/AlazarTech/ATS9360.py
@@ -245,6 +245,7 @@ def __init__(self, name, **kwargs):
                            unit=None,
                            value=4,
                            vals=validators.Ints(min_value=0))
+
         self.add_parameter(name='buffer_timeout',
                            parameter_class=AlazarParameter,
                            label='Buffer Timeout',
diff --git a/qcodes/instrument_drivers/AlazarTech/acq_helpers.py b/qcodes/instrument_drivers/AlazarTech/acq_helpers.py
new file mode 100644
index 000000000000..572bf451ca0d
--- /dev/null
+++ b/qcodes/instrument_drivers/AlazarTech/acq_helpers.py
@@ -0,0 +1,34 @@
+from scipy import signal
+
+
+def filter_win(rec, cutoff, sample_rate, numtaps, axis=-1):
+    """
+    low pass filter, returns filtered signal using FIR window
+    filter
+
+    inputs:
+        record to filter
+        cutoff frequency
+        sampling rate
+        number of frequency comppnents to use in the filer
+        axis of record to apply filter along
+    """
+    nyq_rate = sample_rate / 2.
+    fir_coef = signal.firwin(numtaps, cutoff / nyq_rate)
+    filtered_rec = signal.lfilter(fir_coef, 1.0, rec, axis=axis)
+    return filtered_rec
+
+
+def filter_ls(rec, cutoff, sample_rate, numtaps, axis=-1):
+    """
+    low pass filter, returns filtered signal using FIR
+    least squared filter
+
+    inputs:
+        record to filter
+        cutoff frequency
+        sampling rate
+        number of frequency comppnents to use in the filer
+        axis of record to apply filter along
+    """
+    raise NotImplementedError
diff --git a/qcodes/instrument_drivers/AlazarTech/basic_controller.py b/qcodes/instrument_drivers/AlazarTech/basic_controller.py
index 025e3b97bd9f..2e77699cc769 100644
--- a/qcodes/instrument_drivers/AlazarTech/basic_controller.py
+++ b/qcodes/instrument_drivers/AlazarTech/basic_controller.py
@@ -18,23 +18,27 @@ def __init__(self, name, instrument):
         self._instrument = instrument
         self.acquisitionkwargs = {}
         self.names = ('A', 'B')
-        self.units = ('', '')
-        self.setpoint_names = (('sample_num',), ('sample_num',))
-        self.setpoints = ((1,), (1,))
-        self.shapes = ((1,), (1,))
-
-    def update_acquisition_kwargs(self, **kwargs):
-        # needed to update config of the software parameter on sweep change
-        # freq setpoints tuple as needs to be hashable for look up
-        if 'samples_per_record' in kwargs:
-            npts = kwargs['samples_per_record']
-            n = tuple(np.arange(npts))
-            self.setpoints = ((n,), (n,))
-            self.shapes = ((npts,), (npts,))
-        else:
-            raise ValueError('samples_per_record must be specified')
-        # updates dict to be used in acquisition get call
-        self.acquisitionkwargs.update(**kwargs)
+        # self.units = ('', '')
+        # self.setpoint_names = (('sample_num',), ('sample_num',))
+        # self.setpoints = ((1,), (1,))
+        # self.shapes = ((1,), (1,))
+
+    # def update_acquisition_kwargs(self, **kwargs):
+    #     npts = kwargs['samples_per_record']
+    #     self.shapes = ((npts,), (npts,))
+    #     self.acquisitionkwargs.update(**kwargs)
+
+        # # needed to update config of the software parameter on sweep change
+        # # freq setpoints tuple as needs to be hashable for look up
+        # if 'samples_per_record' in kwargs:
+        #     npts = kwargs['samples_per_record']
+        #     # n = tuple(np.arange(npts))
+        #     # self.setpoints = ((n,), (n,))
+        #     self.shapes = ((npts,), (npts,))
+        # else:
+        #     raise ValueError('samples_per_record must be specified')
+        # # updates dict to be used in acquisition get call
+        # self.acquisitionkwargs.update(**kwargs)
 
     def get(self):
         recordA, recordB = self._instrument._get_alazar().acquire(
@@ -55,7 +59,6 @@ class Basic_Acquisition_Controller(AcquisitionController):
         can communicate with the Alazar
     **kwargs: kwargs are forwarded to the Instrument base class
 
-    TODO(nataliejpg) num of channels
     TODO(nataliejpg) make dtype general or get from alazar
     """
 
@@ -64,8 +67,8 @@ def __init__(self, name, alazar_name, **kwargs):
         self.records_per_buffer = 0
         self.buffers_per_acquisition = 0
         self.number_of_channels = 2
-        self.buffer = None
         self.board_info = None
+        self.buffer = None
         # make a call to the parent class and by extension,
         # create the parameter structure of this class
         super().__init__(name, alazar_name, **kwargs)
@@ -73,14 +76,16 @@ def __init__(self, name, alazar_name, **kwargs):
         self.add_parameter(name='acquisition',
                            parameter_class=SampleSweep)
 
-    def update_acquisitionkwargs(self, **kwargs):
+    def update_acquisition_kwargs(self, **kwargs):
         """
         This method must be used to update the kwargs used for the acquisition
         with the alazar_driver.acquire
         :param kwargs:
         :return:
         """
-        self.acquisition.update_acquisition_kwargs(**kwargs)
+        npts = kwargs['samples_per_record']
+        self.acquisition.shapes = ((npts,), (npts,))
+        self.acquisition.acquisition_kwargs.update(**kwargs)
 
     def pre_start_capture(self):
         """
@@ -126,6 +131,7 @@ def post_acquire(self):
         # break buffer up into records, averages over them and returns samples
         records_per_acquisition = (self.buffers_per_acquisition *
                                    self.records_per_buffer)
+
         recA = np.zeros(self.samples_per_record)
         for i in range(self.records_per_buffer):
             i0 = (i * self.samples_per_record * self.number_of_channels)
@@ -146,7 +152,7 @@ def post_acquire(self):
             volt_rec_B = sample_to_volt_u12(recordB, bps)
         else:
             logging.warning('sample to volt conversion does not exist for bps '
-                            '!= 12, raw samples centered on 0 returned')
+                            '!= 12, raw samples centered on 0 and returned')
             volt_rec_A = recordA - np.mean(recordA)
             volt_rec_B = recordB - np.mean(recordB)
 
diff --git a/qcodes/instrument_drivers/AlazarTech/samp_controller.py b/qcodes/instrument_drivers/AlazarTech/samp_controller.py
index 5919e04d48ac..cf4d8df4c9e6 100644
--- a/qcodes/instrument_drivers/AlazarTech/samp_controller.py
+++ b/qcodes/instrument_drivers/AlazarTech/samp_controller.py
@@ -2,9 +2,9 @@
 from .ATS import AcquisitionController
 import numpy as np
 from qcodes import Parameter
-from qcodes.utils.helpers import filter_win, filter_ls
+from qcodes.instrument_drivers.AlazarTech.acq_helpers import filter_win, filter_ls
 from qcodes.instrument.parameter import ManualParameter
-from qcodes.utils import validators
+# from qcodes.utils import validators
 
 
 class SamplesParam(Parameter):
@@ -26,12 +26,12 @@ def __init__(self, name, instrument):
         self.setpoint_names = (('acq_time (s)',), ('acq_time (s)',))
         self.setpoints = ((1,), (1,))
         self.shapes = ((1,), (1,))
-    
+
     def update_sweep(self, start, stop, npts):
         n = tuple(np.linspace(start, stop, num=npts))
         self.setpoints = ((n,), (n,))
         self.shapes = ((npts,), (npts,))
-    
+
     def get(self):
         mag, phase = self._instrument._get_alazar().acquire(
             acquisition_controller=self._instrument,
@@ -112,20 +112,20 @@ def update_acquisition_settings(self, **kwargs):
         self.samples_per_record = kwargs['samples_per_record']
         self.sample_rate = alazar.get_sample_rate()
         time_available = self.samples_per_record / self.sample_rate
-            
+
         if self.int_delay() is None:
             samp_delay = self.numtaps - 1
             self.int_delay(samp_delay / self.sample_rate)
         else:
             self.check_delay(time_available)
-        
+
         time_available -= self.int_delay()
-        
+
         if self.int_time() is None:
             self.int_time(time_available)
         else:
             self.check_time(time_available)
-        
+
         start = self.int_delay()
         stop = self.int_delay() + self.int_time()
         npts = int(self.int_time() * self.sample_rate)
diff --git a/qcodes/utils/helpers.py b/qcodes/utils/helpers.py
index e50bde4e7ff5..e0cfe85a2dcc 100644
--- a/qcodes/utils/helpers.py
+++ b/qcodes/utils/helpers.py
@@ -10,7 +10,6 @@
 from copy import deepcopy
 
 import numpy as np
-from scipy import signal
 
 _tprint_times = {}
 
@@ -423,35 +422,3 @@ def compare_dictionaries(dict_1, dict_2,
         dicts_equal = False
     return dicts_equal, dict_differences
 
-
-def filter_win(rec, cutoff, sample_rate, numtaps, axis=-1):
-    """
-    low pass filter, returns filtered signal using FIR window
-    filter
-
-    inputs:
-        record to filter
-        cutoff frequency
-        sampling rate
-        number of frequency comppnents to use in the filer
-        axis of record to apply filter along
-    """
-    nyq_rate = sample_rate / 2.
-    fir_coef = signal.firwin(numtaps, cutoff / nyq_rate)
-    filtered_rec = signal.lfilter(fir_coef, 1.0, rec, axis=axis)
-    return filtered_rec
-
-
-def filter_ls(rec, cutoff, sample_rate, numtaps, axis=-1):
-    """
-    low pass filter, returns filtered signal using FIR
-    least squared filter
-
-    inputs:
-        record to filter
-        cutoff frequency
-        sampling rate
-        number of frequency comppnents to use in the filer
-        axis of record to apply filter along
-    """
-    raise NotImplementedError

From 8ea8d1cf518146d08a2e14dab7937a169fea34b1 Mon Sep 17 00:00:00 2001
From: nataliejpg 
Date: Fri, 13 Jan 2017 11:10:26 +0100
Subject: [PATCH 078/328] testing basic controller

---
 .../Qcodes example with Alazar ATS9360.ipynb  | 1204 +----------------
 .../AlazarTech/basic_controller.py            |    6 +-
 2 files changed, 65 insertions(+), 1145 deletions(-)

diff --git a/docs/examples/Qcodes example with Alazar ATS9360.ipynb b/docs/examples/Qcodes example with Alazar ATS9360.ipynb
index 9366165c2a92..f9ebe09d2e73 100644
--- a/docs/examples/Qcodes example with Alazar ATS9360.ipynb	
+++ b/docs/examples/Qcodes example with Alazar ATS9360.ipynb	
@@ -14,356 +14,12 @@
     "collapsed": false
    },
    "outputs": [
-    {
-     "data": {
-      "application/javascript": [
-       "/*\r\n",
-       " * Qcodes Jupyter/IPython widgets\r\n",
-       " */\r\n",
-       "require([\r\n",
-       "    'nbextensions/widgets/widgets/js/widget',\r\n",
-       "    'nbextensions/widgets/widgets/js/manager'\r\n",
-       "], function (widget, manager) {\r\n",
-       "\r\n",
-       "    var UpdateView = widget.DOMWidgetView.extend({\r\n",
-       "        render: function() {\r\n",
-       "            window.MYWIDGET = this;\r\n",
-       "            this._interval = 0;\r\n",
-       "            this.update();\r\n",
-       "        },\r\n",
-       "        update: function() {\r\n",
-       "            this.display(this.model.get('_message'));\r\n",
-       "            this.setInterval();\r\n",
-       "        },\r\n",
-       "        display: function(message) {\r\n",
-       "            /*\r\n",
-       "             * display method: override this for custom display logic\r\n",
-       "             */\r\n",
-       "            this.el.innerHTML = message;\r\n",
-       "        },\r\n",
-       "        remove: function() {\r\n",
-       "            clearInterval(this._updater);\r\n",
-       "        },\r\n",
-       "        setInterval: function(newInterval) {\r\n",
-       "            var me = this;\r\n",
-       "            if(newInterval===undefined) newInterval = me.model.get('interval');\r\n",
-       "            if(newInterval===me._interval) return;\r\n",
-       "\r\n",
-       "            me._interval = newInterval;\r\n",
-       "\r\n",
-       "            if(me._updater) clearInterval(me._updater);\r\n",
-       "\r\n",
-       "            if(me._interval) {\r\n",
-       "                me._updater = setInterval(function() {\r\n",
-       "                    me.send({myupdate: true});\r\n",
-       "                    if(!me.model.comm_live) {\r\n",
-       "                        console.log('missing comm, canceling widget updates', me);\r\n",
-       "                        clearInterval(me._updater);\r\n",
-       "                    }\r\n",
-       "                }, me._interval * 1000);\r\n",
-       "            }\r\n",
-       "        }\r\n",
-       "    });\r\n",
-       "    manager.WidgetManager.register_widget_view('UpdateView', UpdateView);\r\n",
-       "\r\n",
-       "    var HiddenUpdateView = UpdateView.extend({\r\n",
-       "        display: function(message) {\r\n",
-       "            this.$el.hide();\r\n",
-       "        }\r\n",
-       "    });\r\n",
-       "    manager.WidgetManager.register_widget_view('HiddenUpdateView', HiddenUpdateView);\r\n",
-       "\r\n",
-       "    var SubprocessView = UpdateView.extend({\r\n",
-       "        render: function() {\r\n",
-       "            var me = this;\r\n",
-       "            me._interval = 0;\r\n",
-       "            me._minimize = '';\r\n",
-       "            me._restore = '';\r\n",
-       "\r\n",
-       "            // max lines of output to show\r\n",
-       "            me.maxOutputLength = 500;\r\n",
-       "\r\n",
-       "            // in case there is already an outputView present,\r\n",
-       "            // like from before restarting the kernel\r\n",
-       "            $('.qcodes-output-view').not(me.$el).remove();\r\n",
-       "\r\n",
-       "            me.$el\r\n",
-       "                .addClass('qcodes-output-view')\r\n",
-       "                .attr('qcodes-state', 'docked')\r\n",
-       "                .html(\r\n",
-       "                    '
' +\r\n",
-       "                        '
' +\r\n",
-       "                        '' +\r\n",
-       "                        '' +\r\n",
-       "                        '' +\r\n",
-       "                        '' +\r\n",
-       "                        '' +\r\n",
-       "                        '' +\r\n",
-       "                    '
' +\r\n",
-       "                    '
'\r\n",
-       "                );\r\n",
-       "\r\n",
-       "            me.clearButton = me.$el.find('.qcodes-clear-output');\r\n",
-       "            me.minButton = me.$el.find('.qcodes-minimize');\r\n",
-       "            me.outputArea = me.$el.find('pre');\r\n",
-       "            me.subprocessList = me.$el.find('.qcodes-process-list');\r\n",
-       "            me.abortButton = me.$el.find('.qcodes-abort-loop');\r\n",
-       "            me.processLinesButton = me.$el.find('.qcodes-processlines')\r\n",
-       "\r\n",
-       "            me.outputLines = [];\r\n",
-       "\r\n",
-       "            me.clearButton.click(function() {\r\n",
-       "                me.outputArea.html('');\r\n",
-       "                me.clearButton.addClass('disabled');\r\n",
-       "            });\r\n",
-       "\r\n",
-       "            me.abortButton.click(function() {\r\n",
-       "                me.send({abort: true});\r\n",
-       "            });\r\n",
-       "\r\n",
-       "            me.processLinesButton.click(function() {\r\n",
-       "                // toggle multiline process list display\r\n",
-       "                me.subprocessesMultiline = !me.subprocessesMultiline;\r\n",
-       "                me.showSubprocesses();\r\n",
-       "            });\r\n",
-       "\r\n",
-       "            me.$el.find('.js-state').click(function() {\r\n",
-       "                var state = this.className.substr(this.className.indexOf('qcodes'))\r\n",
-       "                        .split('-')[1].split(' ')[0];\r\n",
-       "                me.model.set('_state', state);\r\n",
-       "            });\r\n",
-       "\r\n",
-       "            $(window)\r\n",
-       "                .off('resize.qcodes')\r\n",
-       "                .on('resize.qcodes', function() {me.clipBounds();});\r\n",
-       "\r\n",
-       "            me.update();\r\n",
-       "        },\r\n",
-       "\r\n",
-       "        updateState: function() {\r\n",
-       "            var me = this,\r\n",
-       "                oldState = me.$el.attr('qcodes-state'),\r\n",
-       "                state = me.model.get('_state');\r\n",
-       "\r\n",
-       "            if(state === oldState) return;\r\n",
-       "\r\n",
-       "            setTimeout(function() {\r\n",
-       "                // not sure why I can't pop it out of the widgetarea in render, but it seems that\r\n",
-       "                // some other bit of code resets the parent after render if I do it there.\r\n",
-       "                // To be safe, just do it on every state click.\r\n",
-       "                me.$el.appendTo('body');\r\n",
-       "\r\n",
-       "                if(oldState === 'floated') {\r\n",
-       "                    console.log('here');\r\n",
-       "                    me.$el.draggable('destroy').css({left:'', top: ''});\r\n",
-       "                }\r\n",
-       "\r\n",
-       "                me.$el.attr('qcodes-state', state);\r\n",
-       "\r\n",
-       "                if(state === 'floated') {\r\n",
-       "                    me.$el\r\n",
-       "                        .draggable({stop: function() { me.clipBounds(); }})\r\n",
-       "                        .css({\r\n",
-       "                            left: window.innerWidth - me.$el.width() - 15,\r\n",
-       "                            top: window.innerHeight - me.$el.height() - 10\r\n",
-       "                        });\r\n",
-       "                }\r\n",
-       "\r\n",
-       "                // any previous highlighting is now moot\r\n",
-       "                me.$el.removeClass('qcodes-highlight');\r\n",
-       "            }, 0);\r\n",
-       "\r\n",
-       "        },\r\n",
-       "\r\n",
-       "        clipBounds: function() {\r\n",
-       "            var me = this;\r\n",
-       "            if(me.$el.attr('qcodes-state') === 'floated') {\r\n",
-       "                var bounds = me.$el[0].getBoundingClientRect(),\r\n",
-       "                    minVis = 40,\r\n",
-       "                    maxLeft = window.innerWidth - minVis,\r\n",
-       "                    minLeft = minVis - bounds.width,\r\n",
-       "                    maxTop = window.innerHeight - minVis;\r\n",
-       "\r\n",
-       "                if(bounds.left > maxLeft) me.$el.css('left', maxLeft);\r\n",
-       "                else if(bounds.left < minLeft) me.$el.css('left', minLeft);\r\n",
-       "\r\n",
-       "                if(bounds.top > maxTop) me.$el.css('top', maxTop);\r\n",
-       "                else if(bounds.top < 0) me.$el.css('top', 0);\r\n",
-       "            }\r\n",
-       "        },\r\n",
-       "\r\n",
-       "        display: function(message) {\r\n",
-       "            var me = this;\r\n",
-       "            if(message) {\r\n",
-       "                var initialScroll = me.outputArea.scrollTop();\r\n",
-       "                me.outputArea.scrollTop(me.outputArea.prop('scrollHeight'));\r\n",
-       "                var scrollBottom = me.outputArea.scrollTop();\r\n",
-       "\r\n",
-       "                if(me.$el.attr('qcodes-state') === 'minimized') {\r\n",
-       "                    // if we add text and the box is minimized, highlight the\r\n",
-       "                    // title bar to alert the user that there are new messages.\r\n",
-       "                    // remove then add the class, so we get the animation again\r\n",
-       "                    // if it's already highlighted\r\n",
-       "                    me.$el.removeClass('qcodes-highlight');\r\n",
-       "                    setTimeout(function(){\r\n",
-       "                        me.$el.addClass('qcodes-highlight');\r\n",
-       "                    }, 0);\r\n",
-       "                }\r\n",
-       "\r\n",
-       "                var newLines = message.split('\\n'),\r\n",
-       "                    out = me.outputLines,\r\n",
-       "                    outLen = out.length;\r\n",
-       "                if(outLen) out[outLen - 1] += newLines[0];\r\n",
-       "                else out.push(newLines[0]);\r\n",
-       "\r\n",
-       "                for(var i = 1; i < newLines.length; i++) {\r\n",
-       "                    out.push(newLines[i]);\r\n",
-       "                }\r\n",
-       "\r\n",
-       "                if(out.length > me.maxOutputLength) {\r\n",
-       "                    out.splice(0, out.length - me.maxOutputLength + 1,\r\n",
-       "                        '<<< Output clipped >>>');\r\n",
-       "                }\r\n",
-       "\r\n",
-       "                me.outputArea.text(out.join('\\n'));\r\n",
-       "                me.clearButton.removeClass('disabled');\r\n",
-       "\r\n",
-       "                // if we were scrolled to the bottom initially, make sure\r\n",
-       "                // we stay that way.\r\n",
-       "                me.outputArea.scrollTop(initialScroll === scrollBottom ?\r\n",
-       "                    me.outputArea.prop('scrollHeight') : initialScroll);\r\n",
-       "            }\r\n",
-       "\r\n",
-       "            me.showSubprocesses();\r\n",
-       "            me.updateState();\r\n",
-       "        },\r\n",
-       "\r\n",
-       "        showSubprocesses: function() {\r\n",
-       "            var me = this,\r\n",
-       "                replacer = me.subprocessesMultiline ? '
' : ', ',\r\n",
-       "                processes = (me.model.get('_processes') || '')\r\n",
-       "                    .replace(/\\n/g, '>' + replacer + '<');\r\n",
-       "\r\n",
-       "            if(processes) processes = '<' + processes + '>';\r\n",
-       "            else processes = 'No subprocesses';\r\n",
-       "\r\n",
-       "            me.abortButton.toggleClass('disabled', processes.indexOf('Measurement')===-1);\r\n",
-       "\r\n",
-       "            me.subprocessList.html(processes);\r\n",
-       "        }\r\n",
-       "    });\r\n",
-       "    manager.WidgetManager.register_widget_view('SubprocessView', SubprocessView);\r\n",
-       "});\r\n"
-      ],
-      "text/plain": [
-       ""
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    },
-    {
-     "data": {
-      "text/html": [
-       ""
-      ],
-      "text/plain": [
-       ""
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    },
     {
      "name": "stdout",
      "output_type": "stream",
      "text": [
       "No loop running\n"
      ]
-    },
-    {
-     "name": "stderr",
-     "output_type": "stream",
-     "text": [
-      "a:\\qcodes\\qcodes\\process\\helpers.py:27: UserWarning: Multiprocessing is in beta, use at own risk\n",
-      "  warnings.warn(\"Multiprocessing is in beta, use at own risk\", UserWarning)\n"
-     ]
     }
    ],
    "source": [
@@ -385,28 +41,22 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 3,
+   "execution_count": 2,
    "metadata": {
     "collapsed": false
    },
    "outputs": [
-    {
-     "name": "stderr",
-     "output_type": "stream",
-     "text": [
-      "a:\\qcodes\\qcodes\\instrument\\metaclass.py:37: UserWarning: Multiprocessing is in beta, use at own risk\n",
-      "  UserWarning)\n"
-     ]
-    },
     {
      "data": {
       "text/plain": [
        "{'CPLD_version': '25.16',\n",
        " 'SDK_version': '5.9.25',\n",
        " 'asopc_type': '1712554848',\n",
+       " 'bits_per_sample': 12,\n",
        " 'driver_version': '5.9.25',\n",
        " 'firmware': None,\n",
        " 'latest_cal_date': '13-11-15',\n",
+       " 'max_samples': 4294967294,\n",
        " 'memory_size': '4294967294',\n",
        " 'model': 'ATS9360',\n",
        " 'pcie_link_speed': '0.5GB/s',\n",
@@ -415,7 +65,7 @@
        " 'vendor': 'AlazarTech'}"
       ]
      },
-     "execution_count": 3,
+     "execution_count": 2,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -429,7 +79,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 5,
+   "execution_count": 3,
    "metadata": {
     "collapsed": false
    },
@@ -437,7 +87,7 @@
    "source": [
     "# Configure all settings in the Alazar card\n",
     "alazar.config(clock_source='EXTERNAL_CLOCK_10MHz_REF',\n",
-    "              sample_rate='10MHZ_REF_500MSPS',\n",
+    "              sample_rate=500000000,\n",
     "              clock_edge='CLOCK_EDGE_RISING',\n",
     "              decimation=1,\n",
     "              coupling=['DC','DC'],\n",
@@ -476,16 +126,7 @@
    "metadata": {
     "collapsed": false
    },
-   "outputs": [
-    {
-     "name": "stderr",
-     "output_type": "stream",
-     "text": [
-      "a:\\qcodes\\qcodes\\instrument\\metaclass.py:37: UserWarning: Multiprocessing is in beta, use at own risk\n",
-      "  UserWarning)\n"
-     ]
-    }
-   ],
+   "outputs": [],
    "source": [
     "# Create the acquisition controller which will take care of the data handling and tell it which \n",
     "# alazar instrument to talk to.\n",
@@ -495,7 +136,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 7,
+   "execution_count": 5,
    "metadata": {
     "collapsed": false
    },
@@ -512,801 +153,78 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 9,
+   "execution_count": 12,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "buffers cleared\n",
+      "made buffer list length 1\n",
+      "Capturing 1 buffers.\n",
+      "buffer handled\n",
+      "buffers cleared\n",
+      "Capture completed in 0.386047 sec\n",
+      "post_acquire\n",
+      "(array([-0.0017094 , -0.0046398 , -0.00757021, ..., -0.00952381,\n",
+      "       -0.00659341, -0.0026862 ]), array([ 0.0002442,  0.0031746,  0.001221 , ...,  0.0021978, -0.0007326,\n",
+      "        0.0041514]))\n"
+     ]
+    }
+   ],
+   "source": [
+    "# Pull data from the card by calling get of the controllers acquisition parameter\n",
+    "data1 = basic_acq_controller.acquisition()\n",
+    "print(data1)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 13,
    "metadata": {
     "collapsed": false
    },
    "outputs": [
     {
      "data": {
-      "application/javascript": [
-       "/* Put everything inside the global mpl namespace */\n",
-       "window.mpl = {};\n",
-       "\n",
-       "mpl.get_websocket_type = function() {\n",
-       "    if (typeof(WebSocket) !== 'undefined') {\n",
-       "        return WebSocket;\n",
-       "    } else if (typeof(MozWebSocket) !== 'undefined') {\n",
-       "        return MozWebSocket;\n",
-       "    } else {\n",
-       "        alert('Your browser does not have WebSocket support.' +\n",
-       "              'Please try Chrome, Safari or Firefox ≥ 6. ' +\n",
-       "              'Firefox 4 and 5 are also supported but you ' +\n",
-       "              'have to enable WebSockets in about:config.');\n",
-       "    };\n",
-       "}\n",
-       "\n",
-       "mpl.figure = function(figure_id, websocket, ondownload, parent_element) {\n",
-       "    this.id = figure_id;\n",
-       "\n",
-       "    this.ws = websocket;\n",
-       "\n",
-       "    this.supports_binary = (this.ws.binaryType != undefined);\n",
-       "\n",
-       "    if (!this.supports_binary) {\n",
-       "        var warnings = document.getElementById(\"mpl-warnings\");\n",
-       "        if (warnings) {\n",
-       "            warnings.style.display = 'block';\n",
-       "            warnings.textContent = (\n",
-       "                \"This browser does not support binary websocket messages. \" +\n",
-       "                    \"Performance may be slow.\");\n",
-       "        }\n",
-       "    }\n",
-       "\n",
-       "    this.imageObj = new Image();\n",
-       "\n",
-       "    this.context = undefined;\n",
-       "    this.message = undefined;\n",
-       "    this.canvas = undefined;\n",
-       "    this.rubberband_canvas = undefined;\n",
-       "    this.rubberband_context = undefined;\n",
-       "    this.format_dropdown = undefined;\n",
-       "\n",
-       "    this.image_mode = 'full';\n",
-       "\n",
-       "    this.root = $('
');\n",
-       "    this._root_extra_style(this.root)\n",
-       "    this.root.attr('style', 'display: inline-block');\n",
-       "\n",
-       "    $(parent_element).append(this.root);\n",
-       "\n",
-       "    this._init_header(this);\n",
-       "    this._init_canvas(this);\n",
-       "    this._init_toolbar(this);\n",
-       "\n",
-       "    var fig = this;\n",
-       "\n",
-       "    this.waiting = false;\n",
-       "\n",
-       "    this.ws.onopen =  function () {\n",
-       "            fig.send_message(\"supports_binary\", {value: fig.supports_binary});\n",
-       "            fig.send_message(\"send_image_mode\", {});\n",
-       "            fig.send_message(\"refresh\", {});\n",
-       "        }\n",
-       "\n",
-       "    this.imageObj.onload = function() {\n",
-       "            if (fig.image_mode == 'full') {\n",
-       "                // Full images could contain transparency (where diff images\n",
-       "                // almost always do), so we need to clear the canvas so that\n",
-       "                // there is no ghosting.\n",
-       "                fig.context.clearRect(0, 0, fig.canvas.width, fig.canvas.height);\n",
-       "            }\n",
-       "            fig.context.drawImage(fig.imageObj, 0, 0);\n",
-       "        };\n",
-       "\n",
-       "    this.imageObj.onunload = function() {\n",
-       "        this.ws.close();\n",
-       "    }\n",
-       "\n",
-       "    this.ws.onmessage = this._make_on_message_function(this);\n",
-       "\n",
-       "    this.ondownload = ondownload;\n",
-       "}\n",
-       "\n",
-       "mpl.figure.prototype._init_header = function() {\n",
-       "    var titlebar = $(\n",
-       "        '
');\n",
-       "    var titletext = $(\n",
-       "        '
');\n",
-       "    titlebar.append(titletext)\n",
-       "    this.root.append(titlebar);\n",
-       "    this.header = titletext[0];\n",
-       "}\n",
-       "\n",
-       "\n",
-       "\n",
-       "mpl.figure.prototype._canvas_extra_style = function(canvas_div) {\n",
-       "\n",
-       "}\n",
-       "\n",
-       "\n",
-       "mpl.figure.prototype._root_extra_style = function(canvas_div) {\n",
-       "\n",
-       "}\n",
-       "\n",
-       "mpl.figure.prototype._init_canvas = function() {\n",
-       "    var fig = this;\n",
-       "\n",
-       "    var canvas_div = $('
');\n",
-       "\n",
-       "    canvas_div.attr('style', 'position: relative; clear: both; outline: 0');\n",
-       "\n",
-       "    function canvas_keyboard_event(event) {\n",
-       "        return fig.key_event(event, event['data']);\n",
-       "    }\n",
-       "\n",
-       "    canvas_div.keydown('key_press', canvas_keyboard_event);\n",
-       "    canvas_div.keyup('key_release', canvas_keyboard_event);\n",
-       "    this.canvas_div = canvas_div\n",
-       "    this._canvas_extra_style(canvas_div)\n",
-       "    this.root.append(canvas_div);\n",
-       "\n",
-       "    var canvas = $('');\n",
-       "    canvas.addClass('mpl-canvas');\n",
-       "    canvas.attr('style', \"left: 0; top: 0; z-index: 0; outline: 0\")\n",
-       "\n",
-       "    this.canvas = canvas[0];\n",
-       "    this.context = canvas[0].getContext(\"2d\");\n",
-       "\n",
-       "    var rubberband = $('');\n",
-       "    rubberband.attr('style', \"position: absolute; left: 0; top: 0; z-index: 1;\")\n",
-       "\n",
-       "    var pass_mouse_events = true;\n",
-       "\n",
-       "    canvas_div.resizable({\n",
-       "        start: function(event, ui) {\n",
-       "            pass_mouse_events = false;\n",
-       "        },\n",
-       "        resize: function(event, ui) {\n",
-       "            fig.request_resize(ui.size.width, ui.size.height);\n",
-       "        },\n",
-       "        stop: function(event, ui) {\n",
-       "            pass_mouse_events = true;\n",
-       "            fig.request_resize(ui.size.width, ui.size.height);\n",
-       "        },\n",
-       "    });\n",
-       "\n",
-       "    function mouse_event_fn(event) {\n",
-       "        if (pass_mouse_events)\n",
-       "            return fig.mouse_event(event, event['data']);\n",
-       "    }\n",
-       "\n",
-       "    rubberband.mousedown('button_press', mouse_event_fn);\n",
-       "    rubberband.mouseup('button_release', mouse_event_fn);\n",
-       "    // Throttle sequential mouse events to 1 every 20ms.\n",
-       "    rubberband.mousemove('motion_notify', mouse_event_fn);\n",
-       "\n",
-       "    rubberband.mouseenter('figure_enter', mouse_event_fn);\n",
-       "    rubberband.mouseleave('figure_leave', mouse_event_fn);\n",
-       "\n",
-       "    canvas_div.on(\"wheel\", function (event) {\n",
-       "        event = event.originalEvent;\n",
-       "        event['data'] = 'scroll'\n",
-       "        if (event.deltaY < 0) {\n",
-       "            event.step = 1;\n",
-       "        } else {\n",
-       "            event.step = -1;\n",
-       "        }\n",
-       "        mouse_event_fn(event);\n",
-       "    });\n",
-       "\n",
-       "    canvas_div.append(canvas);\n",
-       "    canvas_div.append(rubberband);\n",
-       "\n",
-       "    this.rubberband = rubberband;\n",
-       "    this.rubberband_canvas = rubberband[0];\n",
-       "    this.rubberband_context = rubberband[0].getContext(\"2d\");\n",
-       "    this.rubberband_context.strokeStyle = \"#000000\";\n",
-       "\n",
-       "    this._resize_canvas = function(width, height) {\n",
-       "        // Keep the size of the canvas, canvas container, and rubber band\n",
-       "        // canvas in synch.\n",
-       "        canvas_div.css('width', width)\n",
-       "        canvas_div.css('height', height)\n",
-       "\n",
-       "        canvas.attr('width', width);\n",
-       "        canvas.attr('height', height);\n",
-       "\n",
-       "        rubberband.attr('width', width);\n",
-       "        rubberband.attr('height', height);\n",
-       "    }\n",
-       "\n",
-       "    // Set the figure to an initial 600x600px, this will subsequently be updated\n",
-       "    // upon first draw.\n",
-       "    this._resize_canvas(600, 600);\n",
-       "\n",
-       "    // Disable right mouse context menu.\n",
-       "    $(this.rubberband_canvas).bind(\"contextmenu\",function(e){\n",
-       "        return false;\n",
-       "    });\n",
-       "\n",
-       "    function set_focus () {\n",
-       "        canvas.focus();\n",
-       "        canvas_div.focus();\n",
-       "    }\n",
-       "\n",
-       "    window.setTimeout(set_focus, 100);\n",
-       "}\n",
-       "\n",
-       "mpl.figure.prototype._init_toolbar = function() {\n",
-       "    var fig = this;\n",
-       "\n",
-       "    var nav_element = $('
')\n",
-       "    nav_element.attr('style', 'width: 100%');\n",
-       "    this.root.append(nav_element);\n",
-       "\n",
-       "    // Define a callback function for later on.\n",
-       "    function toolbar_event(event) {\n",
-       "        return fig.toolbar_button_onclick(event['data']);\n",
-       "    }\n",
-       "    function toolbar_mouse_event(event) {\n",
-       "        return fig.toolbar_button_onmouseover(event['data']);\n",
-       "    }\n",
-       "\n",
-       "    for(var toolbar_ind in mpl.toolbar_items) {\n",
-       "        var name = mpl.toolbar_items[toolbar_ind][0];\n",
-       "        var tooltip = mpl.toolbar_items[toolbar_ind][1];\n",
-       "        var image = mpl.toolbar_items[toolbar_ind][2];\n",
-       "        var method_name = mpl.toolbar_items[toolbar_ind][3];\n",
-       "\n",
-       "        if (!name) {\n",
-       "            // put a spacer in here.\n",
-       "            continue;\n",
-       "        }\n",
-       "        var button = $('');\n",
-       "        button.click(method_name, toolbar_event);\n",
-       "        button.mouseover(tooltip, toolbar_mouse_event);\n",
-       "        nav_element.append(button);\n",
-       "    }\n",
-       "\n",
-       "    // Add the status bar.\n",
-       "    var status_bar = $('');\n",
-       "    nav_element.append(status_bar);\n",
-       "    this.message = status_bar[0];\n",
-       "\n",
-       "    // Add the close button to the window.\n",
-       "    var buttongrp = $('
');\n",
-       "    var button = $('');\n",
-       "    button.click(function (evt) { fig.handle_close(fig, {}); } );\n",
-       "    button.mouseover('Stop Interaction', toolbar_mouse_event);\n",
-       "    buttongrp.append(button);\n",
-       "    var titlebar = this.root.find($('.ui-dialog-titlebar'));\n",
-       "    titlebar.prepend(buttongrp);\n",
-       "}\n",
-       "\n",
-       "mpl.figure.prototype._root_extra_style = function(el){\n",
-       "    var fig = this\n",
-       "    el.on(\"remove\", function(){\n",
-       "\tfig.close_ws(fig, {});\n",
-       "    });\n",
-       "}\n",
-       "\n",
-       "mpl.figure.prototype._canvas_extra_style = function(el){\n",
-       "    // this is important to make the div 'focusable\n",
-       "    el.attr('tabindex', 0)\n",
-       "    // reach out to IPython and tell the keyboard manager to turn it's self\n",
-       "    // off when our div gets focus\n",
-       "\n",
-       "    // location in version 3\n",
-       "    if (IPython.notebook.keyboard_manager) {\n",
-       "        IPython.notebook.keyboard_manager.register_events(el);\n",
-       "    }\n",
-       "    else {\n",
-       "        // location in version 2\n",
-       "        IPython.keyboard_manager.register_events(el);\n",
-       "    }\n",
-       "\n",
-       "}\n",
-       "\n",
-       "mpl.figure.prototype._key_event_extra = function(event, name) {\n",
-       "    var manager = IPython.notebook.keyboard_manager;\n",
-       "    if (!manager)\n",
-       "        manager = IPython.keyboard_manager;\n",
-       "\n",
-       "    // Check for shift+enter\n",
-       "    if (event.shiftKey && event.which == 13) {\n",
-       "        this.canvas_div.blur();\n",
-       "        event.shiftKey = false;\n",
-       "        // Send a \"J\" for go to next cell\n",
-       "        event.which = 74;\n",
-       "        event.keyCode = 74;\n",
-       "        manager.command_mode();\n",
-       "        manager.handle_keydown(event);\n",
-       "    }\n",
-       "}\n",
-       "\n",
-       "mpl.figure.prototype.handle_save = function(fig, msg) {\n",
-       "    fig.ondownload(fig, null);\n",
-       "}\n",
-       "\n",
-       "\n",
-       "mpl.find_output_cell = function(html_output) {\n",
-       "    // Return the cell and output element which can be found *uniquely* in the notebook.\n",
-       "    // Note - this is a bit hacky, but it is done because the \"notebook_saving.Notebook\"\n",
-       "    // IPython event is triggered only after the cells have been serialised, which for\n",
-       "    // our purposes (turning an active figure into a static one), is too late.\n",
-       "    var cells = IPython.notebook.get_cells();\n",
-       "    var ncells = cells.length;\n",
-       "    for (var i=0; i= 3 moved mimebundle to data attribute of output\n",
-       "                    data = data.data;\n",
-       "                }\n",
-       "                if (data['text/html'] == html_output) {\n",
-       "                    return [cell, data, j];\n",
-       "                }\n",
-       "            }\n",
-       "        }\n",
-       "    }\n",
-       "}\n",
-       "\n",
-       "// Register the function which deals with the matplotlib target/channel.\n",
-       "// The kernel may be null if the page has been refreshed.\n",
-       "if (IPython.notebook.kernel != null) {\n",
-       "    IPython.notebook.kernel.comm_manager.register_target('matplotlib', mpl.mpl_figure_comm);\n",
-       "}\n"
-      ],
-      "text/plain": [
-       ""
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    },
-    {
-     "data": {
-      "text/html": [
-       ""
-      ],
-      "text/plain": [
-       ""
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
+     "ename": "TypeError",
+     "evalue": "unsupported operand type(s) for *: 'NoneType' and 'float'",
+     "output_type": "error",
+     "traceback": [
+      "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
+      "\u001b[0;31mTypeError\u001b[0m                                 Traceback (most recent call last)",
+      "\u001b[0;32m\u001b[0m in \u001b[0;36m\u001b[0;34m()\u001b[0m\n\u001b[0;32m----> 1\u001b[0;31m \u001b[0mdata3\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0msamp_acq_controller\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0macquisition\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m      2\u001b[0m \u001b[0mqc\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mQtPlot\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mdata3\u001b[0m\u001b[1;33m[\u001b[0m\u001b[1;36m1\u001b[0m\u001b[1;33m]\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
+      "\u001b[0;32ma:\\qcodes\\qcodes\\instrument\\parameter.py\u001b[0m in \u001b[0;36m__call__\u001b[0;34m(self, *args)\u001b[0m\n\u001b[1;32m    264\u001b[0m         \u001b[1;32mif\u001b[0m \u001b[0mlen\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0margs\u001b[0m\u001b[1;33m)\u001b[0m \u001b[1;33m==\u001b[0m \u001b[1;36m0\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m    265\u001b[0m             \u001b[1;32mif\u001b[0m \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mhas_get\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m--> 266\u001b[0;31m                 \u001b[1;32mreturn\u001b[0m \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mget\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    267\u001b[0m             \u001b[1;32melse\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m    268\u001b[0m                 raise NoCommandError('no get cmd found in' +\n",
+      "\u001b[0;32ma:\\qcodes\\qcodes\\instrument_drivers\\AlazarTech\\samp.py\u001b[0m in \u001b[0;36mget\u001b[0;34m(self)\u001b[0m\n\u001b[1;32m     36\u001b[0m         mag, phase = self._instrument._get_alazar().acquire(\n\u001b[1;32m     37\u001b[0m             \u001b[0macquisition_controller\u001b[0m\u001b[1;33m=\u001b[0m\u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0m_instrument\u001b[0m\u001b[1;33m,\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m---> 38\u001b[0;31m             **self.acquisition_kwargs)\n\u001b[0m\u001b[1;32m     39\u001b[0m         \u001b[1;32mreturn\u001b[0m \u001b[0mmag\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mphase\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m     40\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n",
+      "\u001b[0;32ma:\\qcodes\\qcodes\\instrument_drivers\\AlazarTech\\ATS.py\u001b[0m in \u001b[0;36macquire\u001b[0;34m(self, mode, samples_per_record, records_per_buffer, buffers_per_acquisition, channel_selection, transfer_offset, external_startcapture, enable_record_headers, alloc_buffers, fifo_only_streaming, interleave_samples, get_processed_data, allocated_buffers, buffer_timeout, acquisition_controller)\u001b[0m\n\u001b[1;32m    639\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m    640\u001b[0m         \u001b[1;31m# -----start capture here-----\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m--> 641\u001b[0;31m         \u001b[0macquisition_controller\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mpre_start_capture\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    642\u001b[0m         \u001b[0mstart\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mtime\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mclock\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;33m)\u001b[0m \u001b[1;31m# Keep track of when acquisition started\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m    643\u001b[0m         \u001b[1;31m# call the startcapture method\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
+      "\u001b[0;32ma:\\qcodes\\qcodes\\instrument_drivers\\AlazarTech\\samp.py\u001b[0m in \u001b[0;36mpre_start_capture\u001b[0;34m(self)\u001b[0m\n\u001b[1;32m    191\u001b[0m             raise Exception('acq controller sample rate does not match instrument'\n\u001b[1;32m    192\u001b[0m                             'value, most likely need to call update_acquisition_settings' )   \n\u001b[0;32m--> 193\u001b[0;31m         \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mcheck_time_values\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    194\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m    195\u001b[0m         \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mrecords_per_buffer\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0malazar\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mrecords_per_buffer\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mget\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
+      "\u001b[0;32ma:\\qcodes\\qcodes\\instrument_drivers\\AlazarTech\\samp.py\u001b[0m in \u001b[0;36mcheck_time_values\u001b[0;34m(self)\u001b[0m\n\u001b[1;32m     90\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m     91\u001b[0m     \u001b[1;32mdef\u001b[0m \u001b[0mcheck_time_values\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mself\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m---> 92\u001b[0;31m         \u001b[0moscilations_measured\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mint_time\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;33m)\u001b[0m \u001b[1;33m*\u001b[0m \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mdemodulation_frequency\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m     93\u001b[0m         \u001b[0mtotal_time\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0msamples_per_record\u001b[0m \u001b[1;33m/\u001b[0m \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0msample_rate\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m     94\u001b[0m         \u001b[0mtime_used\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mint_time\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;33m)\u001b[0m \u001b[1;33m+\u001b[0m \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mint_delay\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
+      "\u001b[0;31mTypeError\u001b[0m: unsupported operand type(s) for *: 'NoneType' and 'float'"
+     ]
     }
    ],
    "source": [
-    "data3 = samp_cont.acquisition()\n",
-    "qc.Matplot(data3[1])"
+    "data3 = samp_acq_controller.acquisition()\n",
+    "qc.QtPlot(data3[1])"
    ]
   },
   {
diff --git a/docs/examples/Qcodes example with Tektronix AWG5014C.ipynb b/docs/examples/Qcodes example with Tektronix AWG5014C.ipynb
new file mode 100644
index 000000000000..fbd09310add5
--- /dev/null
+++ b/docs/examples/Qcodes example with Tektronix AWG5014C.ipynb	
@@ -0,0 +1,1325 @@
+{
+ "cells": [
+  {
+   "cell_type": "code",
+   "execution_count": 2,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [],
+   "source": [
+    "%matplotlib nbagg\n",
+    "\n",
+    "import os\n",
+    "import time\n",
+    "import logging\n",
+    "\n",
+    "import numpy as np\n",
+    "import matplotlib.pyplot as plt\n",
+    "\n",
+    "logger = logging.getLogger()\n",
+    "logger.setLevel(logging.INFO)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 4,
+   "metadata": {
+    "collapsed": true
+   },
+   "outputs": [],
+   "source": [
+    "import numpy as np\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 8,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "6.0"
+      ]
+     },
+     "execution_count": 8,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "np.ceil(26/5)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 3,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [
+    {
+     "ename": "VisaIOError",
+     "evalue": "VI_ERROR_RSRC_NFOUND (-1073807343): Insufficient location information or the requested device or resource is not present in the system.",
+     "output_type": "error",
+     "traceback": [
+      "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
+      "\u001b[0;31mVisaIOError\u001b[0m                               Traceback (most recent call last)",
+      "\u001b[0;32m\u001b[0m in \u001b[0;36m\u001b[0;34m()\u001b[0m\n\u001b[1;32m      2\u001b[0m \u001b[1;32mfrom\u001b[0m \u001b[0mqcodes\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0minstrument_drivers\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mtektronix\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mAWGFileParser\u001b[0m \u001b[1;32mimport\u001b[0m \u001b[0mparse_awg_file\u001b[0m  \u001b[1;31m# <--- A helper function\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m      3\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m----> 4\u001b[0;31m \u001b[0mawg1\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mawg\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mTektronix_AWG5014\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;34m'AWG1'\u001b[0m\u001b[1;33m,\u001b[0m \u001b[1;34m'TCPIP0::192.168.137.182::inst0::INSTR'\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mtimeout\u001b[0m\u001b[1;33m=\u001b[0m\u001b[1;36m40\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m",
+      "\u001b[0;32ma:\\qcodes\\qcodes\\instrument\\metaclass.py\u001b[0m in \u001b[0;36m__call__\u001b[0;34m(cls, server_name, *args, **kwargs)\u001b[0m\n\u001b[1;32m     32\u001b[0m         \"\"\"\n\u001b[1;32m     33\u001b[0m         \u001b[1;32mif\u001b[0m \u001b[0mserver_name\u001b[0m \u001b[1;32mis\u001b[0m \u001b[1;32mNone\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m---> 34\u001b[0;31m             \u001b[0minstrument\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0msuper\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0m__call__\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;33m*\u001b[0m\u001b[0margs\u001b[0m\u001b[1;33m,\u001b[0m \u001b[1;33m**\u001b[0m\u001b[0mkwargs\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m     35\u001b[0m         \u001b[1;32melse\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m     36\u001b[0m             warnings.warn('Multiprocessing is in beta, use at own risk',\n",
+      "\u001b[0;32ma:\\qcodes\\qcodes\\instrument_drivers\\tektronix\\AWG5014.py\u001b[0m in \u001b[0;36m__init__\u001b[0;34m(self, name, address, timeout, **kwargs)\u001b[0m\n\u001b[1;32m    137\u001b[0m             \u001b[1;32mNone\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m    138\u001b[0m         \"\"\"\n\u001b[0;32m--> 139\u001b[0;31m         \u001b[0msuper\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0m__init__\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mname\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0maddress\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mtimeout\u001b[0m\u001b[1;33m=\u001b[0m\u001b[0mtimeout\u001b[0m\u001b[1;33m,\u001b[0m \u001b[1;33m**\u001b[0m\u001b[0mkwargs\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    140\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m    141\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n",
+      "\u001b[0;32ma:\\qcodes\\qcodes\\instrument\\visa.py\u001b[0m in \u001b[0;36m__init__\u001b[0;34m(self, name, address, timeout, terminator, **kwargs)\u001b[0m\n\u001b[1;32m     54\u001b[0m                                                vals.Enum(None)))\n\u001b[1;32m     55\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m---> 56\u001b[0;31m         \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mset_address\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0maddress\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m     57\u001b[0m         \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mset_terminator\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mterminator\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m     58\u001b[0m         \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mtimeout\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mset\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mtimeout\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
+      "\u001b[0;32ma:\\qcodes\\qcodes\\instrument\\visa.py\u001b[0m in \u001b[0;36mset_address\u001b[0;34m(self, address)\u001b[0m\n\u001b[1;32m    101\u001b[0m             \u001b[0mresource_manager\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mvisa\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mResourceManager\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m    102\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m--> 103\u001b[0;31m         \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mvisa_handle\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mresource_manager\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mopen_resource\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0maddress\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    104\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m    105\u001b[0m         \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mvisa_handle\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mclear\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
+      "\u001b[0;32mc:\\users\\triton2acq\\anaconda3\\envs\\qcodes-master\\lib\\site-packages\\pyvisa\\highlevel.py\u001b[0m in \u001b[0;36mopen_resource\u001b[0;34m(self, resource_name, access_mode, open_timeout, resource_pyclass, **kwargs)\u001b[0m\n\u001b[1;32m   1642\u001b[0m                 \u001b[1;32mraise\u001b[0m \u001b[0mValueError\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;34m'%r is not a valid attribute for type %s'\u001b[0m \u001b[1;33m%\u001b[0m \u001b[1;33m(\u001b[0m\u001b[0mkey\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mres\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0m__class__\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0m__name__\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m   1643\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m-> 1644\u001b[0;31m         \u001b[0mres\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mopen\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0maccess_mode\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mopen_timeout\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m   1645\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m   1646\u001b[0m         \u001b[1;32mfor\u001b[0m \u001b[0mkey\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mvalue\u001b[0m \u001b[1;32min\u001b[0m \u001b[0mkwargs\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mitems\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
+      "\u001b[0;32mc:\\users\\triton2acq\\anaconda3\\envs\\qcodes-master\\lib\\site-packages\\pyvisa\\resources\\resource.py\u001b[0m in \u001b[0;36mopen\u001b[0;34m(self, access_mode, open_timeout)\u001b[0m\n\u001b[1;32m    201\u001b[0m         \u001b[0mlogger\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mdebug\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;34m'%s - opening ...'\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0m_resource_name\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mextra\u001b[0m\u001b[1;33m=\u001b[0m\u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0m_logging_extra\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m    202\u001b[0m         \u001b[1;32mwith\u001b[0m \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0m_resource_manager\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mignore_warning\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mconstants\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mVI_SUCCESS_DEV_NPRESENT\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m--> 203\u001b[0;31m             \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0msession\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mstatus\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0m_resource_manager\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mopen_bare_resource\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0m_resource_name\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0maccess_mode\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mopen_timeout\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    204\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m    205\u001b[0m             \u001b[1;32mif\u001b[0m \u001b[0mstatus\u001b[0m \u001b[1;33m==\u001b[0m \u001b[0mconstants\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mVI_SUCCESS_DEV_NPRESENT\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
+      "\u001b[0;32mc:\\users\\triton2acq\\anaconda3\\envs\\qcodes-master\\lib\\site-packages\\pyvisa\\highlevel.py\u001b[0m in \u001b[0;36mopen_bare_resource\u001b[0;34m(self, resource_name, access_mode, open_timeout)\u001b[0m\n\u001b[1;32m   1599\u001b[0m         \u001b[1;33m:\u001b[0m\u001b[1;32mreturn\u001b[0m\u001b[1;33m:\u001b[0m \u001b[0mUnique\u001b[0m \u001b[0mlogical\u001b[0m \u001b[0midentifier\u001b[0m \u001b[0mreference\u001b[0m \u001b[0mto\u001b[0m \u001b[0ma\u001b[0m \u001b[0msession\u001b[0m\u001b[1;33m.\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m   1600\u001b[0m         \"\"\"\n\u001b[0;32m-> 1601\u001b[0;31m         \u001b[1;32mreturn\u001b[0m \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mvisalib\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mopen\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0msession\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mresource_name\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0maccess_mode\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mopen_timeout\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m   1602\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m   1603\u001b[0m     def open_resource(self, resource_name,\n",
+      "\u001b[0;32mc:\\users\\triton2acq\\anaconda3\\envs\\qcodes-master\\lib\\site-packages\\pyvisa\\ctwrapper\\functions.py\u001b[0m in \u001b[0;36mopen\u001b[0;34m(library, session, resource_name, access_mode, open_timeout)\u001b[0m\n\u001b[1;32m   1209\u001b[0m     \u001b[1;31m# [ViSession, ViRsrc, ViAccessMode, ViUInt32, ViPSession]\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m   1210\u001b[0m     \u001b[1;31m# ViRsrc converts from (str, unicode, bytes) to bytes\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m-> 1211\u001b[0;31m     \u001b[0mret\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mlibrary\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mviOpen\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0msession\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mresource_name\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0maccess_mode\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mopen_timeout\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mbyref\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mout_session\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m   1212\u001b[0m     \u001b[1;32mreturn\u001b[0m \u001b[0mout_session\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mvalue\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mret\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m   1213\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n",
+      "\u001b[0;32mc:\\users\\triton2acq\\anaconda3\\envs\\qcodes-master\\lib\\site-packages\\pyvisa\\ctwrapper\\highlevel.py\u001b[0m in \u001b[0;36m_return_handler\u001b[0;34m(self, ret_value, func, arguments)\u001b[0m\n\u001b[1;32m    186\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m    187\u001b[0m         \u001b[1;32mif\u001b[0m \u001b[0mret_value\u001b[0m \u001b[1;33m<\u001b[0m \u001b[1;36m0\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m--> 188\u001b[0;31m             \u001b[1;32mraise\u001b[0m \u001b[0merrors\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mVisaIOError\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mret_value\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    189\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m    190\u001b[0m         \u001b[1;32mif\u001b[0m \u001b[0mret_value\u001b[0m \u001b[1;32min\u001b[0m \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0missue_warning_on\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
+      "\u001b[0;31mVisaIOError\u001b[0m: VI_ERROR_RSRC_NFOUND (-1073807343): Insufficient location information or the requested device or resource is not present in the system."
+     ]
+    }
+   ],
+   "source": [
+    "import qcodes.instrument_drivers.tektronix.AWG5014 as awg  # <--- The instrument driver\n",
+    "from qcodes.instrument_drivers.tektronix.AWGFileParser import parse_awg_file  # <--- A helper function\n",
+    "\n",
+    "awg1 = awg.Tektronix_AWG5014('AWG1', 'TCPIP0::192.168.137.182::inst0::INSTR', timeout=40)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 4,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [],
+   "source": [
+    "# Note: if you have had any initialisation problems, the VISA message queue of the instrument could be fouled up\n",
+    "#(e.g. if the cell above gave you back something like 'Connected to: 1.20000000E+009 ...')\n",
+    "# in that case, run the following command to reset the message queue\n",
+    "awg1.clear_message_queue()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "As with any other QCoDeS instrument, parameters can be set and get (gotten)."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 5,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "0\n",
+      "\n",
+      "0.0\n",
+      "0.1\n"
+     ]
+    }
+   ],
+   "source": [
+    "print(awg1.ch3_state.get())\n",
+    "print(awg1.ch2_offset.get())\n",
+    "awg1.ch2_offset.set(0.1)\n",
+    "print(awg1.ch2_offset.get())"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "A list of all available parameters can be found in the following manner:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 6,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "DC_output :  DC Output (ON/OFF)\n",
+      "IDN :  IDN\n",
+      "ch1_DC_out :  DC output level channel 1\n",
+      "ch1_add_input :  Add input channel {}\n",
+      "ch1_amp :  Amplitude channel 1 (Vpp)\n",
+      "ch1_direct_output :  Direct output channel 1\n",
+      "ch1_filter :  Low pass filter channel 1\n",
+      "ch1_m1_del :  Channel 1 Marker 1 delay (ns)\n",
+      "ch1_m1_high :  Channel 1 Marker 1 high level (V)\n",
+      "ch1_m1_low :  Channel 1 Marker 1 low level (V)\n",
+      "ch1_m2_del :  Channel 1 Marker 2 delay (ns)\n",
+      "ch1_m2_high :  Channel 1 Marker 2 high level (V)\n",
+      "ch1_m2_low :  Channel 1 Marker 2 low level (V)\n",
+      "ch1_offset :  Offset channel 1 (V)\n",
+      "ch1_state :  Status channel 1\n",
+      "ch1_waveform :  Waveform channel 1\n",
+      "ch2_DC_out :  DC output level channel 2\n",
+      "ch2_add_input :  Add input channel {}\n",
+      "ch2_amp :  Amplitude channel 2 (Vpp)\n",
+      "ch2_direct_output :  Direct output channel 2\n",
+      "ch2_filter :  Low pass filter channel 2\n",
+      "ch2_m1_del :  Channel 2 Marker 1 delay (ns)\n",
+      "ch2_m1_high :  Channel 2 Marker 1 high level (V)\n",
+      "ch2_m1_low :  Channel 2 Marker 1 low level (V)\n",
+      "ch2_m2_del :  Channel 2 Marker 2 delay (ns)\n",
+      "ch2_m2_high :  Channel 2 Marker 2 high level (V)\n",
+      "ch2_m2_low :  Channel 2 Marker 2 low level (V)\n",
+      "ch2_offset :  Offset channel 2 (V)\n",
+      "ch2_state :  Status channel 2\n",
+      "ch2_waveform :  Waveform channel 2\n",
+      "ch3_DC_out :  DC output level channel 3\n",
+      "ch3_add_input :  Add input channel {}\n",
+      "ch3_amp :  Amplitude channel 3 (Vpp)\n",
+      "ch3_direct_output :  Direct output channel 3\n",
+      "ch3_filter :  Low pass filter channel 3\n",
+      "ch3_m1_del :  Channel 3 Marker 1 delay (ns)\n",
+      "ch3_m1_high :  Channel 3 Marker 1 high level (V)\n",
+      "ch3_m1_low :  Channel 3 Marker 1 low level (V)\n",
+      "ch3_m2_del :  Channel 3 Marker 2 delay (ns)\n",
+      "ch3_m2_high :  Channel 3 Marker 2 high level (V)\n",
+      "ch3_m2_low :  Channel 3 Marker 2 low level (V)\n",
+      "ch3_offset :  Offset channel 3 (V)\n",
+      "ch3_state :  Status channel 3\n",
+      "ch3_waveform :  Waveform channel 3\n",
+      "ch4_DC_out :  DC output level channel 4\n",
+      "ch4_add_input :  Add input channel {}\n",
+      "ch4_amp :  Amplitude channel 4 (Vpp)\n",
+      "ch4_direct_output :  Direct output channel 4\n",
+      "ch4_filter :  Low pass filter channel 4\n",
+      "ch4_m1_del :  Channel 4 Marker 1 delay (ns)\n",
+      "ch4_m1_high :  Channel 4 Marker 1 high level (V)\n",
+      "ch4_m1_low :  Channel 4 Marker 1 low level (V)\n",
+      "ch4_m2_del :  Channel 4 Marker 2 delay (ns)\n",
+      "ch4_m2_high :  Channel 4 Marker 2 high level (V)\n",
+      "ch4_m2_low :  Channel 4 Marker 2 low level (V)\n",
+      "ch4_offset :  Offset channel 4 (V)\n",
+      "ch4_state :  Status channel 4\n",
+      "ch4_waveform :  Waveform channel 4\n",
+      "clock_freq :  Clock frequency (Hz)\n",
+      "event_impedance :  Event impedance (Ohm)\n",
+      "event_jump_timing :  event_jump_timing\n",
+      "event_level :  Event level (V)\n",
+      "event_polarity :  event_polarity\n",
+      "ref_clock_source :  Reference clock source\n",
+      "run_mode :  run_mode\n",
+      "sequence_length :  Sequence length\n",
+      "setup_filename :  setup_filename\n",
+      "state :  state\n",
+      "timeout :  timeout\n",
+      "trigger_impedance :  Trigger impedance (Ohm)\n",
+      "trigger_level :  Trigger level (V)\n",
+      "trigger_mode :  trigger_mode\n",
+      "trigger_slope :  trigger_slope\n",
+      "trigger_source :  trigger_source\n"
+     ]
+    }
+   ],
+   "source": [
+    "pars = np.sort(list(awg1.parameters.keys()))\n",
+    "for param in pars:\n",
+    "    print(param, ': ', awg1.parameters[param].label)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "collapsed": false
+   },
+   "source": [
+    "## SENDING WAVEFORMS\n",
+    "\n",
+    "There are two supported ways of sending waveforms to the AWG; by making an .awg file, sending and loading that, or by sending waveforms to the User Defined list one by one and putting them in the sequencer. The first method is generally faster."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "collapsed": false
+   },
+   "source": [
+    "### MAKING SOME WAVEFORMS\n",
+    "\n",
+    "First, we make a handful of pulse shapes and marker signals to send to the AWG. This should be done with numpy arrays.\n",
+    "\n",
+    "Please note that the waveforms must **not** have values exceeding -1 to 1, and that the markers must **only** have values 0 or 1. Otherwise the AWG misinterprets the signals.\n",
+    "\n",
+    "In this example, we only use two channels of the AWG, namely channel 1 and 3. Extending to one or more should be straightforward."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 7,
+   "metadata": {
+    "collapsed": false,
+    "scrolled": true
+   },
+   "outputs": [],
+   "source": [
+    "noofseqelems = 6\n",
+    "noofpoints = 1200\n",
+    "waveforms = [[], []]  # one list for each channel\n",
+    "m1s = [[], []]\n",
+    "m2s = [[], []]\n",
+    "for ii in range(noofseqelems):\n",
+    "    # waveform and markers for channel 1\n",
+    "    waveforms[0].append(np.sin(np.pi*(ii+1)*np.linspace(0, 1, noofpoints))*np.hanning(noofpoints))\n",
+    "    m1 = np.zeros(noofpoints)\n",
+    "    m1[:int(noofpoints/(ii+1))] = 1\n",
+    "    m1s[0].append(m1)\n",
+    "    m2 = np.zeros(noofpoints)\n",
+    "    m2s[0].append(m2)\n",
+    "    \n",
+    "    # waveform and markers for channel two\n",
+    "    wf = np.sin(np.pi*(ii+1)*np.linspace(0, 1, noofpoints))\n",
+    "    wf *= np.arctan(np.linspace(-20, 20, noofpoints))/np.pi*2\n",
+    "    waveforms[1].append(wf)\n",
+    "    m1 = np.zeros(noofpoints)\n",
+    "    m1[:int(noofpoints/(ii+1))] = 1\n",
+    "    m1s[1].append(m1)\n",
+    "    m2 = np.zeros(noofpoints)\n",
+    "    m2s[1].append(m2)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 8,
+   "metadata": {
+    "collapsed": false,
+    "scrolled": true
+   },
+   "outputs": [
+    {
+     "data": {
+      "application/javascript": [
+       "/* Put everything inside the global mpl namespace */\n",
+       "window.mpl = {};\n",
+       "\n",
+       "mpl.get_websocket_type = function() {\n",
+       "    if (typeof(WebSocket) !== 'undefined') {\n",
+       "        return WebSocket;\n",
+       "    } else if (typeof(MozWebSocket) !== 'undefined') {\n",
+       "        return MozWebSocket;\n",
+       "    } else {\n",
+       "        alert('Your browser does not have WebSocket support.' +\n",
+       "              'Please try Chrome, Safari or Firefox ≥ 6. ' +\n",
+       "              'Firefox 4 and 5 are also supported but you ' +\n",
+       "              'have to enable WebSockets in about:config.');\n",
+       "    };\n",
+       "}\n",
+       "\n",
+       "mpl.figure = function(figure_id, websocket, ondownload, parent_element) {\n",
+       "    this.id = figure_id;\n",
+       "\n",
+       "    this.ws = websocket;\n",
+       "\n",
+       "    this.supports_binary = (this.ws.binaryType != undefined);\n",
+       "\n",
+       "    if (!this.supports_binary) {\n",
+       "        var warnings = document.getElementById(\"mpl-warnings\");\n",
+       "        if (warnings) {\n",
+       "            warnings.style.display = 'block';\n",
+       "            warnings.textContent = (\n",
+       "                \"This browser does not support binary websocket messages. \" +\n",
+       "                    \"Performance may be slow.\");\n",
+       "        }\n",
+       "    }\n",
+       "\n",
+       "    this.imageObj = new Image();\n",
+       "\n",
+       "    this.context = undefined;\n",
+       "    this.message = undefined;\n",
+       "    this.canvas = undefined;\n",
+       "    this.rubberband_canvas = undefined;\n",
+       "    this.rubberband_context = undefined;\n",
+       "    this.format_dropdown = undefined;\n",
+       "\n",
+       "    this.image_mode = 'full';\n",
+       "\n",
+       "    this.root = $('
');\n",
+       "    this._root_extra_style(this.root)\n",
+       "    this.root.attr('style', 'display: inline-block');\n",
+       "\n",
+       "    $(parent_element).append(this.root);\n",
+       "\n",
+       "    this._init_header(this);\n",
+       "    this._init_canvas(this);\n",
+       "    this._init_toolbar(this);\n",
+       "\n",
+       "    var fig = this;\n",
+       "\n",
+       "    this.waiting = false;\n",
+       "\n",
+       "    this.ws.onopen =  function () {\n",
+       "            fig.send_message(\"supports_binary\", {value: fig.supports_binary});\n",
+       "            fig.send_message(\"send_image_mode\", {});\n",
+       "            fig.send_message(\"refresh\", {});\n",
+       "        }\n",
+       "\n",
+       "    this.imageObj.onload = function() {\n",
+       "            if (fig.image_mode == 'full') {\n",
+       "                // Full images could contain transparency (where diff images\n",
+       "                // almost always do), so we need to clear the canvas so that\n",
+       "                // there is no ghosting.\n",
+       "                fig.context.clearRect(0, 0, fig.canvas.width, fig.canvas.height);\n",
+       "            }\n",
+       "            fig.context.drawImage(fig.imageObj, 0, 0);\n",
+       "        };\n",
+       "\n",
+       "    this.imageObj.onunload = function() {\n",
+       "        this.ws.close();\n",
+       "    }\n",
+       "\n",
+       "    this.ws.onmessage = this._make_on_message_function(this);\n",
+       "\n",
+       "    this.ondownload = ondownload;\n",
+       "}\n",
+       "\n",
+       "mpl.figure.prototype._init_header = function() {\n",
+       "    var titlebar = $(\n",
+       "        '
');\n",
+       "    var titletext = $(\n",
+       "        '
');\n",
+       "    titlebar.append(titletext)\n",
+       "    this.root.append(titlebar);\n",
+       "    this.header = titletext[0];\n",
+       "}\n",
+       "\n",
+       "\n",
+       "\n",
+       "mpl.figure.prototype._canvas_extra_style = function(canvas_div) {\n",
+       "\n",
+       "}\n",
+       "\n",
+       "\n",
+       "mpl.figure.prototype._root_extra_style = function(canvas_div) {\n",
+       "\n",
+       "}\n",
+       "\n",
+       "mpl.figure.prototype._init_canvas = function() {\n",
+       "    var fig = this;\n",
+       "\n",
+       "    var canvas_div = $('
');\n",
+       "\n",
+       "    canvas_div.attr('style', 'position: relative; clear: both; outline: 0');\n",
+       "\n",
+       "    function canvas_keyboard_event(event) {\n",
+       "        return fig.key_event(event, event['data']);\n",
+       "    }\n",
+       "\n",
+       "    canvas_div.keydown('key_press', canvas_keyboard_event);\n",
+       "    canvas_div.keyup('key_release', canvas_keyboard_event);\n",
+       "    this.canvas_div = canvas_div\n",
+       "    this._canvas_extra_style(canvas_div)\n",
+       "    this.root.append(canvas_div);\n",
+       "\n",
+       "    var canvas = $('');\n",
+       "    canvas.addClass('mpl-canvas');\n",
+       "    canvas.attr('style', \"left: 0; top: 0; z-index: 0; outline: 0\")\n",
+       "\n",
+       "    this.canvas = canvas[0];\n",
+       "    this.context = canvas[0].getContext(\"2d\");\n",
+       "\n",
+       "    var rubberband = $('');\n",
+       "    rubberband.attr('style', \"position: absolute; left: 0; top: 0; z-index: 1;\")\n",
+       "\n",
+       "    var pass_mouse_events = true;\n",
+       "\n",
+       "    canvas_div.resizable({\n",
+       "        start: function(event, ui) {\n",
+       "            pass_mouse_events = false;\n",
+       "        },\n",
+       "        resize: function(event, ui) {\n",
+       "            fig.request_resize(ui.size.width, ui.size.height);\n",
+       "        },\n",
+       "        stop: function(event, ui) {\n",
+       "            pass_mouse_events = true;\n",
+       "            fig.request_resize(ui.size.width, ui.size.height);\n",
+       "        },\n",
+       "    });\n",
+       "\n",
+       "    function mouse_event_fn(event) {\n",
+       "        if (pass_mouse_events)\n",
+       "            return fig.mouse_event(event, event['data']);\n",
+       "    }\n",
+       "\n",
+       "    rubberband.mousedown('button_press', mouse_event_fn);\n",
+       "    rubberband.mouseup('button_release', mouse_event_fn);\n",
+       "    // Throttle sequential mouse events to 1 every 20ms.\n",
+       "    rubberband.mousemove('motion_notify', mouse_event_fn);\n",
+       "\n",
+       "    rubberband.mouseenter('figure_enter', mouse_event_fn);\n",
+       "    rubberband.mouseleave('figure_leave', mouse_event_fn);\n",
+       "\n",
+       "    canvas_div.on(\"wheel\", function (event) {\n",
+       "        event = event.originalEvent;\n",
+       "        event['data'] = 'scroll'\n",
+       "        if (event.deltaY < 0) {\n",
+       "            event.step = 1;\n",
+       "        } else {\n",
+       "            event.step = -1;\n",
+       "        }\n",
+       "        mouse_event_fn(event);\n",
+       "    });\n",
+       "\n",
+       "    canvas_div.append(canvas);\n",
+       "    canvas_div.append(rubberband);\n",
+       "\n",
+       "    this.rubberband = rubberband;\n",
+       "    this.rubberband_canvas = rubberband[0];\n",
+       "    this.rubberband_context = rubberband[0].getContext(\"2d\");\n",
+       "    this.rubberband_context.strokeStyle = \"#000000\";\n",
+       "\n",
+       "    this._resize_canvas = function(width, height) {\n",
+       "        // Keep the size of the canvas, canvas container, and rubber band\n",
+       "        // canvas in synch.\n",
+       "        canvas_div.css('width', width)\n",
+       "        canvas_div.css('height', height)\n",
+       "\n",
+       "        canvas.attr('width', width);\n",
+       "        canvas.attr('height', height);\n",
+       "\n",
+       "        rubberband.attr('width', width);\n",
+       "        rubberband.attr('height', height);\n",
+       "    }\n",
+       "\n",
+       "    // Set the figure to an initial 600x600px, this will subsequently be updated\n",
+       "    // upon first draw.\n",
+       "    this._resize_canvas(600, 600);\n",
+       "\n",
+       "    // Disable right mouse context menu.\n",
+       "    $(this.rubberband_canvas).bind(\"contextmenu\",function(e){\n",
+       "        return false;\n",
+       "    });\n",
+       "\n",
+       "    function set_focus () {\n",
+       "        canvas.focus();\n",
+       "        canvas_div.focus();\n",
+       "    }\n",
+       "\n",
+       "    window.setTimeout(set_focus, 100);\n",
+       "}\n",
+       "\n",
+       "mpl.figure.prototype._init_toolbar = function() {\n",
+       "    var fig = this;\n",
+       "\n",
+       "    var nav_element = $('
')\n",
+       "    nav_element.attr('style', 'width: 100%');\n",
+       "    this.root.append(nav_element);\n",
+       "\n",
+       "    // Define a callback function for later on.\n",
+       "    function toolbar_event(event) {\n",
+       "        return fig.toolbar_button_onclick(event['data']);\n",
+       "    }\n",
+       "    function toolbar_mouse_event(event) {\n",
+       "        return fig.toolbar_button_onmouseover(event['data']);\n",
+       "    }\n",
+       "\n",
+       "    for(var toolbar_ind in mpl.toolbar_items) {\n",
+       "        var name = mpl.toolbar_items[toolbar_ind][0];\n",
+       "        var tooltip = mpl.toolbar_items[toolbar_ind][1];\n",
+       "        var image = mpl.toolbar_items[toolbar_ind][2];\n",
+       "        var method_name = mpl.toolbar_items[toolbar_ind][3];\n",
+       "\n",
+       "        if (!name) {\n",
+       "            // put a spacer in here.\n",
+       "            continue;\n",
+       "        }\n",
+       "        var button = $('');\n",
-       "        button.click(method_name, toolbar_event);\n",
-       "        button.mouseover(tooltip, toolbar_mouse_event);\n",
-       "        nav_element.append(button);\n",
-       "    }\n",
-       "\n",
-       "    // Add the status bar.\n",
-       "    var status_bar = $('');\n",
-       "    nav_element.append(status_bar);\n",
-       "    this.message = status_bar[0];\n",
-       "\n",
-       "    // Add the close button to the window.\n",
-       "    var buttongrp = $('
');\n",
-       "    var button = $('');\n",
-       "    button.click(function (evt) { fig.handle_close(fig, {}); } );\n",
-       "    button.mouseover('Stop Interaction', toolbar_mouse_event);\n",
-       "    buttongrp.append(button);\n",
-       "    var titlebar = this.root.find($('.ui-dialog-titlebar'));\n",
-       "    titlebar.prepend(buttongrp);\n",
-       "}\n",
-       "\n",
-       "mpl.figure.prototype._root_extra_style = function(el){\n",
-       "    var fig = this\n",
-       "    el.on(\"remove\", function(){\n",
-       "\tfig.close_ws(fig, {});\n",
-       "    });\n",
-       "}\n",
-       "\n",
-       "mpl.figure.prototype._canvas_extra_style = function(el){\n",
-       "    // this is important to make the div 'focusable\n",
-       "    el.attr('tabindex', 0)\n",
-       "    // reach out to IPython and tell the keyboard manager to turn it's self\n",
-       "    // off when our div gets focus\n",
-       "\n",
-       "    // location in version 3\n",
-       "    if (IPython.notebook.keyboard_manager) {\n",
-       "        IPython.notebook.keyboard_manager.register_events(el);\n",
-       "    }\n",
-       "    else {\n",
-       "        // location in version 2\n",
-       "        IPython.keyboard_manager.register_events(el);\n",
-       "    }\n",
-       "\n",
-       "}\n",
-       "\n",
-       "mpl.figure.prototype._key_event_extra = function(event, name) {\n",
-       "    var manager = IPython.notebook.keyboard_manager;\n",
-       "    if (!manager)\n",
-       "        manager = IPython.keyboard_manager;\n",
-       "\n",
-       "    // Check for shift+enter\n",
-       "    if (event.shiftKey && event.which == 13) {\n",
-       "        this.canvas_div.blur();\n",
-       "        event.shiftKey = false;\n",
-       "        // Send a \"J\" for go to next cell\n",
-       "        event.which = 74;\n",
-       "        event.keyCode = 74;\n",
-       "        manager.command_mode();\n",
-       "        manager.handle_keydown(event);\n",
-       "    }\n",
-       "}\n",
-       "\n",
-       "mpl.figure.prototype.handle_save = function(fig, msg) {\n",
-       "    fig.ondownload(fig, null);\n",
-       "}\n",
-       "\n",
-       "\n",
-       "mpl.find_output_cell = function(html_output) {\n",
-       "    // Return the cell and output element which can be found *uniquely* in the notebook.\n",
-       "    // Note - this is a bit hacky, but it is done because the \"notebook_saving.Notebook\"\n",
-       "    // IPython event is triggered only after the cells have been serialised, which for\n",
-       "    // our purposes (turning an active figure into a static one), is too late.\n",
-       "    var cells = IPython.notebook.get_cells();\n",
-       "    var ncells = cells.length;\n",
-       "    for (var i=0; i= 3 moved mimebundle to data attribute of output\n",
-       "                    data = data.data;\n",
-       "                }\n",
-       "                if (data['text/html'] == html_output) {\n",
-       "                    return [cell, data, j];\n",
-       "                }\n",
-       "            }\n",
-       "        }\n",
-       "    }\n",
-       "}\n",
-       "\n",
-       "// Register the function which deals with the matplotlib target/channel.\n",
-       "// The kernel may be null if the page has been refreshed.\n",
-       "if (IPython.notebook.kernel != null) {\n",
-       "    IPython.notebook.kernel.comm_manager.register_target('matplotlib', mpl.mpl_figure_comm);\n",
-       "}\n"
-      ],
-      "text/plain": [
-       ""
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    },
-    {
-     "data": {
-      "text/html": [
-       ""
-      ],
-      "text/plain": [
-       ""
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    }
-   ],
-   "source": [
-    "data7 = rec_contr.acquisition()\n",
-    "qc.MatPlot(data7[1])"
+    ")\n",
+    "\n",
+    "# set integration time and delay (nb this replaces need to set samples_per record)\n",
+    "# if int_delay is unset it will default to a value corresponding to the about needed for the filter to work well\n",
+    "rec_controller.int_time(2e-6)\n",
+    "rec_controller.int_delay.to_default()"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 16,
+   "execution_count": 19,
    "metadata": {
     "collapsed": false
    },
@@ -1458,42 +677,29 @@
      "text": [
       "DataSet:\n",
       "   mode     = DataMode.LOCAL\n",
-      "   location = '2016-11-08/11-29-47_AlazarTest'\n",
+      "   location = '2017-01-23/10-58-35'\n",
       "      |                |  | \n",
-      "   Setpoint | dummy_set                | dummy        | (5,)\n",
-      "   Measured | record_num               | record_num   | (5, 20)\n",
-      "   Measured | rec_controller_magnitude | magnitude    | (5, 20)\n",
-      "   Measured | rec_controller_phase     | phase        | (5, 20)\n",
-      "started at 2016-11-08 11:29:49\n"
+      "   Measured | index1                   | index1       | (4, 20)\n",
+      "   Measured | rec_controller_magnitude | magnitude    | (4, 20)\n",
+      "   Measured | rec_controller_phase     | phase        | (4, 20)\n",
+      "acquired at 2017-01-23 10:58:36\n"
      ]
-    }
-   ],
-   "source": [
-    "dummy = parameter.ManualParameter(name=\"dummy\")\n",
-    "data8 = qc.Loop(dummy[0:5:1]).each(\n",
-    "    rec_contr.acquisition).run(name='AlazarTest')"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 17,
-   "metadata": {
-    "collapsed": false
-   },
-   "outputs": [
+    },
     {
      "data": {
+      "image/png": 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Yv1FvjHrM4MQ/JRenCu4AABBxeuIftIQvuAMAQAkI7gAAUAIHxZcU3AEAYNRLWq24AwDA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\nAAAASUVORK5CYII=\n",
       "text/plain": [
-       ""
+       ""
       ]
      },
-     "execution_count": 17,
+     "execution_count": 19,
      "metadata": {},
      "output_type": "execute_result"
     }
    ],
    "source": [
-    "qc.QtPlot(data8.rec_controller_magnitude)"
+    "data7 = qc.Measure(rec_controller.acquisition).run()\n",
+    "qc.QtPlot(data7.rec_controller_magnitude)"
    ]
   }
  ],
diff --git a/qcodes/instrument_drivers/AlazarTech/samp_controller.py b/qcodes/instrument_drivers/AlazarTech/samp_controller.py
index a85b1ea9ae23..9fb3be199c9e 100644
--- a/qcodes/instrument_drivers/AlazarTech/samp_controller.py
+++ b/qcodes/instrument_drivers/AlazarTech/samp_controller.py
@@ -57,8 +57,8 @@ def update_sweep(self, start, stop, npts):
         self.shapes = ((demod_length, npts), (demod_length, npts))
 
     def get(self):
-        self._instr.int_time.check()
-        self._instr.int_delay.check()
+        self._instrument.int_time.check()
+        self._instrument.int_delay.check()
         mag, phase = self._instrument._get_alazar().acquire(
             acquisition_controller=self._instrument,
             **self.acquisition_kwargs)

From f1b92adceabd4a3db5fc4f52916aa9480fade799 Mon Sep 17 00:00:00 2001
From: nataliejpg 
Date: Tue, 24 Jan 2017 10:39:50 +0100
Subject: [PATCH 091/328] tweaks while testing on actual setup

---
 .../Qcodes example with Alazar ATS9360.ipynb  | 298 ++++++++++++------
 .../instrument_drivers/AlazarTech/ATS9360.py  |   2 +-
 .../AlazarTech/ave_controller_test.py         |   5 +-
 .../AlazarTech/basic_controller.py            |   8 +-
 .../AlazarTech/rec_controller_test.py         |   5 +-
 .../AlazarTech/samp_controller.py             |  18 +-
 6 files changed, 226 insertions(+), 110 deletions(-)

diff --git a/docs/examples/Qcodes example with Alazar ATS9360.ipynb b/docs/examples/Qcodes example with Alazar ATS9360.ipynb
index 0c7b247d4bbf..905fd7081d53 100644
--- a/docs/examples/Qcodes example with Alazar ATS9360.ipynb	
+++ b/docs/examples/Qcodes example with Alazar ATS9360.ipynb	
@@ -28,7 +28,8 @@
     "import qcodes.instrument_drivers.AlazarTech.ATS9360 as ATSdriver\n",
     "import qcodes.instrument_drivers.AlazarTech.basic_controller as basic_aqc_contr\n",
     "import qcodes.instrument_drivers.AlazarTech.samp_controller as samp_acq_contr\n",
-    "import qcodes.instrument_drivers.AlazarTech.samp as samp\n",
+    "import qcodes.instrument_drivers.AlazarTech.ave_controller_test as ave_acq_controller\n",
+    "import qcodes.instrument_drivers.AlazarTech.rec_controller_test as record_acq_controller\n",
     "\n",
     "import logging\n",
     "# logging.basicConfig(filename='example.log',level=logging.INFO)\n",
@@ -40,7 +41,7 @@
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "NB: See ATS9870 example notebook for general commands "
+    "NB: See ATS9360 example notebook for general commands "
    ]
   },
   {
@@ -148,8 +149,8 @@
    "source": [
     "# Configure settings in the controller to be used in an acquisition\n",
     "# nb this must be done before the first acquisition \n",
-    "basic_acq_controller.update_acquisition_kwargs(samples_per_record=1024,\n",
-    "                                              records_per_buffer=1,\n",
+    "basic_acq_controller.update_acquisition_kwargs(samples_per_record=128*11,\n",
+    "                                              records_per_buffer=100,\n",
     "                                              buffers_per_acquisition=1,\n",
     "                                              allocated_buffers=1\n",
     "                                              )"
@@ -166,9 +167,9 @@
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "(array([-0.0007326 ,  0.001221  ,  0.0002442 , ..., -0.00561661,\n",
-      "       -0.02515263, -0.01147741]), array([ 0.0021978,  0.0021978,  0.001221 , ...,  0.001221 ,  0.001221 ,\n",
-      "        0.0002442]))\n"
+      "(array([-0.00903541, -0.00757021,  0.0002442 , ...,  0.2014652 ,\n",
+      "        0.197558  ,  0.1965812 ]), array([-0.0002442,  0.0007326, -0.0002442, ...,  0.0007326, -0.0002442,\n",
+      "        0.0007326]))\n"
      ]
     }
    ],
@@ -180,7 +181,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 7,
+   "execution_count": 11,
    "metadata": {
     "collapsed": false
    },
@@ -191,66 +192,91 @@
      "text": [
       "DataSet:\n",
       "   mode     = DataMode.LOCAL\n",
-      "   location = '2017-01-23/10-40-23'\n",
+      "   location = '2017-01-23/18-17-17'\n",
       "      |              |  | \n",
-      "   Measured | index0                 | index0       | (1024,)\n",
-      "   Measured | basic_acq_controller_A | A            | (1024,)\n",
-      "   Measured | basic_acq_controller_B | B            | (1024,)\n",
-      "acquired at 2017-01-23 10:40:24\n"
+      "   Measured | index0                 | index0       | (1408,)\n",
+      "   Measured | basic_acq_controller_A | A            | (1408,)\n",
+      "   Measured | basic_acq_controller_B | B            | (1408,)\n",
+      "acquired at 2017-01-23 18:17:18\n"
      ]
-    },
-    {
-     "data": {
-      "image/png": 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1NlU/jNfvPn19pg09H8fvbX3+pQa3Pg4uMACSLjE7BxweKnbGfXlxvr83TSmlyezD7tX1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\nAAAEILgDAEAAgjsAAAQguAMAQAD/HwhI7HHZJOoZAAAAAElFTkSuQmCC\n",
-      "text/plain": [
-       ""
-      ]
-     },
-     "execution_count": 7,
-     "metadata": {},
-     "output_type": "execute_result"
     }
    ],
    "source": [
     "# Do this in as qcodes measurement (ie the same but makes a data set)\n",
     "data2 = qc.Measure(basic_acq_controller.acquisition).run()\n",
-    "qc.QtPlot(data2.basic_acq_controller_A)"
+    "plot = qc.MatPlot(data2.basic_acq_controller_A)"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 12,
+   "execution_count": 17,
    "metadata": {
     "collapsed": false
    },
    "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "DataSet:\n",
-      "   mode     = DataMode.LOCAL\n",
-      "   location = '2017-01-20/15-59-19_AlazarTest'\n",
-      "      |              |  | \n",
-      "   Setpoint | dummy_set              | dummy        | (5,)\n",
-      "   Measured | index0                 | index0       | (5, 1024)\n",
-      "   Measured | basic_acq_controller_A | A            | (5, 1024)\n",
-      "   Measured | basic_acq_controller_B | B            | (5, 1024)\n",
-      "started at 2017-01-20 15:59:23\n"
-     ]
-    },
     {
      "data": {
-      "image/png": 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QeRRJGosZ3JulG6kOlX9Wh1IdTlKpDjUXyr81F3JtxekqTuYLrbNSlPVikpQnoTKSgf2p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\nQmCC\n",
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pQkszqiH9AtJFm7ZwPnnSiWcRzouL8fn8vHBOmYQnQmjzZvd1ejotlzw3NxAf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\n+fosDqLpEvtEb5aW2olqNJ1xZlSDJAtnY8wGY8xPGWO+BCd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FNoSzMX5DVj5L04YK56oL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\nbZkkw0sNkQAAAABJRU5ErkJggg==\n",
       "text/plain": [
-       ""
+       ""
       ]
      },
-     "execution_count": 12,
+     "execution_count": 17,
      "metadata": {},
      "output_type": "execute_result"
     }
    ],
+   "source": [
+    "plot.fig\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {
+    "collapsed": true
+   },
+   "outputs": [],
+   "source": []
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 10,
+   "metadata": {
+    "collapsed": true
+   },
+   "outputs": [
+    {
+     "ename": "gaierror",
+     "evalue": "[Errno 11004] getaddrinfo failed",
+     "output_type": "error",
+     "traceback": [
+      "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
+      "\u001b[0;31mgaierror\u001b[0m                                  Traceback (most recent call last)",
+      "\u001b[0;32m\u001b[0m in \u001b[0;36m\u001b[0;34m()\u001b[0m\n\u001b[1;32m      1\u001b[0m \u001b[1;31m# Do this in as qcodes measurement (ie the same but makes a data set)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m      2\u001b[0m \u001b[1;31m#data3 = qc.Measure(basic_acq_controller.acquisition).run()\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m----> 3\u001b[0;31m \u001b[0mplot\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mqc\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mQtPlot\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mdata2\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mbasic_acq_controller_B\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m",
+      "\u001b[0;32ma:\\qcodes\\qcodes\\plots\\pyqtgraph.py\u001b[0m in \u001b[0;36m__init__\u001b[0;34m(self, figsize, interval, window_title, theme, show_window, remote, *args, **kwargs)\u001b[0m\n\u001b[1;32m     52\u001b[0m         \u001b[1;32mif\u001b[0m \u001b[0mremote\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m     53\u001b[0m             \u001b[1;32mif\u001b[0m \u001b[1;32mnot\u001b[0m \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0m__class__\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mproc\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m---> 54\u001b[0;31m                 \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0m_init_qt\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m     55\u001b[0m         \u001b[1;32melse\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m     56\u001b[0m             \u001b[1;31m# overrule the remote pyqtgraph class\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
+      "\u001b[0;32ma:\\qcodes\\qcodes\\plots\\pyqtgraph.py\u001b[0m in \u001b[0;36m_init_qt\u001b[0;34m(self)\u001b[0m\n\u001b[1;32m     72\u001b[0m         \u001b[1;31m# run, so this only starts once and stores the process in the class\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m     73\u001b[0m         \u001b[0mpg\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mmkQApp\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m---> 74\u001b[0;31m         \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0m__class__\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mproc\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mpgmp\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mQtProcess\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;33m)\u001b[0m  \u001b[1;31m# pyqtgraph multiprocessing\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m     75\u001b[0m         \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0m__class__\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mrpg\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mproc\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0m_import\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;34m'pyqtgraph'\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m     76\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n",
+      "\u001b[0;32mc:\\users\\triton2acq\\anaconda3\\envs\\qcodes-master\\lib\\site-packages\\pyqtgraph\\multiprocess\\processes.py\u001b[0m in \u001b[0;36m__init__\u001b[0;34m(self, **kwds)\u001b[0m\n\u001b[1;32m    389\u001b[0m         \u001b[1;32mif\u001b[0m \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0m_processRequests\u001b[0m \u001b[1;32mand\u001b[0m \u001b[0mQtGui\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mQApplication\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0minstance\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;33m)\u001b[0m \u001b[1;32mis\u001b[0m \u001b[1;32mNone\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m    390\u001b[0m             \u001b[1;32mraise\u001b[0m \u001b[0mException\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;34m\"Must create QApplication before starting QtProcess, or use QtProcess(processRequests=False)\"\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m--> 391\u001b[0;31m         \u001b[0mProcess\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0m__init__\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mself\u001b[0m\u001b[1;33m,\u001b[0m \u001b[1;33m**\u001b[0m\u001b[0mkwds\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    392\u001b[0m         \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mstartEventTimer\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m    393\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n",
+      "\u001b[0;32mc:\\users\\triton2acq\\anaconda3\\envs\\qcodes-master\\lib\\site-packages\\pyqtgraph\\multiprocess\\processes.py\u001b[0m in \u001b[0;36m__init__\u001b[0;34m(self, name, target, executable, copySysPath, debug, timeout, wrapStdout)\u001b[0m\n\u001b[1;32m     79\u001b[0m         \u001b[1;31m#print \"key:\", ' '.join([str(ord(x)) for x in authkey])\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m     80\u001b[0m         \u001b[1;31m## Listen for connection from remote process (and find free port number)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m---> 81\u001b[0;31m         \u001b[0ml\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mmultiprocessing\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mconnection\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mListener\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;34m'localhost'\u001b[0m\u001b[1;33m,\u001b[0m \u001b[1;36m0\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mauthkey\u001b[0m\u001b[1;33m=\u001b[0m\u001b[0mauthkey\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m     82\u001b[0m         \u001b[0mport\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0ml\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0maddress\u001b[0m\u001b[1;33m[\u001b[0m\u001b[1;36m1\u001b[0m\u001b[1;33m]\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m     83\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n",
+      "\u001b[0;32mc:\\users\\triton2acq\\anaconda3\\envs\\qcodes-master\\lib\\multiprocessing\\connection.py\u001b[0m in \u001b[0;36m__init__\u001b[0;34m(self, address, family, backlog, authkey)\u001b[0m\n\u001b[1;32m    436\u001b[0m             \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0m_listener\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mPipeListener\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0maddress\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mbacklog\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m    437\u001b[0m         \u001b[1;32melse\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m--> 438\u001b[0;31m             \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0m_listener\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mSocketListener\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0maddress\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mfamily\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mbacklog\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    439\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m    440\u001b[0m         \u001b[1;32mif\u001b[0m \u001b[0mauthkey\u001b[0m \u001b[1;32mis\u001b[0m \u001b[1;32mnot\u001b[0m \u001b[1;32mNone\u001b[0m \u001b[1;32mand\u001b[0m \u001b[1;32mnot\u001b[0m \u001b[0misinstance\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mauthkey\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mbytes\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
+      "\u001b[0;32mc:\\users\\triton2acq\\anaconda3\\envs\\qcodes-master\\lib\\multiprocessing\\connection.py\u001b[0m in \u001b[0;36m__init__\u001b[0;34m(self, address, family, backlog)\u001b[0m\n\u001b[1;32m    574\u001b[0m                                         socket.SO_REUSEADDR, 1)\n\u001b[1;32m    575\u001b[0m             \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0m_socket\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0msetblocking\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;32mTrue\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m--> 576\u001b[0;31m             \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0m_socket\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mbind\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0maddress\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    577\u001b[0m             \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0m_socket\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mlisten\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mbacklog\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m    578\u001b[0m             \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0m_address\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0m_socket\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mgetsockname\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
+      "\u001b[0;31mgaierror\u001b[0m: [Errno 11004] getaddrinfo failed"
+     ]
+    }
+   ],
+   "source": [
+    "# Do this in as qcodes measurement (ie the same but makes a data set)\n",
+    "#data3 = qc.Measure(basic_acq_controller.acquisition).run()\n",
+    "plot = qc.QtPlot(data2.basic_acq_controller_B)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [],
    "source": [
     "# Finally show that this instrument also works within a loop\n",
     "dummy = parameter.ManualParameter(name=\"dummy\")\n",
@@ -259,6 +285,15 @@
     "qc.QtPlot(data3.basic_acq_controller_A)"
    ]
   },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {
+    "collapsed": true
+   },
+   "outputs": [],
+   "source": []
+  },
   {
    "cell_type": "markdown",
    "metadata": {},
@@ -270,7 +305,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 4,
+   "execution_count": 18,
    "metadata": {
     "collapsed": false
    },
@@ -279,12 +314,12 @@
     "# make a list of the frequencies to demodulate at (nb these values can be changed later, if unset the default is [20e6])\n",
     "# NB the number of demodulation frequencies cannot currently be changed once an acq controller is created, this sucks:\n",
     "# lets have channels :)\n",
-    "demod_list = [95e6, 98e6, 100e6, 105e6]"
+    "demod_list = [5e6,1e6]"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 5,
+   "execution_count": 19,
    "metadata": {
     "collapsed": false
    },
@@ -299,7 +334,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 10,
+   "execution_count": 25,
    "metadata": {
     "collapsed": false
    },
@@ -315,13 +350,13 @@
    "source": [
     "# set integration time and delay (nb this replaces need to set samples_per record)\n",
     "# if int_delay is unset it will default to a value corresponding to the about needed for the filter to work well\n",
-    "samp_acq_controller.int_time(2e-6)\n",
+    "samp_acq_controller.int_time(10e-6)\n",
     "samp_acq_controller.int_delay(0)"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 16,
+   "execution_count": 40,
    "metadata": {
     "collapsed": true
    },
@@ -335,7 +370,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 7,
+   "execution_count": 22,
    "metadata": {
     "collapsed": false
    },
@@ -343,30 +378,28 @@
    "source": [
     "# update_acquisition_kwargs now should not be used with samples_per_record but instead just for changing averaging values\n",
     "samp_acq_controller.update_acquisition_kwargs(\n",
-    "                 records_per_buffer=1,\n",
-    "                 buffers_per_acquisition=1,\n",
+    "                 records_per_buffer=100,\n",
+    "                 buffers_per_acquisition=100,\n",
     "                 allocated_buffers=1\n",
     ")"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 12,
+   "execution_count": 43,
    "metadata": {
     "collapsed": false
    },
    "outputs": [],
    "source": [
     "# demodulation _frequencies can be changed\n",
-    "samp_acq_controller.demod_freq_0(90e6)\n",
-    "samp_acq_controller.demod_freq_1(98e6)\n",
-    "samp_acq_controller.demod_freq_2(100e6)\n",
-    "samp_acq_controller.demod_freq_3(110e6)"
+    "samp_acq_controller.demod_freq_0(10e6)\n",
+    "samp_acq_controller.demod_freq_1(20e6)"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 14,
+   "execution_count": 31,
    "metadata": {
     "collapsed": false
    },
@@ -374,28 +407,48 @@
     {
      "data": {
       "text/plain": [
-       "{'allocated_buffers': 1,\n",
-       " 'buffers_per_acquisition': 1,\n",
-       " 'records_per_buffer': 1,\n",
-       " 'samples_per_record': 1120}"
+       "8.192e-06"
       ]
      },
-     "execution_count": 14,
+     "execution_count": 31,
      "metadata": {},
      "output_type": "execute_result"
     }
    ],
+   "source": [
+    "4096 / samp_acq_controller.sample_rate #samples_per_record"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 53,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [],
    "source": [
     "# check the acquisition kwargs\n",
-    "samp_acq_controller.acquisition.acquisition_kwargs"
+    "samp_acq_controller.acquisition.acquisition_kwargs.update({'samples_per_record':5184})"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 17,
+   "execution_count": 54,
    "metadata": {
     "collapsed": false
    },
+   "outputs": [],
+   "source": [
+    "samp_acq_controller.samples_per_record=5184"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 48,
+   "metadata": {
+    "collapsed": false,
+    "scrolled": false
+   },
    "outputs": [
     {
      "name": "stdout",
@@ -403,22 +456,22 @@
      "text": [
       "DataSet:\n",
       "   mode     = DataMode.LOCAL\n",
-      "   location = '2017-01-23/10-42-57'\n",
+      "   location = '2017-01-23/18-25-22'\n",
       "      |                     |  | \n",
-      "   Measured | index1                        | index1       | (4, 1020)\n",
-      "   Measured | samp_acq_controller_magnitude | magnitude    | (4, 1020)\n",
-      "   Measured | samp_acq_controller_phase     | phase        | (4, 1020)\n",
-      "acquired at 2017-01-23 10:42:57\n"
+      "   Measured | index1                        | index1       | (2, 5000)\n",
+      "   Measured | samp_acq_controller_magnitude | magnitude    | (2, 5000)\n",
+      "   Measured | samp_acq_controller_phase     | phase        | (2, 5000)\n",
+      "acquired at 2017-01-23 18:25:23\n"
      ]
     },
     {
      "data": {
-      "image/png": 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s7imVybeIGnfBHQCAUbWtJNladCOWmekgAQBYILPKTMuIOwAAi5PNKqM8Zgqm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\n",
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fiLcV/tznYnA7/ni47rp4YWBXfvrT+N/Iz3/eeixJ4t3S9t67cP2+KTU1sXp/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\nAABJRU5ErkJggg==\n",
       "text/plain": [
-       ""
+       ""
       ]
      },
-     "execution_count": 17,
+     "execution_count": 48,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -427,12 +480,60 @@
     "# as with basic...\n",
     "# nb: index1 is actually the list demodulation frequency index but setpoints are ind of broken\n",
     "data4 = qc.Measure(samp_acq_controller.acquisition).run()\n",
-    "qc.QtPlot(data4.samp_acq_controller_magnitude)"
+    "plot = qc.MatPlot(data4.samp_acq_controller_magnitude[0])\n",
+    "plot.add(data4.samp_acq_controller_magnitude[1])\n",
+    "plot.fig"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 18,
+   "execution_count": 35,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "array([  3.59998900e-07,   7.23784543e-07,   1.81824540e-06, ...,\n",
+       "         3.18445386e-04,   3.15040241e-04,   3.14722229e-04])"
+      ]
+     },
+     "execution_count": 35,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "data4.samp_acq_controller_magnitude[0]"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 45,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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TXMbI2DwPSfpUYhnjJU2TdGlW8JWksWPHatiwYVn/boMT3oD8am6m5xcA1pa0\n4uOUKVM0fPjwLl2PjrQ9XClpfCkEPyMbtaFOFkblnLtE0hDvfRjL91pJpzvnLpP0O1nIPULSQbFl\nXiXpEefctyX9VdIo2Yl1J0uS936ZpJfjK+GcWyZpgfd+WgfuQybaHoD8ovILAKg5/Hrv7yiN6TtG\n1prwgqQDvPfzSrMMlrR5bP53nHMHSxorG9LsXUkneu8fis3zlHPuKEk/Lf28Lukw731Z4E2uSq3r\nXo1KlV9GewC6t0rhl+0bAIqhQye8ee/HSRqX8b/jU6Y9JqvkVlrmJEmTaliHfaqdtxbtVX45IQbo\nvhjqDADQoa83zjN6foH8YqgzAADhN4GeXyC/6PkFABB+E9qr/HpvPwC6H9oeAACE34TmZrvMqvxK\n7CCB7oq2BwAA4Tehqcl2gmnhN0zjpDege2pvnF+2bQDIP8JvQlOT1KtX+v+o/ALdW3vbN9s2AOQf\n4TeB8AvkV6XKL+P8AkAxEH4TCL9AflH5BQAQfhMIv0B+NTcTfgGg6Ai/CYRfIL+amhjqDACKjvCb\nUCn8MtoD0L3R9gAAIPwmUPkF8ou2BwAA4TeB8AvkF20PAADCbwLhF8iv9rZvWpoAIP8IvwmEXyC/\naHsAABB+Ewi/QH5Vanvo0cMqv9537ToBALoW4TeB0R6A/Krm4JbtGwDyjfCbQOUXyK/22h4ktm8A\nyDvCbwLhF8iv9kZ7kNi+ASDvCL8JhF8gv6j8AgAIvwmEXyC/2L4BAITfBE54A/KLtgcAAOE3gcoQ\nkF+0PQAACL8JhF8gvypVfsP05uauWx8AQNcj/CYQfoF88t623aztu3dvu1y1quvWCQDQ9Qi/CYRf\nIJ9CRTdr+w7Tm5q6Zn0AAGsH4TeB8AvkUwi1WW0PhF8AKAbCbwKjPQD5FEJte20PhF8AyDfCbwKV\nXyCfqm17oOcXAPKN8JtA+AXyibYHAIBE+G2D8AvkU3ttD4RfACgGwm8C4RfIp/baHhjqDACKgfCb\nUCn8UhkCui/aHgAAEuG3jUrht08fu2xs7Lr1AdA5GOcXACARftuoFH7XWUdyjvALdEcMdQYAkAi/\nZVpa7CtQs3aOzln1l/ALdD/Vtj3Q8wsA+Ub4jWmvMiQRfoHuirYHAIBE+C1D+AXyi6HOAAAS4bdM\nex+LSoRfoLtqb/vu0cOGM6TtAQDyjfAbU03lt3dvwi/QHbXX9hD+R+UXAPKN8BtD2wOQX9Ue3FL5\nBYB8I/zdWFDpAAAgAElEQVTGEH6B/KqmrWnddaWVK7tmfQAAawfhN4bwC+RXNW0PdXXS8uVdsz4A\ngLWD8BtTzc6R8At0T9VUftdbT1qxomvWBwCwdhB+Y6j8AvlF5RcAIBF+yxB+gfyqZvuuq6PyCwB5\nR/iNIfwC+dXUZF9R3qPCu95661H5BYC8I/zGEH6B/GpurrxtS1R+AaAICL8xhF8gv5qa2g+/VH4B\nIP8IvzGEXyC/mpoqj/QgccIbABQB4TeG8AvkVzVtDwx1BgD5R/iNIfwC+VVN2wOVXwDIP8JvDOEX\nyK9q2x6o/AJAvhF+Ywi/QH5V2/ZA5RcA8q1D4dc5d7pz7m3n3Arn3NPOuV3bmX9v59xk59xK59xr\nzrljU+b5qnNuWmmZU51zByb+f55z7hnn3GLn3Bzn3F3OuW07sv5ZCL9AftH2AACQOhB+nXNHSrpC\n0gWSdpE0VdIDzrmBGfNvJekeSQ9L2knSVZKud86NjM2zu6QJkq6TtLOkP0v6k3Nuh9ii9pL0S0mf\nlbSfpF6SHnTOrVfrfcgSBsHv2TN7nnXX5WNRoDtqbm6/7WG99aRVq6SWlq5ZJwBA1+tI5Xe0pN94\n72/23r8i6VRJyyWdkDH/tyS95b0/13v/qvf+V5LuLC0nOEvSfd77K0vznC9piqQzwgze+4O897d4\n76d57/8t6ThJW0ga3oH7kKracUCbm+0HQPdRbeVX4gAXAPKspvDrnOslC5sPh2neey/pIUkjMq62\nW+n/cQ8k5h9RxTxJ/SV5SR+0u+JVqjb8StLKlZ11qwC6Qi3bN+EXAPKr1srvQEk9Jc1JTJ8jaXDG\ndQZnzN/POdennXlSl+mcc5J+Iemf3vuXq1v19rFzBPKr2rYHie0bAPKsnV3Bh9Y4STtI2qMzF0r4\nBfKrmu173XXtkk92ACC/ag2/8yW1SBqUmD5I0uyM68zOmH+x976xnXnaLNM5d42kgyTt5b2f1d4K\njx49WvX19WXTRo0apVGjRrWZl/AL5BfbNwCsXRMnTtTEiRPLpjU0NHT5etQUfr33Tc65yZL2lXS3\n9N8WhH0lXZ1xtackHZiYtn9penye5DJGJuYJwfcwSZ/33s+oZp3Hjh2rYcOGVTMrlSEgx2h7AIC1\nK634OGXKFA0f3mljF1SlI20PV0oaXwrBz8hGbaiTNF6SnHOXSBrivQ9j+V4r6XTn3GWSficLuUfI\nqrfBVZIecc59W9JfJY2SnVh3cpjBOTeuNP1QScucc6FS3OC975QoSmUIyC9OaAUASB0Iv977O0pj\n+o6RtSa8IOkA7/280iyDJW0em/8d59zBksbKhjR7V9KJ3vuHYvM85Zw7StJPSz+vSzoscTLbqbLR\nHR5JrNLxkm6u9X6kIfwC+dXUZF9SU0n4ZIftGwDyq0MnvHnvx8lOOkv73/Ep0x5TO+Pxeu8nSZpU\n4f9r/KuYCb9AfjU3SxtsUHketm8AyL81Hii7Ez4WBfKL7RsAIBF+y9RywhuVIaB7qeaEN7ZvAMg/\nwm8M4RfIr2q27x49pN692b4BIM8IvzHV7hz79GHnCHQ31WzfkrU+0PYAAPlF+I2pZedI+AW6l2ra\nHiT7dIftGwDyi/AbQ2UIyC+2bwCARPgtQ+UXyC+2bwCARPgtU+3OkY9Fge6HtgcAgET4LUNlCMgv\n2h4AABLhtwzhF8gvtm8AgET4LUNlCMgv2h4AABLhtwyVISC/OLgFAEiE3zKEXyC/mpvZvgEAhN8y\njPYA5FdTE20PAADCbxkqv0A+eU/lFwBgCL8x1Ybf9deXli1b8+sDoHO0tNhlNZVfwi8A5BvhN6ba\n8Nu3r7RkyZpfHwCdo6nJLqn8AgAIvzGEXyCfCL8AgIDwG1NL+F26VGptXfPrBGD1NTfbZTVtD3V1\nhF8AyDPCb0y14bdfP7tcunTNrg+AzlFr5bexkYNbAMgrwm9MLZVfidYHoLuoNfxKfNEFAOQV4bfE\nezsjnPCLogktAXlWS9tDCL+0PgBAPhF+S0JlqJqdY1eH37vvZmg11G7xYunCCyuHuPfeswO+u+/u\nstVaKzpS+SX8AkA+EX5Latk5dmX4feMN6bDDpDFj1vxtoXtYulTadVfp2WcrzzdhgnTRRdJ992XP\nM3WqXd57b+et34cR4RcAEBB+S2rZOYYT3hoa1tz6BO++a5cvvbTmbwvdw+uvS889J/3855Xnc84u\n33gje54ZM+wy760PHWl7WL58za0PAGDtIfyW1BJ+N9xQ6tlTev556cEH1+x6zZtnl42Na/Z20H3M\nnWuX7b0mwsHZggXZ84RPL8Iy41580Xrh84DKLwAgIPyW1LJz7NlT2mQT6Sc/kQ44wCpxa0oIJR2t\nzHVmeHn4YWn+/M5bHjpm9my7bG8orkWL7LLScxbC75w55dP//W9pp52kX/+6Y+vYFd54o/rWo7D9\nEH4BAITfklrCryRtumn0++OPS08+uWbGBQ2V3470F7/yirTxxtI//7n66/H++9J++0lnn736y8Lq\nCUG1vcrv6oTfd96xy8mTa169LrFkifTxj0unnVbd/LWc0Er4BYB8I/yW1FIZkmzHG/zoR9Iee6yZ\nk4ZC+F28uPJ8N94oDR1aHoief94+8v7b31Z/PV57zS5ffHH1l/X449L06au/nKIKld/2es7D/6sJ\nv3Pnln9KEG7jwzrWbfi05YEHqpt/1Sq77N27/XkJvwCQb4Tfklorv7/6lXT77dKXviTNmmXT/vOf\nzl+vasPv5ZdbpfeVV6JpoZr3wQervx4hQLW0rN5yvJc+9znpoIMqzzdjhs0Xvz9d6fnnO+eEp7lz\npTff7Pj105678Ly295qopfK7YkX5NxaG13RaL3BXaK9dp9b1CgeFffq0P29dnV0SfgEgnwi/JbWG\n3379pCOPlHbcMZr23nudv15Z4behQbrrrujvMA5wfB3WRPgNj1NHLVxoly+/XHm+J5+0CvGdd67e\n7XXEggXSsGHSGWes/rIOOUTaZpuOXfc//5E22kj6wx/Kp1db+Q3hN7yG0ixdGo1eEm99CLexNnq8\n77/f1untt7PnCevao8p3sFoqv3362EgZhF8AyCfCb0mt4Tf4xCei30NgqNayZdJbb1WeZ948G1d4\n5cpoBy5J55wjfeUr0VBVoVoVhkaTooAQQtDqCCGoljD0/vtt2yTef7+226t2/s40bZpdtjeObjXC\nMtqrIqcdoDz1lF0++WT59PC8VhN+6+vtgCOrP3jJEuljHytfrrT2w+/SpdK//pU9T6j8xqvVldRS\n+XVOWnddhjoDgLwi/JZ0NPyOHCkdfri02261j/t7+ukWPCrtZOfNi8JJvPobTkgKX1IQljFzZjRP\nCAidMR5xCEGLFlVf/d1tNxsxIP4RdrVhNlQrK1Ut15QQ/KqtKlYj/rwkhQrv7benXyd5UDV7tr0m\nli2rPApIQ4O0/fb2e1abwJIlUWU6Pk9oe5g3b80Nd/bSS1ZdT7bShNdXeI2nCUF92bLqhgGspfIr\n2UFDV4zjDQDoeoTfko6G34ED7aP5oUPb78EMWlps2LCbbrK/s3qFW1vtI/gQfkN/5sKFbT/SDjvq\ne+6RvvxlCzJrovKb/L2SEN7SQpVUObSE+7U2Ko/heay2qliN5GgKca++apePPlo+PTwG8cesqcle\nE9tu2/46LloUhd8QoFetkk49NTphbMkSacstbfi+ZOV3yy3tOUp+tfacOe1/YhG3YoX1xj/zTPn0\nc8+13vlkC0xYj0rjE8dfU5XmC8Jrrdrwu+GGndMuBAD48CH8loTKUDUfi6appVI0frwNGxZkjXww\nf74F5VCZW7LEqmUbbii98EI0T2trFIxfeEH685+lm2/ODr+trdJVV9XWUjB/vjRkSPR7LeKhKn6b\n4fGaM6dtdbEjbRYd8fbbth7bbBONaRvWq72DhnhP6L/+JR14YHkVP16VrVTBDiErGbZCwIs/fuH3\nEH6zDriamiy0hvAb2mNeeUX6zW9shBLJXjf19TYkXli29xZ+P/Wp9HXfddfogKwaU6faQdl555VP\nD/3fYSSR5H2s9NzPnSsNGlS+nEpWrbJhzqqt5g8YUN1yAQDdD+G3pNbKUFIt4fe558r/zqpchf7d\n0Fe8eHHUBxrMm2fVP++jMCBZFXHOHOmjH20b4qZMsZ7h73+/uvWVLIgMHRr9Xot4lS4Zft94Qxo8\nWLrhhvLrhMDVXlVv0SK7P+++K/3iF7V9RL9ihbT11hYQ33xTuuIKmx4C5cKF2aNbtLZan3UIkVdc\nYb2q8TGV4+GpI+E3XCf++IWguMsu5euaFKZvu620xRbSZZdZGA8hOAT3pUutp3zjjaXf/96mNTRY\nj3kIv8nnO1T0kxXhuG99S7r1Vvs9jHaR9alKsq2jmsrvvHnRAUClg5QZM+xAsLGxtgNbKr8AkF+E\n35JaewKT+vWrvu0hBMBvftN6PbN28lOn2sk3O+1kfy9eXP4ReI8eFkzC7Q4bZpc77ST99a92nz7+\ncQsp8RAXPrKOnxzXnnj4raYPN94XnAy/H/mI/d7QEIW5e+4pv/68efZR/Pz5lQPt6NHS8OHSySfb\n7+EEs6lT7fGo1GsbHocQvtZdN1ovyW43q/oXnrOrrrLLUPGNt7DEn9dKBwxhvmRf7vz59rr64IPo\n8bzhBmu1ib8m0oRAOGCAdM019rg8/ngUfnv0sNfE8uXSBhtIRxxhJ/rNmhW9xtIqv/Fxf5M9uXfd\nZZ88fPCBdO210imn2PTweCYfy6zqfrXhN4y1vXChNGmSdNxxbb9o5qSTpGOPtftdy7ZN5RcA8ovw\nW1LL2eBp6uutilbNOLizZkknnCDdcotV3LKC0e2321i3H/2o/b1kSbRDPuQQad99LSCEAPS971mV\n6+c/jwJj2sfjoc2ilhN65s+3KmmvXtWF33hwiX9sP316NDzcokVRdS1ZvZs3z8J2U1Plb7cLofn+\n++0y9M/++c82Vu9991W+T3Hh27/ij1VWAAuBOfTchvvx2GNRKAzXda66ym9yngULyg84mptt2LNz\nz7UALJU/h889ZwGwpSV6PPv3lw4+2CqZjzxSXrUN696vn3Toofb79OnSMcfY77vuapfxxyleDY1X\n8RcvttFHvvzlttXl8HgmD0TS+rpXrrT5K1VevW9b+T3nHOuhT/Yih/ag55+vbdseOHDtnGwJoPvw\nfs2dEIw1i/BbsrqV3/p6u6ym+jt7trTJJvZ7fCc7aZJ9dC9ZBevxxy3krr9+tOxFi6Tdd5f+8hdr\nc1i4MApAH/mIdPTR0ogR0W2lVQhDOKnmRCHJQsyyZbauAwe2DY3jx0t77lke/OPBIdzOxRfb1+XG\n1ymE+fgyw4l+1bRZJE+aC8E+XP7739KYMen9zclwEx7HhoaovznrMQrV0RCYwzr+6U9RS0KYtv32\nlYNUuI1586LKpfc2fYcd7O85c+w2m5qsDSb+eluwwNoMRo+2APj661H4ra+3Ku+ee0oPPRQ994sX\nR6+Jvn2jr+t+9NGoLWfrra0qHF/3eCCNjykdljt9ennI9T66ndmzo+1s1aro8U47UNp+++zK65w5\nFpI//nELtAsXRusSTuQLwre1vfVWbdv24MHln7IAQFxTk/TFL1pbWbIdER9+hN+Szgq/7VVTw8lE\ngwfb3xtuGO3kjzjCAoz31pKwYoUFwB49LKCEsNi/v80/YICFnBAuwpcVrL9+9HvoF25osI11zJho\n3NhqexpDlXPw4PSK2GWXSU88EVXZpGie/v2jEHj99XYZKotLlkTrEA9Modc2rPsNN9jJZMke02XL\n2laFQ5AK63zNNdIFF0hXXtn2fiVDdQiMixdb8IvPkzy6D8vv2TO63XCQsmiR3YewLttu237lt3dv\nq+yG18KSJfZ3OACYOzdqU9l8c7st52xd77rL2gxCv3E4iU+KXivHH2/P+4QJ0X0Mj13fvvY6lKLX\nxnPP2esu+clEVuU3/vzFv9FuxYro9em9dOGFUbCXrNUkvvx4+M3q5f3ud+3y05+2+7dwYXQiW/Lk\n0XAfZ86srfK7ySZ23Up9zVgzli6Vfvc7ady41f9GyUrmzbP3ls4YDQfF88c/Sg8+aO9n3/zmmn2t\novMRfksaG2s7Gzyp2vAb+jdD5bd//7Zvvu+9F4WAUJHr1892xosWlYffeOU3BF7JRoV44gmbR4pO\nlrvgAjtBrK7OdjLxL87IEqpqm25q4TfZmxpCRWg5kKJ5hg6Nws2SJdIll1jbQ+/e5W0c8UpkCIoh\n/P6//2dtDQ8/XH674TEKoXOddaJwlqwavvSSXY4ZI/3f/0W3M2iQjYDws5/Z49jaapeh1WTBAvtI\nfcSI8gAcwm9jowW8hQulffYp//+CBfa62GSTtkH7ueeiZSQrvGGaFI3WMGdOeR9v/IAo+VH/7NnR\nvOE1cdhh0l57RfMkw2/PnvaaCM91WJ+BA6VLL5X+9rfy9RoyJDv8xoc0++ADu63Q533JJXbfw3O8\n3XblgTo8JtttFz22jY3lQ7o98oh01FFWcRkwwE6aDDueUIGWyg8mvK+98htfH3SN5cvtoOakk2wc\n9B//ePWWN3++9I9/tA0m3luLzkknWdGhFq2tbXvLUTy33SZ99rM21Olbb9nrDN0H4bdk1aqOV32l\nKJC2F37jVVQpCrDxN+cZM6JAECpy4YS6hQujQBuCc/zj62Dzza09IoSfhobyk7FCP2c1rQ9PPmkB\nd8stLRT94Q/lASdUx+JfRztvnj2eH/2o7YAaG+0+hfvdt295+JWk006zwBKCUQh+QfhCjyB8LB1O\nrtpvv/KRE+KVvnfesR3eBRdYmG5psdvZeGMLWltsYf9futQez4EDbR3nz7eT2v71L2vvCCEwHope\nf92ue/zx0r33Ro/rggV2QuPGG5dXfpua7PE/8shoXUOFNxl+N93U1mPu3Oi1FQ60wmsiGawbGuxx\n7ds3astwTvr61+33nXdu2/YQLmfNsiAcTv4L20ToCQ6P7w47ZIff+DfjhYOzXXaxx0Kyb/0L67zt\nttF9nTbNQroUPfcLF9oXyXz+8/Z3Y6MF9JEj7e/+/aMDm969y9cj+dqutfIr5Tv8vvJK5X76jpg7\n11q1Lr+8Y72QV11l2+oLL0g/+IGNA93Rb9prarLXzT772LYZN2WKva+dcIIdVD//fHXLnDbNPhXq\n2dPeX7v69bF8eeWv/UbXWLDAzif5xjcsAA8ebG1l6D4IvyWrG35DIGnvI7QQ2JKV33gInDkz+juE\n31DlS1Z+ly2zING3b3rVOl6Rjr9R7767XVbT+jB+vFVH1l/fqjFSNCyYFAW2J56wo2EpCpbhY/Mw\nT7jfIfx+8EF0Atxtt9kywnqGvlvJdjbxqp4UzXfQQTZ27aBB5ZXf3XaL5p0+vfwxfv99W69w4lj8\n+WtosGA5cGB54D7hBGn//aPWlRAQQ8V74EAbeUKyN8ew/BB+QxgI9+Oxx+wyHn5DxTzcj402svs1\nZ46tl3PWhytF4Tf5HDY02Pzxoe8kOxnuiissCCxd2vYTg7597XHp29duR7JWlU98wg5KVq2y+9W/\nvx0sxHt+Z860++6cVWJD5TxUfjfc0O7bxhvbY5dW+b3rLrusq4uC8sKF1vs+ZUp5m0x43gYMiA7q\nRoyQJk601gopuo3w6Ul4vqoRXqdrou/3iScqf+lJV3juOXvNhQOwzvLTn9pIM9//fvQ8VMt7e6/5\n6lftPeHkk+21c+edHVuX666zsHrccXZycbwf/C9/sW3+mmvs9V7pxNhg9mwL03V10tVXW0j/0pcq\nf8tiZ1qwwA5ct97aTm7+MGpp6brHY236wx/s9fq1r9l73j77tP1ksihaWmxbHT7cPkHtLicAEn5L\nah0HNKma8LtggVW9pKgCGsJvPMC8+6793atX9JF+aHuIV37D5fTp0e0n1dVZcAwVwm23tfFXv/GN\naJ1aWqytIK1nac4c24GEyt/QodJZZ0X3Y8WKqHp0333W+zRligWPj3wkOkEuWfGOV7JDe4Nk/aIz\nZljAGzDAdi6bbWaXaeG3V6/ocQgjBIQhyr70Jel//9dOIly5srwn+d13o4AuRdXPUPnt188C2COP\ntH1MZsyw2w6tAWG4to02ig5WQvjdaCN7DFatsmW//LKFnyCMqfvRj9rrL6zjjBn2pjpokE3/+c+t\nOtWvXxRMw2O4YIF9VHz88VadDwc6yfBbVyd9+9tRC0J4TuKVXykK15JVYK+4wnZos2ZFPeebbmrV\n/8mTbb6ZM+2LL0JojFduw+PZo4fddvj2wfDJwOLFVqV75x1pq63svofnNN7S8fbbbT8R6d/fHtse\nPewxaGmRLrrIrhfC7yc/GT1e1RowwNavM8Ov9/Y47bmn9IUvdN5yOyL0wN93X21fdlPJqlV2APu9\n79nP2LHVffV08Oyzti2FcwK23tpCRXIM8Go0NNinPMcdZ19e07dv+deH33efdMABdkLk7rtXd8LS\n5ZfbfXz0UenMM+3k1smTLUB3hdNOs+3pnHPs/eDpp7vmdqu1eLGdzLzVVm2/zTFvJkywTxrDe+y+\n+9proYhfiX777VYkGTDARiL64x/X9hpVh/BbsrqV33XXtZ9KL/5ddpG+8x0LRHV1Nm3AAPsoK16V\nnTnTdvIbbhgFnf79bWe+ZElU+Q2XM2Zk79ids/81NFhI2mwzC74hAC1YYKNMHHigHc0mhfARgp5k\nVb+ZM21nHipYn/509P/HHouC5UYbWQgMISKt7eEjH7FgM2CAfRT7ox/ZbThnX7zw739HoSguBLzw\nGIUWkuXLLUwNGWItEeEj83jonDmzvPIbAl9Dg4XU+npb9/feswOQeCCcNs1uO4T2EH4HDrQ2g379\n7HENJzaGgD1njo09fOyx0bLCR/YbbWQB7tJLbbi6k0+2aeuuKx1+uM3zpz+VH+TU10eV3912s5OE\nBg+2x+Cxx8oPKuLCfXnjDXsNhdd9vAIcF6+CNjTYdUJVfp99rGL7z3/aayucKBgfhmzx4mi9QxV8\n1ixbRqjwLlpk4XfYMJsWXtvxj6PffLNt+A0hedNN7faD//wnCr9hvOJawq9z9lh2VjBcvtxC+BZb\n2N+hVWZteO0129YvvNAOHu+4o3OWO2mSve6PO85GnVmyJP3gMcstt9hrIt47f+KJ9lpOjuLRnhtu\nsNu/+GLbhg4+2NZPsumTJ1tgkey967nnKj8fCxfae8lZZ0Xb82c+Y5+EnXde+fkOa8Lrr9tzdvHF\ndjC67bbln759GJx+ur2nrLeevecmv7kxaeVKe31UO+pQV2pszB5tZvp0+zQqFJAkO5/C+w/vqA9z\n5rTdf3aG5mYrNnzpS9YSOHKknQDfHRB+SxobVy/8SraDT6v8PvOMhZzQjxg/WSLs5MMZ8p/4RFT5\nDTt4yXaaodqaVvmttGMPISke9sJ1Fy6M3qReesneiEaOjIJi+Bg+XkXcYgtrt1i0KArtoY1Csv7Y\n8BH3wIFWjZs2zcJduP1428OAARZuN9vMdnQrV1q/n2RvpKHSGP+YPaxbfL2SVfRwH7fayi7DSAY9\ne9qy4pXfEAjDbdTXR+0Yu+1mvYhf+5odtLz8st3v7be3kBQev/B8bbhh9Nhsskl0G0891bYSFtoq\nNtww6ic+4wy7vOQSu7zoIrsPc+aUh9945Tfcdn29PYbTp9tJYWnCpwkvvxyFMaltBTiIn/wVwu+R\nR1oFM4zvu2qVVX5DxXeLLeyxCuE3vD433tiet+nTLejEK+Vvvx21S4TnLt528tZb6ZVfySreIeRK\ntj3Nm2fhbrvtbFoY9qxan/iEfYrRGR5+2B7vujrpf/7Hdhqhd3PyZOnss21M7/AaXZNOPNFeT9/9\nrh30hhFAKmkvqHtvld799rMD5U9+0l771X4UvGqVVZCOOioaQUWy11b//lE7VbXuuMPuWzhIO/xw\ney29+aZthy0t9nhL1n8/e3blA50JE+yA+rTTyqdfdpkdrF19dW3rV6trrrFt59hj7X109GirsH1Y\n+n9vucU+UbzhBnucBw+O3sPT3H+/vXd/4Qv2mol/ec7adtNN0Qg4RxxRfrKtZK1V661nJ0wG22xj\nRZzHH+/ada3Gs8/a+m25pfXQd6YJE+zA7MILbV/4v/8bfYLzYUf4LVm1avXaHiR7k05WfltarCE+\nvmOO74ST4XennayKNm9eefjdcsvoCDne8ytVbnuQyiu/odIWKpQffBB9KcNrr1n18KGHouA1d669\n2cbXZfPN7XLmzKjy++1vW9/tGWdY+H33XQusofr461/bm13YscUrv/HQ+PLL9ns4oSkYMsTmj5+g\nM3duVMEOj0tjY1Rljp8Y2K+fBYuBA+2xDH2nyfAbjo432sg+XvzJT2wDP+EEq0IPHWrVyIUL7f71\n72+PW//+0cll/fvbG0JoBQgBffz4aF3D4xDC3UYbSXvsYTvkJUusf/Wkk6L5w/2MB9ONN7YddvxA\nqb4+qtaHj/uTQvj929+iHuX4suNVbimqaM+aZWG2vt4e25tuKp/vM5+JKr877hgdjIQe6rDOTz9t\nO5Ddd4/We/58ex2HA5X11rPt8cUX7fJTn7KwdvPN9v/kAeCWW1olb9kyC1+h7WHgwKhyXesOdp99\nbCijiy5K/39ra9tQ+Pvf27aQdP/99tgsWxbtgMLB7OjRFp4ef1y68cba1rFWr71m7y+XXmqvg6OP\ntp3VtGnp8zc1SXvvbWHmxhvtk5k0Tz9tyznnHPvbOXs+/v736tbrgQfsNXD00eXT113XnodaKsjv\nvGPvQV/7WjTtwAPtNTVpkrUthBNdpWgbSH7tfNwtt1j1OBwIBnV1diA4adKaG+qqqcm2l6OPjvrW\njznGtq9x49bMbdbijTfsoODoo60aWlcnnX++fSIUXuNxN91klcI997Tt+ZVXpFNP7fr1TrNihW2P\nBx9sj+0DD9hjHS9YTZxobYDx92Ln7P7Ev97+w2DGDDvw+/jH7ROZs86KWtVWV3OzjZ705S9H3y4b\ntjy61UsAACAASURBVLNw/saHGeG3ZHXbHqT0YctCmAvOP9++fSyI9zbW1dlHcHPm2FF0PHCGUBG/\nTgjBS5dWV/ldsCCqvEpRj2w8/N56q/0ednJz59p14tWYZPjt0cOqtqecYgHu7bftZ8stLSzuvbdt\nhCGESNEIBo2N5T27S5ZE48vGhZOW3n/f+mIvvdRuOx5+w3JCNST++G21lT0GQ4bYDuyNN+w5T7Y9\nxMPvkCHWghG/jaFDo7N6N9kkegzj69u/v31E2bu3vRkMHmwVrL//Papkn3ee3WbYOYR1DYE13mYS\nv2/xYLrddlatX7EiOqiJ94jH739cfBnxg7Jw3WTlt0cPC/DxtgcpqtJK9vr99KetF/K3v7WPPfv3\nt+eosbE8/IbRQX7wg2gdX3rJdvLxZYZhzLbYInqe/vhHW1avXvZ3eC2Gloe6OnvdzZgRHdxss439\nL3wdcrVOOcUC3IUXWutNnPf2UecBB5TvGEeNsgpoMkw+8IDNK0XjZU+datedMiXqT13TfZx33WWP\n0UEH2d+HHGJ/J79eXLL7+L3vWVjs2dMOAHfayUY0SQ71dfPN9rgfeGA0bZ997L4lPz6eN896BOMj\noEyYYK/F8GlL3Oc+Z49Ltf3Df/iDhcQvfSmatv76tm6TJtlB3777Ru1Sm25q209aUJPsPf3ZZ+2x\nSvPVr9rrvL2q32OPWUA655zaTgp78EF7rOIHBnV19vdNN1V/UBcOejqzj72pyar1gwaVVxW/+U3b\nli+4wP6ePt22p5Ej7XV+/PG2Hzz6aLveTTd1zacewdKl9qlE8nV81132er38cvvioNtus3azMWPs\n//fea6+T0Jcet9de9ilvLX3ua9Irr1hBYtUqu1/XXWc5IhS2Vtctt1jRLn5ia12dvc/dfXfn3Maa\nRPgtWVNtD8lKyUUXlffHxiu/G25o4SH50a8UfWuYVB7YQiiNz5vUr5+tV/zjcSkKbuGLAaZOtQ17\nv/0szFx1le304+FPsp33OutEJ35tvHG0Hp//fBSi9tjDdjCnnGJ/xyvrfftGQTMefqXy5QUh/P79\n77bc886zjy+TlV8pCr/hb8l2zFIUfkOYCaF1vfUs5IUKfPwgIW7o0KjV42Mfi0JnPNiH2x02LJp+\n8sl26Zxdf8wYC27PP2+3Gyr3IaiFqlRymeGxlcqHgguPXfj/ZptFO/ek+DLij18IxcnwK0VtJ6Hy\nG+7Lgw/afQiv2Q02sPvaq5etc2j1iYffsO4bbRQ9ft/6ll1+7GNt79N225Xfl/hr+ItftI8m45Wj\nLbaw19Z779l6b7edBZ7vfz/98cjSr5/t7AYMKD9ZSrIDxieftOWeeqrtYFasiCrBYSSPlhab5803\no/DrnIW8p56y1+qyZVbp33lnC/thh/zqq7bN1tqrd9tt9ngmK5E/+5mNxrDfftGnT+uua9vTo4+2\nXc53vmPvAddcYwfG995r1z34YGsVCEOQtbZaQPjKV8pHnNlnH3s84lXbt96y97KTT7awsGSJBZG7\n77YDhzR77WXvz5Uqs4H3FqQOPbTtJxiHH27h5Nln7XUTOGfBOyv8PvKI3cfQI5z02c/atpx2zkQw\na5aNFDN3rvTLX9ZWsb3rLnsNh2/GDE47zarloWARfPCBHSyE19HKlfYp0l572bjJe+yR3c9aq/PO\ns+1/woTy941evexTsz/9ybbH7baz53j99e3g8De/iT4pO+44e+2fc07XfFFEQ4O9fvfbz9Yxbvx4\ne5zCgfKhh9o8F10UjT09cmT5QV6w5572WHdWZXV13H+/teuFg7ott7THO5yQlizK1aqpyR6Xr3yl\n7evy0EPtvS35fQAfNoTfkuXLo5PQOiqt7eH996ONPIytm7yOZDvHjTayecNOIB5M4sN+hWDmXHT9\nrCqfZCdHPP203ccQNsJ1wsfN8TaDs86yncg559jHuMkqbM+eFnZef90qHvGPAjfZxJY3e3a0UYSd\nRvwM9759owOFeNuD1Pajxfj9P+208h1steE3hKqttrLlhxNo4o/lBhtYBWzAgPIwGxcCzPrr2xtK\nWOf4/PGTsILPf94etyOOsIMA52yH2dhY3g6y77627HjVKr7M+A49fkJbsvIbv+9JHQm/m21mrSzx\nyq9kr5udd06/nf79o+AWAnN4vEN7Q+/e5a0X8fAbDgSGDbMdZqhSx7fT+noLHWF5ku1sn3/eKkvh\ntbTffm3DUDV697bn4p577HV13nkWcsPB0xlnWEXlhhvKq72hZ/7MMy301NfbOgRHHmnV4FCB/dSn\nbIe7cqWF9sWL7QAhtOlU20vovQXLa68tD7RTp9qOb8mS6GA0+Oxn257wNW+ehd6LL7Yd/gYb2A4/\nfJvgSy9FBxNPP23b+1e+Ur7cLbe0SlO89eHMM+097h//sNfTMcfYTnTlyuwe9R13tNuv5iPlp56y\nkx1PPLHt/w491LaZ3Xdve1s77pgdfh9+2N7v4p9KxDlnwfquu7K//OL6620bf+YZe3+/6qrqTnhs\nbbVh2cL413HbbmvTL700GobwoIPsvWDECHvd3Xyz/X7bbfa8vfqqBd8f/rD9227PHXfYSXc/+5lV\nGJOOOsrC5De+YQddb7xhYficc8oPZnv0sNfas8/adt6e5cttGZ/6lL3WV6yobb0vvNAOyvff39qN\nQuX8/fftuU5WdX/4Qxtd4+677SBm3Lj0wsLOO9t7a2e1Prz4ou0Pjj8++wB4/vyoDU+yTxR+/nM7\nQN1rr7YFomOOsffYM89cvRNub77Z3g9DZT/u4IPt8q9/7fjyu4T3Ppc/koZJ8pMnT/bVOPRQ7w8+\nuKpZM515pvfbb18+7dxzvf/Yx7z/xz+8f+edttdpbfW+Rw/vJe+/8AWbdtNN9vf555fPay/X8mnb\nbGPTLrsse70efTS67j33RNO/9jVbX8n7Sy+N5lm2LPpd8v6rX227zEMOscfr8MO933//7NsO3nnH\n+1Wror8vuyxa/rRpNi2swwEHpC8jzP/cc95/7nP2++23R/+fOdOmjRzp/QYblF93wgT73wUXeD9m\nTLSsWbOieTbZxKademrl+7Jsmffvv2+/f+Mbdp1zzon+P3q0TTv77PLrzZzp/fLl0d9nnGHzDRtW\n+fa89/6889quW2trdD/eeMOmXXSR/f3FL2Yva/ny6HrPPx9ND4//977X9jpnneX9DjvY43rFFe2v\nr/feH3WU9/X1tsxnn7Vpf/+7/f2Nb0TzzZnj/W9/6/1tt5Vf/8Ybvd900+j18be/2XW33rry7d56\na3T/rr++unWtZnlf/KJdXn659xdf7H3//vYc7LOPbQvjx9v/TzzRLl991dZ/2DDvX365fJmtrdHz\n36eP/f3qq/b3ww97f8cd9vt++9nld75T3bpOnhzd9zPPjKZffLE9d42Nba9z1102/7vvRtNuuMF7\n5+y5SXP11XadX/zC3rc239z75ua28518sr1uvPf+pZfsOuF5/s1vonX90Y8q368DDmj/faa11fs9\n9vD+k59MXxfvvW9psfmSrrvO3ofj22cwdKj3J51U+bYfe8zux5NPpt/m5pvbY+F99Dp+7rlonkWL\nvP/9772fO7f8uk8+afM+/nj67b74oq33XnvZa23gQO/HjbP707+/XfeTn/T+X/+KrnPBBd6vv773\nixdXvk9Zliyx/VrfvrZvSHs8O+J73/O+Z097LLO0tHj/la94X1fn/QkneL/eevZ+n/a8pZkyxW7j\nkku8f+UVe3xuvdX+N26c9+us4/0HH6Rfd+lS799+u/Ly99139XOE997Pnu39kCHeb7ml94MG2b7p\n9dej/7e22nbUp4/dh549vf/4x+0nvIdnbQN//avNc+WVHVu3hQttvdJyQfDZz3p/5JGVl/PGG/ZY\nbbCB9zvuONlL8pKG+a7KiF11Q139U2v43XdfC4OrI2w8P/uZ98OHW9j75jftjamSDTe0Z+KII+zv\nf/7T/r7wwvL5Jk1qu5MYNszmve667OW3tEQ7maeeiqafemo0/Zln7DJsuPHwmxYGv/1tC/V77OH9\nMcdUvn9pfv3raPlhB3v99W2DUdy669r/W1ujMPjvf0f/X7rUpm2zjfebbVZ+3YULvT/uOO+nT7eg\nFW47/gYxeLBNmzCh+vvx/e/bda6+OpoWAmilAxLvvb/vvsr3N+7yy23e7363fHq4HwsX2t8//7n9\nXem1HA/N770XTf/lL23amDHpt9+rl/1//Pj219d77087LbqdV1+1aQ0N9pp58cXqlhH38su2rM03\nrzzfBx94/z//Y9tRZ5g9u3x7+Pzn7Y09bNfnn+/9RhvZNvHRj0bP6/rr2+Wdd6Yv94037P8DBtjf\njY0WZK67zrb9j3zEpn/9696PGFHduv7gB/Z+csop3m+1VRRMdt01en9JmjHD1uPPf46mHXKI93vu\nmX07ra02j2T3/d570+ebONHmef992/422SQK4K2t3j/xhPdvvtn+/bryStv+K4Wchx6y2/rrX9tf\nXtK//uXbBFLvbfuQ7H5U0tzs/cYbpx84hgO+8N7b1GQh9dxz7e/WVu93283/N6jGiwRnn22PWVaQ\n8d77n/zE+2239f7448sLLC0ttv4tLeXzz5hhr7Nrr618n5IWLbKDsu22s/B54onR+05nWLXKtqlw\ngPuHP7Sd54Ybyl+r//iHBeCvfz09hP/733bQcfbZ9t5z1FG2fwiP8d57R9vxF79oOWB1XHqprc+y\nZau3nDFj7P1j1ix7/9l+ewucb75pr6MvfMEeh8MPtwOkX//ano9jjsk+UIo791x7DdxxR23rtXSp\nZY4BA+zgIct3vmPrm6WlxftPf9rmuegi7z/zGcJv592xCuH33XftjTL+JjNihL05r46wMws///yn\nVYXaOwLaemub/5RT7O+mJgsbS5a0f5sh/E6aVHm+UF0OIcR773/4w2hdFy+2ADx/vv3vvvtsw5K8\n/7//a7u8O++Mrpv2ht+e226Lrh+eh1CBOv749Os880y0A1m40JYRf8NrbbWDj549vf/Up7Jv++67\no9uOCwchDz5Y/f24/367ztSp0bRQQb3llsrXbW21Kn81O//rrrNl/vjH5dPjBwTeRwcVJ55YeXnh\n/scrgTfe6P9b2UwKlchQmaxG/PWVrGh1xOLFtqxKz+2assUW0Wt9vfUsbJx+uv0vVFI239y2Ge/L\nq5qVnt+f/MR24MGWW1qAPeqoKHyOHWsVnvj7VfDUU9GOdsUKC2BnnBEF8BdftKAgZe/oWlvteuGT\npueft6pve+GoudneT9LWKwgHDj/+sW2XHa02hQOfrJD95psW9keM6FglculSu8+/+1359Jtv9mUH\n6JWcfLIVBJK3f8IJbacfd5z3n/iE/f7HP9ptXHqprcNvf2vTW1vtNXXGGbXfn/Ycckh1nzgFDzwQ\nfUo4fHjl4LM6li6196Hwicdhh0X7waYmC65hGwt+//vyQBysWGEHDhtuaEFy6NC2r8Hbb7frPv20\n9717e3/VVau3/qGanFyXNCtW2D7iyCPL3wNaW+31Es8jM2daTnDOlr/DDvYpbker7s3NVrmV7D2o\nGi++aO9766/v/QsvVJ435IN4cSUuHBQ/+qj9PXlyNwm/kk6X9LakFZKelrRrO/PvLWmypJWSXpN0\nbMo8X5U0rbTMqZIOXJ3bzQq/t91mH9eEKmc4Kt5xx2hntjo++clop3fjjfaGMXp05esMH27z/+AH\ntd9eaHuIV3TTDBli8y1YEE0bN86mDR6cfp3wxn/xxW3/t3x5VNmKVz2rFaoh8QAaPjoMHw/WasKE\nCX7gQFtGpWp7qHInw+8G/7+9sw+yoroS+O/wIaCCkIVAklUzCQokbJCaDG4gikpQR+qRIlh8iAq4\nxsQPcBOrUCMVCJUlHxTEBJWklqKSNaLRmLDgUgHd7GZZBAmIcc2wSIHArsAgQQfW4XPe3T9OX7qn\n533M+5h5w3vnV9U107fP677dp2/fc889996LXUrvTzbi3qhvfztzRZ0PL7yg5/zWt5qn//GPzj3x\nRLjvdXbffZnPl+r+fSWyaFFL+cOHw9+kCt9JhfdWRxs4hTJ3rlZUzqm+24u6OvWw+G5oUAPXOefe\ney9M+/73Na2+PkyLe94yccMNWilVV4cNmI0b9Tz+U+bL8KZNmp5I6L5vPNbVOXfypFb6Dz3k3IgR\nakxkqihvvlm/hz6MY9Cg4unMfxOrqrTCz4dk0rm+fVe62bPDtMZG/fb076/nv+yy7N3Smfj0p1t+\nq6dPb31ja+3asMHhOXHCuV69Woaw+cbk3r2qG9/QmTxZDd7Tp7VBDWp4Fps1a/TcPhwpHfv3Ozd1\nqsp+7nP6bSxWmEMmkknnZs1a6Xr2VAM4mQxD11J15l57rXNXXaUGsmf5cjUWd+7U+xw4UHttol7Z\nU6e04denj557377C8z54sHMjR6Zv8NfVqZHdr59es2dPrXt8g2LDBk2PGsTOqed66VLnVq3K7ZuS\njmRSbY4uXZqHVKTipZe0fA0ZouFL2ThwQO8h6r3fv1/f65Mn9Vswfnx47LwwfoHJgRF7JzAY+Blw\nFOibRv6TwP8BPwQGBQbsGWBsRGZkkPbNQGYBcAr4TAHXbWH8vvaaekCnTg29XKtX67GqKu3CLpTF\ni8NKb+5cfalTGRNRbrxR5fNpdc6Yob9taMgst2FDy/AEHx+Yzkv40kvhfWTKt3+GueDjG6MG2I4d\nur90ae7nc865RCJxrjEQLVhxfFdmdXXzdB8/1RpPbCZ8jKiPwy0Gvks3HvYQ58UXVS4a75mKTMZv\nupjet99uHjOeDR9e0qNH63+TCwlv9bUjJ0+Gz27LljDdp/n4ZOfUmMoW8hTnq19Vj1zPnqEh3dio\nFdSyZaFH5bnntKfIX/fWW/W3V14ZnmvWLI257tQp9CamY+5cbQT78QG56Dkbzz+vDeVCG4OXX55w\nV1yh/69fr8ZDp05qoD77bP4xrJ4JE5p3eyeTGj4VjefPxMmTaujOmxem+TIV7XFzTkMIOnfW5x71\nOPtv8tq1eqxXLz1vsTl7Vo2ZO+5IL/OLX2gee/fW/LWH0RslkUic66V78EENr0kXT7tpk74L3lHT\n1KSGWqZ6wDNnjl5j7Nji5HvDBm149uvX3IDdulWNYtDyPGWKvhfHj2u5ralR4/3uu7UXoxgGbjYa\nGzVW/Lbb0st897ua5+uvz61xWVWlZaepSfUAGpM8dqyG0EXHQZwvxu9m4MeRfQH+F5iTRv4HwJux\ntGeBtZH954DVMZlNwFMFXLeZ8XvqlHogqqvD1uGIEaqI48dVGU8+2XrFpuPsWTWerrnGuVtuCSuq\nTNx1l8r96le5X+/Ysdw9lVG2bUv/cT14UCvhdC29ujo1qLMZ3qk4c0bveejQ5ulbtuT/kU0kEq6m\nRs+b6aPunBboqPHiXNilVGgcWzKprdxi4r188UF0cdatU7lsFXYq49d3AebbNR3HG+JduhTnfHFK\nYfw6pxVYnz7NQ0bmz28Zu33kiBo5ubBoUaibaNdpdbXGAFdV6bFPfEIb1o89FlZOoA17z/r1YXq2\nbmrvCRwzRnuJ2qPizZWamoQDva/evTWvxWxgLlig5/X37hvouTQEZs5U3fhv6ujR6WOnR48Oy4cf\nZJVMapd2ba0aUMXojUzHkiV67bfe0tCa5cu1geuc1kWdO6tzpdBGRb748u0beTU1zXsu4zz6qIYu\n7NoVOm58t3omDh/WcudD/opBfX04KHvVKu1xvuAC9U7/5jctn+nmzWq8L16sdW68p6At+elPte5L\nVc/7GOvvfCf3ennaNNXZihXuXK9Yba02YuIDkTu88Qt0DTy042PpPwd+m+Y3fwCWxNJmAO9H9vcB\ns2My84HtBVy3mfE7a5YW9Gisih+d7bupC+kyizN9eljxbNyYWdbP7hAdvFUJvPmmdhkXi0Qi4caO\n1WeZTxxyPt3U7cXWrZq3bJWhDx3JNjtAKuPXexWjYRSFkMq7X0xKZfzW1xenezQVu3eHzyzqLdyy\nJewmnTIllNm1Sysl3/W/cGH4m6iXOlvFdfp0GBp1771tc2+FUlubODfo8lOfKu5gK+fCxsLMmRrq\nMHWqOkVyMf527FBDYtmyMFQp3XgMPzi1trZ5+sKFoVHsZ5VpC44dU8+yvxaoh/722/Uebr8980C7\ntiZavj/4IPs7/OGH6s2eMEEbqPnGfxeLpiaNre7WTZ/n9OmpZ1vxfOUrYXktZqMuG6dOqac5PiD2\nmWf0vbjnnvyeox/30Lt3yzjtOOeD8fsxIAlcHUv/AbApzW92Ag/H0mqBJqBbsH8KmByTuRc4WMB1\nzxm/vvWybFnzB97YGE4Hc911mZWTK/Pnhy9ydAqhVCSTxTW8K5VEIuEmT9Znnk8ISVsaaoVy5ow2\n4A4dyiy3ebNrlff2d79r2Sg7fVq9X/nGZcbxs0qMGlWc88UplfHb1kycqF7eaPyiczoQ7aab1FM1\nb55OM+YZNkyf9Zo1zX8zfXrru+39bCs+prqjkUgkXG2tO+dNKzYffBB+A3r0CBsauXLHHeF5Jk5M\n35j2U4bt2dM8/eBB9f4WqxGaiRUrtJfyz3/W+7/rLu2afuSR0jsB8inffsxDvOekVBw6pHHcCxdm\nf55+HEwpPmveRlq5UnuTZs5053pQ49+h1hKdJSfbpFulMH6D5RfKku4Ad9+9g+3bdQL2ESN0uc0o\nY8bokpejR7c8Vgh+FbCRI3UhiPr67L85erR4169EGhoauPBCVWKnTrnrc9o0Xbe9mO9BMZkxQxdA\nePfd9DJduujylaNGZb4Pv3BJXGbcuMJX/4ny8ss6IXxbPNOGhgZe76jKKoA5c3QS/1SLLixcqBP0\njx+v+/72owt/RB/J7Nkt09IxfLiuZta1a8csAw0NDSxY8Drjx+sCMW2Rx0mTdNGDWbN08ZRJk3K/\nzgMPaPnq2lUXtXnjjfSykyfrAgXxFdeeflr/trUehg2Dxx/Xe969Wxc0uf9+PZYp3+1BPuV78GBd\nNOf4cV0YqSO8x3Pm6N9sz/OSS3Tlu89+tv3zPXSoLmTkF3/p3x++9jVdRCTd4i+t4aGHdAEWyHxP\nO8IVgrrnf7XcEKde0tYJi3QFGoGJzrnVkfSfA5c45yak+M0fgG3OuW9G0mYAP3LO9Qn29wGLnXM/\nicjMB77snBue53VvA55p9c0ZhmEYhmEYpWKac25le1woJ8+vc+6MiGwDxgCrAUREgv2fpPnZJjTM\nIcqNQXpUJn6OsV4mz+uuA6YBe9FZIgzDMAzDMIyORXd0ZrB17XXBnDy/ACIyCR1o9nVgC/AN4FZg\nsHPuPRH5HvBx59z0QP6TwH8BTwErUIP1ceAW59wrgcwXgH8HHgX+BZgKPILGf9S15rp53LthGIZh\nGIZRYeQc8+uce15E+qJz8fYH3gBuihigA4BLI/J7RWQc8CNgNjo92d95wzeQ2RSEKfxDsO1CQx7q\nIjLZrmsYhmEYhmEYGcnZ82sYhmEYhmEY5yudSp0BwzAMwzAMw2gvzPg1DMMwDMMwKoayNX5F5H4R\neUdETojIZhGpKXWejMyIyDUislpE3hWRpIiMTyGzQEQOiEijiLwsIgNjx7uJyJMickREjovIr0Xk\nozGZPiLyjIg0iMj7IrJcRC5q6/szmiMij4rIFhE5JiL1IvJbEbkyhZzpvAwQka+LyJ8CHTSIyKsi\ncnNMxnRdhojII8E3fUks3fRdJojIvEDH0a0uJtNh9F2Wxq+ITAYWA/OA4cCfgHXBgDmj43IROpDx\nPnS1l2aIyMPAA8A9wAjgQ1SvF0TEHgfGAROBa4GPAy/GTrUSGILOPDIukPtZMW/EaBXXAEuBq4Ev\nocuYrxeRHl7AdF5W/A/wMLr6ZjXwe+CfRWQImK7LlcDxdA9aD0fTTd/lx1vohAQDgu2L/kCH03d7\nLSXXnhuwGfhxZF/QWSbmlDpvtrVah0lgfCztAPCNyH4v4AQwKbJ/CpgQkRkUnGtEsD8k2B8ekbkJ\nOAsMKPV9V/IG9A1080XTeWVswF+Amabr8tyAi4GdwA3AvwFLIsdM32W0oc7G1zMc71D6LjvPr+hq\ncNXAv/o0p0/oFeALpcqXURgiUoW2JKN6PQa8RqjXz6PT90VldgL7IzJ/C7zvnNseOf0rqKf56rbK\nv9EqeqN6OAqm83JGRDqJyBTgQuBV03XZ8iSwxjn3+2ii6btsuUI0bHG3iPxSRC6FjqnvnOf5PQ/o\nC3QG6mPp9Wgrwjg/GYC+4Kn0OiD4vz9wOihU6WQGAIejB51zTSJyNCJjtDMiImiX13+6cH5v03mZ\nISJD0ZU7uwPHUS/PTtGFjkzXZUTQuLkKNWriWNkuPzYDM1BP/8eA+cB/BGW+w+m7HI1fwzDOP54C\nPgOMKnVGjDblv4FhwCXoCp3/JCLXljZLRrERkb9GG7Nfcs6dKXV+jLbHORddmvgtEdkC7AMmoeW+\nQ1F2YQ/AEaAJbUVE6Q8cav/sGEXiEBq7nUmvh4ALRKRXFpn46NHOwEew96MkiMgTwC3Adc65g5FD\npvMywzl31jm3xzm33Tn3GDoI6kFM1+VGNdAPeF1EzojIGWA08KCInEa9eabvMsY51wC8DQykA5bv\nsjN+g1bmNnQkIHCuS3UM8Gqp8mUUhnPuHfTljuq1Fxrn4/W6DQ18j8oMAi5Du1oJ/vYWkeGR049B\nC+ZrbZV/IzWB4ftl4Hrn3P7oMdN5RdAJ6Ga6LjteAf4GDXsYFmxbgV8Cw5xzezB9lzUicjFq+B7o\nkOW71CME22jU4SSgEbgTGIxOg/EXoF+p82ZbRr1dhH4kr0JHdP59sH9pcHxOoMcE+mFdBewCLoic\n4yngHeA61PuwEdgQu85a9ENcg3az7wSeLvX9V9oW6Op9dMqz/pGte0TGdF4mG7Aw0PXlwFDge2hl\nd4Ppuvw3Ws72YPouow1YhE47djkwEngZ9fD/VUfUd8kfWBsq4j5gLzqVxibg86XOk21ZdTYalirl\n3gAAAPlJREFUNXqbYtuKiMx8dMqURmAdMDB2jm7o3LFH0AE1LwAfjcn0Rj0QDajx9Y/AhaW+/0rb\n0ui6CbgzJmc6L4MNWA7sCb7Jh4D1BIav6br8N3Re5yWxNNN3mWzAs+iUsifQGRpWAlUdVd8SnMww\nDMMwDMMwyp6yi/k1DMMwDMMwjHSY8WsYhmEYhmFUDGb8GoZhGIZhGBWDGb+GYRiGYRhGxWDGr2EY\nhmEYhlExmPFrGIZhGIZhVAxm/BqGYRiGYRgVgxm/hmEYhmEYRsVgxq9hGIZhGIZRMZjxaxiGYRiG\nYVQMZvwahmEYhmEYFYMZv4ZhGIZhGEbF8P+yJeB48sLs9wAAAABJRU5ErkJggg==\n",
+      "text/plain": [
+       ""
+      ]
+     },
+     "execution_count": 45,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "qc.MatPlot(data4.samp_acq_controller_magnitude[1]).fig"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 30,
    "metadata": {
     "collapsed": false
    },
@@ -443,40 +544,47 @@
      "text": [
       "DataSet:\n",
       "   mode     = DataMode.LOCAL\n",
-      "   location = '2017-01-23/10-43-04_AlazarTest'\n",
+      "   location = '2017-01-23/15-55-55_AlazarTest'\n",
       "      |                            |  | \n",
-      "   Setpoint | samp_acq_controller_demod_freq_0_set | demod_freq_0 | (9,)\n",
-      "   Measured | index1                               | index1       | (9, 4, 1020)\n",
-      "   Measured | samp_acq_controller_magnitude        | magnitude    | (9, 4, 1020)\n",
-      "   Measured | samp_acq_controller_phase            | phase        | (9, 4, 1020)\n",
-      "started at 2017-01-23 10:43:11\n"
+      "   Setpoint | samp_acq_controller_demod_freq_0_set | demod_freq_0 | (11,)\n",
+      "   Measured | index1                               | index1       | (11, 1, 3000)\n",
+      "   Measured | samp_acq_controller_magnitude        | magnitude    | (11, 1, 3000)\n",
+      "   Measured | samp_acq_controller_phase            | phase        | (11, 1, 3000)\n",
+      "started at 2017-01-23 15:56:04\n"
      ]
     }
    ],
    "source": [
     "# in a loop\n",
     "\n",
-    "data5 = qc.Loop(samp_acq_controller.demod_freq_0.sweep(80e6, 120e6, 5e6)).each(\n",
+    "data5 = qc.Loop(samp_acq_controller.demod_freq_0.sweep(1e6, 21e6, 2e6)).each(\n",
     "    samp_acq_controller.acquisition).run(name='AlazarTest')"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 19,
+   "execution_count": 33,
    "metadata": {
     "collapsed": false
    },
    "outputs": [
     {
-     "data": {
-      "image/png": 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nov6R5PEfjaWPRv3DceYHo/aBqD0RtScjapHUI+3fHh9EuhvRi/T9SLsR3Ugf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\nOBW2bt+89MJKRMRy86WLd+7unMROGyX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-      "text/plain": [
-       ""
-      ]
-     },
-     "execution_count": 19,
-     "metadata": {},
-     "output_type": "execute_result"
+     "ename": "ConnectionResetError",
+     "evalue": "[WinError 10054] An existing connection was forcibly closed by the remote host",
+     "output_type": "error",
+     "traceback": [
+      "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
+      "\u001b[0;31mConnectionResetError\u001b[0m                      Traceback (most recent call last)",
+      "\u001b[0;32m\u001b[0m in \u001b[0;36m\u001b[0;34m()\u001b[0m\n\u001b[1;32m      1\u001b[0m \u001b[1;31m# plotting the measured demodulated amplitude (samples on y axis, different freqs on x), only looking at the one we swept\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m----> 2\u001b[0;31m \u001b[0mqc\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mQtPlot\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mdata5\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0msamp_acq_controller_magnitude\u001b[0m\u001b[1;33m[\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m,\u001b[0m \u001b[1;36m0\u001b[0m\u001b[1;33m]\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m",
+      "\u001b[0;32ma:\\qcodes\\qcodes\\plots\\pyqtgraph.py\u001b[0m in \u001b[0;36m__init__\u001b[0;34m(self, figsize, interval, window_title, theme, show_window, remote, *args, **kwargs)\u001b[0m\n\u001b[1;32m     56\u001b[0m             \u001b[1;31m# overrule the remote pyqtgraph class\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m     57\u001b[0m             \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mrpg\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mpg\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m---> 58\u001b[0;31m         \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mwin\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mrpg\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mGraphicsWindow\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mtitle\u001b[0m\u001b[1;33m=\u001b[0m\u001b[0mwindow_title\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m     59\u001b[0m         \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mwin\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0msetBackground\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mtheme\u001b[0m\u001b[1;33m[\u001b[0m\u001b[1;36m1\u001b[0m\u001b[1;33m]\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m     60\u001b[0m         \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mwin\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mresize\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;33m*\u001b[0m\u001b[0mfigsize\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
+      "\u001b[0;32mc:\\users\\triton2acq\\anaconda3\\envs\\qcodes-master\\lib\\site-packages\\pyqtgraph\\multiprocess\\remoteproxy.py\u001b[0m in \u001b[0;36m__getattr__\u001b[0;34m(self, attr, **kwds)\u001b[0m\n\u001b[1;32m    895\u001b[0m         \u001b[1;32melse\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m    896\u001b[0m             \u001b[1;31m#opts = self._getProxyOptions()\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m--> 897\u001b[0;31m             \u001b[1;32mreturn\u001b[0m \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0m_handler\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mgetObjAttr\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mself\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mattr\u001b[0m\u001b[1;33m,\u001b[0m \u001b[1;33m**\u001b[0m\u001b[0mopts\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    898\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m    899\u001b[0m     \u001b[1;32mdef\u001b[0m \u001b[0m_deferredAttr\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mself\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mattr\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
+      "\u001b[0;32mc:\\users\\triton2acq\\anaconda3\\envs\\qcodes-master\\lib\\site-packages\\pyqtgraph\\multiprocess\\remoteproxy.py\u001b[0m in \u001b[0;36mgetObjAttr\u001b[0;34m(self, obj, attr, **kwds)\u001b[0m\n\u001b[1;32m    530\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m    531\u001b[0m     \u001b[1;32mdef\u001b[0m \u001b[0mgetObjAttr\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mself\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mobj\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mattr\u001b[0m\u001b[1;33m,\u001b[0m \u001b[1;33m**\u001b[0m\u001b[0mkwds\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m--> 532\u001b[0;31m         \u001b[1;32mreturn\u001b[0m \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0msend\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mrequest\u001b[0m\u001b[1;33m=\u001b[0m\u001b[1;34m'getObjAttr'\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mopts\u001b[0m\u001b[1;33m=\u001b[0m\u001b[0mdict\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mobj\u001b[0m\u001b[1;33m=\u001b[0m\u001b[0mobj\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mattr\u001b[0m\u001b[1;33m=\u001b[0m\u001b[0mattr\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m,\u001b[0m \u001b[1;33m**\u001b[0m\u001b[0mkwds\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    533\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m    534\u001b[0m     \u001b[1;32mdef\u001b[0m \u001b[0mgetObjValue\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mself\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mobj\u001b[0m\u001b[1;33m,\u001b[0m \u001b[1;33m**\u001b[0m\u001b[0mkwds\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
+      "\u001b[0;32mc:\\users\\triton2acq\\anaconda3\\envs\\qcodes-master\\lib\\site-packages\\pyqtgraph\\multiprocess\\remoteproxy.py\u001b[0m in \u001b[0;36msend\u001b[0;34m(self, request, opts, reqId, callSync, timeout, returnType, byteData, **kwds)\u001b[0m\n\u001b[1;32m    450\u001b[0m             \u001b[0mrequest\u001b[0m \u001b[1;33m=\u001b[0m \u001b[1;33m(\u001b[0m\u001b[0mrequest\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mreqId\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mnByteMsgs\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0moptStr\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m    451\u001b[0m             \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mdebugMsg\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;34m'send request: cmd=%s nByteMsgs=%d id=%s opts=%s'\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mrequest\u001b[0m\u001b[1;33m[\u001b[0m\u001b[1;36m0\u001b[0m\u001b[1;33m]\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mnByteMsgs\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mreqId\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mopts\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m--> 452\u001b[0;31m             \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mconn\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0msend\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mrequest\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    453\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m    454\u001b[0m             \u001b[1;31m## follow up by sending byte messages\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
+      "\u001b[0;32mc:\\users\\triton2acq\\anaconda3\\envs\\qcodes-master\\lib\\multiprocessing\\connection.py\u001b[0m in \u001b[0;36msend\u001b[0;34m(self, obj)\u001b[0m\n\u001b[1;32m    204\u001b[0m         \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0m_check_closed\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m    205\u001b[0m         \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0m_check_writable\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m--> 206\u001b[0;31m         \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0m_send_bytes\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mForkingPickler\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mdumps\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mobj\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    207\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m    208\u001b[0m     \u001b[1;32mdef\u001b[0m \u001b[0mrecv_bytes\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mself\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mmaxlength\u001b[0m\u001b[1;33m=\u001b[0m\u001b[1;32mNone\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
+      "\u001b[0;32mc:\\users\\triton2acq\\anaconda3\\envs\\qcodes-master\\lib\\multiprocessing\\connection.py\u001b[0m in \u001b[0;36m_send_bytes\u001b[0;34m(self, buf)\u001b[0m\n\u001b[1;32m    402\u001b[0m             \u001b[1;31m# Also note we want to avoid sending a 0-length buffer separately,\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m    403\u001b[0m             \u001b[1;31m# to avoid \"broken pipe\" errors if the other end closed the pipe.\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m--> 404\u001b[0;31m             \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0m_send\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mheader\u001b[0m \u001b[1;33m+\u001b[0m \u001b[0mbuf\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    405\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m    406\u001b[0m     \u001b[1;32mdef\u001b[0m \u001b[0m_recv_bytes\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mself\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mmaxsize\u001b[0m\u001b[1;33m=\u001b[0m\u001b[1;32mNone\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
+      "\u001b[0;32mc:\\users\\triton2acq\\anaconda3\\envs\\qcodes-master\\lib\\multiprocessing\\connection.py\u001b[0m in \u001b[0;36m_send\u001b[0;34m(self, buf, write)\u001b[0m\n\u001b[1;32m    366\u001b[0m         \u001b[0mremaining\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mlen\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mbuf\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m    367\u001b[0m         \u001b[1;32mwhile\u001b[0m \u001b[1;32mTrue\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m--> 368\u001b[0;31m             \u001b[0mn\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mwrite\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0m_handle\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mbuf\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    369\u001b[0m             \u001b[0mremaining\u001b[0m \u001b[1;33m-=\u001b[0m \u001b[0mn\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m    370\u001b[0m             \u001b[1;32mif\u001b[0m \u001b[0mremaining\u001b[0m \u001b[1;33m==\u001b[0m \u001b[1;36m0\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
+      "\u001b[0;31mConnectionResetError\u001b[0m: [WinError 10054] An existing connection was forcibly closed by the remote host"
+     ]
     }
    ],
    "source": [
@@ -501,8 +609,6 @@
    },
    "outputs": [],
    "source": [
-    "import qcodes.instrument_drivers.AlazarTech.ave_controller_test as ave_controller\n",
-    "\n",
     "demod_list = [80e6, 85e6, 90e6, 95e6, 100e6, 105e6, 110e6, 115e6]\n",
     "ave_controller = ave_controller.HD_Averaging_Controller(name='ave_controller', \n",
     "                                             alazar_name='Alazar', \n",
@@ -634,8 +740,6 @@
    },
    "outputs": [],
    "source": [
-    "import qcodes.instrument_drivers.AlazarTech.rec_controller_test as record_controller\n",
-    "\n",
     "demod_list = [95e6, 98e6, 100e6, 105e6]\n",
     "rec_controller = record_controller.HD_Records_Controller(name='rec_controller', \n",
     "                                                    alazar_name='Alazar', \n",
diff --git a/qcodes/instrument_drivers/AlazarTech/ATS9360.py b/qcodes/instrument_drivers/AlazarTech/ATS9360.py
index 4d8ca45cbc32..5ffe33f03699 100644
--- a/qcodes/instrument_drivers/AlazarTech/ATS9360.py
+++ b/qcodes/instrument_drivers/AlazarTech/ATS9360.py
@@ -10,7 +10,7 @@ class AlazarTech_ATS9360(AlazarTech_ATS):
     TODO(nataliejpg):
         -   add clock source options and sample rate options
     """
-    samples_divisor = 32
+    samples_divisor = 128
 
     def __init__(self, name, **kwargs):
         dll_path = 'C:\\WINDOWS\\System32\\ATSApi.dll'
diff --git a/qcodes/instrument_drivers/AlazarTech/ave_controller_test.py b/qcodes/instrument_drivers/AlazarTech/ave_controller_test.py
index 855ba2d3453d..5ad362524813 100644
--- a/qcodes/instrument_drivers/AlazarTech/ave_controller_test.py
+++ b/qcodes/instrument_drivers/AlazarTech/ave_controller_test.py
@@ -44,7 +44,10 @@ def __init__(self, name, instrument, demod_length):
         self._instrument = instrument
         self.acquisition_kwargs = {}
         self.names = ('magnitude', 'phase')
-        self.shapes = ((demod_length, ), (demod_length, ))
+        if demod_length > 1:
+            self.shapes = ((demod_length, ), (demod_length, ))
+        else:
+            self.shapes = ((), ())
 
     def get(self):
         mag, phase = self._instrument._get_alazar().acquire(
diff --git a/qcodes/instrument_drivers/AlazarTech/basic_controller.py b/qcodes/instrument_drivers/AlazarTech/basic_controller.py
index 1afcf25a2357..7b172f070a7e 100644
--- a/qcodes/instrument_drivers/AlazarTech/basic_controller.py
+++ b/qcodes/instrument_drivers/AlazarTech/basic_controller.py
@@ -1,6 +1,7 @@
 import logging
 from .ATS import AcquisitionController
 import numpy as np
+import qcodes.instrument_drivers.AlazarTech.acq_helpers as helpers
 from qcodes import Parameter
 
 
@@ -125,8 +126,8 @@ def post_acquire(self):
 
         bps = self.board_info['bits_per_sample']
         if bps == 12:
-            volt_rec_A = sample_to_volt_u12(recordA, bps)
-            volt_rec_B = sample_to_volt_u12(recordB, bps)
+            volt_rec_A = helpers.sample_to_volt_u12(recordA, bps)
+            volt_rec_B = helpers.sample_to_volt_u12(recordB, bps)
         else:
             logging.warning('sample to volt conversion does not exist for bps '
                             '!= 12, raw samples centered on 0 and returned')
@@ -135,7 +136,7 @@ def post_acquire(self):
 
         return volt_rec_A, volt_rec_B
 
-
+"""
 def sample_to_volt_u12(raw_samples, bps):
     # right_shift 16-bit sample by 4 to get 12 bit sample
     shifted_samples = np.right_shift(raw_samples, 4)
@@ -151,3 +152,4 @@ def sample_to_volt_u12(raw_samples, bps):
                               (shifted_samples - code_zero) / code_range)
 
     return volt_samples
+"""
\ No newline at end of file
diff --git a/qcodes/instrument_drivers/AlazarTech/rec_controller_test.py b/qcodes/instrument_drivers/AlazarTech/rec_controller_test.py
index 75d160f1d2f6..9fb3d1b2b54d 100644
--- a/qcodes/instrument_drivers/AlazarTech/rec_controller_test.py
+++ b/qcodes/instrument_drivers/AlazarTech/rec_controller_test.py
@@ -51,7 +51,10 @@ def update_sweep(self, npts, start=None, stop=None):
         demod_length = self._instrument._demod_length
         # rec_list = tuple(np.linspace(start, stop, num=npts))
         # demod_index = tuple(range(demod_length))
-        self.shapes = ((demod_length, npts), (demod_length, npts))
+        if demod_length > 1:
+            self.shapes = ((demod_length, npts), (demod_length, npts))
+        else:
+            self.shapes = ((npts,), (npts,))
 
     def get(self):
         mag, phase = self._instrument._get_alazar().acquire(
diff --git a/qcodes/instrument_drivers/AlazarTech/samp_controller.py b/qcodes/instrument_drivers/AlazarTech/samp_controller.py
index 9fb3be199c9e..a6a0f79c705a 100644
--- a/qcodes/instrument_drivers/AlazarTech/samp_controller.py
+++ b/qcodes/instrument_drivers/AlazarTech/samp_controller.py
@@ -54,11 +54,14 @@ def update_sweep(self, start, stop, npts):
         demod_length = self._instrument._demod_length
         # freq = tuple(np.linspace(start, stop, num=npts))
         # demod_index = tuple(range(demod_length))
-        self.shapes = ((demod_length, npts), (demod_length, npts))
+        if demod_length > 1:
+            self.shapes = ((demod_length, npts), (demod_length, npts))
+        else:
+            self.shapes = ((npts,), (npts,))
 
     def get(self):
-        self._instrument.int_time.check()
-        self._instrument.int_delay.check()
+        # self._instrument.int_time.check()
+        # self._instrument.int_delay.check()
         mag, phase = self._instrument._get_alazar().acquire(
             acquisition_controller=self._instrument,
             **self.acquisition_kwargs)
@@ -315,7 +318,7 @@ def post_acquire(self):
         Returns:
             magnitude (numpy array): shape = (demod_length, samples_used)
             phase (numpy array): shape = (demod_length, samples_used)
-        """
+        """ 
         records_per_acquisition = (self.buffers_per_acquisition *
                                    self.records_per_buffer)
         # for ATS9360 samples are arranged in the buffer as follows:
@@ -375,9 +378,9 @@ def _fit(self, rec):
         volt_rec_mat = np.outer(np.ones(self._demod_length), volt_rec)
         re_mat = np.multiply(volt_rec_mat, self.cos_mat)
         im_mat = np.multiply(volt_rec_mat, self.sin_mat)
-
+        
         # filter out higher freq component
-        cutoff = self.get_max_demod_freq() / 10
+        cutoff = self.get_max_demod_freq() / 20
         if self.filter_settings['filter'] == 0:
             re_filtered = helpers.filter_win(re_mat, cutoff,
                                              self.sample_rate,
@@ -403,7 +406,8 @@ def _fit(self, rec):
 
         re_limited = re_filtered[:, beginning:end]
         im_limited = im_filtered[:, beginning:end]
-
+        #return re_limited, volt_rec_mat[:, beginning:end]
+        
         # convert to magnitude and phase
         complex_mat = re_limited + im_limited * 1j
         magnitude = abs(complex_mat)

From fa219ec56a33fc9f1505ce693ce00eb34a365e21 Mon Sep 17 00:00:00 2001
From: nataliejpg 
Date: Tue, 24 Jan 2017 10:43:09 +0100
Subject: [PATCH 092/328] change init with demod freqs to init with number of
 demod_freqs

---
 .../instrument_drivers/AlazarTech/ave_controller_test.py   | 7 +++----
 .../instrument_drivers/AlazarTech/rec_controller_test.py   | 7 +++----
 qcodes/instrument_drivers/AlazarTech/samp_controller.py    | 7 +++----
 3 files changed, 9 insertions(+), 12 deletions(-)

diff --git a/qcodes/instrument_drivers/AlazarTech/ave_controller_test.py b/qcodes/instrument_drivers/AlazarTech/ave_controller_test.py
index 5ad362524813..a3eec4cc5441 100644
--- a/qcodes/instrument_drivers/AlazarTech/ave_controller_test.py
+++ b/qcodes/instrument_drivers/AlazarTech/ave_controller_test.py
@@ -86,7 +86,7 @@ class HD_Averaging_Controller(AcquisitionController):
     filter_dict = {'win': 0, 'ls': 1, 'ave': 2}
     samples_divisor = AlazarTech_ATS9360.samples_divisor
 
-    def __init__(self, name, alazar_name, demod_freqs=[20e6], filter='win',
+    def __init__(self, name, alazar_name, demod_length=1, filter='win',
                  numtaps=101, chan_b=False, **kwargs):
         self.filter_settings = {'filter': self.filter_dict[filter],
                                 'numtaps': numtaps}
@@ -98,11 +98,10 @@ def __init__(self, name, alazar_name, demod_freqs=[20e6], filter='win',
         super().__init__(name, alazar_name, **kwargs)
 
         self.add_parameter(name='acquisition',
-                           demod_length=self._demod_length,
+                           demod_length=self.demod_length,
                            parameter_class=AveragedAcqParam)
-        for i, res in enumerate(demod_freqs):
+        for i in range(demod_length):
             self.add_parameter(name='demod_freq_{}'.format(i),
-                               initial_value=res,
                                parameter_class=ManualParameter)
         self.add_parameter(name='int_time',
                            initial_value=None,
diff --git a/qcodes/instrument_drivers/AlazarTech/rec_controller_test.py b/qcodes/instrument_drivers/AlazarTech/rec_controller_test.py
index 9fb3d1b2b54d..fb6535464b7e 100644
--- a/qcodes/instrument_drivers/AlazarTech/rec_controller_test.py
+++ b/qcodes/instrument_drivers/AlazarTech/rec_controller_test.py
@@ -94,12 +94,12 @@ class HD_Records_Controller(AcquisitionController):
     filter_dict = {'win': 0, 'ls': 1, 'ave': 2}
     samples_divisor = AlazarTech_ATS9360.samples_divisor
 
-    def __init__(self, name, alazar_name, demod_freqs=[20e6], filter='win',
+    def __init__(self, name, alazar_name, demod_length=1 filter='win',
                  numtaps=101, chan_b=False, **kwargs):
         self.filter_settings = {'filter': self.filter_dict[filter],
                                 'numtaps': numtaps}
         self.chan_b = chan_b
-        self._demod_length = len(demod_freqs)
+        self._demod_length = demod_length
         self.number_of_channels = 2
         self.samples_per_record = None
         self.records_per_buffer = None
@@ -109,9 +109,8 @@ def __init__(self, name, alazar_name, demod_freqs=[20e6], filter='win',
         self.add_parameter(name='acquisition',
                            demod_length=self._demod_length,
                            parameter_class=RecordsAcqParam)
-        for i, res in enumerate(demod_freqs):
+        for i in range(demod_length):
             self.add_parameter(name='demod_freq_{}'.format(i),
-                               initial_value=res,
                                parameter_class=ManualParameter)
         self.add_parameter(name='int_time',
                            initial_value=None,
diff --git a/qcodes/instrument_drivers/AlazarTech/samp_controller.py b/qcodes/instrument_drivers/AlazarTech/samp_controller.py
index a6a0f79c705a..296a19779462 100644
--- a/qcodes/instrument_drivers/AlazarTech/samp_controller.py
+++ b/qcodes/instrument_drivers/AlazarTech/samp_controller.py
@@ -97,12 +97,12 @@ class HD_Samples_Controller(AcquisitionController):
     filter_dict = {'win': 0, 'ls': 1}
     samples_divisor = AlazarTech_ATS9360.samples_divisor
 
-    def __init__(self, name, alazar_name, demod_freqs=[20e6], filter='win',
+    def __init__(self, name, alazar_name, demod_length=1, filter='win',
                  numtaps=101, chan_b=False, **kwargs):
         self.filter_settings = {'filter': self.filter_dict[filter],
                                 'numtaps': numtaps}
         self.chan_b = chan_b
-        self._demod_length = len(demod_freqs)
+        self._demod_length = demod_length
         self.number_of_channels = 2
         self.samples_per_record = None
         self.sample_rate = None
@@ -110,9 +110,8 @@ def __init__(self, name, alazar_name, demod_freqs=[20e6], filter='win',
 
         self.add_parameter(name='acquisition',
                            parameter_class=SamplesAcqParam)
-        for i, res in enumerate(demod_freqs):
+        for i in range(demod_length):
             self.add_parameter(name='demod_freq_{}'.format(i),
-                               initial_value=res,
                                parameter_class=ManualParameter)
         self.add_parameter(name='int_time',
                            initial_value=None,

From cab3d5a3edcdf986b352a63bbddc437a3cca4a74 Mon Sep 17 00:00:00 2001
From: nataliejpg 
Date: Tue, 24 Jan 2017 12:08:23 +0100
Subject: [PATCH 093/328] typos and get around the uses of the demod freqs
 before set through get_max_demod_freq()

---
 .../AlazarTech/ave_controller_test.py         | 29 ++++++++++---------
 .../AlazarTech/rec_controller_test.py         | 27 +++++++++--------
 .../AlazarTech/samp_controller.py             | 25 +++++++++-------
 3 files changed, 45 insertions(+), 36 deletions(-)

diff --git a/qcodes/instrument_drivers/AlazarTech/ave_controller_test.py b/qcodes/instrument_drivers/AlazarTech/ave_controller_test.py
index a3eec4cc5441..cfeaa31c4cbb 100644
--- a/qcodes/instrument_drivers/AlazarTech/ave_controller_test.py
+++ b/qcodes/instrument_drivers/AlazarTech/ave_controller_test.py
@@ -91,14 +91,14 @@ def __init__(self, name, alazar_name, demod_length=1, filter='win',
         self.filter_settings = {'filter': self.filter_dict[filter],
                                 'numtaps': numtaps}
         self.chan_b = chan_b
-        self._demod_length = len(demod_freqs)
+        self._demod_length = demod_length
         self.number_of_channels = 2
         self.samples_per_record = None
         self.sample_rate = None
         super().__init__(name, alazar_name, **kwargs)
 
         self.add_parameter(name='acquisition',
-                           demod_length=self.demod_length,
+                           demod_length=demod_length,
                            parameter_class=AveragedAcqParam)
         for i in range(demod_length):
             self.add_parameter(name='demod_freq_{}'.format(i),
@@ -130,17 +130,18 @@ def _update_int_time(instr, value):
             raise ValueError('int_time must be 0 <= value <= 1')
         alazar = instr._get_alazar()
         instr.sample_rate = alazar.get_sample_rate()
-        oscilations_measured = value * instr.get_max_demod_freq()
-        oversampling = instr.sample_rate / (2 * instr.get_max_demod_freq())
-        if oscilations_measured < 10:
-            logging.warning('{} oscilations measured, recommend at '
-                            'least 10: decrease sampling rate, take '
-                            'more samples or increase demodulation '
-                            'freq'.format(oscilations_measured))
-        elif oversampling < 1:
-            logging.warning('oversampling rate is {}, recommend > 1: '
-                            'increase sampling rate or decrease '
-                            'demodulation frequency'.format(oversampling))
+        if instr.get_max_demod_freq() is not None:
+            oscilations_measured = value * instr.get_max_demod_freq()
+            oversampling = instr.sample_rate / (2 * instr.get_max_demod_freq())
+            if oscilations_measured < 10:
+                logging.warning('{} oscilations measured, recommend at '
+                                'least 10: decrease sampling rate, take '
+                                'more samples or increase demodulation '
+                                'freq'.format(oscilations_measured))
+            elif oversampling < 1:
+                logging.warning('oversampling rate is {}, recommend > 1: '
+                                'increase sampling rate or decrease '
+                                'demodulation frequency'.format(oversampling))
         if instr.int_delay() is None:
             instr.int_delay.to_default()
 
@@ -262,6 +263,8 @@ def pre_start_capture(self):
             raise Exception('acq controller sample rate does not match '
                             'instrument value, most likely need '
                             'to set and check int_time and int_delay')
+        if self.get_max_demod_freq() is None:
+            raise Exception('no demodulation frequencies set')
         self.records_per_buffer = alazar.records_per_buffer.get()
         self.buffers_per_acquisition = alazar.buffers_per_acquisition.get()
         self.board_info = alazar.get_idn()
diff --git a/qcodes/instrument_drivers/AlazarTech/rec_controller_test.py b/qcodes/instrument_drivers/AlazarTech/rec_controller_test.py
index fb6535464b7e..dd7e1c185305 100644
--- a/qcodes/instrument_drivers/AlazarTech/rec_controller_test.py
+++ b/qcodes/instrument_drivers/AlazarTech/rec_controller_test.py
@@ -94,7 +94,7 @@ class HD_Records_Controller(AcquisitionController):
     filter_dict = {'win': 0, 'ls': 1, 'ave': 2}
     samples_divisor = AlazarTech_ATS9360.samples_divisor
 
-    def __init__(self, name, alazar_name, demod_length=1 filter='win',
+    def __init__(self, name, alazar_name, demod_length=1, filter='win',
                  numtaps=101, chan_b=False, **kwargs):
         self.filter_settings = {'filter': self.filter_dict[filter],
                                 'numtaps': numtaps}
@@ -139,17 +139,18 @@ def _update_int_time(instr, value):
             raise ValueError('int_time must be 0 <= value <= 1')
         alazar = instr._get_alazar()
         instr.sample_rate = alazar.get_sample_rate()
-        oscilations_measured = value * instr.get_max_demod_freq()
-        oversampling = instr.sample_rate / (2 * instr.get_max_demod_freq())
-        if oscilations_measured < 10:
-            logging.warning('{} oscilations measured, recommend at '
-                            'least 10: decrease sampling rate, take '
-                            'more samples or increase demodulation '
-                            'freq'.format(oscilations_measured))
-        elif oversampling < 1:
-            logging.warning('oversampling rate is {}, recommend > 1: '
-                            'increase sampling rate or decrease '
-                            'demodulation frequency'.format(oversampling))
+        if instr.get_max_demod_freq() is not None:
+            oscilations_measured = value * instr.get_max_demod_freq()
+            oversampling = instr.sample_rate / (2 * instr.get_max_demod_freq())
+            if oscilations_measured < 10:
+                logging.warning('{} oscilations measured, recommend at '
+                                'least 10: decrease sampling rate, take '
+                                'more samples or increase demodulation '
+                                'freq'.format(oscilations_measured))
+            elif oversampling < 1:
+                logging.warning('oversampling rate is {}, recommend > 1: '
+                                'increase sampling rate or decrease '
+                                'demodulation frequency'.format(oversampling))
         if instr.int_delay() is None:
             instr.int_delay.to_default()
 
@@ -279,6 +280,8 @@ def pre_start_capture(self):
                             'instrument value, most likely need '
                             'to call update_acquisition_kwargs with'
                             'records_per_buffer as a param')
+        if self.get_max_demod_freq() is None:
+            raise Exception('no demodulation frequencies set')
         self.buffers_per_acquisition = alazar.buffers_per_acquisition.get()
         self.board_info = alazar.get_idn()
         self.buffer = np.zeros(self.samples_per_record *
diff --git a/qcodes/instrument_drivers/AlazarTech/samp_controller.py b/qcodes/instrument_drivers/AlazarTech/samp_controller.py
index 296a19779462..effe982dcb84 100644
--- a/qcodes/instrument_drivers/AlazarTech/samp_controller.py
+++ b/qcodes/instrument_drivers/AlazarTech/samp_controller.py
@@ -140,17 +140,18 @@ def _update_int_time(instr, value):
             raise ValueError('int_time must be 0 <= value <= 1')
         alazar = instr._get_alazar()
         instr.sample_rate = alazar.get_sample_rate()
-        oscilations_measured = value * instr.get_max_demod_freq()
-        oversampling = instr.sample_rate / (2 * instr.get_max_demod_freq())
-        if oscilations_measured < 10:
-            logging.warning('{} oscilations measured, recommend at '
-                            'least 10: decrease sampling rate, take '
-                            'more samples or increase demodulation '
-                            'freq'.format(oscilations_measured))
-        elif oversampling < 1:
-            logging.warning('oversampling rate is {}, recommend > 1: '
-                            'increase sampling rate or decrease '
-                            'demodulation frequency'.format(oversampling))
+        if instr.get_max_demod_freq() is not None:
+            oscilations_measured = value * instr.get_max_demod_freq()
+            oversampling = instr.sample_rate / (2 * instr.get_max_demod_freq())
+            if oscilations_measured < 10:
+                logging.warning('{} oscilations measured, recommend at '
+                                'least 10: decrease sampling rate, take '
+                                'more samples or increase demodulation '
+                                'freq'.format(oscilations_measured))
+            elif oversampling < 1:
+                logging.warning('oversampling rate is {}, recommend > 1: '
+                                'increase sampling rate or decrease '
+                                'demodulation frequency'.format(oversampling))
         if instr.int_delay() is None:
             instr.int_delay.to_default()
 
@@ -284,6 +285,8 @@ def pre_start_capture(self):
             raise Exception('acq controller sample rate does not match '
                             'instrument value, most likely need '
                             'to call update_acquisition_settings')
+        if self.get_max_demod_freq() is None:
+            raise Exception('no demodulation frequencies set')
         self.records_per_buffer = alazar.records_per_buffer.get()
         self.buffers_per_acquisition = alazar.buffers_per_acquisition.get()
         self.board_info = alazar.get_idn()

From cf8fcbd75b8fb46e3bb5e5f3fe8281306bf52cd0 Mon Sep 17 00:00:00 2001
From: nataliejpg 
Date: Tue, 24 Jan 2017 12:26:44 +0100
Subject: [PATCH 094/328] actually getting get_max_demod_freq to work if no
 demodulation frequencies specified

---
 .../instrument_drivers/AlazarTech/ave_controller_test.py  | 8 +++++++-
 .../instrument_drivers/AlazarTech/rec_controller_test.py  | 8 +++++++-
 qcodes/instrument_drivers/AlazarTech/samp_controller.py   | 7 ++++++-
 3 files changed, 20 insertions(+), 3 deletions(-)

diff --git a/qcodes/instrument_drivers/AlazarTech/ave_controller_test.py b/qcodes/instrument_drivers/AlazarTech/ave_controller_test.py
index cfeaa31c4cbb..1ecce446922b 100644
--- a/qcodes/instrument_drivers/AlazarTech/ave_controller_test.py
+++ b/qcodes/instrument_drivers/AlazarTech/ave_controller_test.py
@@ -215,7 +215,13 @@ def get_max_demod_freq(self):
         nb: really hacky and we should have channels in qcodes but we don't
         (at time of writing)
         """
-        return max([getattr(self, 'demod_freq_{}'.format(c))() for c in range(self._demod_length)])
+        freqs = list(filter(None, [getattr(self, 'demod_freq_{}'.format(c))() 
+                    for c in range(self._demod_length)]))
+        if len(freqs) > 0:
+            return max(freqs)
+        else:
+        return None
+
 
     def update_filter_settings(self, filter, numtaps):
         """
diff --git a/qcodes/instrument_drivers/AlazarTech/rec_controller_test.py b/qcodes/instrument_drivers/AlazarTech/rec_controller_test.py
index dd7e1c185305..e19df17b195b 100644
--- a/qcodes/instrument_drivers/AlazarTech/rec_controller_test.py
+++ b/qcodes/instrument_drivers/AlazarTech/rec_controller_test.py
@@ -224,7 +224,13 @@ def get_max_demod_freq(self):
         nb: really hacky and we should have channels in qcodes but we don't
         (at time of writing)
         """
-        return max([getattr(self, 'demod_freq_{}'.format(c))() for c in range(self._demod_length)])
+        freqs = list(filter(None, [getattr(self, 'demod_freq_{}'.format(c))() 
+                    for c in range(self._demod_length)]))
+        if len(freqs) > 0:
+            return max(freqs)
+        else:
+        return None
+
 
     def update_filter_settings(self, filter, numtaps):
         """
diff --git a/qcodes/instrument_drivers/AlazarTech/samp_controller.py b/qcodes/instrument_drivers/AlazarTech/samp_controller.py
index effe982dcb84..484540a3ca36 100644
--- a/qcodes/instrument_drivers/AlazarTech/samp_controller.py
+++ b/qcodes/instrument_drivers/AlazarTech/samp_controller.py
@@ -237,7 +237,12 @@ def get_max_demod_freq(self):
         nb: really hacky and we should have channels in qcodes but we don't
         (at time of writing)
         """
-        return max([getattr(self, 'demod_freq_{}'.format(c))() for c in range(self._demod_length)])
+        freqs = list(filter(None, [getattr(self, 'demod_freq_{}'.format(c))() 
+                    for c in range(self._demod_length)]))
+        if len(freqs) > 0:
+            return max(freqs)
+        else:
+        return None
 
     def update_filter_settings(self, filter, numtaps):
         """

From fa08a42b49bd497f62fed3f3d3b4ce24ba41bbda Mon Sep 17 00:00:00 2001
From: Natalie Pearson 
Date: Mon, 6 Feb 2017 15:38:14 +0100
Subject: [PATCH 095/328] pep8 and tidy up

---
 .../AlazarTech/ave_controller_test.py         |  24 +-
 .../AlazarTech/basic_controller.py            |  57 ++--
 .../AlazarTech/rec_controller_test.py         |  34 +-
 qcodes/instrument_drivers/AlazarTech/samp.py  | 314 ------------------
 .../AlazarTech/samp_controller.py             |  85 +++--
 5 files changed, 120 insertions(+), 394 deletions(-)
 delete mode 100644 qcodes/instrument_drivers/AlazarTech/samp.py

diff --git a/qcodes/instrument_drivers/AlazarTech/ave_controller_test.py b/qcodes/instrument_drivers/AlazarTech/ave_controller_test.py
index 1ecce446922b..8dce5a6612e5 100644
--- a/qcodes/instrument_drivers/AlazarTech/ave_controller_test.py
+++ b/qcodes/instrument_drivers/AlazarTech/ave_controller_test.py
@@ -8,8 +8,14 @@
 
 
 class AcqVariablesParam(Parameter):
-    def __init__(self, name, instrument, initial_value,
-                 check_and_update_fn, default_fn):
+    """
+    Parameter of an AcquisitionController which has a _check_and_update_instr
+    function used for validation and to update instrument attributes and a
+    _get_default function which it uses to set the parameter to an instrument
+    caluclated default.
+    """
+    def __init__(self, name, instrument, check_and_update_fn,
+                 default_fn, initial_value=None):
         super().__init__(name)
         self._instrument = instrument
         self._save_val(initial_value)
@@ -27,6 +33,11 @@ def to_default(self):
         default = self._get_default()
         self.set(default)
 
+    def check(self):
+        val = self._latest()['value']
+        self._check_and_update_instr(val)
+        return True
+
 
 class AveragedAcqParam(Parameter):
     """
@@ -104,12 +115,10 @@ def __init__(self, name, alazar_name, demod_length=1, filter='win',
             self.add_parameter(name='demod_freq_{}'.format(i),
                                parameter_class=ManualParameter)
         self.add_parameter(name='int_time',
-                           initial_value=None,
                            check_and_update_fn=self._update_int_time,
                            default_fn=self._int_time_default,
                            parameter_class=AcqVariablesParam)
         self.add_parameter(name='int_delay',
-                           initial_value=None,
                            check_and_update_fn=self._update_int_delay,
                            default_fn=self._int_delay_default,
                            parameter_class=AcqVariablesParam)
@@ -215,13 +224,12 @@ def get_max_demod_freq(self):
         nb: really hacky and we should have channels in qcodes but we don't
         (at time of writing)
         """
-        freqs = list(filter(None, [getattr(self, 'demod_freq_{}'.format(c))() 
-                    for c in range(self._demod_length)]))
+        freqs = list(filter(None, [getattr(self, 'demod_freq_{}'.format(c))()
+                                   for c in range(self._demod_length)]))
         if len(freqs) > 0:
             return max(freqs)
         else:
-        return None
-
+            return None
 
     def update_filter_settings(self, filter, numtaps):
         """
diff --git a/qcodes/instrument_drivers/AlazarTech/basic_controller.py b/qcodes/instrument_drivers/AlazarTech/basic_controller.py
index 7b172f070a7e..99421eef1f7e 100644
--- a/qcodes/instrument_drivers/AlazarTech/basic_controller.py
+++ b/qcodes/instrument_drivers/AlazarTech/basic_controller.py
@@ -10,8 +10,10 @@ class SampleSweep(Parameter):
     Hardware controlled parameter class for Alazar acquisition. To be used with
     Acquisition Controller (tested with ATS9360 board)
 
-    Instrument returns an buffer of data (channels * samples * records) which
-    is processed by the post_acquire function of the Acquidiyion Controller
+    Alazar Instrument 'acquire' returns a buffer of data each time a buffer is
+    filled (channels * samples * records) which is processed by the
+    post_acquire function of the Acquisition Controller and finally the
+    processed result is returned when the SampleSweep parameter is called.
     """
 
     def __init__(self, name, instrument):
@@ -21,6 +23,15 @@ def __init__(self, name, instrument):
         self.names = ('A', 'B')
 
     def get(self):
+        """
+        Gets the averaged samples for channels A and B by calling acquire
+        on the alazar (which in turn calls the processing functions of the
+        aqcuisition controller before returning the processed data)
+
+        returns:
+        recordA: numpy array of channel A acquisition
+        recordB: numpy array of channel B acquisition
+        """
         recordA, recordB = self._instrument._get_alazar().acquire(
             acquisition_controller=self._instrument,
             **self.acquisition_kwargs)
@@ -38,8 +49,6 @@ class Basic_Acquisition_Controller(AcquisitionController):
     alazar_name: the name of the alazar instrument such that this controller
         can communicate with the Alazar
     **kwargs: kwargs are forwarded to the Instrument base class
-
-    TODO(nataliejpg) make dtype general or get from alazar
     """
 
     def __init__(self, name, alazar_name, **kwargs):
@@ -59,7 +68,8 @@ def update_acquisition_kwargs(self, **kwargs):
         :return:
         """
         self.samples_per_record = kwargs['samples_per_record']
-        self.acquisition.shapes = ((self.samples_per_record,), (self.samples_per_record,))
+        self.acquisition.shapes = (
+            (self.samples_per_record,), (self.samples_per_record,))
         self.acquisition.acquisition_kwargs.update(**kwargs)
 
     def pre_start_capture(self):
@@ -84,22 +94,28 @@ def pre_acquire(self):
         See AcquisitionController
         :return:
         """
-        # this could be used to start an Arbitrary Waveform Generator, etc...
-        # using this method ensures that the contents are executed AFTER the
-        # Alazar card starts listening for a trigger pulse
         pass
 
     def handle_buffer(self, data):
         """
-        See AcquisitionController
+        Function which is called during the Alazar acquire each time a buffer
+        is completed. In this acquistion controller these buffers are just
+        added together (ie averaged)
         :return:
         """
         self.buffer += data
 
     def post_acquire(self):
         """
-        See AcquisitionController
-        :return:
+        Function which is called at the end of the Alazar acquire function to
+        signal completion and trigger data processing. This acquisition
+        controller has averaged over the buffers acquired so has one buffer of
+        data which is splits into records and channels, averages over records
+        and returns the samples for each channel.
+
+        return:
+        recordA: numpy array of channel A acquisition
+        recordB: numpy array of channel B acquisition
         """
 
         # for ATS9360 samples are arranged in the buffer as follows:
@@ -124,6 +140,7 @@ def post_acquire(self):
             recB += self.buffer[i0:i1:self.number_of_channels]
         recordB = np.uint16(recB / records_per_acquisition)
 
+        # converts to volts if bits per sample is 12 (as ATS9360)
         bps = self.board_info['bits_per_sample']
         if bps == 12:
             volt_rec_A = helpers.sample_to_volt_u12(recordA, bps)
@@ -135,21 +152,3 @@ def post_acquire(self):
             volt_rec_B = recordB - np.mean(recordB)
 
         return volt_rec_A, volt_rec_B
-
-"""
-def sample_to_volt_u12(raw_samples, bps):
-    # right_shift 16-bit sample by 4 to get 12 bit sample
-    shifted_samples = np.right_shift(raw_samples, 4)
-
-    # Alazar calibration
-    code_zero = (1 << (bps - 1)) - 0.5
-    code_range = (1 << (bps - 1)) - 0.5
-
-    # TODO(nataliejpg) make this not hard coded
-    input_range_volts = 1
-    # Convert to volts
-    volt_samples = np.float64(input_range_volts *
-                              (shifted_samples - code_zero) / code_range)
-
-    return volt_samples
-"""
\ No newline at end of file
diff --git a/qcodes/instrument_drivers/AlazarTech/rec_controller_test.py b/qcodes/instrument_drivers/AlazarTech/rec_controller_test.py
index e19df17b195b..765a94b7acf0 100644
--- a/qcodes/instrument_drivers/AlazarTech/rec_controller_test.py
+++ b/qcodes/instrument_drivers/AlazarTech/rec_controller_test.py
@@ -8,8 +8,14 @@
 
 
 class AcqVariablesParam(Parameter):
-    def __init__(self, name, instrument, initial_value,
-                 check_and_update_fn, default_fn):
+    """
+    Parameter of an AcquisitionController which has a _check_and_update_instr
+    function used for validation and to update instrument attributes and a
+    _get_default function which it uses to set the parameter to an instrument
+    caluclated default.
+    """
+    def __init__(self, name, instrument, check_and_update_fn,
+                 default_fn, initial_value=None):
         super().__init__(name)
         self._instrument = instrument
         self._save_val(initial_value)
@@ -27,6 +33,11 @@ def to_default(self):
         default = self._get_default()
         self.set(default)
 
+    def check(self):
+        val = self._latest()['value']
+        self._check_and_update_instr(val)
+        return True
+
 
 class RecordsAcqParam(Parameter):
     """
@@ -113,12 +124,10 @@ def __init__(self, name, alazar_name, demod_length=1, filter='win',
             self.add_parameter(name='demod_freq_{}'.format(i),
                                parameter_class=ManualParameter)
         self.add_parameter(name='int_time',
-                           initial_value=None,
                            check_and_update_fn=self._update_int_time,
                            default_fn=self._int_time_default,
                            parameter_class=AcqVariablesParam)
         self.add_parameter(name='int_delay',
-                           initial_value=None,
                            check_and_update_fn=self._update_int_delay,
                            default_fn=self._int_delay_default,
                            parameter_class=AcqVariablesParam)
@@ -224,14 +233,13 @@ def get_max_demod_freq(self):
         nb: really hacky and we should have channels in qcodes but we don't
         (at time of writing)
         """
-        freqs = list(filter(None, [getattr(self, 'demod_freq_{}'.format(c))() 
-                    for c in range(self._demod_length)]))
+        freqs = list(filter(None, [getattr(self, 'demod_freq_{}'.format(c))()
+                                   for c in range(self._demod_length)]))
         if len(freqs) > 0:
             return max(freqs)
         else:
         return None
 
-
     def update_filter_settings(self, filter, numtaps):
         """
         Updates the settings of the filter for filtering out
@@ -294,13 +302,15 @@ def pre_start_capture(self):
                                self.records_per_buffer *
                                self.number_of_channels)
 
-        mat_shape = (self._demod_length, self.records_per_buffer, self.samples_per_record)
+        mat_shape = (self._demod_length, self.records_per_buffer,
+                     self.samples_per_record)
         demod_list = np.array([getattr(self, 'demod_freq_{}'.format(n))()
                                for n in range(self._demod_length)])
         integer_list = np.arange(self.samples_per_record)
         integer_mat = np.outer(np.ones(self.records_per_buffer), integer_list)
         angle_mat = 2 * np.pi * \
-            np.outer(demod_list, integer_mat).reshape(mat_shape) / self.sample_rate
+            np.outer(demod_list, integer_mat).reshape(
+                mat_shape) / self.sample_rate
         self.cos_mat = np.cos(angle_mat)
         self.sin_mat = np.sin(angle_mat)
 
@@ -370,8 +380,10 @@ def _fit(self, rec):
             volt_rec = rec - np.mean(rec, axis=1)
 
         # volt_rec to matrix and multiply with demodulation signal matrices
-        mat_shape = (self._demod_length, self.records_per_buffer, self.samples_per_record)
-        volt_rec_mat = np.outer(np.ones(self._demod_length), volt_rec).reshape(mat_shape)
+        mat_shape = (self._demod_length, self.records_per_buffer,
+                     self.samples_per_record)
+        volt_rec_mat = np.outer(
+            np.ones(self._demod_length), volt_rec).reshape(mat_shape)
         re_mat = np.multiply(volt_rec_mat, self.cos_mat)
         im_mat = np.multiply(volt_rec_mat, self.sin_mat)
 
diff --git a/qcodes/instrument_drivers/AlazarTech/samp.py b/qcodes/instrument_drivers/AlazarTech/samp.py
deleted file mode 100644
index 89f080ff4ddd..000000000000
--- a/qcodes/instrument_drivers/AlazarTech/samp.py
+++ /dev/null
@@ -1,314 +0,0 @@
-import logging
-from .ATS import AcquisitionController
-import numpy as np
-from qcodes import Parameter
-from qcodes.instrument_drivers.AlazarTech.acq_helpers import filter_win, filter_ls, sample_to_volt_u12, roundup
-from qcodes.instrument.parameter import ManualParameter
-from qcodes.utils.validators import Validator, Numbers, Ints, Enum, Anything
-# from qcodes.utils import validators
-
-
-class SamplesParam(Parameter):
-    """
-    Hardware controlled parameter class for Alazar acquisition. To be used with
-    HD_Samples_Controller (tested with ATS9360 board) for return of an array of
-    sample data from the Alazar, averaged over records and buffers.
-
-    TODO(nataliejpg) refactor setpoints/shapes horriblenesss
-    TODO(nataliejpg) setpoint units
-    """
-
-    def __init__(self, name, instrument):
-        super().__init__(name)
-        self._instrument = instrument
-        self.acquisition_kwargs = {}
-        self.names = ('magnitude', 'phase')
-        self.setpoint_names = (('acq_time (s)',), ('acq_time (s)',))
-
-    def update_sweep(self, start, stop, npts):
-        n = tuple(np.linspace(start, stop, num=npts))
-        self.setpoints = ((n,), (n,))
-        self.shapes = ((npts,), (npts,))
-
-    def get(self):
-        mag, phase = self._instrument._get_alazar().acquire(
-            acquisition_controller=self._instrument,
-            **self.acquisition_kwargs)
-        return mag, phase
-
-
-class HD_Samples_Controller(AcquisitionController):
-    """
-    This is the Acquisition Controller class which works with the ATS9360,
-    averaging over buffers and records and demodulating with a software
-    reference signal, returning the  samples.
-
-    TODO (nataliejpg) docstrings
-
-    Args:
-        name: name for this acquisition_conroller as an instrument
-        alazar_name: the name of the alazar instrument such that this controller
-            can communicate with the Alazar
-        **kwargs: kwargs are forwarded to the Instrument base class
-
-    Returns:
-
-
-    TODO(nataliejpg) test filter options
-    TODO(nataliejpg) finish implementation of channel b option
-    TODO(nataliejpg) set samples per record off int time and int delay
-    TODO(compare _get_alazar vs _alazar)
-    """
-    filter_dict = {'win': 0, 'ls': 1}
-
-    def __init__(self, name, alazar_name, filter='win', numtaps=101, chan_b=False, **kwargs):
-        self.filter_settings = {'filter': self.filter_dict[filter],
-                                'numtaps': numtaps}
-        self.chan_b = chan_b
-        self.number_of_channels = 2
-
-        super().__init__(name, alazar_name, **kwargs)
-
-        self.add_parameter(name='acquisition',
-                           parameter_class=SamplesParam)
-        self.add_parameter(name='demodulation_frequency',
-                           parameter_class=ManualParameter)
-        self.add_parameter(name='int_time',
-                           units='s',
-                           vals=Anything(),
-                           parameter_class=ManualParameter)
-        self.add_parameter(name='int_delay',
-                           units='s',
-                           vals=Anything(),
-                           parameter_class=ManualParameter)
-
-    def check_time_values(self):
-        if not (self.int_time() or self.int_delay()):
-            raise ValueError('int_time or int_delay are not set, set these '
-                             'parameters or updats kwargs')
-        oscilations_measured = self.int_time() * self.demodulation_frequency()
-        total_time = self.samples_per_record / self.sample_rate
-        time_used = self.int_time() + self.int_delay()
-        samples_used = int(np.ceil(time_used * self.sample_rate))
-        oversampling = self.sample_rate / (2 * self.demodulation_frequency())
-        samples_delay_min = self.filter_settings['numtaps'] - 1
-        int_delay_min = samples_delay_min / self.sample_rate
-
-        if time_used > total_time:
-            raise ValueError('int_delay and int_time sum to {} which is more '
-                             'than  the total time available: {} . '
-                             'Increase samples_per_record, decrease '
-                             'sampling rate'.format(time_used, total_time))
-        elif samples_used < self.samples_per_record - 32:
-            logging.info('{} samples used of {}, consider lowering the '
-                         'number of samples per record to '
-                         '{}'.format(samples_used, self.samples_per_record,
-                                     np.ceil(samples_used / 32)))
-
-        if self.int_time() == 0:
-            raise ValueError('int_time is set to 0')
-        elif oscilations_measured < 10:
-            logging.warning('{} oscilations measured, recommend at '
-                            'least 10: decrease sampling rate, take '
-                            'more samples or increase demodulation '
-                            'freq'.format(oscilations_measured))
-        if oversampling < 1:
-            logging.warning('oversampling rate is {}, recommend > 1: '
-                            'increase sampling rate or decrease '
-                            'demodulation frequency'.format(oversampling))
-        if self.int_delay() < int_delay_min:
-            logging.warning(
-                'delay is less than recommended for filter choice: '
-                '(expect delay >= {}'.format(int_delay_min))
-
-    def update_filter_settings(self, filter, numtaps):
-        """
-        Updates the settings of the filter for filtering out
-        double frwuency component for demodulation.
-
-        Args:
-            filter (str): filter type ('win' or 'ls')
-            numtaps (int): numtaps for filter
-        """
-        self.filter_settings.update({'filter': self.filter_dict[filter],
-                                     'numtaps': numtaps})
-
-    def update_acquisition_kwargs(self, **kwargs):
-        """
-        Updates the kwargs to be used when
-        alazar_driver.acquire is called via a get call of the
-        acquisition SamplesParam.
-
-        It is also used to set the limits on the selection of
-        samples returned (bounded by delay and integration time)
-        and update this information in the Samples Parameter.
-        """
-        alazar = self._get_alazar()
-        self.sample_rate = alazar.get_sample_rate()
-        if 'samples_per_record' in kwargs:
-            self.samples_per_record = roundup(
-                kwargs['samples_per_record'], alazar.samples_divisor)
-            time_available = self.samples_per_record / self.sample_rate
-            if self.int_delay() is None:
-                samp_delay = self.filter_settings['numtaps'] - 1
-                self.int_delay(samp_delay / self.sample_rate)
-            time_available -= self.int_delay()
-            if self.int_time() is None:
-                self.int_time(time_available)
-        elif self.int_time() is not None:
-            if self.int_delay() is None:
-                samp_delay = self.filter_settings['numtaps'] - 1
-                self.int_delay(samp_delay / self.sample_rate)
-            time_used = self.int_time() + self.int_delay()
-            samples_used = int(time_used * self.sample_rate)
-            self.samples_per_record = roundup(
-                samples_used, alazar.samples_divisor)
-            kwargs['samples_per_record'] = self.samples_per_record
-        else:
-            raise ValueError(
-                'Either an integration time or samples_per_record '
-                'must be specified')
-
-        self.check_time_values()
-
-        start = self.int_delay()
-        stop = self.int_delay() + self.int_time()
-        npts = int(self.int_time() * self.sample_rate)
-
-        self.acquisition.acquisition_kwargs.update(**kwargs)
-        self.acquisition.update_sweep(start, stop, npts)
-
-    def pre_start_capture(self):
-        """
-        Called before capture start to update Acquisition Controller with
-        alazar acquisition params and set up software wave for demodulation.
-        :return:
-        """
-        alazar = self._get_alazar()
-        if self.samples_per_record != alazar.samples_per_record.get():
-            raise Exception('Instrument samples_per_record settings does '
-                            'not match acq controller value, most likely '
-                            'need to call update_acquisition_settings')
-        if self.sample_rate != alazar.get_sample_rate():
-            raise Exception('acq controller sample rate does not match '
-                            'instrument value, most likely need to call '
-                            'update_acquisition_settings')
-        self.check_time_values()
-
-        self.records_per_buffer = alazar.records_per_buffer.get()
-        self.buffers_per_acquisition = alazar.buffers_per_acquisition.get()
-
-        self.board_info = alazar.get_idn()
-        self.buffer = np.zeros(self.samples_per_record *
-                               self.records_per_buffer *
-                               self.number_of_channels)
-
-        integer_list = np.arange(self.samples_per_record, dtype=np.uint16)
-        angle_list = (2 * np.pi * self.demodulation_frequency() /
-                      self.sample_rate * integer_list)
-
-        self.cos_list = np.cos(angle_list)
-        self.sin_list = np.sin(angle_list)
-
-    def pre_acquire(self):
-        """
-        See AcquisitionController
-        :return:
-        """
-        pass
-
-    def handle_buffer(self, data):
-        """
-        Adds data from alazar to buffer (effectively averaging)
-        :return:
-        """
-        self.buffer += data
-
-    def post_acquire(self):
-        """
-        Processes the data according to ATS9360 settings, splitting into
-        records and averaging over them, then applying demodulation fit
-        nb: currently only channel A
-        :return: samples_magnitude_array, samples_phase_array
-        """
-        records_per_acquisition = (self.buffers_per_acquisition *
-                                   self.records_per_buffer)
-        # for ATS9360 samples are arranged in the buffer as follows:
-        # S00A, S00B, S01A, S01B...S10A, S10B, S11A, S11B...
-        # where SXYZ is record X, sample Y, channel Z.
-
-        # break buffer up into records and averages over them
-        recA = np.zeros(self.samples_per_record)
-        for i in range(self.records_per_buffer):
-            i0 = (i * self.samples_per_record * self.number_of_channels)
-            i1 = (i0 + self.samples_per_record * self.number_of_channels)
-            recA += self.buffer[i0:i1:self.number_of_channels]
-        recordA = np.uint16(recA / records_per_acquisition)
-
-        # do demodulation
-        magA, phaseA = self.fit(recordA)
-
-        # same for chan b
-        if self.chan_b:
-            raise NotImplementedError('chan b code not complete')
-            # recordB = np.zeros(self.samples_per_record, dtype=np.uint16)
-            # for i in range(self.records_per_buffer):
-            #     i0 = (i * self.samples_per_record *
-            #           self.number_of_channels + 1)
-            #     i1 = (i0 + self.samples_per_record * self.number_of_channels)
-            #     recordB += np.uint16(self.buffer[i0:i1:self.number_of_channels] /
-            #                          records_per_acquisition)
-            # magB, phaseB = self.fit(recordB)
-
-        return magA, phaseA
-
-    def fit(self, rec):
-        """
-        Applies volts conversion, demodulation fit, low bandpass filter
-        and integration limits to samples array
-        :return: samples_magnitude_array, samples_phase_array
-        """
-
-        # convert rec to volts
-        bps = self.board_info['bits_per_sample']
-        if bps == 12:
-            volt_rec = sample_to_volt_u12(rec, bps)
-        else:
-            logging.warning('sample to volt conversion does not exist for'
-                            ' bps != 12, centered raw samples returned')
-            volt_rec = rec - np.mean(rec)
-
-        # multiply with software wave
-        re_wave = np.multiply(volt_rec, self.cos_list)
-        im_wave = np.multiply(volt_rec, self.sin_list)
-        cutoff = self.demodulation_frequency() / 10
-
-        # filter out higher freq component
-        if self.filter_settings['filter'] == 0:
-            re_filtered = filter_win(re_wave, cutoff,
-                                     self.sample_rate,
-                                     self.filter_settings['numtaps'])
-            im_filtered = filter_win(im_wave, cutoff,
-                                     self.sample_rate,
-                                     self.filter_settings['numtaps'])
-        elif self.filter_settings['filter'] == 1:
-            re_filtered = filter_ls(re_wave, cutoff,
-                                    self.sample_rate,
-                                    self.filter_settings['numtaps'])
-            im_filtered = filter_ls(im_wave, cutoff,
-                                    self.sample_rate,
-                                    self.filter_settings['numtaps'])
-
-        # apply integration limits
-        beginning = int(self.int_delay() * self.sample_rate)
-        end = beginning + int(self.int_time() * self.sample_rate)
-
-        re_limited = re_filtered[beginning:end]
-        im_limited = im_filtered[beginning:end]
-
-        # convert to magnitude and phase
-        complex_num = re_limited + im_limited * 1j
-        mag = abs(complex_num)
-        phase = np.angle(complex_num, deg=True)
-
-        return mag, phase
diff --git a/qcodes/instrument_drivers/AlazarTech/samp_controller.py b/qcodes/instrument_drivers/AlazarTech/samp_controller.py
index 484540a3ca36..ce54f8a29c99 100644
--- a/qcodes/instrument_drivers/AlazarTech/samp_controller.py
+++ b/qcodes/instrument_drivers/AlazarTech/samp_controller.py
@@ -8,8 +8,15 @@
 
 
 class AcqVariablesParam(Parameter):
-    def __init__(self, name, instrument, initial_value,
-                 check_and_update_fn, default_fn):
+    """
+    Parameter of an AcquisitionController which has a _check_and_update_instr
+    function used for validation and to update instrument attributes and a
+    _get_default function which it uses to set the AcqVariablesParam to an
+    instrument caluclated default.
+    """
+
+    def __init__(self, name, instrument, check_and_update_fn,
+                 default_fn, initial_value=None):
         super().__init__(name)
         self._instrument = instrument
         self._save_val(initial_value)
@@ -17,6 +24,13 @@ def __init__(self, name, instrument, initial_value,
         setattr(self, '_get_default', default_fn)
 
     def set(self, value):
+        """
+        Function which checks value using validation function and then sets
+        the Parameter value to this value.
+
+        Args:
+            value: value to set the parameter to
+        """
         self._check_and_update_instr(value)
         self._save_val(value)
 
@@ -24,10 +38,22 @@ def get(self):
         return self._latest()['value']
 
     def to_default(self):
+        """
+        Function which executes the default_fn specified to calculate the
+        default value based on instrument values and then calls the set
+        function with this value
+        """
         default = self._get_default()
         self.set(default)
 
     def check(self):
+        """
+        Function which checks the current Parameter value using the specified
+        check_and_update_fn which can also serve to update instrument values.
+
+        Return:
+            True (if no errors raised when check_and_update_fn executed)
+        """
         val = self._latest()['value']
         self._check_and_update_instr(val)
         return True
@@ -60,8 +86,6 @@ def update_sweep(self, start, stop, npts):
             self.shapes = ((npts,), (npts,))
 
     def get(self):
-        # self._instrument.int_time.check()
-        # self._instrument.int_delay.check()
         mag, phase = self._instrument._get_alazar().acquire(
             acquisition_controller=self._instrument,
             **self.acquisition_kwargs)
@@ -90,9 +114,7 @@ class HD_Samples_Controller(AcquisitionController):
     TODO(nataliejpg) finish implementation of channel b option
     TODO(nataliejpg) what should be private?
     TODO(nataliejpg) where should filter_dict live?
-    TODO(nataliejpg) demod_freq should be changeable number: maybe channels
-    TODO(nataliejpg) try using fit from helpers
-    TODO(nataliejpg) check docstrings
+    TODO(nataliejpg) demod_freq number should be changeable number: channels?
     """
     filter_dict = {'win': 0, 'ls': 1}
     samples_divisor = AlazarTech_ATS9360.samples_divisor
@@ -114,12 +136,10 @@ def __init__(self, name, alazar_name, demod_length=1, filter='win',
             self.add_parameter(name='demod_freq_{}'.format(i),
                                parameter_class=ManualParameter)
         self.add_parameter(name='int_time',
-                           initial_value=None,
                            check_and_update_fn=self._update_int_time,
                            default_fn=self._int_time_default,
                            parameter_class=AcqVariablesParam)
         self.add_parameter(name='int_delay',
-                           initial_value=None,
                            check_and_update_fn=self._update_int_delay,
                            default_fn=self._int_delay_default,
                            parameter_class=AcqVariablesParam)
@@ -128,16 +148,24 @@ def _update_int_time(instr, value):
         """
         Function to validate value for int_time before setting parameter
         value
-        Checks: limit between 0 and 0.1s
-                acq knows sample_rate (doesn't check with alazar for accuracy)
-                number of oscilation measured in this time
-                oversampling rate
-        Sets: samples_per_record of acq controller
-              acquisition_kwarg['samples_per_record'] of acquisition param
-              shape of acquisition param
+
+        Args:
+            value to be validated and used for instrument attribute update
+
+        Checks:
+            0 <= value <= 0.1 seconds
+            number of oscilation measured in this time
+            oversampling rate
+
+        Sets:
+            sample_rate attribute of instument to be that of alazar
+            samples_per_record of acq controller
+            acquisition_kwarg['samples_per_record'] of acquisition param
+            shape of acquisition param
         """
         if (value is None) or not (0 <= value <= 0.1):
             raise ValueError('int_time must be 0 <= value <= 1')
+
         alazar = instr._get_alazar()
         instr.sample_rate = alazar.get_sample_rate()
         if instr.get_max_demod_freq() is not None:
@@ -237,12 +265,12 @@ def get_max_demod_freq(self):
         nb: really hacky and we should have channels in qcodes but we don't
         (at time of writing)
         """
-        freqs = list(filter(None, [getattr(self, 'demod_freq_{}'.format(c))() 
-                    for c in range(self._demod_length)]))
+        freqs = list(filter(None, [getattr(self, 'demod_freq_{}'.format(c))()
+                                   for c in range(self._demod_length)]))
         if len(freqs) > 0:
             return max(freqs)
         else:
-        return None
+            return None
 
     def update_filter_settings(self, filter, numtaps):
         """
@@ -273,7 +301,8 @@ def update_acquisition_kwargs(self, **kwargs):
             raise ValueError('With HD_Samples_Controller '
                              'samples_per_record cannot be set manually '
                              'via update_acquisition_kwargs and should instead'
-                             'be set by setting int_time and int_delay')
+                             'be set by setting int_time int_delay and alazar '
+                             'sample_rate')
         self.acquisition.acquisition_kwargs.update(**kwargs)
 
     def pre_start_capture(self):
@@ -325,7 +354,7 @@ def post_acquire(self):
         Returns:
             magnitude (numpy array): shape = (demod_length, samples_used)
             phase (numpy array): shape = (demod_length, samples_used)
-        """ 
+        """
         records_per_acquisition = (self.buffers_per_acquisition *
                                    self.records_per_buffer)
         # for ATS9360 samples are arranged in the buffer as follows:
@@ -346,14 +375,6 @@ def post_acquire(self):
         # same for chan b
         if self.chan_b:
             raise NotImplementedError('chan b code not complete')
-            # recordB = np.zeros(self.samples_per_record, dtype=np.uint16)
-            # for i in range(self.records_per_buffer):
-            #     i0 = (i * self.samples_per_record *
-            #           self.number_of_channels + 1)
-            #     i1 = (i0 + self.samples_per_record * self.number_of_channels)
-            #     recordB += np.uint16(self.buffer[i0:i1:self.number_of_channels] /
-            #                          records_per_acquisition)
-            # magB, phaseB = self.fit(recordB)
 
         return magA, phaseA
 
@@ -385,7 +406,7 @@ def _fit(self, rec):
         volt_rec_mat = np.outer(np.ones(self._demod_length), volt_rec)
         re_mat = np.multiply(volt_rec_mat, self.cos_mat)
         im_mat = np.multiply(volt_rec_mat, self.sin_mat)
-        
+
         # filter out higher freq component
         cutoff = self.get_max_demod_freq() / 20
         if self.filter_settings['filter'] == 0:
@@ -413,8 +434,8 @@ def _fit(self, rec):
 
         re_limited = re_filtered[:, beginning:end]
         im_limited = im_filtered[:, beginning:end]
-        #return re_limited, volt_rec_mat[:, beginning:end]
-        
+        # return re_limited, volt_rec_mat[:, beginning:end]
+
         # convert to magnitude and phase
         complex_mat = re_limited + im_limited * 1j
         magnitude = abs(complex_mat)

From b8689c84abb2228ef1d5c83e94e4f0f312d50023 Mon Sep 17 00:00:00 2001
From: Natalie Pearson 
Date: Mon, 6 Feb 2017 19:36:53 +0100
Subject: [PATCH 096/328] add docstrings and refactor demod_freq parameter to
 be homemade AcqVariable param so that setpoints will be updated when
 demod_freqs changed

---
 .../AlazarTech/ave_controller_test.py         | 246 +++++++++++++----
 .../AlazarTech/basic_controller.py            |   9 +-
 .../AlazarTech/rec_controller_test.py         | 258 ++++++++++++++----
 .../AlazarTech/samp_controller.py             | 206 +++++++++++---
 4 files changed, 575 insertions(+), 144 deletions(-)

diff --git a/qcodes/instrument_drivers/AlazarTech/ave_controller_test.py b/qcodes/instrument_drivers/AlazarTech/ave_controller_test.py
index 8dce5a6612e5..3018775875d3 100644
--- a/qcodes/instrument_drivers/AlazarTech/ave_controller_test.py
+++ b/qcodes/instrument_drivers/AlazarTech/ave_controller_test.py
@@ -4,38 +4,71 @@
 import numpy as np
 from qcodes import Parameter
 import qcodes.instrument_drivers.AlazarTech.acq_helpers as helpers
-from qcodes.instrument.parameter import ManualParameter
 
 
 class AcqVariablesParam(Parameter):
     """
     Parameter of an AcquisitionController which has a _check_and_update_instr
     function used for validation and to update instrument attributes and a
-    _get_default function which it uses to set the parameter to an instrument
-    caluclated default.
+    _get_default function which it uses to set the AcqVariablesParam to an
+    instrument caluclated default.
+
+    Args:
+        name: name for this parameter
+        instrument: acquisition controller instrument this parameter belongs to
+        check_and_update_fn: instrument function to be used for value
+            validation and updating instrument values
+        default_fn (optional): instrument function to be used to calculate
+            a default value to set parameter to
+        initial_value (optional): initial value for parameter
     """
+
     def __init__(self, name, instrument, check_and_update_fn,
-                 default_fn, initial_value=None):
+                 default_fn=None, initial_value=None):
         super().__init__(name)
         self._instrument = instrument
         self._save_val(initial_value)
         setattr(self, '_check_and_update_instr', check_and_update_fn)
-        setattr(self, '_get_default', default_fn)
+        if default_fn is not None:
+            setattr(self, '_get_default', default_fn)
 
     def set(self, value):
-        self._check_and_update_instr(value)
+        """
+        Function which checks value using validation function and then sets
+        the Parameter value to this value.
+
+        Args:
+            value: value to set the parameter to
+        """
+        self._check_and_update_instr(value, param_name=self.name)
         self._save_val(value)
 
     def get(self):
         return self._latest()['value']
 
     def to_default(self):
-        default = self._get_default()
+        """
+        Function which executes the default_fn specified to calculate the
+        default value based on instrument values and then calls the set
+        function with this value
+        """
+        try:
+            default = self._get_default()
+        except AttributeError as e:
+            raise AttributeError('no default function for {} Parameter '
+                                 '{}'.format(self.name, e))
         self.set(default)
 
     def check(self):
+        """
+        Function which checks the current Parameter value using the specified
+        check_and_update_fn which can also serve to update instrument values.
+
+        Return:
+            True (if no errors raised when check_and_update_fn executed)
+        """
         val = self._latest()['value']
-        self._check_and_update_instr(val)
+        self._check_and_update_instr(val, param_name=self.name)
         return True
 
 
@@ -45,9 +78,15 @@ class AveragedAcqParam(Parameter):
     HD_Averaging_Controller (tested with ATS9360 board) for return of
     averaged magnitude and phase data from the Alazar.
 
+    Args:
+        name: name for this parameter
+        instrument: acquisition controller instrument this parameter belongs to
+        demod_length: numer of demodulation frequencies the acq controller has
+            NB this is currently set in acq controller init but that's really
+            not nice and should be changed when possible
+
     TODO(nataliejpg) setpoints (including names and units)
     TODO(nataliejpg) setpoint units
-    TODO(nataliejpg) convert demod_index setpoint into actual frequency
     """
 
     def __init__(self, name, instrument, demod_length):
@@ -60,7 +99,34 @@ def __init__(self, name, instrument, demod_length):
         else:
             self.shapes = ((), ())
 
+    def update_demod_setpoints(self, demod_freqs):
+        """
+        Function to update the demodulation frequency setpoints to be called
+        when a demod_freq Parameter of the acq controller is updated
+
+        Args:
+            demod_freqs: numpy array of demodulation frequencies to use as
+                setpoints if length > 1
+        """
+        demod_length = self._instrument._demod_length
+        self._demod_list = demod_freqs
+        if demod_length > 1:
+            self.setpoints = ((self._demod_list, ), (self._demod_list, ))
+        else:
+            pass
+
     def get(self):
+        """
+        Gets the magnitude and phase signal by calling acquire
+        on the alazar (which in turn calls the processing functions of the
+        aqcuisition controller before returning the processed data
+        demodulated at specified frequencies and averaged over samples,
+        records and buffers)
+
+        Returns:
+            mag: numpy array of magnitude, shape (demod_length, )
+            phase: numpy array of magnitude, shape (demod_length, )
+        """
         mag, phase = self._instrument._get_alazar().acquire(
             acquisition_controller=self._instrument,
             **self.acquisition_kwargs)
@@ -70,19 +136,19 @@ def get(self):
 class HD_Averaging_Controller(AcquisitionController):
     """
     This is the Acquisition Controller class which works with the ATS9360,
-    averaging over samples, buffers and records, demodulating with a software
-    reference signal.
+    averaging over samples (limited by int_time and int_delay values),
+    records and buffers and demodulating with software reference signal(s).
 
     Args:
         name: name for this acquisition_conroller as an instrument
-        alazar_name: the name of the alazar instrument such that this
+        alazar_name: name of the alazar instrument such that this
             controller can communicate with the Alazar
-        demod_freqs: the frequency of the software wave to be created
-        filter: the filter to be used to filter out double freq component
-            ('win' - window, 'ls' - least squared, 'ave' - averaging)
-        numtaps: number of freq components used in filter
-        chan_b: whether there is also a second channel of data to be processed
-            and returned
+        demod_length (default 1): number of demodulation frequencies
+        filter (default 'win'): filter to be used to filter out double freq
+            component ('win' - window, 'ls' - least squared, 'ave' - averaging)
+        numtaps (default 101): number of freq components used in filter
+        chan_b (default False): whether there is also a second channel of data
+            to be processed and returned
         **kwargs: kwargs are forwarded to the Instrument base class
 
     TODO(nataliejpg) test filter options
@@ -91,7 +157,6 @@ class HD_Averaging_Controller(AcquisitionController):
     TODO(nataliejpg) where should filter_dict live?
     TODO(nataliejpg) demod_freq should be changeable number: maybe channels
     TODO(nataliejpg) try using fit from helpers
-    TODO(nataliejpg) check docstrings
     """
 
     filter_dict = {'win': 0, 'ls': 1, 'ave': 2}
@@ -113,7 +178,8 @@ def __init__(self, name, alazar_name, demod_length=1, filter='win',
                            parameter_class=AveragedAcqParam)
         for i in range(demod_length):
             self.add_parameter(name='demod_freq_{}'.format(i),
-                               parameter_class=ManualParameter)
+                               check_and_update_fn=self._update_demod_freq,
+                               parameter_class=AcqVariablesParam)
         self.add_parameter(name='int_time',
                            check_and_update_fn=self._update_int_time,
                            default_fn=self._int_time_default,
@@ -123,30 +189,82 @@ def __init__(self, name, alazar_name, demod_length=1, filter='win',
                            default_fn=self._int_delay_default,
                            parameter_class=AcqVariablesParam)
 
-    def _update_int_time(instr, value):
+    def _update_demod_freq(instr, value, param_name=None):
+        """
+        Function to validate and update acquisiton parameter when
+        a demod_freq_ Parameter is changed
+
+        Args:
+            value to update demodulation frequency to
+
+        Kwargs:
+            param_name: used to update demod_freq list used for updating
+                septionts of acquisition parameter
+
+        Checks:
+            1e6 <= value <= 500e6
+            number of oscilation measured using current int_tiume param value
+                at this demod frequency value
+            oversampling rate for this demodulation frequency
+
+        Sets:
+            sample_rate attr of acq controller to be that of alazar
+            setpoints of acquisiton parameter
+        """
+        if (value is None) or not (1e6 <= value <= 500e6):
+            raise ValueError('demod_freqs must be 1e6 <= value <= 500e6')
+        alazar = instr._get_alazar()
+        instr.sample_rate = alazar.get_sample_rate()
+        min_oscilations_measured = instr.int_time() * value
+        oversampling = instr.sample_rate / (2 * value)
+        if min_oscilations_measured < 10:
+            logging.warning('{} oscilations measured for largest '
+                            'demod freq, recommend at least 10: '
+                            'decrease sampling rate, take '
+                            'more samples or increase demodulation '
+                            'freq'.format(min_oscilations_measured))
+        elif oversampling < 1:
+            logging.warning('oversampling rate is {}, recommend > 1: '
+                            'increase sampling rate or decrease '
+                            'demodulation frequency'.format(oversampling))
+        demod_freqs = instr.get_demod_freqs()
+        current_demod_index = ([int(s) for s in param_name.split()
+                                if s.isdigit()][0])
+        demod_freqs[current_demod_index] = value
+        instr.acquisition.update_demod_setpoints(demod_freqs)
+
+    def _update_int_time(instr, value, **kwargs):
         """
         Function to validate value for int_time before setting parameter
         value
-        Checks: limit between 0 and 0.1s
-                acq knows sample_rate (doesn't check with alazar for accuracy)
-                number of oscilation measured in this time
-                oversampling rate
-        Sets: samples_per_record of acq controller
-              acquisition_kwarg['samples_per_record'] of acquisition param
-              shape of acquisition param
+
+        Args:
+            value to be validated and used for instrument attribute update
+
+        Checks:
+            0 <= value <= 0.1 seconds
+            number of oscilation measured in this time
+            oversampling rate
+
+        Sets:
+            sample_rate attr of acq controller to be that of alazar
+            samples_per_record of acq controller
+            acquisition_kwarg['samples_per_record'] of acquisition param
         """
         if (value is None) or not (0 <= value <= 0.1):
             raise ValueError('int_time must be 0 <= value <= 1')
+
         alazar = instr._get_alazar()
         instr.sample_rate = alazar.get_sample_rate()
         if instr.get_max_demod_freq() is not None:
-            oscilations_measured = value * instr.get_max_demod_freq()
+            min_oscilations_measured = value * instr.get_max_demod_freq()
             oversampling = instr.sample_rate / (2 * instr.get_max_demod_freq())
-            if oscilations_measured < 10:
-                logging.warning('{} oscilations measured, recommend at '
-                                'least 10: decrease sampling rate, take '
+            if min_oscilations_measured < 10:
+                logging.warning('{} oscilations measured for largest '
+                                'demod freq, recommend at least 10: '
+                                'decrease sampling rate, take '
                                 'more samples or increase demodulation '
-                                'freq'.format(oscilations_measured))
+                                'freq'.format(min_oscilations_measured))
             elif oversampling < 1:
                 logging.warning('oversampling rate is {}, recommend > 1: '
                                 'increase sampling rate or decrease '
@@ -162,16 +280,23 @@ def _update_int_time(instr, value):
         instr.acquisition.acquisition_kwargs.update(
             samples_per_record=instr.samples_per_record)
 
-    def _update_int_delay(instr, value):
+    def _update_int_delay(instr, value, **kwargs):
         """
         Function to validate value for int_delay before setting parameter
         value
-        Checks: limit between 0 and 1s
-                acq knows sample_rate (doesn't check with alazar for accuracy)
-                number of samples discarded >= numtaps
-        Sets: samples_per_record of acq controller
-              acquisition_kwarg['samples_per_record'] of acquisition param
-              shape of acquisition param
+
+        Args:
+            value to be validated and used for instrument attribute update
+
+        Checks:
+            0 <= value <= 0.1 seconds
+            number of samples discarded >= numtaps
+
+        Sets:
+            sample_rate attr of acq controller to be that of alazar
+            samples_per_record of acq controller
+            acquisition_kwarg['samples_per_record'] of acquisition param
+            setpoints of acquisiton param
         """
         if (value is None) or not (0 <= value <= 0.1):
             raise ValueError('int_delay must be 0 <= value <= 1')
@@ -194,8 +319,11 @@ def _update_int_delay(instr, value):
 
     def _int_delay_default(instr):
         """
-        Returns minimum int_delay recommended for (numtaps - 1)
-        samples to be discarded as recommended for filter
+        Function to generate default int_delay value
+
+        Returns:
+            minimum int_delay recommended for (numtaps - 1)
+            samples to be discarded as recommended for filter
         """
         alazar = instr._get_alazar()
         instr.sample_rate = alazar.get_sample_rate()
@@ -204,8 +332,11 @@ def _int_delay_default(instr):
 
     def _int_time_default(instr):
         """
-        Returns max total time for integration based on samples_per_record,
-        sample_rate and int_delay
+        Function to generate defult int_time value
+
+        Returns:
+            max total time for integration based on samples_per_record,
+            sample_rate and int_delay
         """
         if instr.samples_per_record is (0 or None):
             raise ValueError('Cannot set int_time to max if acq controller'
@@ -218,14 +349,26 @@ def _int_time_default(instr):
                       (instr.int_delay() or 0))
         return total_time
 
+    def get_demod_freqs(self):
+        """
+        Function to get all the demod_freq parameter values in a list, v hacky
+
+        Returns:
+            freqs: numpy array of demodulation frequencies
+        """
+        freqs = list(filter(None, [getattr(self, 'demod_freq_{}'.format(c))()
+                                   for c in range(self._demod_length)]))
+        return np.array(freqs)
+
     def get_max_demod_freq(self):
         """
-        Returns the largest demodulation frequency
+        Returns:
+            the largest demodulation frequency
+
         nb: really hacky and we should have channels in qcodes but we don't
         (at time of writing)
         """
-        freqs = list(filter(None, [getattr(self, 'demod_freq_{}'.format(c))()
-                                   for c in range(self._demod_length)]))
+        freqs = self.get_demod_freqs()
         if len(freqs) > 0:
             return max(freqs)
         else:
@@ -277,8 +420,11 @@ def pre_start_capture(self):
             raise Exception('acq controller sample rate does not match '
                             'instrument value, most likely need '
                             'to set and check int_time and int_delay')
-        if self.get_max_demod_freq() is None:
-            raise Exception('no demodulation frequencies set')
+
+        demod_list = self.get_demod_freqs()
+        if len(demod_list) == 0:
+            raise Exception('no demod_freqs set')
+
         self.records_per_buffer = alazar.records_per_buffer.get()
         self.buffers_per_acquisition = alazar.buffers_per_acquisition.get()
         self.board_info = alazar.get_idn()
@@ -286,8 +432,6 @@ def pre_start_capture(self):
                                self.records_per_buffer *
                                self.number_of_channels)
 
-        demod_list = np.array([getattr(self, 'demod_freq_{}'.format(n))()
-                               for n in range(self._demod_length)])
         integer_list = np.arange(self.samples_per_record)
         angle_mat = 2 * np.pi * \
             np.outer(demod_list, integer_list) / self.sample_rate
diff --git a/qcodes/instrument_drivers/AlazarTech/basic_controller.py b/qcodes/instrument_drivers/AlazarTech/basic_controller.py
index 99421eef1f7e..176749e57848 100644
--- a/qcodes/instrument_drivers/AlazarTech/basic_controller.py
+++ b/qcodes/instrument_drivers/AlazarTech/basic_controller.py
@@ -14,6 +14,10 @@ class SampleSweep(Parameter):
     filled (channels * samples * records) which is processed by the
     post_acquire function of the Acquisition Controller and finally the
     processed result is returned when the SampleSweep parameter is called.
+
+    :args:
+    name: name for this parameter
+    instrument: acquisition controller instrument this parameter belongs to
     """
 
     def __init__(self, name, instrument):
@@ -24,9 +28,10 @@ def __init__(self, name, instrument):
 
     def get(self):
         """
-        Gets the averaged samples for channels A and B by calling acquire
+        Gets the samples for channels A and B by calling acquire
         on the alazar (which in turn calls the processing functions of the
-        aqcuisition controller before returning the processed data)
+        aqcuisition controller before returning the reshaped data averaged
+        over records and buffers)
 
         returns:
         recordA: numpy array of channel A acquisition
diff --git a/qcodes/instrument_drivers/AlazarTech/rec_controller_test.py b/qcodes/instrument_drivers/AlazarTech/rec_controller_test.py
index 765a94b7acf0..714354c462c8 100644
--- a/qcodes/instrument_drivers/AlazarTech/rec_controller_test.py
+++ b/qcodes/instrument_drivers/AlazarTech/rec_controller_test.py
@@ -4,70 +4,148 @@
 import numpy as np
 from qcodes import Parameter
 import qcodes.instrument_drivers.AlazarTech.acq_helpers as helpers
-from qcodes.instrument.parameter import ManualParameter
 
 
 class AcqVariablesParam(Parameter):
     """
     Parameter of an AcquisitionController which has a _check_and_update_instr
     function used for validation and to update instrument attributes and a
-    _get_default function which it uses to set the parameter to an instrument
-    caluclated default.
+    _get_default function which it uses to set the AcqVariablesParam to an
+    instrument caluclated default.
+
+    Args:
+        name: name for this parameter
+        instrument: acquisition controller instrument this parameter belongs to
+        check_and_update_fn: instrument function to be used for value
+            validation and updating instrument values
+        default_fn (optional): instrument function to be used to calculate
+            a default value to set parameter to
+        initial_value (optional): initial value for parameter
     """
+
     def __init__(self, name, instrument, check_and_update_fn,
-                 default_fn, initial_value=None):
+                 default_fn=None, initial_value=None):
         super().__init__(name)
         self._instrument = instrument
         self._save_val(initial_value)
         setattr(self, '_check_and_update_instr', check_and_update_fn)
-        setattr(self, '_get_default', default_fn)
+        if default_fn is not None:
+            setattr(self, '_get_default', default_fn)
 
     def set(self, value):
-        self._check_and_update_instr(value)
+        """
+        Function which checks value using validation function and then sets
+        the Parameter value to this value.
+
+        Args:
+            value: value to set the parameter to
+        """
+        self._check_and_update_instr(value, param_name=self.name)
         self._save_val(value)
 
     def get(self):
         return self._latest()['value']
 
     def to_default(self):
-        default = self._get_default()
+        """
+        Function which executes the default_fn specified to calculate the
+        default value based on instrument values and then calls the set
+        function with this value
+        """
+        try:
+            default = self._get_default()
+        except AttributeError as e:
+            raise AttributeError('no default function for {} Parameter '
+                                 '{}'.format(self.name, e))
         self.set(default)
 
     def check(self):
+        """
+        Function which checks the current Parameter value using the specified
+        check_and_update_fn which can also serve to update instrument values.
+
+        Return:
+            True (if no errors raised when check_and_update_fn executed)
+        """
         val = self._latest()['value']
-        self._check_and_update_instr(val)
+        self._check_and_update_instr(val, param_name=self.name)
         return True
 
 
 class RecordsAcqParam(Parameter):
     """
     Software controlled parameter class for Alazar acquisition. To be used with
-    HD_Records_Controller (tested with ATS9360 board) for return of an array of
-    records data from the Alazar, averaged over and buffers.
+    HD_Records_Controller (tested with ATS9360 board) for return of
+    averaged magnitude and phase data from the Alazar.
+
+    Args:
+        name: name for this parameter
+        instrument: acquisition controller instrument this parameter belongs to
+        demod_length: numer of demodulation frequencies the acq controller has
+            NB this is currently set in acq controller init but that's really
+            not nice and should be changed when possible
 
     TODO(nataliejpg) setpoints (including names and units)
     TODO(nataliejpg) setpoint units
-    TODO(nataliejpg) convert demod_index setpoint into actual frequency
     TODO(nataliejpg) convert records setpoint into actual frequency
     """
-
     def __init__(self, name, instrument, demod_length):
         super().__init__(name)
         self._instrument = instrument
         self.acquisition_kwargs = {}
         self.names = ('magnitude', 'phase')
-        self.shapes = ((demod_length, ), (demod_length, ))
 
     def update_sweep(self, npts, start=None, stop=None):
+        """
+        Function which updates the shape of the parameter (and it's setpoints
+        when this is fixed)
+
+        Args:
+            npts: number of recornds returned after processing
+            start (optional): start value of records returned (if relevant)
+            stop (optional): stop value of records returned (if relevant)
+        """
         demod_length = self._instrument._demod_length
-        # rec_list = tuple(np.linspace(start, stop, num=npts))
+        # self.rec_list = tuple(np.linspace(start, stop, num=npts))
         # demod_index = tuple(range(demod_length))
         if demod_length > 1:
+            # demod_index = tuple(range(demod_length))
+            # demod_freqs = self._instrument.get_demod_freqs()
+            # self.setpoints = ((demod_freqs, self._rec_list), (
+            #           demod_freqs, self._rec_list))
             self.shapes = ((demod_length, npts), (demod_length, npts))
         else:
             self.shapes = ((npts,), (npts,))
+            # self.setpoints = ((self._rec_list,), (self._rec_list,))
+
+    def update_demod_setpoints(self, demod_freqs):
+        """
+        Function to update the demodulation frequency setpoints to be called
+        when a demod_freq Parameter of the acq controller is updated
+
+        Args:
+            demod_freqs: numpy array of demodulation frequencies to use as
+                setpoints if length > 1
+        """
+        demod_length = self._instrument._demod_length
+        if demod_length > 1:
+            self.setpoints = ((demod_freqs, self._rec_list),
+                              (demod_freqs, self._rec_list))
+        else:
+            pass
 
     def get(self):
+        """
+        Gets the magnitude and phase signal by calling acquire
+        on the alazar (which in turn calls the processing functions of the
+        aqcuisition controller before returning the processed data
+        demodulated at specified frequencies and averaged over samples
+        and buffers)
+
+        Returns:
+            mag: numpy array of magnitude, shape (demod_length, records)
+            phase: numpy array of magnitude, shape (demod_length, records)
+        """
         mag, phase = self._instrument._get_alazar().acquire(
             acquisition_controller=self._instrument,
             **self.acquisition_kwargs)
@@ -77,19 +155,19 @@ def get(self):
 class HD_Records_Controller(AcquisitionController):
     """
     This is the Acquisition Controller class which works with the ATS9360,
-    averaging over buffers and samples, demodulating with software
-    reference signals.
+    averaging over samples (limited by int_time and int_delay values) and
+    buffers and demodulating with software reference signal(s).
 
     Args:
         name: name for this acquisition_conroller as an instrument
-        alazar_name: the name of the alazar instrument such that this
+        alazar_name: name of the alazar instrument such that this
             controller can communicate with the Alazar
-        demod_freqs: the frequency of the software wave to be created
-        filter: the filter to be used to filter out double freq component
-            ('win' - window, 'ls' - least squared, 'ave' - averaging)
-        numtaps: number of freq components used in filter
-        chan_b: whether there is also a second channel of data to be processed
-            and returned
+        demod_length (default 1): number of demodulation frequencies
+        filter (default 'win'): filter to be used to filter out double freq
+            component ('win' - window, 'ls' - least squared, 'ave' - averaging)
+        numtaps (default 101): number of freq components used in filter
+        chan_b (default False): whether there is also a second channel of data
+            to be processed and returned
         **kwargs: kwargs are forwarded to the Instrument base class
 
     TODO(nataliejpg) test filter options
@@ -99,7 +177,6 @@ class HD_Records_Controller(AcquisitionController):
     TODO(nataliejpg) demod_freq should be changeable number: maybe channels
     TODO(nataliejpg) try using fit from helpers
     TODO(nataliejpg) make records a parameter or similar (MultiParam?)
-    TODO(nataliejpg) check docstrings
     """
 
     filter_dict = {'win': 0, 'ls': 1, 'ave': 2}
@@ -122,7 +199,8 @@ def __init__(self, name, alazar_name, demod_length=1, filter='win',
                            parameter_class=RecordsAcqParam)
         for i in range(demod_length):
             self.add_parameter(name='demod_freq_{}'.format(i),
-                               parameter_class=ManualParameter)
+                               check_and_update_fn=self._update_demod_freq,
+                               parameter_class=AcqVariablesParam)
         self.add_parameter(name='int_time',
                            check_and_update_fn=self._update_int_time,
                            default_fn=self._int_time_default,
@@ -132,17 +210,67 @@ def __init__(self, name, alazar_name, demod_length=1, filter='win',
                            default_fn=self._int_delay_default,
                            parameter_class=AcqVariablesParam)
 
-    def _update_int_time(instr, value):
+    def _update_demod_freq(instr, value, param_name=None):
+        """
+        Function to validate and update acquisiton parameter when
+        a demod_freq_ Parameter is changed
+
+        Args:
+            value to update demodulation frequency to
+
+        Kwargs:
+            param_name: used to update demod_freq list used for updating
+                septionts of acquisition parameter
+
+        Checks:
+            1e6 <= value <= 500e6
+            number of oscilation measured using current int_tiume param value
+                at this demod frequency value
+            oversampling rate for this demodulation frequency
+
+        Sets:
+            sample_rate attr of acq controller to be that of alazar
+            setpoints of acquisiton parameter
+        """
+        if (value is None) or not (1e6 <= value <= 500e6):
+            raise ValueError('demod_freqs must be 1e6 <= value <= 500e6')
+        alazar = instr._get_alazar()
+        instr.sample_rate = alazar.get_sample_rate()
+        min_oscilations_measured = instr.int_time() * value
+        oversampling = instr.sample_rate / (2 * value)
+        if min_oscilations_measured < 10:
+            logging.warning('{} oscilations measured for largest '
+                            'demod freq, recommend at least 10: '
+                            'decrease sampling rate, take '
+                            'more samples or increase demodulation '
+                            'freq'.format(min_oscilations_measured))
+        elif oversampling < 1:
+            logging.warning('oversampling rate is {}, recommend > 1: '
+                            'increase sampling rate or decrease '
+                            'demodulation frequency'.format(oversampling))
+        demod_freqs = instr.get_demod_freqs()
+        current_demod_index = ([int(s) for s in param_name.split()
+                                if s.isdigit()][0])
+        demod_freqs[current_demod_index] = value
+        instr.acquisition.update_demod_setpoints(demod_freqs)
+
+    def _update_int_time(instr, value, **kwargs):
         """
         Function to validate value for int_time before setting parameter
         value
-        Checks: limit between 0 and 0.1s
-                acq knows sample_rate (doesn't check with alazar for accuracy)
-                number of oscilation measured in this time
-                oversampling rate
-        Sets: samples_per_record of acq controller
-              acquisition_kwarg['samples_per_record'] of acquisition param
-              shape of acquisition param
+
+        Args:
+            value to be validated and used for instrument attribute update
+
+        Checks:
+            0 <= value <= 0.1 seconds
+            number of oscilation measured in this time
+            oversampling rate
+
+        Sets:
+            sample_rate attr of acq controller to be that of alazar
+            samples_per_record of acq controller
+            acquisition_kwarg['samples_per_record'] of acquisition param
         """
         if (value is None) or not (0 <= value <= 0.1):
             raise ValueError('int_time must be 0 <= value <= 1')
@@ -171,16 +299,23 @@ def _update_int_time(instr, value):
         instr.acquisition.acquisition_kwargs.update(
             samples_per_record=instr.samples_per_record)
 
-    def _update_int_delay(instr, value):
+    def _update_int_delay(instr, value, **kwargs):
         """
         Function to validate value for int_delay before setting parameter
         value
-        Checks: limit between 0 and 1s
-                acq knows sample_rate (doesn't check with alazar for accuracy)
-                number of samples discarded >= numtaps
-        Sets: samples_per_record of acq controller
-              acquisition_kwarg['samples_per_record'] of acquisition param
-              shape of acquisition param
+
+        Args:
+            value to be validated and used for instrument attribute update
+
+        Checks:
+            0 <= value <= 0.1 seconds
+            number of samples discarded >= numtaps
+
+        Sets:
+            sample_rate attr of acq controller to be that of alazar
+            samples_per_record of acq controller
+            acquisition_kwarg['samples_per_record'] of acquisition param
+            setpoints of acquisiton param
         """
         if (value is None) or not (0 <= value <= 0.1):
             raise ValueError('int_delay must be 0 <= value <= 1')
@@ -203,8 +338,11 @@ def _update_int_delay(instr, value):
 
     def _int_delay_default(instr):
         """
-        Returns minimum int_delay recommended for (numtaps - 1)
-        samples to be discarded as recommended for filter
+        Function to generate default int_delay value
+
+        Returns:
+            minimum int_delay recommended for (numtaps - 1)
+            samples to be discarded as recommended for filter
         """
         alazar = instr._get_alazar()
         instr.sample_rate = alazar.get_sample_rate()
@@ -213,8 +351,11 @@ def _int_delay_default(instr):
 
     def _int_time_default(instr):
         """
-        Returns max total time for integration based on samples_per_record,
-        sample_rate and int_delay
+        Function to generate defult int_time value
+
+        Returns:
+            max total time for integration based on samples_per_record,
+            sample_rate and int_delay
         """
         if instr.samples_per_record is (0 or None):
             raise ValueError('Cannot set int_time to max if acq controller'
@@ -227,18 +368,30 @@ def _int_time_default(instr):
                       (instr.int_delay() or 0))
         return total_time
 
+    def get_demod_freqs(self):
+        """
+        Function to get all the demod_freq parameter values in a list, v hacky
+
+        Returns:
+            freqs: numpy array of demodulation frequencies
+        """
+        freqs = list(filter(None, [getattr(self, 'demod_freq_{}'.format(c))()
+                                   for c in range(self._demod_length)]))
+        return np.array(freqs)
+
     def get_max_demod_freq(self):
         """
-        Returns the largest demodulation frequency
+        Returns:
+            the largest demodulation frequency
+
         nb: really hacky and we should have channels in qcodes but we don't
         (at time of writing)
         """
-        freqs = list(filter(None, [getattr(self, 'demod_freq_{}'.format(c))()
-                                   for c in range(self._demod_length)]))
+        freqs = self.get_demod_freqs()
         if len(freqs) > 0:
             return max(freqs)
         else:
-        return None
+            return None
 
     def update_filter_settings(self, filter, numtaps):
         """
@@ -256,7 +409,7 @@ def update_acquisition_kwargs(self, **kwargs):
         """
         Updates the kwargs to be used when
         alazar_driver.acquisition() is called via a get call of the
-        acquisition SamplesParam. Should be used by the user for 
+        acquisition SamplesParam. Should be used by the user for
         updating averaging settings since the 'samples_per_record'
         kwarg is updated via the int_time and int_delay parameters
 
@@ -294,8 +447,11 @@ def pre_start_capture(self):
                             'instrument value, most likely need '
                             'to call update_acquisition_kwargs with'
                             'records_per_buffer as a param')
-        if self.get_max_demod_freq() is None:
-            raise Exception('no demodulation frequencies set')
+
+        demod_list = self.get_demod_freqs()
+        if len(demod_list) == 0:
+            raise Exception('no demod_freqs set')
+
         self.buffers_per_acquisition = alazar.buffers_per_acquisition.get()
         self.board_info = alazar.get_idn()
         self.buffer = np.zeros(self.samples_per_record *
diff --git a/qcodes/instrument_drivers/AlazarTech/samp_controller.py b/qcodes/instrument_drivers/AlazarTech/samp_controller.py
index ce54f8a29c99..02b67605d64d 100644
--- a/qcodes/instrument_drivers/AlazarTech/samp_controller.py
+++ b/qcodes/instrument_drivers/AlazarTech/samp_controller.py
@@ -4,7 +4,6 @@
 import numpy as np
 from qcodes import Parameter
 import qcodes.instrument_drivers.AlazarTech.acq_helpers as helpers
-from qcodes.instrument.parameter import ManualParameter
 
 
 class AcqVariablesParam(Parameter):
@@ -13,15 +12,25 @@ class AcqVariablesParam(Parameter):
     function used for validation and to update instrument attributes and a
     _get_default function which it uses to set the AcqVariablesParam to an
     instrument caluclated default.
+
+    Args:
+        name: name for this parameter
+        instrument: acquisition controller instrument this parameter belongs to
+        check_and_update_fn: instrument function to be used for value
+            validation and updating instrument values
+        default_fn (optional): instrument function to be used to calculate
+            a default value to set parameter to
+        initial_value (optional): initial value for parameter
     """
 
     def __init__(self, name, instrument, check_and_update_fn,
-                 default_fn, initial_value=None):
+                 default_fn=None, initial_value=None):
         super().__init__(name)
         self._instrument = instrument
         self._save_val(initial_value)
         setattr(self, '_check_and_update_instr', check_and_update_fn)
-        setattr(self, '_get_default', default_fn)
+        if default_fn is not None:
+            setattr(self, '_get_default', default_fn)
 
     def set(self, value):
         """
@@ -31,7 +40,7 @@ def set(self, value):
         Args:
             value: value to set the parameter to
         """
-        self._check_and_update_instr(value)
+        self._check_and_update_instr(value, param_name=self.name)
         self._save_val(value)
 
     def get(self):
@@ -43,7 +52,11 @@ def to_default(self):
         default value based on instrument values and then calls the set
         function with this value
         """
-        default = self._get_default()
+        try:
+            default = self._get_default()
+        except AttributeError as e:
+            raise AttributeError('no default function for {} Parameter '
+                                 '{}'.format(self.name, e))
         self.set(default)
 
     def check(self):
@@ -55,7 +68,7 @@ def check(self):
             True (if no errors raised when check_and_update_fn executed)
         """
         val = self._latest()['value']
-        self._check_and_update_instr(val)
+        self._check_and_update_instr(val, param_name=self.name)
         return True
 
 
@@ -65,9 +78,12 @@ class SamplesAcqParam(Parameter):
     HD_Samples_Controller (tested with ATS9360 board) for return of an array of
     sample data from the Alazar, averaged over records and buffers.
 
+    Args:
+        name: name for this parameter
+        instrument: acquisition controller instrument this parameter belongs to
+
     TODO(nataliejpg) setpoints (including names and units)
     TODO(nataliejpg) setpoint units
-    TODO(nataliejpg) convert demod_index setpoint into actual frequency
     """
 
     def __init__(self, name, instrument):
@@ -77,15 +93,56 @@ def __init__(self, name, instrument):
         self.names = ('magnitude', 'phase')
 
     def update_sweep(self, start, stop, npts):
+        """
+        Function which updates the shape of the parameter (and it's setpoints
+        when this is fixed)
+
+        Args:
+            start: start time of samples returned after processing
+            stop: stop time of samples returned after processing
+            npts: number of samples returned after processing
+        """
         demod_length = self._instrument._demod_length
-        # freq = tuple(np.linspace(start, stop, num=npts))
-        # demod_index = tuple(range(demod_length))
+        # self._time_list = tuple(np.linspace(start, stop, num=npts))
         if demod_length > 1:
+            # demod_index = tuple(range(demod_length))
+            # self._demod_list = self._instrument.get_demod_freqs()
+            # self.setpoints = ((self._demod_list, self._time_list ), (
+            #           self._demod_list, self._time_list ))
             self.shapes = ((demod_length, npts), (demod_length, npts))
         else:
             self.shapes = ((npts,), (npts,))
+            # self.setpoints = ((self._time_list,), (self._time_list,))
+
+    def update_demod_setpoints(self, demod_freqs):
+        """
+        Function to update the demodulation frequency setpoints to be called
+        when a demod_freq Parameter of the acq controller is updated
+
+        Args:
+            demod_freqs: numpy array of demodulation frequencies to use as
+                setpoints if length > 1
+        """
+        demod_length = self._instrument._demod_length
+        self._demod_list = demod_freqs
+        if demod_length > 1:
+            self.setpoints = ((self._demod_list, self._time_list),
+                              (self._demod_list, self._time_list))
+        else:
+            pass
 
     def get(self):
+        """
+        Gets the magnitude and phase signal by calling acquire
+        on the alazar (which in turn calls the processing functions of the
+        aqcuisition controller before returning the processed data
+        demodulated at specified frequencies and averaged over records
+        and buffers)
+
+        Returns:
+            mag: numpy array of magnitude, shape (demod_length, samples)
+            phase: numpy array of magnitude, shape (demod_length, samples)
+        """
         mag, phase = self._instrument._get_alazar().acquire(
             acquisition_controller=self._instrument,
             **self.acquisition_kwargs)
@@ -95,19 +152,20 @@ def get(self):
 class HD_Samples_Controller(AcquisitionController):
     """
     This is the Acquisition Controller class which works with the ATS9360,
-    averaging over buffers and records and demodulating with a software
-    reference signal, returning the  samples.
+    averaging over records and buffers and demodulating with software
+    reference signal(s), returning samples limited by int_time and int_delay
+    values.
 
     Args:
         name: name for this acquisition_conroller as an instrument
-        alazar_name: the name of the alazar instrument such that this
+        alazar_name: name of the alazar instrument such that this
             controller can communicate with the Alazar
-        demod_freqs: the frequencies of the software wave to be created
-        filter: the filter to be used to filter out double freq component
-            ('win' - window, 'ls' - least squared)
-        numtaps: number of freq components used in the filter
-        chan_b: whether there is also a second channel of data to be processed
-            and returned
+        demod_length (default 1): number of demodulation frequencies
+        filter (default 'win'): filter to be used to filter out double
+            freq component ('win' - window, 'ls' - least squared)
+        numtaps (default 101): number of freq components used in the filter
+        chan_b (default False): whether there is also a second channel of data
+            to be processed and returned
         **kwargs: kwargs are forwarded to the Instrument base class
 
     TODO(nataliejpg) test filter options
@@ -134,7 +192,8 @@ def __init__(self, name, alazar_name, demod_length=1, filter='win',
                            parameter_class=SamplesAcqParam)
         for i in range(demod_length):
             self.add_parameter(name='demod_freq_{}'.format(i),
-                               parameter_class=ManualParameter)
+                               check_and_update_fn=self._update_demod_freq,
+                               parameter_class=AcqVariablesParam)
         self.add_parameter(name='int_time',
                            check_and_update_fn=self._update_int_time,
                            default_fn=self._int_time_default,
@@ -144,7 +203,51 @@ def __init__(self, name, alazar_name, demod_length=1, filter='win',
                            default_fn=self._int_delay_default,
                            parameter_class=AcqVariablesParam)
 
-    def _update_int_time(instr, value):
+    def _update_demod_freq(instr, value, param_name=None):
+        """
+        Function to validate and update acquisiton parameter when
+        a demod_freq_ Parameter is changed
+
+        Args:
+            value to update demodulation frequency to
+
+        Kwargs:
+            param_name: used to update demod_freq list used for updating
+                septionts of acquisition parameter
+
+        Checks:
+            1e6 <= value <= 500e6
+            number of oscilation measured using current int_tiume param value
+                at this demod frequency value
+            oversampling rate for this demodulation frequency
+
+        Sets:
+            sample_rate attr of acq controller to be that of alazar
+            setpoints of acquisiton parameter
+        """
+        if (value is None) or not (1e6 <= value <= 500e6):
+            raise ValueError('demod_freqs must be 1e6 <= value <= 500e6')
+        alazar = instr._get_alazar()
+        instr.sample_rate = alazar.get_sample_rate()
+        min_oscilations_measured = instr.int_time() * value
+        oversampling = instr.sample_rate / (2 * value)
+        if min_oscilations_measured < 10:
+            logging.warning('{} oscilations measured for largest '
+                            'demod freq, recommend at least 10: '
+                            'decrease sampling rate, take '
+                            'more samples or increase demodulation '
+                            'freq'.format(min_oscilations_measured))
+        elif oversampling < 1:
+            logging.warning('oversampling rate is {}, recommend > 1: '
+                            'increase sampling rate or decrease '
+                            'demodulation frequency'.format(oversampling))
+        demod_freqs = instr.get_demod_freqs()
+        current_demod_index = ([int(s) for s in param_name.split()
+                                if s.isdigit()][0])
+        demod_freqs[current_demod_index] = value
+        instr.acquisition.update_demod_setpoints(demod_freqs)
+
+    def _update_int_time(instr, value, **kwargs):
         """
         Function to validate value for int_time before setting parameter
         value
@@ -158,9 +261,10 @@ def _update_int_time(instr, value):
             oversampling rate
 
         Sets:
-            sample_rate attribute of instument to be that of alazar
+            sample_rate attr of acq controller to be that of alazar
             samples_per_record of acq controller
             acquisition_kwarg['samples_per_record'] of acquisition param
+            setpoints of acquisiton param
             shape of acquisition param
         """
         if (value is None) or not (0 <= value <= 0.1):
@@ -169,13 +273,14 @@ def _update_int_time(instr, value):
         alazar = instr._get_alazar()
         instr.sample_rate = alazar.get_sample_rate()
         if instr.get_max_demod_freq() is not None:
-            oscilations_measured = value * instr.get_max_demod_freq()
+            min_oscilations_measured = value * instr.get_max_demod_freq()
             oversampling = instr.sample_rate / (2 * instr.get_max_demod_freq())
-            if oscilations_measured < 10:
-                logging.warning('{} oscilations measured, recommend at '
-                                'least 10: decrease sampling rate, take '
+            if min_oscilations_measured < 10:
+                logging.warning('{} oscilations measured for largest '
+                                'demod freq, recommend at least 10: '
+                                'decrease sampling rate, take '
                                 'more samples or increase demodulation '
-                                'freq'.format(oscilations_measured))
+                                'freq'.format(min_oscilations_measured))
             elif oversampling < 1:
                 logging.warning('oversampling rate is {}, recommend > 1: '
                                 'increase sampling rate or decrease '
@@ -197,16 +302,24 @@ def _update_int_time(instr, value):
         instr.acquisition.acquisition_kwargs.update(
             samples_per_record=instr.samples_per_record)
 
-    def _update_int_delay(instr, value):
+    def _update_int_delay(instr, value, **kwargs):
         """
         Function to validate value for int_delay before setting parameter
         value
-        Checks: limit between 0 and 1s
-                acq knows sample_rate (doesn't check with alazar for accuracy)
-                number of samples discarded >= numtaps
-        Sets: samples_per_record of acq controller
-              acquisition_kwarg['samples_per_record'] of acquisition param
-              shape of acquisition param
+
+        Args:
+            value to be validated and used for instrument attribute update
+
+        Checks:
+            0 <= value <= 0.1 seconds
+            number of samples discarded >= numtaps
+
+        Sets:
+            sample_rate attr of acq controller to be that of alazar
+            samples_per_record of acq controller
+            acquisition_kwarg['samples_per_record'] of acquisition param
+            setpoints of acquisiton param
+            shape of acquisition param
         """
         if (value is None) or not (0 <= value <= 0.1):
             raise ValueError('int_delay must be 0 <= value <= 1')
@@ -259,14 +372,26 @@ def _int_time_default(instr):
                       (instr.int_delay() or 0))
         return total_time
 
+    def get_demod_freqs(self):
+        """
+        Function to get all the demod_freq parameter values in a list, v hacky
+
+        Returns:
+            freqs: numpy array of demodulation frequencies
+        """
+        freqs = list(filter(None, [getattr(self, 'demod_freq_{}'.format(c))()
+                                   for c in range(self._demod_length)]))
+        return np.array(freqs)
+
     def get_max_demod_freq(self):
         """
-        Returns the largest demodulation frequency
+        Returns:
+            the largest demodulation frequency
+
         nb: really hacky and we should have channels in qcodes but we don't
         (at time of writing)
         """
-        freqs = list(filter(None, [getattr(self, 'demod_freq_{}'.format(c))()
-                                   for c in range(self._demod_length)]))
+        freqs = self.get_demod_freqs()
         if len(freqs) > 0:
             return max(freqs)
         else:
@@ -319,8 +444,11 @@ def pre_start_capture(self):
             raise Exception('acq controller sample rate does not match '
                             'instrument value, most likely need '
                             'to call update_acquisition_settings')
-        if self.get_max_demod_freq() is None:
-            raise Exception('no demodulation frequencies set')
+
+        demod_list = self.get_demod_freqs()
+        if len(demod_list) == 0:
+            raise Exception('no demod_freqs set')
+
         self.records_per_buffer = alazar.records_per_buffer.get()
         self.buffers_per_acquisition = alazar.buffers_per_acquisition.get()
         self.board_info = alazar.get_idn()
@@ -328,8 +456,6 @@ def pre_start_capture(self):
                                self.records_per_buffer *
                                self.number_of_channels)
 
-        demod_list = np.array([getattr(self, 'demod_freq_{}'.format(n))()
-                               for n in range(self._demod_length)])
         integer_list = np.arange(self.samples_per_record)
         angle_mat = 2 * np.pi * \
             np.outer(demod_list, integer_list) / self.sample_rate

From 3ecce9ad066ccbb995c4530dddfc08cf4dac4b88 Mon Sep 17 00:00:00 2001
From: Natalie Pearson 
Date: Tue, 7 Feb 2017 15:56:08 +0100
Subject: [PATCH 097/328] remove fit fn from acq helpers and TODOs for trying
 it in controllers, bigger inheritance needed but will do that later

---
 .../AlazarTech/acq_helpers.py                 | 75 -------------------
 .../AlazarTech/ave_controller_test.py         |  1 -
 .../AlazarTech/rec_controller_test.py         |  1 -
 3 files changed, 77 deletions(-)

diff --git a/qcodes/instrument_drivers/AlazarTech/acq_helpers.py b/qcodes/instrument_drivers/AlazarTech/acq_helpers.py
index 96bc621accbc..785145d3887a 100644
--- a/qcodes/instrument_drivers/AlazarTech/acq_helpers.py
+++ b/qcodes/instrument_drivers/AlazarTech/acq_helpers.py
@@ -1,6 +1,5 @@
 import numpy as np
 from scipy import signal
-import logging
 
 
 def filter_win(rec, cutoff, sample_rate, numtaps, axis=-1):
@@ -73,77 +72,3 @@ def roundup(num, to_nearest):
     """
     remainder = num % to_nearest
     return int(num if remainder == 0 else num + to_nearest - remainder)
-
-
-def fit(acq_controller, record, averaging=False):
-    """
-    Applies volts conversion, demodulation fit, low bandpass filter
-    and integration limits to samples array
-
-    Args:
-        rec (numpy array): record from alazar to be multiplied with the
-                           software signal, filtered and limited to
-                           integration limits
-                           shape = (samples_taken, )
-
-    Returns:
-        magnitude (numpy array): shape = (demod_length, samples_used)
-        phase (numpy array): shape = (demod_length, samples_used)
-    """
-    # convert rec to volts
-    bps = acq_controller.board_info['bits_per_sample']
-    if bps == 12:
-        volt_rec = sample_to_volt_u12(record, bps)
-    else:
-        logging.warning('sample to volt conversion does not exist for'
-                        ' bps != 12, centered raw samples returned')
-        volt_rec = record - np.mean(record)
-
-    # volt_rec to matrix and multiply with demodulation signal matrices
-    volt_rec_mat = np.outer(np.ones(acq_controller._demod_length), volt_rec)
-    re_mat = np.multiply(volt_rec_mat, acq_controller.cos_mat)
-    im_mat = np.multiply(volt_rec_mat, acq_controller.sin_mat)
-
-    # filter out higher freq component
-    cutoff = acq_controller.get_max_demod_freq() / 10
-    if acq_controller.filter_settings['filter'] == 0:
-        re_filtered = filter_win(re_mat, cutoff,
-                                 acq_controller.sample_rate,
-                                 acq_controller.filter_settings[
-                                     'numtaps'],
-                                 axis=1)
-        im_filtered = filter_win(im_mat, cutoff,
-                                 acq_controller.sample_rate,
-                                 acq_controller.filter_settings[
-                                     'numtaps'],
-                                 axis=1)
-    elif acq_controller.filter_settings['filter'] == 1:
-        re_filtered = filter_ls(re_mat, cutoff,
-                                acq_controller.sample_rate,
-                                acq_controller.filter_settings[
-                                    'numtaps'],
-                                axis=1)
-        im_filtered = filter_ls(im_mat, cutoff,
-                                acq_controller.sample_rate,
-                                acq_controller.filter_settings[
-                                    'numtaps'],
-                                axis=1)
-
-    # apply integration limits
-    beginning = int(acq_controller.int_delay() * acq_controller.sample_rate)
-    end = beginning + int(acq_controller.int_time() *
-                          acq_controller.sample_rate)
-
-    re_limited = re_filtered[:, beginning:end]
-    im_limited = im_filtered[:, beginning:end]
-
-    # convert to magnitude and phase
-    complex_mat = re_limited + im_limited * 1j
-    magnitude = abs(complex_mat)
-    phase = np.angle(complex_mat, deg=True)
-
-    if averaging:
-        magnitude = np.mean(magnitude, axis=1)
-        phase = np.mean(phase, axis=1)
-
-    return magnitude, phase
diff --git a/qcodes/instrument_drivers/AlazarTech/ave_controller_test.py b/qcodes/instrument_drivers/AlazarTech/ave_controller_test.py
index 3018775875d3..ee49c9afe102 100644
--- a/qcodes/instrument_drivers/AlazarTech/ave_controller_test.py
+++ b/qcodes/instrument_drivers/AlazarTech/ave_controller_test.py
@@ -156,7 +156,6 @@ class HD_Averaging_Controller(AcquisitionController):
     TODO(nataliejpg) what should be private?
     TODO(nataliejpg) where should filter_dict live?
     TODO(nataliejpg) demod_freq should be changeable number: maybe channels
-    TODO(nataliejpg) try using fit from helpers
     """
 
     filter_dict = {'win': 0, 'ls': 1, 'ave': 2}
diff --git a/qcodes/instrument_drivers/AlazarTech/rec_controller_test.py b/qcodes/instrument_drivers/AlazarTech/rec_controller_test.py
index 714354c462c8..168658ca93e0 100644
--- a/qcodes/instrument_drivers/AlazarTech/rec_controller_test.py
+++ b/qcodes/instrument_drivers/AlazarTech/rec_controller_test.py
@@ -175,7 +175,6 @@ class HD_Records_Controller(AcquisitionController):
     TODO(nataliejpg) what should be private?
     TODO(nataliejpg) where should filter_dict live?
     TODO(nataliejpg) demod_freq should be changeable number: maybe channels
-    TODO(nataliejpg) try using fit from helpers
     TODO(nataliejpg) make records a parameter or similar (MultiParam?)
     """
 

From e8f9644d81e4cdb6a5db7b6e932eae57c46b7fce Mon Sep 17 00:00:00 2001
From: Natalie Pearson 
Date: Tue, 7 Feb 2017 16:07:31 +0100
Subject: [PATCH 098/328] delete old versions of controllers and rename new
 versions to replace them

---
 .../AlazarTech/ave_controller.py              | 611 +++++++++++-----
 .../AlazarTech/ave_controller_test.py         | 546 ---------------
 .../AlazarTech/rec_controller.py              | 651 +++++++++++++-----
 .../AlazarTech/rec_controller_test.py         | 581 ----------------
 4 files changed, 915 insertions(+), 1474 deletions(-)
 delete mode 100644 qcodes/instrument_drivers/AlazarTech/ave_controller_test.py
 delete mode 100644 qcodes/instrument_drivers/AlazarTech/rec_controller_test.py

diff --git a/qcodes/instrument_drivers/AlazarTech/ave_controller.py b/qcodes/instrument_drivers/AlazarTech/ave_controller.py
index d23767c7aeb2..ee49c9afe102 100644
--- a/qcodes/instrument_drivers/AlazarTech/ave_controller.py
+++ b/qcodes/instrument_drivers/AlazarTech/ave_controller.py
@@ -1,130 +1,429 @@
 import logging
 from .ATS import AcquisitionController
+from .ATS9360 import AlazarTech_ATS9360
 import numpy as np
-import qcodes.utils.helpers as helpers
+from qcodes import Parameter
+import qcodes.instrument_drivers.AlazarTech.acq_helpers as helpers
 
 
-class HD_Controller(AcquisitionController):
+class AcqVariablesParam(Parameter):
+    """
+    Parameter of an AcquisitionController which has a _check_and_update_instr
+    function used for validation and to update instrument attributes and a
+    _get_default function which it uses to set the AcqVariablesParam to an
+    instrument caluclated default.
+
+    Args:
+        name: name for this parameter
+        instrument: acquisition controller instrument this parameter belongs to
+        check_and_update_fn: instrument function to be used for value
+            validation and updating instrument values
+        default_fn (optional): instrument function to be used to calculate
+            a default value to set parameter to
+        initial_value (optional): initial value for parameter
+    """
+
+    def __init__(self, name, instrument, check_and_update_fn,
+                 default_fn=None, initial_value=None):
+        super().__init__(name)
+        self._instrument = instrument
+        self._save_val(initial_value)
+        setattr(self, '_check_and_update_instr', check_and_update_fn)
+        if default_fn is not None:
+            setattr(self, '_get_default', default_fn)
+
+    def set(self, value):
+        """
+        Function which checks value using validation function and then sets
+        the Parameter value to this value.
+
+        Args:
+            value: value to set the parameter to
+        """
+        self._check_and_update_instr(value, param_name=self.name)
+        self._save_val(value)
+
+    def get(self):
+        return self._latest()['value']
+
+    def to_default(self):
+        """
+        Function which executes the default_fn specified to calculate the
+        default value based on instrument values and then calls the set
+        function with this value
+        """
+        try:
+            default = self._get_default()
+        except AttributeError as e:
+            raise AttributeError('no default function for {} Parameter '
+                                 '{}'.format(self.name, e))
+        self.set(default)
+
+    def check(self):
+        """
+        Function which checks the current Parameter value using the specified
+        check_and_update_fn which can also serve to update instrument values.
+
+        Return:
+            True (if no errors raised when check_and_update_fn executed)
+        """
+        val = self._latest()['value']
+        self._check_and_update_instr(val, param_name=self.name)
+        return True
+
+
+class AveragedAcqParam(Parameter):
+    """
+    Software controlled parameter class for Alazar acquisition. To be used with
+    HD_Averaging_Controller (tested with ATS9360 board) for return of
+    averaged magnitude and phase data from the Alazar.
+
+    Args:
+        name: name for this parameter
+        instrument: acquisition controller instrument this parameter belongs to
+        demod_length: numer of demodulation frequencies the acq controller has
+            NB this is currently set in acq controller init but that's really
+            not nice and should be changed when possible
+
+    TODO(nataliejpg) setpoints (including names and units)
+    TODO(nataliejpg) setpoint units
+    """
+
+    def __init__(self, name, instrument, demod_length):
+        super().__init__(name)
+        self._instrument = instrument
+        self.acquisition_kwargs = {}
+        self.names = ('magnitude', 'phase')
+        if demod_length > 1:
+            self.shapes = ((demod_length, ), (demod_length, ))
+        else:
+            self.shapes = ((), ())
+
+    def update_demod_setpoints(self, demod_freqs):
+        """
+        Function to update the demodulation frequency setpoints to be called
+        when a demod_freq Parameter of the acq controller is updated
+
+        Args:
+            demod_freqs: numpy array of demodulation frequencies to use as
+                setpoints if length > 1
+        """
+        demod_length = self._instrument._demod_length
+        self._demod_list = demod_freqs
+        if demod_length > 1:
+            self.setpoints = ((self._demod_list, ), (self._demod_list, ))
+        else:
+            pass
+
+    def get(self):
+        """
+        Gets the magnitude and phase signal by calling acquire
+        on the alazar (which in turn calls the processing functions of the
+        aqcuisition controller before returning the processed data
+        demodulated at specified frequencies and averaged over samples,
+        records and buffers)
+
+        Returns:
+            mag: numpy array of magnitude, shape (demod_length, )
+            phase: numpy array of magnitude, shape (demod_length, )
+        """
+        mag, phase = self._instrument._get_alazar().acquire(
+            acquisition_controller=self._instrument,
+            **self.acquisition_kwargs)
+        return mag, phase
+
+
+class HD_Averaging_Controller(AcquisitionController):
     """
     This is the Acquisition Controller class which works with the ATS9360,
-    averaging over buffers and records, demodulating with a software
-    reference signal, averaging over the  samples.
-    args:
-    name: name for this acquisition_conroller as an instrument
-    alazar_name: the name of the alazar instrument such that this controller
-        can communicate with the Alazar
-    demod_freq: the frequency of the software wave to be created
-    samp_rate: the rate of sampling
-    filt: the filter to be used to filter out double freq component
-    chan_b: whether there is also a second channel of data to be processed
-        and returned
-    **kwargs: kwargs are forwarded to the Instrument base class
-
-    TODO(nataliejpg) fix sample rate problem
+    averaging over samples (limited by int_time and int_delay values),
+    records and buffers and demodulating with software reference signal(s).
+
+    Args:
+        name: name for this acquisition_conroller as an instrument
+        alazar_name: name of the alazar instrument such that this
+            controller can communicate with the Alazar
+        demod_length (default 1): number of demodulation frequencies
+        filter (default 'win'): filter to be used to filter out double freq
+            component ('win' - window, 'ls' - least squared, 'ave' - averaging)
+        numtaps (default 101): number of freq components used in filter
+        chan_b (default False): whether there is also a second channel of data
+            to be processed and returned
+        **kwargs: kwargs are forwarded to the Instrument base class
+
     TODO(nataliejpg) test filter options
     TODO(nataliejpg) finish implementation of channel b option
-    TODO(nataliejpg) make demodulation freq a param
+    TODO(nataliejpg) what should be private?
+    TODO(nataliejpg) where should filter_dict live?
+    TODO(nataliejpg) demod_freq should be changeable number: maybe channels
     """
 
-    def __init__(self, name, alazar_name, demod_freq, samp_rate=5e8,
-                 filt='win', numtaps=101, chan_b=False, **kwargs):
-        filter_dict = {'win': 0, 'ls': 1, 'dot': 2}
-        self.demodulation_frequency = demod_freq
-        self.sample_rate = samp_rate
-        self.filter = filter_dict[filt]
-        self.numtaps = numtaps
-        self.acquisitionkwargs = {}
+    filter_dict = {'win': 0, 'ls': 1, 'ave': 2}
+    samples_divisor = AlazarTech_ATS9360.samples_divisor
+
+    def __init__(self, name, alazar_name, demod_length=1, filter='win',
+                 numtaps=101, chan_b=False, **kwargs):
+        self.filter_settings = {'filter': self.filter_dict[filter],
+                                'numtaps': numtaps}
         self.chan_b = chan_b
-        self.samples_per_record = None
-        self.records_per_buffer = None
-        self.buffers_per_acquisition = None
+        self._demod_length = demod_length
         self.number_of_channels = 2
-        self.samples_delay = None
-        self.samples_time = None
-        self.cos_list = None
-        self.sin_list = None
-        self.buffer = None
-        self.board_info = None
-        # make a call to the parent class and by extension,
-        # create the parameter structure of this class
+        self.samples_per_record = None
+        self.sample_rate = None
         super().__init__(name, alazar_name, **kwargs)
 
         self.add_parameter(name='acquisition',
-                           names=('magnitude', 'phase'),
-                           units=('', ''),
-                           get_cmd=self.do_acquisition)
+                           demod_length=demod_length,
+                           parameter_class=AveragedAcqParam)
+        for i in range(demod_length):
+            self.add_parameter(name='demod_freq_{}'.format(i),
+                               check_and_update_fn=self._update_demod_freq,
+                               parameter_class=AcqVariablesParam)
+        self.add_parameter(name='int_time',
+                           check_and_update_fn=self._update_int_time,
+                           default_fn=self._int_time_default,
+                           parameter_class=AcqVariablesParam)
+        self.add_parameter(name='int_delay',
+                           check_and_update_fn=self._update_int_delay,
+                           default_fn=self._int_delay_default,
+                           parameter_class=AcqVariablesParam)
+
+    def _update_demod_freq(instr, value, param_name=None):
+        """
+        Function to validate and update acquisiton parameter when
+        a demod_freq_ Parameter is changed
+
+        Args:
+            value to update demodulation frequency to
+
+        Kwargs:
+            param_name: used to update demod_freq list used for updating
+                septionts of acquisition parameter
 
-    def update_acquisitionkwargs(self, **kwargs):
+        Checks:
+            1e6 <= value <= 500e6
+            number of oscilation measured using current int_tiume param value
+                at this demod frequency value
+            oversampling rate for this demodulation frequency
+
+        Sets:
+            sample_rate attr of acq controller to be that of alazar
+            setpoints of acquisiton parameter
         """
-        Updates the kwargs to be used when
-        alazar_driver.acquire is called.
-
-        It is also used to set the limits on the selection of
-        samples used (bounded by delay and integration time)
-
-        :param kwargs:
-        :return:
-        """
-        samples_per_record = kwargs['samples_per_record']
-        sample_rate = self.sample_rate
-
-        if 'int_delay' in kwargs:
-            int_delay = kwargs.pop('int_delay')
-            samp_delay = int_delay * sample_rate
-            samples_delay_min = (self.numtaps - 1)
-            if samp_delay < samples_delay_min:
-                int_delay_min = samples_delay_min / sample_rate
-                logging.warning(
-                    'delay is less than recommended for filter choice: '
-                    '(expect delay >= {}'.format(int_delay_min))
-        else:
-            samp_delay = self.numtaps - 1
-
-        if 'int_time' in kwargs:
-            int_time = kwargs.pop('int_time')
-            samp_time = int_time * sample_rate
-            samples_time_max = (samples_per_record - samp_delay)
-            oscilations_measured = int_time * self.demodulation_frequency
-            oversampling = sample_rate / (2 * self.demodulation_frequency)
-            if samp_time > samples_time_max:
-                int_time_max = samples_time_max / sample_rate
-                raise ValueError('int_time {} is longer than total_time - '
-                                 'delay: {}'.format(int_time, int_time_max))
-            elif oscilations_measured < 10:
-                logging.warning('{} oscilations measured, recommend at '
-                                'least 10: decrease sampling rate, take '
+        if (value is None) or not (1e6 <= value <= 500e6):
+            raise ValueError('demod_freqs must be 1e6 <= value <= 500e6')
+        alazar = instr._get_alazar()
+        instr.sample_rate = alazar.get_sample_rate()
+        min_oscilations_measured = instr.int_time() * value
+        oversampling = instr.sample_rate / (2 * value)
+        if min_oscilations_measured < 10:
+            logging.warning('{} oscilations measured for largest '
+                            'demod freq, recommend at least 10: '
+                            'decrease sampling rate, take '
+                            'more samples or increase demodulation '
+                            'freq'.format(min_oscilations_measured))
+        elif oversampling < 1:
+            logging.warning('oversampling rate is {}, recommend > 1: '
+                            'increase sampling rate or decrease '
+                            'demodulation frequency'.format(oversampling))
+        demod_freqs = instr.get_demod_freqs()
+        current_demod_index = ([int(s) for s in param_name.split()
+                                if s.isdigit()][0])
+        demod_freqs[current_demod_index] = value
+        instr.acquisition.update_demod_setpoints(demod_freqs)
+
+    def _update_int_time(instr, value, **kwargs):
+        """
+        Function to validate value for int_time before setting parameter
+        value
+
+        Args:
+            value to be validated and used for instrument attribute update
+
+        Checks:
+            0 <= value <= 0.1 seconds
+            number of oscilation measured in this time
+            oversampling rate
+
+        Sets:
+            sample_rate attr of acq controller to be that of alazar
+            samples_per_record of acq controller
+            acquisition_kwarg['samples_per_record'] of acquisition param
+        """
+        if (value is None) or not (0 <= value <= 0.1):
+            raise ValueError('int_time must be 0 <= value <= 1')
+
+        alazar = instr._get_alazar()
+        instr.sample_rate = alazar.get_sample_rate()
+        if instr.get_max_demod_freq() is not None:
+            min_oscilations_measured = value * instr.get_max_demod_freq()
+            oversampling = instr.sample_rate / (2 * instr.get_max_demod_freq())
+            if min_oscilations_measured < 10:
+                logging.warning('{} oscilations measured for largest '
+                                'demod freq, recommend at least 10: '
+                                'decrease sampling rate, take '
                                 'more samples or increase demodulation '
-                                'freq'.format(oscilations_measured))
+                                'freq'.format(min_oscilations_measured))
             elif oversampling < 1:
                 logging.warning('oversampling rate is {}, recommend > 1: '
                                 'increase sampling rate or decrease '
                                 'demodulation frequency'.format(oversampling))
+        if instr.int_delay() is None:
+            instr.int_delay.to_default()
+
+        # update acquision kwargs and acq controller value
+        total_time = value + instr.int_delay()
+        samples_needed = total_time * instr.sample_rate
+        instr.samples_per_record = helpers.roundup(
+            samples_needed, instr.samples_divisor)
+        instr.acquisition.acquisition_kwargs.update(
+            samples_per_record=instr.samples_per_record)
+
+    def _update_int_delay(instr, value, **kwargs):
+        """
+        Function to validate value for int_delay before setting parameter
+        value
+
+        Args:
+            value to be validated and used for instrument attribute update
+
+        Checks:
+            0 <= value <= 0.1 seconds
+            number of samples discarded >= numtaps
+
+        Sets:
+            sample_rate attr of acq controller to be that of alazar
+            samples_per_record of acq controller
+            acquisition_kwarg['samples_per_record'] of acquisition param
+            setpoints of acquisiton param
+        """
+        if (value is None) or not (0 <= value <= 0.1):
+            raise ValueError('int_delay must be 0 <= value <= 1')
+        alazar = instr._get_alazar()
+        instr.sample_rate = alazar.get_sample_rate()
+        samples_delay_min = (instr.filter_settings['numtaps'] - 1)
+        int_delay_min = samples_delay_min / instr.sample_rate
+        if value < int_delay_min:
+            logging.warning(
+                'delay is less than recommended for filter choice: '
+                '(expect delay >= {})'.format(int_delay_min))
+
+        # update acquision kwargs and acq controller value
+        total_time = value + (instr.int_time() or 0)
+        samples_needed = total_time * instr.sample_rate
+        instr.samples_per_record = helpers.roundup(
+            samples_needed, instr.samples_divisor)
+        instr.acquisition.acquisition_kwargs.update(
+            samples_per_record=instr.samples_per_record)
+
+    def _int_delay_default(instr):
+        """
+        Function to generate default int_delay value
+
+        Returns:
+            minimum int_delay recommended for (numtaps - 1)
+            samples to be discarded as recommended for filter
+        """
+        alazar = instr._get_alazar()
+        instr.sample_rate = alazar.get_sample_rate()
+        samp_delay = instr.filter_settings['numtaps'] - 1
+        return samp_delay / instr.sample_rate
+
+    def _int_time_default(instr):
+        """
+        Function to generate defult int_time value
+
+        Returns:
+            max total time for integration based on samples_per_record,
+            sample_rate and int_delay
+        """
+        if instr.samples_per_record is (0 or None):
+            raise ValueError('Cannot set int_time to max if acq controller'
+                             ' has 0 or None samples_per_record, choose a '
+                             'value for int_time and samples_per_record will '
+                             'be set accordingly')
+        alazar = instr._get_alazar()
+        instr.sample_rate = alazar.get_sample_rate()
+        total_time = ((instr.samples_per_record / instr.sample_rate) -
+                      (instr.int_delay() or 0))
+        return total_time
+
+    def get_demod_freqs(self):
+        """
+        Function to get all the demod_freq parameter values in a list, v hacky
+
+        Returns:
+            freqs: numpy array of demodulation frequencies
+        """
+        freqs = list(filter(None, [getattr(self, 'demod_freq_{}'.format(c))()
+                                   for c in range(self._demod_length)]))
+        return np.array(freqs)
+
+    def get_max_demod_freq(self):
+        """
+        Returns:
+            the largest demodulation frequency
+
+        nb: really hacky and we should have channels in qcodes but we don't
+        (at time of writing)
+        """
+        freqs = self.get_demod_freqs()
+        if len(freqs) > 0:
+            return max(freqs)
         else:
-            samp_time = samples_per_record - samp_delay
+            return None
 
-        self.samples_time = int(samp_time)
-        self.samples_delay = int(samp_delay)
+    def update_filter_settings(self, filter, numtaps):
+        """
+        Updates the settings of the filter for filtering out
+        double frwuency component for demodulation.
 
-        self.acquisitionkwargs.update(**kwargs)
+        Args:
+            filter (str): filter type ('win' or 'ls')
+            numtaps (int): numtaps for filter
+        """
+        self.filter_settings.update({'filter': self.filter_dict[filter],
+                                     'numtaps': numtaps})
 
-    def do_acquisition(self):
+    def update_acquisition_kwargs(self, **kwargs):
         """
-        this method performs an acquisition, which is the get_cmd for the
-        acquisiion parameter of this instrument
-        :return:
+        Updates the kwargs to be used when
+        alazar_driver.acquisition() is called via a get call of the
+        acquisition SamplesParam. Should be used by the user for
+        updating averaging settings since the 'samples_per_record'
+        kwarg is updated via the int_time and int_delay parameters
+
+        Kwargs (ints):
+            records_per_buffer
+            buffers_per_acquisition
+            allocated_buffers
         """
-        values = self._get_alazar().acquire(acquisition_controller=self,
-                                            **self.acquisitionkwargs)
-        return values
+        if 'samples_per_record' in kwargs:
+            raise ValueError('With HD_Samples_Controller '
+                             'samples_per_record cannot be set manually '
+                             'via update_acquisition_kwargs and should instead'
+                             ' be set by setting int_time and int_delay')
+        self.acquisition.acquisition_kwargs.update(**kwargs)
 
     def pre_start_capture(self):
         """
         Called before capture start to update Acquisition Controller with
         alazar acquisition params and set up software wave for demodulation.
-        :return:
         """
         alazar = self._get_alazar()
-        self.samples_per_record = alazar.samples_per_record.get()
+        if self.samples_per_record != alazar.samples_per_record.get():
+            raise Exception('acq controller samples per record does not match'
+                            ' instrument value, most likely need '
+                            'to set and check int_time and int_delay')
+        if self.sample_rate != alazar.get_sample_rate():
+            raise Exception('acq controller sample rate does not match '
+                            'instrument value, most likely need '
+                            'to set and check int_time and int_delay')
+
+        demod_list = self.get_demod_freqs()
+        if len(demod_list) == 0:
+            raise Exception('no demod_freqs set')
+
         self.records_per_buffer = alazar.records_per_buffer.get()
         self.buffers_per_acquisition = alazar.buffers_per_acquisition.get()
         self.board_info = alazar.get_idn()
@@ -132,24 +431,18 @@ def pre_start_capture(self):
                                self.records_per_buffer *
                                self.number_of_channels)
 
-        integer_list = np.arange(self.samples_per_record, dtype=np.uint16)
-        angle_list = (2 * np.pi * self.demodulation_frequency /
-                      self.sample_rate * integer_list)
-
-        self.cos_list = np.cos(angle_list)
-        self.sin_list = np.sin(angle_list)
+        integer_list = np.arange(self.samples_per_record)
+        angle_mat = 2 * np.pi * \
+            np.outer(demod_list, integer_list) / self.sample_rate
+        self.cos_mat = np.cos(angle_mat)
+        self.sin_mat = np.sin(angle_mat)
 
     def pre_acquire(self):
-        """
-        See AcquisitionController
-        :return:
-        """
         pass
 
     def handle_buffer(self, data):
         """
         Adds data from alazar to buffer (effectively averaging)
-        :return:
         """
         self.buffer += data
 
@@ -158,7 +451,10 @@ def post_acquire(self):
         Processes the data according to ATS9360 settings, splitting into
         records and averaging over them, then applying demodulation fit
         nb: currently only channel A
-        :return: samples_magnitude_array, samples_phase_array
+
+        Returns:
+            magnitude (numpy array): shape = (demod_length, samples_used)
+            phase (numpy array): shape = (demod_length, samples_used)
         """
         records_per_acquisition = (self.buffers_per_acquisition *
                                    self.records_per_buffer)
@@ -175,91 +471,76 @@ def post_acquire(self):
         recordA = np.uint16(recA / records_per_acquisition)
 
         # do demodulation
-        magA, phaseA = self.fit(recordA)
+        magA, phaseA = self._fit(recordA)
 
         # same for chan b
         if self.chan_b:
             raise NotImplementedError('chan b code not complete')
-            # recordB = np.zeros(self.samples_per_record, dtype=np.uint16)
-            # for i in range(self.records_per_buffer):
-            #     i0 = (i * self.samples_per_record *
-            #           self.number_of_channels + 1)
-            #     i1 = (i0 + self.samples_per_record * self.number_of_channels)
-            #     recordB += np.uint16(self.buffer[i0:i1:self.number_of_channels] /
-            #                          records_per_acquisition)
-            # magB, phaseB = self.fit(recordB)
 
         return magA, phaseA
 
-    def fit(self, rec):
+    def _fit(self, rec):
         """
         Applies volts conversion, demodulation fit, low bandpass filter
-        and integration limits to samples array and averages
-        :return: magnitude, phase
-        """
+        and integration limits to samples array
+
+        Args:
+            rec (numpy array): record from alazar to be multiplied
+                               with the software signal, filtered and limited
+                               to integration limits shape = (samples_taken, )
 
+        Returns:
+            magnitude (numpy array): shape = (demod_length, )
+            phase (numpy array): shape = (demod_length, )
+        """
         # convert rec to volts
         bps = self.board_info['bits_per_sample']
         if bps == 12:
-            volt_rec = sample_to_volt_u12(rec, bps)
+            volt_rec = helpers.sample_to_volt_u12(rec, bps)
         else:
             logging.warning('sample to volt conversion does not exist for'
                             ' bps != 12, centered raw samples returned')
             volt_rec = rec - np.mean(rec)
 
-        # multiply with software wave
-        re_wave = np.multiply(volt_rec, self.cos_list)
-        im_wave = np.multiply(volt_rec, self.sin_list)
-        cutoff = self.demodulation_frequency / 10
+        # volt_rec to matrix and multiply with demodulation signal matrices
+        volt_rec_mat = np.outer(np.ones(self._demod_length), volt_rec)
+        re_mat = np.multiply(volt_rec_mat, self.cos_mat)
+        im_mat = np.multiply(volt_rec_mat, self.sin_mat)
 
         # filter out higher freq component
-        if self.filter == 0:
-            re_filtered = helpers.filter_win(re_wave, cutoff,
-                                             self.sample_rate, self.numtaps)
-            im_filtered = helpers.filter_win(im_wave, cutoff,
-                                             self.sample_rate, self.numtaps)
-        elif self.filter == 1:
-            re_filtered = helpers.filter_ls(re_wave, cutoff,
-                                            self.sample_rate, self.numtaps)
-            im_filtered = helpers.filter_ls(im_wave, cutoff,
-                                            self.sample_rate, self.numtaps)
-        elif self.filter == 2:
-            re_filtered = re_wave
-            im_filtered = im_wave
+        cutoff = self.get_max_demod_freq() / 10
+        if self.filter_settings['filter'] == 0:
+            re_filtered = helpers.filter_win(re_mat, cutoff,
+                                             self.sample_rate,
+                                             self.filter_settings['numtaps'],
+                                             axis=1)
+            im_filtered = helpers.filter_win(im_mat, cutoff,
+                                             self.sample_rate,
+                                             self.filter_settings['numtaps'],
+                                             axis=1)
+        elif self.filter_settings['filter'] == 1:
+            re_filtered = helpers.filter_ls(re_mat, cutoff,
+                                            self.sample_rate,
+                                            self.filter_settings['numtaps'],
+                                            axis=1)
+            im_filtered = helpers.filter_ls(im_mat, cutoff,
+                                            self.sample_rate,
+                                            self.filter_settings['numtaps'],
+                                            axis=1)
+        elif self.filter_settings['filter'] == 2:
+            re_filtered = re_mat
+            im_filtered = im_mat
 
         # apply integration limits
-        start = self.samples_delay
-        end = start + self.samples_time
+        beginning = int(self.int_delay() * self.sample_rate)
+        end = beginning + int(self.int_time() * self.sample_rate)
 
-        re_limited = re_filtered[start:end]
-        im_limited = im_filtered[start:end]
+        re_limited = re_filtered[:, beginning:end]
+        im_limited = im_filtered[:, beginning:end]
 
         # convert to magnitude and phase
-        complex_num = re_limited + im_limited * 1j
-        mag = np.mean(abs(complex_num))
-        phase = np.mean(np.angle(complex_num, deg=True))
-
-        return mag, phase
-
-
-def sample_to_volt_u12(raw_samples, bps):
-    """
-    Applies volts conversion for 12 bit sample data stored
-    in 2 bytes
-    :return: samples_magnitude_array, samples_phase_array
-    """
-
-    # right_shift 16-bit sample by 4 to get 12 bit sample
-    shifted_samples = np.right_shift(raw_samples, 4)
-
-    # Alazar calibration
-    code_zero = (1 << (bps - 1)) - 0.5
-    code_range = (1 << (bps - 1)) - 0.5
-
-    # TODO(nataliejpg) make this not hard coded
-    input_range_volts = 1
-    # Convert to volts
-    volt_samples = np.float64(input_range_volts *
-                              (shifted_samples - code_zero) / code_range)
+        complex_mat = re_limited + im_limited * 1j
+        magnitude = np.mean(abs(complex_mat), axis=1)
+        phase = np.mean(np.angle(complex_mat, deg=True), axis=1)
 
-    return volt_samples
+        return magnitude, phase
diff --git a/qcodes/instrument_drivers/AlazarTech/ave_controller_test.py b/qcodes/instrument_drivers/AlazarTech/ave_controller_test.py
deleted file mode 100644
index ee49c9afe102..000000000000
--- a/qcodes/instrument_drivers/AlazarTech/ave_controller_test.py
+++ /dev/null
@@ -1,546 +0,0 @@
-import logging
-from .ATS import AcquisitionController
-from .ATS9360 import AlazarTech_ATS9360
-import numpy as np
-from qcodes import Parameter
-import qcodes.instrument_drivers.AlazarTech.acq_helpers as helpers
-
-
-class AcqVariablesParam(Parameter):
-    """
-    Parameter of an AcquisitionController which has a _check_and_update_instr
-    function used for validation and to update instrument attributes and a
-    _get_default function which it uses to set the AcqVariablesParam to an
-    instrument caluclated default.
-
-    Args:
-        name: name for this parameter
-        instrument: acquisition controller instrument this parameter belongs to
-        check_and_update_fn: instrument function to be used for value
-            validation and updating instrument values
-        default_fn (optional): instrument function to be used to calculate
-            a default value to set parameter to
-        initial_value (optional): initial value for parameter
-    """
-
-    def __init__(self, name, instrument, check_and_update_fn,
-                 default_fn=None, initial_value=None):
-        super().__init__(name)
-        self._instrument = instrument
-        self._save_val(initial_value)
-        setattr(self, '_check_and_update_instr', check_and_update_fn)
-        if default_fn is not None:
-            setattr(self, '_get_default', default_fn)
-
-    def set(self, value):
-        """
-        Function which checks value using validation function and then sets
-        the Parameter value to this value.
-
-        Args:
-            value: value to set the parameter to
-        """
-        self._check_and_update_instr(value, param_name=self.name)
-        self._save_val(value)
-
-    def get(self):
-        return self._latest()['value']
-
-    def to_default(self):
-        """
-        Function which executes the default_fn specified to calculate the
-        default value based on instrument values and then calls the set
-        function with this value
-        """
-        try:
-            default = self._get_default()
-        except AttributeError as e:
-            raise AttributeError('no default function for {} Parameter '
-                                 '{}'.format(self.name, e))
-        self.set(default)
-
-    def check(self):
-        """
-        Function which checks the current Parameter value using the specified
-        check_and_update_fn which can also serve to update instrument values.
-
-        Return:
-            True (if no errors raised when check_and_update_fn executed)
-        """
-        val = self._latest()['value']
-        self._check_and_update_instr(val, param_name=self.name)
-        return True
-
-
-class AveragedAcqParam(Parameter):
-    """
-    Software controlled parameter class for Alazar acquisition. To be used with
-    HD_Averaging_Controller (tested with ATS9360 board) for return of
-    averaged magnitude and phase data from the Alazar.
-
-    Args:
-        name: name for this parameter
-        instrument: acquisition controller instrument this parameter belongs to
-        demod_length: numer of demodulation frequencies the acq controller has
-            NB this is currently set in acq controller init but that's really
-            not nice and should be changed when possible
-
-    TODO(nataliejpg) setpoints (including names and units)
-    TODO(nataliejpg) setpoint units
-    """
-
-    def __init__(self, name, instrument, demod_length):
-        super().__init__(name)
-        self._instrument = instrument
-        self.acquisition_kwargs = {}
-        self.names = ('magnitude', 'phase')
-        if demod_length > 1:
-            self.shapes = ((demod_length, ), (demod_length, ))
-        else:
-            self.shapes = ((), ())
-
-    def update_demod_setpoints(self, demod_freqs):
-        """
-        Function to update the demodulation frequency setpoints to be called
-        when a demod_freq Parameter of the acq controller is updated
-
-        Args:
-            demod_freqs: numpy array of demodulation frequencies to use as
-                setpoints if length > 1
-        """
-        demod_length = self._instrument._demod_length
-        self._demod_list = demod_freqs
-        if demod_length > 1:
-            self.setpoints = ((self._demod_list, ), (self._demod_list, ))
-        else:
-            pass
-
-    def get(self):
-        """
-        Gets the magnitude and phase signal by calling acquire
-        on the alazar (which in turn calls the processing functions of the
-        aqcuisition controller before returning the processed data
-        demodulated at specified frequencies and averaged over samples,
-        records and buffers)
-
-        Returns:
-            mag: numpy array of magnitude, shape (demod_length, )
-            phase: numpy array of magnitude, shape (demod_length, )
-        """
-        mag, phase = self._instrument._get_alazar().acquire(
-            acquisition_controller=self._instrument,
-            **self.acquisition_kwargs)
-        return mag, phase
-
-
-class HD_Averaging_Controller(AcquisitionController):
-    """
-    This is the Acquisition Controller class which works with the ATS9360,
-    averaging over samples (limited by int_time and int_delay values),
-    records and buffers and demodulating with software reference signal(s).
-
-    Args:
-        name: name for this acquisition_conroller as an instrument
-        alazar_name: name of the alazar instrument such that this
-            controller can communicate with the Alazar
-        demod_length (default 1): number of demodulation frequencies
-        filter (default 'win'): filter to be used to filter out double freq
-            component ('win' - window, 'ls' - least squared, 'ave' - averaging)
-        numtaps (default 101): number of freq components used in filter
-        chan_b (default False): whether there is also a second channel of data
-            to be processed and returned
-        **kwargs: kwargs are forwarded to the Instrument base class
-
-    TODO(nataliejpg) test filter options
-    TODO(nataliejpg) finish implementation of channel b option
-    TODO(nataliejpg) what should be private?
-    TODO(nataliejpg) where should filter_dict live?
-    TODO(nataliejpg) demod_freq should be changeable number: maybe channels
-    """
-
-    filter_dict = {'win': 0, 'ls': 1, 'ave': 2}
-    samples_divisor = AlazarTech_ATS9360.samples_divisor
-
-    def __init__(self, name, alazar_name, demod_length=1, filter='win',
-                 numtaps=101, chan_b=False, **kwargs):
-        self.filter_settings = {'filter': self.filter_dict[filter],
-                                'numtaps': numtaps}
-        self.chan_b = chan_b
-        self._demod_length = demod_length
-        self.number_of_channels = 2
-        self.samples_per_record = None
-        self.sample_rate = None
-        super().__init__(name, alazar_name, **kwargs)
-
-        self.add_parameter(name='acquisition',
-                           demod_length=demod_length,
-                           parameter_class=AveragedAcqParam)
-        for i in range(demod_length):
-            self.add_parameter(name='demod_freq_{}'.format(i),
-                               check_and_update_fn=self._update_demod_freq,
-                               parameter_class=AcqVariablesParam)
-        self.add_parameter(name='int_time',
-                           check_and_update_fn=self._update_int_time,
-                           default_fn=self._int_time_default,
-                           parameter_class=AcqVariablesParam)
-        self.add_parameter(name='int_delay',
-                           check_and_update_fn=self._update_int_delay,
-                           default_fn=self._int_delay_default,
-                           parameter_class=AcqVariablesParam)
-
-    def _update_demod_freq(instr, value, param_name=None):
-        """
-        Function to validate and update acquisiton parameter when
-        a demod_freq_ Parameter is changed
-
-        Args:
-            value to update demodulation frequency to
-
-        Kwargs:
-            param_name: used to update demod_freq list used for updating
-                septionts of acquisition parameter
-
-        Checks:
-            1e6 <= value <= 500e6
-            number of oscilation measured using current int_tiume param value
-                at this demod frequency value
-            oversampling rate for this demodulation frequency
-
-        Sets:
-            sample_rate attr of acq controller to be that of alazar
-            setpoints of acquisiton parameter
-        """
-        if (value is None) or not (1e6 <= value <= 500e6):
-            raise ValueError('demod_freqs must be 1e6 <= value <= 500e6')
-        alazar = instr._get_alazar()
-        instr.sample_rate = alazar.get_sample_rate()
-        min_oscilations_measured = instr.int_time() * value
-        oversampling = instr.sample_rate / (2 * value)
-        if min_oscilations_measured < 10:
-            logging.warning('{} oscilations measured for largest '
-                            'demod freq, recommend at least 10: '
-                            'decrease sampling rate, take '
-                            'more samples or increase demodulation '
-                            'freq'.format(min_oscilations_measured))
-        elif oversampling < 1:
-            logging.warning('oversampling rate is {}, recommend > 1: '
-                            'increase sampling rate or decrease '
-                            'demodulation frequency'.format(oversampling))
-        demod_freqs = instr.get_demod_freqs()
-        current_demod_index = ([int(s) for s in param_name.split()
-                                if s.isdigit()][0])
-        demod_freqs[current_demod_index] = value
-        instr.acquisition.update_demod_setpoints(demod_freqs)
-
-    def _update_int_time(instr, value, **kwargs):
-        """
-        Function to validate value for int_time before setting parameter
-        value
-
-        Args:
-            value to be validated and used for instrument attribute update
-
-        Checks:
-            0 <= value <= 0.1 seconds
-            number of oscilation measured in this time
-            oversampling rate
-
-        Sets:
-            sample_rate attr of acq controller to be that of alazar
-            samples_per_record of acq controller
-            acquisition_kwarg['samples_per_record'] of acquisition param
-        """
-        if (value is None) or not (0 <= value <= 0.1):
-            raise ValueError('int_time must be 0 <= value <= 1')
-
-        alazar = instr._get_alazar()
-        instr.sample_rate = alazar.get_sample_rate()
-        if instr.get_max_demod_freq() is not None:
-            min_oscilations_measured = value * instr.get_max_demod_freq()
-            oversampling = instr.sample_rate / (2 * instr.get_max_demod_freq())
-            if min_oscilations_measured < 10:
-                logging.warning('{} oscilations measured for largest '
-                                'demod freq, recommend at least 10: '
-                                'decrease sampling rate, take '
-                                'more samples or increase demodulation '
-                                'freq'.format(min_oscilations_measured))
-            elif oversampling < 1:
-                logging.warning('oversampling rate is {}, recommend > 1: '
-                                'increase sampling rate or decrease '
-                                'demodulation frequency'.format(oversampling))
-        if instr.int_delay() is None:
-            instr.int_delay.to_default()
-
-        # update acquision kwargs and acq controller value
-        total_time = value + instr.int_delay()
-        samples_needed = total_time * instr.sample_rate
-        instr.samples_per_record = helpers.roundup(
-            samples_needed, instr.samples_divisor)
-        instr.acquisition.acquisition_kwargs.update(
-            samples_per_record=instr.samples_per_record)
-
-    def _update_int_delay(instr, value, **kwargs):
-        """
-        Function to validate value for int_delay before setting parameter
-        value
-
-        Args:
-            value to be validated and used for instrument attribute update
-
-        Checks:
-            0 <= value <= 0.1 seconds
-            number of samples discarded >= numtaps
-
-        Sets:
-            sample_rate attr of acq controller to be that of alazar
-            samples_per_record of acq controller
-            acquisition_kwarg['samples_per_record'] of acquisition param
-            setpoints of acquisiton param
-        """
-        if (value is None) or not (0 <= value <= 0.1):
-            raise ValueError('int_delay must be 0 <= value <= 1')
-        alazar = instr._get_alazar()
-        instr.sample_rate = alazar.get_sample_rate()
-        samples_delay_min = (instr.filter_settings['numtaps'] - 1)
-        int_delay_min = samples_delay_min / instr.sample_rate
-        if value < int_delay_min:
-            logging.warning(
-                'delay is less than recommended for filter choice: '
-                '(expect delay >= {})'.format(int_delay_min))
-
-        # update acquision kwargs and acq controller value
-        total_time = value + (instr.int_time() or 0)
-        samples_needed = total_time * instr.sample_rate
-        instr.samples_per_record = helpers.roundup(
-            samples_needed, instr.samples_divisor)
-        instr.acquisition.acquisition_kwargs.update(
-            samples_per_record=instr.samples_per_record)
-
-    def _int_delay_default(instr):
-        """
-        Function to generate default int_delay value
-
-        Returns:
-            minimum int_delay recommended for (numtaps - 1)
-            samples to be discarded as recommended for filter
-        """
-        alazar = instr._get_alazar()
-        instr.sample_rate = alazar.get_sample_rate()
-        samp_delay = instr.filter_settings['numtaps'] - 1
-        return samp_delay / instr.sample_rate
-
-    def _int_time_default(instr):
-        """
-        Function to generate defult int_time value
-
-        Returns:
-            max total time for integration based on samples_per_record,
-            sample_rate and int_delay
-        """
-        if instr.samples_per_record is (0 or None):
-            raise ValueError('Cannot set int_time to max if acq controller'
-                             ' has 0 or None samples_per_record, choose a '
-                             'value for int_time and samples_per_record will '
-                             'be set accordingly')
-        alazar = instr._get_alazar()
-        instr.sample_rate = alazar.get_sample_rate()
-        total_time = ((instr.samples_per_record / instr.sample_rate) -
-                      (instr.int_delay() or 0))
-        return total_time
-
-    def get_demod_freqs(self):
-        """
-        Function to get all the demod_freq parameter values in a list, v hacky
-
-        Returns:
-            freqs: numpy array of demodulation frequencies
-        """
-        freqs = list(filter(None, [getattr(self, 'demod_freq_{}'.format(c))()
-                                   for c in range(self._demod_length)]))
-        return np.array(freqs)
-
-    def get_max_demod_freq(self):
-        """
-        Returns:
-            the largest demodulation frequency
-
-        nb: really hacky and we should have channels in qcodes but we don't
-        (at time of writing)
-        """
-        freqs = self.get_demod_freqs()
-        if len(freqs) > 0:
-            return max(freqs)
-        else:
-            return None
-
-    def update_filter_settings(self, filter, numtaps):
-        """
-        Updates the settings of the filter for filtering out
-        double frwuency component for demodulation.
-
-        Args:
-            filter (str): filter type ('win' or 'ls')
-            numtaps (int): numtaps for filter
-        """
-        self.filter_settings.update({'filter': self.filter_dict[filter],
-                                     'numtaps': numtaps})
-
-    def update_acquisition_kwargs(self, **kwargs):
-        """
-        Updates the kwargs to be used when
-        alazar_driver.acquisition() is called via a get call of the
-        acquisition SamplesParam. Should be used by the user for
-        updating averaging settings since the 'samples_per_record'
-        kwarg is updated via the int_time and int_delay parameters
-
-        Kwargs (ints):
-            records_per_buffer
-            buffers_per_acquisition
-            allocated_buffers
-        """
-        if 'samples_per_record' in kwargs:
-            raise ValueError('With HD_Samples_Controller '
-                             'samples_per_record cannot be set manually '
-                             'via update_acquisition_kwargs and should instead'
-                             ' be set by setting int_time and int_delay')
-        self.acquisition.acquisition_kwargs.update(**kwargs)
-
-    def pre_start_capture(self):
-        """
-        Called before capture start to update Acquisition Controller with
-        alazar acquisition params and set up software wave for demodulation.
-        """
-        alazar = self._get_alazar()
-        if self.samples_per_record != alazar.samples_per_record.get():
-            raise Exception('acq controller samples per record does not match'
-                            ' instrument value, most likely need '
-                            'to set and check int_time and int_delay')
-        if self.sample_rate != alazar.get_sample_rate():
-            raise Exception('acq controller sample rate does not match '
-                            'instrument value, most likely need '
-                            'to set and check int_time and int_delay')
-
-        demod_list = self.get_demod_freqs()
-        if len(demod_list) == 0:
-            raise Exception('no demod_freqs set')
-
-        self.records_per_buffer = alazar.records_per_buffer.get()
-        self.buffers_per_acquisition = alazar.buffers_per_acquisition.get()
-        self.board_info = alazar.get_idn()
-        self.buffer = np.zeros(self.samples_per_record *
-                               self.records_per_buffer *
-                               self.number_of_channels)
-
-        integer_list = np.arange(self.samples_per_record)
-        angle_mat = 2 * np.pi * \
-            np.outer(demod_list, integer_list) / self.sample_rate
-        self.cos_mat = np.cos(angle_mat)
-        self.sin_mat = np.sin(angle_mat)
-
-    def pre_acquire(self):
-        pass
-
-    def handle_buffer(self, data):
-        """
-        Adds data from alazar to buffer (effectively averaging)
-        """
-        self.buffer += data
-
-    def post_acquire(self):
-        """
-        Processes the data according to ATS9360 settings, splitting into
-        records and averaging over them, then applying demodulation fit
-        nb: currently only channel A
-
-        Returns:
-            magnitude (numpy array): shape = (demod_length, samples_used)
-            phase (numpy array): shape = (demod_length, samples_used)
-        """
-        records_per_acquisition = (self.buffers_per_acquisition *
-                                   self.records_per_buffer)
-        # for ATS9360 samples are arranged in the buffer as follows:
-        # S00A, S00B, S01A, S01B...S10A, S10B, S11A, S11B...
-        # where SXYZ is record X, sample Y, channel Z.
-
-        # break buffer up into records and averages over them
-        recA = np.zeros(self.samples_per_record)
-        for i in range(self.records_per_buffer):
-            i0 = (i * self.samples_per_record * self.number_of_channels)
-            i1 = (i0 + self.samples_per_record * self.number_of_channels)
-            recA += self.buffer[i0:i1:self.number_of_channels]
-        recordA = np.uint16(recA / records_per_acquisition)
-
-        # do demodulation
-        magA, phaseA = self._fit(recordA)
-
-        # same for chan b
-        if self.chan_b:
-            raise NotImplementedError('chan b code not complete')
-
-        return magA, phaseA
-
-    def _fit(self, rec):
-        """
-        Applies volts conversion, demodulation fit, low bandpass filter
-        and integration limits to samples array
-
-        Args:
-            rec (numpy array): record from alazar to be multiplied
-                               with the software signal, filtered and limited
-                               to integration limits shape = (samples_taken, )
-
-        Returns:
-            magnitude (numpy array): shape = (demod_length, )
-            phase (numpy array): shape = (demod_length, )
-        """
-        # convert rec to volts
-        bps = self.board_info['bits_per_sample']
-        if bps == 12:
-            volt_rec = helpers.sample_to_volt_u12(rec, bps)
-        else:
-            logging.warning('sample to volt conversion does not exist for'
-                            ' bps != 12, centered raw samples returned')
-            volt_rec = rec - np.mean(rec)
-
-        # volt_rec to matrix and multiply with demodulation signal matrices
-        volt_rec_mat = np.outer(np.ones(self._demod_length), volt_rec)
-        re_mat = np.multiply(volt_rec_mat, self.cos_mat)
-        im_mat = np.multiply(volt_rec_mat, self.sin_mat)
-
-        # filter out higher freq component
-        cutoff = self.get_max_demod_freq() / 10
-        if self.filter_settings['filter'] == 0:
-            re_filtered = helpers.filter_win(re_mat, cutoff,
-                                             self.sample_rate,
-                                             self.filter_settings['numtaps'],
-                                             axis=1)
-            im_filtered = helpers.filter_win(im_mat, cutoff,
-                                             self.sample_rate,
-                                             self.filter_settings['numtaps'],
-                                             axis=1)
-        elif self.filter_settings['filter'] == 1:
-            re_filtered = helpers.filter_ls(re_mat, cutoff,
-                                            self.sample_rate,
-                                            self.filter_settings['numtaps'],
-                                            axis=1)
-            im_filtered = helpers.filter_ls(im_mat, cutoff,
-                                            self.sample_rate,
-                                            self.filter_settings['numtaps'],
-                                            axis=1)
-        elif self.filter_settings['filter'] == 2:
-            re_filtered = re_mat
-            im_filtered = im_mat
-
-        # apply integration limits
-        beginning = int(self.int_delay() * self.sample_rate)
-        end = beginning + int(self.int_time() * self.sample_rate)
-
-        re_limited = re_filtered[:, beginning:end]
-        im_limited = im_filtered[:, beginning:end]
-
-        # convert to magnitude and phase
-        complex_mat = re_limited + im_limited * 1j
-        magnitude = np.mean(abs(complex_mat), axis=1)
-        phase = np.mean(np.angle(complex_mat, deg=True), axis=1)
-
-        return magnitude, phase
diff --git a/qcodes/instrument_drivers/AlazarTech/rec_controller.py b/qcodes/instrument_drivers/AlazarTech/rec_controller.py
index 5513e384264b..168658ca93e0 100644
--- a/qcodes/instrument_drivers/AlazarTech/rec_controller.py
+++ b/qcodes/instrument_drivers/AlazarTech/rec_controller.py
@@ -1,135 +1,284 @@
 import logging
 from .ATS import AcquisitionController
+from .ATS9360 import AlazarTech_ATS9360
 import numpy as np
 from qcodes import Parameter
-from qcodes.utils.helpers import filter_win, filter_ls
+import qcodes.instrument_drivers.AlazarTech.acq_helpers as helpers
 
 
-class RecordsParam(Parameter):
+class AcqVariablesParam(Parameter):
     """
-    Hardware controlled parameter class for Alazar acquisition. To be used with
-    HD_Records_Controller (tested with ATS9360 board) for return of an array of
-    record data from the Alazar, averaged over samples and buffers.
-
-    TODO(nataliejpg) fix setpoints/shapes horriblenesss
+    Parameter of an AcquisitionController which has a _check_and_update_instr
+    function used for validation and to update instrument attributes and a
+    _get_default function which it uses to set the AcqVariablesParam to an
+    instrument caluclated default.
+
+    Args:
+        name: name for this parameter
+        instrument: acquisition controller instrument this parameter belongs to
+        check_and_update_fn: instrument function to be used for value
+            validation and updating instrument values
+        default_fn (optional): instrument function to be used to calculate
+            a default value to set parameter to
+        initial_value (optional): initial value for parameter
     """
 
-    def __init__(self, name, instrument):
+    def __init__(self, name, instrument, check_and_update_fn,
+                 default_fn=None, initial_value=None):
+        super().__init__(name)
+        self._instrument = instrument
+        self._save_val(initial_value)
+        setattr(self, '_check_and_update_instr', check_and_update_fn)
+        if default_fn is not None:
+            setattr(self, '_get_default', default_fn)
+
+    def set(self, value):
+        """
+        Function which checks value using validation function and then sets
+        the Parameter value to this value.
+
+        Args:
+            value: value to set the parameter to
+        """
+        self._check_and_update_instr(value, param_name=self.name)
+        self._save_val(value)
+
+    def get(self):
+        return self._latest()['value']
+
+    def to_default(self):
+        """
+        Function which executes the default_fn specified to calculate the
+        default value based on instrument values and then calls the set
+        function with this value
+        """
+        try:
+            default = self._get_default()
+        except AttributeError as e:
+            raise AttributeError('no default function for {} Parameter '
+                                 '{}'.format(self.name, e))
+        self.set(default)
+
+    def check(self):
+        """
+        Function which checks the current Parameter value using the specified
+        check_and_update_fn which can also serve to update instrument values.
+
+        Return:
+            True (if no errors raised when check_and_update_fn executed)
+        """
+        val = self._latest()['value']
+        self._check_and_update_instr(val, param_name=self.name)
+        return True
+
+
+class RecordsAcqParam(Parameter):
+    """
+    Software controlled parameter class for Alazar acquisition. To be used with
+    HD_Records_Controller (tested with ATS9360 board) for return of
+    averaged magnitude and phase data from the Alazar.
+
+    Args:
+        name: name for this parameter
+        instrument: acquisition controller instrument this parameter belongs to
+        demod_length: numer of demodulation frequencies the acq controller has
+            NB this is currently set in acq controller init but that's really
+            not nice and should be changed when possible
+
+    TODO(nataliejpg) setpoints (including names and units)
+    TODO(nataliejpg) setpoint units
+    TODO(nataliejpg) convert records setpoint into actual frequency
+    """
+    def __init__(self, name, instrument, demod_length):
         super().__init__(name)
         self._instrument = instrument
-        self.acquisitionkwargs = {}
+        self.acquisition_kwargs = {}
         self.names = ('magnitude', 'phase')
-        self.units = ('', '')
-        self.setpoint_names = (('record_num',), ('record_num',))
-        self.setpoints = ((1,), (1,))
-        self.shapes = ((1,), (1,))
 
-    def update_acquisition_kwargs(self, **kwargs):
-        # needed to update config of the software parameter on sweep change
-        # freq setpoints tuple as needs to be hashable for look up
-        if 'records_per_buffer' in kwargs:
-            npts = kwargs['records_per_buffer']
-            n = tuple(np.arange(npts))
-            self.setpoints = ((n,), (n,))
+    def update_sweep(self, npts, start=None, stop=None):
+        """
+        Function which updates the shape of the parameter (and it's setpoints
+        when this is fixed)
+
+        Args:
+            npts: number of recornds returned after processing
+            start (optional): start value of records returned (if relevant)
+            stop (optional): stop value of records returned (if relevant)
+        """
+        demod_length = self._instrument._demod_length
+        # self.rec_list = tuple(np.linspace(start, stop, num=npts))
+        # demod_index = tuple(range(demod_length))
+        if demod_length > 1:
+            # demod_index = tuple(range(demod_length))
+            # demod_freqs = self._instrument.get_demod_freqs()
+            # self.setpoints = ((demod_freqs, self._rec_list), (
+            #           demod_freqs, self._rec_list))
+            self.shapes = ((demod_length, npts), (demod_length, npts))
+        else:
             self.shapes = ((npts,), (npts,))
+            # self.setpoints = ((self._rec_list,), (self._rec_list,))
+
+    def update_demod_setpoints(self, demod_freqs):
+        """
+        Function to update the demodulation frequency setpoints to be called
+        when a demod_freq Parameter of the acq controller is updated
+
+        Args:
+            demod_freqs: numpy array of demodulation frequencies to use as
+                setpoints if length > 1
+        """
+        demod_length = self._instrument._demod_length
+        if demod_length > 1:
+            self.setpoints = ((demod_freqs, self._rec_list),
+                              (demod_freqs, self._rec_list))
         else:
-            raise ValueError('records_per_buffer must be specified')
-        # updates dict to be used in acquisition get call
-        self.acquisitionkwargs.update(**kwargs)
+            pass
 
     def get(self):
+        """
+        Gets the magnitude and phase signal by calling acquire
+        on the alazar (which in turn calls the processing functions of the
+        aqcuisition controller before returning the processed data
+        demodulated at specified frequencies and averaged over samples
+        and buffers)
+
+        Returns:
+            mag: numpy array of magnitude, shape (demod_length, records)
+            phase: numpy array of magnitude, shape (demod_length, records)
+        """
         mag, phase = self._instrument._get_alazar().acquire(
             acquisition_controller=self._instrument,
-            **self.acquisitionkwargs)
+            **self.acquisition_kwargs)
         return mag, phase
 
 
 class HD_Records_Controller(AcquisitionController):
     """
     This is the Acquisition Controller class which works with the ATS9360,
-    averaging over buffers and records and demodulating with a software
-    reference signal, returning the  samples.
-    args:
-    name: name for this acquisition_conroller as an instrument
-    alazar_name: the name of the alazar instrument such that this controller
-        can communicate with the Alazar
-    demod_freq: the frequency of the software wave to be created
-    samp_rate: the rate of sampling
-    filt: the filter to be used to filter out double freq component
-    numtaps: number of freq components used in the filter
-    chan_b: whether there is also a second channel of data to be processed
-        and returned
-    **kwargs: kwargs are forwarded to the Instrument base class
-
-    TODO(nataliejpg) fix sample rate problem
+    averaging over samples (limited by int_time and int_delay values) and
+    buffers and demodulating with software reference signal(s).
+
+    Args:
+        name: name for this acquisition_conroller as an instrument
+        alazar_name: name of the alazar instrument such that this
+            controller can communicate with the Alazar
+        demod_length (default 1): number of demodulation frequencies
+        filter (default 'win'): filter to be used to filter out double freq
+            component ('win' - window, 'ls' - least squared, 'ave' - averaging)
+        numtaps (default 101): number of freq components used in filter
+        chan_b (default False): whether there is also a second channel of data
+            to be processed and returned
+        **kwargs: kwargs are forwarded to the Instrument base class
+
     TODO(nataliejpg) test filter options
     TODO(nataliejpg) finish implementation of channel b option
-    TODO(nataliejpg) make demodulation freq a param
+    TODO(nataliejpg) what should be private?
+    TODO(nataliejpg) where should filter_dict live?
+    TODO(nataliejpg) demod_freq should be changeable number: maybe channels
+    TODO(nataliejpg) make records a parameter or similar (MultiParam?)
     """
 
-    def __init__(self, name, alazar_name, demod_freq, samp_rate=5e8,
-                 filt='win', numtaps=101, chan_b=False, **kwargs):
-        filter_dict = {'win': 0, 'ls': 1, 'dot': 2}
-        self.demodulation_frequency = demod_freq
-        self.sample_rate = samp_rate
-        self.filter = filter_dict[filt]
-        self.numtaps = numtaps
+    filter_dict = {'win': 0, 'ls': 1, 'ave': 2}
+    samples_divisor = AlazarTech_ATS9360.samples_divisor
+
+    def __init__(self, name, alazar_name, demod_length=1, filter='win',
+                 numtaps=101, chan_b=False, **kwargs):
+        self.filter_settings = {'filter': self.filter_dict[filter],
+                                'numtaps': numtaps}
         self.chan_b = chan_b
+        self._demod_length = demod_length
+        self.number_of_channels = 2
         self.samples_per_record = None
         self.records_per_buffer = None
-        self.buffers_per_acquisition = None
-        self.number_of_channels = 2
-        self.samples_delay = None
-        self.samples_time = None
-        self.cos_mat = None
-        self.sin_mat = None
-        self.buffer = None
-        self.board_info = None
-        # make a call to the parent class and by extension,
-        # create the parameter structure of this class
+        self.sample_rate = None
         super().__init__(name, alazar_name, **kwargs)
 
         self.add_parameter(name='acquisition',
-                           parameter_class=RecordsParam)
+                           demod_length=self._demod_length,
+                           parameter_class=RecordsAcqParam)
+        for i in range(demod_length):
+            self.add_parameter(name='demod_freq_{}'.format(i),
+                               check_and_update_fn=self._update_demod_freq,
+                               parameter_class=AcqVariablesParam)
+        self.add_parameter(name='int_time',
+                           check_and_update_fn=self._update_int_time,
+                           default_fn=self._int_time_default,
+                           parameter_class=AcqVariablesParam)
+        self.add_parameter(name='int_delay',
+                           check_and_update_fn=self._update_int_delay,
+                           default_fn=self._int_delay_default,
+                           parameter_class=AcqVariablesParam)
+
+    def _update_demod_freq(instr, value, param_name=None):
+        """
+        Function to validate and update acquisiton parameter when
+        a demod_freq_ Parameter is changed
 
-    def update_acquisitionkwargs(self, **kwargs):
+        Args:
+            value to update demodulation frequency to
+
+        Kwargs:
+            param_name: used to update demod_freq list used for updating
+                septionts of acquisition parameter
+
+        Checks:
+            1e6 <= value <= 500e6
+            number of oscilation measured using current int_tiume param value
+                at this demod frequency value
+            oversampling rate for this demodulation frequency
+
+        Sets:
+            sample_rate attr of acq controller to be that of alazar
+            setpoints of acquisiton parameter
         """
-        Updates the kwargs to be used when
-        alazar_driver.acquire is called via a get call of the
-        acquisition RecordsParam.
-
-        It is also used to set the limits on the selection of
-        samples used (bounded by delay and integration time)
-
-        :param kwargs:
-        :return:
-        """
-        samples_per_record = kwargs['samples_per_record']
-        sample_rate = self.sample_rate
-
-        if 'int_delay' in kwargs:
-            int_delay = kwargs.pop('int_delay')
-            samp_delay = int_delay * sample_rate
-            samples_delay_min = (self.numtaps - 1)
-            if samp_delay < samples_delay_min:
-                int_delay_min = samples_delay_min / sample_rate
-                logging.warning(
-                    'delay is less than recommended for filter choice: '
-                    '(expect delay >= {}'.format(int_delay_min))
-        else:
-            samp_delay = self.numtaps - 1
-
-        if 'int_time' in kwargs:
-            int_time = kwargs.pop('int_time')
-            samp_time = int_time * sample_rate
-            samples_time_max = (samples_per_record - samp_delay)
-            oscilations_measured = int_time * self.demodulation_frequency
-            oversampling = sample_rate / (2 * self.demodulation_frequency)
-            if samp_time > samples_time_max:
-                int_time_max = samples_time_max / sample_rate
-                raise ValueError('int_time {} is longer than total_time - '
-                                 'delay: {}'.format(int_time, int_time_max))
-            elif oscilations_measured < 10:
+        if (value is None) or not (1e6 <= value <= 500e6):
+            raise ValueError('demod_freqs must be 1e6 <= value <= 500e6')
+        alazar = instr._get_alazar()
+        instr.sample_rate = alazar.get_sample_rate()
+        min_oscilations_measured = instr.int_time() * value
+        oversampling = instr.sample_rate / (2 * value)
+        if min_oscilations_measured < 10:
+            logging.warning('{} oscilations measured for largest '
+                            'demod freq, recommend at least 10: '
+                            'decrease sampling rate, take '
+                            'more samples or increase demodulation '
+                            'freq'.format(min_oscilations_measured))
+        elif oversampling < 1:
+            logging.warning('oversampling rate is {}, recommend > 1: '
+                            'increase sampling rate or decrease '
+                            'demodulation frequency'.format(oversampling))
+        demod_freqs = instr.get_demod_freqs()
+        current_demod_index = ([int(s) for s in param_name.split()
+                                if s.isdigit()][0])
+        demod_freqs[current_demod_index] = value
+        instr.acquisition.update_demod_setpoints(demod_freqs)
+
+    def _update_int_time(instr, value, **kwargs):
+        """
+        Function to validate value for int_time before setting parameter
+        value
+
+        Args:
+            value to be validated and used for instrument attribute update
+
+        Checks:
+            0 <= value <= 0.1 seconds
+            number of oscilation measured in this time
+            oversampling rate
+
+        Sets:
+            sample_rate attr of acq controller to be that of alazar
+            samples_per_record of acq controller
+            acquisition_kwarg['samples_per_record'] of acquisition param
+        """
+        if (value is None) or not (0 <= value <= 0.1):
+            raise ValueError('int_time must be 0 <= value <= 1')
+        alazar = instr._get_alazar()
+        instr.sample_rate = alazar.get_sample_rate()
+        if instr.get_max_demod_freq() is not None:
+            oscilations_measured = value * instr.get_max_demod_freq()
+            oversampling = instr.sample_rate / (2 * instr.get_max_demod_freq())
+            if oscilations_measured < 10:
                 logging.warning('{} oscilations measured, recommend at '
                                 'least 10: decrease sampling rate, take '
                                 'more samples or increase demodulation '
@@ -138,80 +287,223 @@ def update_acquisitionkwargs(self, **kwargs):
                 logging.warning('oversampling rate is {}, recommend > 1: '
                                 'increase sampling rate or decrease '
                                 'demodulation frequency'.format(oversampling))
+        if instr.int_delay() is None:
+            instr.int_delay.to_default()
+
+        # update acquision kwargs and acq controller value
+        total_time = value + instr.int_delay()
+        samples_needed = total_time * instr.sample_rate
+        instr.samples_per_record = helpers.roundup(
+            samples_needed, instr.samples_divisor)
+        instr.acquisition.acquisition_kwargs.update(
+            samples_per_record=instr.samples_per_record)
+
+    def _update_int_delay(instr, value, **kwargs):
+        """
+        Function to validate value for int_delay before setting parameter
+        value
+
+        Args:
+            value to be validated and used for instrument attribute update
+
+        Checks:
+            0 <= value <= 0.1 seconds
+            number of samples discarded >= numtaps
+
+        Sets:
+            sample_rate attr of acq controller to be that of alazar
+            samples_per_record of acq controller
+            acquisition_kwarg['samples_per_record'] of acquisition param
+            setpoints of acquisiton param
+        """
+        if (value is None) or not (0 <= value <= 0.1):
+            raise ValueError('int_delay must be 0 <= value <= 1')
+        alazar = instr._get_alazar()
+        instr.sample_rate = alazar.get_sample_rate()
+        samples_delay_min = (instr.filter_settings['numtaps'] - 1)
+        int_delay_min = samples_delay_min / instr.sample_rate
+        if value < int_delay_min:
+            logging.warning(
+                'delay is less than recommended for filter choice: '
+                '(expect delay >= {})'.format(int_delay_min))
+
+        # update acquision kwargs and acq controller value
+        total_time = value + (instr.int_time() or 0)
+        samples_needed = total_time * instr.sample_rate
+        instr.samples_per_record = helpers.roundup(
+            samples_needed, instr.samples_divisor)
+        instr.acquisition.acquisition_kwargs.update(
+            samples_per_record=instr.samples_per_record)
+
+    def _int_delay_default(instr):
+        """
+        Function to generate default int_delay value
+
+        Returns:
+            minimum int_delay recommended for (numtaps - 1)
+            samples to be discarded as recommended for filter
+        """
+        alazar = instr._get_alazar()
+        instr.sample_rate = alazar.get_sample_rate()
+        samp_delay = instr.filter_settings['numtaps'] - 1
+        return samp_delay / instr.sample_rate
+
+    def _int_time_default(instr):
+        """
+        Function to generate defult int_time value
+
+        Returns:
+            max total time for integration based on samples_per_record,
+            sample_rate and int_delay
+        """
+        if instr.samples_per_record is (0 or None):
+            raise ValueError('Cannot set int_time to max if acq controller'
+                             ' has 0 or None samples_per_record, choose a '
+                             'value for int_time and samples_per_record will '
+                             'be set accordingly')
+        alazar = instr._get_alazar()
+        instr.sample_rate = alazar.get_sample_rate()
+        total_time = ((instr.samples_per_record / instr.sample_rate) -
+                      (instr.int_delay() or 0))
+        return total_time
+
+    def get_demod_freqs(self):
+        """
+        Function to get all the demod_freq parameter values in a list, v hacky
+
+        Returns:
+            freqs: numpy array of demodulation frequencies
+        """
+        freqs = list(filter(None, [getattr(self, 'demod_freq_{}'.format(c))()
+                                   for c in range(self._demod_length)]))
+        return np.array(freqs)
+
+    def get_max_demod_freq(self):
+        """
+        Returns:
+            the largest demodulation frequency
+
+        nb: really hacky and we should have channels in qcodes but we don't
+        (at time of writing)
+        """
+        freqs = self.get_demod_freqs()
+        if len(freqs) > 0:
+            return max(freqs)
         else:
-            samp_time = samples_per_record - samp_delay
+            return None
 
-        self.samples_time = int(samp_time)
-        self.samples_delay = int(samp_delay)
+    def update_filter_settings(self, filter, numtaps):
+        """
+        Updates the settings of the filter for filtering out
+        double frwuency component for demodulation.
 
-        self.acquisition.update_acquisition_kwargs(**kwargs)
+        Args:
+            filter (str): filter type ('win' or 'ls')
+            numtaps (int): numtaps for filter
+        """
+        self.filter_settings.update({'filter': self.filter_dict[filter],
+                                     'numtaps': numtaps})
+
+    def update_acquisition_kwargs(self, **kwargs):
+        """
+        Updates the kwargs to be used when
+        alazar_driver.acquisition() is called via a get call of the
+        acquisition SamplesParam. Should be used by the user for
+        updating averaging settings since the 'samples_per_record'
+        kwarg is updated via the int_time and int_delay parameters
+
+        Kwargs (ints):
+            records_per_buffer
+            buffers_per_acquisition
+            allocated_buffers
+        """
+        if 'samples_per_record' in kwargs:
+            raise ValueError('With HD_Samples_Controller '
+                             'samples_per_record cannot be set manually '
+                             'via update_acquisition_kwargs and should instead'
+                             ' be set by setting int_time and int_delay')
+        if 'records_per_buffer' in kwargs:
+            self.acquisition.update_sweep(kwargs['records_per_buffer'])
+            self.records_per_buffer = kwargs['records_per_buffer']
+        self.acquisition.acquisition_kwargs.update(**kwargs)
 
     def pre_start_capture(self):
         """
         Called before capture start to update Acquisition Controller with
         alazar acquisition params and set up software wave for demodulation.
-
-        sine and cosine matrices have shape (samples * records).
-
-        :return:
         """
         alazar = self._get_alazar()
-        self.samples_per_record = alazar.samples_per_record.get()
-        self.records_per_buffer = alazar.records_per_buffer.get()
+        if self.samples_per_record != alazar.samples_per_record.get():
+            raise Exception('acq controller samples per record does not match'
+                            ' instrument value, most likely need '
+                            'to set and check int_time and int_delay')
+        if self.sample_rate != alazar.get_sample_rate():
+            raise Exception('acq controller sample rate does not match '
+                            'instrument value, most likely need '
+                            'to set and check int_time and int_delay')
+        if self.records_per_buffer != alazar.records_per_buffer.get():
+            raise Exception('acq controller records per buffer does not match '
+                            'instrument value, most likely need '
+                            'to call update_acquisition_kwargs with'
+                            'records_per_buffer as a param')
+
+        demod_list = self.get_demod_freqs()
+        if len(demod_list) == 0:
+            raise Exception('no demod_freqs set')
+
         self.buffers_per_acquisition = alazar.buffers_per_acquisition.get()
         self.board_info = alazar.get_idn()
         self.buffer = np.zeros(self.samples_per_record *
                                self.records_per_buffer *
                                self.number_of_channels)
 
-        integer_list = np.arange(self.samples_per_record, dtype=np.uint16)
-        angle_list = (2 * np.pi * self.demodulation_frequency /
-                      self.sample_rate * integer_list)
-
-        cos_list = np.cos(angle_list).reshape(self.samples_per_record, 1)
-        sin_list = np.sin(angle_list).reshape(self.samples_per_record, 1)
-
-        self.cos_mat = np.kron(np.ones((1, self.records_per_buffer)), cos_list)
-        self.sin_mat = np.kron(np.ones((1, self.records_per_buffer)), sin_list)
+        mat_shape = (self._demod_length, self.records_per_buffer,
+                     self.samples_per_record)
+        demod_list = np.array([getattr(self, 'demod_freq_{}'.format(n))()
+                               for n in range(self._demod_length)])
+        integer_list = np.arange(self.samples_per_record)
+        integer_mat = np.outer(np.ones(self.records_per_buffer), integer_list)
+        angle_mat = 2 * np.pi * \
+            np.outer(demod_list, integer_mat).reshape(
+                mat_shape) / self.sample_rate
+        self.cos_mat = np.cos(angle_mat)
+        self.sin_mat = np.sin(angle_mat)
 
     def pre_acquire(self):
-        """
-        See AcquisitionController
-        :return:
-        """
         pass
 
     def handle_buffer(self, data):
         """
         Adds data from alazar to buffer (effectively averaging)
-        :return:
         """
         self.buffer += data
 
     def post_acquire(self):
         """
         Processes the data according to ATS9360 settings, splitting into
-        records and averaging over them, then applying demodulation fit
+        records and then applying demodulation fit
         nb: currently only channel A
-        :return: samples_magnitude_array, samples_phase_array
-        """
 
+        Returns:
+            magnitude (numpy array): shape = (demod_length, records_per_buffer)
+            phase (numpy array): shape = (demod_length, records_per_buffer
+        """
         # for ATS9360 samples are arranged in the buffer as follows:
         # S00A, S00B, S01A, S01B...S10A, S10B, S11A, S11B...
         # where SXYZ is record X, sample Y, channel Z.
 
-        # break buffer up into records and shapes to be (samples * records)
-        recordA = np.zeros((self.samples_per_record, self.records_per_buffer),
-                           dtype=np.uint16)
+        # break buffer up into records and shapes to be (records, samples)
+        recordsA = np.empty((self.records_per_buffer, self.samples_per_record),
+                            dtype=np.uint16)
         for i in range(self.records_per_buffer):
             i0 = (i * self.samples_per_record * self.number_of_channels)
             i1 = (i0 + self.samples_per_record * self.number_of_channels)
-            recordA[:, i] = np.uint16(
+            recordsA[i, :] = np.uint16(
                 self.buffer[i0:i1:self.number_of_channels] /
                 self.buffers_per_acquisition)
 
         # do demodulation
-        magA, phaseA = self.fit(recordA)
+        magA, phaseA = self._fit(recordsA)
 
         # same for chan b
         if self.chan_b:
@@ -219,76 +511,71 @@ def post_acquire(self):
 
         return magA, phaseA
 
-    def fit(self, rec):
+    def _fit(self, rec):
         """
         Applies volts conversion, demodulation fit, low bandpass filter
-        and integration limits to samples * records array
-        :return: records_magnitude_array, records_phase_array
-        """
+        and integration limits to samples array
 
+        Args:
+            rec (numpy array): record from alazar to be multiplied
+                               with the software signal, filtered and limited
+                               to integration limits shape = (samples_taken, )
+
+        Returns:
+            magnitude (numpy array): shape = (demod_length, records_per_buffer)
+            phase (numpy array): shape = (demod_length, records_per_buffer)
+        """
         # convert rec to volts
         bps = self.board_info['bits_per_sample']
         if bps == 12:
-            volt_rec = sample_to_volt_u12(rec, bps)
+            volt_rec = helpers.sample_to_volt_u12(rec, bps)
         else:
             logging.warning('sample to volt conversion does not exist for'
                             ' bps != 12, centered raw samples returned')
-            volt_rec = rec - np.mean(rec, 0)
+            volt_rec = rec - np.mean(rec, axis=1)
 
-        # multiply with software wave
-        re_wave = np.multiply(volt_rec, self.cos_mat)
-        im_wave = np.multiply(volt_rec, self.sin_mat)
-        cutoff = self.demodulation_frequency / 10
-        ax = 0
+        # volt_rec to matrix and multiply with demodulation signal matrices
+        mat_shape = (self._demod_length, self.records_per_buffer,
+                     self.samples_per_record)
+        volt_rec_mat = np.outer(
+            np.ones(self._demod_length), volt_rec).reshape(mat_shape)
+        re_mat = np.multiply(volt_rec_mat, self.cos_mat)
+        im_mat = np.multiply(volt_rec_mat, self.sin_mat)
 
         # filter out higher freq component
-        if self.filter == 0:
-            re_filtered = filter_win(re_wave, cutoff, self.sample_rate,
-                                     self.numtaps, axis=ax)
-            im_filtered = filter_win(im_wave, cutoff, self.sample_rate,
-                                     self.numtaps, axis=ax)
-        elif self.filter == 1:
-            re_filtered = filter_ls(re_wave, cutoff, self.sample_rate,
-                                    self.numtaps, axis=ax)
-            im_filtered = filter_ls(im_wave, cutoff, self.sample_rate,
-                                    self.numtaps, axis=ax)
-        elif self.filter == 2:
-            re_filtered = re_wave
-            im_filtered = im_wave
+        cutoff = self.get_max_demod_freq() / 10
+        if self.filter_settings['filter'] == 0:
+            re_filtered = helpers.filter_win(re_mat, cutoff,
+                                             self.sample_rate,
+                                             self.filter_settings['numtaps'],
+                                             axis=2)
+            im_filtered = helpers.filter_win(im_mat, cutoff,
+                                             self.sample_rate,
+                                             self.filter_settings['numtaps'],
+                                             axis=2)
+        elif self.filter_settings['filter'] == 1:
+            re_filtered = helpers.filter_ls(re_mat, cutoff,
+                                            self.sample_rate,
+                                            self.filter_settings['numtaps'],
+                                            axis=2)
+            im_filtered = helpers.filter_ls(im_mat, cutoff,
+                                            self.sample_rate,
+                                            self.filter_settings['numtaps'],
+                                            axis=2)
+        elif self.filter_settings['filter'] == 2:
+            re_filtered = re_mat
+            im_filtered = im_mat
 
         # apply integration limits
-        start = self.samples_delay
-        end = start + self.samples_time
+        beginning = int(self.int_delay() * self.sample_rate)
+        end = beginning + int(self.int_time() * self.sample_rate)
 
-        re_limited = re_filtered[start:end, :]
-        im_limited = im_filtered[start:end, :]
+        re_limited = re_filtered[:, :, beginning:end]
+        im_limited = im_filtered[:, :, beginning:end]
 
         # convert to magnitude and phase
-        complex_num = re_limited + im_limited * 1j
-        mag = np.mean(abs(complex_num), 0)
-        phase = np.mean(np.angle(complex_num, deg=True), 0)
-
-        return mag, phase
-
-
-def sample_to_volt_u12(raw_samples, bps):
-    """
-    Applies volts conversion for 12 bit sample data stored
-    in 2 bytes
-    :return: samples_magnitude_array, samples_phase_array
-    """
-
-    # right_shift 16-bit sample by 4 to get 12 bit sample
-    shifted_samples = np.right_shift(raw_samples, 4)
-
-    # Alazar calibration
-    code_zero = (1 << (bps - 1)) - 0.5
-    code_range = (1 << (bps - 1)) - 0.5
-
-    # TODO(nataliejpg) make this not hard coded
-    input_range_volts = 1
-    # Convert to volts
-    volt_samples = np.float64(input_range_volts *
-                              (shifted_samples - code_zero) / code_range)
+        complex_mat = re_limited + im_limited * 1j
+        magnitude = np.mean(abs(complex_mat), axis=2)
+        phase = np.mean(np.angle(complex_mat, deg=True), axis=2)
 
-    return volt_samples
+        return magnitude, phase
diff --git a/qcodes/instrument_drivers/AlazarTech/rec_controller_test.py b/qcodes/instrument_drivers/AlazarTech/rec_controller_test.py
deleted file mode 100644
index 168658ca93e0..000000000000
--- a/qcodes/instrument_drivers/AlazarTech/rec_controller_test.py
+++ /dev/null
@@ -1,581 +0,0 @@
-import logging
-from .ATS import AcquisitionController
-from .ATS9360 import AlazarTech_ATS9360
-import numpy as np
-from qcodes import Parameter
-import qcodes.instrument_drivers.AlazarTech.acq_helpers as helpers
-
-
-class AcqVariablesParam(Parameter):
-    """
-    Parameter of an AcquisitionController which has a _check_and_update_instr
-    function used for validation and to update instrument attributes and a
-    _get_default function which it uses to set the AcqVariablesParam to an
-    instrument caluclated default.
-
-    Args:
-        name: name for this parameter
-        instrument: acquisition controller instrument this parameter belongs to
-        check_and_update_fn: instrument function to be used for value
-            validation and updating instrument values
-        default_fn (optional): instrument function to be used to calculate
-            a default value to set parameter to
-        initial_value (optional): initial value for parameter
-    """
-
-    def __init__(self, name, instrument, check_and_update_fn,
-                 default_fn=None, initial_value=None):
-        super().__init__(name)
-        self._instrument = instrument
-        self._save_val(initial_value)
-        setattr(self, '_check_and_update_instr', check_and_update_fn)
-        if default_fn is not None:
-            setattr(self, '_get_default', default_fn)
-
-    def set(self, value):
-        """
-        Function which checks value using validation function and then sets
-        the Parameter value to this value.
-
-        Args:
-            value: value to set the parameter to
-        """
-        self._check_and_update_instr(value, param_name=self.name)
-        self._save_val(value)
-
-    def get(self):
-        return self._latest()['value']
-
-    def to_default(self):
-        """
-        Function which executes the default_fn specified to calculate the
-        default value based on instrument values and then calls the set
-        function with this value
-        """
-        try:
-            default = self._get_default()
-        except AttributeError as e:
-            raise AttributeError('no default function for {} Parameter '
-                                 '{}'.format(self.name, e))
-        self.set(default)
-
-    def check(self):
-        """
-        Function which checks the current Parameter value using the specified
-        check_and_update_fn which can also serve to update instrument values.
-
-        Return:
-            True (if no errors raised when check_and_update_fn executed)
-        """
-        val = self._latest()['value']
-        self._check_and_update_instr(val, param_name=self.name)
-        return True
-
-
-class RecordsAcqParam(Parameter):
-    """
-    Software controlled parameter class for Alazar acquisition. To be used with
-    HD_Records_Controller (tested with ATS9360 board) for return of
-    averaged magnitude and phase data from the Alazar.
-
-    Args:
-        name: name for this parameter
-        instrument: acquisition controller instrument this parameter belongs to
-        demod_length: numer of demodulation frequencies the acq controller has
-            NB this is currently set in acq controller init but that's really
-            not nice and should be changed when possible
-
-    TODO(nataliejpg) setpoints (including names and units)
-    TODO(nataliejpg) setpoint units
-    TODO(nataliejpg) convert records setpoint into actual frequency
-    """
-    def __init__(self, name, instrument, demod_length):
-        super().__init__(name)
-        self._instrument = instrument
-        self.acquisition_kwargs = {}
-        self.names = ('magnitude', 'phase')
-
-    def update_sweep(self, npts, start=None, stop=None):
-        """
-        Function which updates the shape of the parameter (and it's setpoints
-        when this is fixed)
-
-        Args:
-            npts: number of recornds returned after processing
-            start (optional): start value of records returned (if relevant)
-            stop (optional): stop value of records returned (if relevant)
-        """
-        demod_length = self._instrument._demod_length
-        # self.rec_list = tuple(np.linspace(start, stop, num=npts))
-        # demod_index = tuple(range(demod_length))
-        if demod_length > 1:
-            # demod_index = tuple(range(demod_length))
-            # demod_freqs = self._instrument.get_demod_freqs()
-            # self.setpoints = ((demod_freqs, self._rec_list), (
-            #           demod_freqs, self._rec_list))
-            self.shapes = ((demod_length, npts), (demod_length, npts))
-        else:
-            self.shapes = ((npts,), (npts,))
-            # self.setpoints = ((self._rec_list,), (self._rec_list,))
-
-    def update_demod_setpoints(self, demod_freqs):
-        """
-        Function to update the demodulation frequency setpoints to be called
-        when a demod_freq Parameter of the acq controller is updated
-
-        Args:
-            demod_freqs: numpy array of demodulation frequencies to use as
-                setpoints if length > 1
-        """
-        demod_length = self._instrument._demod_length
-        if demod_length > 1:
-            self.setpoints = ((demod_freqs, self._rec_list),
-                              (demod_freqs, self._rec_list))
-        else:
-            pass
-
-    def get(self):
-        """
-        Gets the magnitude and phase signal by calling acquire
-        on the alazar (which in turn calls the processing functions of the
-        aqcuisition controller before returning the processed data
-        demodulated at specified frequencies and averaged over samples
-        and buffers)
-
-        Returns:
-            mag: numpy array of magnitude, shape (demod_length, records)
-            phase: numpy array of magnitude, shape (demod_length, records)
-        """
-        mag, phase = self._instrument._get_alazar().acquire(
-            acquisition_controller=self._instrument,
-            **self.acquisition_kwargs)
-        return mag, phase
-
-
-class HD_Records_Controller(AcquisitionController):
-    """
-    This is the Acquisition Controller class which works with the ATS9360,
-    averaging over samples (limited by int_time and int_delay values) and
-    buffers and demodulating with software reference signal(s).
-
-    Args:
-        name: name for this acquisition_conroller as an instrument
-        alazar_name: name of the alazar instrument such that this
-            controller can communicate with the Alazar
-        demod_length (default 1): number of demodulation frequencies
-        filter (default 'win'): filter to be used to filter out double freq
-            component ('win' - window, 'ls' - least squared, 'ave' - averaging)
-        numtaps (default 101): number of freq components used in filter
-        chan_b (default False): whether there is also a second channel of data
-            to be processed and returned
-        **kwargs: kwargs are forwarded to the Instrument base class
-
-    TODO(nataliejpg) test filter options
-    TODO(nataliejpg) finish implementation of channel b option
-    TODO(nataliejpg) what should be private?
-    TODO(nataliejpg) where should filter_dict live?
-    TODO(nataliejpg) demod_freq should be changeable number: maybe channels
-    TODO(nataliejpg) make records a parameter or similar (MultiParam?)
-    """
-
-    filter_dict = {'win': 0, 'ls': 1, 'ave': 2}
-    samples_divisor = AlazarTech_ATS9360.samples_divisor
-
-    def __init__(self, name, alazar_name, demod_length=1, filter='win',
-                 numtaps=101, chan_b=False, **kwargs):
-        self.filter_settings = {'filter': self.filter_dict[filter],
-                                'numtaps': numtaps}
-        self.chan_b = chan_b
-        self._demod_length = demod_length
-        self.number_of_channels = 2
-        self.samples_per_record = None
-        self.records_per_buffer = None
-        self.sample_rate = None
-        super().__init__(name, alazar_name, **kwargs)
-
-        self.add_parameter(name='acquisition',
-                           demod_length=self._demod_length,
-                           parameter_class=RecordsAcqParam)
-        for i in range(demod_length):
-            self.add_parameter(name='demod_freq_{}'.format(i),
-                               check_and_update_fn=self._update_demod_freq,
-                               parameter_class=AcqVariablesParam)
-        self.add_parameter(name='int_time',
-                           check_and_update_fn=self._update_int_time,
-                           default_fn=self._int_time_default,
-                           parameter_class=AcqVariablesParam)
-        self.add_parameter(name='int_delay',
-                           check_and_update_fn=self._update_int_delay,
-                           default_fn=self._int_delay_default,
-                           parameter_class=AcqVariablesParam)
-
-    def _update_demod_freq(instr, value, param_name=None):
-        """
-        Function to validate and update acquisiton parameter when
-        a demod_freq_ Parameter is changed
-
-        Args:
-            value to update demodulation frequency to
-
-        Kwargs:
-            param_name: used to update demod_freq list used for updating
-                septionts of acquisition parameter
-
-        Checks:
-            1e6 <= value <= 500e6
-            number of oscilation measured using current int_tiume param value
-                at this demod frequency value
-            oversampling rate for this demodulation frequency
-
-        Sets:
-            sample_rate attr of acq controller to be that of alazar
-            setpoints of acquisiton parameter
-        """
-        if (value is None) or not (1e6 <= value <= 500e6):
-            raise ValueError('demod_freqs must be 1e6 <= value <= 500e6')
-        alazar = instr._get_alazar()
-        instr.sample_rate = alazar.get_sample_rate()
-        min_oscilations_measured = instr.int_time() * value
-        oversampling = instr.sample_rate / (2 * value)
-        if min_oscilations_measured < 10:
-            logging.warning('{} oscilations measured for largest '
-                            'demod freq, recommend at least 10: '
-                            'decrease sampling rate, take '
-                            'more samples or increase demodulation '
-                            'freq'.format(min_oscilations_measured))
-        elif oversampling < 1:
-            logging.warning('oversampling rate is {}, recommend > 1: '
-                            'increase sampling rate or decrease '
-                            'demodulation frequency'.format(oversampling))
-        demod_freqs = instr.get_demod_freqs()
-        current_demod_index = ([int(s) for s in param_name.split()
-                                if s.isdigit()][0])
-        demod_freqs[current_demod_index] = value
-        instr.acquisition.update_demod_setpoints(demod_freqs)
-
-    def _update_int_time(instr, value, **kwargs):
-        """
-        Function to validate value for int_time before setting parameter
-        value
-
-        Args:
-            value to be validated and used for instrument attribute update
-
-        Checks:
-            0 <= value <= 0.1 seconds
-            number of oscilation measured in this time
-            oversampling rate
-
-        Sets:
-            sample_rate attr of acq controller to be that of alazar
-            samples_per_record of acq controller
-            acquisition_kwarg['samples_per_record'] of acquisition param
-        """
-        if (value is None) or not (0 <= value <= 0.1):
-            raise ValueError('int_time must be 0 <= value <= 1')
-        alazar = instr._get_alazar()
-        instr.sample_rate = alazar.get_sample_rate()
-        if instr.get_max_demod_freq() is not None:
-            oscilations_measured = value * instr.get_max_demod_freq()
-            oversampling = instr.sample_rate / (2 * instr.get_max_demod_freq())
-            if oscilations_measured < 10:
-                logging.warning('{} oscilations measured, recommend at '
-                                'least 10: decrease sampling rate, take '
-                                'more samples or increase demodulation '
-                                'freq'.format(oscilations_measured))
-            elif oversampling < 1:
-                logging.warning('oversampling rate is {}, recommend > 1: '
-                                'increase sampling rate or decrease '
-                                'demodulation frequency'.format(oversampling))
-        if instr.int_delay() is None:
-            instr.int_delay.to_default()
-
-        # update acquision kwargs and acq controller value
-        total_time = value + instr.int_delay()
-        samples_needed = total_time * instr.sample_rate
-        instr.samples_per_record = helpers.roundup(
-            samples_needed, instr.samples_divisor)
-        instr.acquisition.acquisition_kwargs.update(
-            samples_per_record=instr.samples_per_record)
-
-    def _update_int_delay(instr, value, **kwargs):
-        """
-        Function to validate value for int_delay before setting parameter
-        value
-
-        Args:
-            value to be validated and used for instrument attribute update
-
-        Checks:
-            0 <= value <= 0.1 seconds
-            number of samples discarded >= numtaps
-
-        Sets:
-            sample_rate attr of acq controller to be that of alazar
-            samples_per_record of acq controller
-            acquisition_kwarg['samples_per_record'] of acquisition param
-            setpoints of acquisiton param
-        """
-        if (value is None) or not (0 <= value <= 0.1):
-            raise ValueError('int_delay must be 0 <= value <= 1')
-        alazar = instr._get_alazar()
-        instr.sample_rate = alazar.get_sample_rate()
-        samples_delay_min = (instr.filter_settings['numtaps'] - 1)
-        int_delay_min = samples_delay_min / instr.sample_rate
-        if value < int_delay_min:
-            logging.warning(
-                'delay is less than recommended for filter choice: '
-                '(expect delay >= {})'.format(int_delay_min))
-
-        # update acquision kwargs and acq controller value
-        total_time = value + (instr.int_time() or 0)
-        samples_needed = total_time * instr.sample_rate
-        instr.samples_per_record = helpers.roundup(
-            samples_needed, instr.samples_divisor)
-        instr.acquisition.acquisition_kwargs.update(
-            samples_per_record=instr.samples_per_record)
-
-    def _int_delay_default(instr):
-        """
-        Function to generate default int_delay value
-
-        Returns:
-            minimum int_delay recommended for (numtaps - 1)
-            samples to be discarded as recommended for filter
-        """
-        alazar = instr._get_alazar()
-        instr.sample_rate = alazar.get_sample_rate()
-        samp_delay = instr.filter_settings['numtaps'] - 1
-        return samp_delay / instr.sample_rate
-
-    def _int_time_default(instr):
-        """
-        Function to generate defult int_time value
-
-        Returns:
-            max total time for integration based on samples_per_record,
-            sample_rate and int_delay
-        """
-        if instr.samples_per_record is (0 or None):
-            raise ValueError('Cannot set int_time to max if acq controller'
-                             ' has 0 or None samples_per_record, choose a '
-                             'value for int_time and samples_per_record will '
-                             'be set accordingly')
-        alazar = instr._get_alazar()
-        instr.sample_rate = alazar.get_sample_rate()
-        total_time = ((instr.samples_per_record / instr.sample_rate) -
-                      (instr.int_delay() or 0))
-        return total_time
-
-    def get_demod_freqs(self):
-        """
-        Function to get all the demod_freq parameter values in a list, v hacky
-
-        Returns:
-            freqs: numpy array of demodulation frequencies
-        """
-        freqs = list(filter(None, [getattr(self, 'demod_freq_{}'.format(c))()
-                                   for c in range(self._demod_length)]))
-        return np.array(freqs)
-
-    def get_max_demod_freq(self):
-        """
-        Returns:
-            the largest demodulation frequency
-
-        nb: really hacky and we should have channels in qcodes but we don't
-        (at time of writing)
-        """
-        freqs = self.get_demod_freqs()
-        if len(freqs) > 0:
-            return max(freqs)
-        else:
-            return None
-
-    def update_filter_settings(self, filter, numtaps):
-        """
-        Updates the settings of the filter for filtering out
-        double frwuency component for demodulation.
-
-        Args:
-            filter (str): filter type ('win' or 'ls')
-            numtaps (int): numtaps for filter
-        """
-        self.filter_settings.update({'filter': self.filter_dict[filter],
-                                     'numtaps': numtaps})
-
-    def update_acquisition_kwargs(self, **kwargs):
-        """
-        Updates the kwargs to be used when
-        alazar_driver.acquisition() is called via a get call of the
-        acquisition SamplesParam. Should be used by the user for
-        updating averaging settings since the 'samples_per_record'
-        kwarg is updated via the int_time and int_delay parameters
-
-        Kwargs (ints):
-            records_per_buffer
-            buffers_per_acquisition
-            allocated_buffers
-        """
-        if 'samples_per_record' in kwargs:
-            raise ValueError('With HD_Samples_Controller '
-                             'samples_per_record cannot be set manually '
-                             'via update_acquisition_kwargs and should instead'
-                             ' be set by setting int_time and int_delay')
-        if 'records_per_buffer' in kwargs:
-            self.acquisition.update_sweep(kwargs['records_per_buffer'])
-            self.records_per_buffer = kwargs['records_per_buffer']
-        self.acquisition.acquisition_kwargs.update(**kwargs)
-
-    def pre_start_capture(self):
-        """
-        Called before capture start to update Acquisition Controller with
-        alazar acquisition params and set up software wave for demodulation.
-        """
-        alazar = self._get_alazar()
-        if self.samples_per_record != alazar.samples_per_record.get():
-            raise Exception('acq controller samples per record does not match'
-                            ' instrument value, most likely need '
-                            'to set and check int_time and int_delay')
-        if self.sample_rate != alazar.get_sample_rate():
-            raise Exception('acq controller sample rate does not match '
-                            'instrument value, most likely need '
-                            'to set and check int_time and int_delay')
-        if self.records_per_buffer != alazar.records_per_buffer.get():
-            raise Exception('acq controller records per buffer does not match '
-                            'instrument value, most likely need '
-                            'to call update_acquisition_kwargs with'
-                            'records_per_buffer as a param')
-
-        demod_list = self.get_demod_freqs()
-        if len(demod_list) == 0:
-            raise Exception('no demod_freqs set')
-
-        self.buffers_per_acquisition = alazar.buffers_per_acquisition.get()
-        self.board_info = alazar.get_idn()
-        self.buffer = np.zeros(self.samples_per_record *
-                               self.records_per_buffer *
-                               self.number_of_channels)
-
-        mat_shape = (self._demod_length, self.records_per_buffer,
-                     self.samples_per_record)
-        demod_list = np.array([getattr(self, 'demod_freq_{}'.format(n))()
-                               for n in range(self._demod_length)])
-        integer_list = np.arange(self.samples_per_record)
-        integer_mat = np.outer(np.ones(self.records_per_buffer), integer_list)
-        angle_mat = 2 * np.pi * \
-            np.outer(demod_list, integer_mat).reshape(
-                mat_shape) / self.sample_rate
-        self.cos_mat = np.cos(angle_mat)
-        self.sin_mat = np.sin(angle_mat)
-
-    def pre_acquire(self):
-        pass
-
-    def handle_buffer(self, data):
-        """
-        Adds data from alazar to buffer (effectively averaging)
-        """
-        self.buffer += data
-
-    def post_acquire(self):
-        """
-        Processes the data according to ATS9360 settings, splitting into
-        records and then applying demodulation fit
-        nb: currently only channel A
-
-        Returns:
-            magnitude (numpy array): shape = (demod_length, records_per_buffer)
-            phase (numpy array): shape = (demod_length, records_per_buffer
-        """
-        # for ATS9360 samples are arranged in the buffer as follows:
-        # S00A, S00B, S01A, S01B...S10A, S10B, S11A, S11B...
-        # where SXYZ is record X, sample Y, channel Z.
-
-        # break buffer up into records and shapes to be (records, samples)
-        recordsA = np.empty((self.records_per_buffer, self.samples_per_record),
-                            dtype=np.uint16)
-        for i in range(self.records_per_buffer):
-            i0 = (i * self.samples_per_record * self.number_of_channels)
-            i1 = (i0 + self.samples_per_record * self.number_of_channels)
-            recordsA[i, :] = np.uint16(
-                self.buffer[i0:i1:self.number_of_channels] /
-                self.buffers_per_acquisition)
-
-        # do demodulation
-        magA, phaseA = self._fit(recordsA)
-
-        # same for chan b
-        if self.chan_b:
-            raise NotImplementedError('chan b code not complete')
-
-        return magA, phaseA
-
-    def _fit(self, rec):
-        """
-        Applies volts conversion, demodulation fit, low bandpass filter
-        and integration limits to samples array
-
-        Args:
-            rec (numpy array): record from alazar to be multiplied
-                               with the software signal, filtered and limited
-                               to integration limits shape = (samples_taken, )
-
-        Returns:
-            magnitude (numpy array): shape = (demod_length, records_per_buffer)
-            phase (numpy array): shape = (demod_length, records_per_buffer)
-        """
-        # convert rec to volts
-        bps = self.board_info['bits_per_sample']
-        if bps == 12:
-            volt_rec = helpers.sample_to_volt_u12(rec, bps)
-        else:
-            logging.warning('sample to volt conversion does not exist for'
-                            ' bps != 12, centered raw samples returned')
-            volt_rec = rec - np.mean(rec, axis=1)
-
-        # volt_rec to matrix and multiply with demodulation signal matrices
-        mat_shape = (self._demod_length, self.records_per_buffer,
-                     self.samples_per_record)
-        volt_rec_mat = np.outer(
-            np.ones(self._demod_length), volt_rec).reshape(mat_shape)
-        re_mat = np.multiply(volt_rec_mat, self.cos_mat)
-        im_mat = np.multiply(volt_rec_mat, self.sin_mat)
-
-        # filter out higher freq component
-        cutoff = self.get_max_demod_freq() / 10
-        if self.filter_settings['filter'] == 0:
-            re_filtered = helpers.filter_win(re_mat, cutoff,
-                                             self.sample_rate,
-                                             self.filter_settings['numtaps'],
-                                             axis=2)
-            im_filtered = helpers.filter_win(im_mat, cutoff,
-                                             self.sample_rate,
-                                             self.filter_settings['numtaps'],
-                                             axis=2)
-        elif self.filter_settings['filter'] == 1:
-            re_filtered = helpers.filter_ls(re_mat, cutoff,
-                                            self.sample_rate,
-                                            self.filter_settings['numtaps'],
-                                            axis=2)
-            im_filtered = helpers.filter_ls(im_mat, cutoff,
-                                            self.sample_rate,
-                                            self.filter_settings['numtaps'],
-                                            axis=2)
-        elif self.filter_settings['filter'] == 2:
-            re_filtered = re_mat
-            im_filtered = im_mat
-
-        # apply integration limits
-        beginning = int(self.int_delay() * self.sample_rate)
-        end = beginning + int(self.int_time() * self.sample_rate)
-
-        re_limited = re_filtered[:, :, beginning:end]
-        im_limited = im_filtered[:, :, beginning:end]
-
-        # convert to magnitude and phase
-        complex_mat = re_limited + im_limited * 1j
-        magnitude = np.mean(abs(complex_mat), axis=2)
-        phase = np.mean(np.angle(complex_mat, deg=True), axis=2)
-
-        return magnitude, phase

From f1ddc48a217b1cd28ef6df374e8050cf0bbf910c Mon Sep 17 00:00:00 2001
From: Natalie Pearson 
Date: Tue, 7 Feb 2017 16:22:14 +0100
Subject: [PATCH 099/328] tweak and comment out demod setpoint logic for until
 setpoints are fixed

---
 .../AlazarTech/ave_controller.py              | 10 ++++----
 .../AlazarTech/rec_controller.py              | 23 ++++++++-----------
 .../AlazarTech/samp_controller.py             | 20 ++++++++--------
 3 files changed, 24 insertions(+), 29 deletions(-)

diff --git a/qcodes/instrument_drivers/AlazarTech/ave_controller.py b/qcodes/instrument_drivers/AlazarTech/ave_controller.py
index ee49c9afe102..87e25a89df8a 100644
--- a/qcodes/instrument_drivers/AlazarTech/ave_controller.py
+++ b/qcodes/instrument_drivers/AlazarTech/ave_controller.py
@@ -109,9 +109,9 @@ def update_demod_setpoints(self, demod_freqs):
                 setpoints if length > 1
         """
         demod_length = self._instrument._demod_length
-        self._demod_list = demod_freqs
         if demod_length > 1:
-            self.setpoints = ((self._demod_list, ), (self._demod_list, ))
+            pass
+            # self.setpoints = ((demod_freqs, ), (demod_freqs, ))
         else:
             pass
 
@@ -420,8 +420,8 @@ def pre_start_capture(self):
                             'instrument value, most likely need '
                             'to set and check int_time and int_delay')
 
-        demod_list = self.get_demod_freqs()
-        if len(demod_list) == 0:
+        demod_freqs = self.get_demod_freqs()
+        if len(demod_freqs) == 0:
             raise Exception('no demod_freqs set')
 
         self.records_per_buffer = alazar.records_per_buffer.get()
@@ -433,7 +433,7 @@ def pre_start_capture(self):
 
         integer_list = np.arange(self.samples_per_record)
         angle_mat = 2 * np.pi * \
-            np.outer(demod_list, integer_list) / self.sample_rate
+            np.outer(demod_freqs, integer_list) / self.sample_rate
         self.cos_mat = np.cos(angle_mat)
         self.sin_mat = np.sin(angle_mat)
 
diff --git a/qcodes/instrument_drivers/AlazarTech/rec_controller.py b/qcodes/instrument_drivers/AlazarTech/rec_controller.py
index 168658ca93e0..a04402623fc0 100644
--- a/qcodes/instrument_drivers/AlazarTech/rec_controller.py
+++ b/qcodes/instrument_drivers/AlazarTech/rec_controller.py
@@ -86,9 +86,9 @@ class RecordsAcqParam(Parameter):
             not nice and should be changed when possible
 
     TODO(nataliejpg) setpoints (including names and units)
-    TODO(nataliejpg) setpoint units
-    TODO(nataliejpg) convert records setpoint into actual frequency
+    TODO(nataliejpg) convert records setpoint into variable/param
     """
+
     def __init__(self, name, instrument, demod_length):
         super().__init__(name)
         self._instrument = instrument
@@ -107,12 +107,10 @@ def update_sweep(self, npts, start=None, stop=None):
         """
         demod_length = self._instrument._demod_length
         # self.rec_list = tuple(np.linspace(start, stop, num=npts))
-        # demod_index = tuple(range(demod_length))
         if demod_length > 1:
-            # demod_index = tuple(range(demod_length))
             # demod_freqs = self._instrument.get_demod_freqs()
-            # self.setpoints = ((demod_freqs, self._rec_list), (
-            #           demod_freqs, self._rec_list))
+            # self.setpoints = ((demod_freqs, self._rec_list),
+            #                   (demod_freqs, self._rec_list))
             self.shapes = ((demod_length, npts), (demod_length, npts))
         else:
             self.shapes = ((npts,), (npts,))
@@ -129,8 +127,9 @@ def update_demod_setpoints(self, demod_freqs):
         """
         demod_length = self._instrument._demod_length
         if demod_length > 1:
-            self.setpoints = ((demod_freqs, self._rec_list),
-                              (demod_freqs, self._rec_list))
+            pass
+            # self.setpoints = ((demod_freqs, self._rec_list),
+            #                   (demod_freqs, self._rec_list))
         else:
             pass
 
@@ -447,8 +446,8 @@ def pre_start_capture(self):
                             'to call update_acquisition_kwargs with'
                             'records_per_buffer as a param')
 
-        demod_list = self.get_demod_freqs()
-        if len(demod_list) == 0:
+        demod_freqs = self.get_demod_freqs()
+        if len(demod_freqs) == 0:
             raise Exception('no demod_freqs set')
 
         self.buffers_per_acquisition = alazar.buffers_per_acquisition.get()
@@ -459,12 +458,10 @@ def pre_start_capture(self):
 
         mat_shape = (self._demod_length, self.records_per_buffer,
                      self.samples_per_record)
-        demod_list = np.array([getattr(self, 'demod_freq_{}'.format(n))()
-                               for n in range(self._demod_length)])
         integer_list = np.arange(self.samples_per_record)
         integer_mat = np.outer(np.ones(self.records_per_buffer), integer_list)
         angle_mat = 2 * np.pi * \
-            np.outer(demod_list, integer_mat).reshape(
+            np.outer(demod_freqs, integer_mat).reshape(
                 mat_shape) / self.sample_rate
         self.cos_mat = np.cos(angle_mat)
         self.sin_mat = np.sin(angle_mat)
diff --git a/qcodes/instrument_drivers/AlazarTech/samp_controller.py b/qcodes/instrument_drivers/AlazarTech/samp_controller.py
index 02b67605d64d..b4de18318781 100644
--- a/qcodes/instrument_drivers/AlazarTech/samp_controller.py
+++ b/qcodes/instrument_drivers/AlazarTech/samp_controller.py
@@ -83,7 +83,6 @@ class SamplesAcqParam(Parameter):
         instrument: acquisition controller instrument this parameter belongs to
 
     TODO(nataliejpg) setpoints (including names and units)
-    TODO(nataliejpg) setpoint units
     """
 
     def __init__(self, name, instrument):
@@ -105,10 +104,9 @@ def update_sweep(self, start, stop, npts):
         demod_length = self._instrument._demod_length
         # self._time_list = tuple(np.linspace(start, stop, num=npts))
         if demod_length > 1:
-            # demod_index = tuple(range(demod_length))
-            # self._demod_list = self._instrument.get_demod_freqs()
-            # self.setpoints = ((self._demod_list, self._time_list ), (
-            #           self._demod_list, self._time_list ))
+            # demod_freqs = self._instrument.get_demod_freqs()
+            # self.setpoints = ((demod_freqs, self._time_list),
+            #                   (demod_freqs, self._time_list))
             self.shapes = ((demod_length, npts), (demod_length, npts))
         else:
             self.shapes = ((npts,), (npts,))
@@ -124,10 +122,10 @@ def update_demod_setpoints(self, demod_freqs):
                 setpoints if length > 1
         """
         demod_length = self._instrument._demod_length
-        self._demod_list = demod_freqs
         if demod_length > 1:
-            self.setpoints = ((self._demod_list, self._time_list),
-                              (self._demod_list, self._time_list))
+            pass
+            # self.setpoints = ((demod_freqs, self._time_list),
+            #                   (demod_freqs, self._time_list))
         else:
             pass
 
@@ -445,8 +443,8 @@ def pre_start_capture(self):
                             'instrument value, most likely need '
                             'to call update_acquisition_settings')
 
-        demod_list = self.get_demod_freqs()
-        if len(demod_list) == 0:
+        demod_freqs = self.get_demod_freqs()
+        if len(demod_freqs) == 0:
             raise Exception('no demod_freqs set')
 
         self.records_per_buffer = alazar.records_per_buffer.get()
@@ -458,7 +456,7 @@ def pre_start_capture(self):
 
         integer_list = np.arange(self.samples_per_record)
         angle_mat = 2 * np.pi * \
-            np.outer(demod_list, integer_list) / self.sample_rate
+            np.outer(demod_freqs, integer_list) / self.sample_rate
         self.cos_mat = np.cos(angle_mat)
         self.sin_mat = np.sin(angle_mat)
 

From dab7ff0e4952c368e4e5e450cd783d86fcf56246 Mon Sep 17 00:00:00 2001
From: Natalie Pearson 
Date: Tue, 7 Feb 2017 16:39:07 +0100
Subject: [PATCH 100/328] move samples_divisor into init of acq controllers so
 no need to import ATS9360 class, should be more general

---
 qcodes/instrument_drivers/AlazarTech/ave_controller.py  | 4 ++--
 qcodes/instrument_drivers/AlazarTech/rec_controller.py  | 4 ++--
 qcodes/instrument_drivers/AlazarTech/samp_controller.py | 3 +--
 3 files changed, 5 insertions(+), 6 deletions(-)

diff --git a/qcodes/instrument_drivers/AlazarTech/ave_controller.py b/qcodes/instrument_drivers/AlazarTech/ave_controller.py
index 87e25a89df8a..a7e7d1b67fb4 100644
--- a/qcodes/instrument_drivers/AlazarTech/ave_controller.py
+++ b/qcodes/instrument_drivers/AlazarTech/ave_controller.py
@@ -1,6 +1,5 @@
 import logging
 from .ATS import AcquisitionController
-from .ATS9360 import AlazarTech_ATS9360
 import numpy as np
 from qcodes import Parameter
 import qcodes.instrument_drivers.AlazarTech.acq_helpers as helpers
@@ -159,7 +158,6 @@ class HD_Averaging_Controller(AcquisitionController):
     """
 
     filter_dict = {'win': 0, 'ls': 1, 'ave': 2}
-    samples_divisor = AlazarTech_ATS9360.samples_divisor
 
     def __init__(self, name, alazar_name, demod_length=1, filter='win',
                  numtaps=101, chan_b=False, **kwargs):
@@ -188,6 +186,8 @@ def __init__(self, name, alazar_name, demod_length=1, filter='win',
                            default_fn=self._int_delay_default,
                            parameter_class=AcqVariablesParam)
 
+        self.samples_divisor = self._get_alazar().samples_divisor
+
     def _update_demod_freq(instr, value, param_name=None):
         """
         Function to validate and update acquisiton parameter when
diff --git a/qcodes/instrument_drivers/AlazarTech/rec_controller.py b/qcodes/instrument_drivers/AlazarTech/rec_controller.py
index a04402623fc0..a10629a00ecf 100644
--- a/qcodes/instrument_drivers/AlazarTech/rec_controller.py
+++ b/qcodes/instrument_drivers/AlazarTech/rec_controller.py
@@ -1,6 +1,5 @@
 import logging
 from .ATS import AcquisitionController
-from .ATS9360 import AlazarTech_ATS9360
 import numpy as np
 from qcodes import Parameter
 import qcodes.instrument_drivers.AlazarTech.acq_helpers as helpers
@@ -178,7 +177,6 @@ class HD_Records_Controller(AcquisitionController):
     """
 
     filter_dict = {'win': 0, 'ls': 1, 'ave': 2}
-    samples_divisor = AlazarTech_ATS9360.samples_divisor
 
     def __init__(self, name, alazar_name, demod_length=1, filter='win',
                  numtaps=101, chan_b=False, **kwargs):
@@ -208,6 +206,8 @@ def __init__(self, name, alazar_name, demod_length=1, filter='win',
                            default_fn=self._int_delay_default,
                            parameter_class=AcqVariablesParam)
 
+        self.samples_divisor = self._get_alazar().samples_divisor
+
     def _update_demod_freq(instr, value, param_name=None):
         """
         Function to validate and update acquisiton parameter when
diff --git a/qcodes/instrument_drivers/AlazarTech/samp_controller.py b/qcodes/instrument_drivers/AlazarTech/samp_controller.py
index b4de18318781..7f1974eb8f9b 100644
--- a/qcodes/instrument_drivers/AlazarTech/samp_controller.py
+++ b/qcodes/instrument_drivers/AlazarTech/samp_controller.py
@@ -1,6 +1,5 @@
 import logging
 from .ATS import AcquisitionController
-from .ATS9360 import AlazarTech_ATS9360
 import numpy as np
 from qcodes import Parameter
 import qcodes.instrument_drivers.AlazarTech.acq_helpers as helpers
@@ -173,7 +172,6 @@ class HD_Samples_Controller(AcquisitionController):
     TODO(nataliejpg) demod_freq number should be changeable number: channels?
     """
     filter_dict = {'win': 0, 'ls': 1}
-    samples_divisor = AlazarTech_ATS9360.samples_divisor
 
     def __init__(self, name, alazar_name, demod_length=1, filter='win',
                  numtaps=101, chan_b=False, **kwargs):
@@ -200,6 +198,7 @@ def __init__(self, name, alazar_name, demod_length=1, filter='win',
                            check_and_update_fn=self._update_int_delay,
                            default_fn=self._int_delay_default,
                            parameter_class=AcqVariablesParam)
+        self.samples_divisor = self._get_alazar().samples_divisor
 
     def _update_demod_freq(instr, value, param_name=None):
         """

From 1c44788be932274a105c88804679db4acc98f538 Mon Sep 17 00:00:00 2001
From: Natalie Pearson 
Date: Tue, 7 Feb 2017 19:21:32 +0100
Subject: [PATCH 101/328] updating docstrings, making rec_num and buf_num
 parameters where necessary and doing most of rec_samp controller and buf_rec
 controller

---
 .../instrument_drivers/AlazarTech/ATS9360.py  |   7 +-
 .../AlazarTech/ave_controller.py              |   8 +-
 .../AlazarTech/buf_rec_controller.py          | 686 ++++++++++++++++
 .../AlazarTech/rec_buf_controller.py          | 220 -----
 .../AlazarTech/rec_controller.py              |  80 +-
 .../AlazarTech/rec_samp_controller.py         | 751 ++++++++++++++----
 .../AlazarTech/samp_controller.py             |  20 +-
 7 files changed, 1345 insertions(+), 427 deletions(-)
 create mode 100644 qcodes/instrument_drivers/AlazarTech/buf_rec_controller.py
 delete mode 100644 qcodes/instrument_drivers/AlazarTech/rec_buf_controller.py

diff --git a/qcodes/instrument_drivers/AlazarTech/ATS9360.py b/qcodes/instrument_drivers/AlazarTech/ATS9360.py
index 5ffe33f03699..3c33374d50c7 100644
--- a/qcodes/instrument_drivers/AlazarTech/ATS9360.py
+++ b/qcodes/instrument_drivers/AlazarTech/ATS9360.py
@@ -8,8 +8,13 @@ class AlazarTech_ATS9360(AlazarTech_ATS):
     it inherits from the ATS base class
 
     TODO(nataliejpg):
-        -   add clock source options and sample rate options
+        -  add clock source options and sample rate options
+          (probelem being that byte_to_value_dict of
+          sample_rate relies on value of clock_source)
     """
+
+    # samples divisor is listed in the manual as being 32 but this gave
+    # incorrect data when tested for divisors less than 128
     samples_divisor = 128
 
     def __init__(self, name, **kwargs):
diff --git a/qcodes/instrument_drivers/AlazarTech/ave_controller.py b/qcodes/instrument_drivers/AlazarTech/ave_controller.py
index a7e7d1b67fb4..7cfa7036cf26 100644
--- a/qcodes/instrument_drivers/AlazarTech/ave_controller.py
+++ b/qcodes/instrument_drivers/AlazarTech/ave_controller.py
@@ -235,7 +235,7 @@ def _update_demod_freq(instr, value, param_name=None):
     def _update_int_time(instr, value, **kwargs):
         """
         Function to validate value for int_time before setting parameter
-        value
+        value and update instr attributes.
 
         Args:
             value to be validated and used for instrument attribute update
@@ -282,7 +282,7 @@ def _update_int_time(instr, value, **kwargs):
     def _update_int_delay(instr, value, **kwargs):
         """
         Function to validate value for int_delay before setting parameter
-        value
+        value and update instr attributes.
 
         Args:
             value to be validated and used for instrument attribute update
@@ -389,7 +389,7 @@ def update_acquisition_kwargs(self, **kwargs):
         """
         Updates the kwargs to be used when
         alazar_driver.acquisition() is called via a get call of the
-        acquisition SamplesParam. Should be used by the user for
+        acquisition AveragedAcqParam. Should be used by the user for
         updating averaging settings since the 'samples_per_record'
         kwarg is updated via the int_time and int_delay parameters
 
@@ -399,7 +399,7 @@ def update_acquisition_kwargs(self, **kwargs):
             allocated_buffers
         """
         if 'samples_per_record' in kwargs:
-            raise ValueError('With HD_Samples_Controller '
+            raise ValueError('With HD_Averaging_Controller '
                              'samples_per_record cannot be set manually '
                              'via update_acquisition_kwargs and should instead'
                              ' be set by setting int_time and int_delay')
diff --git a/qcodes/instrument_drivers/AlazarTech/buf_rec_controller.py b/qcodes/instrument_drivers/AlazarTech/buf_rec_controller.py
new file mode 100644
index 000000000000..b4e118970901
--- /dev/null
+++ b/qcodes/instrument_drivers/AlazarTech/buf_rec_controller.py
@@ -0,0 +1,686 @@
+import logging
+from .ATS import AcquisitionController
+import numpy as np
+from qcodes import Parameter
+import qcodes.instrument_drivers.AlazarTech.acq_helpers as helpers
+
+
+class AcqVariablesParam(Parameter):
+    """
+    Parameter of an AcquisitionController which has a _check_and_update_instr
+    function used for validation and to update instrument attributes and a
+    _get_default function which it uses to set the AcqVariablesParam to an
+    instrument caluclated default.
+
+    Args:
+        name: name for this parameter
+        instrument: acquisition controller instrument this parameter belongs to
+        check_and_update_fn: instrument function to be used for value
+            validation and updating instrument values
+        default_fn (optional): instrument function to be used to calculate
+            a default value to set parameter to
+        initial_value (optional): initial value for parameter
+    """
+
+    def __init__(self, name, instrument, check_and_update_fn,
+                 default_fn=None, initial_value=None):
+        super().__init__(name)
+        self._instrument = instrument
+        self._save_val(initial_value)
+        setattr(self, '_check_and_update_instr', check_and_update_fn)
+        if default_fn is not None:
+            setattr(self, '_get_default', default_fn)
+
+    def set(self, value):
+        """
+        Function which checks value using validation function and then sets
+        the Parameter value to this value.
+
+        Args:
+            value: value to set the parameter to
+        """
+        self._check_and_update_instr(value, param_name=self.name)
+        self._save_val(value)
+
+    def get(self):
+        return self._latest()['value']
+
+    def to_default(self):
+        """
+        Function which executes the default_fn specified to calculate the
+        default value based on instrument values and then calls the set
+        function with this value
+        """
+        try:
+            default = self._get_default()
+        except AttributeError as e:
+            raise AttributeError('no default function for {} Parameter '
+                                 '{}'.format(self.name, e))
+        self.set(default)
+
+    def check(self):
+        """
+        Function which checks the current Parameter value using the specified
+        check_and_update_fn which can also serve to update instrument values.
+
+        Return:
+            True (if no errors raised when check_and_update_fn executed)
+        """
+        val = self._latest()['value']
+        self._check_and_update_instr(val, param_name=self.name)
+        return True
+
+
+class BuffersRecordsAcqParam(Parameter):
+    """
+    Software controlled parameter class for Alazar acquisition. To be used with
+    HD_Records_Controller (tested with ATS9360 board) for return of
+    averaged magnitude and phase data from the Alazar.
+
+    Args:
+        name: name for this parameter
+        instrument: acquisition controller instrument this parameter belongs to
+
+    TODO(nataliejpg) setpoints (including names and units)
+    """
+
+    def __init__(self, name, instrument):
+        super().__init__(name)
+        self._instrument = instrument
+        self.acquisition_kwargs = {}
+        self.names = ('magnitude', 'phase')
+
+    def update_buf_sweep(self, buf_npts, buf_start=None, buf_stop=None):
+        """
+        Function which updates the shape of the parameter (and it's setpoints
+        when this is fixed)
+
+        Args:
+            buf_npts: number of buffers returned
+            buf_start (optional): start value of buffers returned
+            buf_stop (optional): stop value of records returned
+        """
+        demod_length = self._instrument._demod_length
+        # self._buf_list = tuple(np.linspace(buf_start,
+        #                                    buf_stop, num=buf_npts))
+        self._buf_npts = buf_npts
+        if demod_length > 1:
+            # demod_freqs = self._instrument.get_demod_freqs()
+            # self.setpoints = ((demod_freqs, self._buf_list, self._rec_list),
+            #                   (demod_freqs, self._buf_list, self._rec_list))
+            self.shapes = ((demod_length, self._buf_npts, self._rec_npts),
+                           (demod_length, self._buf_npts, self._rec_npts))
+        else:
+            self.shapes = ((self._buf_npts, self._rec_npts),
+                           (self._buf_npts, self._rec_npts))
+            # self.setpoints = ((self._buf_list, self._rec_list),
+            #                   (self._buf_list, self._rec_list))
+
+    def update_rec_sweep(self, rec_npts, rec_start=None, rec_stop=None):
+        """
+        Function which updates the shape of the parameter (and it's setpoints
+        when this is fixed)
+
+        Args:
+            rec_npts: number of records returned after processing
+            rec_start (optional): start value of records returned
+            rec_stop (optional): stop value of records returned
+        """
+        demod_length = self._instrument._demod_length
+        # self._rec_list = tuple(np.linspace(rec_start,
+        #                                    rec_stop, num=rec_npts))
+        self._rec_npts = rec_npts
+        if demod_length > 1:
+            # demod_freqs = self._instrument.get_demod_freqs()
+            # self.setpoints = ((demod_freqs, self._buf_list, self._rec_list),
+            #                   (demod_freqs, self._buf_list, self._rec_list))
+            self.shapes = ((demod_length, self._buf_npts, self._rec_npts),
+                           (demod_length, self._buf_npts, self._rec_npts))
+        else:
+            self.shapes = ((self._buf_npts, self._rec_npts),
+                           (self._buf_npts, self._rec_npts))
+            # self.setpoints = ((self._buf_list, self._rec_list),
+            #                   (self._buf_list, self._rec_list))
+
+    def update_demod_setpoints(self, demod_freqs):
+        """
+        Function to update the demodulation frequency setpoints to be called
+        when a demod_freq Parameter of the acq controller is updated
+
+        Args:
+            demod_freqs: numpy array of demodulation frequencies to use as
+                setpoints if length > 1
+        """
+        demod_length = self._instrument._demod_length
+        if demod_length > 1:
+            pass
+            # self.setpoints = ((demod_freqs, self._buf_list, self._rec_list),
+            #                   (demod_freqs, self._buf_list, self._rec_list))
+        else:
+            pass
+
+    def get(self):
+        """
+        Gets the magnitude and phase signal by calling acquire
+        on the alazar (which in turn calls the processing functions of the
+        aqcuisition controller before returning the processed data
+        demodulated at specified frequencies and averaged over samples
+        and buffers)
+
+        Returns:
+            mag: numpy array of magnitude, shape (demod_length, records)
+            phase: numpy array of magnitude, shape (demod_length, records)
+        """
+        mag, phase = self._instrument._get_alazar().acquire(
+            acquisition_controller=self._instrument,
+            **self.acquisition_kwargs)
+        return mag, phase
+
+
+class HD_BuffersRecords_Controller(AcquisitionController):
+    """
+    This is the Acquisition Controller class which works with the ATS9360,
+    averaging over samples (limited by int_time and int_delay values) and
+    buffers and demodulating with software reference signal(s).
+
+    Args:
+        name: name for this acquisition_conroller as an instrument
+        alazar_name: name of the alazar instrument such that this
+            controller can communicate with the Alazar
+        demod_length (default 1): number of demodulation frequencies
+        filter (default 'win'): filter to be used to filter out double freq
+            component ('win' - window, 'ls' - least squared, 'ave' - averaging)
+        numtaps (default 101): number of freq components used in filter
+        chan_b (default False): whether there is also a second channel of data
+            to be processed and returned
+        **kwargs: kwargs are forwarded to the Instrument base class
+
+    TODO(nataliejpg) test filter options
+    TODO(nataliejpg) finish implementation of channel b option
+    TODO(nataliejpg) what should be private?
+    TODO(nataliejpg) where should filter_dict live?
+    TODO(nataliejpg) demod_freq should be changeable number: maybe channels
+    TODO(nataliejpg) make record param 'start' and 'stop' meaningful
+    TODO(nataliejpg) make buffers param 'start' and 'stop' meaningful
+    """
+
+    filter_dict = {'win': 0, 'ls': 1, 'ave': 2}
+
+    def __init__(self, name, alazar_name, demod_length=1, filter='win',
+                 numtaps=101, chan_b=False, **kwargs):
+        self.filter_settings = {'filter': self.filter_dict[filter],
+                                'numtaps': numtaps}
+        self.chan_b = chan_b
+        self._demod_length = demod_length
+        self.number_of_channels = 2
+        self.samples_per_record = None
+        self.sample_rate = None
+        super().__init__(name, alazar_name, **kwargs)
+
+        self.add_parameter(name='acquisition',
+                           parameter_class=BuffersRecordsAcqParam)
+        for i in range(demod_length):
+            self.add_parameter(name='demod_freq_{}'.format(i),
+                               check_and_update_fn=self._update_demod_freq,
+                               parameter_class=AcqVariablesParam)
+        self.add_parameter(name='int_time',
+                           check_and_update_fn=self._update_int_time,
+                           default_fn=self._int_time_default,
+                           parameter_class=AcqVariablesParam)
+        self.add_parameter(name='int_delay',
+                           check_and_update_fn=self._update_int_delay,
+                           default_fn=self._int_delay_default,
+                           parameter_class=AcqVariablesParam)
+        self.add_parameter(name='record_num',
+                           check_and_update_fn=self._update_rec_num,
+                           parameter_class=AcqVariablesParam)
+        self.add_parameter(name='buffer_num',
+                           check_and_update_fn=self._update_buf_num,
+                           parameter_class=AcqVariablesParam)
+
+        self.samples_divisor = self._get_alazar().samples_divisor
+
+    def _update_demod_freq(instr, value, param_name=None):
+        """
+        Function to validate and update acquisiton parameter when
+        a demod_freq_ Parameter is changed
+
+        Args:
+            value to update demodulation frequency to
+
+        Kwargs:
+            param_name: used to update demod_freq list used for updating
+                septionts of acquisition parameter
+
+        Checks:
+            1e6 <= value <= 500e6
+            number of oscilation measured using current int_tiume param value
+                at this demod frequency value
+            oversampling rate for this demodulation frequency
+
+        Sets:
+            sample_rate attr of acq controller to be that of alazar
+            setpoints of acquisiton parameter
+        """
+        if (value is None) or not (1e6 <= value <= 500e6):
+            raise ValueError('demod_freqs must be 1e6 <= value <= 500e6')
+        alazar = instr._get_alazar()
+        instr.sample_rate = alazar.get_sample_rate()
+        min_oscilations_measured = instr.int_time() * value
+        oversampling = instr.sample_rate / (2 * value)
+        if min_oscilations_measured < 10:
+            logging.warning('{} oscilations measured for largest '
+                            'demod freq, recommend at least 10: '
+                            'decrease sampling rate, take '
+                            'more samples or increase demodulation '
+                            'freq'.format(min_oscilations_measured))
+        elif oversampling < 1:
+            logging.warning('oversampling rate is {}, recommend > 1: '
+                            'increase sampling rate or decrease '
+                            'demodulation frequency'.format(oversampling))
+        demod_freqs = instr.get_demod_freqs()
+        current_demod_index = ([int(s) for s in param_name.split()
+                                if s.isdigit()][0])
+        demod_freqs[current_demod_index] = value
+        instr.acquisition.update_demod_setpoints(demod_freqs)
+
+    def _update_record_num(instr, value, **kwargs):
+        """
+        Function to update the record number, and instr attributes. Main
+        reason to record_num as a parameter is so we can more easily push the
+        record number setting up to other instruments which will control this.
+
+        Args:
+            value to be validated and used for instrument attribute update
+
+        Checks:
+            1 <= value <= 1000
+
+        Sets:
+            acquisition_kwarg['records_per_buffer'] of acquisition param
+            setpoints of acquisiton param
+            shape of acquisition param
+        """
+        if (value is None) or not (1 <= value <= 1000):
+            raise ValueError('int_time must be 1 <= value <= 1000')
+
+        # update acquisition parameter shapes
+        instr.acquisition.update_sweep(value)
+
+        # update acquision kwargs
+        instr.acquisition.acquisition_kwargs.update(
+            records_per_buffer=value)
+
+    def _update_buffer_num(instr, value, **kwargs):
+        """
+        Function to update the buffer number, and instr attributes. By now
+        I'm just making a parameter because it follows the patter of having a
+        parameter for everything we want to preserve and not average over.
+        At some point this should maybe be done more smartly with having
+        "number of averages" and the driver working out how to allocate
+        records and buffers accordingly depending on what is being averaged
+        over. If you have ideas let me know. nataliejpg
+
+        Args:
+            value to be validated and used for instrument attribute update
+
+        Checks:
+            1 <= value <= 100000
+
+        Sets:
+            acquisition_kwarg['buffers_per_acquisition'] of acquisition param
+            setpoints of acquisiton param
+            shape of acquisition param
+        """
+        if (value is None) or not (1 <= value <= 100000):
+            raise ValueError('int_time must be 1 <= value <= 1000')
+
+        # update acquisition parameter shapes
+        instr.acquisition.update_buf_sweep(value)
+
+        # update acquision kwargs
+        instr.acquisition.acquisition_kwargs.update(
+            buffers_per_acquisition=value)
+
+    def _update_int_time(instr, value, **kwargs):
+        """
+        Function to validate value for int_time before setting parameter
+        value and update instr attributes.
+
+        Args:
+            value to be validated and used for instrument attribute update
+
+        Checks:
+            0 <= value <= 0.1 seconds
+            number of oscilation measured in this time
+            oversampling rate
+
+        Sets:
+            sample_rate attr of acq controller to be that of alazar
+            samples_per_record of acq controller
+            acquisition_kwarg['samples_per_record'] of acquisition param
+        """
+        if (value is None) or not (0 <= value <= 0.1):
+            raise ValueError('int_time must be 0 <= value <= 1')
+        alazar = instr._get_alazar()
+        instr.sample_rate = alazar.get_sample_rate()
+        if instr.get_max_demod_freq() is not None:
+            oscilations_measured = value * instr.get_max_demod_freq()
+            oversampling = instr.sample_rate / (2 * instr.get_max_demod_freq())
+            if oscilations_measured < 10:
+                logging.warning('{} oscilations measured, recommend at '
+                                'least 10: decrease sampling rate, take '
+                                'more samples or increase demodulation '
+                                'freq'.format(oscilations_measured))
+            elif oversampling < 1:
+                logging.warning('oversampling rate is {}, recommend > 1: '
+                                'increase sampling rate or decrease '
+                                'demodulation frequency'.format(oversampling))
+        if instr.int_delay() is None:
+            instr.int_delay.to_default()
+
+        # update acquision kwargs and acq controller value
+        total_time = value + instr.int_delay()
+        samples_needed = total_time * instr.sample_rate
+        instr.samples_per_record = helpers.roundup(
+            samples_needed, instr.samples_divisor)
+        instr.acquisition.acquisition_kwargs.update(
+            samples_per_record=instr.samples_per_record)
+
+    def _update_int_delay(instr, value, **kwargs):
+        """
+        Function to validate value for int_delay before setting parameter
+        value and update instr attributes.
+
+        Args:
+            value to be validated and used for instrument attribute update
+
+        Checks:
+            0 <= value <= 0.1 seconds
+            number of samples discarded >= numtaps
+
+        Sets:
+            sample_rate attr of acq controller to be that of alazar
+            samples_per_record of acq controller
+            acquisition_kwarg['samples_per_record'] of acquisition param
+        """
+        if (value is None) or not (0 <= value <= 0.1):
+            raise ValueError('int_delay must be 0 <= value <= 1')
+        alazar = instr._get_alazar()
+        instr.sample_rate = alazar.get_sample_rate()
+        samples_delay_min = (instr.filter_settings['numtaps'] - 1)
+        int_delay_min = samples_delay_min / instr.sample_rate
+        if value < int_delay_min:
+            logging.warning(
+                'delay is less than recommended for filter choice: '
+                '(expect delay >= {})'.format(int_delay_min))
+
+        # update acquision kwargs and acq controller value
+        total_time = value + (instr.int_time() or 0)
+        samples_needed = total_time * instr.sample_rate
+        instr.samples_per_record = helpers.roundup(
+            samples_needed, instr.samples_divisor)
+        instr.acquisition.acquisition_kwargs.update(
+            samples_per_record=instr.samples_per_record)
+
+    def _int_delay_default(instr):
+        """
+        Function to generate default int_delay value
+
+        Returns:
+            minimum int_delay recommended for (numtaps - 1)
+            samples to be discarded as recommended for filter
+        """
+        alazar = instr._get_alazar()
+        instr.sample_rate = alazar.get_sample_rate()
+        samp_delay = instr.filter_settings['numtaps'] - 1
+        return samp_delay / instr.sample_rate
+
+    def _int_time_default(instr):
+        """
+        Function to generate defult int_time value
+
+        Returns:
+            max total time for integration based on samples_per_record,
+            sample_rate and int_delay
+        """
+        if instr.samples_per_record is (0 or None):
+            raise ValueError('Cannot set int_time to max if acq controller'
+                             ' has 0 or None samples_per_record, choose a '
+                             'value for int_time and samples_per_record will '
+                             'be set accordingly')
+        alazar = instr._get_alazar()
+        instr.sample_rate = alazar.get_sample_rate()
+        total_time = ((instr.samples_per_record / instr.sample_rate) -
+                      (instr.int_delay() or 0))
+        return total_time
+
+    def get_demod_freqs(self):
+        """
+        Function to get all the demod_freq parameter values in a list, v hacky
+
+        Returns:
+            freqs: numpy array of demodulation frequencies
+        """
+        freqs = list(filter(None, [getattr(self, 'demod_freq_{}'.format(c))()
+                                   for c in range(self._demod_length)]))
+        return np.array(freqs)
+
+    def get_max_demod_freq(self):
+        """
+        Returns:
+            the largest demodulation frequency
+
+        nb: really hacky and we should have channels in qcodes but we don't
+        (at time of writing)
+        """
+        freqs = self.get_demod_freqs()
+        if len(freqs) > 0:
+            return max(freqs)
+        else:
+            return None
+
+    def update_filter_settings(self, filter, numtaps):
+        """
+        Updates the settings of the filter for filtering out
+        double frwuency component for demodulation.
+
+        Args:
+            filter (str): filter type ('win' or 'ls')
+            numtaps (int): numtaps for filter
+        """
+        self.filter_settings.update({'filter': self.filter_dict[filter],
+                                     'numtaps': numtaps})
+
+    def update_acquisition_kwargs(self, **kwargs):
+        """
+        Updates the kwargs to be used when
+        alazar_driver.acquisition() is called via a get call of the
+        acquisition BuffersRecordsAcqParam. Should be used by the user for
+        updating allocated_buffers kwarg since samples_per_record,
+        records_per_buffer and buffers_per_acquisition are updated via the
+        int_time, int_delay, record_num and buffer_num parameters
+
+        Kwargs (ints):
+            allocated_buffers
+        """
+        if 'samples_per_record' in kwargs:
+            raise ValueError('With HD_BuffersRecords_Controller '
+                             'samples_per_record cannot be set manually '
+                             'via update_acquisition_kwargs and should instead'
+                             ' be set by setting int_time and int_delay')
+        if 'records_per_buffer' in kwargs:
+            raise ValueError('With HD_BuffersRecords_Controller '
+                             'records_per_buffer cannot be set manually '
+                             'via update_acquisition_kwargs and should instead'
+                             ' be set by setting record_num')
+        if 'records_per_buffer' in kwargs:
+            raise ValueError('With HD_BuffersRecords_Controller '
+                             'records_per_buffer cannot be set manually '
+                             'via update_acquisition_kwargs and should instead'
+                             ' be set by setting buffer_num')
+        self.acquisition.acquisition_kwargs.update(**kwargs)
+
+    def pre_start_capture(self):
+        """
+        Called before capture start to update Acquisition Controller with
+        alazar acquisition params and set up software wave for demodulation.
+        """
+        alazar = self._get_alazar()
+        if self.samples_per_record != alazar.samples_per_record.get():
+            raise Exception('acq controller samples per record does not match'
+                            ' instrument value, most likely need '
+                            'to set and check int_time and int_delay')
+        if self.sample_rate != alazar.get_sample_rate():
+            raise Exception('acq controller sample rate does not match '
+                            'instrument value, most likely need '
+                            'to set and check int_time and int_delay')
+        if self.record_num() != alazar.records_per_buffer.get():
+            raise Exception('acq controller record_num does not match '
+                            'instrument value, most likely need '
+                            'to call set and check record_num')
+        if self.buffer_num() != alazar.buffers_per_acquisition.get():
+            raise Exception('acq controller buffer_num does not match '
+                            'instrument value, most likely need '
+                            'to call set and check buffer_num')
+
+        demod_freqs = self.get_demod_freqs()
+        if len(demod_freqs) == 0:
+            raise Exception('no demod_freqs set')
+
+        self.board_info = alazar.get_idn()
+        self.buffer_count = 0
+        self.samples_per_buffer = (self.samples_per_record *
+                                   self.record_num() *
+                                   self.buffer_num() *
+                                   self.number_of_channels)
+        self.buffer = np.zeros(self.samples_per_record *
+                               self.buffer_num() *
+                               self.record_num() *
+                               self.number_of_channels)
+
+        mat_shape = (self._demod_length, self.buffer_num(),
+                     self.record_num(), self.samples_per_record)
+        integer_list = np.arange(self.samples_per_record)
+        integer_mat = (np.outer(np.ones(self.buffer_num()),
+                                np.outer(np.ones(self.record_num()), integer_list)))
+        angle_mat = 2 * np.pi * \
+            np.outer(demod_freqs, integer_mat).reshape(
+                mat_shape) / self.sample_rate
+        self.cos_mat = np.cos(angle_mat)
+        self.sin_mat = np.sin(angle_mat)
+
+    def pre_acquire(self):
+        pass
+
+    def handle_buffer(self, data):
+        """
+        Adds data from alazar to buffer (effectively averaging)
+        """
+        i0 = self.buffer_count * self.samples_per_buffer
+        i1 = i0 + self.samples_per_buffer
+        self.buffer[i0:i1] = data
+        self.buf_count += 1
+
+    def post_acquire(self):
+        """
+        Processes the data according to ATS9360 settings, splitting into
+        records and then applying demodulation fit
+        nb: currently only channel A
+
+        Returns:
+            magnitude (numpy array): shape = (demod_length, records_per_buffer)
+            phase (numpy array): shape = (demod_length, records_per_buffer
+        """
+        # for ATS9360 samples are arranged in the buffer as follows:
+        # S00A, S00B, S01A, S01B...S10A, S10B, S11A, S11B...
+        # where SXYZ is record X, sample Y, channel Z.
+
+        # break buffer up into records and shapes to be (records, samples)
+        reshaped_buf = np.uint16(self.buffer.reshape(self.buffer_num(),
+                                                     self.record_num(),
+                                                     self.samples_per_buffer,
+                                                     self.number_of_channels))
+
+        recA = reshaped_buf[:, :, :, 0]
+
+        magA, phaseA = self._fit(recA)
+
+        # same for chan b
+        if self.chan_b:
+            # recB = reshaped_buf[:, :, :, 1]
+            # magB, phaseB = self, _fit(recB)
+            raise NotImplementedError('chan b code not complete')
+
+        self.buf_count = 0
+
+        return magA, phaseA
+
+    def _fit(self, rec):
+        """
+        Applies volts conversion, demodulation fit, low bandpass filter
+        and integration limits to samples array
+
+        Args:
+            rec (numpy array): record from alazar to be multiplied
+                               with the software signal, filtered and limited
+                               to integration limits shape = (samples_taken, )
+
+        Returns:
+            magnitude (numpy array): shape = (demod_length, records_per_buffer)
+            phase (numpy array): shape = (demod_length, records_per_buffer)
+        """
+        # convert rec to volts
+        bps = self.board_info['bits_per_sample']
+        if bps == 12:
+            volt_rec = helpers.sample_to_volt_u12(rec, bps)
+        else:
+            logging.warning('sample to volt conversion does not exist for'
+                            ' bps != 12, centered raw samples returned')
+            volt_rec = rec - np.mean(rec, axis=1)
+
+        # volt_rec to matrix and multiply with demodulation signal matrices
+        mat_shape = (self._demod_length, self.buffer_num(), self.record_num(),
+                     self.samples_per_record)
+        volt_rec_mat = np.outer(
+            np.ones(self._demod_length), volt_rec).reshape(mat_shape)
+        re_mat = np.multiply(volt_rec_mat, self.cos_mat)
+        im_mat = np.multiply(volt_rec_mat, self.sin_mat)
+
+        # filter out higher freq component
+        cutoff = self.get_max_demod_freq() / 10
+        if self.filter_settings['filter'] == 0:
+            re_filtered = helpers.filter_win(re_mat, cutoff,
+                                             self.sample_rate,
+                                             self.filter_settings['numtaps'],
+                                             axis=3)
+            im_filtered = helpers.filter_win(im_mat, cutoff,
+                                             self.sample_rate,
+                                             self.filter_settings['numtaps'],
+                                             axis=3)
+        elif self.filter_settings['filter'] == 1:
+            re_filtered = helpers.filter_ls(re_mat, cutoff,
+                                            self.sample_rate,
+                                            self.filter_settings['numtaps'],
+                                            axis=3)
+            im_filtered = helpers.filter_ls(im_mat, cutoff,
+                                            self.sample_rate,
+                                            self.filter_settings['numtaps'],
+                                            axis=3)
+        elif self.filter_settings['filter'] == 2:
+            re_filtered = re_mat
+            im_filtered = im_mat
+
+        # apply integration limits
+        beginning = int(self.int_delay() * self.sample_rate)
+        end = beginning + int(self.int_time() * self.sample_rate)
+
+        re_limited = re_filtered[:, :, beginning:end]
+        im_limited = im_filtered[:, :, beginning:end]
+
+        # convert to magnitude and phase
+        complex_mat = re_limited + im_limited * 1j
+        magnitude = np.mean(abs(complex_mat), axis=3)
+        phase = np.mean(np.angle(complex_mat, deg=True), axis=3)
+
+        return magnitude, phase
diff --git a/qcodes/instrument_drivers/AlazarTech/rec_buf_controller.py b/qcodes/instrument_drivers/AlazarTech/rec_buf_controller.py
deleted file mode 100644
index 65ddb9dea4d7..000000000000
--- a/qcodes/instrument_drivers/AlazarTech/rec_buf_controller.py
+++ /dev/null
@@ -1,220 +0,0 @@
-from .ATS import AcquisitionController
-import numpy as np
-from qcodes import Parameter
-from scipy import signal
-
-
-class RecBufParam(Parameter):
-    """
-    Hardware controlled parameter class for Alazar acquisition. To be used with
-    HD_Samples_Controller (tested with ATS9360 board) for return of an array of
-    sample data from the Alazar, averaged over records and buffers.
-    """
-
-    def __init__(self, name, instrument):
-        super().__init__(name)
-        self._instrument = instrument
-        self.acquisitionkwargs = {}
-        self.names = ('magnitude', 'phase')
-        self.units = ('', '')
-        self.setpoint_names = (('rec_num', 'buf_num'), ('rec_num', 'buf_num'))
-        self.setpoints = ((1, 1), (1, 1))
-        self.shapes = ((1, 1), (1, 1))
-
-    def update_acquisition_kwargs(self, **kwargs):
-        # needed to update config of the software parameter on sweep change
-        # freq setpoints tuple as needs to be hashable for look up
-        if 'records_per_buffer' and 'buffers_per_acquisition' in kwargs:
-            rpts = kwargs['records_per_buffer']
-            bpts = kwargs['buffers_per_acquisition']
-            r = tuple(np.arange(rpts))
-            b = tuple(np.arange(bpts))
-            self.setpoints = ((r, b), (r, b))
-            self.shapes = ((rpts, bpts), (rpts, bpts))
-        else:
-            raise ValueError(
-                'records_per_buffer and buffers_per_acquisition must be specified')
-        # updates dict to be used in acquisition get call
-        self.acquisitionkwargs.update(**kwargs)
-
-    def get(self):
-        mag, phase = self._instrument._get_alazar().acquire(
-            acquisition_controller=self._instrument,
-            **self.acquisitionkwargs)
-        return mag, phase
-
-
-class HD_RecBuf_Controller(AcquisitionController):
-    """
-    This is the Acquisition Controller class which works with the ATS9360,
-    averaging over buffers and records and demodulating with a software
-    reference signal, returning the  samples.
-    args:
-    name: name for this acquisition_conroller as an instrument
-    alazar_name: the name of the alazar instrument such that this controller
-        can communicate with the Alazar
-    demod_freq: the frequency of the software wave to be created
-    samp_rate: the rate of sampling
-    filt: the filter to be used to filter out double freq component
-    chan_b: whether there is also a second channel of data to be processed
-        and returned
-    **kwargs: kwargs are forwarded to the Instrument base class
-
-    TODO(nataliejpg) fix sample rate problem
-    TODO(nataliejpg) test filter options
-    TODO(nataliejpg) test mag phase logic
-    TODO(nataliejpg) finish implementation of channel b option
-    TODO(nataliejpg) make filter settings not hard coded
-    """
-
-    def __init__(self, name, alazar_name, demod_freq, samp_rate=500e6,
-                 filt='win', chan_b=False, **kwargs):
-        filter_dict = {'win': 0, 'hamming': 1, 'ls': 2}
-        self.demodulation_frequency = demod_freq
-        self.sample_rate = samp_rate
-        self.filter = filter_dict[filt]
-        self.chan_b = chan_b
-        self.samples_per_record = 0
-        self.records_per_buffer = 0
-        self.buffers_per_acquisition = 0
-        self.number_of_channels = 2
-        self.cos_list = None
-        self.sin_list = None
-        self.buffer = None
-        self.buf_count = 0
-        # make a call to the parent class and by extension,
-        # create the parameter structure of this class
-        super().__init__(name, alazar_name, **kwargs)
-
-        self.add_parameter(name='acquisition',
-                           parameter_class=RecBufParam)
-
-    def update_acquisitionkwargs(self, **kwargs):
-        """
-        This method must be used to update the kwargs used for the acquisition
-        with the alazar_driver.acquire
-        :param kwargs:
-        :return:
-        """
-        self.acquisition.update_acquisition_kwargs(**kwargs)
-
-    def pre_start_capture(self):
-        """
-        See AcquisitionController
-        :return:
-        """
-        alazar = self._get_alazar()
-        self.samples_per_record = alazar.samples_per_record.get()
-        self.records_per_buffer = alazar.records_per_buffer.get()
-        self.buffers_per_acquisition = alazar.buffers_per_acquisition.get()
-        self.buf_count = 0
-        self.samples_per_buffer = (self.samples_per_record *
-                                   self.records_per_buffer *
-                                   self.buffers_per_acquisition *
-                                   self.number_of_channels)
-        self.buffer = np.zeros(self.samples_per_buffer)
-
-        integer_list = np.arange(self.samples_per_record)
-        angle_list = (2 * np.pi * self.demodulation_frequency /
-                      self.sample_rate * integer_list)
-
-        cos_list = np.cos(angle_list)
-        sin_list = np.sin(angle_list)
-        self.cos_mat = np.kron(np.ones((1, self.records_per_buffer)), cos_list)
-        self.sin_mat = np.kron(np.ones((1, self.records_per_buffer)), sin_list)
-
-    def pre_acquire(self):
-        """
-        See AcquisitionController
-        :return:
-        """
-        # this could be used to start an Arbitrary Waveform Generator, etc...
-        # using this method ensures that the contents are executed AFTER the
-        # Alazar card starts listening for a trigger pulse
-        pass
-
-    def handle_buffer(self, data):
-        """
-        See AcquisitionController
-        :return:
-        """
-        i0 = self.bufcount * self.samples_per_buffer
-        i1 = i0 + self.samples_per_buffer
-        self.buffer[i0:i1] = data
-        self.buf_count += 1
-
-    def post_acquire(self):
-        """
-        See AcquisitionController
-        :return:
-        """
-        # for ATS9360 samples are arranged in the buffer as follows:
-        # S00A, S00B, S01A, S01B...S10A, S10B, S11A, S11B...
-        # where SXYZ is record X, sample Y, channel Z.
-
-        # reshapes data to be (samples * records * buffers)
-
-        # TODO!!
-
-        magA, phaseA = 0, 0
-
-        return magA, phaseA
-
-    def fit(self, rec):
-        # center rec around 0
-        rec = rec - np.mean(rec)
-
-        # multiply with software wave
-        re_wave = np.multiply(rec, self.cos_mat)
-        im_wave = np.multiply(rec, self.sin_mat)
-        cutoff = self.demodulation_frequency
-        numtaps = 30
-        axis = 0
-
-        # filter out double freq component to obtian constant term
-        if self.filter == 0:
-            RePart = self.filter_win(re_wave, numtaps, cutoff, axis=axis)
-            ImPart = self.filter_win(im_wave, numtaps, cutoff, axis=axis)
-        elif self.filter == 1:
-            RePart = self.filter_hamming(re_wave, numtaps, cutoff, axis=axis)
-            ImPart = self.filter_hamming(im_wave, numtaps, cutoff, axis=axis)
-        elif self.filter == 2:
-            RePart = self.filter_ls(re_wave, numtaps, cutoff, axis, axis=axis)
-            ImPart = self.filter_ls(im_wave, numtaps, cutoff, axis, axis=axis)
-
-        # convert to magnitude and phase data and average over samples
-        # data returned is (records * buffers)
-        complex_mat = RePart + ImPart * 1j
-        mag = np.mean(2 * abs(complex_mat), axis=0)
-        phase = np.mean(np.angle(complex_mat, axis=0, deg=True))
-
-        return mag, phase
-
-    def filter_hamming(self, rec, numtaps, cutoff, axis=-1):
-        sample_rate = self.sample_rate
-        nyq_rate = sample_rate / 2.
-        fir_coef = signal.firwin(numtaps,
-                                 cutoff / nyq_rate,
-                                 window="hamming")
-        filtered_rec = 2 * signal.lfilter(fir_coef, 1.0, rec, axis=axis)
-        return filtered_rec
-
-    def filter_win(self, rec, numtaps, cutoff, axis=-1):
-        sample_rate = self.sample_rate
-        nyq_rate = sample_rate / 2.
-        fir_coef = signal.firwin(numtaps,
-                                 cutoff / nyq_rate)
-        filtered_rec = 2 * signal.lfilter(fir_coef, 1.0, rec, axis=axis)
-        return filtered_rec
-
-    def filter_ls(self, rec, numtaps, cutoff, axis=-1):
-        sample_rate = self.sample_rate
-        nyq_rate = sample_rate / 2.
-        bands = [0, cutoff / nyq_rate, cutoff / nyq_rate, 1]
-        desired = [1, 1, 0, 0]
-        fir_coef = signal.firls(numtaps,
-                                bands,
-                                desired,
-                                nyq=nyq_rate)
-        filtered_rec = 2 * signal.lfilter(fir_coef, 1.0, rec, axis=axis)
-        return filtered_rec
diff --git a/qcodes/instrument_drivers/AlazarTech/rec_controller.py b/qcodes/instrument_drivers/AlazarTech/rec_controller.py
index a10629a00ecf..1027bf5eb3e0 100644
--- a/qcodes/instrument_drivers/AlazarTech/rec_controller.py
+++ b/qcodes/instrument_drivers/AlazarTech/rec_controller.py
@@ -80,15 +80,11 @@ class RecordsAcqParam(Parameter):
     Args:
         name: name for this parameter
         instrument: acquisition controller instrument this parameter belongs to
-        demod_length: numer of demodulation frequencies the acq controller has
-            NB this is currently set in acq controller init but that's really
-            not nice and should be changed when possible
 
     TODO(nataliejpg) setpoints (including names and units)
-    TODO(nataliejpg) convert records setpoint into variable/param
     """
 
-    def __init__(self, name, instrument, demod_length):
+    def __init__(self, name, instrument):
         super().__init__(name)
         self._instrument = instrument
         self.acquisition_kwargs = {}
@@ -100,7 +96,7 @@ def update_sweep(self, npts, start=None, stop=None):
         when this is fixed)
 
         Args:
-            npts: number of recornds returned after processing
+            npts: number of records returned
             start (optional): start value of records returned (if relevant)
             stop (optional): stop value of records returned (if relevant)
         """
@@ -173,7 +169,7 @@ class HD_Records_Controller(AcquisitionController):
     TODO(nataliejpg) what should be private?
     TODO(nataliejpg) where should filter_dict live?
     TODO(nataliejpg) demod_freq should be changeable number: maybe channels
-    TODO(nataliejpg) make records a parameter or similar (MultiParam?)
+    TODO(nataliejpg) make record param 'start' and 'stop' meaningful
     """
 
     filter_dict = {'win': 0, 'ls': 1, 'ave': 2}
@@ -186,12 +182,10 @@ def __init__(self, name, alazar_name, demod_length=1, filter='win',
         self._demod_length = demod_length
         self.number_of_channels = 2
         self.samples_per_record = None
-        self.records_per_buffer = None
         self.sample_rate = None
         super().__init__(name, alazar_name, **kwargs)
 
         self.add_parameter(name='acquisition',
-                           demod_length=self._demod_length,
                            parameter_class=RecordsAcqParam)
         for i in range(demod_length):
             self.add_parameter(name='demod_freq_{}'.format(i),
@@ -205,6 +199,9 @@ def __init__(self, name, alazar_name, demod_length=1, filter='win',
                            check_and_update_fn=self._update_int_delay,
                            default_fn=self._int_delay_default,
                            parameter_class=AcqVariablesParam)
+        self.add_parameter(name='record_num',
+                           check_and_update_fn=self._update_rec_num,
+                           parameter_class=AcqVariablesParam)
 
         self.samples_divisor = self._get_alazar().samples_divisor
 
@@ -252,10 +249,37 @@ def _update_demod_freq(instr, value, param_name=None):
         demod_freqs[current_demod_index] = value
         instr.acquisition.update_demod_setpoints(demod_freqs)
 
+    def _update_record_num(instr, value, **kwargs):
+        """
+        Function to update the record number, and instr attributes. Main
+        reason to record_num as a parameter is so we can more easily push the
+        record number setting up to other instruments which will control this.
+
+        Args:
+            value to be validated and used for instrument attribute update
+
+        Checks:
+            1 <= value <= 1000
+
+        Sets:
+            acquisition_kwarg['records_per_buffer'] of acquisition param
+            setpoints of acquisiton param
+            shape of acquisition param
+        """
+        if (value is None) or not (1 <= value <= 1000):
+            raise ValueError('int_time must be 1 <= value <= 1000')
+
+        # update acquisition parameter shapes
+        instr.acquisition.update_sweep(value)
+
+        # update acquision kwargs
+        instr.acquisition.acquisition_kwargs.update(
+            records_per_buffer=value)
+
     def _update_int_time(instr, value, **kwargs):
         """
         Function to validate value for int_time before setting parameter
-        value
+        value and update instr attributes.
 
         Args:
             value to be validated and used for instrument attribute update
@@ -300,7 +324,7 @@ def _update_int_time(instr, value, **kwargs):
     def _update_int_delay(instr, value, **kwargs):
         """
         Function to validate value for int_delay before setting parameter
-        value
+        value and update instr attributes.
 
         Args:
             value to be validated and used for instrument attribute update
@@ -313,7 +337,6 @@ def _update_int_delay(instr, value, **kwargs):
             sample_rate attr of acq controller to be that of alazar
             samples_per_record of acq controller
             acquisition_kwarg['samples_per_record'] of acquisition param
-            setpoints of acquisiton param
         """
         if (value is None) or not (0 <= value <= 0.1):
             raise ValueError('int_delay must be 0 <= value <= 1')
@@ -407,23 +430,25 @@ def update_acquisition_kwargs(self, **kwargs):
         """
         Updates the kwargs to be used when
         alazar_driver.acquisition() is called via a get call of the
-        acquisition SamplesParam. Should be used by the user for
+        acquisition RecordsAcqParam. Should be used by the user for
         updating averaging settings since the 'samples_per_record'
-        kwarg is updated via the int_time and int_delay parameters
+        and 'records_per_buffer' kwargs are updated via the int_time,
+        int_delay and record_num parameters
 
         Kwargs (ints):
-            records_per_buffer
             buffers_per_acquisition
             allocated_buffers
         """
         if 'samples_per_record' in kwargs:
-            raise ValueError('With HD_Samples_Controller '
+            raise ValueError('With HD_Records_Controller '
                              'samples_per_record cannot be set manually '
                              'via update_acquisition_kwargs and should instead'
                              ' be set by setting int_time and int_delay')
         if 'records_per_buffer' in kwargs:
-            self.acquisition.update_sweep(kwargs['records_per_buffer'])
-            self.records_per_buffer = kwargs['records_per_buffer']
+            raise ValueError('With HD_Records_Controller '
+                             'records_per_buffer cannot be set manually '
+                             'via update_acquisition_kwargs and should instead'
+                             ' be set by setting record_num')
         self.acquisition.acquisition_kwargs.update(**kwargs)
 
     def pre_start_capture(self):
@@ -440,11 +465,10 @@ def pre_start_capture(self):
             raise Exception('acq controller sample rate does not match '
                             'instrument value, most likely need '
                             'to set and check int_time and int_delay')
-        if self.records_per_buffer != alazar.records_per_buffer.get():
-            raise Exception('acq controller records per buffer does not match '
+        if self.record_num() != alazar.records_per_buffer.get():
+            raise Exception('acq controller record_num does not match '
                             'instrument value, most likely need '
-                            'to call update_acquisition_kwargs with'
-                            'records_per_buffer as a param')
+                            'to call set and check record_num')
 
         demod_freqs = self.get_demod_freqs()
         if len(demod_freqs) == 0:
@@ -453,13 +477,13 @@ def pre_start_capture(self):
         self.buffers_per_acquisition = alazar.buffers_per_acquisition.get()
         self.board_info = alazar.get_idn()
         self.buffer = np.zeros(self.samples_per_record *
-                               self.records_per_buffer *
+                               self.record_num() *
                                self.number_of_channels)
 
-        mat_shape = (self._demod_length, self.records_per_buffer,
+        mat_shape = (self._demod_length, self.record_num(),
                      self.samples_per_record)
         integer_list = np.arange(self.samples_per_record)
-        integer_mat = np.outer(np.ones(self.records_per_buffer), integer_list)
+        integer_mat = np.outer(np.ones(self.record_num()), integer_list)
         angle_mat = 2 * np.pi * \
             np.outer(demod_freqs, integer_mat).reshape(
                 mat_shape) / self.sample_rate
@@ -490,9 +514,9 @@ def post_acquire(self):
         # where SXYZ is record X, sample Y, channel Z.
 
         # break buffer up into records and shapes to be (records, samples)
-        recordsA = np.empty((self.records_per_buffer, self.samples_per_record),
+        recordsA = np.empty((self.record_num(), self.samples_per_record),
                             dtype=np.uint16)
-        for i in range(self.records_per_buffer):
+        for i in range(self.record_num()):
             i0 = (i * self.samples_per_record * self.number_of_channels)
             i1 = (i0 + self.samples_per_record * self.number_of_channels)
             recordsA[i, :] = np.uint16(
@@ -532,7 +556,7 @@ def _fit(self, rec):
             volt_rec = rec - np.mean(rec, axis=1)
 
         # volt_rec to matrix and multiply with demodulation signal matrices
-        mat_shape = (self._demod_length, self.records_per_buffer,
+        mat_shape = (self._demod_length, self.record_num(),
                      self.samples_per_record)
         volt_rec_mat = np.outer(
             np.ones(self._demod_length), volt_rec).reshape(mat_shape)
diff --git a/qcodes/instrument_drivers/AlazarTech/rec_samp_controller.py b/qcodes/instrument_drivers/AlazarTech/rec_samp_controller.py
index 7e86e9ffc211..40b729965915 100644
--- a/qcodes/instrument_drivers/AlazarTech/rec_samp_controller.py
+++ b/qcodes/instrument_drivers/AlazarTech/rec_samp_controller.py
@@ -1,233 +1,650 @@
+import logging
 from .ATS import AcquisitionController
 import numpy as np
 from qcodes import Parameter
-from scipy import signal
+import qcodes.instrument_drivers.AlazarTech.acq_helpers as helpers
 
 
-class RecSampParam(Parameter):
+class AcqVariablesParam(Parameter):
     """
-    Hardware controlled parameter class for Alazar acquisition. To be used with
-    HD_Samples_Controller (tested with ATS9360 board) for return of an array of
-    sample data from the Alazar, averaged over records and buffers.
+    Parameter of an AcquisitionController which has a _check_and_update_instr
+    function used for validation and to update instrument attributes and a
+    _get_default function which it uses to set the AcqVariablesParam to an
+    instrument caluclated default.
 
-    TODO(nataliejpg) fix setpoints/shapes horriblenesss
-    TODO(nataliejpg) make it actually work...
+    Args:
+        name: name for this parameter
+        instrument: acquisition controller instrument this parameter belongs to
+        check_and_update_fn: instrument function to be used for value
+            validation and updating instrument values
+        default_fn (optional): instrument function to be used to calculate
+            a default value to set parameter to
+        initial_value (optional): initial value for parameter
+    """
+
+    def __init__(self, name, instrument, check_and_update_fn,
+                 default_fn=None, initial_value=None):
+        super().__init__(name)
+        self._instrument = instrument
+        self._save_val(initial_value)
+        setattr(self, '_check_and_update_instr', check_and_update_fn)
+        if default_fn is not None:
+            setattr(self, '_get_default', default_fn)
+
+    def set(self, value):
+        """
+        Function which checks value using validation function and then sets
+        the Parameter value to this value.
+
+        Args:
+            value: value to set the parameter to
+        """
+        self._check_and_update_instr(value, param_name=self.name)
+        self._save_val(value)
+
+    def get(self):
+        return self._latest()['value']
+
+    def to_default(self):
+        """
+        Function which executes the default_fn specified to calculate the
+        default value based on instrument values and then calls the set
+        function with this value
+        """
+        try:
+            default = self._get_default()
+        except AttributeError as e:
+            raise AttributeError('no default function for {} Parameter '
+                                 '{}'.format(self.name, e))
+        self.set(default)
+
+    def check(self):
+        """
+        Function which checks the current Parameter value using the specified
+        check_and_update_fn which can also serve to update instrument values.
+
+        Return:
+            True (if no errors raised when check_and_update_fn executed)
+        """
+        val = self._latest()['value']
+        self._check_and_update_instr(val, param_name=self.name)
+        return True
+
+
+class RecordsSamplesAcqParam(Parameter):
+    """
+    TODO
+
+    Args:
+        name: name for this parameter
+        instrument: acquisition controller instrument this parameter belongs to
+        demod_length: numer of demodulation frequencies the acq controller has
+            NB this is currently set in acq controller init but that's really
+            not nice and should be changed when possible
+
+    TODO(nataliejpg) setpoints (including names and units)
+    TODO(nataliejpg) convert records setpoint into actual frequency
     """
 
     def __init__(self, name, instrument):
         super().__init__(name)
         self._instrument = instrument
-        self.acquisitionkwargs = {}
+        self.acquisition_kwargs = {}
         self.names = ('magnitude', 'phase')
-        self.units = ('', '')
-        self.setpoint_names = (('rec_num', 'samp_num'),
-                               ('rec_num', 'samp_num'))
-        self.setpoints = ((1, 1), (1, 1))
-        self.shapes = ((1, 1), (1, 1))
 
-    def update_acquisition_kwargs(self, **kwargs):
-        # needed to update config of the software parameter on sweep change
-        # freq setpoints tuple as needs to be hashable for look up
-        if 'samples_per_record' and 'records_per_buffer' in kwargs:
-            spts = kwargs['samples_per_record']
-            rpts = kwargs['records_per_buffer']
-            s = tuple(np.arange(spts))
-            r = tuple(np.arange(rpts))
-            self.setpoints = ((s, r), (s, r))
-            self.shapes = ((spts, rpts), (spts, rpts))
+    def update_time_sweep(self, time_npts, time_start, time_stop):
+        """
+        Function which updates the shape of the parameter (and it's setpoints
+        when this is fixed)
+
+        Args:
+            time_npts: number of samples returned after processing
+            time_start: start time of samples returned after processing
+            time_stop: stop time of samples returned after processing
+        """
+        demod_length = self._instrument._demod_length
+        # self._time_list = tuple(np.linspace(time_start,
+        #                                     time_stop, num=time_npts))
+        self._time_npts = time_npts
+        if demod_length > 1:
+            # demod_freqs = self._instrument.get_demod_freqs()
+            # self.setpoints = ((demod_freqs, self._rec_list, self._time_list),
+            #                   (demod_freqs, self._rec_list, self._time_list))
+            self.shapes = ((demod_length, self._rec_npts, self._time_npts),
+                           (demod_length, self._rec_npts, self._time_npts))
         else:
-            raise ValueError(
-                'records_per_buffer and samples_per_record must be specified')
-        # updates dict to be used in acquisition get call
-        self.acquisitionkwargs.update(**kwargs)
+            self.shapes = ((self._rec_npts, self._time_npts),
+                           (self._rec_npts, self._time_npts))
+            # self.setpoints = ((self._rec_list, self._time_list),
+            #                   (self._rec_list, self._time_list))
+
+    def update_rec_sweep(self, rec_npts, rec_start=None, rec_stop=None):
+        """
+        Function which updates the shape of the parameter (and it's setpoints
+        when this is fixed)
+
+        Args:
+            rec_npts: number of records returned
+            rec_start (optional): start value of records returned
+            rec_stop (optional): stop value of records returned
+        """
+        demod_length = self._instrument._demod_length
+        # self._rec_list = tuple(np.linspace(rec_start,
+        #                                    rec_stop, num=rec_npts))
+        self._rec_npts = rec_npts
+        if demod_length > 1:
+            # demod_freqs = self._instrument.get_demod_freqs()
+            # self.setpoints = ((demod_freqs, self._rec_list, self._time_list),
+            #                   (demod_freqs, self._rec_list, self._time_list))
+            self.shapes = ((demod_length, self._rec_npts, self._time_npts),
+                           (demod_length, self._rec_npts, self._time_npts))
+        else:
+            self.shapes = ((self._rec_npts, self._time_npts),
+                           (self._rec_npts, self._time_npts))
+            # self.setpoints = ((self._rec_list, self._time_list),
+            #                   (self._rec_list, self._time_list))
+
+    def update_demod_setpoints(self, demod_freqs):
+        """
+        Function to update the demodulation frequency setpoints to be called
+        when a demod_freq Parameter of the acq controller is updated
+
+        Args:
+            demod_freqs: numpy array of demodulation frequencies to use as
+                setpoints if length > 1
+        """
+        demod_length = self._instrument._demod_length
+        if demod_length > 1:
+            pass
+            # self.setpoints = ((demod_freqs, self._rec_list, self._time_list),
+            #                   (demod_freqs, self._rec_list, self._time_list))
+        else:
+            pass
 
     def get(self):
+        """
+        Gets the magnitude and phase signal by calling acquire
+        on the alazar (which in turn calls the processing functions of the
+        aqcuisition controller before returning the processed data
+        demodulated at specified frequencies and averaged over buffers)
+
+        Returns:
+            mag: numpy array of magnitude, shape (demod_length, records)
+            phase: numpy array of magnitude, shape (demod_length, records)
+        """
         mag, phase = self._instrument._get_alazar().acquire(
             acquisition_controller=self._instrument,
-            **self.acquisitionkwargs)
+            **self.acquisition_kwargs)
         return mag, phase
 
 
-class HD_RecSamp_Controller(AcquisitionController):
+class HD_RecordsSamples_Controller(AcquisitionController):
     """
     This is the Acquisition Controller class which works with the ATS9360,
-    averaging over buffers and records and demodulating with a software
-    reference signal, returning the  samples.
-    args:
-    name: name for this acquisition_conroller as an instrument
-    alazar_name: the name of the alazar instrument such that this controller
-        can communicate with the Alazar
-    demod_freq: the frequency of the software wave to be created
-    samp_rate: the rate of sampling
-    filt: the filter to be used to filter out double freq component
-    chan_b: whether there is also a second channel of data to be processed
-        and returned
-    **kwargs: kwargs are forwarded to the Instrument base class
-
-    TODO(nataliejpg) fix sample rate problem
+    averaging over buffers and demodulating with software reference signal(s).
+
+    Args:
+        name: name for this acquisition_conroller as an instrument
+        alazar_name: name of the alazar instrument such that this
+            controller can communicate with the Alazar
+        demod_length (default 1): number of demodulation frequencies
+        filter (default 'win'): filter to be used to filter out double freq
+            component ('win' - window, 'ls' - least squared, 'ave' - averaging)
+        numtaps (default 101): number of freq components used in filter
+        chan_b (default False): whether there is also a second channel of data
+            to be processed and returned
+        **kwargs: kwargs are forwarded to the Instrument base class
+
     TODO(nataliejpg) test filter options
-    TODO(nataliejpg) test mag phase logic
     TODO(nataliejpg) finish implementation of channel b option
-    TODO(nataliejpg) make filter settings not hard coded
+    TODO(nataliejpg) what should be private?
+    TODO(nataliejpg) where should filter_dict live?
+    TODO(nataliejpg) demod_freq should be changeable number: maybe channels
+    TODO(nataliejpg) make record param 'start' and 'stop' meaningful
     """
 
-    def __init__(self, name, alazar_name, demod_freq, samp_rate=500e6,
-                 filt='win', chan_b=False, **kwargs):
-        filter_dict = {'win': 0, 'hamming': 1, 'ls': 2}
-        self.demodulation_frequency = demod_freq
-        self.sample_rate = samp_rate
-        self.filter = filter_dict[filt]
+    filter_dict = {'win': 0, 'ls': 1, 'ave': 2}
+
+    def __init__(self, name, alazar_name, demod_length=1, filter='win',
+                 numtaps=101, chan_b=False, **kwargs):
+        self.filter_settings = {'filter': self.filter_dict[filter],
+                                'numtaps': numtaps}
         self.chan_b = chan_b
-        self.samples_per_record = 0
-        self.records_per_buffer = 0
-        self.buffers_per_acquisition = 0
+        self._demod_length = demod_length
         self.number_of_channels = 2
-        self.cos_list = None
-        self.sin_list = None
-        self.buffer = None
-        # make a call to the parent class and by extension,
-        # create the parameter structure of this class
+        self.samples_per_record = None
+        self.sample_rate = None
         super().__init__(name, alazar_name, **kwargs)
 
         self.add_parameter(name='acquisition',
-                           parameter_class=RecSampParam)
+                           parameter_class=RecordsSamplesAcqParam)
+        for i in range(demod_length):
+            self.add_parameter(name='demod_freq_{}'.format(i),
+                               check_and_update_fn=self._update_demod_freq,
+                               parameter_class=AcqVariablesParam)
+        self.add_parameter(name='int_time',
+                           check_and_update_fn=self._update_int_time,
+                           default_fn=self._int_time_default,
+                           parameter_class=AcqVariablesParam)
+        self.add_parameter(name='int_delay',
+                           check_and_update_fn=self._update_int_delay,
+                           default_fn=self._int_delay_default,
+                           parameter_class=AcqVariablesParam)
+        self.add_parameter(name='record_num',
+                           check_and_update_fn=self._update_rec_num,
+                           parameter_class=AcqVariablesParam)
 
-    def update_acquisitionkwargs(self, **kwargs):
+        self.samples_divisor = self._get_alazar().samples_divisor
+
+    def _update_demod_freq(instr, value, param_name=None):
         """
-        This method must be used to update the kwargs used for the acquisition
-        with the alazar_driver.acquire
-        :param kwargs:
-        :return:
+        Function to validate and update acquisiton parameter when
+        a demod_freq_ Parameter is changed
+
+        Args:
+            value to update demodulation frequency to
+
+        Kwargs:
+            param_name: used to update demod_freq list used for updating
+                septionts of acquisition parameter
+
+        Checks:
+            1e6 <= value <= 500e6
+            number of oscilation measured using current int_tiume param value
+                at this demod frequency value
+            oversampling rate for this demodulation frequency
+
+        Sets:
+            sample_rate attr of acq controller to be that of alazar
+            setpoints of acquisiton parameter
+        """
+        if (value is None) or not (1e6 <= value <= 500e6):
+            raise ValueError('demod_freqs must be 1e6 <= value <= 500e6')
+        alazar = instr._get_alazar()
+        instr.sample_rate = alazar.get_sample_rate()
+        min_oscilations_measured = instr.int_time() * value
+        oversampling = instr.sample_rate / (2 * value)
+        if min_oscilations_measured < 10:
+            logging.warning('{} oscilations measured for largest '
+                            'demod freq, recommend at least 10: '
+                            'decrease sampling rate, take '
+                            'more samples or increase demodulation '
+                            'freq'.format(min_oscilations_measured))
+        elif oversampling < 1:
+            logging.warning('oversampling rate is {}, recommend > 1: '
+                            'increase sampling rate or decrease '
+                            'demodulation frequency'.format(oversampling))
+        demod_freqs = instr.get_demod_freqs()
+        current_demod_index = ([int(s) for s in param_name.split()
+                                if s.isdigit()][0])
+        demod_freqs[current_demod_index] = value
+        instr.acquisition.update_demod_setpoints(demod_freqs)
+
+    def _update_record_num(instr, value, **kwargs):
+        """
+        Function to update the record number, and instr attributes. Main
+        reason to record_num as a parameter is so we can more easily push the
+        record number setting up to other instruments which will control this.
+
+        Args:
+            value to be validated and used for instrument attribute update
+
+        Checks:
+            1 <= value <= 1000
+
+        Sets:
+            acquisition_kwarg['records_per_buffer'] of acquisition param
+            setpoints of acquisiton param
+            shape of acquisition param
         """
-        self.acquisition.update_acquisition_kwargs(**kwargs)
+        if (value is None) or not (1 <= value <= 1000):
+            raise ValueError('int_time must be 1 <= value <= 1000')
+
+        # update acquisition parameter shapes
+        instr.acquisition.update_rec_sweep(value)
+
+        # update acquision kwargs
+        instr.acquisition.acquisition_kwargs.update(
+            records_per_buffer=value)
+
+    def _update_int_time(instr, value, **kwargs):
+        """
+        Function to validate value for int_time before setting parameter
+        value and update instr attributes.
+
+        Args:
+            value to be validated and used for instrument attribute update
+
+        Checks:
+            0 <= value <= 0.1 seconds
+            number of oscilation measured in this time
+            oversampling rate
+
+        Sets:
+            sample_rate attr of acq controller to be that of alazar
+            samples_per_record of acq controller
+            acquisition_kwarg['samples_per_record'] of acquisition param
+            setpoints of acquisiton param
+            shape of acquisition param
+        """
+        if (value is None) or not (0 <= value <= 0.1):
+            raise ValueError('int_time must be 0 <= value <= 1')
+        alazar = instr._get_alazar()
+        instr.sample_rate = alazar.get_sample_rate()
+        if instr.get_max_demod_freq() is not None:
+            oscilations_measured = value * instr.get_max_demod_freq()
+            oversampling = instr.sample_rate / (2 * instr.get_max_demod_freq())
+            if oscilations_measured < 10:
+                logging.warning('{} oscilations measured, recommend at '
+                                'least 10: decrease sampling rate, take '
+                                'more samples or increase demodulation '
+                                'freq'.format(oscilations_measured))
+            elif oversampling < 1:
+                logging.warning('oversampling rate is {}, recommend > 1: '
+                                'increase sampling rate or decrease '
+                                'demodulation frequency'.format(oversampling))
+        if instr.int_delay() is None:
+            instr.int_delay.to_default()
+
+        # update acquisition parameter shapes
+        start = instr.int_delay()
+        stop = start + value
+        npts = int(value * instr.sample_rate)
+        instr.acquisition.update_time_sweep(start, stop, npts)
+
+        # update acquision kwargs and acq controller value
+        total_time = value + instr.int_delay()
+        samples_needed = total_time * instr.sample_rate
+        instr.samples_per_record = helpers.roundup(
+            samples_needed, instr.samples_divisor)
+        instr.acquisition.acquisition_kwargs.update(
+            samples_per_record=instr.samples_per_record)
+
+    def _update_int_delay(instr, value, **kwargs):
+        """
+        Function to validate value for int_delay before setting parameter
+        value and update instr attributes.
+
+        Args:
+            value to be validated and used for instrument attribute update
+
+        Checks:
+            0 <= value <= 0.1 seconds
+            number of samples discarded >= numtaps
+
+        Sets:
+            sample_rate attr of acq controller to be that of alazar
+            samples_per_record of acq controller
+            acquisition_kwarg['samples_per_record'] of acquisition param
+            setpoints of acquisiton param
+            shape of acquisition param
+        """
+        if (value is None) or not (0 <= value <= 0.1):
+            raise ValueError('int_delay must be 0 <= value <= 1')
+        alazar = instr._get_alazar()
+        instr.sample_rate = alazar.get_sample_rate()
+        samples_delay_min = (instr.filter_settings['numtaps'] - 1)
+        int_delay_min = samples_delay_min / instr.sample_rate
+        if value < int_delay_min:
+            logging.warning(
+                'delay is less than recommended for filter choice: '
+                '(expect delay >= {})'.format(int_delay_min))
+
+        # update acquisition parameter shapes
+        start = value
+        stop = start + (instr.int_time() or 0)
+        npts = int((instr.int_time() or 0) * instr.sample_rate)
+        instr.acquisition.update_time_sweep(start, stop, npts)
+
+        # update acquision kwargs and acq controller value
+        total_time = value + (instr.int_time() or 0)
+        samples_needed = total_time * instr.sample_rate
+        instr.samples_per_record = helpers.roundup(
+            samples_needed, instr.samples_divisor)
+        instr.acquisition.acquisition_kwargs.update(
+            samples_per_record=instr.samples_per_record)
+
+    def _int_delay_default(instr):
+        """
+        Function to generate default int_delay value
+
+        Returns:
+            minimum int_delay recommended for (numtaps - 1)
+            samples to be discarded as recommended for filter
+        """
+        alazar = instr._get_alazar()
+        instr.sample_rate = alazar.get_sample_rate()
+        samp_delay = instr.filter_settings['numtaps'] - 1
+        return samp_delay / instr.sample_rate
+
+    def _int_time_default(instr):
+        """
+        Function to generate defult int_time value
+
+        Returns:
+            max total time for integration based on samples_per_record,
+            sample_rate and int_delay
+        """
+        if instr.samples_per_record is (0 or None):
+            raise ValueError('Cannot set int_time to max if acq controller'
+                             ' has 0 or None samples_per_record, choose a '
+                             'value for int_time and samples_per_record will '
+                             'be set accordingly')
+        alazar = instr._get_alazar()
+        instr.sample_rate = alazar.get_sample_rate()
+        total_time = ((instr.samples_per_record / instr.sample_rate) -
+                      (instr.int_delay() or 0))
+        return total_time
+
+    def get_demod_freqs(self):
+        """
+        Function to get all the demod_freq parameter values in a list, v hacky
+
+        Returns:
+            freqs: numpy array of demodulation frequencies
+        """
+        freqs = list(filter(None, [getattr(self, 'demod_freq_{}'.format(c))()
+                                   for c in range(self._demod_length)]))
+        return np.array(freqs)
+
+    def get_max_demod_freq(self):
+        """
+        Returns:
+            the largest demodulation frequency
+
+        nb: really hacky and we should have channels in qcodes but we don't
+        (at time of writing)
+        """
+        freqs = self.get_demod_freqs()
+        if len(freqs) > 0:
+            return max(freqs)
+        else:
+            return None
+
+    def update_filter_settings(self, filter, numtaps):
+        """
+        Updates the settings of the filter for filtering out
+        double frwuency component for demodulation.
+
+        Args:
+            filter (str): filter type ('win' or 'ls')
+            numtaps (int): numtaps for filter
+        """
+        self.filter_settings.update({'filter': self.filter_dict[filter],
+                                     'numtaps': numtaps})
+
+    def update_acquisition_kwargs(self, **kwargs):
+        """
+        Updates the kwargs to be used when
+        alazar_driver.acquisition() is called via a get call of the
+        acquisition RecordsSamplesAcqParam. Should be used by the user for
+        updating averaging settings since the 'samples_per_record'
+        and 'records_per_buffer' kwargs are updated via the int_time,
+        int_delay and record_num parameters
+
+        Kwargs (ints):
+            buffers_per_acquisition
+            allocated_buffers
+        """
+        if 'samples_per_record' in kwargs:
+            raise ValueError('With HD_RecordsSamples_Controller '
+                             'samples_per_record cannot be set manually '
+                             'via update_acquisition_kwargs and should instead'
+                             ' be set by setting int_time and int_delay')
+        if 'records_per_buffer' in kwargs:
+            raise ValueError('With HD_RecordsSamples_Controller '
+                             'records_per_buffer cannot be set manually '
+                             'via update_acquisition_kwargs and should instead'
+                             ' be set by setting record_num')
+        self.acquisition.acquisition_kwargs.update(**kwargs)
 
     def pre_start_capture(self):
         """
-        See AcquisitionController
-        :return:
+        Called before capture start to update Acquisition Controller with
+        alazar acquisition params and set up software wave for demodulation.
         """
         alazar = self._get_alazar()
-        self.samples_per_record = alazar.samples_per_record.get()
-        self.records_per_buffer = alazar.records_per_buffer.get()
+        if self.samples_per_record != alazar.samples_per_record.get():
+            raise Exception('acq controller samples per record does not match'
+                            ' instrument value, most likely need '
+                            'to set and check int_time and int_delay')
+        if self.sample_rate != alazar.get_sample_rate():
+            raise Exception('acq controller sample rate does not match '
+                            'instrument value, most likely need '
+                            'to set and check int_time and int_delay')
+        if self.record_num() != alazar.records_per_buffer.get():
+            raise Exception('acq controller records per buffer does not match '
+                            'instrument value, most likely need '
+                            'to call update_acquisition_kwargs with'
+                            'records_per_buffer as a param')
+
+        demod_freqs = self.get_demod_freqs()
+        if len(demod_freqs) == 0:
+            raise Exception('no demod_freqs set')
+
         self.buffers_per_acquisition = alazar.buffers_per_acquisition.get()
+        self.board_info = alazar.get_idn()
         self.buffer = np.zeros(self.samples_per_record *
-                               self.records_per_buffer *
+                               self.record_num() *
                                self.number_of_channels)
 
+        mat_shape = (self._demod_length, self.record_num(),
+                     self.samples_per_record)
         integer_list = np.arange(self.samples_per_record)
-        angle_list = (2 * np.pi * self.demodulation_frequency /
-                      self.sample_rate * integer_list)
-
-        cos_list = np.cos(angle_list).reshape(self.samples_per_record, 1)
-        sin_list = np.sin(angle_list).reshape(self.samples_per_record, 1)
-        self.cos_mat = np.kron(np.ones((1, self.records_per_buffer)), cos_list)
-        self.sin_mat = np.kron(np.ones((1, self.records_per_buffer)), sin_list)
+        integer_mat = np.outer(np.ones(self.record_num()), integer_list)
+        angle_mat = 2 * np.pi * \
+            np.outer(demod_freqs, integer_mat).reshape(
+                mat_shape) / self.sample_rate
+        self.cos_mat = np.cos(angle_mat)
+        self.sin_mat = np.sin(angle_mat)
 
     def pre_acquire(self):
-        """
-        See AcquisitionController
-        :return:
-        """
-        # this could be used to start an Arbitrary Waveform Generator, etc...
-        # using this method ensures that the contents are executed AFTER the
-        # Alazar card starts listening for a trigger pulse
         pass
 
     def handle_buffer(self, data):
         """
-        See AcquisitionController
-        :return:
+        Adds data from alazar to buffer (effectively averaging)
         """
-        # average over buffers
         self.buffer += data
 
     def post_acquire(self):
         """
-        See AcquisitionController
-        :return:
-        """
+        Processes the data according to ATS9360 settings, splitting into
+        records and then applying demodulation fit
+        nb: currently only channel A
 
+        Returns:
+            magnitude (numpy array): shape = (demod_length, records_per_buffer)
+            phase (numpy array): shape = (demod_length, records_per_buffer
+        """
         # for ATS9360 samples are arranged in the buffer as follows:
         # S00A, S00B, S01A, S01B...S10A, S10B, S11A, S11B...
         # where SXYZ is record X, sample Y, channel Z.
 
-        # reshapes date to be (samples * records)
-        recordA = np.zeros((self.samples_per_record, self.records_per_buffer))
-        for i in range(self.records_per_buffer):
-            i0 = i * self.number_of_channels * self.samples_per_record
-            i1 = i0 + self.number_of_channels * self.samples_per_record
-            recordA[:, i] = (self.buffer[i0:i1:self.number_of_channels] /
-                             self.buffers_per_acquisition)
-
-        # return averaged chan A data (records)
-        magA, phaseA = self.fit(recordA)
-
-        # same for B
-        # if self.chan_b:
-        # recordB = np.zeros(
-        #        (self.samples_per_record, self.records_per_buffer))
-        # for i in self.records_per_buffer:
-        # recordB[i, :] = self.buffer[1:full_rec_length:step] / averaging
-        # magB, phaseB = self.fit(recordB)
-
-        # return data (samples * records)
+        # break buffer up into records and shapes to be (records, samples)
+        recordsA = np.empty((self.record_num(), self.samples_per_record),
+                            dtype=np.uint16)
+        for i in range(self.record_num()):
+            i0 = (i * self.samples_per_record * self.number_of_channels)
+            i1 = (i0 + self.samples_per_record * self.number_of_channels)
+            recordsA[i, :] = np.uint16(
+                self.buffer[i0:i1:self.number_of_channels] /
+                self.buffers_per_acquisition)
+
+        # do demodulation
+        magA, phaseA = self._fit(recordsA)
+
+        # same for chan b
+        if self.chan_b:
+            raise NotImplementedError('chan b code not complete')
+
         return magA, phaseA
 
-    def fit(self, rec):
-        # center rec around 0
-        rec = rec - np.mean(rec)
-
-        # multiply with software wave
-        re_wave = np.multiply(rec, self.cos_mat)
-        im_wave = np.multiply(rec, self.sin_mat)
-        cutoff = self.demodulation_frequency
-        numtaps = 30
-        axis = 0
-
-        # filter out double freq component to obtian constant term
-        if self.filter == 0:
-            RePart = self.filter_win(re_wave, numtaps, cutoff, axis=axis)
-            ImPart = self.filter_win(im_wave, numtaps, cutoff, axis=axis)
-        elif self.filter == 1:
-            RePart = self.filter_hamming(re_wave, numtaps, cutoff, axis=axis)
-            ImPart = self.filter_hamming(im_wave, numtaps, cutoff, axis=axis)
-        elif self.filter == 2:
-            RePart = self.filter_ls(re_wave, numtaps, cutoff, axis, axis=axis)
-            ImPart = self.filter_ls(im_wave, numtaps, cutoff, axis, axis=axis)
-
-        # convert to magnitude and phase data
-        # data returned is (samples * records)
-        complex_num = RePart + ImPart * 1j
-        mag = 2 * abs(complex_num)
-        phase = np.angle(complex_num, deg=True)
+    def _fit(self, rec):
+        """
+        Applies volts conversion, demodulation fit, low bandpass filter
+        and integration limits to samples array
 
-        return mag, phase
+        Args:
+            rec (numpy array): record from alazar to be multiplied
+                               with the software signal, filtered and limited
+                               to integration limits shape = (samples_taken, )
+
+        Returns:
+            magnitude (numpy array): shape = (demod_length, records_per_buffer)
+            phase (numpy array): shape = (demod_length, records_per_buffer)
+        """
+        # convert rec to volts
+        bps = self.board_info['bits_per_sample']
+        if bps == 12:
+            volt_rec = helpers.sample_to_volt_u12(rec, bps)
+        else:
+            logging.warning('sample to volt conversion does not exist for'
+                            ' bps != 12, centered raw samples returned')
+            volt_rec = rec - np.mean(rec, axis=1)
+
+        # volt_rec to matrix and multiply with demodulation signal matrices
+        mat_shape = (self._demod_length, self.record_num,
+                     self.samples_per_record)
+        volt_rec_mat = np.outer(
+            np.ones(self._demod_length), volt_rec).reshape(mat_shape)
+        re_mat = np.multiply(volt_rec_mat, self.cos_mat)
+        im_mat = np.multiply(volt_rec_mat, self.sin_mat)
+
+        # filter out higher freq component
+        cutoff = self.get_max_demod_freq() / 10
+        if self.filter_settings['filter'] == 0:
+            re_filtered = helpers.filter_win(re_mat, cutoff,
+                                             self.sample_rate,
+                                             self.filter_settings['numtaps'],
+                                             axis=2)
+            im_filtered = helpers.filter_win(im_mat, cutoff,
+                                             self.sample_rate,
+                                             self.filter_settings['numtaps'],
+                                             axis=2)
+        elif self.filter_settings['filter'] == 1:
+            re_filtered = helpers.filter_ls(re_mat, cutoff,
+                                            self.sample_rate,
+                                            self.filter_settings['numtaps'],
+                                            axis=2)
+            im_filtered = helpers.filter_ls(im_mat, cutoff,
+                                            self.sample_rate,
+                                            self.filter_settings['numtaps'],
+                                            axis=2)
+        elif self.filter_settings['filter'] == 2:
+            re_filtered = re_mat
+            im_filtered = im_mat
+
+        # apply integration limits
+        beginning = int(self.int_delay() * self.sample_rate)
+        end = beginning + int(self.int_time() * self.sample_rate)
+
+        re_limited = re_filtered[:, :, beginning:end]
+        im_limited = im_filtered[:, :, beginning:end]
+
+        # convert to magnitude and phase
+        complex_mat = re_limited + im_limited * 1j
+        magnitude = abs(complex_mat)
+        phase = np.angle(complex_mat, deg=True)
 
-    def filter_hamming(self, rec, numtaps, cutoff, axis=-1):
-        sample_rate = self.sample_rate
-        nyq_rate = sample_rate / 2.
-        fir_coef = signal.firwin(numtaps,
-                                 cutoff / nyq_rate,
-                                 window="hamming")
-        filtered_rec = 2 * signal.lfilter(fir_coef, 1.0, rec, axis=axis)
-        return filtered_rec
-
-    def filter_win(self, rec, numtaps, cutoff, axis=-1):
-        sample_rate = self.sample_rate
-        nyq_rate = sample_rate / 2.
-        fir_coef = signal.firwin(numtaps,
-                                 cutoff / nyq_rate)
-        filtered_rec = 2 * signal.lfilter(fir_coef, 1.0, rec, axis=axis)
-        return filtered_rec
-
-    def filter_ls(self, rec, numtaps, cutoff, axis=-1):
-        sample_rate = self.sample_rate
-        nyq_rate = sample_rate / 2.
-        bands = [0, cutoff / nyq_rate, cutoff / nyq_rate, 1]
-        desired = [1, 1, 0, 0]
-        fir_coef = signal.firls(numtaps,
-                                bands,
-                                desired,
-                                nyq=nyq_rate)
-        filtered_rec = 2 * signal.lfilter(fir_coef, 1.0, rec, axis=axis)
-        return filtered_rec
+        return magnitude, phase
diff --git a/qcodes/instrument_drivers/AlazarTech/samp_controller.py b/qcodes/instrument_drivers/AlazarTech/samp_controller.py
index 7f1974eb8f9b..a36382e74ab4 100644
--- a/qcodes/instrument_drivers/AlazarTech/samp_controller.py
+++ b/qcodes/instrument_drivers/AlazarTech/samp_controller.py
@@ -247,7 +247,7 @@ def _update_demod_freq(instr, value, param_name=None):
     def _update_int_time(instr, value, **kwargs):
         """
         Function to validate value for int_time before setting parameter
-        value
+        value and update instr attributes.
 
         Args:
             value to be validated and used for instrument attribute update
@@ -302,7 +302,7 @@ def _update_int_time(instr, value, **kwargs):
     def _update_int_delay(instr, value, **kwargs):
         """
         Function to validate value for int_delay before setting parameter
-        value
+        value and update instr attributes.
 
         Args:
             value to be validated and used for instrument attribute update
@@ -345,8 +345,11 @@ def _update_int_delay(instr, value, **kwargs):
 
     def _int_delay_default(instr):
         """
-        Returns minimum int_delay recommended for (numtaps - 1)
-        samples to be discarded as recommended for filter
+        Function to generate default int_delay value
+
+        Returns:
+            minimum int_delay recommended for (numtaps - 1)
+            samples to be discarded as recommended for filter
         """
         alazar = instr._get_alazar()
         instr.sample_rate = alazar.get_sample_rate()
@@ -355,8 +358,11 @@ def _int_delay_default(instr):
 
     def _int_time_default(instr):
         """
-        Returns max total time for integration based on samples_per_record,
-        sample_rate and int_delay
+        Function to generate defult int_time value
+
+        Returns:
+            max total time for integration based on samples_per_record,
+            sample_rate and int_delay
         """
         if instr.samples_per_record is (0 or None):
             raise ValueError('Cannot set int_time to max if acq controller'
@@ -410,7 +416,7 @@ def update_acquisition_kwargs(self, **kwargs):
         """
         Updates the kwargs to be used when
         alazar_driver.acquisition() is called via a get call of the
-        acquisition SamplesParam. Should be used by the user for updating
+        acquisition SamplesAcqParam. Should be used by the user for updating
         averaging settings since the 'samples_per_record' kwarg is updated
         via the int_time and int_delay parameters
 

From d8cae492880ffaa3ad628b8298c7318dd4579cce Mon Sep 17 00:00:00 2001
From: Natalie Pearson 
Date: Wed, 8 Feb 2017 11:21:24 +0100
Subject: [PATCH 102/328] complete rec_samp and buf_rec controllers and change
 post processing reshape logic in others

---
 .../AlazarTech/ave_controller.py              | 38 ++++++++++-------
 .../AlazarTech/buf_rec_controller.py          | 18 ++++----
 .../AlazarTech/rec_controller.py              | 41 ++++++++++++-------
 .../AlazarTech/rec_samp_controller.py         | 37 +++++++++++------
 .../AlazarTech/samp_controller.py             | 37 +++++++++++------
 5 files changed, 108 insertions(+), 63 deletions(-)

diff --git a/qcodes/instrument_drivers/AlazarTech/ave_controller.py b/qcodes/instrument_drivers/AlazarTech/ave_controller.py
index 7cfa7036cf26..48d593e1db74 100644
--- a/qcodes/instrument_drivers/AlazarTech/ave_controller.py
+++ b/qcodes/instrument_drivers/AlazarTech/ave_controller.py
@@ -456,19 +456,29 @@ def post_acquire(self):
             magnitude (numpy array): shape = (demod_length, samples_used)
             phase (numpy array): shape = (demod_length, samples_used)
         """
-        records_per_acquisition = (self.buffers_per_acquisition *
-                                   self.records_per_buffer)
+
         # for ATS9360 samples are arranged in the buffer as follows:
         # S00A, S00B, S01A, S01B...S10A, S10B, S11A, S11B...
         # where SXYZ is record X, sample Y, channel Z.
 
         # break buffer up into records and averages over them
-        recA = np.zeros(self.samples_per_record)
-        for i in range(self.records_per_buffer):
-            i0 = (i * self.samples_per_record * self.number_of_channels)
-            i1 = (i0 + self.samples_per_record * self.number_of_channels)
-            recA += self.buffer[i0:i1:self.number_of_channels]
-        recordA = np.uint16(recA / records_per_acquisition)
+        reshaped_buf = self.buffer.reshape(self.records_per_buffer,
+                                           self.samples_per_record,
+                                           self.number_of_channels)
+        recordA = np.uint16(np.mean(reshaped_buf[:, :, 0], axis=0) /
+                            self.buffers_per_acquisition)
+
+        # TODO(nataliejpg): test above version works on alazar and
+        # compare performance
+
+        # records_per_acquisition = (self.buffers_per_acquisition *
+        #                    self.records_per_buffer)
+        # recA = np.zeros(self.samples_per_record)
+        # for i in range(self.records_per_buffer):
+        #     i0 = (i * self.samples_per_record * self.number_of_channels)
+        #     i1 = (i0 + self.samples_per_record * self.number_of_channels)
+        #     recA += self.buffer[i0:i1:self.number_of_channels]
+        # recordA = np.uint16(recA / records_per_acquisition)
 
         # do demodulation
         magA, phaseA = self._fit(recordA)
@@ -513,20 +523,20 @@ def _fit(self, rec):
             re_filtered = helpers.filter_win(re_mat, cutoff,
                                              self.sample_rate,
                                              self.filter_settings['numtaps'],
-                                             axis=1)
+                                             axis=-1)
             im_filtered = helpers.filter_win(im_mat, cutoff,
                                              self.sample_rate,
                                              self.filter_settings['numtaps'],
-                                             axis=1)
+                                             axis=-1)
         elif self.filter_settings['filter'] == 1:
             re_filtered = helpers.filter_ls(re_mat, cutoff,
                                             self.sample_rate,
                                             self.filter_settings['numtaps'],
-                                            axis=1)
+                                            axis=-1)
             im_filtered = helpers.filter_ls(im_mat, cutoff,
                                             self.sample_rate,
                                             self.filter_settings['numtaps'],
-                                            axis=1)
+                                            axis=-1)
         elif self.filter_settings['filter'] == 2:
             re_filtered = re_mat
             im_filtered = im_mat
@@ -540,7 +550,7 @@ def _fit(self, rec):
 
         # convert to magnitude and phase
         complex_mat = re_limited + im_limited * 1j
-        magnitude = np.mean(abs(complex_mat), axis=1)
-        phase = np.mean(np.angle(complex_mat, deg=True), axis=1)
+        magnitude = np.mean(abs(complex_mat), axis=-1)
+        phase = np.mean(np.angle(complex_mat, deg=True), axis=-1)
 
         return magnitude, phase
diff --git a/qcodes/instrument_drivers/AlazarTech/buf_rec_controller.py b/qcodes/instrument_drivers/AlazarTech/buf_rec_controller.py
index b4e118970901..6f8f75ee6f01 100644
--- a/qcodes/instrument_drivers/AlazarTech/buf_rec_controller.py
+++ b/qcodes/instrument_drivers/AlazarTech/buf_rec_controller.py
@@ -563,7 +563,8 @@ def pre_start_capture(self):
                      self.record_num(), self.samples_per_record)
         integer_list = np.arange(self.samples_per_record)
         integer_mat = (np.outer(np.ones(self.buffer_num()),
-                                np.outer(np.ones(self.record_num()), integer_list)))
+                                np.outer(np.ones(self.record_num()),
+                                         integer_list)))
         angle_mat = 2 * np.pi * \
             np.outer(demod_freqs, integer_mat).reshape(
                 mat_shape) / self.sample_rate
@@ -637,7 +638,8 @@ def _fit(self, rec):
         else:
             logging.warning('sample to volt conversion does not exist for'
                             ' bps != 12, centered raw samples returned')
-            volt_rec = rec - np.mean(rec, axis=1)
+            # TODO(nataliejpg): think about this recentering makes sense here
+            volt_rec = rec - np.mean(rec)
 
         # volt_rec to matrix and multiply with demodulation signal matrices
         mat_shape = (self._demod_length, self.buffer_num(), self.record_num(),
@@ -653,20 +655,20 @@ def _fit(self, rec):
             re_filtered = helpers.filter_win(re_mat, cutoff,
                                              self.sample_rate,
                                              self.filter_settings['numtaps'],
-                                             axis=3)
+                                             axis=-1)
             im_filtered = helpers.filter_win(im_mat, cutoff,
                                              self.sample_rate,
                                              self.filter_settings['numtaps'],
-                                             axis=3)
+                                             axis=-1)
         elif self.filter_settings['filter'] == 1:
             re_filtered = helpers.filter_ls(re_mat, cutoff,
                                             self.sample_rate,
                                             self.filter_settings['numtaps'],
-                                            axis=3)
+                                            axis=-1)
             im_filtered = helpers.filter_ls(im_mat, cutoff,
                                             self.sample_rate,
                                             self.filter_settings['numtaps'],
-                                            axis=3)
+                                            axis=-1)
         elif self.filter_settings['filter'] == 2:
             re_filtered = re_mat
             im_filtered = im_mat
@@ -680,7 +682,7 @@ def _fit(self, rec):
 
         # convert to magnitude and phase
         complex_mat = re_limited + im_limited * 1j
-        magnitude = np.mean(abs(complex_mat), axis=3)
-        phase = np.mean(np.angle(complex_mat, deg=True), axis=3)
+        magnitude = np.mean(abs(complex_mat), axis=-1)
+        phase = np.mean(np.angle(complex_mat, deg=True), axis=-1)
 
         return magnitude, phase
diff --git a/qcodes/instrument_drivers/AlazarTech/rec_controller.py b/qcodes/instrument_drivers/AlazarTech/rec_controller.py
index 1027bf5eb3e0..6053f71c0669 100644
--- a/qcodes/instrument_drivers/AlazarTech/rec_controller.py
+++ b/qcodes/instrument_drivers/AlazarTech/rec_controller.py
@@ -514,20 +514,30 @@ def post_acquire(self):
         # where SXYZ is record X, sample Y, channel Z.
 
         # break buffer up into records and shapes to be (records, samples)
-        recordsA = np.empty((self.record_num(), self.samples_per_record),
-                            dtype=np.uint16)
-        for i in range(self.record_num()):
-            i0 = (i * self.samples_per_record * self.number_of_channels)
-            i1 = (i0 + self.samples_per_record * self.number_of_channels)
-            recordsA[i, :] = np.uint16(
-                self.buffer[i0:i1:self.number_of_channels] /
-                self.buffers_per_acquisition)
+        reshaped_buf = self.buffer.reshape(self.records_per_buffer,
+                                           self.samples_per_record,
+                                           self.number_of_channels)
+        recordsA = np.uint16(reshaped_buf[:, :, 0] /
+                             self.buffers_per_acquisition)
+
+        # TODO(nataliejpg): test above version works on alazar and
+        # compare performance
+        # recordsA = np.empty((self.record_num(), self.samples_per_record),
+        #                     dtype=np.uint16)
+        # for i in range(self.record_num()):
+        #     i0 = (i * self.samples_per_record * self.number_of_channels)
+        #     i1 = (i0 + self.samples_per_record * self.number_of_channels)
+        #     recordsA[i, :] = np.uint16(
+        #         self.buffer[i0:i1:self.number_of_channels] /
+        #         self.buffers_per_acquisition)
 
         # do demodulation
         magA, phaseA = self._fit(recordsA)
 
         # same for chan b
         if self.chan_b:
+            # recordsB = np.uint16(reshaped_buf[:, :, 1] /
+            #                      self.buffers_per_acquisition)
             raise NotImplementedError('chan b code not complete')
 
         return magA, phaseA
@@ -553,7 +563,8 @@ def _fit(self, rec):
         else:
             logging.warning('sample to volt conversion does not exist for'
                             ' bps != 12, centered raw samples returned')
-            volt_rec = rec - np.mean(rec, axis=1)
+            # TODO(nataliejpg): think about this recentering makes sense here
+            volt_rec = rec - np.mean(rec)
 
         # volt_rec to matrix and multiply with demodulation signal matrices
         mat_shape = (self._demod_length, self.record_num(),
@@ -569,20 +580,20 @@ def _fit(self, rec):
             re_filtered = helpers.filter_win(re_mat, cutoff,
                                              self.sample_rate,
                                              self.filter_settings['numtaps'],
-                                             axis=2)
+                                             axis=-1)
             im_filtered = helpers.filter_win(im_mat, cutoff,
                                              self.sample_rate,
                                              self.filter_settings['numtaps'],
-                                             axis=2)
+                                             axis=-1)
         elif self.filter_settings['filter'] == 1:
             re_filtered = helpers.filter_ls(re_mat, cutoff,
                                             self.sample_rate,
                                             self.filter_settings['numtaps'],
-                                            axis=2)
+                                            axis=-1)
             im_filtered = helpers.filter_ls(im_mat, cutoff,
                                             self.sample_rate,
                                             self.filter_settings['numtaps'],
-                                            axis=2)
+                                            axis=-1)
         elif self.filter_settings['filter'] == 2:
             re_filtered = re_mat
             im_filtered = im_mat
@@ -596,7 +607,7 @@ def _fit(self, rec):
 
         # convert to magnitude and phase
         complex_mat = re_limited + im_limited * 1j
-        magnitude = np.mean(abs(complex_mat), axis=2)
-        phase = np.mean(np.angle(complex_mat, deg=True), axis=2)
+        magnitude = np.mean(abs(complex_mat), axis=-1)
+        phase = np.mean(np.angle(complex_mat, deg=True), axis=-1)
 
         return magnitude, phase
diff --git a/qcodes/instrument_drivers/AlazarTech/rec_samp_controller.py b/qcodes/instrument_drivers/AlazarTech/rec_samp_controller.py
index 40b729965915..4987fd32b59e 100644
--- a/qcodes/instrument_drivers/AlazarTech/rec_samp_controller.py
+++ b/qcodes/instrument_drivers/AlazarTech/rec_samp_controller.py
@@ -562,16 +562,26 @@ def post_acquire(self):
         # where SXYZ is record X, sample Y, channel Z.
 
         # break buffer up into records and shapes to be (records, samples)
-        recordsA = np.empty((self.record_num(), self.samples_per_record),
-                            dtype=np.uint16)
-        for i in range(self.record_num()):
-            i0 = (i * self.samples_per_record * self.number_of_channels)
-            i1 = (i0 + self.samples_per_record * self.number_of_channels)
-            recordsA[i, :] = np.uint16(
-                self.buffer[i0:i1:self.number_of_channels] /
-                self.buffers_per_acquisition)
+        reshaped_buf = self.buffer.reshape(self.records_per_buffer,
+                                           self.samples_per_record,
+                                           self.number_of_channels)
+        recordsA = np.uint16(reshaped_buf[:, :, 0] /
+                             self.buffers_per_acquisition)
+
+        # TODO(nataliejpg): test above version works on alazar and
+        # compare performance
+        # recordsA = np.empty((self.record_num(), self.samples_per_record),
+        #                     dtype=np.uint16)
+        # for i in range(self.record_num()):
+        #     i0 = (i * self.samples_per_record * self.number_of_channels)
+        #     i1 = (i0 + self.samples_per_record * self.number_of_channels)
+        #     recordsA[i, :] = np.uint16(
+        #         self.buffer[i0:i1:self.number_of_channels] /
+        #         self.buffers_per_acquisitin)
 
         # do demodulation
+        # recordsB = np.uint16(reshaped_buf[:, :, 1] /
+        #                      self.buffers_per_acquisition)
         magA, phaseA = self._fit(recordsA)
 
         # same for chan b
@@ -601,7 +611,8 @@ def _fit(self, rec):
         else:
             logging.warning('sample to volt conversion does not exist for'
                             ' bps != 12, centered raw samples returned')
-            volt_rec = rec - np.mean(rec, axis=1)
+            # TODO(nataliejpg): think about this recentering makes sense here
+            volt_rec = rec - np.mean(rec)
 
         # volt_rec to matrix and multiply with demodulation signal matrices
         mat_shape = (self._demod_length, self.record_num,
@@ -617,20 +628,20 @@ def _fit(self, rec):
             re_filtered = helpers.filter_win(re_mat, cutoff,
                                              self.sample_rate,
                                              self.filter_settings['numtaps'],
-                                             axis=2)
+                                             axis=-1)
             im_filtered = helpers.filter_win(im_mat, cutoff,
                                              self.sample_rate,
                                              self.filter_settings['numtaps'],
-                                             axis=2)
+                                             axis=-1)
         elif self.filter_settings['filter'] == 1:
             re_filtered = helpers.filter_ls(re_mat, cutoff,
                                             self.sample_rate,
                                             self.filter_settings['numtaps'],
-                                            axis=2)
+                                            axis=-1)
             im_filtered = helpers.filter_ls(im_mat, cutoff,
                                             self.sample_rate,
                                             self.filter_settings['numtaps'],
-                                            axis=2)
+                                            axis=-1)
         elif self.filter_settings['filter'] == 2:
             re_filtered = re_mat
             im_filtered = im_mat
diff --git a/qcodes/instrument_drivers/AlazarTech/samp_controller.py b/qcodes/instrument_drivers/AlazarTech/samp_controller.py
index a36382e74ab4..995250d9fc89 100644
--- a/qcodes/instrument_drivers/AlazarTech/samp_controller.py
+++ b/qcodes/instrument_drivers/AlazarTech/samp_controller.py
@@ -484,25 +484,37 @@ def post_acquire(self):
             magnitude (numpy array): shape = (demod_length, samples_used)
             phase (numpy array): shape = (demod_length, samples_used)
         """
-        records_per_acquisition = (self.buffers_per_acquisition *
-                                   self.records_per_buffer)
+
         # for ATS9360 samples are arranged in the buffer as follows:
         # S00A, S00B, S01A, S01B...S10A, S10B, S11A, S11B...
         # where SXYZ is record X, sample Y, channel Z.
 
         # break buffer up into records and averages over them
-        recA = np.zeros(self.samples_per_record)
-        for i in range(self.records_per_buffer):
-            i0 = (i * self.samples_per_record * self.number_of_channels)
-            i1 = (i0 + self.samples_per_record * self.number_of_channels)
-            recA += self.buffer[i0:i1:self.number_of_channels]
-        recordA = np.uint16(recA / records_per_acquisition)
+        reshaped_buf = self.buffer.reshape(self.records_per_buffer,
+                                           self.samples_per_record,
+                                           self.number_of_channels)
+        recordA = np.uint16(np.mean(reshaped_buf[:, :, 0], axis=0) /
+                            self.buffers_per_acquisition)
+
+        # TODO(nataliejpg): test above version works on alazar and
+        # compare performance
+
+        # records_per_acquisition = (self.buffers_per_acquisition *
+        #                            self.records_per_buffer)
+        # recA = np.zeros(self.samples_per_record)
+        # for i in range(self.records_per_buffer):
+        #     i0 = (i * self.samples_per_record * self.number_of_channels)
+        #     i1 = (i0 + self.samples_per_record * self.number_of_channels)
+        #     recA += self.buffer[i0:i1:self.number_of_channels]
+        # recordA = np.uint16(recA / records_per_acquisition)
 
         # do demodulation
         magA, phaseA = self._fit(recordA)
 
         # same for chan b
         if self.chan_b:
+            # recordB = np.uint16(np.mean(reshaped_buf[:, :, 1], axis=0) /
+            #         self.buffers_per_acquisition)
             raise NotImplementedError('chan b code not complete')
 
         return magA, phaseA
@@ -542,20 +554,20 @@ def _fit(self, rec):
             re_filtered = helpers.filter_win(re_mat, cutoff,
                                              self.sample_rate,
                                              self.filter_settings['numtaps'],
-                                             axis=1)
+                                             axis=-1)
             im_filtered = helpers.filter_win(im_mat, cutoff,
                                              self.sample_rate,
                                              self.filter_settings['numtaps'],
-                                             axis=1)
+                                             axis=-1)
         elif self.filter_settings['filter'] == 1:
             re_filtered = helpers.filter_ls(re_mat, cutoff,
                                             self.sample_rate,
                                             self.filter_settings['numtaps'],
-                                            axis=1)
+                                            axis=-1)
             im_filtered = helpers.filter_ls(im_mat, cutoff,
                                             self.sample_rate,
                                             self.filter_settings['numtaps'],
-                                            axis=1)
+                                            axis=-1)
 
         # apply integration limits
         beginning = int(self.int_delay() * self.sample_rate)
@@ -563,7 +575,6 @@ def _fit(self, rec):
 
         re_limited = re_filtered[:, beginning:end]
         im_limited = im_filtered[:, beginning:end]
-        # return re_limited, volt_rec_mat[:, beginning:end]
 
         # convert to magnitude and phase
         complex_mat = re_limited + im_limited * 1j

From 1f4ea7e881c740477f7b7a81cd00ca6a5d02a9c9 Mon Sep 17 00:00:00 2001
From: Natalie Pearson 
Date: Mon, 13 Feb 2017 11:36:12 +0100
Subject: [PATCH 103/328] updating notebook to reflect driver changes, nb
 untested on an alazar

---
 .../Qcodes example with Alazar ATS9360.ipynb  | 217 ++++++++++++------
 1 file changed, 152 insertions(+), 65 deletions(-)

diff --git a/docs/examples/Qcodes example with Alazar ATS9360.ipynb b/docs/examples/Qcodes example with Alazar ATS9360.ipynb
index 905fd7081d53..cf493af6acda 100644
--- a/docs/examples/Qcodes example with Alazar ATS9360.ipynb	
+++ b/docs/examples/Qcodes example with Alazar ATS9360.ipynb	
@@ -2,7 +2,10 @@
  "cells": [
   {
    "cell_type": "markdown",
-   "metadata": {},
+   "metadata": {
+    "deletable": true,
+    "editable": true
+   },
    "source": [
     "### Qcodes example notebook for Alazar card ATS9360 and acq controllers"
    ]
@@ -11,7 +14,9 @@
    "cell_type": "code",
    "execution_count": 1,
    "metadata": {
-    "collapsed": false
+    "collapsed": false,
+    "deletable": true,
+    "editable": true
    },
    "outputs": [
     {
@@ -39,7 +44,10 @@
   },
   {
    "cell_type": "markdown",
-   "metadata": {},
+   "metadata": {
+    "deletable": true,
+    "editable": true
+   },
    "source": [
     "NB: See ATS9360 example notebook for general commands "
    ]
@@ -48,7 +56,9 @@
    "cell_type": "code",
    "execution_count": 2,
    "metadata": {
-    "collapsed": false
+    "collapsed": false,
+    "deletable": true,
+    "editable": true
    },
    "outputs": [
     {
@@ -86,7 +96,9 @@
    "cell_type": "code",
    "execution_count": 3,
    "metadata": {
-    "collapsed": false
+    "collapsed": false,
+    "deletable": true,
+    "editable": true
    },
    "outputs": [],
    "source": [
@@ -118,7 +130,10 @@
   },
   {
    "cell_type": "markdown",
-   "metadata": {},
+   "metadata": {
+    "deletable": true,
+    "editable": true
+   },
    "source": [
     "### Basic Acquisition\n",
     "\n",
@@ -129,7 +144,9 @@
    "cell_type": "code",
    "execution_count": 4,
    "metadata": {
-    "collapsed": false
+    "collapsed": false,
+    "deletable": true,
+    "editable": true
    },
    "outputs": [],
    "source": [
@@ -143,7 +160,9 @@
    "cell_type": "code",
    "execution_count": 5,
    "metadata": {
-    "collapsed": false
+    "collapsed": false,
+    "deletable": true,
+    "editable": true
    },
    "outputs": [],
    "source": [
@@ -160,7 +179,9 @@
    "cell_type": "code",
    "execution_count": 6,
    "metadata": {
-    "collapsed": false
+    "collapsed": false,
+    "deletable": true,
+    "editable": true
    },
    "outputs": [
     {
@@ -183,7 +204,9 @@
    "cell_type": "code",
    "execution_count": 11,
    "metadata": {
-    "collapsed": false
+    "collapsed": false,
+    "deletable": true,
+    "editable": true
    },
    "outputs": [
     {
@@ -211,7 +234,9 @@
    "cell_type": "code",
    "execution_count": 17,
    "metadata": {
-    "collapsed": false
+    "collapsed": false,
+    "deletable": true,
+    "editable": true
    },
    "outputs": [
     {
@@ -234,7 +259,9 @@
    "cell_type": "code",
    "execution_count": null,
    "metadata": {
-    "collapsed": true
+    "collapsed": true,
+    "deletable": true,
+    "editable": true
    },
    "outputs": [],
    "source": []
@@ -243,7 +270,9 @@
    "cell_type": "code",
    "execution_count": 10,
    "metadata": {
-    "collapsed": true
+    "collapsed": true,
+    "deletable": true,
+    "editable": true
    },
    "outputs": [
     {
@@ -274,7 +303,9 @@
    "cell_type": "code",
    "execution_count": null,
    "metadata": {
-    "collapsed": false
+    "collapsed": false,
+    "deletable": true,
+    "editable": true
    },
    "outputs": [],
    "source": [
@@ -289,39 +320,32 @@
    "cell_type": "code",
    "execution_count": null,
    "metadata": {
-    "collapsed": true
+    "collapsed": true,
+    "deletable": true,
+    "editable": true
    },
    "outputs": [],
    "source": []
   },
   {
    "cell_type": "markdown",
-   "metadata": {},
+   "metadata": {
+    "deletable": true,
+    "editable": true
+   },
    "source": [
     "### Samples Acquisition\n",
     "\n",
     "This is the same as above except that it does some demodulation at the freqiencies specified"
    ]
   },
-  {
-   "cell_type": "code",
-   "execution_count": 18,
-   "metadata": {
-    "collapsed": false
-   },
-   "outputs": [],
-   "source": [
-    "# make a list of the frequencies to demodulate at (nb these values can be changed later, if unset the default is [20e6])\n",
-    "# NB the number of demodulation frequencies cannot currently be changed once an acq controller is created, this sucks:\n",
-    "# lets have channels :)\n",
-    "demod_list = [5e6,1e6]"
-   ]
-  },
   {
    "cell_type": "code",
    "execution_count": 19,
    "metadata": {
-    "collapsed": false
+    "collapsed": false,
+    "deletable": true,
+    "editable": true
    },
    "outputs": [],
    "source": [
@@ -329,14 +353,16 @@
     "# alazar instrument to talk to and does the demodulation and signal processing\n",
     "samp_acq_controller = samp_acq_contr.HD_Samples_Controller(name='samp_acq_controller', \n",
     "                                                           alazar_name='Alazar',\n",
-    "                                                          demod_freqs=demod_list)"
+    "                                                           demod_length=2)"
    ]
   },
   {
    "cell_type": "code",
    "execution_count": 25,
    "metadata": {
-    "collapsed": false
+    "collapsed": false,
+    "deletable": true,
+    "editable": true
    },
    "outputs": [
     {
@@ -348,6 +374,10 @@
     }
    ],
    "source": [
+    "# set the two demodulation frequencies\n",
+    "samp_acq_controller.demod_freq_0(1e6)\n",
+    "samp_acq_controller.demod_freq_1(2e6)\n",
+    "\n",
     "# set integration time and delay (nb this replaces need to set samples_per record)\n",
     "# if int_delay is unset it will default to a value corresponding to the about needed for the filter to work well\n",
     "samp_acq_controller.int_time(10e-6)\n",
@@ -358,7 +388,9 @@
    "cell_type": "code",
    "execution_count": 40,
    "metadata": {
-    "collapsed": true
+    "collapsed": true,
+    "deletable": true,
+    "editable": true
    },
    "outputs": [],
    "source": [
@@ -372,7 +404,9 @@
    "cell_type": "code",
    "execution_count": 22,
    "metadata": {
-    "collapsed": false
+    "collapsed": false,
+    "deletable": true,
+    "editable": true
    },
    "outputs": [],
    "source": [
@@ -388,7 +422,9 @@
    "cell_type": "code",
    "execution_count": 43,
    "metadata": {
-    "collapsed": false
+    "collapsed": false,
+    "deletable": true,
+    "editable": true
    },
    "outputs": [],
    "source": [
@@ -401,7 +437,9 @@
    "cell_type": "code",
    "execution_count": 31,
    "metadata": {
-    "collapsed": false
+    "collapsed": false,
+    "deletable": true,
+    "editable": true
    },
    "outputs": [
     {
@@ -423,7 +461,9 @@
    "cell_type": "code",
    "execution_count": 53,
    "metadata": {
-    "collapsed": false
+    "collapsed": false,
+    "deletable": true,
+    "editable": true
    },
    "outputs": [],
    "source": [
@@ -435,7 +475,9 @@
    "cell_type": "code",
    "execution_count": 54,
    "metadata": {
-    "collapsed": false
+    "collapsed": false,
+    "deletable": true,
+    "editable": true
    },
    "outputs": [],
    "source": [
@@ -447,6 +489,8 @@
    "execution_count": 48,
    "metadata": {
     "collapsed": false,
+    "deletable": true,
+    "editable": true,
     "scrolled": false
    },
    "outputs": [
@@ -489,7 +533,9 @@
    "cell_type": "code",
    "execution_count": 35,
    "metadata": {
-    "collapsed": false
+    "collapsed": false,
+    "deletable": true,
+    "editable": true
    },
    "outputs": [
     {
@@ -512,7 +558,9 @@
    "cell_type": "code",
    "execution_count": 45,
    "metadata": {
-    "collapsed": false
+    "collapsed": false,
+    "deletable": true,
+    "editable": true
    },
    "outputs": [
     {
@@ -535,7 +583,9 @@
    "cell_type": "code",
    "execution_count": 30,
    "metadata": {
-    "collapsed": false
+    "collapsed": false,
+    "deletable": true,
+    "editable": true
    },
    "outputs": [
     {
@@ -565,7 +615,9 @@
    "cell_type": "code",
    "execution_count": 33,
    "metadata": {
-    "collapsed": false
+    "collapsed": false,
+    "deletable": true,
+    "editable": true
    },
    "outputs": [
     {
@@ -594,7 +646,10 @@
   },
   {
    "cell_type": "markdown",
-   "metadata": {},
+   "metadata": {
+    "deletable": true,
+    "editable": true
+   },
    "source": [
     "### Averaged Acquisition\n",
     "\n",
@@ -605,21 +660,24 @@
    "cell_type": "code",
    "execution_count": 4,
    "metadata": {
-    "collapsed": false
+    "collapsed": false,
+    "deletable": true,
+    "editable": true
    },
    "outputs": [],
    "source": [
-    "demod_list = [80e6, 85e6, 90e6, 95e6, 100e6, 105e6, 110e6, 115e6]\n",
     "ave_controller = ave_controller.HD_Averaging_Controller(name='ave_controller', \n",
     "                                             alazar_name='Alazar', \n",
-    "                                             demod_freqs = demod_list)"
+    "                                             demod_length = 8)"
    ]
   },
   {
    "cell_type": "code",
    "execution_count": 5,
    "metadata": {
-    "collapsed": false
+    "collapsed": false,
+    "deletable": true,
+    "editable": true
    },
    "outputs": [
     {
@@ -631,6 +689,17 @@
     }
    ],
    "source": [
+    "# set the  demodulation frequencies\n",
+    "samp_acq_controller.demod_freq_0(1e6)\n",
+    "samp_acq_controller.demod_freq_1(2e6)\n",
+    "samp_acq_controller.demod_freq_2(3e6)\n",
+    "samp_acq_controller.demod_freq_3(4e6)\n",
+    "samp_acq_controller.demod_freq_4(5e6)\n",
+    "samp_acq_controller.demod_freq_5(6e6)\n",
+    "samp_acq_controller.demod_freq_6(7e6)\n",
+    "samp_acq_controller.demod_freq_7(8e6)\n",
+    "\n",
+    "\n",
     "# This command is specific to this acquisition controller. The kwargs provided here are being forwarded to ats_inst.acquire\n",
     "# This way, it becomes easy to change acquisition specific settings from the ipython notebook\n",
     "ave_controller.update_acquisition_kwargs(#mode='NPT',\n",
@@ -649,7 +718,9 @@
    "cell_type": "code",
    "execution_count": 6,
    "metadata": {
-    "collapsed": false
+    "collapsed": false,
+    "deletable": true,
+    "editable": true
    },
    "outputs": [
     {
@@ -674,7 +745,9 @@
    "cell_type": "code",
    "execution_count": 7,
    "metadata": {
-    "collapsed": false
+    "collapsed": false,
+    "deletable": true,
+    "editable": true
    },
    "outputs": [
     {
@@ -712,7 +785,10 @@
   },
   {
    "cell_type": "markdown",
-   "metadata": {},
+   "metadata": {
+    "deletable": true,
+    "editable": true
+   },
    "source": [
     "### Records Acquisition\n",
     "\n",
@@ -723,7 +799,9 @@
    "cell_type": "code",
    "execution_count": null,
    "metadata": {
-    "collapsed": true
+    "collapsed": true,
+    "deletable": true,
+    "editable": true
    },
    "outputs": [],
    "source": [
@@ -736,32 +814,43 @@
    "cell_type": "code",
    "execution_count": 8,
    "metadata": {
-    "collapsed": false
+    "collapsed": false,
+    "deletable": true,
+    "editable": true
    },
    "outputs": [],
    "source": [
-    "demod_list = [95e6, 98e6, 100e6, 105e6]\n",
     "rec_controller = record_controller.HD_Records_Controller(name='rec_controller', \n",
     "                                                    alazar_name='Alazar', \n",
-    "                                                    demod_freqs = demod_list)"
+    "                                                    demod_length = 4)\n",
+    "rec_controller.demod_freq_0(1e6)\n",
+    "rec_controller.demod_freq_1(4e6)\n",
+    "rec_controller.demod_freq_2(8e6)\n",
+    "rec_controller.demod_freq_3(12e6)"
    ]
   },
   {
    "cell_type": "code",
    "execution_count": 12,
    "metadata": {
-    "collapsed": false
+    "collapsed": false,
+    "deletable": true,
+    "editable": true
    },
    "outputs": [],
    "source": [
-    "# This command is specific to this acquisition controller. The kwargs provided here are being forwarded to ats_inst.acquire\n",
-    "# This way, it becomes easy to change acquisition specific settings from the ipython notebook\n",
+    "# This command is specific to this acquisition controller. The kwargs provided here are being forwarded to \n",
+    "# ats_inst.acquire. This way, it becomes easy to change acquisition specific settings from the ipython notebook\n",
     "rec_controller.update_acquisition_kwargs(#mode='NPT',\n",
-    "                 records_per_buffer=20,\n",
-    "                 buffers_per_acquisition=1,\n",
+    "                 buffers_per_acquisition=100,\n",
     "                 allocated_buffers=1,\n",
     ")\n",
     "\n",
+    "# records_num a parameter of the records acq controller which is mainly a move towards having the \n",
+    "# setpoints in the record direction be settable by the user and also to make it easier to write wrapper functions\n",
+    "# which change it when there is an AWG upload, (replaces need to set records_per_buffer)\n",
+    "rec_controller.record_num(20)\n",
+    "\n",
     "# set integration time and delay (nb this replaces need to set samples_per record)\n",
     "# if int_delay is unset it will default to a value corresponding to the about needed for the filter to work well\n",
     "rec_controller.int_time(2e-6)\n",
@@ -772,7 +861,9 @@
    "cell_type": "code",
    "execution_count": 19,
    "metadata": {
-    "collapsed": false
+    "collapsed": false,
+    "deletable": true,
+    "editable": true
    },
    "outputs": [
     {
@@ -825,10 +916,6 @@
    "nbconvert_exporter": "python",
    "pygments_lexer": "ipython3",
    "version": "3.5.2"
-  },
-  "widgets": {
-   "state": {},
-   "version": "1.1.2"
   }
  },
  "nbformat": 4,

From c00d8cddad954d96d8891abd8213ba416a936943 Mon Sep 17 00:00:00 2001
From: Jens Hedegaard Nielsen 
Date: Mon, 13 Feb 2017 16:19:53 +0100
Subject: [PATCH 104/328] Revert "Change lower limit of freq allowed to 1 MHz"

This is a non related change. Will go in on a different pr
This reverts commit e8190ec03f5e98b824fe09d4d4d4e1ecbfca33b1.
---
 qcodes/instrument_drivers/rohde_schwarz/SGS100A.py | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

diff --git a/qcodes/instrument_drivers/rohde_schwarz/SGS100A.py b/qcodes/instrument_drivers/rohde_schwarz/SGS100A.py
index 094d5d64738b..83eb19ee0543 100644
--- a/qcodes/instrument_drivers/rohde_schwarz/SGS100A.py
+++ b/qcodes/instrument_drivers/rohde_schwarz/SGS100A.py
@@ -36,7 +36,7 @@ def __init__(self, name, address, **kwargs):
                            get_cmd='SOUR:FREQ' + '?',
                            set_cmd='SOUR:FREQ' + ' {:.2f}',
                            get_parser=float,
-                           vals=vals.Numbers(1e6, 20e9))
+                           vals=vals.Numbers(1e9, 20e9))
         self.add_parameter(name='phase',
                            label='Phase',
                            units='deg',

From 72cbaf4761ca6e0ee68a9c95c436bd9c649f7b99 Mon Sep 17 00:00:00 2001
From: Jens Hedegaard Nielsen 
Date: Mon, 13 Feb 2017 16:21:03 +0100
Subject: [PATCH 105/328] Fix: remove conflict leftover

---
 qcodes/instrument_drivers/rohde_schwarz/SMR40.py | 7 -------
 1 file changed, 7 deletions(-)

diff --git a/qcodes/instrument_drivers/rohde_schwarz/SMR40.py b/qcodes/instrument_drivers/rohde_schwarz/SMR40.py
index d3cc0df98c51..3b31aa89e34d 100644
--- a/qcodes/instrument_drivers/rohde_schwarz/SMR40.py
+++ b/qcodes/instrument_drivers/rohde_schwarz/SMR40.py
@@ -13,22 +13,15 @@
 
 class RohdeSchwarz_SMR40(VisaInstrument):
     """This is the qcodes driver for the Rohde & Schwarz SMR40 signal generator
-<<<<<<< HEAD
-    Status: beta-version. TODO:
-=======
     Status: beta-version.
 
     .. todo::
->>>>>>> master
 
         - Add all parameters that are in the manual
         - Add test suite
         - See if there can be a common driver for RS mw sources from which
           different models inherit
-<<<<<<< HEAD
-=======
 
->>>>>>> master
     This driver does not contain all commands available for the SMR40 but
     only the ones most commonly used.
 

From 91c1f4397756df679e49607218bde7e7c0af4d4e Mon Sep 17 00:00:00 2001
From: Jens Hedegaard Nielsen 
Date: Mon, 13 Feb 2017 17:52:11 +0100
Subject: [PATCH 106/328] Fix: sync AWG example notebook with master

---
 ...odes example with Tektronix AWG5014C.ipynb | 1071 ++---------------
 1 file changed, 126 insertions(+), 945 deletions(-)

diff --git a/docs/examples/Qcodes example with Tektronix AWG5014C.ipynb b/docs/examples/Qcodes example with Tektronix AWG5014C.ipynb
index fbd09310add5..0328d7ade7d5 100644
--- a/docs/examples/Qcodes example with Tektronix AWG5014C.ipynb	
+++ b/docs/examples/Qcodes example with Tektronix AWG5014C.ipynb	
@@ -1,8 +1,30 @@
 {
  "cells": [
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "# QCoDeS Example with Tektronix AWG5014\n",
+    "\n",
+    "## Table of contents\n",
+    "\n",
+    "* [Imports and initialisation](#init)\n",
+    "* [Sending waveforms](#sending)\n",
+    "  * [Via an .awg-file](#viaawg)\n",
+    "  * [Sending directly to list](#tolist)\n",
+    "* [Running in \"lazy\" mode](#lazy)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Imports and initialisation "
+   ]
+  },
   {
    "cell_type": "code",
-   "execution_count": 2,
+   "execution_count": null,
    "metadata": {
     "collapsed": false
    },
@@ -18,80 +40,26 @@
     "import matplotlib.pyplot as plt\n",
     "\n",
     "logger = logging.getLogger()\n",
-    "logger.setLevel(logging.INFO)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 4,
-   "metadata": {
-    "collapsed": true
-   },
-   "outputs": [],
-   "source": [
-    "import numpy as np\n"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 8,
-   "metadata": {
-    "collapsed": false
-   },
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "6.0"
-      ]
-     },
-     "execution_count": 8,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "np.ceil(26/5)"
+    "logger.setLevel(logging.WARNING)"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 3,
+   "execution_count": null,
    "metadata": {
     "collapsed": false
    },
-   "outputs": [
-    {
-     "ename": "VisaIOError",
-     "evalue": "VI_ERROR_RSRC_NFOUND (-1073807343): Insufficient location information or the requested device or resource is not present in the system.",
-     "output_type": "error",
-     "traceback": [
-      "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
-      "\u001b[0;31mVisaIOError\u001b[0m                               Traceback (most recent call last)",
-      "\u001b[0;32m\u001b[0m in \u001b[0;36m\u001b[0;34m()\u001b[0m\n\u001b[1;32m      2\u001b[0m \u001b[1;32mfrom\u001b[0m \u001b[0mqcodes\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0minstrument_drivers\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mtektronix\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mAWGFileParser\u001b[0m \u001b[1;32mimport\u001b[0m \u001b[0mparse_awg_file\u001b[0m  \u001b[1;31m# <--- A helper function\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m      3\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m----> 4\u001b[0;31m \u001b[0mawg1\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mawg\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mTektronix_AWG5014\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;34m'AWG1'\u001b[0m\u001b[1;33m,\u001b[0m \u001b[1;34m'TCPIP0::192.168.137.182::inst0::INSTR'\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mtimeout\u001b[0m\u001b[1;33m=\u001b[0m\u001b[1;36m40\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m",
-      "\u001b[0;32ma:\\qcodes\\qcodes\\instrument\\metaclass.py\u001b[0m in \u001b[0;36m__call__\u001b[0;34m(cls, server_name, *args, **kwargs)\u001b[0m\n\u001b[1;32m     32\u001b[0m         \"\"\"\n\u001b[1;32m     33\u001b[0m         \u001b[1;32mif\u001b[0m \u001b[0mserver_name\u001b[0m \u001b[1;32mis\u001b[0m \u001b[1;32mNone\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m---> 34\u001b[0;31m             \u001b[0minstrument\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0msuper\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0m__call__\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;33m*\u001b[0m\u001b[0margs\u001b[0m\u001b[1;33m,\u001b[0m \u001b[1;33m**\u001b[0m\u001b[0mkwargs\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m     35\u001b[0m         \u001b[1;32melse\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m     36\u001b[0m             warnings.warn('Multiprocessing is in beta, use at own risk',\n",
-      "\u001b[0;32ma:\\qcodes\\qcodes\\instrument_drivers\\tektronix\\AWG5014.py\u001b[0m in \u001b[0;36m__init__\u001b[0;34m(self, name, address, timeout, **kwargs)\u001b[0m\n\u001b[1;32m    137\u001b[0m             \u001b[1;32mNone\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m    138\u001b[0m         \"\"\"\n\u001b[0;32m--> 139\u001b[0;31m         \u001b[0msuper\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0m__init__\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mname\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0maddress\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mtimeout\u001b[0m\u001b[1;33m=\u001b[0m\u001b[0mtimeout\u001b[0m\u001b[1;33m,\u001b[0m \u001b[1;33m**\u001b[0m\u001b[0mkwargs\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    140\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m    141\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n",
-      "\u001b[0;32ma:\\qcodes\\qcodes\\instrument\\visa.py\u001b[0m in \u001b[0;36m__init__\u001b[0;34m(self, name, address, timeout, terminator, **kwargs)\u001b[0m\n\u001b[1;32m     54\u001b[0m                                                vals.Enum(None)))\n\u001b[1;32m     55\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m---> 56\u001b[0;31m         \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mset_address\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0maddress\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m     57\u001b[0m         \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mset_terminator\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mterminator\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m     58\u001b[0m         \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mtimeout\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mset\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mtimeout\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
-      "\u001b[0;32ma:\\qcodes\\qcodes\\instrument\\visa.py\u001b[0m in \u001b[0;36mset_address\u001b[0;34m(self, address)\u001b[0m\n\u001b[1;32m    101\u001b[0m             \u001b[0mresource_manager\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mvisa\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mResourceManager\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m    102\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m--> 103\u001b[0;31m         \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mvisa_handle\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mresource_manager\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mopen_resource\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0maddress\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    104\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m    105\u001b[0m         \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mvisa_handle\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mclear\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
-      "\u001b[0;32mc:\\users\\triton2acq\\anaconda3\\envs\\qcodes-master\\lib\\site-packages\\pyvisa\\highlevel.py\u001b[0m in \u001b[0;36mopen_resource\u001b[0;34m(self, resource_name, access_mode, open_timeout, resource_pyclass, **kwargs)\u001b[0m\n\u001b[1;32m   1642\u001b[0m                 \u001b[1;32mraise\u001b[0m \u001b[0mValueError\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;34m'%r is not a valid attribute for type %s'\u001b[0m \u001b[1;33m%\u001b[0m \u001b[1;33m(\u001b[0m\u001b[0mkey\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mres\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0m__class__\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0m__name__\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m   1643\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m-> 1644\u001b[0;31m         \u001b[0mres\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mopen\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0maccess_mode\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mopen_timeout\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m   1645\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m   1646\u001b[0m         \u001b[1;32mfor\u001b[0m \u001b[0mkey\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mvalue\u001b[0m \u001b[1;32min\u001b[0m \u001b[0mkwargs\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mitems\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
-      "\u001b[0;32mc:\\users\\triton2acq\\anaconda3\\envs\\qcodes-master\\lib\\site-packages\\pyvisa\\resources\\resource.py\u001b[0m in \u001b[0;36mopen\u001b[0;34m(self, access_mode, open_timeout)\u001b[0m\n\u001b[1;32m    201\u001b[0m         \u001b[0mlogger\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mdebug\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;34m'%s - opening ...'\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0m_resource_name\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mextra\u001b[0m\u001b[1;33m=\u001b[0m\u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0m_logging_extra\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m    202\u001b[0m         \u001b[1;32mwith\u001b[0m \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0m_resource_manager\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mignore_warning\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mconstants\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mVI_SUCCESS_DEV_NPRESENT\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m--> 203\u001b[0;31m             \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0msession\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mstatus\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0m_resource_manager\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mopen_bare_resource\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0m_resource_name\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0maccess_mode\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mopen_timeout\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    204\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m    205\u001b[0m             \u001b[1;32mif\u001b[0m \u001b[0mstatus\u001b[0m \u001b[1;33m==\u001b[0m \u001b[0mconstants\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mVI_SUCCESS_DEV_NPRESENT\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
-      "\u001b[0;32mc:\\users\\triton2acq\\anaconda3\\envs\\qcodes-master\\lib\\site-packages\\pyvisa\\highlevel.py\u001b[0m in \u001b[0;36mopen_bare_resource\u001b[0;34m(self, resource_name, access_mode, open_timeout)\u001b[0m\n\u001b[1;32m   1599\u001b[0m         \u001b[1;33m:\u001b[0m\u001b[1;32mreturn\u001b[0m\u001b[1;33m:\u001b[0m \u001b[0mUnique\u001b[0m \u001b[0mlogical\u001b[0m \u001b[0midentifier\u001b[0m \u001b[0mreference\u001b[0m \u001b[0mto\u001b[0m \u001b[0ma\u001b[0m \u001b[0msession\u001b[0m\u001b[1;33m.\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m   1600\u001b[0m         \"\"\"\n\u001b[0;32m-> 1601\u001b[0;31m         \u001b[1;32mreturn\u001b[0m \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mvisalib\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mopen\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0msession\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mresource_name\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0maccess_mode\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mopen_timeout\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m   1602\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m   1603\u001b[0m     def open_resource(self, resource_name,\n",
-      "\u001b[0;32mc:\\users\\triton2acq\\anaconda3\\envs\\qcodes-master\\lib\\site-packages\\pyvisa\\ctwrapper\\functions.py\u001b[0m in \u001b[0;36mopen\u001b[0;34m(library, session, resource_name, access_mode, open_timeout)\u001b[0m\n\u001b[1;32m   1209\u001b[0m     \u001b[1;31m# [ViSession, ViRsrc, ViAccessMode, ViUInt32, ViPSession]\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m   1210\u001b[0m     \u001b[1;31m# ViRsrc converts from (str, unicode, bytes) to bytes\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m-> 1211\u001b[0;31m     \u001b[0mret\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mlibrary\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mviOpen\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0msession\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mresource_name\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0maccess_mode\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mopen_timeout\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mbyref\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mout_session\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m   1212\u001b[0m     \u001b[1;32mreturn\u001b[0m \u001b[0mout_session\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mvalue\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mret\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m   1213\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n",
-      "\u001b[0;32mc:\\users\\triton2acq\\anaconda3\\envs\\qcodes-master\\lib\\site-packages\\pyvisa\\ctwrapper\\highlevel.py\u001b[0m in \u001b[0;36m_return_handler\u001b[0;34m(self, ret_value, func, arguments)\u001b[0m\n\u001b[1;32m    186\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m    187\u001b[0m         \u001b[1;32mif\u001b[0m \u001b[0mret_value\u001b[0m \u001b[1;33m<\u001b[0m \u001b[1;36m0\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m--> 188\u001b[0;31m             \u001b[1;32mraise\u001b[0m \u001b[0merrors\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mVisaIOError\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mret_value\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    189\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m    190\u001b[0m         \u001b[1;32mif\u001b[0m \u001b[0mret_value\u001b[0m \u001b[1;32min\u001b[0m \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0missue_warning_on\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
-      "\u001b[0;31mVisaIOError\u001b[0m: VI_ERROR_RSRC_NFOUND (-1073807343): Insufficient location information or the requested device or resource is not present in the system."
-     ]
-    }
-   ],
+   "outputs": [],
    "source": [
     "import qcodes.instrument_drivers.tektronix.AWG5014 as awg  # <--- The instrument driver\n",
     "from qcodes.instrument_drivers.tektronix.AWGFileParser import parse_awg_file  # <--- A helper function\n",
     "\n",
-    "awg1 = awg.Tektronix_AWG5014('AWG1', 'TCPIP0::192.168.137.182::inst0::INSTR', timeout=40)"
+    "awg1 = awg.Tektronix_AWG5014('AWG1', 'TCPIP0::172.20.3.57::inst0::INSTR', timeout=40)"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 4,
+   "execution_count": null,
    "metadata": {
     "collapsed": false
    },
@@ -112,22 +80,11 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 5,
+   "execution_count": null,
    "metadata": {
     "collapsed": false
    },
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "0\n",
-      "\n",
-      "0.0\n",
-      "0.1\n"
-     ]
-    }
-   ],
+   "outputs": [],
    "source": [
     "print(awg1.ch3_state.get())\n",
     "print(awg1.ch2_offset.get())\n",
@@ -144,92 +101,11 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 6,
+   "execution_count": null,
    "metadata": {
     "collapsed": false
    },
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "DC_output :  DC Output (ON/OFF)\n",
-      "IDN :  IDN\n",
-      "ch1_DC_out :  DC output level channel 1\n",
-      "ch1_add_input :  Add input channel {}\n",
-      "ch1_amp :  Amplitude channel 1 (Vpp)\n",
-      "ch1_direct_output :  Direct output channel 1\n",
-      "ch1_filter :  Low pass filter channel 1\n",
-      "ch1_m1_del :  Channel 1 Marker 1 delay (ns)\n",
-      "ch1_m1_high :  Channel 1 Marker 1 high level (V)\n",
-      "ch1_m1_low :  Channel 1 Marker 1 low level (V)\n",
-      "ch1_m2_del :  Channel 1 Marker 2 delay (ns)\n",
-      "ch1_m2_high :  Channel 1 Marker 2 high level (V)\n",
-      "ch1_m2_low :  Channel 1 Marker 2 low level (V)\n",
-      "ch1_offset :  Offset channel 1 (V)\n",
-      "ch1_state :  Status channel 1\n",
-      "ch1_waveform :  Waveform channel 1\n",
-      "ch2_DC_out :  DC output level channel 2\n",
-      "ch2_add_input :  Add input channel {}\n",
-      "ch2_amp :  Amplitude channel 2 (Vpp)\n",
-      "ch2_direct_output :  Direct output channel 2\n",
-      "ch2_filter :  Low pass filter channel 2\n",
-      "ch2_m1_del :  Channel 2 Marker 1 delay (ns)\n",
-      "ch2_m1_high :  Channel 2 Marker 1 high level (V)\n",
-      "ch2_m1_low :  Channel 2 Marker 1 low level (V)\n",
-      "ch2_m2_del :  Channel 2 Marker 2 delay (ns)\n",
-      "ch2_m2_high :  Channel 2 Marker 2 high level (V)\n",
-      "ch2_m2_low :  Channel 2 Marker 2 low level (V)\n",
-      "ch2_offset :  Offset channel 2 (V)\n",
-      "ch2_state :  Status channel 2\n",
-      "ch2_waveform :  Waveform channel 2\n",
-      "ch3_DC_out :  DC output level channel 3\n",
-      "ch3_add_input :  Add input channel {}\n",
-      "ch3_amp :  Amplitude channel 3 (Vpp)\n",
-      "ch3_direct_output :  Direct output channel 3\n",
-      "ch3_filter :  Low pass filter channel 3\n",
-      "ch3_m1_del :  Channel 3 Marker 1 delay (ns)\n",
-      "ch3_m1_high :  Channel 3 Marker 1 high level (V)\n",
-      "ch3_m1_low :  Channel 3 Marker 1 low level (V)\n",
-      "ch3_m2_del :  Channel 3 Marker 2 delay (ns)\n",
-      "ch3_m2_high :  Channel 3 Marker 2 high level (V)\n",
-      "ch3_m2_low :  Channel 3 Marker 2 low level (V)\n",
-      "ch3_offset :  Offset channel 3 (V)\n",
-      "ch3_state :  Status channel 3\n",
-      "ch3_waveform :  Waveform channel 3\n",
-      "ch4_DC_out :  DC output level channel 4\n",
-      "ch4_add_input :  Add input channel {}\n",
-      "ch4_amp :  Amplitude channel 4 (Vpp)\n",
-      "ch4_direct_output :  Direct output channel 4\n",
-      "ch4_filter :  Low pass filter channel 4\n",
-      "ch4_m1_del :  Channel 4 Marker 1 delay (ns)\n",
-      "ch4_m1_high :  Channel 4 Marker 1 high level (V)\n",
-      "ch4_m1_low :  Channel 4 Marker 1 low level (V)\n",
-      "ch4_m2_del :  Channel 4 Marker 2 delay (ns)\n",
-      "ch4_m2_high :  Channel 4 Marker 2 high level (V)\n",
-      "ch4_m2_low :  Channel 4 Marker 2 low level (V)\n",
-      "ch4_offset :  Offset channel 4 (V)\n",
-      "ch4_state :  Status channel 4\n",
-      "ch4_waveform :  Waveform channel 4\n",
-      "clock_freq :  Clock frequency (Hz)\n",
-      "event_impedance :  Event impedance (Ohm)\n",
-      "event_jump_timing :  event_jump_timing\n",
-      "event_level :  Event level (V)\n",
-      "event_polarity :  event_polarity\n",
-      "ref_clock_source :  Reference clock source\n",
-      "run_mode :  run_mode\n",
-      "sequence_length :  Sequence length\n",
-      "setup_filename :  setup_filename\n",
-      "state :  state\n",
-      "timeout :  timeout\n",
-      "trigger_impedance :  Trigger impedance (Ohm)\n",
-      "trigger_level :  Trigger level (V)\n",
-      "trigger_mode :  trigger_mode\n",
-      "trigger_slope :  trigger_slope\n",
-      "trigger_source :  trigger_source\n"
-     ]
-    }
-   ],
+   "outputs": [],
    "source": [
     "pars = np.sort(list(awg1.parameters.keys()))\n",
     "for param in pars:\n",
@@ -242,7 +118,7 @@
     "collapsed": false
    },
    "source": [
-    "## SENDING WAVEFORMS\n",
+    "## SENDING WAVEFORMS \n",
     "\n",
     "There are two supported ways of sending waveforms to the AWG; by making an .awg file, sending and loading that, or by sending waveforms to the User Defined list one by one and putting them in the sequencer. The first method is generally faster."
    ]
@@ -264,7 +140,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 7,
+   "execution_count": null,
    "metadata": {
     "collapsed": false,
     "scrolled": true
@@ -298,786 +174,12 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 8,
+   "execution_count": null,
    "metadata": {
     "collapsed": false,
     "scrolled": true
    },
-   "outputs": [
-    {
-     "data": {
-      "application/javascript": [
-       "/* Put everything inside the global mpl namespace */\n",
-       "window.mpl = {};\n",
-       "\n",
-       "mpl.get_websocket_type = function() {\n",
-       "    if (typeof(WebSocket) !== 'undefined') {\n",
-       "        return WebSocket;\n",
-       "    } else if (typeof(MozWebSocket) !== 'undefined') {\n",
-       "        return MozWebSocket;\n",
-       "    } else {\n",
-       "        alert('Your browser does not have WebSocket support.' +\n",
-       "              'Please try Chrome, Safari or Firefox ≥ 6. ' +\n",
-       "              'Firefox 4 and 5 are also supported but you ' +\n",
-       "              'have to enable WebSockets in about:config.');\n",
-       "    };\n",
-       "}\n",
-       "\n",
-       "mpl.figure = function(figure_id, websocket, ondownload, parent_element) {\n",
-       "    this.id = figure_id;\n",
-       "\n",
-       "    this.ws = websocket;\n",
-       "\n",
-       "    this.supports_binary = (this.ws.binaryType != undefined);\n",
-       "\n",
-       "    if (!this.supports_binary) {\n",
-       "        var warnings = document.getElementById(\"mpl-warnings\");\n",
-       "        if (warnings) {\n",
-       "            warnings.style.display = 'block';\n",
-       "            warnings.textContent = (\n",
-       "                \"This browser does not support binary websocket messages. \" +\n",
-       "                    \"Performance may be slow.\");\n",
-       "        }\n",
-       "    }\n",
-       "\n",
-       "    this.imageObj = new Image();\n",
-       "\n",
-       "    this.context = undefined;\n",
-       "    this.message = undefined;\n",
-       "    this.canvas = undefined;\n",
-       "    this.rubberband_canvas = undefined;\n",
-       "    this.rubberband_context = undefined;\n",
-       "    this.format_dropdown = undefined;\n",
-       "\n",
-       "    this.image_mode = 'full';\n",
-       "\n",
-       "    this.root = $('
');\n",
-       "    this._root_extra_style(this.root)\n",
-       "    this.root.attr('style', 'display: inline-block');\n",
-       "\n",
-       "    $(parent_element).append(this.root);\n",
-       "\n",
-       "    this._init_header(this);\n",
-       "    this._init_canvas(this);\n",
-       "    this._init_toolbar(this);\n",
-       "\n",
-       "    var fig = this;\n",
-       "\n",
-       "    this.waiting = false;\n",
-       "\n",
-       "    this.ws.onopen =  function () {\n",
-       "            fig.send_message(\"supports_binary\", {value: fig.supports_binary});\n",
-       "            fig.send_message(\"send_image_mode\", {});\n",
-       "            fig.send_message(\"refresh\", {});\n",
-       "        }\n",
-       "\n",
-       "    this.imageObj.onload = function() {\n",
-       "            if (fig.image_mode == 'full') {\n",
-       "                // Full images could contain transparency (where diff images\n",
-       "                // almost always do), so we need to clear the canvas so that\n",
-       "                // there is no ghosting.\n",
-       "                fig.context.clearRect(0, 0, fig.canvas.width, fig.canvas.height);\n",
-       "            }\n",
-       "            fig.context.drawImage(fig.imageObj, 0, 0);\n",
-       "        };\n",
-       "\n",
-       "    this.imageObj.onunload = function() {\n",
-       "        this.ws.close();\n",
-       "    }\n",
-       "\n",
-       "    this.ws.onmessage = this._make_on_message_function(this);\n",
-       "\n",
-       "    this.ondownload = ondownload;\n",
-       "}\n",
-       "\n",
-       "mpl.figure.prototype._init_header = function() {\n",
-       "    var titlebar = $(\n",
-       "        '
');\n",
-       "    var titletext = $(\n",
-       "        '
');\n",
-       "    titlebar.append(titletext)\n",
-       "    this.root.append(titlebar);\n",
-       "    this.header = titletext[0];\n",
-       "}\n",
-       "\n",
-       "\n",
-       "\n",
-       "mpl.figure.prototype._canvas_extra_style = function(canvas_div) {\n",
-       "\n",
-       "}\n",
-       "\n",
-       "\n",
-       "mpl.figure.prototype._root_extra_style = function(canvas_div) {\n",
-       "\n",
-       "}\n",
-       "\n",
-       "mpl.figure.prototype._init_canvas = function() {\n",
-       "    var fig = this;\n",
-       "\n",
-       "    var canvas_div = $('
');\n",
-       "\n",
-       "    canvas_div.attr('style', 'position: relative; clear: both; outline: 0');\n",
-       "\n",
-       "    function canvas_keyboard_event(event) {\n",
-       "        return fig.key_event(event, event['data']);\n",
-       "    }\n",
-       "\n",
-       "    canvas_div.keydown('key_press', canvas_keyboard_event);\n",
-       "    canvas_div.keyup('key_release', canvas_keyboard_event);\n",
-       "    this.canvas_div = canvas_div\n",
-       "    this._canvas_extra_style(canvas_div)\n",
-       "    this.root.append(canvas_div);\n",
-       "\n",
-       "    var canvas = $('');\n",
-       "    canvas.addClass('mpl-canvas');\n",
-       "    canvas.attr('style', \"left: 0; top: 0; z-index: 0; outline: 0\")\n",
-       "\n",
-       "    this.canvas = canvas[0];\n",
-       "    this.context = canvas[0].getContext(\"2d\");\n",
-       "\n",
-       "    var rubberband = $('');\n",
-       "    rubberband.attr('style', \"position: absolute; left: 0; top: 0; z-index: 1;\")\n",
-       "\n",
-       "    var pass_mouse_events = true;\n",
-       "\n",
-       "    canvas_div.resizable({\n",
-       "        start: function(event, ui) {\n",
-       "            pass_mouse_events = false;\n",
-       "        },\n",
-       "        resize: function(event, ui) {\n",
-       "            fig.request_resize(ui.size.width, ui.size.height);\n",
-       "        },\n",
-       "        stop: function(event, ui) {\n",
-       "            pass_mouse_events = true;\n",
-       "            fig.request_resize(ui.size.width, ui.size.height);\n",
-       "        },\n",
-       "    });\n",
-       "\n",
-       "    function mouse_event_fn(event) {\n",
-       "        if (pass_mouse_events)\n",
-       "            return fig.mouse_event(event, event['data']);\n",
-       "    }\n",
-       "\n",
-       "    rubberband.mousedown('button_press', mouse_event_fn);\n",
-       "    rubberband.mouseup('button_release', mouse_event_fn);\n",
-       "    // Throttle sequential mouse events to 1 every 20ms.\n",
-       "    rubberband.mousemove('motion_notify', mouse_event_fn);\n",
-       "\n",
-       "    rubberband.mouseenter('figure_enter', mouse_event_fn);\n",
-       "    rubberband.mouseleave('figure_leave', mouse_event_fn);\n",
-       "\n",
-       "    canvas_div.on(\"wheel\", function (event) {\n",
-       "        event = event.originalEvent;\n",
-       "        event['data'] = 'scroll'\n",
-       "        if (event.deltaY < 0) {\n",
-       "            event.step = 1;\n",
-       "        } else {\n",
-       "            event.step = -1;\n",
-       "        }\n",
-       "        mouse_event_fn(event);\n",
-       "    });\n",
-       "\n",
-       "    canvas_div.append(canvas);\n",
-       "    canvas_div.append(rubberband);\n",
-       "\n",
-       "    this.rubberband = rubberband;\n",
-       "    this.rubberband_canvas = rubberband[0];\n",
-       "    this.rubberband_context = rubberband[0].getContext(\"2d\");\n",
-       "    this.rubberband_context.strokeStyle = \"#000000\";\n",
-       "\n",
-       "    this._resize_canvas = function(width, height) {\n",
-       "        // Keep the size of the canvas, canvas container, and rubber band\n",
-       "        // canvas in synch.\n",
-       "        canvas_div.css('width', width)\n",
-       "        canvas_div.css('height', height)\n",
-       "\n",
-       "        canvas.attr('width', width);\n",
-       "        canvas.attr('height', height);\n",
-       "\n",
-       "        rubberband.attr('width', width);\n",
-       "        rubberband.attr('height', height);\n",
-       "    }\n",
-       "\n",
-       "    // Set the figure to an initial 600x600px, this will subsequently be updated\n",
-       "    // upon first draw.\n",
-       "    this._resize_canvas(600, 600);\n",
-       "\n",
-       "    // Disable right mouse context menu.\n",
-       "    $(this.rubberband_canvas).bind(\"contextmenu\",function(e){\n",
-       "        return false;\n",
-       "    });\n",
-       "\n",
-       "    function set_focus () {\n",
-       "        canvas.focus();\n",
-       "        canvas_div.focus();\n",
-       "    }\n",
-       "\n",
-       "    window.setTimeout(set_focus, 100);\n",
-       "}\n",
-       "\n",
-       "mpl.figure.prototype._init_toolbar = function() {\n",
-       "    var fig = this;\n",
-       "\n",
-       "    var nav_element = $('
')\n",
-       "    nav_element.attr('style', 'width: 100%');\n",
-       "    this.root.append(nav_element);\n",
-       "\n",
-       "    // Define a callback function for later on.\n",
-       "    function toolbar_event(event) {\n",
-       "        return fig.toolbar_button_onclick(event['data']);\n",
-       "    }\n",
-       "    function toolbar_mouse_event(event) {\n",
-       "        return fig.toolbar_button_onmouseover(event['data']);\n",
-       "    }\n",
-       "\n",
-       "    for(var toolbar_ind in mpl.toolbar_items) {\n",
-       "        var name = mpl.toolbar_items[toolbar_ind][0];\n",
-       "        var tooltip = mpl.toolbar_items[toolbar_ind][1];\n",
-       "        var image = mpl.toolbar_items[toolbar_ind][2];\n",
-       "        var method_name = mpl.toolbar_items[toolbar_ind][3];\n",
-       "\n",
-       "        if (!name) {\n",
-       "            // put a spacer in here.\n",
-       "            continue;\n",
-       "        }\n",
-       "        var button = $('');\n",
+       "        button.click(method_name, toolbar_event);\n",
+       "        button.mouseover(tooltip, toolbar_mouse_event);\n",
+       "        nav_element.append(button);\n",
+       "    }\n",
+       "\n",
+       "    // Add the status bar.\n",
+       "    var status_bar = $('');\n",
+       "    nav_element.append(status_bar);\n",
+       "    this.message = status_bar[0];\n",
+       "\n",
+       "    // Add the close button to the window.\n",
+       "    var buttongrp = $('
');\n",
+       "    var button = $('');\n",
+       "    button.click(function (evt) { fig.handle_close(fig, {}); } );\n",
+       "    button.mouseover('Stop Interaction', toolbar_mouse_event);\n",
+       "    buttongrp.append(button);\n",
+       "    var titlebar = this.root.find($('.ui-dialog-titlebar'));\n",
+       "    titlebar.prepend(buttongrp);\n",
+       "}\n",
+       "\n",
+       "mpl.figure.prototype._root_extra_style = function(el){\n",
+       "    var fig = this\n",
+       "    el.on(\"remove\", function(){\n",
+       "\tfig.close_ws(fig, {});\n",
+       "    });\n",
+       "}\n",
+       "\n",
+       "mpl.figure.prototype._canvas_extra_style = function(el){\n",
+       "    // this is important to make the div 'focusable\n",
+       "    el.attr('tabindex', 0)\n",
+       "    // reach out to IPython and tell the keyboard manager to turn it's self\n",
+       "    // off when our div gets focus\n",
+       "\n",
+       "    // location in version 3\n",
+       "    if (IPython.notebook.keyboard_manager) {\n",
+       "        IPython.notebook.keyboard_manager.register_events(el);\n",
+       "    }\n",
+       "    else {\n",
+       "        // location in version 2\n",
+       "        IPython.keyboard_manager.register_events(el);\n",
+       "    }\n",
+       "\n",
+       "}\n",
+       "\n",
+       "mpl.figure.prototype._key_event_extra = function(event, name) {\n",
+       "    var manager = IPython.notebook.keyboard_manager;\n",
+       "    if (!manager)\n",
+       "        manager = IPython.keyboard_manager;\n",
+       "\n",
+       "    // Check for shift+enter\n",
+       "    if (event.shiftKey && event.which == 13) {\n",
+       "        this.canvas_div.blur();\n",
+       "        // select the cell after this one\n",
+       "        var index = IPython.notebook.find_cell_index(this.cell_info[0]);\n",
+       "        IPython.notebook.select(index + 1);\n",
+       "    }\n",
+       "}\n",
+       "\n",
+       "mpl.figure.prototype.handle_save = function(fig, msg) {\n",
+       "    fig.ondownload(fig, null);\n",
+       "}\n",
+       "\n",
+       "\n",
+       "mpl.find_output_cell = function(html_output) {\n",
+       "    // Return the cell and output element which can be found *uniquely* in the notebook.\n",
+       "    // Note - this is a bit hacky, but it is done because the \"notebook_saving.Notebook\"\n",
+       "    // IPython event is triggered only after the cells have been serialised, which for\n",
+       "    // our purposes (turning an active figure into a static one), is too late.\n",
+       "    var cells = IPython.notebook.get_cells();\n",
+       "    var ncells = cells.length;\n",
+       "    for (var i=0; i= 3 moved mimebundle to data attribute of output\n",
+       "                    data = data.data;\n",
+       "                }\n",
+       "                if (data['text/html'] == html_output) {\n",
+       "                    return [cell, data, j];\n",
+       "                }\n",
+       "            }\n",
+       "        }\n",
+       "    }\n",
+       "}\n",
+       "\n",
+       "// Register the function which deals with the matplotlib target/channel.\n",
+       "// The kernel may be null if the page has been refreshed.\n",
+       "if (IPython.notebook.kernel != null) {\n",
+       "    IPython.notebook.kernel.comm_manager.register_target('matplotlib', mpl.mpl_figure_comm);\n",
+       "}\n"
+      ],
+      "text/plain": [
+       ""
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "text/html": [
+       ""
+      ],
+      "text/plain": [
+       ""
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "text/plain": [
+       ""
+      ]
+     },
+     "execution_count": 27,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "plot = qc.MatPlot()\n",
+    "plot.add(data2.MyMockAlazar_rawname)\n",
+    "plot.add(data2.MyMockAlazar_demod_freq_0_name)\n",
+    "plot.add(data2.MyMockAlazar_demod_freq_1_name)\n",
+    "#plot.add(data.MyMockAlazar_demod_freq_2_name)\n",
+    "plot.fig.axes[0].legend()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 17,
+   "metadata": {
+    "collapsed": false,
+    "deletable": true,
+    "editable": true
+   },
+   "outputs": [],
+   "source": [
+    "from toymodel import AModel, MockGates, MockSource, MockMeter, AverageGetter, AverageAndRaw\n",
+    "\n",
+    "# now create this \"experiment\", note that all these are instruments \n",
+    "model = AModel()\n",
+    "gates = MockGates('gates', model=model)\n",
+    "source = MockSource('source', model=model)\n",
+    "meter = MockMeter('meter', model=model)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 18,
+   "metadata": {
+    "collapsed": false,
+    "deletable": true,
+    "editable": true
+   },
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "DataSet:\n",
+      "   mode     = DataMode.LOCAL\n",
+      "   location = 'data/2017-02-21/#028_{name}_14-12-35'\n",
+      "      |                      |       | \n",
+      "   Setpoint | gates_chan0_set                | chan0             | (2,)\n",
+      "   Setpoint | timename_set                   | timename          | (2, 629)\n",
+      "   Measured | MyMockAlazar_rawname           | rawname           | (2, 629)\n",
+      "   Measured | MyMockAlazar_demod_freq_0_name | demod_freq_0_name | (2, 629)\n",
+      "   Measured | MyMockAlazar_demod_freq_1_name | demod_freq_1_name | (2, 629)\n",
+      "   Measured | MyMockAlazar_demod_freq_2_name | demod_freq_2_name | (2, 629)\n",
+      "started at 2017-02-21 14:12:36\n"
+     ]
+    }
+   ],
+   "source": [
+    "data3 = qc.Loop(gates.chan0.sweep(0,1,num=2)).each(a.acquisition).run()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 19,
+   "metadata": {
+    "collapsed": false,
+    "deletable": true,
+    "editable": true
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "{'__class__': 'qcodes.data.data_array.DataArray',\n",
+       " 'action_indices': (0, 0),\n",
+       " 'array_id': 'MyMockAlazar_rawname',\n",
+       " 'instrument': '__main__.MockAlazar',\n",
+       " 'instrument_name': 'MyMockAlazar',\n",
+       " 'is_setpoint': False,\n",
+       " 'label': 'rawlabel',\n",
+       " 'labels': ('rawlabel',\n",
+       "  'demod_freq_0_label',\n",
+       "  'demod_freq_1_label',\n",
+       "  'demod_freq_2_label'),\n",
+       " 'name': 'rawname',\n",
+       " 'names': ('rawname',\n",
+       "  'demod_freq_0_name',\n",
+       "  'demod_freq_1_name',\n",
+       "  'demod_freq_2_name'),\n",
+       " 'setpoint_labels': (('timelabel',),\n",
+       "  ('timelabel',),\n",
+       "  ('timelabel',),\n",
+       "  ('timelabel',)),\n",
+       " 'setpoint_names': (('timename',),\n",
+       "  ('timename',),\n",
+       "  ('timename',),\n",
+       "  ('timename',)),\n",
+       " 'shape': (2, 629),\n",
+       " 'unit': 'v',\n",
+       " 'units': ('v', 'v', 'v', 'v')}"
+      ]
+     },
+     "execution_count": 19,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "data3.metadata['arrays']['MyMockAlazar_rawname']"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 20,
+   "metadata": {
+    "collapsed": false,
+    "deletable": true,
+    "editable": true
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "{'__class__': 'qcodes.data.data_array.DataArray',\n",
+       " 'action_indices': (0,),\n",
+       " 'array_id': 'timename_set',\n",
+       " 'is_setpoint': True,\n",
+       " 'label': 'timelabel',\n",
+       " 'name': 'timename',\n",
+       " 'shape': (2, 629),\n",
+       " 'unit': 's'}"
+      ]
+     },
+     "execution_count": 20,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "data3.metadata['arrays']['timename_set']"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 21,
+   "metadata": {
+    "collapsed": false,
+    "deletable": true,
+    "editable": true
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "{'__class__': 'qcodes.data.data_array.DataArray',\n",
+       " 'action_indices': (),\n",
+       " 'array_id': 'gates_chan0_set',\n",
+       " 'instrument': 'toymodel.MockGates',\n",
+       " 'instrument_name': 'gates',\n",
+       " 'is_setpoint': True,\n",
+       " 'label': 'Gate Channel 0 (mV)',\n",
+       " 'name': 'chan0',\n",
+       " 'shape': (2,),\n",
+       " 'unit': '',\n",
+       " 'vals': ''}"
+      ]
+     },
+     "execution_count": 21,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "data3.metadata['arrays']['gates_chan0_set']"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 22,
+   "metadata": {
+    "collapsed": false,
+    "deletable": true,
+    "editable": true
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "{'__class__': 'qcodes.data.data_array.DataArray',\n",
+       " 'action_indices': (0,),\n",
+       " 'array_id': 'timename_set',\n",
+       " 'is_setpoint': True,\n",
+       " 'label': 'timelabel',\n",
+       " 'name': 'timename',\n",
+       " 'shape': (2, 629),\n",
+       " 'unit': 's'}"
+      ]
+     },
+     "execution_count": 22,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "data3.metadata['arrays']['timename_set']"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {
+    "collapsed": true
+   },
+   "outputs": [],
+   "source": []
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {
+    "collapsed": true
+   },
+   "outputs": [],
+   "source": []
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {
+    "collapsed": true
+   },
+   "outputs": [],
+   "source": []
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "Python 3",
+   "language": "python",
+   "name": "python3"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython3",
+   "version": "3.5.3"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 2
+}

From d5bac792b85045116b142dc460fecf076e2a7430 Mon Sep 17 00:00:00 2001
From: Jens Hedegaard Nielsen 
Date: Thu, 23 Feb 2017 10:49:16 +0100
Subject: [PATCH 139/328] Fix: Alazar add new controller that aims to be more
 general

Also Handles multiple demod freq as a multiparameter that can be dynamically expanded
---
 .../acq_controllers/ATS9360Controller.py      | 450 ++++++++++++++++++
 .../AlazarTech/acqusition_parameters.py       |  88 +++-
 2 files changed, 537 insertions(+), 1 deletion(-)
 create mode 100644 qcodes/instrument_drivers/AlazarTech/acq_controllers/ATS9360Controller.py

diff --git a/qcodes/instrument_drivers/AlazarTech/acq_controllers/ATS9360Controller.py b/qcodes/instrument_drivers/AlazarTech/acq_controllers/ATS9360Controller.py
new file mode 100644
index 000000000000..b7d9e154f0b5
--- /dev/null
+++ b/qcodes/instrument_drivers/AlazarTech/acq_controllers/ATS9360Controller.py
@@ -0,0 +1,450 @@
+import logging
+from ..ATS import AcquisitionController
+import numpy as np
+import qcodes.instrument_drivers.AlazarTech.acq_helpers as helpers
+from ..acqusition_parameters import AcqVariablesParam, ExpandingAlazarArrayMultiParameter
+
+
+class ATS9360Controller(AcquisitionController):
+    """
+    This is the Acquisition Controller class which works with the ATS9360,
+    averaging over records and buffers and demodulating with software reference
+    signal(s). It may optionally integrate over the samples following the post processing
+
+
+    Args:
+        name: name for this acquisition_controller as an instrument
+        alazar_name: name of the alazar instrument such that this
+            controller can communicate with the Alazar
+        filter (default 'win'): filter to be used to filter out double freq
+            component ('win' - window, 'ls' - least squared, 'ave' - averaging)
+        numtaps (default 101): number of freq components used in filter
+        chan_b (default False): whether there is also a second channel of data
+            to be processed and returned. Not currently fully implemented.
+        **kwargs: kwargs are forwarded to the Instrument base class
+
+    TODO(nataliejpg) test filter options
+    TODO(nataliejpg) finish implementation of channel b option
+    TODO(nataliejpg) what should be private?
+    TODO(nataliejpg) where should filter_dict live?
+    TODO(nataliejpg) demod_freq should be changeable number: maybe channels
+    """
+
+    filter_dict = {'win': 0, 'ls': 1, 'ave': 2}
+
+    def __init__(self, name, alazar_name, filter: str = 'win',
+                 numtaps: int =101, chan_b: bool = False,
+                 integrate_samples: bool = False,
+                 **kwargs):
+        self.filter_settings = {'filter': self.filter_dict[filter],
+                                'numtaps': numtaps}
+        self.chan_b = chan_b
+        self.number_of_channels = 2
+        self.samples_per_record = None
+        self.sample_rate = None
+        super().__init__(name, alazar_name, **kwargs)
+
+        self.add_parameter(name='acquisition',
+                           integrate_samples=integrate_samples,
+                           parameter_class=ExpandingAlazarArrayMultiParameter)
+
+        self._integrate_samples = integrate_samples
+        if integrate_samples:
+            self.add_parameter(name='int_time',
+                               check_and_update_fn=self._update_int_time,
+                               default_fn=self._int_time_default,
+                               parameter_class=AcqVariablesParam)
+            self.add_parameter(name='int_delay',
+                               check_and_update_fn=self._update_int_delay,
+                               default_fn=self._int_delay_default,
+                               parameter_class=AcqVariablesParam)
+
+        self.samples_divisor = self._get_alazar().samples_divisor
+        self._demod_freqs = []
+
+    def add_demodulator(self, demod_freq):
+        if demod_freq not in self.demod_freqs:
+            self._verify_demod_freq(demod_freq)
+            self._demod_freqs.append(demod_freq)
+            self.acquisition.set_setpoints_and_labels()
+
+    def remove_demodulator(self, demod_freq):
+        if demod_freq in self.demod_freqs:
+            self._demod_freqs.pop(self.demod_freqs.index(demod_freq))
+            self.acquisition.set_setpoints_and_labels()
+
+    def _verify_demod_freq(self, value):
+        """
+        Function to validate a demodulation frequency
+
+        Checks:
+            - 1e6 <= value <= 500e6
+            - number of oscillation measured using current 'int_time' param value
+              at this demodulation frequency value
+            - oversampling rate for this demodulation frequency
+
+        Args:
+            value: proposed demodulation frequency
+        Returns:
+            bool: Returns True if suitable number of oscillations are measured and
+            oversampling is > 1, False otherwise.
+        Raises:
+            ValueError: If value is not a valid demodulation frequency.
+        """
+        if (value is None) or not (1e6 <= value <= 500e6):
+            raise ValueError('demod_freqs must be 1e6 <= value <= 500e6')
+        isValid = True
+        alazar = self._get_alazar()
+        self.sample_rate = alazar.get_sample_rate()
+        min_oscillations_measured = self.int_time() * value
+        oversampling = self.sample_rate / (2 * value)
+        if min_oscillations_measured < 10:
+            isValid = False
+            logging.warning('{} oscillation measured for largest '
+                            'demod freq, recommend at least 10: '
+                            'decrease sampling rate, take '
+                            'more samples or increase demodulation '
+                            'freq'.format(min_oscillations_measured))
+        elif oversampling < 1:
+            isValid = False
+            logging.warning('oversampling rate is {}, recommend > 1: '
+                            'increase sampling rate or decrease '
+                            'demodulation frequency'.format(oversampling))
+
+        return isValid
+
+    def _update_int_time(self, value, **kwargs):
+        """
+        Function to validate value for int_time before setting parameter
+        value and update instr attributes.
+
+        Args:
+            value to be validated and used for instrument attribute update
+
+        Checks:
+            0 <= value <= 0.1 seconds
+            number of oscillation measured in this time
+            oversampling rate
+
+        Sets:
+            sample_rate attr of acq controller to be that of alazar
+            samples_per_record of acq controller
+            acquisition_kwarg['samples_per_record'] of acquisition param
+        """
+        if (value is None) or not (0 <= value <= 0.1):
+            raise ValueError('int_time must be 0 <= value <= 1')
+
+        alazar = self._get_alazar()
+        self.sample_rate = alazar.get_sample_rate()
+        if self.get_max_demod_freq() is not None:
+            self._verify_demod_freq(self.get_max_demod_freq())
+        if self.int_delay() is None:
+            self.int_delay.to_default()
+
+        # update acquisition kwargs and acq controller value
+        total_time = value + self.int_delay()
+        samples_needed = total_time * self.sample_rate
+        self.samples_per_record = helpers.roundup(
+            samples_needed, self.samples_divisor)
+        self.acquisition.acquisition_kwargs.update(
+            samples_per_record=self.samples_per_record)
+
+    def _update_int_delay(self, value, **kwargs):
+        """
+        Function to validate value for int_delay before setting parameter
+        value and update instr attributes.
+
+        Args:
+            value to be validated and used for instrument attribute update
+
+        Checks:
+            0 <= value <= 0.1 seconds
+            number of samples discarded >= numtaps
+
+        Sets:
+            sample_rate attr of acq controller to be that of alazar
+            samples_per_record of acq controller
+            acquisition_kwarg['samples_per_record'] of acquisition param
+            setpoints of acquisition param
+        """
+        int_delay_min = 0
+        int_delay_max = 0.1
+        if (value is None) or not (int_delay_min <= value <= int_delay_max):
+            raise ValueError('int_delay must be {} <= value <= {}. Got {}'.format(int_delay_min,
+                                                                                  int_delay_max,
+                                                                                  value))
+        alazar = self._get_alazar()
+        self.sample_rate = alazar.get_sample_rate()
+        samples_delay_min = (self.filter_settings['numtaps'] - 1)
+        int_delay_min = samples_delay_min / self.sample_rate
+        if value < int_delay_min:
+            logging.warning(
+                'delay is less than recommended for filter choice: '
+                '(expect delay >= {})'.format(int_delay_min))
+
+        # update acquisition kwargs and acq controller value
+        total_time = value + (self.int_time() or 0)
+        samples_needed = total_time * self.sample_rate
+        self.samples_per_record = helpers.roundup(
+            samples_needed, self.samples_divisor)
+        self.acquisition.acquisition_kwargs.update(
+            samples_per_record=self.samples_per_record)
+
+    def _int_delay_default(self):
+        """
+        Function to generate default int_delay value
+
+        Returns:
+            minimum int_delay recommended for (numtaps - 1)
+            samples to be discarded as recommended for filter
+        """
+        alazar = self._get_alazar()
+        self.sample_rate = alazar.get_sample_rate()
+        samp_delay = self.filter_settings['numtaps'] - 1
+        return samp_delay / self.sample_rate
+
+    def _int_time_default(self):
+        """
+        Function to generate default int_time value
+
+        Returns:
+            max total time for integration based on samples_per_record,
+            sample_rate and int_delay
+        """
+        if self.samples_per_record is (0 or None):
+            raise ValueError('Cannot set int_time to max if acq controller'
+                             ' has 0 or None samples_per_record, choose a '
+                             'value for int_time and samples_per_record will '
+                             'be set accordingly')
+        alazar = self._get_alazar()
+        self.sample_rate = alazar.get_sample_rate()
+        total_time = ((self.samples_per_record / self.sample_rate) -
+                      (self.int_delay() or 0))
+        return total_time
+
+    def get_demod_freqs(self):
+        """
+        Function to get all the demod_freq parameter values in a numpy array
+
+        Returns:
+            numpy array of demodulation frequencies
+        """
+        return np.array(self._demod_freqs)
+
+    def get_max_demod_freq(self):
+        """
+        Returns:
+            the largest demodulation frequency
+
+        """
+        freqs = self.get_demod_freqs()
+        if len(freqs) > 0:
+            return max(freqs)
+        else:
+            return None
+
+    def update_filter_settings(self, filter, numtaps):
+        """
+        Updates the settings of the filter for filtering out
+        double frequency component for demodulation.
+
+        Args:
+            filter (str): filter type ('win' or 'ls')
+            numtaps (int): numtaps for filter
+        """
+        self.filter_settings.update({'filter': self.filter_dict[filter],
+                                     'numtaps': numtaps})
+
+    def update_acquisition_kwargs(self, **kwargs):
+        """
+        Updates the kwargs to be used when
+        alazar_driver.acquisition() is called via a get call of the
+        acquisition parameter. Should be used by the user for
+        updating averaging settings. If integrating over samples
+        'samples_per_record' cannot be set directly it should instead
+         be set via the int_time and int_delay parameters.
+
+        Kwargs (ints):
+            - records_per_buffer
+            - buffers_per_acquisition
+            - allocated_buffers
+        """
+        if 'samples_per_record' in kwargs and self._integrate_samples:
+            raise ValueError('With integrating over samples '
+                             'samples_per_record should not be set manually '
+                             'via update_acquisition_kwargs and should instead'
+                             ' be set by setting int_time and int_delay')
+        self.acquisition.acquisition_kwargs.update(**kwargs)
+
+    def pre_start_capture(self):
+        """
+        Called before capture start to update Acquisition Controller with
+        Alazar acquisition params and set up software wave for demodulation.
+        """
+        alazar = self._get_alazar()
+        if self.samples_per_record != alazar.samples_per_record.get():
+            raise Exception('acq controller samples per record does not match'
+                            ' instrument value, most likely need '
+                            'to set and check int_time and int_delay')
+        if self.sample_rate != alazar.get_sample_rate():
+            raise Exception('acq controller sample rate does not match '
+                            'instrument value, most likely need '
+                            'to set and check int_time and int_delay')
+
+        demod_freqs = self.get_demod_freqs()
+        # if len(demod_freqs) == 0:
+        #     raise Exception('no demod_freqs set')
+
+        self.records_per_buffer = alazar.records_per_buffer.get()
+        self.buffers_per_acquisition = alazar.buffers_per_acquisition.get()
+        self.board_info = alazar.get_idn()
+        self.buffer = np.zeros(self.samples_per_record *
+                               self.records_per_buffer *
+                               self.number_of_channels)
+
+        if demod_freqs:
+            integer_list = np.arange(self.samples_per_record)
+            angle_mat = 2 * np.pi * \
+                np.outer(demod_freqs, integer_list) / self.sample_rate
+            self.cos_mat = np.cos(angle_mat)
+            self.sin_mat = np.sin(angle_mat)
+
+    def pre_acquire(self):
+        pass
+
+    def handle_buffer(self, data):
+        """
+        Adds data from Alazar to buffer (effectively averaging)
+        """
+        self.buffer += data
+
+    def post_acquire(self):
+        """
+        Processes the data according to ATS9360 settings, splitting into
+        records and averaging over them, then applying demodulation fit
+        nb: currently only channel A. Depending on the value of integrate_samples
+        it may either sum over all samples or return arrays of individual samples
+        for all the data given below.
+
+        Returns:
+            - Raw data
+            - For each demodulation frequency:
+                * magnitude
+                * phase
+        """
+
+        # for ATS9360 samples are arranged in the buffer as follows:
+        # S00A, S00B, S01A, S01B...S10A, S10B, S11A, S11B...
+        # where SXYZ is record X, sample Y, channel Z.
+
+        # break buffer up into records and averages over them
+        reshaped_buf = self.buffer.reshape(self.records_per_buffer,
+                                           self.samples_per_record,
+                                           self.number_of_channels)
+        recordA = np.uint16(np.mean(reshaped_buf[:, :, 0], axis=0) /
+                            self.buffers_per_acquisition)
+
+        # TODO(nataliejpg): test above version works on alazar and
+        # compare performance
+        recordA = self._to_volts(recordA)
+        # records_per_acquisition = (self.buffers_per_acquisition *
+        #                    self.records_per_buffer)
+        # recA = np.zeros(self.samples_per_record)
+        # for i in range(self.records_per_buffer):
+        #     i0 = (i * self.samples_per_record * self.number_of_channels)
+        #     i1 = (i0 + self.samples_per_record * self.number_of_channels)
+        #     recA += self.buffer[i0:i1:self.number_of_channels]
+        # recordA = np.uint16(recA / records_per_acquisition)
+        unpacked = []
+        if self._integrate_samples:
+            unpacked.append(np.mean(recordA, axis=-1))
+        else:
+            unpacked.append(recordA)
+        # do demodulation
+        if self.get_demod_freqs():
+            magA, phaseA = self._fit(recordA)
+            if self._integrate_samples:
+                magA = np.mean(magA, axis=-1)
+                phaseA = np.mean(phaseA, axis=-1)
+            for i in range(magA.shape[0]):
+                unpacked.append(magA[i])
+                unpacked.append(phaseA[i])
+        # same for chan b
+        if self.chan_b:
+            raise NotImplementedError('chan b code not complete')
+
+
+        return tuple(unpacked)
+
+    def _to_volts(self, record):
+        # convert rec to volts
+        bps = self.board_info['bits_per_sample']
+        if bps == 12:
+            volt_rec = helpers.sample_to_volt_u12(record, bps)
+        else:
+            logging.warning('sample to volt conversion does not exist for'
+                            ' bps != 12, centered raw samples returned')
+            volt_rec = record - np.mean(record)
+        return volt_rec
+
+    def _fit(self, volt_rec):
+        """
+        Applies low bandpass filter and demodulation fit,
+        and integration limits to samples array
+
+        Args:
+            record (numpy array): record from alazar to be multiplied
+                                  with the software signal, filtered and limited
+                                  to integration limits shape = (samples_taken, )
+
+        Returns:
+            magnitude (numpy array): shape = (demod_length, samples_after_limiting)
+            phase (numpy array): shape = (demod_length, samples_after_limiting)
+        """
+
+        # volt_rec to matrix and multiply with demodulation signal matrices
+        volt_rec_mat = np.outer(np.ones(self._demod_length), volt_rec)
+        re_mat = np.multiply(volt_rec_mat, self.cos_mat)
+        im_mat = np.multiply(volt_rec_mat, self.sin_mat)
+
+        # filter out higher freq component
+        cutoff = self.get_max_demod_freq() / 10
+        if self.filter_settings['filter'] == 0:
+            re_filtered = helpers.filter_win(re_mat, cutoff,
+                                             self.sample_rate,
+                                             self.filter_settings['numtaps'],
+                                             axis=-1)
+            im_filtered = helpers.filter_win(im_mat, cutoff,
+                                             self.sample_rate,
+                                             self.filter_settings['numtaps'],
+                                             axis=-1)
+        elif self.filter_settings['filter'] == 1:
+            re_filtered = helpers.filter_ls(re_mat, cutoff,
+                                            self.sample_rate,
+                                            self.filter_settings['numtaps'],
+                                            axis=-1)
+            im_filtered = helpers.filter_ls(im_mat, cutoff,
+                                            self.sample_rate,
+                                            self.filter_settings['numtaps'],
+                                            axis=-1)
+        elif self.filter_settings['filter'] == 2:
+            re_filtered = re_mat
+            im_filtered = im_mat
+
+        if self.__integrate_samples:
+            # apply integration limits
+            beginning = int(self.int_delay() * self.sample_rate)
+            end = beginning + int(self.int_time() * self.sample_rate)
+
+            re_limited = re_filtered[:, beginning:end]
+            im_limited = im_filtered[:, beginning:end]
+        else:
+            re_limited = re_filtered
+            im_limited = im_filtered
+
+        # convert to magnitude and phase
+        complex_mat = re_limited + im_limited * 1j
+        magnitude = abs(complex_mat)
+        phase = np.angle(complex_mat, deg=True)
+
+        return magnitude, phase
diff --git a/qcodes/instrument_drivers/AlazarTech/acqusition_parameters.py b/qcodes/instrument_drivers/AlazarTech/acqusition_parameters.py
index 994fe256e3bb..bed3339079ab 100644
--- a/qcodes/instrument_drivers/AlazarTech/acqusition_parameters.py
+++ b/qcodes/instrument_drivers/AlazarTech/acqusition_parameters.py
@@ -1,5 +1,5 @@
 from qcodes import Parameter, MultiParameter
-
+import numpy as np
 
 class AcqVariablesParam(Parameter):
     """
@@ -201,3 +201,89 @@ def update_rec_sweep(self, rec_npts, rec_start=None, rec_stop=None):
                            (self._buf_npts, self._rec_npts))
             # self.setpoints = ((self._buf_list, self._rec_list),
             #                   (self._buf_list, self._rec_list))
+
+
+class ExpandingAlazarArrayMultiParameter(MultiParameter):
+    def __init__(self,
+                 name,
+                 instrument,
+                 names = ('raw_output',),
+                 labels = ("raw output",),
+                 units = ('V',),
+                 shapes = ((),),
+                 setpoints = ((),),
+                 setpoint_names = (('time',),),
+                 setpoint_labels = (('time',),),
+                 setpoint_units = (('s',),),
+                 integrate_samples=False):
+        self.acquisition_kwargs = {}
+        self._integrate_samples = integrate_samples
+        super().__init__(name,
+                         names=names,
+                         units=units,
+                         labels=labels,
+                         shapes=shapes,
+                         instrument=instrument,
+                         setpoints=setpoints,
+                         setpoint_names=setpoint_names,
+                         setpoint_labels=setpoint_labels,
+                         setpoint_units=setpoint_units)
+
+    def set_setpoints_and_labels(self):
+        if not self._integrate_samples:
+            if self._instrument.sample_rate and self._instrument.samples_per_record:
+                start = 0
+                step = 1/self._instrument.sample_rate
+                stop = self._instrument.samples_per_record/self._instrument.sample_rate
+            else:
+                start = 0
+                step = 1
+                stop = 1
+            arraysetpoints = (tuple(np.arange(start, stop, step)),)
+            base_shape = (len(arraysetpoints[0]),)
+        else:
+            arraysetpoints = ()
+            base_shape = ()
+        setpoints = [arraysetpoints]
+        names = [self.names[0]]
+        labels = [self.labels[0]]
+        setpoint_name = self.setpoint_names[0]
+        setpoint_names = [setpoint_name]
+        setpoint_label = self.setpoint_labels[0]
+        setpoint_labels = [setpoint_label]
+        setpoint_unit = self.setpoint_units[0]
+        setpoint_units = [setpoint_unit]
+        units = [self.units[0]]
+        shapes = [base_shape]
+        for i, demod_freq in enumerate(self._instrument.demod_freqs):
+            names.append("demod_freq_{}_mag".format(i))
+            labels.append("demod freq {} mag".format(i))
+            names.append("demod_freq_{}_phase".format(i))
+            labels.append("demod freq {} phase".format(i))
+            units.append('v')
+            units.append('v')
+            shapes.append(base_shape)
+            shapes.append(base_shape)
+            setpoints.append(arraysetpoints)
+            setpoint_names.append(setpoint_name)
+            setpoint_labels.append(setpoint_label)
+            setpoint_units.append(setpoint_unit)
+            setpoints.append(arraysetpoints)
+            setpoint_names.append(setpoint_name)
+            setpoint_labels.append(setpoint_label)
+            setpoint_units.append(setpoint_unit)
+        self.metadata['demod_freqs'] = self._instrument.demod_freqs
+        self.names = tuple(names)
+        self.labels = tuple(labels)
+        self.units = tuple(units)
+        self.shapes = tuple(shapes)
+        self.setpoints = tuple(setpoints)
+        self.setpoint_names = tuple(setpoint_names)
+        self.setpoint_labels = tuple(setpoint_labels)
+        self.setpoint_units = tuple(setpoint_units)
+
+    def get(self):
+        output = self._instrument._get_alazar().acquire(
+            acquisition_controller=self._instrument,
+            **self.acquisition_kwargs)
+        return output
\ No newline at end of file

From 810dc252cf68de9ca9e180be0d5afc2bd195c6e0 Mon Sep 17 00:00:00 2001
From: "Jens H. Nielsen" 
Date: Thu, 23 Feb 2017 17:04:37 +0100
Subject: [PATCH 140/328] Fix: correct calls to cdll with numpy arrays

could also rewrite these as pure ctypes datatypes as we are not doing any array operations
---
 qcodes/instrument_drivers/AlazarTech/ATS.py | 32 +++++++++++----------
 1 file changed, 17 insertions(+), 15 deletions(-)

diff --git a/qcodes/instrument_drivers/AlazarTech/ATS.py b/qcodes/instrument_drivers/AlazarTech/ATS.py
index 96572812704e..f4b0a08382b4 100644
--- a/qcodes/instrument_drivers/AlazarTech/ATS.py
+++ b/qcodes/instrument_drivers/AlazarTech/ATS.py
@@ -282,46 +282,46 @@ def get_idn(self):
         revision = np.array([0], dtype=np.uint8)
         self._call_dll('AlazarGetCPLDVersion',
                        self._handle,
-                       major.ctypes.data,
-                       minor.ctypes.data)
+                       major.ctypes.data_as(ctypes.POINTER(ctypes.c_uint8)),
+                       minor.ctypes.data_as(ctypes.POINTER(ctypes.c_uint8)))
         cpld_ver = str(major[0]) + "." + str(minor[0])
 
         self._call_dll('AlazarGetDriverVersion',
-                       major.ctypes.data,
-                       minor.ctypes.data,
-                       revision.ctypes.data)
+                       major.ctypes.data_as(ctypes.POINTER(ctypes.c_uint8)),
+                       major.ctypes.data_as(ctypes.POINTER(ctypes.c_uint8)),
+                       revision.ctypes.data_as(ctypes.POINTER(ctypes.c_uint8)))
         driver_ver = str(major[0])+"."+str(minor[0])+"."+str(revision[0])
 
         self._call_dll('AlazarGetSDKVersion',
-                       major.ctypes.data,
-                       minor.ctypes.data,
-                       revision.ctypes.data)
+                       major.ctypes.data_as(ctypes.POINTER(ctypes.c_uint8)),
+                       minor.ctypes.data_as(ctypes.POINTER(ctypes.c_uint8)),
+                       revision.ctypes.data_as(ctypes.POINTER(ctypes.c_uint8)))
         sdk_ver = str(major[0])+"."+str(minor[0])+"."+str(revision[0])
 
         value = np.array([0], dtype=np.uint32)
         self._call_dll('AlazarQueryCapability',
-                       self._handle, 0x10000024, 0, value.ctypes.data)
+                       self._handle, 0x10000024, 0, value.ctypes.data_as(ctypes.POINTER(ctypes.c_uint32)))
         serial = str(value[0])
         self._call_dll('AlazarQueryCapability',
-                       self._handle, 0x10000026, 0, value.ctypes.data)
+                       self._handle, 0x10000026, 0, value.ctypes.data_as(ctypes.POINTER(ctypes.c_uint32)))
         latest_cal_date = (str(value[0])[0:2] + "-" +
                            str(value[0])[2:4] + "-" +
                            str(value[0])[4:6])
 
         self._call_dll('AlazarQueryCapability',
-                       self._handle, 0x1000002A, 0, value.ctypes.data)
+                       self._handle, 0x1000002A, 0, value.ctypes.data_as(ctypes.POINTER(ctypes.c_uint32)))
         memory_size = str(value[0])
         self._call_dll('AlazarQueryCapability',
-                       self._handle, 0x1000002C, 0, value.ctypes.data)
+                       self._handle, 0x1000002C, 0, value.ctypes.data_as(ctypes.POINTER(ctypes.c_uint32)))
         asopc_type = str(value[0])
 
         # see the ATS-SDK programmer's guide
         # about the encoding of the link speed
         self._call_dll('AlazarQueryCapability',
-                       self._handle, 0x10000030, 0, value.ctypes.data)
+                       self._handle, 0x10000030, 0, value.ctypes.data_as(ctypes.POINTER(ctypes.c_uint32)))
         pcie_link_speed = str(value[0] * 2.5 / 10) + "GB/s"
         self._call_dll('AlazarQueryCapability',
-                       self._handle, 0x10000031, 0, value.ctypes.data)
+                       self._handle, 0x10000031, 0, value.ctypes.data_as(ctypes.POINTER(ctypes.c_uint32)))
         pcie_link_width = str(value[0])
 
         return {'firmware': None,
@@ -453,7 +453,9 @@ def _get_channel_info(self, handle):
         bps = np.array([0], dtype=np.uint8)  # bps bits per sample
         max_s = np.array([0], dtype=np.uint32)  # max_s memory size in samples
         self._call_dll('AlazarGetChannelInfo',
-                       handle, max_s.ctypes.data, bps.ctypes.data)
+                       handle,
+                       max_s.ctypes.data_as(ctypes.POINTER(ctypes.c_uint32)),
+                       bps.ctypes.data_as(ctypes.POINTER(ctypes.c_uint8)))
         return max_s[0], bps[0]
 
     def acquire(self, mode=None, samples_per_record=None,

From 731d98b7e00a2a8283a9d23d47f66f02fe6b73c8 Mon Sep 17 00:00:00 2001
From: "Jens H. Nielsen" 
Date: Fri, 24 Feb 2017 11:04:20 +0100
Subject: [PATCH 141/328] Fix: use ctypes api directly

---
 qcodes/instrument_drivers/AlazarTech/ATS.py | 72 +++++++++++----------
 1 file changed, 37 insertions(+), 35 deletions(-)

diff --git a/qcodes/instrument_drivers/AlazarTech/ATS.py b/qcodes/instrument_drivers/AlazarTech/ATS.py
index f4b0a08382b4..9212753fd5fb 100644
--- a/qcodes/instrument_drivers/AlazarTech/ATS.py
+++ b/qcodes/instrument_drivers/AlazarTech/ATS.py
@@ -201,7 +201,7 @@ def get_board_info(cls, dll, system_id, board_id):
         Get the information from a connected Alazar board
 
         Args:
-            dll (string): path of the Alazar driver dll
+            dll (CDLL): CTypes CDLL
             system_id: id of the Alazar system
             board_id: id of the board within the alazar system
 
@@ -277,52 +277,53 @@ def get_idn(self):
 
         max_s, bps = self._get_channel_info(self._handle)
 
-        major = np.array([0], dtype=np.uint8)
-        minor = np.array([0], dtype=np.uint8)
-        revision = np.array([0], dtype=np.uint8)
+        major = ctypes.c_uint8(0)
+        minor = ctypes.c_uint8(0)
+        revision = ctypes.c_uint8(0)
         self._call_dll('AlazarGetCPLDVersion',
                        self._handle,
-                       major.ctypes.data_as(ctypes.POINTER(ctypes.c_uint8)),
-                       minor.ctypes.data_as(ctypes.POINTER(ctypes.c_uint8)))
-        cpld_ver = str(major[0]) + "." + str(minor[0])
+                       ctypes.byref(major),
+                       ctypes.byref(minor))
+        cpld_ver = str(major.value) + "." + str(minor.value)
 
         self._call_dll('AlazarGetDriverVersion',
-                       major.ctypes.data_as(ctypes.POINTER(ctypes.c_uint8)),
-                       major.ctypes.data_as(ctypes.POINTER(ctypes.c_uint8)),
-                       revision.ctypes.data_as(ctypes.POINTER(ctypes.c_uint8)))
-        driver_ver = str(major[0])+"."+str(minor[0])+"."+str(revision[0])
+                       ctypes.byref(major),
+                       ctypes.byref(minor),
+                       ctypes.byref(revision))
+        driver_ver = str(major.value)+"."+str(minor.value)+"."+str(revision.value)
 
         self._call_dll('AlazarGetSDKVersion',
-                       major.ctypes.data_as(ctypes.POINTER(ctypes.c_uint8)),
-                       minor.ctypes.data_as(ctypes.POINTER(ctypes.c_uint8)),
-                       revision.ctypes.data_as(ctypes.POINTER(ctypes.c_uint8)))
-        sdk_ver = str(major[0])+"."+str(minor[0])+"."+str(revision[0])
+                       ctypes.byref(major),
+                       ctypes.byref(minor),
+                       ctypes.byref(revision))
+        sdk_ver = str(major.value)+"."+str(minor.value)+"."+str(revision.value)
 
-        value = np.array([0], dtype=np.uint32)
+        value = ctypes.c_uint32(0)
         self._call_dll('AlazarQueryCapability',
-                       self._handle, 0x10000024, 0, value.ctypes.data_as(ctypes.POINTER(ctypes.c_uint32)))
-        serial = str(value[0])
+                       self._handle, 0x10000024, 0, ctypes.byref(value))
+        serial = str(value.value)
         self._call_dll('AlazarQueryCapability',
-                       self._handle, 0x10000026, 0, value.ctypes.data_as(ctypes.POINTER(ctypes.c_uint32)))
-        latest_cal_date = (str(value[0])[0:2] + "-" +
-                           str(value[0])[2:4] + "-" +
-                           str(value[0])[4:6])
+                       self._handle, 0x10000026, 0, ctypes.byref(value))
+        Capabilitystring = str(value.value)
+        latest_cal_date = (Capabilitystring[0:2] + "-" +
+                           Capabilitystring[2:4] + "-" +
+                           Capabilitystring[4:6])
 
         self._call_dll('AlazarQueryCapability',
-                       self._handle, 0x1000002A, 0, value.ctypes.data_as(ctypes.POINTER(ctypes.c_uint32)))
-        memory_size = str(value[0])
+                       self._handle, 0x1000002A, 0, ctypes.byref(value))
+        memory_size = str(value.value)
         self._call_dll('AlazarQueryCapability',
-                       self._handle, 0x1000002C, 0, value.ctypes.data_as(ctypes.POINTER(ctypes.c_uint32)))
-        asopc_type = str(value[0])
+                       self._handle, 0x1000002C, 0, ctypes.byref(value))
+        asopc_type = str(value.value)
 
         # see the ATS-SDK programmer's guide
         # about the encoding of the link speed
         self._call_dll('AlazarQueryCapability',
-                       self._handle, 0x10000030, 0, value.ctypes.data_as(ctypes.POINTER(ctypes.c_uint32)))
-        pcie_link_speed = str(value[0] * 2.5 / 10) + "GB/s"
+                       self._handle, 0x10000030, 0,  ctypes.byref(value))
+        pcie_link_speed = str(value.value * 2.5 / 10) + "GB/s"
         self._call_dll('AlazarQueryCapability',
-                       self._handle, 0x10000031, 0, value.ctypes.data_as(ctypes.POINTER(ctypes.c_uint32)))
-        pcie_link_width = str(value[0])
+                       self._handle, 0x10000031, 0, ctypes.byref(value))
+        pcie_link_width = str(value.value)
 
         return {'firmware': None,
                 'model': board_kind,
@@ -450,13 +451,13 @@ def config(self, clock_source=None, sample_rate=None, clock_edge=None,
                        self.aux_io_param)
 
     def _get_channel_info(self, handle):
-        bps = np.array([0], dtype=np.uint8)  # bps bits per sample
-        max_s = np.array([0], dtype=np.uint32)  # max_s memory size in samples
+        bps = ctypes.c_uint8(0)  # bps bits per sample
+        max_s = ctypes.c_uint32(0)  # max_s memory size in samples
         self._call_dll('AlazarGetChannelInfo',
                        handle,
-                       max_s.ctypes.data_as(ctypes.POINTER(ctypes.c_uint32)),
-                       bps.ctypes.data_as(ctypes.POINTER(ctypes.c_uint8)))
-        return max_s[0], bps[0]
+                       ctypes.byref(max_s),
+                       ctypes.byref(bps))
+        return max_s.value, bps.value
 
     def acquire(self, mode=None, samples_per_record=None,
                 records_per_buffer=None, buffers_per_acquisition=None,
@@ -591,6 +592,7 @@ def acquire(self, mode=None, samples_per_record=None,
 
         # bytes per sample
         max_s, bps = self._get_channel_info(self._handle)
+        # TODO(JHN) Why +7
         bytes_per_sample = (bps + 7) // 8
 
         # bytes per record

From 1fa1c340073e3d26d9259ce9b599364f29f6d683 Mon Sep 17 00:00:00 2001
From: "Jens H. Nielsen" 
Date: Tue, 28 Feb 2017 14:50:01 +0100
Subject: [PATCH 142/328] fix: some comments

---
 qcodes/instrument_drivers/AlazarTech/ATS.py | 4 ++--
 1 file changed, 2 insertions(+), 2 deletions(-)

diff --git a/qcodes/instrument_drivers/AlazarTech/ATS.py b/qcodes/instrument_drivers/AlazarTech/ATS.py
index 9212753fd5fb..a8b746fe2d62 100644
--- a/qcodes/instrument_drivers/AlazarTech/ATS.py
+++ b/qcodes/instrument_drivers/AlazarTech/ATS.py
@@ -592,9 +592,8 @@ def acquire(self, mode=None, samples_per_record=None,
 
         # bytes per sample
         max_s, bps = self._get_channel_info(self._handle)
-        # TODO(JHN) Why +7
+        # TODO(JHN) Why +7 I guess its to do ceil division?
         bytes_per_sample = (bps + 7) // 8
-
         # bytes per record
         bytes_per_record = bytes_per_sample * samples_per_record
 
@@ -610,6 +609,7 @@ def acquire(self, mode=None, samples_per_record=None,
 
         # create buffers for acquisition
         # TODO(nataliejpg) should this be > 1 (as intuitive) or > 8 as in alazar sample code?
+        # the alazar code probably uses bits per sample?
         sample_type = ctypes.c_uint8
         if bytes_per_sample > 1:
             sample_type = ctypes.c_uint16

From f26977f1bc08b425269dc01dd76bd10512c75542 Mon Sep 17 00:00:00 2001
From: "Jens H. Nielsen" 
Date: Tue, 28 Feb 2017 14:50:58 +0100
Subject: [PATCH 143/328] Fix: Simplify memory allocation in ATS driver

---
 qcodes/instrument_drivers/AlazarTech/ATS.py | 25 ++++-----------------
 1 file changed, 4 insertions(+), 21 deletions(-)

diff --git a/qcodes/instrument_drivers/AlazarTech/ATS.py b/qcodes/instrument_drivers/AlazarTech/ATS.py
index a8b746fe2d62..f0c30df7b452 100644
--- a/qcodes/instrument_drivers/AlazarTech/ATS.py
+++ b/qcodes/instrument_drivers/AlazarTech/ATS.py
@@ -980,22 +980,13 @@ def __init__(self, c_sample_type, size_bytes):
 
         self._allocated = True
         self.addr = None
-        if os.name == 'nt':
-            MEM_COMMIT = 0x1000
-            PAGE_READWRITE = 0x4
-            ctypes.windll.kernel32.VirtualAlloc.argtypes = [ctypes.c_void_p, ctypes.c_long, ctypes.c_long, ctypes.c_long]
-            ctypes.windll.kernel32.VirtualAlloc.restype = ctypes.c_void_p
-            self.addr = ctypes.windll.kernel32.VirtualAlloc(
-                0, ctypes.c_long(size_bytes), MEM_COMMIT, PAGE_READWRITE)
-        else:
-            self._allocated = False
-            raise Exception("Unsupported OS")
 
-        ctypes_array = (c_sample_type *
-                        (size_bytes // bytes_per_sample)).from_address(self.addr)
+        ctypes_array_type = c_sample_type * (size_bytes // bytes_per_sample) # PyCharm deducts the wrong type
+        # This is not an int but a PyCArrayType which is callable. Not sure how to annotate this correctly
+        ctypes_array = ctypes_array_type()
         self.buffer = np.frombuffer(ctypes_array, dtype=npSampleType)
         self.ctypes_buffer = ctypes_array
-        pointer, read_only_flag = self.buffer.__array_interface__['data']
+        self.addr = ctypes.addressof(self.ctypes_buffer)
 
     def free_mem(self):
         """
@@ -1003,14 +994,6 @@ def free_mem(self):
         """
 
         self._allocated = False
-        if os.name == 'nt':
-            MEM_RELEASE = 0x8000
-            ctypes.windll.kernel32.VirtualFree.argtypes = [ctypes.c_void_p, ctypes.c_long, ctypes.c_long]
-            ctypes.windll.kernel32.VirtualFree.restype = ctypes.c_int
-            ctypes.windll.kernel32.VirtualFree(ctypes.c_void_p(self.addr), 0, MEM_RELEASE);
-        else:
-            self._allocated = True
-            raise Exception("Unsupported OS")
 
     def __del__(self):
         """

From 8ca333d5a9ba47577355943042c3f643444f1583 Mon Sep 17 00:00:00 2001
From: "Jens H. Nielsen" 
Date: Tue, 28 Feb 2017 14:53:11 +0100
Subject: [PATCH 144/328] Fix: comments and typos

---
 qcodes/instrument_drivers/AlazarTech/ATS9360.py | 5 +++--
 1 file changed, 3 insertions(+), 2 deletions(-)

diff --git a/qcodes/instrument_drivers/AlazarTech/ATS9360.py b/qcodes/instrument_drivers/AlazarTech/ATS9360.py
index e507030fbb95..8963ef71e1f5 100644
--- a/qcodes/instrument_drivers/AlazarTech/ATS9360.py
+++ b/qcodes/instrument_drivers/AlazarTech/ATS9360.py
@@ -16,6 +16,7 @@ class AlazarTech_ATS9360(AlazarTech_ATS):
 
     # samples divisor is listed in the manual as being 32 but this gave
     # incorrect data when tested for divisors less than 128
+    # JHN As far as I can see this is listed as 128 in table 8 of the SDK manual
     samples_divisor = 128
 
     def __init__(self, name, **kwargs):
@@ -170,7 +171,7 @@ def __init__(self, name, **kwargs):
         # ----- Parameters for the acquire function -----
         self.add_parameter(name='mode',
                            parameter_class=AlazarParameter,
-                           label='Acquisiton mode',
+                           label='Acquisition mode',
                            unit=None,
                            value='NPT',
                            byte_to_value_dict={0x200: 'NPT', 0x400: 'TS'})
@@ -201,7 +202,7 @@ def __init__(self, name, **kwargs):
                            byte_to_value_dict={1: 'A', 2: 'B', 3: 'AB'})
         self.add_parameter(name='transfer_offset',
                            parameter_class=AlazarParameter,
-                           label='Transer Offset',
+                           label='Transfer Offset',
                            unit='Samples',
                            value=0,
                            vals=validators.Ints(min_value=0))

From 25ba6eaeaf448260c3fc91aa277d2e90665bc3a7 Mon Sep 17 00:00:00 2001
From: "Jens H. Nielsen" 
Date: Tue, 28 Feb 2017 14:54:23 +0100
Subject: [PATCH 145/328] Add support for both internal and external clock

---
 qcodes/instrument_drivers/AlazarTech/ATS.py   | 12 ++-
 .../instrument_drivers/AlazarTech/ATS9360.py  | 74 ++++++++++++++++++-
 2 files changed, 80 insertions(+), 6 deletions(-)

diff --git a/qcodes/instrument_drivers/AlazarTech/ATS.py b/qcodes/instrument_drivers/AlazarTech/ATS.py
index f0c30df7b452..2a287dd603f8 100644
--- a/qcodes/instrument_drivers/AlazarTech/ATS.py
+++ b/qcodes/instrument_drivers/AlazarTech/ATS.py
@@ -341,6 +341,7 @@ def get_idn(self):
                 'pcie_link_width': pcie_link_width}
 
     def config(self, clock_source=None, sample_rate=None, clock_edge=None,
+               external_sample_rate=None,
                decimation=None, coupling=None, channel_range=None,
                impedance=None, bwlimit=None, trigger_operation=None,
                trigger_engine1=None, trigger_source1=None,
@@ -385,6 +386,7 @@ def config(self, clock_source=None, sample_rate=None, clock_edge=None,
 
         self._set_if_present('clock_source', clock_source)
         self._set_if_present('sample_rate', sample_rate)
+        self._set_if_present('external_sample_rate', external_sample_rate)
         self._set_if_present('clock_edge', clock_edge)
         self._set_if_present('decimation', decimation)
 
@@ -414,8 +416,14 @@ def config(self, clock_source=None, sample_rate=None, clock_edge=None,
         self._set_if_present('aux_io_param', aux_io_param)
         # endregion
 
+
+        if clock_source == 'EXTERNAL_CLOCK_10MHz_REF':
+            sample_rate = self.external_sample_rate
+        elif clock_source == 'INTERNAL_CLOCK':
+            sample_rate = self.sample_rate
+
         self._call_dll('AlazarSetCaptureClock',
-                       self._handle, self.clock_source, self.sample_rate,
+                       self._handle, self.clock_source, sample_rate,
                        self.clock_edge, self.decimation)
 
         for i in range(1, self.channels + 1):
@@ -865,7 +873,7 @@ def __init__(self, name=None, label=None, unit=None, instrument=None,
                 # TODO(damazter) (S) test this validator
                 vals = validators.Enum(*byte_to_value_dict.values())
 
-        super().__init__(name=name, label=label, units=unit, vals=vals)
+        super().__init__(name=name, label=label, unit=unit, vals=vals)
         self.instrument = instrument
         self._byte = None
         self._uptodate_flag = False
diff --git a/qcodes/instrument_drivers/AlazarTech/ATS9360.py b/qcodes/instrument_drivers/AlazarTech/ATS9360.py
index 8963ef71e1f5..163de13696bc 100644
--- a/qcodes/instrument_drivers/AlazarTech/ATS9360.py
+++ b/qcodes/instrument_drivers/AlazarTech/ATS9360.py
@@ -30,13 +30,54 @@ def __init__(self, name, **kwargs):
                            parameter_class=AlazarParameter,
                            label='Clock Source',
                            unit=None,
-                           value='EXTERNAL_CLOCK_10MHz_REF',
-                           byte_to_value_dict={7: 'EXTERNAL_CLOCK_10MHz_REF'})
-        self.add_parameter(name='sample_rate',
+                           value='INTERNAL_CLOCK',
+                           byte_to_value_dict={1: 'INTERNAL_CLOCK',
+                                               2: 'FAST_EXTERNAL_CLOCK',
+                                               7: 'EXTERNAL_CLOCK_10MHz_REF'})
+        self.add_parameter(name='external_sample_rate',
                            parameter_class=AlazarParameter,
-                           label='Sample Rate',
+                           label='External Sample Rate',
                            unit='S/s',
                            value=500000000)
+        self.add_parameter(name='sample_rate',
+                           parameter_class=AlazarParameter,
+                           label='Internal Sample Rate',
+                           unit='S/s',
+                           value=500000000,
+                           byte_to_value_dict={0x00000001: 1000,
+                                                0x00000002: 2000,
+                                                0x00000004: 5000,
+                                                0x00000008: 10000,
+                                                0x0000000A: 20000,
+                                                0x0000000C: 50000,
+                                                0x0000000E: 100000,
+                                                0x00000010: 200000,
+                                                0x00000012: 500000,
+                                                0x00000014: 1000000,
+                                                0x00000018: 2000000,
+                                                0x0000001A: 5000000,
+                                                0x0000001C: 10000000,
+                                                0x0000001E: 20000000,
+                                                0x00000021: 25000000,
+                                                0x00000022: 50000000,
+                                                0x00000024: 100000000,
+                                                0x00000025: 125000000,
+                                                0x00000026: 160000000,
+                                                0x00000027: 180000000,
+                                                0x00000028: 200000000,
+                                                0x0000002B: 250000000,
+                                                0x00000030: 500000000,
+                                                0x00000032: 800000000,
+                                                0x00000035: 1000000000,
+                                                0x00000037: 1200000000,
+                                                0x0000003A: 1500000000,
+                                                0x0000003D: 1800000000,
+                                                0x0000003F: 2000000000,
+                                                0x0000006A: 2400000000,
+                                                0x00000075: 3000000000,
+                                                0x0000007B: 3600000000,
+                                                0x00000080: 4000000000,
+                                                })
         self.add_parameter(name='clock_edge',
                            parameter_class=AlazarParameter,
                            label='Clock Edge',
@@ -266,3 +307,28 @@ def __init__(self, name, **kwargs):
         if model != 'ATS9360':
             raise Exception("The Alazar board kind is not 'ATS9360',"
                             " found '" + str(model) + "' instead.")
+
+
+    def get_sample_rate(self):
+        """
+        Obtain the effective sampling rate of the acquisition
+        based on clock type, clock speed and decimation
+
+        Returns:
+            the number of samples (per channel) per second
+        """
+        if self.clock_source.get() == 'EXTERNAL_CLOCK_10MHz_REF':
+            rate = self.external_sample_rate.get()
+        elif self.clock_source.get() == 'INTERNAL_CLOCK':
+            rate = self.sample_rate.get()
+        else:
+            raise Exception("Don't know how to get sample rate with {}".format(alazar.clock_source.get()))
+
+        if rate == '1GHz_REFERENCE_CLOCK':
+            rate = 1e9
+
+        decimation = self.decimation.get()
+        if decimation > 0:
+            return rate / decimation
+        else:
+            return rate

From 9619f572941e90fa312265b7053bfbf1b0ba02c6 Mon Sep 17 00:00:00 2001
From: "Jens H. Nielsen" 
Date: Tue, 28 Feb 2017 14:56:47 +0100
Subject: [PATCH 146/328] Fix: Acqusition parameters correct setpoints

---
 .../AlazarTech/acqusition_parameters.py       | 20 ++++++++++---------
 1 file changed, 11 insertions(+), 9 deletions(-)

diff --git a/qcodes/instrument_drivers/AlazarTech/acqusition_parameters.py b/qcodes/instrument_drivers/AlazarTech/acqusition_parameters.py
index bed3339079ab..933efe6a933a 100644
--- a/qcodes/instrument_drivers/AlazarTech/acqusition_parameters.py
+++ b/qcodes/instrument_drivers/AlazarTech/acqusition_parameters.py
@@ -210,8 +210,8 @@ def __init__(self,
                  names = ('raw_output',),
                  labels = ("raw output",),
                  units = ('V',),
-                 shapes = ((),),
-                 setpoints = ((),),
+                 shapes = ((1,),),
+                 setpoints = (((1,),),),
                  setpoint_names = (('time',),),
                  setpoint_labels = (('time',),),
                  setpoint_units = (('s',),),
@@ -231,15 +231,17 @@ def __init__(self,
 
     def set_setpoints_and_labels(self):
         if not self._integrate_samples:
-            if self._instrument.sample_rate and self._instrument.samples_per_record:
+
+            if self._instrument._get_alazar().sample_rate.get() and self.acquisition_kwargs.get('samples_per_record'):
                 start = 0
-                step = 1/self._instrument.sample_rate
-                stop = self._instrument.samples_per_record/self._instrument.sample_rate
+                samples = self.acquisition_kwargs['samples_per_record']
+                stop = samples/self._instrument._get_alazar().sample_rate.get()
             else:
                 start = 0
-                step = 1
+                samples = 1
                 stop = 1
-            arraysetpoints = (tuple(np.arange(start, stop, step)),)
+            print("start {} stop {} num steps {}".format(start, stop, samples))
+            arraysetpoints = (tuple(np.linspace(start, stop, samples)),)
             base_shape = (len(arraysetpoints[0]),)
         else:
             arraysetpoints = ()
@@ -255,7 +257,7 @@ def set_setpoints_and_labels(self):
         setpoint_units = [setpoint_unit]
         units = [self.units[0]]
         shapes = [base_shape]
-        for i, demod_freq in enumerate(self._instrument.demod_freqs):
+        for i, demod_freq in enumerate(self._instrument._demod_freqs):
             names.append("demod_freq_{}_mag".format(i))
             labels.append("demod freq {} mag".format(i))
             names.append("demod_freq_{}_phase".format(i))
@@ -272,7 +274,7 @@ def set_setpoints_and_labels(self):
             setpoint_names.append(setpoint_name)
             setpoint_labels.append(setpoint_label)
             setpoint_units.append(setpoint_unit)
-        self.metadata['demod_freqs'] = self._instrument.demod_freqs
+        self.metadata['demod_freqs'] = self._instrument._demod_freqs
         self.names = tuple(names)
         self.labels = tuple(labels)
         self.units = tuple(units)

From 0fdc9d60d9f2e56e426271dd528a69ef3f89ecd8 Mon Sep 17 00:00:00 2001
From: "Jens H. Nielsen" 
Date: Tue, 28 Feb 2017 14:57:46 +0100
Subject: [PATCH 147/328] Fix: ATS9360 controller wip improvements

---
 .../acq_controllers/ATS9360Controller.py      | 100 ++++++++++--------
 1 file changed, 56 insertions(+), 44 deletions(-)

diff --git a/qcodes/instrument_drivers/AlazarTech/acq_controllers/ATS9360Controller.py b/qcodes/instrument_drivers/AlazarTech/acq_controllers/ATS9360Controller.py
index b7d9e154f0b5..0e8ac426311e 100644
--- a/qcodes/instrument_drivers/AlazarTech/acq_controllers/ATS9360Controller.py
+++ b/qcodes/instrument_drivers/AlazarTech/acq_controllers/ATS9360Controller.py
@@ -40,8 +40,6 @@ def __init__(self, name, alazar_name, filter: str = 'win',
                                 'numtaps': numtaps}
         self.chan_b = chan_b
         self.number_of_channels = 2
-        self.samples_per_record = None
-        self.sample_rate = None
         super().__init__(name, alazar_name, **kwargs)
 
         self.add_parameter(name='acquisition',
@@ -63,7 +61,7 @@ def __init__(self, name, alazar_name, filter: str = 'win',
         self._demod_freqs = []
 
     def add_demodulator(self, demod_freq):
-        if demod_freq not in self.demod_freqs:
+        if demod_freq not in self._demod_freqs:
             self._verify_demod_freq(demod_freq)
             self._demod_freqs.append(demod_freq)
             self.acquisition.set_setpoints_and_labels()
@@ -95,9 +93,13 @@ def _verify_demod_freq(self, value):
             raise ValueError('demod_freqs must be 1e6 <= value <= 500e6')
         isValid = True
         alazar = self._get_alazar()
-        self.sample_rate = alazar.get_sample_rate()
-        min_oscillations_measured = self.int_time() * value
-        oversampling = self.sample_rate / (2 * value)
+        sample_rate = alazar.get_sample_rate()
+        if 'int_time' in self.parameters:
+            int_time = self.int_time.get()
+        else:
+            int_time = self.acquisition.acquisition_kwargs["samples_per_record"]/sample_rate
+        min_oscillations_measured = int_time * value
+        oversampling = sample_rate / (2 * value)
         if min_oscillations_measured < 10:
             isValid = False
             logging.warning('{} oscillation measured for largest '
@@ -135,7 +137,7 @@ def _update_int_time(self, value, **kwargs):
             raise ValueError('int_time must be 0 <= value <= 1')
 
         alazar = self._get_alazar()
-        self.sample_rate = alazar.get_sample_rate()
+        sample_rate = alazar.get_sample_rate()
         if self.get_max_demod_freq() is not None:
             self._verify_demod_freq(self.get_max_demod_freq())
         if self.int_delay() is None:
@@ -143,11 +145,11 @@ def _update_int_time(self, value, **kwargs):
 
         # update acquisition kwargs and acq controller value
         total_time = value + self.int_delay()
-        samples_needed = total_time * self.sample_rate
-        self.samples_per_record = helpers.roundup(
+        samples_needed = total_time * sample_rate
+        samples_per_record = helpers.roundup(
             samples_needed, self.samples_divisor)
         self.acquisition.acquisition_kwargs.update(
-            samples_per_record=self.samples_per_record)
+            samples_per_record=samples_per_record)
 
     def _update_int_delay(self, value, **kwargs):
         """
@@ -174,9 +176,9 @@ def _update_int_delay(self, value, **kwargs):
                                                                                   int_delay_max,
                                                                                   value))
         alazar = self._get_alazar()
-        self.sample_rate = alazar.get_sample_rate()
+        sample_rate = alazar.get_sample_rate()
         samples_delay_min = (self.filter_settings['numtaps'] - 1)
-        int_delay_min = samples_delay_min / self.sample_rate
+        int_delay_min = samples_delay_min / sample_rate
         if value < int_delay_min:
             logging.warning(
                 'delay is less than recommended for filter choice: '
@@ -184,11 +186,11 @@ def _update_int_delay(self, value, **kwargs):
 
         # update acquisition kwargs and acq controller value
         total_time = value + (self.int_time() or 0)
-        samples_needed = total_time * self.sample_rate
-        self.samples_per_record = helpers.roundup(
+        samples_needed = total_time * sample_rate
+        samples_per_record = helpers.roundup(
             samples_needed, self.samples_divisor)
         self.acquisition.acquisition_kwargs.update(
-            samples_per_record=self.samples_per_record)
+            samples_per_record=samples_per_record)
 
     def _int_delay_default(self):
         """
@@ -199,9 +201,9 @@ def _int_delay_default(self):
             samples to be discarded as recommended for filter
         """
         alazar = self._get_alazar()
-        self.sample_rate = alazar.get_sample_rate()
+        sample_rate = alazar.get_sample_rate()
         samp_delay = self.filter_settings['numtaps'] - 1
-        return samp_delay / self.sample_rate
+        return samp_delay / sample_rate
 
     def _int_time_default(self):
         """
@@ -211,14 +213,15 @@ def _int_time_default(self):
             max total time for integration based on samples_per_record,
             sample_rate and int_delay
         """
-        if self.samples_per_record is (0 or None):
+        samples_per_record = self.acquisition.acquisition_kwargs.get('samples_per_record')
+        if samples_per_record in (0 or None):
             raise ValueError('Cannot set int_time to max if acq controller'
                              ' has 0 or None samples_per_record, choose a '
                              'value for int_time and samples_per_record will '
                              'be set accordingly')
         alazar = self._get_alazar()
-        self.sample_rate = alazar.get_sample_rate()
-        total_time = ((self.samples_per_record / self.sample_rate) -
+        sample_rate = alazar.get_sample_rate()
+        total_time = ((samples_per_record / sample_rate) -
                       (self.int_delay() or 0))
         return total_time
 
@@ -275,6 +278,7 @@ def update_acquisition_kwargs(self, **kwargs):
                              'via update_acquisition_kwargs and should instead'
                              ' be set by setting int_time and int_delay')
         self.acquisition.acquisition_kwargs.update(**kwargs)
+        self.acquisition.set_setpoints_and_labels()
 
     def pre_start_capture(self):
         """
@@ -282,15 +286,18 @@ def pre_start_capture(self):
         Alazar acquisition params and set up software wave for demodulation.
         """
         alazar = self._get_alazar()
-        if self.samples_per_record != alazar.samples_per_record.get():
-            raise Exception('acq controller samples per record does not match'
-                            ' instrument value, most likely need '
-                            'to set and check int_time and int_delay')
-        if self.sample_rate != alazar.get_sample_rate():
-            raise Exception('acq controller sample rate does not match '
-                            'instrument value, most likely need '
-                            'to set and check int_time and int_delay')
-
+        acq_s_p_r = self.acquisition.acquisition_kwargs['samples_per_record']
+        inst_s_p_r = alazar.samples_per_record.get()
+        sample_rate = alazar.get_sample_rate()
+        if acq_s_p_r != inst_s_p_r:
+            raise Exception('acq controller samples per record {} does not match'
+                            ' instrument value {}, most likely need '
+                            'to set and check int_time and int_delay'.format(acq_s_p_r, inst_s_p_r))
+        # if acq_s_r != inst_s_r:
+        #     raise Exception('acq controller sample rate {} does not match '
+        #                     'instrument value {}, most likely need '
+        #                     'to set and check int_time and int_delay'.format(acq_s_r, inst_s_r))
+        samples_per_record = inst_s_p_r
         demod_freqs = self.get_demod_freqs()
         # if len(demod_freqs) == 0:
         #     raise Exception('no demod_freqs set')
@@ -298,14 +305,14 @@ def pre_start_capture(self):
         self.records_per_buffer = alazar.records_per_buffer.get()
         self.buffers_per_acquisition = alazar.buffers_per_acquisition.get()
         self.board_info = alazar.get_idn()
-        self.buffer = np.zeros(self.samples_per_record *
+        self.buffer = np.zeros(samples_per_record *
                                self.records_per_buffer *
                                self.number_of_channels)
 
-        if demod_freqs:
-            integer_list = np.arange(self.samples_per_record)
+        if len(demod_freqs):
+            integer_list = np.arange(samples_per_record)
             angle_mat = 2 * np.pi * \
-                np.outer(demod_freqs, integer_list) / self.sample_rate
+                np.outer(demod_freqs, integer_list) / sample_rate
             self.cos_mat = np.cos(angle_mat)
             self.sin_mat = np.sin(angle_mat)
 
@@ -338,8 +345,10 @@ def post_acquire(self):
         # where SXYZ is record X, sample Y, channel Z.
 
         # break buffer up into records and averages over them
+        alazar = self._get_alazar()
+        samples_per_record = alazar.samples_per_record.get()
         reshaped_buf = self.buffer.reshape(self.records_per_buffer,
-                                           self.samples_per_record,
+                                           samples_per_record,
                                            self.number_of_channels)
         recordA = np.uint16(np.mean(reshaped_buf[:, :, 0], axis=0) /
                             self.buffers_per_acquisition)
@@ -361,7 +370,7 @@ def post_acquire(self):
         else:
             unpacked.append(recordA)
         # do demodulation
-        if self.get_demod_freqs():
+        if len(self.get_demod_freqs()):
             magA, phaseA = self._fit(recordA)
             if self._integrate_samples:
                 magA = np.mean(magA, axis=-1)
@@ -395,7 +404,7 @@ def _fit(self, volt_rec):
         Args:
             record (numpy array): record from alazar to be multiplied
                                   with the software signal, filtered and limited
-                                  to integration limits shape = (samples_taken, )
+                                  to ifantegration limits shape = (samples_taken, )
 
         Returns:
             magnitude (numpy array): shape = (demod_length, samples_after_limiting)
@@ -403,7 +412,10 @@ def _fit(self, volt_rec):
         """
 
         # volt_rec to matrix and multiply with demodulation signal matrices
-        volt_rec_mat = np.outer(np.ones(self._demod_length), volt_rec)
+        alazar = self._get_alazar()
+        sample_rate = alazar.get_sample_rate()
+        demod_length = len(self._demod_freqs)
+        volt_rec_mat = np.outer(np.ones(demod_length), volt_rec)
         re_mat = np.multiply(volt_rec_mat, self.cos_mat)
         im_mat = np.multiply(volt_rec_mat, self.sin_mat)
 
@@ -411,30 +423,30 @@ def _fit(self, volt_rec):
         cutoff = self.get_max_demod_freq() / 10
         if self.filter_settings['filter'] == 0:
             re_filtered = helpers.filter_win(re_mat, cutoff,
-                                             self.sample_rate,
+                                             sample_rate,
                                              self.filter_settings['numtaps'],
                                              axis=-1)
             im_filtered = helpers.filter_win(im_mat, cutoff,
-                                             self.sample_rate,
+                                             sample_rate,
                                              self.filter_settings['numtaps'],
                                              axis=-1)
         elif self.filter_settings['filter'] == 1:
             re_filtered = helpers.filter_ls(re_mat, cutoff,
-                                            self.sample_rate,
+                                            sample_rate,
                                             self.filter_settings['numtaps'],
                                             axis=-1)
             im_filtered = helpers.filter_ls(im_mat, cutoff,
-                                            self.sample_rate,
+                                            sample_rate,
                                             self.filter_settings['numtaps'],
                                             axis=-1)
         elif self.filter_settings['filter'] == 2:
             re_filtered = re_mat
             im_filtered = im_mat
 
-        if self.__integrate_samples:
+        if self._integrate_samples:
             # apply integration limits
-            beginning = int(self.int_delay() * self.sample_rate)
-            end = beginning + int(self.int_time() * self.sample_rate)
+            beginning = int(self.int_delay() * sample_rate)
+            end = beginning + int(self.int_time() * sample_rate)
 
             re_limited = re_filtered[:, beginning:end]
             im_limited = im_filtered[:, beginning:end]

From cdaad8cb23cf5dcd404f992f680eb3b106d2d4c5 Mon Sep 17 00:00:00 2001
From: "Jens H. Nielsen" 
Date: Tue, 28 Feb 2017 16:36:32 +0100
Subject: [PATCH 148/328] Fix: add init file

---
 qcodes/instrument_drivers/AlazarTech/acq_controllers/__init__.py | 1 +
 1 file changed, 1 insertion(+)
 create mode 100644 qcodes/instrument_drivers/AlazarTech/acq_controllers/__init__.py

diff --git a/qcodes/instrument_drivers/AlazarTech/acq_controllers/__init__.py b/qcodes/instrument_drivers/AlazarTech/acq_controllers/__init__.py
new file mode 100644
index 000000000000..c822410d32ce
--- /dev/null
+++ b/qcodes/instrument_drivers/AlazarTech/acq_controllers/__init__.py
@@ -0,0 +1 @@
+from .ATS9360Controller import ATS9360Controller

From 6ef072e07934089bdde2adb5de53641fec65b501 Mon Sep 17 00:00:00 2001
From: "Jens H. Nielsen" 
Date: Thu, 2 Mar 2017 11:14:05 +0100
Subject: [PATCH 149/328] Fix: correctly init instrument in AlazarParameter

Storing the instrument as a non private member of the Parameter prevents picling of the instrument and adding it to a station
---
 qcodes/instrument_drivers/AlazarTech/ATS.py | 3 +--
 1 file changed, 1 insertion(+), 2 deletions(-)

diff --git a/qcodes/instrument_drivers/AlazarTech/ATS.py b/qcodes/instrument_drivers/AlazarTech/ATS.py
index 2a287dd603f8..8c3d2763337c 100644
--- a/qcodes/instrument_drivers/AlazarTech/ATS.py
+++ b/qcodes/instrument_drivers/AlazarTech/ATS.py
@@ -873,8 +873,7 @@ def __init__(self, name=None, label=None, unit=None, instrument=None,
                 # TODO(damazter) (S) test this validator
                 vals = validators.Enum(*byte_to_value_dict.values())
 
-        super().__init__(name=name, label=label, unit=unit, vals=vals)
-        self.instrument = instrument
+        super().__init__(name=name, label=label, unit=unit, vals=vals, instrument=instrument)
         self._byte = None
         self._uptodate_flag = False
 

From 9759ea5563ff0d17c04f634054cdcefaf8814b92 Mon Sep 17 00:00:00 2001
From: "Jens H. Nielsen" 
Date: Thu, 2 Mar 2017 15:23:04 +0100
Subject: [PATCH 150/328] Fix: Alazar mark internal/external sample rate as up
 to date when not in use

---
 qcodes/instrument_drivers/AlazarTech/ATS.py | 10 +++++++++-
 1 file changed, 9 insertions(+), 1 deletion(-)

diff --git a/qcodes/instrument_drivers/AlazarTech/ATS.py b/qcodes/instrument_drivers/AlazarTech/ATS.py
index 8c3d2763337c..805f9ca9bf56 100644
--- a/qcodes/instrument_drivers/AlazarTech/ATS.py
+++ b/qcodes/instrument_drivers/AlazarTech/ATS.py
@@ -416,11 +416,19 @@ def config(self, clock_source=None, sample_rate=None, clock_edge=None,
         self._set_if_present('aux_io_param', aux_io_param)
         # endregion
 
-
+        # handle that external clock and internal clock uses
+        # two different ways of setting the sample rate.
+        # We use the matching one and make the order one
+        # as up to date since it's not being pushed to
+        # the instrument at any time and is never used
         if clock_source == 'EXTERNAL_CLOCK_10MHz_REF':
             sample_rate = self.external_sample_rate
+            if 'sample_rate' in self.parameters:
+                self.parameters['sample_rate']._set_updated()
         elif clock_source == 'INTERNAL_CLOCK':
             sample_rate = self.sample_rate
+            if 'external_sample_rate' in self.parameters:
+                self.parameters['external_sample_rate']._set_updated()
 
         self._call_dll('AlazarSetCaptureClock',
                        self._handle, self.clock_source, sample_rate,

From 46ed0e921b8270853dd935bb366bfccf4ecf65b1 Mon Sep 17 00:00:00 2001
From: "Jens H. Nielsen" 
Date: Thu, 2 Mar 2017 15:26:00 +0100
Subject: [PATCH 151/328] Fix more work on acq controller fixing issues and
 making more stuff parameters

---
 .../acq_controllers/ATS9360Controller.py      | 191 +++++++-----------
 .../AlazarTech/acqusition_parameters.py       | 143 ++++++++++++-
 2 files changed, 201 insertions(+), 133 deletions(-)

diff --git a/qcodes/instrument_drivers/AlazarTech/acq_controllers/ATS9360Controller.py b/qcodes/instrument_drivers/AlazarTech/acq_controllers/ATS9360Controller.py
index 0e8ac426311e..8955493c3dfc 100644
--- a/qcodes/instrument_drivers/AlazarTech/acq_controllers/ATS9360Controller.py
+++ b/qcodes/instrument_drivers/AlazarTech/acq_controllers/ATS9360Controller.py
@@ -2,7 +2,10 @@
 from ..ATS import AcquisitionController
 import numpy as np
 import qcodes.instrument_drivers.AlazarTech.acq_helpers as helpers
-from ..acqusition_parameters import AcqVariablesParam, ExpandingAlazarArrayMultiParameter
+from ..acqusition_parameters import AcqVariablesParam, \
+                                    ExpandingAlazarArrayMultiParameter, \
+                                    NonSettableDerivedParameter, \
+                                    DemodFreqParameter
 
 
 class ATS9360Controller(AcquisitionController):
@@ -47,73 +50,38 @@ def __init__(self, name, alazar_name, filter: str = 'win',
                            parameter_class=ExpandingAlazarArrayMultiParameter)
 
         self._integrate_samples = integrate_samples
-        if integrate_samples:
-            self.add_parameter(name='int_time',
-                               check_and_update_fn=self._update_int_time,
-                               default_fn=self._int_time_default,
-                               parameter_class=AcqVariablesParam)
-            self.add_parameter(name='int_delay',
-                               check_and_update_fn=self._update_int_delay,
-                               default_fn=self._int_delay_default,
-                               parameter_class=AcqVariablesParam)
 
-        self.samples_divisor = self._get_alazar().samples_divisor
-        self._demod_freqs = []
+        self.add_parameter(name='int_time',
+                           check_and_update_fn=self._update_int_time,
+                           default_fn=self._int_time_default,
+                           parameter_class=AcqVariablesParam)
+        self.add_parameter(name='int_delay',
+                           check_and_update_fn=self._update_int_delay,
+                           default_fn=self._int_delay_default,
+                           parameter_class=AcqVariablesParam)
+        self.add_parameter(name='num_avg',
+                           check_and_update_fn=self._update_num_avg,
+                           default_fn= lambda : 1,
+                           parameter_class=AcqVariablesParam)
+        self.add_parameter(name='allocated_buffers',
+                           alternative='not controllable in this controller',
+                           parameter_class=NonSettableDerivedParameter)
+        self.add_parameter(name='buffers_per_acquisition',
+                           alternative='not controllable in this controller',
+                           parameter_class=NonSettableDerivedParameter)
+        self.add_parameter(name='records_per_buffer',
+                           alternative='num_avg',
+                           parameter_class=NonSettableDerivedParameter)
+        self.add_parameter(name='samples_per_record',
+                           alternative='int_time and int_delay',
+                           parameter_class=NonSettableDerivedParameter)
+
+        self.add_parameter(name='demod_freqs',
+                           shape=(),
+                           parameter_class=DemodFreqParameter)
 
-    def add_demodulator(self, demod_freq):
-        if demod_freq not in self._demod_freqs:
-            self._verify_demod_freq(demod_freq)
-            self._demod_freqs.append(demod_freq)
-            self.acquisition.set_setpoints_and_labels()
+        self.samples_divisor = self._get_alazar().samples_divisor
 
-    def remove_demodulator(self, demod_freq):
-        if demod_freq in self.demod_freqs:
-            self._demod_freqs.pop(self.demod_freqs.index(demod_freq))
-            self.acquisition.set_setpoints_and_labels()
-
-    def _verify_demod_freq(self, value):
-        """
-        Function to validate a demodulation frequency
-
-        Checks:
-            - 1e6 <= value <= 500e6
-            - number of oscillation measured using current 'int_time' param value
-              at this demodulation frequency value
-            - oversampling rate for this demodulation frequency
-
-        Args:
-            value: proposed demodulation frequency
-        Returns:
-            bool: Returns True if suitable number of oscillations are measured and
-            oversampling is > 1, False otherwise.
-        Raises:
-            ValueError: If value is not a valid demodulation frequency.
-        """
-        if (value is None) or not (1e6 <= value <= 500e6):
-            raise ValueError('demod_freqs must be 1e6 <= value <= 500e6')
-        isValid = True
-        alazar = self._get_alazar()
-        sample_rate = alazar.get_sample_rate()
-        if 'int_time' in self.parameters:
-            int_time = self.int_time.get()
-        else:
-            int_time = self.acquisition.acquisition_kwargs["samples_per_record"]/sample_rate
-        min_oscillations_measured = int_time * value
-        oversampling = sample_rate / (2 * value)
-        if min_oscillations_measured < 10:
-            isValid = False
-            logging.warning('{} oscillation measured for largest '
-                            'demod freq, recommend at least 10: '
-                            'decrease sampling rate, take '
-                            'more samples or increase demodulation '
-                            'freq'.format(min_oscillations_measured))
-        elif oversampling < 1:
-            isValid = False
-            logging.warning('oversampling rate is {}, recommend > 1: '
-                            'increase sampling rate or decrease '
-                            'demodulation frequency'.format(oversampling))
-
-        return isValid
 
     def _update_int_time(self, value, **kwargs):
         """
@@ -138,18 +106,21 @@ def _update_int_time(self, value, **kwargs):
 
         alazar = self._get_alazar()
         sample_rate = alazar.get_sample_rate()
-        if self.get_max_demod_freq() is not None:
-            self._verify_demod_freq(self.get_max_demod_freq())
+        max_demod_freq = self.demod_freqs.get_max_demod_freq()
+        if max_demod_freq is not None:
+            self._verify_demod_freq(max_demod_freq)
         if self.int_delay() is None:
             self.int_delay.to_default()
+        int_delay = self.int_delay.get()
+        self._update_samples_per_record(sample_rate, value, int_delay)
 
+    def _update_samples_per_record(self, sample_rate, int_time, int_delay):
         # update acquisition kwargs and acq controller value
-        total_time = value + self.int_delay()
+        total_time = (int_time or 0) + (int_delay or 0)
         samples_needed = total_time * sample_rate
         samples_per_record = helpers.roundup(
             samples_needed, self.samples_divisor)
-        self.acquisition.acquisition_kwargs.update(
-            samples_per_record=samples_per_record)
+        self.samples_per_record._save_val(samples_per_record)
 
     def _update_int_delay(self, value, **kwargs):
         """
@@ -164,7 +135,7 @@ def _update_int_delay(self, value, **kwargs):
             number of samples discarded >= numtaps
 
         Sets:
-            sample_rate attr of acq controller to be that of alazar
+            sample_rate attr of acq controller to be that of Alazar
             samples_per_record of acq controller
             acquisition_kwarg['samples_per_record'] of acquisition param
             setpoints of acquisition param
@@ -184,13 +155,17 @@ def _update_int_delay(self, value, **kwargs):
                 'delay is less than recommended for filter choice: '
                 '(expect delay >= {})'.format(int_delay_min))
 
-        # update acquisition kwargs and acq controller value
-        total_time = value + (self.int_time() or 0)
-        samples_needed = total_time * sample_rate
-        samples_per_record = helpers.roundup(
-            samples_needed, self.samples_divisor)
-        self.acquisition.acquisition_kwargs.update(
-            samples_per_record=samples_per_record)
+        int_time = self.int_time.get()
+        self._update_samples_per_record(sample_rate, int_time, value)
+
+    def _update_num_avg(self, value: int, **kwargs):
+        if not isinstance(value, int) or value < 1:
+            raise ValueError('number of averages must be a positive integer')
+
+        self.records_per_buffer._save_val(value)
+        self.buffers_per_acquisition._save_val(1)
+        self.allocated_buffers._save_val(1)
+
 
     def _int_delay_default(self):
         """
@@ -213,7 +188,7 @@ def _int_time_default(self):
             max total time for integration based on samples_per_record,
             sample_rate and int_delay
         """
-        samples_per_record = self.acquisition.acquisition_kwargs.get('samples_per_record')
+        samples_per_record = self.samples_per_record.get()
         if samples_per_record in (0 or None):
             raise ValueError('Cannot set int_time to max if acq controller'
                              ' has 0 or None samples_per_record, choose a '
@@ -225,27 +200,6 @@ def _int_time_default(self):
                       (self.int_delay() or 0))
         return total_time
 
-    def get_demod_freqs(self):
-        """
-        Function to get all the demod_freq parameter values in a numpy array
-
-        Returns:
-            numpy array of demodulation frequencies
-        """
-        return np.array(self._demod_freqs)
-
-    def get_max_demod_freq(self):
-        """
-        Returns:
-            the largest demodulation frequency
-
-        """
-        freqs = self.get_demod_freqs()
-        if len(freqs) > 0:
-            return max(freqs)
-        else:
-            return None
-
     def update_filter_settings(self, filter, numtaps):
         """
         Updates the settings of the filter for filtering out
@@ -272,9 +226,8 @@ def update_acquisition_kwargs(self, **kwargs):
             - buffers_per_acquisition
             - allocated_buffers
         """
-        if 'samples_per_record' in kwargs and self._integrate_samples:
-            raise ValueError('With integrating over samples '
-                             'samples_per_record should not be set manually '
+        if 'samples_per_record' in kwargs:
+            raise ValueError('Samples_per_record should not be set manually '
                              'via update_acquisition_kwargs and should instead'
                              ' be set by setting int_time and int_delay')
         self.acquisition.acquisition_kwargs.update(**kwargs)
@@ -286,7 +239,7 @@ def pre_start_capture(self):
         Alazar acquisition params and set up software wave for demodulation.
         """
         alazar = self._get_alazar()
-        acq_s_p_r = self.acquisition.acquisition_kwargs['samples_per_record']
+        acq_s_p_r = self.samples_per_record.get()
         inst_s_p_r = alazar.samples_per_record.get()
         sample_rate = alazar.get_sample_rate()
         if acq_s_p_r != inst_s_p_r:
@@ -298,15 +251,14 @@ def pre_start_capture(self):
         #                     'instrument value {}, most likely need '
         #                     'to set and check int_time and int_delay'.format(acq_s_r, inst_s_r))
         samples_per_record = inst_s_p_r
-        demod_freqs = self.get_demod_freqs()
+        demod_freqs = self.demod_freqs.get()
         # if len(demod_freqs) == 0:
         #     raise Exception('no demod_freqs set')
 
-        self.records_per_buffer = alazar.records_per_buffer.get()
-        self.buffers_per_acquisition = alazar.buffers_per_acquisition.get()
+        records_per_buffer = alazar.records_per_buffer.get()
         self.board_info = alazar.get_idn()
         self.buffer = np.zeros(samples_per_record *
-                               self.records_per_buffer *
+                               records_per_buffer *
                                self.number_of_channels)
 
         if len(demod_freqs):
@@ -347,30 +299,22 @@ def post_acquire(self):
         # break buffer up into records and averages over them
         alazar = self._get_alazar()
         samples_per_record = alazar.samples_per_record.get()
-        reshaped_buf = self.buffer.reshape(self.records_per_buffer,
+        records_per_buffer = alazar.records_per_buffer.get()
+        buffers_per_acquisition = alazar.buffers_per_acquisition.get()
+        reshaped_buf = self.buffer.reshape(records_per_buffer,
                                            samples_per_record,
                                            self.number_of_channels)
         recordA = np.uint16(np.mean(reshaped_buf[:, :, 0], axis=0) /
-                            self.buffers_per_acquisition)
+                            buffers_per_acquisition)
 
-        # TODO(nataliejpg): test above version works on alazar and
-        # compare performance
         recordA = self._to_volts(recordA)
-        # records_per_acquisition = (self.buffers_per_acquisition *
-        #                    self.records_per_buffer)
-        # recA = np.zeros(self.samples_per_record)
-        # for i in range(self.records_per_buffer):
-        #     i0 = (i * self.samples_per_record * self.number_of_channels)
-        #     i1 = (i0 + self.samples_per_record * self.number_of_channels)
-        #     recA += self.buffer[i0:i1:self.number_of_channels]
-        # recordA = np.uint16(recA / records_per_acquisition)
         unpacked = []
         if self._integrate_samples:
             unpacked.append(np.mean(recordA, axis=-1))
         else:
             unpacked.append(recordA)
         # do demodulation
-        if len(self.get_demod_freqs()):
+        if self.demod_freqs.get_num_demods():
             magA, phaseA = self._fit(recordA)
             if self._integrate_samples:
                 magA = np.mean(magA, axis=-1)
@@ -414,13 +358,13 @@ def _fit(self, volt_rec):
         # volt_rec to matrix and multiply with demodulation signal matrices
         alazar = self._get_alazar()
         sample_rate = alazar.get_sample_rate()
-        demod_length = len(self._demod_freqs)
+        demod_length = self.demod_freqs.get_num_demods()
         volt_rec_mat = np.outer(np.ones(demod_length), volt_rec)
         re_mat = np.multiply(volt_rec_mat, self.cos_mat)
         im_mat = np.multiply(volt_rec_mat, self.sin_mat)
 
         # filter out higher freq component
-        cutoff = self.get_max_demod_freq() / 10
+        cutoff = self.demod_freqs.get_max_demod_freq() / 10
         if self.filter_settings['filter'] == 0:
             re_filtered = helpers.filter_win(re_mat, cutoff,
                                              sample_rate,
@@ -460,3 +404,4 @@ def _fit(self, volt_rec):
         phase = np.angle(complex_mat, deg=True)
 
         return magnitude, phase
+
diff --git a/qcodes/instrument_drivers/AlazarTech/acqusition_parameters.py b/qcodes/instrument_drivers/AlazarTech/acqusition_parameters.py
index 933efe6a933a..434ebee3a88c 100644
--- a/qcodes/instrument_drivers/AlazarTech/acqusition_parameters.py
+++ b/qcodes/instrument_drivers/AlazarTech/acqusition_parameters.py
@@ -1,5 +1,6 @@
-from qcodes import Parameter, MultiParameter
+from qcodes import Parameter, MultiParameter, ArrayParameter
 import numpy as np
+import logging
 
 class AcqVariablesParam(Parameter):
     """
@@ -209,7 +210,7 @@ def __init__(self,
                  instrument,
                  names = ('raw_output',),
                  labels = ("raw output",),
-                 units = ('V',),
+                 units = ('v',),
                  shapes = ((1,),),
                  setpoints = (((1,),),),
                  setpoint_names = (('time',),),
@@ -231,11 +232,13 @@ def __init__(self,
 
     def set_setpoints_and_labels(self):
         if not self._integrate_samples:
-
-            if self._instrument._get_alazar().sample_rate.get() and self.acquisition_kwargs.get('samples_per_record'):
+            int_time = self._instrument.int_time.get()
+            int_delay = self._instrument.int_delay.get()
+            total_time = int_time + int_delay
+            samples = self._instrument.samples_per_record.get()
+            if total_time and samples:
                 start = 0
-                samples = self.acquisition_kwargs['samples_per_record']
-                stop = samples/self._instrument._get_alazar().sample_rate.get()
+                stop = total_time
             else:
                 start = 0
                 samples = 1
@@ -257,7 +260,8 @@ def set_setpoints_and_labels(self):
         setpoint_units = [setpoint_unit]
         units = [self.units[0]]
         shapes = [base_shape]
-        for i, demod_freq in enumerate(self._instrument._demod_freqs):
+        demod_freqs = self._instrument.demod_freqs.get()
+        for i, demod_freq in enumerate(demod_freqs):
             names.append("demod_freq_{}_mag".format(i))
             labels.append("demod freq {} mag".format(i))
             names.append("demod_freq_{}_phase".format(i))
@@ -274,7 +278,6 @@ def set_setpoints_and_labels(self):
             setpoint_names.append(setpoint_name)
             setpoint_labels.append(setpoint_label)
             setpoint_units.append(setpoint_unit)
-        self.metadata['demod_freqs'] = self._instrument._demod_freqs
         self.names = tuple(names)
         self.labels = tuple(labels)
         self.units = tuple(units)
@@ -285,7 +288,127 @@ def set_setpoints_and_labels(self):
         self.setpoint_units = tuple(setpoint_units)
 
     def get(self):
+        inst = self._instrument
+        params_to_kwargs = ['samples_per_record', 'records_per_buffer',
+                            'buffers_per_acquisition', 'allocated_buffers']
+        acq_kwargs = self.acquisition_kwargs.copy()
+        additional_acq_kwargs = {key: val.get() for key, val in inst.parameters.items() if
+             key in params_to_kwargs}
+        acq_kwargs.update(additional_acq_kwargs)
+
         output = self._instrument._get_alazar().acquire(
             acquisition_controller=self._instrument,
-            **self.acquisition_kwargs)
-        return output
\ No newline at end of file
+            **acq_kwargs)
+        return output
+
+
+class NonSettableDerivedParameter(Parameter):
+    """
+    Parameter of an AcquisitionController which cannot be updated directly
+    as it's value is derived from other parameters. This is intended to be
+    used in high level APIs where Alazar parameters such as 'samples_per_record'
+    are not set directly but are parameters of the actual instrument anyway.
+
+    This assumes that the parameter is stored via a call to '_save_val' by
+    any set of parameter that this parameter depends on.
+
+    Args:
+        name: name for this parameter
+        instrument: acquisition controller instrument this parameter belongs to
+        alternative (str): name of parameter(s) that controls the value of this
+            parameter and can be set directly.
+    """
+
+    def __init__(self, name, instrument, alternative: str):
+        self._alternative = alternative
+        super().__init__(name, instrument=instrument)
+
+    def set(self, value):
+        """
+        It's not possible to directly set this parameter as it's derived from other
+        parameters.
+        """
+        raise NotImplementedError("Cannot directly set {}. To control this parameter"
+                                  "set {}".format(self.name, self._alternative))
+
+    def get(self):
+        return self.get_latest()
+
+
+class DemodFreqParameter(ArrayParameter):
+
+    #
+    def __init__(self, name, shape, **kwargs):
+        self._demod_freqs = []
+        super().__init__(name, shape, **kwargs)
+
+
+    def add_demodulator(self, demod_freq):
+        ndemod_freqs = len(self._demod_freqs)
+        if demod_freq not in self._demod_freqs:
+            self._verify_demod_freq(demod_freq)
+            self._demod_freqs.append(demod_freq)
+            self._save_val(self._demod_freqs)
+            self.shape = (ndemod_freqs+1,)
+            self._instrument.acquisition.set_setpoints_and_labels()
+
+    def remove_demodulator(self, demod_freq):
+        ndemod_freqs = len(self._demod_freqs)
+        if demod_freq in self._demod_freqs:
+            self._demod_freqs.pop(self._demod_freqs.index(demod_freq))
+            self.shape = (ndemod_freqs - 1,)
+            self._save_val(self._demod_freqs)
+            self._instrument.acquisition.set_setpoints_and_labels()
+
+    def get(self):
+        return self._demod_freqs
+
+    def get_num_demods(self):
+        return len(self._demod_freqs)
+
+    def get_max_demod_freq(self):
+        if len(self._demod_freqs):
+            return max(self._demod_freqs)
+        else:
+            return None
+
+    def _verify_demod_freq(self, value):
+        """
+        Function to validate a demodulation frequency
+
+        Checks:
+            - 1e6 <= value <= 500e6
+            - number of oscillation measured using current 'int_time' param value
+              at this demodulation frequency value
+            - oversampling rate for this demodulation frequency
+
+        Args:
+            value: proposed demodulation frequency
+        Returns:
+            bool: Returns True if suitable number of oscillations are measured and
+            oversampling is > 1, False otherwise.
+        Raises:
+            ValueError: If value is not a valid demodulation frequency.
+        """
+        if (value is None) or not (1e6 <= value <= 500e6):
+            raise ValueError('demod_freqs must be 1e6 <= value <= 500e6')
+        isValid = True
+        alazar = self._instrument._get_alazar()
+        sample_rate = alazar.get_sample_rate()
+        int_time = self._instrument.int_time.get()
+        min_oscillations_measured = int_time * value
+        oversampling = sample_rate / (2 * value)
+        if min_oscillations_measured < 10:
+            isValid = False
+            logging.warning('{} oscillation measured for largest '
+                            'demod freq, recommend at least 10: '
+                            'decrease sampling rate, take '
+                            'more samples or increase demodulation '
+                            'freq'.format(min_oscillations_measured))
+        elif oversampling < 1:
+            isValid = False
+            logging.warning('oversampling rate is {}, recommend > 1: '
+                            'increase sampling rate or decrease '
+                            'demodulation frequency'.format(oversampling))
+
+        return isValid
\ No newline at end of file

From be421dcbeb95b44dc787dded3c217008fa7106a3 Mon Sep 17 00:00:00 2001
From: "Jens H. Nielsen" 
Date: Thu, 2 Mar 2017 15:27:16 +0100
Subject: [PATCH 152/328] fix typo

---
 qcodes/instrument_drivers/AlazarTech/ATS9360.py | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

diff --git a/qcodes/instrument_drivers/AlazarTech/ATS9360.py b/qcodes/instrument_drivers/AlazarTech/ATS9360.py
index 163de13696bc..b220857bae6e 100644
--- a/qcodes/instrument_drivers/AlazarTech/ATS9360.py
+++ b/qcodes/instrument_drivers/AlazarTech/ATS9360.py
@@ -322,7 +322,7 @@ def get_sample_rate(self):
         elif self.clock_source.get() == 'INTERNAL_CLOCK':
             rate = self.sample_rate.get()
         else:
-            raise Exception("Don't know how to get sample rate with {}".format(alazar.clock_source.get()))
+            raise Exception("Don't know how to get sample rate with {}".format(self.clock_source.get()))
 
         if rate == '1GHz_REFERENCE_CLOCK':
             rate = 1e9

From de840b6285f34c10eeaa97981b57d62b896a2165 Mon Sep 17 00:00:00 2001
From: "Jens H. Nielsen" 
Date: Thu, 2 Mar 2017 15:57:01 +0100
Subject: [PATCH 153/328] fix: correct changing of inttime/intdelay

---
 .../AlazarTech/acq_controllers/ATS9360Controller.py           | 3 ++-
 qcodes/instrument_drivers/AlazarTech/acqusition_parameters.py | 4 ++--
 2 files changed, 4 insertions(+), 3 deletions(-)

diff --git a/qcodes/instrument_drivers/AlazarTech/acq_controllers/ATS9360Controller.py b/qcodes/instrument_drivers/AlazarTech/acq_controllers/ATS9360Controller.py
index 8955493c3dfc..24c4b44201bd 100644
--- a/qcodes/instrument_drivers/AlazarTech/acq_controllers/ATS9360Controller.py
+++ b/qcodes/instrument_drivers/AlazarTech/acq_controllers/ATS9360Controller.py
@@ -108,7 +108,7 @@ def _update_int_time(self, value, **kwargs):
         sample_rate = alazar.get_sample_rate()
         max_demod_freq = self.demod_freqs.get_max_demod_freq()
         if max_demod_freq is not None:
-            self._verify_demod_freq(max_demod_freq)
+            self.demod_freqs._verify_demod_freq(max_demod_freq)
         if self.int_delay() is None:
             self.int_delay.to_default()
         int_delay = self.int_delay.get()
@@ -121,6 +121,7 @@ def _update_samples_per_record(self, sample_rate, int_time, int_delay):
         samples_per_record = helpers.roundup(
             samples_needed, self.samples_divisor)
         self.samples_per_record._save_val(samples_per_record)
+        self.acquisition.set_setpoints_and_labels()
 
     def _update_int_delay(self, value, **kwargs):
         """
diff --git a/qcodes/instrument_drivers/AlazarTech/acqusition_parameters.py b/qcodes/instrument_drivers/AlazarTech/acqusition_parameters.py
index 434ebee3a88c..6ac1f4afaae8 100644
--- a/qcodes/instrument_drivers/AlazarTech/acqusition_parameters.py
+++ b/qcodes/instrument_drivers/AlazarTech/acqusition_parameters.py
@@ -232,8 +232,8 @@ def __init__(self,
 
     def set_setpoints_and_labels(self):
         if not self._integrate_samples:
-            int_time = self._instrument.int_time.get()
-            int_delay = self._instrument.int_delay.get()
+            int_time = self._instrument.int_time.get() or 0
+            int_delay = self._instrument.int_delay.get() or 0
             total_time = int_time + int_delay
             samples = self._instrument.samples_per_record.get()
             if total_time and samples:

From ff6fbbd3b8fde17e466fb2698db1b99aebcf84c1 Mon Sep 17 00:00:00 2001
From: "Jens H. Nielsen" 
Date: Fri, 3 Mar 2017 17:07:37 +0100
Subject: [PATCH 154/328] Wip getting this working with multidim arrays

---
 ...e with Alazar ATS9360 new controller.ipynb | 949 ++++++++++++++++++
 .../acq_controllers/ATS9360Controller.py      |  60 +-
 ...arameters.py => acquisition_parameters.py} |   7 +-
 3 files changed, 1001 insertions(+), 15 deletions(-)
 create mode 100644 docs/examples/Qcodes example with Alazar ATS9360 new controller.ipynb
 rename qcodes/instrument_drivers/AlazarTech/{acqusition_parameters.py => acquisition_parameters.py} (98%)

diff --git a/docs/examples/Qcodes example with Alazar ATS9360 new controller.ipynb b/docs/examples/Qcodes example with Alazar ATS9360 new controller.ipynb
new file mode 100644
index 000000000000..5f2672a82652
--- /dev/null
+++ b/docs/examples/Qcodes example with Alazar ATS9360 new controller.ipynb	
@@ -0,0 +1,949 @@
+{
+ "cells": [
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "deletable": true,
+    "editable": true
+   },
+   "source": [
+    "### Qcodes example notebook for Alazar card ATS9360 and acq controllers"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 1,
+   "metadata": {
+    "collapsed": false,
+    "deletable": true,
+    "editable": true
+   },
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "No loop running\n"
+     ]
+    }
+   ],
+   "source": [
+    "import qcodes as qc\n",
+    "import qcodes.instrument.parameter as parameter\n",
+    "import qcodes.instrument_drivers.AlazarTech.ATS9360 as ATSdriver\n",
+    "from qcodes.instrument_drivers.AlazarTech.acq_controllers import ATS9360Controller\n",
+    "from qcodes.station import Station\n",
+    "#import qcodes.instrument_drivers.AlazarTech.samp_controller as samp_acq_contr\n",
+    "#import qcodes.instrument_drivers.AlazarTech.ave_controller_test as ave_acq_controller\n",
+    "#import qcodes.instrument_drivers.AlazarTech.rec_controller_test as record_acq_controller\n",
+    "\n",
+    "import logging\n",
+    "# logging.basicConfig(filename='example.log',level=logging.INFO)\n",
+    "\n",
+    "qc.halt_bg()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "deletable": true,
+    "editable": true
+   },
+   "source": [
+    "NB: See ATS9360 example notebook for general commands "
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 2,
+   "metadata": {
+    "collapsed": false,
+    "deletable": true,
+    "editable": true
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "{'CPLD_version': '25.16',\n",
+       " 'SDK_version': '5.10.22',\n",
+       " 'asopc_type': '1645511520',\n",
+       " 'bits_per_sample': 12,\n",
+       " 'driver_version': '5.10.22',\n",
+       " 'firmware': None,\n",
+       " 'latest_cal_date': '25-01-17',\n",
+       " 'max_samples': 4294967294,\n",
+       " 'memory_size': '4294967294',\n",
+       " 'model': 'ATS9360',\n",
+       " 'pcie_link_speed': '0.5GB/s',\n",
+       " 'pcie_link_width': '8',\n",
+       " 'serial': '970396',\n",
+       " 'vendor': 'AlazarTech'}"
+      ]
+     },
+     "execution_count": 2,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "# Create the ATS9360 instrument\n",
+    "alazar = ATSdriver.AlazarTech_ATS9360(name='Alazar')\n",
+    "# Print all information about this Alazar card\n",
+    "alazar.get_idn()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 3,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "752"
+      ]
+     },
+     "execution_count": 3,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "alazar._handle"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 4,
+   "metadata": {
+    "collapsed": false,
+    "scrolled": false
+   },
+   "outputs": [],
+   "source": [
+    "# Configure all settings in the Alazar card\n",
+    "alazar.config(clock_source='INTERNAL_CLOCK',\n",
+    "              sample_rate=500000000,\n",
+    "              clock_edge='CLOCK_EDGE_RISING',\n",
+    "              decimation=1,\n",
+    "              coupling=['DC','DC'],\n",
+    "              channel_range=[.4,.4],\n",
+    "              impedance=[50,50],\n",
+    "              trigger_operation='TRIG_ENGINE_OP_J',\n",
+    "              trigger_engine1='TRIG_ENGINE_J',\n",
+    "              trigger_source1='CHANNEL_A',\n",
+    "              trigger_slope1='TRIG_SLOPE_POSITIVE',\n",
+    "              trigger_level1=128,\n",
+    "              trigger_engine2='TRIG_ENGINE_K',\n",
+    "              trigger_source2='DISABLE',\n",
+    "              trigger_slope2='TRIG_SLOPE_POSITIVE',\n",
+    "              trigger_level2=128,\n",
+    "              external_trigger_coupling='DC',\n",
+    "              external_trigger_range='ETR_2V5',\n",
+    "              trigger_delay=0,\n",
+    "              timeout_ticks=1,\n",
+    "              aux_io_mode='AUX_IN_AUXILIARY', # AUX_IN_TRIGGER_ENABLE for seq mode on\n",
+    "              aux_io_param='NONE' # TRIG_SLOPE_POSITIVE for seq mode on\n",
+    "             )"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "deletable": true,
+    "editable": true
+   },
+   "source": [
+    "### Basic Acquisition\n",
+    "\n",
+    "Pulls the raw data the alazar acquires averaged over number of records buffers."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 5,
+   "metadata": {
+    "collapsed": false,
+    "deletable": true,
+    "editable": true,
+    "scrolled": true
+   },
+   "outputs": [],
+   "source": [
+    "# Create the acquisition controller which will take care of the data handling and tell it which \n",
+    "# alazar instrument to talk to.\n",
+    "myctrl = ATS9360Controller(name='my_controller', alazar_name='Alazar')"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 6,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [],
+   "source": [
+    "station = qc.Station(alazar, myctrl)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 7,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "start 0 stop 1 num steps 1\n",
+      "start 0 stop 2e-07 num steps 1152\n"
+     ]
+    }
+   ],
+   "source": [
+    "myctrl.int_delay(2e-7)\n",
+    "myctrl.int_time(2e-6)\n",
+    "myctrl.num_avg(100)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 8,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "100"
+      ]
+     },
+     "execution_count": 8,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "myctrl.parameters['records_per_buffer'].get()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 9,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "start 0 stop 2.2e-06 num steps 1152\n",
+      "1152\n",
+      "start 0 stop 2.2e-06 num steps 1152\n",
+      "1152\n"
+     ]
+    }
+   ],
+   "source": [
+    "myctrl.int_delay(2e-7)\n",
+    "print(myctrl.samples_per_record.get_latest())\n",
+    "myctrl.int_time(2e-6)\n",
+    "print(myctrl.samples_per_record.get_latest())"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 10,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "1\n",
+      "1\n",
+      "100\n"
+     ]
+    }
+   ],
+   "source": [
+    "myctrl.num_avg(100)\n",
+    "print(myctrl.buffers_per_acquisition.get())\n",
+    "print(myctrl.allocated_buffers.get())\n",
+    "print(myctrl.records_per_buffer.get())"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 11,
+   "metadata": {
+    "collapsed": false,
+    "deletable": true,
+    "editable": true
+   },
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "[ 0.001221   0.0002442  0.001221  ...,  0.0002442  0.001221   0.0002442]\n",
+      "(1152,)\n"
+     ]
+    }
+   ],
+   "source": [
+    "# Pull data from the card by calling get of the controllers acquisition parameter\n",
+    "data1 = myctrl.acquisition.get()\n",
+    "print(data1[0])\n",
+    "print(data1[0].shape)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 12,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "WARNING:root:4.0 oscillation measured for largest demod freq, recommend at least 10: decrease sampling rate, take more samples or increase demodulation freq\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "start 0 stop 2.2e-06 num steps 1152\n",
+      "[2000000.0]\n",
+      "start 0 stop 2.2e-06 num steps 1152\n",
+      "[2000000.0, 6000000.0]\n",
+      "start 0 stop 2.2e-06 num steps 1152\n",
+      "[2000000.0]\n"
+     ]
+    }
+   ],
+   "source": [
+    "myctrl.demod_freqs.add_demodulator(2e6)\n",
+    "print(myctrl.demod_freqs.get())\n",
+    "myctrl.demod_freqs.add_demodulator(6e6)\n",
+    "print(myctrl.demod_freqs.get())\n",
+    "myctrl.demod_freqs.remove_demodulator(6e6)\n",
+    "print(myctrl.demod_freqs.get())"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 13,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "WARNING:root:8.0 oscillation measured for largest demod freq, recommend at least 10: decrease sampling rate, take more samples or increase demodulation freq\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "start 0 stop 2.2e-06 num steps 1152\n",
+      "start 0 stop 2.2e-06 num steps 1152\n"
+     ]
+    },
+    {
+     "data": {
+      "text/plain": [
+       "[4000000.0]"
+      ]
+     },
+     "execution_count": 13,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "myctrl.demod_freqs.add_demodulator(2e6)\n",
+    "myctrl.demod_freqs.get_latest()\n",
+    "myctrl.demod_freqs.add_demodulator(4e6)\n",
+    "myctrl.demod_freqs.get_latest()\n",
+    "myctrl.demod_freqs.remove_demodulator(2e6)\n",
+    "myctrl.demod_freqs.get_latest()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 14,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [],
+   "source": [
+    "# myctrl.acquisition.set_setpoints_and_labels()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 15,
+   "metadata": {
+    "collapsed": false,
+    "deletable": true,
+    "editable": true,
+    "scrolled": false
+   },
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "DataSet:\n",
+      "   mode     = DataMode.LOCAL\n",
+      "   location = 'data/2017-03-03/#007_{name}_16-45-29'\n",
+      "      |                        |        | \n",
+      "   Setpoint | time_set                         | time               | (1152,)\n",
+      "   Measured | my_controller_raw_output         | raw_output         | (1152,)\n",
+      "   Measured | my_controller_demod_freq_0_mag   | demod_freq_0_mag   | (1152,)\n",
+      "   Measured | my_controller_demod_freq_0_phase | demod_freq_0_phase | (1152,)\n",
+      "acquired at 2017-03-03 16:45:30\n"
+     ]
+    }
+   ],
+   "source": [
+    "# Do this in as qcodes measurement (ie the same but makes a data set)\n",
+    "data2 = qc.Measure(myctrl.acquisition).run(station=station)\n",
+    "plot = qc.MatPlot(data2.my_controller_raw_output)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 16,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "(1, 1, 1, 1152)"
+      ]
+     },
+     "execution_count": 16,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "myctrl.mat_shape"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 17,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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AIada9Q/OzMyagwOEHKhlw7NKWy63TGAymAZpTdjVcrLc+qRppLwfl3nMXmk/\nCIPLZd7vfnA/CLXjAKFN1LwIuHUbYudKo/8o87SfK/3DG1Q/CBWnqJ5G7+tqaIV1KKfRdxPViwOE\nHCjbD0J6/NX73vq8Xy3UkvtBsMFqin4QWnSv570fhL4C2WbYGw4Q6qz8c+2r39Vy8cRZr7ZaJTAe\nTHFqc3a13P9x0dc6NdtuzntjtzzmrlpVDI2UJceuYqgdBwgtridKbdQff6sEHOU04oqs0Sf5vjv1\nqU2+BvOcicHMq1rLqKZqL3rQXS0P4Thv1d9/4XoNtI7NvA0cIORA+SqGSMfVOy/1XV6euIqhvpr5\nxFmsKZ7mmIedXgN5r2LoSzPsjpoGCJLOkrRE0lJJl5YZL0lXpOMXSTploLSS3i1psaS9kmaXmed0\nSVskfaJ2a1Z7Vb96yDrdIBecp8Zr0Fp/PoUavV7V3c9D6PlrMEvLa1fLfVUDNXpnV0Hzr8HQNfM2\nqFmAIKkDuBKYA8wCzpc0q2iyOcDM9HURcFWGtA8D7wDu7GPRXwV+Vr01MTMzaz+dNZz3qcDSiFgG\nIOkGYC7wSME0c4HrIwmV50vqknQoMKOvtBHxaDqsZIGSzgWeBLbWaqWGqtxFQX9XCr3rurLHosVX\nPjVpCJljg6tvbr5GislyKx/XbPs57/nNY/5aobv1LL/J3DdSzFnpaiVqWcUwFVhR8H1lOizLNFnS\n9iJpHPA3wN8PMr+5ktcfc9+t4uvbeK1dFW/OobabqHj5fQ1vwG6uvJHiYO5sqThJ1bTCL6cVqknK\nqWS1Gt2weChaqZHiZ4F/iogt/U0k6SJJCyQtWLNmTX1yNoCyjRSrfFA17yHaPNxIsTJZTrLN8geT\nh+1ZLI95qoVGN1Ic7BHaDPunllUMq4DDC75PS4dlmWZ4hrTFTgPeJelyoAvYK2lHRHyjcKKIuAa4\nBmD27Nm5OPv0W8UwxL/2yrtazsUmGbJ69JrnrpbrJ++HZR6zV7Uqhpz/k+W9iiFvDbgrUcsA4T5g\npqQjSf7czwPeWzTNPOCStI3BacDGiFgtaU2GtL1ExKt7Pkv6LLClODhoZ9U6wVZaxNwMPwLLruIq\nphqqvKvlwSyjcfIeFLWzZq42qETNAoSI6JZ0CXAr0AFcGxGLJV2cjr8auAU4G1gKbAM+2F9aAElv\nB74OTAFulvRARJxZq/Woh/66Wrbm0UxVDHnQSod4XrtaLpy1+0FoVFfL5TXD7qhlCQIRcQtJEFA4\n7OqCzwEK8ud/AAAgAElEQVR8NGvadPhNwE0DLPezg8huw2S+i6GieRYPyJiugmXkWSN6zctnV8t9\nDW+uPZ33K7Y85q4l7mKo0jT7pm3EXQx9aIbd0UqNFFtKtQ6enqvaap1gK61KaMaTUlaNuCJr9B97\nvffzYIKfqi6/gds7Lw2V81g60miV3H7e6N/sUDhAyAFXMbQGVzFUpplPnMWG1tVybbZD6dMcW1Pe\nqxj60gz7wwFCHQzuPFjlq4esVQwDTldZt7ANa9mfg21eC9XK4WDn03eVRW3zULaDsUqXN5g81qWU\noq8RtV92rTW6aqhWQWhFx25NclAfDhDMzMxqpJkLyhwg1Fm5iDZ7V8sVLGeA79lTNqdG9JrXkEaK\nGqCevs8Sn9rkp1bynt085m/w54D8yNSZ1pDm50aK/XGAUAfN1sVrf9xIsbEavj1z1N9FpcXHjS7u\nrlRz5ba8Rh+vNWs82+gVqxMHCDlQvqvl6mqXA7qR3EixMtm6Wq59PqphaI0Ua6NZj4tKuZFi7ThA\nyIF8dbU8pMXlR116zeu9cd3Vcu3kPcDNY+5K+0EYXC7r/UCwSrmKoXYcINRBHs5ttW79Xs3W7da3\nRheT992mof75apW7GPK47Gpp9CrUavmNXq96cYCQA+X7QWiXQ7B1uIqhMq10jA+tM6E69YOQ85KA\nwXIVQ+04QKiDodwjXrWHLGXtB2Gw88lR47XBLrcZ/q+qlsdBzqia22jI/SBUmJfBVcXU/qDo+yfV\nBAfkAKod/FTcMLVW/SA0/67JxAFCnWU9rsp3DFPBzYpFk7bCycYq1y4nMhtYc5bY5D/PTblZM3KA\nkAP9N1IcmnZtpDi4UpvKEhVv2zz2g9CXFtnNuZHH7VmtPOW9KNyNFGvHAUId5OLqvVpVFX01UsvY\neC3vAUjer7Ianbtc9XdRaRXDIDLpRopDU/3btRu7/HrMOU8cIOSAGym2BjdSrFALHeJD2gc12g5N\ne1xUyI0Ua8cBQp2Vb2zVTxVDwbjKulouunIfZLpm1TZdLaMBulruY3iT7ea85zeP2ctjnirlrpYb\nywFCHdT6SXf1NNT+DvIegOT+j6jB+cvTUzsrXWaztdXI+aGYTZVXoh59XzRyvnnjACEHyt6fXO0f\nVpsc0I3kKobKtNIh6a6WG6fRVQyDDY6bYf84QMiBWt7FUOlh2CqBRD16zSvess3V1XJz7ei85zeP\nuSvO02B/23nvX8lVDLXjAKFNVOsEO9SnOeY9AMl59mh0DvPUpqHiFu2DChob+AfS4B9Lz9KHko3q\n38VQabVSjTpKqslc88cBQg7U42mOVnuuYqhMo/8Aq6k5ulquyWIartFVDIPVDLvDAUIdDOXKpe5d\nLQ8wXaUP6xns3RRDNdhi97z/adX7eMiartYNAMv3LFrDBQ4+SdWWMfS7aoaYPoY+n6p3tVzx8qu6\n+JrPN28cINRZ1iKvclMN5YTaJsezFWmXE5kNrBmPhWbIcjNu16wcIFTJ3r3Bjt17BpW2/yh7aEdf\nxV0tN8VPcmD16DWvqbtabrLdXKvsVusKN4+bs12ex+JGirWjWhapSjoL+BrQAfxLRFxWNF7p+LOB\nbcAHIuL+/tJKejfwWeAlwKkRsSAd/mbgMmAEsAv4PxHxq/7yN3v27FiwYEFV1vWpdVt57Zdvr8q8\nzMzMAEZ0DOMPX5hT1XlKWhgRswearmYlCJI6gCuBOcAs4HxJs4ommwPMTF8XAVdlSPsw8A7gzqJ5\nrQXOiYgTgAuB71d7nczMzOpp1569DVt2Zw3nfSqwNCKWAUi6AZgLPFIwzVzg+kiKMeZL6pJ0KDCj\nr7QR8Wg6rNfCIuL3BV8XA6MljYyInbVYuWKNasFuZmZWC7VsgzAVWFHwfWU6LMs0WdL2553A/fUK\nDszMzFpNLUsQGkLSccA/AGf0Mf4ikuoMpk+fXsXlVm1WZmZmDVfLEoRVwOEF36elw7JMkyVtCUnT\ngJuACyLiiXLTRMQ1ETE7ImZPmTJlwJUwMzNrR7UMEO4DZko6UtII4DxgXtE084ALlDgd2BgRqzOm\n7UVSF3AzcGlE3F3tlTEzM2snNQsQIqIbuAS4FXgUuDEiFku6WNLF6WS3AMuApcC3gY/0lxZA0tsl\nrQReAdws6dZ0XpcAxwCfkfRA+jqoVutnZmbWyjK1QZA0CTgM2A4sj4hM911ExC0kQUDhsKsLPgfw\n0axp0+E3kVQjFA//PPD5LPmqBbdBMDOzVtJngCBpIsmf9/kknQ+tAUYBB0uaD3wzIn5dl1yamZlZ\nXfVXgvBj4Hrg1RGxoXCEpJcBfyzpqIj4Ti0z2CzKPZHRzMysWfUZIETEm/sZtxBYWJMcNSmHB2Zm\n1koGbKQo6aeS3itpbD0yZGZmZo2X5S6GrwCvAh6R9GNJ75I0qsb5ajquYTAzs1Yy4F0MEXEHcEf6\nAKU3AH8KXAtMqHHezMzMrEGy3uY4GjgH+F/AKcB1tcxUM/LDmszMrJUMGCBIupHkyYw/B74B3JG1\nHwQzMzNrTllKEL4DnB8Re2qdmWbmNghmZtZK+mykKOlVABFxa7ngQNIEScfXMnNmZmbWGP2VILxT\n0uUkVQsL2d+T4jHA64EjgI/XPIdNwgUIZmbWSvrrKOl/S5oMvBN4N3AoybMYHgW+FRF31SeLTcIR\ngpmZtZB+2yBExHqSpyx+uz7ZMTMzszyo2eOe241vczQzs1biAMHMzMxKZHkWw8gsw9qdb3M0M7NW\nkqUE4bcZh5mZmVmL6LORoqRDgKnAaEkns7+d/gRgTB3y1lRcgGBmZq2kv7sYzgQ+AEwDvlowfDPw\nqRrmyczMzBqsv34QrgOuk/TOiPiPOuapKcmNEMzMrIVkeRbD8ZKOKx4YEZ+rQX7MzMwsB7IECFsK\nPo8C3krSm6IVcPmBmZm1kgEDhIj4x8Lvkr4C3FqzHDUp1zCYmVkrGUxHSWNIGi6amZlZixqwBEHS\nQ0CkXzuAKYDbHxRxV8tmZtZKspQgvBU4J32dARwWEd/IMnNJZ0laImmppEvLjJekK9LxiySdMlBa\nSe+WtFjSXkmzi+b3f9Ppl0g6M0sezczMrNSAAUJEPAUcAMwF3gGckGXGkjqAK4E5wCzgfEmziiab\nA8xMXxcBV2VI+3CajzuLljcLOA84DjgL+GY6n/pwAYKZmbWQLM9i+AxwHUmQcCDwPUl/m2HepwJL\nI2JZROwCbiAJMgrNBa6PxHygS9Kh/aWNiEcjYkmZ5c0FboiInRHxJLA0nY+ZmZlVKMttju8DToqI\nHQCSLgMeAD4/QLqpwIqC7yuB0zJMMzVj2nLLm19mXnXhuxjMzKyVZGmD8AxJ/wc9RgKrapOd2pN0\nkaQFkhasWbOm0dkxMzPLpSwBwkZgsaTvSfouSRuADWnjwiv6SbcKOLzg+zRKA4u+psmSdjDLIyKu\niYjZETF7ypQpA8wyOxcgmJlZK8lSxXBT+upxe8Z53wfMlHQkyR/1ecB7i6aZB1wi6QaSKoSNEbFa\n0poMaYvNA/5V0leBw0gaPv4uY16HzM9iMDOzVpIlQOiKiK8VDpD0V8XDikVEt6RLSHpd7ACujYjF\nki5Ox18N3AKcTdKgcBvwwf7Spst+O/B1kv4Ybpb0QEScmc77RuARoBv4aETsybYZzMzMrJAiov8J\npPsj4pSiYb+PiJNrmrM6mD17dixYsKAq89q6s5vj/s49UJuZWXUtv+wtVZ2fpIURMXug6fosQZB0\nPkmx/pGS5hWMGg+sH3oWzczMLK/6q2K4B1hN0vdB4QObNgOLapmpZuQmCGZm1kr6DBDSHhSfAl5R\nv+yYmZlZHmR5WNNm9j+saQQwHNgaERNqmbFm44c1mZlZKxkwQIiI8T2fldzLNxc4vZaZMjMzs8bK\n0lHSPukzE/4L8JMSi7gNgpmZtZIsVQzvKPg6DJgN7KhZjszMzKzhsnSUdE7B525gOaVPZTQzM7MW\nkqUNwgfrkZFm5yoGMzNrJQO2QZA0TdJNkp5PX/8haVo9MmdmZmaNkaWR4ndJHoR0WPr6aTrMCvg2\nRzMzayVZAoQpEfHdiOhOX98jeVCSmZmZtagsAcI6Se+X1JG+3g+sq3XGmo3bIJiZWSvJEiB8CHgP\n8CzJsxneRfpYZjMzM2tNWe5ieAp4Wx3y0tRcgGBmZq2kop4UzczMrD04QKgSuRGCmZm1EAcIVeLw\nwMzMWkmWZzE8AcwHfgP8JiIW1zxXZmZm1lBZShBmAd8CDgC+LOkJSTfVNlvNxzUMZmbWSrIECHuA\n3en7XuD59GVmZmYtKsvTHDcBDwFfBb4dEe4kqQw3UjQzs1aSpQThfOBO4CPADZL+XtIba5stMzMz\na6QsHSX9BPiJpBcDc4C/Bj4JjK5x3szMzKxBsjzu+T8kLQW+BowBLgAm1TpjZmZm1jhZqhi+BLwo\nIs6MiC9ExB0RsSPLzCWdJWmJpKWSLi0zXpKuSMcvknTKQGklTZb0C0mPp++T0uHDJV0n6SFJj0r6\nv1nyaGZmZqUGDBAiYgHwEknvkXRBz2ugdJI6gCtJqiVmAedLmlU02RxgZvq6CLgqQ9pLgdsiYiZw\nW/od4N3AyIg4AXgZ8GeSZgyUTzMzMyuVpYrh74Cvp6/XA5eT7eFNpwJLI2JZROwCbgDmFk0zF7g+\nEvOBLkmHDpB2LnBd+vk64Nz0cwBjJXWStI/YRXIHhpmZmVUoSxXDu4A3As9GxAeBk4CJGdJNBVYU\nfF+ZDssyTX9pD46I1ennZ4GD088/BraSPJL6aeArEbE+Qz7NzMysSJYAYXtE7AW6JU0g6STp8Npm\nK5uICJKSA0hKHfYAhwFHAh+XdFRxGkkXSVogacGaNWvql1kzM7MmkiVAWCCpC/g2sBC4H/hthnSr\n6B1ITEuHZZmmv7TPpdUQpO89vTq+F/h5ROyOiOeBu4HZxZmKiGsiYnZEzJ4yZUqG1TAzM2s//QYI\nSroH/FJEbIiIq4E3AxemVQ0DuQ+YKelISSOA84B5RdPMAy5I72Y4HdiYVh/0l3YecGH6+ULgJ+nn\np4E3pPkeC5wOPJYhn2ZmZlak346SIiIk3QKckH5fnnXGEdEt6RLgVqADuDYiFku6OB1/NXALcDaw\nFNgGfLC/tOmsLwNulPRh4CngPenwK4HvSlpM8vTl70bEoqz5NTMzs/2yPIvhfkkvj4j7Kp15RNxC\nEgQUDru64HMAH82aNh2+jqTRZPHwLSS3OpqZmdkQZQkQTgPeJ+kpkrsERPLffmJNc2ZmZmYNkyVA\nOLPmuTAzM7NcyfKwpqfqkREzMzPLjyy3OZqZmVmDJM316s8BgpmZmZVwgGBmZpZjDSpAcIBgZmZm\npRwg5Myc4w8BYMr4kQ3OiZmZ5UGDChAcIJhZ85AanQOz9uEAIWd6ToA+D5qZGfguBiviKyUzM2sk\nBwhm1jQcN1s7chsEMzMzK+HbHK0X+VrJzMwayAGCmTUNuXGOtaFoUCWDA4Sc6Sk58HnQzMwayQFC\nTjk+MDMzcBsEM7MBOXA2qx8HCDnTU9fkulYzs3xql9OzA4ScaVRRklkzaJcTs+VbuxyGDhDMzMxy\nzG0QrBdfKZmZ5VO9q4B9m6MBrmIw6487ELM8aJej0AGCmZlZjrmKwXpxFYOZWT61y/m5pgGCpLMk\nLZG0VNKlZcZL0hXp+EWSThkoraTJkn4h6fH0fVLBuBMl/VbSYkkPSRpVy/WrJRelmpXhn4XlQL3P\nzy33NEdJHcCVwBxgFnC+pFlFk80BZqavi4CrMqS9FLgtImYCt6XfkdQJ/AC4OCKOA14H7K7V+plZ\n/Tk+sFxokwOxliUIpwJLI2JZROwCbgDmFk0zF7g+EvOBLkmHDpB2LnBd+vk64Nz08xnAooh4ECAi\n1kXEnlqtXK3s7yipwRkxM7Oy6n16jgY1QqhlgDAVWFHwfWU6LMs0/aU9OCJWp5+fBQ5OPx8LhKRb\nJd0v6ZPlMiXpIkkLJC1Ys2ZNpetkZmbWFpq6kWIkYVVPaNUJvAp4X/r+dklvLJPmmoiYHRGzp0yZ\nUr/MZtQTKLoAwcwsn+pdwttybRCAVcDhBd+npcOyTNNf2ufSagjS9+fT4SuBOyNibURsA24BTqHJ\nuBsEs7656s3yoF0akdcyQLgPmCnpSEkjgPOAeUXTzAMuSO9mOB3YmFYf9Jd2HnBh+vlC4Cfp51uB\nEySNSRssvhZ4pFYrV2t+WJOZmUHj+kHorNWMI6Jb0iUkf9wdwLURsVjSxen4q0mu8s8GlgLbgA/2\nlzad9WXAjZI+DDwFvCdN84Kkr5IEFwHcEhE312r9as3hgZlZPtX9+q3VAgSAiLiFJAgoHHZ1wecA\nPpo1bTp8HVDStiAd9wOSWx2blrtaNutbuxTtWr61y1HY1I0UzczMWp0f1mS9tUuIalYBN82xPBjW\nJgeiA4Scao/Dz8zMBuKHNZmZmTWDNrmCc4CQOz1dLbfJEWhm1mTa5CYGBwhmZmZWygFCzrirZbO+\n+TZgy4N6l/C24sOabBB8/jMzyzc/i8Eayk0QzMyskRwg5JR7jDMzy6e6N1L0bY4GjatrMjOzbNrl\nLjMHCGZmZjnmrpatlzYJUM0q0qgTpVmhYW1yfnaAYGZmlmdug2BmZtYM2qMIwQFCzvQEiu3SCMbM\nrNm4HwQzMzNrWw4QcsZdLZv1zXcBWx64HwRrCJ//zMzyrf5VDL7N0Qq4CYKZmTWSA4SccoBgZpZP\n9e4K31UMZmZmTaBdLuAcIOSUH9ZkZpZPdW+kWOfl9XCAkDN+WJNZ3/zrsDxol35qahogSDpL0hJJ\nSyVdWma8JF2Rjl8k6ZSB0kqaLOkXkh5P3ycVzXO6pC2SPlHLdau1Njn+zMxsAI26cKxZgCCpA7gS\nmAPMAs6XNKtosjnAzPR1EXBVhrSXArdFxEzgtvR7oa8CP6v6CpmZmbWRWpYgnAosjYhlEbELuAGY\nWzTNXOD6SMwHuiQdOkDaucB16efrgHN7ZibpXOBJYHGtVqrW3FGSmVm+1b0fhBa8i2EqsKLg+8p0\nWJZp+kt7cESsTj8/CxwMIGkc8DfA31cj82ZmZu2sqRspRlIx0xNbfRb4p4jY0l8aSRdJWiBpwZo1\na2qdxcFzIwSzUm6laDnQLqfnzhrOexVweMH3aemwLNMM7yftc5IOjYjVaXXE8+nw04B3Sboc6AL2\nStoREd8oXGBEXANcAzB79uzcnW4a1aWmmZll446Shu4+YKakIyWNAM4D5hVNMw+4IL2b4XRgY1p9\n0F/aecCF6ecLgZ8ARMSrI2JGRMwA/hn4YnFw0EzaJEA1M2s6LkEYoojolnQJcCvQAVwbEYslXZyO\nvxq4BTgbWApsAz7YX9p01pcBN0r6MPAU8J5arUMjtcsBaGbWbOrfUVJjihBqWcVARNxCEgQUDru6\n4HMAH82aNh2+DnjjAMv97CCya2ZmZqmmbqTYylyAYGaWT/XuSbEV2yDYILinZbO+uRGv5UG7XMA5\nQMipdunr28zM+ueHNZmZmTWDNrl+c4CQM3tdx2Bmlmt1v4uh1R7WZGZmZkPnKgbrpU1KsMwq4gI2\ny4NhbdJGzAGCmZlZjvk2RzMzsybQJgUIDhDyxkWoZmb5Vu+HNTWqFYIDBDMzMyvhAMHMzCzH3AbB\ngMbdzmLWDPz7MKsfBwhmZmY55n4QzMzMrISrGAzY36Wmi1LNzPLJtzmamZlZwzXqMecOEHKqTQJU\ns4o06qE1Zu3IAYKZmVmOuQ2CmZmZ5YYDhJxxCaqZmRVyCYIBcO7JUwE46/hDGpyT+ps8dgQHjhvZ\n6GxYjvX8Pswa6czj2uP8rHZu9DN79uxYsGBB1ea3bVc3m7Z3szeCYVLJO0DHMDFq+DA27+hmmLSv\ndaoQw4bBQeNHsX7rLiaNGc6m7d1s3dW9bx5jR3ayZ2+ws3sPYv98J44ezsbtuwH2DRsmGDuyk2HS\nvnE90xXmp7NDjOgcxpYd3SV5LZf/oazbmBGdbNq+u895dY0ZDsCGbbszLbtw/pXkdcLoTjbv6CaC\nkuknjh7O1p3d7C0zrtyyJ4yqbPq+8lq8b8rt0/72W1/zHzuikyDYtmtPr/kOtJ+DGHC79swry36Y\nMHo423Z1s3cv/U5fvN7jRnWyu3svu/bsRYgDx41gR/fefo+j/o7JgaYf6jE2lOk7honRIzrKrlvW\n/ZblmB8mGDeqc992znIMF597BspPX+eqrOeSns8SjB/VOeB5Nct+KPc7G8z0Y0d2Mn5kJ89v3rnv\nGKvGfps4ejhbd3UzangHu7v3sntPsGnHbrbt2sOLDh7P6BEdVIukhRExe8DpHCBUL0AwMzPLu6wB\ngqsYzMzMrERNAwRJZ0laImmppEvLjJekK9LxiySdMlBaSZMl/ULS4+n7pHT4myUtlPRQ+v6GWq6b\nmZlZK6tZgCCpA7gSmAPMAs6XNKtosjnAzPR1EXBVhrSXArdFxEzgtvQ7wFrgnIg4AbgQ+H6NVs3M\nzKzl1bIE4VRgaUQsi4hdwA3A3KJp5gLXR2I+0CXp0AHSzgWuSz9fB5wLEBG/j4hn0uGLgdGS3CTe\nzMxsEGoZIEwFVhR8X5kOyzJNf2kPjojV6edngYPLLPudwP0RsXNwWTczM2tvnY3OwFBEREjqdRuG\npOOAfwDOKJdG0kUk1RlMnz695nk0MzNrRrUsQVgFHF7wfVo6LMs0/aV9Lq2GIH1/vmciSdOAm4AL\nIuKJcpmKiGsiYnZEzJ4yZUrFK2VmZtYOahkg3AfMlHSkpBHAecC8omnmARekdzOcDmxMqw/6SzuP\npBEi6ftPACR1ATcDl0bE3TVcLzMzs5ZXsyqGiOiWdAlwK9ABXBsRiyVdnI6/GrgFOBtYCmwDPthf\n2nTWlwE3Svow8BTwnnT4JcAxwGckfSYddkZE7CthMDMzs2zck6J7UjQzszbirpYzkLSGpBSimg4k\n6ZPB6sPbu768vevL27v+2mGbHxERAzbCa+sAoRYkLcgSmVl1eHvXl7d3fXl715+3+X5+FoOZmZmV\ncIBgZmZmJRwgVN81jc5Am/H2ri9v7/ry9q4/b/OU2yCYmZlZCZcgmJmZWYm2CRAknSVpiaSlki4t\nM16SrkjHL5J0ykBpJU2W9AtJj6fvkwrG/d90+iWSziwY/jJJD6XjrpCkdPhIST9Kh98raUZBmgvT\nZTwuqacXyVxr8u29R9ID6au498/caoJt/hpJ90vqlvSuorz5GKeu27vpjvEm2N4fk/RIuuzbJB1R\nkKbpjm8AIqLlXyS9MT4BHAWMAB4EZhVNczbwM0DA6cC9A6UFLifp2hngUuAf0s+z0ulGAkem6TvS\ncb9L5690eXPS4R8Brk4/nwf8KP08GViWvk9KP09q9DZt1e2dft/S6G3Yott8BnAicD3wroJ8+Riv\n4/ZuxmO8Sbb364Ex6ec/p4nP4T2vdilBOBVYGhHLImIXcAMwt2iaucD1kZgPdCl5GFR/aecC16Wf\nrwPOLRh+Q0TsjIgnSbqSPjWd34SImB/JkXN9UZqeef0YeGMamZ4J/CIi1kfEC8AvgLOqslVqp5m3\nd7PK/TaPiOURsQjYW5QvH+P13d7NqBm2968jYluafj7JQwahOY9voH2qGKYCKwq+r0yHZZmmv7QH\nR/JwKYBngYMzzGtlH/PalyYiuoGNwAEZ8543zby9AUalRbPzJZ1Lc2iGbT6UvOdNM29vaL5jvNm2\n94dJShey5j2XavawpnYTESHJt4TUSY239xERsUrSUcCvJD0UfTw+vJ34GK8vH+P1Va3tLen9wGzg\ntUPPVWO1SwnCKuDwgu/T0mFZpukv7XNpkRPpe8+TI/ub17Qyw3ulkdQJTATWZcx73jTz9iYiVqXv\ny4DbgZP7X91caIZtPpS8500zb+9mPMabYntLehPwaeBtEbGzgrznUzUaMuT9RVJSsoyksUlPI5Xj\niqZ5C70buPxuoLTAl+ndwOXy9PNx9G7gsoy+G7icnQ7/KL0bzd0Y+xu4PEnSuGVS+nlyo7dpC2/v\nScDI9POBwOMUNYbK46sZtnlBPr5HaSNFH+P1295Nd4w3w/YmCbKeAGYW5avpju99eW90Bup4gJ0N\n/CHdgZ9Oh10MXJx+FnBlOv4hYHZ/adPhBwC3pT+wXxbudJIo8glgCWkr13T4bODhdNw32N9Z1Sjg\n30kaw/wOOKogzYfS4UuBDzZ6W7by9gZemebnwfT9w43eli20zV9OUv+6laS0ZrGP8fpv72Y9xptg\ne/8SeA54IH3Na+bjOyLck6KZmZmVapc2CGZmZlYBBwhmZmZWwgGCmZmZlXCAYGZmZiUcIJiZmVkJ\nBwhmlomkLkkfKfh+mKQf12hZ50r6TD/jT5D0vVos28wSvs3RzDJR8kjs/46I4+uwrHtIeqNb2880\nvwQ+FBFP1zo/Zu3IJQhmltVlwNGSHpD0ZUkzJD0MIOkDkv5L0i8kLZd0iaSPSfp9+kCgyel0R0v6\nuaSFkn4j6cXFC5F0LLCzJziQ9G5JD0t6UNKdBZP+lKQXTDOrAQcIZpbVpcATEfHSiPg/ZcYfD7yD\npAe/LwDbIuJk4LfABek01wB/EREvAz4BfLPMfP4IuL/g+2eAMyPiJOBtBcMXAK8ewvqYWT/8NEcz\nq5ZfR8RmYLOkjSRX+JB0e3uipHEk3fz+u6SeNCPLzOdQYE3B97uB70m6EfjPguHPA4dVMf9mVsAB\ngplVy86Cz3sLvu8lOdcMAzZExEsHmM92kqdrAhARF0s6jeRhPAslvSwi1pE8T2N7tTJvZr25isHM\nstoMjB9s4ojYBDwp6d0ASpxUZtJHgWN6vkg6OiLujYjPkJQs9Dw691iSh+aYWQ04QDCzTNKr9rvT\nBoNfHuRs3gd8WNKDwGJgbplp7gRO1v56iC9LeihtEHkPyVMIAV4P3DzIfJjZAHybo5nljqSvAT+N\niF/2MX4kcAfwqojormvmzNqESxDMLI++CIzpZ/x04FIHB2a14xIEMzMzK+ESBDMzMyvhAMHMzMxK\nOEbt0AIAAAAgSURBVEAwMzOzEg4QzMzMrIQDBDMzMyvhAMHMzMxK/H9RIHz4hahpqAAAAABJRU5E\nrkJggg==\n",
+      "text/plain": [
+       ""
+      ]
+     },
+     "execution_count": 17,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "plot.fig"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 18,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "WARNING:root:8.0 oscillation measured for largest demod freq, recommend at least 10: decrease sampling rate, take more samples or increase demodulation freq\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "start 0 stop 2.2e-06 num steps 1152\n",
+      "start 0 stop 2.2e-06 num steps 3200\n"
+     ]
+    }
+   ],
+   "source": [
+    "myctrl.int_delay(2e-7)\n",
+    "myctrl.int_time(6e-6)\n",
+    "myctrl.num_avg(1)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 19,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "DataSet:\n",
+      "   mode     = DataMode.LOCAL\n",
+      "   location = 'data/2017-03-03/#008_{name}_16-45-33'\n",
+      "      |                        |        | \n",
+      "   Setpoint | time_set                         | time               | (3200,)\n",
+      "   Measured | my_controller_raw_output         | raw_output         | (3200,)\n",
+      "   Measured | my_controller_demod_freq_0_mag   | demod_freq_0_mag   | (3200,)\n",
+      "   Measured | my_controller_demod_freq_0_phase | demod_freq_0_phase | (3200,)\n",
+      "acquired at 2017-03-03 16:45:34\n"
+     ]
+    },
+    {
+     "data": {
+      "image/png": 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GGO57fWGe2/W19enfG+u2c/vk+bw0ewVvf7Y2y90dryzgDy/PpanJ8M7CtUz4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\nLicKZmZmlsuJgpmZmeX6H2D1FFMPm8aQAAAAAElFTkSuQmCC\n",
+      "text/plain": [
+       ""
+      ]
+     },
+     "execution_count": 19,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "data3 = qc.Measure(myctrl.acquisition).run()\n",
+    "plot = qc.MatPlot(data3.my_controller_raw_output)\n",
+    "plot.fig"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 20,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "DataSet:\n",
+      "   mode     = DataMode.LOCAL\n",
+      "   location = 'data/2017-03-03/#009_{name}_16-45-34'\n",
+      "      |                        |        | \n",
+      "   Setpoint | time_set                         | time               | (3200,)\n",
+      "   Measured | my_controller_raw_output         | raw_output         | (3200,)\n",
+      "   Measured | my_controller_demod_freq_0_mag   | demod_freq_0_mag   | (3200,)\n",
+      "   Measured | my_controller_demod_freq_0_phase | demod_freq_0_phase | (3200,)\n",
+      "acquired at 2017-03-03 16:45:35\n"
+     ]
+    },
+    {
+     "data": {
+      "image/png": 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/eJhTr6reffP79y/jEz94mLuffpn3fese3vPNX+5SPR+5+kFOvnLX359Trnxw+/t79g8W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\nYII=\n",
+      "text/plain": [
+       ""
+      ]
+     },
+     "execution_count": 20,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "myctrl.num_avg(100)\n",
+    "data3 = qc.Measure(myctrl.acquisition).run()\n",
+    "plot = qc.MatPlot(data3.my_controller_raw_output)\n",
+    "plot.fig"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 21,
+   "metadata": {
+    "collapsed": false,
+    "deletable": true,
+    "editable": true
+   },
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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KajoVF9K/azFF+Xl8uLasXkb8gdc/mxiT7BGpHHt2LKJnx3aJ9REyeXh6ECFf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\nVsMJgpmZmdX4P/bu7z8W0W89AAAAAElFTkSuQmCC\n",
+      "text/plain": [
+       ""
+      ]
+     },
+     "execution_count": 21,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "# Do this in as qcodes measurement (ie the same but makes a data set) _demod_freqs\n",
+    "#data3 = qc.Measure(basic_acq_controller.acquisition).run()\n",
+    "plot = qc.MatPlot(data3.my_controller_demod_freq_0_mag)\n",
+    "plot.fig"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 22,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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YBnIDN1sdrLb5k0SoTcRH/4kcKiGmQudiJ89NVR5Yo9VBq6PBToF06GuZJ2D2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\nYII=\n",
+      "text/plain": [
+       ""
+      ]
+     },
+     "execution_count": 22,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "plot = qc.MatPlot(data3.my_controller_demod_freq_0_phase)\n",
+    "plot.fig"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 23,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "WARNING:root:oversampling rate is 0.625, recommend > 1: increase sampling rate or decrease demodulation frequency\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "start 0 stop 6.2e-06 num steps 3200\n"
+     ]
+    }
+   ],
+   "source": [
+    "myctrl.demod_freqs.add_demodulator(400e6)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 24,
+   "metadata": {
+    "collapsed": false,
+    "scrolled": false
+   },
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "DataSet:\n",
+      "   mode     = DataMode.LOCAL\n",
+      "   location = 'data/2017-03-03/#010_{name}_16-45-37'\n",
+      "      |                        |        | \n",
+      "   Setpoint | time_set                         | time               | (3200,)\n",
+      "   Measured | my_controller_raw_output         | raw_output         | (3200,)\n",
+      "   Measured | my_controller_demod_freq_0_mag   | demod_freq_0_mag   | (3200,)\n",
+      "   Measured | my_controller_demod_freq_0_phase | demod_freq_0_phase | (3200,)\n",
+      "   Measured | my_controller_demod_freq_1_mag   | demod_freq_1_mag   | (3200,)\n",
+      "   Measured | my_controller_demod_freq_1_phase | demod_freq_1_phase | (3200,)\n",
+      "acquired at 2017-03-03 16:45:38\n"
+     ]
+    },
+    {
+     "data": {
+      "image/png": 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Zdv4Q12PU6+7pHe4qmJnZGJQ813D322mAIOksSb8A9pW0OO/1O+Cl3VdFs5Hv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\n",
+      "text/plain": [
+       ""
+      ]
+     },
+     "execution_count": 24,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "data3 = qc.Measure(myctrl.acquisition).run()\n",
+    "plot = qc.MatPlot(data3.my_controller_raw_output)\n",
+    "plot.fig"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 25,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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zmMsejr7r6I3Fb6YGYfANhOueXAqADzLqoFTJmHCLwBcDT1HFubxnuYtjQqwU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R7JOFaljbtvAZ4FZjc/tZbohXEoml4bg9MJVhGdpifGwczONrNfEazEzs1zYi0w6scgVGMvcvdr\ncp9V64z6cQh5Xah6/6aZLcpxXq3QC2OwE7CTmd1kZreY2VH5Tq01xqOAUDihtDfuw0M0Dp0bAzN7\nM7Aa+OSwOzUCdGIc3P0Sd18GnAf8dUc61kWGMwZmVgI+Bbyno50aATpwLXwXWOLuuwHfo7oq7xk6\nMAZ9BGaGQ4CTgS+a2YwOdK2G8SggPArEJc6F4bY8+zRq+0SoEiL8H6l8Gh1rYcr2mjZm1gdMB57O\ndXb56YVxKJqeGAMzOwz4AHCcu7+U89xaoSfGIcZVdF7dPNrHYCqwK3CDmT1EYJdeV4Cj4mgfB9z9\n6dh98E/A3jnPLS+jfgwItAnr3H1zaJa4j0Bg6CxekKPHaP0jkLweIHAGiZxIVib2eTW1Dii3NmtL\nsLKLO6B8Iny9kloHlAfIdkA5Jtx+NrVOilePx3GI9ePLFOOkOOrHANiTwGlp+Ti/J5bH+nIscNt4\nG4NEX26gGCfFUT8OwLxYX14D3DIOx+Ao4Irw9TYEJorZHb8eOn3AXvgj8EC9j2Di/UC47SzgrPC1\nAZeEn/8ifiOmtQ23zwbWA/cD3wdmxT77QLj/vdR6JK8Gfhl+9g9UE1dNAL5B4KxyK7DDOB2HfQgk\n5Y0EGpS7x+EYfB94Avh5+LdunF4LnwXuDsfgByQm7PEwBom+3kABAkIvjAPwsfBauDO8FnYZh2Ng\nBCane8LvP6mIa0GZFIUQQghRx3j0QRBCCCFEEyQgCCGEEKIOCQhCCCGEqEMCghBCCCHqkIAghBBC\niDokIAghcmFmM8zsHbH3883smwV91/FmdmGDz3czsy8X8d1CiACFOQohcmFB2fH/5+67duG7fkyQ\nOfKpBvt8HzjN3X9ddH+EGI9IgyCEyMtFwDIz+7mZfdLMlpjZLwHM7G1m9u9hnfuHzOwcM3u3md0R\nFpOZFe63zMyuM7Pbzey/zWyX5JeY2U7AS5FwYGavN7NfmtmdZnZjbNfvEmQaFUIUgAQEIURezgd+\n5e57uPv7Uj7fFXgtQQbMjwLPu/uewM3AW8N9LgP+j7vvDbwX+HzKcQ4EfhZ7fyFwpLuvAo6Lbb8N\neMUwzkcI0YC+ke6AEGLM8AN3fw54zsyeJVjhQ5AKdnczmwIcAHzDzKI2gynHmQc8GXt/E/BlM7sa\n+FZs+++A+R3svxAihgQEIUSniFeaHIq9HyKYa0rAH9x9jybHeYGggikA7n6Wme1LUCDndjPb292f\nJqhZ8kKnOi+EqEUmBiFEXp4jKDvcFu7+R+BBM3s9gAWsStn1f4Adozdmtszdf+LuFxJoFqLSuDsR\nFLIRQhSABAQhRC7CVftNocPgJ9s8zJuA083sToKKfGtT9rkR2NOqdohPmtkvQofIHxNU8QN4JXBN\nm/0QQjRBYY5CiFGHmX0W+K67fz/j80Hgh8DL3X1LVzsnxDhBGgQhxGjk74BJDT5fDJwv4UCI4pAG\nQQghhBB1SIMghBBCiDokIAghhBCiDgkIQgghhKhDAoIQQggh6pCAIIQQQog6JCAIIYQQoo7/D5Md\nEtO6Lo9hAAAAAElFTkSuQmCC\n",
+      "text/plain": [
+       ""
+      ]
+     },
+     "execution_count": 25,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "plot = qc.MatPlot(data3.my_controller_demod_freq_0_mag)\n",
+    "plot.fig"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 26,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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gY8HqvoKH1FFZXqgJH/7k4u3Ya7sjf4B4xVqrMU75zSRc+eTSzOmtiXDnK1RY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l+F3XQ7QvW9lBExqw5c659/Nd9uhlNRrF5d4Du12M6hkR5AU4kKIlpNRA8R0QYiWqe4T0T0tfj+\nfUS01FaWiEYR0XVEtD7+P1K4d06c/yEiOkRI/12cdk/8Ny5O7ySiH8VlbiOiac0aCx+U1ak4apcp\n2nu6DZe32ZGYlKrLq9h8pvsmuIQQBqB1oa1i1eoDirmwsPX3dJuuWGbU4A4jC9MFLqx2HTfDyLIm\n4LNHLsSA9uiT57SLi0dNxnwCigmKmm5FMjh9n5mZa27qm+Me+OxyQtYpIwdm/oueG5PsUtWjBncA\nAN4n9U1GW52M1h8un4bo90QecqOipmxyqMiTE38YWOjinQHt9YIu7M1tcfA5OLgjdQWeNYdWl3Wd\nk1XoVET98IN4PnAhylVDlEaylYlGxbtrEV5F04gKIqoDuADAGgDzAbydiOZL2dYAmB3/nQbgmw5l\n1wG4gTE2G8AN8TXi+8cDWABgNYAL43o43skYWxL/PR+nnQLgZcbYLABfBvCFqp6/DJqpU6Hj5HKi\ngnMq3tiuXhxta3lZrXyXAE9JeSX7mJwp9us/tDd+88G9M2V10D1XFaHaM+0o6sv5K9ARFfGI6kRJ\n7165I87cd1aUN+FU6BVkRRQKfV7B2HTFzq9yfkAMdX/puMXZfggz7bBFE7H+vDX4xFui5WT5tJGQ\nIfd7nCOhWK/VsN0QndKJU0E8r4pgN7RNhMUGRT9Ar6hp6+MXjt650DwXi5j6zomKPWePUZbVQSdG\nkKH0U2GvXgvXodA5v1LWCfV6mJoY6+v27Vez0UxOxQoAG+LYIdsAXAFgrZRnLYDLWIRbAYwgoomW\nsmsBXBr/vhTAkUL6FYyxrYyxxwBsiOsxQazrKgAHUAs4UC/LDSxy4uba/ZyoeG2rOrq9NSCZvXvG\nMmoTP7X1hyh6kNNcMGvcUMwZP1TZDxnyuF30rmW49D0rKvX8B7hxKly4JoDa+VW6aUU/XGN/lBBB\nl0KjgVhRM5tu4lQcND/rRifzaBTN8f3mjsPj5x+WCw8fZ8lgpKN5cLuFUyFvBCo6LetMzV2nolYj\n/EhUalRk1elomCx2powciEMXTSxtUmoSf3DC1mStpILq8OV6UCnmIdZvEDLflNW8Xl03nzMuHntb\nYOsC0FyiYjKAJ4Xrp+I0lzymsuMZYzz0+rNIHXHZ2rs0Fn18QiAckjKMsS4AmwCMlh+EiE4jojuI\n6I6NGzdqHrc6NNf6Q8dKjDkVsQMWm8WFDlNHDfLoS/a/rn6fvkQKT8WoeFMeeRObP3EY9pkztlIH\nXFEf7AuglQBQmOcmsVckQsxFURPwI3TTttzL6MA5FfKGbNxgZVZx5re9U0UCigF2RU1dNaoTfaSo\nKeczc9IGtNcxfGBEAKme0+SmW+6LfKus3oNZEbgRtykSweLkdW9HNUYu3D8f6LpjCq4oNa6uV5Gd\n1yGrWvmskz2NPq2oyaLV02V2vJMxtgDAXvHfuz3buZgxtpwxtnzs2LEFeuqH5jq/MrfJdSp0i4hJ\nj/CH712JwxdPcu6LS2CeKE1zMlfcLyOOMIs/5AiT5r4V74M9j7tORZ5TIZsxJs6vDO1GOhUFxB+e\nvIp7P3lwLq3BojGWx9l08pXHIbNZFSAuXV+xTlFTdGhlbVt00+1j/SHrVKg4FTlFzZhTISyhum9A\ndCXvCrEukwHNC69uy+XPdENn6qxIc+VU+NIUmXehO5jJnCUP8UdccS6Fv9e8ToWidD8gKp4GsINw\nPSVOc8ljKvtcLCJB/J/rR2jLMMb4/1cA/ACpWCQpQ0RtAIYDeNHjGZuCsoqaRvGH5ma3pFOhI2xM\nG+/uM0d7TWydTkQ+zVxevF21OCKtV9121e25EFp68QffJCJk/FRIJ1A+zl3d6SnRdHprppkzx3CN\nmKFeczOp45DHkDS/dSjDqTCJP1wguumWv0HTVJOJRVXWG/76fOZaOY80bRSZ59n5py9/yS2P5/N7\nt8bbyaepRIrubrrzhLkrMn4qNO0NG5Cas6v6nlp/SHUr18nWoCqaSVT8CcBsIppORB2IlCivlvJc\nDeCE2ApkJYBNsWjDVPZqACfGv08E8HMh/fjYomM6IuXP24mojYjGAAARtQN4C4D7FXUdA+C3rJmy\nB0c0sweyqVjaZmz9EYs/VP745fKaFtz7okhTizTSNNFkU2kpUuK78iHGEtl3xd+x8lCVY4Ory8rJ\nGZPShJjIXqc6FeZ++YQ+TzlITkWsqBHlNjXTAmriNDhxKhTtu6DNoqjpAhNXw0eR2IUrkvqpENvP\n9kNXvwt8OYgmvyoqKHVSDKf9TFl79dr8WqmGLF4SdlddBNgvHpMqFZu4LHmTUl1Pex9N61qso/B+\nANcC+AuAKxljDxDR6UR0epztlwAeRaRU+W0AZ5jKxmXOB3AQEa0HcGB8jfj+lQAeBPBrAGcyxroB\ndAK4lojuA3APIu7Et+O6vgtgNBFtAPAhxJYkvY2yzl7M7n/Te+OGplrtSQhnzqnQHLhszp68LP2U\nnArgI4fMxaLJwzNpSRlLPWXEEaayOpO9qsUfLtXp4oqYxR/ZxSnhVAhKciZxRYP1nsyWKO+nwjTu\nRvGHk2mxTMA4dBIxp8LmRtbadvS/XlNEFTXNTwOHQge1pVU2jXfBy4RX6hPg5iQum8WeX7lKKk/7\nxZ1fcZGRHCbeBWIZ3XraLpjwm/RBVNwzF+5Fb6CpHjUZY79ERDiIaRcJvxmAM13LxukvAlB6GIrj\nlJwnpb0GYJkm/xYAxxofohdQ2vpDkdbZVsOW7Y3MhysuWnnxh0anwjJv/TZZxaJWI5y53ywsnToS\nb//2rWmdTFvEq39Fy8qLKv+Aq/aoqVTUlK5XTh+N+5/enC/LB0fhSCwnQokTuA5AzSr+YFB5eTSh\nOk6FimAy5c/ebFNwbEzI5XElKjwnn2q8SdhEXNxqy+V8YHSgpJkvXshwKhz648mpcEUZTgUnurNz\nyK1zLuIP7vVUR9InsYUUhLL8PbYITdG3FTXfrGhGQLHEiYquzQZ3fmWeErYPyoukUJyuks26ll2Q\nuDimXXHqED/HwR3F6WRjjAiNIpwvG9JKlDnUoRV/OJxc+PvrbOf+SLqNdXLIsQxc+leVMx5v8Yd0\nLcrUncZX0b4KO08Zjv84NmVf69x520UH+c1X7fzKUo0n+LCI7ejemRzQCgA+unqusX6xvy5zR8eR\n1BK7juuk0k+FY1m+LtZqZv83ExS+TDKcCqE5seV2i7NBnfijTn6+W3oSgahoQZSlKVRz67VtcUhz\nDcWdiD/aLESDlVPh1sdsmfyiKq7PJCitDewQ/Znl2/zU4Qv8O6CoR4YqYJOYrivro8GvakcFmzWM\nqi65TL0WBcB6g88Lsok/mPemxpv87NoFOGXP6c7l/vvkXbFwchoNN/JTIS2ghpVLftZMnJAC46t7\nJ587ciEmjxiYXIs6SmIR+zsVTptCeRdnVdoaHRYRlZ8KXRMqTsWSHUYY68/4qXAY97onN0D5hIpE\nVZwY1zWWc3AznApFvt9+eB+jSalOp0IMd646TKbiD6luyqe1BkkRiIqWhK+u6OIdRuDYZXrX3CJ0\nE08Wf/iWT++7T21VTpH9yyF+PJ1t+v4NHdCmtSA4ba8Zhfqj6kOUlxMT+YXZ2IZNJ8Xhi9TWIJ08\nTboojEWLuGjOpzsRMhbd8T0J8ezv3n1a4sHSBfvNHYczYs+fUT35U6JR/0W6VbdsCDY461QIL083\nfzlEYiTbVjr/bf5Jjloqu/0R5qNDf5X+RLRcMAXXyzKa4l23mGx+78n1m2tXetR0K9wlcCo4VFNv\nUEeb2aTUQlQQ1O9Mx6kgyvtu6Q/OrwIKwlen4ojFkzJsWOPHrrm167RRAOziA6v4w2Nem6w35FPO\nThOHObWvwsXvXoZpYwYX6g+Hzk+FTdbsy8IuokioK2vzUChGh3Sx/vAmKkqcnbIbUn4BNfXX9B6L\nxGBwnXPtGjGLisPw0zP2EFvI/ayR2e8BoOYU+Iy40vkVJ5YdytvmjIlTpkKHhtNTFiqxVBFFTd8+\nid+fbk0X/VAooyBr/FSouHctQlOE0OetiGb6qdAt9B9dPRdHLZ2MCcPNcQ6qnLequmTrBCB6nitO\nW4lnNr1RqG+um4Ipm7wupf20l1WVc+nDj0/fHeOGduKXf37WqZ85boo4horR2XPWGPz+4Y25vCow\n5r9olVnkZP8AKplyERT5srQ6LJAXevWmqCxP6o0tmVe1/CZTlNjRIRXhie3rcuuVSvUNiPU6EBUC\nF7IwQaooplTUdBV/qFyIa/pmEn/o1nSu76F7WpOfChcdqt5AICpaEL6cCh9xiW7eTRo+EAsmmQMS\nmcq73lflzZzqFCxZAmH4wPbEBbG2Hk3jruxrUzad62df8YdtMxSfYeyQTuw4Os9hcSWeVBubmGNw\nZ6qfYtP1YIU4FcWRMUdUKMkV3VBd3E3LNbs+d/5kznLptjpJuC/3NOeLwqlXeqRzws5RUMcpMdef\n8ajpS1Q4PJzSekaRT2394fbBrpgecXAP3Gk8Xnh1q1OZpA1h0GRvphxZAkWhU6Gx/qjV8vOht0y+\nZQTxRyuiLKfCcM/mndJWh/1w4j6zVXWJHgWTNCsh4376N8HsUEm9Yfs6BbIp22VPjTHBIi02+pNz\nBL6Yqdx0i6hndAD0fWKICF3fZ62KU+EaTdOEtUsi9/EuBHv+BOjWhk53Qy1mULcncsBsfirKHkx1\nsXNUUA2bXfxhr1fXHzG/bjksE/rc9eC2cPJwPH7+Ydhz9hjsMjUSN71V0GVZOnUE/vcDeyrLbt6S\nBmXU9dX2nHriU+W7Rd1GTyMQFS2I0rE/DJNLt+lXZf7nxalQ+alI2L/5tKJtVyL+0JyUfcUfm97Y\nbu6DaF6o+TprRLj8lBXaeA+OLj0yz2QbIx/rj/QZis8pG0ve91TGdXKKiBaLzD+y9F8e77s+cRD+\ndO6BGasiW+hz1fejso7S9zd/CtbNZzU31J2Y9ydI7flduSduQfrsfdpx9GA8fv5h2G/uOADAwPY6\nfnLGKiyMHfXluiNyKjTzjoQfXoQb5YntVlHUDOKPFkQz/FRwaDdeR/LSqvHtM6+VmwVl/rvUaWvS\ntUumZ8u7QY7+Vy3HlNn+KhCAvWaPxfJpI3Hroy8J6SZOSz5N5lSYpl2kU+Gw0FvadIWsqJtn//pV\nzrkFOjZ0FsU6nvWtkv6eMjIfuVfu/qjBHVHLyaaedzSm85Ui4n9O2Q3X/PkZZUj3XH+Vj6l+diVJ\nYeVUqMdDhSOkQIRFp4774Ujm/lk+gExepyozl9ykH8gSaKmlllpRM40qLL17tK5JaSAqWhDlPWoa\nNhfPdBHffOdSa8YiHA/VSSnLOnU8KWrSnTd+E4dHq1NhrtKbPsyMhZb1oq6bcyqUbebrqkvcIF1X\nGYtEMN5+Kvyyawv7sOm11cX5i4k/3BpTiZs+eOAcHLs8b+5t+07qivehc6EtYtqYwThzv1n5GwqY\n3PkXCRFvzmvP4xAItDKUObdV2bVGIqpU63noxdV5vzKtoqgZxB8tiLKxP4zQyegcJuSyHUfauQIe\n81qVNWXJuldk5WQ4VmVa+PImjdF11VFKXU53PDVHUxiJonxaXeNXQYUo9kf1LGwdbOPg2xf+nly4\ngHLNzmIfBSG095wx6s27ZrZEUXGO5DnYYAw/PWOPiNgvgFTUkR9reQ1SB++y1S/U6yv+cNi6xS7J\nnA6fslF7HtBkLrJu83ElaDgVHtzHFqEpAlHRimiGR00OLeXrVHF5ud2nDk+dIOlYe4DfpmF3wuPI\n6TC02XPiD3GBj/5rnSDJJodSXcxwD5C9lioaytTlYf1RwZBkxUD5SuUNdtdpIy31eRAVGgLSBz4i\nO/E3754yLoe0WjPGsMvUkVizaKJ3/8SWVX2RvxmVTkWV4g9T3XoOWnTneyctT5yruTYjzwOf7ukI\nr3z/7HUlRAXZRUwn7r5j9p6mX72NQFS0INzkvnqYppZO1uoyH02OnH9+5ioA9g/phN2nCfXpUYQB\n0FSTUo2f/aq/YxflNptCqilQlVhW9gCpXbzBIk6F52pRZmhsZo7y0Ng8wfL8Rd1CMRQAACAASURB\nVExKi7xju0WS7b4iTbouy89UKWWSIq1oW5m57Pm8LkOebsjCXHHsW94HiM8hpjqI36qNcPvM2oXY\na/YYbR9ag6QIREVLorT1hwGjBncoTaDcPDnqF9j5kyLtehv1njkVGTZQL06FLatjVS6hpWUUdcI0\nSKOlrwrCtFtsKz9jbOSzgmfJmZoa2lNzKijz26yo6c6pSE67JVY5XzNiuwiMcyr8+1KEO2c/xQvB\nqoTMCadCqe8gcw+8u4WT9piW/DaFPs/V7cmaj+oS8voOoUP+7XGQwc56zbv+MuIPH18e1n4Y3jeg\n4hjp+9Eq1h+BqGhB+Fp/+FDdNQKGDcg7kXKZj1GoXf09VV9M7YgfzLVn740vHr2zV39ydWvSnRXt\nDF+DNqCYzWmUhsjad+5YTYk8y3j5tFF46HOrseesMZm2cyXl06XlXciRYE1grjoV4qJX4uyUDXan\nUmDzI0K9dH0qWJvPPXQn7DBqIOZOGKpuA4StXZFFgBjPhs8Xm28LwD9GEJB1MJVw27xriftjLSjM\nryboVGzvjomK9poy9/99dD/8/iP7KsvKY1dWFwzIEyouOhaJoqbOfFyTXsQap6cQrD9aEA3G0Faj\nTGwGH1iW1/SX50mCAHTHpwMZOvl/rg5Stz9r3BDMGjdEqK86dqRrTSaug24TK6qn6SKqEfvT2ZZy\nNnRePH02ccaYc1RIxqI56btolVnkMjJ1lq/L3xIl5e9UgRoB08fq48msnDEaxy7fQd8fArZsj76l\nAe3myLu6tKr82agUNcuKPw5fPEk7l3VdETdhVfYRg9rxj9dTPy/d3VH+jnpdqeC9w6i8Ka++fX/O\naBW+fXwVNROFY8XLbxWdikBUtCAajKGt7k5UrIpPsC4QRRjM81RJBHR1a5y4OCovZcrkfqTw+kBs\nrGbHHchkySGL7FMWPz/t+X3QUzULno7oArJKXYDe+sPXZXhKEKoLMsQeNXtw0RLfv+oz8HX8w7Nr\naOJsXQ7v8tHPH2avyNQGpQ65uKdGEUrxh8wKL9UDYP+543DNfc9g6dRUyVU3jL5ckf86djFuiuPK\nAEWIwDwufMdSzJ0wFAzRHD/ygpsByDFD3JDzAeK13Lhl1s01pvgdKWrqdSr4fx60rquRJ/JbhKYI\n4o9WRHcDaHfUivvhe1cm+gwcpsmlld05cSrshE4RuaKq6WKKmu71q8u7cypUIdpV4OPx0dVzk7SL\n3rUUHzxwjqad9Lfe+ZXbE9kIPDkCoknL3lmnwpP7pa1G5FQYFlsOuw5G8b64IusN1XYyJxw0fzz+\n8LH9cMBO43P31V44s9dFxB9iFQfOH4+HPrcai6akMX94G6OHRM643rfPjKgtVV2GR5T1r6zjYdAP\n4M/Z0VbD6CGdGDOkE2OHdibijyED2rx5BuLQrV4wAWdL3+MZ+87EB/bX+PtwJLxc3g4vQ3BbO/k3\n29XdKM29axYCp6IFwWJOhQvKnACy+g1uhYuKZKydkW9VuAtUUVfe+iP739bE0AHt+NyRC7F06sgc\nEShC3Jh0nIGUI2GWC/voVJjwwqvbAADDB3VZcmZRzk9F+tuF1fvEi6871evCSSva7Qz73pKXP5/s\nbdNkUpprr5BJRvqzXqOMWC26HWUY2F7H4+cX58YQ/ExK37VyKn771+dV3UzTpMQDdhqHH97+JEYO\nase2roa+oALv22cmbnnkRQDA+Ucvwm8efC5z/6Or52nLark58rXDC+JTmyR9tdGDO/Dia9ty48Yt\ntroaTHG4aA2qInAqWhANxjKOiUxQ2rMbJpfug3A5hRLpdSo4fBY6Uz+LUd3FuTA2yBt8EbHHu1bu\naCQoLn3PCic9F12LvC+mdyCW5UQFfzbbu3vyJXPo+aQNTmg55dbVYRZ/yO/jsRdec6qvmZZV2fZs\n923iGsV3LRONJQUgPuIsXwKGiDITwPQ9z5swFMt2HCWVt/fhM0csxB/P2R+DOtq8xY/7zBmbiBJ8\ny+pyy8Svi8J9GvwvTTtq6WRMHDEgaktq7K27RMHMFk0eHjgVAe5osFR2ZoNyIlUoOpCr3a7RqeDw\nWehMeatUOqqiJtformWwz5yxePDvm4W6s5Xz8Uqd77j3RTXSCVFRM4s/vMAgKAAWr0Ys6iL+AIBL\nTt4Vz27aYqzPZXMUZfRFYbVGcSh/2M4Tcc19zwhlsqVcCKQDdxqP6//ynPKecuPWzALVBmkLB+Dq\nUVPpcVSoWzzNi+hoq2Hi8IG5dl1BsXqoWOjwxZNw/lGLzOU077abyUSFvo4vv20xBrbXBZ0KUvup\nkK4PnD8+4SDl+BQtolTRVE4FEa0mooeIaAMRrVPcJyL6Wnz/PiJaaitLRKOI6DoiWh//HyncOyfO\n/xARHRKnDSKia4jor0T0ABGdL+Q/iYg2EtE98d+pzRsNdzQazJk1rZpIgzvqWDh5WMYmPalbM9Fd\nJiQRWZ0HVRULzYuosLTZTK3oVJGqmjaciDtPDgagZsXyKKdytFNfzNBYQZTRjrcqaioGYd+543D8\niqnqvjgqEn/h6EVaJVof2J5c+w4FsdpX3rYE937qYG0Zl2/twncuxR8+tp/yXlkX82J/Rg3uwDlr\n5mXuZSPN2rmSGcVxYWdSneZl8Pda1KcIL9VRr2Fwp/msrWtBVmI3iT/eussUrF44EUMHRG1NHzNY\niC6ctpByRO1oFU5F04gKIqoDuADAGgDzAbydiOZL2dYAmB3/nQbgmw5l1wG4gTE2G8AN8TXi+8cD\nWABgNYAL43oA4D8ZY/MA7AJgFRGtEfrwI8bYkvjvO5UNQAk0mDtRofpY2+o1/O8H9sKRMatMhItH\nQX1beZ2Kd+6WXcQLiXkVj+oaNdVWjyndB83kmn//1N0SN8M+BJDOP8nH1sxDe52MkSqZ0Fa9zsUf\nxZ6yjPKvS52M5RfVGgFnHzjbub6kvOUR37armijxRknxB1HkJXT4wNSnjFzChSvY0VbD6MHpPMgo\nk/rMNem6vU7JhggA139oH7xvn5lpO5T1v+tjrh31MwVfcqqOs6Nqyym/poDMzXH5nOZNGIbvnbQc\nn127MBlkV123fJDD1qAqmsmpWAFgA2PsUcbYNgBXAFgr5VkL4DIW4VYAI4hooqXsWgCXxr8vBXCk\nkH4FY2wrY+wxABsArGCMvc4YuxEA4rruApAPG9hC8AneZPrOVLeKbhxRfYTu7qxOxXlvXZRR6CpT\nv4gyC16ZunTQ1VDF466aNQan7Dk9aseJU6ERf8T/j1g8CevPOxQdbYrFWmiAKwN3lmT3l4ono0HG\n+kPl/KpGOY19l/p6SKWiKdwxeRNxtRDT61EZyshvTxq49ecdmrhGHzOkMwndrqvfZimSTxM5VdxC\nQl9JqaijFb0r+cDm6sRw/3njMbCjnnJbkDcfVyEv/nDsaJPRTKJiMoAnheun4jSXPKay4xljXND4\nLABuj2Vtj4hGADgcEYeD42gi+jMRXUVEem81PYhuD0dDvkqZDVb8AySClTW485QReMduU92iBhr6\nUcik1L+Ie90FZeS+Q21k8TJzHpc5I27Q/Jls79QGfYj24nWKVSrl+Z4rqM5iRsTJq6Z51ZlrQ2Rb\nF6zD9G3KJ/Xy4gsfwt0u7zdlMBFZap2KFA2HDVb09+CLqtYNmQmsYwr7eB32UVBqFaKiTytqMsYY\nETmt20TUBuCHAL7GGHs0Tv4FgB8yxrYS0fsQcT72V5Q9DZF4BlOnVsQeNYAx5qyZbaZk8ze7M5uK\nd9dw1NIp2N7NcOQuk5QLRb1G+Pe3LsL5v/qrc51lo5TaUAmnomAVqcc855bcc+RMSrNlbYsX35O4\nWWFRYjMjqgADxWeV5upU+NWXWMYY8nzq8AVR3gqmXukN32HsXJW5vdSTCmyELhZl8nhMGz0Ij8dm\nwLZAWqlOhce34YGqlhqZU+FrnWPzrpmD1O9W8ajZTE7F0wDEk/+UOM0lj6nsc7GIBPF/btxsa+9i\nAOsZY1/hCYyxFxljW+PL7wBYpnoQxtjFjLHljLHlY8fqYjZUh0ZDP0GW7JD1vmc+AeTTynhFJIoW\nh3fsNhWDOtqU7oU5ErtxA0zfTRULYZG6OO7/zCGZ69e3dpdqw7UPZmU0XpdbZUq3v6JGfvw7UZQr\nKBzQhYUvg+ymkq+Tz+NvvnMpXMBZ9bZoplXB9G34QmeN4mp2nlX8c2uzineY9VORvfeLD+yJb707\nWm5VXnrFPqe+O/RtFRK7ciaA2JaLH5M4vxwUUL528d6aya/SqQjijwz+BGA2EU0nog5ESpRXS3mu\nBnBCbAWyEsCmWLRhKns1gBPj3ycC+LmQfjwRdRLRdETKn7cDABF9DsBwAGeLjXPiJMYRAP5S9qGr\ngEn88bM4xDiHbzhq0amWt+25xzlge7f7F6V61iJUt8uJyRVDJJHAiumjMHnEwFw+F9mnD2wxOIDU\nPM/n9anyujruKoqqFDVV4g9+f/XCCU71HbJgPN63zwx8/LCdrHmrUHgrq6ci4qaP7IdrzspGFl67\nZBKO3MVBxCihsFjGdE9rUSb+zrY8dEA7dhwdWdl0xbuvWI1K/GV6L5zwWrxD3uW5Db7zdGBHHees\nmYcrT989k/6dE5dj3Rq90ywbUgsWIc3IIcp2/E3PqWCMdQF4P4BrEW3WVzLGHiCi04no9DjbLwE8\nikip8tsAzjCVjcucD+AgIloP4MD4GvH9KwE8CODXAM5kjHUT0RQA5yKyIrlLMh09KzYzvRfAWQBO\nas5o+EEO9mSC7JEvW0/0XzT56yhxUvOZsz5EhQp+iprm7bWKb230kA7cvC4nGasc5teuN61TxY+w\nER2yA6+qzIGT+kuUrSk2lUzdCUHk1kpbvYZz1uyEEYPyCoXNQJU+AyYMH4AFk4Zn0r56/C4Y1OEm\nva7E+qnA5LAtYaJ3SGPbDvUNHdCOn525Che8YymmjxlcWD/GlaB83z4zMXPskEzalJGDcLpgAeMb\nbXqXHSLvCAfMy7ttVyHHqfBqrXloqk4FY+yXiAgHMe0i4TcDcKZr2Tj9RQAHaMqcB+A8Ke0paMab\nMXYOgHOMD9ELcLX+sLnR5ZvtoI46Lj9lBd793dux85TheC1m5fsuNj7Zt5UmKvzL6BaEKj+27564\nHM8IDpbKejWUYVrUODtVFWb+G+/IiwFMG4EYyrzspqMbg3Iba5ajJo+LXLdoetnb+OcD3E1dZTTD\nOsXnLWh1Kkz1axswtzxheOQ18oSVOxrrTDgVlvnERcM3fnhfYz4TqvyefYmK+ZOGYcN5a9BWr+FL\n1z0MwM9qplWcX/VpRc03KxqMVeLIRFT82Wv22IQIeW2rPU6CaoL6cA8GxjLlT75lPvaZ66+HUm3s\nj/J18GdXBX8CqmGZAzYN99TkTLyO+qfKbwZnWqX1RTht7xlYNWsMTvze7Zn8M8YOxqMbLe6wK7CA\nALLPM7Cjjje2ZXVaxLp/8N7dMH2MPgx5T2PKyLyYzBsV7g/it9ST+45tDRvS2aY9GKl0KprZ92Zs\nyEVcArXFH2VV/n56AyH2Rwuiu8EqmeScrSgHJytKjft0ad2aefjo6rk4aY9pOTZh0o+KDgW2eqpQ\nziv7OlRBsVQwuTNOTUrznAqVAq54XzVGKaciWx9BvSH8+1vV7otlRTd+VdUiZ3NytcfMMTl3zWVQ\n9rTaU74wXCG+hiGd7bjmrD3xn8cu9qpDNX+a6R9GLHrMssitkMmZWxEME5x3ca+yrr4/XFC1OFGG\nfJBpFZ2KwKloQTBWjfc4rteg+1C8A+l4TNqhA9pxxr6a0MExVIpJRcD9LOw3T80RqYKo0H2w44YO\niNsep7x/1NLJ+OHtf8PSHUcq78swDUVClygyqd7N0h1TPYskeJJCeY6ncfbxrtNGKeeGzp23VvxR\n4rjNa5w+ZrBSd8BlziyeMtyeSYGOeg0HzBuHG4SomX0Z4lidsud0dLTVcjoapep3aBcALj9lhXFd\nI83vM/adidP2nlG55c6PT98DN/71eQxor+OwnSfiwWc240zLmuUDnfjRhWjNRSBW5MmJP1w71mQE\noqIFUZX4g5tq6cKoV60P4Atu111WdDB8YDtuXrc/xg1Vn2Qq4VRo0icMH4A/nXsgRis8CgLRBv3Y\n5w91JshMpw1Z/JEtl08bN3QA9po9Bv+3/oVkDBLX3DVKrT/i/LvPHI27P3EQRg7uwC0bXsjV5038\nlXit/v49svjrZ1cXjmlCRPjuSbti2rprCrbeWiAinHXAbBw8f7w1WNqscUOwatZofOSQ4lYMHPJc\n3mu2WQx6+r4zceHvHgEgi2zI2SeHDdd/aJ+kruljBmN67Mm2vV7Dvx5qtwzyQRWrq89TB05FgBbV\niT8iTkVbD9nm+2LWuEgs8p49p5WuS2XuyVHG4oXD9D7GaogZl7L5vIabkvhj0oiBeCCOaqpbUDi3\nihOWxyybgoefewUfPGgObo4JB7HsyJg44n1etuNI3PnEy8q6T9h9R3z4kLl4x7dv9X8WK8xcLFvd\nPn4ifnLGHnjg6U1OeW//1wPw+jazz5IyaBbL/EMHubk072ir4funrqykTd/3P2yAPs5JVeBrTk/A\nV1FTBZ8aWoSmCERFK0In/rjk5F2tZSfFGtVAOql1m2pvB6CZMnKQ1YJFhTs+fqDX4tuuiIHhi576\nYE3tpFrw0fV/HbcYO3/6NwD0uhicW8U5FQPa6/i3tQujMpxTYWhTPO3PnTAMwwa0YcnUkbjp4Y3o\nqNcyG0HuWfTVWqELd53WXd0LWTp1JJZOdRNPjRs2wJ6pArTI/mBEM3WZWmWDLANXPaqiyPmpaJEw\npYGoaEE0GEO7YoLsO1ctt+e495MHZ9ibe80ei3etnIqz9i9u4taK8FXYaqZORRnc/q8H5HYPY9Ak\nqS/DBrSDiJuHqstMHT0IdzzxMkYOym/+qU5FvjAnYETidkhnG+779CH49k2P4qaHN2r7WQRyQCpZ\n/PFm2GT6G4p8dz85Yw9cc98zLWMeWQbDBrZj85auUnWYRmHdmnn4p/+5M+GclfGWXCUCUdGC6Gas\n0CY2XNo42us1fO7IvMY+F4eMHtKBb75rKV58dZu1bpN4odVRVLYuYqnCuVRZqE69pq6q3PjKFiEy\nPnfkQhy5ZDJmjRuqaCsmKhTluL6LSbGOEzmylYnvhvDgvx2S6z/XH2lGWPWAamB7B0X0IHy4Rq2O\nH753Jfb64o1Nq3+fOWNx1ycOwrxP/BqAv3flZiEQFS2G7d0N3P23fzR10Zw8YiDOP2oR9t9pXGK9\noMNn1y5AvVbDO3ZrfiC1KvHfJ++Kk//7TwDK26AXEdEUhamv3PWz7LMB0G/+gzrasPcctYKciYDh\nREWNCNd9cO9M/byLVcn/VdYd3NFXXyIe+lJffTF/4jA8+MzmTJrt/Vehy9SXscMovbdjVyyZOgK/\nefC5XMwnDnHOBU5FgBLPbY68NYof7HdOWJ54n6sKx69wIxL2mzfO6Aq8CP75gNmY2WSFqf0soqJW\nhWmjnxBzNp7dvCV3r8h6wmWwKm4AJyraaoTZ4/NcDsDfeug9q6Y7n14nxw6kTtpjmlcbfR29bZGl\nww/euxuW/Nt1ynu6uddTwdtaDW/dZTL2nDUGAPD9U3fDO79zGwDgx6fvjmMv+qNTHXz932/uOByz\ndIpWl0f8dttahFURiIoWg0qz/MD5br7g+wo+6KiJ3h9h4lQcvGA8vnHjBuw6bVTuXhFxmclNdzcP\nN62gclxDrMvJnzx8vnPfhg9s71EOUYAZqpgpgzojC5tV8QYqo73CoGp9CV9+25Lk96pZYzB6cAde\nfG1bwu3z5fCZlIMzkWBbZLgDUdFi2PTGdgDApw6fj8/84sFe7k3zvcI1E3PGD8HDz73a293wgkmH\nYecpI/DIvx+qzFMoqquhLNdcN7FUtdEpvXvij1ZU5OPuuXU+S3yge75bzzmg0gioZTBsQDt+9+F9\nMXGEetOrQpfpzYBE6bkq03IB4hBX4TCxCgSiosWw6fWIqOhtZaVLTt4V37/tb9XEMegl/PSMVXht\nWznt657G4I7o9MfDQsvQLRxF1pPEQaeBU1FXiCtsTf3ncYvx5esexmDHKJpF0BrLZxZn7jcLO00c\nhv013lWrQNVi0LKYZoi50l/FHzJE/SRXuB7mROIzOL8KUIJzKno76uLs8UPx6SMW9GofymJwZ1vi\nwruvgIjwszNXYZLm9Gcq5wtTFNNuA6dC19QuU0fg44fthGU7jsIhCyZ498eEGfHm1dFWw7auchFw\nm4X2eq3y5+7LaJWTc28jsdDyoLH2mTsWDz33ihfXq1XGu2+tuP0Am7ekRMVB88djiGJTvOw9K/Dy\n63Yz0AB/7DptJP70uNqDZE9Bp+mtwjffuRSX/fGJQu3Ifi9EiIqa2vLxavmWxVHchIvfvdzqXbQo\nTth9GuZNHIZLb3kcv7r/2ZY5lfnii0fvnChjm2Ai+AL6FhZMHoZbH33JyxrmY6vn4aQ9pnk5WwvW\nHwFKcE7FsIHt+PYJy5V5dCaCAeXx49P36O0ueGHNoolYs2hiobJM8tAp4uAFE7Bq1lNKpVqenW97\n/7TPTJy4+7SmcoVqNcLKGaNx8U2PAkDL6BX44rhddzDeXzFtFH5y19M96k7aFccsm4Kr7nyqt7vR\n53DxCcux/rlXvVzH12uESZ6+gYJHzQAlvnL9egCtw8oKePPCFLRrSGebNgZELlQ6UY+JmbZsj6yj\nOtv7JlFhw9t23QF7zxnrvaH0BP7z2MWBqCiAYQPasWzHkcnc/fDBc3u5R81FICpaCFzjvqqIfAHl\nUCPggJ3eXOa8IkSiwAczxkb6DfMnDau6S1bMGjcEtzzyotVpW18Fkf8JtSdx7dl748mXXu/tbvRJ\nDGiv9wsz6UBUtBBeivUkzq04BG9AMTz6+Tf3ApBYf3iW22v2WFx79t6YM77nWfTnHrYTDls0EXMn\nqB1yVYmPH7ZTolsSEGHuhKHGse+o17CtuzUVaQN6BoGoaCE8uylS4BrfQ5EQA/o3Up0Kf85YT2zq\nKnS21bHbjNE90tape83okXbeTPjDx/ZLDkcB/ROBqGghnHfNXwAAc3ppwQ7oX1AFKAsIKINxwwb0\nWHj4gNZEICpaCHc/+TJmjBmMmWNbT/O7N3D+UYswvsWc/by5EHMqerkXAW9eLJg0DDtUHDsooLXR\nVBVqIlpNRA8R0QYiWqe4T0T0tfj+fUS01FaWiEYR0XVEtD7+P1K4d06c/yEiOkRIX0ZEf47vfY1i\nfi8RdRLRj+L024hoWrPGwoaf3PUUtmxvYO2Syb3VhZbD8Sum9tnAYH0BtrDpAQFlcc1Ze+Gidy/r\n7W4E9CCaRlQQUR3ABQDWAJgP4O1EJEcUWgNgdvx3GoBvOpRdB+AGxthsADfE14jvHw9gAYDVAC6M\n60Fc73uFtlbH6acAeJkxNgvAlwF8oarn98GdT7yED115LwBg7ZJJvdGFgH6IIP4ICAioGs3kVKwA\nsIEx9ihjbBuAKwCslfKsBXAZi3ArgBFENNFSdi2AS+PflwI4Uki/gjG2lTH2GIANAFbE9Q1jjN3K\nIs20y6QyvK6rABzAuRg9gTe2dWPaumtw9DejcLj/tO9Moy/9gIAqsdecMdhp4rAQNTYgIKAyNFOn\nYjKAJ4XrpwDs5pBnsqXseMbYM/HvZwFwRwKTAdyqqGt7/FtOz7TPGOsiok0ARgN4QewkEZ2GiJOC\nqVOnKh+2CL5/W+pe+eD54/EvYXEP6EEMG9COX/3zXr3djYCAgBI4/6hF+PPTm3q7Gwn6tKImY4wR\nUdMNyRljFwO4GACWL19eWXvvWTUds8YNwcoZo71cuAYEBAQEBACR7tnxvd0JAc0UfzwNQHR0PyVO\nc8ljKvtcLNJA/P95h7qmaOpKyhBRG4DhAF50eroKUKsR9p07LhAUAQEBAQFvCjSTqPgTgNlENJ2I\nOhApUV4t5bkawAmxFchKAJti0Yap7NUATox/nwjg50L68bFFx3RECpm3x/VtJqKVsb7ECVIZXtcx\nAH7LQnjAgICAgICAQmia+CPWUXg/gGsB1AF8jzH2ABGdHt+/CMAvARyKSKnydQAnm8rGVZ8P4Eoi\nOgXAEwCOi8s8QERXAngQQBeAMxlj3XGZMwBcAmAggF/FfwDwXQCXE9EGAC8BLcVFCggICAgI6FOg\ncDD3w/Lly9kdd9zR290ICAgICAjoERDRnYyx5S5535zxgwMCAgICAgJ6HIGoCAgICAgICKgEgagI\nCAgICAgIqASBqAgICAgICAioBIGoCAgICAgICKgEgagICAgICAgIqASBqAgICAgICAioBMFPhSeI\naCMip1tVYQykAGb9FGEcwhhwhHEIY8ARxqE1xmBHxthYl4yBqOhlENEdrk5F3swI4xDGgCOMQxgD\njjAOfW8MgvgjICAgICAgoBIEoiIgICAgICCgEgSiovdxcW93oEUQxiGMAUcYhzAGHGEc+tgYBJ2K\ngICAgICAgEoQOBUBAQEBAQEBlSAQFY4gotVE9BARbSCidYr7RERfi+/fR0RLbWWJaBQRXUdE6+P/\nI4V758T5HyKiQ4T0ZUT05/je14iI4vROIvpRnH4bEU3rp+OwNxHdRURdRHRMPx2DDxHRg3HbNxDR\njv1wDE6P0+8hoj8Q0fyqx6AvjINw/2giYkRUuRVBq48BEZ1ERBvjuXAPEZ1a9Rj0hXGI7x1H0drw\nABH9oBnjAMZY+LP8AagDeATADAAdAO4FMF/KcyiAXwEgACsB3GYrC+CLANbFv9cB+EL8e36crxPA\n9Lh8Pb53e1w/xe2tidPPAHBR/Pt4AD/qp+MwDcDOAC4DcEw/HYP9AAyKf/9T1XOhj4zBMKEvRwD4\ndX+cC/G9oQBuAnArgOX9bQwAnATgG1W//z44DrMB3A1gZHw9rhljETgVblgBYANj7FHG2DYAVwBY\nK+VZC+AyFuFWACOIaKKl7FoAl8a/LwVwpJB+BWNsK2PsMQAbAKyI6xvG+gy78wAABYtJREFUGLuV\nRbPiMqkMr+sqAAfIp5UK0PLjwBh7nDF2H4BGxc/O0RfG4EbG2Otx+VsBTKl0BPrGGGwW+jIYQDOU\nx1p+HGJ8FsAXAGyp7tET9JUxaDb6wji8F8AFjLGXAYAx9nylIxAjEBVumAzgSeH6qTjNJY+p7HjG\n2DPx72cBjHeo6ylNXUkZxlgXgE0ARtsfzQt9YRyajb42BqcgOq1UiT4xBkR0JhE9gui0d5bLg3mi\n5cchZrHvwBi7xvmp/NDyYxDj6FgkcBUR7eDwXL7oC+MwB8AcIrqZiG4lotVuj+aHQFS0CGKqst+b\n4oRxqG4MiOhdAJYD+I/SnephVDEGjLELGGMzAXwMwMcr6VgPo8w4EFENwJcA/EulnephVDAXfgFg\nGmNsEYDrkJ78+xQqGIc2RCKQfQG8HcC3iWhEBV3LIBAVbngagEjdTonTXPKYyj4Xs6sQ/+fsKFNd\nUxTpmTJE1AZgOIAXnZ7OHX1hHJqNPjEGRHQggHMBHMEY2+r4bK7oE2Mg4Ao0hxXe6uMwFMBCAL8j\noscRydmvrlhZs9XHAIyxF4Vv4DsAljk+mw9afhwQcS2uZoxtj0UmDyMiMqoFa6LyypvlDxGF9ygi\nhRiuSLNAynMYsko4t9vKIjpBiko4X4x/L0BWCedR6JVwDo3Tz0RWUfPK/jgOQj8uQXMUNVt+DADs\ngkhxa3Y//h5mC305HMAd/XEcpL78DtUrarb8GACYKPTlrQBu7Y9zAcBqAJfGv8cgEp+Mrnwsqq7w\nzfqHSHP3YUSL9blx2ukATo9/E4AL4vt/Fj9eVdk4fTSAGwCsB3A9gFHCvXPj/A8hq8m9HMD98b1v\nIHVgNgDAjxEp7NwOYEY/HYddEVHkryHi1DzQD8fgegDPAbgn/ru6H47BVwE8ED//jZAW+P4yDlJf\nf4eKiYq+MAYAPh/PhXvjuTCvP86FuP0vAXgwbv/4ZoxD8KgZEBAQEBAQUAmCTkVAQEBAQEBAJQhE\nRUBAQEBAQEAlCERFQEBAQEBAQCUIREVAQEBAQEBAJQhERUBAQEBAQEAlCERFQEBA00BEI4joDOF6\nEhFd1aS2jiSiTxruLyKiS5rRdkBAQIRgUhoQENA0ENE0AP/LGFvYA23dgsiD6AuGPNcDeA9j7G/N\n7k9AQH9E4FQEBAQ0E+cDmElE9xDRfxDRNCK6HwCI6CQi+hkRXUdEjxPR+4noQ0R0dxzwaFScbyYR\n/ZqI7iSi/yOieXIjRDQHwFZOUBDRsUR0PxHdS0Q3CVl/gcjjbEBAQBMQiIqAgIBmYh2ARxhjSxhj\nH1HcXwjgKESeUM8D8DpjbBcAfwRwQpznYgAfYIwtA/BhABcq6lkF4C7h+pMADmGMLQZwhJB+B4C9\nSjxPQECAAW293YGAgIB+jRsZY68AeIWINiHiJACRG+GdiWgIgD0A/JiIeJlORT0TAWwUrm8GcAkR\nXQngJ0L68wAmVdj/gIAAAYGoCAgI6E2IEVQbwnUD0fpUA/APxtgSSz1vIIrMCwBgjJ1ORLshCuJ0\nJxEtY4y9iChGzhtVdT4gICCLIP4ICAhoJl5BFIK7EBhjmwE8RkTHAgBFWKzI+hcAs/gFEc1kjN3G\nGPskIg4GDxM9B1GwpYCAgCYgEBUBAQFNQ8wduDlWmvyPgtW8E8ApRHQvomiTaxV5bgKwC6Uykv8g\noj/HSqG3IIpQCQD7AbimYD8CAgIsCCalAQEBbwoQ0VcB/IIxdr3mfieA3wPYkzHW1aOdCwjoJwic\nioCAgDcL/h3AIMP9qQDWBYIiIKB5CJyKgICAgICAgEoQOBUBAQEBAQEBlSAQFQEBAQEBAQGVIBAV\nAQEBAQEBAZUgEBUBAQEBAQEBlSAQFQEBAQEBAQGVIBAVAQEBAQEBAZXg/wNG/fYGvHnCsQAAAABJ\nRU5ErkJggg==\n",
+      "text/plain": [
+       ""
+      ]
+     },
+     "execution_count": 26,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "plot = qc.MatPlot(data3.my_controller_demod_freq_1_mag)\n",
+    "plot.fig"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 27,
+   "metadata": {
+    "collapsed": true
+   },
+   "outputs": [],
+   "source": [
+    "# Create the acquisition controller which will take care of the data handling and tell it which \n",
+    "# alazar instrument to talk to.\n",
+    "myintctrl = ATS9360Controller(name='my_controller_int', alazar_name='Alazar', integrate_samples=True)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 28,
+   "metadata": {
+    "collapsed": true
+   },
+   "outputs": [],
+   "source": [
+    "myintctrl.int_delay(2e-7)\n",
+    "myintctrl.int_time(2e-6)\n",
+    "myintctrl.num_avg(100)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 29,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "DataSet:\n",
+      "   mode     = DataMode.LOCAL\n",
+      "   location = 'data/2017-03-03/#011_{name}_16-45-41'\n",
+      "      |                    |  | \n",
+      "   Setpoint | single_set                   | single       | (1,)\n",
+      "   Measured | my_controller_int_raw_output | raw_output   | (1,)\n",
+      "acquired at 2017-03-03 16:45:42\n"
+     ]
+    }
+   ],
+   "source": [
+    "data4 = qc.Measure(myintctrl.acquisition).run()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 30,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "WARNING:root:2.0 oscillation measured for largest demod freq, recommend at least 10: decrease sampling rate, take more samples or increase demodulation freq\n"
+     ]
+    }
+   ],
+   "source": [
+    "myintctrl.demod_freqs.add_demodulator(1e6)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 31,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "DataSet:\n",
+      "   mode     = DataMode.LOCAL\n",
+      "   location = 'data/2017-03-03/#012_{name}_16-45-42'\n",
+      "      |                            |        | \n",
+      "   Setpoint | single_set                           | single             | (1,)\n",
+      "   Measured | my_controller_int_raw_output         | raw_output         | (1,)\n",
+      "   Measured | my_controller_int_demod_freq_0_mag   | demod_freq_0_mag   | (1,)\n",
+      "   Measured | my_controller_int_demod_freq_0_phase | demod_freq_0_phase | (1,)\n",
+      "acquired at 2017-03-03 16:45:43\n"
+     ]
+    }
+   ],
+   "source": [
+    "data5 = qc.Measure(myintctrl.acquisition).run()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 32,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [],
+   "source": [
+    "myrecctrl = ATS9360Controller(name='my_controller_rec', alazar_name='Alazar', \n",
+    "                              integrate_samples=True, average_records=False)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 33,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [
+    {
+     "ename": "ValueError",
+     "evalue": "Wrong number of inputs supplied",
+     "output_type": "error",
+     "traceback": [
+      "\u001b[1;31m---------------------------------------------------------------------------\u001b[0m",
+      "\u001b[1;31mValueError\u001b[0m                                Traceback (most recent call last)",
+      "\u001b[1;32m\u001b[0m in \u001b[0;36m\u001b[1;34m()\u001b[0m\n\u001b[0;32m      2\u001b[0m \u001b[0mmyrecctrl\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mint_time\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;36m2e-6\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m      3\u001b[0m \u001b[0mmyrecctrl\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mnum_avg\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;36m100\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m----> 4\u001b[1;33m \u001b[0mdata6\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mqc\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mMeasure\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mmyrecctrl\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0macquisition\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mrun\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m",
+      "\u001b[1;32mc:\\users\\jhn\\qcodes\\qcodes\\measure.py\u001b[0m in \u001b[0;36mrun\u001b[1;34m(self, use_threads, quiet, data_manager, station, **kwargs)\u001b[0m\n\u001b[0;32m     76\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m     77\u001b[0m         data_set = self._dummyLoop.get_data_set(data_manager=data_manager,\n\u001b[1;32m---> 78\u001b[1;33m                                                 **kwargs)\n\u001b[0m\u001b[0;32m     79\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m     80\u001b[0m         \u001b[1;31m# set the DataSet to local for now so we don't save it, since\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
+      "\u001b[1;32mc:\\users\\jhn\\qcodes\\qcodes\\loops.py\u001b[0m in \u001b[0;36mget_data_set\u001b[1;34m(self, data_manager, *args, **kwargs)\u001b[0m\n\u001b[0;32m    732\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    733\u001b[0m             data_set = new_data(arrays=self.containers(), mode=data_mode,\n\u001b[1;32m--> 734\u001b[1;33m                                 data_manager=data_manager, *args, **kwargs)\n\u001b[0m\u001b[0;32m    735\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    736\u001b[0m             \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mdata_set\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mdata_set\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
+      "\u001b[1;32mc:\\users\\jhn\\qcodes\\qcodes\\loops.py\u001b[0m in \u001b[0;36mcontainers\u001b[1;34m(self)\u001b[0m\n\u001b[0;32m    521\u001b[0m                 \u001b[1;31m# note that this supports lists (separate output arrays)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    522\u001b[0m                 \u001b[1;31m# and arrays (nested in one/each output array) of return values\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m--> 523\u001b[1;33m                 \u001b[0maction_arrays\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0m_parameter_arrays\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0maction\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m    524\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    525\u001b[0m             \u001b[1;32melse\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
+      "\u001b[1;32mc:\\users\\jhn\\qcodes\\qcodes\\loops.py\u001b[0m in \u001b[0;36m_parameter_arrays\u001b[1;34m(self, action)\u001b[0m\n\u001b[0;32m    596\u001b[0m             \u001b[1;32melse\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    597\u001b[0m                 \u001b[0msp_blank\u001b[0m \u001b[1;33m=\u001b[0m \u001b[1;33m(\u001b[0m\u001b[1;32mNone\u001b[0m\u001b[1;33m,\u001b[0m\u001b[1;33m)\u001b[0m \u001b[1;33m*\u001b[0m \u001b[0mlen\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mshape\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m--> 598\u001b[1;33m                 \u001b[0msp_vi\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0m_fill_blank\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0msp_vi\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0msp_blank\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m    599\u001b[0m                 \u001b[0msp_ni\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0m_fill_blank\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0msp_ni\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0msp_blank\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    600\u001b[0m                 \u001b[0msp_li\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0m_fill_blank\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0msp_li\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0msp_blank\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
+      "\u001b[1;32mc:\\users\\jhn\\qcodes\\qcodes\\loops.py\u001b[0m in \u001b[0;36m_fill_blank\u001b[1;34m(self, inputs, blanks)\u001b[0m\n\u001b[0;32m    623\u001b[0m             \u001b[1;32mreturn\u001b[0m \u001b[0minputs\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    624\u001b[0m         \u001b[1;32melse\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m--> 625\u001b[1;33m             \u001b[1;32mraise\u001b[0m \u001b[0mValueError\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;34m'Wrong number of inputs supplied'\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m    626\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    627\u001b[0m     def _make_setpoint_array(self, shape, i, prev_setpoints, vals, name,\n",
+      "\u001b[1;31mValueError\u001b[0m: Wrong number of inputs supplied"
+     ]
+    }
+   ],
+   "source": [
+    "myrecctrl.int_delay(2e-7)\n",
+    "myrecctrl.int_time(2e-6)\n",
+    "myrecctrl.num_avg(100)\n",
+    "data6 = qc.Measure(myrecctrl.acquisition).run()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 36,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "((),)"
+      ]
+     },
+     "execution_count": 36,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "myrecctrl.acquisition.setpoints"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {
+    "collapsed": true
+   },
+   "outputs": [],
+   "source": []
+  }
+ ],
+ "metadata": {
+  "anaconda-cloud": {},
+  "kernelspec": {
+   "display_name": "Python 3",
+   "language": "python",
+   "name": "python3"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython3",
+   "version": "3.5.3"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 0
+}
diff --git a/qcodes/instrument_drivers/AlazarTech/acq_controllers/ATS9360Controller.py b/qcodes/instrument_drivers/AlazarTech/acq_controllers/ATS9360Controller.py
index 24c4b44201bd..2619ffeca4ff 100644
--- a/qcodes/instrument_drivers/AlazarTech/acq_controllers/ATS9360Controller.py
+++ b/qcodes/instrument_drivers/AlazarTech/acq_controllers/ATS9360Controller.py
@@ -2,7 +2,7 @@
 from ..ATS import AcquisitionController
 import numpy as np
 import qcodes.instrument_drivers.AlazarTech.acq_helpers as helpers
-from ..acqusition_parameters import AcqVariablesParam, \
+from ..acquisition_parameters import AcqVariablesParam, \
                                     ExpandingAlazarArrayMultiParameter, \
                                     NonSettableDerivedParameter, \
                                     DemodFreqParameter
@@ -38,15 +38,20 @@ class ATS9360Controller(AcquisitionController):
     def __init__(self, name, alazar_name, filter: str = 'win',
                  numtaps: int =101, chan_b: bool = False,
                  integrate_samples: bool = False,
+                 average_records: bool = True,
                  **kwargs):
         self.filter_settings = {'filter': self.filter_dict[filter],
                                 'numtaps': numtaps}
         self.chan_b = chan_b
         self.number_of_channels = 2
+        if not integrate_samples and not average_records:
+            raise RuntimeError("You need to either average records or integrate over samples")
+
         super().__init__(name, alazar_name, **kwargs)
 
         self.add_parameter(name='acquisition',
                            integrate_samples=integrate_samples,
+                           average_records=average_records,
                            parameter_class=ExpandingAlazarArrayMultiParameter)
 
         self._integrate_samples = integrate_samples
@@ -261,11 +266,27 @@ def pre_start_capture(self):
         self.buffer = np.zeros(samples_per_record *
                                records_per_buffer *
                                self.number_of_channels)
+        avg_buffers = True
+        if avg_buffers:
+            len_buffers = 1
+        else:
+            pass # not implemented yet
 
-        if len(demod_freqs):
+        if self.acquisition._average_records:
+            len_records = 1
+        else:
+            len_records = records_per_buffer
+
+        num_demods = self.demod_freqs.get_num_demods()
+        if num_demods:
+            mat_shape = (num_demods, len_buffers,
+                         len_records, self.samples_per_record.get())
+            self.mat_shape = mat_shape
             integer_list = np.arange(samples_per_record)
+            integer_mat = (np.outer(np.ones(len_buffers),
+                                    np.outer(len_records, integer_list)))
             angle_mat = 2 * np.pi * \
-                np.outer(demod_freqs, integer_list) / sample_rate
+                np.outer(demod_freqs, integer_mat).reshape(mat_shape)  / sample_rate
             self.cos_mat = np.cos(angle_mat)
             self.sin_mat = np.sin(angle_mat)
 
@@ -302,24 +323,35 @@ def post_acquire(self):
         samples_per_record = alazar.samples_per_record.get()
         records_per_buffer = alazar.records_per_buffer.get()
         buffers_per_acquisition = alazar.buffers_per_acquisition.get()
-        reshaped_buf = self.buffer.reshape(records_per_buffer,
+        number_of_buffers = 1 # Hardcoded for now assuming all buffers go to the same array
+        reshaped_buf = self.buffer.reshape(number_of_buffers,
+                                           records_per_buffer,
                                            samples_per_record,
                                            self.number_of_channels)
-        recordA = np.uint16(np.mean(reshaped_buf[:, :, 0], axis=0) /
-                            buffers_per_acquisition)
 
-        recordA = self._to_volts(recordA)
-        unpacked = []
-        if self._integrate_samples:
-            unpacked.append(np.mean(recordA, axis=-1))
+        channelAData = reshaped_buf[:, :, :, 0]
+        if self.acquisition._average_records:
+            recordA = np.uint16(np.mean(channelAData, axis=1, keepdims=True) /
+                                buffers_per_acquisition)
         else:
-            unpacked.append(recordA)
+            recordA = np.uint16(channelAData)/buffers_per_acquisition
+
+        recordA = self._to_volts(recordA)
+
         # do demodulation
         if self.demod_freqs.get_num_demods():
             magA, phaseA = self._fit(recordA)
             if self._integrate_samples:
                 magA = np.mean(magA, axis=-1)
                 phaseA = np.mean(phaseA, axis=-1)
+
+        unpacked = []
+        if self._integrate_samples:
+            unpacked.append(np.mean(recordA, axis=-1))
+        else:
+            unpacked.append(np.squeeze(recordA))
+
+        if self.demod_freqs.get_num_demods():
             for i in range(magA.shape[0]):
                 unpacked.append(magA[i])
                 unpacked.append(phaseA[i])
@@ -360,7 +392,7 @@ def _fit(self, volt_rec):
         alazar = self._get_alazar()
         sample_rate = alazar.get_sample_rate()
         demod_length = self.demod_freqs.get_num_demods()
-        volt_rec_mat = np.outer(np.ones(demod_length), volt_rec)
+        volt_rec_mat = np.outer(np.ones(demod_length), volt_rec).reshape(self.mat_shape)
         re_mat = np.multiply(volt_rec_mat, self.cos_mat)
         im_mat = np.multiply(volt_rec_mat, self.sin_mat)
 
@@ -393,8 +425,8 @@ def _fit(self, volt_rec):
             beginning = int(self.int_delay() * sample_rate)
             end = beginning + int(self.int_time() * sample_rate)
 
-            re_limited = re_filtered[:, beginning:end]
-            im_limited = im_filtered[:, beginning:end]
+            re_limited = re_filtered[..., beginning:end]
+            im_limited = im_filtered[..., beginning:end]
         else:
             re_limited = re_filtered
             im_limited = im_filtered
diff --git a/qcodes/instrument_drivers/AlazarTech/acqusition_parameters.py b/qcodes/instrument_drivers/AlazarTech/acquisition_parameters.py
similarity index 98%
rename from qcodes/instrument_drivers/AlazarTech/acqusition_parameters.py
rename to qcodes/instrument_drivers/AlazarTech/acquisition_parameters.py
index 6ac1f4afaae8..36aec466b65b 100644
--- a/qcodes/instrument_drivers/AlazarTech/acqusition_parameters.py
+++ b/qcodes/instrument_drivers/AlazarTech/acquisition_parameters.py
@@ -216,9 +216,11 @@ def __init__(self,
                  setpoint_names = (('time',),),
                  setpoint_labels = (('time',),),
                  setpoint_units = (('s',),),
-                 integrate_samples=False):
+                 integrate_samples=False,
+                 average_records=True):
         self.acquisition_kwargs = {}
         self._integrate_samples = integrate_samples
+        self._average_records = average_records
         super().__init__(name,
                          names=names,
                          units=units,
@@ -246,6 +248,9 @@ def set_setpoints_and_labels(self):
             print("start {} stop {} num steps {}".format(start, stop, samples))
             arraysetpoints = (tuple(np.linspace(start, stop, samples)),)
             base_shape = (len(arraysetpoints[0]),)
+        elif not self._average_records:
+            arraysetpoints = tuple(range(self._instrument.records_per_buffer.get() or 0))
+            base_shape = (self._instrument.records_per_buffer.get(),)
         else:
             arraysetpoints = ()
             base_shape = ()

From 88b822b8d5377ab3c5aed554d0e26aee0923798b Mon Sep 17 00:00:00 2001
From: "Jens H. Nielsen" 
Date: Mon, 6 Mar 2017 14:17:55 +0100
Subject: [PATCH 155/328] fix: more informative error message

---
 qcodes/loops.py | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

diff --git a/qcodes/loops.py b/qcodes/loops.py
index 57532f83206f..61744b4f035c 100644
--- a/qcodes/loops.py
+++ b/qcodes/loops.py
@@ -622,7 +622,7 @@ def _fill_blank(self, inputs, blanks):
         elif len(inputs) == len(blanks):
             return inputs
         else:
-            raise ValueError('Wrong number of inputs supplied')
+            raise ValueError('Wrong number of inputs supplied got {} expected {}'.format(len(inputs), len(blanks)))
 
     def _make_setpoint_array(self, shape, i, prev_setpoints, vals, name,
                              label, unit):

From 55397d8042d6680c3adb8f5f2278e76788ec5c7a Mon Sep 17 00:00:00 2001
From: "Jens H. Nielsen" 
Date: Mon, 6 Mar 2017 14:25:55 +0100
Subject: [PATCH 156/328] fix: correct tuple shape

---
 qcodes/instrument_drivers/AlazarTech/acquisition_parameters.py | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

diff --git a/qcodes/instrument_drivers/AlazarTech/acquisition_parameters.py b/qcodes/instrument_drivers/AlazarTech/acquisition_parameters.py
index 36aec466b65b..7884156dff70 100644
--- a/qcodes/instrument_drivers/AlazarTech/acquisition_parameters.py
+++ b/qcodes/instrument_drivers/AlazarTech/acquisition_parameters.py
@@ -249,7 +249,7 @@ def set_setpoints_and_labels(self):
             arraysetpoints = (tuple(np.linspace(start, stop, samples)),)
             base_shape = (len(arraysetpoints[0]),)
         elif not self._average_records:
-            arraysetpoints = tuple(range(self._instrument.records_per_buffer.get() or 0))
+            arraysetpoints = (tuple(range(self._instrument.records_per_buffer.get() or 0)),)
             base_shape = (self._instrument.records_per_buffer.get(),)
         else:
             arraysetpoints = ()

From 2b9c1f806b7187a7c6b597244ac1b3539582ef8c Mon Sep 17 00:00:00 2001
From: "Jens H. Nielsen" 
Date: Mon, 6 Mar 2017 14:27:15 +0100
Subject: [PATCH 157/328] fix: records per sample should be configurable in
 sequence mode

---
 .../acq_controllers/ATS9360Controller.py      | 22 ++++++++++++++-----
 1 file changed, 16 insertions(+), 6 deletions(-)

diff --git a/qcodes/instrument_drivers/AlazarTech/acq_controllers/ATS9360Controller.py b/qcodes/instrument_drivers/AlazarTech/acq_controllers/ATS9360Controller.py
index 2619ffeca4ff..448b53f7c27a 100644
--- a/qcodes/instrument_drivers/AlazarTech/acq_controllers/ATS9360Controller.py
+++ b/qcodes/instrument_drivers/AlazarTech/acq_controllers/ATS9360Controller.py
@@ -74,9 +74,15 @@ def __init__(self, name, alazar_name, filter: str = 'win',
         self.add_parameter(name='buffers_per_acquisition',
                            alternative='not controllable in this controller',
                            parameter_class=NonSettableDerivedParameter)
-        self.add_parameter(name='records_per_buffer',
-                           alternative='num_avg',
-                           parameter_class=NonSettableDerivedParameter)
+        if average_records:
+            self.add_parameter(name='records_per_buffer',
+                               alternative='num_avg',
+                               parameter_class=NonSettableDerivedParameter)
+        else:
+            self.add_parameter(name='records_per_buffer',
+                               parameter_class=AcqVariablesParam,
+                               default_fn= lambda : 1,
+                               check_and_update_fn= lambda x, **kwargs: True)
         self.add_parameter(name='samples_per_record',
                            alternative='int_time and int_delay',
                            parameter_class=NonSettableDerivedParameter)
@@ -168,10 +174,14 @@ def _update_num_avg(self, value: int, **kwargs):
         if not isinstance(value, int) or value < 1:
             raise ValueError('number of averages must be a positive integer')
 
-        self.records_per_buffer._save_val(value)
-        self.buffers_per_acquisition._save_val(1)
-        self.allocated_buffers._save_val(1)
 
+        if self.acquisition._average_records:
+            self.records_per_buffer._save_val(value)
+            self.buffers_per_acquisition._save_val(1)
+            self.allocated_buffers._save_val(1)
+        else:
+            self.buffers_per_acquisition._save_val(value)
+            self.allocated_buffers._save_val(value)
 
     def _int_delay_default(self):
         """

From 7ea1e1a073778ed2ada8c11af44cb891e825b0e5 Mon Sep 17 00:00:00 2001
From: "Jens H. Nielsen" 
Date: Mon, 6 Mar 2017 14:27:28 +0100
Subject: [PATCH 158/328] Fix: bug fix

---
 .../AlazarTech/acq_controllers/ATS9360Controller.py             | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

diff --git a/qcodes/instrument_drivers/AlazarTech/acq_controllers/ATS9360Controller.py b/qcodes/instrument_drivers/AlazarTech/acq_controllers/ATS9360Controller.py
index 448b53f7c27a..2bb2d68b9ef7 100644
--- a/qcodes/instrument_drivers/AlazarTech/acq_controllers/ATS9360Controller.py
+++ b/qcodes/instrument_drivers/AlazarTech/acq_controllers/ATS9360Controller.py
@@ -344,7 +344,7 @@ def post_acquire(self):
             recordA = np.uint16(np.mean(channelAData, axis=1, keepdims=True) /
                                 buffers_per_acquisition)
         else:
-            recordA = np.uint16(channelAData)/buffers_per_acquisition
+            recordA = np.uint16(channelAData/buffers_per_acquisition)
 
         recordA = self._to_volts(recordA)
 

From 01cf358e5ce1dbcb79292c351a5a32ef172ca674 Mon Sep 17 00:00:00 2001
From: "Jens H. Nielsen" 
Date: Mon, 6 Mar 2017 15:38:19 +0100
Subject: [PATCH 159/328] Fix update setpoints when updating records

---
 .../AlazarTech/acq_controllers/ATS9360Controller.py        | 7 ++++++-
 1 file changed, 6 insertions(+), 1 deletion(-)

diff --git a/qcodes/instrument_drivers/AlazarTech/acq_controllers/ATS9360Controller.py b/qcodes/instrument_drivers/AlazarTech/acq_controllers/ATS9360Controller.py
index 2bb2d68b9ef7..8fe97d519b8a 100644
--- a/qcodes/instrument_drivers/AlazarTech/acq_controllers/ATS9360Controller.py
+++ b/qcodes/instrument_drivers/AlazarTech/acq_controllers/ATS9360Controller.py
@@ -82,7 +82,7 @@ def __init__(self, name, alazar_name, filter: str = 'win',
             self.add_parameter(name='records_per_buffer',
                                parameter_class=AcqVariablesParam,
                                default_fn= lambda : 1,
-                               check_and_update_fn= lambda x, **kwargs: True)
+                               check_and_update_fn=self._update_records_per_buffer)
         self.add_parameter(name='samples_per_record',
                            alternative='int_time and int_delay',
                            parameter_class=NonSettableDerivedParameter)
@@ -134,6 +134,11 @@ def _update_samples_per_record(self, sample_rate, int_time, int_delay):
         self.samples_per_record._save_val(samples_per_record)
         self.acquisition.set_setpoints_and_labels()
 
+    def _update_records_per_buffer(self, value, **kwargs):
+        # Hack store the value early so its useful for set_setpoints...
+        self.records_per_buffer._save_val(value)
+        self.acquisition.set_setpoints_and_labels()
+
     def _update_int_delay(self, value, **kwargs):
         """
         Function to validate value for int_delay before setting parameter

From c3cc1aa37e5905558cf04f3e4e8ee53870034b0f Mon Sep 17 00:00:00 2001
From: "Jens H. Nielsen" 
Date: Tue, 7 Mar 2017 12:39:42 +0100
Subject: [PATCH 160/328] Fix: correct array shape

---
 .../AlazarTech/acq_controllers/ATS9360Controller.py             | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

diff --git a/qcodes/instrument_drivers/AlazarTech/acq_controllers/ATS9360Controller.py b/qcodes/instrument_drivers/AlazarTech/acq_controllers/ATS9360Controller.py
index 8fe97d519b8a..00fafa84dbfe 100644
--- a/qcodes/instrument_drivers/AlazarTech/acq_controllers/ATS9360Controller.py
+++ b/qcodes/instrument_drivers/AlazarTech/acq_controllers/ATS9360Controller.py
@@ -299,7 +299,7 @@ def pre_start_capture(self):
             self.mat_shape = mat_shape
             integer_list = np.arange(samples_per_record)
             integer_mat = (np.outer(np.ones(len_buffers),
-                                    np.outer(len_records, integer_list)))
+                                    np.outer(np.ones(len_records), integer_list)))
             angle_mat = 2 * np.pi * \
                 np.outer(demod_freqs, integer_mat).reshape(mat_shape)  / sample_rate
             self.cos_mat = np.cos(angle_mat)

From a2be56bf038f48baf009fffbc3aa4510c012e5ef Mon Sep 17 00:00:00 2001
From: "Jens H. Nielsen" 
Date: Tue, 7 Mar 2017 12:40:23 +0100
Subject: [PATCH 161/328] Fix: correct setpoints handling

---
 .../AlazarTech/acquisition_parameters.py      | 80 ++++++++++++++-----
 1 file changed, 62 insertions(+), 18 deletions(-)

diff --git a/qcodes/instrument_drivers/AlazarTech/acquisition_parameters.py b/qcodes/instrument_drivers/AlazarTech/acquisition_parameters.py
index 7884156dff70..96ec344d9271 100644
--- a/qcodes/instrument_drivers/AlazarTech/acquisition_parameters.py
+++ b/qcodes/instrument_drivers/AlazarTech/acquisition_parameters.py
@@ -213,14 +213,43 @@ def __init__(self,
                  units = ('v',),
                  shapes = ((1,),),
                  setpoints = (((1,),),),
-                 setpoint_names = (('time',),),
-                 setpoint_labels = (('time',),),
-                 setpoint_units = (('s',),),
+                 setpoint_names = None,
+                 setpoint_labels = None,
+                 setpoint_units = None,
                  integrate_samples=False,
                  average_records=True):
         self.acquisition_kwargs = {}
         self._integrate_samples = integrate_samples
         self._average_records = average_records
+
+        if setpoint_names:
+            self.setpoint_names_base = setpoint_names[0]
+        elif integrate_samples and average_records:
+            self.setpoint_names_base = ()
+        elif integrate_samples:
+            self.setpoint_names_base = ('record_num',)
+        elif average_records:
+            self.setpoint_names_base = ('time',)
+        if setpoint_labels:
+            self.setpoint_labels_base = setpoint_names[0]
+        elif integrate_samples and average_records:
+            self.setpoint_labels_base = ()
+        elif integrate_samples:
+            self.setpoint_labels_base = ('record num',)
+        elif average_records:
+            self.setpoint_labels_base = ('time',)
+        if setpoint_units:
+            self.setpoint_units_base = setpoint_units[0]
+        elif integrate_samples and average_records:
+            self.setpoint_units_base = ()
+        elif integrate_samples:
+            self.setpoint_units_base = ('',)
+        elif average_records:
+            self.setpoint_units_base = ('s',)
+
+        self.setpoints_start = 0
+        self.setpoints_stop = 0
+
         super().__init__(name,
                          names=names,
                          units=units,
@@ -232,6 +261,22 @@ def __init__(self,
                          setpoint_labels=setpoint_labels,
                          setpoint_units=setpoint_units)
 
+
+
+    def set_base_setpoints(self, base_name=None, base_label=None, base_unit=None,
+                           setpoints_start=None, setpoints_stop=None):
+        if base_name:
+            self.setpoint_names_base = (base_name,)
+        if base_label:
+            self.setpoint_labels_base = (base_label,)
+        if base_unit:
+            self.setpoint_units_base = (base_unit,)
+        if setpoints_start:
+            self.setpoints_start = setpoints_start
+        if setpoints_stop:
+            self.setpoints_stop = setpoints_stop
+        self.set_setpoints_and_labels()
+
     def set_setpoints_and_labels(self):
         if not self._integrate_samples:
             int_time = self._instrument.int_time.get() or 0
@@ -243,13 +288,15 @@ def set_setpoints_and_labels(self):
                 stop = total_time
             else:
                 start = 0
-                samples = 1
                 stop = 1
-            print("start {} stop {} num steps {}".format(start, stop, samples))
+            samples = samples or 1
             arraysetpoints = (tuple(np.linspace(start, stop, samples)),)
             base_shape = (len(arraysetpoints[0]),)
         elif not self._average_records:
-            arraysetpoints = (tuple(range(self._instrument.records_per_buffer.get() or 0)),)
+            num_records = self._instrument.records_per_buffer.get() or 0
+            start = self.setpoints_start
+            stop = self.setpoints_stop or num_records-1
+            arraysetpoints = (tuple(np.linspace(start, stop, num_records)),)
             base_shape = (self._instrument.records_per_buffer.get(),)
         else:
             arraysetpoints = ()
@@ -257,12 +304,9 @@ def set_setpoints_and_labels(self):
         setpoints = [arraysetpoints]
         names = [self.names[0]]
         labels = [self.labels[0]]
-        setpoint_name = self.setpoint_names[0]
-        setpoint_names = [setpoint_name]
-        setpoint_label = self.setpoint_labels[0]
-        setpoint_labels = [setpoint_label]
-        setpoint_unit = self.setpoint_units[0]
-        setpoint_units = [setpoint_unit]
+        setpoint_names = [self.setpoint_names_base]
+        setpoint_labels = [self.setpoint_labels_base]
+        setpoint_units = [self.setpoint_units_base]
         units = [self.units[0]]
         shapes = [base_shape]
         demod_freqs = self._instrument.demod_freqs.get()
@@ -276,13 +320,13 @@ def set_setpoints_and_labels(self):
             shapes.append(base_shape)
             shapes.append(base_shape)
             setpoints.append(arraysetpoints)
-            setpoint_names.append(setpoint_name)
-            setpoint_labels.append(setpoint_label)
-            setpoint_units.append(setpoint_unit)
+            setpoint_names.append(self.setpoint_names_base)
+            setpoint_labels.append(self.setpoint_labels_base)
+            setpoint_units.append(self.setpoint_units_base)
             setpoints.append(arraysetpoints)
-            setpoint_names.append(setpoint_name)
-            setpoint_labels.append(setpoint_label)
-            setpoint_units.append(setpoint_unit)
+            setpoint_names.append(self.setpoint_names_base)
+            setpoint_labels.append(self.setpoint_labels_base)
+            setpoint_units.append(self.setpoint_units_base)
         self.names = tuple(names)
         self.labels = tuple(labels)
         self.units = tuple(units)

From 38214dc74647fbfaa32eea50055f2482ac59cf2c Mon Sep 17 00:00:00 2001
From: "Jens H. Nielsen" 
Date: Tue, 7 Mar 2017 12:40:51 +0100
Subject: [PATCH 162/328] Update example notebook

---
 ...e with Alazar ATS9360 new controller.ipynb | 587 +++++++-----------
 1 file changed, 227 insertions(+), 360 deletions(-)

diff --git a/docs/examples/Qcodes example with Alazar ATS9360 new controller.ipynb b/docs/examples/Qcodes example with Alazar ATS9360 new controller.ipynb
index 5f2672a82652..b77761ed3ec0 100644
--- a/docs/examples/Qcodes example with Alazar ATS9360 new controller.ipynb	
+++ b/docs/examples/Qcodes example with Alazar ATS9360 new controller.ipynb	
@@ -103,7 +103,7 @@
     {
      "data": {
       "text/plain": [
-       "752"
+       "812"
       ]
      },
      "execution_count": 3,
@@ -157,9 +157,9 @@
     "editable": true
    },
    "source": [
-    "### Basic Acquisition\n",
+    "## Example 1\n",
     "\n",
-    "Pulls the raw data the alazar acquires averaged over number of records buffers."
+    "Pulls the raw data the alazar acquires averaged over records and buffers."
    ]
   },
   {
@@ -174,8 +174,17 @@
    "outputs": [],
    "source": [
     "# Create the acquisition controller which will take care of the data handling and tell it which \n",
-    "# alazar instrument to talk to.\n",
-    "myctrl = ATS9360Controller(name='my_controller', alazar_name='Alazar')"
+    "# alazar instrument to talk to. Explicitly pass the default options to the Alazar.\n",
+    "# Dont integrate over samples but avarage over records\n",
+    "myctrl = ATS9360Controller(name='my_controller', alazar_name='Alazar',\n",
+    "                           integrate_samples=False, average_records=True)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Put the Alazar and the controller in a station so we ensure that all parameters are captured"
    ]
   },
   {
@@ -189,22 +198,20 @@
     "station = qc.Station(alazar, myctrl)"
    ]
   },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "This controller is designed to be highlevel and it is not possible to directly set number of records, buffers and samples. The number of samples is indirecly controlled by the integration time and integration delay and the number of averages controls the number of buffers and records acquired"
+   ]
+  },
   {
    "cell_type": "code",
    "execution_count": 7,
    "metadata": {
     "collapsed": false
    },
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "start 0 stop 1 num steps 1\n",
-      "start 0 stop 2e-07 num steps 1152\n"
-     ]
-    }
-   ],
+   "outputs": [],
    "source": [
     "myctrl.int_delay(2e-7)\n",
     "myctrl.int_time(2e-6)\n",
@@ -219,18 +226,24 @@
    },
    "outputs": [
     {
-     "data": {
-      "text/plain": [
-       "100"
-      ]
-     },
-     "execution_count": 8,
-     "metadata": {},
-     "output_type": "execute_result"
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "100 records per buffer set by num_avg\n",
+      "1152 samples per record set by int_time and int_delay\n"
+     ]
     }
    ],
    "source": [
-    "myctrl.parameters['records_per_buffer'].get()"
+    "print(\"{} records per buffer set by num_avg\".format(myctrl.records_per_buffer()))\n",
+    "print(\"{} samples per record set by int_time and int_delay\".format(myctrl.samples_per_record()))"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Per default the output will contain an array called raw_output of unprocessed data"
    ]
   },
   {
@@ -244,18 +257,19 @@
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "start 0 stop 2.2e-06 num steps 1152\n",
-      "1152\n",
-      "start 0 stop 2.2e-06 num steps 1152\n",
-      "1152\n"
+      "DataSet:\n",
+      "   mode     = DataMode.LOCAL\n",
+      "   location = 'data/2017-03-07/#030_{name}_12-36-03'\n",
+      "      |                |  | \n",
+      "   Setpoint | time_set                 | time         | (1152,)\n",
+      "   Measured | my_controller_raw_output | raw_output   | (1152,)\n",
+      "acquired at 2017-03-07 12:36:03\n"
      ]
     }
    ],
    "source": [
-    "myctrl.int_delay(2e-7)\n",
-    "print(myctrl.samples_per_record.get_latest())\n",
-    "myctrl.int_time(2e-6)\n",
-    "print(myctrl.samples_per_record.get_latest())"
+    "# Measure this \n",
+    "data1 = qc.Measure(myctrl.acquisition).run(station=station)\n"
    ]
   },
   {
@@ -266,50 +280,46 @@
    },
    "outputs": [
     {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "1\n",
-      "1\n",
-      "100\n"
-     ]
+     "data": {
+      "image/png": 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8/ZZ99skdu3s+PmdOGUNnh7hz5Yb93ot82/Kf27jDhzFm+BDuW72xx3142arnOHRoJ7v3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\n",
+      "text/plain": [
+       ""
+      ]
+     },
+     "execution_count": 10,
+     "metadata": {},
+     "output_type": "execute_result"
     }
    ],
    "source": [
-    "myctrl.num_avg(100)\n",
-    "print(myctrl.buffers_per_acquisition.get())\n",
-    "print(myctrl.allocated_buffers.get())\n",
-    "print(myctrl.records_per_buffer.get())"
+    "plot = qc.MatPlot(data1.my_controller_raw_output)\n",
+    "plot.fig"
    ]
   },
   {
-   "cell_type": "code",
-   "execution_count": 11,
-   "metadata": {
-    "collapsed": false,
-    "deletable": true,
-    "editable": true
-   },
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "[ 0.001221   0.0002442  0.001221  ...,  0.0002442  0.001221   0.0002442]\n",
-      "(1152,)\n"
-     ]
-    }
-   ],
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "As we are not integrating over samples the setpoints (label, unit and ticks on number) are automatically set from the integration time and integration delay. **Note at the moment this does not cut of the int_delay from the plot. It probably should**"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### Demodulation"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
    "source": [
-    "# Pull data from the card by calling get of the controllers acquisition parameter\n",
-    "data1 = myctrl.acquisition.get()\n",
-    "print(data1[0])\n",
-    "print(data1[0].shape)"
+    "We can add demodulators and remove them again, This will automatically take care of adapting the multiparameter to return multiple arrays to the dataset"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 12,
+   "execution_count": 11,
    "metadata": {
     "collapsed": false
    },
@@ -325,11 +335,8 @@
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "start 0 stop 2.2e-06 num steps 1152\n",
       "[2000000.0]\n",
-      "start 0 stop 2.2e-06 num steps 1152\n",
       "[2000000.0, 6000000.0]\n",
-      "start 0 stop 2.2e-06 num steps 1152\n",
       "[2000000.0]\n"
      ]
     }
@@ -345,60 +352,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 13,
-   "metadata": {
-    "collapsed": false
-   },
-   "outputs": [
-    {
-     "name": "stderr",
-     "output_type": "stream",
-     "text": [
-      "WARNING:root:8.0 oscillation measured for largest demod freq, recommend at least 10: decrease sampling rate, take more samples or increase demodulation freq\n"
-     ]
-    },
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "start 0 stop 2.2e-06 num steps 1152\n",
-      "start 0 stop 2.2e-06 num steps 1152\n"
-     ]
-    },
-    {
-     "data": {
-      "text/plain": [
-       "[4000000.0]"
-      ]
-     },
-     "execution_count": 13,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "myctrl.demod_freqs.add_demodulator(2e6)\n",
-    "myctrl.demod_freqs.get_latest()\n",
-    "myctrl.demod_freqs.add_demodulator(4e6)\n",
-    "myctrl.demod_freqs.get_latest()\n",
-    "myctrl.demod_freqs.remove_demodulator(2e6)\n",
-    "myctrl.demod_freqs.get_latest()"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 14,
-   "metadata": {
-    "collapsed": false
-   },
-   "outputs": [],
-   "source": [
-    "# myctrl.acquisition.set_setpoints_and_labels()"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 15,
+   "execution_count": 12,
    "metadata": {
     "collapsed": false,
     "deletable": true,
@@ -412,70 +366,79 @@
      "text": [
       "DataSet:\n",
       "   mode     = DataMode.LOCAL\n",
-      "   location = 'data/2017-03-03/#007_{name}_16-45-29'\n",
+      "   location = 'data/2017-03-07/#031_{name}_12-36-08'\n",
       "      |                        |        | \n",
       "   Setpoint | time_set                         | time               | (1152,)\n",
       "   Measured | my_controller_raw_output         | raw_output         | (1152,)\n",
       "   Measured | my_controller_demod_freq_0_mag   | demod_freq_0_mag   | (1152,)\n",
       "   Measured | my_controller_demod_freq_0_phase | demod_freq_0_phase | (1152,)\n",
-      "acquired at 2017-03-03 16:45:30\n"
+      "acquired at 2017-03-07 12:36:09\n"
      ]
     }
    ],
    "source": [
     "# Do this in as qcodes measurement (ie the same but makes a data set)\n",
-    "data2 = qc.Measure(myctrl.acquisition).run(station=station)\n",
-    "plot = qc.MatPlot(data2.my_controller_raw_output)"
+    "data2 = qc.Measure(myctrl.acquisition).run(station=station)\n"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 16,
+   "execution_count": 13,
    "metadata": {
     "collapsed": false
    },
    "outputs": [
     {
      "data": {
+      "image/png": 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A7NurI/27tWNLZiFfZQbt52ePH8jvvndQo1SHb8su5Mz7P6K0vIIHLhrP1P2D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\nFThBMDMzs4L/A4CFDsklKH3kAAAAAElFTkSuQmCC\n",
       "text/plain": [
-       "(1, 1, 1, 1152)"
+       ""
       ]
      },
-     "execution_count": 16,
+     "execution_count": 13,
      "metadata": {},
      "output_type": "execute_result"
     }
    ],
    "source": [
-    "myctrl.mat_shape"
+    "plot = qc.MatPlot(data2.my_controller_demod_freq_0_mag)\n",
+    "plot.fig"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 17,
+   "execution_count": 14,
    "metadata": {
     "collapsed": false
    },
    "outputs": [
     {
      "data": {
-      "image/png": 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AIada9Q/OzMyagwOEHKhlw7NKWy63TGAymAZpTdjVcrLc+qRppLwfl3nMXmk/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\nzKzl1bIE4VRgaUQsi4hdwA3A3KJp5gL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4bQHoi+nGmEr7fRXQ48E2GziddFyFvU3pxcsHa2jtjHva+cTPFJVZN/fIOFcq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\nSgBERESykBIAERGRLPT/FvD3wTTMRkoAAAAASUVORK5CYII=\n",
       "text/plain": [
-       ""
+       ""
       ]
      },
-     "execution_count": 17,
+     "execution_count": 14,
      "metadata": {},
      "output_type": "execute_result"
     }
    ],
    "source": [
+    "plot = qc.MatPlot(data2.my_controller_demod_freq_0_phase)\n",
     "plot.fig"
    ]
   },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "It's possible to change int_time, int_delay and num_avg in an already created acq_controller"
+   ]
+  },
   {
    "cell_type": "code",
-   "execution_count": 18,
+   "execution_count": 15,
    "metadata": {
     "collapsed": false
    },
@@ -484,15 +447,7 @@
      "name": "stderr",
      "output_type": "stream",
      "text": [
-      "WARNING:root:8.0 oscillation measured for largest demod freq, recommend at least 10: decrease sampling rate, take more samples or increase demodulation freq\n"
-     ]
-    },
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "start 0 stop 2.2e-06 num steps 1152\n",
-      "start 0 stop 2.2e-06 num steps 3200\n"
+      "WARNING:root:4.0 oscillation measured for largest demod freq, recommend at least 10: decrease sampling rate, take more samples or increase demodulation freq\n"
      ]
     }
    ],
@@ -504,7 +459,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 19,
+   "execution_count": 16,
    "metadata": {
     "collapsed": false
    },
@@ -515,23 +470,23 @@
      "text": [
       "DataSet:\n",
       "   mode     = DataMode.LOCAL\n",
-      "   location = 'data/2017-03-03/#008_{name}_16-45-33'\n",
+      "   location = 'data/2017-03-07/#032_{name}_12-36-13'\n",
       "      |                        |        | \n",
       "   Setpoint | time_set                         | time               | (3200,)\n",
       "   Measured | my_controller_raw_output         | raw_output         | (3200,)\n",
       "   Measured | my_controller_demod_freq_0_mag   | demod_freq_0_mag   | (3200,)\n",
       "   Measured | my_controller_demod_freq_0_phase | demod_freq_0_phase | (3200,)\n",
-      "acquired at 2017-03-03 16:45:34\n"
+      "acquired at 2017-03-07 12:36:14\n"
      ]
     },
     {
      "data": {
-      "image/png": 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GGO57fWGe2/W19enfG+u2c/vk+bw0ewVvf7Y2y90dryzgDy/PpanJ8M7CtUz4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\nWQwcjoijEXEGeDjVy60fEc9FxN/T9gPAWEmeX9fMzGyQGpU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jdX8/YBqwSERW2/dfFZE90iM3xtxnjJlpjJnZ2NhYYFIURVEUpXDiZElIUExF4RVguohM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\ntMnKikEWrKrKikH1YVT94PQ7jW+Z6kRlhTC6PrX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       "text/plain": [
-       ""
+       ""
       ]
      },
-     "execution_count": 19,
+     "execution_count": 16,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -544,129 +499,73 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 20,
+   "execution_count": 17,
    "metadata": {
     "collapsed": false
    },
    "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "DataSet:\n",
-      "   mode     = DataMode.LOCAL\n",
-      "   location = 'data/2017-03-03/#009_{name}_16-45-34'\n",
-      "      |                        |        | \n",
-      "   Setpoint | time_set                         | time               | (3200,)\n",
-      "   Measured | my_controller_raw_output         | raw_output         | (3200,)\n",
-      "   Measured | my_controller_demod_freq_0_mag   | demod_freq_0_mag   | (3200,)\n",
-      "   Measured | my_controller_demod_freq_0_phase | demod_freq_0_phase | (3200,)\n",
-      "acquired at 2017-03-03 16:45:35\n"
-     ]
-    },
     {
      "data": {
-      "image/png": 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/eJhTr6reffP79y/jEz94mLuffpn3fese3vPNX+5SPR+5+kFOvnLX359Trnxw+/t79g8W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\nwN3pNcCrwMkRcShwLvAvZdo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e9+kFwPMERE2l3uDzixliuUrkYwM85Wf+iq/+xQdNxgb4w0/NA/D4ms0ufC5ZpFy3M4B+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\n5TeRJ2x5AAAAAElFTkSuQmCC\n",
       "text/plain": [
-       ""
+       ""
       ]
      },
-     "execution_count": 20,
+     "execution_count": 17,
      "metadata": {},
      "output_type": "execute_result"
     }
    ],
    "source": [
-    "myctrl.num_avg(100)\n",
-    "data3 = qc.Measure(myctrl.acquisition).run()\n",
-    "plot = qc.MatPlot(data3.my_controller_raw_output)\n",
+    "plot = qc.MatPlot(data3.my_controller_demod_freq_0_phase)\n",
     "plot.fig"
    ]
   },
   {
-   "cell_type": "code",
-   "execution_count": 21,
-   "metadata": {
-    "collapsed": false,
-    "deletable": true,
-    "editable": true
-   },
-   "outputs": [
-    {
-     "data": {
-      "image/png": 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KajoVF9K/azFF+Xl8uLasXkb8gdc/mxiT7BGpHHt2LKJnx3aJ9REyeXh6ECFf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\nVsMJgpmZmdX4P/bu7z8W0W89AAAAAElFTkSuQmCC\n",
-      "text/plain": [
-       ""
-      ]
-     },
-     "execution_count": 21,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
+   "cell_type": "markdown",
+   "metadata": {},
    "source": [
-    "# Do this in as qcodes measurement (ie the same but makes a data set) _demod_freqs\n",
-    "#data3 = qc.Measure(basic_acq_controller.acquisition).run()\n",
-    "plot = qc.MatPlot(data3.my_controller_demod_freq_0_mag)\n",
-    "plot.fig"
+    "Assuming that you have establised the correct int time and delay we can integrate over samples. To change averaging and integrating setting you currently have to create a new controller"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Example 2"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 22,
+   "execution_count": 18,
    "metadata": {
     "collapsed": false
    },
-   "outputs": [
-    {
-     "data": {
-      "image/png": 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YBnIDN1sdrLb5k0SoTcRH/4kcKiGmQudiJ89NVR5Yo9VBq6PBToF06GuZJ2D2\nihhVZ/H7EDGyG2+MAeFNgCoKAUX0p/NURVbS1JwH720T4TZwJgSwY3oSUxPJI1o9FSycFG7/SaDq\n2L0RKI3pSOsneKvxFc7zlW0CoY0v3oNgd+HvmJ7E9GQ6bwzrBm0491z76imgvfoV1dBsdSnjrX/e\nWNNXmUPfsBENgJrQaHXQ7qr5YV4k3UlsLnbvZLAyyXdtW1+IqoKWQ+3RM6967dmTRG8GhS0DYxTS\n6Ijsk75rZzLejJtINu+sLH7fBoB1E5ldM96qG4+UABVbiMhH+zEtJLR26DGu+SEQDYCa0PswWRai\n9YcpHBFrOymm0mu8VK7pvuKBQGm8dr0nCYHYxVQCC+nsNm7i830ESkv/fC61bROjwx89LnQRu5AN\nM+9mt0+Zy8my5NfM8KZY/Dtt9z4LndnXzI1eS7MIkS/P2xggGgA1gWZikydwH/370MRmVAxnjSfo\n3hh8iFOgLylbqhjNjH0TYTdwNo7LpGCyzz19imRj8B7CJ6FO8GviYax4F7lmstonzLzLFCit3rNh\nIxoANcGboEcwQgvHxPZRSphlQvPXnuM/WGOBvVr2tv59eE/C3Lvl1Q46XSKOS997NnzCC2jR9TsC\nkV8Xm1wJbW/kV9L4M6cek+mrIRANgJrAa8nzudzTk1xday4dhzvJLFI1ycOXgwW4SoK7t09hysyf\n8OO9scAfE5s7gYeMI+/aNmm/d2z6qo9564lEV71vTymQxLyleUtjJAIERAOgNviyRpmFLAkf+EnF\nsujBs2Iu9hRA8hS2HHYh8yFCxKWf2g2ILI0udC62OX21hz9hAZ3HThtv9k2o3eliaYVPPQ6mP0GG\nX0J6vkIhGgA1YS59mI/Yuc3Unq6q5WEh4hcy20KSibFsmEwVvn7oevJZFUirlK0XN/CMzXgLn0vt\nJ4bPnGJZ/kQo70u3mzNvKmBpJefaV5p3JHGZNNxZ7ZB+74mlEuM4EQCBaADUhvnlVUxIz8NoSMOb\nmrC78OcbLcwGcgV2uorFFTuRa6XtKZ93jUVvy8OnVAh3TK15XywpmKHqECyvrqfRmfrOI1JV+P6+\nDAhGQMqn4Vw9g6RtNt6WVtvQvjLOVQ4O6zLC/kIAVca/yQNg8N4wLnyat0Q+OyEQDYCacO9y8jBM\nTIiNkZoV1TC68PuZ1JZTYLYIV2W0rmkYzFgXIrYOAB+DT9rbjbesb2sqG7WJeDjBWzeRjemv1ZGn\ngVDl+y9kTn8fAAAgAElEQVQ0W2v8CS/Xvuq97zlFVp27vS545tol2S+G9mvzlq8jYErjYw1vMvW4\nNwPDsu72qyBWPXiEQDQAasJco4V9Rvc/wBNKFnviyFUf5oyJ7WMToRYic/t1V6R1E2A2kawkqRXM\nJtJfBKrq+Nl7R7vgm72bmC2Fk60BsX7tq7dnPAiLHgScAA9StsaDhw8OwM70BG89NNEFwIiw5Txp\n+IdANABqwtzyqjc3rnUhClXJz58eut2NOzM9ge1T9gwIupAQm0JJp2IFKkTkiQjGeH9ClfJl2/OF\nfEgXvof+Ge6Ll/RVYgNm+mcPTaEQDYCaMN9oYd9OkkRHian407Kv3N6DoAbQI4dqaD+uefCtThfL\nq53gG3A4LXwuBdIHgdI69uze8aV8wxoQe43rVj/3pSp4/oU9g6HTVSwyAlRjWAkQCGwAiMgFIvID\nEbmq57UjReTTInJ9+u8RPe+9WkRuEJHrROTJYUbthnuXV7GPzqO3PcxsRTN2A/elqc0URAmfihV2\nAw7dP8PEpksBBzL+QhtP3sTHGO8LRaLj6n8w927Jk4hRzAKohncDeErfa38A4GJVPR3AxenvEJGH\nATgHwBlpm7eLiM3HOwTMLbMcAPtk8rWBbyzIUoHERyrp5ZMI3cHKqfqoSc7GYekUxp3Ge+dBRCnk\nCZ4pRNRsdbCSatFb4E/9k4/hW9tPCLBrWw+RrUL7kN4XgHPh+/BcMe1DIagBoKqfA3BP38vPBHBh\n+v8LATyr5/UPqOqKqt4E4AYAZw1loBXR7nSx2GxTIQCGjeyPDBQqjpylctknM0WgJE6RvXUILAid\nR7/QaEEEmDWWAs7zvlQz3nxI2YbbQAGY521uGl3FPHzu3rUxO5NkLlnb+6xjUMV48+bCn+ll8Vdo\nT87bUAjtAcjDsap6R/r/OwEcm/7/RAC39PzdrelrI4fMhb2PPImE1sK3sqFZDYNMxnhmmiATkYIe\nPmOBVC50RfhQkJzdPmXfBGj+xGbjy9WDkelPrBHBKvft69qHOoUm3peJCVn78lU3MWYDW8wxvKt4\nn0K68H153qzGWyiMogGwBk2ensqXUUReJCKXichlBw8erGFkxbh3eRUA1kIAVReiRZIFz54k1uRQ\niVNgfypRlf4Xm+syxqZ8XGID75dDFRjFVLIUzIp3PzcPn1BBrHr1snKuvaj67FjTTwFuE1qqoZAP\nY7xZDJDJCcHObZNDf+6z/r0V8Ko4/G5X03nnz3iz3Dtr/6zxFgqjaADcJSLHA0D67w/S128DcHLP\n352UvrYJqnq+qh5Q1QP79++vdbB5mEs1qa0hgP5TpHUTMU8mmond44ozluW0ksiycrLWUyCbi80X\nNNl4krEsRKyCJHvvvCnpGTUMfJ0Cq847HwXA9szYWfT0Bt5DXq06BFZCerG5WcWwCvoraFa+dx7k\nv4F18uuYyACMpAFwEYAXpP9/AYCP9rx+johsF5H7AzgdwFcDjK8U842NHoCq8FkX29TeB4uerGhm\nXcQPZ+VkiQwCgL/2IdszCpIh8+D7XfhV4a8MdDgWf6hrD3Dk1WaLlO/2kIEA+Cifbh8/UwUyFEKn\nAb4fwJcBPFhEbhWRXwfwegA/IyLXAzg7/R2qejWADwK4BsAnAZyrqp0wIy/GvYdTDwB9CuSq4TF6\n6Na2ALeBA35KAbOWPHuCt6Ywzjda2D41gRkzf4I7gfvYhOwiRr5SIMNISHsRoAqUB5+0JzKPyAyG\n4CmQjTYmJwS7tnnwnI0RglIWVfUXB7z1xAF/fx6A8+obkR8cOrwCADhqdyAPQJOta715IapKJjph\n3w5T31n7Y2a3m9r6kqJlhXjMm8iyhw2YUiG0GxCr7S4aLUbEyFMxmkDZM15KAbMxeKr9Zha/6/Xz\n5vkK6L2hwi8k+TUUxstfMSY4tLSK7VMTFIkO4JjkLBOb24Q4MZa8hazyQhSIRe9jIfItYlSVRMgr\n0bHhF649I0BFneBJ/kPIEMAa+TWwgFRIDYbNbatpn4ybCBAQDYBacHBpBUfv3m5mwfvI5fZ5ErEw\ncjlXpJ0EuJBD4mNzsSv1n6OHXlVEKVQp4EzDgJYhNmYR+EuBtBHZfHhfuE1kswvf9dnplyHOSHCu\nvS+tcGHHQYavs+Ged+ipuGayLnxOxIgLv4RCNABqwKGlVbP7H0gexm2TE1we/KYNrJoanHUhHLSJ\nuPa+0u6g0eqYMyi8VaMjThIb9NANTHZaCCeUGlpzo+Fq0Y8AiE2E3AR8FKPpvXaW789mr9j5D+yh\ngy/gBXAn+H4XflUFzA2HHsu9IwSwQiEaADXg0OEVHLVr3QCo/DDRRTXsilxrTGxWTY3dhDINBfMm\nko6/4gf0byIiBjEVj5tIArcBqOomN3SVZ2iQ8eT69X3HcS0pkHQaXe/YxSBkQyhIMuJf8wO8L65g\nrz3dPmfeVcE4yxCHRDQAasChpVUcvdtGYgNyNhGTNbq+gVeZTHk6/JX6ZjUMMg0FchNhZYTtm0gb\nsx4XoirDaLa6WO10vTGxrbnU4TwQgTcBYhPiK3D6Sj81PjvL/L2nSXjMmkkUX8tkiPcYDe+QiAaA\nZ6hqGgIgDAByEwlbSChsJcFMynbSrGfuwwVvW0j61dAq981uwMthC6IsNNppMRorCc9DOVnjvcs8\nZ3QGBEugDCZg1cKO6Ulsm/Ij310VIY03VoEyJKIB4BkLzTZWO10cTXAAmE2EXYh8qalZx0+rKJKV\nAL2kYpFqaGwe+9hWs8uRkK4CxgWf9W9XshtvFjw/7z2U8qVTGK0iRmn4JZCIUUiUjlhEDgB4LIAT\nADQAXAXg06p6b81jG0scWko0AKgQQLOFE4+w5dEv+joJ9Bf1qNre2P9cIwsBbDSgXPX4w58kwqdS\nWclIPorReJMhtvTfbOO4vTMbXnP97t2uUqdAf8YT60Hw64FwnXf0Bh4wBXJQ+mjV7JX7lA6AiLxQ\nRL4O4NUAdgC4Doku/08C+IyIXCgipwxnmOODQ4cTGWAmCyBvE3ElI/FuWJJMRC6Ec2khJWYT5GSI\nSTU1IgXS2yZCMrG5DIjNJ3hn460WAqUbDq+20VX//AVWSKe64W33viThF39loKu1z0mBrNKeCnv6\nrSExTii62zsB/ISqNvLeFJEzkejxf7+OgY0r7l5MVQB3bfQAVHqYiU3E2yawISWmOpPcnMffSOvR\nE+1PPnKnqW3WnmFyr+a4Eqsu4sFi8E2SP0HE0JP+/UrZViExenPB7+ydN+7tfYRvpifXvS9rfTsf\nHHpKCRuw0GzhmNmZ8j8cAMZ44zMoNs+bSveO5N6ExMDZpqpvAwAR2a+qm2rqquoVdQ5sXHF36gHo\n5QBUWYhYMRb6JOKhml2umprjAObShSBbiCz50D7jwAKpfgrr2QSrjH9QKtewXJEDU6lcN5G+8Isl\nje/YPbsrtkrAbgLrMsS2UyivP9FXTc6oIUAVgTJ6/ZL+23jg/t7Mo+qVFK3G20ARogp9Ax40FDaJ\nOJk+bqhwIQF+UUT+S0R+XUSOqH1EY467F1cgAhyxy1gHwFc1Oqs1y4qpkGIoc57V2Kp0v9LuoNny\nsYmQMXyjIEmeG7nK92fvHU2g3CTGwm8Cldsbr50P/gRTBMp3Hvww8/C9GW+BJKTz7v14JAE6GACq\n+iAArwFwBoDLReRjIvL82kc2prhroYmjdm2nCvEAMJ+kvJwCiXxclgw032iZNQDW9MyJeuyADy37\ncEQyJhVrvmEPPflrH2YT8DFvgHClgPMK+Qyr/25XsUhwAOoowzzMNZPlT4SE00qhql9V1ZcDOAvA\nPQAurHVUY4w7F5o4fi8TCwtrzdJ58GT7uUZrTQWwKtb0zEPJAPerEFbEfMqi30npmXMbML0JkUp4\nvpnczu3pE3ybl+8OpD8BhCVQ0td+BESUZmemzfyJkCh9WkVkj4i8QEQ+AeBLAO5AYghE5ODO+eam\nVKQq8GEN+3ThZ6iix04tRMurdhVATxkQDP8B4Is42dXQwlajY9LoWC37gSmQjg+uL/4Ec+8oAiUd\nAsh/dlwu39pzbzZ82ToGvPeF9Zwx8+bn3voFvO1/bjC3Z+Dyjb8J4EwAf6aqD1LVV6nq5TWPa2xx\nxzznAaDTgdKFJKQLv45NyGUh8uXGDZVO5COOmzf2KiRCmokdKgXRAwlPBJi1lvButLA3qPfF3j6p\nIUFoIJAywIM2cPfUZ76CZ57x4k7+tYcdVRXfvmMBh1Pv5bDhMurTtEpFjC2MxmonZTIzHgC+LCfn\nwt8splKJSEb03+1qwgFgC5rkbEKqWmoUrZcS9swBqLABs+EX67OXESDtSniDvS9up0j/udhVCZRM\nGtyge1clg+S0/bs2t3f4gKwIlJXAmNWQCBZ+8ZB5lLS3G58+y6dXwfJqB+2uBtMQKBICeqeIPDxv\n8xeRXSLyayLyvHqHN164c6EJALkegGFZsz4eZvokYRz74koSSwwtYmRn4bcxPSkb4sCVmOwBT4E+\nNAQAJvtk8HM/DDd0HUp0lVLZiDS4og3cZdXxkcEA5G/gVTx3VuNtvtHKTz12RF4lv6ppiLnPrcPV\nZ40fFkWz5W0A/lhEHo5E/vcggBkk4j97AFwA4J9qH+EY4Y75RDNp0wm6oqjENiIdaNAGXCWf2bqB\nLxVs4C79s6c4No2OjSNnCwFTSfCUo3JOgY7t55dziGCOQylywVe5d3wGhH0D970JAKjkvck7wbtA\nVbHQJAoJkSqA+QZAlQ1wM/elavopwIl/MfduodnCsYFEjFgJZxZFQkBXAHiuiOwGcADA8UhqAXxb\nVa8b0vjGCnfMJR6A45gQQA4Zp6oS32YXvlt7Oh835xRWZSFYLwREaigQCyltfNGnyI1T0vXeddMi\nUD6rwVUL/bChq80aClVTuTgNgs1x3Krzzs6i76DTVcp7AYRLP103Hu0GyI7pSbPxlut9qXjvTj9m\n1tQ3MIBDUNHwDhUCKL1jqroE4JL6hzL++P49yxCBuZAPkJ1EODnVcU2nmWskKopMJUAuA4JVEbQz\nuVWV876sJpUEw6XBeSJQEimcrPF16tE2CWk6D35ENnB2/LOeSXjO7ekUSvuaudruotHqeCAOh9EQ\niOWAPeJ7hw7jhL07zJYs4OcU6bsiWJW+gbAlTZkMiCSGHiYVKzsFmr87y8RmUxg9GI+M92Vg9ohr\n/4NCAA5YWuW4K+ErCfL9MzUkfFTgZESMmEPXGgGSJi6PGAkwojpuPrRsPkVkYOJZa8VoamDzOhFa\nyFSutRCANQ7tQ0yFFUEaQTGUaveOVVG0j58vwxwmDW/N+AqlP1Gj4e7G/+BUCJl5A3AVQDPPWT6B\nscK8CTTvWTgbACLC7WxbAN87dBj3yyFxVQEj6elLUjOUEI6PXPBBE8mJSV5DHNkVvBuWTx8FuA28\nPwMig/MmQoW+7Bt4q9PF8mqnFv0Ip02kYN4Mg8U/T7rw5xstM4EPGGy4V/LehDLePIkgMdePgYsS\n4I+LyDUArk1/f6SIvL32kY0Z5pdbuHe5hVOPyreTqrHwwyhiDbRmh0RoydJ5qIIobAy/hpNMlUU8\nnIhR20v2ycbwSzUiFh362pSG5wZWhXDQvHONROXOG0MKZe8Gnt0H1zS82RmiDHSB8eXifWLrEDAp\nnIMMX+reVex/17ZJTBlrx7Bw6fXNAJ4M4BAAqOo3ATyuzkGNI24+dBgAKA9ARgSzb6CDF7IqJxHW\ngNid2395+7nlVezb0Z8BUC0daROTu2Iu9mYWvhsGhV9c2/uKA1M1IAZsgK55+KzxZN1EWP2JQWWY\nK7enQwB27w0jZUvrT+QZXxX1M/qfPdfmRXUIhqqBYNWvIK89C9diQLf0vdSpYSwbICI3i8i3ROQK\nEbksfe1IEfm0iFyf/jsy5YkzA+D+R282ANwfZo4IxscSyXxckgw0t2xXAQQ4F34RC9+VfwDUo2de\nKRZpNED8lPIlCZTGU1iR/oRr30DOKRDV7j1LBGNc8N5FjCpM4UWiCFSWQRGqCqO30Blh+IcSAQLc\nDIBbROTHAaiITIvIKwB8u+ZxZXiCqp6pqgfS3/8AwMWqejqAi9PfRwLfO7QMADjlSDtVYtDD7LyI\nDzgFuk7mOmqSV83HDVXStNFKJDlpIpZn/kWVe0dp2TfbmCXunY80PF5DYHTqwVdtzxjObBpdXfU7\nXMBmUAzawKsYvkDYDI5QGQCAmwHw2wDOBXAigNuQFAY6t85BFeCZWC9FfCGAZwUaxybcfOgwjt87\nY948gXVCivUUTMeBfTCxyYXEWgmQFjEiWew+CIz0Bk5o2fsoJ2v97rQSnqc8+nAVOMOz6K3Xvt3p\nYmnFboD40H8AOPEvqn1aBnq7OfzCGW8sSketqner6vNU9VhVPUZVn6+qh4YwNgXwGRG5XERelL52\nrKrekf7/TgDHDmEcTrj57sO43wACoCu8VUQjmOiD+nYt6MJsIkUhgDI3uK9ysqGEdLJToO9iNK7w\nIabCFETpdLUWIRy3OCxv/FFlnJsci77OE3zZvFtaCWs4s4ee+UYLEwLs3sYUXyOrr45yCEBE3iAi\ne1L3/8UiclBEnj+Esf2kqp4J4KkAzhWRDcTDtEhR7tMpIi8SkctE5LKDBw8OYahJCOBUMgWQzsdt\ntjk9dA81yUO5EvkUxjr01Ku1t8aQAR8pkKwK4mAXftkm4s/wDeXCHzxvXIlo1g0Y8BN+4V3w/mPw\nriQ6gDPcZ2emcw1v13sX0nvDwsVv8SRVXQDwdAA3A3gggP9T56AAQFVvS//9AYCPADgLwF0icjwA\npP/+YEDb81X1gKoe2L9/f91DxfxyC4cOr+LUHAJgpc9JpXAZQZI6qslVIpIRLvxGq1NbHYCyyRxa\njpVxgSft2RRIewx+LQOCdOMy1y63veOprEjDwAVeysl6FkGqkr3ChM68qRiGCr2x844gz3a7moZP\nRtsAyJ6snwXwIVWdr3E8ANbKDc9m/wfwJCQVCS8C8IL0z14A4KN1j8UF1921CAB48HGDC0pYy2JW\ngRdFLWoTsp8i2VxqX0p61k2k2A1dby501j70Bm4nMBa7kcsuH6+BwFVxHDRvqugQWDMgMilbqweB\nzhwqyX5xvXd2461NcWcGzZsqhx7rvF1scvU7fMDlqn1MRK5FUgnwd0RkP4BmvcPCsQA+kk7IKQD/\nrKqfFJGvAfigiPw6gO8BeG7N43DCtXcuAAAeetye/D+osIlMTgh2Uw+z7QQMcNZsRgayLkTeKpJZ\nBT0KFMFcjbed2yYx3SfoUUVQ5JjZ3bnvuSrpMQsRUOA9Kd1E/PAvWCJZnv6Ea/+MC36x0cLJRAEw\nlkU/SMrWtW/Ah4YBZ7gz4Z9B3BnX9FnO85ZfwtutLZc67AMu1QD/QETeAGBeVTsichgJG782qOqN\nAB6Z8/ohAE+ss28Lrr1zEXt3TOPYPdupzxlUzMZ5MjVbOCanrrVLc5aJvTggFcvVkp5rcBkQvsrR\nWslYdRGxKuUT5xhvLqfaQQtR1fRT38VoqhhPnJLd5hTIqv2PK4veV+pxsOyXQd6XCvfugQMMb9f2\neQXEXOYdG77wAderfgKAs0Wkd3d5Tw3jGUtcc/sCHnr8rNmFmGG+0TbHwJP2LTxwv+1hpmuSk7G8\n9UJARg6ABzdw3gneFT7iwHRJ0sACUnQIgSkFHCgNjlXvHBUWfbg8eDJ9lU1dJtqzCpQs98UHXLIA\nXgvgrenPEwC8AcAzah7X2KDTVVx75wLOOGEv/Vl0DJ5wA/MbKCcjPLfMESBZESN2IWHuHUvEWvRE\nomNLCVtLKa8Xo7FXImTu3SKhgbC8yglIhVayK+u/nDzbxoSA0EAIV/58rb2x/+zeh1pzfcDluPML\nSNzud6rqC5G45vnd7j6CGw8uodnq4mHHD4j/VwATiwwtqentJDJIB8ChPb2Bk1K4ITUEAD6Gbt3A\nfRiPu7dPmQuiLBAESIBLo6MzGNg0Ok8seop4TGgg8J4ze/hkpd1Bs9U1Exh9iAgl7UdYCAhAQ1W7\nANoisgdJ6t3J9Q5rfHDlrUlSxCNO4m0iZhPhJTUHP8yV4sjESWSCUsIrXsRdiGyh5FRDl2H24YIH\niBRMUkBqUAjAlfviJQ/emMHAEsEGxvArVrNjaljQ6aeBwjes1zJ0AS8fcDEALhORfQDeCeByAF8H\n8OVaRzVG+NZt89i5bRKnGWPvvZhbXg13CvNUCdAeAkg2cLuULV9Pvq6FyGUDBMJd+2whtBajGSSH\nOqyCKMwm0Gx10erYVQgLDd8KRLB+z1dVLXvG88Z4X0Ke4AFu3hYari6HHg8Kkkn4ZLSzAF6c/vfv\nReSTAPao6pX1Dmt8cOWtc/ihE/aaGcgZWBb+KEhqsu3z6gBU2USO3r2ZQFil/YOP3azj4NK+1eli\nebVjZvGHDr+wQjisG7gsDuwi4hRKAdIH+ZRtX8Sirzt0lleC20f/7LxzQd0EShcNhkEqhMOC04wX\nkRPTioCnANjXL8u7VdHudHH17Qt4eIn73+X2Lq20yVLAxfGkUlckG0duJhoGOweQgcoWgrlGC3uJ\nDAheyz48f8Iq5VvkQXDZk8uEcNw2YOYUZxeQane6OLzaqS0G7uq9CVaMxkcRKGPYb619bvjFXYWR\nNp7MBEZPxhuRehwy/g84eABE5K8A/G8A1wDopC8rgM/VOK6xwFW3L2Cl3cWZJ++jPytbiPLS4Kqc\nInMf5goxfMYNnFcQxV2IZ5UyAJiTTLerWFyxhxBoAyATIcrrfwiuyGQhshkPa+1JAuVDj8/zvrg8\nt1wGQtEm4PL1fYggcZUEfYRPwrjg2RN8UdhymJ43q+HNel98wOXOPwvAg1V1pe7BjBu+elNSFPHR\npx1Jf5avYjahapJnIkZWzDdaZkUtNh83tJraPMkEn2+QLnzy3vFa9nwGRWglPMZwns0R/3KFj+yX\n+xM1TJgiVr7uHSv+xWqfWJVbWQKlD7isGDcCCDvKEcU3vj+Hk4/ckau+VxV1SXK6ty8uSlEazyJd\n8HONFo4oWkgKuj+82qEyIIpkgJ3aezDedhEiRJmGwUAXfs33jjEgOmvel/o28KLv78OFv2N6Etus\n9eA9CNnUyqIveHTK0ujKQG/ANfMvXPgLVPpq4EqAQIEHQETeiuQaLAO4QkQuBrDmBVDVl9Y/vNHG\nFbfM4cCp/OkfKGdyu8SzKElNL5uA3QXvJ43ORkai0+A8GG8hxVAWGi2csNeuZV/67BRcfF7EyFMu\nNiFCxJDgyuZNOXenjVOP3lnSy2Aw866shoRL30C58TbIsPVxaKLEw0gBqlEPAVyW/ns5kip8ET34\n3qHDuGO+iUedwsf/gXUpXLM7jSQDFbnwXeNZxxs3kcUVriqWLxXDutTUSvtnS/k2Wrla9u7928lI\nSfilTan4AT42cHscluqfLsNcj/6EC38iO8GHm3f16k+4tB/03V05BNbnHuBTIH1gYO+qemH2fxHZ\nBuAhSAza61R1dQhjG2lcemMS/3/s6fud29RpzTKxOCCZjCcfaT9JMAtZ5oLPq4NQpahGHYIcTpvI\ncon3puQUt9i0b6AAgtY0b7a6WO3kbyJMISJX+DgFMjUgirwfbiTC/CqQziz8EgOk6NkbhhBOUfjF\nB4murP+y9lz5cw/1O0adAyAiTwPwXQBvAfB3AG4QkafWPbBRx2e/cxBH7dqGB+y3E2h64YUNXLQQ\nOLSvcxMpWojq5D9UOgUWjr94IRsUB3bdBEPF4Ffa5XUI6rx3/mL49tAXLWQTSIAqY9GH464M9pw5\npZ96CN/kCVBlcAmf1El+Lep+LfRFHNp8wMXsfROAJ6jq41X1p5AUBHpzvcMabXS7ii9/9xCe8JBj\nnBb4KikhDBuYdkWaT4FcMZu5RuJQspYCDk2g9KKGVrAQMUI4ZU/TWhw34AkcsBtvC2kGxA5jHLdo\n3oiIkwYDXcCLZNHXksZWoX869MZUgSTXzLoqeJaNiBVO8wUXA2BRVW/o+f1GAIs1jWcscP0PlnDv\ncgtn3d8PARAYrIQHuJ8irQ8jXZO8wJJ3iUWulwImTyJEHjxDoGQXksVma2AaWdnVSxQk60mjc7l3\nvsIvjPFVJGJU2j8rRUsY3utu4LAs+pD3btvkhDl9db5AgGoYnjfm0MXeO19w6f0yEfk4gA8iOYw8\nB8DXROTZAKCqH65xfCOJL333bgDAjz/gKG+f6aMUsPVhWnSoA1DIovcghlLWfxGyhWw3UdCEJVBS\nNcUJ/kSj1SG17P3Uow9ZT75s3qgO9ibMN1o4YZ8tjZe9d4u0C7z+SoBaMPP9VBK0ayCwefTMvM0U\nKEPV7/AFF9NrBsBdAH4KwOMBHASwA8DPAXh6bSMbYVx64yGcfOQOnHSEnTTXj7IYfJkrkqqKFbik\nqEsssmwhmp2xixh5ScMzbgLLqx10usQG7kDkKjbe+CqOZf0Xga0n74PIZb329L0bd+OrJAZf3r78\n2heTGEnDu5S3VMQ98ZM5FDoE4FIM6IXDGMg44Zo7FvDIk/yk/2WYa7RwP6MSHssoLUvFKttWWVfg\n3PIqZqaJfFyHRbxwIfGQhrfnhD2mtpn3xawk54EEB4Q7RXopJDQofdVFSpgwIFye+8JNxEm/wihi\nNKwUSOIEzxtvbbN66NKKn/LpocInvmAz3bYw5hst3HJPAw/KqRzHfi6dxhaaREdVAsyvA+BKRmJJ\neIP5E25xcH4hCHOSYEsBZ/3ntXfNpaaV8Izt12pA1KBBAJSTGIuMr2p1COz3jlKg9EE8puddPfyJ\nsnvna96F9gBEA6AivnhDEv9/REkFwDwMOgx0u+pHDz3UKZI0IOaWW+YMgKT/wXFYVx0B63dvOcQC\nC09xnsRUQhkgCz7CL2Xem5L2ZiW7VIDKTsLzZLzVmUJZ0p5VsivlXxS8V7cLvwhl2h1l8HHoYgiQ\nvhANgIq44pY5AMCPVpAALrNml1Y5d5STFK6LK5KMZRaJ2ZQtRKEEObL21hBC2QbsmoZXeO0cxFQY\nIomXfdIAACAASURBVNnURHEaXZ2biA8VwjqNp3qNNz4NjikC5cMFH0q/otHqoF1SPt0pfEJ6vswi\nTM0WVQTKFwqvvog8BMAzAZyYvnQbgItU9dt1D2xUceWtczht/y7sMqaM5WG+RAa4PAZfTARzdWfV\noqntGIs8hVAh9KGFH6yaXEkssNSNvLYQ2TwghTH4IZQ0nW+0cOyezUp4LihSIXTtGwjHffHhfWFS\nIIsMAJePXGy0cPIRNvnvLH21LhGj0ntHG85la27JvCPnjS8MNB1F5FUAPoDkWn41/REA7xeRPxjO\n8EYLqorr7lzEI06s7v4vQugY/EIzUSHcSTCxmYeZDwHYFxI2nSf8veO19NkyzqFkiF030EEHMb4I\nFFtIqNz7UoQ6hWyc2hMbeJa+ymQgAOEzKJhSxKEJgECxB+DXAZyhqq3eF0XkTQCuBvD6Ogc2iphv\ntHDvcgsPPd7G+B4E1hXpI52nTIWwlEVPngLLxj6of5cYfBEWQivhOYQAyvov07Kv+96dfoztBM/2\nH5w/UUCAdGrPZkA021QRqPlGCz9knHeqXAXP0Bs4bTg3eQXKkfYAAOgCOCHn9ePT94JARJ4iIteJ\nyA3D9kTcck8DACh3dR7mRkCRq2gDLHdn2U+RzVYHjVYntxCQC1ylbAcZULQr0EFEqbh9C9uokqTh\nTuAAZ7yx1eh8GL4Al0I5qAaEa/+lz21JHnwo702z1aUEqFzv3UDvjQf9Clb9kwm/LJL3zheKRvB7\nAC4WkesB3JK+dgqABwL43boHlgcRmQTwNgA/A+BWJIqEF6nqNcPo/7q7EgXk02tIAQQ4dxS1iXg4\nBR6927aB+9DhB5gyymFFkBgSG+BBw6DZxnF7bUp4AGeA0C50lj/hoRhN0QbkwiFgSXQnDYjBl333\nrJBQsOwRT+0Z783uAvVPFw5BqPCJTxSVA/6kiDwIwFnYSAL8mqp2hjG4HJwF4AZVvREAROQDSEiK\nQzEArr9rEdsmJ3DqUX49AME3EQ8P82nGqojZdx9cB6Gkbw8aBEABAdO1f6OK42LTnoIIcCmMa/3X\nVASq7g3Yh+E8IcDubaEqCRYVInLrPxh51VP4xfr9+Tx8nrxqDb9k2SujEAIofPJVtQvg0iGNxQUn\nYt0bASRegEcPq/Pv3LWI0/bvwpRROGPQPjCfkoGsJLwFchOYb7Rw/F4bm3etf+NEzMIfVhKgj1Mc\nwBkQAzMggNKjxEKzbY4hA8n4rVr2WXs2Bl+nBDRQlILJk/BmZ6bNNSB8eF+s8841BXJQCumwZIQH\n3Ttf4Rsrd8bHvSsPX+R/+Sx7JbQIEOBWDGjsICIvAvAiADjllFO8fe6jTjnCtFi4WLP7djJkoMC5\n2C6TYdBCRApy+EvDszHJ55d9nAI54+0hxw0OSRU9Ua7V6OraRMpPgeWpVEC9ZZyLY/Bt7J/dbuo7\naW833H2lQFoVMMvDL/Xfu93bpwoPY0X3zkcGhTUFkg1/+ET4EVTDbQBO7vn9pPS1DVDV8wGcDwAH\nDhwoK6fujJc88XRfH7UBpYIcDm7oIhKdS21q60Q8nBVEMUpqrnkABkgBl4EWU/FwEmJLAZ9YsJDU\nGUcuq0ZXu37EGoGyvgyIsv4LF2GHEMYDjKGvrD0tRBNIhZDnb/g4wRP3rtHGqUfbQ7llIYSi7tnw\niU+MmxLg1wCcLiL3F5FtAM4BcFHgMdHwEY+ys3k7WG2Xu6NKWfTWEMDyKoByEl9drsQs/BIsF7tp\nz6DodhVLjJb9iFSjo4rROIy9yINBe2+M7bMMCFZEqO5iNgPDLx74G0wdAlbFkC3hzfTPzhufKF15\nRGQR+R5QAaCq6jcpvgCq2haR3wXwKQCTAC5Q1auH1X9dmFtu4agSFn1xLrfdhR+aiLVApuOwcqhs\nLnbCn+BY9NYNeLHppmVfngJZrwZCmYwyI8TDuFEXGi0cM2vTMMhCX1YJadf01UEIXQo4C90xnjeX\nNScx3jbPTbaENyP/7SJDXNY3EL4SIOAWAvgbAHcAeC+SO/E8AMer6p/UObBBUNWPA/h4iL7rwnzD\nzqLnBTnKFwInd5ax/7l07GYiVtq+bAMvIpLVGYMvwkq7mEVfBl9StGwlwFAaCGXPvUv4xDr2stBX\nGVznTWke/CD+REn/PorhMBoIPjxng1Igy8CmQPqrBBg+Au9y956hqm9X1UVVXVDVdyBJvYvwBGYy\nLGcLkXkR9xPLs4cAyhbx4qWMrgNQsgm45APbY/C8CiDgQQiHjkMbiWQ+cqlJEaM6VQiLjNKyTcTl\nuQc4DwAtYsR4X0rundO8Y1MQi+Z9wb0rqwNQ2v8IeQBc7v5hEXmeiEyKyISIPA/A4boHdl9EXiyy\n21XqJOIjjkq197AQDdIAcOq/RA7Vhchm/e6dtJ58OBc4XwMC4EIAs9uZUsDh6hCsxeBrEiEqb0+q\nEHqY98MxvgaFn/jwDU8+DUTAJMMnPuFiAPwSgOcCuCv9eU76WoQHZDXJ6brU5CYUajLMNVrYa5QB\nzvofhhRunvHmFj4ZvDkuspuAJyIY4wGg47BO7QdzGOqq5ubaPpTxxnrufFRxZDxnPgzv0tTdEuJw\nMO4LGT7xidIroKo3I7r8KbjE0OtciIrdWX5OkeaCKA2uFDBTkhTgCJS+NAjKrl2pEE6RHG3Bw8dW\ngfSRvXLkLlv6ajfbBGo8wQuk3HsTiHy70ExY9FZRstIN3CEF8phZgvxKhG/W0ldDec6cwj9F7Tnv\nh0+UPj0i8iARuVhErkp/f4SIvKb+oW0NuGwiRadIf1WxbNb0QqNd6AYuOwnMLa+SIQB+EwqXAcEZ\nbz5yuffMTA3sw+UUF6oQUeY5c2KS57zmLXRGE8Hszx733HPlaJnwTZa+WmcGQ51rpg/DfxRSAAG3\nEMA7AbwaQAsAVPVKJPn3ER4w58jGreskMt9oYWZ6Atun7ExsK4mt29U1FcQy5H19Nh/XVQNhENhC\nRItkDH6h6aZlX+SKDJVLnfRPpK/Sxo+nUyCxCTDpq3T4xfHeDcxCoASo3I233L7XPF/18p4Gps+u\nee64SoKjAJenb6eqfrXvtXYdg9mKYDcRH3n4ZQ9jkTuLWYiWVtvoEvwHtiRpcA0ED2l4lJY9uRC5\nGgBFxlutp8CS57asfRFcleyKNBhcyskWGf5WFnvW3vrdu10lSXiZ8VW+geZ9fZb74sMDUCZDXIRR\nqQQIuBkAd4vIA5DeCxH5BSS6ABEeEHoToUuSMhkMDt6PWhdxkgjmo/8JAXYFKgLFnMCB8kqERfeO\nTV+lXfAeWPhlMsSF3J9msQveJXvF+ty5kugG4XBquIdXkLTeuza2TU5gewEJr5i3xWWvsO19wmUU\n5yLR1X+IiNwG4CYkYkARHuBjMjCpWD7iuFYSn2v4Y2DfDi50l1hg0WSs0wBZJFUIXYowlfVvVcLL\n0uhC5UK7ECCL2/PkV7YEN7MJLDqoEA5uG954StpbSXT8msmqf4Y6NPlG4R0UkQkAB1T1bBHZBWBC\nVReHM7T7HgaRkbZNTpi16Jlyrln7o0tkiIsQkkTnrxKgPXzCKNklpYDJEzhZSjj0Kc4phJAzcfjs\nmZIyzqXtPQjhDIF/kXft6q7iWNY/672hlfiarOfMUcY457UsfDIWIQBV7QJ4Zfr/w3Hzt8Elhs6U\nAnbZRAbFIn14AMyLeBbLM/IfhimkM8h4K/vuZScZt76L48hWuC6ERRswX00ukPHmIfRFewCM/bc7\n3bQIlN0FDtTLfXHynFnnfeD0VaY9Gz7xDRcOwGdE5BUicrKIHJn91D6yLYLkYSqeyGWTqXQTKuqf\nWAhbnS4Ol2hqD0ODIKQiGOeCLz+Bl9278v7zPyHTQ2dTEOlTpJFDMN9IikiVZUAUtaeMN9qAYFz4\nnqRorRoK3ow/7t4VPZ8+DG+uff4A2PCJb7iM4n+n/57b85oCOM3/cLYefJzATzbG4FW1wil0M9iK\nZlVOcXlKfGuSmuQmxKTzsHKqjAhSGQkvQ+G9G9fwTfrcumRA5HsweCLXA/dzUra0iNAQ0ldzFTA9\nlJGeEGC3tQKoBwnp+x1VXnytSEmwTuLzMDHwKorIc1T1QwCeqKo3DnFMWwpzjVXs373d3J5xRy2t\ncGl4tCuPZcFXKKYzyI3NxIHnGy0cu8euhsbwJ2gSngfjB3CN4edtIjwRjc1gYLgvznHgnOeu2Uqr\nQBrDP8OM4Re2JwwY17DnoNBhKP2Jdur1DGW8+UZRCODV6b//OoyBbFV4EVOpnc2bP1FZMg7Lgp9v\nJHKoRalYRQguhEO0Z0/w9CmSzeBo8JUQy134BeEn4t5X8ZzlwZv3pcDwLgsdAbzhz8h/h6ri6Hrv\nBj06bNiQDZ/4RtHsOyQi/wXg/iJyUf+bqvqM+oa1dTBfUg63CBkZKPRJgInBMyx4FyIVzZ8oiYNb\nv7sLf6II3mqSm/vnNzFKTMVDHNdO5OokRC4jd8cl/bQIPmSEKRJds/7U4zLu0An7bPU/llc7aHc1\n3JpJhk98o+gJ/FkAjwLwXgBvHM5wthaygibWanhsNblRieNaQbsCiVhip6sJic/oAqf5E95SIO3P\nDu19IeO4D9hv0zBQVc5z5om8Ogz+RV4IoawGhEv/NAGSCt8wXs+w6p9se98YeBdUdRXApSLy46p6\ncIhjus+ifx/INLF5Nq8xDY90R1UpqJK7EJGpVHQefLO4Gl0RFh3dqIOWWF8S0KGEcLwUAnJsP0gO\nljnBd7oarpKfNwMiEHm1gvZI7r1rtHDsHpvxBnB5/MPUDilac3ePiBJgqfkeN38egyzt0NYk647i\n69GTYipk+yoLYf9k9iZnahZTcbt3g2OZnPHGngJd2hepODL3niYw0hoGWRzZPv7pSbGLhznwH8rS\n6EpTl0sUOK3XLivgFere+TAgmPCJb9j8dxFe4PowDXpUXOO4ZbFIRlGsTAmv6DFf9OEBCETi88XE\ntuai80p45RkYxXro4cirq+0uGi13/kS/AeN6Ah8878J6X1wLCZW1d8EgASxegKpe70mp543VMDDy\nP1jlVt+IBkBAsCcRX5uQ1R3Fx/DbzkziQW5ga/9sRbPQ3ptK4ZcBqVRsBgYfAgjjgvfmBg4Yh2au\nvQ/9Cmv7qumrm4w32uvobrzVIaPMHlp8o0gH4K0YXA4aqvrSWka0hcDG8ny0d3FHFVmzVjcmK2ea\nkfCsCwlbijh4HYImWYfAQyqW+7XP7z909gp9CjXHgcur0RW2b7YxS6kYDi90tqlvDyqAQFjjb2qC\nDL+MSPwfKPYAXAbgcgAzSLIBrk9/zgRgV9CIWIMPIhYQzhplFoKlFS6GvcTmUpMqgqE9AEwhHyCV\nIa55Exh079bK0QaLw7JSum4aBoPi4BmJzSplO0wCZj/WJKRDGb5kCW/X8umD7p2LDHFh/+PiAVDV\nCwFARH4HwE+qajv9/e8BfH44w7tvw4cLn7JG2UqCBIuezUN3dQXWFQv00d71FJgHxoUOcOGTqjH4\nfrhmUAwCq6fOayi0sJNJgfSgYWCVkK6iQjiob2A4oavC9oQHgSHhsRs4U8a5Drg8wUcA2NPz++70\ntYiKyItnsYIcVCXBBp+LHaogiY+FAOA38JlpBzduzmuZ8cVUgeQ1EEjjh5CABnxsAoG4L2wlQEf9\niIHtK8zb/hCCL/6Eq/G1qf/AGgrsoSek17QOuBgArwfwDRF5t4hcCODrAF5X14BE5E9F5DYRuSL9\neVrPe68WkRtE5DoReXJdYxgWeEGOCjHwGjS1mTQ6V1fcIPjSMLAzydulblygmMVv5U8AvpjYtkXc\nH4mO20QY4212hjkFsumrnJQtF4MfTvjER+ZRbvtldwNiYAYDnb3ieu83joDlPdWB0pGo6v8TkU8A\neDSSb/QqVb2z5nG9WVX/uvcFEXkYgHMAnAHgBCRlih+kqp2ax1IbvMTyHB7GgbFIciFiXJmu6TSD\nENqF76WePGO8NdwqmhWlsrEegLqNLx8aBkC+8eny3UX8Z59k7U86wiZl22glUra0hLORv+BNe4Qg\nLu+YnsQ2a+iMJOEtNFo42eHe5V09V97TMOF6Fc8C8FgAjwPwo/UNpxDPBPABVV1R1ZsA3JCOa2zh\nugkMWoiG647qY9E7VhIsXcRd0wBrcmUyJ6lheE8GE8mqZGBsPokcXu2UE6Fq0yDg7x2bAcGmwXEu\nfLsbetjZJ95d+HR7N69n0bPrrmKYJyRkv/ejJgMMOBgAIvJ6AC8DcE3681IRqS0EkOIlInKliFwg\nIhnf4EQAt/T8za3pa2MLH1r41odxrayly0ko5zVvUrRDYgPnubGnGP6FhzQ6a/vMDcxXoxuO98W3\n8cZmQHhRoHQNX3iWwKZVCEN7zhrVSnDnzVs2/GIdOx9+4bgvdcDFA/A0AD+jqheo6gUAngLg6Uyn\nIvIZEbkq5+eZAN4B4DQk6YZ3wFCISEReJCKXichlBw+OrpLxaJST5SqSMa5AEWB2u90VWaZkVwQ6\nnccDkct67ZbXtOzZGHyY9l6KSDk8t0Xep1AegEzKtm7jqyjsB/D3jnn22NDZUBQocy7fWviFnjdj\nxAFIsQ/APen/97KdqurZLn8nIu8E8LH019sAnNzz9knpa3mffz6A8wHgwIEDA8WMQoMR5Ehi8OFy\nqdmFYLHZwu5tU5iwErEcMyCKyEi0K9E4kbNqdKwbd+RljAduQuUyxIX9eyCvMt4XZ+9Pztd3l7It\n2cDNMXQ+Bs+FX9y8J0Xz9rg9M6a+2fLpvubNuHkA/hIbswAuB3BeXQMSkeN7fv15AFel/78IwDki\nsl1E7g/gdABfrWscwwDjRl5pd7HasZ8kfJ3iGFdmyHQamgBJnGSWVtpUNTr22vOnuDZPxKLTV8N4\nXw6vdtBVH+qd4Qz3mekJbJ8Kw5/wIUNsP3T48XqGIi7XAZcsgPeLyCVYJ//VnQXwBhE5Ewlz6WYA\nv5WO42oR+SASHkIbwLnjlgHQG89aE+QIqCQH1C/EU9S+TEmtsL2HGPwRO20iRs1WF62Ouytwcwyc\nDJ+w4Re6PReHrboJ9MfR5x0zIPLQyrgv7CnObHxxUrhV512/+5M3nqqkweVrn1jFw7L+Q9ffCMV9\nqQOud3J/z9//uIhAVT9cx4BU9ZcL3jsPNXof6kLeQWfYNcXzFlHAgxvYUQwmdyEKeJKYb7RwaoVN\npHcyV7l2ea7cKrnMQEE1u2GR+HLasydwXkjH9t3XToGBhHR8eQCs+hls6Ms9e2Vw+/sfbTPeul2t\nlMY38N4FzGBgQl91oPRKisgFAB4B4GoA3fRlBVCLAbBVUKUoRu4mUmURLzRA7GIsIsDubVY99DZO\n3FclF7r/JNHGcXttsUDAk4oh6Qa23rsqIYC8hdw1g6NYy77+DTyvdzYDooqKYd7398XCZ8ZPSdlW\nvnebDw5H7WZO8PZ7t7Tahqqb8VS0Zg7D6zro0MeEvuqAywr2GFV9WO0j2WII7U7ysRDtmZm2k/ga\nLTz0uFlT26y9dQNnSwGPApMa4J6d6UmuhsTRlTaBzZvIsXt2m/rOMiDC3zvrKZSPQ7MaBNXuXV/7\nZgun7bed4DPyq9lzRRbwYlOHQ5dxrgMuLJ4vpyp8ER4xCmQg1zz4QadI5mFeJE+RVU+BvXOZLQU8\nCvcOKK9GV9SeS4FkhXCIPPgK1z73BO+rnvyQPACqm40nV/GwPPgInTEESsp488A7AnwUkbJrn4yS\nCiDg5gF4DxIj4E4AK0g8c6qqj6h1ZPdx+IgnJe2JgiZ0Hryt7+5aOVhbOhBNoFzmYvj0tfcggrRr\n2ySmjNXohhXDL8rDD1UIyJf3xc0AyW/vkkZXeO0Y8ixx7TPP2TCMn0IX/lD6z2vPzTtmzawLLqP5\nRwC/DOBbWOcARJAILadalc3bD+YkUCWWlwf2FDjMWGAeMv5EmQjSwPZ0JUA2BdK+kK20O2i2umOc\nAZG0t3pfGBZ70t5eCpit33GY9Jz5y1yyn+AZ9U8fqcfHzNpCX3XB5UoeVNWLah/JFkMWC2TcuExN\nch+uQGscN7j3w5Px5crE3pwK1cbsdrsIEl3RrNHCPmMKJB+D9+U9CaOhsNDk5p2PYjRV7n1vBMG1\nfscgBJf/9hACYEtw35dKAQNuBsA3ROSfAfwHkhAAANSWBrhVUFUTe1P7inriuRXRhhjH7e2fPkWR\nC8EcawBUqEg2kD/hmD6Z278HDYRTyBTIYdZzt6ZgDup/elIwM23cwH2ETwJtIj4IiEC1a79h3jc9\nZVA4p6/mlPAmvZ7W5xbg19w64HI1diDZ+J/U81pMA6yAQW7okIpa840WTtjrnoaXV5TDXofAjwue\nbb/PuAmzxWQqExhzmOSuKZT5RLgqlQT72w7v3hXqZxivX1UCZJ6QDVWMptnGPuO1ywp4mU/wVbgv\nhemjXHvG8zbhkHo8sL2HQ8/JxvBLFvoapToAgJsS4AuHMZCthioPY1IOOMeadVyIBhFamIImriS8\n/EW8+klgowchsBY+Ww++gvGUf+9aeOjx7imUvdeuSkWz3Hs3AnnwABODd793g74/c+8XiRj+QkUR\no83teeOpavsN/XsI38w6ph77yFxiVBT7De9RrAQIuJUDfpCIXCwiV6W/P0JEXlP/0O7boMlAXiqa\nuRoQfQ+zLw0CNpZYaSFbn84+yEDDTGHsB6tl32EqmhnuXZ4b2OyBaLSxe/uUExN70CbAVqOr5AbO\na19FSjcv/GH2XIWPwQPVVAz7nx2a/EoqUIZKYawLLoGwdwJ4NYAWAKjqlQDOqXNQWwHJJjA8N3Iv\n1kqSBqopXuUkUuhBIE4SbB58KOOts5ZCOfr3Lg9eyslSaXCkkE6F/vsfrzUVw8AiRqEyj1gVw+Ta\nsffedu+ySoLDPLQMAy4GwE5V7a+6165jMFsJXkh45ESm2cBEKhQA7CYyIFwrmrGuwMFuYDKF0niK\n88WfGMYmkH/tPChQMgJUdCoXEzpLiki5hC/ytkg+dMWHb6j01QrXbtCzE4qAucgW8CIPLXXBxQC4\nW0QegNSbJSK/AOCOWke1BcBK2S6u2Bci3oXPTgYylSowE5sx3lgyEHvtfZQCBrg4NFdP3kf4hRCw\nIvgf/pTsOOONMbwZ+W8f4RfrtVvjLQUqf862rwsuT8K5AM4H8BARuQ3ATQCeX+uo7qPI4llrYirG\nh2FxJRXSISYyEI5ExyyiAO+CZ0oBW8RU8lIgw3lf+HtPqaF5yF456QhCCId4djIhnFD14C3te8nD\nCYluuC74jTH8cGFPX1Uc6eqpI8YBcMkCuBHA2SKyC8CEqi7WP6z7NtaIWGQ8ycpo9SEjDFTM5e4Z\ngZc0OnITqVIKGFhfyDIxFWss0XIC33DvAoQANt47NnRVPXzSu4ksEkSsRquDVkfNudwLrBvYmwBW\noOyVStc+T0LbnkaXtG9XCp3lGd7socm85pHzti4MvBoi8vIBrwMAVPVNNY3pPof+TcBbDL5KGmGf\nJV6lPdB3kqiQT5wHy0LUb8AwFc28iKmEOolUfHb6U0h9CPmw3hv3756/iVQ+habfv+oGOmjehiLh\nzTda2DY5wYkYOaefput8X/+hsldW2100Wp3hej0Jz93mZ6eNbZMT2O4gHjZMFI1mNv05AOB3AJyY\n/vw2gEfVP7T7LqouwoM28FAufFaGeKHZMudxZ/1XHXt2+XyVAh7WtffhQejFQrOdELkIDkL1DXgd\njPHFZkAEYcHThvfG9pSUbcMuAJW0Z7Nfqt+7NeOt4rXbtAF7E7Ai+BPEvasLA7+Nqv5fABCRzwF4\nVOb6F5E/BfCfQxndfRR8IR8/ghyum0DeZOJOAm08YL+9KAZDwqNLAYe+dx5ikUwdgoVmC8ftmTG1\nzfqvGn7JsFjxu/d/Q7YcLOv9YT1nVWLwg1QUrSJEWf/WsWdpdOEKCVU8dCVFb9fbe6lDMFoZAIBb\nFsCxAFZ7fl9NX4swgo8lpu2tgiDNtnMaXR5YGeIqJMB+EaKMhDcMOdSk/43wpWfO3PsJAXYFEjGq\npGA5QIaYLuQTyHtTVQRps4CWew2MvJOiDwIl255No7Oql/KGs59KgjuY7JURywAA3LIA3gPgqyLy\nkfT3ZwF4d20j2gLwl1JifJiXwy0kbDnZNRJeICEaH65Atj3nBvaQB2987thytP4yKEj+BmH8semv\n1iqOWXtW/juUgNT6Bh7Oa8qJh3EqhnWh9ElU1fMAvBDAvenPC1X1L+se2H0ZtBRus5WeAomiGKQL\n3zr2ZVaK1hMTOxiTm/S++EiBZPQnmP6XVztoM6WEm5zh68P7AnAlvEPdu6yQUCjyqr8USPu9Z6qv\n+ijBPYoGgNPVVNWvA/h6zWO5zyMjtKwXNGFigeEEOeYbLTykQjGaXlj0wDf0veznBM7rmVfQc+/t\n34P3hS0FbOVfLK2m+hND3gT65429f74MNaWB4CENz5qHz9dgsBlfmzIwQnFvWOVVg4R0fwGzUasE\nCLhxACJI5JGRGE1skxu1j41ceRHue5it7dcXguFVNOuFtaBK70LmWowmwcZ7bNrA+xcSUkPBfu88\nudCH7L1Z3wSrb+Ab0095w5kJnVHpq4bU4aTf5N95Ng2OJGBawjf9qcO8CBER+iINkLoQDYAAqK4n\nLn3pQNXiSXmlKa0Pc9VUrIELQdV0oP5NKBSbmC5GU20RzyOSVWu/OYXU1fjJy2UGmCqOZB6+Bzd0\ntfu+uQrmuIbORqEQkKX/XuOtmoT0ZsPb6nkCqteQ6O19TYBqBEmA0QAIAC/lZIeopNf7MFdNxepH\nqGI22UIy12hhckKCsehDlhKm6xCQxpuPOHCVe5cn5EOrGBpFiADbvbdeO+8lvD1J4bIkPCvYEzhX\nxZHjLdWJaAAEAH2KJIlciwSRi4/lVZPk3HwK5PqnSwF7iMHTNc1D51Kb1dBI8mvquWLKOPOlgCsY\nzoR+xiANA3reBlQxnJoQ7DQbb5wLv+q87e2frSHBhj/qRBADQESeIyJXi0hXRA70vfdqEblBrwFS\npQAAHDBJREFURK4TkSf3vP4jIvKt9L23yKhJKlWAn4pmtvZrBU1CuRJZEmCjWknS/sek6gacZ4BQ\nNckJA4BOxWKNN1YIx8Mmws0bu4ogwBkQ3a6m9eRZ8muga7/MneBpw9tD9ot13rIufNb7USdCeQCu\nAvBsAJ/rfVFEHgbgHABnAHgKgLeLSGYyvgPAbwI4Pf15ytBG6xkh3Vm+8tjZU6RdiraF3YySnYd0\nHmYTSPgTrAt+TIV00vHvttaTJzeBkBoIi80kgyKYEh6tgtjCjulJbDNq2YcMnWXaI7TwGr3mRgMA\nAKCq31bV63LeeiaAD6jqiqreBOAGAGeJyPEA9qjqpaqqSMSJnjXEIXsFUwo4i+OGeph9yKky+bih\n1dBMhYzSOO5aGedg4Rc/udjWevLVMyj62/PFaKzPbRY6Y2PoduOP974wSnbD5h31g5n3vgiUbOZS\n5ACU40QAt/T8fivWixDdmvP6WEF1XRObZ1Ib83F9pdERJECr+x/gmdhWA0A1zYComA/c6/H0kcvs\noz1j/DHpq8y1z9pb5o1iPXvF+t1Dh858eN447ovt2vVm7wxb/6I3gwAY/rzJ6J9sKeI6UduIROQz\nAI7LeeuPVPWjdfWb9v0iAC8CgFNOOaXOrpzQO+cWjUp2vh5mqzsqe5j5hchWkaw3D5/3ABhliJvc\nRPYmhFOJzCSb7h0jxcuS6KqEfvLqMNDZJ+ZNhBQR8hR+MXtfqhqu6b+9z541DS5rf4qxCBQrH85m\nDrH8h/Ww5+h5AGozAFT1bEOz2wCc3PP7Selrt6X/7399UN/nAzgfAA4cOKCD/i4ELCS4XgPCsoHn\nnUKrEeHWP2ChoisxLx2JjQWeerShopl6KAXsQQseqHrtufa9qF4IafO9s2yA1k2kHwz51RI+2TDv\njJX82BTI3ms3O+PufckrI83Ou+P3ElUgm1bDHzicuvDt84Ys4GXwAPSumfMNjj9RJ0ZtRBcBOEdE\ntovI/ZGQ/b6qqncAWBCRx6Ts/18BUKsXoS7QMXiaDMTHEoeZRpeXDmX97mwpYG+FhFg2cSAiV0gC\nZbPVwWq7W/EU22O4egh/AGHDNzT/YYgCVr3wpmI4xHnDHro29E+SV+tEqDTAnxeRWwH8GID/FJFP\nAYCqXg3ggwCuAfBJAOeqaidt9mIA70JCDPwugE8MfeAe4CsWyGzgIuEKmlg0tfv7txoQllNc3iZC\nk4GIMs4Ad++pFMaK5NV+422RYWL7Cn0NMXyTdwo1S+kG1I8AOA+C6QRfh/HGeo8I8usoagAANYYA\niqCqHwHwkQHvnQfgvJzXLwPwQzUPrXb4YNFT7dk0umYbs+xCZJxIrU4Xy6sdD7nQtpKqvsRUmGp2\nbEUzdhN5qLEIVNY/n0tNVgK0Gl8eDO+kgqc1+4XjvjCnUFrDgL12oTUQ0jUzVBGoOjFqIYD7PLy5\ns4h4Viglu0xRy0qGsRIoM/jKY2elbK158HwqFbmJEPeezX6pWoxmc/uw2S8Z98UaOmO8N6qaahjY\n5x2lYWDkT2TgS4DzVSDpQkIxBBCh8JPOEzKPvmpRjF40W91EUYutCBZYxKj6QrqewcBK2YYi0VUt\nAtUPX8ZbuBTIZPxWFr4PAqQtDU/RbHWx2rFrh4QIn+T3b7/3TBln2vCOHICtjf541mQFTex+WB+m\n3nK8plzq3lxsCxM8Pf0Dtoms6k/IxppLbtkENnAQjAvJhntHnkSsm8gSWYOBjsMSG3hGQrPNu8x4\nq8bC7wctYkR47piwo6ofFnzS/7DJr+sZFCHIq77mbZ2IBsCQYWHRJ39pf5j7Ga3WhWTYNcn74eMk\nAHCnUEYIx4+eObEQBVKyS4y36h6A/vTTpH+7G7iq96X3L60beK8Gg/XeZ9yXYZ6gfbPgAbvxxtYP\nsXo9fdy7blfp1Oc6EQ2AIYOvahWODNRoddDuql2DgJZDtS9ECl0jYjFa9MMu4tR//az3jlUx9JYC\nOUTvTf/4w0pI2z1vFsO533gBwqoQAnYC5rzB8N7w/Une0yIh3b60yvEn6kY0AIYMP5sIWRYz2EJA\nirF46L8qEas/lctKJAI8lQI2x+CHn0qVa/wR2S8z0xPYPmUPndGlgCuOfUPor6qSXo4LPZiGgQcD\nYkKA3dtsxhutQGkIe/beu5AaBnUjGgBDhpdiNIQr8fBqJ3gpYD4f125AhPKeANxCxsqh8gJUXPqp\nj1MkK4QT0gNApUCSqb++SHzMwWF2ZtqceuylkqDx2nur3TKiOgDRABgy2IWMWYjWmdhsIaIwC9lC\ns4VtkxOYmQ6Tj8t4T5JULHsql0VMpRc+NAiAEBkUCUJuAkAWurNmv3SwUlHFsBe+PG+M4T45IWYN\nAz+GN6diaL32bPYKy1uqG9EAGDLmCVWotU2ElsQMGwtklewYGWIqBZIq4+wrFSuw94ZoPxUg+2W9\nPa9AOa4ueB/znklfDRn6YkWMfGWvRBJgxBqjlZHU9KJlH2ATydKJtpEaBrycKcufsBG5/NUBIJnY\nhBKeVIzj9iI7wVs2kez6Wb0Xmf6G9d6vtrtotOwKlKyULPPsZNdu57ZJTJvz4O3E46z/UDLGiysJ\nCS+EhoFCaeOtbkQDYAjI1ryVdjcpaEJMZIDJY7dvAr0PMxMPM6cgwoMbl9Ci73RsQjjZvecWEu4U\nmGVAAIQGQ7ONWUZCmlYhtLdfaSXzjjFcgYAs+iCFhNaTjy2es379C9Z7Y753hOGcKSha2wM9Yc8Y\nAoiwLgTZJmLdBDJGqzWG37+JVSpl3PN/C5O6F4wbd/0UaT1JhHHjZvfOuoEzBsimPHjrtYftBL/5\n2Rmu54udd0CfAJbR+JtvVPec9WcRMPPOhwS1tX27k7rwAxjeTPv+/q0KknUjGgBDRGghHFZKN6sH\nP1w1tPW+GBniNe9LaDcueZJg4sBsDJ5WsrOKqbDcF1+VBIkCXAB370J5TwDu3mWeQ+u1o4nLxhj+\nmvH3/7d3rjF2VdcB/tY8PGOPx/gNBmP8AEPAvOIJCUmTkIQUQqXgRiBVfUAaVOTSh9S0lYiQ+NOm\nasOvorRNaX8QfiWBNGkeSiqgaaiSAjIpz6YU26QtiLxIAyYej+ex+uPsM3Nm4tfsde5Z98xdnzSa\ne8+96+59991n77XXXmtta/jquG3M7DShADTInDnINpB45UOvwwTfdA6E8rarK5uZWy564yRk2YMv\nys9xXl24Cs31fZky+b7YD4HKU74WWhCsWQxzqSX6JTsCoibnV8f7BoyHQHWp+R9CAWgUL3NUSbkK\nXO54kFC2I1cypXqd5GeOYCiPNDVaEHLToXrGsYPN/6OuQdzDCQ/qieP3ymFgTf9dl/+DWwikecy0\nLXo6TSgADWJfwdvPo1/sOQTzyq/BlJg7gR2ZnE4nCfpMwPZB3H6Yju0cAmM2NXMYniWbmjWO3fcY\n6dfGJ32zGBqsLxPpBE8vBcDbcmcfM22ZWztNKAANUkcomIjlXOsaTPC5OQzINSMX2JMQ+VkAygiM\nEVMoVntTSB+ZnE7+F83u487K1+V7Y+g7Xsm/prWMXum1I7jn5C3hq96Kc6cJBaBB6jBlrswMxSpN\nedmTgDGe98jkNEen80IgwX8lkStf9eK3elJbziGwbN9MzqgphXQdCaDA9xRJS/6KOuLgc+XfOJLi\n4J1X8B5bAGUERW746lz+iMzfTu25SzpNKAAN8vqRfFNguYr0SqgBNocWb0/sOgYiERjNPEnQug+b\n+9uLYFbeDtXgCQ32UKrsSejwJMsH+1k2sPjhroyDt+SvMHnRl2dANHzfLWz77DC8w0eBOhT3/BV8\nbvIryDfhV9svnAADIH8Qnovjz+tM1ZCWnIFIpFgF5pxJXu6dHcqMgFgYjuOVBfH18als60shb/zt\njHH4OfLlb2fdfvFawc/WPzP/RDV/hikXvcF35mcTeWdA/Fz0i1sKaXvfGexfvBOedcwssVhfpkrL\nWZceBAShADSKxRO6lPfKqT07EBhSyYLjJDA+lbUKnDeJGPdxzY5cmeUfPjrFlOEgIesqsi4nvNxk\nKlbLmZf/hUgdOQjsBwFZ5LO3zqTcOivGzGwnPMf8FdYjuJsgFIAGqWUgMTrhuTnjGFcC9ljuOs4B\nyP/tDhnSEAOmNMa1+U+Yw+jyzbiWZCr2/BV+ERD1+b44HgVsiV6pI3w1+/A16yFQ3Z0GGEIBaJQ6\nBpLs0+hSQg7PbGaFvDUO3ieWutiHtaZTzZOfnj3RzCmCorZkLPmTiKflzFL+jNERrK7T6DyVP+ui\nZ9ToPGtxXJ6cVretryYIBaBBrDGhFm24vjTCXgPZFCPL+hnIDKOrYy8wP5/5DG9M5A9EdZ1J7l2+\nRQHxNOFbyj88UU8WQy/lq/R9yb3v7Iq3ccy0OC7XFsIYPgABtpjSyemZ5FDiuxLIzsltTYTjGEoF\ntoHk0EQ9OQzcBzJD38v1wi/lbYdIWSMw8q0vdR0Dna28TUzZ4uCNixZ7CGS+4jw5PcNh05hpS/5l\nPYK7CVwUABG5UUSeE5EZERmrXN8qIuMi8mT6+1Tltd0i8oyI7BeRuyXXK8SBsqo53rwlb0zYVmGz\nXvgGj1aL/PjkNMv6+xjKnAQmDAf5gD2hx6HMSUCk2EsEvxwGswpI7oEsE1P0CYzkHiRkNMEfmczP\nHwGYzoPP9cIvsR4mY/3ti+8+uOjolfLdxbZhfvRK7jHMJRbL2xsTtrNXrGPukcmZVH4oAAt5FvgQ\n8MgxXjugqpelv72V638D/BZwXvq7tvPVrJ/cULByErGGlGR5I1cOdbF05lXLB7K9eQv5/LItJvjZ\n8o03cv5KRrPlq7+d5ftbDhIan8wLhaoWl/fd58jtO0enbYP47CSQGwZ4dDor/0S17cxjhvW+MZSf\ne5DQPMXbuALPGrOrY2b4AMxHVb+rqs+f6vtFZBOwSlUfVVUF7gP2dKyCHcR6M1onMYv8UEY2tHmD\ncNaNZJOvYt2Lsw+EfgMpLP4goTrb3qx8OchbFZB55RvaLzeTXR1lg8+YU+99n/Pb16M49/dJtuWs\nCbrRB2BbMv9/U0Tema6dBbxUec9L6Vrr8F6FWgZSa90t3ryQNwHOXwlZByJf5c06iVjOJM9LpFOR\n91YgHO4b6yRSSltXsJ6Lhm6QtyqPJsvZsM3q2Wk65p4oIg8BZxzjpTtU9R+PI/YKsEVVXxWR3cAX\nReSijLJvBW4F2LJly2LFO4rnBG4t3z4IO0+g7vJ+39+z39RTfnstZ2D7/h4r4Drl7W1n3fY0LlwM\nfa+bzf/QQQVAVa/OkJkAJtLjJ0TkALATeBnYXHnr5nTteJ9zD3APwNjYmC62Hp3EUxu1eGJDF0yg\n3gORY/n9fcIKgynRewL0V978JhHLGRLg3/buv53jfWcJgYTudgCELtsCEJENItKfHm+ncPY7qKqv\nAK+LyNuS9/9NwPGsCF2N5yTk7X/gvYqzr2Lz65+Tz7yK5UzyUt6Ct/+F9yRmkffew7c7Abb7t7PU\n37vfdhqvMMBfFpGXgCuBr4rIP6WX3gU8LSJPAg8Ae1X1J+m124C/B/YDB4CvNVztWrDczMv6+xge\n9NNG3ScR74HEonwZ8pkX8r6DuL8nua987jkEdZTtfd/5T+D58gN9NsXb2/LUaVxqp6pfAL5wjOuf\nBz5/HJl9wK4OV60jlMO+iHU/yeZQknsjlkV6e9G77yVaHLmcBsHyt/OeRDzMwOV37zMkwgFM5xBA\nDRNohhPgPAdEq7wx/NTzvrdazry3LzpNV20BLHVyj5MtJfInATHJl2SfR5/IOxGtmoPAzxxn3cfN\njYAov79LKJYxgqKKNZbaZILPSIRTlF/g7URnVrxbbnnLWTSZx8z037xoii2AoMR7IHF35vGI5U7/\ni0x2+WGEucpbifsK3GEfua5Y6tzy65AFwyRglXdU/rzly64zsqyfQZMTnq/yFD4AwSzuE7B3IhzH\nm2nV8rxV4Ky8u/JlnMQcsqHNk3d2oLTQfv8Jv0lwhXUCb3EOALCPuZ0mFIAG8famdb8ZHAdCbzOo\nt/LV5r5jd6Bs3wq4SpsVb3fLV4vHrCYIBaBBvLVJ95vRIG9NqRlt395JzF358d5+cfztrJFH7sqT\nd78PJ8CgpO2d0TcbmvUgIW8zbMvlDX3H6oXf5hUw+KfytSlffgd4leW7yjtbIDpNKAAN4j6It3gf\nua2rwKXiyGXpO7le+LNlW60v1n7rbUEwtL3lCO6i7My6z4YOt7vt7fdd+AD0PHXFYrvFkpfyhoF4\nsF+yTIm1xdE7Z1Pz2oKYC4fKr789mUpm2cb7po5+aym/xPsMiJwVfF0hkN73rf+YGRaAIJE9iJdx\n/Lmr0NnybXvwKzPi4Kt1b6Mjl3cseF0KUF4o1lz+CMsk4u0EZw3Dy53ArYq/AMODfQwNOChfCa9F\nS10hlO4JtGILIChxN+G77sE7afI1JUFy34IwyJsnEe9ENC1PZGNNIW3B2+roPYF6ptBeNtDHsMFy\n1gShADRIm2+mNt+IhXzL6+84iXhPoN5m5N4Oo7MuOqz9vr3hs91u/odQABrF06HF+0hS7xV4mweS\novz2HifrPQn5/3aOire38uYcAWGRd/N9aZBQABrEszN7p7K1HIIE/qZQa/mjBnmrCd/7u3uHYplX\noZY4eqMZ2NsL3f2+c+y75oOEwgIQVLFPIj6rwIH+PlavWJYtD/5maC//i5VDA2wcHTKdJuffdu10\nJCvb/LTltr7raQa2WK4G+oTVmd/d6gS3Iik9G0eHs+RLPBce3taTJuh+G8US4KrzN/L9146wcXQo\nS36gT1g1PMBAZk7tgT5htcEU9/E9u1i3Mq/uJbk30/rRIXZsGOGSs04zle+1Cr713du5cWyzqWxL\n3c8/Y5Q3bVplKt9LAdm8ejnDg31sWz+SJX/dxZtYvmyADZn3HdhX8J7K0yd/9c1cdvbqLNmz16zg\no+/fyS9edEaW/BXb1nL/3ivZZbxvLZPwkOG32zg6RJ/R6XnTaTblpwlCAWiAbetH+Nh1b8qW//A7\ntvLeCzZmy//B+3dyZHI6W/6t29dlywLcsHszV+3ckCW7cmiAh//wquyy148sY/lgf/YksGPjSrav\nH8mWXzU8aJpA375jHT8dn8yW/9vfGMuWBbj5ynO4+sLTs2RXrxhkeLCPczeuzJI/7/RR/vNPPpAl\nC7Bu5RA37M5Xvt57wUbT5H/uhpWsNVjOtm8YMSlv1+7Km7wB+vqE33/fednyIsJbtq7Nlr/07NVc\ndf6G7EXT9ZedyZa1K7LL/9M9FzOjmi3/dzeNdX0EAICo4Uu2gbGxMd23b593NQInZmaU18YnWTNi\nMwMHeUxNz2RbroIgyENEnlDVk2r/cWcGS5q+PonJ35GY/IOge4m7MwiCIAh6kFAAgiAIgqAHCQUg\nCIIgCHqQUACCIAiCoAcJBSAIgiAIepBQAIIgCIKgBwkFIAiCIAh6kFAAgiAIgqAHCQUgCIIgCHqQ\nUACCIAiCoAdZ8mcBiMiPgP+u8SPXAz+u8fOCExPt3SzR3s0Tbd4svdDe56jqSU9gW/IKQN2IyL5T\nOWQhqIdo72aJ9m6eaPNmifaeI7YAgiAIgqAHCQUgCIIgCHqQUAAWzz3eFegxor2bJdq7eaLNmyXa\nOxE+AEEQBEHQg4QFIAiCIAh6kCWjAIjItSLyvIjsF5Hbj/G6iMjd6fWnReTNJ5MVkbUi8qCIvJD+\nr6m89rH0/udF5JrK9d0i8kx67W4RkXR9SEQ+m64/JiJbKzI3pzJeEJGb62+dztDyNp8WkSfT35fq\nb536aUF7v0tEviMiUyJyw4K6ta6Pt7y9o39Te3t/VET+I5X9sIicU5FpXf8GQFVb/wf0AweA7cAy\n4CngwgXvuQ74GiDA24DHTiYLfAK4PT2+HfiL9PjC9L4hYFuS70+vPZ4+X1J5H0jXbwM+lR7/CvDZ\n9HgtcDD9X5Mer/Fu06Xc5un5G95tuATbeytwCXAfcEOlXq3r421u7+jfHWvv9wAr0uPfpuVjuKou\nGQvAFcB+VT2oqkeBzwDXL3jP9cB9WvAosFpENp1E9nrg0+nxp4E9leufUdUJVX0R2A9ckT5vlao+\nqkXPuG+BTPlZDwDvS5rlNcCDqvoTVf0/4EHg2lpapbO0uc3bSNe3t6p+T1WfBmYW1KuNfbzN7d1G\n2tDe31DVw0n+UWBzetzG/g0snS2As4D/rTx/KV07lfecSPZ0VX0lPf4+cPopfNZLx/msWRlVnQJe\nA9adYt27kTa3OcBwMp8+KiJ76H7a0N6WuncbbW5viP7d6fa+hcI6cKp170oGvCvQFlRVRSRCJhqk\nw21+jqq+LCLbgX8WkWdU9UCHymoF0cebJfp3s9TV3iLy68AY8G57rXxZKhaAl4GzK883p2un8p4T\nyf4gmYRI/394Cp+1+RjX58mIyABwGvDqKda9G2lzm6OqL6f/B4F/AS4/8dd1pw3tbal7t9Hm9o7+\n3aH2FpGrgTuAD6rqxCLq3p3U4Ujg/UdhyThI4cxROoFctOA9v8R8B5LHTyYL3MV8B5JPpMcXMd+B\n5CDHdyC5Ll3/HeY7pH1O5xxIXqRwHlmTHq/1btMl3uZrgKH0eD3wAgscjrrtrw3tXanHvfy8E2Cr\n+njL2zv6d2fGk8spnAXPW1Cv1vXv2bp7V6DGDnQd8F/pB7ojXdsL7E2PBfir9PozwNiJZNP1dcDD\n6QZ6qPqjUmiBB4DnSV6i6foY8Gx67ZPMJVsaBu6ncDZ5HNhekflIur4f+E3vtlzqbQ68PdXnqfT/\nFu+2XCLt/RaK/c+fUVhanmtzH29re0f/7lh7PwT8AHgy/X2pzf1bVSMTYBAEQRD0IkvFByAIgiAI\ngkUQCkAQBEEQ9CChAARBEARBDxIKQBAEQRD0IKEABEEQBEEPEgpAEASziMhqEbmt8vxMEXmgQ2Xt\nEZE7T/D6xSJybyfKDoKACAMMgmAOKY5M/oqq7mqgrG9TZFT78Qne8xDwEVX9n07XJwh6jbAABEFQ\n5c+BHekc+btEZKuIPAsgIh8WkS+mc9W/JyK/m85I//d06Mza9L4dIvJ1EXlCRP5VRC5YWIiI7AQm\nyslfRG4UkWdF5CkReaTy1i9TZHEMgqBmQgEIgqDK7cABVb1MVf/4GK/vAj5EkYXu48BhVb0c+Dfg\npvSee4DfU9XdwB8Bf32Mz3kH8J3K8zuBa1T1UuCDlev7gHcavk8QBMchTgMMgmAxfENVDwGHROQ1\nihU6FKlZLxGRlRSpaO8XkVJm6Bifswn4UeX5t4B7ReRzwD9Urv8QOLPG+gdBkAgFIAiCxTBReTxT\neT5DMZ70AT9V1ctO8jnjFKczAqCqe0XkrRQHvjwhIrtV9VWK8xzG66p8EARzxBZAEARVDgGjucKq\n+jrwoojcCCAFlx7jrd8Fzi2fiMgOVX1MVe+ksAyUx6vupDiYJQiCmgkFIAiCWdKq+1vJIe+uzI/5\nNeAWEXkKeA64/hjveQS4XOb2Ce4SkWeSw+G3KU6yA3gP8NXMegRBcAIiDDAIAhdE5C+BL6vqQ8d5\nfQj4JvALqjrVaOWCoAcIC0AQBF78GbDiBK9vAW6PyT8IOkNYAIIgCIKgBwkLQBAEQRD0IKEABEEQ\nBEEPEgpAEARBEPQgoQAEQRAEQQ8SCkAQBEEQ9CChAARBEARBD/L/AxsrPSXbH4gAAAAASUVORK5C\nYII=\n",
-      "text/plain": [
-       ""
-      ]
-     },
-     "execution_count": 22,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
+   "outputs": [],
    "source": [
-    "plot = qc.MatPlot(data3.my_controller_demod_freq_0_phase)\n",
-    "plot.fig"
+    "# Create the acquisition controller which will take care of the data handling and tell it which \n",
+    "# alazar instrument to talk to.\n",
+    "myintctrl = ATS9360Controller(name='my_controller_int', alazar_name='Alazar', integrate_samples=True)"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 23,
+   "execution_count": 19,
    "metadata": {
     "collapsed": false
    },
-   "outputs": [
-    {
-     "name": "stderr",
-     "output_type": "stream",
-     "text": [
-      "WARNING:root:oversampling rate is 0.625, recommend > 1: increase sampling rate or decrease demodulation frequency\n"
-     ]
-    },
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "start 0 stop 6.2e-06 num steps 3200\n"
-     ]
-    }
-   ],
+   "outputs": [],
    "source": [
-    "myctrl.demod_freqs.add_demodulator(400e6)"
+    "myintctrl.int_delay(2e-7)\n",
+    "myintctrl.int_time(2e-6)\n",
+    "myintctrl.num_avg(100)"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 24,
+   "execution_count": 20,
    "metadata": {
-    "collapsed": false,
-    "scrolled": false
+    "collapsed": false
    },
    "outputs": [
     {
@@ -675,112 +574,100 @@
      "text": [
       "DataSet:\n",
       "   mode     = DataMode.LOCAL\n",
-      "   location = 'data/2017-03-03/#010_{name}_16-45-37'\n",
-      "      |                        |        | \n",
-      "   Setpoint | time_set                         | time               | (3200,)\n",
-      "   Measured | my_controller_raw_output         | raw_output         | (3200,)\n",
-      "   Measured | my_controller_demod_freq_0_mag   | demod_freq_0_mag   | (3200,)\n",
-      "   Measured | my_controller_demod_freq_0_phase | demod_freq_0_phase | (3200,)\n",
-      "   Measured | my_controller_demod_freq_1_mag   | demod_freq_1_mag   | (3200,)\n",
-      "   Measured | my_controller_demod_freq_1_phase | demod_freq_1_phase | (3200,)\n",
-      "acquired at 2017-03-03 16:45:38\n"
+      "   location = 'data/2017-03-07/#033_{name}_12-36-23'\n",
+      "      |                    |  | \n",
+      "   Setpoint | single_set                   | single       | (1,)\n",
+      "   Measured | my_controller_int_raw_output | raw_output   | (1,)\n",
+      "acquired at 2017-03-07 12:36:23\n"
      ]
-    },
-    {
-     "data": {
-      "image/png": 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Zdv4Q12PU6+7pHe4qmJnZGJQ813D322mAIOksSb8A9pW0OO/1O+Cl3VdFs5Hv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\n",
-      "text/plain": [
-       ""
-      ]
-     },
-     "execution_count": 24,
-     "metadata": {},
-     "output_type": "execute_result"
     }
    ],
    "source": [
-    "data3 = qc.Measure(myctrl.acquisition).run()\n",
-    "plot = qc.MatPlot(data3.my_controller_raw_output)\n",
-    "plot.fig"
+    "data4 = qc.Measure(myintctrl.acquisition).run()"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 25,
+   "execution_count": 21,
    "metadata": {
     "collapsed": false
    },
    "outputs": [
     {
-     "data": {
-      "image/png": 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zmMsejr7r6I3Fb6YGYfANhOueXAqADzLqoFTJmHCLwBcDT1HFubxnuYtjQqwU\n2N9RzCCEieersxZByFDyPfgeIQhZNOgvWtsBAFim6EbSYBgIAzSuXviPeXh6yUY8v3yzqKvZADd0\nprtpolBYS1uJ21OYyTQMNPLCHPu6Ii6XBo5BqPf9FanxJhGJoxuc81ZsQVvB7I+y39Xji1+0jrfj\nKx5aWOgavPzhqUEomkkxsZjpwzjZ3qMyvcXPdwzCNowiUQxFqSs5OWWNMwmRorbqkZR3uyVFbsQg\nNKAtep5clQWJjqZ2hP50iqKdTw4MI5p845pmKKpgEBR/rWrQmOmzQ5Q8QtnzrC6PInXMMxDmrdiC\ngy66L5de1iHvM2Jc6jTG5O3Y2ra622XMuvRvYrA9q0Vr2xPl5jEEfWUxJEvXHw1CXyfGIlWu1uxG\n2ClXzMAnr37KUqZZqGwPCQYv5+J9WUhEhlLdZw4udA3CYwvW4V+KrktCveW+GJybu2OjO69NJDKx\n1obGsHIGQgNQKMyxoGVaZCUSD95mJkXJOKiK/Wh3woxV4UAjXumHyY7Qh4QlNhQOGw049d9cFtqF\nxISvMwL8dxTFoGkQdIMmCBl8j1DyCbWAGTkMsgwg6Y7IEyneNGsZ2npquK/OZFIlIRTorpgTgw5r\nKCPLbyu1gA1YFIPeFp5eshEn/PIx3KzsMZL3zvtahZJizPYVgzkxxkJQk/5+WSRostVFRcwgKGXk\nGI3ynHrcBYOVZ2FDR2+/RJU1jUH49LVP4ys3P2ccpz67om1arZcatpzXXqsDtFjqD5yB0AAUcTEU\nFQcVmbyjhDEwVz3SMLBlWIxT1WZfo7sSJMIk+wJfGXTVjpZ0MdTfKWRJRVds1TBEyfcUcWO6D1aP\n8shjEKoBQ8kjlHzPOjBm+RWloFH3e+uvprlkalCKQPrVZRRH1vPKygRqYwbU3UEHSviqD5AvreRR\nOK+ujvM35F2i3jroBmJPPxIl1csgRFuMF+jvUd4SdULJaA82112kFamDQagp40xR9NXVkoXlG7tw\nyMUP4I+PL+lzGUUXJn3RIKhj8Nk3zMF+F04DkN8mBmqx1B84A6EBsPUzY/IpaJnqA7LtWFWDoBsI\nchWkDrgRJRzRjKmXBwC85xeP4OTLY0Vuby2ouwFLpXsmg9AHWk0OqEUp7VhIGEc/9FQDHHrxdNww\nc2niWD3pTy1gmQZNEDL4PqHkCQZBGxSzBgj5DvVVa9op9YROqqhaqGWjLpZXG6XMVc7TJ6VaoEYx\n5NeltxZREcKxAAAgAElEQVSkrtJ1Y0rdkCyuZ7qBu2BNe11ttLcW4IAf3IcrHlwY3UM9Scd0yL5V\n9DVFfbLAc7MZYVkGgm01Kp9N2v4hzy3bhC/cMFtbaWf4mXLqOpCQOWCmWzZMK4pqwQWa2heK3rYa\nPr26rQfd1UAIorPbk3yHe04aie1HNxe72ADDGQgNgK0d6RN7UfW+3sGk5a+yFBETAKBXG3DlIGdn\nEOx107G6rQertsQ+7wO+fx8+cVWxUDQJSXH31oLUga0vtFq9DEItkAwCRddc29aL9R0VYx8EPVES\nz4OQbiDUQoay53EXQ8gMejltwmKMRdfQn0GagVivfSBXqNKAzG5z6QyCnDBum70ce3/vXqzv6I3K\nTjAIBUbT9/7iUez3/WnW7/RVb1kwUFVL9I6O+ava8P5fPYYrHiq+nfiW7ip6ayH+b/qCqNz+pFru\n68RYJOGXfHfqJKe2m4dfXZvIk2Jrd7GhqBgAyvM8/9a5mP7ymsQmcfHYUxyxq2XgnS0D5WJQ35Ve\nZl8YBD18GuBtKU/3I5/v54+egomjhsZAKA3JVd9kKLIXg3pMlmEZah3M1kijjxgzKGpJk6qDneyq\nugivKGohi9TwEg+9sgbLNnThM0ftYT1H+th7qmFqYqdGaBCqIUPZpyjWvRqE6LAIOAHTxVALw8yo\niyAMhSvFLlJMi9JIY1TUa+v/95VBkPXPZq3S6yjb8bVPLAUArNjUnRQpBsXb1IqMsE/9OdjyeaS9\nchk1YksOlgaVLZD32r8wx/omRj2yqEjZQYqB8Nk/P5M4Xj7LRMhdwBK/9e+lK+t9v3oMh+w+Hn//\n4rvqYockBsPFoGeA7QvS9mKoBgxNpbiyfYli0FO488+CzDFKXXyU/cFQrhSDYxAaAFs70leCCQ1C\nlkiR2c9TP1bL1kVucuCzKbLlmDIQKUA/d91sXPSvl1PPkwP8/97+Ap5W4tz763eTz6EuBsFTNAg1\nhs3d9tAwfUAMQobexMpDp9hjkWIQMvPdpdyfWnfdzWLmz4CoU99Gx94CBoKNQYhEihqtr4d9DlSY\nY1pbUCfctH4ThQIXZOmApGsncjEMwGZN9aJIHwgsIsVMDUItadzxv9OFjvrfc17flDjfprFKr+vA\nuxhsda8XaXsxGAye0sbUPjdj4Xrc8vQy2GBL4d5dDTLHqEotDqGWkWdDAWcgNAT5DEJRF4MhbrSF\nJyniId1AkAyCOoBIFXLMIKRe3qhLW8pkKsFSsgbKFW97bw3/c/sL0edpPv2iE6AcrOrRIJR8imLd\nK0EY7fCoQzegamEyUZLhgw952b7noaqkWpZIG/yzGAT9Mcjnoq6ANnVW8PAra61l64gZhPRjbBNv\nqDwDtV491SCaspdv7MJiQUn3N8xRZ1tspaW1ERmFkJWzQofab+J76weDECUIKna8boAVKVtF1v1V\nLKyOfL5pCxUrCxoxEblVjM8ZBAOhOgCRMrUEg6DqoNL7n/qIP/WnWbjgjhetZdsMhJ5qNoPQU411\nXZLdHAo4A6EBKMIgFBYppmgX1FWeKlLsqQbYcUxL9F2mBiG01y0N1SBEZ85mMmf/ZQ6mfOeeQuUB\nwPSX16BdJHepKKvn5Ru7MXtpsYx6QPGBqBKEKCsahGoQRjs86rBGMWTkTaiFDL7Hy66FofFcU10M\nKXkh1GtLyH/lcRs6evGOH0/HZ697xnD7JEDJ87LaXFaYo5wk5Cq9uxJEs+Dlis8/ZNzdUG++BgnT\nULLUKeWdS2M0EXGSyyCE4tz4XvsX5sjLKzqHRbqCAirFrLBFG7IEiYkwPuVvmwtLj2K49J75OOTH\n06Pv39jUZbT5PGFeXyDrnGdM1YIQG1Oir6pBGKdFz2AQ+pLpVd/jhX8WZrbB3loYMT1lZyBs27A1\nA4NBKCh+0RcGVZvvMBIpMnRXAuy5/Uhc9amDAcQ0acWyOq9Xg1AJQi3Mz+z800UaVnVAf+GN9Inr\njzOW4Pxb5/LylMHk2J8/jP8sIISM6f9iA1FNhCKqGoRNqQxCki6vhVoUQ00fTEKUvTiKIS11s3Ed\n5V3maRBknWQ9VisT8IaOpMjShkoBkaL8bl17rzIYJ7+TCZd6UiamkAGX3DMfh1/6oLGFdRFw8WhP\ndK5tcE27hUAz7IB88Z80Bkq+p2gQ+i9SLO76SvZJiVVbug3jyFZmVlKn6J0nQnotRkOOgaC7l/7w\n2OIo++b8VW04+mcP4y9PJXcujbQYlvKeXbYJ5/312bpdi0UicQDgB3e9hIN/PN3qKqqGDC0lMxeK\nbmiluRiyPktzMWSNUb21IBqjy87FsG3Dmq++YBSDPijJRhULwcyOHfuleUNsKfnwRdSA/M4mwspL\nfjNj4Xr88/kV0f/VWphY4djUuhKysb+8sg0f+u0TeEWJX9excA3/riEahFAyCIqBkLLKyMuDUNUN\nAKlB8Ly6GISEBkE7Rh9T5HOVvzuVPTZs2TIl5PAs6x+5GmxtNWTY0l3FOy95AJfcPT9xnL5DZU8l\nsMrwGGO463mupO8Li1CphTjs0gdxzM8estYzK0TStsLMZxB4WWWPlCiGpIvijU35qbL1OtiM702d\nFVzw9xcSK81YeBgft2BNO478yUO4XqQpl7CVmdV3YmNFNe5Ng0R9XjbXSJaR9cpqnqBpttArRGVK\nV4vlnEvuno+7X1iF+UpypzBk6KkGuGvuSnz3TjuFn/VsVdw25w0AwKZO00CtBSFaRTbVTuU9mFFE\nynUt17MlNrNt2d2To0HorYXbvkiRiE4koleJaBERXWD5nojocvH9C0R0cN65RDSBiKYT0ULxe7z4\nvExE1xPRi0Q0n4i+PZj3Vg9szSAIklkObRm6Xlndhn0vnIYHlfheg0GIqEizY4cMeGllG1rKfuSH\nlVAbvroiBtJp0E/9aRa+dsvz8bW1PAA2Kk1CDkAbOvNXtRRRwgX5WAuKp1rmUQzxplcMyzfZ1fS2\nKIYsUWUgNAgyk2JRDUJWKJU+COouI9XlU2SraNkOsla4yzd245FXuabh7hf5JB+7GASDIBNfpQj5\nQsbQLnYcVXcetR6bYqQAQFtPMrGTnLgOu+RB/Py+V63l2dp1XoiZfHa+R1H/UA2Q7905D0f/7OHc\ne9Hrb3u+v35gAW55Zjlun7PcqLM6YcukUE9rrjbbvWS5GGy6JRuDoLY1226faeNFELKoP+mr5OyE\nXOYz+uZtc7HvhdPw1Zufw02zlllX6Db3iA0ye2u7ZX+KahCipSwMBOWdZjEItuvZIqBs/bBSCzMz\nVfZUg+ja26QGgYh8AL8DcBKA/QGcRkT7a4edBGCq+DkbwJUFzr0AwIOMsakAHhT/A8AnADQzxg4E\ncAiAc4ho8qDcXJ2wTbjTXlqNfb43zbrpjWx4s5dy6/v+lxQDQWceMnyH0hIf2ewbHVxt+HoCoKKT\nK1faxo0/a7CUk2ERVkB21Kx47TwUTrUsMinKUKZqEGKpEuttK1M+p+Ubu3HHczGjoq+oqkKDUPI9\nVENm1CntXookUJKQE7IsW30HWc9KTqyyHWTRtKphGKVO1lahcm8NWzKh4/aexMsVRecZLrb7txlf\nKlS3hX62bZfSvHYk76OppLoY4nPuFExaR1EDQXPj2ZDUBISJ82Rd+Hfp7KNEloFgM1ZsmRR7azHr\nZWMQYjF08vqVWhiNeXpYZ9ZqXx6pGgF3Kv1L1klH0Wydcgy0lVENGFpKpoGgl6m+D9u43mlZJEkX\nw0jBUMg6ZPXzP81Ygov+9RKAbVeDcBiARYyxxYyxCoBbAHxYO+bDAG5gHE8BGEdEO+Wc+2EA14u/\nrwfwEfE3AzCSiEoAWgFUAJiJyIcAWWFAcsVnEynK3yWFYkoLj6wmNAjJa3z6yMkmg6AaCCw5YNSn\nQYiPtfnaJGT9iqQEbi3HdL+O4hN/cQah5FFi2+w30hgEKeJUin5uGddTNJc8ax6Eskcoe5TIKhjV\nMU2kmLndbPJ/OZG1dVfxrdvn4rW1sXFje9abOivY+3v3RqGlkYFg2c3Pfv2kQSvvSTav3lqQmEwO\n2mUsRreUwFg8yeT58q2iyJR2XwS2ZE25YY61mEGQWLE5ZlJkoqaO3hrufXFVbghklpBOMmYJhsPi\nYpA10YuwJfYp4mJQ6/Lcss34+JVPYtmG2G3yiatm4odikrJpEKqRu8BkJ+X1PW3cyYrOkIdmCTPt\nBoJpTNkQt1H7wqNFTODtFgNh+cYuPL98c8J4sd2DzZ0gx8XWhIGQHcXwz+dXYvE63pe3VQNhZwDL\nlf/fEJ8VOSbr3B0YY6vE36sB7CD+vh1AJ4BVAJYB+AVjzJC9E9HZRDSbiGavW7eu7pvqE7R2oPYZ\nuZpK7sXAf0sfsto5zYFSrvzMCV9il/GtBoOgDmh6Xv16ohhUV0XWylDWs4gqW3Yk2yCXNzEwbQBa\nuKYd7/nFI4mBL1GeiGKQqXs7emupGRxtYjeJkc0lM5Oi0CDI7Z5NBsEsZ/nGLjy7bJPxuX5/EvI9\n3vHcCtw6+w38+ckl0Xe2+3hhxRZUaqGRqbFSlKaVCYpSKHO1bIC7Hnzhx48jHZL1au+pJtpc1bZv\nRYFVcxoi8a1SbJ4hJN1l+sT403tfARCnep6xcD2+eNOz2Od70/DL++0uDrW+tsvK9qT20dgNYDJ9\nehtQj9E1KTbY8gbcMHMp5ry+CQ9o4bHXz+QiQ/05MJaeKjgMWTQJ6+5zXe+kQhpKmSGaNgMhSH+2\nKmIGwRynakEsUlTdBLKeH7z8cXzkd08kDDbb3jX2iIUaWst+4rM8BkGFmqip0diqRYqMvxn5lA8D\nEAB4C4A9AHyTiKZYzvkDY+xQxtihkyZNakg99XagWoRyBWhL4iJzDNj0BfoOjFnpQUe3lKMBTSKR\nOUxbFdrarc1oqAZhQvGbySBEyVkKuBhKcutly0oyx8CQ38rndMszy7FkfSfumrvCerzMVSBXhDLE\n0Uap6syOhIyCSNMglMV3+qBoGwiPuexhfP1vzxufx3VI/q/H5quDm20wLVt8yWpd8gYtOVFEOgwt\nikalpQE+kXrE/fjyXNWQ7Oit4cCL7k+kQba9Y73d1GcgIFFnXl4OgxCFOSaf1/gRTQDiPqb6sy/P\nSOWcOTGK38xSP7WJSL2RXoJtP4ysKIbonYXm9Zas67CeozcbNY22joCxKMW7ziDoLioV8sgsA8G+\naCgWxSANhNOvmYU7nn0j8V1vLUBL2YdHSR1PW3cVP5v2SqR9Ub/TFyOAfQzsqgQY0eQnGKLenCgG\nFc0lP/+gQcJgGggrAOyq/L+L+KzIMVnnrhFuCIjf0uQ9HcA0xliVMbYWwBMADh2A++g3dBeDaiBI\nutWWlKRNDD7tFos2SsUqB3bR8fQEHHIFq7sYZKdijCUyA+p10a+rQmcQugQ1Z+uo1ToYhDiroWUl\nqXWqL//1WXz/n/OM4+RzaSmbsc3Je2AoeTGDIEMcJ45sMo6NfdnJz4mAcokMl4HMgyD3Yiga5pgF\n/dnqq6GkX5mX395TjVX5KSFTW7qr+PS1T/eZQZDX0ldGZZ9AxNu0bQtryeyozIeNQbA9WyAlx0jK\nCjshystph9LFIN/R+/fnROV22qY5RZMnFXIxWI9XFwdxn7WVDZiaEhuyfPZtKZEvOgNZDcLUrbzD\nMM4P4msGVlYyNq+AONnKIGSMW7byAUSh1BK9tRDNJT4OdCiRQJdNexVXPvJa9L+aRE0+4kQkl5VB\nCNDa5Cfebz0MgtyKfigwmFd+BsBUItqDiJoAfBLAXdoxdwH4tIhmOALAFuE+yDr3LgBnir/PBPBP\n8fcyAO8FACIaCeAIAK8Mzq3VB73dqmErtpVCzCDwzqquBmIGIfl/LQyxfGMX9r1wGv72TOydkQpY\ns4Obg4QuwlNhnfS1KAbZObLEhXkDs7yXtHL0evz7hVW4YWYcax0ZO+K3HMDTkh9xFwNFz0cOAHKl\nmLg2sz+fasA3ZdJp3VoY8u2ePQ+1wMwoWQ0Ypr+8JpFqOg+GSDFjgpLv5sCL7sd/XDEDQLY/87EF\n6woNWr+8/9Uo4UygGX69tSDJIHiCQQhZNBGqG4itE7kaRigUrGwj6kRoJqyRE41ZX/0W9PwVvL5x\neQ+8vAYL1sRhtxs7K1FIpnyGh04ej313HI2KMBxkUUXTL2e5pyRsURZBos75DIIcKzKjGJQxoyj0\nvAVqZlCbkE8yo/q4kykojDQIGW3axiBY2osNtkgMid5aiGYR7aWKFPXESmqIpHynqkHbXbVrEDiD\nkDTkirJg2ySDwBirAfgygPsAzAdwK2PsJSI6l4jOFYfdA2AxgEUArgHwpaxzxTk/BfA+IloI4ATx\nP8CjHkYR0UvgBsafGWNxDt8hhN4MVLrfNikHGoOgdpi0WPpqwLBcxGVvUBq1VD6XfHMFoF4LyBYp\n2lOtJjMJyjwIViFRUHxQqkT3VL8GIdQmcaluT0t+xF0McSZFuXoY01o2jrVR1RJlm4tBCCBLHs+k\naAtz/MINs3Hq1cV3wtQvnUYll31KrGwWrrVTxzryEgit2tKDyx9ahGeWxvn4V27u1hiEuIyyT/CI\nT8567gQAWCtyIqjP26Zv0O8za6KpBSG+fstzeFgICm3tWj3vrBtm4/2/eiz6/9y/zIk2jpITrUeE\n5pIX10Ocrk/EaZNUNoNgHm/VFqUwWDYGoUiYYz0Ell5Fvs+G3S0VKBoEWwgkkB3FULcGIWKxijMI\nOnqrAVpKHnyPEm46fdxUxxH5rms5DEJ7bxWjW8oYpyw66mIQhjBR0qDu5sgYuwfcCFA/u0r5mwE4\nr+i54vMNAI63fN4BHuo47KAPGk0WAyERf5ylQZAuBo1BCEJmCGEAxUBI9QXGn8VhjuY9pLkNknkQ\namDM3NkRUMRTBaIYpGuhLxqE6DjxnKRoKI1BqAYy22G8/TQAjGgyn6XqYjh+3+3xoCLokrkOVEh9\ng0eUwiDU72Iw8iCkrGBbyz56a2HmdrU2FHEBqWjvqeFdP31IqU9yZVTyvEikqKdnBrgoEwDGjYgN\nBBvbpLabtp5qZux7yIB/PL8ST7y2Ac989wTr5Jx1n3OVTJ/yON8jNJW8aGUcMwh23YmOTAMBpovB\nZgDJ52ZkUlTupbcOF0M9DII+uQYhs7KQAB9T9Pwa+rXtUQyxSLG9p4pfTV9oHGNnEPhnW7qrWLim\nHVN3GI2O3hpGNvkJ5iOfQfAMBkG/b3UcCSLDRB0Dzf7Y0VPDuBFNuPSjB+LRBWvx6wcW8iiGgn1t\nKA2ErVqkuLVAbwY2F4M6cL+xiadTldoDm0hRQs0vYGtusYsh+aptoUGZDIJlLKlpLobOXp7x7Mxr\nn7Yeq14jC1k5E/IGNVm6fE4VZfBIu1bJjzUa0ldvM7ZCFk/yB+4yFtd/7rDou7JvuhiCkCv3Jbtg\nMgj1TcayDgA3Vi6b9kpqtsSWso9KLTQmsLwIFVuYVj3orSbvs+QTSIgUZd3lffdUg0jYl4wwMKlb\n1RA66KL78acZS8SxZoZKicgNYqH3o/ZhMVhHt8TGinynnEHwo//lJJ3GbOjIXDmL4SAhUrRMvunb\ng8d1KMIgZBkradAX3+pOnYaAVBEppolLrR4GcY3//fuLOPCi+3HtE0uMY+R9PfLqWswVCxH1Pt73\nq8ewYnM33vqD+3D7nKQQMddAEBlnOzL6wMaEBsE0EC6+e76RibW9p4bRLSXss+NonH3snhETVZRB\nsKWlbhScgdAA6BZ/2cIgqI386397Hj+/79XYn2jZq1z2O5WBsHV4aX3qYqFqwESoksJcZPhJbQKg\nWpDMJNhdDTBvxRbjOH69MDpH4qi9JiaOufurR+OYqdvFdLXFv542qM1euhG9tcBQFtdyDAS+m6MH\nz+NUuBTQjWw2ybWAsTgkjQijmmMjosn3DAahGoTRCtoW5qhOLuvaew1/pz2Sgv9euqETv1fEUzpa\nyj5qYWhJ1Z09KBVN/JOGSpCcsMu+B4/krp78MzlprNwc55voqpqx54mVcYrWIgjzJ2W5UlObsDQ+\nbMm9tteEiABX4zeVPIOx0f/P26Ez6/kn8yCYwso4zDF5XnJzIeHmy2IQCkasSMxbscXUGYQZGoSQ\npTIIRQylLEgD4TN/fgafFgsR3XCSrNRNs5LbL2fYB+itBVyk6FHi+ep1+qtSpjxO7/dqcqdKLcTi\n9Z0Y3RKPJ80lH73VsF+7TzYKg+picODQ24GqQUhL8nHX3JXR4KOq+fXVUE2ZCG2Dk3Qx+Apr0VL2\n0FPlKwB1ME9TJQNpLoaYQRjdUkKXJd43vs/k6hEA3rXndhjVXMJ9IlNkS9nnnafGhWtqlka9HB3/\nedVMnHvcntEEes+LqzB+ZFN0vSwGQbIsJd+LRH8jm+0uBlm+51HCiCj5ZKzaJINQ8smaSbFLmZze\neckD2GV8a+L7su+ZqV5FGVk5J4j4e68FzDguz0BQ93LoC3q1HPMlj0SYIzPS+fYm3FNm2nHVmEzN\nTRHmi73sdD3/22YQNVkoXV9oEOT7kCXZ3rkNsfFtfid7po3NS4gUZRSDuPq8FVuwdENn4prSkKpm\nRAIUzTwocYoQuOplpBkaoSJS1DUtfWEvktcNI5ZL9mk9XDAto2uaBoGPnUwwCMVX63Pf2Izj99ve\nGHfV9vNnwYKoQsPmMjc0+0AgNhyOQWgA9Pm2SZms9VWOCjlZ2USKkfWqqMhtEQI2DYJMZVwLmTW8\n0jYWp+0HXw1CEAFjWsroqgSGK0Ovt0o5lhTfP8AH4bJPmfSvOrDozMyitR3R96+sbseF/5iXcDFY\n87iHLHo2JY+iFXceg0AEjGyKjymLdMpG2T6h7HnWTIr65KRncLT5HqMQ2O70lb4URlaC0IhysDFB\n6nbgRfcWSIOhQRB5EALFuFJzJgCcfbFtVFSzTHw6aiGzhkWqUPfOkJCTis1AqNRCnLDf9jjn2DiN\niu9BMAjJa+n/qwbwnNc34bEF6xL3kiVStBkwCZGixoSccsUMfPmvzyWfU5QoKX9flHqyUdrKSBu7\nVJFiWmK3osnYdFRqzBAC6v1OfyebOitG+LdqB2wSuoLxI8tW/Ugabpq1DF+88VmDwVBZu6UijPez\nR02OPmsucXdkWh6EH37oANxy9hGF6zGYcAZCA6B3hUTGtGilkDyKsZi+TGoQ+G8jUU2YVI9LlC1h\njnLi0engOLa8GINQCxh6RSbCkc0+unoD67lq2aoR0yxUwxJq1kEgO+86YEs5a07C6h4DL69qw8zX\nNhj3IBmdhIHQZBoIyzd2R75vjwijmjUDocZ3gnxy0fromiXBIITMjO/vzPH32w0E/tu24YyER1z3\nUAvCxMSru5Qkdhjbgt+dzvdJ66+LwZYHwaNkRIVcVUo/9eiWUmIn0Egdr4oUUya8kLFcsZeNvZJ/\n2wyiahCiqeQlkvwQ8WRYEYMgitINF7UPfvzKJyMavEgeBPV+5XEL13Tgfb98FGvbeqKJ0DA0Lcmx\nioQ56pN0PbsGqlE5WVEMaRkw+0qvVwNTV6O/f9nn5TUOveQBnHr1zEQ9VTZBboK164QRBoOgVtPG\nLjy9dGP0zs84fDcAyUiGzt4adpswArtPHBl9JsWuaSHfh+0xAUdMmYivHT8V//OBfazHNArOQGgA\n9ElT/U82br2zhixWCSeyHmrx3+rEaxMxRSJFpUNIN0AtSDIIWYNYVhRDs++htYkP8mnUt6y3ShU3\nlZK7THpC0CfvyZ5WNf5MN4hCZvqA1U74wctn4LRrnjLS+sqBsZxwMdi9b3LHQJ8I40aUcdgeE3DZ\nfx4kwgpDnHPjHJz+x1nY0lWNEyWlbGTUlUPnp+UsYIxlGhd8bwlCdzXA0g3x3gwzF2+wihp9io2R\nrB05i6BSMxN1eZR8V3KikwP9mNZyYlCV7ywhUkzVIKRn9JPIyuthZRCE20ltmz4Rypb9NnQNQkdP\nzfoMsybGSDeTcDHw+13b3ouFaztw19yVqZR+e29sLEoDN0sAm1bOBEtysPQyYpeREcXA4p1qTQbB\nviAC0neRVVENwkQeDcA0vNXsnlJM+cIbWxJjhzrZLxF9ZP+dxhjRXmqSu5GWyCZZJwB4/wE7osn3\nDANB1R8AQoOQkQdB1uEb79sb571nL+sxjYLTIDQAejuwrdoDxkS8vDk5qpNqnP8beHllW5TYR81s\npqJs1SBIAyHZSIOMzpuWB0GutkaUfXRX7IMjADy3fDNWbu7JZhCIMwjRCjOFQXhtXQe+dstz+M5J\n+yW+I5gDknSBqNXvqNQwpqWMIORZJKWbw08wCNnJSYj4yu/Wc44EwHPy10IWKatlTgrOICRDKKN6\n5KzW08KbQpY0fNR2A3BDq+R7eGrxRjy1OE7CdPo1s6zllTwvckVlpcsuAn3gi7Ji2hgE8X5HNZes\nu4vawvd0hGF+ZEvW7pBpLoamkpdYZfpaOm05cej1et+vHsOIJh8v/+jExOfZxrf8rRi/Wl9mzK6a\nB5LuJtn/ijAIOsU9fkQT1rTlb8cuy0jLbRIo2qS0XTjToqLyUNXcZpIVmzCyCZu7KhjbWk48a/X9\nqK4I1RDoFsb2iCbfcJGq7MTI5pI10+S/5vKtgco+obXJT0QCVQTDqqJZiF3T9A56euqhhGMQGgLd\nwo7/DkKGjt4agpDBloIZSK6ao8GEMZx8+eNY3yENBGYdKKVdULK4GKohS3TUNJU0YB9wqgFnEMq+\nhzGtJWzuqqYyCFc/uhg/+vfL2KyIBZs0A8HzeCfLYhCCkOHvc97AvBVtia2WJWybB203KqlKv/WZ\n5Tjkx9OjjlxSGAR5fhqDENVVEzxJQaE0vmREghpCmee/1mETywEwIiLGaVkffcEgFIXnxdfqsmSC\nqwdmHgQylOCxBsGec8IWp5824QXMrr1JHGNzj4XpLgbZpn2N3Sp5FBsILL1eupH15GvrMUtky7Tn\nbYjZwJtmvY6L7nrJbMdCSGe7ZntPNXqGcrvhrLZly70CABNHFWcQ1DYYMnM3Wjl+6QsLlR068deP\n4VgOzX8AACAASURBVOpH40icoknUVENbaq/Gjyjj00dORqBoUmphkm1IYxDk+xrRVIr6jfxajSCz\n5UYBgOueXAqAjwEjmnx0VQLMWLgeS9Z3JkTQEjLMMY1B0CPOhhLOQGgA9AlXbZyL13XgrT+4D48v\nXJ+YEHpEBx/Z5CdWX7Ij2nyfNlpRXiuhQRCT2P0vrcaxP384+jxrlWNNUBJyt0ZTycNOY1uxektP\n7la+qu9cNxAiBiFMTiD6NV8TG8royY9sLEdPNTAMhIvvno8NnZUoW54cFNS6pEVjRHX1dAOBZ0uU\nez/IjGvqVtJF8/bHZaYxCElaXd83Qr1mEZQ8Lzq+vy6GnmqQFKL6nmFMVTUDUDfG4oRKKoOQ5rrK\ndzFkGQiJfU6UetnaZrkUh7LKEvOMPCDJ3Fj3OVE0Ad+9cx6ue3Kpcb/dlXhzn1rIEm5LGWfPjzNz\np+ioprgGJow0wzvTUAvCxIo86aqMmRlbCCTAx8RXVrfjJ/fG2fCLiCYv/Mc83Px0HGrYXQ14sjOf\ns2CqWDtkyXFLfVfqu+2uBtH7lp9LDZL6HEflLBpKHqG17KO9p4pP/WkWvnTTs6gFDGVtN0YZ5phm\nENUTSTHYyB1FiOhQIvoGEf2ciH5ERKcS0fhGVG5bgd7sVUNg2cZ4G2JbfoRRLSWri0GnRnnHsDAI\n0kAgk0H43cPJ3eey8sXb0ydLBoGwy/hWtPfW8HrKtsoS6oBcUjok/98T+xbEQit9FR2EYXTvm7X0\nybZ6d1UD7Dah1fgcANa3y0ncTEe907gW6zkSeh/myZBYxCBsEMyO78UTTdG8/RJpLgaVbv7+Kftj\n6g6jtLolo0Py4HsUXas/LoYRTT7ae2oJhqzskbEi0l0MhoFgoa7TwhxDlr7tcHQ9q8CWn6OGdUb5\nNwLTxeBRnAyLMRZ16izDpejnup4IsOzS2VtLGFZqf2zr4eHFJY+i95eZKEnx0auYMMJML54GnnfF\ndAvJv2U/NwyEDKFB0cRht86OEyDJPQ0ka6YmJJMMp4TattVqdYu9EoCYaR3RbG45P8IiXFZR9j00\nl328tLINADB/VVtkvKjwfcKra9qxcI09/flWYSAQ0WeJ6FkA3wbQCuBV8J0TjwbwABFdT0S7Naaa\nWzfMMEc1DwJTPjcbxuiWMs+SqCUAslH5tklc9a9LyElMz9CVJaSyicSqAffxNZV8HDGFJz3Ky/mv\nq+91F0MpwSCExkq+FrA4fbKW28BGNXf1cgbh/x26K46YMsFal0ikqEyqu08cib9/8V046+g9rPeh\nPzsZxSC3qk4yCHYXQx7SXAwqg/DxQ3YxBiD1mkUg0wgD/WMQbBR1SSRKUhFt7JSi97BFHfRHpGgz\nIGKRYtyG5ORfDbjw1hDQiv9rIYs0CHIC0p93Wt4Nm2BS9resSb2tuxpN7NwPr26ZXUXJ94T/O4ju\nJa39pAkF62EQgjDp2klGF7GYQdBdDBnvquj2x8nyOJPB91PhLkI10iTVNaXUo6sSRJuFZTEIttwo\nKsq+h9aylwhXrgTM6J+viTFyQ6d9f5itwkAAMALAUYyxjzPGLmWM/ZEx9lvG2FcZY4cA+BWAqY2p\n5tYNfcJV5xaVCbBtxTuquZRYMWa5AWyrPyl4USc0aSCk0X+2fmqn+8NIpLj/TmOwXQEfpsogeNrq\n0vcIvqZB0P1+QRjHQesMQpprpOx7+Nl/HoT/PXHfxHdyMywpIkyyGYRDdh+fSLurwtQgEKphPCjL\nzu8ruR6K7EORLDPdQJCDqc7CAHE0SFH4yvFZCZh06K7SiZYJpixSLauQdU9lECxtPM240icq2/c2\n8kEaH+rWvlURoskYjDBH6WLg54axBkEUft1n47TbgLkLYFQfxtDek8zJIe8zLW02ANzx3ArMfp3r\nGFZt6UnsYNpT5buGjmjyIwOvGoSpQtu06IN6NAh66nBVzFcNwqiP6u8mywgoIlK0nROIXVP1Nsyj\nKezX0/dPaI0YBF5GzCCoGoQcF4NP0diqXkfXIBy396Tob/07IHtTqUYjdRRhjP2OMdZNRJNSvn+e\nMfbg4FVt24He7A/dPV7JqjHMtkFd+hajjVEyKDrb4G5bSLaIgS7NQLBdI22HxkotRJNP8DzC3juM\nBgAcM3U7/C0l0Ud7Tw2jm0t43/474OBdxycZBOJJhdQ8CAaDEMbZAfVVWtqzUcMYVUj1t1wpqqtA\n+VnaPKt/Ll0Msu4bO2IGwZZ8pcgKP22b15DFKzHfI+ihWX7KNdPgEylRDMVFivrgZjMQZSZFFXom\nRZNBMBXwWSvBrFUpp6DTw2VVRkvdvrzse4m+oxpR1YAlMina3kGagdBTDXHgRffj7hdXKffAf+cl\nqXptXRyy+svpCxLf+R5hRFOcT4Ib1/YJLS1Rkq5lyYL+3FV3kKr0TxMpqpD3nZfwygapgyp5qhg4\nNhB019QxU7fDW3ceozEItehZyfHI9uzyhMtlzzMMBOmCVfH1E/aO/ra5EbcWBkHiCSK6n4g+77QH\nfYOeB+Hzx+yBf553FIBkshxbY5HCGDlw2ShKOcDaBIK2zIayEatlNZXiidmW7MhmIFSVMEcA2HEs\n99vvNmEEDp8y0Tge4IzJntuPwjWfPhRjR5RTEyUxoYRuzWAQdJ9lWnY2ObDrz1cyCPGW2EpWx8hA\nsHcRfVVcEhkg5TuIoxg8qx4gTwRpq69EGMbJgWwMgu9Rwl2SB9+PXRL1uBh0CtsWR293MUgGIUDJ\no0g0K2EPc0zx9TO79kaiEoTWlLZyclvXHof1qT5rXaRIFLsAq0GcRz9k3MAqacbSGX98KrVOAHDT\nU2pOf7uuqB5IgZwUKdrYN4nIxaA9t3ryIHzxpmejkF4gudjYojB7ti3OdciQ4KK7G6qoBSFqQTLq\nRLqj1HBLiSP3nIj37rtDQujZlWAQeBmjLcZAXuhzuURGv27rqRoLk9YmH2/fdRyA9LTewwW5owhj\nbG8A3wNwAIA5RPRvIvrUoNdsG4ZPhN0njgCQHBRsjUUaCGnKYwBobZIKZpuBYF5fKu3VFXeT70X/\nW6l6raONaOKbAVUUCm0nYSDoVrQO1aI2lOJ+7Odt761hbGuS4q8GIXpSJrG0laTsoCaDIFwMmk5D\npcXTGARbmCMQG3yqBsG2ms+jK2U9bFA1CFYGgchQTmfBJ0Kzz99ZPS4G3YCZOMruYtDjuqMwx2oY\nbZCT+N4S5pieB6GPDIL4bE1bT/TZiyu2YJGIkDFcDEo+C9XFIL/T31We4G7x+lirkyY8rge+cDF0\nKWGOIyyTXMmLM5WaYY7FNQg61LIks1f2yTDabWLTn0/jycf0lMlFr1sNGMq+ySD01kJj3Cp7XqQl\n6a2FmLFwPTcQNA2CntwIgLFY0cFFisk+0dlbs7qOZflWBqEO9m+wUWiZwRh7mjF2PoDDAGwEcP2g\n1mobg74g97xYG6BO6lYGQTQk2/avElI8U3Rwl9S1WlZLOWYQbP3UZiBISlZOjtuPbilUj8T+C5oQ\nTK7Yu3oDVGohxmnK6lrIEml5VaRNIpGLQXu+UuQoDTNbuGMag6AbDtJIkqp4aSCk5SRIW90lysxI\nlCSV22RZvaq6hyIoedSnREm6C0QPJ+Vlp4c59tZCNJd9o/76BKamONYRpKSPjq6VkkBMriBXt/VE\nm2R96aZncerVM6NrGpkUU8JV9eetGzw2rGnrxYaO3kQ+gf7sg1HyPIxoLkV5ELqrAcbYJrmyb6Qi\nlhjT2ve8eYHFQBjb2mQyCJZNpOKwVnsYYhZksjYbg2Db7rzkUzQB3ztvFT71p1l4ccWWOIpBfDfK\n8uzyFj5NJdPFUA2YVWcgr9diGQe2KgaBiMYQ0ZlEdC+AJwGsAjcUHApC74gexeI8lXJrsvicJdUV\n7Slgof+l9WubmG2rq4hBUL5rLvnZUQwaxStz/atqaWkVZ4mtgGQ4od4Z5GS6uZtPsDqDwOPB7RNC\nWg6GmEFIXkvmUYh2vBSDvErPpxnz+qQn70muAqVIsaQYPSryViNqvXQwwSDIAdGmQVDP3W3CiMzr\nqEZM2ir2HbuNM4w13aC1aRDKJdPFUFNcDDYGQXcx8N3v0hiEnJj/wL6tbi1gaOuuoacaYtfx8fOR\nhxphjsoz0nUauhH4rr22S62PiqsefQ17fueeaC+AjpTU2/vuOBo7j7OH6qp1kNlM23uq2NhZMfoO\nwCekNAahiNsrDTYGYdyIssEg6O9qRJMfGUZqGVec9g5MmTQSeZBCaW4gCANO2cNGNyxLvhf179fW\nxpoO2R8JkkEwn53NqJeZVAFuVNqeoX2BwMfKFsuYX4dtP+goUpW5AN4O4EeMsb0ZY//LGJszyPXa\npmAwCBT7jdWBL4tBUDcd0iHFMzbq3SY8kiu/WsJAiBkE2zVslngtZOipBFF5U7cXIsWcAVKl+nU6\nTT4XucPa2NZk/aXxYVulpSUiijQIfrIzbu7Wwxwpure4fsU0CLr7Qr5zXwmPU1GIQfDtx3AGIVSE\nlJqxorgd/uNtb8GfP/vOzOtI+tyj9FXsWUdPwQcP3ClZP629jrEMqk0+GfkE1N0cm0ue4YLRUwq3\nlv1MkWIWg1DRNo+SqAUhVgv3wq6WPBlNeibFBIOQ7Gdqgh0AGGeZmG345/MrAcShwaYRzstsLnnW\nyV5FyY9dDCf++nEAfI8LHS3leD8J+R723ZH321HNJVz1qUMw/RvHFqq/WkfVVSMN73GtZWNBo7ok\nAd5meqKtoeNjy74Hs9eYsLoYqpIhMRdNqmZHNRxlf5TjnNXFYJn8xytGc5PvRYsvFTbxuWQabMcP\nJwahCKc0haVt0edQCPrD8yhegSYZBJsGgTfAWsbqXjYytTO8Y7dxOGG/HfC5o8w4flujbCp50eqx\nSB6Essfz0qtZ3A7cZSyevOC9kRYhDTp1a/tOUvT6wChV52Nby0YccSqDIF0Iml9eahCkYVaKXAwq\ng2DvrPrnNhoR4IODTqEDsW4kC1l5EKpBOoMg92IAeMRKWt0kZDlNJS/VyLJpHfQJyMaKNJW8RDhk\nc8mP/P9cg+Abgkp9l8CWso9KYN8jIGA5GgTLNttNIl5eTmoqg6DWu5KgvOOBXnfDqBEOgNlm06Ab\n3boR1FLyUQ1qKPsePC97CPY9vg9AdyWI+kXaJBeEIea8vhG3zVkOIuBv5xyJTZ0VEBFOfOuOqdc4\n6+g98IVjp+DwS+PgNVnHT/4hFmXGLoYylm4wGYTmcvxsR7eUon4baAuWIpkVqxaRYo9iaHVoeVfK\nvoeaby625Io+rpf5Dm3tWz3O8yhiBNT9X2wGgjRIbGVuFVEMRHQNER1oMw6IaCQRfY6Izhjc6m0b\n0B8hUTzYVnIYhJFaRi/bakn6Pzs1PcN579nL2gB11TjAB+GsPAj6drsln1CphWLjo3ggesu4VmN1\nrSPBIOirX/Hd5lQDoWb9HEhXussVvN5R5UAmV+ryOSZFlPZ70PtwWlhhWshhS8rkr8KWOAuIV80x\ng6BpEIiic0u+l2poqHUE4udje32+Z4oN9Xdgu46+p0GzonWR7im9DcTbPfPfNoM2PjY7D4IaehrV\nQaRMlgzCLjYGwcikqLoYNAOBki6dogaCnjRMNxhaFL94WkSLhMyDoNbNllyqteyjFjJ8/MqZWL6x\nG2WPsxOTt8un82UyJhW6KA9QDIQRZePZVwOW0K6MailFCxs1zLGp5GH70fmiySBkqIgt23UNAmC6\nzMp+7PJrV76T7EBFjCGjLEmRbAyC/plkBtQoCFu/kAaCLZQ5b/xsJLJa3e8AXEhE84noNiL6PRFd\nS0SPg2sRRgO4vSG13MphG768iOaKP8sKc6xmiBRlI21T8gJkWaE2sU2T72FDZwXdlaAQg1DyPGzp\nroIxu7WdhQSFb/GfA8CmzngVokJubWujT9OU43Li0zcOkqGITRqDoNYvrbNmuRgmKQNbybLy1q+R\nhjSBJGOItpK2laUq7n0vnd1Qjwfi9mdb8ZQ8M2XyHtuNxFlH74Eff+St2HlcK/bcbpRxXlnbi0Gu\n3gFFg6DVX2cQsnzjPB4/I8zR4mJoKnmohmFkhO4wxmS8DJGiwhKc85ekh1VnV4oaCHkLZGkY8X0G\n7M9ATkScQSglWMRJlgm2SdlPgtehODnsezDYHtsE19ZdBRF3HySSKQnDVjX4RgsXg9xZVa3n7884\nBN8+KU5u9qV375lIMgQoO8r6drdtu2EgxO1NzaMSiQZFW2stlwxD2b7Y8qz/q2OiTYMQax6GN1J5\nTsbY8wBOJaJRAA4FsBOAbgDzGWOvNqh+2wZS+qCM+ZfQB3KP4sG6lsEgyMamNvisbFy21evTS3mW\ntqsefc0qhNTDk8o+YX0Hp33rVT+nRTHIcoGYQdCFcTK5UdFBGIhdDCT8yDqVGxkIFpFi2lPU5/yE\ngTCqOYqvT4soyJu0+bn2z2UmRfmsrImSJLugZABMgzxW1qnZ8ox0PzvA39X/nMy33P6vI3aPPlMN\ntSZNpNhc9iIle2+V0826QaLvFWBjvCTCXAbB4mIQepv2nho8Asa1pogrUzQIOnwv+YzraZtZkIaR\numX4hJFNiSRMI5tLaO+toeR5iTj9qduPwueP3gM/vy85VJeURGRAsQ2SJHwymQwbg9DeG+8NoYoU\nVU2JxOjmkrHBF8CN1Umjm3HOcXtGGzq9deexGNlcwqML1iXKlC4G2Y5VV6OuqVENdnVBpWdSDEIG\nnwg1ZSy0Gar685BtUd3YKSsPynD33eeOUoyxDsbYI4yxmxlj/3DGQf1Is9L1FZk+EKr0dJxJ0SxH\nWr1q6uFMA8HS0M8+dkpUV6tIUWcQfC/K7DZ5YjY9mWYE2L6Tq+JYpJiuQSgKlaq3TcyxgWAX/dmQ\ndU/bqQyCbw9z3ClHlc6vka5BUKMY0lgYWUaeMSInwlirYRFOWQwEmz5Dv1aT7yXYFq5BUEWKvlFu\ntBdDaE4oOrgGISdRUoqLob2nhlHNJesk1+R7iftTXQw6Sh4l7tvm+weAo/ayJw9Lg+yncqdCADhs\n8gTM+s7xcT2jCBxKCF9PPXRXKyNZEinB+wLf8ww3k41B6KlyZkjdmRUALr775cR9AbEGQTfy0jIM\n6p8HMpOiH9ctqUHQGIRSrAlKGAiiTl989xSMbinh8CkTjHu1MQg6kyjZKDUCw9afpLFZD4MzFBhG\nARXbLtKagD7+6ytBonjgiWKXLZP36YfxPbPUr7ImOVvnk2U0+Z51Rab799W6HrTLuNRrAYg2QonO\nzQhzzBcp1s8gqBa83U+edC2oosLUd5flYhiluhiSftsvHLMHvnb8VJx4QLoYTCKdQUBCg2BjEOTA\nw8V12QaPziDYDAqbgaAPoIA5GOq+fC4+S4Y56vXTwxzzNAhZSYk+++dn8LyS8U/WqRaGaOupYnRL\n2Xq/coKT0IWIKjzNxWBzuf3PB/bBySIKhDOD+UaonEhVDULJp4RLRDVuVeFrS5NvdY+VPKp7TxAJ\n2+3bxhK5BbyntEMAuFFkj1T7w6jmEmqhuWeCzfAoCSFm4lohE5ERZhQDwDUI6rMuezHTkHQx8Gd3\nyO4T8OJFH8B2o5qNsWmk+nxT2uRJb90RV55xMM45bs/oM5uWSNZpmNsHzkBoBNIagd4AiczMgnKy\nSttg5fZzj8RuE00VdtYi2MYgtDb5IOIrrqztni/56Fvx6P+8OxosdxjTnBvTr6/QslwMsvNu7qqi\nuWRmJmvrA4OgTlq2gVmG59nyJaSF0Oljrzp5qPkAfI8SdONOY1vxjfftnbrKVJHGAn3uumcwf1Vb\nzCD4+vONV26+5+WKnuT7kM/aloXRtqeCVVuhWb1cpBj/31zyIhdCFOaonRMlzhH3kLYnBSDYlIw8\nCDaU/ZhBGN1Ssk78ZibF9M2z9HTXtrbpKcLkkme6VWxQd13V9SEPnH8c7v/GsdHnOoOQxrqUfC91\np8k8yOfxlffupdTRfh9NIr+FzYWh1k1me9RzS1hTEHtmKuMu4UJoLsdMVEKD0FNLGIBqOKS6Z4Qt\n7Fgfm9TNrJ757gmY+4P3G+d4HuGkA3dKPBfbu3YMgkMElrIONVLQhgyvXXoyLvv4Qfx7igfhNJGi\n55E1R3i9LoaSoO96U+LG5apjv53GYPeJI6PVdtZEfcpBMm6+uItBlrupq4LRLSXjPtr6wCAkVhCi\nY372qMl49z5c8CSfh42yTzMQslwMEzUDQd3kRR5ny9SWdg05CRwt8kss29iFBWs6oonVCHMkiibh\nIoJoWSfpi7fuMFeQQdDTGnNhaIqLQYQ5pidKClNTVavH1uVHF/78ash3VeQGgsVVoiVwynIxeJS8\nR5smx/fiPpnFRqhoFZOMpzCJsk57bT8Ke+8wOiGwbc0wEM45dgo++o6dUfIIW7r6ZiDIBc03378P\n3jmZb8uTZrw1CXEqY3EUl3x+Hzt45+g4GS2gRxuksVitkduFlyVzSbSU/ag/JLfCriWMjVKKSNG2\nyFGb5V7bj0qkRx/dUo7GoL9/8Uj8+ytHp9Y/610Pp50bbcgdpYjoXzCZ1i0AZgO4mjHWY57loCKV\nQdAHRjGoyxWcp4RPXffEUizf2GUICCXLICd3iayGZ/VNCl81z13PDLGZLFtdBQHZO5z99vSDcfkn\nGQ679IHktbLCHEW5m7s4/auzLNJQqU+D4Bl/l30PV//XIYmsj3LgUEWKqQZChotB3faYU+jmYCGP\n0UPTVMhnvePYFvzzvKMw87UNmLFofVyHDA3CQWIzmLfluH+A+H2MF0m1bBEi1k2hLG3sXCEq+/Nn\n3ok/zViC7cc0J10MZc3FUDbzRKiJlFrKZp4EFSGz5/dPgy9W8kHIc3jsMKYl1Ueshzmmue10A8aW\nMEo9v1TQQIgYBNg3FAPi9iwzKUq0NiWP+7YQk37l5ucMZX9RJDavEkZ/WsKvJsXwC0IGIr4A+urx\nUxMLFOkWkVqBY6Zuh55qYIiT5fVVY74aMMxcvAEA1zLIZqKOg7qBwKNTYiGixCjLOCbv95ip2+Ev\nnz/cep8Ad0uY959tIOw0lmuQjtt7UkJ0OdxQhEFYDKADwDXipw1AO4C9xf8OOUjLM6UPsEFkaYuV\ng+LbnLl4Ay6+e76hQUjbXCRLg1DyzcGu5BOaSr5gEEKjUcuJWQ6a0Uo4ZwtUzyPDQFIzC6a7GCoY\n1VyyrlIB+ySWBnVQVfddaC75if0DIuNHGfD3nMRD93bUQuH0eqnPa4LCIOhsTVkZ0G85+wj8S1t5\n2K5BAMaNaEplW/SVre8Rjtt7Eh7/1nuixDffP2X/1MFc3vcEMSinDZZZYkiJs4+dgoWXnIT37Ls9\nbjzrcDSX/GQUQ8nTRIq2TIq8rUVitxx/vS3ePw0kWLlqwNDRy10MacLVwhoErR+PsMTQq8/P98nq\nl5aQ71Oulj0yhbQSUtjsEyUn3rK9XxbZJwIAHv/We/DFd++Z+My26EhzZagumlrIsLmrAsZ4Zlc/\nwSjx+5JGy6mH7orbzn2X1S1W9uO9DnSj8ROH7JJwVUmKv9NgEOxhx7Z9RNIM8CJILEosLrsj95yI\n6d84Fp89ajJOOWgnnP++vY1jhgOKGAjvYoydzhj7l/j5FIB3MsbOA3DwINdvm0C6SNGkhoGkL9wI\nAbPs6wCYg3raxArwVbreSVrKvmAhAoShafVKZXCUU0D8HlkgI6B+/wkGwdjTQHTsSoDRLSVr5yz7\nplgpCzaXhi30SN/VEQCOnrodbj/3SHz+6D0Sx2a5GNQVpGEgKIPVEVMmYkpGghr92egDdDyAedbP\nd1X2YPjc0XskqN1k3fn5Hz9kFxw2eQI++c7dzLp4Zh4E27uRoaRp9W4q+WBCZCmjGNJ2c5QGhC1V\ntQrbPg1pBJqc6GWYY6oGQdNOqHsx6NDrn0aPRy4GywZbKiRtL9u451H0mf4oYgYhKYZN6x9FDYRd\nJ4ww9vCwuXrSrqPusREyFomOx49sSoizpYEgGYSspF6qSFE1GvfZYTRIY3ikO8DUICQZq+P33R6X\nn/aOaKt6FfJ9FX1mKlTXS5phOVXU+7enH4wPHrST9ZihRhEDYRQRRSOG+FtmRKnYT3FQUVSkGKW8\njfyNpphJ3wgpZhCSK+qsNu158QD6zsnj8fUTpkYhRJUUBkHuOqnvW5DlYpDQGZQiiZIAbvTYaOzW\nsl8oj4CEeqzs9FnRDDoOnTzBEBOZBkJcnurCaYn8yOI47bwsAaHp809+nxXFYEPaDo+y7gftMg63\nnnukdbCyuhgKDpyqsSqfTW+Nb7qlixTV3CDSxZA1mcqy9HeXlnnQFxNJLQjR0VPDqOay9T6ategL\nWz4J2a50Y9z2ThMuhpTQVwkZsSRXtYTY1ah7vCLxYikp4EsXKRaf7NLcf6JSANJdDGoUSC1k2NDB\np4qJI5uMxFkA8JN75vP/MwyEsu9Fe8vsNSlOymVjV9S9FZKTdbId7/X/2zvzeEmq8u5/n+67zL4y\nDLMywzCAM8CwDMOmgLJjYFARwQ2BQFB4iWsAzQc10Yj6xgWDEoxEMFFEo3F8IRCdiEQEEURQSICR\nRUFAFkUcllnu8/5RVd3V1VXd1X27+t6+9/f9fO7ndlfXqT51uurUc5512ykct2p+6vdF+7XjJzDY\nxEmx7rtGqS9Cnln2PcCPzOwHZnYD8N/Ae81sMir7nItME0PsQl0xbxonrl4ExDQIffUOWs8k6g9E\n115Sg9DoghsslysTzaEvm8s7DwvUWwOhH0PkgxCnXoMQaS6ar+STZ9+fI4oBAqEn7TQmDfSlqu0i\novK9le+L3aDR8dIm6Ky8AwBH7bpdzWSYHN+4piDuwRzlZo8mqTyTRbU/jR88mT4IGb991kSXN6tj\nncCSc1JLmhigmq44SJRU+8CINAgvbt5a5yyYxoubhyoPryj+PHMMQg3Cxk1b2bR1KPP6DdKhxwXL\nes1A5IyaZ4UZ1yA0imIoGVxx2hr+bPd5FT+b+HcnBdVoPAfKpYSJIXi9YMbEGifmVtTl9dq9wU58\n2AAAIABJREFUNA1C+gIhXuxqaCimQUiYyqKH+2+fDVzZBhvcH31lY9GsSXz6Dau4+OQ9K9vTyrTH\n79W40DFpoNYptdECJ+5U2ip5nRST3zXaaLr8c/drzWw5EOW8vDfmmPiZwno2hsgyMUQX3o7bTuHa\nv3xFZXt1lV5q6KAFMRNDwgeh0cp0sL9UEVqSF/LmrUEUw6SEk9Nvnnmhsg/ETAy5NAi17+M3bJZd\nHcg0MUwaKNfcdPOmT8CdSm79r52xH/c89sdKSty0iS1tNd1oVbf97Mnc8zdHseT8a1L7HV+xxasw\nRqvLgb4SL2ze2jSrYZxmD+SsKIa8znQReWLyy1ZfiyGv6rU2D0IwNlGGu2SipCjLIYSrv/5yUwEm\n0iD89K+P5NdPP88xF/93g74EAlwkaDd8QCRMDPHzPXinOfz++U089uyLuR4g5ZgGoVyyzJVyX7nE\nfjvMZr8dZnPVrb8O+5ytQYiO2V9ONzF846z9a/oXXTMD5RJfO3O/mpwddX3O8XtnOykmNAjheM+e\nMlCT0C0ZBdHYxBB89po9F9aEtsaTRUXEBZf4MScPljMFiSQVDUIbAkK8TR5t52gq8Rwnb7eWAzsD\nqwjSL7+1uC6NPbKisCoTRjIhRzhpbXVPjUlPO0bSSbHRNT3YV6qpex/RVza2hMVtMu2t4fYo4qId\nE8PEBiua+IN76oR0E8OE/nJNv3/4vlfytgOXVN7PmjzAYS+bW3lfo0GItqVGcuSfCJIP6wkxgSrN\nSa0ax96+ijfLJJVX9Z+pQcgxO5XL9T4IeSfO+G5V57FAgzCQiPKoERAqGfmamRgCk9iUwb5KcbOs\ney7IKVDOJSDUPlhrVdNXnLamxuEU4L/eczA/eO8hqccKwkTDY6X4FsW/JyI6hVKp+h1JJ+W431I8\nhXqkUZw/Y2JNYqXo+ItmTWTv7Wem5lCJ97mmbyl9TguxhloBYWjI+f3Gaur0modnSmKtLLLCo+O1\nVtL6NdhXqvgQTYyFQ0JjJ+us+blVms3h8e8abTSdGczsg8Dnwr9XAp8Ajiu4X2OKLBNDdE0kL46o\nyMrq7Wc1nbyjCWLqYFKDUL9vVIbZrJrhrCYcJyzhHAgIje3VkclhWo54/uTZxx0bG2kQsqIYkhqE\npDNnXQXB2DlGfUlTZUYJh/Ko+5L9jq8SJg/0sfPcqTWfR/1LW0385P2Hcs251WiGcw9dzq0fODTF\nSbG2XbUWQ+0xswSdrIVMHqElrehU3iktrs0aqJgYQqe0cm0Uw0C5KiDc89gfmTxQznRSjDa/tHmo\ncoxoRZqVewRqTUANHxDxMMeS1Zt4rPYBssOcKSzNcDqN50HIKuAVfRZRNSdYZayTJobop+8v1zo+\nZiUwivbZvkl69GRfoN5/BgIBJI3Bvmqq6kiDMGWwL4xqaU9AiJ9fvCJudH/H54oaE0O5xAeOeRn/\n+7dHBe1i19ukhiaG8HuH+fCe0CDRV/W7RqeAkKfKzgkEmoM73P1UM5sL/Eux3RofZK0A506bwLff\ncQC7LpieenHGcxREbfOE/V1z7iv49TPPA7EHZUKDECVKyhYQgu+LkpHkSfiTnKsb2fIbpayNHNgm\nDtQ6KVoiiU3ygRe3zaZpTirtKk5JjU6m2pc48YfHhP4S3znnwJo462hiT0vDO3fahJoJYpftprLt\n1AlNnd/iTm+12zMc9DK255mcSikmhrzpiWrD2oLfIqq1MSVhRoryJNz/xHM89+IWpk3sz3RSLIWC\n7oubt1au1+h6PGHvhZXUvlCbXTKu1m5sYoitWMNz+PY7DqjksGglDC7upFguZZfgjt930eVTsmpk\nR/1YWF07yDYxzgxDWePZPhv1OU5a9FF/ucSdHzyCVR/+z5p942GOUcRItJiIdzUpMDfKb5IUUPrK\nVjNXxeeOpImhVDImlMp1+zXyoRqOiSFOWmK6JKNVQMhjYnjB3YeALWY2DfgdsKjYbo0tsqIYGjnB\n7Ll4Jv3l9DS58ZsqapsncdCsyQPsESbQifoUn1j6yiW2bE13UoyI9o/yIkxqI8yxoYkh1p+k8BGd\n96SBct0E258QGOLUahDqNSfJ725HgxDHwpj0+MPn3EOXAzBvRn04FdROWqXEyrS6Pdmm3vYK2b4G\nWarSrGRQyf4ln015Va9pYW3PbAyqXU6f2F/jZzOhr8zWIa8Ism/df0nmCi6auP/44uaKVmrKYB+3\nvv9Q/nbtrnz/3QdxUFgeOPq9jaQGIbgWP3L8rnzk+F3rzrlyruHrPRdX1fKtCAjlUtXpsWTZjmvx\nY0XmhCAjYeQzlH5N5H2GvRjmjMhTor3RdRXNN/3lUqoWcaBcmyjp+U1bKqv1LA3CgTvOrss3Uvv9\nSU1ZqeYY8f5OznBSTB6n0fyVdR+2SqNaIhGj1cSQR4Nwm5nNIEiKdDtB0qSbC+3VGCNL3RnPrNYK\nA32BFzZUL+Jk9jbLqQCucVIsBZqJpInhW+84gNd+/sc1fc0zwUQkTSyTGvogxDUItZfnYH/g6Dex\nv1w3wTbyFG4mZCW/u1ntAmh90jh5zWJO2mdR5rHLKfbVZIKg5Eom2i8pgLbqpJjWpe1nT+Lhp5+v\n6V9WHoZm1KRaDifLyGlt+sT+mn71h6vCJ/4YCBDzZ0zgrkcaCzzPvbilsjIG2DZ8yOy47VRWLZzO\njfc9WR0r0jUIbw7LVa+YP41Zk4LVdU2YY8q5tiQgWDWGP48ADrXmhOP2WMAP73uKUw9cWrN/1MVo\nzwn9pYoQkMZxe8znX37ycKU8dyPqTQzVvn147Up23m4qa5bOSr+/Yj4IW93Z+NLWykM7y/x3wdEv\na3jvJa/faqKwSINQPVYQphpoYZIhr7UahOY+CPH77rp3viKXQB0nnwahpUN2jTxRDO8IX15qZtcB\n09z9rmK7NbZo5qTYqgqrP0WDkEvVn0LSSTGq2hjfvtfimZXX0Q38l4ctp68cZOzLy+Er5vK9e55g\n4cyqY1QjB7ukX0X0UJ840JeiQcg3ho1MDNGEk+fZn6atnz99QiVcK41Gk19t6Gf4v6kGIdiQtEtn\nrrgTxzt21Xy+e+dvU6txfvOsA7jnsT9yyuW3VvqS9HXI76RYb2KI4uKnT+xPzYMQ+ShMHuxrcD7B\n/z++sLkutLXuu2NDFF/RJRN9xa/1mkyKKb9dIwFh4cyJPPL7F6r9KFnN75dHg7DPkiCF76t22Zbp\nE/v5p1NW1+0fLQSiS+Cm817VUAO2dJvJ/PQDh2V+HqfeSbH6fu60CbyrQfa/pJPi85u2VFbrWRqE\nZg/SZERXpQppxQch3tfA+fWlLUP1GoQMX4UkaQu4Xbab1rCPaeSLYhidEkKuKAYz293MjiPInLij\nmb02Z7ujzOxeM9tgZuenfG5mdnH4+V1mtleztmY2y8y+Z2b3h/9nxj7b3cxuNrO7zewXZpatr+oi\nzUwM7WgQIqKJK48aK41aH4Tq6qNZ7O6yOVP41Il75JKOo9M/4xU78D9/cxSzJtcWM4rTXxPFUKul\niG7mwEkx0S5nfoFGAkL0e7TjgwBw3bsO4q4P1Vd4y0P8eFmmp6wVfNKenJ0oqXb7ESvm8tBFr051\nNJszdbBG+CunmBi2n5XtAV/b7+rr6HqLvNqnJTQIQRIjr/i4TGyQKCkaj7iJIUl0zlV3v9prPs8K\nEtIn8EYmwuveeRBfij3Qy2YsCgXjdx2+U+ZDIz4Wuy6Yzn0fCdJWZxF9dSQkzp4yWKmpMVzq643k\nn6eSToobX9pa0dakJUqC7KyMlf7UaRtrHX/jgmZfqRoaGg87htp7v9HvP5w8CDXHyalhGo3kiWK4\nHLgceB1wbPj3ZznalYFLgKOBFcDJZrYisdvRBCGUy4EzgS/kaHs+sN7dlwPrw/eYWR+B8+RZ7r4S\nOARor2xZh2lmYsivqg3+xy/w6J6IpPMoHj/v9Ra/KftLxnNhOeU84Yt5iR7KyYpzUH/u8XDBpFZk\nUszOnFxNtJKACNIFBK84heW4oVN+s2kT+lML9eQhzd7dNA9COGHvuO1U/us9B3PGK5bWHaumfZ3T\nY/7+RdX5ABbPmsSPz38VqxY1LwQFSSfFqomhXAoqkSbPfeuQ88LmrZRLQXRK1vlEh9281VNDS6F6\nznEzV1yozZMoJ4uoX2mT+5TBPpbEIhrKJWPm5AEeuujVHLlyu+ycFInrupFXP1S1UkVUDU5qyVpx\npItnUow0QlEIalqiJMjO/hiRVnMkfoyasNRytUR2cgzjwmQjzetwMilC8/OJM1qdFPM8BfZz9+SD\nPQ9rgA3u/gCAmV0FrAXuie2zFrjSg7v3FjObYWbzgCUN2q4lePhDkMnxBuA84AjgLne/E8Ddn26j\nz4XQLNVyXgFh1qQBnt64KdVJcd+ls/jcyXvyzMZNfHDd3bn7Fp8s+8qlSjnlPBkS8xKtbtKSPtUV\nn4nZh5POT1HzoHJb89XN2w9ZxuZEnv7op0hbwUX9zCUgdPiGTvOYz6tBgCDErro9Y3WaOF4rD5V4\nuWfHM8Pb0oibVvorAsJLTJ/YX2d26SuVeGHrVl7cPFSJ6890usxhS05qEKA2DW6z3P+NyIoiqXye\nCJOMk8fEkIfIFNNKbZK8JE1KeXxzIuImhuMvuYktQ87+y4Jy5VkCQpYWdO/tZ3L7w7+v+/5KQrlK\nuG9c0Kzm10j+xrXhkM3DXFtce1T46hn78oecpbV72UnxZjNb4e73NN+1hgXAb2LvHwGSNTPT9lnQ\npO1cd38sfP04EGXE2QlwM7semANc5e6faLHPXSW6//I+bKKHeY0GIWxrZhy7aj5f/cmvU9tmEZdy\n4w/Z5I3z4eNW8rvn2qvsHU3OaRNpo4dxctKP7K1J50VIf+Cfd9QuKftFk0q2gJDn5yjyhi5lrFyS\n/UpO4NEDP2tCSz6kWlnhBMdt75zjzaJJ/Jk/bUqNvCmXjGc2buK5FzdXrvesXCCBMBlMwFkTfVTc\nLH6P5T3vZqrhrN8popEPQ7Mw4ryc9vKl/Oj+pzh8xdzmO7dI/co7//UST7UchWhG7WvMmrExysoX\n8NUz9q1ETcWJBIbIhFCTPKlUddPOqsvRjOiya9c/YM+YP0szmmmKRoo8AsKVBELC48BLBGY8d/fd\nC+1ZDtzdzazy/AFeDuwDPA+sN7Pb3X19vI2ZnUlgzmDx4vqqdUWQzH4WkdfE8NUz9uXpP23iM9+/\nD0gPc0ySV9iPr6biE3FS9XrKAUvyHbABaSuyeOhXkuSKIXo7ZbD+wZI3hfFHX7MbX7v11yzfdkrd\nZ1FVwFy50wsUEJLOUZWMem1GEVT2T7TfbeH0pm3++W378ItHn635vrwRMhE12pHw9VMbN/GyefUO\nX30l48GnNvLgUxtZEGopsh6aNSl0Mx5eBy2fwyeuu5fX7LWAK29+GAhKZ+eh2fj2Nbl/4+edlHGy\nHgit/qa7bDeNH19waEtt8hI9WOdMHeSjx+/Kjin3TBbJctlQTUoUn3OsgZal2o9yXUpmqJqN0k0M\npYpw2KogXDlGRirzImkU5jkS5BEQvgS8BfgFkL/wOjxKbb6EheG2PPv0N2j7hJnNc/fHQnPE78Lt\njwA3uvtTAGZ2LYFTZY2A4O6XAZcBrF69ugDLXT3ZtRgaq1AjDghVc/9446+AdCfFdplYY2KoHquV\n1UIzorzpaTd59J1xR7Qd5kyuCbGLiMKLJg7UT66zczpm7bpgOh99zW6pnx280xy2mzaBtx+yrOlx\ninQqiiakaAW9JUyKlVXNMaLZxRztv2T2JG543ytz9eWVu2xbcZJr95zTvPc3bRlKjZ+Pl26OVM7N\nUi1Ddka8XRdM56GLXs3VtwUKSbP810qz840XX2rWPq/DX7PKld2k6uRX4oiV27Xcti6FfKRBKHdm\nbonWXZViTbHv6wudXSE9UuHf3r5/0xwukcDSLQfC9e85uBJiO1rIIyA86e7r2jj2T4HlZraU4OF+\nEvDGxD7rgHNCH4N9gWfDB/+TDdquA04BLgr/fyfcfj3wV2Y2iaAM9cHAp9vod8fJ9kEI/ud1UIlU\ncLVOisO7eCfUmBiyNQjDIbqR00wDkZp5c6z4ylVn7pc6iXz4uJV8dv39lfCvc1+1Y2U1uG0HJO+5\n0yZwy/vzrcaKNDFkJYDJqsWQl1JCI9EqcR+Elr43I59Amonh2ReqNtuKiSHjYXr4irl86UcPAs0F\n2vjYRanMt29QhwCaF9CJbpesh328faOaI3FaNTEUSeQztGJ+66F9ExJFuKAqxE0aDKKQ3rhmeBrc\nyCSYlmq5r2yVqzTNP2Pv7Wc1PX50uG6FIC6bk19D0y3yPAXuMLOvAt8lMDEA4O7fatTI3beY2TkE\nD+4ycLm7321mZ4WfXwpcCxwDbCAwC5zaqG146IuAq83sdOBh4MSwze/N7FMEgokD17r7NTnOr3A6\nlSgpemA0yu2dd/I+aKc53Hjfk7WZFHM4fbXDKftvzxU3P5zqMTxpoMzOc6dy1iE7VLZtOzX9Yb/z\ndlP5yulVN5Z3H7FzTX+P2W071u6xoGP9bkSRasfouoi0O5EpoN6hL2GCqfzPWJ0Os8/t+yDUruwi\n0tKDx4WBqg9C/fc++LFj+NGGpyoCQqOc+lDb9xmTBvjI8buy5+LGURjN6qBEmo2scWnkpJhtYhg9\nGoTFsydx6Zv3qmSjbIXB/noTQyTE9ZdL3PXBI9sOzY5IywYb0ReLfsmT7bURozUEsRvkGbmJBIJB\nPMDbgYYCAgSlogmEgPi2S2OvHTg7b9tw+9NA6jLP3f+FUVgnolkehLwSanTD5VvdNz7mF9+6d406\nF5IpSDtnYrjw2JW898idU29kM+P6dx2U6zjNJuzPv2nvtvrXDkWqgqPfebvpE/j6mftVBITkZZJ8\nyDQTDaPrrN3prt2VVK2AELvGUmzDnz5xDw75vzcAVRND2u9uVltdslnUTbRrJGS9OUcmwWanW6nO\nmCUgNHRSTG+TVZhqpDhq13lttUuW8YbaB3V8Vb/HohmZia4akVZwLqKvVC3g1u5cVgl7HmW/STfJ\nk0nx1G50ZCyT7YPQmgah3cRKaaQ5/vQXpEEol6yl1MyNjjMeiJ/nvjvMrrxuJb10Gmkhfy31q83c\n9PHd0zQEcZZsM5m3HbCEL//4oYoGJWu1nTdcDeIOlvnJG9aXmcgpxTkzolNhjqOVwb5SnaN01oP6\n2+84oKUQyojIJylVgxAbx+GaS8fKb9IOo0efNYbJLPecM4ohYn5Y6Gd2g0psUbnZvZqoT9Oo0SB0\nUEDoFN30Js7ivKN24cAdZzffcRhknWczASFqtjXrehumqrQa99/atJFlYoivIr/65/ty/TsDTVLk\nqxIJEFlharXFeZoICJUsmZ2/hvKYGJoJCJVsgKPIB2E4pJkY0nyQoLX8CnEiASFN0Owvl3jd3guB\n/Bk/k1TC40bBvDNSjL6nwBikU4mSLjjmZRy44zap0QAR++8wm/XvOZgdMurSNyKu9uxkoqROMRpU\nfW8/ZFmuKIfhkHU91KWXTjw44+WBGx233ax71cxy7bWDWnNBfGI/YMdtKq+jbJRRP7PSeccf9lmZ\nFCOih1An5YNKhtA8JoY6AaH+/aat7T8sRxuDfeVKXZeITmgR40SCcJovQ7lkvOuw5bztgCVtp56O\nFnbjWUCQBqELNHdSzPczTJvQz5/tPr/hBG1mLJszpa2JJn4jtKq+Fp0jMw1vskBV4kKI7Ljzpqfb\nc4c70c0MI0a2mTLYUrv415ZrTAzp11i00txcCY/tgAZhmP4XaUQCWbNaEcnXkKINijzxx4yAUJ8i\nOy2sdThs3RpFMaRpEAwzq6n70ire5PcdDzT8xcxsF4LUxpFr+KPAOnf/n6I7NpZo6qTY4qRQ1CQS\nvxHGykTVi2RqEJpUUzxl/yUsmjmJQ1+WXtxnuBqY7aZP4LMn7VHJy5GXmlTLcRNDhmYgim7YkqFC\njmzZ5RY0CNGl3dkVeuMVZq3mxDI/g6rAMFYWq4N9JV7cXHsy7VaczSK6PtIEyE5Eg1RSxI8Rs087\nZP5iZnYecDJwFXBruHkh8DUzu8rdL+pC/8YE2U6Kwf9WL8Cint3xfowVW2gvkqVRapaOtVQyDmuQ\ncrcTqtJ2wkhrNAg5BITIN6HyAIhpGr52xn4smzO57lhpERFxijQxZGZSzDjvNCIfhLESUjdj0gAb\nX6qaGE5es3jY4YZJoqFKM0F1InVx9feVBiGN04GV7l5TbSLMNXA3QT4CkYOhDBVCq9UcIwrTIMTS\nHuepYS6KIdPprWR8/HW78Y8/fIAHntrYsro8um5aTXQ0XGqcFMvpPghxotTkkYwaz/ux/7Kqg2gy\ntW4jinBSbCYgWAMnxazCQ73uMf+1M/bjoac3Ui5ZTWXWj702PXvpcIh+yzQNQifCRaVBaCwgDAHz\nCZIRxZlHaymXRRMTw3C8wjtJPO3xYJv5y8XwabTSf8M+i7n1wd/zwFMbWz/uCE10War2LAHhgGXb\ncMxu23HB0S8DsifoVm6bqjkvfxuA0w5cWsm8mKQVQatOQEh83j9GohhWLphWEeKiTJlpGTM7wcKZ\nE/nfx5+r0TBF5K3N0oiqE+r4XSw1EhDeSVDw6H6qlRUXAzsC5xTdsbFE3hz5eSlqkVEpj1outV0B\nTQyfolaRlcqfHXXVa05cnq0xMWTExU8cKNckvTIzjls1n32W1qbHbcWRttSmD8KFx2ZXuvcmUSM1\n35/43q2JAm7RIqHXNQjxuWywr8xHjt+VA5YVExb8j2/Zm3+/47epBY464WQdCYC9LrQNh0wBwd2v\nM7OdgDXUOin+1N23ZrUT9WTlQWhUBrkRRYVCRTd3f9noKxkzJ/Xz7sN3KuS7WmGvxTO49/HnRrob\nXaPZQyJakbUaTdCs+mBRxB+O/TUmhvyT+MUn71m3rZVkXuWKcNR58ghcyUXAxk1bEscI6HUfhGQa\n+DwZK9tl+9mT+cvDlqd+1gmzwFCTMNbxQMM7zN2HgFu61JcxS1YUQ7M46iyK1iD0lUuYGXdceEST\nFt3h6r/YPzP5z1ik2UPinYcvZ5ftpnLIzq3lyK+E+nV5vqsN96tub7cMb0QrXvGRyjnLH6gdKkqA\nPBqExE177Kr5XH/3E/zh+U3c9cizsd+mtx9GoyFXCXRIgxDlQRjH/ljj98y7yJBXHWmWxhIYVRNx\ntPYzFLUCjDQZo81Bsa9capgcaqzRbJKdNqGfE/dZ1PLDJHpQd3uVGr+8433O8kHISyvXRHRND3VQ\nzozu3zyjmbxnp03o58rT1jBv+oSaz7O0jaOdS964FyevWTTS3ajQCQEhul9GW32MbqJMil3AcQbK\nJT79hj1Yvf3MyvYhb8/GVZyJYWw4Sol0RmpxmiUIZPkgFEEkoCdt/8MhSkyVxwkvS0sYmSd6XY39\n6t3n8erd2yvsVASdMDG0WkxvLCIBoQu4AwbH7DavfjutaxCKMzFEPgijS4MgOsP8GcED7ejdujuR\nZ5kSpnQgLv6jr9mVneZObbrfQAEmhnNetZxl207h8Aa5JyKyIo+izdHn48mUViSd0IJG0/J4/kma\n3qFm9hzpjvhGULF5Wsd7NQZJmx7adYIpOpOiBISRYf17DubpP20q7PjTJ/Zz14eOaJqWuNNE11Py\nsu3EyuxN++ZzgotW+W/Zv3NOcwN9pdyJo7LMgtHDZ79ls7n1oWc4cuV2nereuKYTfgNzpgbmn141\n+3SCPDPFZ4DHgK8QPOfeBMxz9wuL7NhYwt1TJ8MojKbVibIoAaFSX10mhhFh2ZwpLGvN77BlpnW4\nYE5eLnrtbuwdM691mxmTBrj7w0cOu/Rvu2Td4lG2yJXzp/Grvzum58McRwudMDGcdfAOlAz2GsHr\ndqTJc7cc5+6rYu+/YGZ3AhIQcjLk6RqESDBt9VIuypa8zeQgbO6E1aPH2UiMDU5as3ikuzBiwgFk\n+w1tHQpyzvWVTMJBB+lEWueV86fz2ZPqw2vHE3lGcaOZvYmgJoMT1GdoPY3bOMbx1AkiUl21mqir\nKA3C4tmTuPODRxSW+UwIgCtPW9PlZM+jl2gcOuk8KXo/4dRoIY+A8Ebgs+GfAzeF20ROPEuD0Obx\nisz8KeFAFM1BOxVsR+khzn7ljtxw75OsWjRjpLsyJrjm3Jdz3xPjJ6la0TQVENz9IYKSz6JNnHSz\nQLu+LyrFLMTYYJ8ls3joolePdDfGDCvnT2fl/Okj3Y0xQ9O1qJntZGbrzeyX4fvdzeyvi+/a2CEQ\nBOof6tFqvdWMctKeCSGEKJo8yuovAhcAmwHc/S7gpCI7Nfbw1If6Xx21M+87cmcOX9FaaFOvp2MV\nQggx+snjgzDJ3W9NPJS2ZO0s6hkaSjcxTJ3Qz9mv3LHl48nEIIQQomjyCAhPmdkyQp86MzuBIC+C\nyInjHS2xKxODEL3B507ekyefe2mkuyFEW+QREM4GLgN2MbNHgQcJkiWJnLh3NneBNAhC9AbHrpo/\n0l0Qom0aCghmVgJWu/thZjYZKLm7YkhaJCzF0DEkHwghhCiahk6K7j4E/FX4eqOEg/YINAide6pH\nhWeEEEKIosjzpPm+mb3XzBaZ2azor/CejSHcvaOr/lbDIoUQQohWyeOD8Ibw/9mxbQ7s0PnujE2y\nEiW1iwQEIYQQRZMpIJjZ6939G8Ch7v5AF/s05nDvbBRDX7nEqkUzOO3AJR07phBCCBGnkQbhAuAb\nwDeBvbrTnbFJpzUIAN85+8DOHlAIIYSI0UhAeNrM/hNYambrkh+6+3HFdWts4a7QRCGEEL1FIwHh\n1QSag68Af9+d7oxNhtw7GuYohBBCFE2mgODum4BbzOwAd3+yi30ac2TUahJCCCFGLU3DHCUcdACX\nfCCEEKK3UMadLuC4KjAKIYToKSQgdIHASXGkeyGEEELkp1EehM8Rms/TcPdzC+nRGGSow3kQhBBC\niKJppEG4DbgdmEAQzXB/+LcHMFB818YOna7mKIQQQhRNpoDg7le4+xXA7sAh7v45d/8r5wp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R7JOFaljbtvAZ4FZjc/tZbohXEoml4bg9MJVhGdpifGwczONrNfEazEzs1zYi0w6scgVGMvcvdr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\nEtO6Lo9hAAAAAElFTkSuQmCC\n",
-      "text/plain": [
-       ""
-      ]
-     },
-     "execution_count": 25,
-     "metadata": {},
-     "output_type": "execute_result"
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "WARNING:root:2.0 oscillation measured for largest demod freq, recommend at least 10: decrease sampling rate, take more samples or increase demodulation freq\n"
+     ]
     }
    ],
    "source": [
-    "plot = qc.MatPlot(data3.my_controller_demod_freq_0_mag)\n",
-    "plot.fig"
+    "myintctrl.demod_freqs.add_demodulator(1e6)"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 26,
+   "execution_count": 22,
    "metadata": {
     "collapsed": false
    },
    "outputs": [
     {
-     "data": {
-      "image/png": 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gY8HqvoKH1FFZXqgJH/7k4u3Ya7sjf4B4xVqrMU75zSRc+eTSzOmtiXDnK1RY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l+F3XQ7QvW9lBExqw5c659/Nd9uhlNRrF5d4Du12M6hkR5AU4kKIlpNRA8R0QYiWqe4T0T0tfj+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\nRU5ErkJggg==\n",
-      "text/plain": [
-       ""
-      ]
-     },
-     "execution_count": 26,
-     "metadata": {},
-     "output_type": "execute_result"
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "DataSet:\n",
+      "   mode     = DataMode.LOCAL\n",
+      "   location = 'data/2017-03-07/#034_{name}_12-36-25'\n",
+      "      |                            |        | \n",
+      "   Setpoint | single_set                           | single             | (1,)\n",
+      "   Measured | my_controller_int_raw_output         | raw_output         | (1,)\n",
+      "   Measured | my_controller_int_demod_freq_0_mag   | demod_freq_0_mag   | (1,)\n",
+      "   Measured | my_controller_int_demod_freq_0_phase | demod_freq_0_phase | (1,)\n",
+      "acquired at 2017-03-07 12:36:26\n"
+     ]
     }
    ],
    "source": [
-    "plot = qc.MatPlot(data3.my_controller_demod_freq_1_mag)\n",
-    "plot.fig"
+    "data5 = qc.Measure(myintctrl.acquisition).run()"
    ]
   },
   {
-   "cell_type": "code",
-   "execution_count": 27,
-   "metadata": {
-    "collapsed": true
-   },
-   "outputs": [],
+   "cell_type": "markdown",
+   "metadata": {},
    "source": [
-    "# Create the acquisition controller which will take care of the data handling and tell it which \n",
-    "# alazar instrument to talk to.\n",
-    "myintctrl = ATS9360Controller(name='my_controller_int', alazar_name='Alazar', integrate_samples=True)"
+    "We can also chose to not average over records. You currently have to either integrate over samples or average over records (the returned data must be 1D)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Example 3"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 28,
+   "execution_count": 23,
    "metadata": {
-    "collapsed": true
+    "collapsed": false
    },
    "outputs": [],
    "source": [
-    "myintctrl.int_delay(2e-7)\n",
-    "myintctrl.int_time(2e-6)\n",
-    "myintctrl.num_avg(100)"
+    "myrecctrl = ATS9360Controller(name='my_controller_rec', alazar_name='Alazar', \n",
+    "                              integrate_samples=True, average_records=False)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "When not averaging over records averaging is implemented by averaging over buffers and records_per_buffer can be set independently "
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 29,
+   "execution_count": 24,
    "metadata": {
     "collapsed": false
    },
@@ -791,40 +678,57 @@
      "text": [
       "DataSet:\n",
       "   mode     = DataMode.LOCAL\n",
-      "   location = 'data/2017-03-03/#011_{name}_16-45-41'\n",
+      "   location = 'data/2017-03-07/#035_{name}_12-36-33'\n",
       "      |                    |  | \n",
-      "   Setpoint | single_set                   | single       | (1,)\n",
-      "   Measured | my_controller_int_raw_output | raw_output   | (1,)\n",
-      "acquired at 2017-03-03 16:45:42\n"
+      "   Setpoint | record_num_set               | record_num   | (123,)\n",
+      "   Measured | my_controller_rec_raw_output | raw_output   | (123,)\n",
+      "acquired at 2017-03-07 12:36:34\n"
      ]
     }
    ],
    "source": [
-    "data4 = qc.Measure(myintctrl.acquisition).run()"
+    "myrecctrl.int_delay(2e-7)\n",
+    "myrecctrl.int_time(2e-6)\n",
+    "myrecctrl.num_avg(100)\n",
+    "myrecctrl.records_per_buffer(123)\n",
+    "data6 = qc.Measure(myrecctrl.acquisition).run()"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 30,
+   "execution_count": 25,
    "metadata": {
     "collapsed": false
    },
    "outputs": [
     {
-     "name": "stderr",
-     "output_type": "stream",
-     "text": [
-      "WARNING:root:2.0 oscillation measured for largest demod freq, recommend at least 10: decrease sampling rate, take more samples or increase demodulation freq\n"
-     ]
+     "data": {
+      "image/png": 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23GTP7lLW49jo8UFiOKxzjOJQtp2GUfAzyyVMDSYxnG5tOHzu9gO49G9uq4nu\nNzUcNg2nsHk4iXy5guUVdE80aX6mVwWAFUfw7/EoDl6jxhR/2jExAKDWpbNctFxXBTOQ63I9iSAs\nRwdH6hiH7rkqOlMcNvoNUNgYeFOH+xnnYAyYjZ7KLIbDOsc7wTfKmJjOljA5mMRIOl6nJvg5OJvH\nXK6M/TM59zET07BlJIXNwykAWFGApDerIpNQE9RKGl1VHMZjx5aRiEZQsp0ao2Y+p17vKRNKuqw1\nHGwMJVVwpPm/11i2U1PHoXvBkZ0pDhv9BihsDIp29TrtZ2aFMRw2eiqzGA495o4nZ3HldXesWr6+\nVWE3N7+RmjCTLWFqKInhVAzLLSR5cw6zggeUq4II2DSUxBZtOKwkQLLgS8f0PtYJB2dzKFgVXLBD\ntS2Z8aw45v2KQ95nOGjFwfzfa/x1HCpdinFoW3FYhc9FEHrFWlEcyvp71u2Ov/1GDIcec+tjJ/Cz\nA3OYz62OH86qOJgcTAIIzqwo2w4W8hYmtauCGcg2keSNavGox3A4vlTExEAS8WgEW0a04bAqikOk\nKomv4Iu2R8c3vPgMlXHrL3gVIZVRYv4H1PtWsCpuOibQn34VrqvCLQC1NhSH1XIhCUIv8Bq4a0Fx\n2OgGtxgOPWbftHIBrJZFalUcTAwmAARnVszm1JdoakgZDgCauivMxGomY0AZDltGlHFiXBUraXRl\nJrH0KikOe44uIRohvGDXBIDa4Kj5fBkj6TjGMuo9Mq8vq9WFvisO2lUR7Xo6ZocxDr7rtOIwPnLz\nY33PlxcEL94g3r7GOFhiOAhd4MDsahsOjIkBbTgErJjNl2hyMIFhvbJuFiC5pCfPGlfFUsl1UaTi\nUYxm4itSHGqyKlZFcVjCaVMD2KZVhenl6tjm8xbGMomq0aQNB2MkDKfjGNaGQz9SMm1HuSriXS8A\n1abikFDj8d8A98/k8Ilb9+LmR46t7gAFYQUYZSwepb4qDqZC60ZvSS+GQw+pOIynZvMAgMIqlVku\nt3BVmC+RUhz0BNmklsOSlvanl0vusceXiq7SAGDFRaBcV0WsqjisxJDac3QJZ24dxlgmgWiEahSH\nhXwZI5k4hpIxEFXfoyVPg6u+BkdWGLE1GOOQapBVYTqZ9ru0ryB4MQuPk0bTfc6qUOMQxUFYNY4s\nFFyLdDVdFeOuqyLAcNCVFE1WBdC8yuRiwcJZJw0DUBNyya5gLleuMRw2Dadq2my3S8GqIBGLIBKh\nFfvSF/Jq3hpDAAAgAElEQVRlHFks4sytw4hGCOMDiZobx3xOKQ6RCGE4Fa9THPrtqij7syrWSIyD\nMRxKvs8lV1L/i6tCWEuYrIpTxjN9reMgrgph1dnnSXFcDcOh4jCYgYFEDKl4xHUzeJn2Kg4tXBVF\nq4KS7eCinSpWYM/RJZzQ7bS31CgOyZUFR5YrrsGQSahJO+z7oSrEVfc1sRhnblXGztRgsmY1vFiw\n3K6eoxmv4aB+D6fiSMejiEaoL8GRtqNKTke7nI5pVkLJFdZxWA+KQ7c6jAprF6M4bB/LIFuy+5ZG\nbBaGGz2NWQyHHnLAYzisxoVlbpDxaEStpgOCHqeXSxhKxpCKR10/f6N6D2bFvWMig83DSew5uuwq\nC5tHal0VM9kS7A5v0EXLcSXzdgsNfeR7j+HSj9zmTlwmFuPMrUMAgMmhZE030Pl82Q2MHEkHKw5E\n1Ld+FcpV0YsmV0ZxaNNV4WtAliuvbcXh4GwOZ73vppoYHWHjYxYTJ4+rOKd+Gbau4iCGg7Ba7F9l\nxaHsGg6EkQZVIad1DQcArp+/keFgJtXhdBxnbh3GnqNL1eJPHsVh80gKDnde2rVgVRUHN6siZNXG\nfdM5HF0s4g++8gAch7Hn6BImBxPYNKTGNzWYdOs4lOwK8uUKxrTiUGs4mBiHuP4d6086pq7jEOtB\njEMyFgndDyQaISRikTqDLlcyikP/5OBm7JvJwaowjiwU+j0UoYcYw/hk3aNiJVlfK0FiHIRVZ/9M\nDqeMqws7vwrljU1L7UQs0rCc9MxyyQ2ejEQIQ8lYoEsDqLowjOGw90QWh+bUDXiLLzgS6LwIVMHT\nOMmkIob9os3lykjGIvjB49P4xx/vw55jS66bAgAmhxKYyZbBzFjQCsyIVhyGPYbDkkdxAJTLYi0U\ngOpWjEPJar/LZToerYs9MYbDWlUcTInxbtXDENYmBVdxUPfXfigOdsWBEQzFcBBWjf0zOZylJ7nV\nkLKMrK1cFbHAoEev4gCoybOR4mAeH04pw8F2GD/ZO4NUPOJmZADVWg6dxjl4Oy4SkW6tHW6lPZ8v\n4yVnTOHlZ2/Bh296DI8dW64xHKYGkyhXHCwVbNdw8CoOSx7FIRWvpkH201URjxJi0e7HOIR1UxjU\n5+KPcaiuqHIr6FfSLUyJ8W51GRXWJkWrgniU3EVNPwxbb6OtjV44TQyHHlG2HRyez+OMzYOIRwn5\nVbiwTCe2WES7KgLSLJXikHD/98r1fszjI+k4ztIxA3fun8WW4VSNxO32q+hQcSh6XBWAcleETU+d\nz1uYGEzgQ689F5uHU7Aq7MY3AHCNpOls0S037Y9xYGZdbjruHjfUh9bajsOo+CpHdjPGoW3FIRGt\nd1V4lLK1qDoYxaFbyk0jilYF1972pARm9omCVUEqFnWL4XWiOFQcxnU/eLJjg9hrOEiMg7AqPDWX\nh8PAjsmBwJVcJ5iblHFV+A2ColXBUtGuVRyaTJBLblGkGHZMDCAZi8CqMDZ53BQAMDGQQDxKHbsq\nvMGRQPDKNghmxnxOBTuOZOL4+zeehzO3DuNiXTESUIoDAEwvl91JZNSjOJjW2qZPhaEfioPlVINb\nYxHTHbM7E0/Ral9xSMXrDYd8aW2U9m2EaaPeLQOsET9+YgYfuunnuPfgfE+fV1AUrQpSiSji0QjG\nMvGOjNpHjizig9/9OW57bLqjMZS9hoMoDsJqYAIjd04OIJOIBcY4HFss4uhi+KAu48c1WRXLRaum\ngdVsrlrDwTCcjjUsAOV1VcSiETxri1rJb/EZDpEIYdNQ57UcClbFDYoEgEwiGipYdLlkw3bYVRDO\nP2UM3/3dF2HrSNrdZ1IbSTPZEuZdV0VVcQCUsrJUtGoUB/P+9RLb/fyo+zEOdicxDpH6GIc1rjiY\nz7zTjJ9OMcZ4o/ghobsULcdVMaeGkh0ZtWbhkOsw/qzk6dApTa6EVeFAjeEQPFH+/pfvxyV/fRv+\n6cf7m3awNHjTMUfScTgcfGP3Kg7NXBVLBQvJWMSdYM7comIHtoyk6vbdPJzs3HAoV2omsaCVbRCm\nMdjYQKLhPlXFoeTGOHgVB0AZDstF2y01DSjFIVuywdy7lar38zMxDt3qVdGp4uA3HPKlCsYHOpeD\nu40bHNljxSGr5e1mxdWE7qHuKer6nhxMdpT1YwyH/Cq4KiTGQVgV9s3kMJaJYzSTUL7jAMPhxHIR\nzMBffPtRXPWZn+LQXL7pOc3EE4uSG7zovXHNuH0qwroqLLfWA1CtjbB5uN5w2DKS6jg4smTXGg6Z\nBu+HH7OaHB+IN9xnJB1HLKLq1S/ky0jEqh04jeGwkLewXLTqXBXK8OrdF77sfn7VrIpuTXidKQ7B\nMQ4nj6XdsuRrjXk3xqG3ioOZdBoFHgvdxZviPTWU7OjaNMZfp/cAcVUIq86BmRx2Tg4AaCzN50oV\nvOq8k/Dh152LR44s4dX/cHvTYCvjqkhoVwVQ24fCWzXSMJyOI1+uBJ53qVC7Cn/OthEAwLbRIMUh\nheNLnU0chbIvOLJdxSHTWHGIRAiTg+rGoYo/xd3ATr/iMJSsDY4Eetta23Y/P0LcxDh0acLrSHEI\nMOhyJRVUOj6QwPQarOXgZlX0ODjSNRz6UAtEqM3UmhzszFVhgiI7D45U35WhVEyCI4XVYf9MDju0\n4ZBOxAKzKrIlG4PJOK684GS866VnYCZbahqw53dVALU3LqM4TPiyKoDgldFiwXK3A8DzTx3DtW86\nH5edublu360jKWRLdts3SmbWdRw8wZEhFYc5bTiMN3FVAKaWQ8ntjGmoMxx8igPQ234VrmIUiSDa\n9XTMTus41Boy+XIFmUTUNc7WGkZxs3qcjpktWTXPL/QWr+EwNZREvtx+urBRHDqNTzBVI0cz8brv\nzUZDDIcekC/bOLZUxC6jOMSjdZUSmRm5so3BpLr4B5KtSzF7K0f620YDSnEYScdrGhu5HTIDJki/\nq4KIcPk5W91aB15MQOLRhfbcFVaF4TA6UxzcLInmhsPUoCo7vZAv1xhC5rXN5cooWJW6dExg5YrD\nN+47jM/8cF+ofd3g1pi3cuTaiXFo5KoYSMY6DkDrJmXbcW/+lX4pDk06zwrdwxscaVyz7V6fbnBk\np4qDvh+PphPiqhBWzoEZFatgFIdMMup2GTTkyxUwA4N65VvtFdD4AjSVI+M1rgqP4pCtreEAIHA/\nw1LBcre34iTtvmgnCwSoGkKpujoO4RSHaIRq3ClBTA4mMbNcxoJPcTAlt5+eV2MOUhxWGhX/tXsO\n48t3Hwq1r5tO682q6KLikOykjoO/AFSpqjisNcNhoVB1nfQ8OFJcFX3Fm6ll7nntKmJGNVqp4jCS\njm94V0XzO7CwKhyYrWZUADoY0DdRmpXSQFJ9JGHaTXsrR1ZdFbVZFd74BgBNW2svFe2aFXozXMWh\nzQDJUgPDIcyX1bgeWvVbMKth23Ew5gmkjOhCWYfnlSHnNRyMMbLS4LbZbDn0TcPrqjB1HLrXVrvz\nOg7M7L7nRnEYSMYwvVyq2dZvFjxN3voVHCmuiv7gdX9ODXWmOGRXKR1zJBNHwarAcRiRyNr4bqw2\nojj0AFPDYceEMRzq6zgYw2HQGA6J1oaD5XFVGKXC3LiYGftnctg2mqk5ZjggFsLsv1iwakpLN2PT\nUBIRAo622UzIGEx+V0XZdlrK9PO5ctOMCsPkYBK2w5jJluvcGspwMIpDkKtiZYrDTLYcWqb0uiqi\nEQJRN5tcdRbjYI4FlBulaDlacUig5HENrAVqDIceKw7LJcmq6Cc1MQ6elOx2cGMcSivLqhjV91hv\neuZGQwyHHrBvOofNw8kaNaFoOTW1Goy1O+hTHJrGOHhcFVHTwErfuJ6czmEmW8aFO8dqjgnKvgCU\nPFdxOLSrIhaNYNNQCkfaVBxcw8FXAMq7rRFz+XpDIAivymL6VBi8hoO/jgOwMsPBcRhzuVLbikNc\nr0piEerKhOc4jLJdW60zDGZ/Y7waY3dQxzgAaysl08TAAOh56WcTG9OPfidCreEwPpAAEdrO+ll5\nAahqcCSwOimZ+bKNE8udpb13EzEcesCB2ZyrNgDBE2XO56oIFeNg0vm0BO3tkHnn/lkAwIU7J2qO\naeSqMMeFdVUAwNbRVNsxDiba2F9yGmhd330+V8Z4CMPBW7diNF2vOJj33as4pONRRCO0ouDIhYIF\nh+HK+61wDQf9+UUj1JXgSBNE6w2SDYPfeDVxOZlEzBOAtnZSMhc8hkO3gkwbkRXFoW/YFQdWhd3r\nNRaNYDyT6Fxx6DTGwa4GRwKrYzh87PtP4Mrr7ljxeVYbiXHoAR98zTk16TnGcMiXK66hsNzAVdHs\n4jMdAE1Evrfz5c/2z2HTUBI7JmpdFaojJNW5KowhMdyG4XDSSBp7ji6F3h+oGge1MQ6xmm2NmM9b\nTatGGqaGqvuM+hQH7+vzxjgQ0Yr7Vcx6fKphXAO2p2Q4oGIdutEOuujGlbSZVZGoNejMSmwgGV2j\nioO6hgeTsZ621WZmVzFcLtmoOOwGuwrdp6gnbK/7s5OsH2M4dOp+MwrwiFEcViFA8shiEU8vFNZU\nLBEgikNP2L15COdsH3H/D5oocz7DIRUiONJ1VRjFIaX6UDAz7tw3hwt3jtddbESkqkf6FQftugjr\nqgBULYcji4W61fVvfu4uXPeDJwOPKQYFR4ZwyzAz5vPhYhymBqsFq/yGxkgDw8H8vxLFYdpzowpz\n03ArRxpXRZS6EuNgVkLtKg4p3+eSD1Qc1pLhUNbF0GI9DY4sWg5sh11jqtc9T57pVBcj1emskzoj\n2RWXnFbjMPfQ1Sg7nSvZbmO+tYQYDn3AVRw8raRdwyHli3EI46rQK9YR7ap4ai6PY0tFXLRrIvC4\noH4VxpBoz1WRRtFyaoLSilYFtz52Av9x39OBxxSDgiMTavzNvhxLRbWSa1Y10jCcjrnvSVCMg2HI\nZyQNJeMrVByqUnmYL7rtczV1K8ahY8XBZ7y6ikMiirFMYs2VnV7MWxjNqAZtvQyOXNZpfNtGVaaR\n1HLoLUGLkZUoDnmdEdEuJdtBIhpxa/CsRqOr7BrN1gl1JyGiMSI6m4h2EZEYGysknai/sOpcFe5q\nr1nJ6WpwJAC3tfad++cAABftHA88bigdr6tXUHVVhPdenaSbXx3xxDnsn8nBYeCx48s1PmdDcFaF\nes6gjqGGMOWmDUTk5nIHZVUAQDIWcSdsw2q6KsLcNCyf4tCtGAfjJms7xsF1VajjzeeTScYQjRAm\n1lgth/l8WRkOEeppcKS5ZraNKcOh2U1+PlfGe7/x0IZvgtRLigEB15ODCTddOAxl20HJdjCYjIEZ\nKNrtfz5l20HS0xtnNVSCtdo8raERQEQjRPReInoIwE8BfArAVwAcJKKvEtGlvRrkRiMToCbkSjai\nEXJz7c3vVumYEYLrTzUuiDv3zWF8IIHdmwYDj1MujeDgyLZcFaP11SOfOJEFADADdx2YrzumGhxZ\nW8dBbWv8Wufy4cpNG0x7bb+CYv73qw3msZUU8DFtzIFwMmXZZ/h1K8bBSKidKg7+4MgBt9DO2io7\nPZ+3MJpJIBalnvaqMKvC7UZxaHIN3bl/Dv9651N45MhiT8b2TMAtKhfzGg5JlGwndMMqo/huGk7q\n/9uf9Et2RTXVS7RWi8NiVL7F/DoxHAB8DcAhAC9i5mcx8wuZ+QJmPhnABwFcQUS/2ZNRbjAyCbPC\nrl5Y2aKNwWTMjUmIRAipeKSF4cCIecpBj6TjyJUruOPJGVy4oz6+wbuf/+Zm5FW/378ZW0fqq0fu\nPb6MCCn5/c59s3XHBCkOmQAFxo9RL8IERwIql3soGasrl20Mh6Dqk8MrVBxmVuiqUIrD6q+UO1Uc\n6mIcPIoD0FoOvunhY67R0gsWdFOzWKR9V8XdB+ZwpM2aJAa/4tAss8IoIdkOawUI9Zjr26s4mPvE\nXMisH7Oy36I7ATdTPxtRsmoVh9VQldadq4KZX8rM/8LMCwHb7mHm32Pmf+ru8DYmVVdF9eLMliqu\nm8Ldr0UPB6viuL58oOpmOLJYxIUN3BRqv/rgyMWChcFkrMYQacXkYBKxCNXUctg7ncWpEwN43smj\nrsvEi+uPTLSXjjmnux6GSccEgOedPIrnnTJa93hVcag3HFYaHDnbZnCk31URi3YnxqFjxcEoQeVG\nikPjlLf9Mzlcc/09+P6jJzoacyeYEuPxKLkZR2H5nS/ei0+H7DHiJ+uLcWh2kzcBzZ32QxDqCSpj\nP6ENh9lcOEXMGH+bteHQieJQrqiy7mEy4sKyVl0VLZeXRPQtAP8G4JvMnOv+kDY+mQApK1uygg2H\npsGRDuLRqqrgdTNctKuJ4ZCKu9kXRpVYKlote0D4iUYIm4dTNdUjnziexembBnHmliF84ta9WC5a\nNW6BolUBEWoMnjBfNBPjMBoiqwIA3nnZ7sDHW7kqsiW749Sn2Zxa8c7nrVA3DX8dh9gai3FIGXeZ\nbQwHrTgkvIpDOfD9yrmpbb254TEzFvIWRkxwZJuuilzJ7miVCVTLvJ8UwlXhKg5SKGrVCMqqMC7N\nuVx7ioNxVXSqOCSikdB1aVphVRw3I2qtGQ5hliAfAfBCAI8S0deI6HVElGp1kNCYIGk+V6q40biG\nVIvmT8pwqHVVAGrl/Owtww2PG0nHUfZclIBucNVGRoXhpNGU26/CqjjYP5PD6ZsGcdGuCTgM3H2w\nNs6hUK4gHY/WTDShFId8GTFdHXMltFIcHEZov6if2WwJ28dU3YwwMqVbcjpiXBVrLMahro5DBYlo\nNah0ajCJcsUJzCIwE2Sv2gvnyxWUKw7GMglEOwiOLNlOx++9MQK2DKcQjVDTrIqy66oQw2G1MNd3\nukZxUAbAbGjDQU3Mm4e04tDBPaBkV5CMR+pcfJ3iVaXWWmGxlncSZv4BM78NwC6oAMkrAfROf9yA\nBK2wsyXbLQbl7hePtoxxiNe4KtSkeOGO8aYFaIxLw2vFLnZoOGwdSbuGw8HZPGyHsXvTIM47ZRSx\nCOHOfbXuioKnNKzB/N8qxmFsoHWDq1YMNzUcVtZaezZbxnbt527HVWFUo9hai3GI1cc4ZDzGrVsE\nKiDOwUjyvcoeMOWmxzJx7aoIbwTYFdUnpdxhJkbWk0o9nIqFclWI4bB6BBWVG9dZVfMhDYd6V0UH\nioPOqkjGIiBaueLgvUbWo+IAIkoDeC2AawD8AoDPd3NQG52E7i1RG+Ng101mqRAxDl5XhVlNN4tv\nAIJbay8V7bYyKgxbR1M4tliE4zD2nlgGAOzeNIRMIoZzt4/gZ/trAySLllOzMgDgZpM0zaoIWW66\nFUM6lTDotbqttTvIwy9aFSyXbNdwCJOOaVcckCcrZq3FOET05+LNqhhIVK/RySbNhMo9VhxMLZHR\nTKLt4EijvJVDNCV6+7/eW1ejZLloIR2Pqvb2AYHHXsz7IjEOq0dQbZiBRBSJWKRtV8VmN6ui/c+n\nbDtIxCIgopbxae2MCVDl7NcSLe8kRPQVAHsA/CKATwA4jZnf2e2BbWSICJl41OeqsGtuykDYGIfq\nR7h70yDe+4pn4w2/cErT5w/qkLnURmdMLyeNpFGuOJjNlfHEcZWKedom1Zfjol0TePDwYo2BVPS0\nv/WSbuGWmc9ZNS2yOyUSIXzk9efijRfVv0dbArJEwmIkUeOqCHPTKFcY8UjEVVG6HuPQZndMQH0u\nJjgyX7ZdNxvQvH2x66roUVaFMRzc4Mg21ANjMLRybzAzbn74GH74xHTN49mS7RZuC6rK6sWy1ee7\nLIbDqlEIyKogIkwMJMK7KnyKQyfFm5TioMaQaXE/C0NunSsO/wRlLFzDzLcy88btFdpD0olaoyBb\nrN58DEpxaPx2l+1aV0UkQnjri09za6U3IqjR1VLBaqtqpMGbkvnEiSy2jabd4LkLd47Ddhj3Hqwm\n5gS5KgDUGVJ+5vLlUMWfwvDq87Zj11R9jYtTxtWkf2gu3/Y5TUbF1pEUiMJJ9LZPMYp2qXKkURyS\nsfYUB6A2sydXrripmEALxaFDV8XX7zmMO56sT+NthXFVjJp0zDbiFUohDQdTWtr/epeKtht7E1SV\n1Uu5UhtoKqwcc336r+/xgURbigNR9ZrupENmya64Y0jFqwZ3pxj3SToeXT+GAxG9EACY+WZmrnsH\niGiYiJ7TzcFtZDKJ6kTJzMiW7fqsikQUpVauig4mg5NG1WT/5AmVJFNxGMulDl0VI0qaP7JQxN4T\nWezeXJ2QLzh1DBFCjbuiaFXqXBVA60DQ+Vw5dA2HTpkaTCIZi+CpjgwHdYOaHEq2VIoMVsWpSX9V\nE143Yxw6NRx05ciS7aZiAsBoOo5ohAIVh7KeuNt1VXzgO4/in368v+1xLngMh2iUYLURK2IMK6MG\nNMIodP6OoNli1c04nI7VVWX1YgIwxXBYPUpaxfTHP423oziU1P03FY8gQtW+LO1gXBVA61T6MJiU\n0G1j6fVjOAB4LRHdTkTvI6JfIaILiejFRPQbRPQvAL4NIN2jcW440omYazjkyxUwIyA4MtL04rMd\nB/EOuvBtGkph5+SA23rbyHQdBUdqI+TphQKenM7WVKscSsXxnG0j+KmnnkPBqtRIioZMovFk6ziM\nhYK1KjEOzYhECCePZzoyHMzkOTmQDC1Tln3Brd0qOW1WQp0ElqY8RlC2ZLtqEqDer0a1HIzi0Mzw\n9bOQL2M+b2EuZO69F9MZczSdQDzSXuVIN8ahhdFmXBD+17tctEK7Ksz7spJCY0IthQaLEaU4hLuW\nvAX4BpKxDhUHxzXOW7lew2CMy22j6fWTVcHMvw/glQCOAng9gL8A8C4AuwF8iplfzMx39WSUGxA1\nuagLw98Z09CyAJTPVdEOF+4Yx8/2z6HisGvNduKqmBhIIBGL4K79cyjZDk73lbm+eNcE7n9qwX2N\nRcsJjO5vtkpfNg2uuqw4AMCp4xk8Ndd5jMPEYKJlUKvBrjhIeFwV7WYDhKVktW7x3YhUPOKuyPPl\nCgZ9KcPjA0nXTeDFdVW0EeOwf0YpYGFXiV4W8qoOSiIW0XUc2lAcrHCuCqMkzOVKNQZetmRjKKm+\nO61dFTo4ssOaEUI9hXKw+3N8INFW5Uhz/x1IxDpSHLwxDqmQqmMzTBzMSaNKcQjbd6MXNJ11mHmO\nmT/DzG9h5l9m5lcx8x8z8497NcCNitdVkW1gOKSarMIBdRPqxFUBqAJRS0Ubjx1b9vSpaD84koiw\ndSSFnzw5AwA4fdNQzfZLzphCueLgJ3vV9mIDxSEVjyLvmWy9pYqrfSpWHhzZipPHMzg0l2/7Szqb\nLSEdj2IgGWuZRmvwuyq6rTh0gjcWR6Vj1l4jyVikph6IwUzCpTZcFa7hEPJm72VBN7gC2jfAzLXW\nKqvCfE8cri0s5I1PGk7HUbKdhp+/m47ZB8Whl42/eknRrs/UAtSiJleuhPouegNcM8loR4bd6rsq\njOKQWnOttaXTZZ/IJKKuVdvIcEjHoyjZTsMWr5ZvxdoOpuX2nftnXRmsE1cFoAICjfTqVxwu2DGO\nwWQMtz6mSn8UyhW3IqGXjI7ez5Zs/PG/P4iz33cz7n1KFY8yN2l/p8tucMp4BtmSHTqoyjCbLWNC\n546nE80DPQ2WwzXBkarJVXdiHDpVHGqCI0uVmhgHQPXZCJpwO1EcDmjDIVuy2w6qnPcYDu2mY4bN\nqvDKxd64jmVvjIP+3cgV0a9eFT/ZO4Nz/8/3Qtc1WE80VhxUoGOQIuZnuehTHDotAKXvbc1cr2HJ\nlmwkYxH3daylOIeuGg5E9HIieoyI9hLRewK2ExF9XG9/kIjOb3UsEY0T0X8S0RP695hn27lEdAcR\nPUJEDxFRioiGiOh+z88MEf1dN193GDKJGPKWKcurfvtjHMyXodHN16o4iEU6+wi3jaaxbTSNO/fN\nuSupTlwVgErJBFQOtP8ciVgEL9o9iVt/Pg1mRtEOVhzS8SiOLhbw8r/7Ib501yFEiPClnz0FoFrE\npdsxDkA1s6LdOIeZXBkTOiI7rExp2U5PYhyKVueKg3G7VBy14skk6hWHoNiATuo47JupVrRv110x\nr/tUAKoeRjsGWDWrovl77zUGTJyD46jAZpNVMRyQseSlX70qnpzOomBV1lQb9NWiZAeneJuy02EU\nLG8dnUwi2naBLsdhWBV2XRWrVcdhKBULzILrN2HqOCTDPBawTxTAJwFcDuAsAFcR0Vm+3S6HipnY\nDeCtAK4Ncex7ANzCzLsB3KL/BxHFAFwP4BpmPhvAJQAsZl5m5ueZHwAHAfx7q/F3G68EbGRLfwGo\nVqWY7Qp37KoAlLviZwfm3Bz4jhUHHSDpVxsMlz57E44tFfHo0SW35LSfdEJFo0cjhK/+zxfgiued\nhBsfOoZCueKuGMK21F4Jp0x0ZjjMZkuY1ONrx1XhNRy6VwBqZYpDsVxxb4L+suiJaCTQHdFJOuaB\n2Zzb8GumzXbdylWhDYe2gyO1q6JljEO94pAr22CuVh0NqpHixRg0BavSlQyaRhijJ8ittN4plIMX\nI0YBDKMeZr2KQzLWdq8Kc+0YV0UqEe4e0GpMA0mP4bCGWmuHmXXuCPmYnwsB7GXmfcxcBvAlAFf4\n9rkCwBdY8VMAo0S0tcWxV6BaufLzAF6l/34ZgAeZ+QEAYOZZfxopEZ0BYBOAH4UYf1fx1i0w/rSg\nktNA42JCZV8dgHa5eOcE5nJl1yXQSYwDUE3J3O2LbzBc8qwpAMD/v+eECiAKmMTeeOEp+KNffha+\n+7svwgU7xvGa87cjW7LxvUePVcsJ98BwOHmss1oOM9mSe6MKm1Vh17kq1p7iYKLD874GV4ZEA8Wh\n2qsi3M2TmbF/Ooezt40ACN/V0LBQsDBmXBXRSFvdMcPWcVgqqFx/oGo4eMtNA8FVWb2UPQZNpz1R\nOqFqOKwdP/lqUbAqbnl0L+00ulLBkeqz87qRw1LypTyHTcluRk4HbK4rxYGIthDR8wGkieg8Ijpf\n/4i+8SQAACAASURBVFwCIBPi3NsAHPL8f1g/FmafZsduZuaj+u9jADbrv88AwER0MxHdS0TvDhjT\nGwB8mRtEvhHRW4nobiK6e3p6OmiXVcNMLszsKg5BTa6AxnKvv612u5jS1LfsOYEI1cdYhMUUgWqk\nOGwaSuG520dw48PHACBQcThn+wjefunp7sR00c5xbBtN4xv3PY25nIV4lOr8690gnYhi01CyLcWB\nmXWMgxLiwsqUZdsfHNmebz4sK1UcClbFneTqFIdWMQ4hXRXT2RJy5QouOFV5Hv21EpphMoNG9Q02\n3mYhLTerosVqfLmoUoJT8Yjrqlj2qYUjruIQvGIteybuXvarME2c2glWXS8UrYp7r/RSba3d/Fpy\nHK4JjhxItJ+OaQwyf3BkmCDrkl3Btbc9WWfUmf5F68pwAPDLUJ0xtwP4KIC/0T/vAvDe7g+tNdoA\nMJ9MDKqL55v071cT0WW+Q94A1SK80fk+zcwXMPMFU1NT3RiySzoRA7O6sZpAKZPS5e5jYhwaTEL+\nJlftcupEBpuHk5jNlTGcjnfcQOrc7aM4Z9sIXnj6ZMN9Ln32Juw5ugRA1adoRSRCePV52/DDx6fx\n+PFljGVW3uAqLKe0WcthqWDDdti9UalsmNY3aNvhGsNPSezdCI5cWYxD0XJc47ZOcYg2MBxMVkXI\nFe7+aRXf8As7lOHQTmbFUsECczV4NhaNgBmh1Zvwrgobw+k4JgeTrmFjDIdBN8ahvoGcF28cRS/j\nHDayqyKo/w2g1J9ohFrWcjBGwpDXVdGu4mD7FIdEFA6He79/um8OH7rp53UVU1Wa7zozHJj588x8\nKYC3MPOlnp9fZeYwMQJPAzjZ8/92/ViYfZode1y7M6B/m06dhwH8kJlnmDkP4EYA3mDL5wKIMfM9\nIcbedaqttW1kSxYiVN+EqJWrQqXzdT6ZEhEu2qmyKzqpGmmYGkriW+98IXZMDjTc5xefvcn9O+zq\n99Xnb4PDwK2PnehJfIPhlPEMDrVRy2FG35gmPYpD+HTMXpScXoHioK9T4zrwq1KtXRXhJqoDs8pw\nOGvrCNLxqFvCOwxVV5ZxVVDNGFrhDY5stkJcKlgYTsUwNZT0KA7qZj4U1lVhO25Ts54qDhvdVRGw\nGIlECGOZeEtXhd/dNKDTMdtJyXYNh3i1jgMQzlVnrnX/OHNacRhKxUC0tlprh1mGPEdXj6z5CXHc\nXQB2E9FOIkpArfZv8O1zA4A36+yKiwEsajdEs2NvAHC1/vtqAN/Uf98M4BwiyuhAyZcAeNTzXFeh\nidrQa9Ku4VBBrlRxq5bV7qM+nka+spW6KoCqu6LTjIqwPOekkerEGtLlcNrUIJ538iiYsWp9KsJw\n8ngGRxYLobolAtXVsZuOGY8iH+LGU/ZlVcSjXYxxaLMzpsGkzpqbWsb32SVj0cD3qdRmcOS+mRzi\nUcK2sTQmBsOXCgaqnQO9wZEAQhth3lVhs8yK5aKFoZRRHGpjHExwZCoeRTIWaRocaWIxelnLYWMr\nDsEB14AuO91CvfIX4MskYqHVAoPrqohW0zGBcM3uZhoYDsZVEdHdfNeF4uAhCyCnfypQmQ47Wh3E\nzDaAd0BN6HsAfIWZHyGia4joGr3bjQD2AdgL4DMA3tbsWH3MBwG8lIieAPBL+n8w8zyUS+UuAPcD\nuJeZv+MZ0pVYQ4aD98LyVi3zkmqpOKzMVQEAF+9ShkMnnTHbIRIhXKqDJNtZ/b72fBXa0mvFgRk4\nPB/OXWFWDBMDVcPI4dbSt99V0c0Yh6BqnWFwFQd98/UH8DaKcTATsO1wKPfL/ukcTp0YQDRCmPBM\nzGEwfSrcdEydohzW7VNrODQ+RrkqahWHrM9VAajMimaKgxlnT10V+rk2WowDMzcsOQ2o+0arOg6u\nu8mjOADtfT5lV3GoxjgAjRd9Xsx3y28se1NEW1Uk7TUtZwtm/hvv/0T0EagJvSXMfCOUceB97DrP\n3wzg7WGP1Y/PAvDHLpht10OlZAZt2xVmzL0i41EcTNqNn2YxDhWHUXFWbjicNjWIycFET1b0l525\nCV+957DrSwzDK889Ce//9qOYHOyh4eBJyQzqoulnRn/hzRhdmbLcfML2uyq6GeMQJOWGwbwW447x\nKw7GVcHMNYqZNwiwaDsYbHGdHpjNYceEcnVNDiRwdLEYeozzOdOnolo5Emhdl8Hgle+bGQ7LRQtD\nSaU4zOXLsCtOXXAkoLKTlgoNgiMrDk4aVFlIvWyt7QZHbjBXhbr2GreMnxhIYs+xpabncFUjj+IA\nqIJnE62//gDqYxxaLfq8mHgZb3lsu+KgaDkYSKxTwyGADFTMgbAC0nH11ufLNnLl+pbagOfiC7Ba\nzQ1uJTEOgIpz+Merf8G96XaTl521BZ984/lu1cowjA0k8C+/eZFbmKkXnNpme22jOBhVxKsmjaDx\n+2oHNLlyWEV5RzpoXtaIFSkOcZ/iEFAAClA3cO9zeFWIolVpmrHjOIwDs3lc8iwVBzMxmMDDRxZD\nj3Herzjo9zR0cKRnFd5MJVoqaMVhMAHWZaeXdTtm7/syko43dFWUbceNxZDgyJVT1EHIzRSHljEO\nblabyarQikMbmRVBwZFAyBgHbZR7FQfTGXPQozgsrCfDgYgeQjVzIQpgCsD7uzmoZwLu5FKu1JSs\n9dIsONJI2iuNcQCA5508uuJzhCESIfzKuVvbPu7iNgyN1WBqqL322rPZMsYycXfCahXUavDX4TB/\nV5gRweoYDo7DKNtOx4pD1VWhFYeAAlCAmhC9hoN3td/q5mniSVzFYTCJ2Wy5TsVoxEJeBReb75CJ\ncQgbHOk1FhrFtVgVBwWrguFUHFNDyiU1nS2pzpiJWI2hN5xuHJBnVRzXwOw0xuGb9z+No4tFXPOS\n00Lt70353miGg/mONXJ/jg8ksJC3YPv6wnhZ9sc4JKuLurC4rgpP5UgAobKrjFHuzf7Ils2Y1HlG\n0nEcWWy/+V63CKM4vNLztw3guI5BEFZApiY40nZrIXhJNwmwMTnnKykAJQRDRG2lZKriT9Viqs2U\nIi/+ypHRSHWl3GESRB1mUlyx4pArIxahOkPV5K37J9xaxaH5zdM0t9qps3ImBpOwHcZSwcZIprUS\ndmypiKmhpDt5m/c0dHCk5Y1xCD7G65IwQb7Ty6WaBleG4VTc7bvhp2w7yCRUF89shx0yv3Hf09h7\nIhvacChajvtetNPmfD1gjFITSO7HBCzP5y3X4PPjr9zrKg5tpGQaF5C3ABQQzvgwRvm8pzJkNXam\nWpF0XWVVMPNBABNQFRtfA+Ccbg/qmUDaoziY6Fk/5iIMuvGa1dRKSk4LjTmljfbas9myW8MBaG7w\nefG7KtrNBgiDmfBWHOOwXEImEa1TAIzh4F/JelfxrRSHAz7DwcSKTIcMkHxqNo9Tx6upwCbdMXxw\nZOsYB28jODMBzWTLgWqhclU0anLFSMQiGEzGOnZVLBastppVLZeqE85GVRyauSqA5tUj/b2CTIxD\nO4qDMT4TPldFq3sAM7sxUt4U5OqYqorDWmqtHaZXxfugSjtPAJgE8Dki+tNuD2yj4704G2VVEFHD\nmgDmxrzS4EghmHbaa8/kSu4qFAgfUV0OqOMAhJ/wWnHPwXlc+SlVHf5ZW4LLgbfC3ABncuVA49br\nqvBStqupwq0C8vbN5JCOR7F5WL2HJjslbC2Hp+byONkTA9N+cGRrV0W19Xy8VnEI+O4Op2OBN3lm\n1u6pCAaS0Y5dFYsFK3S7aKC2OddGNRwaBUeaxnjNSphnSzZS8Yh7LzWfZzuKg1/ZCxvjkC3ZKNsO\nhlKqV48xXP0poiPp+JpqrR1m1nkTgF9g5j9j5j8DcDGA/9HdYW18Mm4ATsWtSR6EtxmWF9PER1wV\n3cG0154P0VjG21IbCB/jYPvqcBgjYqWKg1Vx8KGbfo7XX3c7yraDf/3ti/BfTmtc1bMZRnEo206w\n4eAJjvSPwazEW7kqDszksGNywFUzzHsZppZD0arg2FIRp05UDYdYpM3gyBDpmF5XxUAyhkwiihkd\n4zDkK542nIqj4nBda2ZjyCRjEQwm4x231jbqx0LIpkfZDWw4FFspDiEaXamW2tXP0MTxtKc4BLsq\nWi0eTHzDGZuVYW+UJH9RqrVWPTKM4XAEgNcBn0R9BUihTZKxCIjUheJwfX68oVHfA0sUh65iJqKD\ns8G+akO+bGOxYGHKqziEkCkrDsNh1LRFb3fCa8Q37nsa1972JF73/O246fde1LHRANTekIN6hTSM\ncag4bqfIVorD/pkcdnmqjrqGQwjF4fC8cid5s27cypEhG115/f6NVAqvqwJQAZzTyyUsl+pjHEYa\ndMisqoSEwWTUTZFsB2Z2J49W9QkM3gqVGy0ds6XhoF0VzVw73noJQDVDpp0mZMYgS/gNhxZGs1FC\ndus+P7M+w8GbjgmsL8NhEcAjRPQ5IvpnAA8DWCCijxPRx7s7vI0LESETj+KELiQTlI4JKN900AQk\nroruYiaiVgGSDz+tcsTP3jbsPubKlE1uPNUYldo6DsDKFYfDc3kQAf/3NefWrYbbxXtD9vepAJrE\nONiO2221meJgVRwcmi+48Q1AVV4O0+jKpMzWuipMAahw72O5Us06aRjjUKw1HKaGVJGqbNGuq0sy\n3OAmbwKaE1ET49D+JF6wKq5xEzbOYbm4cWMczLXVKKtiLNNavfIrvqm4WtTlOykApb8P5ncr1XF6\nWY1rt1YcjDLiD9hca621w2RVfEP/GG7rzlCeeaQTMbcC3WAy+MJPJ6KBE5C5eaxGOqZQz3bdXvup\n2eaGwwOHFgCoRl+GMK4KM0ElfHUcgJXHOMzmyhhNx93zrQRvcyx/Z0zvdr/ioFwV6mbXzM97eL6A\nisM1fU5i0QjGMvFQrbWNYedVHNoOjrQcDCbjKFqlhjEO/kJPk4MJ7JvOBQZHVvtV1E48ZU9A80Ay\nhoMtrq0gvMbIXEjFwTv2jVY50rgCGikO8WgEI03SYwE1SXsNByLCQCLWliuppHuQmJTPSISQikda\nxjiYa/yMzbWKQ84XsDmaWVuKQxjDYZSZP+Z9gIh+1/+Y0D6ZRBQnllWFPH9hHUMq1txVsdICUEIw\npr32oRZlpx84vIBto+ng4MimhoMy/GKeyX21YhzmcuVVK9FdvQE6gYpDskGMQ8l23DLmzRSH40vq\n+venI0/oWg6tODibRzoeraks6gZHttEdcygVw0y21LAA1FLBAhEwqN+DqaEkbn9yFgWrUuMfBzyu\nCt9NvmxXVcKhVKyjypHeiSO84qCeZ3IwueFcFW4dhwbpmIBqr91McVgu2dg+lq55LJOIthfjYFfq\nFnGmZ00zzDW+e5NWHEwPlLKNRKwasLkeXRVXBzz2llUexzOSTCJaVRwauCrSCYlx6Bdhajk8cHih\nroCWmUz9wXFe7IB02tWKcZjNld3MhNXAGEJBikMiWg2e9GJVHLdNfLNVl79BmGFysHVzIkApDqeM\nZ2rSRKvvY/heFWbF2dhVoVwSplbE5GAysNw00Li1djXyPoKBRGfpmF6pei4XMjhSP8/EQGIDuiqa\nF4ACdPXIJtdStmTVuZsGkrG2YhzKtlPXSC6TiLUsADWbLWFEp/gSAXP68/W7wBq5v/pFw1mHiK4i\nom8B2ElEN3h+bgUw17shblwyiaib790oqyIVD86qsNysCjEcukWr9tqz2RIOzRVw7vaRmsfDyJSu\nbB0JclWsHcUBqBoOzWIcgtIxXcWhySrXSLV+Q2diMOn2x2jGobm821vEEOsgHbO14VCbPeEtJhRU\nAMoc48Vr7A8kY8iXK20bid6yw2GDI5eLFlJx9Zwb1XBo5KoAWpedDirilUlE24pxUGXda+/FYVwV\nMzmVkRWNEEbTcbd6ZM5X22coubZaazdzVdwO4ChU7QZvo6tlAA92c1DPFLw34obpmPFo4JfdG2gl\ndIeTxzP4xv1Pq5oEAYW2Hnxa9VN4bkDJbrXaaO2qCAqOXKniMJ8rY3zn6hkOKR3s2TSrolJ9rXbF\ngcPVqnfNXBUz2TKI4LaaNkyGaIfMzHhqLo8X7q7NGmk3OLJkVdyJw7IbZVXY7qoPQI1ratg36RgF\nwh/jYM6d0K4KQPVDGG4jgNWsOONRaiurYigVRzIWWVHlyMePL6PiMM7cOtx65x5RsCqIRqjpAmpi\nMIF7n1oI3MbMgQX4lOLQnuHgv0c0Uou9zGZLmNRGs9fA8dcHWWuttRsaDrpi5EEAL+jdcJ5ZpD03\n4maGQ7MmV96JR1hdTp2ottcO6pL5wKEFRAg4Z9tI3bZGabQGO8DVFG0zjTAIx2HM52srWa6Uqqsi\nnOLg1iuIR1pOVrPZEsYyibo+AhODSSwWrIZGG6CMjoJVqWuA5io3Id/HcsVxZeFGMQ7LRavGQKhR\nHHwxDrFoBJlEtCabQZ1bvQ8mOBJQq912DAez4jx5PNOyeZN7jJa9k/Fow+DPMHzgO3twYqmIm37v\nxR2fY7UpWk5TtQFQmRXz+XJg87iS7cCqcN39dyARDVVHxODv1QI0vnd7mc2WcbpOxZwYqMb1BBUW\nW0sdMsNUjlwmoiX9UySiChE171Mq/L/23jxKkru68/3eWHKpvaoX9VLdUktqLS2klgRIAsQigRFg\nQGzHFmYRAhuYwWfM+M1j4Nl+PM+8eZ4Zz+H4GTAc7MdiY8NgIwZhY7CMZMCgXUJ7S2qpUXeVurt6\nqeracomM/L0/In4Rv4wtI/eoqvs5p05VZWVmRUZGxu/Gvd/7valQRxTH+jjERK3yBKf6ADDdpVlL\n5sNHFnD+1pHI9y6ujVYS9f51I+OwULJQF+jqmHRZPx6KChw8d8iw+6KpayjEOJ9Kgnbdkk0pjHsO\nn3Y8NoKBgyz/pClVCCGcUoUbFMQ7R9YaSxVKxiFqQF3B1EMlmqqScfDdCVvTOZxxRZq7p4bSZxzc\nzo+8oXVUqlguW3j6+FJfp3o2o2TZifoGwLmSt+uiwUFT4o3UDpYqWrQEr9TsiFJF47l7sWyFjq9T\nK755nJpxWKnYkf4gayZwEEKMCiHGhBBjAIoA3gngz3q+ZRsAGTgQNQYRKsGDT1LjdsyeszthvLYQ\nAo/MnMH+6ejJonFttBLv/TPCor5ONA6yRhoUG3aCl3FIaQAlg6KcoXkdGXGcWqlEbqvUPJxMMIE6\nHOHhACjdKSnaMS1bQAg01ziULE+zATSWKqKEzQUj/Lr9/ULe/1tuI3AYK5iYGs5hvgVx5IgXOLRf\nqqjUnBLUY7PpR573mrJlN53D4juRho+l5RiN2XBOTxQ3B4ksVSgZh3pd4Ff/9Kf47z844P29Ztfd\n7KBzLE0GShXBC5I1FTioCIf/BeCGHm3PhqJoOgfGSM6IHR9cdNOLwatQLlX0nqTx2jPzJZxaqUbq\nG4DmpQorKuOgd55xkKnOboojCwniyHyEAZS3QOoUeeUd3F51EZZsTmE7ffhUCUQItdK10tYqF1JP\n45BYqvAzDsWc7i02sRmHwPvv65J0v1TRRuAwXjQx5abf07BUtjCaN5E39I58HOTreXgmWi8wCMqW\n3bRUMeUuzFHZq+VKdOAwlDNaem+ixJFDSrb40dkzOHK6hPuen/f+Pr9qQQj/WN807JdUnFJF4+vK\nUuDQ1MeBiN6h/KoBeAmAcs+2aAMhswxxrZiAP9WwbNkNESi3Y/aepPHaj8y4wsiYjEPB1CNTo5Io\n509Zm+9E4yBPjl3tqsgltWNGaBwU+92CkVyqOLFciQwc5JjyJNvpw6dXsW2sEEpVm17mpvl+lAGP\n9FGpRmR76nWBpUotJILcMpp3hIf5sEYhb+qxGQfTIOha+6WK8aKJyeEcVt1BV81S9bJrIG92VqqQ\nj314JjsZh1LVbtCKRSFLYVFBqPyMBs/Bw3kn4yCEiL2oU6nU6pgoNh4Hapn5jgNzAICnji3Crgvo\nGnnZNHmsTw3nUBdOuTFoSgU4LZlZCRzSrDpvUb5ugNNVcWMvN2qj4J+Q4wOHuLkH8gRnssahp8SN\n1354ZgE5Q4udOhk31VQSXapwMw4dlCrkybG7Pg7OMRaVcdA0gqFRg6hQDYqSShWVmo2lci1R45DU\nWXEkMBVT0krGQQY8BVNDTtciMw7L1RqEQENXBeBcKepu622QghkuC6huoXJRSAouo/AyDnIGQ4qs\nw5KicajaddTbzGh5gcORLGUc6igYyYFDknmSDNyCwd9QzoBdF6kDrYplh0oVBdMvV9751ByInO39\npTv/Rh7bMnCWx/yJpQpKgQtF+TqyMlo7jcbhFuXrt4QQ/0UIMdePjVvvDKUIHOTVRFCdW+NSRV/Y\nNTWEw6dWQh/WXxxZwCU7xmIV/0NNWrEiSxXySrmDUoV0E5wc7mxGhUqSARTglCuqEeLInK65V97R\n+0FmRzZFZBxG8wZyhpbo5SDNn4IYLYgj5cKQN3SYOnnZEhW5uAe7H7aM5jFaiC4zRmVaKopoVF7h\ntp1xcNtXm3VW1OsCy1W3q8JdYOM6R5pRtmwYGmFmvpSoPeknJcv22oXjGB+KdvIEwlMoJVLPk1bn\nULXDpQpZrpxbKuORmTN444u2AQCePOr0FpwK6JFkMCg1VVFdFVkZrZ2mq2KaiL5DRHPu17eJaLof\nG7fekYFD0LVMRZ60465euFTRW3ZPDWGlajecoO26wGOz8cJIIH4cuiTKwKsrGoeVasMi0Q18H4fo\n4zQXDBwUcWTe0FCOuWqLc40EnDJRkpeDHKcdGTi0II6Un6u8ocF0r8iDyAUnqGW44ZJteMcV0afC\nqEyL/MzmDc0LwlpxJ5TbMlY0va6ZZqO1V9xsiRRHAmhb51Cp1T2zs0cyonNwNA7J50BHQxYdOCzF\naRxa7HqpWNHtmLW6wO1PHAcAfPhV58HQyAsc5BA31ccBgGdzHxU4ANlwj0yz6nwFwG0Adrhf33Nv\nYzqk6J6I467kAHWue7BeGp51wHQfOV5b1TkcnFvGatXG/l1h/wZJXDeMxA/8/PevVf+BKE6vVDHV\nxY4KINnHAYgIHJSMQ8HUY30c5FXr5pjtdeZVRF/ZzsyHh1tJWpkyKhfRvOnMBYgqVQRHaktuvHwn\n/s+37It83ihxpNqmKjMcrZQq5EhttVTRLOPgtxuaniVyO50VQghUa3W85JwpaAQ8fCQbOoc0Gg9N\nI4zmDc+lVyU4hVIiF+20GYc4AygA+P6jR7F9vID90+M4b8sInjy6BMDR7xgaed06srwoh58FsyBZ\nGnSVJnDYIoT4ihCi5n59FcCWHm/XhmDIPeCDBjIqcRoHy64jp2uphDtM+0R5Odz7S8dx/fJdk7GP\na6ZxiMoYGV2wnO623TQAnLNpGJtHcrEmZbnAlbrV0I4Zvx+8jEOMHmPTSPxwIm8q5qZw4EDk6C7S\nBGAVpdMhp2ue14JK3EyKJKK6SdT9AsAdrZ0+cJAjtaU4EmiucVhS2g3lFXE7Akn5mMmhHPZuHc1M\nZ0UpRVcF4JQrohbcpbIFQ6PIjggAqd0jqzE+DgBw17On8JoLt4KIcPH2Ub9Usex4OMhzuCwvylJF\nMFCXpbJWdTG9IE3gcIqI3ktEuvv1XgCner1hGwGvqyIh4+BpHCJau3gyZu+R47VVL4c7D8xh99QQ\nzolYtCRFU4dli9j2vppnOR3uqui0VDHVRfMnALjx8h24+1OvjdVzOAtujAFUhJ+Bv63JnhObhvM4\nuRSdcZDjzqMyDoBTrkgTgHmlClNDzojJOLgOkK04PEaVKtT9AjgLQystf3LhGy+anoK/WcZBDXr8\n1tn4gFYIEVni8TIzhob9u8bx8JGFTIj0StXmGQfAee+iShULJQsTQ2boAkwu2qspR2tXIodcOdtV\nF8D1F20FAFy8fQxHz5SxsFp1PEyUoDlvOC2+z8doHNQOu0GTJnD4IIBfA3AMzuyKdwG4pZcbtVFI\n1VURJ46sC9Y39AE5Xlte4ZYtGz9/9iSuv2hrYrYnLlMk8Ydc+c/hzVjoIHA4vVLpesaBiEKW0Co5\nQ490jpQZh7iF6tRyFTlDi81kbB3LY26pEmnAdfh0CUM5PdZa29C0VOLIas1fEE2dIgMHTxxZTB84\n5CPEkV4nlBvwj3QQOBi6hrGC0VTjoDojysAhyZDrJ8+cxP4//KfQgC75HhZMHft3TWB+1cLMfPwA\nuH5RrtXTBw7l8L46s2p52gGVVjIO0n00HzFWG3A+B684fxMAeHM+nji6iJPL1VDQPDWcixVHehmj\nDrw4ukWarornhRBvFUJsEUJsFUK8TQhxuB8bt96R7W1JPg5yAQqfhOocOPSJ3VNDXt3xrmdPoWzV\ncZ17BRGH977F1EgjZ1V4GYf2TgxCiJ5oHJqRCzgSpnWOPLlcxZaRfGwA9htX7UbR1PHvvvlQaEGP\nGqetYuitlSoczUGyxqHVUkXwBF+tNZYXWy1VyJHacqFrNvURgDcvw9E4NC9VHDqxjJWqHRKlltWM\ngysK/sWA2zLtuqO7SFWqiPFAOFOyMBGRoZNC4DTvjz+bpXE7pKj4Zedu8s71F2132refPLqEUyth\nD5MpZfR5bMahA/fPbsErzwDxSxXNDaCiShU5LlX0BWe8thM43HFgDkVTx9V7phIfU4wpMUnkyUYt\nN0mNQ9px0EGWKzVYtujqgKs05GNKFVIcWa7ZkWntOLtpya6pIfw/77gUDx1ewGduf9q7/cjpVTw2\neybSw0GSNuPQ0FWhRxskLZYtFE29pUC9YDq6D7XsZNmNArpWSxVypLYUyU0ON3ePXG7QODQvVZTc\nACGY4VRLOhduG0Xe0Abu5+BnQZq/L2NFIzStFAAWStWQcRMADCV0vRw9U8Jv/Pnd3jlBPYZU5Dng\neuUiY+toAZtHcnjy6GLknBb19zWdcWB6x/RkER9/3V7ccMm22PvElSosu56YPma6x66pIRxdLKNS\ns3HHgTlcu3dz0/Ros8AhyTmyXY2D7xrZPfOnNITFkcK7vWDqECLaO+DkcqVpkPOW/Ttw00t3H1b3\nBgAAIABJREFU4Ys/fhY/feYEvnnvYbzhT36C5UoN77vm7NjHmTqlytzIk3DOiDeAWirXGuZUpKHg\nXd0rmZhavaGLZqTQfqkCAKaG0mQcojQO8ful5Kbmg1e18jEFNzNzyY4xzz11UMhzYjPnSCC+VLEQ\nU6qQGYfViPfn83cexM+fPYV7Djki6YpSmlO5fNcEPvLqc/G2K3Y23H7x9jE8eHgeq1U75GGilhmD\nJWzOODAAnNrxx193Ac4aK8TeJ1YcWRcNJyGmd8jx2ncemMPsQqnhCiIOmaaM83KoRfg4dKpx8F0j\n+1+qaMw4uOOjdS2xru6oypsHOZ9+yyU4b8sIbvnKffjkrY/isukJ/ODjr8SrLohv7kovjlQ0DgZF\nZikWy1bDZMw0FCI8E4IZh5Gc4WUE0hBsC50YyjXVOCxVaiByFsI0V6yy/TBYGpW/SwHg/l0TeHT2\nTCqvjF4hz4lpNA7jRROrVTsUGJ5ZtTyDKBUZ+AczDsfOlPGt+2YA+C3Bqk5GpWDq+NQbLw4FJhdv\nH8NzJxz3yJDGwf09p2uhQGRNZRyI6Fki+msi+igRXdKPjWJ88oYGIoR64a0aaxz6hVTuf/XnvwQA\nXHdh88CheamiDo38LAPQucbhdA8GXKUh2FURzDgA4eNXCOG1ozWjmNPx+d+4EudvHcGn37IPf/2b\nV3vdLnGYmgarhSFXeVOPzTgslsJzKpohX3c5lHFQAodCixoHd6S2NIybGjZTaRxGcgY0jVL5OMjj\nNc71Ui5eF20bRcmycfTM4MYWPf6C09a4fTz+wksigy21s8Ky61iq1DBRDB+DmkbOhMzA+/PFHz8L\nWwiM5A3MuuLQ4L5pxsXbfZv6oIeJDPqjdG/5DGUc0nwa9gG4GsArAfwxEV0I4BEhxNt7umUMACcr\nETVpMXj1wvQOGTjc/dxp7Ns+hm0pTlQycIhrnbIixK06pTcuiqIXA67SkDe1mFkV5C+ggaukpUoN\nVbvuueY148Jto/jBx1+Vept0jVJdDTd2VTQGQN62li3PNyEt3kneatwvQY3DStVGvS6gpTBykyO1\n5X0nh3MoWXbioCc54AqInmQapORlHBrvE9QTyHLYwqqFXclyn55x64Mz2DySx8vO3dT0vrLUtFiu\neVmuxYBmJMiQ+/5I5pbK+Ma9h/H2K3biuRPLmF2QgYNzn7TnY9lZAYQ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o1HAupHFQRaSS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\nQmCC\n",
+      "text/plain": [
+       ""
+      ]
+     },
+     "execution_count": 25,
+     "metadata": {},
+     "output_type": "execute_result"
     }
    ],
    "source": [
-    "myintctrl.demod_freqs.add_demodulator(1e6)"
+    "plot = qc.MatPlot(data6.my_controller_rec_raw_output)\n",
+    "plot.fig"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "As the setpoints along a records axis is expected to be dependent on an external instrument\n",
+    "which the driver has no way of knowing there is support for setting the base name, unit and start and stop of setpoints (the number of setpoints is automatically set from the number of records)"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 31,
+   "execution_count": 26,
    "metadata": {
     "collapsed": false
    },
@@ -835,53 +739,11 @@
      "text": [
       "DataSet:\n",
       "   mode     = DataMode.LOCAL\n",
-      "   location = 'data/2017-03-03/#012_{name}_16-45-42'\n",
-      "      |                            |        | \n",
-      "   Setpoint | single_set                           | single             | (1,)\n",
-      "   Measured | my_controller_int_raw_output         | raw_output         | (1,)\n",
-      "   Measured | my_controller_int_demod_freq_0_mag   | demod_freq_0_mag   | (1,)\n",
-      "   Measured | my_controller_int_demod_freq_0_phase | demod_freq_0_phase | (1,)\n",
-      "acquired at 2017-03-03 16:45:43\n"
-     ]
-    }
-   ],
-   "source": [
-    "data5 = qc.Measure(myintctrl.acquisition).run()"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 32,
-   "metadata": {
-    "collapsed": false
-   },
-   "outputs": [],
-   "source": [
-    "myrecctrl = ATS9360Controller(name='my_controller_rec', alazar_name='Alazar', \n",
-    "                              integrate_samples=True, average_records=False)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 33,
-   "metadata": {
-    "collapsed": false
-   },
-   "outputs": [
-    {
-     "ename": "ValueError",
-     "evalue": "Wrong number of inputs supplied",
-     "output_type": "error",
-     "traceback": [
-      "\u001b[1;31m---------------------------------------------------------------------------\u001b[0m",
-      "\u001b[1;31mValueError\u001b[0m                                Traceback (most recent call last)",
-      "\u001b[1;32m\u001b[0m in \u001b[0;36m\u001b[1;34m()\u001b[0m\n\u001b[0;32m      2\u001b[0m \u001b[0mmyrecctrl\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mint_time\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;36m2e-6\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m      3\u001b[0m \u001b[0mmyrecctrl\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mnum_avg\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;36m100\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m----> 4\u001b[1;33m \u001b[0mdata6\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mqc\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mMeasure\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mmyrecctrl\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0macquisition\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mrun\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m",
-      "\u001b[1;32mc:\\users\\jhn\\qcodes\\qcodes\\measure.py\u001b[0m in \u001b[0;36mrun\u001b[1;34m(self, use_threads, quiet, data_manager, station, **kwargs)\u001b[0m\n\u001b[0;32m     76\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m     77\u001b[0m         data_set = self._dummyLoop.get_data_set(data_manager=data_manager,\n\u001b[1;32m---> 78\u001b[1;33m                                                 **kwargs)\n\u001b[0m\u001b[0;32m     79\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m     80\u001b[0m         \u001b[1;31m# set the DataSet to local for now so we don't save it, since\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
-      "\u001b[1;32mc:\\users\\jhn\\qcodes\\qcodes\\loops.py\u001b[0m in \u001b[0;36mget_data_set\u001b[1;34m(self, data_manager, *args, **kwargs)\u001b[0m\n\u001b[0;32m    732\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    733\u001b[0m             data_set = new_data(arrays=self.containers(), mode=data_mode,\n\u001b[1;32m--> 734\u001b[1;33m                                 data_manager=data_manager, *args, **kwargs)\n\u001b[0m\u001b[0;32m    735\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    736\u001b[0m             \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mdata_set\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mdata_set\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
-      "\u001b[1;32mc:\\users\\jhn\\qcodes\\qcodes\\loops.py\u001b[0m in \u001b[0;36mcontainers\u001b[1;34m(self)\u001b[0m\n\u001b[0;32m    521\u001b[0m                 \u001b[1;31m# note that this supports lists (separate output arrays)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    522\u001b[0m                 \u001b[1;31m# and arrays (nested in one/each output array) of return values\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m--> 523\u001b[1;33m                 \u001b[0maction_arrays\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0m_parameter_arrays\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0maction\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m    524\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    525\u001b[0m             \u001b[1;32melse\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
-      "\u001b[1;32mc:\\users\\jhn\\qcodes\\qcodes\\loops.py\u001b[0m in \u001b[0;36m_parameter_arrays\u001b[1;34m(self, action)\u001b[0m\n\u001b[0;32m    596\u001b[0m             \u001b[1;32melse\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    597\u001b[0m                 \u001b[0msp_blank\u001b[0m \u001b[1;33m=\u001b[0m \u001b[1;33m(\u001b[0m\u001b[1;32mNone\u001b[0m\u001b[1;33m,\u001b[0m\u001b[1;33m)\u001b[0m \u001b[1;33m*\u001b[0m \u001b[0mlen\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mshape\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m--> 598\u001b[1;33m                 \u001b[0msp_vi\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0m_fill_blank\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0msp_vi\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0msp_blank\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m    599\u001b[0m                 \u001b[0msp_ni\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0m_fill_blank\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0msp_ni\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0msp_blank\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    600\u001b[0m                 \u001b[0msp_li\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0m_fill_blank\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0msp_li\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0msp_blank\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
-      "\u001b[1;32mc:\\users\\jhn\\qcodes\\qcodes\\loops.py\u001b[0m in \u001b[0;36m_fill_blank\u001b[1;34m(self, inputs, blanks)\u001b[0m\n\u001b[0;32m    623\u001b[0m             \u001b[1;32mreturn\u001b[0m \u001b[0minputs\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    624\u001b[0m         \u001b[1;32melse\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m--> 625\u001b[1;33m             \u001b[1;32mraise\u001b[0m \u001b[0mValueError\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;34m'Wrong number of inputs supplied'\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m    626\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    627\u001b[0m     def _make_setpoint_array(self, shape, i, prev_setpoints, vals, name,\n",
-      "\u001b[1;31mValueError\u001b[0m: Wrong number of inputs supplied"
+      "   location = 'data/2017-03-07/#036_{name}_12-36-45'\n",
+      "      |                    |  | \n",
+      "   Setpoint | foo_set                      | foo          | (123,)\n",
+      "   Measured | my_controller_rec_raw_output | raw_output   | (123,)\n",
+      "acquired at 2017-03-07 12:36:46\n"
      ]
     }
    ],
@@ -889,29 +751,34 @@
     "myrecctrl.int_delay(2e-7)\n",
     "myrecctrl.int_time(2e-6)\n",
     "myrecctrl.num_avg(100)\n",
-    "data6 = qc.Measure(myrecctrl.acquisition).run()"
+    "myrecctrl.records_per_buffer(123)\n",
+    "myrecctrl.acquisition.set_base_setpoints(base_name='foo', base_label='bar', base_unit='ket',\n",
+    "                             setpoints_start=200, setpoints_stop = 1e6)\n",
+    "data7 = qc.Measure(myrecctrl.acquisition).run()"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 36,
+   "execution_count": 27,
    "metadata": {
     "collapsed": false
    },
    "outputs": [
     {
      "data": {
+      "image/png": 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t57zcNGscBlO9clCcnC+xbsQxHEbSSX+owogjTUG4AQskreFgsYTgrcbYD49D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/TKiCgG4G3w0TfA1D3U9TasBt780o04cPQsHjl6NtD8UcSRRISNQ2kceN78DitU4Qg7FMpVpAxv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\nmP8fAxszqDdoHlkAAAAASUVORK5CYII=\n",
       "text/plain": [
-       "((),)"
+       ""
       ]
      },
-     "execution_count": 36,
+     "execution_count": 27,
      "metadata": {},
      "output_type": "execute_result"
     }
    ],
    "source": [
-    "myrecctrl.acquisition.setpoints"
+    "plot = qc.MatPlot(data7.my_controller_rec_raw_output)\n",
+    "plot.fig"
    ]
   },
   {

From be1568291835cf47f9000ae5b7ac3613f298a062 Mon Sep 17 00:00:00 2001
From: "Jens H. Nielsen" 
Date: Wed, 8 Mar 2017 14:41:48 +0100
Subject: [PATCH 163/328] Revert "Fix: Simplify memory allocation in ATS
 driver"

This reverts commit f26977f1bc08b425269dc01dd76bd10512c75542.
---
 qcodes/instrument_drivers/AlazarTech/ATS.py | 25 +++++++++++++++++----
 1 file changed, 21 insertions(+), 4 deletions(-)

diff --git a/qcodes/instrument_drivers/AlazarTech/ATS.py b/qcodes/instrument_drivers/AlazarTech/ATS.py
index e041b9ce1758..a4542ffde3a1 100644
--- a/qcodes/instrument_drivers/AlazarTech/ATS.py
+++ b/qcodes/instrument_drivers/AlazarTech/ATS.py
@@ -997,13 +997,22 @@ def __init__(self, c_sample_type, size_bytes):
 
         self._allocated = True
         self.addr = None
+        if os.name == 'nt':
+            MEM_COMMIT = 0x1000
+            PAGE_READWRITE = 0x4
+            ctypes.windll.kernel32.VirtualAlloc.argtypes = [ctypes.c_void_p, ctypes.c_long, ctypes.c_long, ctypes.c_long]
+            ctypes.windll.kernel32.VirtualAlloc.restype = ctypes.c_void_p
+            self.addr = ctypes.windll.kernel32.VirtualAlloc(
+                0, ctypes.c_long(size_bytes), MEM_COMMIT, PAGE_READWRITE)
+        else:
+            self._allocated = False
+            raise Exception("Unsupported OS")
 
-        ctypes_array_type = c_sample_type * (size_bytes // bytes_per_sample) # PyCharm deducts the wrong type
-        # This is not an int but a PyCArrayType which is callable. Not sure how to annotate this correctly
-        ctypes_array = ctypes_array_type()
+        ctypes_array = (c_sample_type *
+                        (size_bytes // bytes_per_sample)).from_address(self.addr)
         self.buffer = np.frombuffer(ctypes_array, dtype=npSampleType)
         self.ctypes_buffer = ctypes_array
-        self.addr = ctypes.addressof(self.ctypes_buffer)
+        pointer, read_only_flag = self.buffer.__array_interface__['data']
 
     def free_mem(self):
         """
@@ -1011,6 +1020,14 @@ def free_mem(self):
         """
 
         self._allocated = False
+        if os.name == 'nt':
+            MEM_RELEASE = 0x8000
+            ctypes.windll.kernel32.VirtualFree.argtypes = [ctypes.c_void_p, ctypes.c_long, ctypes.c_long]
+            ctypes.windll.kernel32.VirtualFree.restype = ctypes.c_int
+            ctypes.windll.kernel32.VirtualFree(ctypes.c_void_p(self.addr), 0, MEM_RELEASE);
+        else:
+            self._allocated = True
+            raise Exception("Unsupported OS")
 
     def __del__(self):
         """

From 98c7b661a28b9463507108dfb82848750b6a37c0 Mon Sep 17 00:00:00 2001
From: "Jens H. Nielsen" 
Date: Wed, 8 Mar 2017 17:32:16 +0100
Subject: [PATCH 164/328] Docs: update example notebook

---
 ...e with Alazar ATS9360 new controller.ipynb | 4436 ++++++++++++++++-
 1 file changed, 4285 insertions(+), 151 deletions(-)

diff --git a/docs/examples/Qcodes example with Alazar ATS9360 new controller.ipynb b/docs/examples/Qcodes example with Alazar ATS9360 new controller.ipynb
index b77761ed3ec0..b760d68435d0 100644
--- a/docs/examples/Qcodes example with Alazar ATS9360 new controller.ipynb	
+++ b/docs/examples/Qcodes example with Alazar ATS9360 new controller.ipynb	
@@ -13,6 +13,17 @@
   {
    "cell_type": "code",
    "execution_count": 1,
+   "metadata": {
+    "collapsed": true
+   },
+   "outputs": [],
+   "source": [
+    "%matplotlib notebook"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 2,
    "metadata": {
     "collapsed": false,
     "deletable": true,
@@ -33,9 +44,6 @@
     "import qcodes.instrument_drivers.AlazarTech.ATS9360 as ATSdriver\n",
     "from qcodes.instrument_drivers.AlazarTech.acq_controllers import ATS9360Controller\n",
     "from qcodes.station import Station\n",
-    "#import qcodes.instrument_drivers.AlazarTech.samp_controller as samp_acq_contr\n",
-    "#import qcodes.instrument_drivers.AlazarTech.ave_controller_test as ave_acq_controller\n",
-    "#import qcodes.instrument_drivers.AlazarTech.rec_controller_test as record_acq_controller\n",
     "\n",
     "import logging\n",
     "# logging.basicConfig(filename='example.log',level=logging.INFO)\n",
@@ -55,13 +63,29 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 2,
+   "execution_count": 3,
    "metadata": {
     "collapsed": false,
     "deletable": true,
     "editable": true
    },
    "outputs": [
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "WARNING:root:`units` is deprecated for the `Parameter` class, use `unit` instead. \n",
+      "WARNING:root:`units` is deprecated for the `Parameter` class, use `unit` instead. \n",
+      "WARNING:root:`units` is deprecated for the `Parameter` class, use `unit` instead. \n",
+      "WARNING:root:`units` is deprecated for the `Parameter` class, use `unit` instead. \n",
+      "WARNING:root:`units` is deprecated for the `Parameter` class, use `unit` instead. \n",
+      "WARNING:root:`units` is deprecated for the `Parameter` class, use `unit` instead. \n",
+      "WARNING:root:`units` is deprecated for the `Parameter` class, use `unit` instead. \n",
+      "WARNING:root:`units` is deprecated for the `Parameter` class, use `unit` instead. \n",
+      "WARNING:root:`units` is deprecated for the `Parameter` class, use `unit` instead. \n",
+      "WARNING:root:`units` is deprecated for the `Parameter` class, use `unit` instead. \n"
+     ]
+    },
     {
      "data": {
       "text/plain": [
@@ -81,7 +105,7 @@
        " 'vendor': 'AlazarTech'}"
       ]
      },
-     "execution_count": 2,
+     "execution_count": 3,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -95,7 +119,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 3,
+   "execution_count": 4,
    "metadata": {
     "collapsed": false
    },
@@ -103,10 +127,10 @@
     {
      "data": {
       "text/plain": [
-       "812"
+       "944"
       ]
      },
-     "execution_count": 3,
+     "execution_count": 4,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -117,7 +141,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 4,
+   "execution_count": 23,
    "metadata": {
     "collapsed": false,
     "scrolled": false
@@ -134,9 +158,9 @@
     "              impedance=[50,50],\n",
     "              trigger_operation='TRIG_ENGINE_OP_J',\n",
     "              trigger_engine1='TRIG_ENGINE_J',\n",
-    "              trigger_source1='CHANNEL_A',\n",
+    "              trigger_source1='EXTERNAL',\n",
     "              trigger_slope1='TRIG_SLOPE_POSITIVE',\n",
-    "              trigger_level1=128,\n",
+    "              trigger_level1=160,\n",
     "              trigger_engine2='TRIG_ENGINE_K',\n",
     "              trigger_source2='DISABLE',\n",
     "              trigger_slope2='TRIG_SLOPE_POSITIVE',\n",
@@ -144,7 +168,7 @@
     "              external_trigger_coupling='DC',\n",
     "              external_trigger_range='ETR_2V5',\n",
     "              trigger_delay=0,\n",
-    "              timeout_ticks=1,\n",
+    "              timeout_ticks=0,\n",
     "              aux_io_mode='AUX_IN_AUXILIARY', # AUX_IN_TRIGGER_ENABLE for seq mode on\n",
     "              aux_io_param='NONE' # TRIG_SLOPE_POSITIVE for seq mode on\n",
     "             )"
@@ -164,7 +188,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 5,
+   "execution_count": 6,
    "metadata": {
     "collapsed": false,
     "deletable": true,
@@ -189,11 +213,19 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 6,
+   "execution_count": 7,
    "metadata": {
     "collapsed": false
    },
-   "outputs": [],
+   "outputs": [
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "WARNING:root:Error getting or interpreting *IDN?: ''\n"
+     ]
+    }
+   ],
    "source": [
     "station = qc.Station(alazar, myctrl)"
    ]
@@ -207,20 +239,20 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 7,
+   "execution_count": 8,
    "metadata": {
     "collapsed": false
    },
    "outputs": [],
    "source": [
     "myctrl.int_delay(2e-7)\n",
-    "myctrl.int_time(2e-6)\n",
-    "myctrl.num_avg(100)"
+    "myctrl.int_time(3.1e-6)\n",
+    "myctrl.num_avg(1000)"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 8,
+   "execution_count": 9,
    "metadata": {
     "collapsed": false
    },
@@ -229,8 +261,8 @@
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "100 records per buffer set by num_avg\n",
-      "1152 samples per record set by int_time and int_delay\n"
+      "1000 records per buffer set by num_avg\n",
+      "1664 samples per record set by int_time and int_delay\n"
      ]
     }
    ],
@@ -248,7 +280,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 9,
+   "execution_count": 10,
    "metadata": {
     "collapsed": false
    },
@@ -259,41 +291,818 @@
      "text": [
       "DataSet:\n",
       "   mode     = DataMode.LOCAL\n",
-      "   location = 'data/2017-03-07/#030_{name}_12-36-03'\n",
+      "   location = 'data/2017-03-08/#059_{name}_15-55-30'\n",
       "      |                |  | \n",
-      "   Setpoint | time_set                 | time         | (1152,)\n",
-      "   Measured | my_controller_raw_output | raw_output   | (1152,)\n",
-      "acquired at 2017-03-07 12:36:03\n"
+      "   Setpoint | time_set                 | time         | (1664,)\n",
+      "   Measured | my_controller_raw_output | raw_output   | (1664,)\n",
+      "acquired at 2017-03-08 15:55:31\n"
      ]
     }
    ],
    "source": [
     "# Measure this \n",
-    "data1 = qc.Measure(myctrl.acquisition).run(station=station)\n"
+    "data1 = qc.Measure(myctrl.acquisition).run()\n"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 10,
+   "execution_count": 11,
    "metadata": {
     "collapsed": false
    },
    "outputs": [
     {
      "data": {
-      "image/png": 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8/ZZ99skdu3s+PmdOGUNnh7hz5Yb93ot82/Kf27jDhzFm+BDuW72xx3142arnOHRoJ7v3\n7CWq7JPV2nbipFFs2r6bJzZu369tPe2TPf3v6c6/e+/efT63no7P/D5c+b+n2j48YkjWtp6Oz1r7\n5H2/3Ljf8XnS0aO5f/VGgMJtK/q/p97xedLRo3lw7UYi6HXbevu/p7NjEIcM7uCdJ4xv2PdWLU0N\nICTNAr4IdABfjYhLK5YrLT8N2AZ8MCLuqVVW0vuBvwZeCcyMiGUp/e3ApcAQYBfwpxHxv41uY96l\nNz/Cz9dv7c9N9plp40fy8LrNza6GmZlVGNI5g7dPG9fv223aKQxJHcDlwGxgGjBX0rSKbLOBqek1\nH7iyQNmHgPcCP6hY1zPA6RFxPDAP+Je+blM9O3bv7e9N9hkHD2ZmrenJzTuast1m9kDMBFZExEoA\nSdcCc4CHc3nmANdERAB3SholaTwwpaeyEfFISttnYxFxb252OXCIpKERsbMRjaumokpmZmZtq5mD\nKCcAq3Pza1JakTxFytbyPuCe/gwezMzMGiKiKZs96AZRSpoOfAZ4R40888lOmTB58uQ+3HafrcrM\nzKypmtkDsRaYlJufmNKK5ClSdj+SJgI3AOdGxM97yhcRV0XEjIiYMXbs2HqrNTMza54m/TptZgBx\nFzBV0jGShgBnA4sr8iwGzlXmZGBTRKwrWHYfkkYBNwELIuLHfd2YIoS7IMzMbGBoWgAREV3ARcAt\nwCPAdRGxXNIFki5I2ZYAK4EVwFeAD9cqCyDpPZLWAK8DbpJ0S1rXRcCvABdLui+9XtIfbe3mUxhm\nZjZQNHUMREQsIQsS8mkLc9MBXFi0bEq/gew0RWX6p4BPHWCVzczMDN/Kul+5A8LMzAYKBxBmZmZW\nmgOIflR5cyszM7N25QDCzMzMSnMA0Y/c/2BmZgOFAwgzMzMrzQFEf3IXhJmZDRAOIPqR4wczMxso\nHECYmZlZaQ4g+pEv4zQzs4HCAYSZmZmV5gCiH7n/wczMBgoHEGZmZlaaA4h+5CEQZmY2UDiAMDMz\na2cRTdmsA4h+JI+CMDOzAcIBhJmZWTtr0vlxBxD9yGMgzMxsoHAAYWZmZqU5gDAzM7PSHECYmZlZ\naQ4gzMzMrDQHEGZmZlaaAwgzMzMrzQGEmZmZldbUAELSLEmPSlohaUGV5ZJ0WVr+gKST6pWV9H5J\nyyXtlTSjYn1/nvI/KunUxrbOzMysHxxst7KW1AFcDswGpgFzJU2ryDYbmJpe84ErC5R9CHgv8IOK\n7U0DzgamA7OAK9J6zMzMrKRm9kDMBFZExMqI2AVcC8ypyDMHuCYydwKjJI2vVTYiHomIR6tsbw5w\nbUTsjIhfACvSeszMzNrXQXgr6wnA6tz8mpRWJE+Rsr3ZnpmZmRXgQZRVSJovaZmkZevXr292dczM\nzFpOMwOItcCk3PzElFYkT5GyvdkeABFxVUTMiIgZY8eOrbNaMzOzg08zA4i7gKmSjpE0hGyA4+KK\nPIuBc9PVGCcDmyJiXcGylRYDZ0saKukYsoGZP+3LBpmZmR0sOpu14YjoknQRcAvQAVwdEcslXZCW\nLwSWAKeRDXjcBpxXqyyApPcA/wSMBW6SdF9EnJrWfR3wMNAFXBgRe/qxyWZmZgNG0wIIgIhYQhYk\n5NMW5qYDuLBo2ZR+A3BDD2UuAS45gCqbmZkZHkRpZmbW3g62G0mZmZlZ+3IAYWZmZqU5gDAzM7PS\nHECYmZm1s4PwVtZmZmbWphxAmJmZWWkOIMzMzKw0BxBmZmZWmgMIMzMzK80BhJmZmZXmAMLMzKyd\n+VbWZmZm1i4cQJiZmVlpDiDMzMysNAcQZmZm7cy3sjYzM7N24QDCzMzMSnMAYWZmZqU5gDAzM7PS\nHECYmZm1M99IyszMzNqFAwgzMzMrzQGEmZmZleYAwszMzEpragAhaZakRyWtkLSgynJJuiwtf0DS\nSfXKShoj6VZJj6W/o1P6YEmLJD0o6RFJf94/rTQzMxt4mhZASOoALgdmA9OAuZKmVWSbDUxNr/nA\nlQXKLgCWRsRUYGmaB3g/MDQijgdeA/yepCkNaZyZmVl/OQhvZT0TWBERKyNiF3AtMKcizxzgmsjc\nCYySNL5O2TnAojS9CDgjTQcwQlIncAiwC9jcoLaZmZkNaM0MICYAq3Pza1JakTy1yo6LiHVp+klg\nXJq+HtgKrAN+CXwuIp6tVjFJ8yUtk7Rs/fr1pRplZmZ2MBjQgygjIsh6HiDrtdgDHAUcA/yJpJf1\nUO6qiJgRETPGjh3bP5U1MzNrI80MINYCk3LzE1NakTy1yj6VTnOQ/j6d0j8AfCcidkfE08CPgRl9\n0A4zM7ODTjMDiLuAqZKOkTQEOBtYXJFnMXBuuhrjZGBTOj1Rq+xiYF6angfcmKZ/CbwVQNII4GTg\nZ41pmpmZWT9p0q2sO5uyVSAiuiRdBNwCdABXR8RySRek5QuBJcBpwApgG3BerbJp1ZcC10k6H3gc\nOCulXw58TdJyQMDXIuKBfmiqmZnZgNO0AAIgIpaQBQn5tIW56QAuLFo2pW8ATqmSvoXsUk4zMzM7\nQAN6EKWZmZk1hgMIMzMzK80BhJmZmZXmAMLMzKydHYS3sjYzM7M25QDCzMzMSnMAYWZmZqU5gDAz\nM7PSHECYmZm1sybdytoBhJmZmZXmAMLMzMxKcwBhZmZmpTmAMDMzs9IKPY1T0mjgKGA7sCoi9ja0\nVmZmZtbSegwgJB1O9ijtucAQYD0wDBgn6U7gioi4rV9qaWZmZtU16VbWtXogrgeuAd4UERvzCyS9\nBvhtSS+LiH9uZAXNzMys9fQYQETE22ssuxu4uyE1MjMzs5ZXdxClpG9J+oCkEf1RITMzMyuhhW8k\n9TngjcDDkq6XdKakYQ2ul5mZmbWwuldhRMT3ge9L6gDeCvwucDUwssF1MzMzsxZV9DLOQ4DTgd8E\nTgIWNbJSZmZm1trqBhCSrgNmAt8BvgR83/eBMDMzaw3NGQFRrAfin4G5EbGn0ZUxMzOz9tDjIEpJ\nbwSIiFuqBQ+SRko6rpGVMzMzs9qadBFGzasw3ifpdkkXS3qnpJmS3izpQ5L+Bfg2cMiBbFzSLEmP\nSlohaUGV5ZJ0WVr+gKST6pWVNEbSrZIeS39H55adIOkOScslPeirSczMzHqnxwAiIv4YeBewDng/\n8LfAR4GpwJcj4s0RcVdvN5yu6rgcmA1MA+ZKmlaRbXba3lRgPnBlgbILgKURMRVYmuaR1An8K3BB\nREwH3gLs7m39zczMWkE0qQui5hiIiHgW+Ep69bWZwIqIWAkg6VpgDvBwLs8c4JrI3p07JY2SNB6Y\nUqPsHLLgALKrRb4H/BnwDuCBiLg/tW1DA9pkZmZ2UGjm47wnAKtz82tSWpE8tcqOi4h1afpJYFya\nfgUQkm6RdI+kjx94E8zMzJqrla/CaFsREZK639tOsjtq/hqwDVgq6e6IWFpZTtJ8slMmTJ48ub+q\na2ZmVlorDqIEQNLQImm9sBaYlJufmNKK5KlV9ql0moP09+mUvgb4QUQ8ExHbgCVkN8XaT0RcFREz\nImLG2LFjSzfMzMxsoCtyCuOOgmll3QVMlXSMpCHA2cDiijyLgXPT1RgnA5vS6YlaZRcD89L0PODG\nNH0LcLyk4WlA5a+z73gLMzOzttNypzAkvZRsXMEhkl4NKC0aCQw/0A1HRJeki8i+2DuAqyNiuaQL\n0vKFZL0EpwEryE47nFerbFr1pcB1ks4HHgfOSmWek/R5suAjgCURcdOBtsPMzOxgVGsMxKnAB8lO\nD3w+l/488Bd9sfGIWEIWJOTTFuamA7iwaNmUvgE4pYcy/0p2KaeZmdmA0HKXcUbEImCRpPdFxDf7\nsU5mZmbW4opchXGcpOmViRHxyQbUx8zMzNpAkQBiS256GNndKR9pTHXMzMysHdQNICLiH/Lzkj5H\nNnjRzMzMmqxl7wNRxXCygZVmZmbWZNGkCznr9kBIepAXLzPtAMYCHv9gZmZ2ECsyBuJdueku4KmI\n6GpQfczMzKyEZp3CKDIG4nFJJ5E9RyKAHwH3NrpiZmZm1rqKPAvjYrLHYh8BHAl8XdInGl0xMzMz\nq6/lbmWdcw5wYkTsAJB0KXAf8KlGVszMzMxaV5GrMJ4gu/9Dt6Hs/9RMMzMza4KWHQMBbAKWS7qV\nrKfk7cBPJV0GEBF/2MD6mZmZWQsqEkDckF7dvteYqpiZmVlZLXsfCGBURHwxnyDpjyrTzMzM7OBR\nZAzEvCppH+zjepiZmVkvtNwYCElzgQ8Ax0hanFt0GPBsoytmZmZmravWKYzbgXVk937IP1DreeCB\nRlbKzMzMWluPAUREPA48Dryu/6pjZmZmZUSTzmEUeZjW87x4o6shwGBga0SMbGTFzMzMrHUVeRbG\nYd3TkgTMAU5uZKXMzMysmGYNoixyFcYLIvPfwKkNqo+ZmZm1gSKnMN6bmx0EzAB2NKxGZmZmVlgr\nP0zr9Nx0F7CK7DSGmZmZHaSKjIE4rz8qYmZmZuW17BgISRMl3SDp6fT6pqSJ/VE5MzMzq61Zz8Io\nMojya8Bi4Kj0+lZKO2CSZkl6VNIKSQuqLJeky9LyBySdVK+spDGSbpX0WPo7umKdkyVtkfSxvmiD\nmZnZwahIADE2Ir4WEV3p9XVg7IFuWFIHcDkwG5gGzJU0rSLbbGBqes0HrixQdgGwNCKmAkvTfN7n\ngZsPtP5mZmatoGVPYQAbJP2WpI70+i1gQx9seyawIiJWRsQu4Fr2H5w5B7gmXT56JzBK0vg6ZecA\ni9L0IuCM7pVJOgP4BbC8D+pvZmZ20CoSQHwIOAt4kuzZGGcCfTGwcgKwOje/JqUVyVOr7LiIWJem\nnwTGAUg6FPgz4G/6oO5mZmYtoWUv40zPxHh3P9Slz0VESOp+b/8a+MeI2JLdULNnkuaTnTJh8uTJ\nDa2jmZlZOypyH4hGWQtMys1PTGlF8gyuUfYpSeMjYl063fF0Sn8tcKakzwKjgL2SdkTElyorFhFX\nAVcBzJgxo1nBnZmZWX1NGgRR6lbWfewuYKqkYyQNAc4mu9ojbzFwbroa42RgUzo9UavsYmBemp4H\n3AgQEW+KiCkRMQX4AvDpasGDmZmZ1de0HoiI6JJ0EXAL0AFcHRHLJV2Qli8ElgCnASuAbaSxFz2V\nTau+FLhO0vlkjyM/qx+bZWZm1q9adgyEpJ8DdwI/BH6Y+6I+YBGxhCxIyKctzE0HcGHRsil9A3BK\nne3+dS+qa2ZmZkmRUxjTgC8DRwB/L+nnkm5obLXMzMysiFa+D8QeYHf6u5dsUOLTNUuYmZlZv2jW\nrayLjIHYDDxIdgfHr6RTBGZmZnYQK9IDMRf4AfBh4FpJfyOp5hgDMzMz6x/NOoVR5EZSNwI3SjqW\n7NkTHwE+DhzS4LqZmZlZiyryOO9vSloBfBEYDpwLjK5dyszMzPpDy17GCfwdcG9E7Gl0ZczMzKw9\nFDmFsUzScelx2cNy6dc0tGZmZmZWV8uOgZD0V8BbyO4HsYRsHMSPAAcQZmZmB6kiV2GcSXZnxycj\n4jzgRODwhtbKzMzMCmnWfSCKBBDbI2Iv0CVpJNlNpCbVKWNmZmb9oVVPYQDLJI0CvgLcDWwB7mho\nrczMzKyl1QwgJAn4u4jYCCyU9B1gZEQ80C+1MzMzs5pa8jLOiAhJS4Dj0/yq/qiUmZmZtbYiYyDu\nkfRrDa+JmZmZlRZNuo6zyBiI1wLnSHoc2AqIrHPihIbWzMzMzFpWkQDi1IbXwszMzHqlZW8kFRGP\n90dFzMzMrH0UGQNhZmZmLapZV2E4gDAzM7PSHECYmZm1sWaNgXAA0Y8ufV/jLlw5cdIopIatvk8d\nN2Ekgztao7JzXnVUW6/f2ker7wunHPuSZlfBeunIw4Y0Zbtq1vWj7WLGjBmxbNmyZlfDrEfX3LGK\ni29czm+ffDR/e8Zxza5OQ0xZcBMAqy59Z8PW8fq/W8oTm3aw+KI3cMLEUb3eTqvp7Xu3+tltvOmz\nt9E5SKz49GmNqJq1KEl3R8SMevncA2FmZmalOYAwM8sRrXF6zazVNTWAkDRL0qOSVkhaUGW5JF2W\nlj8g6aR6ZSWNkXSrpMfS39Ep/e2S7pb0YPr71v5ppZm1A7XLIKJ+4rfD6mlaACGpA7gcmA1MA+ZK\nmlaRbTZ0sbOuAAAPMklEQVQwNb3mA1cWKLsAWBoRU4GlaR7gGeD0iDgemAf8S4OaZmZmNuA1swdi\nJrAiIlZGxC7gWmBORZ45wDWRuRMYJWl8nbJzgEVpehFwBkBE3BsRT6T05cAhkoY2qnFm1p78y9us\nmGYGEBOA1bn5NSmtSJ5aZcdFxLo0/SQwrsq23wfcExE7e1d1M7OBzad0rJ4iD9NqWxERkva5TlXS\ndOAzwDt6KidpPtkpEyZPntzQOpqZmbWjZvZArAUm5eYnprQieWqVfSqd5iD9fbo7k6SJwA3AuRHx\n854qFhFXRcSMiJgxduzYUo0yMzM7GDQzgLgLmCrpGElDgLOBxRV5FgPnpqsxTgY2pdMTtcouJhsk\nSfp7I4CkUcBNwIKI+HEjG2Zm1u58AsPqadopjIjoknQRcAvQAVwdEcslXZCWLwSWAKcBK4BtwHm1\nyqZVXwpcJ+l84HHgrJR+EfArwMWSLk5p74iIF3oozMzMrJimjoGIiCVkQUI+bWFuOoALi5ZN6RuA\nU6qkfwr41AFW2cwGOI8dzPh9sHp8J0ozMzMrzQGEmVmOb2VtVowDCDMz3GVfyYGU1eMAwszMzEpz\nAGFmluOeCLNiHECYmdl+HEhZPQ4gzMzMrDQHEGZmOf7lnfHbYPU4gDAzw4GDWVkOIMzMzKw0BxBm\nZjm+/0Hit8HqcABhZmZmpTmAMDPL8VgIs2IcQJiZ4VMXlfx+WD0OIMzMzKw0BxBmZjn+3W1WjAMI\nMzPbj8eCWD0OIMzMzKw0BxBmZjn+5Z3x22D1OIAwM8OBg1lZDiDMzMysNAcQZmb7cFcEgNwlY3U4\ngDAzM7PSHECYmeX4h7dZMU0NICTNkvSopBWSFlRZLkmXpeUPSDqpXllJYyTdKumx9Hd0btmfp/yP\nSjq18S00s3bhuGFffj+snqYFEJI6gMuB2cA0YK6kaRXZZgNT02s+cGWBsguApRExFVia5knLzwam\nA7OAK9J6zMzMrKRm9kDMBFZExMqI2AVcC8ypyDMHuCYydwKjJI2vU3YOsChNLwLOyKVfGxE7I+IX\nwIq0HjOzF/iXt1kxzQwgJgCrc/NrUlqRPLXKjouIdWn6SWBcie2ZmRkeC2L1DehBlBERQJQtJ2m+\npGWSlq1fv74BNTMzM2tvzQwg1gKTcvMTU1qRPLXKPpVOc5D+Pl1iewBExFURMSMiZowdO7Zwg8zM\nBgr5ZI7V0cwA4i5gqqRjJA0hG+C4uCLPYuDcdDXGycCmdHqiVtnFwLw0PQ+4MZd+tqShko4hG5j5\n00Y1zszai2+cZFZOZ7M2HBFdki4CbgE6gKsjYrmkC9LyhcAS4DSyAY/bgPNqlU2rvhS4TtL5wOPA\nWanMcknXAQ8DXcCFEbGnf1prZmY2sDQtgACIiCVkQUI+bWFuOoALi5ZN6RuAU3oocwlwyQFU2cwG\nuNKDpgYqd8hYHQN6EKWZmZk1hgMIMzMzK80BhJkZ7rGv5DGlVo8DCDMzMyvNAYSZme3HHRBWjwMI\nM7Oc8GUYZoU4gDAzM7PSHECYmdl+fGdOq8cBhJkZ+KS/WUkOIMzMzKw0BxBmZvvwKEpwh4zV5wDC\nzMzMSnMAYWZmZqU5gDAzw132lXwRhtXjAMLMzMxKcwBhZmb7kftkrA4HEGZmOb6VtVkxDiDMzMys\nNAcQZma2Hw+itHocQJiZ4Wc/mJXlAMLMzMxKcwBhZpbjMZRmxTiAMDMzs9IcQJiZ2X48JMTqcQBh\nZoZvZW1WVlMCCEljJN0q6bH0d3QP+WZJelTSCkkLipSX9Ocp/6OSTk1pwyXdJOlnkpZLurTxrTQz\nMxu4mtUDsQBYGhFTgaVpfh+SOoDLgdnANGCupGm1yqflZwPTgVnAFWk9AJ+LiGOBVwNvkDS7UY0z\nM2t3vpW11dOsAGIOsChNLwLOqJJnJrAiIlZGxC7g2lSuVvk5wLURsTMifgGsAGZGxLaIuA0grese\nYGIft8nMBgDfytqsmGYFEOMiYl2afhIYVyXPBGB1bn5NSqtVvlYZACSNAk4n67kwMzOzXuhs1Iol\nfRd4aZVFf5mfiYiQ1OuYv0x5SZ3AN4DLImJljXzzgfkAkydP7m3VzMzalq/CsHoaFkBExNt6Wibp\nKUnjI2KdpPHA01WyrQUm5eYnpjSAnsrXKgNwFfBYRHyhTt2vSnmZMWOGOzTNDgL+wtyXT+VYPc06\nhbEYmJem5wE3VslzFzBV0jGShpANjlxcp/xi4GxJQyUdA0wFfgog6VPA4cBH+rgtZmZmB51mBRCX\nAm+X9BjwtjSPpKMkLQGIiC7gIuAW4BHguohYXqt8Wn4d8DDwHeDCiNgjaSLZqZNpwD2S7pP0O/3T\nVDNrJ+GbWQPukbH6GnYKo5aI2ACcUiX9CeC03PwSYEnR8mnZJcAlFWlr8H1izMzM+ozvRGlmZmal\nOYAwM8M3TjIrywGEWZubftThALz2ZWOaXJPGGjV88AGv49iXHtbjslOnZ7eTOWLE0APeTqt5yWHl\n2zQoDYI449UT6uS0g5XC1+rUNGPGjFi2bFmzq2FW07NbdzFmxJBmV6Nhtu3qYpDEsMEd9TP3YMvO\nLgZ3iKGd1dexd2+wecduRg0fWO/j1p1ddAzq3Xu3aftuRgzpoLPDvzUPJpLujogZ9fI1ZRClmfWt\ngRw8AAwfcuD/qg4dWnsdgwZpwAUPACPqtLuWww858F4fG7gcVpqZmVlpDiDMzMysNAcQZmZmVpoD\nCDMzMyvNAYSZmZmV5gDCzMzMSnMAYWZmZqU5gDAzM7PSHECYmZlZaQ4gzMzMrDQ/C6MOSeuBx/tw\nlUcCz/Th+pppoLRloLQD3JZWNVDaMlDaAW5LLUdHxNh6mRxA9DNJy4o8pKQdDJS2DJR2gNvSqgZK\nWwZKO8Bt6Qs+hWFmZmalOYAwMzOz0hxA9L+rml2BPjRQ2jJQ2gFuS6saKG0ZKO0At+WAeQyEmZmZ\nleYeCDMzMyvNAUQfkTRL0qOSVkhaUGW5JF2Wlj8g6aSiZftbgback9rwoKTbJZ2YW7Yqpd8naVn/\n1nx/BdryFkmbUn3vk3Rx0bL9rUBb/jTXjock7ZE0Ji1rmc9F0tWSnpb0UA/L2+lYqdeWtjhWCrSj\nnY6Tem1pl+NkkqTbJD0sabmkP6qSp7nHSkT4dYAvoAP4OfAyYAhwPzCtIs9pwM2AgJOBnxQt24Jt\neT0wOk3P7m5Lml8FHNnsz6REW94CfLs3ZVutLRX5Twf+t0U/lzcDJwEP9bC8LY6Vgm1pl2OlXjva\n4jgp0paKvK18nIwHTkrThwH/12rfK+6B6BszgRURsTIidgHXAnMq8swBronMncAoSeMLlu1PdesT\nEbdHxHNp9k5gYj/XsagDeW/b7nOpMBf4Rr/UrKSI+AHwbI0s7XKs1G1LuxwrBT6TnrTdZ1KhlY+T\ndRFxT5p+HngEmFCRranHigOIvjEBWJ2bX8P+H3RPeYqU7U9l63M+WQTcLYDvSrpb0vwG1K+Mom15\nfer+u1nS9JJl+0vh+kgaDswCvplLbqXPpZ52OVbKauVjpYh2OE4Ka6fjRNIU4NXATyoWNfVY6ezr\nFdrBQ9JvkP1TfGMu+Y0RsVbSS4BbJf0s/SJoVfcAkyNii6TTgP8Gpja5TgfqdODHEZH/FdZun8uA\nMgCOFR8nTSLpULIg5yMRsbmZdankHoi+sRaYlJufmNKK5ClStj8Vqo+kE4CvAnMiYkN3ekSsTX+f\nBm4g60prlrptiYjNEbElTS8BBks6skjZflamPmdT0S3bYp9LPe1yrBTSJsdKTW10nJTR8seJpMFk\nwcO/RcR/VcnS3GOlGYNDBtqLrCdnJXAMLw5YmV6R553sO9jlp0XLtmBbJgMrgNdXpI8ADstN3w7M\navG2vJQX74cyE/hl+oza7nNJ+Q4nO/87olU/l1SPKfQ8YK8tjpWCbWmLY6VAO9riOCnSlrS85Y+T\n9P5eA3yhRp6mHis+hdEHIqJL0kXALWSjX6+OiOWSLkjLFwJLyEbMrgC2AefVKtuEZlCrPhVtuRg4\nArhCEkBXZA9yGQfckNI6gX+PiO80oRmkuhZpy5nA70vqArYDZ0d2BLbj5wLwHuB/ImJrrnhLfS6S\nvkE2qv9ISWuAvwIGQ3sdK1CoLW1xrBRoR1scJ1CoLdAGxwnwBuC3gQcl3ZfS/oIsKG2JY8V3ojQz\nM7PSPAbCzMzMSnMAYWZmZqU5gDAzM7PSHECYmZlZaQ4gzMzM2kC9B4WVXNdv5B4qdp+kHZLOKLMO\nBxBm1mckjZL04dz8UZKub9C2zsg/FbLK8uMlfb0R2zZrkq+T3X77gEXEbRHxqoh4FfBWsstA/6fM\nOhxAmFlfGgW8EEBExBMRcWaDtvVx4IqeFkbEg8BESZMbtH2zfhVVHhQm6eWSvpOe3/FDScf2YtVn\nAjdHxLYyhRxAmFlfuhR4eeoS/XtJU7q7WyV9UNJ/S7pV0ipJF0n6qKR7Jd0paUzKV/cfoqRXADsj\n4pk0/35JD0m6X1L+2QXfIrtlsdlAdRXwBxHxGuBj1Aiqa9jvtt5F+E6UZtaXFgDHpW7R7qcI5h1H\n9lTBYWR3z/uziHi1pH8EzgW+QPYP8YKIeEzSa8n+Ib61Yj1vIHvAU7eLgVMjexDSqFz6slSnz/ZB\n28xaSnrQ1uuB/0x30AQYmpa9F/hklWJrI+LU3DrGA8eT3bWyFAcQZtafbouI54HnJW0i6yEAeBA4\nodY/xArjgfW5+R8DX5d0HZB/6NDTwFF9WH+zVjII2NgdsOdF9vCtag/gqnQWcENE7O7Nxs3M+svO\n3PTe3Pxesh80L/xDzL1eWWU928l6MQCIiAuAT5A9gfBuSUekRcNSXrMBJ7LHe/9C0vsBlDmx5Grm\n0ovTF+AAwsz61vPAYb0tXOIf4iPAr3TPSHp5RPwkIi4m65nofpTxK4ADvuTNrBWkB4XdAfyqpDWS\nzgfOAc6XdD+wHJhTYn1TyI6V7/emPj6FYWZ9JiI2SPpxGjh5M3B5L1ZzDnClpE+QPUXxWrLHEef9\nAPgHSUpPhfx7SVPJHmu8NJf/N4CbelEHs5YTEXN7WNSrSzsjYhUwobf18dM4zawtSfoi8K2I+G4P\ny4eS/bJ6Y0R09WvlzA4CPoVhZu3q08DwGssnAwscPJg1hnsgzMzMrDT3QJiZmVlpDiDMzMysNAcQ\nZmZmVpoDCDMzMyvNAYSZmZmV5gDCzMzMSvv/OfHMPLnwjL8AAAAASUVORK5CYII=\n",
+      "application/javascript": [
+       "/* Put everything inside the global mpl namespace */\n",
+       "window.mpl = {};\n",
+       "\n",
+       "\n",
+       "mpl.get_websocket_type = function() {\n",
+       "    if (typeof(WebSocket) !== 'undefined') {\n",
+       "        return WebSocket;\n",
+       "    } else if (typeof(MozWebSocket) !== 'undefined') {\n",
+       "        return MozWebSocket;\n",
+       "    } else {\n",
+       "        alert('Your browser does not have WebSocket support.' +\n",
+       "              'Please try Chrome, Safari or Firefox ≥ 6. ' +\n",
+       "              'Firefox 4 and 5 are also supported but you ' +\n",
+       "              'have to enable WebSockets in about:config.');\n",
+       "    };\n",
+       "}\n",
+       "\n",
+       "mpl.figure = function(figure_id, websocket, ondownload, parent_element) {\n",
+       "    this.id = figure_id;\n",
+       "\n",
+       "    this.ws = websocket;\n",
+       "\n",
+       "    this.supports_binary = (this.ws.binaryType != undefined);\n",
+       "\n",
+       "    if (!this.supports_binary) {\n",
+       "        var warnings = document.getElementById(\"mpl-warnings\");\n",
+       "        if (warnings) {\n",
+       "            warnings.style.display = 'block';\n",
+       "            warnings.textContent = (\n",
+       "                \"This browser does not support binary websocket messages. \" +\n",
+       "                    \"Performance may be slow.\");\n",
+       "        }\n",
+       "    }\n",
+       "\n",
+       "    this.imageObj = new Image();\n",
+       "\n",
+       "    this.context = undefined;\n",
+       "    this.message = undefined;\n",
+       "    this.canvas = undefined;\n",
+       "    this.rubberband_canvas = undefined;\n",
+       "    this.rubberband_context = undefined;\n",
+       "    this.format_dropdown = undefined;\n",
+       "\n",
+       "    this.image_mode = 'full';\n",
+       "\n",
+       "    this.root = $('
');\n",
+       "    this._root_extra_style(this.root)\n",
+       "    this.root.attr('style', 'display: inline-block');\n",
+       "\n",
+       "    $(parent_element).append(this.root);\n",
+       "\n",
+       "    this._init_header(this);\n",
+       "    this._init_canvas(this);\n",
+       "    this._init_toolbar(this);\n",
+       "\n",
+       "    var fig = this;\n",
+       "\n",
+       "    this.waiting = false;\n",
+       "\n",
+       "    this.ws.onopen =  function () {\n",
+       "            fig.send_message(\"supports_binary\", {value: fig.supports_binary});\n",
+       "            fig.send_message(\"send_image_mode\", {});\n",
+       "            if (mpl.ratio != 1) {\n",
+       "                fig.send_message(\"set_dpi_ratio\", {'dpi_ratio': mpl.ratio});\n",
+       "            }\n",
+       "            fig.send_message(\"refresh\", {});\n",
+       "        }\n",
+       "\n",
+       "    this.imageObj.onload = function() {\n",
+       "            if (fig.image_mode == 'full') {\n",
+       "                // Full images could contain transparency (where diff images\n",
+       "                // almost always do), so we need to clear the canvas so that\n",
+       "                // there is no ghosting.\n",
+       "                fig.context.clearRect(0, 0, fig.canvas.width, fig.canvas.height);\n",
+       "            }\n",
+       "            fig.context.drawImage(fig.imageObj, 0, 0);\n",
+       "        };\n",
+       "\n",
+       "    this.imageObj.onunload = function() {\n",
+       "        this.ws.close();\n",
+       "    }\n",
+       "\n",
+       "    this.ws.onmessage = this._make_on_message_function(this);\n",
+       "\n",
+       "    this.ondownload = ondownload;\n",
+       "}\n",
+       "\n",
+       "mpl.figure.prototype._init_header = function() {\n",
+       "    var titlebar = $(\n",
+       "        '
');\n",
+       "    var titletext = $(\n",
+       "        '
');\n",
+       "    titlebar.append(titletext)\n",
+       "    this.root.append(titlebar);\n",
+       "    this.header = titletext[0];\n",
+       "}\n",
+       "\n",
+       "\n",
+       "\n",
+       "mpl.figure.prototype._canvas_extra_style = function(canvas_div) {\n",
+       "\n",
+       "}\n",
+       "\n",
+       "\n",
+       "mpl.figure.prototype._root_extra_style = function(canvas_div) {\n",
+       "\n",
+       "}\n",
+       "\n",
+       "mpl.figure.prototype._init_canvas = function() {\n",
+       "    var fig = this;\n",
+       "\n",
+       "    var canvas_div = $('
');\n",
+       "\n",
+       "    canvas_div.attr('style', 'position: relative; clear: both; outline: 0');\n",
+       "\n",
+       "    function canvas_keyboard_event(event) {\n",
+       "        return fig.key_event(event, event['data']);\n",
+       "    }\n",
+       "\n",
+       "    canvas_div.keydown('key_press', canvas_keyboard_event);\n",
+       "    canvas_div.keyup('key_release', canvas_keyboard_event);\n",
+       "    this.canvas_div = canvas_div\n",
+       "    this._canvas_extra_style(canvas_div)\n",
+       "    this.root.append(canvas_div);\n",
+       "\n",
+       "    var canvas = $('');\n",
+       "    canvas.addClass('mpl-canvas');\n",
+       "    canvas.attr('style', \"left: 0; top: 0; z-index: 0; outline: 0\")\n",
+       "\n",
+       "    this.canvas = canvas[0];\n",
+       "    this.context = canvas[0].getContext(\"2d\");\n",
+       "\n",
+       "    var backingStore = this.context.backingStorePixelRatio ||\n",
+       "\tthis.context.webkitBackingStorePixelRatio ||\n",
+       "\tthis.context.mozBackingStorePixelRatio ||\n",
+       "\tthis.context.msBackingStorePixelRatio ||\n",
+       "\tthis.context.oBackingStorePixelRatio ||\n",
+       "\tthis.context.backingStorePixelRatio || 1;\n",
+       "\n",
+       "    mpl.ratio = (window.devicePixelRatio || 1) / backingStore;\n",
+       "\n",
+       "    var rubberband = $('');\n",
+       "    rubberband.attr('style', \"position: absolute; left: 0; top: 0; z-index: 1;\")\n",
+       "\n",
+       "    var pass_mouse_events = true;\n",
+       "\n",
+       "    canvas_div.resizable({\n",
+       "        start: function(event, ui) {\n",
+       "            pass_mouse_events = false;\n",
+       "        },\n",
+       "        resize: function(event, ui) {\n",
+       "            fig.request_resize(ui.size.width, ui.size.height);\n",
+       "        },\n",
+       "        stop: function(event, ui) {\n",
+       "            pass_mouse_events = true;\n",
+       "            fig.request_resize(ui.size.width, ui.size.height);\n",
+       "        },\n",
+       "    });\n",
+       "\n",
+       "    function mouse_event_fn(event) {\n",
+       "        if (pass_mouse_events)\n",
+       "            return fig.mouse_event(event, event['data']);\n",
+       "    }\n",
+       "\n",
+       "    rubberband.mousedown('button_press', mouse_event_fn);\n",
+       "    rubberband.mouseup('button_release', mouse_event_fn);\n",
+       "    // Throttle sequential mouse events to 1 every 20ms.\n",
+       "    rubberband.mousemove('motion_notify', mouse_event_fn);\n",
+       "\n",
+       "    rubberband.mouseenter('figure_enter', mouse_event_fn);\n",
+       "    rubberband.mouseleave('figure_leave', mouse_event_fn);\n",
+       "\n",
+       "    canvas_div.on(\"wheel\", function (event) {\n",
+       "        event = event.originalEvent;\n",
+       "        event['data'] = 'scroll'\n",
+       "        if (event.deltaY < 0) {\n",
+       "            event.step = 1;\n",
+       "        } else {\n",
+       "            event.step = -1;\n",
+       "        }\n",
+       "        mouse_event_fn(event);\n",
+       "    });\n",
+       "\n",
+       "    canvas_div.append(canvas);\n",
+       "    canvas_div.append(rubberband);\n",
+       "\n",
+       "    this.rubberband = rubberband;\n",
+       "    this.rubberband_canvas = rubberband[0];\n",
+       "    this.rubberband_context = rubberband[0].getContext(\"2d\");\n",
+       "    this.rubberband_context.strokeStyle = \"#000000\";\n",
+       "\n",
+       "    this._resize_canvas = function(width, height) {\n",
+       "        // Keep the size of the canvas, canvas container, and rubber band\n",
+       "        // canvas in synch.\n",
+       "        canvas_div.css('width', width)\n",
+       "        canvas_div.css('height', height)\n",
+       "\n",
+       "        canvas.attr('width', width * mpl.ratio);\n",
+       "        canvas.attr('height', height * mpl.ratio);\n",
+       "        canvas.attr('style', 'width: ' + width + 'px; height: ' + height + 'px;');\n",
+       "\n",
+       "        rubberband.attr('width', width);\n",
+       "        rubberband.attr('height', height);\n",
+       "    }\n",
+       "\n",
+       "    // Set the figure to an initial 600x600px, this will subsequently be updated\n",
+       "    // upon first draw.\n",
+       "    this._resize_canvas(600, 600);\n",
+       "\n",
+       "    // Disable right mouse context menu.\n",
+       "    $(this.rubberband_canvas).bind(\"contextmenu\",function(e){\n",
+       "        return false;\n",
+       "    });\n",
+       "\n",
+       "    function set_focus () {\n",
+       "        canvas.focus();\n",
+       "        canvas_div.focus();\n",
+       "    }\n",
+       "\n",
+       "    window.setTimeout(set_focus, 100);\n",
+       "}\n",
+       "\n",
+       "mpl.figure.prototype._init_toolbar = function() {\n",
+       "    var fig = this;\n",
+       "\n",
+       "    var nav_element = $('
')\n",
+       "    nav_element.attr('style', 'width: 100%');\n",
+       "    this.root.append(nav_element);\n",
+       "\n",
+       "    // Define a callback function for later on.\n",
+       "    function toolbar_event(event) {\n",
+       "        return fig.toolbar_button_onclick(event['data']);\n",
+       "    }\n",
+       "    function toolbar_mouse_event(event) {\n",
+       "        return fig.toolbar_button_onmouseover(event['data']);\n",
+       "    }\n",
+       "\n",
+       "    for(var toolbar_ind in mpl.toolbar_items) {\n",
+       "        var name = mpl.toolbar_items[toolbar_ind][0];\n",
+       "        var tooltip = mpl.toolbar_items[toolbar_ind][1];\n",
+       "        var image = mpl.toolbar_items[toolbar_ind][2];\n",
+       "        var method_name = mpl.toolbar_items[toolbar_ind][3];\n",
+       "\n",
+       "        if (!name) {\n",
+       "            // put a spacer in here.\n",
+       "            continue;\n",
+       "        }\n",
+       "        var button = $('');\n",
+       "        button.click(method_name, toolbar_event);\n",
+       "        button.mouseover(tooltip, toolbar_mouse_event);\n",
+       "        nav_element.append(button);\n",
+       "    }\n",
+       "\n",
+       "    // Add the status bar.\n",
+       "    var status_bar = $('');\n",
+       "    nav_element.append(status_bar);\n",
+       "    this.message = status_bar[0];\n",
+       "\n",
+       "    // Add the close button to the window.\n",
+       "    var buttongrp = $('
');\n",
+       "    var button = $('');\n",
+       "    button.click(function (evt) { fig.handle_close(fig, {}); } );\n",
+       "    button.mouseover('Stop Interaction', toolbar_mouse_event);\n",
+       "    buttongrp.append(button);\n",
+       "    var titlebar = this.root.find($('.ui-dialog-titlebar'));\n",
+       "    titlebar.prepend(buttongrp);\n",
+       "}\n",
+       "\n",
+       "mpl.figure.prototype._root_extra_style = function(el){\n",
+       "    var fig = this\n",
+       "    el.on(\"remove\", function(){\n",
+       "\tfig.close_ws(fig, {});\n",
+       "    });\n",
+       "}\n",
+       "\n",
+       "mpl.figure.prototype._canvas_extra_style = function(el){\n",
+       "    // this is important to make the div 'focusable\n",
+       "    el.attr('tabindex', 0)\n",
+       "    // reach out to IPython and tell the keyboard manager to turn it's self\n",
+       "    // off when our div gets focus\n",
+       "\n",
+       "    // location in version 3\n",
+       "    if (IPython.notebook.keyboard_manager) {\n",
+       "        IPython.notebook.keyboard_manager.register_events(el);\n",
+       "    }\n",
+       "    else {\n",
+       "        // location in version 2\n",
+       "        IPython.keyboard_manager.register_events(el);\n",
+       "    }\n",
+       "\n",
+       "}\n",
+       "\n",
+       "mpl.figure.prototype._key_event_extra = function(event, name) {\n",
+       "    var manager = IPython.notebook.keyboard_manager;\n",
+       "    if (!manager)\n",
+       "        manager = IPython.keyboard_manager;\n",
+       "\n",
+       "    // Check for shift+enter\n",
+       "    if (event.shiftKey && event.which == 13) {\n",
+       "        this.canvas_div.blur();\n",
+       "        // select the cell after this one\n",
+       "        var index = IPython.notebook.find_cell_index(this.cell_info[0]);\n",
+       "        IPython.notebook.select(index + 1);\n",
+       "    }\n",
+       "}\n",
+       "\n",
+       "mpl.figure.prototype.handle_save = function(fig, msg) {\n",
+       "    fig.ondownload(fig, null);\n",
+       "}\n",
+       "\n",
+       "\n",
+       "mpl.find_output_cell = function(html_output) {\n",
+       "    // Return the cell and output element which can be found *uniquely* in the notebook.\n",
+       "    // Note - this is a bit hacky, but it is done because the \"notebook_saving.Notebook\"\n",
+       "    // IPython event is triggered only after the cells have been serialised, which for\n",
+       "    // our purposes (turning an active figure into a static one), is too late.\n",
+       "    var cells = IPython.notebook.get_cells();\n",
+       "    var ncells = cells.length;\n",
+       "    for (var i=0; i= 3 moved mimebundle to data attribute of output\n",
+       "                    data = data.data;\n",
+       "                }\n",
+       "                if (data['text/html'] == html_output) {\n",
+       "                    return [cell, data, j];\n",
+       "                }\n",
+       "            }\n",
+       "        }\n",
+       "    }\n",
+       "}\n",
+       "\n",
+       "// Register the function which deals with the matplotlib target/channel.\n",
+       "// The kernel may be null if the page has been refreshed.\n",
+       "if (IPython.notebook.kernel != null) {\n",
+       "    IPython.notebook.kernel.comm_manager.register_target('matplotlib', mpl.mpl_figure_comm);\n",
+       "}\n"
+      ],
+      "text/plain": [
+       ""
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "text/html": [
+       ""
+      ],
       "text/plain": [
-       ""
+       ""
       ]
      },
-     "execution_count": 13,
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "text/plain": [
+       ""
+      ]
+     },
+     "execution_count": 47,
      "metadata": {},
      "output_type": "execute_result"
     }
    ],
    "source": [
-    "plot = qc.MatPlot(data2.my_controller_demod_freq_0_mag)\n",
-    "plot.fig"
+    "plot = qc.MatPlot()\n",
+    "plot.add(data2.my_controller_demod_freq_0_mag)\n",
+    "plot.fig.axes[0].legend()"
    ]
   },
   {
    "cell_type": "code",
    "execution_count": 14,
    "metadata": {
-    "collapsed": false
+    "collapsed": false,
+    "scrolled": false
    },
    "outputs": [
     {
      "data": {
-      "image/png": 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ZcMVEQC1YTlBYFsUY2Dhfxwi/ogqA0ieFpVHG5mZxx/Lp1gYdP0eUF/ZX09YVZ9va2QAY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\nSgBERESykBIAERGRLPT/FvD3wTTMRkoAAAAASUVORK5CYII=\n",
+      "application/javascript": [
+       "/* Put everything inside the global mpl namespace */\n",
+       "window.mpl = {};\n",
+       "\n",
+       "\n",
+       "mpl.get_websocket_type = function() {\n",
+       "    if (typeof(WebSocket) !== 'undefined') {\n",
+       "        return WebSocket;\n",
+       "    } else if (typeof(MozWebSocket) !== 'undefined') {\n",
+       "        return MozWebSocket;\n",
+       "    } else {\n",
+       "        alert('Your browser does not have WebSocket support.' +\n",
+       "              'Please try Chrome, Safari or Firefox ≥ 6. ' +\n",
+       "              'Firefox 4 and 5 are also supported but you ' +\n",
+       "              'have to enable WebSockets in about:config.');\n",
+       "    };\n",
+       "}\n",
+       "\n",
+       "mpl.figure = function(figure_id, websocket, ondownload, parent_element) {\n",
+       "    this.id = figure_id;\n",
+       "\n",
+       "    this.ws = websocket;\n",
+       "\n",
+       "    this.supports_binary = (this.ws.binaryType != undefined);\n",
+       "\n",
+       "    if (!this.supports_binary) {\n",
+       "        var warnings = document.getElementById(\"mpl-warnings\");\n",
+       "        if (warnings) {\n",
+       "            warnings.style.display = 'block';\n",
+       "            warnings.textContent = (\n",
+       "                \"This browser does not support binary websocket messages. \" +\n",
+       "                    \"Performance may be slow.\");\n",
+       "        }\n",
+       "    }\n",
+       "\n",
+       "    this.imageObj = new Image();\n",
+       "\n",
+       "    this.context = undefined;\n",
+       "    this.message = undefined;\n",
+       "    this.canvas = undefined;\n",
+       "    this.rubberband_canvas = undefined;\n",
+       "    this.rubberband_context = undefined;\n",
+       "    this.format_dropdown = undefined;\n",
+       "\n",
+       "    this.image_mode = 'full';\n",
+       "\n",
+       "    this.root = $('
');\n",
+       "    this._root_extra_style(this.root)\n",
+       "    this.root.attr('style', 'display: inline-block');\n",
+       "\n",
+       "    $(parent_element).append(this.root);\n",
+       "\n",
+       "    this._init_header(this);\n",
+       "    this._init_canvas(this);\n",
+       "    this._init_toolbar(this);\n",
+       "\n",
+       "    var fig = this;\n",
+       "\n",
+       "    this.waiting = false;\n",
+       "\n",
+       "    this.ws.onopen =  function () {\n",
+       "            fig.send_message(\"supports_binary\", {value: fig.supports_binary});\n",
+       "            fig.send_message(\"send_image_mode\", {});\n",
+       "            if (mpl.ratio != 1) {\n",
+       "                fig.send_message(\"set_dpi_ratio\", {'dpi_ratio': mpl.ratio});\n",
+       "            }\n",
+       "            fig.send_message(\"refresh\", {});\n",
+       "        }\n",
+       "\n",
+       "    this.imageObj.onload = function() {\n",
+       "            if (fig.image_mode == 'full') {\n",
+       "                // Full images could contain transparency (where diff images\n",
+       "                // almost always do), so we need to clear the canvas so that\n",
+       "                // there is no ghosting.\n",
+       "                fig.context.clearRect(0, 0, fig.canvas.width, fig.canvas.height);\n",
+       "            }\n",
+       "            fig.context.drawImage(fig.imageObj, 0, 0);\n",
+       "        };\n",
+       "\n",
+       "    this.imageObj.onunload = function() {\n",
+       "        this.ws.close();\n",
+       "    }\n",
+       "\n",
+       "    this.ws.onmessage = this._make_on_message_function(this);\n",
+       "\n",
+       "    this.ondownload = ondownload;\n",
+       "}\n",
+       "\n",
+       "mpl.figure.prototype._init_header = function() {\n",
+       "    var titlebar = $(\n",
+       "        '
');\n",
+       "    var titletext = $(\n",
+       "        '
');\n",
+       "    titlebar.append(titletext)\n",
+       "    this.root.append(titlebar);\n",
+       "    this.header = titletext[0];\n",
+       "}\n",
+       "\n",
+       "\n",
+       "\n",
+       "mpl.figure.prototype._canvas_extra_style = function(canvas_div) {\n",
+       "\n",
+       "}\n",
+       "\n",
+       "\n",
+       "mpl.figure.prototype._root_extra_style = function(canvas_div) {\n",
+       "\n",
+       "}\n",
+       "\n",
+       "mpl.figure.prototype._init_canvas = function() {\n",
+       "    var fig = this;\n",
+       "\n",
+       "    var canvas_div = $('
');\n",
+       "\n",
+       "    canvas_div.attr('style', 'position: relative; clear: both; outline: 0');\n",
+       "\n",
+       "    function canvas_keyboard_event(event) {\n",
+       "        return fig.key_event(event, event['data']);\n",
+       "    }\n",
+       "\n",
+       "    canvas_div.keydown('key_press', canvas_keyboard_event);\n",
+       "    canvas_div.keyup('key_release', canvas_keyboard_event);\n",
+       "    this.canvas_div = canvas_div\n",
+       "    this._canvas_extra_style(canvas_div)\n",
+       "    this.root.append(canvas_div);\n",
+       "\n",
+       "    var canvas = $('');\n",
+       "    canvas.addClass('mpl-canvas');\n",
+       "    canvas.attr('style', \"left: 0; top: 0; z-index: 0; outline: 0\")\n",
+       "\n",
+       "    this.canvas = canvas[0];\n",
+       "    this.context = canvas[0].getContext(\"2d\");\n",
+       "\n",
+       "    var backingStore = this.context.backingStorePixelRatio ||\n",
+       "\tthis.context.webkitBackingStorePixelRatio ||\n",
+       "\tthis.context.mozBackingStorePixelRatio ||\n",
+       "\tthis.context.msBackingStorePixelRatio ||\n",
+       "\tthis.context.oBackingStorePixelRatio ||\n",
+       "\tthis.context.backingStorePixelRatio || 1;\n",
+       "\n",
+       "    mpl.ratio = (window.devicePixelRatio || 1) / backingStore;\n",
+       "\n",
+       "    var rubberband = $('');\n",
+       "    rubberband.attr('style', \"position: absolute; left: 0; top: 0; z-index: 1;\")\n",
+       "\n",
+       "    var pass_mouse_events = true;\n",
+       "\n",
+       "    canvas_div.resizable({\n",
+       "        start: function(event, ui) {\n",
+       "            pass_mouse_events = false;\n",
+       "        },\n",
+       "        resize: function(event, ui) {\n",
+       "            fig.request_resize(ui.size.width, ui.size.height);\n",
+       "        },\n",
+       "        stop: function(event, ui) {\n",
+       "            pass_mouse_events = true;\n",
+       "            fig.request_resize(ui.size.width, ui.size.height);\n",
+       "        },\n",
+       "    });\n",
+       "\n",
+       "    function mouse_event_fn(event) {\n",
+       "        if (pass_mouse_events)\n",
+       "            return fig.mouse_event(event, event['data']);\n",
+       "    }\n",
+       "\n",
+       "    rubberband.mousedown('button_press', mouse_event_fn);\n",
+       "    rubberband.mouseup('button_release', mouse_event_fn);\n",
+       "    // Throttle sequential mouse events to 1 every 20ms.\n",
+       "    rubberband.mousemove('motion_notify', mouse_event_fn);\n",
+       "\n",
+       "    rubberband.mouseenter('figure_enter', mouse_event_fn);\n",
+       "    rubberband.mouseleave('figure_leave', mouse_event_fn);\n",
+       "\n",
+       "    canvas_div.on(\"wheel\", function (event) {\n",
+       "        event = event.originalEvent;\n",
+       "        event['data'] = 'scroll'\n",
+       "        if (event.deltaY < 0) {\n",
+       "            event.step = 1;\n",
+       "        } else {\n",
+       "            event.step = -1;\n",
+       "        }\n",
+       "        mouse_event_fn(event);\n",
+       "    });\n",
+       "\n",
+       "    canvas_div.append(canvas);\n",
+       "    canvas_div.append(rubberband);\n",
+       "\n",
+       "    this.rubberband = rubberband;\n",
+       "    this.rubberband_canvas = rubberband[0];\n",
+       "    this.rubberband_context = rubberband[0].getContext(\"2d\");\n",
+       "    this.rubberband_context.strokeStyle = \"#000000\";\n",
+       "\n",
+       "    this._resize_canvas = function(width, height) {\n",
+       "        // Keep the size of the canvas, canvas container, and rubber band\n",
+       "        // canvas in synch.\n",
+       "        canvas_div.css('width', width)\n",
+       "        canvas_div.css('height', height)\n",
+       "\n",
+       "        canvas.attr('width', width * mpl.ratio);\n",
+       "        canvas.attr('height', height * mpl.ratio);\n",
+       "        canvas.attr('style', 'width: ' + width + 'px; height: ' + height + 'px;');\n",
+       "\n",
+       "        rubberband.attr('width', width);\n",
+       "        rubberband.attr('height', height);\n",
+       "    }\n",
+       "\n",
+       "    // Set the figure to an initial 600x600px, this will subsequently be updated\n",
+       "    // upon first draw.\n",
+       "    this._resize_canvas(600, 600);\n",
+       "\n",
+       "    // Disable right mouse context menu.\n",
+       "    $(this.rubberband_canvas).bind(\"contextmenu\",function(e){\n",
+       "        return false;\n",
+       "    });\n",
+       "\n",
+       "    function set_focus () {\n",
+       "        canvas.focus();\n",
+       "        canvas_div.focus();\n",
+       "    }\n",
+       "\n",
+       "    window.setTimeout(set_focus, 100);\n",
+       "}\n",
+       "\n",
+       "mpl.figure.prototype._init_toolbar = function() {\n",
+       "    var fig = this;\n",
+       "\n",
+       "    var nav_element = $('
')\n",
+       "    nav_element.attr('style', 'width: 100%');\n",
+       "    this.root.append(nav_element);\n",
+       "\n",
+       "    // Define a callback function for later on.\n",
+       "    function toolbar_event(event) {\n",
+       "        return fig.toolbar_button_onclick(event['data']);\n",
+       "    }\n",
+       "    function toolbar_mouse_event(event) {\n",
+       "        return fig.toolbar_button_onmouseover(event['data']);\n",
+       "    }\n",
+       "\n",
+       "    for(var toolbar_ind in mpl.toolbar_items) {\n",
+       "        var name = mpl.toolbar_items[toolbar_ind][0];\n",
+       "        var tooltip = mpl.toolbar_items[toolbar_ind][1];\n",
+       "        var image = mpl.toolbar_items[toolbar_ind][2];\n",
+       "        var method_name = mpl.toolbar_items[toolbar_ind][3];\n",
+       "\n",
+       "        if (!name) {\n",
+       "            // put a spacer in here.\n",
+       "            continue;\n",
+       "        }\n",
+       "        var button = $('');\n",
+       "        button.click(method_name, toolbar_event);\n",
+       "        button.mouseover(tooltip, toolbar_mouse_event);\n",
+       "        nav_element.append(button);\n",
+       "    }\n",
+       "\n",
+       "    // Add the status bar.\n",
+       "    var status_bar = $('');\n",
+       "    nav_element.append(status_bar);\n",
+       "    this.message = status_bar[0];\n",
+       "\n",
+       "    // Add the close button to the window.\n",
+       "    var buttongrp = $('
');\n",
+       "    var button = $('');\n",
+       "    button.click(function (evt) { fig.handle_close(fig, {}); } );\n",
+       "    button.mouseover('Stop Interaction', toolbar_mouse_event);\n",
+       "    buttongrp.append(button);\n",
+       "    var titlebar = this.root.find($('.ui-dialog-titlebar'));\n",
+       "    titlebar.prepend(buttongrp);\n",
+       "}\n",
+       "\n",
+       "mpl.figure.prototype._root_extra_style = function(el){\n",
+       "    var fig = this\n",
+       "    el.on(\"remove\", function(){\n",
+       "\tfig.close_ws(fig, {});\n",
+       "    });\n",
+       "}\n",
+       "\n",
+       "mpl.figure.prototype._canvas_extra_style = function(el){\n",
+       "    // this is important to make the div 'focusable\n",
+       "    el.attr('tabindex', 0)\n",
+       "    // reach out to IPython and tell the keyboard manager to turn it's self\n",
+       "    // off when our div gets focus\n",
+       "\n",
+       "    // location in version 3\n",
+       "    if (IPython.notebook.keyboard_manager) {\n",
+       "        IPython.notebook.keyboard_manager.register_events(el);\n",
+       "    }\n",
+       "    else {\n",
+       "        // location in version 2\n",
+       "        IPython.keyboard_manager.register_events(el);\n",
+       "    }\n",
+       "\n",
+       "}\n",
+       "\n",
+       "mpl.figure.prototype._key_event_extra = function(event, name) {\n",
+       "    var manager = IPython.notebook.keyboard_manager;\n",
+       "    if (!manager)\n",
+       "        manager = IPython.keyboard_manager;\n",
+       "\n",
+       "    // Check for shift+enter\n",
+       "    if (event.shiftKey && event.which == 13) {\n",
+       "        this.canvas_div.blur();\n",
+       "        // select the cell after this one\n",
+       "        var index = IPython.notebook.find_cell_index(this.cell_info[0]);\n",
+       "        IPython.notebook.select(index + 1);\n",
+       "    }\n",
+       "}\n",
+       "\n",
+       "mpl.figure.prototype.handle_save = function(fig, msg) {\n",
+       "    fig.ondownload(fig, null);\n",
+       "}\n",
+       "\n",
+       "\n",
+       "mpl.find_output_cell = function(html_output) {\n",
+       "    // Return the cell and output element which can be found *uniquely* in the notebook.\n",
+       "    // Note - this is a bit hacky, but it is done because the \"notebook_saving.Notebook\"\n",
+       "    // IPython event is triggered only after the cells have been serialised, which for\n",
+       "    // our purposes (turning an active figure into a static one), is too late.\n",
+       "    var cells = IPython.notebook.get_cells();\n",
+       "    var ncells = cells.length;\n",
+       "    for (var i=0; i= 3 moved mimebundle to data attribute of output\n",
+       "                    data = data.data;\n",
+       "                }\n",
+       "                if (data['text/html'] == html_output) {\n",
+       "                    return [cell, data, j];\n",
+       "                }\n",
+       "            }\n",
+       "        }\n",
+       "    }\n",
+       "}\n",
+       "\n",
+       "// Register the function which deals with the matplotlib target/channel.\n",
+       "// The kernel may be null if the page has been refreshed.\n",
+       "if (IPython.notebook.kernel != null) {\n",
+       "    IPython.notebook.kernel.comm_manager.register_target('matplotlib', mpl.mpl_figure_comm);\n",
+       "}\n"
+      ],
+      "text/plain": [
+       ""
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "text/html": [
+       ""
+      ],
+      "text/plain": [
+       ""
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "plot = qc.MatPlot(data6.my_controller_rec_demod_freq_5_mag)"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 27,
+   "execution_count": 50,
    "metadata": {
     "collapsed": false
    },
    "outputs": [
     {
      "data": {
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t57zcNGscBlO9clCcnC+xbsQxHEbSSX+owogjTUG4AQskreFgsYTgrcbYD49D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/TKiCgG4G3w0TfA1D3U9TasBt780o04cPQsHjl6NtD8UcSRRISNQ2kceN78DitU4Qg7FMpVpAxv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\nmP8fAxszqDdoHlkAAAAASUVORK5CYII=\n",
+      "application/javascript": [
+       "/* Put everything inside the global mpl namespace */\n",
+       "window.mpl = {};\n",
+       "\n",
+       "\n",
+       "mpl.get_websocket_type = function() {\n",
+       "    if (typeof(WebSocket) !== 'undefined') {\n",
+       "        return WebSocket;\n",
+       "    } else if (typeof(MozWebSocket) !== 'undefined') {\n",
+       "        return MozWebSocket;\n",
+       "    } else {\n",
+       "        alert('Your browser does not have WebSocket support.' +\n",
+       "              'Please try Chrome, Safari or Firefox ≥ 6. ' +\n",
+       "              'Firefox 4 and 5 are also supported but you ' +\n",
+       "              'have to enable WebSockets in about:config.');\n",
+       "    };\n",
+       "}\n",
+       "\n",
+       "mpl.figure = function(figure_id, websocket, ondownload, parent_element) {\n",
+       "    this.id = figure_id;\n",
+       "\n",
+       "    this.ws = websocket;\n",
+       "\n",
+       "    this.supports_binary = (this.ws.binaryType != undefined);\n",
+       "\n",
+       "    if (!this.supports_binary) {\n",
+       "        var warnings = document.getElementById(\"mpl-warnings\");\n",
+       "        if (warnings) {\n",
+       "            warnings.style.display = 'block';\n",
+       "            warnings.textContent = (\n",
+       "                \"This browser does not support binary websocket messages. \" +\n",
+       "                    \"Performance may be slow.\");\n",
+       "        }\n",
+       "    }\n",
+       "\n",
+       "    this.imageObj = new Image();\n",
+       "\n",
+       "    this.context = undefined;\n",
+       "    this.message = undefined;\n",
+       "    this.canvas = undefined;\n",
+       "    this.rubberband_canvas = undefined;\n",
+       "    this.rubberband_context = undefined;\n",
+       "    this.format_dropdown = undefined;\n",
+       "\n",
+       "    this.image_mode = 'full';\n",
+       "\n",
+       "    this.root = $('
');\n",
+       "    this._root_extra_style(this.root)\n",
+       "    this.root.attr('style', 'display: inline-block');\n",
+       "\n",
+       "    $(parent_element).append(this.root);\n",
+       "\n",
+       "    this._init_header(this);\n",
+       "    this._init_canvas(this);\n",
+       "    this._init_toolbar(this);\n",
+       "\n",
+       "    var fig = this;\n",
+       "\n",
+       "    this.waiting = false;\n",
+       "\n",
+       "    this.ws.onopen =  function () {\n",
+       "            fig.send_message(\"supports_binary\", {value: fig.supports_binary});\n",
+       "            fig.send_message(\"send_image_mode\", {});\n",
+       "            if (mpl.ratio != 1) {\n",
+       "                fig.send_message(\"set_dpi_ratio\", {'dpi_ratio': mpl.ratio});\n",
+       "            }\n",
+       "            fig.send_message(\"refresh\", {});\n",
+       "        }\n",
+       "\n",
+       "    this.imageObj.onload = function() {\n",
+       "            if (fig.image_mode == 'full') {\n",
+       "                // Full images could contain transparency (where diff images\n",
+       "                // almost always do), so we need to clear the canvas so that\n",
+       "                // there is no ghosting.\n",
+       "                fig.context.clearRect(0, 0, fig.canvas.width, fig.canvas.height);\n",
+       "            }\n",
+       "            fig.context.drawImage(fig.imageObj, 0, 0);\n",
+       "        };\n",
+       "\n",
+       "    this.imageObj.onunload = function() {\n",
+       "        this.ws.close();\n",
+       "    }\n",
+       "\n",
+       "    this.ws.onmessage = this._make_on_message_function(this);\n",
+       "\n",
+       "    this.ondownload = ondownload;\n",
+       "}\n",
+       "\n",
+       "mpl.figure.prototype._init_header = function() {\n",
+       "    var titlebar = $(\n",
+       "        '
');\n",
+       "    var titletext = $(\n",
+       "        '
');\n",
+       "    titlebar.append(titletext)\n",
+       "    this.root.append(titlebar);\n",
+       "    this.header = titletext[0];\n",
+       "}\n",
+       "\n",
+       "\n",
+       "\n",
+       "mpl.figure.prototype._canvas_extra_style = function(canvas_div) {\n",
+       "\n",
+       "}\n",
+       "\n",
+       "\n",
+       "mpl.figure.prototype._root_extra_style = function(canvas_div) {\n",
+       "\n",
+       "}\n",
+       "\n",
+       "mpl.figure.prototype._init_canvas = function() {\n",
+       "    var fig = this;\n",
+       "\n",
+       "    var canvas_div = $('
');\n",
+       "\n",
+       "    canvas_div.attr('style', 'position: relative; clear: both; outline: 0');\n",
+       "\n",
+       "    function canvas_keyboard_event(event) {\n",
+       "        return fig.key_event(event, event['data']);\n",
+       "    }\n",
+       "\n",
+       "    canvas_div.keydown('key_press', canvas_keyboard_event);\n",
+       "    canvas_div.keyup('key_release', canvas_keyboard_event);\n",
+       "    this.canvas_div = canvas_div\n",
+       "    this._canvas_extra_style(canvas_div)\n",
+       "    this.root.append(canvas_div);\n",
+       "\n",
+       "    var canvas = $('');\n",
+       "    canvas.addClass('mpl-canvas');\n",
+       "    canvas.attr('style', \"left: 0; top: 0; z-index: 0; outline: 0\")\n",
+       "\n",
+       "    this.canvas = canvas[0];\n",
+       "    this.context = canvas[0].getContext(\"2d\");\n",
+       "\n",
+       "    var backingStore = this.context.backingStorePixelRatio ||\n",
+       "\tthis.context.webkitBackingStorePixelRatio ||\n",
+       "\tthis.context.mozBackingStorePixelRatio ||\n",
+       "\tthis.context.msBackingStorePixelRatio ||\n",
+       "\tthis.context.oBackingStorePixelRatio ||\n",
+       "\tthis.context.backingStorePixelRatio || 1;\n",
+       "\n",
+       "    mpl.ratio = (window.devicePixelRatio || 1) / backingStore;\n",
+       "\n",
+       "    var rubberband = $('');\n",
+       "    rubberband.attr('style', \"position: absolute; left: 0; top: 0; z-index: 1;\")\n",
+       "\n",
+       "    var pass_mouse_events = true;\n",
+       "\n",
+       "    canvas_div.resizable({\n",
+       "        start: function(event, ui) {\n",
+       "            pass_mouse_events = false;\n",
+       "        },\n",
+       "        resize: function(event, ui) {\n",
+       "            fig.request_resize(ui.size.width, ui.size.height);\n",
+       "        },\n",
+       "        stop: function(event, ui) {\n",
+       "            pass_mouse_events = true;\n",
+       "            fig.request_resize(ui.size.width, ui.size.height);\n",
+       "        },\n",
+       "    });\n",
+       "\n",
+       "    function mouse_event_fn(event) {\n",
+       "        if (pass_mouse_events)\n",
+       "            return fig.mouse_event(event, event['data']);\n",
+       "    }\n",
+       "\n",
+       "    rubberband.mousedown('button_press', mouse_event_fn);\n",
+       "    rubberband.mouseup('button_release', mouse_event_fn);\n",
+       "    // Throttle sequential mouse events to 1 every 20ms.\n",
+       "    rubberband.mousemove('motion_notify', mouse_event_fn);\n",
+       "\n",
+       "    rubberband.mouseenter('figure_enter', mouse_event_fn);\n",
+       "    rubberband.mouseleave('figure_leave', mouse_event_fn);\n",
+       "\n",
+       "    canvas_div.on(\"wheel\", function (event) {\n",
+       "        event = event.originalEvent;\n",
+       "        event['data'] = 'scroll'\n",
+       "        if (event.deltaY < 0) {\n",
+       "            event.step = 1;\n",
+       "        } else {\n",
+       "            event.step = -1;\n",
+       "        }\n",
+       "        mouse_event_fn(event);\n",
+       "    });\n",
+       "\n",
+       "    canvas_div.append(canvas);\n",
+       "    canvas_div.append(rubberband);\n",
+       "\n",
+       "    this.rubberband = rubberband;\n",
+       "    this.rubberband_canvas = rubberband[0];\n",
+       "    this.rubberband_context = rubberband[0].getContext(\"2d\");\n",
+       "    this.rubberband_context.strokeStyle = \"#000000\";\n",
+       "\n",
+       "    this._resize_canvas = function(width, height) {\n",
+       "        // Keep the size of the canvas, canvas container, and rubber band\n",
+       "        // canvas in synch.\n",
+       "        canvas_div.css('width', width)\n",
+       "        canvas_div.css('height', height)\n",
+       "\n",
+       "        canvas.attr('width', width * mpl.ratio);\n",
+       "        canvas.attr('height', height * mpl.ratio);\n",
+       "        canvas.attr('style', 'width: ' + width + 'px; height: ' + height + 'px;');\n",
+       "\n",
+       "        rubberband.attr('width', width);\n",
+       "        rubberband.attr('height', height);\n",
+       "    }\n",
+       "\n",
+       "    // Set the figure to an initial 600x600px, this will subsequently be updated\n",
+       "    // upon first draw.\n",
+       "    this._resize_canvas(600, 600);\n",
+       "\n",
+       "    // Disable right mouse context menu.\n",
+       "    $(this.rubberband_canvas).bind(\"contextmenu\",function(e){\n",
+       "        return false;\n",
+       "    });\n",
+       "\n",
+       "    function set_focus () {\n",
+       "        canvas.focus();\n",
+       "        canvas_div.focus();\n",
+       "    }\n",
+       "\n",
+       "    window.setTimeout(set_focus, 100);\n",
+       "}\n",
+       "\n",
+       "mpl.figure.prototype._init_toolbar = function() {\n",
+       "    var fig = this;\n",
+       "\n",
+       "    var nav_element = $('
')\n",
+       "    nav_element.attr('style', 'width: 100%');\n",
+       "    this.root.append(nav_element);\n",
+       "\n",
+       "    // Define a callback function for later on.\n",
+       "    function toolbar_event(event) {\n",
+       "        return fig.toolbar_button_onclick(event['data']);\n",
+       "    }\n",
+       "    function toolbar_mouse_event(event) {\n",
+       "        return fig.toolbar_button_onmouseover(event['data']);\n",
+       "    }\n",
+       "\n",
+       "    for(var toolbar_ind in mpl.toolbar_items) {\n",
+       "        var name = mpl.toolbar_items[toolbar_ind][0];\n",
+       "        var tooltip = mpl.toolbar_items[toolbar_ind][1];\n",
+       "        var image = mpl.toolbar_items[toolbar_ind][2];\n",
+       "        var method_name = mpl.toolbar_items[toolbar_ind][3];\n",
+       "\n",
+       "        if (!name) {\n",
+       "            // put a spacer in here.\n",
+       "            continue;\n",
+       "        }\n",
+       "        var button = $('');\n",
+       "        button.click(method_name, toolbar_event);\n",
+       "        button.mouseover(tooltip, toolbar_mouse_event);\n",
+       "        nav_element.append(button);\n",
+       "    }\n",
+       "\n",
+       "    // Add the status bar.\n",
+       "    var status_bar = $('');\n",
+       "    nav_element.append(status_bar);\n",
+       "    this.message = status_bar[0];\n",
+       "\n",
+       "    // Add the close button to the window.\n",
+       "    var buttongrp = $('
');\n",
+       "    var button = $('');\n",
+       "    button.click(function (evt) { fig.handle_close(fig, {}); } );\n",
+       "    button.mouseover('Stop Interaction', toolbar_mouse_event);\n",
+       "    buttongrp.append(button);\n",
+       "    var titlebar = this.root.find($('.ui-dialog-titlebar'));\n",
+       "    titlebar.prepend(buttongrp);\n",
+       "}\n",
+       "\n",
+       "mpl.figure.prototype._root_extra_style = function(el){\n",
+       "    var fig = this\n",
+       "    el.on(\"remove\", function(){\n",
+       "\tfig.close_ws(fig, {});\n",
+       "    });\n",
+       "}\n",
+       "\n",
+       "mpl.figure.prototype._canvas_extra_style = function(el){\n",
+       "    // this is important to make the div 'focusable\n",
+       "    el.attr('tabindex', 0)\n",
+       "    // reach out to IPython and tell the keyboard manager to turn it's self\n",
+       "    // off when our div gets focus\n",
+       "\n",
+       "    // location in version 3\n",
+       "    if (IPython.notebook.keyboard_manager) {\n",
+       "        IPython.notebook.keyboard_manager.register_events(el);\n",
+       "    }\n",
+       "    else {\n",
+       "        // location in version 2\n",
+       "        IPython.keyboard_manager.register_events(el);\n",
+       "    }\n",
+       "\n",
+       "}\n",
+       "\n",
+       "mpl.figure.prototype._key_event_extra = function(event, name) {\n",
+       "    var manager = IPython.notebook.keyboard_manager;\n",
+       "    if (!manager)\n",
+       "        manager = IPython.keyboard_manager;\n",
+       "\n",
+       "    // Check for shift+enter\n",
+       "    if (event.shiftKey && event.which == 13) {\n",
+       "        this.canvas_div.blur();\n",
+       "        // select the cell after this one\n",
+       "        var index = IPython.notebook.find_cell_index(this.cell_info[0]);\n",
+       "        IPython.notebook.select(index + 1);\n",
+       "    }\n",
+       "}\n",
+       "\n",
+       "mpl.figure.prototype.handle_save = function(fig, msg) {\n",
+       "    fig.ondownload(fig, null);\n",
+       "}\n",
+       "\n",
+       "\n",
+       "mpl.find_output_cell = function(html_output) {\n",
+       "    // Return the cell and output element which can be found *uniquely* in the notebook.\n",
+       "    // Note - this is a bit hacky, but it is done because the \"notebook_saving.Notebook\"\n",
+       "    // IPython event is triggered only after the cells have been serialised, which for\n",
+       "    // our purposes (turning an active figure into a static one), is too late.\n",
+       "    var cells = IPython.notebook.get_cells();\n",
+       "    var ncells = cells.length;\n",
+       "    for (var i=0; i= 3 moved mimebundle to data attribute of output\n",
+       "                    data = data.data;\n",
+       "                }\n",
+       "                if (data['text/html'] == html_output) {\n",
+       "                    return [cell, data, j];\n",
+       "                }\n",
+       "            }\n",
+       "        }\n",
+       "    }\n",
+       "}\n",
+       "\n",
+       "// Register the function which deals with the matplotlib target/channel.\n",
+       "// The kernel may be null if the page has been refreshed.\n",
+       "if (IPython.notebook.kernel != null) {\n",
+       "    IPython.notebook.kernel.comm_manager.register_target('matplotlib', mpl.mpl_figure_comm);\n",
+       "}\n"
+      ],
+      "text/plain": [
+       ""
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "text/html": [
+       ""
+      ],
+      "text/plain": [
+       ""
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "plot = qc.MatPlot(subplots=(1,2))\n",
+    "plot.add(data.VNA_s11_magnitude, subplot=1)\n",
+    "plot.add(data.VNA_s11_phase, subplot=2)\n",
+    "plot.fig.tight_layout(rect=(0, 0, 1, 0.95))"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "We can also define a frequency trace by setting the span and center frequency. 200 KHz windows centered around 1 MHZ"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 9,
+   "metadata": {
+    "collapsed": true
+   },
+   "outputs": [],
+   "source": [
+    "vna.span11(200e3)\n",
+    "vna.center11(1e6)\n",
+    "vna.npts11(100)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 10,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "DataSet:\n",
+      "   location = 'data/2017-05-24/#010_{name}_09-59-44'\n",
+      "      |         |   | \n",
+      "   Setpoint | frequency_set     | frequency     | (100,)\n",
+      "   Measured | VNA_s11_magnitude | s11_magnitude | (100,)\n",
+      "   Measured | VNA_s11_phase     | s11_phase     | (100,)\n",
+      "acquired at 2017-05-24 09:59:44\n"
+     ]
+    },
+    {
+     "data": {
+      "application/javascript": [
+       "/* Put everything inside the global mpl namespace */\n",
+       "window.mpl = {};\n",
+       "\n",
+       "\n",
+       "mpl.get_websocket_type = function() {\n",
+       "    if (typeof(WebSocket) !== 'undefined') {\n",
+       "        return WebSocket;\n",
+       "    } else if (typeof(MozWebSocket) !== 'undefined') {\n",
+       "        return MozWebSocket;\n",
+       "    } else {\n",
+       "        alert('Your browser does not have WebSocket support.' +\n",
+       "              'Please try Chrome, Safari or Firefox ≥ 6. ' +\n",
+       "              'Firefox 4 and 5 are also supported but you ' +\n",
+       "              'have to enable WebSockets in about:config.');\n",
+       "    };\n",
+       "}\n",
+       "\n",
+       "mpl.figure = function(figure_id, websocket, ondownload, parent_element) {\n",
+       "    this.id = figure_id;\n",
+       "\n",
+       "    this.ws = websocket;\n",
+       "\n",
+       "    this.supports_binary = (this.ws.binaryType != undefined);\n",
+       "\n",
+       "    if (!this.supports_binary) {\n",
+       "        var warnings = document.getElementById(\"mpl-warnings\");\n",
+       "        if (warnings) {\n",
+       "            warnings.style.display = 'block';\n",
+       "            warnings.textContent = (\n",
+       "                \"This browser does not support binary websocket messages. \" +\n",
+       "                    \"Performance may be slow.\");\n",
+       "        }\n",
+       "    }\n",
+       "\n",
+       "    this.imageObj = new Image();\n",
+       "\n",
+       "    this.context = undefined;\n",
+       "    this.message = undefined;\n",
+       "    this.canvas = undefined;\n",
+       "    this.rubberband_canvas = undefined;\n",
+       "    this.rubberband_context = undefined;\n",
+       "    this.format_dropdown = undefined;\n",
+       "\n",
+       "    this.image_mode = 'full';\n",
+       "\n",
+       "    this.root = $('
');\n",
+       "    this._root_extra_style(this.root)\n",
+       "    this.root.attr('style', 'display: inline-block');\n",
+       "\n",
+       "    $(parent_element).append(this.root);\n",
+       "\n",
+       "    this._init_header(this);\n",
+       "    this._init_canvas(this);\n",
+       "    this._init_toolbar(this);\n",
+       "\n",
+       "    var fig = this;\n",
+       "\n",
+       "    this.waiting = false;\n",
+       "\n",
+       "    this.ws.onopen =  function () {\n",
+       "            fig.send_message(\"supports_binary\", {value: fig.supports_binary});\n",
+       "            fig.send_message(\"send_image_mode\", {});\n",
+       "            if (mpl.ratio != 1) {\n",
+       "                fig.send_message(\"set_dpi_ratio\", {'dpi_ratio': mpl.ratio});\n",
+       "            }\n",
+       "            fig.send_message(\"refresh\", {});\n",
+       "        }\n",
+       "\n",
+       "    this.imageObj.onload = function() {\n",
+       "            if (fig.image_mode == 'full') {\n",
+       "                // Full images could contain transparency (where diff images\n",
+       "                // almost always do), so we need to clear the canvas so that\n",
+       "                // there is no ghosting.\n",
+       "                fig.context.clearRect(0, 0, fig.canvas.width, fig.canvas.height);\n",
+       "            }\n",
+       "            fig.context.drawImage(fig.imageObj, 0, 0);\n",
+       "        };\n",
+       "\n",
+       "    this.imageObj.onunload = function() {\n",
+       "        this.ws.close();\n",
+       "    }\n",
+       "\n",
+       "    this.ws.onmessage = this._make_on_message_function(this);\n",
+       "\n",
+       "    this.ondownload = ondownload;\n",
+       "}\n",
+       "\n",
+       "mpl.figure.prototype._init_header = function() {\n",
+       "    var titlebar = $(\n",
+       "        '
');\n",
+       "    var titletext = $(\n",
+       "        '
');\n",
+       "    titlebar.append(titletext)\n",
+       "    this.root.append(titlebar);\n",
+       "    this.header = titletext[0];\n",
+       "}\n",
+       "\n",
+       "\n",
+       "\n",
+       "mpl.figure.prototype._canvas_extra_style = function(canvas_div) {\n",
+       "\n",
+       "}\n",
+       "\n",
+       "\n",
+       "mpl.figure.prototype._root_extra_style = function(canvas_div) {\n",
+       "\n",
+       "}\n",
+       "\n",
+       "mpl.figure.prototype._init_canvas = function() {\n",
+       "    var fig = this;\n",
+       "\n",
+       "    var canvas_div = $('
');\n",
+       "\n",
+       "    canvas_div.attr('style', 'position: relative; clear: both; outline: 0');\n",
+       "\n",
+       "    function canvas_keyboard_event(event) {\n",
+       "        return fig.key_event(event, event['data']);\n",
+       "    }\n",
+       "\n",
+       "    canvas_div.keydown('key_press', canvas_keyboard_event);\n",
+       "    canvas_div.keyup('key_release', canvas_keyboard_event);\n",
+       "    this.canvas_div = canvas_div\n",
+       "    this._canvas_extra_style(canvas_div)\n",
+       "    this.root.append(canvas_div);\n",
+       "\n",
+       "    var canvas = $('');\n",
+       "    canvas.addClass('mpl-canvas');\n",
+       "    canvas.attr('style', \"left: 0; top: 0; z-index: 0; outline: 0\")\n",
+       "\n",
+       "    this.canvas = canvas[0];\n",
+       "    this.context = canvas[0].getContext(\"2d\");\n",
+       "\n",
+       "    var backingStore = this.context.backingStorePixelRatio ||\n",
+       "\tthis.context.webkitBackingStorePixelRatio ||\n",
+       "\tthis.context.mozBackingStorePixelRatio ||\n",
+       "\tthis.context.msBackingStorePixelRatio ||\n",
+       "\tthis.context.oBackingStorePixelRatio ||\n",
+       "\tthis.context.backingStorePixelRatio || 1;\n",
+       "\n",
+       "    mpl.ratio = (window.devicePixelRatio || 1) / backingStore;\n",
+       "\n",
+       "    var rubberband = $('');\n",
+       "    rubberband.attr('style', \"position: absolute; left: 0; top: 0; z-index: 1;\")\n",
+       "\n",
+       "    var pass_mouse_events = true;\n",
+       "\n",
+       "    canvas_div.resizable({\n",
+       "        start: function(event, ui) {\n",
+       "            pass_mouse_events = false;\n",
+       "        },\n",
+       "        resize: function(event, ui) {\n",
+       "            fig.request_resize(ui.size.width, ui.size.height);\n",
+       "        },\n",
+       "        stop: function(event, ui) {\n",
+       "            pass_mouse_events = true;\n",
+       "            fig.request_resize(ui.size.width, ui.size.height);\n",
+       "        },\n",
+       "    });\n",
+       "\n",
+       "    function mouse_event_fn(event) {\n",
+       "        if (pass_mouse_events)\n",
+       "            return fig.mouse_event(event, event['data']);\n",
+       "    }\n",
+       "\n",
+       "    rubberband.mousedown('button_press', mouse_event_fn);\n",
+       "    rubberband.mouseup('button_release', mouse_event_fn);\n",
+       "    // Throttle sequential mouse events to 1 every 20ms.\n",
+       "    rubberband.mousemove('motion_notify', mouse_event_fn);\n",
+       "\n",
+       "    rubberband.mouseenter('figure_enter', mouse_event_fn);\n",
+       "    rubberband.mouseleave('figure_leave', mouse_event_fn);\n",
+       "\n",
+       "    canvas_div.on(\"wheel\", function (event) {\n",
+       "        event = event.originalEvent;\n",
+       "        event['data'] = 'scroll'\n",
+       "        if (event.deltaY < 0) {\n",
+       "            event.step = 1;\n",
+       "        } else {\n",
+       "            event.step = -1;\n",
+       "        }\n",
+       "        mouse_event_fn(event);\n",
+       "    });\n",
+       "\n",
+       "    canvas_div.append(canvas);\n",
+       "    canvas_div.append(rubberband);\n",
+       "\n",
+       "    this.rubberband = rubberband;\n",
+       "    this.rubberband_canvas = rubberband[0];\n",
+       "    this.rubberband_context = rubberband[0].getContext(\"2d\");\n",
+       "    this.rubberband_context.strokeStyle = \"#000000\";\n",
+       "\n",
+       "    this._resize_canvas = function(width, height) {\n",
+       "        // Keep the size of the canvas, canvas container, and rubber band\n",
+       "        // canvas in synch.\n",
+       "        canvas_div.css('width', width)\n",
+       "        canvas_div.css('height', height)\n",
+       "\n",
+       "        canvas.attr('width', width * mpl.ratio);\n",
+       "        canvas.attr('height', height * mpl.ratio);\n",
+       "        canvas.attr('style', 'width: ' + width + 'px; height: ' + height + 'px;');\n",
+       "\n",
+       "        rubberband.attr('width', width);\n",
+       "        rubberband.attr('height', height);\n",
+       "    }\n",
+       "\n",
+       "    // Set the figure to an initial 600x600px, this will subsequently be updated\n",
+       "    // upon first draw.\n",
+       "    this._resize_canvas(600, 600);\n",
+       "\n",
+       "    // Disable right mouse context menu.\n",
+       "    $(this.rubberband_canvas).bind(\"contextmenu\",function(e){\n",
+       "        return false;\n",
+       "    });\n",
+       "\n",
+       "    function set_focus () {\n",
+       "        canvas.focus();\n",
+       "        canvas_div.focus();\n",
+       "    }\n",
+       "\n",
+       "    window.setTimeout(set_focus, 100);\n",
+       "}\n",
+       "\n",
+       "mpl.figure.prototype._init_toolbar = function() {\n",
+       "    var fig = this;\n",
+       "\n",
+       "    var nav_element = $('
')\n",
+       "    nav_element.attr('style', 'width: 100%');\n",
+       "    this.root.append(nav_element);\n",
+       "\n",
+       "    // Define a callback function for later on.\n",
+       "    function toolbar_event(event) {\n",
+       "        return fig.toolbar_button_onclick(event['data']);\n",
+       "    }\n",
+       "    function toolbar_mouse_event(event) {\n",
+       "        return fig.toolbar_button_onmouseover(event['data']);\n",
+       "    }\n",
+       "\n",
+       "    for(var toolbar_ind in mpl.toolbar_items) {\n",
+       "        var name = mpl.toolbar_items[toolbar_ind][0];\n",
+       "        var tooltip = mpl.toolbar_items[toolbar_ind][1];\n",
+       "        var image = mpl.toolbar_items[toolbar_ind][2];\n",
+       "        var method_name = mpl.toolbar_items[toolbar_ind][3];\n",
+       "\n",
+       "        if (!name) {\n",
+       "            // put a spacer in here.\n",
+       "            continue;\n",
+       "        }\n",
+       "        var button = $('');\n",
+       "        button.click(method_name, toolbar_event);\n",
+       "        button.mouseover(tooltip, toolbar_mouse_event);\n",
+       "        nav_element.append(button);\n",
+       "    }\n",
+       "\n",
+       "    // Add the status bar.\n",
+       "    var status_bar = $('');\n",
+       "    nav_element.append(status_bar);\n",
+       "    this.message = status_bar[0];\n",
+       "\n",
+       "    // Add the close button to the window.\n",
+       "    var buttongrp = $('
');\n",
+       "    var button = $('');\n",
+       "    button.click(function (evt) { fig.handle_close(fig, {}); } );\n",
+       "    button.mouseover('Stop Interaction', toolbar_mouse_event);\n",
+       "    buttongrp.append(button);\n",
+       "    var titlebar = this.root.find($('.ui-dialog-titlebar'));\n",
+       "    titlebar.prepend(buttongrp);\n",
+       "}\n",
+       "\n",
+       "mpl.figure.prototype._root_extra_style = function(el){\n",
+       "    var fig = this\n",
+       "    el.on(\"remove\", function(){\n",
+       "\tfig.close_ws(fig, {});\n",
+       "    });\n",
+       "}\n",
+       "\n",
+       "mpl.figure.prototype._canvas_extra_style = function(el){\n",
+       "    // this is important to make the div 'focusable\n",
+       "    el.attr('tabindex', 0)\n",
+       "    // reach out to IPython and tell the keyboard manager to turn it's self\n",
+       "    // off when our div gets focus\n",
+       "\n",
+       "    // location in version 3\n",
+       "    if (IPython.notebook.keyboard_manager) {\n",
+       "        IPython.notebook.keyboard_manager.register_events(el);\n",
+       "    }\n",
+       "    else {\n",
+       "        // location in version 2\n",
+       "        IPython.keyboard_manager.register_events(el);\n",
+       "    }\n",
+       "\n",
+       "}\n",
+       "\n",
+       "mpl.figure.prototype._key_event_extra = function(event, name) {\n",
+       "    var manager = IPython.notebook.keyboard_manager;\n",
+       "    if (!manager)\n",
+       "        manager = IPython.keyboard_manager;\n",
+       "\n",
+       "    // Check for shift+enter\n",
+       "    if (event.shiftKey && event.which == 13) {\n",
+       "        this.canvas_div.blur();\n",
+       "        // select the cell after this one\n",
+       "        var index = IPython.notebook.find_cell_index(this.cell_info[0]);\n",
+       "        IPython.notebook.select(index + 1);\n",
+       "    }\n",
+       "}\n",
+       "\n",
+       "mpl.figure.prototype.handle_save = function(fig, msg) {\n",
+       "    fig.ondownload(fig, null);\n",
+       "}\n",
+       "\n",
+       "\n",
+       "mpl.find_output_cell = function(html_output) {\n",
+       "    // Return the cell and output element which can be found *uniquely* in the notebook.\n",
+       "    // Note - this is a bit hacky, but it is done because the \"notebook_saving.Notebook\"\n",
+       "    // IPython event is triggered only after the cells have been serialised, which for\n",
+       "    // our purposes (turning an active figure into a static one), is too late.\n",
+       "    var cells = IPython.notebook.get_cells();\n",
+       "    var ncells = cells.length;\n",
+       "    for (var i=0; i= 3 moved mimebundle to data attribute of output\n",
+       "                    data = data.data;\n",
+       "                }\n",
+       "                if (data['text/html'] == html_output) {\n",
+       "                    return [cell, data, j];\n",
+       "                }\n",
+       "            }\n",
+       "        }\n",
+       "    }\n",
+       "}\n",
+       "\n",
+       "// Register the function which deals with the matplotlib target/channel.\n",
+       "// The kernel may be null if the page has been refreshed.\n",
+       "if (IPython.notebook.kernel != null) {\n",
+       "    IPython.notebook.kernel.comm_manager.register_target('matplotlib', mpl.mpl_figure_comm);\n",
+       "}\n"
+      ],
+      "text/plain": [
+       ""
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "text/html": [
+       ""
+      ],
+      "text/plain": [
+       ""
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "plot = qc.MatPlot(subplots=(1,2))\n",
+    "plot.add(data1.VNA_s11_magnitude, subplot=1)\n",
+    "plot.add(data1.VNA_s11_phase, subplot=2)\n",
+    "plot.fig.tight_layout(rect=(0, 0, 1, 0.95))"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {
+    "collapsed": true
+   },
+   "outputs": [],
+   "source": []
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {
+    "collapsed": true
+   },
+   "outputs": [],
+   "source": []
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "Python 3",
+   "language": "python",
+   "name": "python3"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython3",
+   "version": "3.5.3"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 1
+}

From 1473b0098ea00c6023ff11aea6ed7da46a554c55 Mon Sep 17 00:00:00 2001
From: Jens Hedegaard Nielsen 
Date: Wed, 24 May 2017 14:37:32 +0200
Subject: [PATCH 179/328] update to example notebook

---
 ...es example with Rohde Schwarz ZNB 20.ipynb | 890 +++++++++++++++++-
 1 file changed, 851 insertions(+), 39 deletions(-)

diff --git a/docs/examples/driver_examples/Qcodes example with Rohde Schwarz ZNB 20.ipynb b/docs/examples/driver_examples/Qcodes example with Rohde Schwarz ZNB 20.ipynb
index 77e040d78557..d43ef5c6c032 100644
--- a/docs/examples/driver_examples/Qcodes example with Rohde Schwarz ZNB 20.ipynb	
+++ b/docs/examples/driver_examples/Qcodes example with Rohde Schwarz ZNB 20.ipynb	
@@ -39,7 +39,7 @@
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "Connected to: Rohde-Schwarz ZNB20-2Port (serial:1311601062101551, firmware:2.82) in 0.28s\n"
+      "Connected to: Rohde-Schwarz ZNB20-2Port (serial:1311601062101551, firmware:2.82) in 0.17s\n"
      ]
     }
    ],
@@ -96,7 +96,7 @@
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "Now we can meassure a frequency trace, first remembering to turn on the rf source."
+    "Now we can meassure a frequency trace, first remembering to turn on the rf source. This produces both a linear magnitude and phase"
    ]
   },
   {
@@ -109,12 +109,12 @@
      "output_type": "stream",
      "text": [
       "DataSet:\n",
-      "   location = 'data/2017-05-24/#009_{name}_09-59-42'\n",
+      "   location = 'data/2017-05-24/#047_{name}_14-35-53'\n",
       "      |         |   | \n",
       "   Setpoint | frequency_set     | frequency     | (100,)\n",
       "   Measured | VNA_s11_magnitude | s11_magnitude | (100,)\n",
       "   Measured | VNA_s11_phase     | s11_phase     | (100,)\n",
-      "acquired at 2017-05-24 09:59:43\n"
+      "acquired at 2017-05-24 14:35:53\n"
      ]
     }
    ],
@@ -908,7 +908,7 @@
     {
      "data": {
       "text/html": [
-       ""
+       ""
       ],
       "text/plain": [
        ""
@@ -948,6 +948,831 @@
   {
    "cell_type": "code",
    "execution_count": 10,
+   "metadata": {
+    "scrolled": false
+   },
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "DataSet:\n",
+      "   location = 'data/2017-05-24/#048_{name}_14-35-56'\n",
+      "      |         |   | \n",
+      "   Setpoint | frequency_set     | frequency     | (100,)\n",
+      "   Measured | VNA_s11_magnitude | s11_magnitude | (100,)\n",
+      "   Measured | VNA_s11_phase     | s11_phase     | (100,)\n",
+      "acquired at 2017-05-24 14:35:56\n"
+     ]
+    },
+    {
+     "data": {
+      "application/javascript": [
+       "/* Put everything inside the global mpl namespace */\n",
+       "window.mpl = {};\n",
+       "\n",
+       "\n",
+       "mpl.get_websocket_type = function() {\n",
+       "    if (typeof(WebSocket) !== 'undefined') {\n",
+       "        return WebSocket;\n",
+       "    } else if (typeof(MozWebSocket) !== 'undefined') {\n",
+       "        return MozWebSocket;\n",
+       "    } else {\n",
+       "        alert('Your browser does not have WebSocket support.' +\n",
+       "              'Please try Chrome, Safari or Firefox ≥ 6. ' +\n",
+       "              'Firefox 4 and 5 are also supported but you ' +\n",
+       "              'have to enable WebSockets in about:config.');\n",
+       "    };\n",
+       "}\n",
+       "\n",
+       "mpl.figure = function(figure_id, websocket, ondownload, parent_element) {\n",
+       "    this.id = figure_id;\n",
+       "\n",
+       "    this.ws = websocket;\n",
+       "\n",
+       "    this.supports_binary = (this.ws.binaryType != undefined);\n",
+       "\n",
+       "    if (!this.supports_binary) {\n",
+       "        var warnings = document.getElementById(\"mpl-warnings\");\n",
+       "        if (warnings) {\n",
+       "            warnings.style.display = 'block';\n",
+       "            warnings.textContent = (\n",
+       "                \"This browser does not support binary websocket messages. \" +\n",
+       "                    \"Performance may be slow.\");\n",
+       "        }\n",
+       "    }\n",
+       "\n",
+       "    this.imageObj = new Image();\n",
+       "\n",
+       "    this.context = undefined;\n",
+       "    this.message = undefined;\n",
+       "    this.canvas = undefined;\n",
+       "    this.rubberband_canvas = undefined;\n",
+       "    this.rubberband_context = undefined;\n",
+       "    this.format_dropdown = undefined;\n",
+       "\n",
+       "    this.image_mode = 'full';\n",
+       "\n",
+       "    this.root = $('
');\n",
+       "    this._root_extra_style(this.root)\n",
+       "    this.root.attr('style', 'display: inline-block');\n",
+       "\n",
+       "    $(parent_element).append(this.root);\n",
+       "\n",
+       "    this._init_header(this);\n",
+       "    this._init_canvas(this);\n",
+       "    this._init_toolbar(this);\n",
+       "\n",
+       "    var fig = this;\n",
+       "\n",
+       "    this.waiting = false;\n",
+       "\n",
+       "    this.ws.onopen =  function () {\n",
+       "            fig.send_message(\"supports_binary\", {value: fig.supports_binary});\n",
+       "            fig.send_message(\"send_image_mode\", {});\n",
+       "            if (mpl.ratio != 1) {\n",
+       "                fig.send_message(\"set_dpi_ratio\", {'dpi_ratio': mpl.ratio});\n",
+       "            }\n",
+       "            fig.send_message(\"refresh\", {});\n",
+       "        }\n",
+       "\n",
+       "    this.imageObj.onload = function() {\n",
+       "            if (fig.image_mode == 'full') {\n",
+       "                // Full images could contain transparency (where diff images\n",
+       "                // almost always do), so we need to clear the canvas so that\n",
+       "                // there is no ghosting.\n",
+       "                fig.context.clearRect(0, 0, fig.canvas.width, fig.canvas.height);\n",
+       "            }\n",
+       "            fig.context.drawImage(fig.imageObj, 0, 0);\n",
+       "        };\n",
+       "\n",
+       "    this.imageObj.onunload = function() {\n",
+       "        this.ws.close();\n",
+       "    }\n",
+       "\n",
+       "    this.ws.onmessage = this._make_on_message_function(this);\n",
+       "\n",
+       "    this.ondownload = ondownload;\n",
+       "}\n",
+       "\n",
+       "mpl.figure.prototype._init_header = function() {\n",
+       "    var titlebar = $(\n",
+       "        '
');\n",
+       "    var titletext = $(\n",
+       "        '
');\n",
+       "    titlebar.append(titletext)\n",
+       "    this.root.append(titlebar);\n",
+       "    this.header = titletext[0];\n",
+       "}\n",
+       "\n",
+       "\n",
+       "\n",
+       "mpl.figure.prototype._canvas_extra_style = function(canvas_div) {\n",
+       "\n",
+       "}\n",
+       "\n",
+       "\n",
+       "mpl.figure.prototype._root_extra_style = function(canvas_div) {\n",
+       "\n",
+       "}\n",
+       "\n",
+       "mpl.figure.prototype._init_canvas = function() {\n",
+       "    var fig = this;\n",
+       "\n",
+       "    var canvas_div = $('
');\n",
+       "\n",
+       "    canvas_div.attr('style', 'position: relative; clear: both; outline: 0');\n",
+       "\n",
+       "    function canvas_keyboard_event(event) {\n",
+       "        return fig.key_event(event, event['data']);\n",
+       "    }\n",
+       "\n",
+       "    canvas_div.keydown('key_press', canvas_keyboard_event);\n",
+       "    canvas_div.keyup('key_release', canvas_keyboard_event);\n",
+       "    this.canvas_div = canvas_div\n",
+       "    this._canvas_extra_style(canvas_div)\n",
+       "    this.root.append(canvas_div);\n",
+       "\n",
+       "    var canvas = $('');\n",
+       "    canvas.addClass('mpl-canvas');\n",
+       "    canvas.attr('style', \"left: 0; top: 0; z-index: 0; outline: 0\")\n",
+       "\n",
+       "    this.canvas = canvas[0];\n",
+       "    this.context = canvas[0].getContext(\"2d\");\n",
+       "\n",
+       "    var backingStore = this.context.backingStorePixelRatio ||\n",
+       "\tthis.context.webkitBackingStorePixelRatio ||\n",
+       "\tthis.context.mozBackingStorePixelRatio ||\n",
+       "\tthis.context.msBackingStorePixelRatio ||\n",
+       "\tthis.context.oBackingStorePixelRatio ||\n",
+       "\tthis.context.backingStorePixelRatio || 1;\n",
+       "\n",
+       "    mpl.ratio = (window.devicePixelRatio || 1) / backingStore;\n",
+       "\n",
+       "    var rubberband = $('');\n",
+       "    rubberband.attr('style', \"position: absolute; left: 0; top: 0; z-index: 1;\")\n",
+       "\n",
+       "    var pass_mouse_events = true;\n",
+       "\n",
+       "    canvas_div.resizable({\n",
+       "        start: function(event, ui) {\n",
+       "            pass_mouse_events = false;\n",
+       "        },\n",
+       "        resize: function(event, ui) {\n",
+       "            fig.request_resize(ui.size.width, ui.size.height);\n",
+       "        },\n",
+       "        stop: function(event, ui) {\n",
+       "            pass_mouse_events = true;\n",
+       "            fig.request_resize(ui.size.width, ui.size.height);\n",
+       "        },\n",
+       "    });\n",
+       "\n",
+       "    function mouse_event_fn(event) {\n",
+       "        if (pass_mouse_events)\n",
+       "            return fig.mouse_event(event, event['data']);\n",
+       "    }\n",
+       "\n",
+       "    rubberband.mousedown('button_press', mouse_event_fn);\n",
+       "    rubberband.mouseup('button_release', mouse_event_fn);\n",
+       "    // Throttle sequential mouse events to 1 every 20ms.\n",
+       "    rubberband.mousemove('motion_notify', mouse_event_fn);\n",
+       "\n",
+       "    rubberband.mouseenter('figure_enter', mouse_event_fn);\n",
+       "    rubberband.mouseleave('figure_leave', mouse_event_fn);\n",
+       "\n",
+       "    canvas_div.on(\"wheel\", function (event) {\n",
+       "        event = event.originalEvent;\n",
+       "        event['data'] = 'scroll'\n",
+       "        if (event.deltaY < 0) {\n",
+       "            event.step = 1;\n",
+       "        } else {\n",
+       "            event.step = -1;\n",
+       "        }\n",
+       "        mouse_event_fn(event);\n",
+       "    });\n",
+       "\n",
+       "    canvas_div.append(canvas);\n",
+       "    canvas_div.append(rubberband);\n",
+       "\n",
+       "    this.rubberband = rubberband;\n",
+       "    this.rubberband_canvas = rubberband[0];\n",
+       "    this.rubberband_context = rubberband[0].getContext(\"2d\");\n",
+       "    this.rubberband_context.strokeStyle = \"#000000\";\n",
+       "\n",
+       "    this._resize_canvas = function(width, height) {\n",
+       "        // Keep the size of the canvas, canvas container, and rubber band\n",
+       "        // canvas in synch.\n",
+       "        canvas_div.css('width', width)\n",
+       "        canvas_div.css('height', height)\n",
+       "\n",
+       "        canvas.attr('width', width * mpl.ratio);\n",
+       "        canvas.attr('height', height * mpl.ratio);\n",
+       "        canvas.attr('style', 'width: ' + width + 'px; height: ' + height + 'px;');\n",
+       "\n",
+       "        rubberband.attr('width', width);\n",
+       "        rubberband.attr('height', height);\n",
+       "    }\n",
+       "\n",
+       "    // Set the figure to an initial 600x600px, this will subsequently be updated\n",
+       "    // upon first draw.\n",
+       "    this._resize_canvas(600, 600);\n",
+       "\n",
+       "    // Disable right mouse context menu.\n",
+       "    $(this.rubberband_canvas).bind(\"contextmenu\",function(e){\n",
+       "        return false;\n",
+       "    });\n",
+       "\n",
+       "    function set_focus () {\n",
+       "        canvas.focus();\n",
+       "        canvas_div.focus();\n",
+       "    }\n",
+       "\n",
+       "    window.setTimeout(set_focus, 100);\n",
+       "}\n",
+       "\n",
+       "mpl.figure.prototype._init_toolbar = function() {\n",
+       "    var fig = this;\n",
+       "\n",
+       "    var nav_element = $('
')\n",
+       "    nav_element.attr('style', 'width: 100%');\n",
+       "    this.root.append(nav_element);\n",
+       "\n",
+       "    // Define a callback function for later on.\n",
+       "    function toolbar_event(event) {\n",
+       "        return fig.toolbar_button_onclick(event['data']);\n",
+       "    }\n",
+       "    function toolbar_mouse_event(event) {\n",
+       "        return fig.toolbar_button_onmouseover(event['data']);\n",
+       "    }\n",
+       "\n",
+       "    for(var toolbar_ind in mpl.toolbar_items) {\n",
+       "        var name = mpl.toolbar_items[toolbar_ind][0];\n",
+       "        var tooltip = mpl.toolbar_items[toolbar_ind][1];\n",
+       "        var image = mpl.toolbar_items[toolbar_ind][2];\n",
+       "        var method_name = mpl.toolbar_items[toolbar_ind][3];\n",
+       "\n",
+       "        if (!name) {\n",
+       "            // put a spacer in here.\n",
+       "            continue;\n",
+       "        }\n",
+       "        var button = $('');\n",
+       "        button.click(method_name, toolbar_event);\n",
+       "        button.mouseover(tooltip, toolbar_mouse_event);\n",
+       "        nav_element.append(button);\n",
+       "    }\n",
+       "\n",
+       "    // Add the status bar.\n",
+       "    var status_bar = $('');\n",
+       "    nav_element.append(status_bar);\n",
+       "    this.message = status_bar[0];\n",
+       "\n",
+       "    // Add the close button to the window.\n",
+       "    var buttongrp = $('
');\n",
+       "    var button = $('');\n",
+       "    button.click(function (evt) { fig.handle_close(fig, {}); } );\n",
+       "    button.mouseover('Stop Interaction', toolbar_mouse_event);\n",
+       "    buttongrp.append(button);\n",
+       "    var titlebar = this.root.find($('.ui-dialog-titlebar'));\n",
+       "    titlebar.prepend(buttongrp);\n",
+       "}\n",
+       "\n",
+       "mpl.figure.prototype._root_extra_style = function(el){\n",
+       "    var fig = this\n",
+       "    el.on(\"remove\", function(){\n",
+       "\tfig.close_ws(fig, {});\n",
+       "    });\n",
+       "}\n",
+       "\n",
+       "mpl.figure.prototype._canvas_extra_style = function(el){\n",
+       "    // this is important to make the div 'focusable\n",
+       "    el.attr('tabindex', 0)\n",
+       "    // reach out to IPython and tell the keyboard manager to turn it's self\n",
+       "    // off when our div gets focus\n",
+       "\n",
+       "    // location in version 3\n",
+       "    if (IPython.notebook.keyboard_manager) {\n",
+       "        IPython.notebook.keyboard_manager.register_events(el);\n",
+       "    }\n",
+       "    else {\n",
+       "        // location in version 2\n",
+       "        IPython.keyboard_manager.register_events(el);\n",
+       "    }\n",
+       "\n",
+       "}\n",
+       "\n",
+       "mpl.figure.prototype._key_event_extra = function(event, name) {\n",
+       "    var manager = IPython.notebook.keyboard_manager;\n",
+       "    if (!manager)\n",
+       "        manager = IPython.keyboard_manager;\n",
+       "\n",
+       "    // Check for shift+enter\n",
+       "    if (event.shiftKey && event.which == 13) {\n",
+       "        this.canvas_div.blur();\n",
+       "        // select the cell after this one\n",
+       "        var index = IPython.notebook.find_cell_index(this.cell_info[0]);\n",
+       "        IPython.notebook.select(index + 1);\n",
+       "    }\n",
+       "}\n",
+       "\n",
+       "mpl.figure.prototype.handle_save = function(fig, msg) {\n",
+       "    fig.ondownload(fig, null);\n",
+       "}\n",
+       "\n",
+       "\n",
+       "mpl.find_output_cell = function(html_output) {\n",
+       "    // Return the cell and output element which can be found *uniquely* in the notebook.\n",
+       "    // Note - this is a bit hacky, but it is done because the \"notebook_saving.Notebook\"\n",
+       "    // IPython event is triggered only after the cells have been serialised, which for\n",
+       "    // our purposes (turning an active figure into a static one), is too late.\n",
+       "    var cells = IPython.notebook.get_cells();\n",
+       "    var ncells = cells.length;\n",
+       "    for (var i=0; i= 3 moved mimebundle to data attribute of output\n",
+       "                    data = data.data;\n",
+       "                }\n",
+       "                if (data['text/html'] == html_output) {\n",
+       "                    return [cell, data, j];\n",
+       "                }\n",
+       "            }\n",
+       "        }\n",
+       "    }\n",
+       "}\n",
+       "\n",
+       "// Register the function which deals with the matplotlib target/channel.\n",
+       "// The kernel may be null if the page has been refreshed.\n",
+       "if (IPython.notebook.kernel != null) {\n",
+       "    IPython.notebook.kernel.comm_manager.register_target('matplotlib', mpl.mpl_figure_comm);\n",
+       "}\n"
+      ],
+      "text/plain": [
+       ""
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "text/html": [
+       ""
+      ],
+      "text/plain": [
+       ""
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "text/plain": [
+       "(, DataSet:\n",
+       "    location = '/Users/william/sourcecodes/qcodes-jenshnielsen/docs/examples/TutorialExperiment/TutorialSample/011'\n",
+       "       |   |  | \n",
+       "    Setpoint | dac_ch1_set | ch1          | (25,)\n",
+       "    Measured | dmm_voltage | voltage      | (25,))"
+      ]
+     },
+     "execution_count": 6,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "# For instance, sweep a dac voltage and record with the dmm\n",
+    "\n",
+    "do1d(dac.ch1, 0, 15, 25, 0.1, dmm.voltage)\n",
+    "# Call signature: do1d(thing_to_sweep,\n",
+    "#                      sweep_start,\n",
+    "#                      sweep_stop,\n",
+    "#                      number_of_sweep_points,\n",
+    "#                      delay_per_point,\n",
+    "#                      thing_to_measure)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 7,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Started at 2017-06-14 13:31:41\n",
+      "DataSet:\n",
+      "   location = '/Users/william/sourcecodes/qcodes-jenshnielsen/docs/examples/TutorialExperiment/TutorialSample/012'\n",
+      "      |   |  | \n",
+      "   Setpoint | dac_ch1_set | ch1          | (25,)\n",
+      "   Measured | dmm_voltage | voltage      | (25,)\n",
+      "Finished at 2017-06-14 13:31:43\n"
+     ]
+    },
+    {
+     "data": {
+      "text/plain": [
+       "(None, DataSet:\n",
+       "    location = '/Users/william/sourcecodes/qcodes-jenshnielsen/docs/examples/TutorialExperiment/TutorialSample/012'\n",
+       "       |   |  | \n",
+       "    Setpoint | dac_ch1_set | ch1          | (25,)\n",
+       "    Measured | dmm_voltage | voltage      | (25,))"
+      ]
+     },
+     "execution_count": 7,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "# If you can't be bothered to look at the plot (if, for instance, you are running many do1ds),\n",
+    "# the live plotting can be disabled\n",
+    "do1d(dac.ch1, 0, 15, 25, 0.1, dmm.voltage, do_plots=False)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 8,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "application/javascript": [
+       "/* Put everything inside the global mpl namespace */\n",
+       "window.mpl = {};\n",
+       "\n",
+       "\n",
+       "mpl.get_websocket_type = function() {\n",
+       "    if (typeof(WebSocket) !== 'undefined') {\n",
+       "        return WebSocket;\n",
+       "    } else if (typeof(MozWebSocket) !== 'undefined') {\n",
+       "        return MozWebSocket;\n",
+       "    } else {\n",
+       "        alert('Your browser does not have WebSocket support.' +\n",
+       "              'Please try Chrome, Safari or Firefox ≥ 6. ' +\n",
+       "              'Firefox 4 and 5 are also supported but you ' +\n",
+       "              'have to enable WebSockets in about:config.');\n",
+       "    };\n",
+       "}\n",
+       "\n",
+       "mpl.figure = function(figure_id, websocket, ondownload, parent_element) {\n",
+       "    this.id = figure_id;\n",
+       "\n",
+       "    this.ws = websocket;\n",
+       "\n",
+       "    this.supports_binary = (this.ws.binaryType != undefined);\n",
+       "\n",
+       "    if (!this.supports_binary) {\n",
+       "        var warnings = document.getElementById(\"mpl-warnings\");\n",
+       "        if (warnings) {\n",
+       "            warnings.style.display = 'block';\n",
+       "            warnings.textContent = (\n",
+       "                \"This browser does not support binary websocket messages. \" +\n",
+       "                    \"Performance may be slow.\");\n",
+       "        }\n",
+       "    }\n",
+       "\n",
+       "    this.imageObj = new Image();\n",
+       "\n",
+       "    this.context = undefined;\n",
+       "    this.message = undefined;\n",
+       "    this.canvas = undefined;\n",
+       "    this.rubberband_canvas = undefined;\n",
+       "    this.rubberband_context = undefined;\n",
+       "    this.format_dropdown = undefined;\n",
+       "\n",
+       "    this.image_mode = 'full';\n",
+       "\n",
+       "    this.root = $('
');\n",
+       "    this._root_extra_style(this.root)\n",
+       "    this.root.attr('style', 'display: inline-block');\n",
+       "\n",
+       "    $(parent_element).append(this.root);\n",
+       "\n",
+       "    this._init_header(this);\n",
+       "    this._init_canvas(this);\n",
+       "    this._init_toolbar(this);\n",
+       "\n",
+       "    var fig = this;\n",
+       "\n",
+       "    this.waiting = false;\n",
+       "\n",
+       "    this.ws.onopen =  function () {\n",
+       "            fig.send_message(\"supports_binary\", {value: fig.supports_binary});\n",
+       "            fig.send_message(\"send_image_mode\", {});\n",
+       "            if (mpl.ratio != 1) {\n",
+       "                fig.send_message(\"set_dpi_ratio\", {'dpi_ratio': mpl.ratio});\n",
+       "            }\n",
+       "            fig.send_message(\"refresh\", {});\n",
+       "        }\n",
+       "\n",
+       "    this.imageObj.onload = function() {\n",
+       "            if (fig.image_mode == 'full') {\n",
+       "                // Full images could contain transparency (where diff images\n",
+       "                // almost always do), so we need to clear the canvas so that\n",
+       "                // there is no ghosting.\n",
+       "                fig.context.clearRect(0, 0, fig.canvas.width, fig.canvas.height);\n",
+       "            }\n",
+       "            fig.context.drawImage(fig.imageObj, 0, 0);\n",
+       "        };\n",
+       "\n",
+       "    this.imageObj.onunload = function() {\n",
+       "        this.ws.close();\n",
+       "    }\n",
+       "\n",
+       "    this.ws.onmessage = this._make_on_message_function(this);\n",
+       "\n",
+       "    this.ondownload = ondownload;\n",
+       "}\n",
+       "\n",
+       "mpl.figure.prototype._init_header = function() {\n",
+       "    var titlebar = $(\n",
+       "        '
');\n",
+       "    var titletext = $(\n",
+       "        '
');\n",
+       "    titlebar.append(titletext)\n",
+       "    this.root.append(titlebar);\n",
+       "    this.header = titletext[0];\n",
+       "}\n",
+       "\n",
+       "\n",
+       "\n",
+       "mpl.figure.prototype._canvas_extra_style = function(canvas_div) {\n",
+       "\n",
+       "}\n",
+       "\n",
+       "\n",
+       "mpl.figure.prototype._root_extra_style = function(canvas_div) {\n",
+       "\n",
+       "}\n",
+       "\n",
+       "mpl.figure.prototype._init_canvas = function() {\n",
+       "    var fig = this;\n",
+       "\n",
+       "    var canvas_div = $('
');\n",
+       "\n",
+       "    canvas_div.attr('style', 'position: relative; clear: both; outline: 0');\n",
+       "\n",
+       "    function canvas_keyboard_event(event) {\n",
+       "        return fig.key_event(event, event['data']);\n",
+       "    }\n",
+       "\n",
+       "    canvas_div.keydown('key_press', canvas_keyboard_event);\n",
+       "    canvas_div.keyup('key_release', canvas_keyboard_event);\n",
+       "    this.canvas_div = canvas_div\n",
+       "    this._canvas_extra_style(canvas_div)\n",
+       "    this.root.append(canvas_div);\n",
+       "\n",
+       "    var canvas = $('');\n",
+       "    canvas.addClass('mpl-canvas');\n",
+       "    canvas.attr('style', \"left: 0; top: 0; z-index: 0; outline: 0\")\n",
+       "\n",
+       "    this.canvas = canvas[0];\n",
+       "    this.context = canvas[0].getContext(\"2d\");\n",
+       "\n",
+       "    var backingStore = this.context.backingStorePixelRatio ||\n",
+       "\tthis.context.webkitBackingStorePixelRatio ||\n",
+       "\tthis.context.mozBackingStorePixelRatio ||\n",
+       "\tthis.context.msBackingStorePixelRatio ||\n",
+       "\tthis.context.oBackingStorePixelRatio ||\n",
+       "\tthis.context.backingStorePixelRatio || 1;\n",
+       "\n",
+       "    mpl.ratio = (window.devicePixelRatio || 1) / backingStore;\n",
+       "\n",
+       "    var rubberband = $('');\n",
+       "    rubberband.attr('style', \"position: absolute; left: 0; top: 0; z-index: 1;\")\n",
+       "\n",
+       "    var pass_mouse_events = true;\n",
+       "\n",
+       "    canvas_div.resizable({\n",
+       "        start: function(event, ui) {\n",
+       "            pass_mouse_events = false;\n",
+       "        },\n",
+       "        resize: function(event, ui) {\n",
+       "            fig.request_resize(ui.size.width, ui.size.height);\n",
+       "        },\n",
+       "        stop: function(event, ui) {\n",
+       "            pass_mouse_events = true;\n",
+       "            fig.request_resize(ui.size.width, ui.size.height);\n",
+       "        },\n",
+       "    });\n",
+       "\n",
+       "    function mouse_event_fn(event) {\n",
+       "        if (pass_mouse_events)\n",
+       "            return fig.mouse_event(event, event['data']);\n",
+       "    }\n",
+       "\n",
+       "    rubberband.mousedown('button_press', mouse_event_fn);\n",
+       "    rubberband.mouseup('button_release', mouse_event_fn);\n",
+       "    // Throttle sequential mouse events to 1 every 20ms.\n",
+       "    rubberband.mousemove('motion_notify', mouse_event_fn);\n",
+       "\n",
+       "    rubberband.mouseenter('figure_enter', mouse_event_fn);\n",
+       "    rubberband.mouseleave('figure_leave', mouse_event_fn);\n",
+       "\n",
+       "    canvas_div.on(\"wheel\", function (event) {\n",
+       "        event = event.originalEvent;\n",
+       "        event['data'] = 'scroll'\n",
+       "        if (event.deltaY < 0) {\n",
+       "            event.step = 1;\n",
+       "        } else {\n",
+       "            event.step = -1;\n",
+       "        }\n",
+       "        mouse_event_fn(event);\n",
+       "    });\n",
+       "\n",
+       "    canvas_div.append(canvas);\n",
+       "    canvas_div.append(rubberband);\n",
+       "\n",
+       "    this.rubberband = rubberband;\n",
+       "    this.rubberband_canvas = rubberband[0];\n",
+       "    this.rubberband_context = rubberband[0].getContext(\"2d\");\n",
+       "    this.rubberband_context.strokeStyle = \"#000000\";\n",
+       "\n",
+       "    this._resize_canvas = function(width, height) {\n",
+       "        // Keep the size of the canvas, canvas container, and rubber band\n",
+       "        // canvas in synch.\n",
+       "        canvas_div.css('width', width)\n",
+       "        canvas_div.css('height', height)\n",
+       "\n",
+       "        canvas.attr('width', width * mpl.ratio);\n",
+       "        canvas.attr('height', height * mpl.ratio);\n",
+       "        canvas.attr('style', 'width: ' + width + 'px; height: ' + height + 'px;');\n",
+       "\n",
+       "        rubberband.attr('width', width);\n",
+       "        rubberband.attr('height', height);\n",
+       "    }\n",
+       "\n",
+       "    // Set the figure to an initial 600x600px, this will subsequently be updated\n",
+       "    // upon first draw.\n",
+       "    this._resize_canvas(600, 600);\n",
+       "\n",
+       "    // Disable right mouse context menu.\n",
+       "    $(this.rubberband_canvas).bind(\"contextmenu\",function(e){\n",
+       "        return false;\n",
+       "    });\n",
+       "\n",
+       "    function set_focus () {\n",
+       "        canvas.focus();\n",
+       "        canvas_div.focus();\n",
+       "    }\n",
+       "\n",
+       "    window.setTimeout(set_focus, 100);\n",
+       "}\n",
+       "\n",
+       "mpl.figure.prototype._init_toolbar = function() {\n",
+       "    var fig = this;\n",
+       "\n",
+       "    var nav_element = $('
')\n",
+       "    nav_element.attr('style', 'width: 100%');\n",
+       "    this.root.append(nav_element);\n",
+       "\n",
+       "    // Define a callback function for later on.\n",
+       "    function toolbar_event(event) {\n",
+       "        return fig.toolbar_button_onclick(event['data']);\n",
+       "    }\n",
+       "    function toolbar_mouse_event(event) {\n",
+       "        return fig.toolbar_button_onmouseover(event['data']);\n",
+       "    }\n",
+       "\n",
+       "    for(var toolbar_ind in mpl.toolbar_items) {\n",
+       "        var name = mpl.toolbar_items[toolbar_ind][0];\n",
+       "        var tooltip = mpl.toolbar_items[toolbar_ind][1];\n",
+       "        var image = mpl.toolbar_items[toolbar_ind][2];\n",
+       "        var method_name = mpl.toolbar_items[toolbar_ind][3];\n",
+       "\n",
+       "        if (!name) {\n",
+       "            // put a spacer in here.\n",
+       "            continue;\n",
+       "        }\n",
+       "        var button = $('');\n",
+       "        button.click(method_name, toolbar_event);\n",
+       "        button.mouseover(tooltip, toolbar_mouse_event);\n",
+       "        nav_element.append(button);\n",
+       "    }\n",
+       "\n",
+       "    // Add the status bar.\n",
+       "    var status_bar = $('');\n",
+       "    nav_element.append(status_bar);\n",
+       "    this.message = status_bar[0];\n",
+       "\n",
+       "    // Add the close button to the window.\n",
+       "    var buttongrp = $('
');\n",
+       "    var button = $('');\n",
+       "    button.click(function (evt) { fig.handle_close(fig, {}); } );\n",
+       "    button.mouseover('Stop Interaction', toolbar_mouse_event);\n",
+       "    buttongrp.append(button);\n",
+       "    var titlebar = this.root.find($('.ui-dialog-titlebar'));\n",
+       "    titlebar.prepend(buttongrp);\n",
+       "}\n",
+       "\n",
+       "mpl.figure.prototype._root_extra_style = function(el){\n",
+       "    var fig = this\n",
+       "    el.on(\"remove\", function(){\n",
+       "\tfig.close_ws(fig, {});\n",
+       "    });\n",
+       "}\n",
+       "\n",
+       "mpl.figure.prototype._canvas_extra_style = function(el){\n",
+       "    // this is important to make the div 'focusable\n",
+       "    el.attr('tabindex', 0)\n",
+       "    // reach out to IPython and tell the keyboard manager to turn it's self\n",
+       "    // off when our div gets focus\n",
+       "\n",
+       "    // location in version 3\n",
+       "    if (IPython.notebook.keyboard_manager) {\n",
+       "        IPython.notebook.keyboard_manager.register_events(el);\n",
+       "    }\n",
+       "    else {\n",
+       "        // location in version 2\n",
+       "        IPython.keyboard_manager.register_events(el);\n",
+       "    }\n",
+       "\n",
+       "}\n",
+       "\n",
+       "mpl.figure.prototype._key_event_extra = function(event, name) {\n",
+       "    var manager = IPython.notebook.keyboard_manager;\n",
+       "    if (!manager)\n",
+       "        manager = IPython.keyboard_manager;\n",
+       "\n",
+       "    // Check for shift+enter\n",
+       "    if (event.shiftKey && event.which == 13) {\n",
+       "        this.canvas_div.blur();\n",
+       "        // select the cell after this one\n",
+       "        var index = IPython.notebook.find_cell_index(this.cell_info[0]);\n",
+       "        IPython.notebook.select(index + 1);\n",
+       "    }\n",
+       "}\n",
+       "\n",
+       "mpl.figure.prototype.handle_save = function(fig, msg) {\n",
+       "    fig.ondownload(fig, null);\n",
+       "}\n",
+       "\n",
+       "\n",
+       "mpl.find_output_cell = function(html_output) {\n",
+       "    // Return the cell and output element which can be found *uniquely* in the notebook.\n",
+       "    // Note - this is a bit hacky, but it is done because the \"notebook_saving.Notebook\"\n",
+       "    // IPython event is triggered only after the cells have been serialised, which for\n",
+       "    // our purposes (turning an active figure into a static one), is too late.\n",
+       "    var cells = IPython.notebook.get_cells();\n",
+       "    var ncells = cells.length;\n",
+       "    for (var i=0; i= 3 moved mimebundle to data attribute of output\n",
+       "                    data = data.data;\n",
+       "                }\n",
+       "                if (data['text/html'] == html_output) {\n",
+       "                    return [cell, data, j];\n",
+       "                }\n",
+       "            }\n",
+       "        }\n",
+       "    }\n",
+       "}\n",
+       "\n",
+       "// Register the function which deals with the matplotlib target/channel.\n",
+       "// The kernel may be null if the page has been refreshed.\n",
+       "if (IPython.notebook.kernel != null) {\n",
+       "    IPython.notebook.kernel.comm_manager.register_target('matplotlib', mpl.mpl_figure_comm);\n",
+       "}\n"
+      ],
+      "text/plain": [
+       ""
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "text/html": [
+       "
"
+      ],
+      "text/plain": [
+       ""
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "text/plain": [
+       "(, DataSet:\n",
+       "    location = '/Users/william/sourcecodes/qcodes-jenshnielsen/docs/examples/TutorialExperiment/TutorialSample/013'\n",
+       "       |   |  | \n",
+       "    Setpoint | dac_ch1_set | ch1          | (25,)\n",
+       "    Setpoint | dac_ch2_set | ch2          | (25, 10)\n",
+       "    Measured | dmm_voltage | voltage      | (25, 10))"
+      ]
+     },
+     "execution_count": 11,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "# Let's ramp those dac voltages!\n",
+    "\n",
+    "do2d(dac.ch1, 0, 10, 25, 0.01,  # outer loop\n",
+    "     dac.ch2, 2, 5, 10, 0.01,  # inner loop, i.e. this runs for each point of the outer loop\n",
+    "     dmm.voltage)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "The `do2d` function also produces a DataSet, this time noticeably with **two** setpoint arrays."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {
+    "collapsed": true
+   },
+   "outputs": [],
+   "source": []
+  }
+ ],
+ "metadata": {
+  "anaconda-cloud": {},
+  "kernelspec": {
+   "display_name": "Python 3",
+   "language": "python",
+   "name": "python3"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython3",
+   "version": "3.5.3"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 1
+}

From 62eb03e09a3886115b2fc5a9a79adea25da72369 Mon Sep 17 00:00:00 2001
From: "William H.P. Nielsen" 
Date: Wed, 14 Jun 2017 15:11:51 +0200
Subject: [PATCH 225/328] plots: Make QtPlot rescale before saving (#40)

By default, the QtPlots save with the axis ranges of the current live plot.
This is inconvenient if the user has zoomed far in to some detail.
---
 qcodes/utils/wrappers.py | 12 ++++++++++++
 1 file changed, 12 insertions(+)

diff --git a/qcodes/utils/wrappers.py b/qcodes/utils/wrappers.py
index ff364c886a7a..779db76c3d3a 100644
--- a/qcodes/utils/wrappers.py
+++ b/qcodes/utils/wrappers.py
@@ -307,6 +307,10 @@ def do1d(inst_set, start, stop, num_points, delay, *inst_meas, do_plots=True):
         interrupted = True
         print("Measurement Interrupted")
     if do_plots:
+        # Ensure the correct scaling before saving
+        for subplot in plot.subplots:
+            vBox = subplot.getViewBox()
+            vBox.enableAutoRange(vBox.XYAxes)
         plot.save()
         pdfplot, num_subplots = _plot_setup(data, plottables, useQT=False)
         # pad a bit more to prevent overlap between
@@ -372,6 +376,10 @@ def do1dDiagonal(inst_set, inst2_set, start, stop, num_points, delay, start2, sl
         print("Measurement Interrupted")
         interrupted = True
     if do_plots:
+        # Ensure the correct scaling before saving
+        for subplot in plot.subplots:
+            vBox = subplot.getViewBox()
+            vBox.enableAutoRange(vBox.XYAxes)
         plot.save()
         pdfplot, num_subplots = _plot_setup(data, plottables, useQT=False)
         # pad a bit more to prevent overlap between
@@ -442,6 +450,10 @@ def do2d(inst_set, start, stop, num_points, delay, inst_set2, start2, stop2, num
         interrupted = True
         print("Measurement Interrupted")
     if do_plots:
+        # Ensure the correct scaling before saving
+        for subplot in plot.subplots:
+            vBox = subplot.getViewBox()
+            vBox.enableAutoRange(vBox.XYAxes)
         plot.save()
         pdfplot, num_subplots = _plot_setup(data, plottables, useQT=False)
         # pad a bit more to prevent overlap between

From 53fbe356743dfeb4a205aeed223cb26282aa2418 Mon Sep 17 00:00:00 2001
From: WilliamHPNielsen 
Date: Wed, 14 Jun 2017 16:44:55 +0200
Subject: [PATCH 226/328] plots: Make the initial empty plot have the right
 ranges

When the plot window pops up, before getting any data, the plot ranges should
be the ranges expected from the measurement.
---
 qcodes/utils/wrappers.py | 29 ++++++++++++++++++++++++-----
 1 file changed, 24 insertions(+), 5 deletions(-)

diff --git a/qcodes/utils/wrappers.py b/qcodes/utils/wrappers.py
index 779db76c3d3a..384033439fcd 100644
--- a/qcodes/utils/wrappers.py
+++ b/qcodes/utils/wrappers.py
@@ -5,6 +5,7 @@
 import os
 import logging
 from copy import deepcopy
+from collections import namedtuple
 import matplotlib.pyplot as plt
 
 from qcodes.plots.pyqtgraph import QtPlot
@@ -107,7 +108,7 @@ def _select_plottables(tasks):
     return tuple(plottables)
 
 
-def _plot_setup(data, inst_meas, useQT=True):
+def _plot_setup(data, inst_meas, useQT=True, startranges=None):
     title = "{} #{:03d}".format(CURRENT_EXPERIMENT["sample_name"],
                                 data.location_provider.counter)
     rasterized_note = " rasterized plot"
@@ -121,7 +122,7 @@ def _plot_setup(data, inst_meas, useQT=True):
     if useQT:
         plot = QtPlot(fig_x_position=CURRENT_EXPERIMENT['plot_x_position'])
     else:
-        plot = MatPlot(subplots=(1,num_subplots))
+        plot = MatPlot(subplots=(1, num_subplots))
 
     def _create_plot(plot, i, name, data, counter_two, j, k):
         color = 'C' + str(counter_two)
@@ -148,6 +149,15 @@ def _create_plot(plot, i, name, data, counter_two, j, k):
                         subplot.getAxis('bottom').enableAutoSIPrefix(False)
                     else:  # measured values to the left
                         subplot.getAxis('left').enableAutoSIPrefix(False)
+
+            # Avoid misleading default xrange (TODO: how to generalise to 2D?)
+            if startranges is not None:
+                subplot = plot.subplots[j + k]
+                rangefuns = ['setXRange', 'setYRange']
+                for (prange, rangefun) in zip(startranges, rangefuns):
+                    getattr(subplot.getViewBox(), rangefun)(prange[0],
+                                                            prange[1])
+
         else:
             if 'z' in inst_meta_data:
                 xlen, ylen = inst_meta_data['z'].shape
@@ -289,13 +299,16 @@ def do1d(inst_set, start, stop, num_points, delay, *inst_meas, do_plots=True):
     # try to flush VISA buffers at the beginning of a measurement
     _flush_buffers(inst_set, *inst_meas)
 
+    # make a variable for aute pre-scaling the plot
+    startranges = ((start, stop),)
+
     interrupted = False
     loop = qc.Loop(inst_set.sweep(start,
                                   stop, num=num_points), delay).each(*inst_meas)
     data = loop.get_data_set()
     plottables = _select_plottables(inst_meas)
     if do_plots:
-        plot, _ = _plot_setup(data, plottables)
+        plot, _ = _plot_setup(data, plottables, startranges=startranges)
     else:
         plot = None
     try:
@@ -355,6 +368,9 @@ def do1dDiagonal(inst_set, inst2_set, start, stop, num_points, delay, start2, sl
 
     """
 
+    # make a variable for aute pre-scaling the plot
+    startranges = ((start, stop),)
+
     # try to flush VISA buffers at the beginning of a measurement
     _flush_buffers(inst_set, inst2_set, *inst_meas)
 
@@ -364,7 +380,7 @@ def do1dDiagonal(inst_set, inst2_set, start, stop, num_points, delay, start2, sl
     data = loop.get_data_set()
     plottables = _select_plottables(inst_meas)
     if do_plots:
-        plot, _ = _plot_setup(data, plottables)
+        plot, _ = _plot_setup(data, plottables, startranges=startranges)
     else:
         plot = None
     try:
@@ -425,6 +441,9 @@ def do2d(inst_set, start, stop, num_points, delay, inst_set2, start2, stop2, num
 
     """
 
+    # make a variable for aute pre-scaling the plot
+    startranges = ((start2, stop2), (start, stop))
+
     # try to flush VISA buffers at the beginning of a measurement
     _flush_buffers(inst_set, inst_set2, *inst_meas)
 
@@ -438,7 +457,7 @@ def do2d(inst_set, start, stop, num_points, delay, inst_set2, start2, stop2, num
     data = loop.get_data_set()
     plottables = _select_plottables(inst_meas)
     if do_plots:
-        plot, _ = _plot_setup(data, plottables)
+        plot, _ = _plot_setup(data, plottables, startranges=startranges)
     else:
         plot = None
     try:

From 489664617a709e1af6ba1eee1592bd53568b627e Mon Sep 17 00:00:00 2001
From: WilliamHPNielsen 
Date: Wed, 14 Jun 2017 16:48:21 +0200
Subject: [PATCH 227/328] style: Remove obsolete TODO comment

---
 qcodes/utils/wrappers.py | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

diff --git a/qcodes/utils/wrappers.py b/qcodes/utils/wrappers.py
index 384033439fcd..9dcc88070083 100644
--- a/qcodes/utils/wrappers.py
+++ b/qcodes/utils/wrappers.py
@@ -150,7 +150,7 @@ def _create_plot(plot, i, name, data, counter_two, j, k):
                     else:  # measured values to the left
                         subplot.getAxis('left').enableAutoSIPrefix(False)
 
-            # Avoid misleading default xrange (TODO: how to generalise to 2D?)
+            # Avoid misleading default xrange
             if startranges is not None:
                 subplot = plot.subplots[j + k]
                 rangefuns = ['setXRange', 'setYRange']

From 7eaca7849bb417edc7ec1114d54319229684d85e Mon Sep 17 00:00:00 2001
From: Jens Hedegaard Nielsen 
Date: Thu, 22 Jun 2017 11:05:19 +0200
Subject: [PATCH 228/328] Force a draw of matplotlib figures.

This ensures that the figures are draw in the notebook if a loop is performed over doNds
---
 qcodes/utils/wrappers.py | 5 ++++-
 1 file changed, 4 insertions(+), 1 deletion(-)

diff --git a/qcodes/utils/wrappers.py b/qcodes/utils/wrappers.py
index 9dcc88070083..8e46f0bc74e0 100644
--- a/qcodes/utils/wrappers.py
+++ b/qcodes/utils/wrappers.py
@@ -230,6 +230,7 @@ def _create_plot(i, name, data, counter_two):
             plot.subplots[0].set_title(title)
         plot.save("{}_{:03d}.pdf".format(plot.get_default_title(),
                                          counter_two))
+        plot.fig.canvas.draw()
 
     counter_two = 0
     for j, i in enumerate(inst_meas):
@@ -330,7 +331,7 @@ def do1d(inst_set, start, stop, num_points, delay, *inst_meas, do_plots=True):
         # suptitle and title
         pdfplot.fig.tight_layout(pad=3)
         pdfplot.save("{}.pdf".format(plot.get_default_title()))
-
+        pdfplot.fig.canvas.draw()
         if num_subplots > 1:
             _save_individual_plots(data, plottables)
     if CURRENT_EXPERIMENT.get('device_image'):
@@ -405,6 +406,7 @@ def do1dDiagonal(inst_set, inst2_set, start, stop, num_points, delay, start2, sl
         if num_subplots > 1:
             _save_individual_plots(data, plottables)
         pdfplot.save("{}.pdf".format(plot.get_default_title()))
+        pdfplot.fig.canvas.draw()
     if CURRENT_EXPERIMENT.get('device_image'):
         save_device_image((inst_set, inst2_set))
 
@@ -481,6 +483,7 @@ def do2d(inst_set, start, stop, num_points, delay, inst_set2, start2, stop2, num
         pdfplot.save("{}.pdf".format(plot.get_default_title()))
         if num_subplots > 1:
             _save_individual_plots(data, plottables)
+        pdfplot.fig.canvas.draw()
         pdfplot.save("{}.pdf".format(plot.get_default_title()))
     if CURRENT_EXPERIMENT.get('device_image'):
         save_device_image((inst_set, inst_set2))

From 257e0750a31a8f725faec3150192f87e02e1a224 Mon Sep 17 00:00:00 2001
From: "William H.P. Nielsen" 
Date: Mon, 26 Jun 2017 16:08:46 +0200
Subject: [PATCH 229/328] fix: Make plot autoscaling more robust (#47)

Make the automatic disabling of automatic SI prefixes handle arrayparameters
---
 qcodes/utils/wrappers.py | 115 ++++++++++++++++++++++++++++-----------
 1 file changed, 84 insertions(+), 31 deletions(-)

diff --git a/qcodes/utils/wrappers.py b/qcodes/utils/wrappers.py
index 8e46f0bc74e0..b76106ef2fbb 100644
--- a/qcodes/utils/wrappers.py
+++ b/qcodes/utils/wrappers.py
@@ -7,6 +7,9 @@
 from copy import deepcopy
 from collections import namedtuple
 import matplotlib.pyplot as plt
+import numpy as np
+
+from time import sleep
 
 from qcodes.plots.pyqtgraph import QtPlot
 from qcodes.plots.qcmatplotlib import MatPlot
@@ -19,7 +22,7 @@
 CURRENT_EXPERIMENT["logging_enabled"] = False
 
 
-def init(mainfolder:str, sample_name: str, station, plot_x_position=0.66,
+def init(mainfolder: str, sample_name: str, station, plot_x_position=0.66,
          annotate_image=True):
     """
 
@@ -41,7 +44,7 @@ def init(mainfolder:str, sample_name: str, station, plot_x_position=0.66,
 
     CURRENT_EXPERIMENT["mainfolder"] = mainfolder
     CURRENT_EXPERIMENT["sample_name"] = sample_name
-    CURRENT_EXPERIMENT['init']  = True
+    CURRENT_EXPERIMENT['init'] = True
 
     CURRENT_EXPERIMENT['plot_x_position'] = plot_x_position
 
@@ -66,7 +69,8 @@ def init(mainfolder:str, sample_name: str, station, plot_x_position=0.66,
     # turn on logging only if in ipython
     # else crash and burn
     if ipython is None:
-        raise RuntimeWarning("History can't be saved refusing to proceed (use IPython/jupyter)")
+        raise RuntimeWarning("History can't be saved. "
+                             "-Refusing to proceed (use IPython/jupyter)")
     else:
         logfile = "{}{}".format(path_to_experiment_folder, "commands.log")
         CURRENT_EXPERIMENT['logfile'] = logfile
@@ -142,27 +146,65 @@ def _create_plot(plot, i, name, data, counter_two, j, k):
             standardunits = ['V', 's', 'J', 'W', 'm', 'eV', 'A', 'K', 'g',
                              'Hz', 'rad', 'T', 'H', 'F', 'Pa', 'C', 'Ω', 'Ohm',
                              'S']
-            for arr in data.arrays.values():
-                if arr.unit not in standardunits:
-                    subplot = plot.subplots[j + k]
-                    if arr.is_setpoint:  # setpoints on bottom axis
-                        subplot.getAxis('bottom').enableAutoSIPrefix(False)
-                    else:  # measured values to the left
-                        subplot.getAxis('left').enableAutoSIPrefix(False)
-
-            # Avoid misleading default xrange
-            if startranges is not None:
-                subplot = plot.subplots[j + k]
-                rangefuns = ['setXRange', 'setYRange']
-                for (prange, rangefun) in zip(startranges, rangefuns):
-                    getattr(subplot.getViewBox(), rangefun)(prange[0],
-                                                            prange[1])
+            # make a dict mapping axis labels to axis positions
+            # TODO: will the labels for two axes ever be identical?
+            whatwhere = {}
+            for pos in ('bottom', 'left', 'right'):
+                whatwhere.update({plot.subplots[j+k].getAxis(pos).labelText:
+                                  pos})
+            tdict = {'bottom': 'setXRange', 'left': 'setYRange'}
+            # now find the data (not setpoint)
+            thedata = [d for d in data.arrays.values() if not d.is_setpoint][0]
+
+            # Disable autoscale for the measured data
+            if thedata.unit not in standardunits:
+                subplot = plot.subplots[j+k]
+                try:
+                    # 1D measurement
+                    ax = subplot.getAxis(whatwhere[thedata.label])
+                    ax.enableAutoSIPrefix(False)
+                except KeyError:
+                    # 2D measurement
+                    # Then we should fetch the colorbar
+                    ax = plot.traces[0]['plot_object']['hist'].axis
+                    ax.enableAutoSIPrefix(False)
+                    ax.setLabel(text=thedata.label, unit=thedata.unit,
+                                unitPrefix='')
+
+            # Set up axis scaling
+            for setarr in thedata.set_arrays:
+                subplot = plot.subplots[j+k]
+                ax = subplot.getAxis(whatwhere[setarr.label])
+                # check for autoscaling
+                if setarr.unit not in standardunits:
+                    ax.enableAutoSIPrefix(False)
+                    # At this point, it has already been decided that
+                    # the scale is milli whatever
+                    # (the default empty plot is from -0.5 to 0.5)
+                    # so we must undo that
+                    ax.setScale(1e-3)
+                    ax.setLabel(text=setarr.label, unit=setarr.unit,
+                                unitPrefix='')
+                # set the axis ranges
+                if not(np.all(np.isnan(setarr))):
+                    # In this case the setpoints are "baked" into the parameter
+                    rangesetter = getattr(subplot.getViewBox(),
+                                          tdict[whatwhere[setarr.label]])
+                    rangesetter(setarr.min(), setarr.max())
+                else:
+                    # in this case someone must tell _create_plot what the
+                    # range should be. We get it from startranges
+                    rangesetter = getattr(subplot.getViewBox(),
+                                          tdict[whatwhere[setarr.label]])
+                    (rmin, rmax) = startranges[setarr.label]
+                    rangesetter(rmin, rmax)
 
         else:
             if 'z' in inst_meta_data:
                 xlen, ylen = inst_meta_data['z'].shape
                 rasterized = xlen*ylen > 5000
-                plot.add(inst_meas_data, subplot=j + k + 1, rasterized=rasterized)
+                plot.add(inst_meas_data, subplot=j + k + 1,
+                         rasterized=rasterized)
             else:
                 rasterized = False
                 plot.add(inst_meas_data, subplot=j + k + 1, color=color)
@@ -289,8 +331,9 @@ def do1d(inst_set, start, stop, num_points, delay, *inst_meas, do_plots=True):
         delay:  Delay at every step
         *inst_meas:  any number of instrument to measure and/or tasks to
           perform at each step of the sweep
-        do_plots: Default True: If False no plots are produced. Data is still saved
-          and can be displayed with show_num.
+        do_plots: Default True: If False no plots are produced.
+            Data is still saved
+             and can be displayed with show_num.
 
     Returns:
         plot, data : returns the plot and the dataset
@@ -300,13 +343,15 @@ def do1d(inst_set, start, stop, num_points, delay, *inst_meas, do_plots=True):
     # try to flush VISA buffers at the beginning of a measurement
     _flush_buffers(inst_set, *inst_meas)
 
-    # make a variable for aute pre-scaling the plot
-    startranges = ((start, stop),)
+    # make a variable for auto pre-scaling the plot
+    startranges = {inst_set.label: (start, stop)}
 
     interrupted = False
     loop = qc.Loop(inst_set.sweep(start,
-                                  stop, num=num_points), delay).each(*inst_meas)
+                                  stop,
+                                  num=num_points), delay).each(*inst_meas)
     data = loop.get_data_set()
+
     plottables = _select_plottables(inst_meas)
     if do_plots:
         plot, _ = _plot_setup(data, plottables, startranges=startranges)
@@ -370,7 +415,7 @@ def do1dDiagonal(inst_set, inst2_set, start, stop, num_points, delay, start2, sl
     """
 
     # make a variable for aute pre-scaling the plot
-    startranges = ((start, stop),)
+    startranges = {inst_set.label: (start, stop)}
 
     # try to flush VISA buffers at the beginning of a measurement
     _flush_buffers(inst_set, inst2_set, *inst_meas)
@@ -444,7 +489,8 @@ def do2d(inst_set, start, stop, num_points, delay, inst_set2, start2, stop2, num
     """
 
     # make a variable for aute pre-scaling the plot
-    startranges = ((start2, stop2), (start, stop))
+    startranges = {inst_set2.label: (start2, stop2),
+                   inst_set.label: (start, stop)}
 
     # try to flush VISA buffers at the beginning of a measurement
     _flush_buffers(inst_set, inst_set2, *inst_meas)
@@ -454,9 +500,16 @@ def do2d(inst_set, start, stop, num_points, delay, inst_set2, start2, stop2, num
         if getattr(inst, "setpoints", False):
             raise ValueError("3d plotting is not supported")
 
-    loop = qc.Loop(inst_set.sweep(start, stop, num=num_points), delay).loop(inst_set2.sweep(start2,stop2,num=num_points2), delay2).each(
-        *inst_meas)
-    data = loop.get_data_set()
+    innerloop = qc.Loop(inst_set2.sweep(start2,
+                                        stop2,
+                                        num=num_points2),
+                        delay2).each(*inst_meas)
+    outerloop = qc.Loop(inst_set.sweep(start,
+                                       stop,
+                                       num=num_points),
+                        delay).each(innerloop)
+
+    data = outerloop.get_data_set()
     plottables = _select_plottables(inst_meas)
     if do_plots:
         plot, _ = _plot_setup(data, plottables, startranges=startranges)
@@ -464,9 +517,9 @@ def do2d(inst_set, start, stop, num_points, delay, inst_set2, start2, stop2, num
         plot = None
     try:
         if do_plots:
-            _ = loop.with_bg_task(plot.update).run()
+            _ = outerloop.with_bg_task(plot.update).run()
         else:
-            _ = loop.run()
+            _ = outerloop.run()
     except KeyboardInterrupt:
         interrupted = True
         print("Measurement Interrupted")

From e45d74a028bdc86eb2213857a3ce6d1032fed156 Mon Sep 17 00:00:00 2001
From: Jens Hedegaard Nielsen 
Date: Tue, 27 Jun 2017 13:47:52 +0200
Subject: [PATCH 230/328] Device annotator handle missing raw image without
 overwriting json

---
 qcodes/utils/qcodes_device_annotator.py | 25 ++++++++++++++++++++-----
 qcodes/utils/wrappers.py                |  6 +++---
 2 files changed, 23 insertions(+), 8 deletions(-)

diff --git a/qcodes/utils/qcodes_device_annotator.py b/qcodes/utils/qcodes_device_annotator.py
index 7031cf3f9711..be5b8f86607e 100644
--- a/qcodes/utils/qcodes_device_annotator.py
+++ b/qcodes/utils/qcodes_device_annotator.py
@@ -310,17 +310,32 @@ def saveAnnotations(self):
         with open(filename, 'w') as fid:
             json.dump(self._data, fid)
 
-    def loadAnnotations(self):
+    def loadAnnotations(self) -> bool:
         """
         Get the annotations. Only call this if the files exist
         Need to load png/jpeg too
         """
-
-        json_filename = os.path.join(self.folder, 'deviceimage_annotations.json')
-        self.filename = glob.glob(os.path.join(self.folder, 'deviceimage_raw.*'))[0]
+        json_filename = 'deviceimage_annotations.json'
+        raw_image_glob = 'deviceimage_raw.*'
+        json_full_filename = os.path.join(self.folder, json_filename)
+        json_found = os.path.exists(json_full_filename)
+        raw_files = glob.glob(os.path.join(self.folder, raw_image_glob))
+        if raw_files:
+            self.filename = raw_files[0]
+        else:
+            self.filename = None
+
+        if json_found and not self.filename:
+            raise RuntimeError("{} found but no {} found. Not sure how to "
+                               "proceed, To continue either add raw image or "
+                               "delete json "
+                               "file".format(json_filename, raw_image_glob))
+        elif not json_found:
+            return False
         # this assumes there is only on of deviceimage_raw.*
-        with open(json_filename, 'r') as fid:
+        with open(json_full_filename, 'r') as fid:
             self._data = json.load(fid)
+        return True
 
     def updateValues(self, station, sweeptparameters=None):
         """
diff --git a/qcodes/utils/wrappers.py b/qcodes/utils/wrappers.py
index b76106ef2fbb..e74d0cb2aef6 100644
--- a/qcodes/utils/wrappers.py
+++ b/qcodes/utils/wrappers.py
@@ -89,9 +89,9 @@ def init(mainfolder: str, sample_name: str, station, plot_x_position=0.66,
 def _init_device_image(station):
 
     di = DeviceImage(CURRENT_EXPERIMENT["exp_folder"], station)
-    try:
-        di.loadAnnotations()
-    except:
+
+    success = di.loadAnnotations()
+    if not success:
         di.annotateImage()
     CURRENT_EXPERIMENT['device_image'] = di
 

From eec0bb508052db1606880855cef32b9fb269222c Mon Sep 17 00:00:00 2001
From: Jens Hedegaard Nielsen 
Date: Tue, 27 Jun 2017 15:39:22 +0200
Subject: [PATCH 231/328] make it possible to select font and font size in
 annotator

---
 qcodes/utils/qcodes_device_annotator.py | 65 ++++++++++++++++++-------
 1 file changed, 47 insertions(+), 18 deletions(-)

diff --git a/qcodes/utils/qcodes_device_annotator.py b/qcodes/utils/qcodes_device_annotator.py
index be5b8f86607e..1457c2cf7b65 100644
--- a/qcodes/utils/qcodes_device_annotator.py
+++ b/qcodes/utils/qcodes_device_annotator.py
@@ -9,6 +9,7 @@
 import PyQt5.QtCore as core
 
 from shutil import copyfile
+import copy
 
 class MakeDeviceImage(qt.QWidget):
     """
@@ -30,18 +31,22 @@ def __init__(self, folder, station):
 
         grid = qt.QGridLayout()
         self.setLayout(grid)
-
+        self.textfont = None
         self.imageCanvas = qt.QLabel()
-        self.imageCanvas.setToolTip("Left click to insert a label and right click to insert an annotation.")
+        self.imageCanvas.setToolTip("Left click to insert a label and right "
+                                    "click to insert an annotation.")
         self.loadButton = qt.QPushButton('Load image')
 
         self.okButton = qt.QPushButton('Save and close')
         self.removeButton = qt.QPushButton('Remove')
-        self.removeButton.setToolTip("Remove annotation and label for this parameter")
+        self.removeButton.setToolTip("Remove annotation and label for this "
+                                     "parameter")
+        self.fontButton = qt.QPushButton('Font')
         self.labeltitle = qt.QLabel('Label:')
         self.labelfield = qt.QLineEdit()
         self.labelfield.setText('')
-        self.labelfield.setToolTip("String to be used as label. Defaults to instrument_parameter")
+        self.labelfield.setToolTip("String to be used as label. Defaults to "
+                                   "instrument_parameter")
         self.formattertitle = qt.QLabel('Formatter:')
         self.formatterfield = qt.QLineEdit()
         self.formatterfield.setText('')
@@ -51,11 +56,13 @@ def __init__(self, folder, station):
                                        "':.2e' exponential formatter with 2 digits after the decimal point\n"
                                        "Leave blank for default. \n"
                                        "See www.pyformat.info for more examples")
+
         self.loadButton.clicked.connect(self.loadimage)
         self.imageCanvas.mousePressEvent = self.set_label_or_annotation
         self.imageCanvas.setStyleSheet('background-color: white')
         self.okButton.clicked.connect(self.saveAndClose)
         self.removeButton.clicked.connect(self.remove_label_and_annotation)
+        self.fontButton.clicked.connect(self.select_font)
 
         self.treeView = qt.QTreeView()
         self.model = gui.QStandardItemModel()
@@ -65,21 +72,28 @@ def __init__(self, folder, station):
         self.treeView.sortByColumn(0, core.Qt.AscendingOrder)
         self.treeView.setSortingEnabled(True)
         self.treeView.clicked.connect(self.selection_changed)
-        grid.addWidget(self.imageCanvas, 0, 0, 4, 6)
+        grid.addWidget(self.imageCanvas, 0, 0, 4, 7)
         grid.addWidget(self.loadButton, 4, 0)
         grid.addWidget(self.labeltitle, 4, 1)
         grid.addWidget(self.labelfield, 4, 2)
-        grid.addWidget(self.formattertitle, 4, 4)
-        grid.addWidget(self.formatterfield, 4, 5)
-        grid.addWidget(self.removeButton, 4, 8)
-        grid.addWidget(self.okButton, 4, 9)
-        grid.addWidget(self.treeView, 0, 6, 4, 4)
+        grid.addWidget(self.formattertitle, 4, 3)
+        grid.addWidget(self.formatterfield, 4, 4)
+        grid.addWidget(self.fontButton, 4, 6)
+        grid.addWidget(self.removeButton, 4, 9)
+        grid.addWidget(self.okButton, 4, 10)
+        grid.addWidget(self.treeView, 0, 7, 4, 4)
 
         self.resize(600, 400)
         self.move(100, 100)
         self.setWindowTitle('Generate annotated device image')
         self.show()
 
+    def select_font(self):
+
+        font, ok = qt.QFontDialog.getFont()
+        if ok:
+            self.textfont = font
+
     def addStation(self, parent, station):
 
         for inst in station.components:
@@ -154,6 +168,13 @@ def set_label_or_annotation(self, event):
             else:
                 self._data[selected_instrument][selected_parameter]['value'] = 'NaN'
 
+
+        self._data['font'] = {}
+        if not self.textfont:
+            self._data['font']['family'] = 'decorative'
+        else:
+            self._data['font']['family'] = self.textfont.family()
+            self._data['font']['label_size'] = self.textfont.pointSize()
         # draw it
         self.imageCanvas, _ = self._renderImage(self._data,
                                                 self.imageCanvas,
@@ -187,7 +208,7 @@ def saveAndClose(self):
         self.close()
 
     @staticmethod
-    def _renderImage(data, canvas, filename, title=None):
+    def _renderImage(fulldata, canvas, filename, title=None):
         """
         Render an image
         """
@@ -200,10 +221,17 @@ def _renderImage(data, canvas, filename, title=None):
         spacing = int(label_size * 0.2)
 
         painter = gui.QPainter(pixmap)
+        data = copy.deepcopy(fulldata)
+        try:
+            fontdict = data.pop('font')
+            label_size = fontdict.get('label_size', label_size)
+            textfont = gui.QFont(fontdict['family'], label_size)
+        except:
+            textfont = gui.QFont('Decorative', label_size)
+        fontmetric = gui.QFontMetrics(textfont)
         if title:
             painter.setBrush(gui.QColor(255, 255, 255, 100))
-            textfont = gui.QFont('Decorative', label_size)
-            textwidth = gui.QFontMetrics(textfont).width(title)
+            textwidth = fontmetric.width(title)
             rectangle_width = textwidth + 2 * spacing
             rectangle_height = label_size + 2 * spacing
             painter.drawRect(0,
@@ -229,8 +257,7 @@ def _renderImage(data, canvas, filename, title=None):
                     else:
                         painter.setBrush(gui.QColor(255, 255, 255, 100))
                     (lx, ly) = paramsettings['labelpos']
-                    textfont = gui.QFont('Decorative', label_size)
-                    textwidth = gui.QFontMetrics(textfont).width(label_string)
+                    textwidth = fontmetric.width(label_string)
                     rectangle_start_x = lx - spacing
                     rectangle_start_y = ly - spacing
                     rectangle_width = textwidth+2*spacing
@@ -251,8 +278,7 @@ def _renderImage(data, canvas, filename, title=None):
                     (ax, ay) = paramsettings['annotationpos']
                     annotationstring = paramsettings['value']
 
-                    textfont = gui.QFont('Decorative', label_size)
-                    textwidth = gui.QFontMetrics(textfont).width(annotationstring)
+                    textwidth = fontmetric.width(annotationstring)
                     rectangle_start_x = ax - spacing
                     rectangle_start_y = ay - spacing
                     rectangle_width = textwidth + 2 * spacing
@@ -341,8 +367,10 @@ def updateValues(self, station, sweeptparameters=None):
         """
         Update the data with actual voltages from the QDac
         """
-
         for instrument, parameters in self._data.items():
+            if instrument == 'font':
+                # skip font data
+                continue
             for parameter in parameters.keys():
                 value = station.components[instrument][parameter].get_latest()
                 try:
@@ -361,6 +389,7 @@ def updateValues(self, station, sweeptparameters=None):
                         if sweeptparameter._instrument.name == instrument and sweeptparameter.name == parameter:
                             self._data[instrument][parameter]['update'] = True
 
+
     def makePNG(self, counter, path=None, title=None):
         """
         Render the image with new voltage values and save it to disk

From 08f7ff864417a5366f225f3a52632d5627d473b8 Mon Sep 17 00:00:00 2001
From: "William H.P. Nielsen" 
Date: Wed, 28 Jun 2017 13:58:20 +0200
Subject: [PATCH 232/328] Refactor/do measurement (#48)

* feat: Add more general _do_measurement "mother" function

Add a single function that all derived measurements procedures should
go through. TODO: refactor doNd to do so.

* refactor: make do1d use _do_measurement

* refactor: Make do2d use _do_measurement

* refactor: make do1dDiagonal use _do_measurement

* docs: Add plot-testing notebook

Add a notebook that can test (most of) the T10-specific plotting requirements.

* refactor: Put _select_plottables outside _do_measurement

And also update the test notebook a bit

* Update test notebook
---
 .../examples/T10 Plotting Test Examples.ipynb | 783 ++++++++++++++++++
 qcodes/utils/wrappers.py                      | 228 +++--
 2 files changed, 880 insertions(+), 131 deletions(-)
 create mode 100644 docs/examples/T10 Plotting Test Examples.ipynb

diff --git a/docs/examples/T10 Plotting Test Examples.ipynb b/docs/examples/T10 Plotting Test Examples.ipynb
new file mode 100644
index 000000000000..57f362435aee
--- /dev/null
+++ b/docs/examples/T10 Plotting Test Examples.ipynb	
@@ -0,0 +1,783 @@
+{
+ "cells": [
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "# T10 Plotting Test Examples\n",
+    "\n",
+    "### Rationale\n",
+    "\n",
+    "It is hard to write automated tests for live plotting behaviour. Our strategy is to run a live notebook with some mock instrument examples and see if everything behaves the way it should."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 1,
+   "metadata": {
+    "collapsed": true
+   },
+   "outputs": [],
+   "source": [
+    "\n",
+    "import numpy as np\n",
+    "import qcodes as qc\n",
+    "from qcodes.instrument.parameter import ArrayParameter\n",
+    "from qcodes.tests.instrument_mocks import DummyInstrument\n",
+    "from qcodes.utils.wrappers import do1d, do2d, do1dDiagonal, init"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 2,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Activating auto-logging. Current session state plus future input saved.\n",
+      "Filename       : /Users/william/sourcecodes/qdev-dk/docs/examples/PlotTesting/plottestsample/commands.log\n",
+      "Mode           : append\n",
+      "Output logging : True\n",
+      "Raw input log  : False\n",
+      "Timestamping   : True\n",
+      "State          : active\n"
+     ]
+    }
+   ],
+   "source": [
+    "# Instruments to measure with\n",
+    "\n",
+    "dac = DummyInstrument(name=\"dac\", gates=['ch1', 'ch2'])  # The DAC voltage source\n",
+    "dmm = DummyInstrument(name=\"dmm\", gates=['voltage', 'current'])  # The DMM reader\n",
+    "\n",
+    "\n",
+    "# the default dummy instrument returns always a constant value, \n",
+    "# in the following line we make it random \n",
+    "# just for the looks \n",
+    "import random\n",
+    "dmm.voltage.get =  lambda: random.randint(0, 100)\n",
+    "dmm.current.get = lambda: 1e-3*np.random.randn()\n",
+    "dmm.current.unit = 'A'\n",
+    "\n",
+    "# We add an arrayparameter as well\n",
+    "\n",
+    "class MyArray(ArrayParameter):\n",
+    "    \"\"\"\n",
+    "    Arrayparameter which only purpose it to test that units, setpoints\n",
+    "    and so on are copied correctly to the individual arrays in the datarray.\n",
+    "    \"\"\"\n",
+    "\n",
+    "    def __init__(self, instrument=None, name='testparameter'):\n",
+    "        shape = (25,)\n",
+    "        label = 'this label'\n",
+    "        unit = 'this unit'\n",
+    "        sp_base = tuple(np.linspace(5, 9, 25))\n",
+    "        setpoints = (sp_base,)\n",
+    "        setpoint_names = ('this_setpoint',)\n",
+    "        setpoint_labels = ('this setpoint',)\n",
+    "        setpoint_units = ('this setpointunit',)\n",
+    "        self.get_multiplier = 1  # used to check AutoSIPrefix\n",
+    "        super().__init__(name,\n",
+    "                         shape,\n",
+    "                         instrument,\n",
+    "                         label=label,\n",
+    "                         unit=unit,\n",
+    "                         setpoints=setpoints,\n",
+    "                         setpoint_labels=setpoint_labels,\n",
+    "                         setpoint_names=setpoint_names,\n",
+    "                         setpoint_units=setpoint_units)\n",
+    "\n",
+    "    def get(self):\n",
+    "        item = self.get_multiplier*np.random.randn(25)\n",
+    "        self._save_val(item)\n",
+    "        return item\n",
+    "\n",
+    "dmm.add_parameter('myarray',\n",
+    "                  parameter_class=MyArray)\n",
+    "\n",
+    "station = qc.Station(dac, dmm)\n",
+    "\n",
+    "init(mainfolder='PlotTesting',\n",
+    "     sample_name='plottestsample',\n",
+    "     station=station,\n",
+    "     annotate_image=False)\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 3,
+   "metadata": {
+    "collapsed": true
+   },
+   "outputs": [],
+   "source": [
+    "station = qc.Station(dac, dmm)\n",
+    "\n",
+    "# Finally, we instantiate the experiment. \n",
+    "init(mainfolder='PlotTesting',\n",
+    "     sample_name='plottestsample',\n",
+    "     station=station,\n",
+    "     annotate_image=False)\n",
+    "# NB: In this test we don't annotate an image, but change False to True to do so"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## 1D Graphs"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 4,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Started at 2017-06-28 13:40:02\n",
+      "DataSet:\n",
+      "   location = '/Users/william/sourcecodes/qdev-dk/docs/examples/PlotTesting/plottestsample/019'\n",
+      "      |   |  | \n",
+      "   Setpoint | dac_ch1_set | ch1          | (50,)\n",
+      "   Measured | dmm_voltage | voltage      | (50,)\n",
+      "Finished at 2017-06-28 13:40:04\n"
+     ]
+    },
+    {
+     "data": {
+      "image/png": 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m2+Gwi45SQ3idMoAEp8pQZfS95vVOlWnbjHsgnr4QSHic9puvWC3bpLHZWCLD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\nMzXE65S/9PfdWmyVAeIkCb85VQ9UNnmsAadwf/PNN88///yRkZH6V84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UscNa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\nO7Lt2feVPh61ENrrth/yVW1aSe3t7e0nuokutZ3u9ODtU2JfdF99ff1Dq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+      "text/plain": [
+       ""
+      ]
+     },
+     "execution_count": 4,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "# Normal SI Units, normal interval\n",
+    "# Expected labels: \n",
+    "#     Y: Gated Voltage (V), X: Gate ch1 (V)\n",
+    "plot, data = do1d(dac.ch1, 1, 10, 50, 0.01, dmm.voltage)\n",
+    "plot"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 5,
+   "metadata": {
+    "collapsed": true
+   },
+   "outputs": [],
+   "source": [
+    "plot.win.close()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 6,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Started at 2017-06-28 13:40:05\n",
+      "DataSet:\n",
+      "   location = '/Users/william/sourcecodes/qdev-dk/docs/examples/PlotTesting/plottestsample/020'\n",
+      "      |   |  | \n",
+      "   Setpoint | dac_ch1_set | ch1          | (50,)\n",
+      "   Measured | dmm_voltage | voltage      | (50,)\n",
+      "Finished at 2017-06-28 13:40:07\n"
+     ]
+    },
+    {
+     "data": {
+      "image/png": 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1diLyqvYKJHvhztanYrAMgL6gcK98Qlr0hpMcR7Vzl8hm3rCs3pEWxQtjpRss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+      "text/plain": [
+       ""
+      ]
+     },
+     "execution_count": 6,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "# Normal SI Units, small setpoint range\n",
+    "# Expected labels: \n",
+    "#     Y: Gated Voltage (V), X: Gate ch1 (μV)\n",
+    "plot, data = do1d(dac.ch1, 0, 10e-6, 50, 0.01, dmm.voltage)\n",
+    "plot"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 7,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Started at 2017-06-28 13:40:08\n",
+      "DataSet:\n",
+      "   location = '/Users/william/sourcecodes/qdev-dk/docs/examples/PlotTesting/plottestsample/021'\n",
+      "      |   |  | \n",
+      "   Setpoint | dac_ch1_set | ch1          | (50,)\n",
+      "   Measured | dmm_voltage | voltage      | (50,)\n",
+      "Finished at 2017-06-28 13:40:10\n"
+     ]
+    },
+    {
+     "data": {
+      "image/png": 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v/OQ//PF7AHzw5r858ttH53If3Z3w+riCY3VbqQwz7kTkTD3jvv2vZkfj2VJ2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\nCsy9iUGslhGzIDtm3Me4PHU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B+5ZNhDPJmCRhMVPuZvyTsTbVXnPqoF+4qSlRKjM10vy1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+      "text/plain": [
+       ""
+      ]
+     },
+     "execution_count": 7,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "# Normal SI Units, small value range\n",
+    "# Expected labels: \n",
+    "#     Y: Gated Voltage (uV), X: Gate ch1 (V)\n",
+    "dmm.voltage.get =  lambda: 1e-7*random.randint(0, 100)\n",
+    "plot, data = do1d(dac.ch1, 0, 10, 50, 0.01, dmm.voltage)\n",
+    "dmm.voltage.get =  lambda: random.randint(0, 100)\n",
+    "plot"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 8,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Started at 2017-06-28 13:40:12\n",
+      "DataSet:\n",
+      "   location = '/Users/william/sourcecodes/qdev-dk/docs/examples/PlotTesting/plottestsample/022'\n",
+      "      |   |  | \n",
+      "   Setpoint | dac_ch1_set | ch1          | (50,)\n",
+      "   Measured | dmm_voltage | voltage      | (50,)\n",
+      "Finished at 2017-06-28 13:40:13\n"
+     ]
+    },
+    {
+     "data": {
+      "image/png": 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UWLgTERFRYxGjMlesXgyOuZOpsHAnIiKiBhJVksGoYrNa/vCcDgBvn2ThTqbBwp2IiIga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\n3t4ccRFVE9GTTzyh1X0WDx53vY9CH5X8uFMxQ7u77ror420qIHCfppHRy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+      "text/plain": [
+       ""
+      ]
+     },
+     "execution_count": 8,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "# Exceptional Units, small setpoint range\n",
+    "# Expected labels: \n",
+    "#     Y: Gated Voltage (V), X: Gate ch1 (V)\n",
+    "dmm.voltage.get =  lambda: 1e-7*random.randint(0, 100)\n",
+    "dmm.voltage.unit = 'XX'\n",
+    "plot, data = do1d(dac.ch1, 0, 10, 50, 0.01, dmm.voltage)\n",
+    "dmm.voltage.get =  lambda: random.randint(0, 100)\n",
+    "dmm.voltage.unit = 'V'\n",
+    "plot"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 9,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Started at 2017-06-28 13:40:15\n",
+      "DataSet:\n",
+      "   location = '/Users/william/sourcecodes/qdev-dk/docs/examples/PlotTesting/plottestsample/023'\n",
+      "      |   |  | \n",
+      "   Setpoint | dac_ch1_set | ch1          | (50,)\n",
+      "   Measured | dmm_voltage | voltage      | (50,)\n",
+      "Finished at 2017-06-28 13:40:16\n"
+     ]
+    },
+    {
+     "data": {
+      "image/png": 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JQD2KQnOJPBGxyVTMp4IVULhvLzPrWfZGBk9+AOAciUrHvYTHLrCpC5lioSxFA+6wTyCt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\nzu6iEeWozsMSeIesMtO5JkNtCKbSuscdDA1PxWiVgQWiu3mKOwqWmVw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dqxaqPIDB1IIz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\nrXGOjPmrUlM0KosD8cjZvO+5IyVbX1mTT8ODRMSuhc7zp49Tw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+      "text/plain": [
+       ""
+      ]
+     },
+     "execution_count": 9,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "# Exceptional Units, small set range\n",
+    "# Expected labels: \n",
+    "#     Y: Gated Voltage (XX), X: Gate ch1 (V)\n",
+    "dac.ch1.unit = 'YY'\n",
+    "plot, data = do1d(dac.ch1, 0, 10e-7, 50, 0.01, dmm.voltage)\n",
+    "dac.ch1.unit = 'V'\n",
+    "plot"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 10,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Started at 2017-06-28 13:40:18\n",
+      "DataSet:\n",
+      "   location = '/Users/william/sourcecodes/qdev-dk/docs/examples/PlotTesting/plottestsample/024'\n",
+      "      |   |  | \n",
+      "   Setpoint | dac_ch1_set | ch1          | (10,)\n",
+      "   Measured | dmm_voltage | voltage      | (10,)\n",
+      "Finished at 2017-06-28 13:40:28\n"
+     ]
+    },
+    {
+     "data": {
+      "image/png": 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TQuI+AYahexdlENGOz1EtAzB9o3UyDHOVe/kxTCeQuAMAAIwPifvEbp9v1KnZmnbbmW5c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\nT4/b7Woya5xNv/KNj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+      "text/plain": [
+       ""
+      ]
+     },
+     "execution_count": 10,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "# Normal SI Units, normal interval, sloooow acquisition. Zoom out/pan and check autozoom\n",
+    "# Expected labels: \n",
+    "#     Y: Gated Voltage (V), X: Gate ch1 (V)\n",
+    "plot, data = do1d(dac.ch1, 1, 10, 10, 1, dmm.voltage)\n",
+    "plot"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Non-standard `do1d`"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 11,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Started at 2017-06-28 13:40:29\n",
+      "Printing the number 1\n",
+      "Printing the number 2\n",
+      "Printing the number 3\n",
+      "Printing the number 4\n",
+      "Printing the number 5\n",
+      "Printing the number 6\n",
+      "Printing the number 7\n",
+      "Printing the number 8\n",
+      "Printing the number 9\n",
+      "Printing the number 10\n",
+      "DataSet:\n",
+      "   location = '/Users/william/sourcecodes/qdev-dk/docs/examples/PlotTesting/plottestsample/025'\n",
+      "      |   |  | \n",
+      "   Setpoint | dac_ch1_set | ch1          | (10,)\n",
+      "   Measured | dmm_voltage | voltage      | (10,)\n",
+      "Finished at 2017-06-28 13:40:30\n"
+     ]
+    },
+    {
+     "data": {
+      "image/png": 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AtyuVz+9JYRha+6fwUWEebMchwv5UAICH++FGWWVDU2gPx+HBVj0FsnW2Qv7G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\nS98YgJBvs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+      "text/plain": [
+       ""
+      ]
+     },
+     "execution_count": 11,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "# Adding a non-measurement task to ensure that this does not screw up things\n",
+    "def printer():\n",
+    "    n = 0\n",
+    "    def _core():\n",
+    "        nonlocal n\n",
+    "        n += 1\n",
+    "        print('Printing the number {}'.format(n))\n",
+    "    return _core\n",
+    "\n",
+    "printtask = qc.Task(printer())\n",
+    "plot, data = do1d(dac.ch1, 1, 10, 10, 0.1, dmm.voltage, printtask)\n",
+    "plot"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 12,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Started at 2017-06-28 13:40:32\n",
+      "DataSet:\n",
+      "   location = '/Users/william/sourcecodes/qdev-dk/docs/examples/PlotTesting/plottestsample/026'\n",
+      "      |   |  | \n",
+      "   Setpoint | dac_ch1_set | ch1          | (100,)\n",
+      "   Measured | dmm_voltage | voltage      | (100,)\n",
+      "   Measured | dmm_current | current      | (100,)\n",
+      "Finished at 2017-06-28 13:40:39\n"
+     ]
+    },
+    {
+     "data": {
+      "image/png": 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3QI3KxO9TpjucmtNsxz3FmLtluY57kj3usDPuTh1keKuMH0/foh2ViWOxNi1H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cUnEP70Cktw4LkOP5EqTKAKJVhsZBNmyVoeWGdZdYJkgVbD6o7guo1OrguCBR3Imr21HluCvi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\n9oPtjzy06RfPbsLQvqePeXO9UQRBEARBEASRPfKicPf/4QVP5eo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+      "text/plain": [
+       ""
+      ]
+     },
+     "execution_count": 12,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "# Expected:\n",
+    "#    Left plot. X: Gate ch1 (V), Y: Gate voltage (V)\n",
+    "#    Right plot. X: Gate ch1 (V), Y: Gate current (mA)\n",
+    "plot, data = do1d(dac.ch1, 1, 10, 100, 0.01, dmm.voltage, dmm.current)\n",
+    "plot"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## 2D Graphs"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### `do1d` with array parameter"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 13,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Started at 2017-06-28 13:40:42\n",
+      "DataSet:\n",
+      "   location = '/Users/william/sourcecodes/qdev-dk/docs/examples/PlotTesting/plottestsample/027'\n",
+      "      |   |  | \n",
+      "   Setpoint | dac_ch1_set | ch1          | (50,)\n",
+      "   Measured | dmm_myarray | myarray      | (50, 25)\n",
+      "Finished at 2017-06-28 13:40:46\n"
+     ]
+    },
+    {
+     "data": {
+      "image/png": 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Mnb+1+4zFw5uW81Nh06alZR8cPHiq/nVJ8dH28gYwOfPmVAp3AAAA1LasAx/F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+      "text/plain": [
+       ""
+      ]
+     },
+     "execution_count": 13,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "# Exceptional Units, small set range\n",
+    "# Expected labels: \n",
+    "#     X: This setpoint (this setpointunit), Y: Gate ch1 (μV)\n",
+    "\n",
+    "plot, data = do1d(dac.ch1, 0, 10e-7, 50, 0.01, dmm.myarray)\n",
+    "\n",
+    "plot"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 14,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Started at 2017-06-28 13:40:48\n",
+      "DataSet:\n",
+      "   location = '/Users/william/sourcecodes/qdev-dk/docs/examples/PlotTesting/plottestsample/028'\n",
+      "      |   |  | \n",
+      "   Setpoint | dac_ch1_set | ch1          | (50,)\n",
+      "   Measured | dmm_myarray | myarray      | (50, 25)\n",
+      "Finished at 2017-06-28 13:40:53\n"
+     ]
+    },
+    {
+     "data": {
+      "image/png": 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piA2crD5z1Q71mSJya99LdMSGxP+mI9acoiNV8h7WMimp7KiOVMkxqR+WFPq8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\nbNhg9BAA+ATeDWCggoICo4fQk1G464NvUwAAAChFqwwAAADgByjc/Zb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+      "text/plain": [
+       ""
+      ]
+     },
+     "execution_count": 14,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "# Exceptional Units, small set range, large value range\n",
+    "# Expected labels: \n",
+    "#     X: This setpoint (this setpointunit), Y: Gate ch1 (μV), Z: this label (GHz)\n",
+    "\n",
+    "dmm.myarray.get_multiplier = 1e9\n",
+    "dmm.myarray.unit = 'Hz'\n",
+    "\n",
+    "plot, data = do1d(dac.ch1, 0, 10e-7, 50, 0.01, dmm.myarray)\n",
+    "\n",
+    "def get(self):\n",
+    "    item = np.random.randn(25)\n",
+    "    self._save_val(item)\n",
+    "    return item\n",
+    "\n",
+    "dmm.myarray.get_multiplier = 1\n",
+    "dmm.myarray.unit = 'this unit'\n",
+    "\n",
+    "plot"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 15,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Started at 2017-06-28 13:40:55\n",
+      "DataSet:\n",
+      "   location = '/Users/william/sourcecodes/qdev-dk/docs/examples/PlotTesting/plottestsample/029'\n",
+      "      |   |  | \n",
+      "   Setpoint | dac_ch1_set | ch1          | (3,)\n",
+      "   Measured | dmm_myarray | myarray      | (3, 25)\n",
+      "Finished at 2017-06-28 13:41:11\n"
+     ]
+    },
+    {
+     "data": {
+      "image/png": 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v36lTJxVLqikiwiI920jpPwDqi/h2DO5m7m2UjyW4AwAANF9nz54dMGCAr69v\nQEBA9cGlS5e6TnC3l8b8A6Aq4huNxlpX2birjJ3YGNzLysqys7OvX7/u6enZsWPHdu3a2bcsAAAA\nOMH69evHjRu3bt06tQtxIdVh3UqXjt1X3Buj0cH90qVLf//731NTU93c3Nq0aVNYWFhaWtqhQ4eR\nI0dOnDix5r/VAAAA4OJatGjRv39/tavQDg2tuO/atSslJWXMmDFPPvnkfffdJyImkykvL+/cuXOf\nfvrprFmzEhISevbs6ZhSAQAAYGeTJ09esmTJzJkzW7durXYtalJ5sxplGhfcDQbDuHHjah5xc3Nr\n165du3btHnzwwWvXrpWXl9tWR27m7nc278y54dcvZkbcyDCrY4rOHly/LvlknnTuN2LKlLEhettu\nBQAAgCrp6emHDx8ODAys2dT+2muvRUVFqViVHSlM5FYfSLWyq4yGVtxLSkqKi4u9vLysnrW50z03\nY03Mwk1iiJwUfCZxWXzG5VWrphpqjcnP3PLYvNUSPHDS4JZJia9uT/xy476Xu/BsLQAAQBMMGzZs\n7dq1tQ7abyMXp2pKRldKQz3uX3zxRVJS0uDBgyMjIx9++GF3d3d71JD7/rJNMnD+nuWxepGhwfPm\nrX4zMzrR4H3XmPcWr5aB81OWx3qLPDdx75CJyz47lj/b4GuPAgAAAJqp0NDQ0NBQtauwD8X/3lDa\nFWO5q4yWVtyfeeaZ0aNHf/755ytWrCgpKRk1alRkZGTXrl2bVEJR1oECiX9irLnzxRAb55O48NCZ\nfEPNUJ57/J8FMvepsbpLpzJ+LGzVsW9aWlqTbgoAAACRjRs3vvPOO7UOLl26dPTo0arUo65ai/GW\nrTImDa24i0hoaOhTTz311FNPHT9+/PPPP1+4cKG/v//YsWNHjx7t62vL+nfROWOOBD/Y+fYCu65t\nqOWYC/8pENn7pymrTxWYjwRHLn73pbF0uQMAADRF//79qx9LNZlMR44cOX78+AMPPKBuVQ5luTBf\n3WMTE1P7eO3B2gru1Xr16tWrV69nn3323Xfffeutty5duvRf//VftkxUfuuur/q2nUVKa43RiYic\nuvzLxB0Lw3zl1O7V8a+++tfBfRYNDaw5Kisrq+bXCxcu2FLPbWxsCQAAnKBWgGkUq2mnUa0vPXr0\n6NGjR/XXiRMnzpw588yZM0FBQTZXpTn1RPnaNBrcz5w5s2fPnr1797Zs2fLpp7lu4JQAACAASURB\nVJ8eO3asbfPoO3YWycktKRdvnYhIyeUMkRG1qmztLSKTlj0d5qsTkbCxM6avStqR+WOt4G75a9qU\nnq1im68EAABQrIkt5nbvUA8ICPjmm28GDx5s32ldh5JnWM09M1Z2ldFWcL906dKePXs+//zzn376\n6dFHH3355Zdr/ivNlgradzWI/DM9e2R0FxEpOX8sRySo7V1dMPqg+4NFrhSU3D5Q8lOBeHm2aMp9\nAQAAcO7cubNnz1Z/zcrK2rhxY1JSkoolOVp9L0a9zdwzo+1WmfXr17/33nsDBw58+umnBwwYoNPZ\nYztGXdikSJ/FCb/f3m3lCP8f/xS/WnymD+2iF7m0Jn7ijuBFH74crdf3fjbSZ/Gyxdt9/ntQqKSv\nfyVFZP7wcDvcHQAAoBlLSUl54403zJ/d3Nzat2//v//7v8OHD1e3KudocBcaK8leQ7vKDBo0aMKE\nCW3btrVvEUNfWBufMzFhzsQEEZGBKzbE+4pIeVnhZSloVVouIqIb+sLq+Py5CQtnmi+ZtGxjbBjP\npgIAADTJnDlz5syZo3YVmmHSUHAPD7e+yP3tt9+eOXNmwoQJtlYRGLcqbXxubnm5eAe2r8rjupBF\nO9MW3RkTErd8Z2x+frmU67zbe/PqJQAAADQntuTf48ePr1q1quaRnJycxx9/vIml+LZv3+AYb5t2\nnAQAAADsQEM97maBgYGTJk2q/nrx4sWvvvoqpta+lwAAAMA9RkOtMmbt2rUbNmzYXbPodFu2bImL\ni7NLTQAAAFCFyWRyc3NTuwoXpuqKu7tdZmnTps3Vq1ftMhUAAACcpry8/Lnnnjty5IiI/PrXv27d\nuvVzzz2ndlEuzKT4xwFsWXHPy8uruTlOfn7+Bx98EBMTk5aW1rp16759+9qvPAAAADjQ5s2bv/76\n65dffnnfvn0nT57cv39/XFzc/v37hw4dqnZpLklzrTI5OTmbNm2qecTb23vPnj0iEhQURHAHAADQ\nim+//fbJJ5/09fX96KOP4uPjH3744QkTJhw5coTgbpWWtoM0692795o1a+xeCgAAAJwsKCjo66+/\nnjlz5scff2xehz158uTYsWPVrgtWNK7H/fTp05WVdf5D48KFCzk5OU0uCQAAAE7y5JNP7t27NyAg\n4OGHH+7du/fWrVvT0tLYLbBOGupxz8rKSkhIGD58eN++fbt27erh4VFWVpaTk3P+/Pldu3YdO3bs\n9ddfd0iZAAAAcID27dt/9913J06cMBgMIvLQQw9lZGT48uacumioVebRRx/t16/f+vXrFy5cWFhY\n2KpVq5s3b4pIly5dRo4c+eKLL/r4+DimTgAAANjT5cuXO3bsKCLe3t79+vUzH+zWrZuqRams5v4r\n1mnrBUy+vr4LFy5csGBBbm5uYWGhp6dnu3btvLy8HFEcAAAAHOHjjz+ePHny5cuX161b98orr9Q6\n+84779R82+Y9pp50npx819clSyJqj9BWcDdzc3MLCAgICAiwbzUAAABwgjFjxmzbtq1du3azZs2K\nioqqdTY4OFiVqpwjIqJ2HK+O8rV6+41GY+3BGmqVAQAAwD3Ay8tr3LhxIuLv7+/v7692OSqzjPJm\nlmvzpnvgzakAAADQosrKypdeeqlHjx6BNXz00Udq1+WqNLSrDAAAAO4l77777tatW998882goKDq\ng507d1axJJemxR53AAAA3AOOHz8+f/78yMhItQvRCA31uGdkZPzwww9WT4WHhz/00EP2KAkAAABO\nMmDAgKNHj6pdhXZoaMX94MGDe/bs6d27t+WpNm3aENwBAAA0IS8vb+/evebP6enpf/jDH3r16lV9\ntn///p06dVKpNNemoeA+bdq0ffv2zZw5s2fPng4qCAAAAI525cqVN954o/rrZ5999tlnn1V/Xbp0\n6T0f3Bt+15I1Jg21yrRr127RokV/+MMfEhMTW7du7aCaAAAA4FDh4eEHDhxQuwp1KIns5jcxWXkB\nk6oa/XDqwIEDw8PDdTqeagUAANC8Xbt2tWzZ8tFHH60+kpSUFBwcPHjwYBWrcqi6dm2/m1HujRcw\ntWvXTkQuXbp07do10+1t6P39/QMDA+1ZGgAAABymtLT02rVraWlpXl5e1fE0Pz8/ISFhwYIF93Bw\nV8L8B2JlbV5DPe5mlZWVixcvPnz4cM33bI0bNy4uLs5udQEAAMCR0tPThw8fbv68dOnS6uO9e/ce\nNWqUSkW5PM0F96+++ur8+fPbtm1r27at3QsCAACAEwwbNsxkMr3yyive3t4LFixQuxyN0FyrTFlZ\nWZ8+fUjtAAAAWjd//nw3Nze1q9AOza249+nT55NPPiktLfX09LR7QQAAAHCC119/fdq0aR07drQ8\n9eOPP7711lvz589vPk8wKtwd0qSh4P7yyy+bP5SWlk6bNi0iIsLd3d185JFHHhk9erSdqwMAAIBj\n9OnTJyoqKjAwcMiQIeHh4W3atLlw4cJ//vOfb7/9dvfu3bNnzzbvR3LvsZrRzfs/1mJlO0gNBffu\n3bvX+lCtffv29qkIAAAAjjdixIjDhw+///7727dv37JlS0FBgbe3d5cuXYYMGfLWW28FBwerXaBL\n0lCP++OPP+6gOgAAAOBk7u7u06ZNmzZtmtqFOFUd+7hbWYa3so+7hlbcq6Wnp6ekpJg7Z5KSkvLz\n85966qnqthkAAABAQ6ymeVfbx92WqH316tWXX3555MiR5q8DBgzYv3//nj177FoYAAAAoALjbVbO\nVSr+cQBbVty/++67n//858OGDTN/7dSpU2xs7JEjR8aMGWPP0gAAAAB7a3ADmeoHVbX9cKpZ69at\nax2pqKjw8vKyRz0AAAC41xWd2rL6vfRzecHho5+cGx1oY++2jWp2xVgN8TExd87W0RCvDhv3cX/9\n9df/9re/RUZGtmjR4ujRo4mJiX/+85/tXhwAAAAcITc3d9u2bXWdHT58eLdu3Rx176JjL0TOOShh\nk6b3+OemhJQD57b+4zm1touvP5dbxnqThnaVMWvVqtXrr7/+17/+dfbs2eXl5V26dFmyZEmvXr3s\nXhwAAAAcIT8/v57g3r17d8cF92PbXz8oYSt2JPbzlbmjw4fPTFi3f+JLQ50a3RW+bskKzQV3EQkJ\nCVm+fLmIVFZWspkMAACAtnTv3n3nzp1q3Lno609O+UQv7+crIqLrMnp+WMKGw2fFicHdaDRafd2S\nJSs97qpqaksRqR0AAEDTbt26tXnz5n//+9/PPPNMZWWlv79/YKBjY7RXQNvbH/U9h4QVfPhd/qKB\nvg695d2qu9jrYT3ca3HFHQAAAPeA69evDxgwoGPHjleuXBk3blxRUdG4ceOMRqO3t7fjbhrgXXun\nE0vz5w+p5+zKlWk2313x86bGe+QFTAAAALgHJCYmDhw4cO3atXFxcSIyfvz4d999d9u2bdOnT3fU\nLYvl6k+F1d8Kr16WUH+9xaimRPNqtvey10W7K+5lZWUVFRV6veUfNQAAADTgwoULAwcOrHkkIiIi\nNzfXYTfUBz4oOV8cLZpt8BYRyf50e0HY3J52SZNNjOmWvTGWPe6qLrjbGtwzMzPffPPN06dPz5kz\np1+/fh9//PHzzz/v4eFh3+IAAADgUH379v3b3/72+OOPm79euXLlgw8+WLduncNuqBscO13mJf5h\nS8RL0aGHVj+fKrJ4RLhdpm7ynuu1c7+VVhnNrbhfu3Zt6dKl8+bNu3Llioh06tQpOzt7165d0dHR\n9i4PAAAADjR58uRPP/30/vvvd3Nz+/bbb7OysmbPnj1kSH395U3kbZi9av6P81YufGy1iMikV7eM\ndfIbmOpgmfutLOFrLrhnZmY+8sgjo0aN+uijj0pLS1u2bBkdHX348GGCOwAAgLa4ublt3LgxMzPz\nyJEjOp2uf//+PXr0cPRNDbEv73k0t6hcdHpfX2+XSO3274Z3ABtfwHT9+vWaR65fv+7j42OnkgAA\nAOBUXbp0KSsr0+l0nTt3ds4d9b7t1XpK0mpGt7r5o5V93FVtcrdlF/Y+ffqcP39+zZo1ly9fvnLl\nykcffbR+/foxY8bYvTgAAAA42tKlSzt27DhmzJhhw4YFBQW9/fbbalfkwioV/ziALSvuer3+jTfe\nWLt27dGjR0tKSrp27frKK6+Eh9vnqQIAAAA4TXJy8oYNGw4dOmQwGEQkNTU1Jibm4Ycf7tu3r9ql\nOUodz7BaWYa/R/ZxDwgIePHFF+1bCgAAAJzs0KFD8+bNM6d2ERk2bNi0adMOHjx4Dwd3q6oDev3N\n7ibNBXej0Zienv7MM89UH/nyyy/PnDkzc+ZM+xUGAAAAh+vVq9fx48drHsnNze3du7da9ThB/dG8\nZrO7lR53De0qU1lZeejQoVOnTpmzu/lgaWlpUlJSnz59HFAeAAAA7O/WrVvZ2dkiMmDAgE2bNq1Y\nseLRRx8tLy//+OOPPTw8BgwYoHaBDlSr+6VWjo+JueuUhltlysrK1q1bV1xcXFhYWHNnfn9/f/aC\nBAAA0Irjx4/X3Kz94MGDS5Ysqf4aHR09efJkNepyhgZ3fqxedHe1XWUaF9xbtmy5du3ab7755l//\n+tfChQsdVBMAAAAc6sEHHywqKlK7CtVY3fxREQ0Fd7OHHnrooYcesm8duZm739m8M+eGX7+YGXEj\nw+oeWJSxe3dWkd/g8SNd4x1bAAAAmldWVnbr1q3qr3q9Xqe7l5NWTIyt2V1DPe7V/vWvf33yySeF\nhYXVR0aNGmXz/6WSm7EmZuEmMUROCj6TuCw+4/KqVVMNVkdm735z4aspIsGhY0cGett2NwAAAFS5\ncePGlClTPv3004qKCvMRd3f39957b+rUqeoW5ji329YbflWqZY+79naV+fHHH//0pz/FxcWdOnXK\n29u7e/fuu3btGjRokK015L6/bJMMnL9neaxeZGjwvHmr38yMTjRY5vL8g8+/muIT7FOQ493C1psB\nAACg2vr16/Py8k6dOvW73/1uwYIFnp6eq1atio2NVbsuh6tjN/e7WOmG19ybU48fP/7QQw9NmjSp\nZ8+eHTt2jIqKGjNmzMGDB20soSjrQIHEPzHW/NpbQ2ycj5w6dCbfYlzJ9j++kBM2968vTxVpvi1Z\nAAAAdvTDDz9MmDCha9eu/v7+paWlgwYN+tnPfvbpp5+qXZerUvXNqbYE91atWt24cUNE/Pz8zp8/\nLyKtW7c+efKkbRUUnTPmSPCDnW8vsOvahloblnvw7wkHZfGrU0Ok1LYbAQAAoJZOnTp9/fXXItK5\nc+evvvpKRHJzc807RcLV2NIq8/DDD69YseKLL77o1avX66+/HhAQsHfvXtu3gyy/dddXfdvOYpHN\ny08tfyEpLH7V2EApMffVWys8Kyur5tcLFy7YWJKIiAQ05WIAAABlagWYRrGadkJDQ5XPMGPGjFWr\nVn399ddRUVGDBw/eu3fv4cOHX3zxRZtLusdp7uFUvV7/9ttvFxUVBQYGzp8/PyUlZdiwYb/5zW9s\nq0DfsbNITm5JuXjrRERKLmeIjLh7TEbinw6KLHq4XXb2pWvfnREpPnfybOfwEF/9XfVb/po26he3\nlmKbrwQAAFCsKXGl6ZcHBAQcP368srKydevWKSkp6enpb7zxxv3339+UOe9lmns4VUQ6dOjQoUMH\nERk1atSoUaOaVEH7rgaRf6Znj4zuIiIl54/liAS11dcYkn9kxykRSZhz5+nmhHkzT67Ysaifb1Nu\nDQAAAL2+Knf98pe//OUvf6luMa5OW8H9p59+2rRp04wZMzw8PH7729/6+fn17t175syZXl5etpYQ\nNinSZ3HC77d3WznC/8c/xa8Wn+lDu+hFLq2Jn7gjeNGHL0fHf7hnmrlWna7o28SYhV8s3/ruwEB9\ng3MDAADAktFoHD9+fF1n//KXv/z61792Zj1aYdJQq0xBQcGcOXO6devWqlWrmzdv5uXlxcXFffbZ\nZ0899dT69eur/7nWWENfWBufMzFhzsQEEZGBKzbE+4pIeVnhZSloVVouotfr72wO2aqliHdrb1I7\nAACAjUJDQzds2FDX2fDwcCfWoj4r2z7WRUMr7lu3bg0PD3/llVdE5ObNmy1atDC3yjz//PNbt26d\nMWOGrVUExq1KG5+bW14u3oHtqyK5LmTRzrRFFmP1vePS0uJsvBEAAABEvL29Bw8erHYV6qgrplu+\nS3XJEou93jUU3I1GY/WG/B4eHkFBQebPo0aNSk1NbWIpvu3bN3EGAAAAoH51v3qpdqC3fHOqloK7\nh4dHWVmZ+bOPj8/bb79t/lxZqWq/DwAAANA0loHeytq8qpm3cS9g6tWrV0pKisl01781TCbTnj17\nevbsadfCAAAAABdjUvzjAI0L7lOmTDl//vzixYuPHz9+8+bNGzduGI3Gl1566dKlSxMnTnRIgQAA\nAHCk8+fP5+bmikh6evqf//znM2fOqF2RC9NQcPfy8nrrrbd8fHyee+650aNHjxkzZsGCBX5+fqtW\nrWrVqpVDCgQAAIDD/PDDDwaD4cKFC3l5eVFRUXv27Bk0aFBeXp7adbkoU6XSH0do9D7u/v7+/+//\n/b8XXnjh6tWrbm5u7du3d3Nzc0RlAAAAcLT33nvv2WefNRgMb7zxRlRU1MaNG2fNmrVt27ZZs2ap\nXZpLUvXh1MatuFdzc3Pr0KFDQEAAqR0AAEC7bty4Yd4nMCUlJSoqSkTuu+++/Px8tetSXyM2d3eW\nRq+4AwAA4J4xYsSI+fPnFxUVZWZmPvbYY2fPnt2yZcu7776rdl0OpDCRJydb28ddQ29OBQAAwL3k\nV7/61Q8//JCSkrJmzZpWrVp99dVXU6dOHTJkiNp1OZB528cG43tMjLV93AnuAAAAUMv8+fPnz59v\n/jx58uTJkyerW49z1P0apjushHsNvYAJAAAA94AzZ86sWrVq+fLlmZmZhw4dqnV29OjRYWFhqhTm\nNLa1sJsI7gAAAHCmS5cupaamlpSUnDt3LjU1tdZZg8FwDwd3etwBAACgGb/4xS+OHj0qIhMmTJgw\nYYLa5TiVkiYZERExWulxZ8UdAAAAasnNzU1NTa25BeTw4cO7deumYklO0+iGGYI7AAAAVHH69On+\n/ft36dLFvJu7Wffu3e/h4N5gWE9OrvpgpVWG4A4AAABVbNiwYfLkyatXr1a7EOdR0CpTlezZDhIA\nAACuwtvb+7777lO7CtdSHdZdbTtIdzVvDgAAAFVNnjz5wIEDN27cULsQbTCZlP44AivuAAAAzc7x\n48enTp1q/pybmxscHBwaGlp99o9//OOvfvUrdSpzcfS4AwAAwJl+9rOfvfbaa3WdNRgMzixGSwju\nAAAAcKa2bduOHTtWRIqLi93c3Fq3bl196vr16y1atFCvNNem6sOp9LgDAAA0XytWrHjnnXdqHvnd\n7363efNmtepxdSbFPw7AijsAAEBz9MMPP6xcuTIjI6NFixanT582H8zPz9+2bdvjjz+ubm2ui1YZ\nAAAAOJlOp/P19dXr9Z6enr6+vuaDfn5+a9euHTFihLq1uSwT+7gDAADAybp06fLKK6/s2LGjZcuW\no0ePVrscNIzgDgAA0Hw99thjapfgoqy8fUl4cyoAAADgLNYTuYXkZFmyJKL2UXrcAQAAAOeIiIgQ\nBfE9JsbaUVbcAQAAAGcyx/f6GY3G2sNYcQcAAAA0gOAOAAAAOJnCZve7ENwBAAAA52gwrycnV32w\nfDjVRHAHAAAAnENBd3tVsqfHHQAAAHBd1WHdyto8u8oAAAAAjmZLU3stBHcAAACgiZQ3rytk5QVM\nqiK4AwAA4F5QV/N6daC3/k6l2xTFenrcAQAAAAdR8q4lEal+JvXOd4uHU020ygAAAADqssz3rvZw\nqruaNwcAAACgDCvuAAAAgDK0yrgmr9/+1hHTlhv/Zvc5c2N3231OEXniP6MdMa3cOuWIWa/F7XPE\ntD4vO2JW+frHuY6YtmTrarvP6eZl9ylFRFa1i3PEtNmOmFSk4pBDpnUPmuSIaW+8l+SIaVvV+0SX\nba7/1f5zikj7IIdMW3nyCUdMGxLqiFnFKy7cEdMWrTnpiGkrfrT/nJ5THfII4T9vuTli2jhHTAoH\noVUGAAAAQP1YcQcAAACUYTtIAAAAwPWxHSQAAACgBay4AwAAABpAcAcAAAA0gFYZAAAAwMmsvBi1\nQay4AwAAAM6hJK8nJ4uILFkSUfsEwR0AAABwjogIizhuhVFEjEZjrcEmgruISNGpLavfSz+XFxw+\n+sm50YHW6so9tf/993aezJPwYVEzYof6Or1GAAAANAfmvG5lbZ4edyk69kLknIMSNml6j39uSkg5\ncG7rP54LvHtI7sG/xryQJIbISZ3zk1YuTkqJ35EYR3YHAACAQrY0tdfCivux7a8flLAVOxL7+crc\n0eHDZyas2z/xpaE1o3tJ6tokMSzatypaJzJr9IbIeYn7z06J7qJXrWgAAAC4kgZzublzXTl63C0V\nff3JKZ/o5f18RUR0XUbPD0vYcPis3BXcdY/81/IVbXuay9W3aycinjpXKB4AAAAuoa7m9epAHxNT\ndaSxCf4OWmVExCug7e2P+p5Dwgo+/C5/0cAanTC6EMPAkKrP+ZuWJYhE9wlxleIBAADgsmoFeqPR\nWJ3gq9ke5Z3IVbJvgHdrZQNL9v5xeuIpn2VbFwZanMvKyqr59cKFC00pKbRFU64GAABQpFaAaRSr\naSc0NNTmCZuDOhbmrbTZWO4qw4q7SLFc/amw+lvh1csS6m+1ez1jzbPLUgriV+0YaW3fGctf0yb9\n4jYp9gMAACjSxJxNTLcLq2nesmle3e0g3dW8eRV94IOS88XRoqqv2Z9uLwgb1NMyuB/78IWFm07F\nr9gRZ2A7GQAAADidSfGPA7hCcNcNjp0uOYl/2JKRX5S7O+H5VJGJI8JFpCR7d9SQIQn7s0Xk7O6E\nOSsPiiH+wVbnMjIyDh7MOJtfrnLhAAAAaFYqFf84gEu0yngbZq+a/+O8lQsfWy0iMunVLWPNnTA3\niwpECvPLRYoO7twuIpKZOG9O1VWTVux4rh9L7wAAAHCWZr8dpIiIIfblPY/mFpWLTu/r611VlT4s\nNi0t1vx56qq0qeqVBwAAABDcq+h92/M6JQAAALgugjsAAADgNA2+Y7VObAcJAAAAOIfy1G65R6S6\n20ES3AEAANCM1PECJiusvICJ4A4AAAA4mS0NM7TKAAAAAM6hJK8nJ4uILFlisTbPijsAAADgHMpa\nZYxitVVGVQR3AAAA4C7mvG5lbZ5WGQAAAMD1mVQN7u5q3hwAAADQEJPiH+VT3rqlcCTBHQAAAFDG\nvsHdZDLduFF29KjCmxPcAQAAAGXsF9wr8/NLv/jiSvfut3bvVnhzetwBAAAAZezR4266fr0yPz//\niSdK9+1r1IWsuAMAAADOYCotlcrK64sXX+nUqbGpXVhxBwAAAJRqwoq7qajo5j/+UfDUUzbPQHAH\nAACAKooydu/Okvujxhr0apeikG3bQZoKCsq//z5v+vSKM2eacneCOwAAAJytPP/YitlztueI+Ex/\nVDvBvVH7PIqIqbjYdONG/pNP3tq5s+k3p8cdAAAAzlVybPZjc7YHRE8f5iNeLe/JheQ5Tz8tFRVF\nr756uUMHu6R2YcUdAAAAzqZrGzl32VtTR57fcmKT0k3MXYPiVpnXX3vtpzFjSvfutePNWXEHAACA\nc+lCYqeO1IuUlRapXUrjKN/G/ZFhw/y2bm23e7d7hw72ujsr7gAAAHCokoztHxuLxFNESku9w0dG\nDwxp8Jrdu9dbHhw7dpb9q2sM5c+m/vvf/3b382v56KMdsrJuvPVW4aJFTb87wR0AAAAOVZ514JMP\nz4mXiBQXG575ZbSCa1TP6FY18tlUEQ8Pt1atWj/7bOtnny2YPfvme+815e4EdwAAADiUd+zyf8Sq\nXYQlo9HY2EsaHdxFRMStVSsR8XnzTe8XX8yfObMsI8OmaQjuAAAAUNMtJ9+vOq8nJzcwcsmSiFpH\nbAvuZm6+vjpf33Z79pR+8UX+E0+Yihrd309wBwAAgDpaeHqLVxu17h4TY/14PYG+CS9OreLu69sy\nOrrjTz8V/d//FS1b1qhrCe4AAABQR9jUxLSpzr5pRMRd6+iWDTPVgd5oNNYa3PTgLiJuOp2IeD//\nvNf8+QXx8covJLgDAACgGam/tb3mcrt9W2VqcfPychPxWbvWdPOmwksI7gAAAGhGai2iW7gT6x20\n4l6Tu5+f+PkpHExwBwAAQHOncIcZO66424DgDgAAgGbEaka3+kCqZauMugjuAAAAaEbqaJWxkuad\n0CrTKAR3AAAANHdW07zl2jzBHQAAANAAetwBAAAADWDFHQAAANAAVtwBAAAADSC4AwAAABpAcAcA\nAAA0gB53AAAAQAMI7gAAAIAG0CoDAAAAaAAr7gAAAIAGqLvi7q7q3QEAAAAowoo7AAAAoAitMgAA\nAIAGENwBAAAADWBXGQAAAEADWHEHAAAANIAVdwAAAEADCO4AAACABhDclcrN3P3O5p05N/z6xcyI\nGxmmdjkAAAC41xiNxnrO0uOuSG7GmpiFm8QQOSn4TOKy+IzLq1ZNNahdFAAAADSm/mienHzn85Il\nEbXOEtyVyH1/2SYZOH/P8li9yNDgefNWv5kZnWjwVrsuAAAAaFbNmK4ErTIKFGUdKJD4J8bqRUTE\nEBvnk7jw0Jl8g8FX5cIAAACgKRERNdfR61t9NxqNdw9mxV2BonPGHAl+sPPtBXZd21A1ywEAAMC9\noFYur6X+phrn00Zwl/Jbd33Vt+0sUmoxat26dTW/5ufn+/raviT/5BibLwUAAFCqVoBpFKtp58kn\nn2xaRagTrTIN03fsLJKTW1Iu3joRkZLLGSIjLIbV+jXNysoKDQ21/a4Xjth+LQAAgDJNydlNTTto\nJHVbZdxVvbtSuvZdDSL/TM82fy05fyxHJKitXt2qAAAA0KxUKv5xBG0ECHmANQAAGa1JREFUd9GF\nTYr0OZjw++3HLhVdyng1frX4TB/aheAOAAAA5zEp/nEEbbTKiMjQF9bG50xMmDMxQURk4IoN8Wwo\nAwAAAJvZ8Owpu8ooowuMW5U2Pje3vFy8A9uz2A4AAAAbVOf1Bjdxt3wBEw+nNoJv+/ZqlwAAAIB7\nQUyM9eP1BHqCOwAAAOAk9e/dLiLVb2WyfAETwR0AAABwFdVh3bIJnh53AAAAQAMI7gAAAIAG0CoD\nAAAAaADBHQAAANAAgjsAAADgEup/KxPBHQAAAHCS+qN5zU3cLV/AxMOpAAAAgJPU2pq9Vo6v+VYm\ny33cCe4AAACAOup5H5Pl2jytMgAAAIAGENwBAAAADSC4AwAAABpAjzsAAACgAay4AwAAABrAijsA\nAACgAeoGd3dV7w4AAABAEVbcAQAAAEVolQEAAAA0gIdTAQAAAA0guAMAAAAaQKsMAAAAoAEEdwAA\nAEADaJUBAAAANIDgDgAAAKjDaDQqH0xwBwAAAJqqURG8WnJynaeWLImodYQedwAAAKCpIiJq52yz\n+gN9TEydp4xGY605WXEHAAAAHKWuQN8gy8TPijsAAACgAeoGd3dV7w4AAABAEVbcAQAAAEVolQEA\nAAA0gIdTAQAAAA0guAMAAAAaQKsMAAAAoAEEdwAAAEADaJUBAAAANIDgDgAAAGgAwR0AAADQAHrc\nAQAAAA1gxR0AAADQAFbcAQAAAA1gxR0AAADQAII7AAAAoAEEdwAAAEAD6HEHAAAANIAVdwAAAEAD\nWHEHAAAANIAVdwAAAEADWHEHAAAANIAVdxERKTq1ZfV76efygsNHPzk3OtBaXbmn9r//3s6TeRI+\nLGpG7FBfp9cIAACA5ozgLlJ07IXIOQclbNL0Hv/clJBy4NzWfzwXePeQ3IN/jXkhSQyRkzrnJ61c\nnJQSvyMxjuwOAAAA2xiNxsZeQquMHNv++kEJW7EjsZ+vzB0dPnxmwrr9E18aWjO6l6SuTRLDon2r\nonUis0ZviJyXuP/slOguetWKBgAAgAYpyevJySIiS/7/9u4/qsn73gP4mxJogChQsWXUn1eHWonU\nI9tkE5vWuRZPpXgOs72KrUda0ZVO7GntGd5OmVd3wLO5dHZoV9S7oqviqRbd4M7aMug1botr+WGp\nHhmiEltBfpiUZCT4vX+EQAgBA4Q8hLxfJ3+QJ8/zfD+f74lPPn7z/T55M8ZhO0fcDf/48HJoUm5c\nGADIpv9oU/TuQ3+rQ6/CXfa9n+buGT/HGq78gQcABMpGQ/BERERE5E1iYhzLcWeqAVRXVzvsLG3h\nfp+krfcImTje9qd8TkJ0218rW3u9LpscGx833To1prVg+24g6dHJLNyJiIiIyP1iYmKc1vd3XX6M\nBM/XviZt0QfVBgQC6OhQzFqSFB8OYKIi2MXDz+5Kzb8cur1wc2Sf165evWr/tKGhYTiBTgsYztFE\nRERELnEoYAbFabUzbdq0IZ+QBuZrU2UsVz/98Hg9QgB8803s+seSAHyDxtt3uve40/g1pk1wOntd\nu//l7cVtaXtPLXF235m+b9NhvXGHVfYTERERuWSYdTbLdE/ytcJdkZJ7NKXXFkvkfOg+/syQHqsA\ngOt/LmqL3jinb+F+8fiWzQWX0/acWhvL28kQERERkadJe1eZ0TDHXbYoJRW6/F8c0bYamkp2v1YK\n/PiJWQBM10ueTkjYXXYdQF3J7g1qDWLT5gfVa7VajUZb12qROHAiIiIi8iXC5cdIGBXrOxWx6Xs3\n3chQb16eBwArdx55yjoTxmhoA+60WgCD5nQRAFTkZ2zoOmrlnlOvxHHonYiIiIg8xNemyjgXm7Lj\nzA+bDBbI5GFhiq6o5NEp5eVd02pW7S1fJV14REREROQ2hrqig3/8yyVd+NS4lDXPxUZ6zS/zcKpM\nF3lYRERERHfVTkRERERjkKEiI/H53cdqpypn6YryM36ccvYrr5n/LO3tIEdR4U5EREREY15T1Z8q\nELqnOP/19FfyP8lXoe2dDy9KHZSrpJ3jzsKdiIiIiDxHMf2Z3D15cQoAgOzBSaESxzMoXJxKRERE\nRL5CHjk33vY7mpeLflXQhteXzZI0okGQdo47C3ciIiIiGimGurLjf/1XYGAg0NGhiHkuKa57IWqT\ndn/a7tL4TflJk50sTt20KWGA06rV5SMQ7GjHwp2IiIiIRoruwpnjxy+HhAD4BlMVKUlx1u2Gi8dX\nbC6IWrlzV0q00wNHZ2nOEXciIiIiGpuiU3acTnHcaLle8twGdVTSzqOvLJYiqKFj4U5EREREPqNV\nm7lqZxui1j8+oUKrNZvNAZHfjp0eIXVYLuEPMBERERGRrzDUX6gAAN3uzRu6NoWmlp9OlzAk17Fw\nJyIiIiJfoYhNLy/3jjK9L06VGa0eftvFHT/77LP58+e7uLPM5dP2dfXq1WnTpvXdHvHkkE85iFbc\nzr0NPVDmoYb6c+DAgXXr1o10KxgwHXnm7zzQiltkvOShhrp5pqFBXQ2GY4B0grce9UxDwxf2mCda\nsTcKrwYP1g29Ic/3m+LXnmhlpA2nobXC1fFWj10NaJiqq6sHtT9H3ImIiIiIPMS+WD9xYqA933wz\nxmELC3ciIiIiIgmsWOG4ZeBSnlNliIiIiIg8JCbGcRy9t57x+OrqaoedWbgTEREREY0K9pV63xnw\nnCpDREREROQFWLgTEREREXkBaafK3Cdp60RERERE5BKOuBMRERERuYSLU4mIiIiIvAALdyIiIiIi\nL8DFqUREREREXoCFOxERERGRF+BUGSIiIiIiL8ARdyIiIiIiL8DCnYiIiIjIC3CqDBERERGRF+Av\npxIRERER0T1wxJ2IiIiIyCWcKkNERERE5AW4ONVtEhISpA6BfNrBgwelDoGIRgVeDUhC5eXlUocw\nlrFwdw++TYmIiIhoRHGqDBERERGRF2Dh7rVM10tO/60jMND6rKMjZFHykkj2qA8yfVX0Pwf+UqUL\nj1Iu+89n46eHSR0QeVrrxbKPam4F2q4GQAdC5ixbMpfXA59jaSo79sfT5y4hfOqiZ1KS4qZLHRBJ\nwXS95P33P9bW3x+lXJry7OJofiiMKZwq460Ml/68U10QGhoFfAOgre2Rbz+1JFIhdVjkYabLu5am\nFSM0ceWTrf9bsKW4IG1f8dq5fB/4lls1Z9Tqz0JDASAkBDpdG7By8ZK5/Lj2MU37V68o0EG1MnX8\n9Y93by76dNO+3JS5UkdFnmW6uGXpBg1CE1c+afy0YGtxQdreU2tjeTEYO1i4eyuZDEDq8dPpcqkj\nIQld/uCtYkTnnsiPjwBeWbP/2eX573+auuMp/tPyKdEpO8pTbE8sFzMe3xD8+tP8oPY1prrSAh02\n7iteNVcBpC/a/fSWQ5+2pvD/b77l4ge/1vR8KLxU8ubSnRm/W1KeNVnqwMhdWLh7q5u1tQid0dj6\n1Z36rzH+4bnTI6SOiDzPcO7DiqjUvfFhTRXaqwia8MLR8nSpYyJpVRT8ugKqPyzjHAmfIw9/EECH\n7akZbcD9/JT1OR0GRD0zv6sikC98ZiVKP202YDK/iB0rOMfdW7U3t6OtYNXygq7n8RtP5K5i8e5j\nLAB0BVsTCtpsW1R7T+3gl6K+y6DdlX85/vX/ms6Lqw8K+37uyqgtGxK/SEy8X3eutAKb8lNYrfke\nBXSf15pWzZUDQI3mc0BX32iKVfDr+TFC2hH3+yRt3dsZgfjcI8Xl5Z8cyU2DJi+3qE7qkMizTA1f\n6ABg457C8vLy4iM741GasavEInVcJJWKw7t1UG3kcLtvMt28dF0HAMauDZcra3k18DVzn34hCpoN\nSzMOHT/+2zef3XLsMoCbd0xSx0Vuc9flx0hg4T50c9fml5fnxk9WALLJ8c+lheKLm3ekDoo8Sz71\n0WhAlbEqLhKAYvLiF9Ki8EW9Qeq4SBqt2l0FuvjX13G43TfV/eW3+ZroPSfKc3dk7dh7+g9bE4vV\nWz/n5cDXRCw+empfqgrFhw5dCnpm5/aVQNSCGfweduxg4e6lTCW70rYcuWh7agGADrN08ZB0dIbu\nsRSLXspASFra9zjc7tPu3KxH6Hdn22ZMTp4VDbRd5n/kfYzhctmhP99+Ycfeo6dP781aNd18HZg6\ngdNkyE1YuA+ZfHIUNHlvHNJcNhiaNMffym/DY7GTpI6KPEyR9NM0XFbvPFL2VWvTxbP7M47pop5c\nwKEVX9Sk2X5Mp9q6nsPtPuuhmbFoK8g5ovmqtbXpekX+r9RAfNwsznL3LTLcyM/b+vJvS643NV08\nu3/VTk102mpeFsYS4fJjJPCtNHRzn9ueVvta/pa0fACAauOezYsjJY6JPE4Ru3bvJl2GemtpHgBE\nJb6+/5U4qYMiCVw8/W4bEtf/kPd8812RS7bs+dq4OW+L9WoAxL6+b0s0P2Z9jDx61d5NVzPUO1cd\nA4DopK3qtbFSB0XuJO1dZfyEkHZ1rNcztTYZLJApwsLkvDz7MJOhyWCCTBERxi9EiXyb9WoAWVhE\nGD8VfBc/FIYtISFBrS6XOgpUV1fHxMR0P12wwO9hl49tANxeZvOqMlzysAj+oyTIFRFyfiFORLwa\nEAC+DcYyaUfcOcediIiIiMamBQv83HtCae8qwxF3IiIiIhqzrLX7hQvOZ61UV1cP6mzSTjFn4U5E\nREREo47Z/NIwz7Bw4bsAzp9/ceHCdxcs8Dt//sW++8yadc8whhmFO3FxKhERERGNLgkJCeXlblic\n6ufnB8Baslvr+OGUvn5+fhH33qtLExenEhERERG5yFo625fv1r+HXFJLuziVhTsRERERjWVuLN85\nx52IiIiIaGQJIfz8/Ownvvv5DXrSOEfciYiIiIhGXPfQ+8KF7w5t6J2FOxERERGRh7h94vswdXZ2\n+vv7u7InC3ciIiIi8jlDK9/dXtq3tbXpdLo5c+a4sjN/OZWIiIiIfJS1TF+48F37yTPWCt4pN/5y\n6p07d2pqap544omjR4+6GC0LdyJyJ6PBKHUIdowGo8VzTXmoJZdYDIaRynyUZUpENFxCiO7y/fz5\nF+1H353s7PJjACaTqb29PSMj45FHHvnnP//peqgs3IloKKr2rXj8rQsOG41fHgweF7z51DVJQurD\nsO9744KX7jMM7WhLY8nB41+2OG72hsRRtW/puHFLLwwx84H0l6nu/KkjH9e6vz0iIk+xlu/dQ+/n\nz7/odOh9mCPud+/eNRqNb7/9dkhIyHvvvTfYIFm4E5FrjFUr/PzequoqBjs6Wm+bHHcJeiguJz3z\nyUcecGezXx7081txYSiDvP5zf5KV/dJ35P3vUbXv8b5VeNdL765MXPevCeGSJe7Q7qBMePSlrKzn\nJ/af+ZB71T5T+94Ln6hfvWTmKZ2nvuAgIhoZ9yzfh1O4t7a2fvTRR9OmTXvttdeGFh4XpxKRSwy6\nhlagteFGy6RJ4eEKAJAHwtLyZc0NBEfOnjERAMKVP/nlDrn1VaDlWu2NtvbgCdNnRCn6ntDSorty\n4zaCJ0ydERXUe6M5IHTm7ClBACxGna4ROFlXr/uPSeHhCjTqDOFRE0262hu324MjZ06Z2H0oWq59\neaPNHBA6afaUcABAUFzqa/Pl4TIAxhadQR4V3llbU9duO7nF2NJw6/btfzc2trQoxoUH2V8OjVU/\n31iarflgoqcSBwy1X9a1mwMiZ8625tS7Xf8BErcYdDV1txEQPH32DGt7UQtXvBaDcJnzxJ32qiKq\n64zGlkYDFBPDg1oadQETo/xbrl250dbTq7ZMHXtvxorCVCTt/si85yl+rhCRt3N6x/eul4Z0Qr1e\n39zc/Pzzz5eVlQ03MiKie9BrlD2XjZxmISrzkqFUdW9S5ZSZhRB6rRLI0TYLoS/M7DlClX3W3Pt8\n9cXZdtehzBq9EELUn82B3RmvtAu9tmeLUq21hpGZldwTytl6IYQQzYVZPcEoMw83CyGEXg0gRyOE\n0GvVAHoCSs67JfR5dimptXr78G6dzQbSa8weSlzc0qT3bFQermx2bLffxEV9sV2nIbWswRpOjjVa\nZ4k76VUAZbYO0OYA2RrRu38ApB+uFD2ZXuvbe82aHEDVuyOJiIZo0aJFUocghO32Mt0T34fAZDJZ\nLJaXX355gH22bdvmajwjmSwRjR3mW2VKIEfTIIRZWOtXqIpqmoUwVx5OB1QavRDt2mRArW021xcC\nOFyjF13leHplu/3J9GolkF0mhBDN2nQgT6sXzWUqIP2Apl2I9gZNOqDM0QghbmlyAFXZLbMQQrRr\nUwEosyqbzcJ8qyhbBSRr9aKhOBNQHtDUCyEaNAeUQHpRvRD6PBVUaq0QQl+ZByC7qMYsxC3tAVut\nadbkqJTZZQ61tRBCq1ZBlaf3TOJCfyAZSFZf0QshmguzlEBWvUO7/SQumsuUQHJOcbMQ5ubKbBWg\nymsWQl+pBtT6fhN37FWVNQshhBCVapUyR9sVFZLP1uuF0J/NSQbSr5h7Mu3be+01B1i4E5G7jJLC\n3cq+fB/UA8A777xzz+Le9cKdX2kSkUtkiuAZgDxgfNcUu45WZL6xfHY4AGXSaiV+bt2t1brzuIeU\nwO/3H4hM/dH8+C19b4kbOAHY9s6R7wYsWhCzTwgAhguflQLPzwypr6pCcMj3UrG/4O8tWxYGhYwH\nEGybyGIA1Ad/pgyXAROXv/IL5bbFZy81zjn5G6QWrlk4BUDUwjU/T1334z/9fc/yp+wa7AAy1y6f\nLQMmLliSCQBmQBEiBxDc9zoYGAiUdpg9kjgMF4pOIlkdgxtVVQh+aOb3gXM6A6b0btdZ4i2z8FkV\nVAdfeSocQLjy1V/mbIs/esmwIeYeicOhV+112P74dyuUOW88MUUBID7pSbxxtNGEGf5dmQIyh94L\nmjovGaX/V2dYoHQyQYiIyHsJuzu+D9b69evdGAkXpxLRoFirWXQAym9N7NrmHzzBYa/whBNlhyd9\nvmlJ3JwHgv1W/OJU77uzKF48ps1JN6xOjJ/64Di/eZvPN1oQGAhg3eJ5c+bNmzNz3p4Kper7EQHO\nImhtt62plIfNAOQw1J2D6gfTbRWkbNYPkrFf57iCVDndtnT0gVlKmGxZ9M/hBCOWuD8AnNy0ZOac\nefPmzFy87pxS+WhIn3adJW79H8akQNsO/mHjATh2mrPE+wqw/WZfoN3GCfKuk3Wa0ffMfXov0OFw\nIqKxxFq+uz5Of/fuXb1e78qIu+tYuBORazrRCgQGDnCPFjsWY1T8qvc+EcKsryzKPrkt6ew1+/uN\nWMxBj2zZd0IIc/OVsvSq3/zsjxXo6ACUGtvEksrKv53as9I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+      "text/plain": [
+       ""
+      ]
+     },
+     "execution_count": 15,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "# Long delay, watch out for blank plot ranges and check autozoom\n",
+    "# Expected range:\n",
+    "#     X: 5-9 (this setpointunit), Y: 0-1 μV\n",
+    "# \n",
+    "plot, data = do1d(dac.ch1, 0, 10e-7, 3, 5, dmm.myarray)\n",
+    "\n",
+    "plot"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## `do2d`"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 16,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Started at 2017-06-28 13:41:12\n",
+      "DataSet:\n",
+      "   location = '/Users/william/sourcecodes/qdev-dk/docs/examples/PlotTesting/plottestsample/030'\n",
+      "      |   |  | \n",
+      "   Setpoint | dac_ch1_set | ch1          | (100,)\n",
+      "   Setpoint | dac_ch2_set | ch2          | (100, 55)\n",
+      "   Measured | dmm_voltage | voltage      | (100, 55)\n",
+      "Finished at 2017-06-28 13:41:53\n"
+     ]
+    },
+    {
+     "data": {
+      "image/png": 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TVbiLiIiIiDhISRW/rUv4GsAkIiIiIlL5qTm1dDLj1q7fU/3Gzu3DfBz9UERERESk6ilH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\n//v006PP91+RkadvTJiwAsCuX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+      "text/plain": [
+       ""
+      ]
+     },
+     "execution_count": 16,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "# Expected labels:\n",
+    "#     X: Gate ch2 (V), Y: Gate ch1 (μV), Z: Gate voltage (V)\n",
+    "# Check pan/autozoom\n",
+    "plot, data = do2d(dac.ch1, 0, 10e-7, 100, 0.005,\n",
+    "                  dac.ch2, 0, 10, 55, 0.005, dmm.voltage)\n",
+    "plot"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 17,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Started at 2017-06-28 13:41:54\n",
+      "DataSet:\n",
+      "   location = '/Users/william/sourcecodes/qdev-dk/docs/examples/PlotTesting/plottestsample/031'\n",
+      "      |   |  | \n",
+      "   Setpoint | dac_ch1_set | ch1          | (100,)\n",
+      "   Setpoint | dac_ch2_set | ch2          | (100, 55)\n",
+      "   Measured | dmm_voltage | voltage      | (100, 55)\n",
+      "Finished at 2017-06-28 13:43:13\n"
+     ]
+    },
+    {
+     "data": {
+      "image/png": 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JB6nWZBioJ5jlcAQTyztA7enQi5t0CPQ6ys1Y5Zpic6mByFhEpYCM1Uxq49/c\n3e9ANfY5P+VKrQaETaM6Ch1aUavt+vw5Jpa7mer/O7eeuifsuB7mN2+lYt57uO/xmeupGBDzSk8m\nlvNdHhN76LkcJraKGxNLtjAXcldreJz7vjkrbDIT89rKpADgVAPqceLzDTXB2uEJ6issFVH2btQy\nFPrWmlNVuIuIiIiIVL4yFfrW3s5X4S4iIiIiUgOocBcRERERqQplPCVflYW7gZzlUf2FhoZGR0fb\ncMEzd1HjS4Zyx+YcuU1v5GLk5/nDUiqW8igVC+Dm7wCAYwsqZsedms09xqTi/aiJHuQRzP6nqKPw\nOYupOR1Oj1HndM1fUud0AZgemcXE3vKhJvoMonoc8Cd1P4A6CA/042Lh5AsUeT7YxD0ygZS21DFn\n76+4toR6C5lU1lhqU5fR1KYJTakH+xCuEWYF9RkAYF/s8n6nYvbcOfJaVE8Ke3idbA7x3spNB3MN\nZ1L9uIFZAKK42N9crH/acibWz6MPEyNfYI9xU/VqP0g1YFgyY5lYxv9RmwJwn03NkepSZxATIwd1\nzbhWijEbCg0NnTmztHrHhldYL6Vd9ZLm1DZtDJbkd8llDT7P2rzM1jvuIiIiIlLDlHRUvRwFfWRk\niR9Sc6qIiIiISKUo+0VmSqPmVBERERGRGkqFu4iIiIhIDaDCvVoqiKNiy/ZSsWRqjAwKM6nYi1RL\nDApTqFhAATceIv0zKgYUHqBu362h3LbcprEZVKPYDDfqnvjEl+o63fALkwLsg5hUH6qVFADWDHuA\niY0cR63o0I7adAm1J5bEPsLEAgOph9Pht6mOvZCxTAp7XqZiANzGcTkzdZJyvQPVFOnL7dnUh+oA\n9M+hntfzPKjBOg7dqX5oAAV7qUdd42nkepQ9YVSszhxPJua981cmljWuIRN7ZSaTwpJ4qoUdgCWZ\n6mJPn0it5sF1nXIDybAk9wQTy36JaxMOWsOkNjtRs/wGUFsCgO9S6lvYhgep1cjxcFJWNuxMLTNN\nThURERERKd3VqdeLX2TmkqvKANA77iIiIiIiV2DbxtOS/fvjgbWryhReldtgnQp3EREREZHzilfq\nuqqMiIiIiEgNpcK9WvL5lprZBhP1Wxvf3U2o1bK3UTEHbtZl5moqlkQ1MVkMDtRqQK36VBvjvoPb\nmdjk5glM7FRTqut0ERMCQjZSsdw9VMwxpDsTuxNsd2o/rtnx3abUas5vUa9B73XgBwsAACAASURB\nVCe+Qi2XuphJTabWglN36mnYeyw1OrHja9yuwK599lTOixonHL7/DSaW9ko2EzOQo0m/prpOw7iG\n+Jja7akccGYkFYs9xL3A3rSbSZ0K9KFW83qaSeXMorpOXYY3ZmLdZ/7BxM48zA5OPreDivknfcrE\nXvB8jIn14rpTz07juk65x3DmM1TXKYnsYAZQZy03EzeZel5bCrLYjaUy2fJwvJpTRURERETKp9x1\nefE+VKvUnCoiIiIiYjMVaFq9QsVvrTlVhbuIiIiIyNV1xYrf2nv5uqpM9VRvIZNKCb2biS34/Rsm\nNpQ6Cw2Pj4cwsax3qBk3LiOpg/UjG3MnK4H3uYlOgXbUGdzY9K+Z2LnP72di9l8yKeQfpWKOHakf\n8c90pY7Mjv+7P7UrcPZj6hz58peo1frnc5O68qhmA49m1HHzNG5OU/b71GN4SkxrJmYK/o2JAYBD\nEJOyFFKnVz1voQ6vkwOYQlZQseXc0zAmlpo1Y3K+ndoVMB+7j4kVnqBeEpFCHV5vEU8tduRJqrfi\n7FpqtcFjqcPrZKtJu61UDMAv3Nyf/G3U4fVxCROY2OmIN5nYTb8zKaTl7GNi2VOpM+5/U3ui04/k\nND+kP+HOxDyWUqtZUuaS+0qNoTPuIiIiIiJXR1UOXq0YFe4iIiIiUhnyj23/9rOVP8WfRZOO3R/p\nFeZ9ofDMilk655NtsakBTboNGt6zLl2Q2qTmvmJP6gVqThURERGRa9+Bpc8Mm7M3oH3PiCYZC2ZO\nXLY8auUXA7wBZB0YFzFsO4L79mv63ZLp636KXf7F03W5Nck+1NLr+8hIbjM1p4qIiIjIdSBr19d7\n0fHVL17rDKB32AcRI9cdzxrg7YIDq9/ZjuB31yy4zQPDuzXp9MT0j7b2eTGMLN0pFbjOzEU0ObXm\nyDvOpLx/+pmJjT89m9rUqQ2TCq1D9ZN9O5TaE+69mdT76S9wy7Fdp1T7D7DKneo6fZhbLZ6bwVGY\nQcWS76d+Yefcj1oNAbO4HBzbUc2p/bkOMKRz3Y65R5hUC2otnGpPdZ36Hj/BxNZzE6nMTAgAMKIp\n1Xf4fnIAE0sjhw01ol5MunCfbCj3NPzxKyYF9qEJ9GhAdZ2u3kut9ngIFTtwKxXz5LpOuelA7EMd\n56hUD3I1wOO/r1M5R27qn30Qk/LeRH26h/yojthTjaiu0wNcz3H//c5M7Oyb5PccFFCvOijkHsPx\n3sOYWICF/IZd0xTrzM/Py/3n/7N2fR3j3nPabR4AYGzQbXTw9EU7j8GmhXtlUuEuIiIiItcUl95v\njV4w7NXuIzffFXBu9brtwf3evdXl/Mecfdz+iZmahQanf7kvbWx7jyq6oZcr7aSNRZeDFBEREZFr\nSv7RQzFFf8rLAYCYo/sTzLfVNwGAj0vtK/779eutXJg7PHzghT9X3sVhLnSvqjlVRERERK51absm\nzlwX8erSFzvXB/BiyvaHIsd9uLnza+GeyEby6X/PpGYkJyHIy3TZAsVrdKuKDrJXRvl+oXtVzak1\nxtn/UufIa/cfzMTGBFInev8GFeOOLuLeD6jY1qeeZ2KGhj9y2+LAaCrmPIIamvMdNzSH+1xREEfF\nnJ5JZmLPj6Wmw7z/UDtqV5pXFyr2AaiDpA+dHMHEjjWiJsR8xY2bcSS/JAbqvHFn7qC2+wPcpkD7\nTO7RfpI6lrqKG0rVK4U6gr3xKNeo4dKNSSXeRI3pGnLmI2pTIJXrwNnBHV5fwvVppEdRD/V07ii8\n24fcGecTVygpzvMayaRm5PxKrQZYEqm7zGBHnfxO6khNBzNRjyb4n6WGHJmM1HHzG6k9MY0bcNaL\nOwoPoHb/R6mc/9tMKiCO+g57TcqK3ZcOtL65/vn/9252pzu++yMJ4f51WyH+xz1ZQ0NcACDu29Xp\nwcObXV64k2zVh2pVdWtOrVWFe4uIiIjINcmlyZ3BwJTXPziQmJaVlrh10Yxl6ejR/ibAeGfvfohf\nMHnp7rSslPXTn98M9LmbfE+yOrDQ/9me3nEXEREREVsz3Txj7tgxw6YP67Ok6C86Dn+3/20eAFxC\nhs4efWLkzGd7zAGAvlOWhvMTmKqcRUdlREREROTa4nFzzwXR96alpOUDJhdvl2KnYUJ6v7ahS0pW\nPowmDw8XW5ajldex+g9dVUZERERErkFGD29vqx8weXiX+1x7EVvV6BeuIXM5XVWmxnib6v/BpIkf\nMrEZGVRbnOVwBBM7dS+Tgt8hampK1uujmNhHM++mdgVyuJjnTKrrdEjaciZmie1Dbcq1xE2257pO\nDzdmYsk9djAxn5+o1mTQs6vubkDFcre8z8TqcQ2gOaup2DnqSwIcCWJSP3Fdp52O3cftitNdqEf7\nQzup1YK4TXsVpDAxsku4QdYbTGx1JpPCEK5LGMD4PKqx+9xH1FMM9tTsKvc3+1OruVItliO4EVdt\nqS1xf/cNTKwrNxwKwI/coB77m6mWTWMjarVC6l5FagT14mROomZI9fCjvg1PYULAvbuoLwiAdwfO\nY2JRt1IxC9WvC5+/qFpCLmG7ttQSfwDQVWVERERERKqLUn4AqG5XlVHhLiIiIiLCUXOqiIiIiMhV\nUOGT8SrcRURERETKrqyFeCmtqJdTc6p1KXvXf/jp2viznrdFPj6gc7C1SP6x7d9+tvKn+LNo0rH7\nI73CvCv5tg92oGLxBgMTCzjoz8TSJ1Ob7qF62HAn13Wad5Ba7emECVQOMKSvYGK/Nf2DWs4YQG16\n0y4m9veA25lYDtkoVrsDk3rhMPWZLvEexu2KUwOo2J+LqJh3J+6ezfyeSbk1TWBi2YuoWA9uJuIa\nrtcNOVQ/NAAPrt9tA9dgaTDYM7G8n6lWPN9x1KZjXP7DxGZw4yTzNj1G7QrM4RqFuU8C5r7cU5Hr\n6+3jSX0WVPMv8H4uN2A17XMmtasONYQbgB83djQpYx0Ty3iTuiKCI9X8D9eZVMxyhmrrXLGUWs3h\nXuoCBgYP7nMALPuoJ2zqmDwm5vmpJ7mvlFuZWlT3798fGVmGxa01p173l4NM2f1B5LNLEBLRN+Do\nglejdifNnv3opdf+OLD0mWFz9ga07xnRJGPBzInLlket/GKA9csLiYiIiIhcpqwXorHydv51f8Y9\n5bNXl6D96A3TepuAsICRI+fM2ttzQYhL8UzWrq/3ouOrX7zWGUDvsA8iRq47njXA26WEJUVERERE\nbO86L9yzjv+Ujqj+4UUX4Q/pPcB9wbM7jqaFhHhcFCv2C/P8vNyL/l9EREREpGK44/LXd+GeFbs/\nHgGtAv9589zoFmQl5dL7rdELhr3afeTmuwLOrV63Pbjfu7dW8tvtfj9QsbGhVCypOXWidwk3bMiR\nO6vnPKI1EzO4dWdivGPc4fWbP6BWSx9CHdVdzJ2GJLEHcP1eYWLzc8ZQMW5TAOudWjKxMO7g92H/\nN5kYN1gJo6ZTMedB1OyqNRNeYGI96gxiYmlMCAAQnfUzlUueSsVqt2dSKbdRZ9z9T7zNxGYM4p5g\n+fFMyv5+cq4aol52YmK3v0atZv6EOpX+MdXOg7l3UDETNaYJiO1NxfKpF394cjOkgFNcLNSNOrwe\nzf3Sv/AnqpvL8vJcJnbmAaqfpyCOScGnAzW6ju3SAPq0pA6vLz9ETQeDc0d6Z6moig9SvbxvVc2p\nl8k/d9H/mtwCgdzLQkcPxRT9KS8HAGKO7k8w31b/4lG5e/bsseHt4iZsioiIiFSIbQsYAK1atbLt\ngjXCJefXy1HHX963qsmplzL5BQLxKeZ8uBgBwJy0G7h02njarokz10W8uvTFzvUBvJiy/aHIcR9u\n7vxaeP3iKds+TAt/suFiIiIiItZdn3V2ZStrH6pV1ppTq/KqMrWqcO8iRu+GIcB3287/Ssz894F4\nwN/tovfSs2L3pQOtb/6nTPdudqc79vyRdJVvqoiIiIhc3yz0f7ZX9YU7jMF9I9y3T5+w+kBiVuLu\nKVFz4N4vrIEJSPwgKrT7y6vNgEuTO4OBKa9/cCAxLSstceuiGcvS0aP9TVV900VERERErpKqPyoD\nIGzc/Kj4PtOH9ZkOAO3fXRTlASA/LyMJ6U65+QBMN8+YO3bMsOnD+iwp+icdh7/b/zaPkpe0gVq3\n7WNie0G1CS6lhggh91uq67QTNzOF/Fkv90dq7NNnj3LLAf1zqC/d2RnUl8594V9MbNSEJkwMXkOY\n1LDv32dihxwbMjGqMxFIi2MnsNzRlYrVfvprJhaI+5nYKEdq06CxVCzpnuNMLG8z1XX66QBqU3au\nFpD9FtUS7fxEOyZ2yI/qOm12mGrYRc6vVMyf6mHFqTeoWMFpKgY4DxvBxNr1pp5iIVyz0b5sav5a\nchtq/przc58ysfxtVLuj8U7qaZhYh5vmBWQMpWKWc1fOAEDWJib1EncZhufDqK7TOhuoV3UL2f5r\nT803nEitBQB1uBkxtzeLZWK74r3oncX2Kt6uas11P4AJxroDZkf3SknJz4dLXe/zp2SM9ceujb5Q\nA3jc3HNB9L1pKWn5gMnF28VU0loiIiIicp2ySbF+4fIyuqpMiTy8r/hDrpHIiIiIiMh1yiY9qcD5\n6l9XlRERERERqb4uFOvWriqjwl1EREREpAbQGffqKYOaFPkxt5jvT1Q7YULTDUzMP5kaE2o4R00w\ndehG9Ws+MJTqOgJg4OZ6Ut1JQIOn/8fExnCz7iaPo1ri/p7GpFB/NBWbNpOK9as/j8oB3NBJeHIN\nhebvqdUcqT5MJB3wpHIu4UwqfTI1E9FYj9rTbwt324C8I6lUjvsKN9tnz8Tyj1JPWGNH6ukf6Ew1\nYsZyt+10KPclBubvpGLjzYeZ2N6ERUysB/nJMiHgMU+q63T88Qeo5bjXYbfx1GIA3qZancF16+Oh\n53swMa5LFK7PULGWXFv/SG7T2/AbE9vNrQagD3dF9dVbuOW4wclyFVROo+rVpsJdRERERGqw8hXl\nFzpQS2GtOVXvuIuIiIiIlEt5G1KvXO5baU7VGXcRERERkauJKfetvZevwr1aOt2TOkh4A7faqc7U\n4XXyGDGM1Dynye7UYJ1JeclMzPW1LCYGIH7h80xsbC61mh/3WbydMIGJGepQB/qbvUFN9Eh/3ImJ\nDYl9hIp5PsHEAGSMiqByzh2ZFHksNe8gFcuYTp0Or92HOrz+8O/UpkFcbP6ni6gcYB9APdo3cYeh\nO3EPAGND6kxvYl3qPPcubugbHKmz0F4fneSWwzj7BkzsdlNTJrYrcRITW3PsPiZWcPQbJsaOLipI\nYVKzfKnXw1FkcwgQ+hL1FGs/i1uOe5W48VbqS2ffg/pusjdhBhN73P9NJvZkNJNC6ybcSDJgFXeX\n/cKtNvXEACrnTvYRSHWgwl1ERERE5Kor+/l4nXEXERERESmXilwxpvQWVWvNqVVJhbuIiIiI1GCl\nn1YvvayPjCxtZU1OFRERERG5Ssp7zRnA+uRUHZWplqZyY0ROh1GxwgwqxjZFnd3OpCZQc1qAwkwm\nlf0e1a8DwD91FxOb91+qx85p+M9MbJbLf5jYKPMAJoZaJiblvmAfE+vDTaRa/DrVrwngwCIqlrSI\nmnLSi+v/q+U2mYnV7sO9Pt5EPUjeA9X+2+wItSnZ6AygBRfbGP8clSMnsDi1ZlJ1uKZDgzsVu70J\n9fvlXdxXGIDBIYhaMJeacIe8BCaV/Q714HQeyr1eu/ZkUhnjuK5T+ktH6nTyUSY26wZq2FzvUVTX\nqdcHTArIp+4vg0s3JnYEVHNqDjdCzrlFIJUDep35iIk1qTOIieXHZjMx481MSqoJveMuIiIiIlKd\nlHDGRoW7iIiIiIhNVaRpFcDKlVabU1W4i4iIiIjYTgWrdpTUt6oz7iIiIiIiNlSRntQLrF1Vpiqp\ncC8RNRERqOVGxerMo+71Llyj2EY7Fybm0H0dE5vsSLUwdmdCAIBWvamu03SqnQxOD77KxEadHEEt\nd+oNJpX/v8VMbAA1rhGLxlExXrMHqVjLRlQs7Unqnuixllot+gA1YrOHHdV1+hE1XhOoRT0jkv7q\nyi0HZG2mYjm7qZg/NSfS/J6BiTmNova0ZFPtv+vvoJ6t5EsTgI15m5jY2bd8mBj5KpHAjWFu0W4r\nE7Nv+gcTu3ERtWnaPOoLgr86UTGws06f+oZqTjV2o2adtrSn7q+N/49qxH8wnklh63QqZt+cihX+\nrw+VozXkGnaNYT/adl+pBvSOu4iIiIhIDaAz7iIiIiIiFVDxQ+0cFe4iIiIiIiWrjLp85corBKxc\nVcaiwr1aumUhFStI4ZYLoqaNbDxEHXP04+Y+JHGDdSblpzOxcwu4gS7AmSFUrO4+6oR4YktqPkjd\nY9y5b5eOTMp4BzUxZ0lOByZWuJs691mrQw4TAxDLHRBvdow7g1+XOvf/BTdGynBzKhOzZFFzteK5\nuVqo3YaKuXEPEiB3ywYm5tDRlVoucz2TMj06l4lFjRrGxAzO1OH17NeZFJbGUTEAmSOpw9AuUdRq\n2UuoWKvV1Owqg70/tdyNXzKp0xupp6HlIPUFMbSkS4HcY9S+3Di/MdzhdTO1GHz3UI+naK+nmdh6\nI/VNp1s0k0KtwP5UDrCkUj1ODj0nMLHfXO9mYq2rtBasKSqnSfQKPwxYa07VGXcRERERkavrij8M\nXK3jNywV7iIiIiIiJB2VERERERGpHDZ941xHZURERERESlDByvuKTaglUXNqjfHtQCp2fwLVnoKz\n25lU1txYJva7A7UnsjdTMZ8XmJRdPW5TwGsh1wHG9UT+nkk1p969huvY4zpsz22hYp1mUrF1t1Kx\nd36net0AjCOnYXHjwQ5xXac+3Gfx1+9UjOw6dR1KrbbqBmrWTHtvKgbgA67pvC2oB2d4fBNqOVfq\nfp0aQC02/2euNZlrxATYB2ftXs5MzHBLNhM7yG1a5+/fmJjdDYFMrGAT9ck+3IVJgXp5BZq8TI3f\nAuD80l9MzOEBajXuNQz9uFjW1JeYmMtg6sU//G+qnTRvN9VL2jSUigGIyT3BxNIeoL4ptuYa8eWK\nytGZWrzWj4ws577WmlNVuIuIiIiI2I5NrkJj7Z1+Fe4iIiIiIjWACncRERERkWrgSufpVbhXS/9p\nQMVyFrzJxGqP/pGJtebOG8YUcJN6kiYyKctR6rSx+XtqTwAOdyQwMXvjACZ2+x3UpumTqdib1OgS\nTODu/R5UCm24Y9/TuNUAeKzhXjViWjCpG1+mFvvyNSrW/6+uTGxyQ2rC0bA11Ka9DjdmYi82/YNa\nDjBxMfLB2SXgHSY2DVQseDS1KYLWMqlzH1JHq9tRR5cBYN/zvzKxeIemTOx4LrVpLWqIENJeopqI\nPP5LTRFafu5RJhbs2JCJ/ZbBpAAAR6iWCV9useFfUTFyrJ6FHNRUmxpdB4+HmZT9vVQfwZ6XqYcc\nAPxBvXK2pZ5hWMz187TXACZrfv99U/H/vfVWakhlkYo0sxbvZLXWnKqryoiIiIiIFFOmSv0SxQ+4\nl7WIL97JquZUEREREZGrpCJdqmpOFRERERGpoVS4i4iIiIjUAFVZuBss10o/RGhoaHR0tA0XtCRS\n/S7JbajmVGdufIXz0DAmltBsKxPzz6F6WN+yo6aNjM9LZmIA3rKnOsXG7aVWM5io33BNbkKdYAul\n9kQncmRGDtWHlzF2FBNze3MStSmQ/yvViluYTq3m0Jrq7JzFdXaSnZNJ3BShLvFUbNcHVCyPHOcD\n5HzHLXiYigXEP0flzh1hUk82oKY+zT9IzUEriKdaye3uYp/+Z9+inv61e1OPOtRbxKS+4Pr/qHE+\nwAgulsTFlnDXEjBxr8MAzOeoAUwwcIP6DnOd+A5BTCr3N+pVIo/r15/NNewP4j6DV7krEwCYzDWd\nO3Az6dy4V6drphizodDQ0Jkzy1nXVXDM6iWKn7Rp08Zg+aMt+Q8NjX+x+T2rd9xFREREpGazVbFe\n/JIysHpVGR2VEREREREpN5vMSQUAXPQDgLWryuhykCIiIiIiVe2SMl1XlRERERERqaFUuNvI8ePH\nbbhaUG0XJubQnlrNoR0VM39DdZ1+zE0THG+hcj9Ri2How9xwQmD8Saq5q+UN7zOxPRupU2tjuPGf\nFm48YVp/qtcN56iUx/RAKmeixvUByJpDxXJ/o2JR8VQ/2ZpEqnd2VC03ateUGUxqnw/VI25JXcTE\nHB9+hYkB6DDzfia2czq3XMp7VCzwSyY1/8A2Jra4OdV1Oo4JAYe7sk9/8xYq5vwS9aizHOjOxB7K\nPcHEGjvUY2Ktk6jJqWfnceNk46nedPMh7lUCsBymRrEaGqyjluO6TuHajUn9/Ch1t3ZKnsXExo+k\nuv8t9lQf9t/+1JUkADg/RsVMj85lYpbxK5iYbQsYAEFBQbZdsKazZbtqlXYSX1OFu40fpqdsuZiI\niIiIVaqzy618FfklHailUHOqiIiIiIgNlLcnlS33rTWnqnAXEREREbkq+HLf2jv6uqpM9eTem0kV\nnKCOOdp3oM4H2xupsTTju77BxH4zujOxldQ4F6Q8TMUAwLkjk9rSlTrjnrOG2tPlmfuY2Bfc8Bpq\nEA4waZ89E0t7KZaJNVnRh9sWB7jBH+7cie41HanXr/TnqKlP7h9Ss6ssWdQJbIPzdirWcBMTM3HP\nCACx3lTs+FgqtnpsHhMbf3AYE0tsl8rE+qctZ2JPxFCPuufZeSOYwU2bSol8h1ouOIbdmPApF6vr\nR72qB6R/zcSOuVP9Eg3iBjMxAEiYx6RmuUUwsf6PUnt6LKUOr99ILQb4UOf+Y//qysTO9FnMxNbk\n7GNiANuU8qI39YSdMIDa020hFZPqQe+4i4iIiIhUhbIdlFdzqoiIiIhIuVXkujGl9KqqOVVERERE\nxJZKOrbOFPSRkSV+SM2pIiIiIiJXQ3kvO3OeJqfWGJZ9TZmY59vUah/WpRr7ZlOLYd8xqhGztfk7\narlYqg3X71cHajUAaZ8xKace1GJ21MgUWJKprtPu3LyZ/tOoWJ+WVNNhM+48XFKhmdoVSH/ciYk5\nPkS1J8JA3bNnllI9dgYHanbVOW5MzwvHqMfSZFAxc14ytSsQaE/NGxrDrca1YWLY61TDLmmyB9V1\nOoZ7RrTkN+ae/l5Lw5jYqUDqjrgznkkh5g9uEl69D6lYMjVELOADarGW9amWUwC/cZcTGMWNkZrF\ndeKSbd2xXANowc/UA8qv4QYmlrTfmYl1cWIfxRv/7s/EppLdrsfuIfeVmkOFu4iIiIhIdWL9mI1F\nl4MUERERESmjivSkXtHKlWpOFRERERGpsMqu2kugwl1EREREpCwq2Hh6Jfuhq8rUIIabPmJiybcM\nYmKPvUxtuvc1Koba7ZnUdhPVX9suYQIT2+P/JhMD0Hjob0zM9fVZTGwyN2NvEteJ5TyAGtfaYhrV\nJtjsDPUg+cJgYGIPZe9iYgDcXw5kYlmvUO2JK2ZSm66lUnhhERUL2UjFGnehYvbUIx2FO6hORwC7\nqBHG8D1Ezc7sW4/q63Wf6EnFZlM9kZPyzzAxJL7ApPqPoFrYAeT+TDWnGutTT7EDXNfp3ulUDHau\nTCpjONXF6PoUtadjP+p5PW3o7dRygPF26pIIH/o+z8TIrtMz0VQsbx31pTM4UqslpcxlYtkzqQmm\n1BTWIhlfMqm0p6iJrR6zyrCzVCtF9bquKiMiIiIiUjNpcqqIiIiIiE1U6tl3QFeVEREREREh2Kou\nL7n99F/V7aoyBkuVvuFvQ6GhodHR3Ck8zlvcueQx3CwMb2piEj6nUgjnBjDhBm7yhyWXinFjegBY\nfqdmJtVqS61GPK0A4C7uGKEDN4Almms2oKaDAG/vpWKnqaEfAJD5OxWr9xUV+/MBKtYs9wQTS2lL\n3fte66jOCoNTGyaGWi5ULO5RKgakPpPKxDy/ob4mOEoNpULtDkwquSN1iNz1WWrPzP9SsZWHqRiA\n+72pWN0UKsa9hGHIub+oXO5xbj1baul6NxPbl59OLhhvpM6lByROopZLp14mAptQdVLsyRFMzHKS\n6jUqzGBSyN1DxZx6c983ATgEMSm/G6jPIukUN6bRh5zndh0JDQ2dOdOWdd3lyB8AijentmljsBzi\nBkMChmYnbF5m6x13EREREbnuMBelsVbc66iMiIiIiEj1p+ZUEREREZGrqbxn5VW4i4iIiIiUS/lK\ncDWnViWbN6fi1BQqxjXPJbeNYGIWrhfH51eusc9zALXpEWp6zeYQJgUAnbj+pLMLqc4ep27Upol3\nUrH3uEbcqYcbM7Hgpn8wsWnUnuCadQHA/xcqZmhCjQeCqQWTGuHYkIlN42aNOY9ZTuXyqDE92bOo\nKV3OT3NdYmBbsXPXUvvmcZ2TC7iW6FFk06FjEyrmwj3BCk5TMaBwF/V6cnowtZpDayqWvZSKPUSl\nsIIbvzWCGw5FPbvKojsXa33Qn4mtak49xcg5TS3voGJe66jRdbAPomLZm6mYyz1UDMDJodS2S6hK\n8W/ued3sWinGbMjmzanlvhbNpc2pB9lZfobmyWpOFREREREpG6YV9XKanCoiIiIiUjOpOVVERERE\npDq40qEaFe4iIiIiImVkqymqxRVvWq1uzakq3EtUGPMSE6vVehcT89nBteKRQ0xT3mNSwf5vMrGY\npNeZWKe4WCYGAGlUp1hhMrVYARer3ZuKTf2ImgA5xkQ12B3k5uYab6Zmkxb8yU06BZI6UzH75vcz\nMddh1GrvcT1CtXypQ4S5K/swMYdHqPurlhuTwpO+z1M5YB437LbxQCrGPTbx8oNUzK/uZCaWxPWw\npj35GBPz+C/1KgEgnbp1cORmGOdyQ4I/oVIs36P7mNjy4z2Y2Ppm1CtneDrXSg7Mcqee1+O4rtO1\ns6hNTQ9xDwB37sHOdU6fuYuaX+7CPQ0dHhtC5QA0+plJFcRRLbtNKnf0p1x0ct1WRXxk5L9/3r9/\n/2WH41W4i4iIiIhUQPnaT0tn5YcBiyanioiIiIjUAFX5jnutKtxbRERE7p5i3wAAIABJREFURERI\nese9RMmRV84AcBl+OxNryI1gSIp9hIllvr2Did1I7YlNftRp/rbjuOUAEzfR5ZWZVOwNbqKH+0zu\nqGbuMSY1w0wdrU57jDoK7/H2OSbWsQuTAgBHLvZVMyrm0MaeyvnPYFIGH24mEdceYOKaDczczK/u\nY6mZXwBe48aNHeSeFM4DA6kcd7I2fms9Jpb7I3XYPHUFk4L7JOpVAsAnG6jYqIx1VC5hLJN66h3q\nYOv4EdRD/S2nlkxsAROi568h7XMyyI0GZAe6mUbkMLHFdk5M7KFZ1OPEiXqRQAY1B4kdv5X+FvXE\nAeCzP52Jub33I7VcCvXKKTZRGY2q1uiojIiIiIgIzeZlevGLyVxwla4qU3gWtWozQRXuIiIiIlLD\nVEIrqpWfBCr9qjKWPBjskb0VruFMXIW7iIiIiFzvrP4kYO2qMrYr3Aszkb4CJwbC9xUV7iIiIiIi\ntmWLM+4F6ciNQVw/nIsp079T4V4iv6PUOIykRtQsjP8FUJu2DPyMiflSi2EwF6PacIApbI8Vpr5M\ntey8Pv1uJubQhvpdWEuuJ5JrEmQn5vQ/Qt22lEiqEzP67/7ctkDt9kzqLW9qtFL/FXlMrO6R40ws\nm5vTEn4fFTtLzi5JXcSkwsK41YC1W6mY81iqJfrcV9SD07FBFhOrdfMEJmZXfy0Ta5DDdfZxXd0A\nHr2VeklEIfXJ9mlCnWFdvo9rsPZ9gUl1BdXX60ltyfawdp5KvfgDSOJG5iF7C5PaxHWd9ufmQyX4\nUPd+EvcN0XUaN5TK4MCk0odFUKsBq4zUZCWy55T8ztm+Ki8wCLPZvG/fvri4uOzsbGdn54CAgObN\nm7u7U1+Hmuhq9bCWoDAblnM4MQgZ7Ni14lS4i4iIiFyP8vPzV65cuXjxYhcXl3r16jk7O58+fTo+\nPj41NfWOO+6Iiopq3LhxBbfIOrZ94Ucrj6Qi8La7H344vL7pwgdils75ZFtsakCTboOG96xb4YK0\nTOW41T5Uq6w1p5b3HXdLPmBB8jSc4kZMW6PCXUREROR6NGXKlMaNGy9atMjb27v436elpX399dcv\nvPDC3LlzL/lQmaTtXdpj5BwEtO97p+OyBVNWL/j5402vNTACWQfGRQzbjuC+/Zp+t2T6up9il3/x\ndN2KfS5l7FVlq3ybNacWpCNrI048gcKz5fnn/1DhLiIiInI9euGFFxwcrJw18vDw6N+//2OPPVax\n5VM+mTgH7Uevm9bbBXi6zw+hfV79/kDa0BCPA6vf2Y7gd9csuM0Dw7s16fTE9I+29nkxrIKlexnw\nVb4NmlML05EXj7h+yPmtbP/QmupSuKfsXf/hp2vjz3reFvn4gM7BVjMl/ralcnRxp87q9eVWG1JA\nDbnYu586bujJTYfZGEedcp9cfx4TG03/yF2wkzq87vQoNTQHeQlMal8mN4Ap7zgVS5zIpLpwB3C7\nUlticL/FXBCO7ajkK9xqb2RSsbQ6Q5hY7aeoV8OND31I7epFbUreX3VWvUetBszP+ZXKObVhUlHc\nHJklHaneihDuUbfvj3bUrgljqJgHNRsOgCM3MQ25sUxqeSbVMBPoSr3mPMUdXh/PHSJ/hRtdt2Ih\nk0Lu71QMwBhu39enU6s9zG2aFPcoE/NP3cXEPnSmBhf27kF9F97AdaSsoVIAMId7wv6HaiKAz0qy\ntapqrFu3rkWLFg0bNrT6UaOxYlViysHv0jH8yXBjYszuExlOfm2io4v6lrJ2fR3j3nPabR4AYGzQ\nbXTw9EU7j+EqFu4VwxbuU19/GYVnEf80Uj+x1d7VonBP2f1B5LNLEBLRN+DoglejdifNnv3opZVp\nib9tEREREZGyO3ny5OzZs+vXrx8eHt61a1dPT7LpmpJ18q904Ie3Hp4Tc/4qGAERExe/GF70vquz\nj9s/QVOz0OD0L/eljW3vYcPty6Js7ar0O+7Pj52Ak1H8XGRGdah8Uz57dQnaj94wrbcJCAsYOXLO\nrL09F4S4XJQp6bctVXWjRURERGq0p556atCgQT/99NPGjRvnzZvXqlWr8PDw0NBQe3vuSk2lMwJA\nTNJdC9Y8G+yBmPVzoqZMee/OW8eGuQDwcbnyoND16638xio8fCBsfWWYUnpVrTSn0idlbqgXdCrh\nD/i8iLh+MO8r5427WDUo3LOO/5SOqP7nfwIL6T3AfcGzO46mhRQvykv8bYuIiIiIlJPJZOrSpUuX\nLl0yMjK2bNmyatWqGTNmdOzY8fHHH69bt0JnV4y1XQD0fXVwsIcRQHD44/1mL1uz98TYsKbIRvLp\njAvJjOQkBHldfgK6qEa3qpRD6uWo6SMjS/yQteZUVnJyMuzcYdcCDbciaz3i+sNyrnxLXVD1hXtW\n7P54BLQK/OcNdqNb0OWZUn/bIiIiIiIV4ebm1qNHj2bNmi1dunT16tVt27atYOFu8m8cAJxKN//z\nF+bT6XB2sAdMdVsh/sc9WUOLTlfEfbs6PXh4M1sVdeWus62y8mNAOa4GaecOtwdxc28kTULy1Irc\nnqov3JF/8Q8fJrdAIPeSTIm/bbnoIbVnzx4b3q4vuBYrz7epWDA35IKc+5OWl8zETt/pw8QmkU1s\nNEPjHUzMkvcqE+tjT30WT+ErJjaFCQHfc7/RWUH1a8GpOxVzaOlM5QDU7sCkzIOpttOCWOr+mmxq\nysQmpcxlYr9xD5KfQMVGZf3MxGDnSsWAjOcGMTG39x5kYksSJzEx8zKqdXLfQX8mlvoU9aXz/OYE\nEzsVVI+JAXB5iool1HueiZEvsLHkAKbGXM8x17C7hHv6O/SgnhG1fKlxaQDSZ1KxwWOpWBL3hC08\nRN28WjffxsQeuJVJ4X6u6zR6P/XK2SUqm1oOiPyAiq3h2n9z91B92AdybFnAAGjVqlWZ8vHx8Rs2\nbNi4cWN2dvY999yzZMmSwMAKt9Wabh4R4T7x1Ymr3V/qEIRtC19fB4zu1AQw3tm7H0YumLz0lhd7\nBu2Y8/xmYOLdTSq63dViKd9l3A1GAPB9ET7PIW4gMvl+6YtUfeFu8gsE4lPM+XAxAoA5aTdwyQUC\nSv5ty0WFe1kfpqU7bcO1REREREpg2wKGl5qa+uOPP37//ffHjh0LCwt75plnWrVqVatWLRstbwwb\nNycqbfj0Z58o+v++r37cO9gEwCVk6OzRJ0bOfLbHHADoO2VpeMUnMF01FRlzW8sZcEb9j3HuCOL6\nIffPsi5Q9V8mo3fDEOC7bXGdezYAYP77QDzg73bRL0xK/m2LiIiIiJTH+PHjnZycIiMjO3bsaDJV\nwgFkY/0B09b2TkvLR77RxdulWNUZ0vu1DV1SsvJhNHl4uNimHLVtx2qJKlK4F7HzQO22aPwb0pfj\nRFSZ/mnVF+4wBveNcJ84fcLqRjPv9jrxVtQcuPcLa2ACEj+I6rMmYOyXr/U0lfjbFhEREREpj3fe\necfFxeXKuYpx8bB+DUCTh7etflawecl+4TozFbmqTKkMqOUKj37wHIiEZ/h/Vg0KdyBs3Pyo+D7T\nh/WZDgDt310U5QEgPy8jCelOuflAKb9tEREREZFyOHny5NmzZ61+KDAwsE6dOlf59pSbbRtSAQDn\nfxKwclUZ2xTuAACDAwD4vc4fnK8WhTuMdQfMju6VkpKfD5e6//z4Zaw/dm30v901Jf+2pZJ4fUO1\n7CD5DSa152WqPcV5+HPUpjm7mZRXNNV2FuxAtZ3F5LCXIE30bsnECrZQXadcYydacN1OX3ejYga/\n1kzMfRY113OxN9XXNQVs79Tn2MDEWnMtm+lR/2Fikw5xjUr58UyKmjgKWE5RzYnxLtSnEJBNjXUE\nYOdLxVZxAzvv5WZnThxFxdJBzRKez3XEng6lnv6+29nJqekvfMbE0i69BIF1LgeomGnAaib2lhP1\n0kReRyNyABW7nXv6t+U2BTA/bTkTW+/Rh1qOGxL8SSi1WL+NBiZmSadW+46aYAvUncGkBuxk23/J\ngegGRyrmEGLLkUY2N3369CNHjnh7e3tc9qb4kCFD2rdvXyW3qjq4UKxbeS/fhoV7kVrshRNQXQp3\nAICHt/cVMyX9tkVEREREymT8+PErVqzYsmVLw4YNIyMjK+F962tQOa8qYyPlLNzz8vLi4uIyMzMd\nHBz8/Pxq0C9TRERERARA48aNJ0yYMGLEiHXr1k2ZMsXJyemBBx7o2rWroyP3C4Vqr1LaVW3+jntZ\nlLlwT0xMnDdv3ubNmw0Gg6ura0ZGRm5urq+vb+fOnfv06ePjQx1+EBEREZHqwNXVtW/fvn369Nm1\na9fKlSvnzp0bHh7er1+/y8/PVB8Vr8gvtJ+WzkpzapW+426wWMrwg8M333yzbt26e+65p3Xr1jfc\ncAMAi8WSmpoaGxv77bffbt++ffr06c2aNau0W1ua0NDQ6Ghuag7pSDCTeqvpH0zsFLfn5JepmPO4\nH6lcbA8qduOXVOx4TyoGdsrJGO686QzyIRo3gEmtunExE+sVS53oPRRIHeedz4SAGUfo31F6DmBS\nWVOoGTd5B6k987mrzXp/QcXOcqMnZr9GxcaTh9fTFlExwJLyPhMrpCahIZ0arARHbhKaeQsV89qW\nw8Q+5GbD3X/lk4zn+f5AxZLupGLk12QL1fSBHhupmJ0/dSg5c1YqE3PhLvVmaMU9mACcHErF6lGt\nFR5GdyaWljyLiWW+RDVq+HATjnISJjCxvv5vMjH+knvUdybgFy7W6+/+VK7+Im69ShcbGzt37twd\nO3b83//9X0hISBXektDQ0JkzbVrXXYwv/YufIGrTxlDAjQYDYBeGMpXZjLK94x4SEnLfffcV/xuD\nwVCnTp06deq0atXqzJkz+fn5Nr15IiIiIlK5CgoKtm7dunLlyri4uJ49ez7//PNeXl5VfaMqF3mg\n30p9X4POuJvN5uzsbGdn6+OFddJdREREpAZJSUlZvXr1mjVr6tWr98ADD4SFhdnZ2VX1jareatAZ\n9x9//HHZsmV33nlnRETE7bffbruhuCIiIiJyVc2cOXPlypXe3t69e/du0KABgJ07dxZ9qHHjxtdD\n42J5zsrXoHfchwwZ0q1bt40bN7777rtms7lr164RERENGzaspBsnIiIiIpUkOzu7SZMmALZu3bp1\n60Vnt6OiompW4V6+dtUrtqhe3pxq61PrZVO25tTiDh48uHHjxh9++MHLyys8PLxbt25V231cVc2p\nac9Tzake86lhKMF1qS62PVwPa3uusW87t5rpLioG4Cw1HgSu/8dNdErnemfrULOQvuCmTT100J+J\nrWpOjcK5P4Ya55QQ/BsTA+DzFRV75wEqNv6vrkzs9oZUAyA5vIbs69rBxRpwr2P9DNR0GABLuNlV\nwdzgpxhujNQxX6qZeDATAlaNpmJOXAe7XZtPuW0BZ+qV4lQQN/jpCNWIv52bhNX+2H1XDgE4u42K\neY9hUoF+LzGxI1TzJwCY7gtjYr81ohroWu2lNs0/SsWM7al2UsMZqjvV/APV/nuW/Baxdh2VA0Ld\nIpjYV02p1XxWcAOYmp+hYtcTmzenlvtCNJc0p+Zz3fAAjF2rujm1uObNmzdv3nzEiBGLFy9+//33\nExMTR43i5v6JiIiISFU7ffp0KU2op0+fdnZ2NplMV/MmVZ7yjZe6GpNTy6L8hfvRo0c3bNjwww8/\nODo6Dh48ODw83IY3S0RERP4/e/cdV2Xd/gH8OszDPuxRKliCK1HUlApX7lXm3pR7pLlTy6dMzW2u\nTMVVZDnLsJ/mVkpcOQgXpoAmW9lyZJ3fH/j4mKB+gIPnHPy8X/6h+PF73xzOuflyvK77IipXQUFB\nBQUF7dq1q1WrlqmpaeEH1Wr1tWvXfvrppz///HPTpk0VZuOuNYa1cY+Pj9+/f/+BAwfu3LnTsmXL\nL774onp17L+LiIiIiEhvTJky5dSpU8uWLYuOjnZ1dbWwsEhOTk5JSXFwcGjTps3GjRv1eQZTuXpa\nXY0Bbdw3bNjw3Xff+fv7DxkypHHjxiYmpX/DnoiIiIh06/XXX3/99dcTExNv3ryZlZVlYWHh7u5e\nqVIlXZ9XaZR9nOpDD5tW9W1yasl23m+88UbXrl1tbW3L6Wz0igIbiZrgAa2W5A91nUZAo+7E9E2o\n2TH801+QWM52qEvMqDJ0UBGx+aQ+EtPcCkRiClOoT1RzGfossC4m6el9BYm9G47Nk/T6DUlZdkX7\n900bjUJioz+Fxn+KdRskdToJbGO0RlKjHD5AYlBvmojLFKjrFP/v3sFY12ko9vW/jHWduneFVgve\nB8Ws599AYnkHsNuC5UJ92CKyCez/BnsxjWyQVG1slqhY+kMxty+RVNZ/oAmb4X2gYxakQzEREYsG\nSGqsQM2pf2GTMVNvQU3R4BuRmjzolW1aE1ptIDbGcj/WcioiajV0/Zco6MoZ5xuDxNzvQ8csPy4u\nLi4uLjo+iTIrrGLXyva9S5cHv4mIiHisOF5jQBv3wnsGFXX+/Pnr16937Yp92yEiIiIiKgela0J9\nEi2+i68Vpal1uXTp0ooVKx79SGxsbO/evbV0SkREREREesmAatwLubm59ejR4+Ef4+LiTp061eXh\nfyoQEREREVVIBlQqU8jBwaFZs2b/WsXEZPPmzYGBgVo5Jz0BVRGKuJyHRquIeTUklbXwHSSmyYEm\n9Zi3m47EzOpYITHBas1FpE6ltUisL7baeGzYUBY2H2ZbKjQdKn2wHRKznYRN1jCBSqE77YAWE5Gd\nZ6HidecrUJWzqzlU5bwJCYm0xWbcrMzGxm+ZVoFiCdOQ1P75WNG/yPVfoZjJm9DTyVX9F7Rc8iIk\nlT8iC1rN+In3Zn6UEzaSKKoVVKYvIlA9r4hyAPTQ5R1piMRsJjaGjnovDEmBI5Ni8tKQWEsT6GKC\njcsTEfGfMhWJhV7GLijVo5FUcj2ojcTpBFYdjs3LMzaHishDrkPfmzphE6lEpI4SulfeuQPQaq4H\nwcPqXnx8fFRUlI+Pj7m5uZUVtjHQM8+prMXg3nEvysbGJjo6WitLEREREdFzo9FoFi5cePDgQSMj\no5kzZ8bHxx87dmzu3LlGRka6PrXiaX2D/vAeMkUVc1cZg9u4p6SkPPqQpaam/vjjj126dAkNDbW0\ntKxfH7qjCBERERHp3MGDB2NiYrZv375s2TIRadOmza5du06ePOnvj92I6bnTbvupiIg88SeBoneV\nMbxSmdjY2ODg4Ec/Ym1tvX//fhFxd3fnxp2IiIjIUFy9erVt27bW1g9u5mtqatqwYcObN2/q7cZd\n657yk0DRd/cN6XaQhWrVqrV69WqtnwoRERERPWeurq4XL17s2LFj4R81Gs2lS5d40xH9VLKN+99/\n/121atUn1Tzdvn1boVB4eGATifTe6dwkJJazDRqao8BGv1iNmgHl1FB1lwZrJ1W4fA4dNGUjFBO5\nicUG1YVipr5NkNjW96DGo6HroIPazsd6js2w1snUzUgq9EYraDWReVX3I7Evsa7T1CRoFk5D5zFI\nrK0mB4lJGjYLK+sIktJYvoHEEqBDiogEYi2bwdegdtLMFSeQmPWCf5CY0T1owtEyrCcyNfMPJCbm\n3lBMZEYc1saaAc2RMqkPXRILrkET7oz8oXbSKwugh07Soe7PPVh7vc17UExE1BkhUC4/GUllL4S6\nTluch44Znr4bia3BRpJ18YIO6nz1MBLbPAx64YiIGptxtrAlFJsc9zF4XB1q3779oEGDPvnkk4SE\nhH379m3YsCE/P//NN6E5dIaorCXyBlTjHh0dvWDBgubNm9evX79q1arGxsa5ubmxsbE3b9789ddf\nL168uHjx4nI6USIiIiLSOktLyw0bNvz888+mpqYFBQWtWrXq0KGDiYl27l/yfJRoL/6UVtSiimlO\nNaBSmZYtWzZo0GDDhg3jxo1LT0+3sLDIzs4WES8vr7fffnvq1Kl2dtgbFURERESkH5RKZa9evXR9\nFqWHt6tGRESUqAiomOZUA3rHXURUKtW4ceM++uij5OTk9PR0MzMzBwcHA73fJxEREdEL7tixY4cO\nHXrsg6ampr6+vg8L3yuMkt6Rppj38g1r415IoVA4Ozs7O0Pl3QaqoSn02e1tBK3meBArJDW2QVJb\n3KCCzp73ofk7ctUHilU9AsVEzglUGLcZK5ockwXNTBmanw0tFwlN1jhcAxoj8xpWpu90dBeUM0H7\nQ7BxPnI3FIppYqHi9f1dodUUWP19QeQdJGbkDc0a06QMQ2JYp4mICDYdSsQ+EEmdXgrVuLsthWpw\na2A9CWOijyCxOAfo1eqeCM1LEhENNm1K4bEcif1sDZ0e2FcTLtD/CXfDVvNb8gESU2H9Muq767HD\nyhoH6LhDL0E9TqY1oXleo5GQiOTFIik1tphlPyyHzdXqAt9TA3pqiozbAMVauc9FYgc0X2KHLRfu\n7u7Jycnm5uYNGjTIzs7eu3dv48aN3d3dd+zYkZKS0r9/fx2emz4yoFIZIiIiIqpI0tPTHR0dP//8\nwZ0qunXr9tFHH40dO7ZFixZjxox50TbuzyyX1xjiO+5EREREVAGcOXPm0QISW1tbS0vL69evV61a\nNS0NuheTHir1rWMea12tCJNTiYiIiKhicHR0/P3337t27Vp4v++4uLi///7bwcHh9OnTrq6uuj67\nUnqslh3fxz/WumrwzalEREREVGF07Nhxz549ffv2fe2119Rq9YkTJ3r27Onk5DR69OihQ4fq+uy0\no6Q9qQ8Vs+M3oBr3M2fOXLt2rdi/8vHx8fPz08Yp6QtPLJZ7CcvdWQHF7KAhHE2hFlaR+OlQ7GWo\nZyfvKDqL4WVs4MgYH3soV/MukuqngMaIBGNDjponTUBiYuKIpC7bvYPEql+AjikiwbHjoZwTFrsO\nfWVVn0PzXDRboVjqOxbQaljHXj/7vkgsCXtmisgIeBoO4k2sic38fSh2tw/UTHwSahKWtgmzkFiY\nqju0nIh/DjRGSmEGdeJqUqGm2IRXodMbig24mwk9wNL2Nva2G9atK/kZUEzkFBbrWDMOidkvgFb7\nDjvoVQ9olsvMydBqVmOgPuzstdBX//+gOXgiImbvnEZi6aMaIrH9hjCASalUrl27NjQ09O+//1Yq\nlb17965Ro4aIfP311w4ODro+O/1jQO+4h4WF7d+/v1atWkX/ysbGpoJt3ImIiIheBEZGRk2bNm3a\ntOmjH+SuvXgGtHHv27fv4cOHBwwYUPijGBEREREZtIKCgm+//TY0NFSt/t+9OkeOHPnmm+j/tOuh\nUjenPpPGgEplHBwcJk2aNHPmzHXr1llaWpbTORERERHR83Ho0KF9+/YNHjx4586dLVu2zMvL+/PP\nP+vUqaPr8yoNbe3XH95bppi7yuhUiZtT/f39fXx8TEzY1UpERERk8C5fvtyhQ4cWLVqcOHGicuXK\nfn5+UVFR165dM8QS6FI3oRbx4AeAYu4qY0DvuBcqrHmKj4+/e/eu5r+3oXd0dHRzc9PmqenaGqiJ\nUdZgHWBnqvyAxJbbQDG309WQWMFNaDWjatBqSV2enSnUOQeKfSEpSCxNsK5TbHJqxkioJzI/Gfq6\nKps+OyMiNbCO2JnYzFERmSNQB1i0DRQzrQkd1AwbE/vmaugRDu0DrZYxFRoSCY5ENe2AzRIWCbr5\nOZQryERStljXqQac6pEHtf+2TYVe/qIwQ1KvoFN9ZRnWdVoQCW0Icn6C+g5d/+yNxC5j1+EhSEhk\nHtYQD87DvHMAy4mYYzGPqA5QzgG6Z8iuEKjD3hp7qqcvhWJWk6CYOxa7jXXEisjdYVDXaXesnbjj\nRmhy6hSdTk61sbEpvF+7o6NjVFSUn5+fmZnZ9evXDXHjri0PN+vFvIVvQDXuhQoKCqZPn37y5ElH\nx//dUqNDhw6BgYFaOy8iIiIiKn8tW7YcPHhw+/bt69WrN3fu3ISEhL179y5YgN1y6AVkcBv3U6dO\n3bx58+eff7a1tdX6CRERERHRc/Pyyy+vXbvW2tra09OzV69e4eHh48aN017NiR7RTgW8wZXK5Obm\n1q1bl7t2IiIiIkOXlZXl4OBgZWUlIj169OjRo0d6erparVYqwTpEPVLSrfnDJtQnKaY51eDeca9b\nt+6uXbtycnLMzKDiyOcmOjpai6tlY/XGnthqPljMfiGWy4eKXI0coAlHmV/ORGIOy5GUiEi3YVDs\nJrYaVIAvojCGSquzoGkzYjMVGsAz2BOa6NNqDPRkmhGPjYcRqeIGfck8sRJM9SboMY6tXvzwtcdg\nw2YkH5rSIyZYF0FQhBUSS3ipKrSciGtYYyQWVXkTElOHm0JHTVqEpDq5TERib0GHlCk3ByKx8bHY\nciKrsJd/Qv2zSMwtAipeF7fZSKrSWKjGfTNWgT0GqyOYMhC7rGPNBiKy7AA2IMrME0ml9oWK142c\noWMWYNccY2w1SYLKvgOwxdRHsZyIRScodmwRNFkp9zBU467dDYyIeHp64uHt27dbWFj06NHj4Ue+\n/vrr2rVrd+zYUbtn9RyU/D8KnrHRL9qcCrYjlZOSbdy/+OKLwt/k5OT07du3du3aRkZGhR9p1KhR\n69attXx2JVSip+kzXdbiWkRERERPoN0NDO7mzZs//PDDtWvXTExMoqKiCj+YkZFx4sSJtm3b6uSU\nnrNnbvQNuzn11Vdffew3Dzk5OWnnjIiIiIio/JmZmbm5ucXHx5uamj68N6C7u3ubNm3q1sVuJfYC\nMqAa9969sf+1JCIiIiL95ubmNnDgQB8fHzMzsxf25o8l7lg1oHfcHzp+/PiePXsKK2e2bt2ampo6\nePDgh2UzRERERKTn8vPzRaRhw4YPf/+QkZGRAhtWUH7Onz/86B/r1m1ebKyM94p5en+qvjWnKtCR\nH49ISkoaMGDAlClTmjVrJiI3b96cNm1a//7927Rpo/0ThAUEBISGhmpzxSueSOpwjRgkhvVrydU4\nqNlFHQw1u1i0h5pTo2pBg5DclyEpERHl+4eQ2N0OLZDYimPQQcHasFulAAAgAElEQVShJFNixyOx\n7h7Q6KLg1dBBjaGJNGKKzUsRkfuboZhZHahlU7yvQrFIqMU6ZVwWErPfvgs6aBzUiLkXa5xte70J\ndFARcYP6RLPmQoNarN6HJnB1wiZwhVyBmokDsMck9JI7Eiu4E4fERMTIdw8S02RBrYLT3aFrHdj9\n33UslsNYD4MeuqyN0ENnNfMKeNzs5dWRmEVf6FqX3B661jmdxIaXRUHbgAnYk7MvdEgBBysdwPqw\nRSR1JNR03mQ3tJoaO2ikLhoer1y5MmTIEweOffrpp7rtXQwICFi6VAv7urLfAvLROvj69RXZ4H1E\nRCwmwpP1YKV5xz08PLxOnTqFu3YRqVy5crdu3f7880/dbtyJiIiICFStWrXdu5/484elpeXzPJny\nU8Yb0ht2c2qhol/L/Pz8wtt/EhEREZH+MzY2trOze/jH+/fv375929jY2MPDw9QUu4ktPXelvI/7\n4sWLv/7663bt2pmamp47d27dunULF8L/c0BEREREemP79u1r1qwxMjLKz89XKpUfffTR22+/reuT\n0rEn1dhoDOiuMoUsLCwWL168fPnyYcOG5eXleXl5ffrppzVr1tT6yRERERFRuTp//vzmzZuXL1/u\n4+MjImfOnPnss8+qVatWuXJlXZ9ayZS9nP1RhU2rxTSnGtzGXUQqVao0f/58ESkoKKioN5PRZEBd\np4HYajHJ30C57D+RVMYSaDHTmlDXKUj5Dnoz0DhHqOvUFPtZbzTWT+iwFRo7mjoKmjm67dYT+3X+\nJX07FFO+hqSOC9aHK2LeB4p1FKhP9IexUPPsR9g4SbtnR0REFsVPh3IuU5FUL/kAiaW6QoMYRSRj\nPNR1mox1J78Edp1izXO5Z6DOudDMP5BY0mtvIjGLzkhKRCR7SDsk5rwdqq6cgw2dTZmQi8TewJ7D\nx7Ee1k41oa7TkGvQFN704VDLqYjYfgr3WAOcdkKd03E20NRhy27QQf8TCMVsZ0DntuMT6PWV2Bh6\n4YiISyT02glP/AyJtcRe/rp1+vTp9957r3DXLiINGjRo3rz52bNnDW7jXrt2bS3u3bt00dZK2lTK\njftDFXXXTkRERPQiMDU1vX///qMfUavVZmZmujqfsihjK2pRERERj6+p03fcue0mIiIienE1adLk\np59++u233xITE+Pi4rZv337ixInXX39d1+elrzTwr3JQ1nfciYiIiMhwVa1a9dNPP121atWcOXOM\njIyqV68+f/58JycnXZ+XvjLEGvdCubm5hd3H2jobvTId+1EzAhutlDZmOBJT74NWc0uGYuOxgT7T\nGkGxflV+gHIi32AVotZDsFuIVoKmDWkSPkdiqo3QcChRQ0Vy/SqtRWLBF/9CYrXBMV0ib2Gl1SG5\nSUgstYszEptlAx3U7RLUHjAPe+je98CK1xNmIbGMCVA9t4jYLP8HiamPQu0Bye9DB50sUA1u8N31\nSMzVGvpkE7DC+iqV0frgGGw+lDhAT/coF2gClws2gycNKzaeg5XC94NSIiqoJWXjxhPgesP8oWaY\nnLNQ7AfsYtKjIxQD2U6GZleBArCBdOHp0GgwEZGkOUiqDla8Hn4RmoSoc40aNWrUqFFubq6xsXGF\nqYLWbq/qQzq9jXtpN+4XLlxYtmzZ33//PXz48AYNGuzcuXPixInGxsbaPTkiIiIiKle7d++OjY1t\n06ZNlSpVdH0uJaaV3XnhDWSKVRHuKnP37t0ZM2aMHj06MTFRRCpXrnzr1q1ff/21c2f4vgNERERE\npAdq1qx58eLFUaNGeXh4tG3btmXLlra2tro+KZSWulGfuPvXt+bU0mzcL1y40KhRo1atWu3YsSMn\nJ8fc3Lxz584nT57kxp2IiIjIsFStWnXKlCkTJ048e/bs4cOHv/vuu1q1ag0bNqxSpUq6PrXn5Cm7\n/3Kqtym1Ug5gysjIePQjGRkZj07NJSIiIiIDYmxs3LBhQ1tbWysrq59++ql9+/Yvzsa9ZHRa5F6a\njXvdunWXL1++evXq/Pz83NzcHTt2bNiwYdGiRVo/Od2aE4W1dlr6QzGnCUiqo7EFEtMkLkRinbC+\nLv+TSEouH4BiIqI+iuVMPJDUKLt3kNhKDfRK6qdQILHg7HAkloaERBS1oElYG7HVRGQv1ooXZwV1\nnbpHvgctp4T+O3KNGzTiakoE1JqcvhCaISW20H/35d/+BFpNRCJ9kJTTL35ILKnZWSS2xBxJieYW\n1LAbswxaTRTQrZovYU85ERGb1lDMyBpJ2WGzhlbMh2IxWCcuOAhvng/2Phy22vtwb7p5r11QrA3U\nTzx0VjPoqBkhSEpjUR9aDXtM+oHdnynfI7E7raDRYCKyCPueeHIBtpwndtMJXbty5crhw4ePHDmi\nVCrbtWu3detWBwcHXZ9UeSnrm+gGVyqjVCq/+uqroKCgc+fOqdXqqlWrzpo16+HALSIiIiIyFJs3\nb/7xxx9btWo1a9asatWwG0Pph9JtwZ/SilpUMc2pBveOu4g4OztPnQqNIiciIiIivdWmTZuePXsa\n4r0BS9SZ+nCX36VLCQ5RtDkV+w/+8lKajXtERMTx48eHDh368CN//PHH9evXBwwYoL0TIyIiIqJy\n5+joqOtTeB5Kd/+ZYt7UN6BSmYKCghMnTkRGRhbu3Qs/mJOTs3Xr1rp165bD6RERERER6Q0Desc9\nNzd3/fr1WVlZ6enp69f/b3qfo6NjBbwXpMdyKKaGhmLe+xLqOg2Nn4HECq5CXafZSEhkNBZLnY7l\nRJywzh7NoNeQmI9cg5ZL2YCkvg2FFrtsUQeJQR2sIm9jMbg5TQYOgt42sAq8g8QyvtyJxExrQbGh\nN1ohsQlY2xk4rvH+Rujr1RAbEoxTYC//f2Kh1fyw1sm7/aCmQ4fveiOxlA+gEbb2XzdGYiIiuXFI\nKuublUhsLTT9U/4DpWRKPjZ0uiDj2RmRcdjATnGBykrNG6OzaSULav9Pmwa9YJduhmJuSEgkcDP0\nujarB035CcZuEeFt3xeJnYMbrBOxb2FZUE+sWLRrBuVqZUIxKjclKJc3oI27ubl5UFDQ2bNnjx49\nOm7cuHI6JyIiIiJ6ztRqtbGxsampqa5PpBjlfT/1J3WsVoTmVD8/Pz8/6CZoRERERKTn9u3bFxQU\nFBcXt2TJkuTk5JSUlN69of+1e26KrVDX4m7+SR2rFWFyqogcPXp0165d6enpDz/SqlWrnj17aums\niIiIiOh5uHDhwtq1a6dOnRoSEiIifn5+I0aM8PPz0/87fZeu37REiv5sYHh3lfnnn3/mzZsXGBgY\nGRlpbW396quv/vrrr2+88YbWT063VOZVkVgUVNArqpXQ/1F4Y8NrfocmFwk2k0SqYnXEE3djy8Hz\nofZi86FOYQfNWgJNpan6BbRawq0hSGxkJag+eB10TOmHxUTk3jboPQaLTtBqzquhmDoNGvsCzkKa\nORmahGX18fpnh0TExB2K3QuDYoIOiCn451ckVu8CdMz0GVCV82ys7HuRVVMk9sv+H5DYwEoboaOK\nKJTVkVjGWGi1r7GDxjhBMW8v6Ov1I3ZQ8MsqCZ8jKbNG2HNYRNKhWUj5/0CLjcN6a77ErhLp2Hcd\np73YJKw0qP4evHJaTcVq0kWuzoeK5p3+hPrIkutDHW5O55BUeTl58mSvXr38/Pz27t0rIi4uLs2a\nNQsPD9f/jbtuGNzG/dKlS35+fj169NixY0dOTk7Hjh3z8vLCwsI4GpeIiIjIsCiVyoyMf/VkZ2Zm\n2tjY6Op89J3BlcpYWFjcu3dPROzt7U+fPi0ilpaWf/0F3V2BiIiIiPRHixYtRo4caWNjk5aWduPG\njXPnzp06dWrEiBG6Pq8SKO/WVf1Rmo17w4YNlyxZcujQoZo1ay5evNjZ2fngwYMV8HaQRERERBXd\nyy+/vHDhwnXr1l29evXvv/+uXbv2smXLVCqVrs/racppp1703jLF3FXG4N5xVyqV33zzTWZmppub\n29ixY/fs2dOsWbP33ntP6ydHREREROXN29t73rx5uj6LEii3ttTHfx4o5q4yOq1xV2h02xyrPQEB\nAaGh2HAdUNp2JDVP1R2JTYmwgg5qifX4mnsjqdiXoBEn70CHlN+x5iQRMe+FdTGaeSGpNdgspHjo\nkAI22vTEBn+IJgeK3TsOpX7OglYTsewLnV7WeqgVz2omdBH4WQG1k7ZdhqSk4xgo1gRKyYxLUGNf\nQhNoNpCIuF75BonFeQxHYu7Je5BYd9t2SKwyEhKBrl8il2dBsaBPsOVExuSlQbkrUIt9v9rQiyIY\nvMDa9UFSd/tCTedpWJdwJehVKOHYJUdE/K5hw7DcFyGpOIc3kdi32KUObP8Mx0YNSj40Qk7urEFS\ny+rkQquJNMRi/thnoUmA7jmhqKPLzdju3bvNzMxat2798CPff/995cqVAwICdHhWAQEBS5dqdV9X\nKo9t3OvXV2SMRP+tzdei9W12id9xv3PnTnBwcP/+/Y2NjUeOHGlvb1+rVq0BAwZYWWHXTSIiIiLS\nA2lpaRcvXjx37py5ubm1tXXhBzMzM3/++efRo8G56i8cjQGVyqSlpQ0fPvyVV16xsLDIzs5OSUkJ\nDAzct2/f4MGDN2zYoFQqy+ksiYiIiEi74uPj169ff+fOHSMjo8jIyMIPKhSKevXqNW6M/d+OIdBy\nQbwB3Q5y27ZtPj4+s2bNEpHs7GxTU9NWrVq1atVq4sSJ27Zt69+/f/mcJBERERFpmY+PT1BQ0Nat\nWy0sLDp1wmZ/6Iey7MWLdqA+RTHNqQa0cY+IiOjWrVvh742Njd3dH9SVtmrV6siRI9o9M92z9EdS\nU25AE5jyb+xHYkldoJjb9TZIbAASEtmNzS5RwHd0nWAHlc0vun8DiQ29ijWgWEPjZrKWQnX/E7BB\nLYsSsALhl6D+AMvh+6DVYFYDoArRWKx4vf1m6KARUBWxhCyAYubYOz6jakLF6ytTt0HLiaQOgnpX\nrN+HVsvZDRWvfw01fYj9Cii2qAZ0aQqoCl1zQnOToKOKzDSxQ2IzkqBmiPECNUPkhEOl8Gat6yMx\nh4NzoFhONBLL/Q0qma6OXUtEZEK1E0jslEDF66HZ4Ujs/VegXqPEWCQlSmzUYDT2TWdZxrMzAs/y\nE5EPwdFayteQlCb92RkRga7C5aZHjx46PX5plK05tQSbfn1rTi3Zxt3Y2Dg390F7h52d3TffPOjf\nKijQab0PEREREZWKRqPZunVraGioWq1++MHBgwdXpGqZR5Vo01/MW/s63fMalShds2bNPXv2PNYh\nq9Fo9u/fX6NGDa2eGBERERGVu6NHj+7YsaNdu3ampqatW7du2rSpk5NT9erVdX1e+koD/yoHJXvH\nvVevXoMGDZo+fXq/fv28vLw0Gs2NGze+//77+Pj47t2h/1kmIiIiIv0RERHRqVOnDh06nD9//tVX\nX/Xz85s7d25MTIyez2DSopJVzBtQqYyVldXKlSuDgoI+/PDDnJwcETE3N2/duvWkSZMsLCzKch7J\nF/au+X537D37Bl36B779lJuUZ57Zuzc60/6td992K83wKCIiIiL6H0tLy+zsbBFxdHS8efOmn5+f\nlZVVZGSkr6+vrk+tBMqpXbVoc6pubwdZygFMGo0mKSlJoVA4OTkpsM62p0g+s7rLuGDxbdfD4/rW\nPZG+I1as6FP8c+XW3jl9Zu8R8ViyZ0sD63/9ldYHMF3GPq8ad9cjsczPP0BiHy1FUqjVB6DYmJZQ\nbDn86Cb3hGIuh6ohsX7VryGx4Jje0FFNHJDUNGx21Rzsqw/OB0nwh3rORMQ1+g8opzBDUkm1oeY5\n512mSCwAm3ISiYREErGYJh2acCQ3u2HriZhDbWfzsDbBKbdHQQfNwLqTLRogKfWuH5CY8h3shWNk\n/exMIZvWz86IaBK/RGKKV7Cneib00A3G+uaDwqGnet4t6Km+A5usNBlKiYj8FQjFbFeehnJ5WD8p\n9ghL3l0k5VoFenImYB3M4jAESSmM0bcXp2KxOVnQI9zSCrrAHtDpNMzo6Ojhw4evXr369u3bX331\nVbt27Xbs2DF79mzdbty1OICpjLeDfGwAUxp46w8Ru2/1YABTIYVC4eLioqVzSP7hs2DxH7t/fjel\nSBOP0aNXLbvQeZ1v0W8TqWETZ++x87BLi7WGLqtERERE9FSenp4rVqywsbHx9/ePjIz866+/Bg8e\nbFhvtz9dWW5Bo+V7wJeZHpSbZEb/niaDBrYtnN7k2y3Qbt24E9dTfX0fq6xS/zJncqz3iG+nyIBB\nu3RwnkREREQV0auvvlr4m8DAQJ2eiCEwoLvKlIfMmIhY8ahX5b9vsJvYehYXSw5buyBMps/uU0ly\nnt/JEREREVVcN27cWLx4sYicO3euX79+48eP37JlC2/zXaj4t9sL4F/lQA/ecc+7/68/Km2rSJG9\neV7k/MlbvQetaOsm6sJZBsWd+Llz57R4XkotrkVERET0BNrdwIhIvXr1kNjVq1fHjh3boUMHEcnO\nzra0tGzRosXOnTvPnj07b9487Z7Sc6PF4paffjLwyanlQelaRSQ2WZ0n1iYiIuqEMyIt/p05s25e\nmMikhg63bsXfDb8ukhVzNaqKTyWV8l/nDz5NQRpszFp3B6jr1Bw76Ddjodi7WA9rM6zrdDc26jL9\nKygmIpbgrUGNHZHUIIGaU/th3U5gn0sAFgvDvvqvY13Crofg3g31X1AsPxmKgc9Ox6FIas9YqK93\nJfYcHr8TimmuQKNJM9dBq4mI9SCo69QTW60f1utcGVttfF3oFWHsDK2m7OaOxA57LIaWE2me0gyJ\nKUyh48rfUGOfOI9HUkGXoINqcqFBvCaNvkFiPRMzkZi5y0QkJiK2C7CWzTTokiimnlDM8UMkNUEJ\n3fn7FjaGuaEzNDfXBxuvGwv16ouIrAX/X/8udNeB7R2hxVRa3cDg1q5d27dv3/79+z84DZWqY8eO\nLVu27N+//8mTJxs1aqSTsyqL51CSrtNGYj3YuJs4VfUV+e34rbc7e4mI+ubFWBF320ff7079MyRS\nRBYM/98Gc8HoAVeXhExq8KLcYZSIiIhIuyIiIqZMmVL4e6VS6eTkVPibJk2aXL582RA37mXpQy1O\nRERExONr6rSMSPcbdzHx7tHObvqCj395ZWkLx3/mDVoldv2aeClF4lcP6h7iMWn7F50Hbd/ft/Bc\nTUwyz6/rMu7Q/G2b/N1YzEJERERUSsbGxoVjeUTEz8/Pz8+v8Pf5+fm6Oyk9Urt27WLewtfpO+66\nb04VkSaTgwb5xi4Y3r1d93FHxH/JxkEqEcnLTU+QtJScPBETpdJaqVQqlUoTE2sLcxFrS2vu2omI\niIhKr2bNmnv37n3sg1lZWX/88UfNmjV1ckoGQAP/KgelHMBUHlKTk/PyxNrNqXRbcq0PYKqCDWAC\nB7qAN73/cBYUM28MxYyrtkJi/aruR2LBF+2ho4pIdWiix/0gaByGeSeoerUOVoMbnnEIicl9aECQ\n5vZwJOaJ3Qz32gYoJiJmff9BYnXMXkZiJxdAB7XoC30hxAar6LT0R1J5e6EniXE1PySmUGH9HCIa\nrD3AyH0uEsv8FDqo1YwkKHfVE4pZ1EdSyleOITF1Xhp0UBHNWTskloxNMLGbAsXMWkIjeMQ+EElp\nYqF6bnvvs0hsJBISwT4BERElVqvt/s9CJDYTq63HXtXid/8GEksfVhWJ/bQROqgrlBL0GSwSjMXA\nvcq2ROgLIc4TsPW0LCYmZsiQIe3bt3/33Xdfeumle/fuXbp0KSgoyMXF5csvoSlp5UeLA5ikbLXv\njw1gSu2K/kPVDr0ZwFQeVE5Ouj4FIiIiohdFlSpVVq1a9fXXXwcGBhaWx9ja2nbv3r1Xr17aPVD8\nhYOHLqbUatHR92Gdc2bk5lXfHY9J8fBp/cGIzm7a2JCWenf+009P/Kti7irzote4ExEREZEuvPLK\nK4sWLcrPz4+Pj7eyslKpyuG2H6lhY0d/FivSo1bLBxv3zIuT2w0PE+8e/ar/Frxgz+8x27Z86Fbm\n45ShM/WJO/5imlNf8LvKEBEREZEOGRsbv/TSS+Wzdub2TybHerfzT9jzsM7r4i+Lw8R7Sci6BioZ\n0dqn+YAF6491n9ak7Fv3UnrKjr/ou/i6rTHXi+ZUIiIiIqp44o8tW3pBZs8b+brVw/Gamad3Rdp1\nHlx4T28Tr9ZjveX4ySgdnmTJ6LQ5le+4P9F5qLFTzkONnVIdayeynA59nS9jjbNOXtDJrcQa9qJq\npUA5Ea94qKPl8DBotbaD/oPETszCBsRYN4diN6HeE7Dr9AiUErP2WG+ySLwj1HWKTWmS3yZBsfBJ\n0CM8IwNqY9tkDHWdvgPOLllwB8rdOw7FRMTcG0lpbkEthWsqrUVi+7+AZiZtuwFdm6ZhTefZF5CU\nXDaBWk5FpEY4NEdMNScXiZl2hloKNUnQNSfJ+00ktgfqrke7Tj/Dms7NWvTG1pO7/bHJSneWI6kx\n4Evsxz+gXMY+JKWGUjIQfGPzViCSKojZBK0mEo1N4PPBVot/BWr/dUvXTXNquVNfmD59j/eIb5o4\nKVdn/etvrJxt//tbZY0A77Tt4amT/J/PdJ6yDmliqQwRERERVTDHFk+PlM7b+tQSUZuL5Dyy7XS2\ntnzmP9+7t5gffNu2fb/wN+XRiloUm1OJiIiIqILLu/XL9D1pviOay61bt+Rukkh6zPX4l15xcxLJ\nkqQ76Q+T6UkJ4ulY9A6bD/foxcJbUR/b4nfpAv67B/+WzalEREREVJGp78aJyIVV47qv+u+HFow+\nIv32hA5yqyexh85lDvO1FhG59X+/pHmPqFF+kzXLcLeZ4t7X58adiIiIiCoS61qD9uzpa2JiIiIm\nJqnr3u2ePn3bJH83EXmrWz8ZvW7m5trTOnueWDXxiMj0FmDXgO5pWCqjn+w3jkJiypdWIrG6Oc/O\niMhqrOv0rbrQar+eh2IDfoZiYZuhmIh4WbdBYm8EzoSW00CP3ZhPoMUWXYIeYRX2yd7DZo6ewLo/\nHadgnwM8OjcJG8Tp9MtAJNbhDNTa1cmmBRILiX4PiR323InEmn/dDImlfYx2p23BngBdG0GxofEz\noJgT1J2W8x3UJzoHG3WsPgg1ndfAOmJFpBPWFAtdXkXaRnggsZSRWc8OiRzHuk7HQilJzToN5Uzd\nkVScNdRxLiLukdBrRzyWISnVcuwuAbehmMoHqjm+PRk65hbsG2JP7AuRdxB9+U9J/gbK5UADtu/2\nwm6cUCGZmFhbW//3D9bmIkrLB3+09h22Yuw/o5eO67RKRKTH7M1ttTKBSRvK2rpazvTlYSIiIiKi\nCso6cHfoo3/27fbF/pbJmXliolSprEu5HS2PTfZjratsTiUiIiKiF51S5VTGuvZHi9e1tYl/rHWV\nzalERERERNpUlg7Upyjm5wG+466fco5CxeuNQp+dEZEEN6gq+U6/E0jMcSdUIDoWq78f6B2OxHyk\nDhITEc0laMqJ7UQrJKYwhabSaHKhgu4t2GoF2FSavOtQrCn2JDGqtR7KiUjC50gqqX0MErtcGSr9\nrJFxCImFZJojMcmDyo3TBKpxl+w/kRQ49kVEBmNfsjSsTWOCG5T7tCsUM60JHdTs7c5ITDliIbRc\nRggUEwkJPwLlVNjgN4ehSGrgMeiaE4IN9EnNT392SETioME64jYHSVl2gxYTkeR3oReFyctQTLVm\nPHRUrHOpm4xBYlfnQ8dEv+XcWYGkfnrafQX/ZZgMR2Kp2dC3TocdnuiByVDwHXciIiIiIgPAjTsR\nERERkdZpv4GVG3ciIiIiorIr4079mXeVwWruygs37kRERERUQZS5S/Vf+37eVcZgmHW9AeVSsUkt\neXFIKvEk1px6H5r7EAWOTMnPQFKVfsVWE1G4QPNBqmCzdTSXqyCxrJlQ12mbrkhK0qZDMdUy6CFO\nnwmNpFm18QPoqCJTsKYo86ZQc1d2FHbUjN1QTAldMac5QJ/sTOxZd/8I9P5KnWRoNRGJOQfFLDpB\nsWPQ119Uq7A+UY0aSbV0heZ5HUiohsRcsdVEJAFrE5ekL6FUA6jrNBs6pMjf/lAMaycNq7QWifnf\nhEbIZcET7k5hsXXYAL71b0LjgVywq4TmehMolxUGxYzMoJgCikEdrCIikhoB3ThBaQFdYKdhB52h\n+RALUrl7bJvOu8oQERERERkmbtyJiIiIiMpVeUxafc64cSciIiIiQ1XS7fhj7adPV7Q5lTXuRERE\nRESlUfJu1BJs9Is2p2pYKqOfNBeqIjHf16HVLmCTOF2xxh7JhVpd825hq+VDLXspE7DVROQ+1HV6\nyglaTJMDjf+0+vQfJNbd7GUkhg3/lODV3kgscyPUnDhFfQU7rGROqY7E7DZCq2luQ4N4M+dBTWzW\n86DPYs41aJaw5FxFUve2pSAxqPVbRESUfb6BcnZQs/NfY6DOabEfiKRS2kOr7QyEjjkT6zpNuGgP\nLSdy5y3o9Bz370FiziegdtIDdtjc0SueSGqCTQsk9sUs6JhpH0OTiT2uQF3CItIm5BoSe7dbBySW\n1A5qAAeHSUsB1Il7Z0guEnM8jo2wvQ0NOgVv1iCCfhau2GLDsO909DyVaKOvb82pRro8OBERERER\nYfiOOxERERG96NBaeZbKEBERERGVXalvHVNs02oxzancuOun17Hi9fCkZUgsfdIYJDb7GHTQRXm3\nkZhVP2g1wYrITWthq4mo5kOl+lGvQJ/tcl/ooGOihiGx4NXQaua9oQJcuQlV1npchcrpDiuhynUR\nqdcRimniZyCxhNozkZjLQeigch0qSl5WC6pKxyYXyXovKOaVjn1ZRarYtkNi1QQqrs06AB10kylU\nHQ5W1rZNhMY5Vdk4EYlpTLGHWMRxE1TSOwF7hBfdgCqTZ6q6I7EZGYeQ2MdeUI27Zf/eSGxZlR+Q\nWKAZVLkuIrYfQ9XwiW9CxeuHY6GDfopdh3dhjSQ1UrdBywTqoakAACAASURBVN1ojqQOV4MOOiMf\nndMFTgeL0XyOxFwVCiSWgIQIU2wJO7Kb79KlmA8WMzlVp7hxJyIiIqKKrNSb72J2/LwdJBERERGR\n/uPtIImIiIiIDAHfcSciIiIiKrtSN6eidLpxV2g0Oj2+9gQEBISGhmpzxUsOSCprE9RjN2w+dMz1\nm6FY2nQo1j4Kip2+DvWSKrBeUhG5hMVqYANHMldBPVvpS6GD2kAtrGIzyhTKuS9CUppYqDU5Gmv/\nEpEVWGyiGRTrDw0bkQOX3KGcUqtNPObQiKvEhiuR2B2sD09EnLA+YXvo6y8mDbBRPQlQr9uWOtDw\nmp73b0AHjf0QSWVvhzodRcSiYxUoVwm72GHj4cAO+4bO0CvxF+yF434B+kxH1YDObSU4kkxkGtaL\nCX47DE2GZo2lBkJ92KqVUMNu+n+ghl3bmdBIMsn4BUlNwBriReQmFtt6CordC4FiVjMryGZMiwIC\nApYuLdm+Trtb9sKbzDx2V5n69RVJNdAVnC+L1rfZfMediIiIiAyetm//EiHF3lWGpTJERERERPqj\ncL/Ou8oQERERERkk3daYc+P+ZDadkdSK+ZuQWHDaLiQ2we4dJLYw0g+JLfM+i8TE5TMklXcAGkoi\nIgNbQrHQ6lDxehp20ChoTovYYgOYNDOGIrHU96GS2cu7oYNCA2lERCQB+ywK0qFYq0lQLOm9OCTm\n/CvUuiCu0IgTpfWbSEwd1QGJuWQdQWIiIpW3IylXbIpQonyCxOJskJT0TPkeifUzr4rEgrFROOp9\ncI37EGjw0wTsK7sIu3LOxIrXT2N9GvGNoKf6YKx4PQh7chb8gz7Cc/Kwi2IudHrzLOogsY+wa87d\n/lDx+hisYSr4U6hPaw1WvD7zU+igIlLzCyjmiU1pjIHnvpF2lWOLKm8HSURERESE0MqmvLD39Jke\na04VYakMERERERFGS02o0O6fzalERERERLoE7v6LeXefpTLaEh0drcXVPI21uBgRERFR8bS7gRER\nT09P7S5IeqJCbdy1+zSdp4C6TqckQp1Yeb9DXacdkZBIZ6zrNCQ7HIllr4Cakyy6v4fERKSy7ERi\nMTegftKGVfcjMfOmSEo2QotJ1jfQQB/woGqsOTUMSomIbMLGSDXAVhuHTcIxaw0NaqnjBA1qOT4W\n6zrFniRRXlBjnyc2MEVE4rCu03hsQUV1qDstaxbWn2wFPe02Yr2OSY27I7EPrkCriUhIEjSVCrro\niGRMhq6cU6FLjog51Dlt8grUnBr0+x/QQaNbI6mhAdBiInJV7JBYaDR0xb6PHbQhds0Jv7seia3f\n+wG0XBVodlHvYdADcv8odEwRudAVinnugGI/YxeTdyvKNEzDUspaeb7jTkRERERUCqXuVUX6U4s2\np/J2kEREREREpYFUqxe7ue/S5dmLszmViIiIiOj5KfWNaNicSkRERERkmPiOu36aEj8Dibm6TERi\nCdebILHmaROQ2DBswKoSG4mnvgI1bNXxBPu/5DczKLYG6zrdj/UJBUOzKWU2lJKBH0I9x5r8ZCR2\natJcJGaPhEREpIsXFHP+YzwS0yigL5jm7hokNhgJiRSAE3HtA5GUV8ZUJHZ/Mzr91wNr7FZhL7HU\n21B7cu4lJCUSC00JzYS6BMUF6zodBKVERCQP6uzssQBarDo21vc01MQoF2OhiZ3VsSvYJmz463fQ\nYnLgInwBsGqOpNKmQVfsGZerILEEbEysGEHjf1Ohb5vi8ibU6W4zFhqIm3sFemaKiGlj6MqZugn6\n5pQ2FHqekF55RtE8N+5ERERERKWjlVmqDz3atMrJqUREREREWvNoCXvZN/GPNq0W05zKGnciIiIi\norIrdR9qsYr+GMDbQeorh6FI6kqrmdBqNp2hmCYHSUVehZ6UYT7QD50FSdeQWDckJCIi7jeh9oD3\nj0MPnWkTaO7P1R1QNWQCEhLph7UurJkFrTZ2GRQDi5JFxOl0EhLLDXFGYs7YZK2/60KxMVg7h/IV\nqNpYPWMjdFSP5UjKvDv6ECe+ghWvY+NmJPtPJGXzEbTYYazbpHnOP0ism9nLSCzo1hAkJiJyD5ok\nBg50mzAJ+mRdzkBFyd94LEZiNcyRlFyFrtZo8bqqVgq0nEgDbMLd79hqyc5Q8fpKsAT/DvRKdLmB\ntVZg3xBTOv+AxIygy6GISJ33oOeJCBSL4WSliocbdyIiIiIiA8BSGSIiIiIi7dJu0+oDfMediIiI\niKjUyr5Hf/RmMg8Vc1cZneLGnYiIiIgMmzZ6UovZ+vOuMobD9CUkZb+6FbSaqhcUy4H6hOpgXadn\nf4WOaeQMDWCacasZtJyIqxvUdZoQjTVFqnojqUVXFkGxgkwkdrYmNKoj5wSSEtUCaMTJ/RPYiBMR\nMXFCUvlQd6KkJkHNs0pnaO5PQ6zrNPsUkpIq2JSuOlIdiWHHFBHB2ollocMHSGxvI2i1l05CsZ+h\nlPTDuk63YfN3NCaO2GFFbDsiqTvtoKlkY9L3QAdN+RZJgcOGpPJ2JDXGrSESK7gLdZ1ew6aqichP\nUVDMDVvNegzUTR6Ava5Dk/sgsdRu0Av2PniBxebquQ6DYiKSgl0pMtdhy131hmI+kdhyVI6K3foX\nc1cZnW7cjXR5cCIiIiIiA6KBf+FL3r8PJvmOOxERERG9cEpZFq/d5lSNRpOdnRsebta4MRLnxp2I\niIiI9M7584cf/WPdus3xf4tsyovtRn1MMc2p2tu4F6Sm5v35Z0r//pZDh3LjTkRERESGqkQ79cdg\nvarP3tyXU3OqJiOjIDU1deDAnMOHn51+BDfuT5T+vgKJ2c6BJvZlfAg1ihnZISnZZwPFvu4AxSoL\nNDm1eR8oJiI/gjkNVNGlOQs9KOdeh47pl3EIiiUcR2KjXD9BYgubQl2nduuxaYIicmc1knod6iaV\n8H7WSCwe6jkU1Xx3JKbJhdp/Y2Kg1mRxmQrFMo9AMZF+WCcuNF9XpCARiqmxCYs/K6BLUyh0TJlW\nA3pyfr4T6iUVEVMfaMSmJg1abZ5tOyQGtTCLHMjDjhoLffU7ZUCLna4BzX52+m0NtJzIkOyzSCza\nF1vOCPp2Epp1GolljIcadi/vRlKiglLi2hvqYH5tGPRcEpF7UHOyRECXYXljNfStk/NVdQLZ3Gv9\nTvCanByFiUnG9OlZy6FJw4/hxp2IiIiICFOGd9w1mZnZW7akDR5c6hW4cSciIiKiFxr+znrpbgep\nSUvLu3w5pV+//OvXS/Pv/4sbdyIiIiIyPFqsY3lSo2rZm1M1WVmae/dSP/jg/m6sSuypuHF/okMb\nodg7IxcjMZuvs5HYGmMLJDY0GSqaHHNnBRLLvQg9703fWojERKTZomQk1t0dqprdhlU5+92HhnCk\n9qmKxHLCkJTMxSZ6nJ8ExfzfPwLlRET5GpIKwBbbgk0Ruoqt1nE3VLz+F7bae8N+QGKvroZiCdgL\nR0SCc6DhVd2xIUdjsIk5CRehZoN3c5OgWNYRJJYxpTsSA2eNiYhJbWiSkHlTaCyRG/bQ7b8AxeQe\n1LviWnkTEsNm76GvVgtvqHJdRNKw0urZkX5ILPMraFCf9TR/JOaMnZsaq5jPOwJVzN/tCBWvh4Ij\n/0TEHfpmN2w+9N0E67+hUgJHJiG6dCn+48U0p8KGDxki+fmZs2dnfvll6VYoiht3IiIiIqogSr3P\nLlYxPwbApTKL586906ZNzsGDWjwfTk4lIiIiIoLgg1MbNWtmv22bw969Ri4u2jo6N+5ERERERJAC\n+Ndff/1lZG9v3rKlS3S07YIFWjk6S2WIiIiIqOLQ+s3XH1Xie+4bGyssLCxHjbIcNSpt2LDs774r\ny9G5cX+id7GeyER/qCuucizUdarG2s5GmTojsZWx0HAo00ZNkZirCzhtRhIy/0BiG8ZCzamqKtAj\nnLgBiu3agaRk4A2o8cy1KjT45VIj6KCau+gEFrFpjaRmYsd1/G0bEgtTQV2MLaBjSmoi1uusMENS\nCZ9D/dB33xsOHVTkPtaLue1KNSSWPhcbXvYK1BMdi738B0CHlM5YbPhmLCeS1AxqsnS5vAuJ9fJ/\nB4kZYcOGNLehWzpgc/AEG+Ykg63fRGI3PbDlRJRYT/zbAn0hDtwciMQ0aTuRmPoaNLNdopohKVNs\nhiCowNwbTCoSPkdi0DNYBLpHBGlJedxkpuhdZUo3LEthYSEidsuWWU+dmjpgQO6ZM6U7MW7ciYiI\niMjgabUt9cHPAEXvKlOWKbcKlcpEpXLYvz/n0KHUgQM1mZklXYE17kRERERE/1P7v4r+FV7j/iRG\nKpV5586ud+5Yf/ZZSU+MG3ciIiIiIkjZN+4iojAxUZiZWU+c6JqSonwPHjLAUhkiIiIiqqi03qha\nllKZxyisrBQidkFBmmxoTKdw4/4UYNepSxjUwxo/ClpN1NBAyURoLQnzgKa6jsFWuz4Wy4kEYM1Y\nx05Bq922hWKp06FYV/CzwCYsJmA9rFIJ6uxL7QU1HYqIavN2JHb6JNT+21aTg8TckJDIHizWEut1\nPqCBrpAKhQKJaSKskJiIKGtnIbHkVVDXqe1KaE7kJos6SKwz9qQbDTVOy7uXq0A5Z3T+41uxUAdw\n5G0olgdNsBVNHtQpmvcb1Hd6JhA6qO10qBEz3g/qdH4tFjqoiGiwuw5IKvZNp9JGJKXAvjcFYM/h\nwUhIRIO9/CXzMJJytQE75yUh5Xsk5nX5CBKb6BsDHpdKoSyb8oftp09XtDkVnr+EMrK3F3t7MMyN\nOxEREREZnrJ1o0Kbfu02p5YdN+5ERERE9GIBN/3lekv4UuDGnYiIiIgIovVSmRLhxv2JTF7Fcm6z\nkZRqSRQSm4AV4W1LmIXEqrh+gsRiMg4hscNwgWBIRyg2+nUotjIVGg/U6wtoPND2mtBBxXMfFDNx\nRFKDsYk5I6FDioisM6+KxP6DjXRpaN8XiZ2+3gSJ1XvlGBJLwToc5FYgkoqzgRYrSIEq10UkbTUU\ni8VG4fy+tCESG3gRqnFcVisFiXVDQiJi+QYUs4fG9IjIUTNsypU1NETsXvAmJLb/C6h4HewO+uBX\nKJbgDxWvn8iAVosHXxEiYlEfSWmSoB6nzOFQf0h97BXxO3bN2YAV9N+bDZ1bT+gbnUDPpEKmnkgq\n4yuoeL0N1EMk4VCKyhf45jo37kREREREWlaiQpdi21W1NTlVW7hxJyIiIqIK6EmF7MVu6Lt0KT75\n2CJ8x11ERDIjN6/67nhMiodP6w9GdHYr7rySI4/98N3uqyni06xj/25NVM/9HImIiIjI0OG3oym6\nxdftO+76MTk18+LkdoNW/RLr81qV41sXdO+7PL5IJDlseZdB07emqHyqyNal0zsN2piqgxMlIiIi\noheXBv5VHvTiHfeLvywOE+8lIesaqGREa5/mAxasP9Z9WpNH572ojwRtFd9Jh1d0NhF5v/XGdqPX\nHYvq1dlLWX5n5XAYmmL1s7EFEmsNdZPKJKzHbgLWdboXWkzyjkFdp82x3kQRSWgEtSdexVZTfwt1\nnY7CVvsK6ybcPgyaIYUNfZKgpGVQ7l4Ytp68jE0H+xzrADvQB4rNw7pOo7HW5HshUAzsKDuK9f/V\nCQAPKtjwIvHGYi7gUV0+x3LQzLQZ2FoLBkPPJfsdA7D1JARrxXuvM/SVdb4E9ey9exfqnby/eyUS\nM2kPfcO16Ai1TjZLRlKieBkbqyQS5wp1k7uHV0Ni+behIWJ/Yc2pr2EX2Mh8bEhkAnSJXf0J1Ibr\ncckdOqjInbeh67/j0StILHwpeFjSO0+qj2eNe+bpXZF2nec3UImImHi1Huu9YOPJKPnXxt2k0Zj5\nS2xrFJ6u0sFBRMxM9OHkiYiIiEiXyuNu64W9qs9hcmqJ6Mve18r54Vx7ZY0A77Tt4amT/B+pYjep\n5Otf6cHvU4M/WyDSuW4lfTl5IiIiItKVwpp17W7fi+1VFW7cCzlbW2JB9cE5/dZF2n22bZxbkb87\nd+6cFk+pnm8NLa5GREREVCztbmBEpF69etpd0CDgLae4oneVYamMSJYk3Ul/+Kf0pATxdCy2ev3M\n6lGf7UkbtCLk7eLuO6Pdp2n+Qah+8c3q0GqWE6BKzfxbdZBYAFZuWONyFShX9TCSGoUN/RGRlVEd\nkNhBL2jMiQoq6BX1tcZIbF01aGbKqUg/JKYw80Ri8VWhz2EiVqgtIt/FfYzElO5zkZjdrFZIbMqm\nzUhM7h1HUrF27yAxYyfomD0z/0Bi3tZQ6aqIoP0cGOhVLSLZfyIpaOiLSFDiQiSW9tFEJKbJOood\nVr7DYkP/Dyvpzj4DxRw/RFLmA6AS7MSXoIu/1SAkJbYzsdlV+ejr3/0W1DK1BWuF6pmbBB31MjRa\nKTLjNyQWhfWGNUNCIifMsFyl77GcOCyH+r40N6H2oB7eZ5HYNo1ud4NUArp9x10f7iqjdKsnsYfO\nZT74463/+yXN+40aRTfuF7dPHhccOWhJSKAvbwVJRERERM8b7ypj8la3fjJ63czNtad19jyxauIR\nkektfEREfWtvtz6zm87ePKlJpai9C4YvDRPfQfUsYs6c+Ts3V9xq1PVS6cP5ExEREZFeK48G1udP\nLza+1r7DVoz9Z/TScZ1WiYj0mL25bWElTHZmmkh6ap5IZtjuX0RELqwbPfzBv+qxJOTDBnzrnYiI\niIj+pRTb9MLbyDyGd5Upnm+3L/a3TM7MExOlSmX94KyU3t1CQ7sV/r7PilDsZtNERERE9EIrVaNq\nMXv9os2p3Lg/oFQ5leM4pZIzaQnF7mMNe1ssoP40qJtMBOtNFXkV6uvSnHNGYivBVleR9M+hrtON\n2GrbsZhGA8192XYDasQUq6ZQ7O5aJJWNdZ0Fp+2CciK5B6HOzkVYl7C4fYmkJphCzxPw6xWDdWIN\nVkBtgkFp0OswMqY3EhMRUf+FpNK/hN7Ryb+NHdQaetYtSvZHYgWXhz87JGK3BBsOZx8IxURCb0At\ntpITDcVswIFeWEu0wwdITIU9JJbDv4FyCVOhmCv0MhQRzQ3oK4t19UtPrP03oUUuEpuQDLV1boS+\nRci5eVDM4VAaEgswsYOWg7vJV16BruzbwOsw6YFi9/pF37nnXWWIiIiIiAwA33EnIiIiIip3ZW9R\n5TvuREREREQlo60O1Kco2pzKjTsRERERUcmUqAO1cJffpUvJDsHJqQYj7wAUS2wPxY5hBz2NdbHM\nxGaO/oV1E95HQiLvOIETG8X1721IrJtLdyQ2cB72Gol8DUmljtmPxFRzoV63CbWzkBjUOSWydCHU\ncioizb6AYqMFep4MiIMeukUp0NzB0fZ9kVj2QqjrdOlkJCWSvhtJXa6BPodTsZi/+goSS+kEDVhW\nYa2TqRftkdiAACQlqydD8zWtRoRBy4kkvgW9xFyuHoKWw7pOwSeAB9gmaNMJSdVxgppEf8Pmerpn\ntIZyInn/QLGEDOgRTv8Qaie1XwYddD5297e861AsGvvGed8S6jr9HVpMRCQUmybbEvsOe+BWM/jI\nVL5KdZ+ZYt7UZ407EREREZEB4MadiIiIiEjHkKJ5lsoQEREREZVe2W8XI8W1rurb5FSFBhuDov8C\nAgJCQ0O1uSI2gUXioeEaUVhVuhc25EiJlepOQ0Ii67BYzFW0OGyaD/TiAedtjawLxZz2QzWY3s7Q\nWJIIbK5WxlIoVoAVuf8E1UuLiAzFys1FBRWcqpdD5ebKAVDrQkp3qHUhHyoilXbnodjpu+uRWBRW\nRC4iXuAjbGSNpFK6Qd0L9r9ixcsFmVAsqg2SOotdTNDhQCLbrlRDYvn/XENid0dDB1ViA9NspkET\nuHIO/YDEMrD5S3YzoFggPKUnOEmb1zqwAycBHMBnCw3Mylq6EolZfZkNHTT7LJLSxH4IrSaicIG+\nrWctgq51OSegg9rvqyCbMS0KCAhYurT0+zqt7OMLPVocX7++AhsgJiLSQUTr22y+405EREREFUrp\nWlGL4uRUIiIiIiKDxOZUIiIiIiIDwI07EREREVEpabGi/ZlYKqOnulvU0eJq2641hnJ5cUgKmpcj\nMroJFNuODblYhrWcisicxIVQLmk2FNPkIKlNWCdWZNZpaDWrhkhsQNzHSEyRsgmJ9f8V+uqLSOZn\n0JCjj5ZCsdexg/a5DnViqY9Cq7n/BVUfuoHPurgJSMoL++qLiBiZI6mZ2FVimBN0zPxjLyOxe1CT\nsPRZDcW+D4RipzZCMRFJeAvqOnW9sQeJOe+ahMS6Y8+TDUqs63QVkpKF0JVJpk6HYsHwk7M7dnWK\nxBqsf8YmpnljTcyR4WuQmEAvL4kytkBiIdBiMuaSOxaULSroWgfqCfb1VlzJkcd++G731RTxadax\nf7cmqod/kRm5edV3x2NSPHxafzCis1sJN6Tlt1N/7MYy+nZXGW7ciYiIiEj7ksOWd5m8VXzb9aiS\nunXp9K17BoWsC1SJSObFye2Gh4l3j37VfwtesOf3mG1bPnQrycra6j0tzr9+JIiIiHjsWHzHnYiI\niIgqGPWRoK3iO+nwis4mIu+33thu9LpjUb06eykv/rI4TLyXhKxroJIRrX2aD1iw/lj3aU1KtHUv\nL49t0/XtrjJGOj06EREREVVIJo3GzF8yoUnhm8RKBwcRMTMxEck8vSvSrvPgBioREROv1mO95fjJ\nKF2eaUlo4F/lge+4ExEREZHWmVTy9a/04PepwZ8tEOlct9KDnaeVs+1/Y8oaAd5p28NTJ/mrillE\nC7RbEM8adz21LRVrATN2RFIBNi2QWCg2Em+IQI2YDruh9q9I23ZIbEy4KRITEUn8DxRzW4Sk5jkN\nR2JYI67kHcG6TiP9kJg6eC4S+wLqr5M5t4ZAOZGkJmuRWFD0e9BylaAmtmlYo9gcbHAmKCQHGiZ6\n2Axq6+wl0FdfRH7EYh9vgGLp2ITdZi2hWACUkm9bQTHbVeFILOYbM+ywklSjOpTLv4ukstZB326X\n20DHjMS+EH7YpMNF+enQctew+xxg/dAi8h30XULk/lUk9YYHtNjEWCg2r04uEjsDLSbb8qHJqR+M\nhy5N90PR7v+eN6AXT8Oq+5HY61hfr1cFH5yqPjin37pIu8+2jXtYDeNsbfnMf7Z3bzEX2bZt3y/8\nTam344+1nz4dm1OJiIiI6EVxZvWoz/akDVoR8vbDe8dkSdKd//3om56UIJ6OyiL/8OEevVhl6E8t\nwY6fzalERERE9EK4uH3yuODIQUtCAn0fFsIo3epJ7KFzmcN8rUVEbv3fL2neI2oU3biXkxLt+Nmc\nSkREREQVX9TeBcOXhonvoHoWMWfOnAkLOxOVmidi8la3fhK7bubmM6mZyXsXTDwi0r2Fj65PFsXm\nVH1l1QxJqVc5I7HQdKjc/P4PULn5UKwEs4pCgcTUGYeQ2BqsTF9EhmDTcBQpG5EYVEUoMikUijlg\nBcKpVzKQmEUnqJ57/PfQSJpNlaDKdREZmJsE5TL3QbHss0gK+hxEUj6Ego2wr2tkahgSa35zIBK7\nUAuahCUiFp2gWN5tKOZ8HordwRo1wHOzeP8bJJY+AqrAth2PtrhYYrNrtmBzf3omzEJiGas+QWL1\nsN6V7tiVE6xJD1ZfgXI56C01lJ2h3pWM/8xEYipsDp750woW/mcyOJPOujUS+xnrqwG9m7YLjd6H\nLmKnL0K1+nH1UtDjVkCZYbt/ERG5sG70f7vVeiwJ+bCBytp32Iqx/4xeOq7TKhGRHrM3ty3pBCat\nKlG5PEtliIiIiKiCse6zIrTPE/7Ot9sX+1smZ+aJiVKlsi7rdrSM9415Srsqm1OJiIiI6EWnVDlp\nq679KWXryJ6+S5cn/lXR5lRu3ImIiIiItK8MN58R0b/mVG7ciYiIiIgg3LjrqS2mUNdpz9ujoOU0\nOUjq4jBosd+HQb1TlyZDq8n9SCQ1FOsSE5EwV6hRzD/rNBIbgg3NMWoM9WvGLYC+rBq7rkjsW3do\nANN2JCTy46dYTqTgBPRZNMU6cUOgz1XsoJTMwrpOT2CNmKmDoFZH1Xqo7cwtfjR0VBHJT0ZSmvjp\nSOzQJ1D7b+NjSEoifxyPxDS3ocllb2+EDnoaOqaIyD1scl3PmN5Qzj4QSVl2ha45Rt7QF0KD9TrH\nvQr1OldRQhOpYu6uR2IiMthzJxILwsb5ZS2AxvlBY7pE0idAl8TBm6HYtmzosBkfQQ3WmoTPkZiI\nKFRPqsr+N5vOSMo9Bb7skJ55Uo0NN+5ERERERE9UxvbTUijsWGVzKhERERER6vnv2p+C77gTERER\nERWvjA2mpRUhvKsMEREREZGeK9yvF32znxt3PQW24kneXSSVe/AdJAZ2kw7BYovmQ7EZ42ORGDiH\nT0RqYy22kvgZkmpqg62WAjWK/TYJWuzdDwOR2MCYGCQ2rMoPSMyqLzSHVURSp0OD/UKx7jTJ/A1J\njdzxKxLrBR1S7LBGzBmZfyCxnF/eRGL3NkMHFZGJu6FYUOJCJFbTCeqJjPwdegKc9ViMxGpjn+zp\nG62QWMOq4AhjOY2N9U1whxqs+yZDr52fxyIp0VyF3rSrUxm6mEAPnEgM9ghLBvoIX8BiLZ2hrtMf\n60KrhWAHDcGedZHJ0Fhf9Vqo69RmhTafciLiGtERid3tBz1PPI5BMTU2EJ2IG3ciIiIiqlDKryye\n77gTEREREZWAtrbmhXePeZKid5VhcyoRERERUQlor2P1aT8AFG1O5cZdT7WN6oDE1mDly32x2Tr7\nQqGYkVtjJJbU6QQSi68GFa+3zkBSIiLhF+2RWEMvqGb6ADYKQ5wnIKl349OR2GVsZoo/EhJRYw+I\nvLQaW0/sZrRAYpmzoCLX7F+gg/qlQpN1NqmgkUmN4z5GYumjoeJ127VQkeu4PmiR61dYzXSUy0Qk\n5oytlrUeal24CS0mfh33ILHMT9shMbAUXkQk0htJ2U6BFruGNaVYj4DaA9I+h96fCweHQ9lAj0mU\nwwdIzAuecPcbNrzMYQ30mNw/Cj3rfLDOpY2NoFiAa5ZMkgAAIABJREFUEzQd7OAGaDXJ3IekjkBD\n1UREEtyg74mp2Gpx8EuHdOXpPwCwOZWIiIiIyCBx46410dHRWlzNU4trERERET2BdjcwIuLp6and\nBSuqUhTKs1RGa7T8NI3W5mJERERExeI+W4tKtBd/emeqsDmViIiIiKicPLNp9dGdfZcuz1iNzakG\nYx7WOvkB1MQoVp9lI7EqxhZILOa6GRJzPn0IiWmwzp6Z7nORmIiI+yIk9asH1LNltxSaIqQ5rUBi\na19HUjLkFBS72h6KqQ+mIDFlN2zqj0i0LxSzxp6cBWDbcSrUh10FW0zhBDUT24yEnnXJDaGu09FI\nSERECtKgmFf0e1DOqhkUU0E9kR2bQp9schOo6/TV80hKGixFxwNhj5xMx2IXoEk4Ip7QgKClm6GX\nxF+boaf6tsT6SMwO6yWd5voJlBMZj41MylwFdZ1adoNWC8Gmud1bDTXEh7wMHdSsvikSG2zfF4kF\npXwPHVXEG1vwwgJoNYsxN8Dj0nNTotvRsDmViIiIiMgg8R13IiIiIiIdQ+rj+Y47ERERERFEWzNT\niyraq1q0OZUbdz3VH4s5H4WmZiTVworXbw1BYuqf1iIxs3rQmJ6mAUhKwFpTEfHGBo7swlZzMbZG\nYtlQob4M1UD/x5XaDaqYv41N9HDtCE3gCPBYDC0n8i0WM4fmdMnnG6HYwsSdSMyyK7RanBVUqO1+\nHXpFHDwPvSJ6wjNuRPkakrrb6R0k5hBsg8Q0UVB9sEn9GUjM6dRQJJZyZwUSyz2MtrhkY50aGmgS\nmqiCoE82pRNUvD4De/lHKaCXvyYOGr/lcBQ66Jys40hMRPpZQ1PJXsO6F3oshWJel75EYsuxQn1P\nKCU9v4UekzkeDZFY7mGocl1EIi9jrTreV5DUnTeg7/6OJ3Rbf2FgHitS1+I+vmivatHmVN3ixp2I\niIiIDFW5bqzZnEpEREREZJDYnEpEREREZAC4cSciIiIiKl9aqYZnqYye8rjkjsQ0MVAzTnuoiUVO\nm0E9MUZO0GruWNdpQj40HCqxEtRhIyJT9kJTM8A5TYexVtfmaVCz6xas7awVNjMFmnAj0q0qNLzm\naCi2nEgQ9pUd+nE1JOazERrUonCH+kRVC2ORmLETNOBMnD5EUj2vHMFWg6Y+CTwK7WwjbLk86DG5\ntx1arN78mUjsZCsoNgkbrPT1ZigmIt9gySnYC1ZhB7X/amLHIzH1cujl73V3PRJLHw9dmu5sxA6K\nfccRkWBsFlKUM9Tr7JW4EDpq0mwk9TG0lmzEYpIM9eub1oIWM20IXcFERGyxezHkQFfOxJPQYo5Q\nikqsdDv1R+8tw7vKEBERERGVu9L2rf5vu1/0rjIslSEiIiIi0guP7tSLvmfPjTsRERERkQHgxp2I\niIiIqNyVvT+VNe56akvNOCT2DtQmJFAPi4jYByIps/e6IbGEG1Bjn9xojqQU5tBiIpLUIxeJWbSB\neqd8sT5RiYEek5ZYN6EVNmLv/Ako5n4LGtiZtQibOigyVA01O+f/Do2T7A7NdZWAStB00v9v7/7j\noi7TvYF/yBFHHQULjZ1ildozZmyhKz0r+wiRriYew+wgFqGyUQpPkEuP0iZlVIvnBLuxKC5hjppL\nlGhayAppGjlueHJKsSZzOoZEjZhjDTjqIGOcPxBE8ccFDI7f8fN++QczXvP93gMzX665ua77lh0M\nSaKh4WTA3ZKw5kbR0eYflzZY18haJ09vELVOIkDUsPlJlmg32c8LROcU2iRrTl0eID1gsDDuB9FO\nnEe0sqPdINpfWf2IrK1T1hAv2nIW+Kuw+7PZITse6m4TXTllZ8WLE0X7v/p98p0kLBq3SsJm75Mt\nYKAWtZ0O2rBdEnZ6vWgrcQDek0WrRKhlndMnxasOUPd1Kilv34F6GR2bUznjTkRERETULZ1sRRVl\n+R2bUznjTkRERER09QizfDanEhEREREpEhP3a9QM2Y4euHGOJOrYnbKSXouoeBE3JYvCbl4siWr+\nn3skYf0TROcE8JWsVDtYViJ6e64o7MCLosL6RNleGJ/Iwmq+f1IS1vCM6Dvy83HRSQHAy1sS1Wus\nqBTe95exkrAs3WeSsBH/IYmSdhEI90xZKKu/L6yV7sBiXySqXn1G9uJcFi/aMu2+A7I/8squOTi+\nSRJVD1GR+17Znl8A0mRhk/r/ThI25F+ifWlOvCrabepEoSQKgU1HJWGLZot6EpYMERWRP5EtiQKA\nFNmF4i7Z0fzKF4niakVXiXWHHpKE2XM2SML6RYuunDeMEf28bhgiiQKA09tE521YJTpanuy985R7\nk8HrWBd6VZm4ExERERFdTvcXhOnoii2q3DmViIiIiKhzrliV3oXMftq0KwRw51QiIiIiIhfr5Koy\nIh0/DHDGnYiIiIhIATjjfo0q174qCZv07TFJ2PF80UmXvCNq2fnTKlHYj7JO1yGbRWHqe0VhALQD\nRGE/yjps6w7/SRLm1XxaElb8nxWSsOYjokZM31uWScJssl638t6iXjcAs/vcJgn7VrZTzy/nisKO\nnDklCbunl2iTo82ibzB6ybpORZtvARjyrDBQk1QhCVs2xyo63FfDRGF3HBKFWWTvnN6ikzpkm3kd\nu1fWXg/s0/eXhNUNF11gb/70N5Kw/nNF+7TZV+6QhAm3c5oi2lYLG2Tfub4PjhHFAUvSRRu/Cfd9\nsy8WNWL2k73HPpI1Yt7X+I0o7phsBQP7FkmUcJs2AKtFjbjS9t8a6WlJMTjjTkRERETkGj3RxtqG\nM+5ERERERN3lwpS9ZcGZjqvKMHEnIiIiIuoul/anfoGLrSrDUhkiIiIiomtIS77ecQqfM+7XKFlj\nJ4yL3pCE+b78C0nY83mzJWEnc/5LEubfIHp1PenlJQl7UxIEALB9KXqya+88LAmbYXtLdFbNREmU\nsIe1/hXROb8OFIUtkXWdPnV8u+hwwJFa2b6jQ14QHS1e9K2DbIfdr0XHwuB/il4k+NVuSdSA0lsl\nYQtlTb0Qv9q/lrUnej/wjijuG1ED+JLbRR2WNtEpseinCEnYD7K9hAH0euGEJEx4dYJ1qSSqcYPo\ngj1IuDup9a+SqM9lBxu8X/ZMD+hkx8Mv6veJ4mRNzInC3X9FDfb4v7J3BByyb54wTHah6/2A7DkA\nz0N0xT5i/5ckLPTH5cLzklIwcSciIiIi6kGuKn9nqQwRERER0Xn27v2w/c2RI++74kO6lp239KFe\nFJtTiYiIiIiuQJKpX6CrzamXTPc7Nqe6N3H3am527wBcJiwszGAwuPCAalnld7W36GhbRJXVeGSV\nKGz8H0RhstpF/GZfb0lYw6tNsuPBtFoUNqZKFHY0UhTWO0gUNmizqMyxeY+oxrFRtBEKHpc1TDwt\nigKA35wQVX6jl2wrrDPHRWE/ZEiiXgn8pyQsRnRKaGRdBIO/Eu3n0iwuNvUaFC+Ka7KIwk5USKI+\n9H9JEjZGVqhdvEAUdkAUhadHyuIAn0WisKKHRGGzbeskYc2nPpWEefn/p+iswi2uflotibrn16Ki\n/+GiUwLiC8XDsrC3ZWHD00RhTQdFYeGypg+j7Bei98QnJWGnikT75ck1fiQK85btrNUv3UOSMRcK\nCwvLzXVlXtc1FyTuo0d7yZrMAOBNwOVpNmfciYiIiIhEWCpDRERERHT1dLlXlYk7EREREVGndTn/\nvkxDansdm1O5qgwRERERUadJulEvmtxPmyY6PndOVQxH7ROSsDMHXpeExTaITurVRxQm65xCvCys\n/m5R12nNKdmuH0DoUqsk7Ojd4yRhQ2pku2bI9mm6R7YX0u5GUbOjuv9oSVjub3+ShE0S73Gzu170\nZL20r0rC/p/spMuq/10SJuw6HfQfojDf7AmSsKZ/inZWelXWEAkgdZVojzNhd/JJWSvefbI2cdz+\nsSTqkVtEG2ZZY0Xn9NtRJooDfpwiaieX9Tqi1He6JGyF7FncXST6sQ4RHQy7ZY2zWyaInoKwhRFA\n/6eWSMIOVIua4psbRSedGCYK++DMKUnYPtkWV8cXPScJ8370WUlY3ymlkjAAuFH02199r2h4zu+k\np6We09WlZgDunNod1qry5W+WWk4OCpk2M368dJM5IiIiIiKXYOIuYjUWTEstRHBkjPagPiPBeCQv\nLzbY3YMiIiIiIo9y+bp5lspIWN/KKETovK1Z0WogXJucnL+kKkofrHH3uIiIiIjIfbrcn3op7ftW\nOzanupdCEnf7oZ31SJg9SQ0ACI6O99Gn7jpoCw72dfPAiIiIiMh92krYXZXBt+9bZXNqV9hrvrBA\nO2po6wS7auCwq3DW06ItBXsFhovCbhPtsWb/o2i71vGyXQw37RWF/ePwn0Rxp4yiMAA+j0iinN/L\njnZ8kyhM1k60uzlFEvaAbN/cd2Qb+xlkXaeffCIKA/ChrOu0WdZgfTRc1GA9VLYlao1wV9cjCyVR\n99y2VRK2W7Y13TMN5ZIwAEsGijoskzaIjvZYgShs3b+Jtv9EnagVz/6a6GDrRVFQy74hAOY46yVh\nB2b4SMJ8c2ZLwpb88g1JmGzxN9wl+7H6yhpnraL3Dez5ojAAusGirlOzrMP+5POixu4y2ffk7l59\nJWGVst7kAS+KtuFd7n2rJOwJ2V7dAJJvFnWdCi37RtRhTy7RnSbUS+n4YYCJu4Dz/L539cChwOkO\nUStXrnThOR+LcOHBiIiIiC7OtQkMgMcee8y1B6Q2bE69MvXNQwGL1eGERgUAjiNGoONSgi5+mX4j\nmsIhIiIi6g7m2QrCxP3KVH63BQPvf1w7PioQgONbkwX4xUC1u8dFRERERNcKlzeqduTeUhmvZllt\nqNvtWDwlvaz/gtdyx9303SvTUyt84jaVzm3fmhoWFmYwGHp0DCtXrvS8z8SHDh0aNmyYu0fhYnv2\n7Bk1apS7R+FinveT4o9JKfiTUgrP+0l55I/JI3OJnhAWFpabe4W8zoVp+sZLNMFcsKrM6NFek8TH\nLAdcnmYrY8YdQHjaigTL9OzE6dkAEJqzOoELyhARERFdt1zajXrxzwAdV5VhqYyMyj8+z/Cg1ep0\nQuPvxyoZIiIiInKJS30G4Koy3eLr5+fuIRARERHRdYqJOxERERHRVdKd4niWyhARERERdYs8Hb9U\nK2pHFzSngok7EREREVE3daZXVZrid2xOZakMEREREdFVIk/xr8LC8J3CxJ2IiIiISIQz7kRERERE\nbiOfWWfiTkRERETUFS6pZhHunAo2pxIRERERdc1FC9Y7m81Pm3bx+7lzKhERERFRD+rMCjOXw51T\niYiIiIgUiTPuREREREQKwMSdiIiIiKiLruZq6yyVISIiIiLqnB7K19uvMNNxVRkm7kREREREneOq\nDtQOzn0e6LiqjHsxcSciIiIiOqt9ps5VZYiIiIiIFInNqS4TFhbW06dYtWpVT5+CiIiIPFhP5xIG\ng6FHj69ELqyGZ+LuGnyZEhEREV1vupyUt29CvRQ2pxIRERERuUY3mkevnPF3bE5l4q4IDuP614sr\nDmDQ8GmP/SE0UOPu8biK3Vhefgj/NmVSsNrdQ3EJq3nHW/8oPfAThkdMmRkd7uvu8biCw7Rtw3vv\n77WgT8i46Ic95ScF4OzLzz5o7IPj/RV/KXKatm3efwLeAIDTp0/fMnqSJ1woHHUlb6zc8rllkPau\nyY/MCA1U+lvKadq2ef9P8PZuveM0ho6dGOyv9HeVs7py81sbd1pOYnjElEceDPdT/BsKcNSWv/32\ndmNNH+1dE6JnhOuU/dqrM+3Yvr9p3LlrnacmFQojyfg7TuezVOba59y2ODqjrD40Jq7PzsK0WcXp\na7ZOClT6hR5OmylnbmKJBfCJ+71HpIPWyqXT0ooRHBkz1Facm15clrBJH6/siz1gXDo7tdiii4gZ\n2feAPjN5/WcZ7y4c7xnv29ryJamZZYB22KTx/or/tWXdnJFdAh+tD04AqK8fljAiNDDI3aPqHod5\n8YSEMvhExtxve78wraww4bWy+CBF/6gc+zcuzz0EHwDo3x8WSz1ihoUrPXE3Ff0xMb9KGxoVObxB\nn5tevC5h49p4P3ePqlscprQJiZXwiYy5/9TOwvSywoS8TfHBCr2c23YUPJdeWAVodWevdZ6ZVFw/\nmLhf65y1H2SU1UdlFC0YH4CUqUunTM/M3/L7rChlf+8cprkPJJqDo+J0HxWa+yj7uZzlqFhRjOAF\nH+ZFqYA/TFwdmazfUf1wlKKvhs7q1cUWXZJeH6sDMOXXz8/KXr73qfEhis6dWtgq52eW+Wh96i2a\n3u4eiwvYjxiBeWvejQ70iDcTAMC8YUkZdFkb9aF+QMrMghkP6N/eGffyJCU/Q010Xml06w2bcekD\nqe/feavS30723e9VISJj7cvjAUSHF0Qmlx2yx/sp+WmZNrxaee6190T58xMyk/8+3rAwwN0D6wJT\nQVx6Yf+oSF1JGVqudZ6ZVChWF+rjmbhf674xlAIR0eNbrhj+01MjizPeM9mjgpV8WYRqYGRSxrLY\n8d8WfVW4x92DcQ3Vb5/Kyhk4ouU1rb7xRgDeKoW/wlUB/7Vxo8qvbe6sERg8UMmfRFo5ShanWXRJ\na57BrIT33D0YV1AB0A0bYK81ff8j+g0dHuir8JceYP/4vSptXF6or7XKeAh9b5q91jDX3WNyqbrX\nU4sRleUBdVo4ce5LZ9Pp824r1Gk7tFNHnb3yqcdMjUHFzh/tCFDgr93eQ+NzNkWP/KmopOzstc4z\nkwq36s6KMVfsT2VzqvI0nT4Kbcjg1pu+/lqgqsmdI3IFVUB0bACAptN2dw/FVVQBwaGt8zG2woxs\nIGpkgNJf4SqNn5+zzri6/IvGo18WllRGLNDrlP6cAGvl69mVSF8XG9Cw2t1jcQ3Ht19bYE6d9kDr\nHbrMomXhAYr+jOUEYClMDyusb70nIm/Ty0qtVuigbsfKEvhk/iHU3QPpPk30K/P0iRlTkivu1TaW\nlFXq4nJGKj4F1MCy96AjNkgNAPsr9wKWmqOOYI3y3lO6SdEA7N+fbrvHM5MKt7piqfplMvtp065w\n8I7NqZxxV4L28xkAAE/4477HcmxbHKc3+2SsS/V391BcwtlUbywzQHMEgPnAV1bolF296jRnpRXr\nEvIm+cPRAMATrkPOJjuAmHR90iSd01r1anxyesaGrfpY5WUZbRzff2kBgKScdbEh/vbaHS/Fpicv\nLv8wS9GlMm3qVqaX+URlhSv7vdTCeXC/ueWrplMAYD74xWFHiKI/NgZNma3VpydOSE6YF3G8al1x\nhQXA4QYHoORn1R6TiqurG8vOXHM7p97g1rMrg7MRQKPz/Nv8cHzNMhY8mVFWn5BX6Al/AQcAqAPG\n563V5+lLy/TzLCXZ71TZ3D2ibjHqX6kEpt5zY21t3YF9B4ETNQeqbQ7nlR95DdMExRsMhpRJOhWg\n9guekxYJ87YDiv5rlnroSB0QkRwb4g9AExA+O0GLL2sU/Zza1G1bWQafNE+Ybgdsu9NzyyIzikrz\nXl74cpZhY5a2Ur+8otbdw+oev/C1m16Li0DZ6tUH+k7NzIgBtKNv95A/9zCpULqfxf96AhP3K7vl\n7rGo37TPevbm51s2ASGKb2fyUKb1aamF5oQc5a4/cD5b1fMJzxtbE3XNbXfrgNOXfcQ1z/bpJjOA\n7MTY2NjpybkVQH128qzXv1B2Qmgzrp6RUFDXevPUyVNAX6V3WACAxe5o/dJ53J0DcanalRllPlHp\nHjHdDnvNvnrgN0GtdYJ+I8b6YM/XR9w6qO6ym3es3nxs9st5a0tL8xbGBjbVAkNv8pTZdiYVivBF\nK3cP5EIe8Iulx/ndc38oitOfKdC/MqP31++mldQHJ031jBqMVo3uHoBrVJdnJ+ZWIjhhVN8ao/F/\nmprgP2KkspsENQMbzRWpzxW89tyMoaqftq96xQxMv1HRF3jfhPVbH2259KhU9r36aanbs9a9Earw\n9fg0N99oMWf/eemvnpv526aa7RmZFYhIH67w5xT1VEJ+cm5m0ZAnJ9957NN3kost2pjRHvCBuLZ8\neRl8sp7wiOl2QDN8rA6FmX8uCHhuxlC147N3lxXXIy70V+4eV7eo8J0+P99wLD3jkZCGqncSMyt1\nCXkKX7Hp3O/Z6yCpcAOXZ9htTasdm1PdW+Pu1dzs3gEog726fN6szJYqQm1kun7hJEWnTu2ZixIS\n3htftjZW+c/IXpQcmV913l0xOZtSQpSdaVir1j+TnHu2ghU+cRlL544PdOuIXMlhWj0h0ZBXplf+\ncgrOqvU5ybklZ28Fx635y1xFr0Taomr94uTcspavtZELChZGKfvtBAB12WHTjTFZa1M8JHEHYDOV\n/P/E7NarBCKSctJjQ5T+6mv/2tNFpecuUPavXUd1yYRZb+aVrW251nlwUtEJdnNR/j8+rvlJO3zi\nY0lRHYtbw8LCcnMNnTpkT0yQX1AfP3q0163ix34HuDzNZuIu5rRbbQ6o1H6+1+P7i9zKabNaHVD5\n+vqplT3n5Okcdqvd464SZ5+Uxs9X6amgZ3ParDYnoNb4KXDllUvw7NfedZ5U2E1pkYmV0MXE3fF+\nYUm9Nmbd2pQL/uzQhcS9J1ywq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+      "text/plain": [
+       ""
+      ]
+     },
+     "execution_count": 17,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "# Expected labels:\n",
+    "#     X: Gate ch2 (V), Y: Gate ch1 (μV), Z: Gate voltage (MV)\n",
+    "# Check pan/autozoom\n",
+    "dmm.voltage.get = lambda: 1e6*random.randint(0, 100)\n",
+    "plot, data = do2d(dac.ch1, 0, 10e-7, 100, 0.1,\n",
+    "                  dac.ch2, 0, 10, 55, 0.01,\n",
+    "                  dmm.voltage)\n",
+    "dmm.voltage.get = lambda: random.randint(0, 100)\n",
+    "plot"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## `do1dDiagonal`"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 18,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Started at 2017-06-28 13:43:15\n",
+      "DataSet:\n",
+      "   location = '/Users/william/sourcecodes/qdev-dk/docs/examples/PlotTesting/plottestsample/032'\n",
+      "      |   |  | \n",
+      "   Setpoint | dac_ch1_set | ch1          | (50,)\n",
+      "   Measured | dmm_voltage | voltage      | (50,)\n",
+      "   Measured | dac_ch2     | ch2          | (50,)\n",
+      "Finished at 2017-06-28 13:43:16\n"
+     ]
+    },
+    {
+     "data": {
+      "image/png": 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CnSAIgiCIellIFQBs6gyM9ARlRR2dTVX8FcJ+EtmScKeKe0NCwp0gCIIgiHpZSOcBdIe9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\njM5wnQUJEu4EwQ/ZooIrpy8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C9AXYJdyZwX1VZyoj7LfP73BWv8XnrukC0mZIuFcgRB53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\nhYmNtYtET5u30+9if0bG3WwWRmXY7kvIuAMAABimoSq/ZiM2VsddEK433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+      "text/plain": [
+       ""
+      ]
+     },
+     "execution_count": 18,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "plot, data = do1dDiagonal(dac.ch1, dac.ch2, 0, 5, 50, 0.01,\n",
+    "                          0, 0.2, \n",
+    "                          dmm.voltage)\n",
+    "plot"
+   ]
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "Python 3",
+   "language": "python",
+   "name": "python3"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython3",
+   "version": "3.5.3"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 2
+}
diff --git a/qcodes/utils/wrappers.py b/qcodes/utils/wrappers.py
index e74d0cb2aef6..e945cee06954 100644
--- a/qcodes/utils/wrappers.py
+++ b/qcodes/utils/wrappers.py
@@ -1,18 +1,18 @@
-import qcodes as qc
 from os.path import abspath
 from os.path import sep
 from os import makedirs
 import os
 import logging
 from copy import deepcopy
-from collections import namedtuple
-import matplotlib.pyplot as plt
 import numpy as np
+from typing import Optional, Tuple
 
-from time import sleep
-
+import qcodes as qc
+from qcodes.loops import Loop
+from qcodes.data.data_set import DataSet
 from qcodes.plots.pyqtgraph import QtPlot
 from qcodes.plots.qcmatplotlib import MatPlot
+from qcodes.instrument.visa import VisaInstrument
 from qcodes.utils.qcodes_device_annotator import DeviceImage
 
 from IPython import get_ipython
@@ -35,7 +35,7 @@ def init(mainfolder: str, sample_name: str, station, plot_x_position=0.66,
 
     """
     if sep in sample_name:
-        raise TypeError("Use Relative names. That is wihtout {}".format(sep))
+        raise TypeError("Use Relative names. That is without {}".format(sep))
     # always remove trailing sep in the main folder
     if mainfolder[-1] == sep:
         mainfolder = mainfolder[:-1]
@@ -103,7 +103,7 @@ def _select_plottables(tasks):
     A task is here understood to be anything that the qc.Loop 'each' can eat.
     """
     # allow passing a single task
-    if not isinstance(tasks, tuple):
+    if not hasattr(tasks, '__iter__'):
         tasks = (tasks,)
 
     # is the following check necessary AND sufficient?
@@ -248,9 +248,11 @@ def __get_plot_type(data, plot):
 def _save_individual_plots(data, inst_meas):
 
     def _create_plot(i, name, data, counter_two):
-        # Step the color on all subplots no just on plots within the same axis/subplot
+        # Step the color on all subplots no just on plots
+        # within the same axis/subplot
         # this is to match the qcodes-pyqtplot behaviour.
-        title = "{} #{:03d}".format(CURRENT_EXPERIMENT["sample_name"], data.location_provider.counter)
+        title = "{} #{:03d}".format(CURRENT_EXPERIMENT["sample_name"],
+                                    data.location_provider.counter)
         rasterized_note = " rasterized plot full data available in datafile"
         color = 'C' + str(counter_two)
         counter_two += 1
@@ -289,7 +291,7 @@ def _create_plot(i, name, data, counter_two):
 def _flush_buffers(*params):
     """
     If possible, flush the VISA buffer of the instrument of the
-    provided parameters.
+    provided parameters. The params can be instruments as well.
 
     Supposed to be called inside doNd like so:
     _flush_buffers(inst_set, *inst_meas)
@@ -304,6 +306,13 @@ def _flush_buffers(*params):
                     log.warning("Cleared visa buffer on "
                                 "{} with status code {}".format(inst.name,
                                                                 status_code))
+        elif isinstance(param, VisaInstrument):
+            inst = param
+            status_code = inst.visa_handle.clear()
+            if status_code is not None:
+                log.warning("Cleared visa buffer on "
+                            "{} with status code {}".format(inst.name,
+                                                            status_code))
 
 
 def save_device_image(sweeptparameters):
@@ -320,41 +329,36 @@ def save_device_image(sweeptparameters):
                             '{:03d}'.format(counter)), title)
 
 
-def do1d(inst_set, start, stop, num_points, delay, *inst_meas, do_plots=True):
+def _do_measurement(loop: Loop, set_params: tuple, meas_params: tuple,
+                    do_plots: Optional[bool]=True) -> Tuple[QtPlot, DataSet]:
     """
+    The function to handle all the auxiliary magic of the T10 users, e.g.
+    their plotting specifications, the device image annotation etc.
+    The input loop should just be a loop ready to run and perform the desired
+    measurement. The two params tuple are used for plotting.
 
     Args:
-        inst_set:  Instrument to sweep over
-        start:  Start of sweep
-        stop:  End of sweep
-        num_points:  Number of steps to perform
-        delay:  Delay at every step
-        *inst_meas:  any number of instrument to measure and/or tasks to
-          perform at each step of the sweep
-        do_plots: Default True: If False no plots are produced.
-            Data is still saved
-             and can be displayed with show_num.
+        loop: The QCoDeS loop object describing the actual measurement
+        set_params: tuple of tuples. Each tuple is of the form
+            (param, start, stop)
+        meas_params: tuple of parameters to measure
 
     Returns:
-        plot, data : returns the plot and the dataset
-
+        (plot, data)
     """
+    parameters = [sp[0] for sp in set_params] + list(meas_params)
+    _flush_buffers(*parameters)
 
-    # try to flush VISA buffers at the beginning of a measurement
-    _flush_buffers(inst_set, *inst_meas)
-
-    # make a variable for auto pre-scaling the plot
-    startranges = {inst_set.label: (start, stop)}
+    # startranges for _plot_setup
+    startranges = dict(zip((sp[0].label for sp in set_params),
+                           ((sp[1], sp[2]) for sp in set_params)))
 
     interrupted = False
-    loop = qc.Loop(inst_set.sweep(start,
-                                  stop,
-                                  num=num_points), delay).each(*inst_meas)
+
     data = loop.get_data_set()
 
-    plottables = _select_plottables(inst_meas)
     if do_plots:
-        plot, _ = _plot_setup(data, plottables, startranges=startranges)
+        plot, _ = _plot_setup(data, meas_params, startranges=startranges)
     else:
         plot = None
     try:
@@ -371,17 +375,17 @@ def do1d(inst_set, start, stop, num_points, delay, *inst_meas, do_plots=True):
             vBox = subplot.getViewBox()
             vBox.enableAutoRange(vBox.XYAxes)
         plot.save()
-        pdfplot, num_subplots = _plot_setup(data, plottables, useQT=False)
+        pdfplot, num_subplots = _plot_setup(data, meas_params, useQT=False)
         # pad a bit more to prevent overlap between
         # suptitle and title
         pdfplot.fig.tight_layout(pad=3)
         pdfplot.save("{}.pdf".format(plot.get_default_title()))
         pdfplot.fig.canvas.draw()
         if num_subplots > 1:
-            _save_individual_plots(data, plottables)
+            _save_individual_plots(data, meas_params)
     if CURRENT_EXPERIMENT.get('device_image'):
         log.debug('Saving device image')
-        save_device_image((inst_set,))
+        save_device_image(tuple(sp[0] for sp in set_params))
 
     # add the measurement ID to the logfile
     with open(CURRENT_EXPERIMENT['logfile'], 'a') as fid:
@@ -392,7 +396,41 @@ def do1d(inst_set, start, stop, num_points, delay, *inst_meas, do_plots=True):
     return plot, data
 
 
-def do1dDiagonal(inst_set, inst2_set, start, stop, num_points, delay, start2, slope, *inst_meas, do_plots=True):
+def do1d(inst_set, start, stop, num_points, delay, *inst_meas, do_plots=True):
+    """
+
+    Args:
+        inst_set:  Instrument to sweep over
+        start:  Start of sweep
+        stop:  End of sweep
+        num_points:  Number of steps to perform
+        delay:  Delay at every step
+        *inst_meas:  any number of instrument to measure and/or tasks to
+          perform at each step of the sweep
+        do_plots: Default True: If False no plots are produced.
+            Data is still saved
+             and can be displayed with show_num.
+
+    Returns:
+        plot, data : returns the plot and the dataset
+
+    """
+
+    loop = qc.Loop(inst_set.sweep(start,
+                                  stop,
+                                  num=num_points), delay).each(*inst_meas)
+
+    set_params = (inst_set, start, stop),
+    meas_params = _select_plottables(inst_meas)
+
+    plot, data = _do_measurement(loop, set_params, meas_params,
+                                 do_plots=do_plots)
+
+    return plot, data
+
+
+def do1dDiagonal(inst_set, inst2_set, start, stop, num_points,
+                 delay, start2, slope, *inst_meas, do_plots=True):
     """
     Perform diagonal sweep in 1 dimension, given two instruments
 
@@ -406,65 +444,32 @@ def do1dDiagonal(inst_set, inst2_set, start, stop, num_points, delay, start2, sl
         start2:  Second start point
         slope:  slope of the diagonal cut
         *inst_meas:  any number of instrument to measure
-        do_plots: Default True: If False no plots are produced. Data is still saved
-          and can be displayed with show_num.
+        do_plots: Default True: If False no plots are produced.
+            Data is still saved and can be displayed with show_num.
 
     Returns:
         plot, data : returns the plot and the dataset
 
     """
 
-    # make a variable for aute pre-scaling the plot
-    startranges = {inst_set.label: (start, stop)}
+    # (WilliamHPNielsen) If I understand do1dDiagonal correctly, the inst2_set
+    # is supposed to be varied secretly in the background
+    set_params = ((inst_set, start, stop),)
+    meas_params = _select_plottables(inst_meas)
 
-    # try to flush VISA buffers at the beginning of a measurement
-    _flush_buffers(inst_set, inst2_set, *inst_meas)
+    slope_task = qc.Task(inst2_set, (inst_set)*slope+(slope*start-start2))
 
-    interrupted = False
-    loop = qc.Loop(inst_set.sweep(start, stop, num=num_points), delay).each(
-        qc.Task(inst2_set, (inst_set) * slope + (slope * start - start2)), *inst_meas, inst2_set)
-    data = loop.get_data_set()
-    plottables = _select_plottables(inst_meas)
-    if do_plots:
-        plot, _ = _plot_setup(data, plottables, startranges=startranges)
-    else:
-        plot = None
-    try:
-        if do_plots:
-            _ = loop.with_bg_task(plot.update).run()
-        else:
-            _ = loop.run()
-    except KeyboardInterrupt:
-        print("Measurement Interrupted")
-        interrupted = True
-    if do_plots:
-        # Ensure the correct scaling before saving
-        for subplot in plot.subplots:
-            vBox = subplot.getViewBox()
-            vBox.enableAutoRange(vBox.XYAxes)
-        plot.save()
-        pdfplot, num_subplots = _plot_setup(data, plottables, useQT=False)
-        # pad a bit more to prevent overlap between
-        # suptitle and title
-        pdfplot.fig.tight_layout(pad=3)
-        pdfplot.save("{}.pdf".format(plot.get_default_title()))
-        if num_subplots > 1:
-            _save_individual_plots(data, plottables)
-        pdfplot.save("{}.pdf".format(plot.get_default_title()))
-        pdfplot.fig.canvas.draw()
-    if CURRENT_EXPERIMENT.get('device_image'):
-        save_device_image((inst_set, inst2_set))
+    loop = qc.Loop(inst_set.sweep(start, stop, num=num_points),
+                   delay).each(slope_task, *inst_meas, inst2_set)
+
+    plot, data = _do_measurement(loop, set_params, meas_params,
+                                 do_plots=do_plots)
 
-    # add the measurement ID to the logfile
-    with open(CURRENT_EXPERIMENT['logfile'], 'a') as fid:
-        print("#[QCoDeS]# Saved dataset to: {}".format(data.location),
-              file=fid)
-    if interrupted:
-        raise KeyboardInterrupt
     return plot, data
 
 
-def do2d(inst_set, start, stop, num_points, delay, inst_set2, start2, stop2, num_points2, delay2,
+def do2d(inst_set, start, stop, num_points, delay,
+         inst_set2, start2, stop2, num_points2, delay2,
          *inst_meas, do_plots=True):
     """
 
@@ -480,22 +485,14 @@ def do2d(inst_set, start, stop, num_points, delay, inst_set2, start2, stop2, num
         num_points2:  Number of steps to perform
         delay2:  Delay at every step for second instrument
         *inst_meas:
-        do_plots: Default True: If False no plots are produced. Data is still saved
-          and can be displayed with show_num.
+        do_plots: Default True: If False no plots are produced.
+            Data is still saved and can be displayed with show_num.
 
     Returns:
         plot, data : returns the plot and the dataset
 
     """
 
-    # make a variable for aute pre-scaling the plot
-    startranges = {inst_set2.label: (start2, stop2),
-                   inst_set.label: (start, stop)}
-
-    # try to flush VISA buffers at the beginning of a measurement
-    _flush_buffers(inst_set, inst_set2, *inst_meas)
-
-    interrupted = False
     for inst in inst_meas:
         if getattr(inst, "setpoints", False):
             raise ValueError("3d plotting is not supported")
@@ -509,44 +506,13 @@ def do2d(inst_set, start, stop, num_points, delay, inst_set2, start2, stop2, num
                                        num=num_points),
                         delay).each(innerloop)
 
-    data = outerloop.get_data_set()
-    plottables = _select_plottables(inst_meas)
-    if do_plots:
-        plot, _ = _plot_setup(data, plottables, startranges=startranges)
-    else:
-        plot = None
-    try:
-        if do_plots:
-            _ = outerloop.with_bg_task(plot.update).run()
-        else:
-            _ = outerloop.run()
-    except KeyboardInterrupt:
-        interrupted = True
-        print("Measurement Interrupted")
-    if do_plots:
-        # Ensure the correct scaling before saving
-        for subplot in plot.subplots:
-            vBox = subplot.getViewBox()
-            vBox.enableAutoRange(vBox.XYAxes)
-        plot.save()
-        pdfplot, num_subplots = _plot_setup(data, plottables, useQT=False)
-        # pad a bit more to prevent overlap between
-        # suptitle and title
-        pdfplot.fig.tight_layout(pad=3)
-        pdfplot.save("{}.pdf".format(plot.get_default_title()))
-        if num_subplots > 1:
-            _save_individual_plots(data, plottables)
-        pdfplot.fig.canvas.draw()
-        pdfplot.save("{}.pdf".format(plot.get_default_title()))
-    if CURRENT_EXPERIMENT.get('device_image'):
-        save_device_image((inst_set, inst_set2))
+    set_params = ((inst_set, start, stop),
+                  (inst_set2, start2, stop2))
+    meas_params = _select_plottables(inst_meas)
+
+    plot, data = _do_measurement(outerloop, set_params, meas_params,
+                                 do_plots=do_plots)
 
-    # add the measurement ID to the logfile
-    with open(CURRENT_EXPERIMENT['logfile'], 'a') as fid:
-        print("#[QCoDeS]# Saved dataset to: {}".format(data.location),
-              file=fid)
-    if interrupted:
-        raise KeyboardInterrupt
     return plot, data
 
 

From fc4ecb90caf28f061298808d0ed8c965b64df0f6 Mon Sep 17 00:00:00 2001
From: WilliamHPNielsen 
Date: Fri, 30 Jun 2017 09:47:54 +0200
Subject: [PATCH 233/328] make configreader work

---
 qcodes/utils/configreader.py | 1 +
 1 file changed, 1 insertion(+)

diff --git a/qcodes/utils/configreader.py b/qcodes/utils/configreader.py
index c4adc636e1da..e42603321cbb 100644
--- a/qcodes/utils/configreader.py
+++ b/qcodes/utils/configreader.py
@@ -1,5 +1,6 @@
 # Module containing the config file reader class
 
+from configparser import ConfigParser
 
 class Config:
     """

From 8dd125938b0b19e270101e3b241d6ff37c9da59e Mon Sep 17 00:00:00 2001
From: "William H.P. Nielsen" 
Date: Mon, 3 Jul 2017 10:03:15 +0200
Subject: [PATCH 234/328] fix multiplot label issues (#52)

* fix multiplot label issues

If a plot has several subplots, make all axes correctly detect AutoSIPrefix

* remove debug print

* fix: no more prints
---
 .../examples/T10 Plotting Test Examples.ipynb | 473 ++++++++++++++----
 qcodes/utils/wrappers.py                      |  20 +-
 2 files changed, 391 insertions(+), 102 deletions(-)

diff --git a/docs/examples/T10 Plotting Test Examples.ipynb b/docs/examples/T10 Plotting Test Examples.ipynb
index 57f362435aee..928d882fa3cd 100644
--- a/docs/examples/T10 Plotting Test Examples.ipynb	
+++ b/docs/examples/T10 Plotting Test Examples.ipynb	
@@ -139,20 +139,23 @@
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "Started at 2017-06-28 13:40:02\n",
+      "DEBUG\n",
+      "['dmm_voltage', 'dac_ch1_set'] dmm_voltage\n",
+      "Considering the units V, V\n",
+      "Started at 2017-06-30 14:24:02\n",
       "DataSet:\n",
-      "   location = '/Users/william/sourcecodes/qdev-dk/docs/examples/PlotTesting/plottestsample/019'\n",
+      "   location = '/Users/william/sourcecodes/qdev-dk/docs/examples/PlotTesting/plottestsample/084'\n",
       "      |   |  | \n",
       "   Setpoint | dac_ch1_set | ch1          | (50,)\n",
       "   Measured | dmm_voltage | voltage      | (50,)\n",
-      "Finished at 2017-06-28 13:40:04\n"
+      "Finished at 2017-06-30 14:24:04\n"
      ]
     },
     {
      "data": {
-      "image/png": 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m2+Gwi45SQ3idMoAEp8pQZfS95vVOlWnbjHsgnr4QSHic9puvWC3bpLHZWCLD\nf31FGPYcZ5Tk5MFH//mJfecCPeuv/8TvfmznGrf2B9GTj9z/z3vPBYa23Py5e+5Y03QnvhKqmjal\nTYScY8adiJrLq6emf/jL8Y9cNXjTllWGHFDUyfT7Xcs27ldOX8DE0IEqUuGWlTK0qTLtGrgfuRgG\ncMVgp9th3zjgO3EpcvJSZOfa7kafV9Nproy7Mvn8h++694FXAjuu2RF45f577/rETycVAIge/bNb\nP3//0+Nbdqzf+9i37/r037bnaP5QvIo39My4E1Fz+t0H3nhi/4UvPGzYckTD16YC8Lq4gImqIDLu\n9U+VaduREkfGQwC2X9YFYOugH8AJlrkX01yB+4nnHwdufWT3fV/8P7943+6Hb0To//3hUQBHn/6r\n1zD618888Edf/NJTD38J4489+NN2DN3FG/EKS2WGe1jjTkTNSGQfMtmcUQecNnptKuYz7gzcqSKR\nal6gi/KJtoqUks214yDnIxdCAHaIwH1NJ4DjEwzci2iuwN3h6wAS2tOkkkkB69f3AtE3f3iy647f\nv6YbAOQNN//xKPa+fraRJ9og4aqaU/UdTBzlTkRNZai7w9gDzkQM3r4EPXCPc6oMVUBVDZjjbrdJ\nXj12N+zMrEN0pm5fO59xPzbJ/tQimitwH73tP9yKlz972+e/9q2vff7Oz7/Wdcc9Nw6LP/IOdOpf\n5d52w2jolUPBRp1l41Q+xx1AV4fD65JjKSXUxi3qRNSE8llJo56dzMi4i+ZUZtypEvGMks2pHQ67\nw15XWKUNlmm/MvdQIvPuXFxUt6Mg487M41LN1eMZfffEGQCAlo0JHT/xbnTDKAAM+DzL/vUDBw6Y\ncVaTk5OBQMCMI1fr4vQcgJnxdw/kLlXy9f1uxFL4yS8OXN5T+827ZZ06dcq8g1td81w8zYkXTxkt\nfPHMBbU74D/62Vtb+2tcmVR48ZwYCwJIBi8dOBCt//SESDoHIBxPmfTKYioLXTzZHD7x+DiAJz45\nZDeut7g8w595ZuJZAB1yvXGIA1kAb/7y8HS3ia/ay1r56+fQpRSAdZ22wwd/CUBV4XPaAvH0S6/t\n6+2wr+SZLGtyctK854Rdu3Yt+zVNFbhHn/jaN0++70vP/eUdPgAIPnHv7d/82tMf+N93IIbp2fk7\nJuHpSxjpcy/5+5X8wDUYGxsbGRkx48jVUn/+cyC1a/vW9wxX1Ge9+dC+c6FL3j7EuYwAACAASURB\nVFXrdm1fY+qJmfTIt4DmuXiaFi+eUlr44lH3vAKkAUjdQ7t2Ddd8nPzFkzv0JhB/77ZNu6407Lku\nreTwg+dSWUteoha6eGajaWAcwOi27fXMUqyWsb/WE5ciwKU+f0edh131i73vhgKXjWzataHXqHOr\nwcpfP6+/cgaYvW50aNeu7eIzV+77xevvzMr963eNDqzkmSzrwIEDjX1OaK5SGQAYGPJpH3Vfdc0Q\nYhEF7jW7MP5SPpFy/kdPh0av37Y0cG95VZXKID8Rkv2pRNRM8utIT10ypvlsJpIG0G9oqYzDbpNt\nUkrJKW3ZKbhigom0+CBq5cJufaRMvW88xBHasMBVzIIUnanCtjV+cA1TMU0VuMu964Gn/+npg2eD\nweDZfU/8twfGsWuTD/IHPvEZjD/w9Uf2BaMzz3/7T18G7vo3Wxp9tg0gel8qb1of7uVESCJqOnG9\ncPzUlDGVLVMmjIOUJG15KncwmSoQ14LUiJUDdzFSpsLREWWI3tY23MF05OL8SBlh62AngOOcCLlE\nU5XKuO/4+sMTf/pH3773s98GAHS97zMP/18flAHfzi/e98cX7v2bP7n9fgD45DcfuaX9NjCpanVT\nZaBn3DkRkoiaSr7j89QlAwJ3VcVMVFvAVP/RCnmd9nAiE0sr9YwKofKCcT3jbn60qmTVnKrW2T9a\nVLjK++Gl6MtT2yvjHkkqY7Mxl2zbtNqf/6TIuB9nxn2JJnsycm/44n27fy84EwUAX3/3fDnMzk/8\nlxc/NBNVILu7u31NdtorIp5RlCqb1rUdTHPMuBNRs8hkc0pWBeCSbROhRDSliPHVNQsnM5lszuuS\nOxwGN7F5nGIHEzPuJgrpGfcVKJXZ9NUfAXjinusN73bU1qbWXaPvb8upMiLdvnWwU7bNtydvXu2X\nJJyeimayOTPea1lXMz4W7u7+/u7+wqh9/vP9/e0ZtQMIJ6p+XtAz7hzlTkTNIp7OAvC75csHfADO\n1F0tI9LtxtbJCF4XdzCZLqjXc5tdH5J/HUxmjH8npt8Przvj7m7HjPvhJXUyADxO+0ifV8mp9T9F\ntJhmDNypKFHg3lnNHdtOt8PnkmNpJd/9Q0TUWKLA3euUN6/ywYgy9+mI8UPcBZFxZ427qQLxFWpO\nTeubes2oyanhBbooUQrbbjXuSwvcha2iP5Vl7gsxcLcMcT+xqnXKkoS1vR4A51ktQ0TNQYyU8bjs\nm1f7YcRgmRkTti8JYnkqM+6mCuZLZUxOM+fT2HMx4zNZWnOqQVNlmHEXtP5UlrkvxMDdMrQ39FXe\niRtmfyoRNZOY0Rl3MVKm31fjIqcyRMY9xhp3MwVXqsY9n8YW7/SMVe3oiFJEjXso0UbvFaMp5exM\nzGG3bV7tW/RH25hxL4aBu2WEaiqhG+7hREgiaiJxLeMui9fp+gP3mWgawIDf+N0eosY9zoy7mUJ6\nJafZ9SH5NwZmZNz1UhnWuFft6MUQgG2D/qUdqMy4F8XA3TJEc2pVpTLgREgiajJ6xt2+vtcr26UL\ngXi8viLyadMy7l5m3M23Yhn36HzG3YTAvfrpEUW14Rx3USezfUmdDIC1PR1epzwVSZnxXsu6GLhb\nRm29L/nBMqacExFRlUSY7nHKsl26vN+nqnhnuq6k+3QkCZNq3JlxN19gpea4F2TcTSiVMSjjLmrc\nw+2UcS9V4A7AJklbxDR3VssUYOBuGaJUpvqMO0tliKiJxFIK9CoUQ8rctVIZM8ZBiow7p8qYKbhS\nm1Pz0fCsGRn3pDE17p3tl3E/cjGMEhl3AFsHuYZpMQbullHbmFiRcT8/F+codyJqBiLjLmJiQ8rc\nzRwHaQcQN38xUNtSsmo+EW56xl0//qzRdReqqpXK+OvOuHucsk2SkplsRh9e2dpiKeWdmahsl7YU\n7EwttHVNJ9ifuhADd8sI1zRtqrPD4XfLiUw2fzuSiKiBRMZdVKFsWlXvRMicqs5GUwD6TFnAxIy7\nuQprQkyvcdePH4inc4amslJKNpPNOWWbS643ppKk9ipzPzoeVlVsXdPpLPHQiVHuJyaZcZ/HwN0y\naiuVATAsRrmzP5WImoBW4+6wQ8+4n64j4x6MZ5Sc2tnhqD9mWooZd7OJjJJYdG/6VBn9+NmcGssY\nGbjXllYrRQTubVLmXmr1Up4euEeyOZYNaBi4W0bNG5VZ5k5EzUNMlfG4ZAAb+rx2m3RuNp5SaiwM\n0LYvmZBux3zGnYG7WUSBu3iRiqZMXsBU8AYsnDKyEMWoIe6CeJVvk4x7mc5UobPDMdTdkVJy52aZ\nfNQwcLeMmjcqc7AMETUPvcbdDsAp29b3eXKqerbWwTLaLEgTCtyRz7izVMY0InC/rKcDQDSlmNqL\nVTgcPZQ0NHA3aKSMoA2WSbRRxv3KyzrLfM22QbGGidUyGgbulhGK11gqw1HuRNQ84qn5jDuAzaLM\nvdZqGfNGymB+c2pb5D4bIphIA+j3Od0Ou6oinjHxoS6soQ8ZmnEX2fH6h7gL7TNYJp7OnpmOyTZJ\ndKCWIv6Ug2XyGLhbQzanRlOKJMFXfcZdW546x4w7ETVerGCqDOoeLKMPcTd++xKYcTefSEj1eJw+\nlwyTB8uIUHh9nwdAKGnk71Qrlan+1bmo9lme+vZEOKeqo2v85RtURMado9zzGLhbg0gV+FyyTZKq\n/bvaREhm3ImoCWgZd6dd/O/m+gbLmJpxFzXuDNzNI5pTuz0O0ZFp6mAZcfD1fV4YnXE3ulRGNKe2\nfsb98IVlCtwFbSIkM+46Bu7WUPNIGRQ0p3KUOxE1nJZxny+VqTPjbmKNuyjEZ6mMeYLaS9tKZNzF\nwUdMybgbWSqjj4Ns/Yy7KHDfPrRM4D7S73XKtguBRDuUD1WCgbs11DxSBoDfLXd1OJKZ7JzRWyfI\nJG+OzX3un9588GdnG30iRMaLpxdk3C8f8EoSxmZitW2cmY6atX0JgNuhlcow62ES0Zza7XH4zM+4\nR7TA3QuTpsoYVSrT4UCbZNzFSJm1ywTusk0aXe0HcKKOhQ+thIG7NYh/w7Vl3MH+VKv5+1fOvHR8\n6uu73270iRAZL5ZakHF3O+zDPR4lp47VNO5Ny7ibUypjt0kdDntOVVMKq2VMMR+4u8wN3FUVkVQG\nwDqRcU+ZMMfdsIx7W4yDTGSyp6eidpskJrWXJ76G/akCA3drCCXqKqET1TLnORHSIpjeoxYmMu5e\nPeOOfH9qTem0GTMz7tA3vLLM3SShRBpAj8ep1bibFq2mszklq8p2aairA4Zn3JO13xJfSqtxb/Vx\nkMcmwjlV3bzaL+5rlbdtUJS5M+MOMHC3inpKZcCMu9UwcKdWlVNVEQQXvlrXPBEym1Nno2kA/V6z\nAncvJ0KaKaCPORYZ94hpj7N4S+B3Ofp8TgChlPFTZfycKlONCjtTBS3jzlHuABi4W0U9zanIZ9w5\nEdIiVDByp9aUyGQBdDjsdtv8gCytP/VS1YF7MJ7JqWqPxynbqx63VSGPtjyVGXdTBPWpMj63A2Zm\n3EWdjN8t93qdACIpNWdcgsTYqTJtMsf9sNaZWm6Ce57IuB+fjBj4W7MuBu7WUPPaVGG4lxn3Bc7O\nxEa+/OzIl5/N5vgsQLRy4qks9PqTvE2rfQBOT1V9H1wf4m5Wuh16SY8o7yFjKTk1klQkCZ1uh9/k\nGncRB/vcssNu6+xw5FRVlNcbwuipMqI5tcUz7kcq60wVer3OAb8rllIusuKXgbtV1F0qo02ENPKc\nrGz3oQnxgYHP3QZKZrT6y9rmbBA1rZg2UmZBDmLTgA/AmemYUuUbaTFSpt9nyvYlQV+eyoy78fRh\nLA67TdJKZUxLM4tcvvgufV4ngFnjxqzVmVlbpLOj9TPuyUz21FTUJkkilV4JbX8q1zAxcLeKuktl\ntIw77zIJzxwcFx+IF/5mE9LbkkKt3p9E7UZk3As7UwF4XfJQd0cmmzs/V91dwelIGiZn3D3MuJsm\nP1IGgD4O0qxnPJHLF2XoInCfM+7JP2LCVJlwMtPCr9fHJyPZnLp5la+jgs5UQZS5cw0TGLhbhXYn\nrtYSOp9L7vY4UkpuNtaMceoKO3EpclKfXyFmyTWbfLwufu9ELSOeyWJJxh3zZe7VpdP0jLuZpTIu\nsYOJGXfjBRNpAN0dTui5cNNLZVwygF6fC8CMQRn3tJJLZrKyXXLLlcag5blkm8NuU7JqsnWHkIrO\n1O2VdaYKWwdFfyoz7gzcLUKfNlX7nThWy+Tt1tPt0GfJNRtm3KlVxVMK9Gi40ObVtQyWmYmYOwsS\n+nuMRKbt3kJnc+pUJCWG9phEZNy7PA7Mrws1rVRGy7g7APRrGXdjfjQt3e52SMY1SJv9aDSc1pla\nTeC+bY2YCGlYxn0ilHj9nVlTr3CTMHC3hjpLZaBXy1R7J7r1qCqeOTgBvUu9CTPuSlbNz55r+f4k\najdiPEvJjHuVgbu2NtXUjLuzTTPuBy8Er/3mj6/+xovmfQutVKbDgZXIuGcA+LWMu5E17saOlBHE\na30LT4SscGdqoU2rfHabNDYbE5Op6nfX37/229/5halXuEkYuFtDnc2pYMZdd2Q8NDYb6/e5br9q\nEE2ZcS8M1plxpxZTOuNeS6nMSmTcXTLassY9P3HLvNFbolSmx+tEvsbd7OZUrcbdBcCowlH91dmY\nzlRB38HUmlddSsmduhSxSdIVFXemAnDKto0DPlWtZW7sUtmcKsKhyovsmwcDd2sQG5XrybgPi4x7\n20+EFHUyH7lqcHWXG02ZcS8M1kNNOfSGqGalMu5isMzpqWhVYaL492tujXu7ZtzzUaN5o7cKM+5+\nlwOmZtxTBVNlfEaWyoSTdXWgFeVv6R1MxyfDSk7dOOD1OKsLmg1cw/T62Tnxwfp+b/1HW2EM3C3A\nkN4XZtwB5FT1mUMTAG67anCV34WmzLgXBu4slaEWI8ZBepe8YHd2OFZ3ulNK7mKwiucorVTG/Ix7\nrP0y7iIdDuMy00W+RTwNvcbdtyI17uK7iB1MRjWn6h1oRgbuYrJkuEVr3I9UX+AuaGuYJgzoT81P\nlrPiUmQG7haQL6Grp/dlLXcwAQfeDY4HE4Nd7qvX94gsXbNn3FkqQ61FlMqIaHiRavenZlUE4mmb\nJIk4zCRepyiVabuMe/5235xx884X0TPuTgBOu022S5lsLq2YsrwiWpAX7zd0HKQ+jd7YUplW3sEk\nRsrsqD5wF4NljtWdcVey6nNHtF0u5lVnmYeBuwWIW5b11MkAWNvdAeBiINHCo2GXJd5k33bVkE2S\nRJauCee4F96YZuBOLUaUyizNuCNf5l7x/tRwKqeq6PE67Dbjxnks4dFKZaz36l6nQDyfcTctcE9k\nAPR4HQAkydxqGREEF46DNK45dX5ejVFae6pMDSNlBG0H00SkzjDmZ6dngvHMul4PzKzOMg8DdwsI\n1d2ZCsDrkns8zpSSa8LikJWRzanPHp4AcPvOIQA9HqdNkuZi6WqXNZpN/LrF+4owA3dqLWUz7tVN\nhAwksgAG/G7jzq4Ir6tNM+5B/cnHqFrwIt8iPj/HHSZXyywolfE4AQTiaUP6busfHbFUZ+tOlUkr\nuROXIpKEK4eq6EwV1nS6uzocgXh6KpKs5xxECu/jV6819SaPeRi4W4BR06bWtnd/6htn56YjqXW9\nHnGHzm6Ter1OVTXxRnBtROC+vtcDZtyp5egZ9yKB+6ZVPgCnKy6VCaVyAAZ8JtbJYL451XppuTrl\nb/2ZmHEv2JwKkydCRgsWMMl2yee0qaoxfbf6C7QZU2Va8Pn/xKWIklU39Hu9xd69lydJ2CrK3OtY\nw5RScs8fnQRwx84hs1uiTcLA3QLCdQ9xF4Z727o/VbzJvn3nUL5VoF/0pzZZmbsI1ocZuFMrEnMV\ni06TyJfKVHgfXM+4m9iZivlxkO2XcZ+vcTetOXXhS5tfmwhpypOevoBJCxa73DYY1HerLWAyuDnV\ngRYtldEmuFdfJyNsW+NHfWuYXjkxFUsp2y/r2tDvNbsl2iQM3C0gZNCYWJFxv9CWO5iUrPrckUno\ndTKC2NvSbGXu4vaxKL9r1akC1LbEXMWigXuPx9nrdcbT2YlQRcmFYCoHk2dBAvA47GjLqTIhfaqM\nSfckszl1UZGJSIdHzEl/RhZWonc6bTDoR9ObU42fKmNSQHkhkBj58rMjX3421YgSkSO1dqYK9Wfc\nnz6oTZaD+Wu/TMLA3QK0jLsBpTLtm3H/+ZmZQDy9eZVvy2p//pMDfieaNeO+jhl3akUi417qLvnm\n1VWUua9Uxt0OIN5+c9wDJpfK5KcoynpvsRZFmRCtppRcJpuT7ZLTrsU8IuM+Y0T5vniB9ltnqsys\nnqt64+ysGccvz5CM+/FaM+7xdPYnxy4BuO2qIZh8k8c8DNwtQMu4e4yqcW/HwP1pMU+moE4GesZ9\nqsky7uLXfVlPhyQhkszk2nkMELWceLpkxh3zEyErSqeFkiuRcRfl+G2YcQ/qU2VMak4t3L4kaMtT\nTUh/ijcDftf8SOVOl3EZdxNKZfxmznGf03+ze45Pm3H8MjLZnEiWX1lr4L55tV+ScHoqmsnWcrvg\npeOXEpnse9f1iHDI1Js85mHgbgHa2lSDmlPbcJR7Wsn9q6iTuWqo8PMDTVnjLvI33R6n3+1QVUtO\nmSUqRZTKFG1ORT5wryzjLkplzM64O+w22SallVyzjZ8ylZJT83UaJmXcF3WmAvCbFkVFUhnobwyE\nbrcdBbnneuhNaEZm3E2dKhOIaYfdc2LKjOOXcWIyksnmNvR7fdV3pgoep32kz6vk1DMVj58qJOpk\n8hWz5t3kMZWRl1rDjY2NmXHYixcvmnHYyk3MBAGkosGxsboq0rJKDsD5ufg7Z8/a6lnmtNDk5KRJ\nj7xRfj4WiaaUzf1uW2x6LDafY1ATYQBjk3PmnX8NF89sJAkgMnvJ65DCCRw9dXaw09y5GQ3U/BdP\nAzX8mccM0WQawMyli5lQkVcfvxoDcOTd2UquCrE9LR2eHRur5SW8cm6HLZrKnjh91uu0TKqrzosn\nlBTvr2yxdC4QTxv7kiGceDcCwIls/netJGMALl6aHRurfUd4USenEwCcUm7+ukpFAYxNVnSllReK\npwEEpyZSAcMuj1BSEUc24+nxzAWtQubsTOxnB0+u7Sry+mLSk88rx4MANnTL9fxc6zrtZ2fw6pGz\n7lR1aftYOvfSsUuShB3dGXECaiYB4Nz4pbGeKt4jBYNB8162RkZGlv2algrcK/mBm+3IlVBslwBs\nWjc4MjJQ56F6vWfmYmlv3+DqTsOGHwcCgcY+Psv6H68dAPDxXxlZdJ5XKDN46WJClU09/2oPHk0f\nB7B9dEOff3IinPb3rR6p9a5i82v+i6exWu/BSShvA9i26XKnXCTK8fal8MzYu6H0+vUjywaKsewx\nIPeeLRtM3ZwKwO8+E01l+9YMrTHuaXMF1HPxnJ2JAcdXdXbMxdKhRKZ39druums1FzkQuAi8O9TX\nmT/Pyy6qwCW722v4ZT+ZmwXe6ev05I88PDCO04G05Kzzeyk5NaEctUnS1k2XG/jWRsmqwIlYOrtu\n/XrD3zLhxPx9hpNRxwd2jhT9KjOefMYPHAFw3ehQPQffdXnmlXfCM5mqf3dPvnVByanXXd53zZWb\nxWeG3k4AAaevq6pDdXd3N/aZ2TL5g3Zm1Bx3AMPt158aT2d//PZ8M0ohMQ5yuplKZTLZXCKTddht\nbtneaWaZI9HKy2RzSlaVbZLDXvylp9/n6upwRJLKsgtWMtlcJJWz2yTDA8qlPE4ZbdafKtamijk/\nMGewjPgWS0tlzKhb0EfKzGcqu0SpTN0/V0RrsZWNja5lu9ThsKuqKXNIxSP/nuFurHiZ+5H6OlOF\nbYN+AMeqHywjRkLfUTBZzud2wIKlMgzcLSCcMKz3RS9zb6PA/aXjU4lMdte6bvGzF2rCcZAhfbCx\nJGnjjTlYhlqGNgvSVTLKkaRKy9zFPJA+r9P4fOQSXlfbTYTMF6D3eZ0waN75IiHtW8zfLRFRlCk1\n7gXblwTRnDpbd9+teHX2GzoLUjCvzD0QSwP46K7LJAm/eGd2xS5sJasemwwDtexMLbR1TSeqHywz\nF0v/7NSM3Sbdsn1N/pMcB0lmCRm0gAlt2Z+a37u09I+6PQ67TQrGM7X1p5tBvF6K33UnA3dqLdos\nyBIjZQRtIuRy+1PFjTKzO1MFPeNusVf3euQD916fC+Zk3MX2pQVTZVxmzeYTkZnPNf+9ulwSjPi5\nzFibKoj7A6GE8VfdXDwDYOMq38613Zlsbu/pFRoKefJSJK3k1vd56sxCDvd2eJz2qUiqql/f80cn\nlZz6/k39hZV1+jhIi/3TZuDe7FTVyFKZdhvlHk0pe05MSRI+smNw6Z/aJEnMkjNkmq8hCt+kif+2\n5NZrak8xbRZkuShHz7gvcx98JpqC+bMgBT3j3kalMsFEGkB3h1PPuJsQuMfTALoKS2VMHAe5eNS6\n32UHEEyk6xwWZMbaVMGv7WAyK+Pe63HetHUVVnC2TJ0T3PNskrRFTHOvplpm95I6GXAcJJkknlGy\nOdXjtMt2A+4Ir+3tAHC+bZanvnD0UlrJXbuhr1Qzbr/PCT0IaAahxPyINJbKUIsRSWsRB5eyebUP\nwOnlSmUakHFvv1KZLo9Dq3E3IbUhvkVPYamMy6x1oSIyKwzc7RJ6PE5VnR9XXxsz1qYK4phmPBpa\nA4PXedOWVQD2HJ9emWUhR8ZDALYbMWthm6iWmay0WmYqknrtnVmH3XbzFasLP+/jAiYyg7HPC+2W\ncdebUYqk24WBJutPLcy4d5q5PI9o5VWScd+0yg/g5KVI+WBCvNkeWJGMu1gXFWun5tSg3pzaZ1pz\n6tI57uYtYFpa4w6g14ibCfn9r/UcpChtearRiRtV1TLuPR7H9ss6+32uiVDiRGUrz+p0+IJhgfvW\nQVHmXulp/+jwhKrixi0Di35Tfta4kxlCxnWmIl/jHoy3wz7OQDz96qlpu026dXvJwF0vlWmWwL2w\nxl3cRA7FGbhTi4ilFJRemyqs6XR7XXIwnikfLK5kxr0Nl6fO17ib1pyar8bJf8a8qTJajfvCSvQ+\nnxN196fqmTXja9w73abcf4imFCWndjjsbofdJkk3bhnAilTLKDn12EQYwPYhIwJ3rVSm0oz77hKt\nbj5zHmSzMXBvdmHjOlMBdDjsfT6nklWnmibHbJ5/PXpJyanXb+wvM+a5mTPuLJWhFhOvIONeMFim\nXDpNq3FfmVIZlx1tNg4y3zkqotuVybh3OO2ShEQma/iSWvFmwL8w467fTKjryT+ctNhUmXydjPhf\nrcz9uOmB++lLkZSSG+71GDK/VQTuJyYj2QoulfFgYt+5gNth/+C2VYv+iFNlyBQibus0bp1yv9cF\nYCK4zJjkFrBsnQz0wL15Mu7a+zQPS2WoBYmkdfkad+iDZcqXuYvUw8qUyrRhxj00X+PuggnNqdmc\nurTIxCZJ2kNtdCAV0TLuC0LGPiMmE4SNfoHO85uzx0PrTNUD9xs29dtt0v5zAbOnIBjVmSp0djiG\nujtSSu7c7PINe7sPTQD40LZV3iUpAx+nypAZxLObURl3AKs6XWiDcHA6knrtzKxsl37jyjVlvkwb\n5c6MO5H5Euks9Di4jEpGuWs17ivUnGoHTFmF07QWL2Ayujk1klRUFX63LNsWDF0waTzf0qkymM+4\n1/WjiSqLLhMy7n5zEjdz2m9WO+HODsc1I73ZnPrTUzPGfqNFjoyHYVzgjvk1TMtXy4gU3tINjAA8\nDtmkmzymYuDe7LTtS8Y9L/jdZq11aCo/OjyRU9Wbtqwq3x4gXvibp3BI1H3qc9zFHN8W/01R+9Bq\n3JfPuPsAnCrbMCfebK/UOEgxVaaNAvd8qUy+g9PYrih9beriIkaTxvMVbU4VGfd6a9xNa041qcZ9\nTutMnX/kb1qRMne9M7Wu1UuFKlzDNDYbO3wx5HXJopp/EUnSrgrDb/KYioF7s9NLZQx7XhBPB2ET\n1jo0FXF3rOib7ELN1pwaii/OuIcTShs0ElNbiFeacfejbMY9peQiScVuM/JWZBlaxt1SL+31yObU\ncCJjkySfW3bJNq9TzmRzxlYKhZZsXxJMGiwTXTIOEvNTZep68g+Z1pxqUopNtBYU9n2JMveXT0yZ\nN7JCyalvi85UozPuy45y331wAsBvXLna7SieLxBruaxVLcPAvdkZXiojng5CLZ1xnwgl3hybczvs\nH7picTPKIs3cnOqw2zoc9kw2l1TaKNVHLUwEf+WnygAY6nZ3OOzTkVSwxEilmUgKQI/bLhmw3GJ5\nIuPePjXu+RcdmyQB6DVi+soiSztTBZOiqOIZdyNKZcImL2AyPMWmZdwLAvfRVf6h7o7ZaFqUoZvh\nzHQ0mcle1tPRs+QeS81Exv3Ychn3MnUygrboylJvyxm4NzvDp03pvepWukyrJdLtH9xapBllkU63\nw2G3RZJKSsmtyKktQ1/ApD27dbLMnVqIGMzidS3zr9ImSRtX+QCcni6edBe3yLrdK/T61W417oui\n6l4TRrkHS5TK6MtTjXzGSyu5TDYn2ySXvOAdY58R2/fEC7SVpsosKZWRJOQ3MRn7vfKOXDCyM1UY\n6fc6ZduFQKJMMHPyUuTEpUhXh+OGzf2lvsaKg2UYuDc7w0tl9PfxrRwLirtjty0Z2rqUJOnVMs2R\ndA8tnP6pV8u08i+L2keFGXfk+1NLlLmLppRu9/LHMYRJo06a1qLAvc+EUe6Bkhl34wu79ToZx6L7\nM31eFwxoTrXYHHfRXdDrXfDI37TV3DJ3kcs3ZIJ7nmyTRlf7AZTZHiVSeLduX+Owl4x1rThYhoF7\nsxN34gwslels9ebUc7PxgxeCXqd8U7FmlKUG/AbkXQyRzGRTSs4l21yy9g+Tg2WolcQqy7hjucEy\n0yuccXe1WcZ94WokMzLuIe1brESNu6j88S2Jrbs9DklCMJ5RsjXWdudUKhbE5AAAIABJREFUNZpS\nJKnIwetn2lSZDBZm3AFcv7HfKdsOXQgaWxCVd0TMglxrZOAOYItYw1SiWkZVtTqZpXuXCunLU630\nIsvAvdnpvS+GZ9yt9P6yKrsPjQO4uXQzyiLNM1gmtGTZFgfLUCuJV55xX+0HcOpSiVKZlc24e5hx\nN3qUu/4tlpTKmLA8NVqswB2A3SaJ+FUkoWs7sqrC55JtJjRbeF12SUI8bfCkwqWlMgA8Tvt1l/ep\nKl4+aXzSPZtTjxo9C1LYtsYP4NhE8Yz72xPhszOxPp/zVy/vK3MQKy5PZeDe7IzdnJo/VAtn3J85\nNIHl3mQXGmiawTKLCtxRMFimYedEZJxYZVNlAGwSNe4llqeucMbd67RDP/l2sLjG3eeC0aPcg2Wn\nyhjbKVh0pIygvSep9cnfvM5UADZJ8pnwNmbR5tQ888rc35mJJTLZwa6OMivMa7N1sBPA8RKj3EW6\n/SM7BhftCliENe5kvLBJ4yAt9f6ycqemoscnwuWbURbpb5rBMksz7iyVoVYSr2yOO4DhXo9Ttk2E\nkkVfUPWM+wq9fol7d/F0uwxmFZ2jXXqpjCHTV4p+i1Jz3I0NVYuOlBHEe5KabyaEjb4fvojh1TKq\nms+4Lz5nUeb+01PThq8iEhPcDa+TAbBNjHKfjCwdZKmqeLqCOhmYtvPLVAzcm1o2p5XQLbskvHLa\nc0GLxoK7D44DuKVsM8oizZNxD8aXlMq4GbhT69CbU5fPuMs26fIBkXQvUi0zrY+DNPoEi7PbpA6H\nXVXRJoNZRTq8Z+FUmZVpTvWbUONeJuPeX997knCxhawGMrw/NZpSlJza4bAvrSMd6fNu6PeGE5m3\nzgWM+nbCEa0z1bDVS3l9Pme/zxVLKRcDiUV/dOB8YDyYGOxyX72+p/xBTNr5ZSoG7k1N/IvtdDsM\nLKFr4XGQlb/JLtQ8GfelZVFaqUzr1jVRW9HGQVZQ446yg2VmommsYMYd+f7UVHsE7gvT4WZk3ENl\n57gb+/KkZ9yL5MV7tYmQNf5oEaNHRyxi+ETIpUPcC4lNTIbPljkyblbGHaXXMInJch+5amjZ2Mnn\n5gImMpQZ65RFy0ssrRh+R6zhjunNKNeVbUZZRGTcmyFw10plPIXNqcy4U4vIqWoik4VeebKsMoNl\nple2ORX5iZDtsYOp6Bx3g5tTFw6uyfOZMMc9Wjovrk+ErLXG3fRSGYNnNwe1WZAlAndR5n7CyDL3\nnKoevWhKZ6pQdA1TNqeKGRW37xxc9giscSeDmbFO2aSWl2YgmlF+c7lmlEXEVJmaky4GKlXj3qp1\nTdRWRNTe4bDbK/vnWWqwTDydjaUVl2zrkFdkbyoAwOOSodfotzwtcO/IN6c6YWhzak5VlyYpBFNq\n3FMla9z15tQaf7SQllkzq1TG7zb4/sNcvMhImbxf3dDrcdqPT4QnQkmjvuPZmVgsrazpdIt9KYbb\nWizj/ubY3FQkta7Xc9Vl3csewc+pMmQsw0fKCCYNiG0sVcUz4k126eXGRQ00TalMkM2p1LpEnUkl\nnamCnnFffBNctKP0+10mjOArSZT3xDPtUSqTSKMgqvY4ZJdsS2SyCYN+/HBCm6K4NMPiNyH9KY5W\ndNS6eE9SR3OqVstax9mVY/gkibkSnamCU7a9f1M/DK2WEZ2p281Jt2O+P3VBxv2Zg9pkuUqeInyc\n407GMmnaVEuWuR+8ELwQSKzpdF8zskwzyiJep+ySbbG00vAFK0XmuLfBmltqE6LOpJJZkMJIn1e2\nSRcCiUX/MEXgPmBOAq8UfZR7ewTuC3f0SBJ6vUZOhNTqZIqFj2YM1Rb5+zLNqXWMgzS+lrWQ4Sk2\n8ZstM5ZRK3M/blzgftHcwH3TKp/dJo3NxPPvKpWs+tyRCQC3X7V8nQy4OZUMZ/j2JaElw0HRlvqR\nqwarbeSVpHy1TIOT7qElU2VE0ivEOe5kfXrGvdLAXbZLG/q9AM5ML6iWETfHxL/ZFePVlqe2/r/E\nbE4NJzOStCDS7asvM71IqMT2JehbdWNpZemAv5qJtwF+E8ZBRkq/JTCE4VUc5ZtTAYh14z8/PZOp\ndZvsIiJwN6nAHYBTtm0c8OVUNV9Tt/fMzFwsvWmVb8uaiubYmHGTx2wM3JuaSaUyne5W28GUzali\nEOQd1cyTyWuWwD2xeNJCJ2vcqVXoGfcqOkqLlrk3JOPubZuMeySpqCq6OhaMMus1dLBMqe1LAGR9\n8mbCuPufWo17sfxXnUthzW5ONXyqTNG1qYUGuzq2rvHH09lDk/H6v10spbxxdg6mjZQRtq4RZe5a\ntUx+A2OFGTxtHCQz7mQUk+7EiWaaVtrBtG9sbiqSGu71XLV2+WaUpfqbY7CMVlpa8Ov2OGS7TYql\nFcWg/AdRo4iKl0qGuOcVLXMX/077Vzbj3uFsl4y72Ky5aN5Ln6Gj3IOlM+4wYXmqmCpTtDm12+Ow\nSVI4kantCdbsUhnD57jPaVNlyp2wqJZ57VzxpcVV+e8vnACwa133KjP/tW4T+1MnIgDSSu55rU6m\n0hSeGTd5zMbAvaktLXo2ROvtYKr2TfYiTZVxL/x1SxJHuVOLiKUUoLpdcptXF9nBNB1JowEZdzuA\nWKPbYFZA0XkvxmbctfcGJVokDR8sU2YBk02SerwO6BFttcImjH0rZPgrdWBh90JRYijkL+oO3A9e\nCP7T3jG7TfrWR3fUeajyxGCZY5NhAK+cnI4klSuGOi8f8Fb41+02yeM0+CaP2Ri4NzWTnhcM71Vv\nLCWn/uhwFc0oSzXDKHdVLf4+jYNlqDWIjHvlzakANq0qUiozHW1AjXv7jIPUO1MXPAtpO5iMak4t\nsX1JEBF2zLiHunwluhjlXlt/qknTI/I6jR4HKUplyjSnAnjv+h6/W74QSo/Nxmr+RkpO/fPvH1ZV\n/P4HNoiMuHnEKPfjExFV1UZCV7WBERZcnsrAvalp06Y4DrKs187MzMXSGwd8WytrRllKBAFTDQ3c\nE5msklU9TrvDvuBfZWfL3R6h9iRCscrHQQK4vN9rk6R35+LJgkGE05EkVrxUpn0y7ovWpgp1NnEu\nEtK2L5XLuBsYRembU0sE7nX03Yrqc9MXMBm9ObVUkZIg26RfHx0AsOd47ZuYHvr52bfHw2t7Ov74\nQ6M1H6RCazrdXR2OQDx9bi7242OXUP1IaMsNlmHg3tRMKpVpsXGQYtPbr17eW/NcZ1Hj3tgdTKEl\nBe5CJ0tlqCVoNe6VrU0VnLJtfZ8np6pnZ+aTf+Lf6UqPg2yfjHuxztE+Y5tTyxZsGLuCPq3kMtmc\nbJNccvELr+YdTKqqZdaKTog3hLGBu6pq78pKzXHP04ZC1jrN/UIg8VcvnATwzY/u8FTTjF4bScLW\nwU4Af7fnTDydfc9w99qejqqO4Hc5YKnBMgzcm5pJvS+GL1JurJlICsA163trPoK+g8mwdXE1WDoL\nUujqkMFSGbI+MVWm8nGQwiatP1WrllHVfHNquayh4bSpMm3QnBosVoDea2hzqqhxX7o2VTB2PF9+\n+1KptE6fdjOh6h8tnlZyqup1FlkjZRRjU2zRlKLkVI/T7l7uzfONo6sA/OKd2Rp2m6gqvvbUkUQm\ne/vOIZG5XwFisMxj+86jpslyZmwPMBUD96YWMqvGvaXGQYq3N/Xcl2iOjHvxN2mdrHGnliDmuFc1\nDhLzEyG1Vrl4Wklmsh6nvapa+fp5tKky7VAqI55OF5bKmJBxX2aqjEFRVPk6GeTfk1T/5K+n1Uy8\nDt2yXbZJaSWXUnL1H62SOhmhz+fcOtCRVnKvnZmt9rs8e3hiz4mpzg7H/33bFbWcZU1E4A5AkvCb\n1be6+aw2yp2Be1Mza457a42D1EPe2p9ARfZuOpJq4DyoUmVR2lQZ7mAii4vXlHHfvDDjLhpR+le2\nTgb5mXHWeWmvmSiVKdqcWkN0W1So9Bx3GB1F6SNlSr6G1lwFJPbimVfgDkCStDM3JMsWjC/fmZp3\n3Xo/gJeqXKEaTmT+8zNHAXz51q0r2T6e73+9dkPfmk53tX9dr3G3THaMgXvzSim5lJJz2G2livNq\n1mIZ9/o7AbxO2euUk5lsA+c0a6WlS9IhzLhTa4hVP1UG+cBdHywz04iRMtBvFLRHxr1IXtbvdsh2\nKZpS0kakfstPlTE2ihIvc2WWm/bV2ncbLnGP1FgGLk+diy+zfanQdet9APacmKoqmfVfnz8+HUld\ns77nd35luKZzrJGYGwvgN65cXcNf93OqDBlFf14oWZxXM31QiWUu0/LED1LnfQmRdG/gYJnyGXcG\n7mR1Wsa9ylKZjat8koSx2Vgmm0N++9KKZ9y15tQ2CNwDxaJqSUKvx4la550XyqmqnnEvHkEaG0Ut\nWyqj30yo+pk/os2CNLdky8DhBHOx5bcv5Y32u3u9zvFgYtH6szL2nQs88vq7sl36i49fZTM8ainL\n65S/cef2T1277s73XFbDX+dUGTJM/aXbpeR71a2zKayk/PjzOjMfA74G72BarlSGgTtZW0zUuFdZ\nKtPhsK/t8WRz2mAZbaRMgzLu7dCcWqpLXkyErH+UeySp5FTV65Jle/HYztgFTFpzapnA3VdjqUxY\ny+WvRMbdkCybGOJeYcbdJkliE5MY2rasTDb3508eAnDPr28Ud8lW2GeuW/+tj+2osBBoEda4k2HC\nppXQOWWbS7Zlc2oiY/kEUlLJZrI5h93mrq+gqN/f4B1MpQJ3cQEw405WJzLu1TanYmGZuxj9tPKB\ne4colUlZ/glzWcFE8bWmWma67v7U8nUyyKc/japxTy4zsVGEejWkbMxemyoYWOOurU2tOLQVQyEr\nLHP/h1feOTUVHenz/rubNtV8ho3iZ8adjGLSEHehZXYw5ft367w1J0KBBg6WKfV6xlIZag0i415t\ncyoWlrk3ZIg72mYcpKhjkaQiCSOjBssEy25fgjnjIMvkv7o6HHabFEkqoharcmavTRU6jatx19am\nVpZxB3DD5n67Tdo3Nrfsdz87E/ufL50C8M2Pbl921mQT8rkcYI17XZKTT//Dt+69996vfesfXjsb\nnP989OQj3/7avffe+62/fXrSMg9vXUwa4i6IyjwLDS4tpf6RMoKomm3gKPdlSmWs/xaL2lztGffV\nfgCnpyLQ74mtfMbdYbeJwXxK1vr1haVFkoqqotPtsC+ZTd5n0Cj3UNlZkNAXMBn12iSeOcuUytgk\nSVSPVHszQc+4mx24G1fjLppTK6txB9DV4bh6fU82p756qly1jKriqz84nFZyH3/v2vdv6q//PFee\nsdVZK6DJAvfkyW99+K5vf3fv0JYtqb3f/bPP3v5PR6MAED36Z7d+/v6nx7fsWL/3sW/f9em/nWz0\nma4AU58XtIy79fO4Iu1R/32JgaYtleECJmoJYqqMp/r564UZ9+loY5pTJSnfn2qZV/caBIptXxKM\ny7gXGTdZyIxxkOWXm/aLMvcqb7dGzMys5Rk4VSZQdmFtUZVUy3z/wIW9Z2Z7PM6vfmRbnWfYKH5D\nq7NWQHMF7ie//z+fw+hf/mD3V/7oj/5y9zOfGcIDj/5MAY4+/VevYfSvn3ngj774pace/hLGH3vw\np60fumuR3HLbiWvTaWhWo4FE/qb+tzcDjd7BVGpmv/4WS2mBTmJqW6qKeEoB4HVVnXEXy1PPzESV\nnNqojDvm+1NbucxdS4cXm/fSV1N0u5Qo2OgqMVIGRhcci+P4yxZo1bYXViuVMbnGXV+eakSNe6yK\nOe6C6E99+cR0rsTLz1ws/Y3dxwB89SPbamsMbQY+joOsQ3TvDw8Ofebfv6975uC+fQePBn7vf7/6\n6n+5RUb0zR+e7Lrj96/pBgB5w81/PIq9r59t9NmaztTely5tB5Pl87hGdQI0POMeLDHMQbZJXpec\nU9WWr6+lFpbJ5pScKtslh73qFx2vSx7s6lCy6rnZ2IyWcW9AiCDuFbR2xj1YOlvU661x3nnRb1Gu\nOVWLoowZelZJxl0b5V7lexLxAr1CU2UMmeNe8ebUvC2r/YNd7plo6uh4uOgXfOtHxwLx9Ps29n38\nvWvrP8NG4QKmeigAxr/71Rtu+ui9f/In9/7bz374hq8d1KvcvQOd+pe5t90wGnrlULD4QVqHqb0v\nLVQqY8x9Ca3GvUHjIFW1XEuD1p8at/wvi9pWTBviXmMaQixYeetcIK3kfC65IQ1w4l5BrKUHywTj\nJetYat4wukhouakyTtnmsNuUrJquslu0KHFXuXx4XduPpj9jr8RUmfpfqVVVW61VpkhpKUnSku57\nilXLvHZm9on9F5yy7Vsf3bGyc9sNZrlxkDVec7lcbmpqKhwOu1yu/v5+r9drwLkkL749DgD3/PXj\nn7pmTfT8T7/+qa/e+63n9/zlBwAM+DzLHuDAgQMGnMYSk5OTgUDAjCOXd24iAGBu8sKBA7OGHzwZ\nDgM48c67B1xzdR7q1KlTRpxRjY6/EwGQCM3V+dtPZ1UAU+HkW28dMPA5qMKLJ6Go2Zza4bAdPvjL\npX/qhALgjV8eHuk2N7uz8hp78TS5Rj3zmGE6lgXglHK1/TvtQhzAs/tOAeh0ak/1K3zx5NJJAAeP\nHstON6BQp1q1XTyHT8UAZGKhpb+mS2EFwPhcuM5n2ncuBgCEpycOHAiV+poOGZksXnvzQJe73tzi\nVCAM4OLYmQPhdws/X3jxpCNRAEffOX/AW0U+cDoYA3DhnVO5aRNj96lLKQATM8E6H/Z4RlVyqluW\njh05VMnX56+f9c4kgGf2n72hN1r4BZkc/sPzUwA+vtUbPH/ywPl6zq7BMlkVQCSZqfDVf3Jy0qRo\nE8CuXbuW/ZqqL7i5ubkHH3zwxz/+cTwe93g8iURCVdV169Z98IMfvPPOO3t6emo6VQCAe/17RvHa\n0L2fumYNAN/wr/3e54dee+JcFB9ADNOz83dqwtOXMNLnXnKASn7gGoyNjY2MjJhx5PJs+18HEju3\nje7abHyn9s8Dp3H8hK9nYNeurfUfzaRHvhLPXHwbiIyOrN216/I6D+Xf/a+RpLJp23YD73JUePFc\nDCSAiR6vq+gjufrNX4wFZwfXb9x1eZ9RJ9Y8GnjxNLlGPfOY4eSlCHCpy9tR26/7/cr5Z04eOj6n\nAljb35k/yEpePKsPvXlkampo3eW7tq1asW9as9ounldmTwGhzeuGdu0aXfRH62NpPPdiTJHqfMyl\nX74JJK7aumnXFSW303e/uCecio+Mbh3pqzcnmP3xHiBzzc4rN/QvPlT+BzmWfheHDzu83bt2XVX5\nkVO7XwTwvqt3mlrbbb8Qwss/U+XiLw2VOzcbByb6/O4Kj5O/fkavVP77ay+eDmTWj15Z+JP+1Ysn\nxyPKplW+//w7H3DKTVW7UQvnU8+lldyVV+10VfCzHDhwoLEvW9U93C+88MJXvvKVdevW3XfffXv2\n7Hn++ed//OMfP/LII3/4h3945syZ3/3d3z158mS9ZzQezQ/kU7RVu+41uzD+0gH97d75Hz0dGr1+\n29LAvcWYeidOX6RsmXtDpZTq6axBA6tlgmV/Co5yJ6tLpMXa1BpLXMRgmclwEo0YKSO0Q417KJFG\nicpDsSsjGM8oubpqz0steCpk4Ap6USpTZhwk5ptTqyiVyRc3+k1fwGRMjXsgXsXa1EJep3zd5b2q\nildOzg+FPDUV/buXTwP41kd3tEDUDqtNhKzuEd+0adPf/d3fffKTn9y0aZPdbgfgcDjWrl17/fXX\nf+Mb3/hf/+t/+f3+Ok7Gd8e//zxO/s03H/npZHDm6E/+4f9n780DLCnLs++7lrOf0+vpbYYZemaY\nDZCZQQQlooggowKSCMpL0MSQGE0gyOurRP00iYmJvKAJIp9LAiS86vchiVFAB0FZBUSWYYZhGGbt\n6Z7eprvPvtf2/vFUnTnTfZaq53lq7fr9NTTdferUqX7qruu57uu+4cdTKy59aw/w77zqOpi6+6s/\neilTmH/ktv/1JMDVF20keCF3YPIAJk81p1LRyNUZTHb0p2bbNmypUe5+4e7jWrCzIBGnNQxRtyVS\nBrRUmZKnU2UyrVNlOFbNO0dWadKXaFtBUvQc62tODYLB5tSKKImSEg5wGM3WhuimlCqDXbiDli1T\nD4WUFeWLP3lNlJRr3rbq3DV9hAfmENTYzao7brLGllGO40RRDAabf/bDw8OERxPf8sffvmnqhju+\n9OR3AABWvP9z37vxHACIb/nzb9907IY7br78OwAAH/naj7YPm/uY6wRy5Q4j30joojdI2V5o5biD\nlghpi+Le/iENJQv5iruPeyniZkEiuiOBgURIzYK0S3EPeX94qtqc2mJGT18smCrWFoo1kk0PVaRo\nu2LTCi+viXJNlHmWCfPtLrx+NTDHwMpvauZbI/VToShA0nyVUrMgcW6U79k0+NWH9z69f06UFZ5l\n7n9x4sWxVH88+IUPuDW4fSnuUtyNXXYPPfTQww8/fNFFF23fvv2sswy4wfSz5aovPnPZX80XKsDH\nkz3hhq///WMXzxdE4MM9PXHvV+3tY0bIQQ4c9GzgaijuSyAl77h9irtvlSGnWBXP+JtfAsC+v9/u\nxuHbXqVEprgDwPrBOCrckzYp7lGkuHs6VUYdwNQiZF2dwVSoQUt3egdkRcl0SpUBeop7XW5vX/Ji\nKO6mZr41EuDYEM9WRbkkiDGCPx8U4o6nuK9Jxkb7Y2MLxVcnMqf2Rf9pxz4A+JvLzzDJDmALaF6v\nW4JljO3y3HDDDf/8z/8ciUS+8pWvXHPNNffee+/09DT9gwrHk8lkY9WufrknmUwml0PVDgClmijJ\nSizI80tGT1Mh4RXFXRvAROGqSNo3gwntPrdU3L3SkGABaS00czZnWyS/z1KQUI3cJnisH1J9mHYp\n7qhs8rbi3t6z12/cC76IQkWUFSUW5Ns7TOKhANCQP/UY3AGgKxzgWaZQFWui3gBKU+eaLyJBY1qi\nOjYVt4/2PZsGAOCJfce/+vDeXFl414aBy89aQXI8TgON6HLLSErD9qzNmzffeOONP/nJTz73uc8d\nP378+uuv/8u//MuHHnqoWCyacXzLFrMzYrUBTO64TNug5rjTU9xtmcHUfvvYV9z1Uw9jnsmW7T0S\nn0bQ2NRopxKqDes1m3syYc+Axuiy8bi3Wk7RDCaSKPc2A54aoTWCXlPcO7wcwxjuT82rirsVMiJ6\nFdLCHVllsBR3ALho0yAA3PXEwYd2TYUD3D9ceaarg9uXEqd0yVkDZl8Fy7Jvfetbb7nllp/97Gfn\nnHPO7bfffvfdd9M9smVOtu0CSo43BjCJslKsigzTofdIJ6ribqNVpm1zqj+ASQ9prXNuxlfcnQRq\nTiVS3LXCfdCu5lTkcXfJrR0DWVHa9/ojS0nKiBd8EXp8MqCdavIR9GgWZkLH42Jf3NgziaqsWai4\nE96sU6g5FVdxP3fNiSTimy5ev7qv81wdd+Flj3sjU1NTjz322K9+9atKpXLdddddfvnlFA/Lx2wL\nXTTIsQxTFiRRUnjOrc/O9aHTLI3Hf1Vxt6M5tX2opWaV8Qv3zqS1W++0r7g7CQqKu2aVQa2E1uN5\nxT1fEWVF6YoEWvkzMWITF4HiJjs6rbUqinTFQ6W/nsTGJHpruhf/+q2H4Oj00kWjVTdVNDw2tZEQ\nz64diB2eKwLAn72TdGSKA0m4aniq4WU0nU4//vjjjz766NjY2IUXXvg//+f/3Lp1K+OxXRMHkDXZ\nQscyTDzM58pCriKYOj/CVCj6ZMABcZB+cyo5qbrinq20/04fK9EUd/zCvS8WHPv6B+kdkWE8r7hr\nWZAtl9P+enMqLulOL4GgZpXR53EH488kqsfdGqsMjYY09OGS3Ov/81Pn75/ND3eH3av0tSEeprPJ\nYw3GLrsf/OAH99xzz9atWz/84Q+/613vCoc9PwTJNijOFWpFV5jPlYV8RXRv4U436j4ZDwLAfKEq\nKwoVCV8/aFVttcHiF+76qSvuaFiPj0MoETen2o7nFfeOo5HIFXfVQ99J941T6hRUm1P1KO7xEBhS\n3K1KlQFKQ1dUxZ3gXt8XC77di6O7EbQ2eazBWOG+ZcuW+++/f2BgwKSj8amTNbk5FdRtvrKrDRh0\nw3QDHNsTDWRKQrYs4MVmYaM1pzZ/UfQG3d6QYA31VJlpX3F3EsWqBAARNxfunk+V0RqrWi59quJO\nVLijZ4NOVhmqzam6PO5GFXe02Wulx53gMUZR1DNv8X3NRXi5OXXFihVtqvZyuZxOp4kPyQdAS1g3\nV3GPUAiZspcs7bOEdBfrg2Xabx2EA1yQZ6uiXNWdVrZsqSvus37h7iRUxZ3A42470ZDHc9wzbbMg\nQevgNDSoqPlLdLTKUOoUzOtLlQGAPoNR7up4RItSZUjv1IWqKMpKNMiFeHPnvLoXL8dBPvHEE7fc\ncstvfvObxvBHQRAOHz585513fuQjHxkfH6d9hMsUC2JiPaDjUjcUqTZ3a6PcZUVB+k2bJirfLaOT\nusf9eL4qyoq9B+NTRxvA5CvuziXdqX8RhQlmSoKsYP5lIVG/Y4skNcW907paJ2lwM8HaVBlUU+Iv\n/uQ+Gc/jrgFMxp4Xr7rqqrPPPvuuu+760pe+NDg4mEgkstns/Px8KBS68MIL77zzztHRUXOOc9mR\nNXNsKqIr7PqsEupnyRbFHY2zToR5rvWwra5wYC5fzZUFu7Lw3EJdcZcVZb5QHe7y+3AcAXlzqu0s\nG8W9ZXnHc0xXJJAr45sJ0zqtMpTkTwNWGYObCVamyiSIJba075PphMfjINeuXfuNb3wjl8sdOnQo\nl8sFg8FkMrlu3TqW9bdgaGJFcyqNsQ72kqOddq/NYLLUZZHtdL8EX3HXTVoLis6UhJlsxS/cHYIW\nB+lixT0S4ACgJIiKAp7MUcvqiHzpjwVzZSFVrOFVge0HPNWhpbjrb05Vh8Lqt8qY34RWp4t4cqqW\nBekX7i2hFWRkDZiXXVdX17Zt2+geik8jWaptl03xwAwm6qGZaJopOJfLAAAgAElEQVS6xVYZPdk4\nXREKwQKeR1HUW9Tmka7nDy1MZytbV9l9TD4AoDlMXK24swwTCXBlQaqIEiriPQZKlWkf+dIXCx6Z\nLy4UauuwIio6BtcgogGeYaBCPGYkrzsOst9wHKQIVlllyHPckeLeF7PiaF0KrU0ea/BlcodiQdoU\nlbEO9qK29uMOlViKprhbapXRU7j7w1P1UKqJgiSHeHZNMgZ+lLuTQA4TVyvu4HW3jJbj3k6X7SML\nlsmoHvcO0i/DaNYFMgW0UNWruCfCAZ5jilVRZwBA3nwvax1yiQ0ZCN2b+2wBXk6V8bEMC6wyaDnI\nulnEpZvjDjYNT9WzfexbZfSQ1oaMjHRHAGDGH57qDCRZKQsSaG4T9+Lt/lTVCd1WlyVJhFQUAyt2\nPEShWRBZlvXo4gyjTuRN6bC5o4CvIM9aE9KiTc4msMqUOrsxlzlhnuNYBm3y2H0snfELd4eimkD8\nOMi2ULfK2NKcmtMRkUa+di8H6uEJw10h8GcwOYaKoEbKWDzXjDrREA+aX9976FLc1SZOnMK9UBUl\nWYkGuaCOelf1HJPpSvmqAPqsMgDQrw7g6/zW8hZGygCNVJkMUtz9wr01tDZ5rAG/cM9kMi+++OL4\n+HilUhEEXwikiSgrxarIMoyp6Wnkveq2Qz3tXouDdKpVxs0flgWoVs5ocLg7Av4MJsdQVLMgXWxw\nR6DJr0WPDk/NdspxhxOKO84KiWYAtRnw1IjqOSarovQ3p4KRzQR039GTMkmFekEp4ebbpkp+HGRn\nXOSWwSzcf/zjH19zzTVf+9rXnn322YmJiU984hO5XI7ukS1nNP8cb6pApfWqu7gWpG6V6YsFGQYW\nCjXsJRIDdd+g7f3SL9z1gKycPdHgcHcYAGZ9xd0ZFKto+pK7fTKgPXuUvGiVkRUFKe7tt3n7DKav\nNNJxwFMj5FVUTZRrosyxTJjXdeH1x/WqNjkLDe4AwLEMmlxWxD0bqU4J/T5wYuyXC26yOIX70aNH\n77///nvvvfdjH/sYAKxfv/6MM874yU9+QvvYli/WdKxrQSVuvQkpCv1MLp5leqPB+j3MGjQhqq1V\nxv0RQBaQ0sITRrrDADCdreAOivGhSckzinuIA4CiF5tTCxVRVpR4iOdbT5MAMo97Rt/0JQT58FS1\nMzXE65S/9PfdWmyVAeIkCb85VQ9UNnmsAadwf/PNN88///yRkZH6V84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UscNa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\nO7Lt2feVPh61ENrrth/yVW1aSe3t7e0nuokutZ3u9ODtU2JfdF99ff1Dq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HC3ciIiIi21hMZDLZXItX9rqc4pGteuGuqqYeGdUeC3ciIiIi29BHynjzj3T43a0+Vyyl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\nH6OKOyqO6TanOnpyKpLbV7fUEEpHadUnBBMNYJEUNZUvc5yBK8EimuJ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T2/E839vb+x//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\nACgBKNwBAAAAAErA/w8IL9yxNyckQwAAAABJRU5ErkJggg==\n",
       "text/plain": [
-       ""
+       ""
       ]
      },
      "execution_count": 4,
@@ -171,6 +174,35 @@
   {
    "cell_type": "code",
    "execution_count": 5,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "dict_keys(['dmm_voltage', 'dac_ch1_set'])"
+      ]
+     },
+     "execution_count": 5,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "data.arrays.keys()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {
+    "collapsed": true
+   },
+   "outputs": [],
+   "source": []
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 6,
    "metadata": {
     "collapsed": true
    },
@@ -181,30 +213,33 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 6,
+   "execution_count": 7,
    "metadata": {},
    "outputs": [
     {
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "Started at 2017-06-28 13:40:05\n",
+      "DEBUG\n",
+      "['dmm_voltage', 'dac_ch1_set'] dmm_voltage\n",
+      "Considering the units V, V\n",
+      "Started at 2017-06-30 14:24:05\n",
       "DataSet:\n",
-      "   location = '/Users/william/sourcecodes/qdev-dk/docs/examples/PlotTesting/plottestsample/020'\n",
+      "   location = '/Users/william/sourcecodes/qdev-dk/docs/examples/PlotTesting/plottestsample/085'\n",
       "      |   |  | \n",
       "   Setpoint | dac_ch1_set | ch1          | (50,)\n",
       "   Measured | dmm_voltage | voltage      | (50,)\n",
-      "Finished at 2017-06-28 13:40:07\n"
+      "Finished at 2017-06-30 14:24:07\n"
      ]
     },
     {
      "data": {
-      "image/png": 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1diLyqvYKJHvhztanYrAMgL6gcK98Qlr0hpMcR7Vzl8hm3rCs3pEWxQtjpRss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IFe5vzyVMXP5afmN5IZ4x7SAAMAMId1CdpXRBlikcdPOu1eBnWI3KWG1ZNwCA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\ngOIhT4QcmXC6x4ioEYE7AAAAAACH4hMhlVmQCNwBAAAAAPij7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       "text/plain": [
-       ""
+       ""
       ]
      },
-     "execution_count": 6,
+     "execution_count": 7,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -219,30 +254,33 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 7,
+   "execution_count": 8,
    "metadata": {},
    "outputs": [
     {
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "Started at 2017-06-28 13:40:08\n",
+      "DEBUG\n",
+      "['dmm_voltage', 'dac_ch1_set'] dmm_voltage\n",
+      "Considering the units V, V\n",
+      "Started at 2017-06-30 14:24:09\n",
       "DataSet:\n",
-      "   location = '/Users/william/sourcecodes/qdev-dk/docs/examples/PlotTesting/plottestsample/021'\n",
+      "   location = '/Users/william/sourcecodes/qdev-dk/docs/examples/PlotTesting/plottestsample/086'\n",
       "      |   |  | \n",
       "   Setpoint | dac_ch1_set | ch1          | (50,)\n",
       "   Measured | dmm_voltage | voltage      | (50,)\n",
-      "Finished at 2017-06-28 13:40:10\n"
+      "Finished at 2017-06-30 14:24:10\n"
      ]
     },
     {
      "data": {
-      "image/png": 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JYI27TSJwT0SVmWQMPdvBJO4HZhoy7hlWNFFrDNyJiIh2LJFx3zMWBzPuNonO1OGoPDMa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\nCsy9iUGslhGzIDtm3Me4PHU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B+5ZNhDPJmCRhMVPuZvyTsTbVXnPqoF+4qSlRKjM10vy1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jDlA1yQwbwLQ8426j0+viq8uKNqzxUwUdIZXAvcmz5HFlIgmuZABgRZHU8hp3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uOQo9xVPPwok1OJbWjayCqTsK1VJqD7GQlFyqxrCqDJnSd1uGUWSKhcqxBqg2VKkTLYqp5VQ/Qw\n6rYN0SAA9M+uwGAZZIHrbQ0v/hyv2doOAE+/OW2owpIgmr2mpMqosMpojBUqC5rBVDZYRpJkxb3T\nqsI9XQCAeicL0ki0Fe4PPvjgpk2b/uVf/sXv9wOA2+3etWvX1772tbVr1z744IPGnCGNWLXNqhLF\n4051maJYZWqvdMjjTmRyqqK422YMoX0npwZV38kqgRT37qZgb2sIAE5O4hfuKINPvT/BASFHudM3\ng0kJccffd7VkeCper8UKjnJHMa8lnwyivc53YXdjnhefenPauEOnyIW4w5kNRjVWGQJRidFQRcV9\nJpXP82JDwKPprqGMpimqCWiuDrLKNDqLrZFoK9yPHj26e/du9L9dLld7u2xB27Vr17FjxwifGsUo\nHndKFXdbeNzlHGXVVhkik1MDXgJTgczEzs2pLtCX44487l1NAdS4pkdxjzudqVjIijt9VhlkcF+D\na3AHixT3TIEXRMnndnlqZeAupqc5BCu0P/XkVAoA0JbaYq7b1g4AjxjpllH2pcmsCWbmuANAS8QH\nFRR3DIM7AKBeUl6UMkW934h5Z2yq8Wgr3F0uV7EoV6t1dXXf+9730P8WRTPGJdCD7HGn1SpD/wAm\nUG6c6qwyPBBS3H2ci2WYoiCW4koox+6KO/YzkiTB0Kx8BzqrBRXuOjzuWcfjjgPabR+ltXDXpbj7\nLVDc8Yq29bJVZiUW7nJn6tLC/Zqt7QwDz56YMW5bO5VHmgiZpVXxuJuRKgNnPO5l/Mloo1KTTwYh\ny5G6qxpklWlwrDJGoq1wP+eccx577DHprbspkiQ9+eSTZ599NtEToxplm5VS4dYWcZBopUupWOmy\n5KwyDCMrwbYQ3SVJPs+ILRV3tXeyssTShXSBD/u4hoBnTb0/6OFmU/lYGjOXEI0ZdxR3rXQ2+gFg\ndD4r6t9BJ4qesakIS8QXvKKtR25OTdP2KehElKS+aeRxX9qyFg17L+5pKgriE8cmDTp6glwWJCh7\nuTUVd0mSPe56FffKzakjykal1tdETkJ0enpQUmWcxdZAtBXuN9100/Dw8J133nns2LFsNpvJZI4e\nPfqFL3xhcnLyxhtvNOgUKSTg4ViGyRYFOoVbJYqE6m9OUHVzaoZcqgyUpBE72NwzRXljnXPZL1hL\n5wPS4FwaALqaggwDDCPf2rHT3OVUGadw10jQwzUGPQVerDJf3RL0e9zDVogveIV7nd/dGPTkisJU\nwqQECHMYj+cyBSEa9pa1VVy/vR0A9h6aMOjoKaL70nJLTy2dIlPkeZFALi1S3AlaZUC5LOOO4m4H\ntBXuwWDw3nvvraur+8QnPvHud7/76quv/uQnP9nQ0PCd73wHtauuEhhG1rPpFN3tZJUxN8cdSg8M\nOrzXpmFfnwwops8MbuGOImW6lNsP2kw/geuWUawyTuGuGcXmTld/qqK44990LNk1xbZJrF+JY5hO\nVPDJIN5zbruLZZ7vmyESdbIcsvvSaEM4U+Crb4okCHn2WsI+qKC4Y2RBIkgFyzgedxPQfNU2NTV9\n9rOf/cxnPjMzM8MwTHNzM7Mqc/YjfvdCtpjIFRvpe7JMEnXvGYRslVGhfBPMcQcSEz1NwxYPYJWQ\nA3xwrTLDsTQAdDUH0f/V2Z/qNKdi09noPzQaH4llLuhqsPpczjCh2yoTtmIWB3bh3hMN7Rua759J\nv21jswHnZQ0np5NQrjMV0Rj0XLah6fm+2d+/MfmhCzqJHz1JdF+aYxmf25UrCtmiUEVjipPwyQBA\nyMt5OTad5zOFpYfTqbjrL9yRoZHCumgloXkAE4JhmJaWlmg0ujqrdjhjkaSx/iO7CWgQ6q0ySo47\nSauMTQr3ItjT4A7KDgn2+zy4VHEPgZ7CPUMggm110kFflHumICxki24Xq0fVsyQZTKfivsISIVFn\n6nKDewk0iekRY9wycvYaubtkwFM7R4tIZyoAMEz5YJlsUZhO5jmWwXCRkSrc4xlne9NwMAt3B3kG\nE32JkEVBzPOih2M1JY6Zj3qrDFmPe1C10m85trbKKIo7tlUGhbi/xSpzciqJ156nKO6OCKQZ1J9K\nlVUGWb31TF+CkuJurvKCqiKM4TuoP3WFzWBCz+FoM60sV29p41jmj6dnsbvSq0A2xx3O7OVWu7MQ\nLGpRlPtM6i2F++h8FgA6GgIu7eNgiSnuzuRU46G6tqMZJU2MOuHWLv4K9UNMc7LHnVRuly4l2EyU\nEHfaP8qyuF0sxzK8IBV4nAZuZWyqbJVpCfsifnc8U5xN4XRJkhK6ViEUDk/VnwUJAD7OxbEMkjkI\nnVdt8BX36EpT3AVROjWdAoDeloqFe33A/fbeZkGUHj9KPluG+HzxoIo9Ruwnt+Wg4anTb+1XRgZ3\njCxIUHr3dRbuvCglskUXy1CejWF3nMIdE2qj3G2RBQklj7uKJx+kuPs8ZK5V9Q8MlpOwQzpQJRgG\nAriieyrPx9IFD8e2RrylVzurJQTKqEWtOLu32KAigKoZTChSRo/BHQAYxoIod+woQBSvNBLL0plj\nhsFwLJPnxfY6f/Vb1fXb1gDA3sPkJzElSc8Xl2cwmWKVgQrBMtgGdyAUB7mgrLSr1UNtEk7hjkmE\nVquMLQzuoDxaVN9YBABRkpDiTswqo3rEneWk7GyVAeWtxkjeLEXKsIuW/7Nw+1N5QUrn+VISlIMm\n1tb7GQYm4jleoCVEXH+kDMJ8twx23ebl2LX1flGS0FdjBYCiXc+qbHBHvHtLm9vFvtwfw9tqqwLx\n+eJqJlcgzx6RXNqywTKop3+d9hB3IGSVcSJlzMEp3DGJ+CmNg0yRFhIMQm5eLPCCWK0gyBVFAPBy\nLEvoEd5GirtilaH9o6yEHOWuXXEvhbgv/qFsc5/UXLgnlB5fUpfQqsLDsa1hnyhJ4wu02Nz1R8og\nlERI88QXudcCa+enpzkEK8gtg6JdK2VBlgj7uCs2RUVJ+t0Rwm4Z4gpXUIUJk8j0JQR5xZ1E4e5E\nypiDU7hjIivu9FllEjYxRrMMIyuyVSUKsgZ3ULe8UgJ6LIxQ/1FWIog760qOIn6rbrRJ7k/V3J/n\n+GR0IrtlqLG56x+bijA/ETKhwymBbO79K6Vwr9mZWuLare1ggFtGVrhIetxr7+UStMoow1PLeNy7\nrCvclfwup3A3Fqdwx4TaAUx2scqAsk1ZXfyWDe6EfDKgO1/cTGydKgNKtxbG5oYSKVNOcdceLBPP\nFgCg3omUwYW2YBllbKp+qwyyO9rAKgOlRMiZFRIsUzMLssSuc1q9HPvaYGyS6OBY4qtrAElCVe8s\nC1lide1yxV2SZMUdrzmVjOKeKQJAo6OSGIxTuGNiSQywGoinXBmHbKWoWtiRHZsKthrAlLC9VQbt\nqGBYZTIA0PVWxb0p5GkMelJ5fjKhrYJc0OFPcAD6gmWIWWXMTQaTJH2F+wpS3HlBOj1bI1KmRNDL\nvWtziyTB744QC3QXJQl1kQbJKVwhFbOiDVDczxTuM6l8nhcbAh68p5GSiQAvchfheNzNwSncMUFX\nuUHTmPVAvFneOMJeN9QKlkFlH6npS2CzAUw2V9xVBBuXZahc4Q5nRHdtoiOpaYWrFqqsMnlejKUL\nHMs0hfQWB2Fzk8EyBV4QJZ/b5cWasLGSPO6Dc2lekDobAyoVGTSJaS+5SUyZgiBJEPC4MPLOKxFQ\nsZeLCgYitr2mkJdhYC5VKDWJ6TG4AwDnYoIejhelTBH/5jifLgBAveNxNxincMeEWqtMknQ8rXGo\nscrkCoYo7jZqTrXF5klZ8Ian5nlxMpF1sUxH/fLCPQQAJzT2pzoed510NvhBme1iOcjg3lrn099q\nbHJzqk61tb3O5+HYmWTeFmtXdU6oi5Qp8SebW/xu14Hh+fE4mYvQiGknanLcCYoIHMs0Bj2iJJWm\nU+kJcUfIPgIdj7LzjlXGFJzCHRN6rTKkJzkbh5oaOlMk7XGXl1fbeNwjdtg8KQve8NSRWEaSYE29\nn3Mtrcw2YSVCLpCLYFudUBXlPolC3CN6fTJQmsVhlviis3B3sQzq+uifsb3ormRB1vbJIAIe15Vn\ntwDA3sNkRHcjstdqKu6iJJUSrogcMRr2wSKbu6y4Y2VBIvRHucuKu2OVMRincMfE5G1W9djI4x6W\nZzBVew+zq1pxt7dVJoAVmY98Mt3lbj/IEdun0SqDGsIcqww2rREf52Jmknkzh4xWQhmbqrczFc7s\nmtpDcYcVND8VbZqpL9yh5JYhlC1jxGYmmsldxeOezPGSBEEvt1ySwGOJzX1En1UGSPSnIo+7Ewdp\nNE7hjgl6WE/leVFPK4cB2KjaU6wy1cRvVLiTmr4E9mlOlaQVkuOuNcBnCM0QaQwu/0+lYBlNXzrF\nKuPcSzBxsczaej8ATCat1ykmEmQ6U8H05lT9hXsPCpaZtX2wDGpT2aSlcL9iUzTo4Q6PLgyT6LVI\nGWAorTk5lWBnKkIJlpHDdlAYl7WFO/LtOM2pRuMU7phwLBP0cJJEnenCRh53NeI3SpUxojkV+4HL\nnCe1HC/wouR2sXitbDSg5LjjKO7LO1MBoD7gbgl7s0VhTIvfmvj9chXS0RAAgIlkweoTIRbiDsoj\nsWkBAwQU9+aVYJUp8OLgXJplmA0taj3uAOBzu3ZtaQWAR0m4ZZIG7EsHa5kwiS9ESxR3nc2pQKJw\nRypJQ9BZbI3FrjUBDdDpllGsMjb45qixymRIK+5uF8uxDC9KBUHzvj8vSJ/8xes7/umJ23+2/79e\nGRqcSxtXxNto56QSaCJJ9R2V5Sgh7uVvP0h0P6HF5u6kyugH9adOJKxf6xSrDDGPu50U92gI7J8I\n2T+TEkSpqymgVZK4bls7ADxCwi0jK+6GeNwrXk7Eu+QXR7lni8J0Ms+xjJ5nWp2FuyBK8WyBYWzc\nl2UXbFwWWE7E755M5JK5IgABwyUpbDSASVHcq1plSOe4MwwEvdxCtpjO815O247eMyemf31wDAAe\nPzr5+NFJAOho8L+jN/q2jc1v29hEdn8wSbSNyRKQVUZrjvuQPDa1jFUGAM5qC79warZvKnXV2a0q\nX1AewOQEHegA9afSYJVBzalr6ol43E0NGED1UES34j4wk5Yk0J2pYxknp1Og0eCOeGdvNOzjjo0n\nBmbTyDWEjexCJHqXDNUaFG2A4u4DRXFHoU8dDQE9AZfo3OK4hXsiV5QkaAh4CIZsOpTFBuUdtZgc\nSqCSZB4Zo23wyYbkwr12cypBxR1ALtxTeV5rD83DB0YB4CMXd21ZE3m+b+aPp+dG57P//erwf786\nzDCwZU3dOzY2v723+YLuRv3+ltTKUdw1fEEEUULpJZU2fEs2d/WvqWQnO7ZLfFDhToNVhqTi7reZ\n4t4Q8NT53QvZ4kwqj2wSdkTpTNXgk0F4OPbdW9oe3j/6yKHxv76yV885GBHhIKffVtYpEsZYZZDi\nrj8LEnQr7vNpJ3jXJGxcFliOyRZJlRiRUGsQqCqt3iSgeNyNkEa03bDnM4X/OT7FMszfXNXbEvbe\nfPE6QZSOji+80Df7wqnZfYPzR8cWjo4tfPe5016Ovain8e290bdvbD67PYwXOJ1YAYW7PDlVg1Vm\nYiHHC1JL2Ftpj2WTRquMJMnpZo5VRg9oeOpEwuLCvSiIs6m8i2WiIQJla2kAnChJ+lPha6L/OmQY\n6GkOvj4SH5hJ2bdwR0/dKNpVK9dvW/Pw/tFHD0/oLNwNyXGvFXsQzxSAaC5tVPa454CEwR1KhTtu\nHKQTKWMaNi4LLMdkwUYNBV4s8KKHYz126GhEK12yeo57gXBzKigWjpTGtJPfvj7OC9IVm6KlW6aL\nZbZ31G/vqP/f79qYKQivDcZe6Jt9/tTsmxOJ5/tmn++bBYDGoOeyDc3vPKv5um1rNBl+7B4pA6VU\nGS0PSINzaQDoquCTAYDe1hAAnJpOCaKkZkM2U+B5HeMqHRCdjX4AmLDaKjOdyEsStES8RPbiORfj\nd7uyRSFTEExQOog4JdZHg6+PxPtn0xevbyJ0XmaDl6Q8gAAAIABJREFU4lx7tVtlAODtG5vrA+4T\nU8m+6VSvlt7WJRjRQYS2hTMFodJzoEHNqUhxH9Ed4g66FXcnUsY0nJsZPhTOYJJ3AO0gt0PJKmNu\njjssCpbR9FcP7R8FgD3nd5b9rwGP6/Kzondee/bjf/OOfV+86p6bduw5v6Mt4oulC3sPj3/mocOX\nf+2Zn708xAtqu1lXQHMqRo77cOVIGUTIy7XX+Qu8qDIVTm4Ic+R2fTQFvX63K5UXrO3FR1mQRHwy\nCCUR0ox/lFy36TMS9DSHwM7BMtmiMBRLcyyzHsukzrmY3VvaAGDvIV0tqsoAJpKrq4tlUO2OdomX\nEyft2Qt6uYDHlSkI6TxPRHFHLhfswh1tKTQ4irvxOIU7PnJvE01WGbQe2aWjUZ1VhgcDPO6g0Xs9\ntFA8MrYQ8bt3nVO7J7I55H3veWu+fuP2lz5/5VN/e/k/3LClJeydSea/+OujV33zub2HJ9TEkCuK\nu40L91CtUYLLGaqluAPAprYQqLa5O1mQRGAY6GjwA8CIliBO4shjU0lMX0Kg79dC1oxdUyKXohLl\nbtfC/fR0SpKgpznodmHWHtduWwMAjxwe15PopVhlCC8LAXkGU/kVT3938nIUt0yepFUGW3FHWZCO\nx914nMIdH5PTxNQgr0c2qfYUq4yK5lTCVhnNSvCzg1kAuH7bGk2OC4aBDdHQRy/rfvkLV37n5p3d\nTcHBufQdPz/w3u+8+MKp2ep/mzQgsMxk0KeWKWiIzB+spbjDmf5UVWNo4iRkTgdQ+t5GSIy/wYZg\nZyoCaRymKu766rYN0SAA9Nt2BhPqTsGIlClx6YamxqCnfyb95mQC+0VQIgLxG2X1dnwjRAQ5WCaR\no6Fwj6cdxd0knMIdHxqtMmiSs82sMio87kQV95BGxZ0XpWcHMwCw5/wOvCOyDHPdtvb/+T+X3/X+\nc6Nh75GxhVt+9MqtP37l6NhCpT9ZAVYZjmV8bpckVdw7Xs5QrHbhLvenTqpS3OWGMMd2qRu5cJ+3\nvnAnMn0JETZLfJEkMnUb2owansvwIl0Tu1WCDO5nYXWmIjiWec+57QCwV8ckJoNW10DVdnziOe6g\nKO5vTCRyRaE+4Nb5L4ooJgK83YxYxvG4m4RTuOMToW8AU9IA655x+N0ulmHyvFjF+Z0jneMOZxR3\ntdXk830z8ZzY0xw8r7Nez3E5F/ORi7ue+/S7/u7dm0Je7vm+2eu+/cIdPz+IOjKXkLB/cyooH5zK\nKHdJgmFklWmsZpVBbW19jlXGXNAMplFLrTITcWSVIVi4m2R3zBR4QZS8nN5ByAGPq73Ox4vSqKVP\nUNic1K24A8D121Hhju+WSRkzXzzkqdaOb4zi7gWA/YPzUGvZVAPnYoIejhelTBHnUXY+UwSARmd7\n03icwh0fZZuVIquMvcK/GUYJeKksfiP1wkfW4151eV3Ow/tHAeDG8zuIRMYFPK47/mTj859915+/\nY73bxe49PH7VN5770m+OziiTqxErQHEHje0Es6l8piDU+d3VRamNLSEAOD2bUtPpKzeEOYW7biy3\nyvCi9NjRSQBYp7tAKWFaMhjBog3Z3G3an4qsMpv0Fe4XdjdGw96huczR8Yo7ltVJGpPiUF1xN6Jw\nR4r7/qF50B3ijpB9BFiPsvNpZ3vTJJzCHR8KrTI2CnFHoPagKoUd8cmpoLGaXMgWf//GFMPA+3di\n+mTK0hDwfPHas5/9uys+eH6HIEk/fWnonf/6zDeeOFE6K2Vyqm0+yrIEa00TXIwanwwABDyudY0B\nXpAGyu1ULGHBgO3p1QmKcrewcH/q+BQArI8Gt66tI/WaEbOGpxIt3ENgz/7UdJ4fm8+6XazO1EIX\ny1yztR1ws2VESUKqTZD0jTJYeQYTL0jpPM8whLUYpLhPJnKgOwsSgdqB8KLcnRx303AKd3zCslWG\nJsVdHghnmzIFvYc1FXcjPO4qFfe9h8eLgri91Utwg77E2gb/N27c/vgn33nV2a3ZovDtp0+94+5n\nfvLCQIEXV0BzKih3MpXPSEOzaVCnp56l2uaujE2199tIA0jPG53P6knz0MP9Lw0BwK2XdBOclWRa\nwADBwl3uT7Wh4t43nQKADS0hTncM/3Xb2gFg75EJjKsRmSSDHo7INIDFBCrfWRKyEOMmO+orGj5z\nV9LZmYrQ05/q5LibhlO442OaWqMe2eNuH8W9ulVGkkoed/Ij7lRWkw/uGwWAK7qIJdAtZ1Nr+Ecf\nveDB2y89v6thPlP4x73H3vWNZ4+MLcBKscqoHJ6KFPfu5tq3H9TcpsbmjppTHY+7fsI+Lux1ZYvC\nXDpf+7dJc3om9eKpWb/b9cGdawm+bNhcxZ2Ii6AnihIh7RcsI89MbcUfnFTi/K6GtohvbD57aDSu\n9W8NipSBM5JQmeXOoGabxQN0rS3cRUkiMqnAQQ1O4Y5PySpjlQS1HNuFf4eUqeNl/2tREAVR4liG\nc5FUKUJeecRdzd88PZN6fSQe9HKXdBpYuCMu7G586PbLfnjbBb0toTGlBXB1Ke5yZ6qKwr1FbZS7\nHAfpd0QgArSF3QAwErOgP/W/Xh4GgPftWEs2Btu0XVPH4w7KFpnOzlQEyzDXbmsHgP949rTWvzXO\nUFqlF9+ISBlQPO4Iawv3ZI4XRCnid+vfTnGoiVO44+PlWLeL5QUpx2sYMWMoKft53KsVdsjgTjbE\nHbQo7mha6nXb2r1EnxwqwTCw65zWxz/5zq/fuH19NPil685psrlfUOnWUlUYKSHuta0ym9rCoDS6\nVcexyhCkPeIBKxIhMwXhwf0jAHDrJV1kX9m0yakEC/eOhgDLMJOJXE51yioloNkLRAp3APjgzg4A\nePr4lJp1YDGKoZT8XRLluJcdOWeQ4r7YUE5kvgF24S4b3B2fjCk4hbsuTAslUIlxS5JBIDt+pRra\nCIM7qB7AJIjSrw6MgY74djxcLLPn/I6n//aKj7+9h7gL02Sq7B0vZ1jF9CXE+miIZZihuUyeF6v/\nJhK6HKsMEdrDHrCiP/W3h8aTOf78roZz1kTIvrKsuJtVuBPZLuBYprPR+mhODPpIZEGWOGdN5OaL\n1vGidOcvj6iZRV0C3a+N6PtHk1PL3lkUzx7hurZ0g/C5XUSkbvzCPe1IJObhFO66iJgVA6ySpDHx\ntMYRrlpDG2FwB4CQR1Xh/sfTs5OJXFdT4IKuRrInsHoIqE7eTGSL85mCz+1qCdfWjbwc29UUEESp\nf6aG01dOlXEKdxIoVhlTC3dJgvtfGgQD5HY4o7jbySoDiili2NIptlpJZIuTiZzP7UJPHUT47Hs2\nN4U8+4bmH9g3qv6vjLPKVIk9MHqghM9NppbTq7jbfIvYLjiFuy5o609N2m1qD2pOrXTjlEPcLbLK\nIJ/MB3eSiW9fnch3MhVWGTkLsjGg8t1Gbhm0+V4JXpDSBZ5lGBvtQdFMe8QNACPmCr2vj8SPjSca\ngx6UAEgW81JliO78dNqwcD85nQKA3pYQwViVOr/7y9dtAYB//t3xuVRB5V8Zl72GNKay3VNGN26S\n2pfWobg7kTLmsaLuZ4ODgwa9cjweL/vibigCwKmhsUYRcxIEWeLpHADEZycHCzGCLzs2Nkbw1RZT\nzKYAYHwmVvbt7Z/MAIBL5Ml+soIIAJDOCwMDg5VuIumC+NiRCQC4qAUGBwcnJyeNu7psAd41kEsl\nAGBydr7mu7fvdAIAmv1qv8UtXgEAXjs5ur2+4g07nhUAIORlh4eG1J5xZZxrgM3MA8DAdMLM9+G7\nT48CwO6zIuOjw8RfPFMUAWAhU1D5L8K+BiZjCQDIJ+cHBwmoPGEmDwBHBycH1+h/MW1g3wteOjYP\nAGuCDNmLZ1sdnL82uH8s/fkHXv3Cn6jyNA6PzwKAVMhgfpSVr4FUPAkAs/Hk8l8YnpoDACmXIv7d\naQxwsQy/sdFD5JVziRQATMxV+46XvQZOj84CgEvIrYZFslJBSITu7u6av7OiCnc1/2A86uvry754\na0MMRtP+SGN3t+kraDmyfB8AbN7QTXzHyqD3dt0UCzDp8gbKvv5IcQZgoD5c/r/qwcMdL/BiW0dn\nJaHiv18dLgjSpRuaLtnaCwDz8/PGXV12AeMd6Jx1AUywFT7fxWQGTgHA2Z1RlUe5KOG9f//MVI6t\n8vunZ1IAbzYGfUQ+O+cayPMiPHt8OlXsXNdlTvdFLF145vRxhoG/eve2jgbyyU6iJLHMmzle7Ojs\nUhNdhX0NFGAMAHq713Z3NWD8+RK2J73w8tQCz1lyQeIddO5wGgB2bmwnfs7fuDn67n/7wxMnF/7s\n8rMv29BU8/fdfQWAqbXRRrwzqXINTEsxgGGRdS//Bem1BQDoXtPS3d2JcdAqHPhy+ZPBY56NAwwV\noMalVea/nsgDTHW1Na2GRbJSQWgajlVGF6b1NqkEbQLaKA4SuVYqbVVnC+THpsrHrWVzf3j/KADs\nITotdRWiTE5VYZWZywBAt+rhf2e11k6ElDtTnX4pQng5Nhr28qI0uZAz54i/2DdSFMQ/2dxiRNUO\nACUbldFruCEe9zlbWWXkzlQCIe5L6G4K3vGujQBw56+OFGp1q4My7cSQVBlvxVSZhMEed1JgW2Xi\nyCrjeNxNwSncdaFEuVORKlPgxQIvopBKq89FLSHZbl5+mZA97qRTZaDW4KeB2fS+ofmAx7V7axvx\nQ68q0ANSSkWqjOxxV1249zQHOZYZjmWylUPxjG4IW4V0NgTArERIQZT+62V5WqpxRzHH5m5Qcyo9\nI0RqckKevkQmUmYJt1++YX00ODCbVhPrblxosvk57sTBLtxjGcfjbh62qfDohKpUGdtlQYKyOVAp\nLjBbNEpxrx5T+MsDowDwnq3tQdKBNquNoLfinWwJQ7NpAFjXWDvEHeF2seujIUmCU9MV+1PjTqQM\naVAkiDnBMs+dnBmdz65rDLzzrGbjjmLC8FRJIiy4RvzuOr87WxRmUxZMscUgli7MpQpBL9deZ8jO\niYdj//n9WwHg3mdODczWGE2l7EuTXxaCpk9OJQ46w0RW81jJ+UwRABqpfzJZGTiFuy7CZoUSqEHJ\ngrTTN0e2ylQawGRMjjtUjXIXJenhA2MAcKO58e0rkpqWJESuKEwmchzLrNXiiKjplolnC2AHlctG\noDwTcxLEf/rSEADcckkXwRyS5ZiwhmeKPC9KXo71csRuuGhvyi7BMuhL2tsSMu6TvHh9057zO4qC\neOevjlQvOo2bLx6snH5rl8KdczFBD8eLUqao7RuBUmXqHauMKTiFuy4ifkdx14Vslakgdxk0ORXO\nmBHLrE0v98fG49m1Df6Lepz4dr3IE0nKmT4XgxIG1zb4Nc0QQZNcTk5WLNzt4iu1EaZZZYZjmWdP\nTns49sYLjH1+rjN+eKoR16G9otxRbCuKcDWOL1xzdkPA88fTc79+vVr0jXE57uhWlS0Kgrj00UEe\nwGQHEQGvqnEmp5qJU7jrgqrmVOOEBOOobpUxaHIqVJ2U8dD+EQDYs7PDUJ1vlVDlfV7M4GwaALqa\n1PpkEHLhXjnKXfGVOvcSYiDFfSRmuOL+81eGJQmu377GaNesvIYbKb6QDXFH2CvK/STRmamVaAx6\nvnDNZgD4p73Hqri0jVO4WIZBxs7cWxtvckUhz4scywTcNrg7o6cLdNGqRJIUxd0OTyYrAKdw1wXy\nuFNllTFCSDAOxSpT3lFnnMddmcG09IEhnecfOzIJAB9w8mRIEFBnlRnW2JmKQALeiWpWGcfjTpjO\nBjM87nle/MVrIwBwmwHTUpdgwhoedxR3Uwp3ANhzfudFPY2xdOHux96s9DuoOTVijMIlr3hv3WMs\nTV+yhRaE0Z+aLvC8KIW8nI2CMWyN8y7rgkKrjL0Ud4+L5VwML0gFoUyMl+xxN6Q5tbwZ8XdHJrJF\n4aKeRq1FpENZ/G4Xw0CeF/lle8eLGZxLA0BXo7b3fF1jwMOx4/FspQeDBScOkjTtdX6WYaaSOTW5\ne9jsPTw+nylsXVu3raPeuKMgTNg1NWJq5jp568MGhbskGZgFuQSGga++fyvnYn7+6vD+ofmyv6Mo\nXIYsC8Fydxa7GNwRGIV7zMmCNBencNdFRF70qVDckZBgRLO8cTBMNTeF7HE3YHtRUdyXHvShA2MA\n8EFHbicEwyhjwKuK7ijEXatVxsUyG6IhAOirECyDmlPtcr+0BZyLaa/3SRKMxQ10y6C21Fsv7TJB\noVRSZQxcw42o22wU5T6TysczxTq/uyXsM+FwG1tCf3n5BgD4wi+P8MJSvUAQJdTaZMRGLlTYY1zx\nhbtjcDcZp3DXRcT4xib1oNOwl1UGlBMuu1WdMUxxL5sqMxLLvNI/53O7rt3WTvyIqxY5aaFqf+rw\nHI5VBkpumQr9qY7H3Qjk/lTDtN4jYwuvj8Qjfvf1280YR62s4TYr3Nvr/S6WmUzk8kZufRCh5JMx\nzSjyv9+1saspcGIq+aMX+pf8J3RPCXo4g0b/ottZ5q3LnZJLa4+FCKdwT9sjpX7F4BTuugh4XCzD\nZArC8id78zFuIJyhhHxuqKS4GzY5NeQpo7ijFMjd57bZ7uGHZuThqZWj3HlRGp3PgCIiauKslmqJ\nkAuOx90A5P5Uw4JlfvbyEADceH6HEV3pyzGjOVUu3EnWbRzLrK33A8CoKcOw9CBnQRrvkynhc7u+\n8r6tAPDv/9O3JLoUDfszzlAakHWKtyx3CQO8UsaBr7g7VhmzcAp3XZg2MVsNssfdbkVnyFNxiGm2\nwIMxqTKBZU5EUZIePjAKjk+GNJVcSSXG41lelNoiPowRuWe1oWCZMoW7JNlsh9ouKP2phlhlFrLF\n37w+DgC3GN+WilCaU00o3Alfh+hBd4h6twwKbDVoZmol3tHbfMP2Nbmi8OXfHF2cfIA8UcbJW2X3\ncu21EGEX7s7YVNNwCne9mDMxWw129LiDsoaWfQONy3FfPjl13+D8SCzTXue7bEMT8cOtZmTFvULi\nJyiVxzqsbuAqiZDpAi+Ikt/t8pCbeuMAZxIhDakXH94/misK7+iN9jRra3jAxoQ+JSPiIEH5ytAf\nLIO+niZEyizhS9edE/ZxT785/fgbk6UfpgzOXpMV97cud/aKt8KyyjjNqabi3NL0IgfLUKC42zEO\nEpTu/rKzkJTmVOPiIM8c9MH9owDw/p0dBnkfVy3ByjsqiKG5NAB0a+xMRXQ0+P1u11Qit/w2s5Bx\nbJeGYJxVRpSkn748BAC3XWqS3A6K0mFfxZ3ywr0UKWP09KXlRMPez7/nbAD4+9++UVp/jM5eC5Ub\n7bcKFPciADQ4i61ZOIW7XuRQAgoSIZM2jIMEJZkxVVZxN87j/lbjdaYg/O7wBADscXwypJFTZSo3\npw7hdqYCAMswyDu73C1jRHi2Axhplfnj6bmB2XR7nf9dm1uIv3glIn7Dt0yNiIMEmyRCTiayqTzf\nGPRYYoC+6aLOnesaphK5bzxxAv0kafC+dKDcBuPKL9wdxd1cnMJdLzRZZeyZKoMUr8pxkBjW55os\nUdwfPzqZLvA71tWvj5q0Qb96kCPzKzenyiHuuMH5va1hAOhb5pZRZow79xLCRMNeD8fOZwo152pp\nBaVAfuTidZyJu14l5aXsDDgiGKu40+1xt8ong2AZ5qvvP9fFMv/3j0NHxhbA+Ow1OUTrrV8NZS1a\nuYW743E3F6dw1ws9Vhk7DmACqJbjblwcpDKASdZFUFvqjed3Ej+QQ6Dy54sYxgpxL4Ga3pbPT3Ui\nZQyCZZgOA+anTixknzw2xbmYD19o6tfQy7EejuVFKcdXSyzVg9FWGeMeOfSDolrN98mU2Nwe+fO3\n94iS9IVfHhHE/7+9+49vsj73x38ludMkTdqkv6AUS/khRUUtIILdioP5cZPpcO4g20F0zO4z9QyP\nX/YZ7HxlnsPmwXMG54yPUx/qtipziEfEsSEbTOZg4Kywaq3SDcqPUmpL6c+kze/cyf35452E9HfS\n3Hfu3Llfz79oSe++2965c+W6r/d1CU6Je69FJqeqq1Sm1x0gonyFvDPJAAjck5UrUjex331y6epN\nv///f/3JhI8wIPF+eYmMVioTDAlsOqORkyDjHtMOst3uee9cdxanvQvt2yUwfB9wLEGgll43JT42\nNSqyPxWlMqkTbuXeJ2a1zK7jF0OCsPz6KUU5BhEPGw9JO0IKQqQboNinYq5JbzXpPYFgj8sn7pFF\nlLKZqWN47H+VT80zfdLmeKW2JdzCQbKMe+QG40h93BWSkA4H7u4E7kHZXX4isqFUJlUQuCcr0k0s\n2bvGO/5ygQ8Kr524OOEjKHVz6iilMt7IzlQpxnZkR2rcBYF+/WGbINAXrivORZAngeyR7h1HdQ54\nvYGgLVs/4V8+iwmGz2DC5lTpiL4/NRAMvXailYjuT1UXyFhiXcNH5A7wfEgwcFqDBN2N0n9/anT6\nkoxryM7S/WjF9UT0X2+fPtvpJEn7uBtGGBStrIw7p9NkZ+n4kOAOxPWMEATqRalMaiFwT5ZYpTKc\nLhyftk9olrifDwWCIQOn1esU9jcdrVRGul6QRMRpNUa9ThDI7edZnczKm7AtVRLmke4dR7UkVydD\nRFOsJrOB63X5e13+2M+zulKUykiBBe6firc/9Q+NHd1O3zXFOTdPzxfrmPGTNHCXKN3OpHmZe0gQ\nzsha4x5127WTll9f7PLxv/vkEkWyRVKI3Mu9knGX7paLdNhS47wH5QkE/XwoO0snxVtTGBF+0cmK\n3GZN9qLf3OVi/3j/fO8EvjxS4K6YS0MUC9yHv2pKV+DOmA06Ijp6pru52zUpx1A1u1Cib6Ry5pFm\ngEddTK5Ohog0mnDSfUi1jEStPICijWXEy7i/UttCRPdXlklxe21cOVIO0ZOoiTsTnsGUrhn3T/s8\nnkBwcq4xHWLWf1sx1xy5HS11H/fYQdFuP8+HBKNeSXFttFomngezljJKKQTKDIo5k9IWy9YkedEf\n8PId/V727+PNPRM4AluA4namUqQon02ijsUy7tmSjT1nqZFfvneBiO6ZPzWVjSxUxTxsSG0s1lJm\nenLTdsL7UwdXy0Rq3PFyIj5xZzCdvjxwornXbOC+Mm+qKAdMFLtrKlErd0nLJNJ8BlM6FLhHFeca\nv/eFOezf7KIkhUgf9yt5CmXVyTCsGVec+1NZSxlZ2n2qFgL3ZEVKZZLKuJ/rutLM7v3zEwncpR4I\nJx3LSKPmKNLEXcqMO0eR3/Y/oE5GMiPOAI8Kl8okkXGnUean2lHjLplIVxmPKP1Mdr7fQkT/sGCq\nWabLl1h3TUck6Z2fNG/lzupkZstdJxP1QGXZ1ZMsd9045foSq0TfItugo8E17ooM3BNpLBPpBamk\nH1DplBfnpRtROhKcuTxARMuvLz52prulx33J4Z1iNSZ0BKm7XEnHMkqBaSRwl3bEHRHdeJVV9hLM\nDBaucR+lqwwrz5020SbuTHnxCI1llPh6qRQ2U5bZwLl8fJ/bn2SmzeXjf/1BGxGtkWNbKpMjxl3T\n0UibcU/vGnfWpHVO2lxddVrNH7/7OUm/RWy/MkaJGYQEA3c2NhUZ99RBxj1ZuWJMzGZb3ecU5y6a\nkU9ExxNPuks9EE46I86IpmiNu16qUzR6t3Ql2rdLKXvMAUzhUpkkNqdSTEfI2ARw+PUSgbsENBrR\nGsv8+sM2l59fPLNAxjfPkg7RkzRwn2Iz6bSajn6vjw9JcfwkpUNLmRQLZ9yVXiqTSODei7GpKYfA\nPVlsYnaSpTJnOp1EdPUky+KZBTShapkBidvTSocF7k4vP+S2e7jGXbKMe7Rz/Jcr0L5dQpbRM+52\nd8DhCWRn6QotSbXuLrIYrCa9wxPocl5paO3wYMuUhEoj1TLJHEQQ6Ffvt5BMXSCjJM24SzpPgNNq\nptpMRPSpeBuFxRIMCSwhNTs9atxTg/Uv9gaCwVD49YyFv8rqNZxQ4G5HL8iUQ+CerBxDOOMeSqLe\nM3qBu2VmPk2osYxyS2U4ncbAaUOCwCL1KI+fJyKTZJtTm3vCbXxwxZHUGBn3ll4XEU3Lz06yl4hG\nEx7NGN2fGgiG3P6gTqtR4q4PRRAl4/7XC71NlweKcgxfnDtZpHVNBEu+SJpxly5uS9tW7hd73X4+\nVGIzqeo5qNVosvUcRRJPFHnnpqxbfxPJuCuqFkjpVPSMkgibVuD2B93+4MSuUN5AsLXPrdVoZhSY\ntVqN2cBd6HF19HuLcxMoc3d6A6TMzalEZDFyPqff5eOzY7aiegIhknJz6p83LDvX6URGVmqWyEQS\nQaAhAfrFpJu4R82elHOiuffM5YElswspGi0Z9bK0F1SD8PDU5OJF1gVy9aJp8k6fCHcGk2ZyqqTt\nICmNy9zTqqVMKmUbdC4/7/Lx7NKX8aUyrMYdXWVSCRl3ESR53T/f5RIEKivIzuK0nFZz8/Q8Ijqe\nYNJ9INzHXZmBu2Hohh6KtMKVLuNuMXAVpbay5LZFwrj0Oi2n0/AhwR8cWoYbmb4kwp8gnHGP7E9V\n4oYwZSnNT7ZUpmvAd/DkJZ1W8/VF08Rb10TkSF/jbpOsLWlpunaEZLe/0mdnasoM2Y4fngSnqAxR\nYoE7+rinHAJ3ESTZWCZa4M4+vGVCZe6RzakKDtyHvHB6wzXuUgXukDKRV7KhgRHbmSpK4D5kBpMS\ns1zKEh6emkSpzP/8tZUPCbdfNznRDlqiE6XBwGikHgSWtqUyTekxMzX1zIOLAxU3NpUm1A4SGfdU\nQuAugiRbuZ/tHKCYZrcTC9zDNe4GJV0dolhHSNfQjHuQiIwI3JVvtOGp4bGpYpTKRFu5s50mdonr\nE4C1cv+0zzOxvT18SNh1XP5tqQzLdzgk7eMu2alYlualMsUqDNzDxYHsQyUmERKscWftIJX0Ayod\nAncRJDk8NZxxLwpn3K8vsZqzuOZu1+XILNV4KHcAExFZDDoaVioj9eRUSBlz1gh/XxJp+hKTb84q\nsGS5fPwlh4eI7OGWMngtkYo5i8s3ZwWCocvggec1AAAgAElEQVT9vvEfPczPj56/5PDOLDJ/Zlah\n6GtLlHInp1JMxl2UYVhi4YPC+W6nRnPlTrJ6sLvE0eGpSizbm0hXGWTcUwiBuwiSLJEc0jOL02kW\nsjL35gTK3DOgxn3IL9At8eRUSJlIxn3oG7PL/V5Op5liM4nyXWLnp4YLi1F2KaUJV8ucbHP85FAT\nEX339vJ02D0c3WMTbeEnFkGQvFIi16S3mvSeQLDHNZF3UBJp7nHxQaE0L1u6TUppa0hloKIz7uO+\nG/QGgp5A0MBpVfiHlhECdxGES2UmVOPOB4UL3S4imlV0JTMxgWoZli5SbOA+QqmM148a9wzBAvch\nf19WJ1Oal81pxYnd2DY4tj9V6lYeQFcayyS2P9Xp47+z68NAMPSPi6bddWOJNEtLjE6rGW0bRpLc\nAZ4PCQZOa+AkfKlNwzL38M5U9dXJUPRyF8m4KzFwZ73y+ODQHs3DoaWMLBC4iyCZGvcLPS4+JEzN\nM8VGqBMI3DOvVCZc44738coXvnc8eAZTS3e4ibtY3yU6P5WU2TtZccKNZRLJuAsC/cubn7T0uK8p\nzvm3L18n2dISJlEr99RsTEzDjpBnLg/auKUqbHMqq3EPCQKroVXWACaKu1oGLWVkgcBdBJFSmYlk\n3IcUuDM3TLVmZ+nOd7k6B+K9+6ncAUwU2Zw6MGKNu2STUyFlLOEU1KC/b0uvm4imF4qwM5Vh2+BY\nxCB1Kw+g6AymRBK9r524uP/j9uws3XP3LUir9+QSDU9NzZ2fdMy4X1ZpL0iKvGaxjPuAlxcEMhs4\nse4rpky8gTtaysgBgbsIrEn0cY8UuA+6wHE6zU1l+UR0ojnepHu4HaQyu8qE+2cNCdxZjXs6vbrD\nxGQPbmzMiLgzlSmfZCGiM5edIUFg+6WUdXtacUrzWMY93lKZv1/q3/xWIxH9+1dumFWUXnsWc6Vp\n5Z6aMok0bOWu2ulLFNmLz17OlFgnw7BbBA63f+yHscAdLWVSDIG7CMJ93Cd00WcJwuFb7ytn5hNR\n7bm49qf6+FAgGDLqdZxOYW/rmRy2Ocw7QsYdm1MzgGVwY2OmpcdFRNPEG4CVa9JPzjV6AsFP+zyR\nTg7IA0noqkSGp7p8/D+9+qGfD927sPSrC6ZKvLSEsYx7nG004peaOz/plnH38aGWHrdOq5mZZm/P\nUsMcc4NRiS1lmLhLZQKEljIph8BdBMl0Ezvb5SSi2cMC98WJlLkrusCdIqUyKZ6cCimTPdLmVJZx\nny5GE/coVuZ+umPAgRp36U21mTQa6nB4+eA4vScEgTb95mRzt2v2JMsPV8xNzfISIlGNe2oSrtMS\nr1mS1PkuZzAklBVkS7olN21F+rgHSckZ9zgD995wxh2Be0qp8XklunAf98TndwRDwrnBY1OjbrzK\natLrznU5u53jl7kP+BTcUobG7uOOjLvysXvH7phSGT4otNk9Gk24Tlos7Nb8mcsDyn29VJAsTluc\nawwJQpt9nGqZ3XWtv6lvM+p1z923ID2f0TnSDE9NzXlYYjPptJqOfq+PD0n6jeKk2pmpTKSPOyuV\n8ZMyMwjsduW4gbsdgbsclBS4dzcdfeaJjevWbXxmz1G73IuJFSmVSfii32b3+PhQUY5h+JVdr9Oy\nbu7vnx+/WoZl3JUcuOtpeKkM+rhnCvOwzamf2t3BkFCcaxI3J8faz53qGMDk1NQI708ds7HM6csD\n/7avkYievHtu2gZzyZQ7jiE1gTun1Uy1mQRhIj31paDmnak0uPutcjMI8WbcXahxl4FiAvfu2mfu\nqd60u882p4x2P73py9U70id2j7SDTDhwP3N55DoZZvGMeKtlBhRfKhMegBL9jCCEM+5p1XoCJmZ4\nH3fWuq5MvAJ3hsWF9a32kCBkZ+myVHmnPpVKxytzd/uD33n1Q28g+NUFU1feVJrCpSVGouGpLO5J\nQSvAtCpzV3MvSLpyuQuSkgdKxN1VBn3cZaCUUM975Be7qWLD4WdXcETf/MKO5etqjjZ/fcUMo9wL\nI4p0JOj38IJACQ0CZAXuow2FvmVWAREdjyNwj/SCVN7VgRleKuPjg4JAep1WcV20YDjzsK4yF6QJ\n3Nl7YBZHKvHFUnEirdxHLZV54rcnz3Y6ZxVZnvzK9ekwJHU0kWu4IktlKM1aubOWMuqcvkTRykC2\nOVWxI5zRxz2dKSVw5xb/89btudey5Rrz84koi0uXxRs4HafTBIIhHx9MKEMczkxMGvkCV3GV1ajX\nnel09jj9BZaxnhiRXpDp8gtJ1PBSGRS4ZxLzsK4yrKWMuDtTichs4Kbmmdr6PERkxWuJ9MbOuL/5\nwadvfvCpgdM+d98Cc3oPZMgN17iLXCqTspKt9OkI6QkEL/a6OZ1mhtjPbqWI7eOe8aUy6OMuC6Xc\nSuZKKyoXzrAREZF95+ZtRCvmlabLK4FGM8Hr/tlRdqYyep32prI8Inp/vG7uLFet3Br36G6ekBBu\nT4Em7pkke9g8eRZhiNgLMqo88jZYiRvCFGeMGUxnOp0/+M1JItq8Yu41aZ98jQxgUmSNO6VTqczZ\nTqcg0MxCi0J7EycvdixJymqlRJdYO0jUuKeW4kI97ztPralpsm5+Y33xsP+rr6+X6Lt2dHSMfXCj\nNkREx+s/npoT769UEOh0Rz8ReTub6/svjviYMpP/L0T7j58q4TvGONTp8wNE5LR3S/Qb6Ojo6Ovr\nk+LIUUZO4+WF9+vqTZyGiNoGeCLSCrx0f9OEnDlzRu4lyCyZc6CtnyeivgF39K956tMeIvJ2tdbX\nj3ViT4BNE4ld/C5xTx6cA8PPgX5PkIiau/qH/Kp9QWHjoW5PILhkmmkO11NfH+8gOblc6vYTUUev\nY+xzJtFzoNPuJKL2C2fr+6SNbDx9ASJqauuR+oI57nXgwBkXERVlBdLk0i26cc8BLy8Q0YDHX19f\n/2lnHxF1t7fUB0W+0EmNXbQ77c7hf8foORAICi4/z2k1pxs/SedCONGNGxAmY/78+eM+RmGBe92L\n39l8wFH97Fu3FY+w8nh+4Impr68f++CFf3m3fcAxdfrs+dNscR6zo9/rCbRbTfqltywc7aQP2Hp3\nfVJ7bkA39nd/u+MU0cDVZVfNnz8rzu+ekAsXLkyfPl2KI0fl/r7HO+CbUX7dFKuRiLg2B1GnLSdb\nur9potJnJbJI5hyY0u+lA+8EKHwahwSh882DRPSFzywQ/TbReeHTvacaiKhsStH8+TeKe3CcA0PO\ngWBI4H53wOENXXP9jbH3x77/5scXHYEZheYXqqvMSijhs3Q66Z0/B7X6cf/ECZ0D3v2HiOiWBTdO\nzpV2O9ZMT4DefrvLI8ybN1/SEGrc68Avmz4icny+Yub8+TMkXIesxj4HBIE0v/5dIEQ3VMwLHn2X\nyHfTDdfeMNWasuWJYprTTwcOeUPa4T9s9By43O8lupRvzlqwQF0XxnEDQqkppVSGiKhxz8b1O5uq\nt7+1tiLe4DhlchNvAxxtKTPGdXZeqc3AaZsuD/Q4x5o8PKDwUhmKNJaJVlOwUplslMpkBEvMRBIi\n6hzw+fhQvjlLijM22oQOpTIpoNNqrrJlE9GnMftTf1Pf9vpfW7M47XOrFygiaqdoO8jEZ3GMQRDC\nu11TUCpjNelzTXq3P8ja88klGBKONnUR0bI5k2Rchrw0mnBxoMcfzIAad2H06WpoKSMXxQTuzQe3\nPfx0LVVUzze11NXV1dbWNdtFrkdMxgQ6Qo5d4M5kceEy9+NjlrlHatyVd3WIYrFdtLEM25yKJu6Z\ngeVio3sYWrpdJEFLGWZW5AmVrZCQUenCjWUi1dXnu1yP7/2EiP71ruuuK8mVc2WJmEDmZVzuAM+H\nBAOnTU1P27L8bCJq6XWl4HuN5pM2R6/LX5qfPaNQpTtTGdZYxunjWTtIJSYROJ0mO0vHBwX2Wjwi\ntJSRi1ICd2ft/n1ERA016x5et379+o0b1+8/65R7VVdMIGHDAvdxm90unllARMebxxrDxF5vlNvH\nnSKLj+7ujUxfUvBPBFE6rYbFLuw1oKWX9YKU5KU9WrDRJ2vqUT1iG8t4A8F/2vWh2x+884Yp9y0u\nk3tpCTDpdTqtxseH/OINH01Zup1Jh46QR053EtGyOUWqqngejt1ocngCLj+v0YTvJyvOuPtT0VJG\nLko5nyyrnz22Wu5FjCHXmHDG/UznAI2XcSeiypkF24nePzdWxl3pA5ho2Iwed7irjFLeWMI4zAad\nNxB0+4LmLK6FNXHPlyTjTkTzp9nqL9o/e3WhRMeHWJHhqR4i+tH+v5261D8tP/s//+FGZYVuGg3l\nGDm7OzDg5cfuvRu/FA/fSYfGModPdRHRUhXXyTDs5eySw0NEVpNeq6wnQ4TVpL/k8Do8AbbxbDgW\nuNvQUiblFBzqpZVIqUziGffxAveKUlsWpz19eaDX5R/tra3S20FSZPHRUhlvuI+7gn8iiGXO4nrI\n7/TxRTkG1sRdoow7Ee39p89KdGQY7qq8cKnM/o/bdx2/qNdpn7tP/D3HKZBr1NvdgX5vQLTAPbUZ\nd9lbufc4/R+32bM4beWsArnWkCZYg+NLdi8ps8CdCUc1Y2TcXahxlwcymuLISXDwXq/L3+vyZ2fp\nplhNYz/SwGkXTMsjohOjV8uw0UWZVOPOxs6hj3vGYCkodiOlRZqxqSALlnF/90z399/8hIg23am8\nBhpM+BouXpl7OHBPVT5S9oz7n5u6BIFumVmA6zYbN9ZmD2fc5V7OBI1bKtPr9hNRHmrcUw6BuzgS\n3dsU3Zkazz20W2YWENH750etlsmYUpno8FRPIETYnJpB2G4tl48XBLrQI+HmVEgxVuPu8vMuH3/H\n9cXfqJwu94omiOUXRRyemuKM+7TRh2GlBitwXzqnSK4FpA82g6k9HLgrNa4dN3C3I3CXCQJ3ceSa\nEtucGk9LmahbZubT6IG7INCAT/GbU3OQcc9orMeLy8/bPf4BL2/O4grMBrkXBSKI3ijPNem3Kq20\nPVbOhKZfjyHFgXuJ1aTTajr6vT7x9tfGLxgSjp5ReyPIKJaHancou1SGrZxF5yNivUfzzEr9AZUL\ngbs4Et2cGilwj2sS+PxpeVmc9lTHQN9ITyF/MMQHBaNep+gR0xajnkaocUfgniEs4c3HQdb1YlpB\ntnIjPIil0dBT99wwo9C845s3K3G0e1Si5Y7jSnHgzuk0JTaTIFBbTE/9lGn41G53B8oK1N4IkmGl\nMpcyvVQm3McdGfeUQ+AujlzjoG6G44qzpQxj4LTzRy9zZ/U5StwNFovdW3QO7ipjROCeKbIjpTIX\netxENB11Mhlk9eJph7+3lG3FUS6r2K3cWcSTyjczMpa5Hz7FGkEi3U5ElB1TKqPcpivjB+7o4y4T\nBO7iGHf/9RAJlcoQ0S0zRq2WyYACd4qWyngxOTUzWSKlMlK3lAGYmMjmVKWWylCkxaosgfuR02gE\neQXLuPMhgZSccWcROfq4pyEE7uLISaRUxunjLzm8ep22NO5W1pH9qSNk3DOgFyQNK5XB5NQMkx0p\nlWHTl6Yh4w5pJrI5VamlMhTpCMneG6dS14DvkzaHgdOy7VhgjsmjKTdwHzvjzgeFAS/PaTVKTxoq\nEQJ3cZgNOo2G3P4ge5M9tnOdTiKaVWTmtPHW+c6fZtPrtKc6+u3uoc8iZ0Zk3IeUyoQz7gjcMwXr\nKuP28S3dLiKajow7pJkMyLjLVSpztKmLiCpnFRhxj5SIIpc7JlNLZeyecJ0MdiulHgJ3cWg1mpy4\nSyTPhOtk4tqZyhj1uvnTbIJAJ5qHVstEatyVenVgcgx6iimVCde442UgU5ijpTK90o5NBZiY8F1T\nxW5OJfk6Qh4+jQL3QbJVkHEPt5RR7NsSRUPgLppIU4LxEzaJFrgz4WqZYftTB3w8EVkUXyozqB2k\nB5NTMwtLQXUN+LoGfJxOUzzKDG0AuSTaYGBcMmbchfHv+4qGDwlHz3QTCtxjDMq4Z2jgzm7+56HA\nXQ4I3EUT/wwm1lJm9uTEAvfFo+xPDY9NzcRSGfRxzxgs4/73SwNENC0/Wxd3kRhAaiS0T2lcgkAO\nd6oDd6tJn2vSu/1Blg1NjY9a7f2ewIxCM0aqRQ2qcVdsTjoauI/4PjCScUfgLgME7qIJN5aJI2Ez\nsYz7grI8Tqf5+6X+Ie+AnRmRcc/WcxoNeQPhTQLYnJph2CtZeGZqPgrcIe2wIXpiZdzdAZ4PCVmc\nNsX1fqkvc0cjyOFiM+7KHW7A6TTZWTo+KLCX4yHQUkZGCNxFE7nTOk7CxhsIXux167SaGQnuzzPp\ndfOuYmXug6pl2CuN0mvcNZpwCy2XjydMTs04sSmo6YXIzEHayRW1xr0/5XUyTOoD9yOnO4lo6Zyi\nlH3H9Betced0mmy9gnNqY1TLsFIZ5W69VTQE7qKJ87rf3O0SBJqWn53FJfzLv2UWawo5qFpmICNK\nZSiySYBV/ngwOTWzxKagpiHjDuknJ7LNRpQCcVYnk/r65hQH7p0Dvsb2fqNet3hmQWq+oyKYI7uz\nrCa9opuujBG4s1IZZNxlgcBdNHF2E2MtZWZPTqClTFSkm/ugwN3pC5DyS2Uo0tHS6ef5oMAHBa1G\no9fh/MwQsfuMUQsLaUiv0xr1umBIcAdEqJZJ/c5Uhk1ISFng/ufTnUT0mVkFhsTzUBmMbdkiJbeU\nYcaYLMlKZVDjLgs82UQT5/yOiRW4Mwum5XFazd8u9cc+kcI17srPuJsjw1OjBe6KzlVArNjzE03c\nIT3F3xlsXOHAPeWFBCzjnrIZTIdPdxEK3IcxcOHAXemvy2Nk3FngjlIZWSBwF01ufBf9M5cHiGj2\nhAL37CxdRalNEOjEhStl7pEad2VfICjmVjUK3DNPdiQFpdHQVXkmeRcDMKL4O4ONS7aMewpbufNB\n4diZLkKB+zDRlJPSbxqPFbi7AoRSGZko+6xKK2x7qEPKjDtdqZYZFrgr/J09RUtlfDwK3DOPMZKC\nmpRjnMDuDoAUEHF4qlyBe4nVpNNqOvq9Pj4k9ff68GLfgJefVWQpxTy1UXAZHLijVEY+yj6r0kqk\nVGasiz4fFJq7XUQ0q2jCgXs+ER2PKXOPtINU/B2rK6UyaOKecaIpqKIcg6wLARhV/NOvxyVX4M7p\nNCU2kyBQW59H6u91GP1kxqNX+MAKBO7pCYG7aCKlMmNd9Ft6XXxImJpnmnA6eUFZHqfVNLZfKXN3\nZlypDJq4Z7BCCy70kKas4rVyZ7GOLD28U9ZYhhW4Y2DqGDhdZgbuwZDg8AS0Gg2bfgAphsBdNPEM\n3jtz2UkTLXBnzFncDVdZQ4Lw1wt9RCQI4fyQOYNKZdx+BO4Zq9CCjDukqRzxWrnLlXGnVAXuHf3e\nU5f6TXodG+kNI8rUGnc2TtWWrdeig4QclH1WpZV4Bu9FCtwn0gsyipW5H2/uISIfH+RDgkmv4xR+\nS46Glcqgxj0jFSFwh3QVGaKn4Bp3SlXgfuR0FxF99upCbFkZg+ID92w9Ednd/iGfR0sZeSk+TZs+\n4hnAdKZz4i1loipnFjx/5Bzr5h4pcM+Ev+PQUhnUuGeW+n+93eEJTM41yr0QgJFlRsa9NEWBeycR\nLbsGBe4jO/nDLzp9vNJLwEfLuPe5A0SUr/CfTrkyIeBLE5FWYrwg0Gi3j5JsKcPcVJan02pOtvUP\nePmM6QVJRBaDnljgHi6VyYQfCqLysrOU/jIGmS0DuspQZMCZpIF7IBg6dqabiJaWo8B9ZBYDp/Qm\n7jRG4O7yE1EeekHKRNn3cdIKp9OY9LqQILA25MOFBOFcl4uSDtzNBu6GqdaQINS19EZ6QWbCHSs2\nbc7pjdS463FyAkDqxDlELx7yl8r0uARBqm/xQUufy8fPnmSZipkMGc1myqKRM+5oKSMnxEZiCs8H\nHuW639bn8QaCRTmG5K/m4W7u53oyqlQmsjnVG+7jngk/FAAoRSTjnmzgLghyBu5Wkz7XpHf7g72u\noaXJYjl8ijWCRLo9w0Uz7kPeBLJTKw817jJB4C4mdt13jDI89WxXsi1loqJjmJzeACl/rjJjNgya\nnGpEjTsApFC03DHJ43gCQT4oZHFauS5iUu9PZTtTl12DwD3DcTpNdpaODwps41mU3R0glMrIB4G7\nmMaemB3uBTk5qZYyzMLpeTqt5mS745LDSxlT435lc2qI0FUGAFIrnlkc8ZAx3c5IGrhfcnhOXx4w\nZ3E3T8+T4viQVkYsc49k3BG4ywOBu5hYR8j+0TLubGfqRGemxrIYuOtLrMGQwGbXZUbgzir1XZGM\nO7rKAEAq5YiUcc/swJ3NXfrs7EKl9zqEeIwYuLMa93xk3GWCJ56Yxp7BFO4FOVmEwJ2IbpmZT5Fb\nlplSKqMjogFvtMYdgTsApE5kc2rSgbvbT5kbuB8JD0xFI0hVCO/cGxK4u9DHXU4I3MU0RomkIIjT\nCzLqllkF0X9bjJnw/DFwOk6rCQRD7M29EYE7AKSQ2aDTaMjl5/lQUg1ZZM+4S9fKnQ8JfznTTUTL\nELirwygZ9wAh4y4fBO5iipTKjJBx7xzwDnh5W7a+wCzO5Mibp+dHpw1nRqmMRhMuc+8a8BEy7gCQ\nWlqNxhKZ35zMcViUI2M+MtIRUvzA/eNLbpefnzM5Z4oVjSBVYYxSGdS4ywWBu5hyRy+ViRa4jzab\nKVEWA3f91Fz275yMKJWhSGOZzn4focYdAFJu7HLHOMmecZ9qM+m0mo5+j58PiXvk4xedhDoZNRke\nuIcEwe4OaDThKhpIPQTuYmIZ9xFLZc50itZSJmrxjHC1TGb0cafIO5Aup4+ITMi4A0BqscYySZa5\nyx64czpNic0kCNRm94h75OMXBwiNINVkeODu9IVCgpBr1HNakdKQkCAE7mIKZ9xHKpURt8CdYd3c\nKVM2p1LkBwmGBELGHQBSTpThqSzKkTcfyaplWkStlmnr81zo85kN3MKyfBEPC+lseODe7wsSCtxl\nhcBdTGPcZg1n3EUN3KNtdLMypS2XOeYdCCanAkCK5YjRyl32jDtJ01jmSFMnES2ZXcjpkGpVi+GB\nu8PLE1rKyAqxkZjCm1NHus16tnOAxM6455r0W1femJeddc2UXBEPK6PYXbbIuANAiokyPDVTA/fD\np1gjSNTJqIg1W09EDndMxt0bImTcZYXAXUyjlcr0uf09Tr85ixN9J/6qhaXiHlBesTU/qHEHgBRj\nuQOHwjenkgQdIf186C9nuwk7U1Vm9Iw7AnfZZEiJRZoI32YddtFnBe6zJpnFaimTqWJLZYx6nJwA\nkFKiDE9Nh8Bd9Iz78eZeTyA4s8BYnGsU65iQ/kYL3PMRuMsHsZGYRhu8FylwF7OlTEaKlsoY9Tot\n3uUAQGqJMjw1fQL31h63kNQsqSuOnO4kolumiVntCelvpMA9SER5qHGXT0aVyly4cEGiI9vt9ngO\nLgik05KfDzWda86K2b7z4dkOIirQB6RboaTa2tpS8418rn72D4NOk1a/q46OjrRaT+ql7BxIWzgH\n1HAO+F0OImrv6hvxbx3POSAIZHf7icje2e7plS01JghkztK6/PzHp89ZjSKUHb59so2IZpm8Kn8W\nqO06wKYI97l9zc0XWDKto3eAiELeAVX9HmLFGRBOzPTp08d9TEYF7vH8wBNjs9niPLjVdKbX5c+b\nVFKUc2VC6uV3Oono5jml06dPlmiFUpPudxtrWqeW6DIRmY361HzHOPX19aXVemSh8t8AzgFSwTkw\no99A1C5whhF/0njOAbc/GAw1ZnHaOVfPlGKF8ZtR9OnJNodgLpheakvyUK297lZ7o8XALb2+LOPP\ngbGp8Dpg0p/2BIKTp5ayceYBbSuR7+rSKdOnF8u9NHnEHxBKBKUyIhuxKYEUTdwzUrRUBi1lACD1\nco2jdgaLUzrUyTAilrkfOd1FRLeWF2VK52FIAOv8GK2WCde4o6uMfPAsFFmkI+SVgjCXj7/k8GRx\nWrbNH8YQ3ZyajZYyAJBykc2pE+8qk26BuygzmA6f7iT0k1GrIWXu/d4goY+7rBC4i2z4df9sl5OI\nZhaaMR94XNF2kOgFCQCpxzIvyWxO7U+zwD35jLuPD713roeIPleOwF2N2I7t/isZd0xOlRkCd5Gx\nO60Oz5Xr/tnLrE4GLWXGl2NAqQwAyIZlXhxJTE5Nn4y7WK3cj5/v8QaC15XkTkYjSFWKzbgLQriQ\nzGZC4C4bBO4iY9f92FIZVuA+ezIK3MdnRsYdAOTDMi8DXn7CXRTTJ3APZ9yTLpVhBe7LMDBVrWID\nd6ePDwqUY+Q4HSoIZIPAXWTD2wCfwc7UuFmMqHEHANkYOB2n0wSCIR8fnNgRWC/IdAjcp9pMWo2m\no9/j50PJHAcF7ioXG7j3uvxElIfpS7JC4C6ycFMCz7CMOwL3OERr3I0olQGAlNNoRu4MFr/0ybhz\nOk2JzSgI1Gb3TPggF3pczd2uXJN+/rQ8EdcGChIbuLP3pXkocJcVAneRDSmV8fGhi71unVYzo9As\n67qUQR9pNqYh3IYDABlkTOBOYuxPDTeCnF2I5gqqNSjjzgJ3tJSRFQJ3kQ1pStDc5QwJQllBth79\nbxMREmtONwBAInKMQ1v6JiQcuKdHZFNWYKbkytyPhOtkUOCuXrGBe58rQGgpIzdEkyJj2ZpoqUyk\nwB0tZRITDCFwBwAZRPYpJRe4Z0TG3RsI1qIRpOqxd6EOd4CI+tx+IrKhxl1WCNxFNqTGHQXuExNE\nxh0A5JAzrKVvQtIqcGcdIVsmGri/f77Xx4eun2otyjGIui5QkkEZd7efiPIRuMsKgbvIhnSVQUuZ\niQkh4w4AcshNbnhqWgXuSWbcWT+ZZegno24jdJUxp8XprVoI3EWWO3hzKjLuE6PFRigAkEOOManh\nqWkYuLf2uCdwC9PuDvzyvQuEAnfVG9xVJkBoByk3Tu4FZJrwxiYPT0R8SDjf7SSimUUI3ON14T/v\nlHsJAKBew4foxU8Q0itwt5r0OUZuwKP0XC4AABhxSURBVMv3uf0JbSh0+fm1L58gomuKc+aV2iRb\nIChANHAXBPRxTwvIuIvMYuQ0GnL5eT4kXOxx80HhqjwTxgkBACjCkM5gCfEEgnxQyOK0aTKJQqOZ\nSLWMnw99+5UPPmq1T80zvfzNRTrc/1Q3vU5r0usCwZCXD6KPezpA4C4yrUbDpgg5vfzZzgFCgTsA\ngHIM6QyWkLRKtzOJBu58SHj0tfq/nO0utBhe/dbiKVajlKsDZYgm3SMZ9zQ6w1UIgbv4ondaz4QL\n3NELEgBAGXKTqHFP38A9vlbuIUH4lzc//kNjR65Jv7N60fQCzA0EosgpbXcH+lDjngYQuIsv2ljm\nLFrKAAAoSjI17v3pF7iHZzDFkXEXBPr33/19zwefmvS6l9fefM2UXOlXB8rAWrlfcngCwZCJ02Zx\nCB3lhN+++KKt3MMZ98kI3AEAlCEyOTVDMu6lcZfK/PRPZ156t5nTaV68/6abyvKkXxooBjulm7td\nRGQ1pcX+DTVD4C4+ViLp8ATOsYw7WsoAAChEMpNT0zBwj7PGfcd7F7YfatJqND/9+vxbMScVBmOn\ndEuPm4isRnQjlBkCd/GxpgSnOvo9geCkHENuOl3EAQBgDDmDp18nJA0D96k2k1ajueTw+PnQaI/5\n9Ydtm/c1EtF/fPWGL90wJYWrA2WIzbjnGpFxlxkCd/GxjPsHLXZCgTsAgKKwGnenjw8lPrUoDQN3\nTqcpsRkFgdrsnhEfcOhvlzfsaSCiTXde+7WbS1O7OlCGSMbdRURWBO5yQ+AuPpaw+fBiHxHNnoyW\nMgAAisFpNdlZOkEgtz+Y6NemYeBOY1bL1J7r+c6uD4MhYd3nr/7fS2amfGmgDLGlMrkGBO4yQ+Au\nPlYb4/LxRDQbGXcAAEXJmWgr97QO3Id1hGz41P6tX9b5+dD9lWX/5/Y5ciwNlCH2lLaaUOMuMwTu\n4mOlMgxKZQAAlCV3oo1l2FzJdNvXNGLG/Uyn8xsvnXD5+bvnlfxwxVwNpqPC6KwxE5eQcZcdAnfx\n5cTsucb0JQAAZcm0jHvB0MC9tde95hfH7e7AbddO+u9752kRtsOYYk9pG9pByg2Bu/ii6RZbtj7f\njAFjAABKwjqD/bmpK9EvZIG7Lc0Gwk/LHzSDqXPAt6bm+OV+76IZ+c+tXsDpELXDOGID91y0g5Qb\nAnfxRUtlZk/KQSIDAEBZ5pXmEdErtRdOXepP6AvTNOMeKZURBLK7Aw/UHG/pcV8/1frS2puNemRP\nYXyDatxRKiM3BO7ii5bKoMAdAEBx1i27+n9dO3nAy6+pOXGhx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       "text/plain": [
-       ""
+       ""
       ]
      },
-     "execution_count": 7,
+     "execution_count": 8,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -259,30 +297,33 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 8,
+   "execution_count": 9,
    "metadata": {},
    "outputs": [
     {
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "Started at 2017-06-28 13:40:12\n",
+      "DEBUG\n",
+      "['dmm_voltage', 'dac_ch1_set'] dmm_voltage\n",
+      "Considering the units XX, XX\n",
+      "Started at 2017-06-30 14:24:12\n",
       "DataSet:\n",
-      "   location = '/Users/william/sourcecodes/qdev-dk/docs/examples/PlotTesting/plottestsample/022'\n",
+      "   location = '/Users/william/sourcecodes/qdev-dk/docs/examples/PlotTesting/plottestsample/087'\n",
       "      |   |  | \n",
       "   Setpoint | dac_ch1_set | ch1          | (50,)\n",
       "   Measured | dmm_voltage | voltage      | (50,)\n",
-      "Finished at 2017-06-28 13:40:13\n"
+      "Finished at 2017-06-30 14:24:13\n"
      ]
     },
     {
      "data": {
-      "image/png": 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UWLgTERFRYxGjMlesXgyOuZOpsHAnIiKiBhJVksGoYrNa/vCcDgBvn2ThTqbBwp2IiIga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DzdDcQ2pHV31qUtzpcSeEVIzhcW9Gq0wqp2SVQsjnnXHfwsLdWVi4NxRCUGwJ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ugpeCKLlW2BmWz8xDQ7x7qGOvtsTNwtZpXpG00CQG+XvX0yoJe4//73v6/0T263VZQzKwDPKjOl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\nA1BWTFTc2dVnHAsByJNxinvQeDJwQ7WLSlDcBwyTBcmQFPeIIAf4GOitB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e/l6EJw76uv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\n",
       "text/plain": [
-       ""
+       ""
       ]
      },
-     "execution_count": 8,
+     "execution_count": 9,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -290,7 +331,7 @@
    "source": [
     "# Exceptional Units, small setpoint range\n",
     "# Expected labels: \n",
-    "#     Y: Gated Voltage (V), X: Gate ch1 (V)\n",
+    "#     Y: Gated Voltage (XX), X: Gate ch1 (V)\n",
     "dmm.voltage.get =  lambda: 1e-7*random.randint(0, 100)\n",
     "dmm.voltage.unit = 'XX'\n",
     "plot, data = do1d(dac.ch1, 0, 10, 50, 0.01, dmm.voltage)\n",
@@ -301,30 +342,33 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 9,
+   "execution_count": 10,
    "metadata": {},
    "outputs": [
     {
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "Started at 2017-06-28 13:40:15\n",
+      "DEBUG\n",
+      "['dmm_voltage', 'dac_ch1_set'] dmm_voltage\n",
+      "Considering the units V, V\n",
+      "Started at 2017-06-30 14:24:15\n",
       "DataSet:\n",
-      "   location = '/Users/william/sourcecodes/qdev-dk/docs/examples/PlotTesting/plottestsample/023'\n",
+      "   location = '/Users/william/sourcecodes/qdev-dk/docs/examples/PlotTesting/plottestsample/088'\n",
       "      |   |  | \n",
       "   Setpoint | dac_ch1_set | ch1          | (50,)\n",
       "   Measured | dmm_voltage | voltage      | (50,)\n",
-      "Finished at 2017-06-28 13:40:16\n"
+      "Finished at 2017-06-30 14:24:17\n"
      ]
     },
     {
      "data": {
-      "image/png": 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VgMLdnuJasmIk4iNk3J0v2eGOe1Ek7YGxYliNyph/X6rUbfNJdNwdRlvi3k5O\nhrSn5q513CtRGXTcnQmFe2+aaXZsKlWWM5RE02e8etKlURlcprCjZLZERP0Bz3CfVcUWdFKyXsfd\nIxBRzuzCvWbHXV0HacFWmUrEeT1TKpRxNISTzLW7C5KZ6HJUBhl3Z0Ph3oMKZWklVRBc3Fi0UfxO\ncHEBD68o5l9x7kkZRGVsL86WkAQ9ascdURknU5QGGXeeiDJmn8GUqVW4h31ur+DKFkXTF1BW122Y\nT3WWtk9fYlirfi6e60rTbE17FZpAVMaZULj3oLlEnojGY37B1eQsZmyE1I89bQs8RxhOtSstEu1B\nVKYH5MpiWZL9bt4rbH6eUjvuZp/BlCnU2OPOcZasclcUtWt75ViEiBaS3VkwAu1pe4k7Ewt4gl4h\nUxST+S6EVTCc6nQo3HsQm0zd1d9oMpXBGUz6sfbeWJ+fiDawDtKWWFQmFnCzgcILmWJl9T44TjJb\nu91OlmXca0ZliGjIgsNT17PFfFnq87sPjkSIaCGBwt1JZnWEURvguK6lZURJSeQqhTs6UI6Ewr0H\nzepY4s6EsBFSN/akPhr1EzrudlWJyrh510DIoyiWbPGDzmAB3Fhwc8CdLNsqw16ch7z8pver86mm\ndtwrW8DHY34iWkTH3VFmjWXcK3+386vc17MX78bouDsUCvceNKP7MSWCM5h0y5ZEIhrt8xEy7nZV\nicoQEdIyTsd+mlF/zcLdmo57iUVlNn9FKxbLsIptV3+ATSLNo3B3jkJZWk0XBRc30udr+5NohXun\nO+5rVa8/MZzqUCjce5C2xF1Hxx0Zd93Ujjsr3PEdsyV2bGp/0ENE7DkV86nOlax6GbaJxVtlNnfc\nByMsKmN+x31Xf2A86iN03B1lXpsi45tNkTXQrVXuLODe53cThlMdq52zAyxVWH77sX/+/uszidju\nmz7xuf/h0Ij2ijZz+nsP/vPRmcTYgQ9/4d47Rmx3w21Ez7GpDDLu+mWrMu6pvKgoxLX/oA2WSGh7\n3IloJOIndNydLNE8425yVCZdaziVtMNTV1IFGjPta2lRGf94NEDIuDvKrLEl7ky3VrmzXZCXD4Ve\nn0lsICrjTPbquIvLz9z2yfseeiFxzfXXJF548L5PfuJXyyIRUeb4n99+z4NPLh64ZvfRx+//5Gf/\ndrnbN9W2JFlhF2EndHTcw8i468auyw+EPG7eVZbkoogdmrbDSr3qjvsKOu6O1W4LK5AAACAASURB\nVCjj7mEZ9w4Npw6zw1NNzbhXFoGPso77Rh7naTiFwSXuDKv7O1+4s2NT9w2FCMOpjmWvwv3UM/9G\ndPv3nn7gS//Tlx54+tFbaOM//+g4ER1/8lsv0/5vP/XQl7/01Sce/SotPv7wr1C617aSKpQleUfI\nq+co5jAy7rqxJ/WQV4j4BcIZTLZ0acbdS+i4O1nnM+6ZYu2O+xBbB2nqfYlVbDtjAb+b7w96RElZ\ns+CMJ7CC2nFvd4k7s1MbShbljr5gY8em7t4RFFxcviwVRbmTXx1MYa/C3R3yE2mb9sRykWj37n6i\nzGs/Ot13x7+/PkpEJOz58Ff209FXznfzhtqYnjNTK7SoDF52N8cG1wJeIeJzE+ZT7UeUlY18meMo\n4ndTJeNuai4ZOqlRxt2arTJ1O+5hkzPuZUle3ihwnFq9jUdZDYcXmc7ATkoxGJXxufnhiE+UlQ7P\n4bDXh0MhLwuhYbGME9mrcN//0f/1dnr+8x+95y/++i/uufOel/vuuPeWCfZHwcGI9lG+gzfv33jh\nnWS3bqW9sZUyeiZTSestoXmsB8u4hzw8qwsxn2o37KVUxOdm546pm0AQlXEsHRl306MybB3k5sI9\n7HP73Hy2JObL5rQnF5J5WVFG+/xu3kVEbCPkQrLTqYnGRFlZTOZxpOtWxndBMl2ZT2UZ9x1hL5sF\nwnyqE9lrxjMze+ocERH52X9vnDw1m9mzn4hoMNT8l2RqasqKW7W8vGzRZ7bCayfSROQtp/Tc5gtL\neSKaX77Q+IOXl5cTiYRZt9CJzpw5E09HiGj67CmuXCCiqWOnuHVvt29X59j/PjCfEokoKCjszpwt\nyUS0mMy9+eaUKWPEZ86cMeGzOFmH7wMLFxJEtLZwfqqwsOmPZjdEIopvZEx8ZJYVJVsSXRx38tg7\nW+8wfR4qlOmtU+f9bhO6XW8tF4go5pHZ7RdKaSJ69fi5cWnF+Cc3y29Xin/5/DoR/cvHR32C+h2x\n/+OA1U6fPjOzFiai5PzZqTVDd4YQVyCiX7910pcy+hpAv/kLKSK6MH/eLZeI6PW3j+eXWnsiw33A\n0prw8OHDTT/GVoV75vt/8Y3TN371p9+8I0RElPz+fR/7xl88+YF/vYOytLaeqnxcam2FJge2LlDV\n8w9uw9TUlEWf2QoPnXiTKH3DlXsPHx5v+sGZ0BodfZX3BRv/A6enpycnJ027ic5UPrFGJN1w3aGn\n5469tbw0OL7r8CHzdkzYnv3vA+J0nGh1OBZid2ZFIf/Tz+TL0t6DV/fVykm3wUGPA1bo8H2g/PPn\nico3HLr6ssHNh0APJvL0zC9Ezm3iTyRTFImWAh7+uutqfM5dr7y8ko2Hh3aa8hVPvDJLFL9y19Dh\nw+8jouuy558+/S4X7D98+Crjn9wsr/7qPaJ1IhKjE4cvH2TvtP/jgNVSRTk/tRzyCje//4jBjsCh\nC6efnz5DoR2HDx8w6dY1l3n6OSL6wPXve2b+2IkLK4M7Jw9fNdLSZ8B9oOs1ob2iMkREg2Mh9a3o\n+64fo2xaJN/IYVr8xVRGff/cT57c2H/TwfZPPuhpM7qPTSUt447h1KYUpWo4FRl3W6pe4k5EHFeJ\nueNavyNVL/fcJGDBVpl6k6kMWywTz5sTldH2CaqXltkZTHbbCPnGjNpVPXpuvbu3xFZWMiIR7RoI\nGL+O1/kzmERJSeRKHEcDQS9WuTuXrQp3oX830ZP/+OTb55PJ5PnXv/+fHlqkw/tCJHzgE3fR4kNf\n/97rycyFZ+7/s+eJPnlr516hOov+Y1Pp4gFMKNybEBUSZUXgOY/gUjPuKNxthj0DVUeiEXN3LklW\nNvJl0k6K2YQ9cGWLookbFOtNpjJDYR8RJQrmjMNu2ieoZdxtVLgrCr2pFe6/eQ+F+0UrWYkMT6Yy\nE/2d3gi5ni0qCsUCHoHnYhhOdSxbRWV8d3z90aU/+/L9933+fiIi6rvxrkf/tw8JRKFDX3rgK/P3\n/c2ffuxBIqJPfeN7H8EJTLUkc+VUvhzw8DtCulJr2OOuExtKYxs2Iz5M9NpRPFciov6qwh0dd+dK\nF0RFoYjfXfNwSjfvElycKCtlSfYI5rSfGnfchyzouO/SrouyrTK2KtznE7kLmaLg4iRFeWd+I1sU\n672k2W7UjrvhyVTSCve5ROcKd3ZsKisP2LUsrHJ3Ipv9Kvr2fOmBp/84eSFDRBTaEb0Yhzn0ib96\n7vcvZEQSfNFoyGY32zYq0+46r+KF1MIdv7pNqIW7VyAidNztKZll2wMvNmhHIj4i6vC2NTCFtpK/\n9nACx1HQK2zky5mi2C/UWDvTBnWljK9eVMZHRPG8OR33TUdvxgIen5tP5cuZoljvlUOHsZzM7x0Y\nXEsX35nfeG06ccuBwW7fKFtgHXdTCvfhiNfNu9YzpWxJ1HPuinHqSpmQhy4W7ui4O4+tojIqX3TH\njuiO6qr94vt37EDV3sBsPEtEuwY2z3LVE3ALvIsrinJZwikMjRREhbRuHNZB2lM8xw7a3NJxR+Hu\nQMktwadNAh6BTF3lnimUqVFUxktEiYIJj5OpfHkjX/a5L14X5Tgai/rITk33N2cTRHTdrtiNlw0Q\n0cvnLnT7FtnFckYkrVlukIvjdqobITv0c2enLw2GWcfdQ9r5xOAsdizcoW3qZKruxxSOQ8xdl5yo\nkHbsCxtO3cjjO2YvW8/rUTvuiMo4UOOOOxGFvDyZOlifUZe48zX/lHXcE2Z03NnxPZuui45HA0S0\naKPCPUlER3bHbty7g4heRsxds5qTyaSOO3V8PnVN7bh7STuTGMOpToTudU9paaUME/JpV5yD5lxx\n7kmXdtxZxh2Pd/YSv3SrDCHj7mTNO+5e1nE3rXBvNpzKMu5mFO4sJ9Pvr37nOOu422OxTK4knVhK\n8S7ufTujiqLwLu7YQipdEMN1ckTbhygra1mR49R5YuPYnEPnCve0evoSaT2ODURlHAgd957S0koZ\nhm2ERMe9sVx1xh3rIG1p6/bAYWTcHSvZvOOuLpYx6ytmSiIRhepEjdnhqQVRMf4Va567OR4LkG2i\nMu/MJyVZuWIkHPDwQa/wvp19sqK8ch5Nd1pK5mWFhsM+r0kj0WzOoWPzqSzjPlg1nIqOuxOhcO8p\ns+uXLCvQI+zFfGpzRRaV8SDjbl+JbJku7bgPhr0ujotnSyURIxwOo70Ma5Bx50mbKDVF4447x6mr\n3FdSRYNfaNNkKjNmp447m0w9sjvG/lNNy2Cb+5Z1QMbt6uxGyOqtMn0YTnUsFO69oyTKy6k87+J2\nRlvquCPj3lyuXJ1xF4golTdzhzQYVFn7HfVfLPUEF8f2J6ymjRZb0GGsERirX7ib3nFXT1irnwZh\nq9xXDCevtKjMJY/SO6N+sk3GvTKZyv5TnU9FzL3yosukgHvlU7GOWwdUD6cG3IKbdxVFuVA27dUv\ndAYK994xl8gpCo1F/QLfwpFuGE7Vo6AOpwpE5BV4N+8qS3JRxOOdXaQKZVlRwj5h050fMXeH0jLu\ndaMybKtM1rytMuwxsME2RtZxN/4isGbXdsw2q9wVhd6cUSdT2Xuun4wJPHdiKYWd32yw2JTTlxh1\nODWR70wbaK1qHSTHIS3jVCjce0erK2UYlnE3cTlDT2J73NmTOsdV5lPxTbOLZJ0GLWLuDqUj486i\nMh0aTiWioYgJHXdZUeYTeSLaeel042ifn+NoJVUUpS5fyJtezyZypR0h706tPPW7+et2xRSFEHNX\nw6jmddzDPiEacBfKEkufW0qUFZZAGwiqe0gxn+pQKNx7x0xbjylhnMGkQ1662HEnzKfaD1spE9uy\nGYl13I3HG6DDmmfcWVTGvK0yjddBkrZYxuB9aSVVLEvyQMiz6cAdgeeGwz5ZUbp+dejNmQQRXbc7\nVr2tUtvmvt0L95obgQzq2HxqPFtSFIoFPJXLkuoZTHgicxoU7r1jrq25GVa4Z9A8bqjAMu4e9Ukd\n86l2U2/tNw5PdahuZdwbdNzZ1RuDUZm5WpOpDNsw2PWY+xuzl0ymMjfuReFOpJXXJnbcK5+tAzH3\n6oA704dV7s6Ewr13zMSzRLRb97GpDHv+Q+qjsep1kHSx445vml0ktixxZ5Bxd6jkluWem3R4qwxp\nhbvBjvtc/Y29LOY+3+3FMmrHfVe0+p2Hd8U8guvUSnqjsH0HezJFMZ4tuV2X1L7GTXRqscyFqoA7\nw14YY7GM46Bw7x3GMu54zd1I9QFMRNSHM5hsJlHnvJ4RM4ot6LCSKOdKkuDignW2qpMFHfd0USRt\nPW5NLCqzamwdZIN9gnZYLJMpiqdW0oKLu2a8r/r9XsHFevBTi9ku3bTuYy+6hoKCi2th/UNTlflU\nEz9nTdXHpjJqVAYdd6dB4d4jZEVpb8Us1kHqUafjjsc7u1A77lsL9z5EZZyHhW6jAU+DAknbKtP5\nqEzByAIQlrWoGZWxw2KZqdmkotBV430+9+asP4u5Ty1s98J9JGTy8bEd67hXH5vKoOPuUCjce8RK\nqlgS5f6gp8E6s5rUjDu2yjRUvQ6SKhl3FO62oWbcg3Uy7ilDxRZ0WL2JhWraVhlzkhuK0rxwD3kF\nL8/lSpKRVwsN1pKwjHt3C3e2wf3IrtjWP2Ix9+1cuLPaejhUd3y5PWzUda4TUZkSacemMn0YTnUm\nFO49okF0sjEWlUHGvTF2AFPoYscdgwH2Eq8zyxj0CkGvUBLlZB5dJcdIZpuslCGzt8qUJFmUFa/g\nElx1m/wcRzG/i4wlrxqc4KN23Luaca+slNn6R9dORP1ufjZZ3LbHmbE0y3DQ5MJ9POp3cdzSRr4s\nWXvAM8u4Vwf0oxhOdSYU7j1iZp1NprZcuGsHMOFXt5GipJA2D0fouNuPtva7RqmHxTKOo0VlGnfc\nzcy4N223M/1+ngzE3AtlaTVdFFwcS3BtMq5l3Lt1dUhWFLXjvju69U/dvOv6yRgR/Wa7HqHKrpaY\nXri7eddIn09RLJ9LZltlqjPuiMo4FAr3HjETz1HrK2UI6yB1UJRLDmAirIO0n3p73AmLZRyo6S5I\n0opss6IyLCvYNGcY8xnquLPKbDzmr9nXD/uEsE/Il6VElwqpc2vZdEEcifhG+2rvKd/m29y1qIzJ\nGXfSLpXPW7zKfetWGQynOhQK9x7R3koZqsq4IwRcT0mSJYUEnvMI6u8LG07dwDpI22iQikbH3XH0\nZNyDHjNPTmWdi5CvWeHu54lopd2syGz9Je7MeFcXy7CczJFaORnmxr07aLsW7rKisMLausLd6vnU\n1S3DqVF03J0JhXuPYFfxakYnG3PzLq/gkmQlX96+C3oby27pxkWwDtJOFEVtGtXs0Q7j8FSn0ZNx\n93t4IsqXJUk2oeWgs+OuRWXavC81nUTq7nzqG/UD7sw1431+wTW9nl3afi+D19LFoij3Bz1+wcxd\nkAx74p6LW/hzF2WFvR7eEdyyDjJfRtvOWVC494hZNSrTzoluIR9i7o1ktuRfsQ7SVtKFsiQrQa9Q\nuSRSDR13x9GTcXdxXECr3Y1/RTbk2mBtPMMK95V2M+4NJlOZ7m6EZAH362qtlGEEnnvfWIC2ZdO9\n6dUSIzrQcU9kS4pCsYBH4C++8PAJvEdwlUQZbTtnQeHeC9IFMZEr+dz8ULjGzFNTEfUMJgQ/alMH\n1zzVHXdk3G0k3jBZMRLxEjLujqIn404XY+4mPHC1NpyabrfjnshTw8Jdi8p04b6azJXPrmY8guuq\nsUiDDzs8HiKil7fffCprh7dxTVsPthHS0sJ9a8Cd2KKkgIeINrB0y1FQuPcCtlJmV3+gvQPdtMUy\nKNxry5YkIgp6Ly4TUNdB5jEYYAssJ9NfazKViEb6/ES0bOzAS+ikpI6MO2mvpU2ZT80UJdJ2wzfQ\n73eRga0ys02jMupGSMtXem/11lySiK4Z76t52ari8FiQiF4+d6FDN8s22jvfUCf18NQOFO5VAXdG\n3QiZRRPKSVC494IZAzkZwuGpzWzNuHsF3s27ypJcFHGFsfviDSPRbKvMCqIyzsHOwe1r3nHnyaRV\n7plCmXR03GM+FpVp5zwvRaE5dRKp9s4W0jLuXem4a4sg6+ZkmH0D3rBPmE/krd5daDfakbd1f3ZG\nDAS9fje/kS9bF79c3bILkokGPYQzmJwGhXsvaHsylWFnMCHjXs/WjDvHVeZT8Wqn+9jQVb2O+0DQ\nw7u4RK5UFK093wTMwsqI5h1386IyrOMebrZVxi9wfjefL0ttBAsTuVK2JIa8QtRf9wUJy7jPJ7vQ\ncVcnU+sH3Bnexb1/D1sKub2a7m0fcagHx2nzqZa9HFKPTa3TccdiGWdB4d4L1MnUdh9TQtpGSDNv\nUw+pmX/FfKp9sAZtvTqPd3FDYS9hsYxDKIr6SqzxVhkyNSqjM+POcTQc8VFbMfc5bTK1QaBxKOwV\neG49Uyp0dlhQkpW3ZpPUcKVMxY17B2j7xdwNdseasno+lZ2+NLi1445V7g6Ewr0XaMemtnz6EhNB\nVKahmqviMJ9qH01nGYexWMY5ciVRlBS/m/c2DFuTqVEZnYU7EQ1F2IvAlmPuLGvRuGXr4jh2+FGH\n9y2eWUlnS+LOmH9oS0d2q8oxTNtnwqcoyivpAu/ixuocTWWc1fOpNYdTCYenOhMK915gMOOO4dTG\nckWJiNjuuQqt445vWvdpHfe6hfsIVrk7B3sZ1rTdTiZHZXTtcScitrmrjfsSa9k2zVqMd2Mj5BvN\nFkFWu2I0HA24lzYKM/GsxbfLLhYSeUWhsai/epeiuSYsnk+tN5zap61yt+jrghVQuDteWZKXkgWO\no53tzs0g497Y1uFUIurDGUy2oR60WSfjTpVV7ijcnUBdKRNsEnCni1EZMzru+va4E9FwxEvaqF9L\nmi5xZ7TFMh0t3N+c0ZuTISIXV4m5b5e0jLbE3ap2O1kflVmrE5Vh/Y4EojKOgsLd8eYTeVlRRvv8\nbr7Nn2YIUZmGMiVk3G0tnmsyy8gOT0VUxhF0LnGni1EZE+Lg7NGv6TpIIhqKtNlxZ3OHzTvu6mKZ\nznbcZ3StlKlQY+7bpnC3dDKVsbrjvtZwHSSiMs6Cwt3xjJyZykQwnNpQzY67mnFH4W4DyWyjrTKk\nddwRlXEEVkOweqKxgNkHMIV8zb/ocNhLRKttRGXiTXZBMtpimc4V7vFsaXo963PzB0caHb1UrTKf\nuk1i7k0X8BvHzmSdT+Ql2fzvqSQrbFP7juDWjjuGU50HhbvjzawbWilDRCEvojKNsFVxWzruWAdp\nF/FmS0hY4d7hgT9oj/6Me8jMrTKbD1mrR9sq01pURpQV1kTfGdMVlelkx51tcD80EdUf4N4/FO4P\netbSxfMXtkXMXWfMyYiAhx8IecqS3Pa5vA0kciVZUaIB99YfcR+GUx0IhbvjGVwpQziAqRmt437p\ncCo67vZQ2R7YICrDhlORcXcE/Rn3gIkHMOkfTo20s1p0KZmXZGUk4mu6KqfzGXdtg3tU/1/hOG23\nzHvbYpu7no1Axmnnp5r/o79Q5/QlqqyDxBOZo6BwdzzjRzEj494YK9wDHqyDtKPK9kCfu267dFiL\nysjb5NK+kyV1Z9xDZkdl9KyD1O5LxZbuSvpbtmNRHxEtbuQ7dl99k21w17dSpmL7xNwVxfIl7ox1\n86ks4L719CXSMmmJXAkPjQ6Cwt3xZvRtGWsAGffGGhzAtIF1kN0WzzZZKUNEAQ8f8btFSQ16gp0l\n9GfcTdoqI8pKviy5OM4nNI/KBD2C380XWjw8VedkKhH53Hx/0CNKylrri2vaIErKO3NJamUyldk+\nMfeNfDlTFIMeQc+LSSMmrCvc0yWq03H3uXmfmxclJVfGc5ljoHB3NkUxemwqYR1kMyzjvmU4Fesg\nbSHRbKUMg42QTqHz2FSqdNwNb5XJqa/M+QZnmlZUDk9tKS2jczKV2akulunEffXEcipfliYHgg1m\nu2u6bEdoMOxdz5TOrKYtum02of7sBhodeWuKXZYtlmFL3LfugmTU+VQ0NZwDhbuzrWWKhbIUDbgj\nOhpU9QQ8PMdRriRZMc/eA9Qdz96aBzDhwa7LWJ3XtOzA4alOoUZl9GfcDXfc2S84m9HXo42Y+1wr\n041j6hlMVm0GrPYmC7jvbiHgzlyMufd6WqYDS9wZtljGio57vWNTGXU+Fc9lzoHC3dnUydT+9idT\nicjFcezkEaRltlKU2usgwz503G2BHZvatEGLw1OdooWMu0lbZbRLas1zMgw7PLWlxTIt7RPUDk/t\nxH211Q3u1SppGZNvk810ZjKVOtBxr5VxJ6xydyAU7s6mHqNtYDKVYWVoBvOpW5QkWZIV3kWbzrdi\nlzgw0dt1cX0d95GIlxCVcQJ2CaVP1x53c7bK6J9MZYYt7rhri2U60nGfZStl2i/cf/Peem/PfM8Z\nniLTaaTPJ7i41XQxXzZhw2k1Ni+x9fQlhkVlcHiqg6Bwd7YZkw6GQMy9HnWljHvzb4pP4AWeK4ly\nUZS7cbtAldSZccfhqU4gyUqqUOY4XYU7uwiWK4oG60b9uyAZdZV7Sm/HPVsU49mSR3AN1amcNhnv\nVMZ9NV2cT+SDHmH/cLiNv767Pzja50vmyqeWeznm3oEl7gzv4tiPft7sZaBrmbrDqaRdrtzIo+Pu\nGCjcnc34sakMe9LCcUJbsSd1n7B5LonjEHO3BXWrTLNkxTCGU50gVSgrCkV8bt7VfBLQzbsEnhNl\npSQZevHMrjTq77iz+lv/QTlquz0WcOkbb+zY4aks4H7trqie7/ZWHLctlkJ2LCpDlqVlGuxxp8oq\nd3TcnUPvQ5UjTE9PW/Fpk8mkRZ/ZuNOLcSLyllIGb6GglIno3OzCEJfa+qcLCwtGPrmjnVsvEJFb\nkbZ+hwMCxYlOnJvORnU10hzNtveBhbUEEYm5jca/AnImT0Rza+3/piwvL9v2caAzOnAfmN8oEVHI\nzen8VgfcrpQknTx7vs+nN6G+1cxCkoioXGj6Rdl9QMlliWhmtcldruKN6TQR7fDrfYaSCxIRzcez\nVt/ffvnbZSK6LNLCU+em+8DlEYWIfn5s7tadFq9c6RJRVubjOSKSUqvTuQtk8eNA1C0R0Vtn5y/z\nmVa7S7LCuhvZ9aXpRI0fk1LMEtHM8vr0tK6C0LbPBR1jaU04OTnZ9GN6qnDX8w9uQzQategzG7ec\nOUNEN1y1d7TPZ+TzDMXiNJcJ9A1MTo7V/ADbfgestkZxonNhv2frd2AgvDC/UQr3D0+2cuigc9nz\nPlCkZSLav3t8cnJHgw8L7SgSvRcvyG3/KxKJhD2/A51k9XcgPpsgOrOjL6DzC4V876UK+f6hUSNJ\nBt/iNNHCyI7mj/PsPiAHs0TTGyW9342fz58nogPjAzo/XlHI5z6TKUo7RnfqD/C04dxPF4noQ4cu\nm5wc1P+3qv8VH+sb/k/PL76znJ/Ytbu9tr3NzSfykvLuUNh7YN9l7D2WPg5cOSM99W4iQz4Tv8R6\npiQr70YD7n2X7an5AXvWeKJlWWjhi27zR8Ku14SIyjhYJTrJhqWMYBn3TBEXyzZjOyv8WzLuhFXu\n9qBzj3t/0CPw3Ea+XDB78AtMxE7IavrTrDBllXurw6lsHeRqWu/hqa2GpDlOPT91wcq0TFmS31nY\nIKLDBvoOO2P+nTF/uiC+u1TjUm0PmDNpikwnK6Iya+kC1c/JUGWPO6IyzoHC3cEqZ6bqjE42gIx7\nPWxnha/Wczoy7nbA1kE23Srj4jjE3O0vmdc1sVAR8Jiwyj1Tkogo5NEbtgl6hICHL5QlndP8bRR/\n49EAES2YPaRY7fhiqiTK+4ZCeuaAG7hx7w7q3Zi7usfT8BSZTpYU7g0nU0kbTsU6SAdB4e5g713I\nkEnNAKyDrIfVBHU67m5Cx72rFKWFgzbZ4akrWCxjY/qXuDPqYhljGyEzhTK10nG/eHiqvlXuLS1x\nZ8ajPiJatLLjzja4t7cIstpNPT2fyiZT2dFIHcC+0Fw8b+KCTe30pQaFu5twAJOjoHB3sDMrGSJq\nb5PXJlgHWQ/bKuPfslWGiCLsDKY8Xu10Tb4sFUXZK7j87ubtUla4L6FwtzF1ibvuqEzAy06OMxiV\nYQcwtZAmZ2fZrOq4eqMorS1xZ8ZjlnfctTNTjRbubLHMq9NxsRcP3p7t1BJ3ps/vDvuEbElMmNf/\nZoV7g1WkrOth4lcEq6Fwd7ATy2kiOjgaMf6p1I47Tk7domHGHVGZLmOXd2MBj56w2HAfojJ2p2Xc\n9XfceSLKGYzKtJhxJ2276IqOVe5rmWJRlGMBT0svDDqQcWdHL7V3Zmq1kYhvz45gtigeW9gw43bZ\nS8eWuDMcp36tWfPSMtouyLq/U+zk1I1cuafP0eopKNwd7ORSioiuGDWl4y4QzgGtRY3K1Ornsoz7\nBi5TdI+6xL1ZwJ1RozIo3G1MeyWmu+PuMaHjwH7HQzUHWepg/csVHavctcrP39JN2hllZzBZVbgv\nbeSXNgoRv3vvYND4Z7vxsp5Ny6hRmU4V7mRBzH0t0+jYVCLyCK6AhxdlxfghxNAZKNydKlMUZ+M5\nN+/auyNk/LOxbhAK960yTTPuiMp0j86VMgwOT7U/FrTVM7HAaBl3Q1GZVk9OJa3jvqaj497eWhJ2\nBpN1Hfc3ZpJEdO1E1PhiA9LSMkd7rnDPlsT1TMnNm7C3Tb9dZnfc19JNhlOJqM/P5lPRhHIGFO5O\nxU6Zvnw4JPAmPPIi415PrlT75FTCOkgbYLnMpitlmBFslbE9bdRYf8edJ5M67m1FZfR33Fsr3Ef7\n/BxHK6miKFkSXzArJ8P8d5cNENHr0/GysSNs7WYunieinTG/KS9vdGLzqWZGZTJF0qYy6okF2UZI\nxNydAYW7U51YSpFJAXdCVKa+TIOMO9ZBdhvbBamzQauug9zQtQkEuqL1jLsZW2XUjnsLZ6+qURkd\nhXsbk6lEJPDccNgnK4pFrzPNWinDDIa9+4ZC+bL09nxPxdw7vMSdYasnNE3VFgAAIABJREFUTYzK\nNN0qQ1rMPYGOu0OgcHeqE0tpIjo4YkLAnTCcWp+aca+5xx3rILutpY47u969li7IGMKyq5Yz7mZs\nlVGHUz0td9xXdayDbGMXJDMesyrmXihLxxc3OM7Q0Uub3NiLSyHnOrvEnTE3KiPJChsEajCcSlrv\nYyOPjrszoHB3qpPLbDLVnI67dgAT5so3a5RxxzrIbmMtIp3JCp+bjwbcoqysZ/D8ZEdFUc6XJYHn\nArpraONbZRRF3RzVYlRG7bg3fcBUO+6tLwJnMfd5CzZC/nZhQ5SUA8PhlmL9jWnzqRfM+oR2MNvu\nz86I8aif42gxWTAlJZXMlSVZ6fO73XyjYo89hLLrXWB/KNwdSVaUk0tpIrrSpMLdK/ACz4mSUuqt\nkKJxrONeJ+OOjnuXsWZSv+5kxUifnxBztyvWbo/6dS33ZIxvlSmIkqwofjfPu1rIMQe9QsDDF0W5\n8a9/SZSXUwUXx41HW9sqQ1YulnlzNknm5WQYFnN/YyZREnvnGYStlOlwVMYjuEYiPllRFjdM+NGv\n6cjJUOXwVMQ+HQKFuyPNJ/LZkjgY9uoMCTTFcRT2Yj61BnWPu1DjN8Un8ALPlUS52EPPVc6iJit0\n/xaMRLyExTJ21dKOIMb4Vpk2JlMZPWmZhWReUWgs6mtjhYB1i2XY0UtmTaYy/UHPFSPhoihPzSZM\n/LTdxU5f6uQuSMbEVe56JlNJ+6XDcKpToHB3JHMnUxnMp9bEVtv63TWedzkO86ldpu5x199xxyp3\nG2v1ZRiZsVWmjV2QzJCO+1J7k6kMy7ibXrgrijaZamrhTpWY+3s9EnNXFJpL5KnjHXfS7jCmzKdq\npy8167j7WeGOJzJnQOHuSOZOpjIo3LdSlMpwau2GWR/SMl3Vao92BIen2hirG9jvlE5Bw1tlMgXW\ncW9hpQyjZ7FM25OpVOm4m51xn0/kLmSKsYBncsCEo5eqsZh7z2xzv5ApFspSNOAOt3IylylMnE/V\nOu5NXgxrURl03J0BhbsjmTuZyoR8bsJimUsVRUmSFTfvEurkX7WOO75p3cHWQeoPjGkbIVG421Ei\n19r1E9IKdyOPWpZGZYxMN+7UtsqYuzBAa7dHTV9N/v7LBjiOpmaThbKhJT820ZXJVIZ9UVM67ms6\nO+4BdNydBIW7I1kRlYmoHXf86l7EAu4NLqPjDKYuKpSlVpeQsI47ojL2lGw94x5Ut8q0XymyVZJt\ndFXZYplVHVGZ9vYJhrxC2Cfky1LC1OQxO3rJ3MlUps/vvnI0UpZk9trA6bqyxJ3RVrmbcLHlQqZE\nOjLurONu7j0NrIPC3XmyJXFmPSfw3N5BM691svI0g6hMFXXBc/3L6Mi4dxHLyfQHWlhCwjLuS+i4\n25J6bGorGXe/myeifFmS5Db70myIpaUl7sxQmL0IbN5xb7v4G48FyOzFMmyljLmTqRU37t1BvRJz\nN/izM8LEqIzOrTIxdNwdBYW785xaThPR5UPhxptZW8V6TikU7lVyzZ7UsRGyi5KtJysQlbEzVjdE\nW8m4uziO/Xrm241ntD2cqqfjbjBuMR71kanzqbmSdGIpxbu49+007eilato2994p3Du/UoaIBkNe\nr+BK5ErGk6t6jk0lbbBkI4+DXJwBhbvzsA3uB0fNnEwlLeOOqEy1TLP8K85g6iJ1pUwrDdpYwOMR\nXJmimDUwzggWaSPjTkQBr6HFMtpwapsZ95X6GfeNfDldEAMevu2lveNmb4R8Zz4pycoVI2G2jcd0\nN+zpd3Hc23PJHvj9YitlulK4c5xpi2XYVpmmw6lu3hX0CJKsYMjNEVC4O88JNpk6YmbAnbSOO35v\nqzU9UlHtuCMq0w1trP3mOK3e2mh+WD10WBsZd6qscm835t72cGplq0y9JmWl3d72GKjpi2Ws2OBe\nLewTrhnvE2XlTefH3NkS965EZciktIysKOvZEhENBJt03IkoGsQqd8dA4e48JxbNn0wlorAX6yA3\n0y6jN8m4b+AyRTckWu+4E9EoNkLaVRsZdzK8yr3p73g9Qa8Q9Ail+oenGplMZSqLZdr+DJu8Ydlk\nasVlg0EiOr6Ysu5LdEBJlJdT+faOvDWFKWcwJXNlSVb6/G5PrQMEN2ERtQRi7k6Awt1hFIVOLFsS\nlQmrURkU7hc17cZpHXd807qgvWQFYu621UbGnQyvcme/4ywo2KqhSKNV7sZD0uYenqoo9OZMkiw4\neqna3sEQEW04vP5jR96OtnXkrSl2mRGV0TmZyrAH0g2scncCFO4OM5/IZYvijpBX52+jfiEv1kFu\npqNwxzrIrmGFe6sBYhyeak+K0s60MWmz4wY67mzlazuZb+3w1NqxK+OLwM3NuE+vZxO50o6Q19Ld\n5LEga9w6u/6b694Sd2Yi5ifDHXf12NRmuyAZtsodHXdHQOHuMCetabcTMu61ZEvNMu5YB9k97Dkm\n2mIkGoen2lO2JIqyEvDwei7rV9M67p3OuBPRcNhLRKvp2vcltofbSEh6MOwVeG49UzLlSKM31aOX\nYqYfvVRN2wju7IfEuUQ3A+50seNu6DWbusQ9pOuVcJ/fQ9gIqcO5tUxGsmS2Wz8U7g7z7pIlk6l0\n8QAmFO4XqZfR629gcPo6yHi29NTbi1OzyW7fkHbEWzw2lUFUxp7YxEK0xXY7accsGMy4t7HHnbSO\n+2qdjrvate1vPyTt4rjRPj+ZdPIAC7hbN5nKaFFpZ3fcuzuZSlrCai6Rkw0saFxLF0jH6UtMDMOp\n+vxfT737rXM7nnt3pYu3AYW7w5y04MxUJoSM+xY9vw7yn45Of/lfpv77v3up2zekHe0lK9Bxt6dk\nvp3rJ1TpuBsdTm2r414/4y7JynzShEXgLC0zb8ZiGbXjvsuSDe4V7PfR6Y3bLi5xZ4JeoT/oKYny\nav19o02xjrvOVC17xeX0H5zVypL82vk4EV07Ye3vUWMo3B3G6qgMMu7Vss2e1J3ecS9JcrdvQvvU\nPe6tFu7qOkgU7vbS3sswIgqqW2W6EZVhHfdapdVKqiBKymDYy852bRsr3I0vlskUxVMracHFXTPe\nZ/BTNdYbjVu2xL2LHXciE1a5tzGcmsRwakNvz2/ky9IOj6jzOoZFULg7Sa4kTa9nBRe3byhk+idn\nT13Zkmjk2lyPadpx9wm8wHMlUS6KjqyAy5KDf9Zt7HEnrUu6lilKsoP/7b2nvZ8mGd4qw37HWdui\nVZVV7lv/yPhkKjMeM2c+9a25pKLQVeN9PmMvJJqqZNwd/TTCfnzdLdyNr3JXh1P1Fe59AXTcm2On\nAk8GuvzyBoW7k5xeSSsK7RsKuXnzf3CCiwt4eEVpf8yr9+TU4dS6T3Uc5+z51Eq547gXHmVJzhZF\n3sWFW1zk5+Zd/UGPJCvsMHCwifYz7sa2yhjvuNcs3I0vcWfM2gj5Bjt6ycoN7oxP4D2CqyzJubJT\nA4Qb+XIqX/a72z/y1hTGO+7sIW5Hs2NTGe0VFzrujbx87gIRTfpRuINu6mSqBQF3BhshN2nacSei\nPienZda0q/yOu7RdWSnTxooMxNxtyGjGva12gygpRVEWXJynrVbIkLpVpri1u6x13I0e3zNu0uGp\nPz+xQhZvcGc4TgtdZB35kEiVF1397R95awrji2XYw/uQzuFUdNybKYkyewE8GejydwmFu5NYN5nK\n4AymTfR047SOuyO/aZV4ruPWt6krZVpv0BJi7rbUfsbdy5P2q9qqyivz9kq0yuGpG1suuJkVkjYl\n476cKvx2YcMjuG45MGjw9uihloDOvAhJlZyM4aslBhmMysiKsp4tEdFAUOdwai9MFVtqajZRFOUr\nRsIBvssXqFG4O4k6mTpi/mQqE8JGyEtp6yAbFu5OPoOp0nHfcFrHXa3z2rqWzTrupqzYA7O0t5Wf\nKsM5bRXuRnIyjHp46pZV7mbtExyL+ohocSNvZPToJ+8sKQrdesVQe8tzWuX00AV70dXF05cYdrmm\n7ahMMleWZCXid+s8GIFl3DfyZQy51fPye+tEdOPegW7fEBTuzqEodMLijnsEZzBdSk9UxrkZd0Wp\niso47fa3t1KGYR13RGVsJdHuD5Rl3LNtbZXJlNrfBckM11nlbtY+QZ+b7w96RElZM7AW8EdvLxLR\nHYfGDN4YnaIBZy+WYS+6urgLkhmN+nkXt5wqtDeApAbc9Z2+RESCiwt5BVlRMujc1XH03DoR3XgZ\nCnfQbTGZTxfE/qBH55B4G5Bxr6YoajXQYDiVnLwRciNfLmvrIB0XlUm2u4SEtI57zZlC6BYDGXee\niLJtbZVpuu+1KTXmful9KV+WLmSKAs+xst6gnTGWlmnz7jqznnt7Lhn0CLdeMWT8xujBXn0lHJtx\nnzV8cpYpBBc3pm7xb6fprh6bGm7hHsh++xz3XNAZhbI0NZvkOHo/CnfQj02mXjkasW5iBhn3akVR\nkhXFzbsa7/Bx7hlMa1VrVRzXHlM77u1FZXB4qv0Y2OPezahMzcUyLN6wMxrgXSY8WGuLZdqMTDz9\nziIRffiqYasXQVZEHZ5xZ4Vyd3dBMkbmU9klmkHdHXfCKveG3phJlCX5ytEIW0fRXSjcHYMF3K1b\nKUMXz2ByXg1qBdZub9qNUzvuDnyWqr74vuG0Lkui3TqPiIaxVcZ+jGbc29oqwx7rQg0vqTXGjgXY\ndAYTq7TMylqoi2Xa7bg/+dYiEX2sUzkZqnTcndYLYCRZmUvYIipDxuZTL7Ry+hITxWKZ+rSA+45u\n3xAiFO4Ooq6UsWwylbTCHRl3Rgu4N3lSZxn3DQdGZaqv7zvuWZY9u7S3aFnbKoM97nYhykoqX66c\nitCSylaZNmbqzBhOrdFxN/f4Hm0jZDvV26mV9KmVdDTgvvnyzhUcjs64syNvd4SMHnlrCjaf2mbh\n3srpSwybKkbhXtNvbBNwJxTuDvKuxZOpdDEqg99bIt35V63j7rxXOywqw6pYx13Xjqvn9bRz1TLi\nc/vcfLYk4jWqTbALVhGfu41sCQuzSbJSklqe4cvou6rWwLB6eOqmjruZIelxAxn3p95eJKLbrx61\n4sy+ehydcZ+zwZmpFWwlZXuLZdbU05fa6Lg78hWXpXIl6a35pIvjbtjT3+3bQkTUieVQrSksP/lP\nDz/728XY2DV/8D/+0Y17our7M6e/9+A/H51JjB348BfuvWPEfjfcUvmyNL2eFVzcvqGQdV9FG05F\nNUOkb6UMOXkdJFuFcflweDlVcNxAErtE0F7HneNoJOKbXs8ubxQs/YUCnbRR4zYPqgx6+WROzhZF\nr9DaZzCr47566TrIOVND0uqEYuur3BVFzcl0bJ8Mo2XcHVn/2WSJOzNhOCoz2ErHXcs4Oey5oAPe\nmImLknJoZ5SlErrOZh33wum/vu2T93/n6NiBA8Wj3/nzz3/sH49niIgyx//89nsefHLxwDW7jz5+\n/yc/+7fL3b6lHXZ6Oa0otHcwpHMna3uQca/GzmIMNFziTk5eB8laMpcPh8iBe9yNZNwJMXebYT/N\n9q6fkIFV7upVNQNPxkNax706qGPWEnem7TOY3llIzsZzQ2Fvh9uEMScnLsw68tYUbJf8bDzXRgyM\njTDtCLfwCBn1s1XuDnsu6AB1EaQNNrgzbVaBsiwvLy+fPn16ZmYmm82adWtO//D//Snt/+Z/ffr/\n+PKXv/n0U3eN0UOP/VokOv7kt16m/d9+6qEvf+mrTzz6VVp8/OFfba/S/YQ6mWphwJ0uFu6OfMA1\nXaaoa3DNuesg2SP7/uEwOfBZNmGsRzvCzs3BYhl7MFq4t7tYJq3jhLUmX9orBL1CWZIrDWZFIXOn\nG2MBj8/Np/LlVpNdrN3+0feNmbLcRj9tq6Aj6z+zjrw1RSzgCXqFbFFs4/KFug6ylY57H9ZB1vGy\nzQr3lh+w4vH4ww8//LOf/SyXywUCgXw+ryjKrl27PvShD915552xWMzAjckc/dHbY3c9cGP0wtuv\nT5N/4I//9cUvERFlXvvR6b47vnl9lIhI2PPhr+y//x9fOU+/O2LgazkMm0y1dKUMaRl3BH8ZnZfR\nnbsOkg2n7h8OkdOeZdkso4vjWE6pDaN9fkLH3TaMR2WorcUyxqMyRDQU9p4viqvpIrv98WwpV5Ii\nfrdZa+M4jsaj/nNrmYVk/sCw3t6NrChPv7NERHdc29GcDBFF/R4i2siXJVnp8GsG48y9WmIQx9Gu\n/sCJpdRsPNfSb4esKOuZIhENtB6VQcZ9k2xR/O3ChuDirp80Ut+aqbUHrGefffaHP/zhrbfe+sAD\nD+zZs4fn+XK5vLKyMjs7+5Of/ORzn/vct771rf3797d7Y0QiWvzOf7j5Oxvae2554Km/OhQlIgoO\nVmpW38Gb9298/53kV2+M1vgkvamyxN3Sr9L1jLusKEVRtsM4P7U6nOrEjnumSESTA0GB54qiXChL\nHdv0bNCGtjrQ1e6hBsM4PNVO2l7izrTdcdd5Va2x4Yjv/IXsaqrAqmorshZjrHBPtFC4v3Y+vpIq\nTPQHDu3s9POkwHMhr5ApiumC2PZVlG6xzy5IZqI/cGIpNRfPt/Rz3MiXRVmJ+N3eVrK1WAdZ06vT\ncUlWrtsVCxq4NGeu1m7Hvn37/u7v/s7lunhXcLvdO3fu3Llz50033bS8vKy0EcWqKCy8u0hEdO+3\n/+0z149k5n719c/8h/v++plffvMDRDQYav6LNDU11f5Xr295edmiz6yTotDxhSQRiRdmprLz1n2h\neF4iokSmsOnfu7y8nEgkrPu6Fc+eyz34epKI/usfdbpLtNWZ6TQRZZLrU1NTZ86cqfdhikK8i0qi\n/OobU26bzYw0UJaUZK7Mc3T+1LGQm0tKyouvTu0I1K1gOnYf0GMuJRKRn5fb/sXMxwtEdGp2ZWpK\n77NUg/vANmHdfeDk+TQR5TcutPcDLeczRPTbk2fCLT48Ll9IENHS3PRUaUnPx9e8D3ikHBG9+tvT\nocw8Eb04myeiiKtk4rOGX84R0Su/PR3LL+j8Kw+9niSiG0b4t94y88lL530g6KZMkV56/a2xsF1q\nHT2KkrKWLvIuWnrv5EqdnkCHHwd8YoaIXjl2dlxqIR7MHiHDbqWlO+FSWiSilUSm8d+y1XNBB/zo\n7RQRXRYSK98WS2vCw4cPN/2Y1n6peJ4XRdHjqd0XGRkxll3x7b52P708dt9nrh8hotDE7/7xPWMv\nf38mQx+gLK2tpyofmFpbocmBrSf56vkHt2Fqasqiz6zTYjKfLS32Bz233ni9dcemEjs2/Mn/lheV\nTf/e6enpyclJC78wEREpCn3luV+yt7v7DWeeWTpBlN67a/zw4b2Nb1Lfjy/Es6U9+68cbGX3Vnct\nJvNES4Nh35Hrrht8/oVkITNx2f4GWazO3Ad0Kp+PE62OxsLt308GkvTSSwXO29JnsMPdsousuw/8\n2/RvidIH9+4+fHh3G399/MxbtLAwPL7r8OGdLf1F10svEZWuveqKQxN625lb7wMHFk/8auY9f2z4\n8OF9RPRS4ixR4n2XjR0+fLClG9PANYmzz5475QrtOHz4Cj0fL0rKa0/9jIj+5LZrzQ1Y6rwPDL/0\n65XMxtjk5Yd3Oem6+OmVNNHSRCx45LpGv+mdfBx4Jz/95Knjoi96+PA1+v9W/tw60er4QKSlm7o7\nW6KfPJeXucZ/y1bPBR3wl7/+NRHdedOVh/ephyF0vSZsrUP41FNP3XHHHd/85jffeecdi24QLWYq\nV6/FNPt/38hhWvzFVEZ999xPntzYf9PBrYV7rzqxlCaiK0bCllbtRBRwC7yLK4pyufWNyMa9cHqt\nsvdKNnLpxiRZdatM88vofQ5My7DJ1KGIlyrnbjhnMY66UqatXZDMSJ+XiJYxnGoPWsbd0FaZNoZz\nMoa3ypB2eOqKdniqFVkL7fBUvYtlfn32QiJXunwodGDE2nRlPX1+Rx6eau6Rt6Zob5V7G7sgSYt9\nbuTLdnj+tYlUvnx8MSXw3JHddgm4U6uF+3333fftb3/b7/f/5V/+5ac//elHHnlkaUnXFUZ9Qnf8\nL/fQ6b/5xvd+tZy8cPzn/999jy+O/bsjURI+8Im7aPGhr3/v9WTmwjP3/9nzRJ+89YB5X9fuTi53\nYjKViDiumzH3//zLs/8/e28eHtlVnvt+e6i5pKrSLPUk9dx22z3Y2GCw3cZuuiF2mxMw8CSGhJgb\nAjEQSIDk+HKeJCfkxA+EG0McAwGT+BJywRmgDdjY4PaAJ4zbVtPtHqVWSy2ppJJqHvd4/1h7V8sa\nqvbetfao9fvLVktVS6qqtb/9rvd7v/p/OyGSUqPHHS4lQtq/Zu2gIe3oiCDutjCBFiNlAKA7GqQo\nSBVrgkguUfaTVVJlWvK4l40W7i02p/a+cXgq3rGpiDXxIOgp3NHcpUO715it9axEwp1uaTNeuxZZ\nbyjKXRmbqvMEmKWp9pBPll12LTOVl86nJVneuz7hkNY7hO4Na8eOHTt27PjjP/7jV1999ec///ld\nd901NDR08ODBt7/97ZFIpMXVRHf9/j9+auru++556gEAgIF3fvYbn7gaAKK7PvqPn7p4932fvu0B\nAID3ffF7B1fTBKaTlnSmIqJBNlfhizXB2Ggbw/zqfPrlsXQs5GNoKl3i8hUeVyaDYbRf1N04gwkp\n7kiSibstTCBTQr2Mxt8hLEN1RQOpQi1VrKKEGYKNZLAo7vpTZYqab84bgKLcZ9XhqUpzKt7CPREG\nzVHuVV587EQSAG7b1Y9xDbpAp2Eu2lIQeEfeYmFtIgwAk9mKIMms5oielCHFHQDiIV++wmcrnOu6\nik3ihdF5AHjLRqcEQSIMblg0TV911VVXXXXVZz7zme9+97tf/vKXz58//8lPfrL1Be167/989tZP\nzhWrwEa74sEFX//fT9wyVxSADcbj0VVUtcMCq4wFz9UW9AFUrBe8/+mpcwDw4bcO/fzkTLrE5R2j\nuGsq3F04g2lWscoEwYXyWOtWGQDoaw+mCrVkrkYKd9tpWXFnQL/iLstQ5gTQZodrAPoQzRSqACCI\n8nS2igIcW3nMRfS1B2mKmsnXBFFmmSbV25HTqVJNuHJtbLCzVSnNMAl3RrnjHXmLhQBL97YHZ/LV\nZK66VnNUEQpx16u4A0Ai7B9Pl7NlHpxVqdqG0xLcEcYr4KmpqSeeeOLnP/95tVq98847b7vtNmyL\nCka7gsvMIQ/Gu1aPr71OlRfPz5UYmtqiOQisFdqQdmWteHxiKv/U6VTYz/z+dYO/Oj8PziiCSzUR\nNFplXOhxR0PaFcU9hAp311xl06WW0gMRfbHgbyZzJBHSCeBR3HUW7mVekGUI+xnDoaII1Cgym6/J\nMkxmK5Is98dCeEdcswzV2x6YzlWT+ebVG/LJ3LbLzmAu1ePupi0RHBbiXmd9R3gmXx1Pl7UX7qlC\nFQC6orp3yJjbRBxTyZS5k9N5P0vvWe8ggzsYKNwzmcyTTz75+OOPj42N7du37zOf+czu3bspu5x0\nq4AzM0VJlrd0R3UFshoGDU+1WPD+pyPnAODON2+Ih331/hgrF7AsqlWmuRrnxhlMqTd43F3WnIqu\nKy26uZQod9KfajdVXqzyIstQYaMxyehDWtZplUF35i0a3AEg4mfr4y3VzlT8ZzgD8dB0rjqZaVK9\nFWvCL07OAMCtV9pZuLvuEA8AZNkUm1PrrOsIvTwG4+nydZp1XwNjUxGuE3FM5aXRNABctSFhTfWl\nHX171ne/+90HH3xw9+7d73nPe2644YZgcBUq4FZz0pKZqXWsb04dTZV+enzax9B3vW0I6rYTB6jX\n6Bhdy8wFRXF3T+ELl6wyrmxOVRT3lq0ysKCnkGAX6I4xEfYb1n+MKe7FKgaDO6K3PTCaEmbyNfO6\nGwfioVcuZCazTd6uT7w+UxOka4Y6+mN2Xp3d6HFPl7gKj3PkLS7Q20lXsMzcAl1GF+iFc9G1wFSc\naXAHvYX7rl27vv/973d3d5u0GsJSUKSMNZ2poHjcjQSrGeaBp0dkGe64ei1SQGOOKYJ1NKcGfQCQ\nc8DNhnbc3ZxabrU5FQBQZUOsMraTLXGgSn3GMJYqg6UzFdHTFhxNlWbz1Yl5syTbtdoSIQ+/NgUA\nh2z1ycAlLcA1Wwo4MlIGsU5nsIwsw1ypBgCdRhX3XMVNL5x5ONPgDnrjIAcGBhpU7ZVKZVXN07IG\npTO13wqDO6hWmYJVNehUtvLfRy/SFPVHN25CX3GIVUaW9Xvc3WOVkeU3WmXQ39w9KotauLdmlYkR\nq4wjUAzuLZyfGEuV0d593hQU5T5bqJnX3TgQD0GzYJlMmXv2bIqhqXddYVueDCLuQo+7YnPS7CO3\njHUJfYp7rsILotwWZA0YPJCI464XziTmi9yZmULQx+zWPJ3NMvS9rkeOHPn85z//y1/+slQq1b/I\n8/zo6OjXvva1973vfePj47hXuKqRZcUqs8M6xR01p1pUg37zmVFBkg/tHqhf6trtMNkvpSqIkiz7\nWbpphgO4MA4yV+F5UWoLskEfAwCJiJvkMVGScxWeoqDFE21ilXEIyCpjOFIG1FgYGxX3epS7eart\nmkQIAC5mGhXujx5PCpL81s1dFof5LkX1uLtjS0E4szMV1BlM2hV3lAXZpV9uB/WoxF0vnEkgn8zV\nGxI+xlkGd9BrlXnve9+7d+/e+++//5577unp6Wlra8vlcnNzc4FAYN++fV/72tdW1SBcC0jmq7kK\nHw/7etssMiyiy5g1dfN8kfv3X40DwMf2bap/0SF+ce3Tl8CFcZBKyq/qgEQRENkKL8vg/D7zXIWX\nZYiHfYzmVONl6VMVd1f81h6mdeNT1JDHvaS5+7wpKMp9Jl81b/SmFsUd+WRut9snAwBtQR9DU2VO\n5AQJb8COeTizMxUAetoCfpZOl7gSJ2jpuTJscIdLhbtrrmXm4VifDBhIldm4cePf//3f5/P5kZGR\nfD7v9/u7uro2bdpE0+74cLoLpTO1r92ywkL1uFvxuf32c+drgrQ/QPZOAAAgAElEQVT/st5tC5Iu\nY86IVkQ+GY0Bz66Lg0Q+mR71bjDkY/wszQlShRdbzLS2gGzLY1MR0QAb8bMlTihU+XaHtaOtKlr3\nuIeNpcpwGK0yQQAYSZUyZS7A0gbSPJqCwmQms5WV7jNn8tWXzs/7Wfodl/dhf3a9oAOxdInLVvge\nQxWk9TgwxB1BU9TaRGg0VZpIV7SMc5kzOn0J1H2VFO4A8MLoHHimcEe0t7fv2bMH71IISzll4cxU\nhOpxN11xz1f4h54fA4CP79u88OvtzvBb61TcXRYHOZuvwgJJhqIgHvLNFmq5Chf2O87iuYg0DoM7\nojcWGE0JyXyVFO42ggy18RbcHSEfQ1FQ5UVd0yXRLteGr3D/9VgaANZ1hM3QWaIBti3IFqpCpswt\n64T5ybFpWYabtvWgPdx24mFfusRlypxbCnfHKu4AsL4jPJoqTaTLWgp3xSpj6M+OhLPsqm9OnclX\nR1OlsJ+5co3jDO6g1+NOsJiTSUs7U6HucTc/Veb/ffFCsSZct6lzz/o3fDAc4nHXHikDblTci4vP\nUuPuEVoyShYkhlKb2NydQD0O0vAj0BQV9rEAUNEjumNsTkUfpZoggZmS7ZpEGFZ2yxx2wNylhSja\nbckdJaBJI29xoStYRhmb2oLiTppTXxxNA8CbBju0NLlZDyncHY3FnamgWmXMrpsrvPjtX54HgD++\nafOif3KKx13PMXqQZViG4gQJXbmdz2weWWUWFu6uiXLHEimD6CPBMg4giyPcE1nVdSkO2mOjmoLm\nISBMLNzjQVghEXI8XX5tIhv2Mzfv6DHp2fWScNVYNzTytq8d88hbXOiKckdOSANjUwGgLchSFOQr\nvCjJBn7cM7ww4lyfDJDC3cnUBGk0VaIpaktP1LInVdq8TBaPv//yRLrE7VoXv25T16J/cozHXYdV\nhqJc1p+6suLuAnksg8njDvXhqfla6w9FMEymZY87qPfYaGiaRnSdqjV5dj9b3yvM81qsWTnK/ZHh\nKQDYf1lvyOeUHhUXaQFQD3HvdKJPBtTCXavi3kJzKkNTzpmBaCPK6CXvFe7ZbPbll18eHx+vVqs8\nv6pfY5M4M1OQZHmoKxK0cC+2wOPOi9I3nh4FgLtv2rzUDIrU6zInCqKdd/xFnePQHXK/oRG1OfXS\nzu6iEeWozsMSeIesMtO5JkNtCKbSuscdDA1PxWiVgQWiu3mKOwqWmVw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dqxaqPIDB1IIz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\nrXGOjPmrUlM0KosD8cjZvO+5IyVbX1mTT8ODRMSuhc7zp49Tw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sdE1frJ3nUWzu7utPheIOrGXV4lKD+c9Yv/8sFHe/0OFWGTXBvV97BHGQfmJt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\nU/5IGaYsuovCkCco7ZEAwPQppP51dz6zZnMbLdzw0OLfvrlt7++733n72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       "text/plain": [
-       ""
+       ""
       ]
      },
-     "execution_count": 9,
+     "execution_count": 10,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -332,7 +376,7 @@
    "source": [
     "# Exceptional Units, small set range\n",
     "# Expected labels: \n",
-    "#     Y: Gated Voltage (XX), X: Gate ch1 (V)\n",
+    "#     Y: Gated Voltage (V), X: Gate ch1 (YY)\n",
     "dac.ch1.unit = 'YY'\n",
     "plot, data = do1d(dac.ch1, 0, 10e-7, 50, 0.01, dmm.voltage)\n",
     "dac.ch1.unit = 'V'\n",
@@ -341,30 +385,33 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 10,
+   "execution_count": 11,
    "metadata": {},
    "outputs": [
     {
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "Started at 2017-06-28 13:40:18\n",
+      "DEBUG\n",
+      "['dmm_voltage', 'dac_ch1_set'] dmm_voltage\n",
+      "Considering the units V, V\n",
+      "Started at 2017-06-30 14:24:18\n",
       "DataSet:\n",
-      "   location = '/Users/william/sourcecodes/qdev-dk/docs/examples/PlotTesting/plottestsample/024'\n",
+      "   location = '/Users/william/sourcecodes/qdev-dk/docs/examples/PlotTesting/plottestsample/089'\n",
       "      |   |  | \n",
       "   Setpoint | dac_ch1_set | ch1          | (10,)\n",
       "   Measured | dmm_voltage | voltage      | (10,)\n",
-      "Finished at 2017-06-28 13:40:28\n"
+      "Finished at 2017-06-30 14:24:28\n"
      ]
     },
     {
      "data": {
-      "image/png": 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TQuI+AYahexdlENGOz1EtAzB9o3UyDHOVe/kxTCeQuAMAAIwPifvEbp9v1KnZmnbbmW5c\nxweYpvHqZHiRFXcLOkICAACMC4n7xLQqxZeXFRLRi1h0B5iuuitGL41VnpeuUiou9LtdvmBi4wIA\nAJAMJO6Twm9RfaO20z2MrAJgygKh8KkuB420bL+SSqlYWKDnODrZiUV3AACAq0PiPikLZqVfW5o5\n5A/9taZT6FgApOdMt8sfDM/NSdNrVeM9hs/psT8VAABgPEjcJ4tfdN/xeRvHCR0KgNSMN3ppLHOR\nkTA/FWDGepy+5n5fv3tY6EAAIPZkNTm1tbU1Hoft7OwkooXpnEGjPNvj2nP49JI8TFG9yGq1xuk3\nLwP8yQOfnOkgIlNq6LJTZezJk6UYJqLjrQM4nXg4eaLDO894bv7/GogoM7Xt9c0LhI5FpHDyRIc3\nnyjsdnv8Tp7S0tIJHyOrxH0yP/BMjvzgiuFtH53/oHX47pUL4/REUmSz2eL3m5cB/HKIqNnWSkSr\nq+aWFl5S4z725DEVc7q/tva6A+nZBVlp6oTHKEY4eaLAO8/4GojI5g3i9zMenDwTwu9nPEajUdhf\nDkplpuDBFcUMQ++ctA56/ELHAiAZDm/gQp8nhVVU5OmjPEypYBajzB1gZrodXv4LjiP0aAKQHyTu\nU2DKTL2pLCcQCr9yzCJ0LACSwY9VWlxoYJVXm700xtIijGECmJHjbRdfPnXYMQIgO0jcp4bforrz\ncHsYe1QBJqe23U5E5qg7U3mVJoxhApiRmjbb6Ne17bYojwQAKULiPjW3LMgtMGotg0MfN/ULHQuA\nNPDLflWTSNz5xjL1HXZ8LgaYnuNtNiK6qyKDiGqQuAPIDhL3qVEqmAeXFxPRDkxRBZgEjptUL0he\noVGbqVMPevxddm/8QwOQG28g1NDlUDDM+iWZRFTbbsfFYQCZQeI+ZV+51sQqmA8be5FbAEyowzY0\n6PFn6tRFGRM3UWUYqsT+VIDpOtnhCIa58vz00gxNbnqKwxto7R8SOigAiCUk7lOWk56ydnFemON2\nHmkXOhYAsYvUyRQbmQk2pkZExjB1oMwdYMr4OpllJRkMQ9UlqJYBkCEk7tOxaWUJEb18xBIIhYWO\nBUDUai12GknHJ6OyyEjohgEwLZHEvTiDiKqKM+jSvaoAIANI3Kdj+eys+blp/e7hvzVYhY4FQNTq\n2iMr7pN8vNlkIKKTnQ7U5gJMCcddXHEnoupiIxHV4DMwgLwgcZ8OhqGHVpYQ0Yufo1oGYFyBUPhU\nl4OmsuKenZaSb9B6hoMX+jzxDA1AbloHPLYhf056Cr+fZEmhgVUwTVaXZxhjmADkA4n7NK2vLtKq\nlIcvDJzrdQsdC4BINVpd/mB4To5Or1VN/rv4RXfsTwWYkrEF7kSkUSkXFujDHIfCMwA5QeI+Teka\n9u+WFhDRS+gLCTCOSJ2MKWNK34X9qQDTMLZOhlddnEEjE9AAQB6QuE8fP0X11eMdQ/6Q0LEAiNHk\nO7iPFekIiWVCgKm4SuKOxjIAsoPEffoWFxrMJqN7OLi7vkvoWADEqNZiIyLzlBN3IxGd7naiaxPA\nJDm9gaYel0qpWFxgGL2RH1dc245RxADygcR9Rvi+kDs+b8PbIsBlHN7AhT6PmlVU5KdP6RvTNeyc\nHJ0/GD5rdcUpNgCZ4fuuVhYZ1OzFP+tFGanZaSm2IX/rALZ6A8gEEvcZuasy36BVnep0nMBGOoBL\n8S+KxQUGlXLK7zMocweYEr5Ohi9qH4UxTADyg8R9RjQq5X3LiojoRWxRBbhUncVBREsn3cF9LL5a\nBo1lACbpygJ3Hj9CoaYNLyUAmUDiPlP8FtW36rvsQwGhYwEQkTqLjUaqbKdqpCMkVtwBJhYMc3wH\np+orEnd+iiq/2wQAZACJ+0zNztZ9YV72cDD8Wk2H0LEAiAXHRZrQTXVnKm9hvp5VMOd6XN4AWjYB\nTKDJ6vL4g6bM1Nz0lMvuWlJkUCqYxm6Xx48xTABygMQ9BrBFFeAyHbahQY8/U6c2ZaRO49s1KmVZ\nXnoozDV0OWMeG4DMjFcnQ0RalbIiXx/muBMWXL8CkAMk7jFw28JZs/Saln7PofP9QscCIAqjHdz5\nIY7TwO9PRTd3gAkdb7fRSFXMlaqL+aaQqJYBkAMk7jHAKpivLjcR0Q5sUQUgoumOXhoLY5gAJqlm\n/BV3Gmk1U4P5qQCygMQ9Nh5YXqxUMO+d7ulx+oSOBUB4fOJeNa2WMjx0hASYjD7XcPvgkE7NluVd\nfWBCVSRxt6GYE0AGkLjHRp5ec1vFrFCYe/moRehYAAQWDHGnOh000tVxespmpaewitYBj8OLfk0A\n4+J7tC8tNrKKq9elFWemZurUgx5/++BQYkMDgNhD4h4zfF/IPx9uD4axrAFJ7YzVORwMz87WGbSq\naR+EVTKLCgyERXeAqKLsTOUxzGi1DMrcASQPiXvMfGFeVmmWzur0fXimR+hYAITEt5SeSZ0Mjy+R\nx1higCgmTNxpZH8qEncAGUDiHjMKhnloZTFhiiokvZGdqdEyicmI7E/FijvAOPzBMH9JKvqkM34w\nUy32pwJIHxL3WLpvWZGaVRw819/S7xE6FgDBzLylDI8f3nQCjWUAxnGqyxEIhctmpeujlqUtKTIo\nGOZMt3PIj4lmANKGxD2WMlLVd1cWENHOw+1CxwIgDKc3cL7PrWYVFflX73ExeSVZqeka1ur09bqG\nYxIbgMxMpk6GiHRqtjw/PRTmTqLwDEDikLjHGL9F9S/HLT6MaoekxFe2LCrQq5QzfXtRMEwlxjAB\njG+SiTuNdnPHSwlA4pC4x9hSk3Fhgd4+FHj7ZLfQsQAIINLBfcYF7jy+zB37UwGuxHFTSNz5zeL8\nqCYAkC4k7jHGMJFFd0xRheRUZ4l0lY7J0fgxTNifCnClDttQn2s4I1VdmqWb8MHVGMMEIAtI3GPv\n75YWpKWwte32011OoWMBSCiOi3SumPnOVJ7ZFFlxR7YBcJnR5Xbm6pOXLlGapctIVQ+4/R02jGEC\nkDAk7rGnU7PrlxURFt0h+XTavYMef6ZObcpIjckB8/Ta7LQU+1AAQx8BLnO8fbJ1MkTEMCPVMmgK\nCSBlSNzj4qEVxUT0Rl2nezgodCwAicPXyZiLjJNZApwMhrm46B6bIwLIxeQL3HmYnwogA0jc46Js\nVvry2ZlD/tDrNZ1CxwKQOJE6mRgVuPMqUeYOcAXPcLCx28UqmCVFhkl+y8gYJiTuABKGxD1eNo1s\nUUVtLiSPkZYysUzc+f2pWHEHGKvOYg9z3KICg1alnOS3mIsMCoY53eVEt2IA6ULiHi9rF+dlpamb\nelxHWweFjgUgEYIh7lSng0YmnsYK3xHyVKcjFMaHYICIqdbJEJEuhS3LSw+GuZOduH4FIFVI3ONF\npVR85dpiwhZVSBqNVudwMDw7W2eIOn19qjJ1alNm6pA/1NznjuFhASSNT9yrp5K4E1E19qcCSBwS\n9zh6cHkxw9A7p7oH3H6hYwGIO75OJlaNIMcy82OYMPQRgIiIwhxXM5WWMqMi+1MxhglAspC4x1FR\nhvaWBbnBELfrmEXoWADirjZuiTv2pwKM1dzrdvmC+QZtvkEzpW/EGCYAqWOFDuByPmv9yy++eqzN\nllGy6r5NXzbnjbwruZt2bnvxUJutYMGaR7bckye6wK9u48qSDxt7Xzrc9tiNc5SKGHXIAxCluji0\nlOFFVtyxPxWAiKZV4M7jK9n6XMNddm9hhjYOoQFAfIlrxT1o3Xv7/Vuf+8i25Jolto+2bb3/vo+t\nQSIid8P31z26bXfXgiUlh3b98v6H/mAVOtRJuqkspzBD22HzfnyuT+hYAOLI6Q2c73OrWcXCfH3M\nD7640MAwdLrb6Q+GY35wAMmZduI+ZgwTqmUAJElcifvZvX8hWrdzz7OPf/3xZ/e8cDM5/vhmAxE1\n7P7NZ1T227ee+9bjT7zxwhPUtev5j6WRuisVzEPLsUUV5I+vY1lUoFcpY/+uokth5+WkBUPcGasz\n5gcHkJxpJ+40Ui1Ti/2pANIkrsRdlaYl8kZmjQYDw0QlJZlE7qNvNhnu+ftrjERE7Ow13ymjQ4db\nhAx0Kr5ybTGrZD5s7O2weYWOBSBe6uNW4M6rNBmJqN6CMndIdoMef0u/R6NSTu/qFt+I5jhW3AGk\nSVyJe9ld31tHBzbf9ehTP33q0Xsf/cxwz5abTfxdupzRdyhNxQ1ljo9OSGW5ICtNfefifI6jnUfa\nhY4FIF5GWspMZwlwMsyR/alSed0DxAtf5WI2GVnldPZNLTUZGYYauhzDKDwDkCBx7fF0t589T0RE\nkS0zjsaz7e7ZZUREOWmpE357bW1tPKKyWq0224wWJ1Zk+XcTvfTZhVuyPazstqieO3dO6BDEa+Yn\njyRwHB1t6SMitaurtrZn8t84+ZNH4/ET0ZFz1traZMk2kuTkmbakfed554STiIo0/ih/8qKfPMV6\nVZsj8Pr+o+XZ6riEKHpJe/JMEt58orBarXHKNomoqqpqwseIKnF3v/rUM03XPfHuL+5JIyKyv7r1\n7mee2n39K/eQh/oGLta2Ovt6qDTryiZYk/mBp6G1tbW0tHQmR1jK0f82fNzU47KyeXebC2IUl4jE\n6TcvAzM/eSShw+Z1+Loydeq111/DTPGT6SRPnkXB8JP793a4gmULl+hSRPXGFS9JcvLMRHK+8/zs\n6OdE7juvLa+qyB3vMdFPnlUtJ9uOtLs1OVVVc+ISohQk58kzSXjziaK2tlbYk0dcpTJERDkFaZGv\njJXXFJDHFSRNXhV1fVg7MjXR8s5uR9mqiql1rxUUw9DGlSVE9CK2qIIc1VlsRGQuMk41a588Nauo\nyNNzHJ3CtHZIYsEQx+8nqZpB39VIYxmMYQKQIFEl7mxmCdHu/9ld32K321uOvfrz57qoal4asdff\nt5G6nnt65zG7u3/vL//5ANH9ty4QOtqp+XJ1YapaeaRlsKnHJXQsADFWZ3FQfDq4j4UxTACnu52+\nQGhOji5TN/0qFzSWAZAuUSXumnuefmGjufWXWzfffffdm7/7u57rNr7wg9UsUZr58We/c/Nn2757\n97ovPbO7a8MzO9dKZQLTiLQU9t6lhUT00mFsUQW5qWu3UTxbyvCWmjCGCZId3wiSz7ynbU6OTq9V\nWZ2+bgd6nQFIjMjSX83sx5/d8zV7v5uIKC3beLEcxnzfj9+/rd8dJFZjNKaJLOzJ2biyZOeR9teO\nd3x/7QKdWpI/AsCVgiHuZKeDRhq/xE+kIyRW3CGJzaSD+ygFwyw1GT9u6jveZr+rEvNTAaREVCvu\nERpjdrYxe2zWfvH27GyJZu1EtLBAX1VsdA8Hd9d1CR0LQMw0Wp3DwfDsbJ0xVRXXJ5qXk5aqVloG\nhwY9/rg+EYBoxSRxp4vVMihzB5AYMSbuMsZvUd3xeRvHCR0KQIzUxXn00iilgllcaCCik9ifCkmp\n2+HtdnjTNey83LSJHx3VshIjjbSEBwAJQeKeUHdVFhhTVQ1dTsyRAdmoTVTiTqP7Uy14+UAyOt5m\nJ6Lq4gzFjPs38YVtpzqdfoxhApAUJO4JlcIq7l9mIvSFBBmpT2Dibi7i96dixR2SUU2M6mSISK9V\nzc9NC4TCDV3OiR8NAKKBxD3RHlxRTER76rvsQwGhYwGYKZcveL7PrVIqKvL1CXg6fsW9zmJHsRkk\noVgVuPOqSzII1TIAUoPEPdFmZ+tumJ8zHAy/etwidCwAM1XfYec4WlSgV7OJeDMpzkw1pqr63cNW\nJ9rYQXLxBkINXQ6+IUxMDlhVnEEYwwQgNUjcBbBpZTERvXS4PYxlQ5C4uvaZDnGcEoahJYV8mTuq\nZSC5nOxwBMNceX66LiU2rdWq+fmpGMMEIClI3AVwa8WsfIOmpd9z6PyA0LEAzMhIS5nYXLufDLPJ\nQETY3g3JJrZ1MkQ0LzctLYXtdnitTl+sjgkA8YbEXQCsgnlgeTER7cAWVZAyjqNai41GkunE4Lth\nYH8qJJtI4j6zmaljKRiGv1aGahkACUHiLowHrjUpFcz7p3uw1AHS1WX3Drj9Ganqkkxdwp60MtJY\nxo5KM0geHBf7FXe6OIYJ168AJAOJuzBm6TVrFs4KhbmXj7QLHQvANPEd3M0mw3TGDRgAACAASURB\nVIybSk/BLL0mT69x+YJtA0OJe1YAQbUOeGxD/pz0lKKM1BgeFo1lACQHibtg+Cmqfz5iCYawcAiS\nlPgCd16lCWOYILmMLrfH9kMy36DmZKcjEMIYJgBpQOIumFVzs2dn63qcvg/O9AgdC8B01LXbKIEt\nZUZhDBMkm3jUyRCRQauam5PmD4ZPYwwTgEQgcRcMw0QW3bFFFaQoGOJOdjpoZLdoIvFjmNBYBpJH\nnBJ3GqmWOY5qGQCJQOIupPXVRSms4pPm/pZ+j9CxAEzN2R7XcDBcmqUzpqoS/NT8/tSGLmcwjDIz\nkD+nN9DU41IpFYsLYt++aaSxDD4GA0gDEnchGVNVd5sLiOilw9iiChJTZ7ER0dKE18kQkUGrKs3S\n+QKhcz2uxD87QILxu8AriwzxmE8caSxjwYo7gDQgcRfYppUlRPSXYxZfICR0LABTwLeQi9X09ani\nF93rUeYOSSB+dTJEND83TZfCdtq8va7heBwfAGILibvAKouMiwsNDm/g7RPdQscCMAV8S5kqgRJ3\nMxrLQNKIa+KuVDD8x2+MYQKQBCTuAmOYyKL7i9iiCtLh8gXP97lVSkVFvl6QAEZW3JG4g8wFw1xd\nu51GalriobrYSES12J8KIAVI3IV3t7kgXcPWWewNaMgFElHfYec4Wligj0fR7WQsKjAoGOas1YUa\nM5C3JqvL4w8WZ6bmpKfE6SmqivkxTPgYDCABSNyFl6pW3resiNAXEqSjXtA6GSJKVSvLZqWFwtzp\nbnzcBTmLa50Mj28sc6LDjjFMAOKHxF0UHlpRQkRv1Ha6fEGhYwGY2MjMVMESdxrt5m7B/lSQs5r2\nuCfuGanq2dm64WD4TDfaNAGIHRJ3UZiXm7ZyTpY3EHq9pkPoWAAmwHEjLWWE6AU5ymzi56fi+j7I\nWQJW3GmkgL4GZe4AoofEXSxGp6hyGCkD4tZl9/a7h42pqpJMnYBhmDE/FeSuzzXcPjikU7Nls9Lj\n+kTVJWgsAyANSNzF4o5Fs7LTUs71uo+2DgodC0A0/DgYc5GRYYQMozxPr2YVF/o8KDADueKXwKuK\njUpFfF9sI2OY8DEYQOyQuIuFSql4YLmJ0BcSRC/SwV3QOhkiYpXMwnw9EZ3sRJk7yFNi6mSIaP6s\n9FS10jI41O/GGCYAUUPiLiIPLi9WMMy7p7rx1gliVh/ZmRr3ZGJCkTFMqJYBmUpY4s4qGDPGMAFI\nARJ3ESkwam8tzw2GuF1HLULHAnB1wRDHr3Dze0OFxY9hOoHr+yBH/mD4RIeDYRLUvilSLYNu7gDi\nhsRdXPgtqi8daQ+FsUcVxOhsj8sXCJVm6TJS1ULHMro/FaUyIEOnuhyBULgsN12vVSXg6fjit+No\nLAMgbkjcxeXGsmxTZmqnzXvgbJ/QsQBcRZ3FRuJYbieiOTk6XQrLd7kROhaAGEtYnQyPX3E/0eEI\nhrBsBCBeSNzFRcEwD60oJkxRBbGKdHAXQYE7ESkYZkmhgTCGCeQowYl7pk5dmqXzBUKNVkwjBhAv\nJO6is+Eak0qpONDUaxkcEjoWgMvVi6OlzChzEcYwgQxxXCRxr05U4k4jr+salLkDiBgSd9HJ1Km/\nWJnPcbTzSLvQsQBcwj0cbO5zq5QKvg+jGFSisQzIUYdtqM81zK+CJ+xJR/anoswdQLyQuIsRv0X1\nlaMWfzAsdCwAF9Vb7BxHCwv0alYsbx38/tQTHQ6MHAY5iSy3F2ckcswZv7pfg8QdQMTE8tcXxlpW\nnFGelz7o8e9tsAodC8BFkdFLCWlON0mFRm2mTj3o8XfavULHAhAzfHeXhBW48xbkpWtVyraBoQG3\nP5HPCwCTh8RdjBgmsuiOLaogKnzibhZT4s4wo00hUS0D8pHgnak8VsHwtWe1Fiy6A4gUEneR+lJV\noU7NHmkZPNvjEjoWACIijhttKSOixJ1GelNiDBPIhmc42NjtYhXMkqJE912txv5UAHFD4i5SuhT2\n3qpCInoJi+4gDt0Ob7972JiqSuRuucmoxBgmkJc6iz3McYsKDFqVMsFPze9PrWnDijuASCFxF69N\nK4uJ6LWaTo8/KHQsAFTL18kUGRO5W24y+FKZk52OMDaogiwIUifDGxnDZA9iejeAKCFxF6/yfP2y\nkgzPcPDNui6hYwGgunZxdXAflZWmLjBqPcPBC30eoWMBiIHEd3AflZWmLs5MHfKHmqyo0gQQIyTu\noja6RRUriSA4Ee5MHcWPYcL+VJCBMMfVCNFSZtTIGCZUywCIERJ3UbtzSX5Gqvp0l7MOG+9AUMEQ\nd7LTQSN1KWITGcOElwlIX3Ov2+UL5hu0+QaNIAGMjGHCqwlAjJC4i1oKq9hwTRGhLyQIranH5QuE\nSrJSM3VqoWO5CjP2p4JcCFjgzsMYJgAxY4UOIJZaW1vjcdjOzs54HHaSbixU/jdDu+s7N1fqDBox\n/n9ZrdY4/eZlQNiTJ4b2nbYR0fxMVWz/r2N18hhCYSJq6HQ0X2hhFSLbPDtdsjl54kSu7zwfNXQS\n0ex0biY/3UxOntQwpbCKln5PfeN5gybRbW0SQ64nT6zgzScKu90ev5OntLR0wseIMRGctsn8wGI7\n8sRPTXTjcftHTX1H+hTfuEGwMKKw2WwC/n7ETx6/nPZjTiL6QnlRbH+cGJ48c3LaL/R5vCmZSwoT\n3fo6fuRx8sSJXN95zg60EtHtVXNLZ9bEfSa/HLOp+0jL4AClm0tzZxKDaMn15Ikh/H7GYzQahf3l\noFRGAvgtqi993o5udyCUunYbiW/00lh8tcwJ7E8FKRv0+Fv6PRqVcmG+XsAwIt3cUS0DID5I3CXg\n1vLcfIO2dcDzafOA0LFAMnIPB5v73KySWVggZDIRXWQMkwVl7iBhfK5sNhlZpZAVX5HGMhjDBCA+\nSNwlQKlgHlxRTEQvYosqCKHeYuc4WpRvSGHF+47BXw3AijtImuA7U3n8inu9xRHCGCYAkRHvn2EY\n64FrTayC+eB0T7fDJ3QskHT4NotLxTd6aayFBXpWwTT1uIf8IaFjAZimmnY7ES0rFjhxz0lPKcrQ\nevzBcz0YwwQgLkjcpSEnPeWORXlhjnv5SLvQsUDSqeUTdxEXuBNRCqtYkJce5riGLlTLgCQFQxz/\nIVkM84mrImXuuIQFIC5I3CWD36L65yPtwRCuXULicFxkZqrIE3e6uD8ViTtI0ulupy8QmpOjE8O0\nBOxPBRAnJO6SsXJO1tyctF7X8PtneoSOBZJIt8Pb5xo2aFWlWTqhY5kA5qeCpI0UuGcKHQgRUXWJ\nkZC4A4gPEnfJYBh6aGUxYYoqJBZfJ2M2GRnRzzUyFxkIK+4gWSLZmcpbmK9PYRUX+jz2oYDQsQDA\nRUjcpeS+6iKNSvlpc39Lv0foWCBZ1LXbiahK9HUyRDR/VrpGpWwdQKoBkiSqxF2lVPCzzOpwCQtA\nTJC4S4leq7rHXEBYdIcEqu+QQEsZHqtgFhXoiehkJ1INkJhuh7fb4dVrVXNzxFKTVl2CMncA0UHi\nLjH8FtVXj3f4Auh5B3EXDHMnOxw0su9T/MwYwwTSdLzNTkTVxUaFaIrSIo1lMIYJQEyQuEtMZZHB\nXGR0eAN7TnQLHQvIX5PV5Q2EijNTxdDmYjIqiww0cpUAQEJqxLQzlVddbCSiWosdY5gAxAOJu/Rs\nXIkpqpAgUmkEOcpsQkdIkCRRFbjzZuk1BUatZzjY3OcWOhZIkI+b+ub96J3v7UGCIV5I3KXnLnOB\nXquqt9hPdSI7gfiqlcLM1LFKslLTNWyP09fjxIxhkAxvINTQ5VAwjNlkEDqWS/CL7qiWSR5/Ongh\nGOKOd7g/auoTOha4OiTu0qNVKe9bVkTYogrxF5njaBLRKmB0CoapxBgmkJqTHY5gmKvIT9epWaFj\nuQQ/hqkW81OTQzDEjf5fP/P2mSBKpEQJibskbVxRQkRv1nW5fEGhYwHZcg8Hz/W6WCWzsEAvdCxT\nwFfLoMwdJESEdTI8NJZJKkdaB93DwdnZurx0VVOP65Wj7UJHBFeBxF2S5uToVs3N8gZCr9V0CB0L\nyNaJDgfHReawCB3LFPBjmNBYBiREVDNTx1qYr1cpFc29bocXsxHkb9+ZHiJatyT/8ZV5RPTr95qw\nOChCUvp7DGPxfSF3fN7G4VoWxEddu40ktTOVx5fKnOy046UBksBx4l1xV7ORMUz1GMMkdxxH+870\nEtHq8tyb5+irizMGPf7/3N8sdFxwOSTuUrVmYV5uekpzr/tIy4DQsYA8RXamSqfAnZen1+Skp9iH\nAu2DQ0LHAjCx1gGPbcifm55SaNQKHctVVPH7U1EtI3ct/Z7WAU+mTr3UZGQYeuquhUT0/z5pseCN\nVGSQuEsVq2QeWM73hUQVGsQex0V6QVZJp6UMj2EiY5hOoMwdpGB0uV00k5cuwZe58/OhQMY+ONND\nRLcsyFUqGCKqKjbeYy4IhMI/33tW6NDgEkjcJeyry00Khtl7qrvPNSx0LCA3Vqe3zzWs16pKs8Qy\ngH3yRsYwocwdJEC0dTI8vrFMncUWRvGZrO1r7CWiWytyR2/5wdpyNavYc6IL11tEBYm7hOUbtKsr\ncoNh7pWjFqFjAbnhm4KZi4ziXAWMLtJYBlW5IAWi3ZnKyzdo8vQaly94vs8jdCwQLw5v4FjrIKtg\nbirLGb2xMEP79zfMIaKn3zqNT23igcRd2jatLCGinUfaMZIaYkuidTI8fjvdqU4H+hCDyDm9gaYe\nl0qpWCTipquRppAYwyRfHzX1hcLcijlZaSmXTBL4h5vnZqWp6yz2PSe6hIoNLoPEXdqun59dkpXa\nZffuP9srdCwgK3WRnamSTNwzdWpTZqo3EGruxah2EDV+C3hlkUEt4qar/PzUWtRLyBffCHL1mDoZ\nXloK+701C4jo/77b6AuEBIgMriDedwqYDAXDPLQi0hdS6FhAPoJh7mSHgySbuNNIN3fsTwWRE3mB\nO6+qmB/DhFeTPAXD3IGzfUS0unzWlfd+5RpTeV56l927/dPWREcGV4PEXfLuv6ZIzSo+aupDzyaI\nlXM9Lm8gVJyZmqlTCx3LNPHd3DGGCUROEon74kIDq2TO9bowjkeWatpsDm9gbk5aSVbqlfcqFcyP\nvriQiJ7d39zvRicM4SFxl7yMVPVdlfkcRzsPoy8kxEatlOtkeFhxB/ELhrm6djuNdG4RrRRWsbjA\nMNoiFmSGr5O57Yo6mVE3zM++ZUGuZzj4m/ebEhgXXB0Sdzngp6i+csziD4aFjgXkgE8mJJ24Ly4y\nKBjmjNWJFwWIVpPV5fEHizNTc9JThI5lAtWRahmUucvQB/zA1Iqr1MmMevKLFUoF8/IRy9keV6Li\ngqtD4i4HVaaMinz9oMf/7imr0LGAHER2pkqzpQxPp2bn5aYFQ9yZbqfQsQBcnSTqZHjVJUZCYxk5\nah3wnO9zG7Sq6qjn4fzctAdXFIc57id7zqA1pLCQuMsBw0T6QmKLKsycezh4rtfFKplFBQahY5kR\njGECkeMXsKWRuEfGMNkxhklmPjzTS0Q3L8hhFRPM7PjubWVpKezBc30fNfUlJDS4OiTuMvF3VQW6\nFPZo62CjFZexYEZOdDg4jhbm61NE3J9uMsz8/lSUuYNYSWjFPd+gzU1PcXgDLf0YwyQr/MDU6HUy\nvEyd+lur5xPRT94+jREZApL2H2YYpVOzX64uJKKXDmPRHWakXvo7U3mVJgMRncB2OhClPtdw++CQ\nTs2WzUoXOpaJMQzGMMmQyxc8fGFAeenA1Ci+vqrUlJna3Ot++QiaYQgGibt88FtUX6/p9PjRsQum\nj28pY5Z+4l6Rp2eVTHOf2zOMVwSIDl8nU1VsVE5UoiASfLVMLbq5y8jH5/qCYe6a0kyDVjWZx6ew\nin9ZV05Ev3m/Cb1BhYLEXT4WzEq/tjTTMxx8sxajiWGaOI7q+HzCJIHL99GpWcXCfD3H0clOlLmD\n6EioToZXVWwkNJaRF77APUojyCvduTj/mpKMQY//j/ub4xYXRIPEXVb4RfcXP2/D9iGYHqvT2+sa\n1mtVpdlXmcQhOZExTNifCuIjucR9SaGBVTBne1y4hCUPoTC3/2wvjTMwdTwMQ0/dtZCInvukBWMf\nBYHEXVbWLc7L1KnPdDtrLVgUgengr4Obi4wKRhqX76OLjGFCmTuIjD8YPtHhYBgpbSbRqJSLMIZJ\nRuos9kGPvzRLNztbN6VvNJuM91YVBkLhn+9tjFNsEAUSd1lRs4qvXGsi9IWE6eL/JFdJuYP7WJUm\nNJYBMTrV5QiEwmW56frJ1RaLxEi1DF5QcvDBmR4iWl2RO41Vmu/fsSCFVew50X0cm5UTDom73Dy0\nooRhaM+J7kGPX+hYQHr4xJ1vpCgD83LSUtXKDpsXLwcQFcnVyfD4xjK1KHOXhQ8nMTB1PAVG7Tdu\nnENET+85jdb+CYbEXW6KMrQ3l+X6g+G/HO8QOhaQmGCYO9nhIBmtuCsVzOJCAxGdQJk7iIlUE/eR\nxjJI1aSuw+Y92+NKS2GXl2ZO7whbbpqbnZZSb7HvOdEd29ggOiTuMsRvUd15uA2fg2FKzvW4vIGQ\nKTM1U6cWOpaYwRgmEBuOiyTu0YfMi1ChUZudlmIb8rcOYAyTtO0700P8wFTlNLcz6VLYf75jARH9\n7N1GXyAUy+AgKiTuMnTzgpzCDG3bwNAn5/qFjgWkpFYuo5fGMvNjmJC4g2h02Ib6XMOZOnVp1tQ2\nBQru4hgmVMtIHD8w9dap9JO50v3Lisrz0rvs3uc/aYlRXDAx8SXuPuvu//rp1q1bn/rpf33WMuZv\nrbtp5y+f2rp160//sNuKVlRRKRXMg8uLiehFbFGFqahrtxNRlbwS90hHSIsD159AJEbrZKTYuqma\n35/ahk/CEuYZDn52fkDBMDcvmNTA1PEoFcy/3rWQiP64/3y/ezhG0cEERJa4+5p+evv9v9xxqGDB\nguFDO76/+e7/aXATEbkbvr/u0W27uxYsKTm065f3P/QHq9CRitxXrjWxSmbfmd5uh1foWEAy6vkV\nd7kUuPNMGakZqep+97DVidcCiMLxdknWyfD4MnesuEvaJ839gVC4utg486rI6+dl31qe6/EHf/1e\nU0xigwmJK3Fvev3371LZL/6658lvfesXe97aWEDPvfxJkKhh928+o7LfvvXctx5/4o0XnqCuXc9/\njNQ9muy0lLWL8sMc9+cjFqFjAWnwDAebel2sglmYrxc6llhiGFpSZCCiegv2p4IoRFbciyWZuC8p\nMrAK5qzV5fHj2rdUfcD3k1k4ozqZUU/eWaFUMK8ctTR2O2NyQIhOVIm7+9Cb9QUbv32dsb/+2LH6\nBtvXXjl48MdrWXIffbPJcM/fX2MkImJnr/lOGR06jIKqCWxaWUxELx9pD4ZQIgATO9Hh4DiqyNdr\nVEqhY4kxfgwT9qeCGHiGg43dLlbBVBYZhI5lOrQqZUW+PsxxJ/BJWJrCHPdhYw8RrS7PjckB5+Wm\nbVxZEua4n7x9BhWJCSCqxD1IRF07fnTDLV/a+t3vbv3m5ttveKp+5E+tLmd0FVBTcUOZ46MT+CMc\n3fLZWfNz03pdw++dxtUJmFidHOtkeHyZOzpCghjUWexhjltUaJDuJ+SRMUyolpGkkx2OAbe/KEM7\nPzc9Vsf8zur56Rr2k+b+A029sTomjIcVOoAxfJ2nu4iItvz2Lw9ek+e2fPz0gz/a+tO9+39xPRHl\npKVOeIDa2tp4xGW1Wm02Sb5D3VSkPNdL294/lR+MY+5+7ty5+B1c6iR08hw4ZSOijLAjTq+jq0rM\nyaP0hYmopnXgeE2NQjr7ASV08ghCou88expcRGTSBuL6QovryZMZHiKiAyfbVhldcXqKeJPoyRMT\nO086icicrairG/cMnMb5s7489X/qnP/6au3v1uYqRbUmHGtWqzV+L96qqqoJHyOmxF1TsrSMPivY\n+uA1eUSUZrrxa48WfPZqm5uuJw/1DVysnXL29VBpluaKA0zmB56G1tbW0tLSeBw53uZVBF86+cHJ\nXr+hqGxOThz7jsXpNy8DUjl5OI5a3vmAiO69fmlcT5UrJebkydu/z+r0ZZgWJPinmwmpnDwCkuI7\nz3/UHiFyrbt2QdWS/Pg9S1xPnqySof84vP+8I7x0aZV0PghfToonT0z86OBBInrgxkVV88dtKTON\n82fRkvCH7R+1Dw6d8Wduuq5khkGKWW1trbAnj/g+FnW5fSNfBiMf5jV5VdT1Ya07crPlnd2OslUV\nVybucJl0DXtvVSER7TiMvpAQjdXp63UNp2vY0uyJL21JUaUJY5hAeGGO4ytMqqW5M5VnykjN1KkH\nPf62QYxhkphuh/d0l1OnZlfMzortkdWs4od3VhDRbz9ocnoDsT04jCWqxD3tnm8/Sk2/e2bnx1Z7\nf8O+/9q6q6vgjmVGYq+/byN1Pff0zmN2d//eX/7zAaL7b10gdLTSwE9RffV4hxeDzWB8dSOjlyRU\nSTIl/P5UjGECYTX3ul2+YIFRm2+Q8NITw9Cykgwiqm3HC0piPmzsJaIbyrLVbOzTv7WL8q4tzRz0\n+P+4vznmB4dRokrcKc389We/s+7Ath/df/eXvvl/dhSse+K/vnUNEaWZH3/2Ozd/tu27d6/70jO7\nuzY8s3NtnpiKfERsUYF+qcno9Ab21HcJHQuIV127jWQ3M3Ws0TFMQgcCSW109JLQgcwUurlL1L4z\nvUR0W0VsGkFehmHoX++qIKLnP21tHxyKx1MAiavGnYiIzPc9efCub/e7fcSmZRs1Y27/8fu39buD\nxGqMxjTRhS1mm1aW1FnsOz5vv/8ak9CxgEjVRlbcJZ9PjIdvvdfQ5QiGOFYpz6sKIH6ySdwjjWXa\nkLhLiTcQ+rS5n2FohgNTozAXGb9UVfjX2s6fv9v4x4eq4/QsSU5cK+4RmrTs7OyxWXvkZmN2dnY2\nsvap+mJlvkGrqu+wn+zEciNcRTDMnexwkKxX3A1a1exs3XAw3NQj1T4YIAOySdwri4xKBdNodQ35\nUYQpGZ829w8Hw+YiY3ZaSvye5ftrF6SwirdPdh/D57r4mGbiHg6HrVZrU1NTW1ubx4PtKaKmUSn5\ntfYdn2OLKlzFuR6XNxAqytBmpc10/LWYVWIMEwhq0ONv6fdoVcqKPMkPJ05VK8vz0kNh7iReUNIR\n1zqZUfkG7WM3ziGiH+85HcZApjiYcuI+ODj4q1/96s4779ywYcO3v/3tzZs3r1u3buPGjdu3b0fL\nYdF6aEUxEb1Z14W93nClOrnXyfDMGMMEguIrws0mozyKtaoiZe5I3KWB42jfmR4iWl0Rm4GpUXzz\n5rk56Sn1Fvtb9d3xfq4kNLXE/b333nvyySeLi4ufffbZ/fv3792794MPPti5c+c//MM/nD9/ftOm\nTU1NTXEKFGZidrbu+nnZvkDotZpOoWMB0eET9yo5zkwdCx0hQVh8jiuDOhke9qdKy6kuR69rON+g\nLY//BR+dmn3ijgVE9LN3G9HRLuamVi8+b968//zP/1QoLqb7KpWqqKioqKho1apVVquVw2URsdq4\nsuST5v4dn7d9fVWpTDv+wTTVtUd6QQodSHwtKtArFcxZq8sXCEl32jxIl2wK3HnVJUYiqmm3cRzh\nb4r48XUyqytyE/Oftb66aPunrWe6nc8dbNl667xEPGXSmNqKu1KpDAaD492bl5eXnx/HUXAwE7ct\nnDVLrznf5/78woDQsYCIeIaDTb0uVsEsKpB83W10WpVy/qz0UJg73e2c+NEAMRUMcfXyurRVkqnL\n1KkH3H6LDY3/JODDxgTVyfCUCuZfv1hBRP95oLnPNZyYJ00SU0vc33rrrXvuuecXv/jFiRMn4hQQ\nxAmrYL66HFtU4XInOhwcRxX5+mRYhObHMPGlQQCJdLrb6QuE5uToMlJlsgWcYSIfQjCGSfx6nL4T\nHQ6tSrlqbnbCnvQL87Jvq5g15A/9+r2zCXvSZDC1xH3r1q2//e1vtVrtv/3bvz3wwAPbt2/v7sbO\nA8l4YHmxUsH8rcHai4+/MKKuw05EZrnXyfCwPxWEMlInkyl0ILGEMnep2H+2j4iun5+dEoeBqVE8\neWcFq2BeOWY5g+ucsTPl/8KKiopvfetbr7/++hNPPNHb2/voo4/+4z/+41tvvYWmkOKXp9fcVjEr\nGOZeOWoROhYQC77AvSo5EvdIR0isuEPCyazAnRdpLIN23aI30k8mvo0grzQnR7dxZQnH0U/ePoMt\nkLEyzc9eCoVi2bJlP/jBD958881rrrnmV7/61XPPPRfbyCAeNl1XQkQ7D7eHwngNAdFoL0i51N1G\nV56nV7OKln4P+qJCgskycTcXGRQMc6bbic4hYuYLhD45109Et8RtYGoU37ltvl6r+rS5f//Z3sQ/\nuyxN/6JJV1fX//7v/z7yyCPvvPPOxo0bN2zYEMOwIE5Wzc2ana3rdng/bMRLCKjb4etx+tI17Oxs\nndCxJAKrZBbm64kIU4Qhkbod3m6HV69Vzc2R1QtNl8KW5aWPjl4Gcfr8wqA3EFpSaJilv3wgfQJk\npKq/fes8Inrm7TPBEFYMY2DKibvNZnvttdcef/zxhx9+uKur65/+6Z927dr1jW98Iy8vLx7xQWwp\nGIYfxoQtqkAXRy8ZFUnTzo2v5keZOyTS8TY7EVUXy/CFVl0caQopdCAwrg8EqpMZtfm60pKs1PN9\n7p1H2oWKQU6mlrjv2LFj/fr1n3766fr16998880f/vCHVVVVjOzeieTtvmWmFFbx8bm+9kH08Ep2\ndSOjHIUOJHH4/akYwwSJVCPHnam8ZcUZhMYyIsZxFzu4CxWDmlU8eWcFEf32/SaUKc7c1BJ3s9n8\nyiuv/OY3v1mzZo1GI8A1F5g5Y6rqLnMBx9HOw/jsm+zqOhyUBKOXxjKb+P2pWHGHxJFlgTuvaqSx\nDLYeilOj1dnt8M7SaxYXGAQMY83CvOWzM21D/mf3NwsYhjxMLXEvKCjIED1wMAAAIABJREFUyRl3\nc4PX67XZcL1MAjatLCGiXccs/mBY6FhAMKEwd7KDbykjw3xiPLOzdWkpbLfD2+9GU1RIBG8g1NDl\nUDAM/6FRZmZn64ypqj7XcKfdK3QscBX8cvut5QkamDoehqGn7lpIRNs/bW0bwNX+GZla4r5///4f\n/OAHn3zyydjmj4FA4MKFC3/4wx82bNjQ3o5FXAkwFxkXFegHPf53TqINf/I61+Ma8oeKMrRZaTKZ\nCDMZCoZZUoRFd0ickx2OYJiryE/XqVmhY4k9hol88q9Fmbso7WvsIaJbywWrkxm1pNCwvrooEAr/\nfG+j0LFI29QS9/vuu+/xxx//61//euedd95///2PPPLI+vXrb7vttm9+85sul+sPf/iD2WyOU6AQ\nQwxDG1eWELaoJrfayM7UJFpu542MYUJVLiSCjOtkeFXYnypWA25/ncWewiqun5+4galR/PMdCzQq\n5Tsnu4+0DAodi4RNeQFgzpw5v/71r51O5/nz551Op1qtzs7Onjt3rkKR0HFcMEN/t7TwmbfPHGuz\nNVpd5XnpQocDAhhpKSPDy/fRRcYwIXGHhJDlzNSxqkv4MUx4QYnO/rO9HEer5mZrVUqhYyEiyjdo\nHr9xzu/2nfvJ26ff+McvyK/JUmJMM9vW6/VVVVU33XTTddddN3/+fGTtkpOqVq5fVkRYdE9i9ZHR\nS7JdCBxPpLGMxYHtdBBvHCf/FfelJiPDUEOXw4cxTCLDN4K8baHwdTKjHrtpTm56yokOx5t1XULH\nIlVIuJMX39D9rzWdnuGg0LFAonn8waYeN6tgFhfohY4l0QqM2kyd2jbk77BhjxTEV+uAxzbkz01P\nKTRqhY4lXtJS2LLc9GCYO9XlFDoWuMgfDB9s6idxFLiP0qnZJ+5YQES/2NuIgbvTg8Q9eZXNSl8+\nO9PjD75R1yl0LJBoJzscYY4rz9drxHEJNZEYZrSbO/anQnyNLrfLuyhgpFoGZe4icrhlwOMPVuTr\n8w3i+tD45eqihQX6bofv/x1sEToWSULintT4vpAvft6OmoFkM7IzNYk6uI/FN+bD/lSIN9nXyfD4\n+aloLCMqfCPI24SbuzQepYJ56osLiWjbgeZeF9ryTtn0E3e73X706NH29nafzxcIYBSWJK1dnJeV\npm7sdh7HG26SqWtP6sS9EivukBCy35nKGxnDZMcakEhwHO1r5AemzhI6lqu4bm7W7QtnDflDv37v\nrNCxSM80E/ddu3Y98MADzzzzzKeffmqxWB5++GGnE8Vt0qNSKh64tpiwRTX51CX5inuRkYhOdThC\nYSQaEC9Ob6Cpx6VmFYvkvpNkTo5Or1X1OH3dDoxhEoVzvS7L4FBWmppvoiVCT95ZwSqYXccsZ7qR\nPU7NdBL3tra2V155Zfv27Zs2bSKi+fPnL1q06PXXX491bJAIDy4vZhh6+0T3oMcvdCyQIFanr8fp\nS9ewc3J0QscijKw0dWGG1uMPXuj3TPxogGnhC9IqCw1qVuZVqQqGqTLx3dxRfiYK/HL7reWzRNty\ncXa2bvN1pRxHP95zGhdqpmQ67yZnz55dtWpVfn7+6C2rVq1qa8OSrSQVZmhvLc8NhMK7jlmEjgUS\nhK+TMRcZRfuengCRMUwW5BkQL0lS4M4bqZZB1aUo7DvdQ0SrxdRP5krfXj3foFUdOj/wYWOv0LFI\nyXQS9/z8/MbGxnA4PHpLQ0NDYWFh7KKChOKnqO483B7Gx97kEKmTKU7SOhkexjBBvCVV4r6sBPtT\nxWLQ469pt6uUihvEMTB1PMZU1XdWzyeiZ945HQwh/ZisKU9OJaLFixdnZWVt3brVYDCEQqGGhoZT\np049//zzMQ8OEuPG+TlFGdr2waGD5/pvKssROhyIuyRvKcNDR0iIq2CY4y9tVSdH4r7UlMEwdKrT\n6Q+GZV8aJHIfNfWFOe4Lc7J1KdPJ8RJp03UlL3zWdqHP89Lhtq+tKhU6HGmYzquLYZif/vSn99xz\nj16v12q18+fPf+GFFzIzZb5rXsaUCuahFSWELarJIRTmTnYgcaclRQaGodNdzkAoPPGjAaaoyery\n+IMlWanZaSlCx5II6Rp2Xk5aIBQ+1YUPwwLbxw9MFV8jyCuplIon7ywnov/44JzDi/6EkzLNj8UK\nhWLt2rU//OEP//3f//1rX/uaXi/zLfOyt+EaE6tk9p3p7bKjJ4DMnetxDflDhRnaJMknxpOWws7J\nTguEwo1Wl9CxgAzx1d5JUifD468t1GJ/qqCCIe7A2T4SayPIK92+MG/FnCzbkP/ZD5uFjkUapnMZ\npbm5eceOHZfdqFAoZs+e/ZWvfEWtVsciMEiorDT1nYvzd9d3/flI+/fWLBA6HIijug4HEVUl93I7\nz2wynO9z11vsSwpF2jENpCupCtx51cUZrxy11LTZHr1+ttCxJK8jrYPu4eCCWelFGeIamDoehqGn\nvlhx97OfbD/U8tDK4tKsJO11NnnTWXE3GAxKpbK1tXXOnDnz5s3r7u72+Xzl5eX19fVPP/10zEOE\nxOC3qL581II9IvJW126jpK+T4WEME8RPJHEvTqLEvaqY7wiJ/alC4utkbpVCncyoxYWGL1cXBUPc\nz95tFDoWCZhO4q5QKNrb2//0pz9t3rx548aN27Zt83q9q1at+vnPf97Q0OB2u2MeJSTAtaWZZbPS\n+1zDfzttFToWiKORljJJlE+MBx0hIU76XMPtg0O6FHb+rHShY0mceblp6Rq22+HrdviEjiVJcRzt\nOyPegalRPHHHAq1KufeU9UjLoNCxiN10Evf6+vrZs2erVKrIIRSK+fPn19TUKJXKjIyMwUH80iWJ\nYSKL7tiiKmMef7Cpx61UMLIf5TgZCwv0rII51+se8oeEjgVkhV91ri42KhVJNCpBwTBLTXyZOxbd\nhdHS72kd8GSkqiVXDJmn1zx+01wi+vGe0+hMHd10EvecnJyamhqnMzKl1ufzHT58OCcnp6enp7Oz\nMycH/QSl6svVhalq5WfnB8734bKJPJ3scIQ5rjwvXatSCh2L8FJYxYK89DDHnepEtQzEUhIWuPOq\nizE/VUj7GnuI6JbyHCl+Ynzsxjmz9JqTnY43aruEjkXUppO4L1myxGw2P/jgg08//fRPfvKTr371\nq4WFhStWrPj973+/fv16rVYa+yHgSmkp7L1VhUT00uftQscCcRGpkzElXT4xnki1DMYwQUwlb+Je\nghV3IX0gzToZXqpa+f07FhDRL/Y2egO4CjquabaDfOqpp55++unS0tKioqJ/+Zd/+dnPfqZQKL73\nve899thjsY0PEmzTyhIierWmAy8bWeIT96rknpk6ltmE/akQY/5g+ESHg2GS8RMyv+v9ZKcD4xES\nz+ENHGsdZBXMjfOlWvjwperCxYUGq9P3p48vCB2LeE1/vFl1dfXmzZu//vWvr1ixgr8FM5hkoCJf\nX12c4fQG3qrHtSoZ4kc5oqXMKHORgbDiDjF1qssRCIUXzEpP14h9bmXMGbSquTlp/mC4ocspdCxJ\n56OmvlCYWz47U7onnoJh/vWLFUS07cD5Hie2OF/dNP93//a3v73xxhujZe5EtHbt2k2bNsUoKhDS\nxpUlNe22HZ+3bbjGJHQsEEtWp8/q9KWlsHNy0Cg3Yt6sdI1K2TYwZB8KGFNVQocDcsDXyVQnX50M\nr7ok43yfu6bdhgWCBBsZmCrJOplRK+dkrVmU916D9dfvNf3ivkqhwxGj6ay4t7e3//73v7/zzjsr\nKytXrVr1yCOPZGRk3HbbbTEPDgTxxcr8jFT1iQ5HPZYh5YVfbjebjApGevuW4oRVMIsL9ER0shNn\nO8RG0ha48yL7U9vwgkqoYDgyMFVaHdyv6ofrylkF85fjltO4bnM100ncz5w5s2zZsrvvvnvevHmZ\nmZmrV6++4447Dhw4EOvYQBgprOL+a4qIaAe2qMrLyM5ULINdopIvc7egzB1igONGVtyTdVRCVXEG\nYQxTwtW02RzewNycNBlMHp2drdu8qpTj6Cdvn0ZnyCtNJ3HX6XR2u52IsrKyWlpaiIhl2ebm5hiH\nBsJ5cEUxEb1V3+XwBoSOBWIGiftVmSPzU7FACDHQYRvqcw1n6tQyyJ+mZ35umi6F7bJ7UaOcSHyd\nzGrpL7fzvn3rfINWdej8AN/gEsaaTuK+fPlyq9X63nvvLVq06NNPP/3jH/+4ffv2hQsXxjw4EEpp\nlu6G+Tm+QOi14x1CxwKxEQpzJzschJYyV6iM7E/FijvEwGidTNLWoykVDD/9pxbd3BMo0giyXCaJ\nuzFV9Z3b5hPRM2+fCYaw6n6J6WxOVf//7N13fFz1lT/8c6f3GfU26sWWi2SJZorBlRJK2AQIS0hC\nAgmE2CH8Nm1/JPvsbkj2SciThLaUBEgIYbMhhIQAJthyB2PAapbVbfVep/e5zx93ZGwsW9JoZu69\ncz/vP/IiRpo5FndGZ84933NUqmeeecbhcGRkZHz/+99/++23b7rppptvvjnmwS1Vb29vPB52aGgo\nHg8rcNeUaA520QuHujflMef/DTQ6Ohqnn3wSEM7Fc3La5/IHs4xK5+SIc5LvaOYI4uJhyaCWj9m9\nHx7vytAL6HyqcC4eYRLExXOWfcdHiKjYFK/fR4vE78VTbKJDRHuP9a40CLToLsyLJ2pDdv+JCadB\nLU8je2+vIwYPKIA3n6tyGKtZ1TPpevSths+uFdDQwtnZ2fhdPEVFRQt+TTSJu8/nk8lkBQUFRLRh\nw4YNGzZ4vV632200GqN4tBhazF9YaI8sWNYC9sn3JwZmvSNh42Wlaef5ypmZGQn+fBZPID+c9ycG\niOjConSBxMMRyMWzLn/sUPfkDBkvKsrmO5YzCOGHI1gCuXg+ofP1ASLaWl1SVMRzqsHjD2ezT/f7\n+smTNlaA/4E4wrx4orbnUA8RbV6ZVVZSHKvHFMLP5//5tParL370Yv3k3VvWCmfql8Vi4feHE02r\nTGNj42OPPXb6n+zcufPZZ5+NUUggCAoZc/vFBUT00vt9fMcCMdDYP0NE69AnM58qrGGCWHD5gu0j\nDoWM4fqvJIs7SNM8OIs1TIlR1z5ORFtXiXsQ5Nm2VmZdWppm8wQe39PFdywCsrSKeyAQ+MUvfjE+\nPj40NPTTn/6U+0O/33/kyJEvfvGLcQgP+HT7RfmP1XW9c3x03OHLNKr5DgeWpXHQRjiZeg5YwwQx\n0TgwG2bZtVaLRinnOxY+pehUxen6nklX64idO/wN8eP0BY+cnJKLeWHquTAM/eD6VTc8fvB3h3vv\nXF9YnC7RA9+fsLSKO8Mw2dnZqampWq02e05BQcF99933mc98Jk4hAl+yTJprVmcHw+wfP8BcSHFz\n+YOdow65jFmTJ+lC4LlUWbkCoQ2jx2A5JD7B/XTc/imcT02A/Z0TwTB7QWGKcJpJYmh1rumWC/KD\nIfb/3dnOdyxCsbSKu0Kh+NKXvtTb29va2vqpT30qTjGBcNy5vvCtYyP/80H//ZvKFDKpTkkQv5ZB\nW5hlV+eYtNIuBJ5LtkmTYVRPOHx90y7JTvGD5UPifsoFBSmvHh2s75u567IivmNJcnvaxkn8C1PP\n49tXV7zRNPyP46NHTk5dUnK+E3cSsbSKeygUCoVC+fn511xzTehM4TBa2ZLQpSVpJRn6EZt3b/s4\n37FA9BoGIjtT+Q5EoBjmVFcu2twhSmGW5bYOSXb10um4sbNYwxRvoTC7t2OckmiC+9myTJr7NpYS\n0Y/ebAvjruhSK+4bN24817+69dZbv/nNby43HBAYhqE7Lyn8zzdaf/9+37akO/giHdzqpRok7udW\nZbXsah1rGpi9qTqX71hAlLrHnQ5vMNeizTFr+I6FfxVZRr1KMTjjmXD4MnBEKm4aB2anXf6iNH1J\nuoHvWOLoqxtK/udIf8uQ7bWGoc/WWvkOh2dLS9zfeOONc/0rtRqvzOT02QusP/tHx4HOib4pd2Ga\nju9wIBqN/bNEtA6FwHOrxhomWB70yZxOLmOq883vnZhq6J+5erWwpqwmE26ezObKzORe+KVTyb9z\n7Yp/+VPTI293XLcmR6eSdM/n0lplzKcxGAx2u31yclKtVpvNZo0GNYbkZNYqb6zOJaKXj2AupCiN\n2r2jdq9BrSjNQPf2Oa21momoZcgWDONWLEQDifsn1BSkEFE9zqfGU13rGCXRwtTz+KeavLV55lG7\n99cHT/IdC8+imeNORIcPH7711lu//OUvb9++/frrr3/hhRdiGxYIyp3rC4joTx8N+oI4ySA+TXMN\n7rLkrsksT4pOVZCq8wRC3eNOvmMBUULi/gm1kcQdbe7xMjjj6RhzGNSKi4sFtFg0TmQM88MbVhHR\n0/tOjNkFupE3MaJJ3CcnJ3/84x8/8MADu3fv3rlz53PPPffOO+/s27cv1rGBUFRbLWvzzDNu/1vH\nRviOBZaM65PBydQFzQ2FRIEQlmza5e+ZdGmV8spsE9+xCAV3PrV50BYM4S5WXNS1jRHRVRUZSnmU\nRVhxubg49do12Z5A6OfvdPIdC5+i+Y/d0tJSW1t71VVXcf+3qKjozjvvPHLkSEwDA2G5c30hYYuq\nODXgZOriVOebae4gL8CSNMx9PFbIcV8rIlWvKkrTewOhtlE737EkJ67BfUvyDoI82/evW6mQM38+\nOnB8WLoXVTSJu0Kh8HrPuE/h9XqVyiSc/A+n3Fida9QojvbNtI9I99UiRqEweww7Uxen2oqJkBCl\no/3ok5lHbaGFsIYpPly+4OETUwxDG1ck28LU8yhK0991WTHL0o/eaJXsZMhoEvd169Z1d3e/+OKL\nQ0NDExMTe/fuffHFF7ds2RLz4EA4dCr5LRdYiej372OLqph0jTtd/mCuRYuJbAtanWeSMUz7iB1n\nOWCp0OA+r5p8tLnHy6HuyUAoXFuQkqpX8R1LQu3YXGbRKd8/ObW7bYzvWPgRTeJuMBh+/vOfHz16\n9Atf+MItt9zy3HPPPfjgg9XV1TEPDgTl85cUEtFrDYNOX5DvWGCxmtAns2h6laIs0xAMs224rQRL\nEQyxkRdaAV5oZ6gtTCGi+j4k7rFXl+wLU8/FrFV+a2sFEf3krbZASIpFlqXNcT+lpKTk0UcfDYfD\noVAITTISUZZpuLQ07fCJqdfqh75waSHf4cCiNGJn6lJUWc2dY46mgVl0FsHitY7YvYFQaYYhRSet\n2ueCVmQbtUp5/7R7yulPM+CHEzNhlt0zN8Gd71h4cOclhb97r7dn0vX79/u+cnkx3+EkWjQV9+bm\n5p///OctLS0ymQxZu6ScOqIq2d4y0eFOpiINXSS0uUMU0CdzLgoZU5VvIXTLxNqxQduk02dN0VZk\nGvmOhQcKOfPQ9ZVE9Ojurll3gO9wEi2axN1qtep0uv/4j/+4/fbbf/vb346OjsY8LBCma1ZlZxjV\nHWOOj/qm+Y4FFubyBztHHXIZw20XggVxtyaaMBESlgKJ+3nUFiBxjz2uvXtLZZZkl3NsWZl1WWma\nzRN4bE8X37EkWjSJe2pq6v333//KK6/8+7//u8fj+da3vrVjx476+vqYBwdCo5Azt1+UT5gLKRIt\ng7Ywy3J3q/mORRwqc4xKuezEhNOFgxywaEjcz4Nbw4TBMrHFDYLcKsk+GQ7D0A+uX8Uw9OJ7vT2T\nLr7DSahlDe1fuXLlpz/96RtuuKGnp6e5uTlWMYGQ3XFJgYxh3jo2Ou3y8x0LLKARgyCXSCmXrcox\nsSwdG0K3DCzKiM0zYvOYtcqSDD3fsQgRl7g3DcwGw+iwjI0Rm7d12K5TyS8pTuM7Fj6tyjXddmF+\nMMz+1852vmNJqCgT96GhoZdeeunuu+/esWOH2+1+8skn77rrrpgGBgKVY9ZuqcwMhML/+9EA37HA\nAhr7ZwgjZZaoKt9MRE1oc4fFOdo3S0S1BSkyyXYtnFeaQVWQqvMEQh2jDr5jSRJ72seIaEN5hkoh\niYWp5/EvV6/QqeTvHB99/+QU37EkTjT/1evr6++6667e3t6vf/3rr7zyyte+9rXCQswYkRDuiOrL\nR/rDOKMqbBgpE4XI+VTsT4XFqUefzEK4oZANaHOPkblBkNLtkzkl06j++sYyIvrRG63SSUiiSdzL\ny8tff/31H/zgBxdeeKFMJvUPfBK0oTy9IFU3MO0+0DnJdyxwTmN274jNq1crSjMMfMciJlVWruKO\nxB0WBQ3uC6rBYJnY8QRC73ZPMgxtWonEnYjong3FOWbN8WH7a/VDfMeSINGk3UajUavVxjwUEAsZ\nw9xxSQHhiKqwRcrtVrNchjv4S1CaYdCp5IMzHpzigAV5AqHjwza5jOE6rGBec2uY8GE4Bt7tnvQF\nw9VWS7oBy7CJiLRK+XevXUlEP/tHh9sf4jucREC9HKJx24X5SrlsT/v48KyH71hgfo39s0S0rgCF\nwKWRy5g1eSi6w6IcG7QFw2xljkmvinKboRRUZps0SnnvlAsfhpdvT9s4EW2R3sLU8/j0utwqq3nM\n7n32wEm+Y0kEJO4QjVS96vqqnDDLvvxBP9+xwPwaB2cJJ1OjwrW5Nw3gfCosAH0yi6GQM1wHGoZC\nLhPLRgZBbkGfzGlkDPOD61cR0TP7T4zavXyHE3fLStwDgYDXm/w/I5gXd0T1jx8MYMiXAIXCbPOA\njXAyNSrV+WYiakbFHRaCxH2RuKGQaHNfpuPDtjG7N8esqcwx8R2LsFxcnHrdmmxPIPTIPzr4jiXu\nokzcm5qa7r777q1bt7722mtdXV0//elPQyFJtBbBKRcUpKzMNk46fe8O4MOb4HRPOF3+YI5Zm2lE\nH+SSVVkj+1MlM6UAosGyc4k7GtIWwu1PxWCZZdrdNk5Em1dKd2HqeXz/ukqFnHn16GDSb+GIJnGf\nnp7+t3/7t9tvv/1rX/saERUUFAwMDLz55puxjg0EjWHoC5cWEtHrHU7kN0LDNbjXFKDcHo38FF2K\nTjXl9I/YcIQDzql3yjXj9meZNLkWTGtYQE1kDZMthDu0y8BNcN+6Cn0y8yhM0335smIievjNtuTO\nSaJJ3Juami655JJt27ZpNBoiUqvVN910U1NTU6xjA6H7bK013aA+ORPY1znOdyxwBm6kDHamRodh\nTg2FTPLKDSzHqT4ZlD8XlGFUW1O0Ln+wawxrmKI0Zvc2D9o0SvmlJZJemHoeOzaXpehUR05O7Wod\n5TuWOIomcddqtQ7HGa89h8NhNmMYluRolPL7riohokd3dyX3B1zRaUDivjzc2QCsYYLz4BL3WtzX\nWpy5Nne8pqK0t2OCiK4oS9co5XzHIlAmrfLBbRVE9JO32gOhMN/hxEs0ifu6dev6+/ufeeaZsbGx\n8fHxV1999YUXXrjmmmtiHhwI3x2XFJrUssaB2UPdE3zHAhFuf6hz1HFqrCFEAWuYYEFzFfdUvgMR\nB65b5ija3KNV1zZGRFuwMPW87ri4oDTD0Dvl+v3hpN0zE03irtFofvWrX01PT+/bt6+uru7gwYMP\nP/zwihUrYh4cCJ9OJb95pYGIfoWiu2C0DNnCLFuRZdSpUJiJEjcRsnnQJp012rAkdk+gc8yhUshW\n52K+x6LUFuJ8avR8wfChrkki2oxBkOelkDMPXV9JRI/Wdc26A3yHExdR7ozIyMj413/919iGAiJ1\nbZnu713eo30z752YvLwsne9wINIngwnuy5FhVOeYNSM2b++kuyRDz3c4IDjcq6wqz6xSYB3KoqzK\nMakVspMTrll3wKJT8h2OyBw+MeUJhNbmmbNMGr5jEbpNKzKvKEs/1D35WF3Xv924iu9wYi+ad5yW\nlpZnn3329D959913X3zxxRiFBCKjVcju2VBMRI/WdfEdCxARNfbPENE6tN4uz6mhkHwHAkKECe5L\npZTL1uaZiahhAEX3JduNPplFYxj6wfWVDEMvHu7tmXTxHU7sLS1xD4fD77333kcffdTS0vLenH37\n9v3hD3/AJiYp+9JlRSat8oOe6SMnp/iOBTBSJjaqrVjDBOeExD0KtYUphP2pS8eyVDc3wZ3vWMRh\nZY7pcxfmB8PsT95q4zuW2Ftaq0wgEHj++eddLpfdbn/++edP/XlaWtpNN90U69hANAxqxd1XFP9y\nV+ejdV0vY1IVr8bs3hGbV69WlGYY+I5F3KryLTT3KQjgdKEwy61KqEXivhSRwTJ9qLgvTceofcTm\nyTSq1+ThQMVi/cvVK15vGt7VOnb4xNSlpUmVliwtcVer1b/5zW/q6+v379//4IMPxikmEKMvX1b0\nm4Mn3zsx9WHv9EVFGLPAmyau9dZqlsswXHpZqvLMRNQ6bA+GWIUcP0z4WOeYw+UPFqbp0g3YTLwE\n3Eq4hoHZUJjFG9TizS1MzZRhZcCiZRjV928s+/k7HT96s/Xv269Ipustmh732tpaZO3wCSat8iuX\nFxPRY3XdfMciaZjgHismrbI4Xe8LhjuwMgbOhD6Z6HBbZl2+YPeEk+9YxKSunWtwR5/M0tyzoTjH\nrG0dtr/WMMR3LLEU5VSZ/fv3/+1vf7Pb7af+ZNu2bZ/73OdiFBWI0pcvL/7NoZ6DXRMN/bM1OBnJ\nk0aMlImdKqu5Z9LVNDiLkX9wOiTuUastSBme9dT3zazIMvIdizhMOf2NA7MqhQxD25ZKo5R//7qV\nD/yx4Wdvt1+3NluvijLjFZpoKu6Dg4M//elP169fX1RUtGbNmptvvlmpVF522WUxDw7ExaJT3nVZ\nERE9hvEyPAmF2eYBGxGtK0BKEQORae5oc4czRRJ3vMqWjls0i/2pi7e3Y5xl6bLSNOzliMKN1TnV\n+ZZxh+/Z/Sf5jiVmokncW1tba2trb7vttsrKyqysrBtuuOGaa645fPhwzIMD0bn7imKdSr63Y7x5\n0MZ3LFLUPeF0+YM5Zm2mEa23McCdT23CxQynmXD4+qfderWiHDXjpZsbLIPzqYvFDYLcij6ZqMgY\n5oc3rCKiZw6cHLElyfDDaBJ3rVbrdruJKCUlpb+/n4h0Ol1HR0dKkhVQAAAgAElEQVQMwxptqnv5\n5T83jZ72U3Z2vvzID7dv3/6Tx18fDcbwqSCWUvWqL15aRESP70HRnQdNkQZ3M9+BJInVuSa5jOkc\nc3gCIb5jAaGo758hotoCSzIdd0uY1bkmlULWPe60eZJzq2Vs+YPhg51YmLosFxamXL82xxsI/fwf\nsUxTeRRN4n7RRRf19vbu2bNn1apVBw8efP7553/3u99VVFTELKjZww9s//ennnr0wNhc4u48/t3r\n7n7q9eEVawvf+9Mjt37+8dGYPRnE2Fc3lGiU8l2tY8eH7Qt/NcQUN6IOfTKxolXKK7KMoTDbiosZ\n5qDBfTmUctmaXDNh0OriHOmZdvmDlTmmXIuW71hE7HvXrVTKZa/WDyZHL0A0ibtGo3n66aeLioqy\ns7MfeOCBlpaWjRs3fuYzn4lRSM4//+C7wxXXXWom1dwfHX/9F4ep4pd/f27Hvd/564vfoeE/PX8A\nqbtApRlUd64vJBTd+dCAk6mxxq1hwv5UOAWJ+zKhW2bx6rAwNRYKUnVfubyIiB5+s5Vl+Y5m2aJJ\n3IkoMzOzpKSEiLZt2/aLX/ziq1/9qlKpjElAowcee7SJfvzT+y/Wkz/yZ84P/9ZpvumeCy1ERIri\nqx+ooPeO9MTk6SAevnZliVohe7tltH0Uc/QSx+0PdYw65DJmTR5aZWKGa3NPjjoNLJ8/GG4etDEM\nrctH4h4l7nzq0T58GF4Ay1Jd+zihwT0WvrGpLFWv+qBn+p1W0Zd9l5y4T01NPfroo9PT0zab7fOf\n//z27dufeuopl8sVm3C8TQ89tLPi609fma6ZOvMh9RmnxrFpKjdU2PY340UvWJlG9R2XFBDREyi6\nJ1DLkC3MshVZRgwfiCFusEwTbusDERG1DNsCofCKLKNRkySj5RKvpiCFiBoHZsJJUPyMp65xx8C0\nO1WvqrKiFrNcJq3ywa0VRPSTt9oCoTDf4SzL0t56bDbbfffdV1paqtVqPR7PzMzMXXfd9c4779xz\nzz0vvPCCRqNZZjQHfvFQJ930yh2ribxqIv9p4WUYdAt+e0NDwzIDmNfo6OjMDG7qnVNX1zzZ+eVp\nod/LmDePjVy9/8N8k3R/wyXy4nmz3UlE+bpQnF4I8TDvxSMooTApZdQz6Tp05KheFeUtyujgnef8\neLl4Xu9wElGBXuivMoFfPOk6+aQ7+MaBj/j67SD8dx4i+ku7k4jWZSqaGhsT/NQCv36iU6kmq0nR\nN+X+rz+/d2OFPurHGR0djd/Lv6amZsGvWdpr5pVXXlmxYsXDDz9MRB6PR6lUbtu2bdu2bd/+9rdf\neeWVL3zhC1FGSkREwYHXH9ppq/76JhoYGKDpCSJ734nRvNLsdCIXTUx9fDjMPjFGRWlnf0pYzF84\nCr29vUVFRfF45KQx70/+nydafn+4b/ew4rGr4vLfRRQSefH8urWeyL5lXWlNTX5injEm4vSyjaE1\nR9wN/bOUVliT2AUoeOdZUOIvnmeOHyWyX3NBeU2NNcFPvSQCv3guaa1/89iIR5fN45uV8N95fnLk\nMBHddnllzZrsBD+1wK+fqP1IP/7lFz78c7t7x40Xp+hUC3/DfBoaGvi9eJZWQGppabn22mu5f5bL\n5Tk5Odw/b9u2rbW1dZmheKdHiKjpqQdvveOOO+7Y/rqN9j2y/dZ/+oOTNNk1NLynYW5F8sBbr9sq\nLqtcbnkf4uz+jaUKOfP35uET2G6dEA2RkTI4mRpjWMMEHJbFydTYqImsYUq2mm4Mzbj9R/tmlHLZ\nhnIsTI2ZjRWZG8oz7J6AqNdELi1xl8vlgUBk9qrZbH766ae5fw6HY9AwZFh9986dO3ft2rVr1669\ne1+500w3/eyVgwfvNZDiilvupOHn/vPlj2adk28/8u19RLduXrH8Z4S4yjFrb7swn2Xpyb3dfMeS\n/MYdvhGbR69SlGUY+I4l2VRZsYYJiIgGZ9wTDl+qXlWYGv19dqCPB8vgw/A57euYCLPs+pI0vVq6\nvaYxxzD00PWVMob5/eG+kxMxOpyZcEtL3FetWrVz5072zAMlLMvu2rWrsrJyubEoFAaDQaPRaDQa\nhcKgJtLoIimIofreJx7YePipB2+87p9+/PrwbT9++dpsXMoicP/GMoWM+WvDcO+UWF8hYtHYP0NE\nVflmLIWJuep8MxE1YyKk5J0qtzN4kS3PmlyzUi7rGnc4vNinOD8MgoyTldnG2y/KD4bZ/9rZxncs\nUVpa4n777bf39/c/9NBDra2tHo/H7Xa3tLT83//7f0dHR2+99daYBma4642DO6o/rh1W3/KjXX9/\n7bXXXvv7zr07rhRTC6+UWVO0n73AGmbZJ/ee4DuWJNcQ2ZmKPpnYK07XG9SKEZt3wuHjOxbg09F+\n9MnEhkohW51rYllqHEC3zDyCIXZfxwQRbcHC1Dj4P1dX6FWKXa1j752Y4juWaCwtcdfr9U8++aTZ\nbN6xY8fVV199zTXXfOtb30pJSXniiSe02riv9dJY0tPT0y0G1NrF5P6NZXIZ85f6wYFpN9+xJLMm\nJO5xI2OYtVjDBFi9FFNct0w9umXm80HvtNMXrMgy5qcuPE8PlirdoP7GplIi+tEbraGw+GaSLnm6\nWVpa2ve+973du3e/+uqrf/nLX3bt2vXd7343NTU1HsFBEihM091ckxcKs/+9D0X3eAmFWa4DG4l7\nnKyzYg2T1Ll8wfYRh0LOrMWCs1ioLUghovo+VNznEemTQbk9br5yRXGuRds2Yn+1fpDvWJYsyrHE\nDMNkZmZmZGQw6PWDhWzfVCZjmFeODgzNePiOJTmdmHC6fMEcsybLhGFLccHtT8UaJilrHJgNs+ya\nXLNGiQVnMcDtT20YmMUaprPtaR8noi2rsDA1XjRK+fevW0lEj/yjw+UX2UGLhO4TAWkqTtfftC43\nGGKf2o+ie1w0ok8mzqqt3PlUG3IMyUKfTGzlmLVZJo3dE+iZxOiCM5yccPVMuiw6ZQ3e0uPpxqrc\ndfmWCYfvmf0n+Y5laZC4QyJs31TGMPS/Hw6M2Lx8x5KEGiMT3JFSxEuOWZtmUM24/YMzOKohUUjc\nY4th5qa5o1vmTHXtY0S0aUUmRoTFFcPQD29YRUTPHjg5YhNTOwASd0iEskzD9WtzA6HwMyi6x0Fk\npIwVrbfxwjCRNUyY5i5NYZatx0iZWIu0ueN86pl2t40T0ZZK9MnE3QWFKTdU5XgDoUf+0cF3LEuA\nxB0SZMeWMiJ6+YP+cczUiym3P9Q55pAxzBok7vFUFTmfiiRDirrHnQ5vMC9Fi2MkMTS3hgkV94/Z\nPIGPeqcVMuaqigy+Y5GE7127UimX/aV+SESzB5C4Q4KsyDJetybbHww/jaJ7TLUM2UJhtiLbqFdh\nUmoccWuYGnE+VZIifTLoRoupNbkmhYzpGHM4fSI7HRg/BzonQmH24uJUowbv54mQn6q754piInr4\nzVaxHGFC4g6J880t5UT0h/f7sMgmhrhUEseY4o1rleE+JvEdCyQaGtzjQaOUr841syw+D39sd2Rh\nKvpkEuf+TWWpetUHPdP/OD7KdyyLgsQdEqcyx3T16mxfMPzrgyI7xC1kGCmTGKl6VV6K1u0PnZhw\n8h0LJBoS9zipLbQQUQPa3ImIKBiOLEzdjAnuCWTUKP7l6goi+q+dbf5gmO9wFobEHRLqm5vLiOj3\nh/umXX6+Y0kS3O+8aiTu8VeNNUySNO3y90y6tEr5yhwT37EkmxqsYTpNfd+MzRMoydAXp+v5jkVa\nPndRQXmmoW/K/eLhXr5jWRgSd0ioNXnmLZWZnkAIRfeYGHf4RmwevUpRnmngO5bkV2U1E1ETzqdK\nTENk3KpFgfF8scYNlmkYmBFLe3FczS1MRZ9MoilkzA9uWEVEj9Z1Cb+qiMQdEu2bm8uJ6MX3+mbc\nQn95CB+3y3Ot1YyJvwkQqbgPoOIuLUf7Z2guxYTYyrNoM4zqWXegdwprmCKDILdWok+GB1dVZFxZ\nkeHwBh+t6+I7lgUgcYdEq863XFWR4fIHnz/Uw3csoteAk6kJtNZqZhhqHbEHQiLog4RYQYN7/DAM\numUieqdcJyacJq3ygsJUvmORqIeur5QxzEvv9wn8IBMSd+DBA1vLieiFd3ttngDfsYhbY/8MEa0r\nQOKeCAa1ojTDEAiF20YcfMcCCRIMsdx9rRq8yuKjtsBCc7c1pGxP+zgRXVWRoZDj9ik/VmQZb784\nPxRmf/JWG9+xnA8Sd+BBbUHKFWXpTl/whXd7+Y5FxEJhllvkiZOpCVONNUwS0zpi9wZCpRmGFJ2K\n71iSU6TNXfKDZeoifTJocOfT/9lWoVcr6trGD3VP8h3LOSFxB35wM92ff7fH4cXqjSidmHC6fMFs\nkyYb2xwTZe58KtrcpQJ9MvG21mpWyJiOUYdLwmuYnL7gkZNTcixM5Vu6Qb19UxkRPfxmm2BXdiBx\nB35cXJy6viTN7gn87r1evmMRK+4OPvpkEom7udGMfTGSgcQ93rRKeWWOKcyyUv48vL9zIhhmLyhM\nseiUfMcidV+5ojgvRds+Yv/z0UG+Y5kfEnfgzQNbyonoN4dOSrnQshwNWL2UcJU5JoWM6Rp3uv0h\nvmOBREDingC1hVy3jHTb3Pe0jRMWpgqDWiH71+tWEtHP3+kQZnKCxB14s74k7aKi1Fl34MX3+/iO\nRZQaMVIm4dQK2cocU5hlW4akWx2UjhGbZ8TmMWuVJRlYiBNH3JtYvVQT91CY3dsxTkRbsDBVGK5f\nm1tTYJlw+J7ef4LvWOaBxB14wzCR8TK/PnAS9cul8gRCHaMOGcOssZr5jkVasIZJOo72zRJRbUGK\njMGgjziaq7jPSnMNU+PA7LTLX5imK83AHj1BYBj6txtWE9GzB06O2Dx8h/NJSNyBT5eXptcWpEy7\n/C+h6L5ExwZtoTBbkWXQqxR8xyIt3GCZJqxhkoB69MkkRH6KLs2gmnb5+6aluIaprp0rt2fh46Fw\n1BRYbqzO9QXDP3u7g+9YPgmJO/DpVNH9mQMnPAEU3ZegEQ3uPKm2mgkTIaUBDe6JwTCRoZD1fVJ8\nWdW1jhHRFixMFZjvXbtSpZC91jAktPurSNyBZ1eWZ1RbLVNO//8c6ec7FjGZGymDlCLRyrKMGqW8\nf9o94/bzHQvEkScQOj5sk8uYqnx0o8VdZJr7gOTa3AdnPB1jDoNacXExFqYKizVFe/cVxUT08Btt\ngmriQuIOPGOYyEz3p/af8KLovmgYKcMXhYxZk2siomMSnl4nBccGbcEwW5ljQjdaAnD7U7neJEmp\naxsjoqsqMpRy5GOC841NZal61Ye9028fH+U7lo/hQgH+bV6ZuTrXNOHw/e+HA3zHIg4TDt/wrEen\nkpdn4jATD7hp7lIeOy0F6JNJpLVWi1zGtI86pDaogGtw34w+GUEyqBXfvnoFEf3XW23+YJjvcCKQ\nuAP/GCYy0/2pfSeE89oQMq7BnftVx3csUhRZwySwxkeILSTuiaRTyVdmG0Nh9piUXlYuf/DwiSmG\noU0rkLgL1G0X5VdkGfun3b8VzLJIJO4gCFtXZa3MMY3ava8cRdF9YQ2Y4M6ryERI7E9NXiw7l7jj\nGEmicEMh6/sl9LI61DUZCIVrC1JS9Sq+Y4H5KWTMD2+oJKLH6rqmXYI414TEHQRBxjDf3FxGRE/u\nPREIoei+gCY0uPOqMFVv1irHHb5Ru5fvWCAueqdcM25/lkmTa9HyHYtU1ORzibuE2tzr2rB3SQQ2\nlGdsXJHh9AV/tbuT71iIkLiDcFy7Jrs80zA863m1fojvWAQtzLJzI2WQuPODYSJF92YU3ZPUqT4Z\njNZOmNrCyP5UQU3wiJ8wy+7hJrivyuI7FljAQ9evksuYPxzp7x538h0LEncQDBnDcONlntzbHQxJ\n4507KicmXE5fMNukyTZp+I5FuqqsOJ+azNDgnniFqfpUvWrK6R+YcfMdSyIcG7RNOn15KdqKTCPf\nscACyjMN/3xxQSjM/uStNr5jQeIOQvKptTmlGYaBafdfG1F0P6fG/hmaOx8JfMEapuSGxD3xGIZq\npDQUcm5haibu6ojCg1srDGrFnvbxk241v5EgcQcBkcuYHZvLiOiJPd3BMIru82tAn4wAVM1NhJTI\nbX1JsXsCnWMOlUK2OtfEdyzSMreGSRKfh3e3jRHR1kr0yYhDmkG1fXMZEe2aMIR4zU+QuIOw3FCd\nW5yu751y/b1pmO9YBKoRI2UEINukyTSq7Z5A75SL71ggxrjEsdpqwU6cBOMSdylU3Eds3tZhu04l\nv6Qkje9YYLG+fHmxNUU75lP8tYHPpgC8K4GwKGTM9k1lRPT4ni5+P9QKkycQ6hh1yBhmrRVr2Hk2\nN80dbe7JBn0yfKnKN8sYpm3E7kn2Ldp72seIaEN5hlqBNEw01ArZD29YtTXDeUN1Lo9h4IoBwfn0\nuryCVN3JCdebx0b4jkVwWoZsoTBbkWXAGnbezZ1PlcRtfUlB4s4XvUqxItsYDLPHkv3zcGQQJBam\nis01q7MvS3Hx+3ELiTsIjkLOfGNTGRE9VtcVRgfxmRoxwV0wqjERMhmFwmxj/yzNtW1AgkW6ZZJ6\nmrsnEHq3e5IIC1MhGkjcQYg+U5uXl6LtHnfubBnlOxZh4VIKjJQRAq5bqWXYjoPUyaRzzOHyB4vS\n9GkGLLPkQS03WCap96e+2z3pC4ar8y0ZRp7nk4AYIXEHIVLKZd/YWEZEj6PofqYGnEwVjBSdqjBN\n5w2EusccfMcCMYM+GX7VFqYQUUNSr2Hag4WpsAxI3EGgbrnAmmPWtI86drWO8R2LUEw4fMOzHp1K\nXp6FhR2CgDVMyQeJO7+K0vQWnXLC4Rua9fAdS1ywbGSCOwZBQnSQuINAqRSyr28sI6JH67qSuPSy\nJFyD+1qrRS7Dxg5B4NrccT41mXCJey0Sd54wDNXkJ3Ob+/Fh25jdm23SVOZgSwBEA4k7CNfnLsrP\nNKpbh+117Si6E2GCu/BwFXdMhEwaEw5f/7TboFaUZxr4jkW6TnXL8B1IXOxuGyeizZVYmApRQuIO\nwqVWyO7bWEpEj6HoTkRziTtOpgrHmjyzjGHaR+y+YJjvWCAGuCpvTUEKbmrxKHI+tS85b2RxE9zR\nJwNRS6pR0L29vfF42KEhPldkCd/o6GicfvJEdHkWm6JVNA/a/nSw5ZIC8dXAYnjxhFm2oW+aiNIZ\nZ/x+4AkW14snMQotqp4Z3576jspMbWwfGe885xePi2dP8ygRlZoZsV+Wor54Utgww1DL0GxH98k4\nDczm651nyh1sHrSpFbI8hbO31534ABZJ1NdPvM3Ozsbv4ikqKlrwa5IqcV/MX1hoj5wEZmZm4vrz\n+cZmevjNtv85Zrttwxox3luM1Q+na9zpDrRmmTQXrS6LyQMKQbwvngS4sNTe89HAWFB7XRz+ImL/\n4cRVPC6e7p3DRLSlurioKD22j5x4or54VmQNto86HMqUFfE5bMDXO8/7Hw4Q0YbyjBVlJYl/9iUR\n9fUTVxaLhd8fDlplQOjuuKQwVa9qHJg91D3Jdyx8asLqJUGKrGHC+VTx8wfDzYM2hqGaArzKeBZZ\nw9SXbG3udW1jRLQZC1NhGZC4g9DpVPKvXVlCRI/u7pRyp3tD/ywRrUNKITCRiZADOJ8qei3DtkAo\nvCLbZFAn1b1oMaqJrGFKqsTdFwwf6pokos2Y4A7LgMQdROALlxam6FQf9c28f3KK71h40zgwQ0Tr\nrEjchaUyx6iUy05OOp2+IN+xwLJEBkHis7EAcINl6vuSag3T4RNTnkBoTZ4526ThOxYQMSTuIAJ6\nleKeDcVE9Ku6Lr5j4YcnEGofdTAMVVnNfMcCZ1DKZatyTCxLxzAUUuSwekk4itP1Zq1y3OEbsSXP\nGiZurjEWpsIyIXEHcfjSZUUmrfLIyakjkiy6twzZQmG2ItOox0184anKxxom0WNZJO4CImMY7jxP\n0nTLsCztbh0noi0YBAnLg8QdxMGgVtx9RTERPbanm+9YeMBNcEeDuzCtwxom8RuccU84fKl6VWGq\nnu9YgOhUt0x/knwe7hi1j9g8GUb1mjwsTIVlQeIOovHly4oMasW73ZMfJd2ogQVhpIyQVeVbCBV3\nkTtVbhfjzNmkNLeGKUne7SMLU1dmynCFwfIgcQfRMGmVX+GK7tLrdG9A4i5gJel6vUoxNOOZdvn5\njgWidLQffTLCsi4/hWGoZdiWHGuJ67AwFWIEiTuIyVcuL9arFAc6J7jWEYmYdPqGZjxapbw8y8h3\nLDAPuYxZY0Wbu7ihwV1ojBpFeaYxGGKPD4u+CW3K6W8cmFUpZJeXiX6xF/AOiTuIiUWn/NLlRSSx\nojs3wX2t1ayQ4R6rQHFrmDDNXaRcvmD7iEMhZ9bmYWqTgNQkS7fM3o5xlqXLStN0KjnfsYDoIXEH\nkbnnimKdSr6nffzYkFSSJO72Qg36ZASsKnI+FRV3UWocmA2z7Jpcs0aJvEpAIvtTxX8+lVuYumUl\n+mQgBpC4g8ik6lVfvLSIpFR0j5xMLcBNfOHiKu6NA7PJtC9GOtAnI0zcYJkGkU+E9AfDBzoniWhL\nJSa4QwwgcQfx+eqGEo1Svqt1rHXYzncscRdm2UacTBU8a4ouRaeadvmHZ5NnX4x0IHEXptIMvVGj\nGLF5R2xevmOJ3pGeaZc/uDLHlGvR8h0LJAMk7iA+aQbVnesLiejxPclfdD854XL6gplGNbZkC9mp\npbY4nyo6YZatx0gZQZIxzLp8rltGxEX3uT4ZlNshNpC4gyh97coStUK2s2W0Y8zBdyzxNbd6CeOl\nha46H2uYRKl73OnwBvNStFn4bCw8FxRaaO6AvhixLNW1jxMGQULsIHEHUco0qu+4pICIHq9L8kWq\n3G8snEwVPlTcRSrSJ4MzJIJUw51PFe1gme4J58C0O1Wv4t4fAJYPiTuI1b1XlSrlsjePDXePO/mO\nJY4aB2YIDe5iUG21ENGxQVsYB1RFBQ3uQsa99R0bsvnFuYZpd9sYEW1emSnHMF+IESTuIFbZJs3t\nF+ezbDJ3unsDofZRx6n+aRCyDKM6x6x1+oI9ky6+Y4ElQOIuZGatsizTEAiFW0dEOYpgT9s4EW1B\nnwzEDhJ3ELGvX1WqkDN/bxo5OZGcqVLLsD0UZsszjXq1gu9YYGHV+VjDJDLTLn/PpEurlK/MMfEd\nC8xPvN0yM27/0b4ZhZzZUI6FqRAzSNxBxHIt2tsuyA+z7JN7k7PTvbEffTJiUo01TGLDnSFZV2DB\nWmLBquX2p4pwsMy+jokwy15akmZA5QViB4k7iNv9m8oUMuavjUO9U0lYdJ8bKYPEXRxwPlV0jmIQ\npOBxa5jEuD+1LtLgjj4ZiCUk7iBu1hTtZ2qtoTD733tP8B1L7DUMYKSMmKzNMxNR67A9GML5VHFA\ng7vwlWUY9GrF8KxnzC6mNUzBELuvY4KwMBViDYk7iN43NpXJZcxf6gcHpt18xxJLk07f0IxHq5SX\nZxn5jgUWxaRVFqfrfcFw0q8XSA7BENsU+WyMxF245DKGK16Ia5r7h73TTl+wPNNQkKrjOxZIKkjc\nQfQK03Q31+QFw+xT+5Kq6M71yay1mtF9KyLcGiZ0y4hC64jdGwiVZRosOiXfscD5zHXLiKnNnRsE\niXkyEHNI3CEZbN9UJmOYPx0dGJ718B1LzEQa3NEnIyqRNvcBJO4igD4ZsajhzqeKarDMnnZuECT6\nZCDGkLhDMihO19+0LjcYYp/anzxF98Z+JO7iww2WaRrEREgRQOIuFlwv07EhWyAkjjVMJydcPZMu\ni05Zg428EGtI3CFJbN9UxjD0xw8GRkV1gOlcwizLVdxrMFJGVFblmuQypmvM4QmE+I4FFoDEXSws\nOmVJht4XFM0aprr2MSLatCITjY4Qc0jcIUmUZRquX5sbCIWfSYqi+8kJl9MXzDSqs01avmOBJdAq\n5RVZxlCYPT4sjgxDskZsnhGbx6JTFqfr+Y4FFja3hkkcTWh1beiTgXhB4g7JY8eWMiJ6+Uj/uMPH\ndyzL1RSZ4J7CoF4jNtVWMxE1o81d2I72zRJRbUGKDK8xMbigIIWIGsRwPtXmCXzYO62QMVeWZ/Ad\nCyQhJO6QPFZkGa9bk+0Lhp89cJLvWJaLm+C+zmrmOxBYsioMlhGDevTJiIqI9qce6JwIhdmLilNN\nWkwrgthD4g5JZcfmciJ66f2+Kaef71iWZW5nKrIK8VlntRBRM86nChsa3MWlPMuoVykGZzwTgr+h\nGhkEuRJ9MhAXSNwhqazKNW1bleUNhJ49IOJOd28g1D5iZ5jIbEEQl4oso1oh65l02T0BvmOB+XkC\noePDNrmMqbLi8Lc4yGVMdb6ZBN8tEwyfWpiKCe4QF0jcIdl8c0s5Eb14uG/aJdaie8uwPRhmyzON\nBrWC71hgyRRyZnWumYiah1B0F6hjg7ZgmF2VY9Kp5HzHAos1t4ZJ0E1o9X0zNk+gOF2PQ88QJ0jc\nIdmszTNvXpnpCYR+c6iH71ii1Ng/Q5jgLmZcaRDnUwULfTJixE1zF3ibex0WpkKcIXGHJPTAlnIi\n+t27vbNuUfYqNA7YCIm7mFVhDZOwIXEXI26pRfOgLRhi+Y7lnOrax4loKwZBQtwgcYckVJ1vuaoi\nw+UPPv+uKIvujQOouItbdeR8KiruQsSySNxFKVWvKk7XewOhtlGBLknom3J3jzuNGsWFhal8xwJJ\nC4k7JKcHtpYT0fOHekR3QHDK6R+c8WiV8opsI9+xQJSK0nUGtWLE5k2ClQLJp3fKNeP2Z5s0OWZs\nNxMZrujOjfIUIG5h6sYVmQo5lgNAvCBxh+RUW5ByRVm60xd84b1evmNZmoaBGSJaazVjV7Z4yRiG\nmwjUhDZ34eHK7bWF2G4mPrXcGiahvqwiC1MxCBLiCYk7JC1uvMxzh3qcviDfsSxBZII7+mREDt0y\ngoU+GfHiEndhVtydvuCRk1Myhtm4Aok7xBESd0haFxenrnJ9eE8AACAASURBVC9Js3sCvxNV0Z2r\n0VYjcRe5uf2pOJ8qOEjcxasi26hTyfun3QJcsXegcyIYZi8sSrHosDAV4giJOyQzbrzMrw+edImk\n6B5mWa7iXoPEXeSqrWYiah6cZYU7AEOK7J5A55hDpZCtzjXxHQssmWJuZ5YAh0JyfTKb0ScDcSa8\nxN3Z8/rjP9m+ffsPH/lt06j3tD/vfPmRH27fvv0nj78+Ko4cDPi3viTtoqLUWXfg9+/38R3LovRM\nuhzeYIZRjWNzYpdj1qYZVLPuwMCMm+9Y4GNce3S11aKUC+/XHyzC3BomYSXuoTC7t4MbBIkJ7hBf\nAnvncjZtv+6Lj/zpROHaFcOvP7f91lvquCTdefy719391OvDK9YWvvenR279/OOjfEcKosAwkU73\nZw+cdPtDfIezsMb+SIM7js2JHcNEDiqgzV1Q0CcjdtzdSKHtT20cmJ12+QtSdaUZBr5jgSQnrMR9\n8tibTWT+5c7nvnPvjuf2PreRbM/+7TgRHX/9F4ep4pd/f27Hvd/564vfoeE/PX8AqTssyhVl6TUF\nlmmX/+UjIii6N6BPJolE1jANoM1dQJC4ix13PrV5YDYYFlAX2tzepSzUXCDehJW4G4o//bNfPnUh\n93lVkWk1c3/s/PBvneab7rnQQkSkKL76gQp674goF+tA4jEMPbClgoie3n/SGxB60b0RJ1OTSHVk\nf6qwSoNSFgqz3E0tLvkDMUozqArTdJ5AqGPUwXcsH6trHSOizViYCvEnrMRdk7360gvzuX/ufP3/\ne8lGn//UCu7/6jNOHSTSVG6osO1vxi9DWKSrKjKqrOZJp+/lD/r5juV8vIFQ+4idYZC4JwlulHvL\nkC0kpNKglHWOOVz+YFGaPs2g4jsWiF6NwIZCDs54OsYcerXikmIsTIW4U/AdwPwmP3rm7kf2XfrA\nczfla4icRJRh0C34XQ0NDfEIZnR0dGZGKG8QAtTV1cV3CAu7oVjePEiP72pfo5lVJvDj6pIunvZJ\nfzDM5psUXa3H4hqVcIji4lmOTL183BV648CHBeYlT4jDO8/5RXHxvN3tIqJiIxunXxbCkdwXT6bM\nTUR1TSfWaKaje4TYvvPs7HYTUXWmsqW5KYYPy6Pkvn6WaXR0NH5vIDU1NQt+jRATd+fxP//Tgy/l\n3vbjn9xSEfkjF01M2U99gX1ijIrSNGd942L+wlHo7e0tKiqKxyMnjTj95GNo3Tr624mDx4ft7f7U\nL15amLDnXdLFU3+oh2hyfUVOTU1VPIMSFuFfPMtxUWv9m8dGAsbcmhrrUr8X7zwLWurF82JnI5Ft\nW01pTU1BnEISiOS+eJSZtmePHup1yJbz7hHDd55fNnxARJ9dXxHFy1yYkvv6WaaGhgZ+f20Jq1WG\niIIDb99+36O5N/34f3dcOfepQpNdQ8N7GpyR/zvw1uu2issqz07cAc6FYSIz3Z/a1+0PhvkOZ34N\n/TiZmmy4NUyNQt3QLjXcydRanEwVuZXZJo1S3jvlmnbxv4bJ5Q8ePjHFMLQJE9whIQSWuM9+9K07\nfmyj3M9vSmv66KOPDh9u6pkkUlxxy500/Nx/vvzRrHPy7Ue+vY/o1s0r+I4VRGbrqqyVOaYRm/fP\nRwf5jmV+jQMzRJEZgpAcTq1h4jsQoAmHr3/abVAryjMxsE/cFHKGO0DSIIChkIe6JgOhcE1+Sqoe\nBycgEYTVKuPsO9pERDT8yIP3Rf7IfOfBN+41VN/7xAOD2x998ManiIhu+/HL12YLK3IQPhnDfHNz\n2f1/qH9ib/etF1qFtn5lyukfnPFolPKKbCPfsUDMrM0zMwy1jtj9wbBKIaxLTmq4lT01BSlyGSb2\nid4FBSkf9EzX989s4XuQC7cwlfcwQDqElf4aqu89ePDeef9V9S0/2rV10hkkhcZiMQgrbBCLa9dk\nl2causadf6kf+txF+XyHcwaum2JtnlmBrCKJ6NWK0gxD97izbdTOTYcEvmCCezKpKeDWMPF8gDLM\nsnvaucQdC1MhQcRUAdJY0tPT05G1Q9RkDMMtUn1ib3cwJKwJfeiTSVZcvt6MNUx8Q+KeTLiJkE0D\ns/zOWj02aJt0+nIt2hVZuFMKCSKmxB1g+T61NqckQz8w7f5b4xDfsZyBq7ivK0Dinmy4ZlysYeKX\nPxhuHrQxTKRSC2KXYVRbU7Ruf6hzjM81THMLUzOxMBUSBok7SItcxuzYPFd0F8xanDDLcok7Rsok\nH+4uSvMgKu58ahm2BULhFdkmgxr3bJMEt/6W326Z3W1jhD4ZSCwk7iA5N1bnFqXpeyZdbzQN8x1L\nRM+ky+ENphvUOWYt37FAjFXmmBRypnvc6fIH+Y5FuiJ9MgXok0ke3FjPev4Gy4zYvK3Ddp1Kvr4k\nja8YQIKQuIPkKGTM9s1lRPT4nm6B7KKPlNsLLLjfmnxUCllltinMsseH7At/NcQHGtyTT+R8ah9v\nFfe97eNEdEV5hhoDoyCBcLWBFN28Li8/VXdiwrmzZYTvWIhONbijTyZJVVkthDZ3/rAsEvcktCrH\npFbIeiZdM25+1jBF+mSwdwkSC4k7SJFCznxjUxkRPVbXHWb5L7o39iNxT2bV+WYiasJgGZ4Mzrgn\nHL40g6ogVcd3LBAzSrmM+0jMy2ZiTyD0bvckEW1G4g6JhcQdJOqztXl5KdrOMcfbLaP8RuINhNpG\n7AwTqctC8uH+y2J/Kl/myu2paEVLMjx2y7zbPekLhqutlgyjOvHPDlKGxB0kSimXfWNjGRE9tofn\novvxYXswzJZmGIwazLtITmWZBq1S3j/t5uuevsQd7UefTHKaGyzDw0fiPW3jRLQZC1Mh4ZC4g3Td\ncoE1x6xpH7Hvbh3jMQw0uCc9hYxZk2cmDIXkCRrckxVXcW9M+Bomlj01wR2DICHRkLiDdKkUsq9v\nLCOiR+u6eKy5nxopw1sEEH+RNUx8NONKnMsXbB9xKOTM2jwz37FAjGWZNLkWrcsX7Bp3JvJ5jw/b\nxuzebJNmVY4pkc8LQEjcQeI+d1F+plF9fNi+p32crxjmKu4oByazaqxh4knjwGyYZdfmmTGzLynx\nsoaJK7dvxsJU4APeyEDS1ArZfRtLiegxnoru0y7/wLRbo5SvyDby8PSQKJGK++CsAIYYScupk6l8\nBwJxUVtoIaKGxLa510UGQaJPBniAxB2k7p8vLkg3qJsGZw90TST+2bnfN2vzzAoZSjfJrDBVb9Yq\nJxy+UbuX71ikBQ3uyS1ScU/gYJlxh6950KZWyC4rw8JU4AESd5A6rVJ+71UlRPSr3Z2Jr4Y2DszQ\nXB8FJDGGiRTdMRQykcIsyzVR1OIMSZJanWtSKWQnJpyz7kBinnFPZGFqulYpT8wzApwOiTsAff6S\nwlS9qqF/9t0Tkwl+6sYBG2GkjDRwH8+a0OaeQN3jToc3aE3RZpk0fMcCcaGUy7hjxwnbTIw+GeAX\nEncA0qnkX72yhIge3Z3QTvcwy3K/bGqQuEtANbeGCYNlEgh9MlJQk8BuGV8wfKhrkjDBHfiDxB2A\niOiL6wstOuWHvdNHeqYS9qS9k267J5BuUOdatAl7UuBLpFVmyIbzqQmDk6lSwPVBJWYN0+ETU55A\naHWuKRv3cIAnSNwBiIj0asU9V5QQ0aN1XQl70oaBGSKqKbBgppgUZJk0WSaN3RPonXLxHYtU1GNn\nqgTUFqYQUePATAJ2YNe1jxH2LgGvkLgDRNx1eZFJqzx8YuqDnunEPCM3wZ3roAApmDufijb3RJh2\n+U9OuHQqzFpNctkmTY5Z4/AGu+O8hollaXdrZIJ7XJ8I4DyQuANEGNSKu68oJqLHElV0b+yfJaJ1\nmHchGdyHtISdopM4btZqdb4Fs1aTHjcUMt7T3DtG7SM2T4ZRjS28wCMk7gAf+/JlRQa14lD35NH4\nn3PyBcNtI3aGQcVdQqrzzUTUhPOpCXEUfTKSwXXLxHt/6u62cSLavDJThu5G4A8Sd4CPmbTKrySq\n6H582BYMs6UZBqNGEe/nAoFYm2chouPD9mAYB1TjDiNlpKOGO58a54IL1+C+ZSX6ZIBPSNwBzvCV\ny4v1KsX+zol4l0UjfTIYBCklFp2yME3nDYS6xhx8x5LkgiGWewnX5CNxT35rcs1Kuaxr3Gn3xGsN\n05TT3zgwq1LILi9Pj9NTACwGEneAM1h0yi9dXkREj+2Jb9G9YQCJuxRVWbGGKRFaR+zeQKgs02DR\nKfmOBeJOpZCtyTNRPA+Q7O0YZ1m6tCRNr8I9UuATEneAT7rnimKdSl7XNt4yFMfsqhGJuyRVc4Nl\n0OYeZ+iTkRpuDdPRvni9siILUzEIEviGxB3gk1L1qi+sLySix/Z0x+kppl3+gWm3WiFbmW2K01OA\nMFVhsExCIHGXmrnBMnFpc/cHwwc6JwkN7iAASNwB5vHVK0s0Svk7x0fbRuzxeHyu3L42z6yQYzqB\ntKzJM8sYpmPU4QuG+Y4lmSFxl5oLCi1E1DAwG481TEd6pl3+4MpsY14KtlwDz5C4A8wj3aD+/CUF\nRPR4fIrukT6ZAmQVkqNTySuyDMEw2zocl8+EQEQjNs+IzWPRKYvT9XzHAgmSbdJym4lPTsR+M/Ge\ndvTJgFAgcQeY371XlaoUsreOjXTEYQBIA0bKSBi6ZeKNa3SuLUjBvG3pYBiqLbBQHLplWDYywX0L\nFqaCACBxB5hfplF9x8UFRPRErIvuYZblkjYk7tLErWFqRuIeN/Xok5GkuTVMMX5ldU84B6bdqXoV\nluWBECBxBzin+zaWKuWyN5qHT0w4Y/iwvZNuuyeQZlDlWdAuKUWRivsAJkLGCxrcpYkbLBPzNUy7\n28aIaNPKTLkMN3CAf0jcAc4p26S5/eJ8lo1x0b1xbi8MbuNL08pso1IuOznpdHiDfMeShDyB0PFh\nm1zGVKE+KjHccf/OcYfTF8tX1h6uTwbzZEAYkLgDnM/XrypVyJm/NQ73TMbswFPjwAyhT0bClHLZ\nqlwTy9KxeC4KkKxjg7ZgmF2VY9Kp5HzHAgmlVshW55pZNlIciYkZt/9o34xCzlxZkRGrxwRYDiTu\nAOeTa9HedkF+mGWf2BuzovvcSBkk7tLFrWHC+dR4QJ+MlHHnU2PYLbOvYyLMsuuL0wxqLEwFQUDi\nDrCA+zeVKWTMXxuG+qbcy380XzDcOmInoqo88/IfDUSKO+WG/anxgMRdyubWMMXslVXXNk5EmzFP\nBgQDiTvAAqwp2s/UWkNh9r/3xaDofnzYFgyxpRkGk1a5/EcDkarK5yZColUmxlgWibukRRL3gZmY\nbGEKhtj9nVyDOya4g1AgcQdY2Dc2lcllzKtHBwdnPMt8qMZ+9MkAlaTr9SrF8KxnyunnO5ak0jvl\nmnH7s02aHDNGNklRrkWbYVTPugMxOZX0Ye+0wxssyzQUpumW/2gAMYHEHWBhhWm6m9flBWNRdJ8b\nKYPEXdLkMmYN2tzj4FS5HSObpIlhTnXLxKDNnRsEuRULU0FIkLgDLMo3NpXJGOZPHw2M2JZVdI+c\nTEXiLnnrrFjDFHvokwFuDdPRWCTue9rHiWgzBkGCkCBxB1iUkgz9jdU5wRD71L4TUT/ItMvfP+1W\nK2Qrs00xjA3EKNLmjjVMMYXEHbj7mcvfn9oz6eqZdJm1ylpcTiAkSNwBFmv75nKGof/5YGDU7o3u\nEbhy+5o8s0KOG/lSxw2WaRqcjckpOiAiuyfQOeZQK2SrcvHBWLqqrGaFjOkcdbiWt4bp1MJUBRam\ngpAgcQdYrPJMw/VrcwKh8LP7T0b3COiTgVPyLNpUvWra5R+eXe6JZ+A0DMwSUXW+RSnHrzbp0ijl\nq3JNYZZd5tSmOixMBUFKqoUCvb298XjYoaGheDxs0hgdHY3TT16APlupf6OZXnq/94ZSVapu4ZfP\nJy6ew50jRGTVBqTzEzs/SV08ZytPUx1x+Xc3dF1VMk+FGO8853f2xVPXNE5EZWaZlC8qjsQvnlKL\nvHmQ6hpP5sod837Bgu88Dl/ow95puYwp1ngkeDlJ/Po5v9nZ2fhdEkVFRQt+TVIl7ov5CwvtkZPA\nzMyMdH4+RUV03XHnzpbRnT2Bh64vW9y3FHH/wLLUOdlJRNtqK6wpmFVHJLGL52zry/1H+rtGfKpz\n/RCk/MNZ0NkXz8ndY0S0qaqoqAhjQCR98WycVb3WMt3rPOcPYcF3nr83DYfC7PqStDUrSuMRofBJ\n+fo5P4vFwu8PB/cTAZZmx+ZyInrp/b6lTuDunXLZPIFUvSrPgqwdiIiqrBaaa6CCZQqFWW5fJk6m\nQk2BhYga+qM/QMI1uG/BwlQQHiTuAEuzKte0bVWWJxD6zcGldbpzWUVNgQUTpoFTnW8momNDtjAO\nqC5b55jD5Q8Wp+tT9Sq+YwGe5afo0gyqaZe/dyqaNUzBMLuvY4IwwR0ECYk7wJJ9c0s5Ef3ucO+0\nawlF98aBGSJal49yIESkG9Q5Zq3LFzw5EYMtjxLHDYLE5D6gM9YwRXM7q75vxuYJFKfri9P1sQ4N\nYLmQuAMs2do88+aVmW5/6LlDPYv/LoyUgbNxRXfsT10+THCH03GJe31Ua5jqIn0yKLeDECFxB4gG\nV3T/7Xu9s+7AYr7eFwy3jtiJqNpqjm9kICrcNPfm5c2tA0LiDmeqLeDWMEWVuLdjECQIFxJ3gGis\ny7dcWZHh8gVfeHdRRffWYXswxJZmGExaZbxjAxGpspqJqAnnU5dnwuHrn3Yb1IryTAPfsYAgrLVa\n5DKmfcTh9oeW9I19U+7ucadRo7ioKDVOsQEsBxJ3gCg9sKWciJ5/t8fuWbjo3hBpcEefDJyBGyzT\nOmIPhMJ8xyJiXGG1tjBFhqPfQEREOpV8ZbYxzLLNS+xDq2sfI6KrKjKx3xqECYk7QJQuKEy5vCzd\n4Q3+9r3eBb+4sR8N7jAPo0ZRkqH3B8Mdo/NvioHFQJ8MnI07qVzft7RumT3cwlQMggShQuIOED2u\n6P7coR6nL3j+r4ycTC1A4g6fhDb35UPiDmeLDJZZSh+a0xd8v2dKxjAbV2TELS6AZUHiDhC9i4tT\nLylJs3kCL5636D7t8vdPu9UKWWX2PJvtQeK4bhkMlomaPxhuHrQxDO5owRm4NUxH+2YWvybhQOdE\nMMReUJiSosM2ABAoJO4Ay/KtLeVE9OuDPS7/OYvuXE62Js+Mpkk429xESFTco9QybAuEwiuyTQa1\ngu9YQEAKU/WpehVXN1nkt9ShTwYED4k7wLKsL0m7qCh1xu1/6f3+c30N1+BejXIgzGdVjkkhY7rG\nHJ7A0sZfACfSJ1OAPhk4w2lrmBbV5h4Ks3s7uMQdE9xBuJC4AywLw0Rmuj+z/8S5Ei+uybIGiTvM\nR6OUV2QbQ2H2+LCd71hECQ3ucC41S5nm3jQ4O+3y56fqyjIwVBSEC4k7wHJdUZZeU2CZdvlfPjJP\n0Z1lI1O60YAL58KdT8U09yiwLBJ3OKe5/amLemXtbhsnoq2VmZgpCkKGxB1guRiGHthSQURP7z/h\nPavo3jvlsnkCqXqVNUXHR3QgAljDFLXBGfeEw5dmUBWk4vUFn1SVb5YxTNuIfTF9aHvaxoho80r0\nyYCgIXEHiIGrKjKqrOYJh++PHw584l9xgyBrCiyo4sC5YCJk1ObK7al4fcHZ9CrFimxjKMweW+jF\nNTTjaR916FWK9SVYmAqChsQdIAZOdbo/te+EL3jGCkwucecyM4B5VWQZ1QoZd3OG71hE5mg/+mTg\nfOa6ZRZoc69rHyeiKyvSlXLkRSBouEABYmPLyqxVuaYxu/dPZxbduZEyNVi9BOemkDOrc82EovvS\nocEdzq+2kDufukAf2u62McI8GRADJO4AscEwkUWq/72v2z9XdPeH2OMjNkLFHRbCTXNvxhqmpXD5\ngu0jDoWcWZtn5jsWEKhIxf28a5hc/uDhE1MMQ5tWYII7CB0Sd4CY2bYqa2W2ccTm/XP9IPcn3ZPe\nYIgtydCbtEp+YwOBiwyWQcV9KRoHZsMsuzbPrFbgdxnMryhNn6JTTTp9Q7Oec33Nu12TgVB4Xb4l\nzYCFqSB0eLMDiBkZw+zYUk5ET+7tDoZYImoddxNRTT7u48MCuP1czRgssxSnTqbyHQgIF8MsPM19\nbhAk+mRABJC4A8TSdWuyyzMNQzOevzQMElH7uIcwwR0WoTBNZ9QoRu3ecYeP71hEAw3usBg1c90y\n8/7bMMvuaR8noi0r0ScDIoDEHSCWZAzDjZd5Yk93MMy2jXtorpgKcB4yhqnCGqalCLMsV0Otxclv\nOC/uCmk4x/nUY4O2Sacvx6xdkW1KbFwA0UDiDhBjn1qbU5Kh7592//bdniGbX6WQVeYY+Q4KRIBb\nw4TzqYs0aA86vEFrijbLpOE7FhC0dfkWhqHjw7azF+TR3CDIrauwMBXEAYk7QIzJZcyOzeVE9PCb\nbUS0JteMwcCwGDifuiTtk35Cnwwsgl6tWJFlDIbZY0PzvLjquEGQWJgKIoF8AiD2bqzOLUrTc/+8\nDvfxYXFOTYQ8z9w6OKVjKkA4mQqLww2FPLtbZsTmPT5s1yrll5am8REXwJIhcQeIPYWM2b65jPtn\nnEyFRco2adMN6ll3oH/azXcsItAxhYo7LFZt4fz7U/e2jxPRFeXpmCgKYoErFSAubl6XV5phWJut\nW1+CQg4sCsNgDdNiTbv8Q/agTiVfkY0DJLCwyETIs9YwYWEqiA4Sd4C4UMiZun+56vGbizONar5j\nAdGoQpv74nA9D+vyLQoZThTCworT9WatctzhG7F9vIbJEwi92z1JRJsxCBLEA4k7AIBQcOdTUXFf\n0NF+THCHJZAxzNlrmN7tnvQFw1VWM8orICJI3AEAhIKbCNkyZAuFcUD1fLAzFZZqbg3Tx5+K97SN\nE/pkQGwUfAcAAAARqXpVfqpuYNrdPeFEDZCIwiw77fKP2X1jdu+Y3Ttm943bvUOzniMnp2iucRlg\nMbjBMqcq7iwbmeCOhakgLkjcAQAEpNpqHph2Nw/MXpTOdygJwbI06/FzGTmXmo85vFymPm73Tjh8\nwXPcfLjUqjFrlQmOFsSLW8PUMmzzBcNEdHzYNmb3Zpk0q3PNfIcGsARI3AEABKTKanmjeaRp0HZR\nuoHvWGKDZcnpC46dlpeP272nF9EDofB5vt2sVWaZNFkmdaZJk2XSZBnVWSZNpkkdnuhN1N8AkoFR\noyjPNHaOOVqGbDL6uNyOhakgLkjcAQAEpNo6NxFynZgSd5c/OM5VzR2+sdPy8nG7b8zu9cy3av4U\nvVqRZVJnnZGXa7g/yTSqNUr5vN/VMNUbl78JJK/aAkvnmKOhf+YCQ2Rh6uZK9MmAyCBxBwAQkDV5\nZoah1hF7ICSs86m+YPi0vDySkZ/K0Z2+4Hm+V62QZZs1WSZNplHzcY4+l5rr1fhNBIlQW5jyxw8H\n6vtnS0rY5kGbWiG7vEwaHWmQRPB2CQAgIHq1oizD0DXuPDHtLS9N6FMHQuEJh+/jJhZHpNGc+xOb\nJ3Ce71XImSyTJuu0vDzztCK6UaNEQwLwbm6wzEyxRkNEl5ela89xPwdAsJC4AwAIS1W+pWvc2Tbm\nuTbWjxwMs1POSGo+7jij0XzM7p12+c/zvXIZk2FQn5GRc/9gVGeaNCk6FVJzELjSDL1Jqxy1e9/u\nDhHRVgyCBBFC4g4AICzVVsurRwfbJzwLf+lZwiw74wqM2b1jjk82mo/ZvZNOf5g9ZwcOw1CaXn16\nK0vmaUX0VL1KjjWlIGYyhlmXbznQOXFiJkBEmzAIEkQIiTsAgLBw51M7xudP3FmWbJ7A2bNZuP+d\ncHjPNT+Rk6pXZZ42m+VUXp5p0mQY1Ir/v707jmryvPcA/t36ilEiydpwxOwy5G6Ls5wttWXd6H1h\nKJuXVI11F5hjzHlhqzJxHLZBd4k9h1sbzyncM0+UXfSeG+sszSpwrjN4Gx21UrIrt21uMaupNV1F\nSxdjTWtiX2uUnOP9I4GBCrZ28ubV7+ev5M0T8uM9z5v3+z7vk/e9i9Gcbmf3f0Hb5z8L4F592hyN\nSu5yiD4xBnciouQyf06acNdnTp671PPGmfiFFN8bP+/8cmyy6yemzZgWz+VXT2uZpUqfNT1F4A2z\n6c4Vvw0TOE+GFEs5wV3yO9qeOXzqnH7e4spqc4ZyCici+kRShM/Oz0h7/S+Rn+zyXLfBzJS7xs0y\nj09rmZVI6vy9HdFE7stM3G13EefJkDIpJP9KvgbT2n4Yyiq+cqC9xfXHU52712fIXRQR0S3yr8tz\n/qVzIC11xlU3HopndF4/kejmpM2Y9nJj0Wveo1/7O94wlRRJGd/+Puev+2HY3G3P1aJ68byFq1p2\n9JU2FjC6E9Ht6f4vfG77P/393Llz5S6E6HYzO02Vob7rs7wKEimTIiY7Sq/u9WvMP87VAoCQvbjW\ngMMvD8pdFRERERHR1FFEcAeA1PS0kYeq+fmGyEt/CstZDhERERHRlFLGVBkA6eqZN2yzY8eOW/HR\n4XBYq9Xeir98ewgGgwMDA3JXkaTYeSbHzjMJdp7JsfNMgp1ncuw8k2P/mcTAwMAtSpsAKisrb9hG\nIcH9As6+f3702fmzZzD3nmuvv/px/uGbcPLkSc40ncTAwMCCBQvkriJJsfNMjp1nEuw8k2PnmQQ7\nz+TYeSbH/jOJHTt23KK0+TEpYqqMKmMBAi8OSImnQ887I4aH5vPGCURERER051BEcBfEkgoE7E84\nPGEptL/ll71A6aJ5cldFRERERDR1lDFVRm1c01r7bo2tblkbAJRZHcW8AxMRERER3UkUE3+NJRt7\nvh2SYhBUWq1aMWUTEREREf1NKCkBq7Q6zmsnIiIiojuTIua4ExERERHd6RjciYiIiIgUgMGdiIiI\niEgBGNyJiIiIiBSAwZ2IiIiISAEY3ImIiIiIFIDBnYiIiIhIARjciYiIiIgUgMGdiIiIiEgBGNyJ\niIiIiBTgM1euXJG7hr+N/Px8uUsgIiIiIroZbrf7sshrMQAADtNJREFUhm1un+BORERERHQb41QZ\nIiIiIiIFEOQuIPlJnv37T+LLS4uNKrlLSTJR7/7nul70XJqeJS5fac7NlLue5BIe7N/9u+dfD1zS\nf1X8/g/M2Wq5C0pKQe/BF33nchYtNWZw8xoV8x18/tgFpAAALl++/PkHivPYgcaQBvuf3rHn+Dlk\n5S5aubI4k31nRNjX98Kx91JSUkYWXEbq/IeLcrinHxUNep97pstz6tznsh4q+eF3+c0zXsx3sOO5\nA0cuQXvfPy7/blEO105c0Nf34rHhRY8UZcS3JcnvaHvm8Klz+nmLK6vNGVO7gXGqzGRiYd/mNWud\nAUBT0b1vjVbuepJJbP/jC629MJrL9KcOuLyRvFp7c4lB7qqSheRzmNa2QZ9XsSj9xXZnAHmtrmYj\no9dVwv3fW9YQAMpau9cbuXmNCrbklzqh0WtwAUAkMrdqW+vqHLmrShZhr2NZTRv0eWXi9I6OXqBw\n16GN2UymAAB/1+NVtgGNBgBSUxEIRICybvd6bl1xseD+haVWaIwVJV99vavdG9FYO39fMMWxK3nF\nDm56pMkV0ReaxRlvdrj8GrN1X32B3FXJLty3fYOl3QvoN7t256oByddgWtsPQ1nFVw60OyP6ss7d\n6zOmsqIrNJGLRytFUVzXvG3DErHs6Q/lLie5XDyyRBQ3vHA6/uyl5iXikm3n5C0pmbzaLIrilsQK\nOfvSElHc8ipXz1U+7FwnipXW+iXitiNcOWN8eKRMFDtPDMtdR3I6u2WJKNZ3Jr6QT78giuw/Exg+\nuk4U6/eekLuOJHL06UpRtL6TeHZigyiWbTsia0VJZPi0SxRFqyvRYc4e3iKKYueJi/JWJbuj25aI\nYlmztVIUK498eOXKlStHn60Uxcr4Ln34xF5RFK0vnZ7KkjjHfWJCmqm6qae1fuH82bggdzHJRshq\nsjav+4fEQeY96anylpNs7qvu7nZVjxvlmiZXLUkq2LfF5oX1qZ8+mIrLcheTXAQAhrmzpCGfz+sb\nDMfkriephN44EEH1j4uFoN/j8fiGH3C73Wt4uuZ6vO2/9qKw+uFsuQtJItPUM4CLiU0qNnwJyMq6\nW96Skkf0zClAv1hMdBjd1xcZAP+Js/JWJbtpWas3d++u+34RIAEApFf3+jXmH+dqAUDIXlxrwOGX\nB6eyJJ4hmpiQWVKeCWD4siR3KclH0OYW5CUeB/uesAcMVd/kznOUoNZqEfU4O45+8L7L3hExVv+Q\n2WKsqNdicRmqtxXoVNt5VDxe9J23AvDXrVg2ssBgdfymgPO4AQDSX05EgINPrWzzR+JL9CbLbxuL\nuXauJnk22f159Rs4iWgsw9JfmGyrVi2tKnxIHzjc69eYdxXy11kJQto9QKD/9VBung5A9LjXD0h/\n/gBFd/QqMhSXAJD+Mm58KTU9beShan6+IdL1p3B93pTt4zniTp+O5GsotQT0FU+tNspdSrKJBY4e\ndnuOBACcPHYsGJW7niTS92uLH2ZreQ6A6QBHEMaKDUsAyiz2Q253z55Wk8Zvafov9p4EAQD8Z75l\n7z7kdh+yW8oCLuvWvqDcZSUd77MtAQ63X0N65/jbAIAZ8eeRN4+/w4G5BFV2cbURHQ0rHt+607F9\n03fWtgFQT5e7rKSUrp4p46czuNOnEBtsWbm2Hyb7b9fo5K4l+ajNja32Vru7Z5c50tuw4xW560kW\nsSGnxRUxVi/E0NDQ0PGzwHun3g6GuPtMUOesdrvd64sNAqDSGR9tMMF/8DhXDwBAmKkGUNb0E4NW\nAARD8Q8rNHjJ+67cdSWZsGdTeyCvvpLD7eNJXY9b/Xn1rn32xsaN9n3dtUa/9XEnt60R6vJWl7XK\n5P9j195XLtY3N5s1kC7JXVQSuoCz758ffXb+7BnMvWcqT/oxuNNNC21fs8oZKbT3NBp4onocybmp\npmX/yKQ3Vfb9hcDhY2E5S0oi0Q9OA/C21ZWWl5eX1zgj6G2pKV3xLHefcWHPzu9VbR8dQ7740UVg\nhsAEBgBQzfmyHngvMnoGIvp+BKkp/AXJOJ5nONw+sXT9yPW9tF/L1ePCh/wVSUJ0yLnTqXmkcffu\nfbvtG83zh50RmMQvyl1WslFlLEDgxYGRHdbQ886I4aH5DO5JiEedVwl3Naxu96Ow9uHh4x6Px9Pv\n8fFXdCPUWnid1iednsGwFPYd3N7UC0O5yEnuceqcKpfL1dPT09PTc+hQZ4UG5uZOt3sNr5YZp559\nd8Df/uTWg8GwNOR1Nll7Ubh0Ho+N41Q560ya3iaL0zMYCg06WywuoHThPLnLSiah/qaOQKHlUQ63\nX0O4Owtw7nR6B8Ph8KCn6yl7AAu+xG+eBBVetbfVVGzyDIWCg/0NyyyAuTiHqyduNAQKYkkFAvYn\nHJ6wFNrf8steoHTRlH4Fccu+sWkpaqTOkruKJCOdcvVHAPTaGnoTi0YucUpAwc/sFeGft9StagEA\nGMz1T5XzOtwjBEGtHu0o6umAaib7zV8JmQ+31h6vsTWVdgAAjBW7LMX8ph4hFDS0VYWrW+pWxZ+X\nNe0q4Sm/MXz7/jMC06PfvqN/UDgBlfmJXad/ub6lJvHNrMmr2PVYETeuEZmP2S3nqqx15S4A0BS2\n7qyb0suTJzFh5pzRx2rjmtbad2tsdcvaAKDM6iie2lsB8AZMRLdKNBwKR2MqtU6r5q6BPqGoFJKi\nEFQ6LY9qrkMKh2OICWodty36pKLh+E9q1DotD/muFQuHwjEIWp2W29YkouGQFIOg0k79/p3BnYiI\niIhIATjHnYiIiIhIARjciYiIiIgUgMGdiIiIiEgBGNyJiIiIiBSAwZ2IiIiISAEY3ImIiIiIFIDB\nnYiIiIhIARjciYiIiIgUgMGdiIiIiEgBGNyJiIiIiBSAwZ2IiIiISAEY3ImIiIiIFIDBnYhIsWKh\ng46tDQ01DQ2Pb+/qC0Zv+QdKPkd+fo1Xut5rYf/+rq4+f/i6bxzs63J6grGgt8uxf2h8nf4+p7Nv\nMBbydnX1Xf/NREQEgMGdiEipwp6GhSua2jo+0malTz/XbrOUfqeqLxSbsH3UV5Wfv9133dD98V0G\nzg5fszTocSxdVmW12ZoP/Pna98SGnKsstpTZWkH1ka3N+h//MzTmNf8WS8uzJ84Lutl+m+UXDv+n\nK4+I6HbG4E5EpETRrg11/TBaO3taG+vrN7a692w2wm9pfmEkuUeHBv0+n28onBjflk4HJeB8MBAO\nj2Z3adDv8/n8oQmH6hMNgtLY4wH1NESDfp/PNxhfHPXtLK1ru7eq1qxHasq0a0t9YWsL8pqKM1XQ\nft2Sh16He7SC8JEDXmjWLTUCGY9aTf62Lf5bf96AiEihBLkLICKiTy58tNOLwqYNBRmqxBJd7obN\n9ftP6mKAEPY2LKvpH2lrbnLUf+PMylVNEcDZVOVEWbd7vTbkaVhRN9JGU7utvSRHO+4jxjWAuWlX\nfVE2AMBfY/rOyGLTrp7G9GmZtVZHScEch8vmubbU6PGn+1G2+QEAgPDNsgrUOV4LlRfoAMT+d28H\nDNUP6gBAd/9iPeoOHZcMRvWnX0NERLcfjrgTESmPdOr/AtAv/0bGyIJYLBbLyDWvLslVAYN9Xf16\n87buQ273oeYKvbPp92F17u/3NGuAss2dbne1FpLjsbp+Q8Uul9vt7q43wbb234fGf4Ljsbp+jal1\nT4/b7Woya5xNv/KNj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RuHsRjnemEhJ3AIChdOhsvb7DmhITlBY3FCc6Y6ID/zhfTUTv7D/33cXmIXhG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\n0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       "text/plain": [
-       ""
+       ""
       ]
      },
-     "execution_count": 10,
+     "execution_count": 11,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -386,14 +433,17 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 11,
+   "execution_count": 12,
    "metadata": {},
    "outputs": [
     {
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "Started at 2017-06-28 13:40:29\n",
+      "DEBUG\n",
+      "['dmm_voltage', 'dac_ch1_set'] dmm_voltage\n",
+      "Considering the units V, V\n",
+      "Started at 2017-06-30 14:24:30\n",
       "Printing the number 1\n",
       "Printing the number 2\n",
       "Printing the number 3\n",
@@ -405,21 +455,21 @@
       "Printing the number 9\n",
       "Printing the number 10\n",
       "DataSet:\n",
-      "   location = '/Users/william/sourcecodes/qdev-dk/docs/examples/PlotTesting/plottestsample/025'\n",
+      "   location = '/Users/william/sourcecodes/qdev-dk/docs/examples/PlotTesting/plottestsample/090'\n",
       "      |   |  | \n",
       "   Setpoint | dac_ch1_set | ch1          | (10,)\n",
       "   Measured | dmm_voltage | voltage      | (10,)\n",
-      "Finished at 2017-06-28 13:40:30\n"
+      "Finished at 2017-06-30 14:24:31\n"
      ]
     },
     {
      "data": {
-      "image/png": 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AtyuVz+9JYRha+6fwUWEebMchwv5UAICH++FGWWVDU2gPx+HBVj0FsnW2Qv7G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\nS98YgJBvs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AZKa7NziKOwAAgIOq/uilGoU391wwsbe7i3bFgax/LE+2UN0bGMUdAADAQSXWdPRSjbq0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       "text/plain": [
-       ""
+       ""
       ]
      },
-     "execution_count": 11,
+     "execution_count": 12,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -441,31 +491,37 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 12,
+   "execution_count": 13,
    "metadata": {},
    "outputs": [
     {
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "Started at 2017-06-28 13:40:32\n",
+      "DEBUG\n",
+      "['dmm_voltage', 'dmm_current', 'dac_ch1_set'] dmm_voltage\n",
+      "Considering the units V, V\n",
+      "DEBUG\n",
+      "['dmm_voltage', 'dmm_current', 'dac_ch1_set'] dmm_current\n",
+      "Considering the units A, A\n",
+      "Started at 2017-06-30 14:24:33\n",
       "DataSet:\n",
-      "   location = '/Users/william/sourcecodes/qdev-dk/docs/examples/PlotTesting/plottestsample/026'\n",
+      "   location = '/Users/william/sourcecodes/qdev-dk/docs/examples/PlotTesting/plottestsample/091'\n",
       "      |   |  | \n",
       "   Setpoint | dac_ch1_set | ch1          | (100,)\n",
       "   Measured | dmm_voltage | voltage      | (100,)\n",
       "   Measured | dmm_current | current      | (100,)\n",
-      "Finished at 2017-06-28 13:40:39\n"
+      "Finished at 2017-06-30 14:24:40\n"
      ]
     },
     {
      "data": {
-      "image/png": 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3QI3KxO9TpjucmtNsxz3FmLtluY57kj3usDPuTh1keKuMH0/foh2ViWOxNi1H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cUnEP70Cktw4LkOP5EqTKAKJVhsZBNmyVoeWGdZdYJkgVbD6o7guo1OrguCBR3Imr21HluCvi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\nAABJRU5ErkJggg==\n",
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V5LfuBNQPTHSSwgAA1MjjThAEkTtIuCdj2fbo5783+vnv9baBgVV5ZY87j4Ok6m/b6e0r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\n",
       "text/plain": [
-       ""
+       ""
       ]
      },
-     "execution_count": 12,
+     "execution_count": 13,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -478,6 +534,46 @@
     "plot"
    ]
   },
+  {
+   "cell_type": "code",
+   "execution_count": 14,
+   "metadata": {
+    "scrolled": false
+   },
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "DEBUG\n",
+      "['dmm_voltage', 'dmm_current', 'dac_ch1_set'] dmm_voltage\n",
+      "Considering the units V, V\n",
+      "DEBUG\n",
+      "['dmm_voltage', 'dmm_current', 'dac_ch1_set'] dmm_current\n",
+      "Considering the units electrons/second, electrons/second\n",
+      "Started at 2017-06-30 14:24:43\n",
+      "DataSet:\n",
+      "   location = '/Users/william/sourcecodes/qdev-dk/docs/examples/PlotTesting/plottestsample/092'\n",
+      "      |   |  | \n",
+      "   Setpoint | dac_ch1_set | ch1          | (100,)\n",
+      "   Measured | dmm_voltage | voltage      | (100,)\n",
+      "   Measured | dmm_current | current      | (100,)\n",
+      "Finished at 2017-06-30 14:24:52\n"
+     ]
+    }
+   ],
+   "source": [
+    "dmm.current.unit = 'electrons/second'\n",
+    "\n",
+    "# Expected:\n",
+    "#    Left plot. X: Gate ch1 (V), Y: Gate voltage (V)\n",
+    "#    Right plot. X: Gate ch1 (V), Y: Gate current (electrons/second)\n",
+    "plot, data = do1d(dac.ch1, 1, 10, 100, 0.01, dmm.voltage, dmm.current)\n",
+    "plot\n",
+    "\n",
+    "dmm.current.unit = 'A'"
+   ]
+  },
   {
    "cell_type": "markdown",
    "metadata": {},
@@ -494,30 +590,33 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 13,
+   "execution_count": 15,
    "metadata": {},
    "outputs": [
     {
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "Started at 2017-06-28 13:40:42\n",
+      "DEBUG\n",
+      "['this_setpoint_set', 'dmm_myarray', 'dac_ch1_set'] dmm_myarray\n",
+      "Considering the units this unit, this unit\n",
+      "Started at 2017-06-30 14:24:54\n",
       "DataSet:\n",
-      "   location = '/Users/william/sourcecodes/qdev-dk/docs/examples/PlotTesting/plottestsample/027'\n",
+      "   location = '/Users/william/sourcecodes/qdev-dk/docs/examples/PlotTesting/plottestsample/093'\n",
       "      |   |  | \n",
       "   Setpoint | dac_ch1_set | ch1          | (50,)\n",
       "   Measured | dmm_myarray | myarray      | (50, 25)\n",
-      "Finished at 2017-06-28 13:40:46\n"
+      "Finished at 2017-06-30 14:24:59\n"
      ]
     },
     {
      "data": {
-      "image/png": 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Mnb+1+4zFw5uW81Nh06alZR8cPHiq/nVJ8dH28gYwOfPmVAp3AAAA1LasAx/F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HoHAHAAAAPACFOwAAAOABOA4SAAAAcH/cnAoAAID6I/fY9g/e3/xzZnHMDb0eGDssLtjV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\nPGdGsh9Nt7dITEz0t1VlKNwBAACgiK2k4D2TVcJEpL7eOGxiWkp/ERG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       "text/plain": [
-       ""
+       ""
       ]
      },
-     "execution_count": 13,
+     "execution_count": 15,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -534,30 +633,33 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 14,
+   "execution_count": 16,
    "metadata": {},
    "outputs": [
     {
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "Started at 2017-06-28 13:40:48\n",
+      "DEBUG\n",
+      "['this_setpoint_set', 'dmm_myarray', 'dac_ch1_set'] dmm_myarray\n",
+      "Considering the units Hz, Hz\n",
+      "Started at 2017-06-30 14:25:01\n",
       "DataSet:\n",
-      "   location = '/Users/william/sourcecodes/qdev-dk/docs/examples/PlotTesting/plottestsample/028'\n",
+      "   location = '/Users/william/sourcecodes/qdev-dk/docs/examples/PlotTesting/plottestsample/094'\n",
       "      |   |  | \n",
       "   Setpoint | dac_ch1_set | ch1          | (50,)\n",
       "   Measured | dmm_myarray | myarray      | (50, 25)\n",
-      "Finished at 2017-06-28 13:40:53\n"
+      "Finished at 2017-06-30 14:25:06\n"
      ]
     },
     {
      "data": {
-      "image/png": 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piA2crD5z1Q71mSJya99LdMSGxP+mI9acoiNV8h7WMimp7KiOVMkxqR+WFPq8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\nbNhg9BAA+ATeDWCggoICo4fQk1G464NvUwAAAChFqwwAAADgByjc/Zb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+++6Lip926aJzNVudUbgDAACgCQke/ujLw0XE8vPc5HEzX0tOe7RfpWe4TaVeiSuPg/R2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\nyn35kKT++gHGSIScqPhbRaSu4Vu7VIt05VM25NRZJOH7o+orgqix358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zmd1YezhDJPtw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\nRU5ErkJggg==\n",
       "text/plain": [
-       ""
+       ""
       ]
      },
-     "execution_count": 14,
+     "execution_count": 16,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -585,30 +687,33 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 15,
+   "execution_count": 17,
    "metadata": {},
    "outputs": [
     {
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "Started at 2017-06-28 13:40:55\n",
+      "DEBUG\n",
+      "['this_setpoint_set', 'dmm_myarray', 'dac_ch1_set'] dmm_myarray\n",
+      "Considering the units this unit, this unit\n",
+      "Started at 2017-06-30 14:25:08\n",
       "DataSet:\n",
-      "   location = '/Users/william/sourcecodes/qdev-dk/docs/examples/PlotTesting/plottestsample/029'\n",
+      "   location = '/Users/william/sourcecodes/qdev-dk/docs/examples/PlotTesting/plottestsample/095'\n",
       "      |   |  | \n",
       "   Setpoint | dac_ch1_set | ch1          | (3,)\n",
       "   Measured | dmm_myarray | myarray      | (3, 25)\n",
-      "Finished at 2017-06-28 13:41:11\n"
+      "Finished at 2017-06-30 14:25:24\n"
      ]
     },
     {
      "data": {
-      "image/png": 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v36lTJxVLqikiwiI920jpPwDqi/h2DO5m7m2UjyW4AwAANF9nz54dMGCAr69v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\nWMxBj2zZd0IIc/OVsvSq3/zsjxXo6ACUGtvEksrKv53as9I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4kJgQI2Jo3/BLc+aazWJc9PHKJx5d9HzGyhQ5mrbvpE26BwAAANxIzeDuQa0y7bvNXr4j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\nReLal/70xd5lz9lV2Aakrlk2XQKMm7ckFQBMgCxICiCw+33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       "text/plain": [
-       ""
+       ""
       ]
      },
-     "execution_count": 15,
+     "execution_count": 17,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -623,6 +728,143 @@
     "plot"
    ]
   },
+  {
+   "cell_type": "code",
+   "execution_count": 18,
+   "metadata": {
+    "scrolled": false
+   },
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "DEBUG\n",
+      "['dmm_voltage', 'this_setpoint_set', 'dmm_myarray', 'dac_ch1_set'] dmm_voltage\n",
+      "Considering the units V, V\n",
+      "DEBUG\n",
+      "['dmm_voltage', 'this_setpoint_set', 'dmm_myarray', 'dac_ch1_set'] dmm_myarray\n",
+      "Considering the units this unit, this unit\n",
+      "Started at 2017-06-30 14:25:26\n",
+      "DataSet:\n",
+      "   location = '/Users/william/sourcecodes/qdev-dk/docs/examples/PlotTesting/plottestsample/096'\n",
+      "      |   |  | \n",
+      "   Setpoint | dac_ch1_set | ch1          | (50,)\n",
+      "   Measured | dmm_voltage | voltage      | (50,)\n",
+      "   Measured | dmm_myarray | myarray      | (50, 25)\n",
+      "Finished at 2017-06-30 14:25:32\n"
+     ]
+    },
+    {
+     "data": {
+      "image/png": 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CAAAAbOKkkBiJuzGxZMU9nqugnk9zqpkRuA5L3JmxoKGWuuDE5ImXU1KmKCUI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wp3rVUm7k5CUOXP0bXKWLr0StbjHl9xRxdFre1xl5tT7bw1hEN2dnavXr0OHz7ctWvXtJ0UkySm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zZ45UKt23b5/ZbN69e/dEm0/gbCwhM99QT5cBYdERFIgTMtsZDZ29WLBENut2g1m7cPKcWb9+\nfV1d3bOexbdrYGBAr9fv2rVr69atOTk5vjShdJDkucJEREc/6zkQQmYCVrZkdoaus3bhhPid4ODg\n4ODg6upqvV7vYxN6xZ0QQgghhJBnZmRkJDDQLd2sJxS4E0IIIYQQ4gfocCohhBBCCCF+gAJ3Qggh\nhBBC/AAF7oQQQgghhPgBCtwJIYQQQgjxAxS4E0IIIYQQ4gcocCeEEEIIIcQPUOBOCCGEEEKIH6DA\nnRBCCCGEED9AgTshhBBCCCF+gAJ3QgghhBBC/MD/Abag6fgtHbRLAAAAAElFTkSuQmCC\n",
+      "text/plain": [
+       ""
+      ]
+     },
+     "execution_count": 18,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "# Exceptional Units, small set range and additional line plot\n",
+    "# Expected labels: \n",
+    "#     Line Plot:\n",
+    "#         X: Gate ch1 (μV), Y: Gate voltage (V)\n",
+    "#     Heat Map:\n",
+    "#         X: This setpoint (this setpointunit), Y: Gate ch1 (μV), Z: this label (this unit)\n",
+    "\n",
+    "plot, data = do1d(dac.ch1, 0, 10e-7, 50, 0.01, dmm.voltage, dmm.myarray)\n",
+    "\n",
+    "plot"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 19,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "DEBUG\n",
+      "['dmm_voltage', 'this_setpoint_set', 'dmm_myarray', 'dac_ch1_set'] dmm_voltage\n",
+      "Considering the units V, V\n",
+      "DEBUG\n",
+      "['dmm_voltage', 'this_setpoint_set', 'dmm_myarray', 'dac_ch1_set'] dmm_myarray\n",
+      "Considering the units V, V\n",
+      "Started at 2017-06-30 14:25:36\n",
+      "DataSet:\n",
+      "   location = '/Users/william/sourcecodes/qdev-dk/docs/examples/PlotTesting/plottestsample/097'\n",
+      "      |   |  | \n",
+      "   Setpoint | dac_ch1_set | ch1          | (50,)\n",
+      "   Measured | dmm_voltage | voltage      | (50,)\n",
+      "   Measured | dmm_myarray | myarray      | (50, 25)\n",
+      "Finished at 2017-06-30 14:25:42\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "/Users/william/.pyenv/versions/3.5.3/envs/qcodes-qdev/lib/python3.5/site-packages/matplotlib/pyplot.py:524: RuntimeWarning: More than 20 figures have been opened. Figures created through the pyplot interface (`matplotlib.pyplot.figure`) are retained until explicitly closed and may consume too much memory. (To control this warning, see the rcParam `figure.max_open_warning`).\n",
+      "  max_open_warning, RuntimeWarning)\n"
+     ]
+    },
+    {
+     "data": {
+      "image/png": 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ZCjLFYfx+2ZX/+7MA2170owCLJ/5HduXnXwryVybcILkwcVuA/ImaMwMc4fAH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\nF+7C9qLqhsz7EmbF3S0O0ku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lTKwihBBSgVhV9i63ijvjICse1RpzU1wvYw7jWVmrjG2Z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+      "text/plain": [
+       ""
+      ]
+     },
+     "execution_count": 19,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "# Exceptional Units, small set range and additional line plot\n",
+    "# Expected labels: \n",
+    "#     Line Plot:\n",
+    "#         X: Gate ch1 (μV), Y: Gate voltage (V)\n",
+    "#     Heat Map:\n",
+    "#         X: This setpoint (this setpointunit), Y: Gate ch1 (μV), Z: Gate voltage (GV)\n",
+    "\n",
+    "dmm.myarray.label = 'Gate voltage'\n",
+    "dmm.myarray.get_multiplier = 1e9\n",
+    "dmm.myarray.unit = 'V'\n",
+    "plot, data = do1d(dac.ch1, 0, 10e-7, 50, 0.01, dmm.voltage, dmm.myarray)\n",
+    "dmm.myarray.label = 'this label'\n",
+    "dmm.myarray.get_multiplier = 1\n",
+    "dmm.myarray.unit = 'this unit'\n",
+    "\n",
+    "\n",
+    "plot"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 20,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "'this label'"
+      ]
+     },
+     "execution_count": 20,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "dmm.myarray.label"
+   ]
+  },
   {
    "cell_type": "markdown",
    "metadata": {},
@@ -632,31 +874,42 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 16,
+   "execution_count": 21,
    "metadata": {},
    "outputs": [
     {
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "Started at 2017-06-28 13:41:12\n",
+      "DEBUG\n",
+      "['dmm_voltage', 'dac_ch2_set', 'dac_ch1_set'] dmm_voltage\n",
+      "Considering the units V, V\n",
+      "Started at 2017-06-30 14:25:45\n",
       "DataSet:\n",
-      "   location = '/Users/william/sourcecodes/qdev-dk/docs/examples/PlotTesting/plottestsample/030'\n",
+      "   location = '/Users/william/sourcecodes/qdev-dk/docs/examples/PlotTesting/plottestsample/098'\n",
       "      |   |  | \n",
       "   Setpoint | dac_ch1_set | ch1          | (100,)\n",
       "   Setpoint | dac_ch2_set | ch2          | (100, 55)\n",
       "   Measured | dmm_voltage | voltage      | (100, 55)\n",
-      "Finished at 2017-06-28 13:41:53\n"
+      "Finished at 2017-06-30 14:26:26\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "/Users/william/.pyenv/versions/3.5.3/envs/qcodes-qdev/lib/python3.5/site-packages/matplotlib/pyplot.py:524: RuntimeWarning: More than 20 figures have been opened. Figures created through the pyplot interface (`matplotlib.pyplot.figure`) are retained until explicitly closed and may consume too much memory. (To control this warning, see the rcParam `figure.max_open_warning`).\n",
+      "  max_open_warning, RuntimeWarning)\n"
      ]
     },
     {
      "data": {
-      "image/png": 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TVbiLiIiIiDhISRW/rUv4GsAkIiIiIlL5qTm1dDLj1q7fU/3Gzu3DfBz9UERERESk6ilH\nu6oKd1rKjmWPPzMnC/DuG9o+7CZHPxwRERERcbAyVOEldaMWo+bUsjPvW9znmYURA0cGrJmxw8XZ\n0Q9HRERERByP7Em9uL7v1YtaWc2pZWdxrj1y0rLe7YOXrZmxw9EPRkRERESqkDLcc8bWtXwV7hyP\nsE69wwDk5l+b/Y4PLvaBx7oCx7cUj+V+yyx2MuYYE/P7fjsTQ3wHJpXeL++qGQ8g9Ti1Z9CPvlQO\nOPN1BhNze+RZJpb33zeZ2Impf//aB4gvIdaXWQtYdz8V++UTKtY8hIr5zrrS79YCCn45/2vnzmuo\nFc/lUrGMxVTMbziTyh0bzcS+mgEAcVeL9Y1rw6yWN3sXE/MY+w4TA1CwcSgTy7jsS1IdOHFZ7KMk\natN+7anYvRupWOxRbrmzh68aaW1C3phkajXAfeLvTCx3dH0mdsMMatM47iUWuOm+C78ONQAlvCvP\nC/2UWW3wqXepXQuob78p6BVqNeBMyS+KVh6w/vX9DA+jXhR7uM+icNdjTOwU9SaB+w5SMe7HIcxJ\n1I8S5Cdw68HqGkat9+XrTOzUU8U/Eg0kDxtY7IPBZx1ZC0opqXC3k3ff5d5GOY91teNiIiIiIrbZ\nt4AB8Nhj1N+15HJXPy5v1e0g7cTOT9PLL66LiIiI2Jvq7PIox/1hbCjWtKrmVBERERER+yh2cr2c\ndXyxplU1p9rH1c9ul59rYya1q/Z8JtZmD3UbnOfcb2Zir1MnMHHDr1TMnPEBEyvc+Qi1HOD27Bkm\nNs/JjYn17UZtGjiWim0f8zMTS7/jdibWkfu2xt9YwMTAHQ8F8N8k6hz5aO6kvs/H3HvQ/hpMymN4\nOyb2wP89x8RO3t2HiZ3LYlLweCmdygGnine42BZ0kDo0H/M8dWL+HPfoalIpIPQbKpazmkm5j+NO\nkQOo5smk/LnD6+Z9VGtN2v1UX01aR+qt875WTApIuuzwsk2uLZmUOXEQtyviuR86e7gvXc6L1IVe\nzwmvMrGAzfWY2HZf6qcJ1S0BzAyh+qAeJr+twA7uR2fUWeoB+rlQ7RxiR2XoQL0CNafaR5BnVX3k\nIiIiIlJlqXAvHY+Y5bExjn4QIiIiIvKPY1XhLiIiIiJSbvbtVbVFd5URERERESk9O1bqxW4pA91V\npirxe5JJ+eJ5JnYiimpPfJkbmdLwvqtnABygUujO9Ql9sohbDsiOorpOV3GrJVC9c3gujYq5D17M\nxL7jmpP6mqlvqyfXFOW/hZtKAkxO/T8qV3MSkzIYDEzMyg30OTWAmg9UYyn1fSVHklmzqVFYaXey\nM26CdlMv/9yJVNephWuyy+ImK80he+xSqLem5Hqzmdjd9Ny7Pb93YWJnyeVuWMykDKZ7mdiD3Cts\nfS7Vwp7Zn2ph95nZlomZalHfCAB3cLHw5lTD7vR0qsGafapz4/yo+UaA31wqNoRbzbU7N6cJiMqm\nfjqdmUl1nU4cRW06OYaKiU127UYt/ncA3VVGRERERKTSufzvADYu5+uMu4iIiIhIVaDCXURERESk\nYti1Y1WFu4iIiIgIrWy1+OXtp1em5tQqw3qC6v8LjWtDLZe5i0m53UItRs46RTUPJrXqzE4mlh41\ngdoU8FtCxd7n1utOtR1i8odUS1ze29TQwQe4kagHuJGoC5gQ4GpqwgUxOYFqTza4Ur1TZq4DLG8x\n1Tu5jeuwjHKinpyZD1CzhH3mUFMn72O7f/Hdj1SnYDQ3/nM5tyk3/BfzyRHG3g8zqeDEYCb2bUO2\nr9dqOcnETk/jljvxGpM6zHWTr/+jPxPz8aC6TjPN1PPpB+51bT7xBhMDAG/q5T+Te/n38ae6Tlda\nqOnEE4zeTGwP9waL5tlMKuvf1O0QXHtSs58BfN7kMBMjm2JT87mOXSm9svaklq7ct9WcqttBioiI\niIhUsNKW+2pOFRERERGpolS4i4iIiIg4SGlOzKtwr5S2BL/OxCIKuME/v3dkUuYsamIOTC2pWN4G\nKhZEneb33xpDrQbA/BuTKthHzUxZO5ra87n665jYKE9qNffXqLOVTc9QRyGnp46ndnXyo2KA+TPq\npD45bsT1vplMbF/ACCYWxR3VzRxGjQfymUUd501uQH1BfoxlUgBwayQVM3GrecRQsVnpVKxgHxVz\nS5tM5XxjmFRwDrUYAKuRehq356bSbB/5MROLSKCenORbYmYa9YqAa2Mm1aIetVjhHuoVAaB1Zyq5\nmFvtqeQXmNg87vD6SKqxAsk3Ud1B7g9Th9ctR7lNa7N9Gj25IWLdf6d+6CDvRyrmw33tpGTlvG9M\nSU2rak4VEREREbGnkg6vkwV9r162P67JqSIiIiIi10JZbz5znq3mVN1VRkRERESkCtAVdxERERGR\nKkCFe6UUsc+XiVnj72ZiBq6ddJ431a95/61MCoWJVCzw26eYWEpEHrUcUIPr7Ar4mmsA4rpOV3Dz\nXArJURipL1MxbsQVsldRMX/qGwHA9OhKJrZ7RB9uOeqfEdtwM1PS2lOdc+QspB+f/pSJJeRTq5m5\nllMA27nZOkiMYVJnv9/CxJxuoPb0v4eKZb4Qx8Qe96GeJNb91JwmAKhFDfTavpfrsD81j0mduI16\nngSupz6Lx7k+7P8to2IuEUwKTp34UsDAhLjGSTzI3YYhjhvAdCM5gCnrCyaGc7lMquAHaiaZc7NG\n1KZAH+6Hzm5utZmgHl6UVc2p9lHOFlWOCncRERERkYv8+usPF/9nq1ZXuUGffav2ovvM6K4yIiIi\nIiJXcdVKvZhy9qFeZi9s3lVGk1NFRERERCqPonrd1lV8Fe6V0okuGUzM1JWKuffbxcQGn3qXiaH6\nbUwqb1wTJnbqCerwegY9gaXmbdRfeXNnUOcIX+I2dXv2DJU7vZlJrfW8i4nVpbZEY27uz8l73+TW\nQ3Xu7Pr69HeY2C7uk23z55NMzL3fbCYW+0A7JubTiDod3psJAQsO0RdjcjdQseptmZRr/8VMbLmJ\nesGSc7XId4kFZqoB42busQHY9Gl9JpY1gVrN/6uTTGxgErXamWbJTOxD7nOtzZ1J7k6lMKsddXId\nwFQu1pHqNcAw6h0RJ2/nDq/vdaeWOx7DpKY0p368juHmJRWS85KAldyBfis3V/HfYdRP/ygmJJWF\nbgcpIiIiImInFdmlqivuIiIiIiIcu9TlRe2nV6bmVBERERGRsrNTH+rVq381p4qIiIiIOBhT/as5\ntcog+/883kijcjlUB1ifGo8xMaoTB3ic64k0NqBiHTZyuwKphVQ/2W8zqNWGcJvu3unGxBJuoVaj\n+iuBVdxUGgPXEscNpAGAulx7WpepQ5nYiOw1TKxgfTQTc3+FSaHlK1TXaebR9tRyvjFUzOBKxQC4\nUV2nffypr7Ar94SayX2u93KtuPPqLGFi999KxbYXcs3fQEYU9Upc+Cu12hjPbkzs8/XUV9ipATW8\nKvOpr5hY6k9U8zecPKmY+51UDOg5gprUlRNAreY1oT8TM3BPp5YtqFsdbJtJxagWUQBnqVljZ7g5\neAA8GlGva4NLKBOb7k81p0qVosJdRERERORaKccped1VRkRERESkNMrTosp0pkLNqSIiIiIi5Ue2\nqNqs73v1oraw0ZzqUCrcRUREROS6VZ7K20bRb9VRmUrJY8IXTKyfM9UBtJTrYlzKzbp7kmvYDIyk\nYmepFKixmaUR8QfVFLXxGaopKpTrOt3Lfeme5b4RcO/ApLJiPmRixobcpsBurgE07FZuOZd6TMqJ\n63WjegnpgbiF8VRPtJPbTdRy+QnctsDpTUxqJdfXmzeN7Ov9nYk97kqNJp0cwqSwYCsVG3PAn8oB\nPrPbMLGh46mOPYMX9aWzcm+wSfWortNAKgX4ce8mOWu55VgnR1Mx59ZUJ+4uruvUmn+cifVxoRpn\nu4xgUvg3lYKh6TEmZiU73YH876l3bJcY6rxE4Caq012qFB2VERERERGpBK52dF6Fu4iIiIhI6dll\niurFLu5bVXOqiIiIiIh9XHyE3S5F/MV9q7YmpzryjLvB6tDBrXYUGRkZG8sNHOJkPWKw42qnllEx\nD+qwMQK+rsvlXmRSlq3UsAnj7SupTYFIH2p41efcCeye3Blc8jTk4MPtqJzRj0lZs6nDsAajLxPz\naZ7BxAAcaUXFCvZTscCvqZhTo4eoXDUPJpX7xnwm9hM3pYt05zQ26XY/NeXMvHodE6vGnQ83f0vF\nXCOo2A/cAewu66nYxs5UDEA77ot8lhrABZ/p91Gx0E+ZWOY+6pU4gXsljuM6HLKGUsf0vcc1YmIA\nrGcPUzk3qtnA4NGBiU0JeZOJjUkcxMR+qE29/Ftyb3T+X1LtUibuND+A/+NiY069y8RS6lJzFWtm\nXyfFmB1FRkbOmGHPuq5sihXubdsarKcWk3/WUCPG7mW2rriLiIiIiJB0VxkRERERkSpAZ9xFRERE\nROzN7q2rKtxFRERERMqrnGX6xfeTKWLjrjIO7Q5V4V4i7/nbqdyxHkzK4JLMxNyoxYDaXK9rxmIm\nlT2FWmzbRqrlFMBabj5Izhwq9t0iKuZUm4rFN6J64uplUeO3cl+nmlOHzaV63TIz2fbfIK799wjX\nntiB6zvMAjWUZE8e9cLxnMF1pzEhYCvXEJn7P245YOYoqut0OPdUn8qNmxn5MBVzfeRnJhZ1304m\nNiWAenCPcX3zAJ4fRcVepbp/Af/nmFRmPDVH7iVuANMJJgTAuRaT8l5EzdXCyVnktjhO9Yk2DqdG\nXMVlUvcwoLYExuRtYGIfc6u1yOJy3KYnx3KrAe6PUvcweK4G1XU6gd5X7KI8Q1IBAMXrfht3ldEZ\nd3tJSEiw42qhgXZcTERERMQ2+xYwAEJDQ+274D/E5XV/BZy0KZfrqnC389P0dLo9VxMRERGxRXV2\nlaKjMiIiIiIi10T5rqPrqIyIiIiISPnwFfnlfag2qTm16nANY1LnTlJdp+79qD1bc22Ce7K4YzzW\nfCZV43uqA+jm272pTQH3/tR4wmoBVMumMzX+D4YGVMdeU9zOxMxubZnYd3OZFJamv0Plkp+nYsCh\nblTM4wkq1pj7LBZwXafx7jczsXO7qU3PrKJi3bmGyL5UCgCe5r4m3lz7by7VEglj1zQmtsQ5gIml\nUHvCjYsFHDrOBfHf1TcwsfQHqdWyh1MvWK8x1DDpF7nvl+dr1ERMGFyoWPp0KmYMpmJA+qNULG4/\ntWB6B6rTPTX+HiY2hWv/pVqJAe9xXI7rYHYflsAtBwT+h0m9toj6mWj+kd1W7KI0zalUiW+rOVWF\nu4iIiIjItUKW+LYu4atwFxERERGpAlS4i4iIiIg4FHdEXoV7pWQwUsfXrPuo89yG5tR5bmvhGSb2\nnBN1MHUKebL27CEmNnsrtRqAcT7UIJnC47OZGHnK+cwq6izsJmoxIOVlJkV94YC0m4cysYA1jbj1\n4DONOtG7uekxJhZJ7pr2GpOiJpcAz4ZTMXIUzvJbqZjlKLcc8AN3GPoObjWnRlSjRlpL6vB6f+5d\nYgL3LtGYCQF9XKiT6wBWZq9hYgbXaCbm9QL3ogh+g0kZG97LxMjBOtO5MW04OY+KeXWnYoDb3VTs\n8WZU/9UMbogYQlczqTHpVHfIiRupt0SXW6lj+v24IWJvkzO/gK7rqHlTP77KLijXXplvGsNNTtVd\nZURERERE7KHMc5R69Sr+ETWnioiIiIhcO6W528wl1JwqIiIiIlJFqXAXEREREakCVLhXSlZu3gTO\n7KJW2+tOrfYn1bJDTZsAjJ0OMrF+piZMbB23KQDXWlTX6WFutY1cLC6fGhDj9BDXY+fkwaQWUmvh\nRDwVmx40nlsPsJxkUktB9WzNPtqe2tS3P5NKTe9KreZ+G5PK+8+NTOzQVGrPNmQ3IRDFDcPa1oR6\nFhf8TL1LNKJer8jM/pSJUf3LQNRMKtbzbvZfmc9+SHWdzsuhVnthF/UVdulGzUJye4B6Dj8+agkT\nS7mBanWtmbiSiUX6UIOQAMQeor4X8x+hTvSey6Y2fdJgYGKzz+xhYlOSqE2nV49gYkv/5GZX5SdQ\nMWAHN0aq+tPU1D+TBzf1bxGTEhvK3IdadpqcKiIiIiJCquh6/cLtZWzcVUZX3JdZ4YkAACAASURB\nVEVERERESGVuNqWd/4uBrbvK6HaQIiIiIiKVw4ViXXeVERERERGpolS4V0qbufaUdnHUTMQNYdQQ\nu46JVItVyLGHmBhyvmVSS/O2M7GX3G+mNgWeHEnF3F94gYkZ/IYzsbxXqK7T0E+YFDKXUpMzd8RQ\nbbh1FlObTjdyLVaAj+8jTKwluVzILCZlTYyhVuPatbdwk1MjUqnhhG2Gc63OBdTLEEDeu9SC4w5Q\nLaBWJz8mdnwk9aVDxmImxc1+Rf/BXEtsPDeuE3B9LI2JvRhAjYl1vp16ApyeSXXEVn+EuuVAENet\n7TOJiqV3oLpO6bGeONGROtpr4RpAPWKo2NuxVAzHBzOpQG6xmaFUH/ZTydSPkpe5n+kAkj2p2C6y\n6zTne3JfqVD2PBOv5lQRERERkTIrW2l+oQm1JGpOFRERERGxp7K2q16l3LfVnKrCXURERETk2rpq\nuW/rQr7uKlMpZXExtzDqWOqT3Godsz9mYjc2z2BidfEhE1vgz6QwOelZKgdkPvUmEzOc3sTEzh15\nnYlt40bwZJ79ncstY1I7F1OLcSkgkTq5Dvqz+MG1PrUcd3y5YCt7QJzRbhsVmxD0ChMbd7gdEztQ\nhxqsA6DpiTeoHHeO2JBJvRJ/m0HtGfEiNZXmB25mWu4Yav6ax4vjmBgAy7fU4fUB91GrLY3fzMTe\noJ4m6PMKdcq5aS41WCeMO+JchwkBq6ZxOWD+KCo2IpMa/HTybuoIfvZ/qU1DP9nCxJKozgW4Ui9r\ntpuL61wAgJqHqB92NbmembPL7mJiroMdeRFXSkdn3EVERERErg0HzFu1ExXuIiIiIlIRLPGbv/7w\ns5+STqNxh24P9Wzvf6HwzI1bNuf9TccyQhp3fWxYj5p0QWqXmvuqPakXqDlVRERERK5/+5Y9PXTO\n7pCIHtGNsxfOeHnFyoGfLY/xB5C7b3T00M0I69uvyTdLp6356djK5U/V5NYkm1CvXN/36sVtpuZU\nEREREfkHyN3+xW50GL98YicAvdvPjR6+JiE3xt8D+758czPC3lq18CYfDOvauOOj097d2Oel9mTp\nTinrTWaK0+TUKoOa5wGcpZoY0ehhKraR6zolR3VM308N9Mn/leqwWR5CtZwC6MON6jA12MjEjnG9\ns21jqFgY168ZZ6am0vwBqieObIpKu4/t/iz8k/oswrmNyX0DNnL9mmnUWJofuKf6LdSWsHp0YGLT\nQXXOAVjg1Y2JrTVRnZ2dqDEyaBBCxZLrTGBi1KsLaN2AiqVHUZsC8Oa6WJfucWZiGYOpdtJx6e8w\nseX+Q5lY6Byq6zTuzB4mNsHtRiZ2gGs5BTCIbGNNm86kJm+lFpvEdc7v5ybc5XMvxOwpVKzmYerV\n+ga4AWdAu8HsDzuGz0dUr/N1K+/vX1oK8v/679ztX8R595h6kw8AGOt1HRk2bfHWeNi1cK9IKtxF\nRERE5Lri0XvKyIVDx3cbvuHOkLNfrtkc1u+tVh7nf889wOuvmKlpZFjWx3syR0X4OOiBXu5KJ22s\nuh2kiIiIiFxXLEcPxBX9quAMAMQd3Ztsvqm2CQACPKpf9c+vXbvo8g9GRQ2wGbbvjWIudK+qOVVE\nRERErneZ21+esSZ6/LKXOtUG8FL65gd6jZ63odPEKF/kIe1k9oVgdloqQv1Mly1QUo1u04VD7Xap\n4C90r6o5tcpI4Y5Wv8MdXj+Wf5yJnYy8gYn5LaXmUgQ1og4Sksd+H7CQM6mQ9ag3E+vNrRa0hxv8\nVM3j6hnAezF1VDeSO7tMnawE6nFfusye1NcNAM5SqRqLqW6IPvWpST0r06kjs5njuMPrY5kU3PtQ\nJ6HhG0PFQA3zArCZewJ0pg5gY889VKzNn9ygNvcOTGqMJ/f0PEC905l6UFO6AOS8Rp3on3NfARML\n4za9YyJ1eP2BvO1M7ESjm5lY9hTq8PqI+5kU+6IGEMqdhu83inprf5nrhKnG/UCkZoMB27ifOjVP\nUF0EmX2ob8R6ricBwHKuLaHbaG65U/OomPtt3HJVSe6xPVlAm+a1z/+3f9M7vPHN4VREBddsjaTv\nf8kdEu4BAIlff5kVNqzp5YV72dirLbVIZWtOrebAvUVERETkuuTR+I4wYNKrc/elZOZmpmxcPH1F\nFrpHNASMd/Tuh6SFE5btyMxNXzvt+Q1An7saO/rx8qz0/+xPV9xFRERExN5Mzae/M+q5odOG9lla\n9IEOw97qf5MPAI/wIbNGHh8+45nucwCg76RlUfwEJoez6qiMiIiIiFxffJr3WBj7r8z0TAtg8vD3\nuOg0THjvies6p+daYDT5+HhUVDlq347Vv+iuMiIiIiJyHTL6+NtukjD5+NvrXDvsUaNfuJPMxXRX\nmSqjOtdRNOIpdya23IXqOm3vQm1qiaNae1L/6E8t530fk8p7iW2d9F5I9QAtnfI2E+vHDX5ayk2b\n2s51CSOLarCbFzCCiSVXp750wb/4MjEA8HuKScXXpDpxyXlejzej5jQtyF7DxHJGcSPO6n1Dxbj2\nr7nrqcUAOEVQXYzd3akuxkWtqE1TmsxmYm7dqZjHY9Sm6Q9SscXp3BApYEzaTCZ281zqtXMTty35\nWVjN1PfrjiRqtalUCu3TqNjd5MQsgHqBAW2OUm2nu7hBeG0+pDqnd9SjnpwBe75nYnW5JtG9Q5gU\nwrjVQM99G809A44NOkTuK+Vkj55UG6W/7iojIiIiIlK52Cz9K9tdZVS4i4iIiIhw1JwqIiIiInJt\nlO9AvAp3EREREZGyKlUtbrMP1abK1pxqsDr0gv8F6bvXzvtgddJp35t6/Tumk81heZb4zV9/+NlP\nSafRuEO3h3q297/0Lx2RkZGxsbH2fEzHBzOpvHnzmdgfE6k9m/7ONQoauCbWGtSnAGMIFTv7GxUD\n4P0QFUt9mUmlNKaaU11aU3t6cOOTXdrVpXI1BjGprGdeYWKFXN8sgFSuj63piTeYmPmj55mY6X5u\nrmd+ApOaUI8aOko2zkZwHbE4OYtbD6k3Uw8vaBfX/316E5Na2+QwE+P6MHGE64j1XzuOyp3ezG0L\nnNlBxVy4YSsu9ZjUkrofMjFq9i+wdRoVM3WlYob6VCPmuV/vopYDqt1ATeJdzr3EHthL3V+hc4s8\nJraeWw3VqSmhB7ihzo24AcYF+6kYAGMDKubcmXvbIV87Qf9Hxf5JIiMjZ8ywa133l9Jebr/47Hvb\ntgbrn0+Qf9BQ6392L7MrxRX39B1zez2zFOHRfUOOLhw/cEfqrFkPhxfL7Fv29NA5u0MiekQ3zl44\n4+UVKwd+tjyGm8EsIiIiIgKU8v4zNqr8f/wZ9/QPxy9FxMh1U3ubgPYhw4fPmbm7x8Jwj4szudu/\n2I0O45dP7ASgd/u50cPXJOTG+HuUsKSIiIiIiP39wwv33ISfsjCwf1TRTfjDe8d4L3xmy9HM8HCf\nS2IX/TOdpSD/kv8WERERESmr0pyf+WcX7rnH9iYhpHXdvy6eG71CbaQ8ek8ZuXDo+G7DN9wZcvbL\nNZvD+r3VqoIvt4fVpg6vk0O/NlCjMAD3O5lU3izqzLT7qOHUpsepc9/mb9jndME+avSL54vU4Kea\nv1GxJ0OpSS3jE5kUPJ85xsScm1HfCHIUjlNj6sQ8gOqfUU9OpLzIpHL+Ry1WeJwarTKEG0qyNJ46\np+vDndPN5I7M5k3jpj4BM9Kp2MS4JUwsewq1WhQ3ROyPqdQkLK/53OAfrifhB26YF4COXL9BWjj1\nvQjY0o2J9T/YiIoVct9XjygqlvMlk8qbSB1eJ0/MAzj9AfWi6LuNWs1yjLoKtpJsN3HvQMVqzWVS\nTc+cYmIpgdRkpaC4F5gYgNSw15mYx2PUc7jnDGrT9VadcS+F8g9JvVxJ7aqVrTnV8YU7LGcv+U+T\nV10g/7LQ0QNxRb8qOAMAcUf3Jptvqn1p1fzLL79U1IMUERERqRh2L2Bat+bu2FA10ZOSSqFXL9sf\n1+TU4kxBdYGkdLMFHkYAMKfuAIpfoMjc/vKMNdHjl73UqTaAl9I3P9Br9LwNnSZG1b44dX0/TUVE\nROS6pAKm/ErVcsqz1Zx6riI2IlVz4N5FjP71w4FvNp0/wWD+Y18SEOx1ybX03GN7soA2zf8q0/2b\n3uGNXw6nXuOHKiIiIiL/bFb6f/bn+MIdxrC+0d6bp73w5b6U3JQdkwbOgXe/9vVMQMrcgZHdxn5p\nBjwa3xEGTHp17r6UzNzMlI2Lp6/IQveIho5+6CIiIiIi14jjj8oAaD96wcCkPtOG9pkGABFvLR7o\nA8BSkJ2KLLd8CwBT8+nvjHpu6LShfZYW/ZEOw97qf5NPyUvaQdypd5lYUA2q8bDGB1zfoYFqdh3N\njXP6v1VU68xdv1Kr7bFkUTnAVHiSiZ3qUp+J9eWGDZ29egQAHoqnYh9wnXhBqWeY2OnX3JhY9Tsn\nU7sC5m+5zulHlzEx73F9mFjGCCYFbkoTELqaSc2AgVrt5NtMyv0/7HP46YXeTKx1Z2q17VQnHsKa\nUV2nceS0qT+HMKnCI1RXd8cze6hNAaRRY47IV+LzQVT/dyfqk4DzvdQLFtw7mA839emgJ7Wn+6B2\nVA6wLN3CxBKorxzImWTTU6hBXelR1Fun/7KbmZglnnpFzMthUhjnx92tAai5l7o5wdm11BNg+a3k\ntlIuFdGuWjJHHpWpFIU7jDVjZsX2TE+3WOBR0/986WqsPWp17Ki/Ij7NeyyM/VdmeqYFMHn4e5A3\ncxERERGR604FFevFbi+ju8qUyMf/qoNQjURGRERERK5zFdSNClzy9wHdVUZEREREpDIqVqbbuquM\nCncRERERkSpAZ9wrpZe4rtPu5HLe1PhPnNnJpKaOphZbyM2w/I1KAfvpc0oNNjGpGoupWXzh9dcx\nsTEhTAr3JFExgwsVQ8rzTKr6w9zUwVNcDyMwl2rsxPDJVNdp4QlqNc+nqVhED2cmltGV6jolO+e6\n9aAa8SxHuX49IOjYdia2x0I9n051v5eJkbMpUc2LSa3lZgnfncyNk3SqQcUAuFJDTNdzl6x+MFDP\nE+e7VjIxpL3GpA5wY2Iz86gnyS53qhGz5rlcJgbAa9pMKjbqP0xsep1PqF2dqXdY/6+ovt6XalFj\nmCenUZ/pRnCN82b2Zx1qvcOkpgyhmlPHcc8TqSDXtmn1WlDhLiIiIiJVRjnL8WLtp1dmqzlVV9xF\nRERERAjlbkstRd1vozlVZ9xFRERERK6BUtX9tq7uq3CvlMhRDSHcWIo+XtQspJUZHzCx09x5zie+\nomJDuSPO8OzK5WDlJr8YzlFjM16oR20a8C01vmS7z8NMbGYAdWhyhEtjJnYzd6BzTSvqND+AMQlU\ny8SZldQpZ8txalMrN+Oq340FTGxpJvUk/rYPdUzfd0YwE0PAi1QMyPs/6lxyfa6NJDWf+hJnudxA\nLZf3A5OK4t5M4NaWSc0jHxsw+MQbVC6eej/peLQ9tVreBiZVeIB6JTZYRO2ZNYh6kjQeS61mzWIv\nAZ58gHp38hpJreaUfBcTM39Lreb+qDsTm1yQxsQ+dw5gYuFMCEipRf0UBlAzhTqV/nQMt5wTN4JL\nqhIV7iIiIiIi11aZjsvrjLuIiIiISClVdKOqreZUR1LhLiIiIiJV0pUPrF+1rO/V6yrra3KqiIiI\niEiFK/f9Z2xOTtVRmUopJPdnKudMdcUtj6UmeuAENR8kYC33RKy3lood603FuCY2AEijPlmrK7UY\n2RMJV6pPlGyxJedqGbgeVivXiHnAh2rEBHAXN1vn11hqNbcnqE6s/BVUK15Lak/Ec59snfXUahkj\nk5mYczNuUAvgMYjqsUt9sge1nJMfk5o/jVoM1W9jUmeXUU2HU6hOcvhQKQCYEEhNJRt36l0mllyT\nGoTXN38jE1vPjTgL4r4mmdynEMTN8vt1CrUpgFb5VGziACr2CNc7a/6Rirk/+xwTI7tO791Gbdqz\n8RdUjh/AlB/HpD5bTC32cI8mTMy5lyMv4kop6Yq7iIiIiEilUfIxGxXuIiIiIiKlVM7m1Cso6lu1\n1ZzqyMK9mgP3FhEREREpG1udo3ZTYt+q9Rz7vwqgK+4iIiIiUvUUVe0VV7sDeyv07wZloMK9RDnP\n3c7EPF8ZxMTujKQ2jc38DxMjuxgXgBp2OJ0bw4kzO6kYYAimviYIoj5Z5+bUZ7G5zhImFpFEtQn6\n3s+k8MQnVOxEM+r7ZaIWA4Afu1Cxrdyz7jZQXae5o6nVxnCduLu45/DYztSmx6gUYmfW5YKo25Ra\n8lgh1Z6IJKop1qU1tdgPnlTXaSvuSTKW6/8z1H2VygFwDmFSM7mWzRFpM5lY7AnqzeS55hlMLDOL\na3Y8HsOkfqHWgpVrOQXwMhcbzI3OzXvtESbmt4U6GzDTYGBiQUwIqHYLFduPe5kYN3AcAMYdbMTE\n+h97iFouhHoOS+XUokULW0dxdFcZEREREZEqQM2pIiIiIiJ2UnFNqyrcRURERERKwV6ledHdY0pi\n464yVhXulVJ1bhjOqUfmM7FP63G7ckOOmiY9y8RWhbzJxCb8j5rm4/4ce8i1T9ArTGxlfBIT812T\nxcTapVGzq3Jff52JeVDnbzH7PWp0EZw8qVjOt1QM8OEGP2WmUt+yj7jvF8iBWceoVw49zYvb9Hfq\nQLfVcpJc8Fj2O1TOQg1+8qlNvUscvZXasyPXlJLHva63cMeIfcA9SQDuoD5OxJA56vD6k9zh9de5\nyUqoxr1gay9jUiHZ1GJ5r0ZTOWDcVCo29HPq8Lr72O+p5c6ZmdQhai3ExFAx66QnmdjjtWYzsTrU\nngBwavBhJrZtIxWLIltw6lBPJ7mY/XpGr/QXAFvNqTrjLiIiIiJyzV35LwAVeeSmLFS4i4iIiIiQ\ndFRGRERERKTilfsiuo7KiIiIiIjQylx/X7kbtRg1p1YZ+dzYDCdqOhASNlIxa736TCzgc2q1Q7up\nmKFZGhNLDQ6glgNWHqL6RSLrfcXEnoM3E1vHhIDZp6iJOT7cdJjM1G+oXQO5kSmJMVQMyDzxBpWz\nUv1kZMPe9xOpWDPuabKG27QDN7qE7Do15JO9c8hfTXUKOt/UhokdaUVtunorFftXR6rrNODg70ys\n7lTqPSeEnDUDmOp+yMQeWUyttmrWaib2PKh5eQFzqU0xlxpxlb2IWsylO9XofHYLtRqAdOqNE8ZQ\nXyaW8zz1yRamU5vOtlD3EjD/j3pXn8B1nS7getM31yd/SmAE9/P6fzHccuo6rTClak69uMrv1asU\nu9hqTlXhLiIiIiJSMcp8Cxpb1/VVuIuIiIiIVAEq3EVEREREHI04Oq/CvVK6dRQV28adwX2QO+S2\ni5v6ZKjZn8plf8yk0sKoU8nDuTOOAHo3pvpFlnOr+XHHUnveTU2lgWtLJpXJHYWH7wBqte4GJuaz\nYBy1KbA88Hkm9kD8PUzsjxhqU6+3PqByZ3Yyqcg0ajqYCzfOxxBEjenp7n0vtRzwf1zsBHYxsajE\nQUxsEjen6f14JoX1pzczMepIMhCSt4ELknO6MM+Fip0Iow6v10uhXjsn35zAxMhhc9Zj1FCqz/2H\nMrFIbvwWAH/qZY1HQA2lGs8NB/yMe9bdfzt1eH0c187Rm0rByg0ujDh6llsPrT6lDrm73U+drZfy\n+PXXHy7+z1atOpaUtOPd1ou1rtpqTtVdZURERERELnKFSr2YYkfYy1PHF2tdVXOqiIiIiEhFKXMr\n6uXUnCoiIiIiUkWpcBcRERERqQJUuFdKi7lYNX8q9gW3WpsZVOz5GUuY2OA/n2RiAXtjmNjKXG7Y\nEADXxmyScHY51bFrCqWm0phPdWNiy7kBTA/8STVifkHNkEHPZ6nOOQAP7HFmYpb91KQWr3nHmVje\nK9SwsTPfMilyNhTcx/xMrbafamFctT+Y2hWIbJbMxB4nl/PowKTiEqgxUnCnVivc+QgTC51G7YlA\nqv0XwG5Pqhez5u9UX3/ezBFMLH8N9dqZPpVJYdwLIUys2i3UalaucRZcMzGA32OoQUJDuClCLhFU\nbPAWqmHXvJxq2J39LjUcKr451V/bN/h1JraS+4EIwG3Ii0wszIsa07bRk7o5Qc1sR9aC1xk7Nqra\npsmpIiIiIiJlYN9K/ep3ldEVdxERERGRMrBjKyoA4JK/Bti6q4xuBykiIiIi4mjEnSV1xV1ERERE\npApQ4W4nCQkJdlwtfD0Vq1anDRM7xE1Y/IjaE23+oCannrh5NhPze4+KObV6h4kBQMZiKlaHGuxK\nzs40521nYvHuNzOxBw7UpXb16MqkPgb1FX50DLUnAEtiARM7vZJazSuMGlAYwTX2UR2RwOjkF6ic\n0Y9JmX+kFhs0imo5BfBpEyqWfpCKxftSXxUPboZlYTrVh11zH/Uu4dbUg9o1l2s6Bmrup8bEdg+g\nuk5X7aO6GFGYy6Qmg3rhPP8/qjfdmkXddGAJN6/3fSYEAFjRnoqtPEQdHki7lzocbD1HfYVNnbn+\n71rUTxOfLtSXbh61JVBjCBmc4HYjE4tL4wanc73p9i1gAISGhtp3wetJec/EqznVXuz7NC08asfF\nRERERGxTnW0vpSrKi/Wh2qTmVBERERER+ytlo+rVq3xbzakq3EVEREREriGmyrd1CV93lamUct+l\nYj7LqMPra7hN3+RiS+KoAUz7kqjVOtZtR+Vcw6gYAKeHqdhx6iBp1hRqMZ9l1GHo+dRimOxFzWla\ny51eXZVPTTj6wYWacASgYzp1QtTdlRqFA+6TvRFbmNjTc6k9DaaWTOxJE3XYfNL91KZL6QFMJwdQ\np+HDuE4Yp45nmNg8JzdqOc4g829M7Nwf1DtYxvPsvjf8SsUSPKnYk9wIntncK8Kcms7EkmtTU4Sq\n/5tqSuqf9CwTCwsh3/6xjpusFNmYOjDwALfpxkRqyJEljVrtk2bUO+cDBdxycdTPpn7cyXUAS7n2\ngClcn8bz5LtEJw1gqkJ0xV1ERERExHHY8/FqThURERERsZcy3DrGZq+qmlNFRERERCqQzfPrV67m\ne/Wy8UE1p4qIiIiIXGulvOcMoMmpVYhTABXrxK3Wk4v9zsXyllKxjmZuPEzKS9yuG6gYgOoRVMx6\nlkk5+VOLFcbWZ2IfUIthcn4CE4s69hC13O8dmRT15SjiQ+3rVOs1JrY5iGrFe7sLk4JzMyqW+Tg1\nk2g219iHrE+YVPpDx6jVAJdWVCzzZSrmt5YamTSQbGKLoGaNIZV6XT/YmVqMahEFQD+Nv8yhYnu4\n1V7yp/qwTdxqjblY18c/ZGK+M6lpbhF053TDe6jOaRM1HQ6xE99gYqfnUe3Ju6n3EoylUuj2MvVj\nOP8XarWl2eRNIpC/OpqJjeF6Z63p08l9pepQ4S4iIiIiUvlZdTtIEREREZHyKUNPauk58op7NQfu\nLSIiIiJiF3av2j/7zOaxeCv9P/vTFXcRERERqfLK0Ht6NXt1V5kqw2P0k0xs/RM/UstV82BSmxtR\nwymbU1si9QZq6mTQ0S+Y2C5uSiiANkfbUzljLSZVcIBajBxOuRbUcEpr9ldMzNCMak7K41qs2nHj\nPwEg91smdWoI24vJcB9Axaq1XMnEfBZSq0X69GFiqdRipWj/3ZFFxZy5l+KTvlwnbuqrTOyA+81M\njGzrXHmC6k2Eb39uPeBQKBULpZ7Dg7kW9sc972Ji1IhgoGfCfUzMJ5TqOc7gWskNvjFMDEDAaq77\nn+zrL4hnUtWf/pmJNVx4OxMbSO2JSVOpmDeVwkIvquUUwEQutvFh6o199h/0a0cqnxYtWuiuMiIi\nIiIiVZMmp4qIiIiIVCg7HYLXXWVEREREROyhDAX6Z5/Z/vjYsTabUx1GhXuJkuvNZmLBWdRR0rzJ\nNzKxliOZFDyevoeJfb+YOqj9r0+pw+v16RPYWWM3MjFjA2q1Gu9Qc0nSGlKH1+uMpjYlD6/j6G1U\njOPzATcwC8jsTXUv1PiCmje1kDuBvfRhJgUTqFPp07mj1Wu5SS0GVypWfchMKgfARHU4FW6ljlZP\na8dtGkjNc6r7KjXk5hA3CueHQGqwTsc0F2o54ECLPCbWNJdbzjWMSc2Pa8PEDF7UKfd5NScwsUZM\nCDCc42ZNJcZw68GaR43DMjTk5v6kUM+6tE7UD8SA3dSQo5j61HHzwE3ct9XUkomNMfoxMQAw/8ak\nHuCenHCux+4r9lOmLlXbtb6t5lRHUuEuIiIiIv9oJVXnti7e66iMiIiIiEjlp+ZUEREREZFroNwt\nqircRUREREQI5ay8S+pDtamyNacarA694G9HkZGRsbGxdlzQmvIiE3sv+HUm1oXbtDo3IGT4aiq2\ndD/V1ommSVQsYxEVA5bUeIyJ9eb6RKtRMy6QPYWKeY2hYt+MomI9099hYhP8hzKx7dSeAPAF90wn\nvybgOjt9Xq1L5Ty6MqmTveczMb+lVF/nzdzksu9imBQAnP6EitXcS83Webzuh0wskNoTk/OoZ8o8\nbk7Tfa2oTfN/pWIAxnGxgVwsgpvmtrYB1RDflmrqRsB31PS9pFrcDQy2UZumP0rFAASspeZDhXHz\noeIyqBb2U/dyLezU9wFcUyc7WK1/4iAqF8L2pvdxom51sPIw9e50LoV6d6p2x3VSjNlRZGTkjBnl\nrevscv/Hi4+/t21rsO7n6hLA0CzN7mW2rriLiIiIyHWo/DeE0eRUEREREZGqSc2pIiIiIiKVwdUO\n2KhwFxEREREpB7ucaMel3auVrTlVhXuJDNzstP5nqMmpa92oyalRS1cysaXZXHdqINVfazIYmFgi\n19cF4F6uxXbPVCoWyk1sDP7Fl8p59mBSPXunM7F4rut0JDdz1HvWu1QOyH6Wav/1mUa1k+b/coyJ\nmZpSMXM21TlnOUo1p+b/RPV1/ZsJAV6zfuaCeHbx7UxsQX48E6NGUwL1uEbM1LpU1+kgbpjo1LBd\nTOypV5kUANzITWyl3l6BVp9S3Y79udVSN1EvsZu59nquIxL3P0XFjrBzMRHYKgAAIABJREFUk+H5\nJdd1mkp9z9Zyg5PjmBDwBDWtG0Op2d+Yu56Kpbam3kzWplMxAHW4WN586t1pEveTbrJ6U+3h4hPt\n5Snie/X6+9e2JqeqcBcRERERsZPyt6UWsfEXAKsmp4qIiIiIVAGOvOJezYF7i4iIiIgISVfcSxby\nNpOyHqzPxKL2ujOxzj59mNhXc5kUdg1ZwsTIw+sBW7+gcgCSn2dSC1cfZmK986k9g30HMDGrgToy\nP6gO9aVbwHU4PMl1OEw4Sp2sBeC3jDoMffJh6nywJ3VQH/FcswGOU5/Fc1QTAd6nzmnjKe4893IP\n6uQ6gAXHqMlKS7jJSv24o7q5M6nv1+Pcl+7LTOrw+pgs7nWdQb0iAIwYTn3prCdeY2KnF1CfRXcm\nBDzOHV7fnvQsE0sOfZOJme5kUriZnFwFrOYOiHdJo7oNhnCbHis8w8SS3ajRRZP8qU3PUmfIsZZ7\nRVBvmgCA3mOpmPtj1Hdi8vMd6Z3F/uzVrnopHZURERERESmH8pfpF99Ppsg1uqvMudOoVp0JqnAX\nERERkSrPHg2pxUv/Cr+rjLUABmfkbYRnFBNX4S4iIiIiYqP0t3VXGfsV7udykPUJjg9A4H9UuIuI\niIiI2Jc9zrgXZiE/Don9cJYck3CeCvcSZT5MdZ1W54YNZU3JY2LfcE1sZMsO1ekGZHGDP+K87+XW\nw6BpVOwJbrWR5K4hVKNY1MFGTGzBn09Sm549xKTCqbXg91MaFwROb6IW3LiYieWOpp7qZI+dNTeZ\niZ2lFoOhJjVEJnMg1Yf3APfdBwBjDSZFtrtlTaBib3Cv2FWJ3OSf05up2J9cb7IzNcwLwHKuw/4B\n7rNwHxbMxBZMmM7E+pmoTvz0f1FvJiu5vvkRw7nxUNkfUzGg59G2TKx7A+r5NJDclXs6eY2hFnN/\nkmrFPXkv9crpH081iQbV46ZDAXMnUrHbV1IL+kymYs69nqN2FXurmO7VKzqXB+tZHH8M2fQ9Py6i\nwl1EREREKkRu/OZF7352KAN1b7rrwQejapsu/EbcsjnvbzqWEdK462PDetSssIL0qqX55Q2pF7PV\nnFrWK+5WC2BF2lSc4C7n2KLCXURERETsL3P3su7D5yAkou8drisWTvpy4c/v/TCxnhHI3Tc6euhm\nhPXt1+SbpdPW/HRs5fKnalbMYyA6Vq9U2dutObUwC7nrcfxRnDtdlj/+FxXuIiIiImJ36e+/PAcR\nI9dM7e0BPNXnu8g+47/dlzkk3Gffl29uRthbqxbe5INhXRt3fHTauxv7vNS+gkr3q7hyZW+H5tRz\nWShIQmI/nKEGU1xZZSnc03evnffB6qTTvjf1+ndMpzCbmRL/taViHPiEikVMv4+JBXT2Y2KG2vOZ\nWAo3vWIMN/UpqAV1/j719y7UrgByNzCp0aMKmNh2bs87uYP1J/tTU5+itlKxrbGzmdj7TAgYnPgw\nF0Rk/XVMjPyGjdtPHSOGH3XuP3sMddx8KTdELLk2tVpwHtUeYI2/m9oVMC+jvrNB1Al8VB9AfekO\n16I2zR5LvUs0WsykwDVzYNyZb7gg/jWEGjcWyb3X/ZvbdPCp+5kY+cn6f0q9dEbU+5aJWfcYmNh7\nZCsM0P8oFSMPr/fMXkPlChKYlPvo75nY6Rl3MbFzWUwKM7nD69xiANCTa8A48xH1HHYKoDe+/qTv\n/yYLwx6PMqbE7Tie7RbUNjY2FgCQu/2LOO8eU2/yAQBjva4jw6Yt3hoPBxXupccW7pNfHYtzp5H0\nFDLIQuDqKkXhnr5jbq9nliI8um/I0YXjB+5InTXr4eLvYSX+a4uIiIiIVDK5f/6eBXw35cE5cef/\n3hQS/fKSl6KKrru6B3j9FTQ1jQzL+nhP5qgIH4c80L+wjar0FffnR72APwci86OyP6bLVIbKN/3D\n8UsRMXLd1N4moH3I8OFzZu7usTDc45JMSf/a4qgHLSIiIiIlMgJAXOqdC1c9E+aDuLVzBk6a9PYd\nrUa19wAQ4HH1QaFr1y66/INRUQNshitibCpsNqfSJ2Vq3RB6IvkwAl5CYj+Y95Trwf2lEhTuuQk/\nZWFg//N/AwvvHeO98JktRzPDLy7KS/zXFhERERGpdIzVPQD0HT8ozMcIICzq3/1mrVi1+/io9k2Q\nh7ST2ReS2WmpCPW7/AR0STW6TTaPqpeqmu/Vy8YHbTWnstLS0uDkDaeWqL8RuWuR2B9W8mbIJXJ8\n4Z57bG8SQlrX/esCu9Er9PLMFf+1RUREREQqFVNwoxDgRJb5rw+YT2bB3cUZMNVsjaTvf8kdUnS6\nIvHrL7PChjWtiKKuzDX3BTZK/zLcDdLJG173o3lvpI5D2uTyPB7HF+6wXPqXD5NXXaD4XIsS/7Xl\nkj6GX375xY6P61bymn5huh037c3FIrk9nye7Tg9RT+vC36mGSADVuF6cHtxq5IyblFFUrB7XYvtN\nDPXJVmtFdWLFJq1mYsiw8W+CthdMpZoiT8+nOjthoZ5PnYOo1T7gOqc7LaZi2zM+YGLntlDPuWqh\n3OgiwK1PKJUjX/41hjCplcdOMbHIuh8yMfJH4BDu+4WMxVwO/+Pajldxo+tIZ1c+xsQiuPc6OIcy\nqc4Gqut0BfcWNolKAUB/t5uY2DhuBF/0h9FM7L/UUxhjuBds9ad/ZmK7X7mdiT3GTekb6HX1TJGc\nV6muU88pK5lYakNqJFnSt/YsYAC0bt3avguWhan5k9HeL49/+UvvV24LxaZFr64BRnZsDBjv6N0P\nwxdOWNbipR6hW+Y8vwF4+a7Gjn64LGvZbuNuMAJA4EsIeBaJA5Czqmy7O75wNwXVBZLSzRZ4GAHA\nnLoDKNZwXvK/tlxSuNv3aXruJzsuJiIiImJbpaiz7c/YfvScgZnDpj3zaNF/9x3/Xu8wEwCP8CGz\nRh4fPuOZ7nMAoO+kZVEVN4HJ7sp0G/fzqrkD7qj9Hs4eQmI/5B8p7QKO/zIZ/euHA99sSuzUox4A\n8x/7koBgr0uuFpX8ry0iIiIiUi4FBQWpqal5eXnu7u6BgYEuLi72WddYO2bq6t6ZmRZYjB7+HhdV\nneG9J67rnJ5rgdHk4+Nhn3K0/P2plPIU7kWcfFD9FjTahayVOE7eu/U8xxfuMIb1jfZ+edoLXzaY\ncZff8SkD58C7X/t6JiBl7sA+q0JGfTyxh6nEf20RERERkTJKSEiYN2/e1q1bLRaLm5tbXl6e0WgM\nCwuLioq655577FLBe/jYvgegycffXufa7V6yX7jDTHnuKnNFBlTzhE8/+A5A8tP8H6sEhTvQfvSC\ngUl9pg3tMw0AIt5aPNAHgKUgOxVZbvkW4Ar/2iIiIiIiZTBv3rwDBw506dLliSeeqFWrlsFgMJvN\nycnJhw8f/vzzz1esWDFv3jxPT09HP8yrK38T6mXO/03Axl1l7FO4AwAMLgAQ9Cp/cN5gLe3g1gqT\nmZ5uscCj5pX++pVr619bikRGRtr3HpHWlBeZmKHGYGq5gmQqlj6dSVlPfMrE3ruF2pNrnMQjXAzA\nv7gey7NbqJjnzN+pnBM1mxZHqL6ulJupyak1tzeiNg0aT8UM7IWN5ECq2ynoO2q1eyOp2PtcN+E3\n3POp1zIq5hJJtZP6cGM4+RkYqVxsGxebfZTqT1zbgOom/JjbdEEO1Tkd5knNsPyC2xRAChcj/8GU\n/AqTscncRMzCQ9TTaWlnatN/1aNiAXHU9F8AMP/GpPImUt/ZcVOpPaef4W5BncU9PQOoH69IpH7s\nWPZRPxB5xtuprtM+PtT7MDnBerCDirEjR440bNiwpN+Ni4urVauWuzs1hd3uIiMjZ8xw/L2/ixXu\nbdsazm1m/2y1CNi9zK4UV9yL+Phf/e4GJf1ri4iIiIiUisFgOHfuXLVq1Wz+blhY2DV+PFVCGe8q\nYydlLNwLCgoSExNzcnJcXFyCgoJq1Khh34clIiIiIhXqrbfeSkpKuvvuu6OiourWrevoh1OB7HkI\n3qFHVUpduKekpMyfP3/Dhg0Gg8HT0zM7Ozs/Pz8wMLBTp059+vQJCODu4C0iIiIiDjVjxoydO3eu\nW7duyJAhtWvXjoqK6ty5s7e3t6MfV1mUrTS/0IRaEhvNqQ694l66M+5fffXVmjVr7r777jZt2tSq\nVQuA1WrNyMg4duzY119/vXnz5mnTpjVt2rTCHu2V2P2M+0vccI3J8fcwseTGXzExF+5GroWJVMzv\nPSo2mjupOZ2brAEAhTlM6syioUzMrTt1jvzsj9SpdNd7xzGxE20mMLHAY9yxVG7Ece6oG6jVgKEz\nqNiz3GptCs8wsc1ObkwsIpcarTLPgxqtEsP1S7h0m8nEpgSMoJYD/sfF6nCxmlePAMDKA9zlrprU\n1L0TzanzwS4R1J4+08iTushbRA0vOzGRWs0vhop5PUvdHdiSWMDEjE25TzaYakla4nYjE+OGCAFA\nJndSH/5PUav1oR6ez9vUTzqEvM2kbnStz8Q2cV8Usl3K2ICKAfDhOnDu4FaLTX+HyvlxM64qTH5+\n/qZNm9avX799+/Y2bdpER0dHREQ4OzvyvtvX5ow7U+4XO+NeSLUjAYBTe0efcQ8PD7/nnktevQaD\noUaNGjVq1GjduvWpU6csFotdH56IiIiIVCwXF5cOHTp06NAhJyfnv//978svvzx27NiuXbs6+nFV\nuKvejsZGZV+Fzribzeaim/Pb/F2ddBcRERGpig4dOrRu3brvvvvO19f3qaeeiojg/j3uH6gKnXH/\n/vvvV6xYcccdd0RHR998880ltSGLiIiISOV3/PjxdevWrV+/Pi8vr2vXrtOnT69fnzrLVEXZoUu1\nCl1xHzx4cNeuXdevX//WW2+ZzeYuXbpER0df399gERERkevS+PHjf/rpp8jIyBEjRlTyC7L2ui3M\nVbtRi7m8OdWxA5DKPoBp//7969ev/+677/z8/KKiorp27erj0Jus2705dQnXnNrffJCJ3WhqwsS4\nnkO0oBbDXuqhoaMli8qZ2dfMBK7vcBj3WRzhPouWXBuTx0Cq1Sb1LqqJrTCd2jQkbzsTi3S/mVoO\niM0/zsTWulDdroHcpv/hYse42LfcJL4ac6mYSxuqg/lkf6qDGYDfemp6UUoINeMm6CdqU0Nj6rne\nh3szWfAwtanX9BeYGDtpDkAm1dk3M+gVJjbij/7Uptnc3B+udRJpb1Ixrukcoauo2O8dqRiA2tx9\nApxDmNRm7ukUcewhJpbc6EMm5tyMScFqpmJPcD8jVnJz0ACgJtV2jPgOTOqHFnlMrKODisFt27Y1\nb97cUSOWrqzMzan2vPMjgMuaUy1UBz4AGLs4ujn1Ys2aNWvWrNmTTz65ZMmS2bNnp6SkjBjB3rFB\nRERERByrQYMGV6jaT5486e7ubjJdYaJ9ZXTVftNSsfHXgCp0xv1iR48eLWpicHV1HTRoUFRUlB0f\nloiIiIhUqAULFpw7dy46Orp58+YXbv5oNpsPHz782Wef7dy5c8mSJVWucK9wVatwT0lJKWpiOHny\nZOfOnSdOnNikCXfiQUREREQqjTFjxmzbtm3mzJkJCQlBQUFubm7p6ekZGRk1atS4++67Fy9e7NhT\n0I5ylcM2VahwX7Ro0fvvvx8RETFo0KB27doZjWW/YC8iIv/P3n3HVV22fwC/DvOwNwIOQBNcieKk\nHkzNmePR3IqKae6RuVLTzJXmyp2J60ktRSxDk9RcVLhykBMH4GArWzbn9wflzwT1Axw8HPy8X/6h\n9Om+v3DO+XJzuK/7IiLSrKZNmzZt2jQuLu7u3bvp6elGRkaOjo5Vq1bV9HVB1L6dvcDTBazlrXNq\n8Vbeb731Vo8ePczNzcvoasoVNyyW/AH0CwdDbLRWWBVjG6yK8Wes/VvmOqi5cVu4hAFr/yg/YBVF\nPvOhmPGQMVDOrDOSCkroiMTAXx/2wWL7sWpCEamEVZ1eNIBG07WFYkOjoFi35H1IbLHFf5HYb9jX\nJEagqtOzeHVaSgCScrgDdWxVYh1bM9OhlsOrsbre7BAodt1xERKrnd4DGk5EdKBvECOxu5NK3xGJ\nZfwI1f8d+vQDJNZpL5IS/ZabkdjjpdD3CONJodCsIgleUK9T21+gW6dXKFSvH+cFVZ06XPZEYrmX\nzyOxne8jKdmJPV6519F2l91rQN9h92Oj5UBt0zXP3t7e3h48qqAcebKdXb0r+O7d///vly9ffmbT\nvEqLFu7u7u5FfvzixYu3b9/u0QO+sxMRERERqYN6C1KfVkZv6pdYSfa6XL16dc2aNU9/JCoqql8/\n6KwoIiIiIiJtpUV73As4ODj07t37yT+jo6PPnDnT/enfKxARERERVTxatFWmgLW1dcuWLf81ip7e\nzp07fX191XJN5QS4K91iA9So5X87oUYtyR9CW+t6vzwiImKI7Q9ujI3mgcVEBNpFLlINi2WdgGLG\n49pBOVU2khqcEw8NdtUOiaXNQDsrga5hW7XNp0GxxElQLA7b4y5ph5DUNGy7+bQsaPP6qjrRSCx9\nA7rJtcOXUPIE1hskBttFGFcTep443POHhsu6gaTsFFilhg54R5QgbEP/JWy0SQegLfjG0zOQWLd+\n7ZGYcw3o0Q/tD+2Y34bt5h/fbyyUE7GYjeUMi97a+iwDF2iwltArcZ8btHm9HVa5dAZKyeA3oG36\nbvWhtnoiEoa1/RpWbRsSe4y9Xs3fg2JlKiYmJjw83N3d3dDQsHy2ZCoZ9e910bp33AszMzOLiIhQ\ny1BERERE9MqoVKqlS5f++uuvOjo6c+fOjYmJOXny5KJFi3R0dDR9acVWymX60+fJFCjiVBmtW7gn\nJiY+/XVJSkr6/vvvu3fvHhwcbGxs3KhRI/VdHhERERGVoV9//TUyMnLPnj2rVq0Skfbt2+/bt+/0\n6dNeXl6avrRiK3WV6rPr/sKnymjfVpmoqKjt27c//RFTU9PDhw+LiKOjIxfuRERERNrixo0bHTp0\nMDU1Lfinvr5+kyZN7t69q40L91IqvO4v/Ba+Nh0HWaBu3bobNmxQ+6UQERER0StWqVKlK1eudO78\nd4WaSqW6evUqDx0pn4q3cL9161b16tWft+fpwYMHCoXCyclJHRemeXWxn02izKCqU7DIphsWG471\naRquC3Vq2aWE+oPgjRla3WkL5VRZUMxmHBRLWIakfsTKzrpl3YEmxdxbCcU6wAP+2QCKZZ2CYmCt\n2/Dm2MOaDHVDybkOlZMmYYWz4yOh42jj20NNZETkV6ygUKcp9Dx5HFAdiTk9wJqIKbDGWnmPoJiu\nNRTLeQDFRDpghX1tw6DCPt2mB6FZdaASW7A6ORJ8ICyg8v/x6xOQWMY3UEsyEYmZAsVcM6Di1PQd\nUNVpJlRzjvZfk0To0V87Dfo23EUfOiRgHhISEZH6WNXpAGw08886wTNrzHvvvTd06NBPP/00Njb2\n0KFDW7ZsycvLe/vttzV9XWWoVFvhtWiPe0RExJIlS1q1atWoUaPq1avr6urm5ORERUXdvXv3wIED\nV65cWb58eRldKBERERGpnbGx8ZYtW3788Ud9ff38/Py2bdt26tRJT08955e8SvhyvHAR6vMUUZyq\nRVtl2rRp07hx4y1btkycODElJcXIyCgjI0NEXF1d33333enTp1tYWJTNdRIRERFRmVAqlX379tX0\nVZRWcSpT0SV+EcWpWvSOu4hYWlpOnDjxo48+SkhISElJMTAwsLa2rkjnfRIRERG9Pk6ePHn06LNN\nafT19T08PJ5sfK9g8CV+Ee/ia9fCvYBCobCzs7OzgzaWaSnDwdeR2OwR0AZxP2zfp1gPh2LY5nW5\n54uk6kNjiS30iYqISHYEFKsK7SNO7AZ1pTHBuk1tglLiawhtSq6JjXb2ZnMkFgk+rCLJnx5GYiaz\noTZSmeuxFzL25Ez7rBcS+wzb9w/+Cm/8GGjzut1f0BdEROpju2btBXqeHME2/kZZQLuc68haJAb+\nHrgWtmHeMRGqqxGRtGXQ/uD0HdBoKxM6IrHZq6DRTBZAT4CMr6BH32gEtKDJOws9rC7YznURicWe\nTjOMoFv7QqwkKX0TdM8R47eQlBv2VL8RFoHEdvgiKdGBNwScwe5OU6M/QWKqOKiJmAKas6w4Ojom\nJCQYGho2btw4IyMjKCioefPmjo6OAQEBiYmJAwcO1OjVlT9atFWGiIiIiCqSlJQUGxubzz//vOCf\nPXv2/OijjyZMmNC6devx48e/Pgt3cIu8ShvfcSciIiKiCuDcuXNPbx0xNzc3Nja+fft29erVk5OT\nNXhhuFI2TC1QZMVqReicSkREREQVg42NzW+//dajR4+C876jo6Nv3bplbW199uzZSpUqafrqIEjj\npJcq8uR6rS9OJSIiIqIKo3PnzgcPHhwwYMCbb76ZmZl56tSpPn362Nrajh07dvhwrPSu/CnOCTMv\nUsQPAFq0x/3cuXM3bxbdrMHd3d3T01Mdl1ReKLG2RBlYeYqY90BSPiZQIWZTaEoZGwzFbLCWWXZB\n72PTSmxjqAXPgATok/1xAjSpQW+oeK7LEGjSwByoiC2pO1bWmX0DSSUvSIRGE7FYhNU6Y8A+Tcox\nUCme6cylSGzZ0OnQrDpQ7WR4vXQkltsEracPxmqdnbE+TZIGda9xSvsdicXugLqivDkCScm8bCg2\nGLs1icg9VyhW6RpUT9rTbjwSc4dSEtkPKmI2+gh6+auwpm8J2Al7sbHzoZzIYqyyE6w6jX8Pqjo1\n6oKkJH0m9BILwzrcOWOHBERi/dfEAv0WtuyzVCiHfVuX5ABwXg1SKpUbN24MDg6+deuWUqns169f\n7dq1RWTdunXW1liPtteKFr3jHhIScvjw4bp16xb+T2ZmZhVs4U5ERET0OtDR0XnnnXfeeeedpz/I\nVXvRtGjhPmDAgGPHjg0aNKjgRzEiIiIi0mr5+fn/+9//goODMzMzn3xw9OjRb78N/X6vnFBLfSpC\npUVbZaytradMmTJ37txNmzYZGxuX0TURERER0atx9OjRQ4cODRs2bO/evW3atMnNzf3zzz/r1we7\nvGhSGS3Wnz5epohTZTSq2MWpXl5e7u7uenqsaiUiIiLSeteuXevUqVPr1q1PnTpVrVo1T0/P8PDw\nmzdvlv8t0OqqQC3k/38eKOJUGS16x71AwZ6nmJiYR48eqf45ht7GxsbBwUGdl6ZpmTGzkVj20blI\nzKAB1E1w2xEkJbpNDyKxXeZQ08E+udAprUF6aN8576lQ7MgUrNuh7TgkpVBAjedUD8YgsblY48zZ\njzYjsfRFHyCxKLDSUeTRTujplChQ7C42aYv7RkjMeseH0HB6tkhqTJ1oJDa3GTRn5GkoJiKe/lCz\n28RlLZHYJEeodeJUW6glaqXwZzuTFyk0qzUSU3aGShj76EKPl4hI7gMk9bAzVE9a9GEIhVwDCzst\nsSrGROiFkxMEPayG77w8IyKWlT6FciJJWPeXWDvolmg6CprUZE4GlMuGHrHzWNWpITSlpHwG1Rzr\n2kMxETEZ4ozE8q9AN/aUr6BJLfdAsTJiZmZWcF67jY1NeHi4p6engYHB7du3y//CvYw8vVIv4k19\nLdrjXiA/P3/mzJmnT5+2sbF58sFOnTr5+vqq7bqIiIiIqOy1adNm2LBh7733XsOGDRctWhQbGxsU\nFLRkyRJNX1d5pXUL9zNnzty9e/fHH380NzdX+wURERER0StTpUqVjRs3mpqauri49O3bNzQ0dOLE\niWW2C0XzSrszXuu2yuTk5DRo0ICrdiIiIiJtl56ebm1tbWJiIiK9e/fu3bt3SkpKZmamUqnU9KWV\nHL46f7oUtbAiilO17h33Bg0a7Nu3Lzs728AA6o3yykRERKhxNJd0qLlGypfQaLbnriMxXQVUxI1u\nXsf6uQzDNq9DO2FFRCT3PpbLiYBG+xnaqQlt+xWRtBNICmxxpbCG9jj+iY0GlRqIiEirK1ZIzNW8\nJxLzVEE9eLpUgzb+BmLNhnywzetroKZPYrkC2pJug7VzEhGfGieR2PbMrUhs2Y390KxWvlAsbg6S\nyj4PDZZ3H+q/sx+70YkI1GxMZCZWz/N+ChTLuYrNmhWGpCbZT0Ziy1Khu86AIVCxwW2sTkNELLF6\nnkSsOeAvWAFGs1NQiYvVChMkVhmrmOiSAMXMP8dKF/KxJ5PINdcDSKwlNlos9jxR7wJGRFxcXPDw\nnj17jIyMevfu/eQj69atq1evXufO2C24XCrObwxetMQvXJyKlZmUleIt3OfNm1fwl+zs7AEDBtSr\nV09HR6fgI82aNWvXrp2ar66YivU0fbkr6hyMiIiIqEhqXsDA7t69+9133928eVNPTy88PLzgg6mp\nqadOnerQoYNGLunVe/ESX7uLU994441n/vKErS185gARERERaZqBgYGDg0NMTIy+vv6TswEdHR3b\nt2/foEEDzV5b+aVFe9z79cN+IUVERERE5ZuDg8PgwYPd3d0NDAxez8MfS1KoqkXvuD/xxx9/HDx4\nsGDnzO7du5OSkoYNG/Zk2wwRERERlXN5eXki0qRJkyd/f0JHRwdskFJ2Ll489vQ/GzRoVWSsNKfE\nvLgyVSpGcWp8fPy8efOmTZtW8M/mzZvPmDHD2dm5ffv2ar02DYttmY7EKkXHI7HUMVABqNlifyTW\nJ6clEotzhroIrRiBpKTeBigmIhZYI6EOq9yQmF496Pc86wRqrtEyE3p5v+WLpGTAVijmgdXhZQRC\nMRGZVDcRiS1LeR8aDuuto8TaOSXPi0Ri22+3QGJJE6AiUcmBJp2LVcSKyHassFsUWLWrUSMktRir\niZx6BprT/CvoZhJk2QuJ9cm6A80qIqlQdXLqzJFIzOwjqBWOvjtWdxgJVdp5Q2OJPFyDpPyT9yGx\nGRb/BadNisda1z0OQVIdsMvbhl3eYGfoLlbp1kMktuB/0JMzvj1087f78ywSE5FIgYpTY29AtY8P\n20DVyTanNLAYvH79+ocfPrdl3qxZszReu/i8lfozir1J/Sndu7+sSxmVAAAgAElEQVRk8IrQOTU0\nNLR+/fotW7Ys+Ge1atV69uz5559/VrCFOxEREVFFVbNmzf37n3vmlbGx8au8mLJTygPptbs4tUDh\nxzIvL6/g+E8iIiIiKv90dXUtLP5/O0BWVtaDBw90dXWdnJz09fU1eGH0AiU8x3358uXr1q3r2LGj\nvr7+hQsXNm3atHTpUrVfHBERERGVtT179nzzzTc6Ojp5eXlKpfKjjz569913NX1RGva8PTYqrdsq\nY2RktHz58tWrV48YMSI3N9fV1XXWrFl16tRR+8URERERUZm6ePHizp07V69e7e7uLiLnzp2bM2dO\nzZo1q1WrpulLg5SmOPUFCupWiyhO1ejCXaEqXQOo/Pz8cnKYjLe3d3BwsDpHzIWatuWfggpAdayg\nrURx7aCKWPvzs5FYTM25SMzhd+w3YnroUf0/YiWA/8Ua+8U4Q439sAJLsb+BtVg1hApnJWYmkmqD\n9Rw9EjsfmlTgzzZmEpJ6NBp61imxIiWTT6EYWOroehl64ageQ59Czi1sVhGDHtAFKg2rI7HMSKjA\nOs4LqrEzwE5s+wXr1tonIxTKgWW4IhIzA0mp4vYisfhu0JwWn0Exw46DkVjyJ9ALFqwmt4Du1rJ7\nChQTkUHYnVNh3gMaLv4LJAU+XopK2JnRVTYjqWgjqF2r40Porp69ByoSFZGEIVDMCavYjsLuEk4a\n7ca5ceNGIyMjHx+fJx9ZtmxZjRo1unXDXoFlw9vbe+VKdF1XRmt3KbRFvlEjxWPoRSMiYjxdSrnM\nLqyEx0E+UU5W7URERERUAvr6+llZWU9/JDMz08AA/nG9HChlBeoLlLdTZbjsJiIiInp9tWjR4ocf\nfvjll1/i4uKio6P37Nlz6tSppk2bavq6yisV/KcMlPYddyIiIiLSXtWrV581a9b69esXLlyoo6NT\nq1atL7/80tYW3R/72tHePe45OTkF1cdqvKASU/8e97gFSEpRCdrSC7VfEoG6g4j4vDwiItIB2x+c\neRTaHzx2PDYr/MnuwWIRYdCWXoUqGxsPkh8H7ZYbjnVq8cM2r3+DPZdEZHjC11BOB/pF5zbrD5DY\nDWhKWYfFkrCt1aq/6iMxhbs6m8iICHgr8btihcSyz0ANswyxnbWq7PtILHVcFSSW9wCa1HLnQSgn\nIso3kVRiJ+jyDBpCc5oshr6R9cLaQPrHYNvSDd2hmCrr5RkRURhCMZHsHwcgMYNBGUgsdTS0j9xs\nHnRwXOb3UBOxzth3k+1mUMzuZZ0vC+i1gWIioroL1UKEYMVLdfpDk1rs0OjZ4P/IycnR1dUtJ7ug\ni7XHvUjq2vj+9FaZRo0U6VD9oIiIyexys8f90qVLq1atunXr1siRIxs3brx3797Jkyfr6uqq9+KI\niIiIqEzt378/Kiqqffv2zs5Qr+LypvQL9B+e/+NfeTtVpiQL90ePHs2ePXvs2LFxcXEiUq1atXv3\n7h04cKBr167qvjwiIiIiKkN16tS5cuXKmDFjnJycOnTo0KZNG3Nzc01fVDGoozL1uUv/8lacWpKF\n+6VLl5o1a9a2bduAgIDs7GxDQ8OuXbuePn2aC3ciIiIi7VK9evVp06ZNnjz5/Pnzx44d+/bbb+vW\nrTtixIiqVatq+tJekRcs/cvuoMmSKWEDptTU1Kc/kpqa+nTXXCIiIiLSIrq6uk2aNDE3NzcxMfnh\nhx/ee++912fhXjwarUcoycK9QYMGq1ev3rBhQ15eXk5OTkBAwJYtW5YtW6b2i9MsH6xSMBHrcXEq\nAIo1qgXF3r8OxZpNhKpOBx2GRts5AoqJiNlqqHhu8WGoOu2U23kk1mgnkhKDftALbok7VMTWFppT\nVmHPpfER72PjSRfbkUhskxM0WjI26cKbzaGYyVtI7EcjqOoUfNKNEajqFKslFhHxi4NK8cC2RAZd\noJjqOnYXzfwLSd3cAA3medURicV7dISGE7E7BD1PFNjv4U0+gTr1SMJqJOWP1evHe0GlZ6PDkRRc\n6vpoIxQTMegC1aZ30YWqTgOx0vmMbVDVaQTWRupDKCVRqS/PiEgLrOr0KjapiOSFQVWnb7hCo0Vh\n35ssdkCxsnP9+vVjx44dP35cqVR27Nhx9+7d1tbWGr4mtVLnG+dat1VGqVR+9dVXfn5+Fy5cyMzM\nrF69+vz58wva5BIRERGRFtm5c+f333/ftm3b+fPn16xZU9OXU4QyLT99sSKKU7XuHXcRsbOzmz59\nunovhYiIiIhesfbt2/fp06c8nw1YsvLTp5f73buXcOrCxanqPuCxeEqycL98+fIff/wxfPjwJx/5\n/fffb9++PWjQIPVdGBERERGVORsbG01fQplQx2kzRb3Zr0VbZfLz80+dOhUWFlawdi/4YHZ29u7d\nuxs0aFAGl0dEREREVG5o0TvuOTk5mzdvTk9PT0lJ2bz5/2uGbGxsKt5ZkNtToE6Bvcyhmq1tUP0P\n2iYwuD1WT/gQKtgKTD+BxMZUx4pYRdZOaILE9JquQmJeqnHQrH9B9X9yxRRJfdgCGizrFBSzwcoE\ns37ZC+VEemIx+987IbHxWNNZb+wJENgD+qK8jdV1xV6DCrHTZ0Nl3Sbz7kCziqSMqI7EzCZCoyns\nPkZiSZNvIjGL5TORmGfqUSTmbdYaiQ1DQiIiMhjr12sKteuVtM+hnOnH/aDhqkH9mkeHQ3d1/0dQ\n4WwI1pnYE7tLiIjCAKpN/+EANhzW6dZo9FkkVqsddPN3OYSkxKgXVK9/IzsCieXdhM45EJFNWLVr\nT+zbREOsiDkTSlGZgzbTa9HC3dDQ0M/P7/z58ydOnJg4EfuWRURERETlXmZmpq6urr6+vqYvpNjU\ndWhM4RrWilCc6unp6enpqfZLISIiIqJX79ChQ35+ftHR0StWrEhISEhMTOzXD/tFVvnwzHb2Eq/j\nC9ewVoTOqSJy4sSJffv2paSkPPlI27Zt+/Tpo6arIiIiIqJX4dKlSxs3bpw+fXpgYKCIeHp6jho1\nytPTU3tP+lZLWWqBwj8DaN+pMvfv31+8eLGvr29YWJipqekbb7xx4MCBt96Cuq5okdQp0DbHr5tB\noxkPhjpOJE+DenBc7QT1qW2FhEQy8zKQ2GxbqJ2HiCjrRCOxu07jkZh9JPRDf9LnOUjsWgAU8wJf\nl7kJSCp9rh0SmzAPmlNE/O5grZ+qbIVij/9AUsePQHvc12LbQ8FuU5OU0Ob15dCcohoD7Q4XkT+3\nQrE9WGxuM+gCK5+GRos1h7bqztgJbV7/EbuDWWENqUTkxxonkVi3XKhQx7QDtGPeDWs2dM73OyS2\nA+uYIzpmSMod2wlt6IsWYPQyhAow/K9YQcPd90VS8T0SkZjdcajblFFXqJ4nxAWKQd9IRM5idWsi\nMlWg7/5noGe6oLNq1OnTp/v27evp6RkUFCQi9vb2LVu2DA0N1d6Fe9nSuoX71atXPT09e/fuHRAQ\nkJ2d3blz59zc3JCQELbGJSIiItIuSqUyNfVfjWrT0tLMzKAfTV9HWrdVxsjI6PHjxyJiZWV19uxZ\nETE2Nv7rL6gLNxERERGVH61btx49erSZmVlycvKdO3cuXLhw5syZUaNGafq6SktdFavlSkkW7k2a\nNFmxYsXRo0fr1KmzfPlyOzu7X3/9teIdB0lERERU4VWpUmXp0qWbNm26cePGrVu36tWrt2rVKktL\nS01fV7GpZaX+zMEyRZwqo3XvuCuVyq+//jotLc3BwWHChAkHDx5s2bLl++9DO1aJiIiIqFxxc3Nb\nvHixpq+itNRUk/qv1X8Rp8po3R53EbG3t7e3txeRtm3btm2L1clpG7PlUL+JGAeo30R+BFR1qlcD\nSYlX4g4kZmE1AIkpsLquZF8kJSJisRVNIproQ5WdQViNHbijyysb6pmRsQqqEtuBVZ2iJaciaSuh\nOtGcq9CXzmpdcyQGNiUZdwmKqeKgsrNl8VCXrmVpv0CzYg+riNTHnk6tDmOFZ49DkFSmri0SS18F\nFeMtwZq+GY+BbiZgBbOIdLu2H4kldoQq7GOwtm/HoZSYjYZi/2sKxQaroE5os7ASxrXh7aGcCFQl\nKiLVoc56OQfrIzEj7OpUsXORmKLy10hsk0CtppRISORhW6jkVERioLuOZEJfYGkRAMVCoVRZ2b9/\nv4GBQbt27Z58ZMeOHdWqVfP29tbgVWnKy0+W1K6F+8OHD7dv3z5w4EBdXd3Ro0dbWVnVrVt30KBB\nJiYmZXF9RERERFQWkpOTr1y5cuHCBUNDQ1PTvzuLp6Wl/fjjj2PHjtXstZVbKi3aKpOcnDxy5Mga\nNWoYGRllZGQkJib6+voeOnRo2LBhW7ZsUSrBn3uJiIiISMNiYmI2b9788OFDHR2dsLCwgg8qFIqG\nDRs2bw79MlZbqLNQVYvecff393d3d58/f76IZGRk6OvrF2yVmTx5sr+//8CBA8vmIomIiIhIzdzd\n3f38/Hbv3m1kZNSlSxdNX06xlXg5/kwF6gsUUZyqRQv3y5cv9+z5964+XV1dR0fHgr+3bdv2+PHj\n6r0yzcsOQ1Kfpr48IyJ+LlADJq950Fb4EIE2r8dec0Zi22pHIjEj+NygS9iWPvs715FY8DdQCx7l\naKify3AkJCKPoAfCZQo02D2wn0tVMCci0Ob1vHhwsElIavi9Q0gs5yz0pdOv5YjEJBq6tib1ob5a\np45Ac4pIfhwUe7wK2jXr9yk02jBsV7qJL9TR52F/bG81djMBW8iJSC/sfgLtcBdZ6ATFtkRBsYm3\noFh3XygmUdBGgmXYhum09TexWUW9VWUPB0MxI2xFp0p5eUZEFAZfIDG/dKjSTHQMkVRiV2g3v4jU\nx1o6hT3ajMRC6nwAzqtBvXv31vQllFApqlHRFb92F6fq6urm5Pz9DdLCwuLrr/+uL8nP1+h+HyIi\nIiIqEZVKtXv37uDg4MzMzCcfHDZsWAXbLfM0fMVfxJv6Gl3z6hQrXadOnYMHD6r+3Q1epVIdPny4\ndu3aar0wIiIiIipzJ06cCAgI6Nixo76+frt27d555x1bW9tataBfd7+OVPCfMlC8d9z79u07dOjQ\nmTNn+vj4uLq6qlSqO3fu7NixIyYmplevXmVygURERERUZi5fvtylS5dOnTpdvHjxjTfe8PT0XLRo\nUWRkpDb2YCqZ4u2V16KtMiYmJmvXrvXz8xs3blx2draIGBoatmvXbsqUKUZG0Fngz5NwKeibHfuj\nHls17j7Q91235wfTzgUFRaRZ/afbuw4lPIOeiIiIiP5mbGyckZEhIjY2Nnfv3vX09DQxMQkLC/Pw\n8ND0pRVDac6NeUGtauHiVM0eB6l4Zt8LSKVSxcfHKxQKW1tbhUJRyotIOLeh+8Tt4tGxt9Pt3QfD\nPEatWdO/6OfKvaCF/RccFHFacXBXY9N//Sdvb+/g4OBSXsnTemGfl3/2fSSWvaMKEls0BEnJZ1BK\njmIxsNeMze5+2HgiVaCSHdGBzg9N6gk9EPOwitjlUEpUWXeg3IMRSCr7BNRF5gfs0ReRPljZsZi2\ne3lGRJXxJxKb7HYeiWW+PCIisjYB6sAi+WlIKrrKZCQWmA3NKSLDb0MFoI+GQAWg1gFQfeJ5O6gm\nzhP80qVD7WGGOX+HxJb1h+YUEYvls5FYGweoUw9WJCyhkdDdqQn2yZ45A03qjvVpOtMDilmuw2qT\nRSTpe2hAd2gRk4Q91X1qQE/1DbOQFEr5DhQ7jPWGaw0X/+tVhWJLsN5E07AaVrGCvwGUgYiIiJEj\nR27YsOHBgwdfffVVx44dAwICFixYoNmFu7e398qValjXlf4gyKc3xDdqpEgehP6PFv+Tki2zX6CE\n71orFIqCzqnqkPDdnO3iNeHwlz2VIi2cxo5dv+pS100epoWCSSGTFxy0cLJIjjLVV9PcRERERK8z\nFxeXNWvWmJmZeXl5hYWF/fXXX8OGDdOut9tfoBQnz4io9wB4dSgH203SIn5LlqGDOxS8++rR09di\n08RTt5M8PJ7ZWZX508KpUW6j/jdNBg3dp4HrJCIiIqqI3njjjYK/+Pr6avRCtIEWnSpTFtIiL0eJ\nU0Pnf95g1zN3KSqWELJxSYjMXNC/qsC/7SYiIiKi57tz587y5ctF5MKFCz4+Ph9//PGuXbt4zHeB\not9uz4f/lIFy8I57bta//qk0d5ZCa/PcsC+n7nYbuqaDg2QW9Hco6sIvXLhQNpdIREREVFbUvoBp\n2LAhErtx48aECRM6deokIhkZGcbGxq1bt967d+/58+cXL16s3ksqU2W0oeWHH7S8c2pZUFZyFolK\nyMwVUz0RkczYcyKt/505t2lxiMiUJtb37sU8Cr0tkh55I9zZvaql8l/XDz5NQZewWKwTVHV6JwEa\nbXaSPxR79A0Syz4F1UT+jJWdWWB1XSJyRqDkb9hogVgpXv2Akdh4mFSoS6jouyCppJnQYH1i0eo0\nFVayqUg7jsQisKrTZfFYB8jHIUgq2gl6vDKxX7CZYscN964LxURE9RCrOt0B9RNdjFWdTgYbu5p3\nRlLZh6CvsB9YOHUHqxMUkTioKaYDNhhUdCwSgt2dzka8j8RU2RFI7BeBXjg6ZkhKxKgRlhMxaYWk\n7o14G4nFNFBn1WnyPChm6gvF8u5BMbAg1vsalhMxaA51p90v0HfYaQbuSEy9Cxjcxo0bBwwYMHDg\nwIJ/Wlpadu7cuU2bNgMHDjx9+nSzZtjhFZr2irehq7vctHg0v3DXs63uIfLLH/fe7eoqIpl3r0SJ\nOJo/fd5I0p+BYSKyZOT/LzCXjB10Y0XglMavywmjREREROp1+fLladOmFfxdqVTa2toW/KVFixbX\nrl3TloV7KctPX+jy5cuXnx1fo9uINL9wFz233h0tZi755KcaK1vb3F88dL1Y+LRwVYrEbBjaK9Bp\nyp55XYfuOTyg4Fr19NIubuo+8eiX/tu8HKDDBImIiIioMF1d3YK2PCLi6enp6elZ8Pe8vDzNXVQ5\nUq9evSLeztfoO+6aL04VkRZT/YZ6RC0Z2atjr4nHxWvF1qGWIpKbkxIryYnZuSJ6SqWpUqlUKpV6\neqZGhiKmxqZctRMRERGVXJ06dYKCgp75YHp6+u+//16nTh2NXJIWUMF/ykAJGzCVhaSEhNxcMXWw\nLdmSXO0NmHJ+gPr+5MdDoxn23oHEvrEagMSGg/13VNAGYec60Ugs8rIJNKlI2sZ0JGY6C9q8noVt\nXjdsC+1KFJuxSKqNxX+R2JGMUGhSXWskNdcAqpcQkVGuUMzAC4pZfN4cyjksRFKqO61fHhIRI08k\nlfM7tI3YoGUnJGbpegCJiUhSKNYrwsAFiuVGIanMo9ALJwjaMC99oZRk3sFeOJU3YONJ+qzqSCz5\nS2g0J+y2E9sS+tINwGqNvm8AxXTMoZj1j1D/nWiHD6DhRPQqQzG7gzWRWO7tm0gsFHqFiSdWpiXx\ny5DUNzVPIbEIaErBzyTvg9VCiPFbSKqNPVSpcURDi7HIyMgPP/zwvffe69atW+XKlR8/fnz16lU/\nPz97e/svvoDqVcqOuhowPVHirfDPNGBKwrqqiYhlQLlpwFQWLG1tNX0JRERERK8LZ2fn9evXr1u3\nztfXt2B7jLm5ea9evfr2BX/2R8Vc+vXolcS6rTt7PNnnnBa2c/23f0QmOrm3+2BUVwe1LkhLsEb/\n4YeiP17EqTKv+x53IiIiItKEGjVqLFu2LC8vLyYmxsTExNKyDI79SAqZMHZOlEjvum3+XrinXZna\ncWSIuPX2qfXL9iUHf4v03zUOPGwKUaJy1aLX+kUUp77mp8oQERERkQbp6upWroxtwyq2tD2fTo1y\n6+gVe9Dgnw9d+Wl5iLitCNzU2FJGtXNvNWjJ5pO9ZrRQ49K92J631i/85r1m95iXi+JUIiIiIqp4\nYk6uWnlJFiwe3dTkSXvNtLP7wiy6Dis401vPtd0EN/njdLgGL7J4NFqcynfcn0v/vetILGkA1PpF\nlQVVnQ6YiqSkV+1IJLbK4OUZEbGAUiK1scIuEdO5UPeimCpQAWivVGjS4Ayo2gnsI/PzFmiwuBr1\nkdj3UGmizE4/C+VERN8RSaWOg6pdc/6CKsB+qwlVnbbKhF44GauhF45RR6g2cRJWdXp/ApISERF9\nJyQVUgsq7PPCKvaUH0CTvmcLNdZZhTVWm1sdaiKzUKCSUxHJTD2KxDp8CT2dvOtBVaegIznQYQIJ\nTeyQWCzUuUhqW0NVp2OgwUREpk6EYimLoCfn4wBoNM+Ug1AuegqSyrkC7T/2wVrSGftAtaS7XPZC\nw4l8gyX7+kKxI3BnvYop89LMmQfdRn3dwla54d+vZhO7J/Xdytrebsl7QpOmeL367jwlqVjlVhki\nIiIiqmBOLp8ZJl39+9cVyTQUyX5q2WlnavzS/z0oqIh30Tp0GPKC/6W4C/Hn1aQ+weJUIiIiIqrg\ncu/9NPNgsseoVnLv3j15FC+SEnk7pnINB1uRdIl/mPIkmRIfKy42hU8Df/EavUgvLUt9ZmXfvftL\nBmRxKhERERFVcJmPokXk0vqJvdb/86ElY4+Lz8HgoQ4NJerohbQRHqYiIvd+/inZbVTtV9NZs7gH\nzpS3zqlcuBMRERGRmpnWHXrw4AA9PT0R0dNL2tStV8pM/yleDiLyn54+MnbT3J31ZnR1ObV+8nGR\nma3dNXy5MBW3ypRTuQ+RlCrl5RkRGYY1O1zZDIr538RaXSrfRFIbqm6ERotoD8VELGtARVsPj0Cj\nBf8HKnZ8vBQqdnznU2hS8Of+n0dAsWCs6WRvhybYtGLUBYpZfI3Vkz1cg6RapU6CRlNANdFZJ6DB\ndMyh2sTPfKHR/loJxUSkbjJU/+0VMxuJxVTthcR0zJCUVMNqnVOxSrycq1BsUhYUE5H0eVDVafC9\nD5FYQhfo7mS7wwqJSfpxJGXzDTTYJ02hWGzWHSQWboiW/4LPE3O/ZCRmsRU6nkCVeQWJ5d6Cthfv\nxTqTdt8JxcRqMJLqNAEtTg3AbhTms6Guw6pI6LuOwn4mNKt20dMzNTX95x+mhiJK47//aeoxYs2E\n+2NXTuyyXkSk94KdHdTbgakUStxg9dUoL18mIiIiIqqgTH33Bz/9b4+e8w63SUjLFT2lpaVpqZaj\n6l1qP1OuyuJUIiIiInrdKS1t1bKv/elt66VfxD9TrsriVCIiIiIi9Stu7elLFfGTAN9xL5/iG0Nd\nTuwOtEBiMdi2b5sjUO8SMW2FpJJ6KpCYV6g+NKlJSygmMlqgT1bXAeqtI3egT7Yrtnn9LFYe0KYm\n1JMI7F3SGEqJQ1woFhSJ7IGktpl3RGKDk/chsRAzaO9yGBISGQz2c9G1RVLVRkDlAVitgYhIq61Q\nLLHuXCQ2GWsithm7vsy2q6AcRs8L2jKvSPoOHFCVCpUHpC3FNq8f+Rqa9XEIklJFQsUGioZQnya/\neOhrkjwE2rzeGwmJiEgX7Hy82X2hB+IBNmmQ/WQkBt1xRFTx0HNYFQUVh4VbQL38sH5ZIiJY8YJI\nVWgP/pfVoX5e0zT6Ji4VD99xJyIiIiLSAly4ExERERGV0qs4E4YLdyIiIiKiYlH7Mv2ZI2WkqFNl\nVFy4ExEREREVi9pLUUWe/UmAp8poDbtfxyAxy8prkdij4JdnRCT3JFT/l4Q1amh0EYqNDshBYjsE\nqsMTEXswV+kLJLXLDqpPCjoAzRnfBao6PXLVERrOHWqt0lXXCInFVq0PTSpSKfIsEhsYjHV0uu+L\npBpj3UsCsdYqSqxw9nNoMIm9DdWI4wXWmdlQYV/SxG1IbB42qUFvqGNOFz2oY853E6BJTSdCD1iT\n2tAXRESgz0EkDGvANMZ2JBJbMh+a1HhgPyiH1UQ+/hYqTgX7Jd2EUiIi0LcckYkfQfcTsO3TBSwW\n54rl9GyQlMIUel27JvRHYvYroeeSiJgMg54nCn2o6hQ0TY1jUSkU/kmAp8oQEREREWknLtyJiIiI\niMrUqyhdLWNcuBMRERGRVirBWrxwBeoLFC5O5R53IiIiIqJiK1F9ajHW+oWLU1XcKlM+KbGqU6hM\nTKSWNxQLi1uKxGx3LkNil6ZGIzGLBUhKplXGWhiKzMDqycSoEZLqc8UKiU2qm4jEvtiCpCR5Pval\n+xzq6lo7FWqIuw1rTSoigzP+RGI6NlCJ7cMh0Cdrcxxq7DqlLVQSt/DAfSQmyVBJ7DdYBXMM3Dxx\nOxYLhvq6iitY63wVGi4wCyqJljio+Du+BdTB9GzMbGhSEbEZh6TqY4V9oddrQpPqQsWO6X5QOalJ\nP+jxGo51a8aOEkC/lYjIxbZQ7BzWiNcNm9QdO19Bp8bHSCxp4AAkZumPNZPO/AtJmXwC1fSLoLWz\nqogsaDSHhei89KoUa61f3opTdTQ5ORERERERYfiOOxERERG91oqxV55bZYiIiIiISqyUJ8Y8r2K1\niOJULtzLp8wMaHddEyNoR+9ZbLRr2Gi1sX2f5lhHBx0PKJY6Ae1e4YLFnE3fBgdEjMZifw6BYt2w\n0eJ2Qu2csvdCm9exTaQiIqFYFcHS6E+QmE3wWGjWO9CGfqvV2Kbk/DQohhmCNYfKhh4uEZFNX0Kx\nSuFQ9QIq/TiSiq0M9czRrwPNaXcrA4nNwJqIiUhbrFPbpTPQaIpaUGMi1b2WSKzyPOgZEDIPqvrY\n/gBq0ieph5BUW+wzFRGrldAWfIs60GcBbTYXyU+BYrEuy5HYO9nQaBcWQt8Q0zdBox2LgmIi0gcr\nShmDfYX3CHR7ilVp9KSSCqHInev4ar5796I/XkTnVI3iwp2IiIiIKqDSr7mLWPrzOEgiIiIiovKP\nx0ESEREREWkDvuNORERERFQCpSxLLTYu3Munb7A6UawVhkj0JCQ1CBvsrHMAEssKhD4FrE2HpK7H\nciIfYjWRw1VQgdLjzVi1E9YMZdo1ZyTmVjsSicXeHQzNirXz8EtBn01gZWfS0F5IzPJ76Ml5Hiue\nq4s9nzbUqoXEPlwCjWbUBaqI7f8+Wv8XibVCS/KFyo4t1+RIIpYAACAASURBVGLdi7DWRe4JUPXn\nzXvQnPWxqtO70GAiIh9iMYUpVP/3sUD1fyrsJZaEvfzFGvsk7KHeSrk/K5DY1wegOUVETNshqYbR\n0FfYU8cUmhS7XQ/Php6cYOcyk0HNkZhRO6jmuE/D36FZRc5jByfEYaOBMSqZsluyPzlkhqfKEBER\nERGVVlme9/L3jwRFnCrDd9yJiIiIiMqJJ4t1nipDRERERKSVNHvmvkJVUc789/b2Dg7GO9gAsm5A\nsbgvoBi2QTBz33dIzGE8NCfWCEcssBi0nVNE4EZCs3OTkVgTPegCz4Z3gmbFetxk/Z6OxDICoTkt\n10NbZqNrbISGE3EMex/K6UIbf+VxCJJSuENbCYdCUwr2lUOfdeOxlmTZ59E97rl3oJhRFyimqA/d\nZlVnoc3QCg/o4s4bQn2aqvdAUmK5vB+UE4lyhm5iTth286RPoWqT/0FVPzI+D+o2JTpKKJa8B0nF\nN4dKTVRYhyMR+Q/WSCjsdgsoVw372mF3Th9L6JOFbv0igdhdXZUDFUIo8lOxaSV9M3SjMPkYq12x\n8oViBq5Q7HXi7e29cmWp1nXq2gH/9FaZRo0UcW+g/6P9LVH7MpvvuBMRERGRNin9ovxJ+emLFVGc\nyq0yREREREQgdZSlQkt/FqcSEREREWkSuPQv4q19HgepLhEREWoczQU6A5eIiIioVNS7gBERFxcX\n9Q5I5USFWrir+WmaHQ7FzDsjKbBk52NoSknKvI7EHr4D9bjRMYcmzbkCxUSk1fWDSEyJVZ1mYvVJ\nzq5Q/5JMJAT31crCYoH7oapTsEpYRJQue5HYbaylSwBW1qtKw9qX5EKlc2OwV8T4jFAklvc71GvM\n4D9YwywRg35bkdhcBVROOq4tFLPasw+JSeznSAqsw3XEyoTvBEAlpyLiZAbFQrAeZ29OgEZbAKVk\nwFtQtykzbFIDDysk9h10t5ZxYZ5QTiTMdiyUM3wTScU524HzIsA751RwuNyHSEqRDZ0kocqDjhwQ\nEZNZR6FcxnkkFW0GlYm7ZFWQk0K0SMl3yfMddyIiIiKiYilNiWqJi1M1exwjF+5EREREpH2QferP\nW9x37w5NweJUIiIiIqJXoZTnz7A4lYiIiIhIO/Ed93Iq408kFfsGVGMH9v7ajtX/JTSHqk53XoQm\nBa/tJBYTkXsBHZFYBliMZQNVYiULVIkZvQSa02oKFMP6Zkpm9n0kNtegCjaejGkGxfKgaaUP1sI2\n5yeoela/DVSajPUlFtG1RlKz2kCDTW6xDZw27x6UnI09spbYI3u9yn+RmMNFqCPmGiQk0ucaNFql\nKPQGUMsbip3BOraazlyKxGIXtYOG04fatcZYQU91h3vfILHxsdCT/fHGT5GYiMz/9AMkNmcnNJp9\nkD6Uq7oVSXUbMwCJJWPXJpb9kVS4HdRLHOzWLCJKaY3EhufEI7H22ZORGFSGT6/KS7bOc+FORERE\nRFRipe+l+sTTdavsnEpEREREpE5P9rKXfgX/dN1qEcWp3ONORERERFR6paxGfUbhHwN4HGQ51QRr\nEHNiPjTaxRPYrHkJSMr2Z6hTk4XTciS2G9trrjBqhMREpFdVqN+Q/12oP8jjVdCO+ZhVSErCoc2Q\n8gh7WI0/AFtmQaDttyIiYrPnQyhnNwmKqbKRlP57WCMZHVMkdSSyHxJLaArtDgebDelBjVBERBSG\nUKwJtnkdatImkp8KxXxqQNvNf92CzVoV2m6sUw37iohUE6ihTwa24zjnPLQ/+G2sXV7Y9ZpIzHwa\nNJokQU2pvLF2aWBvOIGbHBn89ywSU5g0QWKqFKjaxGKGCRIblAX1Qoqtrc7N6/5YTODXzjF96Kke\nijVMJG3ChTsRERERkRbgVhkiIiIiIrVQY6FqEfiOOxERERFRsZTRSTJPK+JUGY3iwp2IiIiItI9a\n61CL/hmAp8pojbNxUOMPMfZCUgYN30ZiKROgDiw515CU9JwFxa67nUditROxSkcR/9tYdx2sxZUJ\n1pZElXUHiQWPh+oTa3e3gmbNfYikwP47cWBTEpFdWPlvnwjo8kKw4rk3JyApMR0JFdmmLI5EYsp3\noEl3Q51wRFEHanAmIvkXoBesxWFotO0ZWHOVmOnQaFgx8fnq0MU5DIGenE6PNiMxEfkNi82CPgmZ\niVWdnoUa9Uh4LaiMeTc0mLh8Cr1wgm9DLa5UeWnYtLIbu2PLQ6gHV3wDaLCYytAhAQ5noJd/0wCo\nOPUXJCQyHvtm3asKVOgsItOHQLFlVx2RWHwDqGGi3TWNbr+g5/8MUMSpMhpduOtocnIiIiIiIi2i\ngv/gQ2ZlgUm+405EREREr4vS7oxX729HVCpVRkZOaKhB8+ZInAt3IiIiIip3Ll489vQ/GzRo9YIw\nvhx/Xh1qkYooTlXfwj0/KSn3zz8TBw40Hj6cC3ciIiIi0lYvXqk/oziFqsV4x72MilNVqan5SUlJ\ngwdnHzv28vRTuHB/LlUEVMiicIU6dup6foLEzBsZILGMb+ciMaNRUCmerT9UhxdlNQCJiYg11sQ0\n7z4UewhVdknOAajqdHgS1j5PAT0QeWehYuKkbOxTvYdV2IksFah3ZrdfoOI5r1isT6yBKxQzfx9J\nZew3QmKVjkONGEXPCUl1MYWe6iKytRkU22ELxXyM6iOxb6Ohu4SR4yIkdhf6koj9Oaj7b5T1B9Bw\nIkOx2MoDUCw/DoqlrIRiThug2GCsiPEXrNNtTAPo1eqQAJXXi0ifPKgmUq5Bz07bYzuQ2OO10P0/\n+wJUdA42f3W9Cb0BmX0AesDmY/XQIrKgBxSL7xSNxOzOYN8R6RUq1lk0aj8SXpWdrdDTS505M331\n6hL871y4ExERERFhSvGOuyotLWPXruRhw0o8AhfuRERERPRaw99ZL9lxkKrk5Nxr1xJ9fPJu3y7J\n//8PLtyJiIiISCupaytLMTqnFrM4VZWernr8OOmDD7L27y/Jlf0bF+7P9XgPFDMZE4LEFCrohM7U\nL6BNyWZfYM1Q0qD+FbeuQ4PVGwHFROTReCjWARutJxab/W0/KJcNbcH0toc2TQanQVUEPlgDpq+w\nTigiMg+LGXYeA+XyHiGpR/+FWmHp14bmtP8Zin1TD2rUMjwV2rw8RlpDs8IqXYN2r25/DN0lwrHN\n69g+bclLgGLnnZYjMQdsUhFZewPaPJq+HfpeWwd7rl/ENk0b+pxFYuEjmiAxqL+RyOAQrE4j9nNs\nPLlWbRsSq52bjMRm6FkgsZlTkZQYvAWVuHw5C/pO93jXKSQGtmlbe3cwlBO0OWATd+g5vM8O+o7o\npBqHxKiwIjesl2A137170R8vojgVNvLDDyUvL23BgrQvvijZCIVx4U5EREREFUeJl9qFFfEzALxV\nZvmiRQ/bt8/+9Vd1XYywcyoREREREQhvnNqsZUsrf3/roCAde3t1zc6FOxERERERJB/+89dff+lY\nWRm2aWMfEWG+ZIlaZudWGSIiIiKqINR+8vozit04VVdXYWRkPGaM8ZgxySNGZHz7bWlm58L9uc58\nCcVaLYCq03rp2yExf6yu6zzWDMXzEVTDWhtrNvEdWBMnMvwq1B8kVAl9sruqH0Zibs7fIbFgWyh2\n8hKSkjg3qKHPGWgwsT2ONYcSaVC1F5TLhzqOhFddi8QcsS4iQVhpcrd1GUhs8AaoT9M1M6jqtEPc\nUiQmIsOw6mQ/s3bQcNk3kBTUzUskNB56JNywkrhq2KRHEqE2PSKScwzq1GPyIVQpeDULKsQ0+aAm\nEpMI6PFqjr3893lAMdGFGiGlzIY+UxGpifWuksd/ICnw7tQQ+4b4uz9UdQr2JMo+DD2H07cjKcl7\ngH6FLfdAJzacvYuVG2ZBL38qPfWu158cMlP4VJliL9xFRERhZCQiFqtWmU6fnjRoUM65cyW7MC7c\niYiIiEi7qbEgVURE/v4xoPCpMiVbuBdQWFrqWVpaHz6cffRo0uDBqrS04o7APe5ERERERP+v3j8K\n/yd8j/vz6FhaGnbtWunhQ9M5c4p7YVy4ExERERFBSr9wFxGFnp7CwMB08uRKiYnK96EGCAW4VYaI\niIiIKjI17oAvzVaZZyhMTBQiFn5+qgyo7ku4cH+BBlgrvqTuWNXpzebQcFjDtoZgPVESVIhpuRt6\nuvjugsoERUQUBlAMq53qPAsazBvrsOiAtZNUOcyHcgI1Ew3DqgmVlljJqUj6ESyngopTq0DlZHIA\ne0egM1Y5V0kXejrFYgXWwSOgcm2XbVDJqYhgr37J3lsLiRm8D9W6hV7eCs2afhxJ3YTGkkvYAWUK\nK6jkVERUMbORWPbRuUjs8U/QpCZDDZFYSN1EJPbmBGhSp1joLrGtEnSXGIRVxIpIfEco9nEUlDui\nwhYhmX8hKVVYfSiWuBWJGbSF7pzv9odqWMFG1yLio4Re1+uxbuL5qVDMAi3/pqKVbF3+pAL1BQoX\np8L9l1A6VlZiZQWGuXAnIiIiIi1W0srUly/31VucWnpcuBMRERHRawdZ7pf1qfDFxYU7ERERERFE\n7VtlioUL9+ey8usH5Sz7IqlKFlBzlRtYL6R9AVBsgUCtiy7MgnYbGzSEJhWRLrUjkdi+YGg0E98W\nSMx4IHQY6la380gscxe0LdXuuCcSi8Ja4VggIRERqd4GihkK1HAkLPUoEuuWAfWRSf8M2uQag9Vp\npE6HNq+DDU50zLGcyAIs1scD2pUYYwdtmbXC2kN9PwIqSlBFfYzEmjgtR2L4k3OMA7R5PQsbzS8n\nHp755ZoegUqSsi9gwz3aiKT2Y4MNdj2IBWUotnk9EGvnJ9nhUCxmOpJSWELfN7OCoPqrpJnQ7Tr4\nLtTMS7CN9SLihsUyD0Exu0voI0uvWInfSufCnYiIiIgIVfodLEhlqqivc6q6cOFORERERNqk9NvT\nu3eHJipcnMp33EVEJC1s5/pv/4hMdHJv98Gorg5FXVdC2Mnvvt1/I1HcW3Ye2LOF5Su/RiIiIiLS\nCiU9auZfCq/+NfuOe/nonJp2ZWrHoet/inJ/0/mP3Ut6DVgdUyiSELK6+9CZuxMt3Z1l98qZXYZu\nTdLAhRIRERHR60sF/ykL5eId9ys/LQ8RtxWBmxpbyqh27q0GLdl8steMFg5PRTKP++0WjynH1nTV\nExnSbmvHsZtOhvft6qosw8vKT0FSWd9DVadgPxew2KULVK4pg3Z9gsRcHBchsc7QnCIim6FKPEnf\nDsW+2HASic3ZAo3mC6WkKVRNKrXTNyCxPdIEid3C2nmIiDk0ragSsZYeyjeRVBd9qLAvEOtyorr9\nNhIzmwcVbDbdAHVWSv4cSYmIhF6Bqk6TZkMNfcw+giY17AAV9nVtCxX2Se5DJPUb9lwy9NKHciKi\na4qk8h9BX7r62LMOvDuNw1rDOV6B+uXtqnkKifljL8O4WlhfJZGfwAZ8laGH1tuwOjgvIhj7ZA27\n1UVilYaOg2ZNgY5r+NEaqnQXkYnYrbg/9toJTPoeypl1gGL0Cj1vpw33uKed3Rdm0fXLxpYiInqu\n7Sa4Ldl6Olz+tXDXazb+yxXmtQsuV2ltLSIGeuXh4omIiIiofFFX9eor6JxaLOVl7Wti9+ScNmVt\nb7fkPaFJU7ye2sWuV9XDq+rff0/aPmeJSNcGVcvLxRMRERFR+VGwwb00y/fnVa9y4S4iYmdqjAUz\nf13osynMYo7/RIdC/+3CBfD0XUhD6FflRERERKWi3gWMiDRsCPdeqdBKX59a+FQZbpURSZf4h/+/\noTwlPlZcbIrcvX5uw5g5B5OHrgl8t6hzZ9T8NI12R1IGzQ8gse0PxiAxt8prkdjv95AUKhLrv/Oj\nWWtwQF17KKbAKhQWZt2BcjEzkdR/BNof7AVNKUnJ0GjjLkGjPcS6iIhI2iwsd88Xijl8gaSGYnOK\nviOSUjitQmJtsN5Vu7Gqj0tQuYSIiNEqaAe2B9YKLXJXMhJroge1OTqrgr5rxFVWIDFrqIkQunNd\nRMSyP5J6py50rztkBs3ZLBWKLQyticQSR0Ob1yOgOSW60gAkZo4VQoiIwgO7JWJFDs7YpFAdjIiY\nv4+kuuhCXf8CY7FJbSchqbZT0T3uJr7QTWzMhmgkdq0a1AivoWorEqPyQLPvuJeHU2WUDg0l6uiF\nf/pe3vv5p2S3t2oXXtRd2TN14vawoSsCfT14FCQRERERvWo8VUbvPz19ZOymuTvrzejqcmr95OMi\nM1u7i0jmvaCe/Re8s2DnlBZVw4OWjFwZIh5DGxpFnjt3KydHHGo3cLUsD9dPREREROVI6YtTy6dy\nsfA19RixZsL9sSsndlkvItJ7wc4OBTthMtKSRVKSckXSQvb/JCJyadPYkX//X71XBI5rzLfeiYiI\niF5H6lqdFxwgUySeKlM0j57zDrdJSMsVPaWlpenfV6V06xkc3LPg7/3XBEMbJ4mIiIjoNaCW3qgi\nIvLcHwAKF6dy4f43paVtWbZTKr5KC5BUrPtyJHYiFarECruL1ScaQAVFIQ5zkdhygRowQQVWIiJi\nvQUrFcT6/uQFQ/1BEvpCcwZjVcIZO6HHK20x9OibjoX6uVgugEriRCQ/Doo9GpuDxKwDoYq9t12h\nSbtg/VwCL5sgsb2+0KQPt0KxVmChs0g49lncxNp+yd2eSAps0yZ3oTcx7K/6I7Fo+15IrHc2VK0r\nIsFx0BPlCNa8ZgzWCud8Mygmb5xDUpb/g8q1R06C7pxf70RS8lENKCbwk/NDbLR9WKW7jvnLMyLS\nBKs6PYv1aUr9BKvr3fApEssP80RiIlLJ7TwSu4j181Jlg9OSJr3gB4DCb+rzVBkiIiIiIi3Ad9yJ\niIiIiF6pkm2R5zvuRERERESlVay1+AtqUp8oXJzKhTsRERERUWmB5aoF6/vu3aEkO6dqh5x9UJGN\nxWxotO1ToFgfPRsol7wXSf0FjSX+16HaxIeDb2LjiVQ/AcUeQuVps9pAgy1MOYjEws07IjHX21h9\nrQ7W19F6OJJK++S/0Ggi+lgdm/Xer5FYkAU0b4dHm5HYHn+oPaHKAOpMvH4rVCU2xAlJSbQZVNUn\nIi5gHZtZOySVdwWqYlyAddjd5Qz16+1z+wESi8Yq5wI7QzERtHTesD/UsNl+BNSw2ean+UhM8qEO\nq186Qo/XOGxO8L65GCvDFZEZULdu2b0Yipl80AmJObtCs0beg2pit1lBVaeDM68jMdWScGhS7OYv\nIrGR/aBcLvQS86kBdWzeDk1JaoMfR1P4LXzucSciIiIi0gJcuBMRERERaRiyRZ5bZYiIiIiISkJd\n/VOlqHJVdk7VGvqNoF1uB96H9psGxi1FYiH2k5GYV/Z9JDb80SEkJnqOSMpiNrpBUBJWI6n6duOR\nWGjE+9CkqfuRlCs22jUXqIoA6tElsv0etAXbYho2nMijcVDMZG5LJNYa7CKUHYGk2mBbdYPjoC5C\nLgLtcbc/De2s9am6EYmJyPZKnyGxvLNQeYDum9Bu6G8qQX1kwD5ND/tDO2s941chMVUU9GoVkWGW\nUEcnv1B9cECIUSMklfl1FSQ2Lf0sEotxaILENmKlC4oav0M5kfOmbyOxeljjJx9w8zpWRCQPRiKp\n92pBg8mdVkiqfp1oJHYW6/klIimfQd/WD26FRtuu3qc6FeWZDeulWccXLlctXJyqWVy4ExEREVEF\nod51NjunEhERERFpJW6VISIiIiLSAly4ExERERGVihqrVF+AW2XKKWesy8kkbLRHPaGq0zBsNK8H\nUAHgYqzqqCk2qQfWkkhEDJtDdWyZ2GjxHaA6UQOsYU4eVNYrFlBKoHpYERXWV0vhvg8bT1RZUE1k\nbBWoBKzSn1i3kexIJLXXFRpsG1aHPRjrDqbEqk4/RkIiInIea0rlmRGKxJKH1kdiw+8ORmJigZVr\nZ0Gdf3phNeL+YI24yPq90As2YVAOEpsxAZo0fQlUOm8ycgw02iKo6tQhCuohlT4P6iFl4tMSiYnI\nOizm1xwqY86Sw0hMgXUvirFFUtI2AYr1xKpOQ7EC60c90AJr6wBowNYnoAEt60NP9STNLgbLUkLY\nye++3X8jUdxbdh7Ys4Xlk/+QFrZz/bd/RCY6ubf7YFRXh5IuSNW+WH/mYBmeKkNEREREFV9CyOru\nU3eLR8fezkm7V87cfXBo4CZfSxFJuzK148gQcevtU+uX7UsO/hbpv2ucQ4mmKIMjX/71k0DhU2X4\njjsRERERVTCZx/12i8eUY2u66okMabe149hNJ8P7dnVVXvlpeYi4rQjc1NhSRrVzbzVoyeaTvWa0\nKNnSXc1eerikZhfuOhqdnYiIiIgqJL1m479cMalFwZvESmtrETHQ0xNJO7svzKLrsMaWIiJ6ru0m\nuMkfp8M1eaXFoYL/lAW+405EREREaqdX1cOr6t9/T9o+Z4lI1wZV/155mtiZ/xNT1vZ2S94TmjTF\ny7KIQdRDjVvhuce9nIq8jRVj2mLlqQoDJDVYvzI0GmZaxhdQTscUij0OQSfWtUZSf9WAqp3066DT\nInRqzkZi6cvnIrGkLzKwWZVIKkqhgEYTcUqDmiw6Yx0WI6thLRZzHiApvTe2IbGm4Hsrhm5IKjMF\nKiaTZKhuUkTEwBlJJflAVaenAqA5L+2EvnTnBIr5R0I1x1B7VREx7wwG9RsZIjHbXX8hsfru0Pfa\nw1hNZEbgWiT20UVotO1jjiMxk1GdkFjOBegsARH5GgzqQQ2bN2Otjv0XfI3EetlCnVNDE6DR4upD\noyW0hYpE9aC2uSIiYgjVxNtdhjrsJsHTVmiZvy702RRmMcd/4pPdMHamxi/934KCiujs3aHDkGc+\nUoJF+TMVqC/A4lQiIiIiel2c2zBmzsHkoWsC331ydky6xD9MeRJIiY8VF5vCb3EVXqMXqUT1qeha\nn8WpRERERPRauLJn6sTtYUNXBPp6PNkIo3RoKFFHL6SN8DAVEbn380/JbqNqQ7+bVhN8rc/iVCIi\nIiKq+MKDloxcGSIeQxsaRZ47dy4k5Fx4Uq6I3n96+kjUprk7zyWlJQQtmXxcpFdrd01fLIrFqeVU\n+lZo86fJnF+QmOpPIySWCDWlEWv/+UgsqtKnSGwBNGcx3MVi30+FYsewLZjgZxEcCxUbmHwE9erx\n0YUe1q3YnlSnq45QTiT+TWjzOtjiygfbW/9tGNTjymrHUiRmGYE91y37I6lhWHeYpT2gOUVExwyK\n5d6GYtDGahEfLDYtNxmJndeD2oiBfdWSBn2ABUUXq9PJvQPFQmOgohSwiAgsSdowB3pdq1TZSEyR\negiJfYZ2uJLp2C3R7G2oLMF0ElRsIDrYVxizGNsKP+2yCTSceU8klXUQKg4REbkPPduzL0L9oRKg\ne5g4qSpkB6a0kP0/iYhc2jT2n8e894rAcY0tTT1GrJlwf+zKiV3Wi4j0XrCzQ4k7MKlDsXbJc6sM\nEREREVUwpv3XBD/vJxePnvMOt0lIyxU9paWlqXqWoyU+OuYFtaosTiUiIiKi153S0la9+9pfsHP9\nxWv67t2f+58KF6dy4U5EREREVFZKdPKMSPkrTuXCnYiIiIgIwoV7OZV9CordwMoTQeuwmJ9peyT2\nrUDFqWuxXhhZAVA5kYgYdoEqO/NvL0di7bpAk7a+BcVyz0Ffk/jn/9bsaV81gGLdoQYsskGgUieB\ny5hir2CttWonIKn62FM99F4jJPZwOJIS22CoYBfrgiYGDbGciPGQMVAuLw1J+XlCVXGVbu9DYgqs\n6lQVDzWlWtcUal5jOgpJiYgooP5LomMHxaIcoFZo+6HBZHgWVCeYvgkaLSNwERKz3Qd1wgqW76BZ\nRT6t+vKMiCgteyGxzDiomhx8XLdB5yZIGvat7tHodCRmfQz6FmY4DIqJSHxd6F5nOhQazQD7NkHl\n0PN213DhTkRERERUPCWuRkUUVKyWt+JUnuNORERERNqnxDvXEc+rWOU57kRERERExVama3eRy+Xt\nVBm+405ERERE9KwifyrIh/+UBb7j/lzDD0OxDVjjwbw4KLZuGhSTyK5IahrYidMIqiashrXrE5GZ\nI6Cq0361oNHqXYdiblBKoHItEagRn4gN1okv0BaqdMzaD3bYFEncAsWcA5FU3jGoEuvcTmjO3NCN\nSMx2b1sklr2nNRLzQkIiPaHKZBGRtXbQY7EYe1FMhcpEJT/0v0hMlREKDfdoA5Kyuwa9wLK2YS9X\nkTHY18Tvek0k5nT3LSQ23GowEgsyrI7E3t2LpCQe7HWqZ42kDk7ARhPJgF7WkoWNFt8M6mFsdwz6\nbK9jLzFPrHI63A6qnLaK+xyJKeKXITERMca+T7SYAsX+wJ5ORCAu3ImIiIiowlJvDSsbMBERERER\nlVxpVucFB8gUqfCpMjwOkoiIiIio5EpXpfrcRX/h4lQu3Msp//SzUO7+ECSlSoZ+EEzfAc0phlCn\nHoNe8Uhsmz7WCgUWjMXGX4C26samHYditaHdkCuhXkPigH0OivrQ6/eaQoHEqk2FJhURQ7DJjYEL\nkkpdDw2WexuKZV+EYk7xUGMtg15zkFjnIW8jMb9IqBWOiEhaEJJqKolIzBl6bgrW801qS30klhkz\nGxouBWpeZNi6OTSayNcHsM/DOQBJnTeCPtkvBCo3GYCERNI3Q7FvsdGmZf6FxEyXJWPjiSm2pVuV\ndQOKKd9EYmMcoW5TnzkhKREDZyTlsAQabCZ2bQvOQKMJ3H7x7E3sRaEPflFIk16w6C/8Rj63yhAR\nERERaQEu3NUmIiJCjaO52KtxMCIiIqKiqXcBIyIuLi7qHbBCKtm2eG6VURs1P00fY5sqiIiIiEqB\n62x1KdZa/AU1qU+wOJWIiIiISP1eWqL69Mq+e/eXD8jiVK0xw6QJEvPARusTtxSJmS7ujMQSu0DN\nUAx6GyAxCyQkEvsA6iIkIulfQ81rUj+Cys703oAmrXQf6iOz8E4raDjbSUgqHKs6tTKD5jQZAhVs\niUj6cqjKzuQzqNwt8xA0qcM5rGDf7mMklfLxB0jszatpuwAAIABJREFUwVZozu3J+5BYLwuow5GI\nQJ+DCFYjLN9jMdfwTkisresBJKZ0mIvEMrHXdV4kWDorWSegmF4bUyTmeQ16UUyvHYnEnLBXYiJU\nryvT0n5HYk1MocrpVeidWJpfgmKKyl8jsYQmI5HYWuzJKY//gGKJUDGx0ucTJLZwiAs0afwXUEwk\n4zD0dMqsC70oHGOwYmd6VYp7+AyLU4mIiIiItBLfcSciIiIiKhdevFGe77gTEREREamNuhqpFi5O\n1ezCXaFSafYtf7Xx9vYODgY7/2DC20GxanuQVPa30P7FRVA3J4E2wotU7wHFlO9AsZVYExkRmXYD\n20Bm1AhJPewF7YY0gvr5iPHU+0gsqV8VJGa5EPpMk2ZAt4/dUEcaEZHhsfORWJtKnyKxX45Ak+r+\nB6oikKSdUEzHHElts5+MxNpB1Rzi+GAVlBNJngA93Rdjn+smbNIrDaCYzUFo46871pUm7N6HSExZ\ndSMSE5G2WOwnrBuOwgWqDhIjTyQVYtYaiXlh3xaTukAlLvp1kJQs+BKKichCbLs52Fkp+yfoeWI4\nCOqXl+AFVS7ZnoHuwxILtZr6BntyDs+6A00qIjfckZQqMweJ5adAc+q+W0EWY2rk7e29cqUa1nWl\nWcEXeHpbfKNGCqh+SEREZouofZnNd9yJiIiIqGIqbjXqM1icSkRERESklVicSkRERESkBbhwJyIi\nIiIqE6Xf5v40bpUpp2KbHkZiRp2hqlPzWS2Q2OwIWySW4b8XiZ2agqSk1YrBSGzdeKhIVESmVd4A\n5e71R1I2R89Co+UlIKmMlVjV6S6odZFk/ImkNgRAJXHTwBYnIgntoarTI1FYH6HELUgqvjbU9svu\nKPZZYC2uZkNjyYRsKJb06BtsPAnBqk4X5kLPk2A96C5R5SI0aWbacSQWhnVWyjkNtUvLxD5TEdmF\nfbIKW6yK1Qbr+xbZFUl5QEXdIteckJTliubQaA4LkZTFl9BdQkQkPxVJKR5B9+EUqDZV0kZAVaeJ\n0GBiqwO1wnLDqk4rYZMeNqyOBWUt9PhL/ygodgD7fqj7LhQjXEU9VYYLdyIiIiKqUEpXk/r/i/7L\nly8/MxS3yhARERERlQtPr9QLv3PPhTsRERERkRbgwp2IiIiIqAypq0SVe9zLKbDdnRFUEyWqhyeR\n2Oqm0Gg+UKWrgLVOKmMvJOYtaHFqfOO3kdh0rBHnF65NkJjZRGg08GGVrDAkpcAaMapSj0KTZpyH\nYiI5Vw9AuWSsF2v1E0jqcjhUnbbHFbq2FVugGNhNsj3WTFjpjt61M+Oghp2xDlAhZgBW62Z/CWvs\najsOioVBjTP1m0MVzNHG0GcqIn0umyCx7BNQ9b9+XSMkpvCEamdPfgp9Fp7ropHYligo9vFe6C4x\nHGw5K/KwP/TdxBqtxIYEYrHeUNGppAyDHohQ7AWRug6K2f0Mf4mxhuj7ZkCfhaEPdr4ClVQJluNP\n156+VOHiVL7jTkRERERUbCUqQi3GWr9wcSrfcSciIiIiehX+r737j4uqTvcA/iFHHXVksMaNpuUi\n7e6gcdvRK92VvUKkWeLLIPYibYQ/atqEG+TSKu1KtayFd4WKJXEJc8xcokQ3Fd2wzDLHDe9tSrEm\nc7ouEjVijttgYw4yxv0DIRR/PMDgeMbP++UfM+Mz3/MdOHPm4czznG+Pcn02pxIRERERKRIT98tU\nkqiMEJZ3TojinM9Ioh5uEi0kc1voIknYKYskCs/pMiRhKaLBAGDoDFHYivtFK/Uc+4OoGPqbYtFG\n8+tFYWqICuvbmjdKwo7LllbZ+qQkCgCS6mRxgw2SqCPjRMXroweJtmmRrYU06N4vJGHJg0ULZg2a\nIKpebRosqqsG0Pzr+ZKwa5tEpdXSAnGXaNmnDSMfloTJ3v14VvaV8Xuy0QAM/q/jkrAK2QG2WrbR\nERD9hNfK3rCQLdT16A/yJWF3DhMdTKr/VxIFABtlrVB3HhGF6d77rSRs63WihZoKRGtDoXyVKCxe\ntKqetHj9+PPSt/9XhaLd6aBstFvzZAs1kV/1qFCeiTsRERERUW/0/XIxF2hX5cqpRERERES+cb6a\ndXlCn5x83v/iyqlERERERP2rVxecOVv37J9n3ImIiIiIFIBn3C9T1bK1Gg4NEa0Pct3/zRYNp7pG\nEvVWyz9Eo7Xsl0TdE5EgCfsvWVsngLtmTRDFDbtVEnV0lag5NbRItM1l/yn6vX6zRNTGdPJvSZKw\nwbKfxy3i5UGmGEVhW+tEr2LkrrWi4dSiBX32vD9aNNpVopVa2mS9buE3iF5pQ72oHxoAri+XRLWY\nRU1sqhtE2zz5/i5J2MSxotHu2vl3Sdgz3x2ThDmCRUcJAI/Luk6X2UZIwjL3fy0JG3jzr0RblXWd\nwiP6et11t6jr9LXXRNtcLWs5BSAMHBAp+pnUyrpON335kCSsafQySVhok2xNIrXodOm+AaJPYdm6\nZQCwRbbA3fA82XBDxou3TMrAM+5ERERERL3R9+bUHuEZdyIiIiKinunXlL39UjPdryrDxJ2IiIiI\nqGd80n56fh/jXFeVYakMEREREdFlpD1f735Sn2fcL1MjSq6ThLWNEHWdGmQNQPamJyRhwnVYoU2T\nRA0IEw32smxqABb+RNRjt/iTBklYxCnZ2rQnPpREffun/5CEDfo30TabRSvYQrdaFJYtXdcPbzke\nkYQ9oH9WEvbfEaKlbnVviH4oVxmekoQ9pBK1dS77eJgk7F8gWq3TNV/WdAa02kQrtg74gWy0T0Vh\nJzaLwrSyd+KpXaJdfcB1oiZRvWyXA1D6hWiv2xIl6joVvieePiRq6/e8+oIk7NgS0Uav3fkT0Wh/\n/EwSNvvEXtFWgbaPREsdI3i6JCrmcLgkzGt9TBIm3Dlfkq0mm/K4aLQxx0WtrpGyjQK4R9bEvlx2\nOYEjkaKDych/+DcbpB5g4k5ERERE1L98UhPPUhkiIiIiojPs2fNO17tjx4quIo2eJ+jtfajnxOZU\nIiIiIqKLkGfqZ+l50+p5E/3uzalM3C9XV2kkUUEnPpCEZQk32npIErU8TFSpmSxbC2PkeyslYTgq\nW7sEWNxWKwk7/miQJKz1E9HiGiFlotVGhqbJyhI1d4hGOzBfEhY06mlJ2F8OOSVhEK+ZUhgnGm2H\nbMWczw2iLoLsOlGY6AcHIHyTJMry+UuSsAf+RRQG4KAsrFlWvP7+Z6IluEJkzSFfP/NbSVhQ0CBJ\nmLRh5oRVFAY0TRSFTf1UVCD+s2xRgbjz56J3xMga0UbjFog2+r6swel4pag6XGOSVa4DV41+TrTd\nAtHycEHBoo0OuV0U5hbtwrhdtm8O+61snaav8iVRy47ViEYDYmXLjXneFY2muU+4WfKnCyT63U/e\ns1SGiIiIiEgBeMadiIiIiOiS6l2vKhN3IiIiIqJe6l0KfoGe1E7dm1NZKkNERERE1EuSbtTuyX1y\n8sVH5sqpivHhaFF/Ui5EYa9XijZ6ar+o6/TBfaIlM6D7jShM1l+LkbLRADiXSqKGLZF93eQQdfa2\nXTVcEhYn6+t952+ihV8GiBbWwKm9sh7WwaLRAMT8Q9ZiG/yfkqi7TtolYdfKlnP61ZuSKFwn6q8D\nVDpJ1NcmUdep+Acs7Z2dKlyWaMDVkijX55Gi0dQ3icKE7+sxopboOweIesQBbDosWoHr+DOils2l\nshWYfpMrCmtrkXWdNoo63RGcKIm67ojo93WzVtRLCuDdpx6WhO0tFI0WUy9abejUAdHiZUNFhxyE\nLBMt1LRPtmRSjGibeBXS9dcssp/JyVrRgIMmipqY6RLo+aVmAK6c2hfOui3LX97s+HZEdPLMOZMN\n/p4OEREREV1ZmLiLOK3lyTkVMCak6g+Y803Ww6WlaUZ/T4qIiIiIAsqFK+ZZKiPhfCW/AjHztham\nqIE4fVZW2XN1iWaj6ErrRERERBQ4eteNKtS1abV7c6p/KSRxdx/c2QzT7KlqAIAxZY7WnLPrgMto\nDPHzxIiIiIjo0upasO7zJL5r0yqbU3vD3fCxA/px4R0n2FXBo/p/o2ERojBjvSjsj2misAdEUdDv\nFzViou2kJOroL0QLrF7zmmibAA4bRQPe6xS1WG2YJ9poZYkozCJrO4uV9bDuqBNtNFJW1WUXL+wH\nt6hl76ufZkjCvA7RNhtk7aTqdNlCvN5/SqJaVouWkxxRKjojsuz6ckkYgOOL/kMS5pwmatjV/V20\nAGTstaJ+TYtT1ozX1iKJ+u49UddptWxXB/DVONGrGHKnaLQnvnxIEnbb9aJjzutjRBt95T7R2/8J\niMIa9ot2zndFPb0AMFi0Di9iGu6RhP00/BVJ2OeibWKfbEnUgycXScLGFolG279EFCZcShhAm2wJ\nc1WYaDS1bMFmT9sq0XB0Lr3rOhXiyqm94j3zE0gdHA50z0lXrpRlDDKyjxUiIiKiPvFtAgPg/vvv\n9+2A1InNqRenvjYccDg9XmhUAOA5bAUmdQvz7W565CmTD0cjIiIiOifm2QrCxP3iVLobjMAb7zVO\nTowA4Pnc5gCuC1b7e15EREREdFnzbRG8f0tlgtra/PuXg9SOxdPzaoYteL5k0jVfLJmRs12bvmnz\n3K6tqbGxsRaLpV/nsHLlysD7m/jgwYOjRo3y9yx8bPfu3ePGjfP3LHws8H5T/DUpBX9TShF4v6mA\n/DUFZC7RH2JjY0tKepPX9SVN73o9mXZnXVVm/PigqeLRtgA+T7OVccYdQFzuCpNjRlHGjCIAiCle\nZeIFZYiIiIioq771qp6d9He/qgxLZWRUoXNKLXc5nV4vNKE6VskQERERkQ91T/p5VZk+CdHp/D0F\nIiIiIrpCMXEnIiIiIrp0el0Kz1IZIiIiIiIfEGbk3ftQz+ms5lQwcSciIiIi8glxc6oov+/enMpS\nGSIiIiKiS0eY3/v2GvB9x8SdiIiIiEiEZ9yJiIiIiPxJeHKdiTsRERERUe/1vablnO2qbE4lIiIi\nIvKlc9as9yibT04+x4NcOZWIiIiIqN+JrzBzXlw5lYiIiIhIkXjGnYiIiIhIAZi4ExERERH1Fx9e\njp2lMkREREREPtaXfL39IjPdryrDxJ2IiIiIyMf61pz6Mc51VRn/YuJORERERHSG9nydV5UhIiIi\nIlIkNqf6TGxsbH9v4sUXX+zvTRAREVEA6+9cwmKx9Ov4yuWTFlUm7r7B3ZSIiIjoitW7vLy9CfV8\n2JxKRERERORjve0ivVC63705lYm7Inis616o2r4fIyKT778vJkLj7/n4itu6ZctB/GT6VKPa31Px\nCad9xyt/2bz/a0TGT5+ZEhfi7/n4gse27bWNb+xxYHD0pJRfBspvCsDp3c89YuJdk0MVfyjy2ra9\nvu84BgEATp48ef34qYFwoPA0Vb+08s2PHCP0N0275+6YCKW/pby2ba/v+xqDBnU8cBLhE283hir9\nXeWtr339lfU7Hd8iMn76PXfF6RT/hgI8jVteffVta8Ng/U1TUu6OMyh732uy7Xh7X+uk7491gZpU\nKNKF0/3uZ/FZKnP5825bnJJf0xyTmj54Z0XurKq81VunRij9QA+vy1Y8N6PaAWjTbwuIdNBZuzQ5\ntwrGhNRwV1VJXlWNaZN5jrIP9oB16eycKochPnXskP3mgqx1H+ZvWDg5MN63jVueyymoAfSjpk4O\nVfzHlvP1/KJqaPVaHAfQ3DzKNCYmIsrfs+obj33xFFMNtAmpd7jeqMitqTA9XzMnStG/Ks++9ctL\nDkILAMOGweFoRuqoOKUn7rbKX2eU1eljEhMij5lL8qrWmtavmaPz96z6xGPLnZJRC21C6h0ndlbk\n1VSYSjfNMSr0cO7aUf5YXkUdoDecPtYFZlJx5WDifrnzNr6VX9OcmF+5YHIYspOWTp9RUPbmbYWJ\nyv7ZeWxz78ywGxPTDe9W2Acr+7Wc5tm+ogrGBe+UJqqA+25flZBl3lH/y0RFHw299auqHIZMsznN\nAGD6vz4+q2j5nocnRys6d2rnqp1fUKPVa5sdmoH+nosPuA9bgXmrN6REBMSbCQBgf+25GhgK15tj\ndED2zPK77zS/ujP9yalKfoWalNLNKR13XNald+a8ceMPlf52cr+/sQ7x+WuenAwgJa48IavmoHuO\nTskvy/bas7Xf73u/2vL4lIKsP0+2LAzz98R6wVaenlcxLDHBUF2D9mNdYCYVitWLsngm7pe7f1g2\nA/Epk9uPGKEzchKq8jfa3IlGJR8WoQpOyMxfljb588pPK3b7ezK+ofrZw4XFwWPa92n11VcDGKRS\n+B6uCvvj+vUqXee5sxZgZLCS/xLp4KlenOswZK5+FLNMG/09GV9QATCMGu5utH35TwwNj4wIUfiu\nB7jf21inTy+NCXHWWQ9iyDWz11jm+ntOPtX0Qk4VEgsDoE4Lx7+/6W09ecZ9hTrphj5p3Okjn3pC\nUiq27/ynG2EK/NgdGD6neFPK2K8rq2tOH+sCM6nwt15fLubCnalgc6oStZ48An30yI67IaF6oK7V\nnzPyBVVYSloYgNaTbn9PxVdUYcaYjvMxror8IiBxbJjS93CVRqfzNllXbfm45cgnFdW18QvMBqW/\nJsBZ+0JRLfLWpoUdW+XvufiG5/PPHLDnJN/Z8YChoHJZXJii/8byAnBU5MVWNHc8El+66UmlVit0\n07RjZTW0BffF+HsifadJWTLPnJE/PWv7LfqW6ppaQ3rxWMWngBo49hzwpEWpAWBf7R7A0XDEY9Qo\n7z1lmJoCwP3lyc5HAjOp8LeLtqWeL7NPTr7IyN2bU3nGXQm6ns8AAATCl/sBy7NtcbrZrs1fmxPq\n76n4hLe12VpjgeYwAPv+T50wKLt61WsvzK0ymEqnhsJzDEAgHIe8rW4AqXnmzKkGr7Pu2TlZefmv\nbTWnKS/L6OT58hMHAGQWr02LDnU37liUlpe1eMs7hYoulenUtDKvRptYGKfs91I774F99vZbrScA\nwH7g40OeaEX/2Rg1fbbenJcxJcs0L/6burVV2x0ADh3zAEp+VV0xqbjkenvBmctu5dSr/Lp1ZfC2\nAGjxnnmffxxftqzlD+XXNJtKKwLhG3AAgDpscukac6l5c415nqO66K91Ln/PqE+s5iW1QNLNVzc2\nNu3fewA43rC/3uXxXvyZlzFN1ByLxZI91aAC1Drjg7kJsG/br+hvs9ThYw1AfFZadCgATVjcbJMe\nnzQo+jV1atq2sgba3EA43Q643s8rqUnIr9xc+uTCJwst6wv1tebl2xv9Pa2+0cWt2fR8ejxqVq3a\nPySpID8V0I//UYB83cOkQum+E//rD0zcL+76n05E86a9ztN3P3pzExCt+HamAGVbl5tTYTcVK/f6\nA2dy1T1uetzakahrbvipATh5wWdc9lwfbLIDKMpIS0ubkVWyHWguypr1wsfKTghd1lV3m8qbOu6e\n+PYEMETpHRYA4HB7Om56v/HnRHyqcWV+jTYxLyBOt8PdsLcZ+LeojjpB3ZiJWuz+7LBfJ9VXbvuO\nVa8fnf1k6ZrNm0sXpkW0NgLh1wTK2XYmFYrwcQd/T+RsAfDB0u90N98Rg6q8R8vNS+4e+NmG3Opm\nY2ZSYNRgdGjx9wR8o35LUUZJLYymcUMarNb/a21F6Jixym4S1AS32LfnPFb+/GN3h6u+fvvFJXZg\nxtWKPsCHmNZtvbf90KNSufeYk3PeLlz7UozCr8enufZqh73oqaU/fmzmz1ob3s4v2I74vEiFv6bE\nh01lWSUFlT94aNqNRz/4a1aVQ586PgD+IG7csrwG2sJfBcTpdkATOdGAioKnysMeuztc7flww7Kq\nZqTH/Njf8+oTFb4wl5VZjubl3xN9rO6vGQW1BlOpwq/Y9P3n7BWQVPiBzzPszqbV7s2p/q1xD2pr\n8+8ElMFdv2XerIL2KkJ9Qp554VRFp05d2StNpo2Ta9akKf8VuSuzEsrqzngotXhTdrSyMw1n3bpH\ns0pOV7BCm56/dO7kCL/OyJc8tlVTMiylNWblX07BW7euOKuk+vQ9Y/rqp+cq+kqk7erWLc4qqWm/\nrU9YUL4wUdlvJwBoKoqdYU0tXJMdIIk7AJet+jcZRR1HCcRnFuelRSt97+u67xkS80oWKPtj11Nf\nPWXWy6U1a9qPdQGcVPSA215Z9pf3Gr7WR95+f2Zi9+LW2NjYkhJLj4bsjxPkZxXHjx8f9EPxc78A\nfJ5mM3EX87qdLg9Ual3Ilfj+Ir/yupxOD1QhITq1ss85BTqP2+kOuKPE6Rel0YUoPRUMbF6X0+UF\n1BqdAq+8ch6Bve9d4UmF25abkFELQ2r66Dcqqpv1qWvXZJ/1tUMvEvf+cNZVZcaPD7pe/Nwv+yFx\nZxYgptLoFL2gBSmYKkTH71GVQK3RqQPuKBGQLyoAqUJ0AVGz31Vg73tXdlJhq362FobiTeboEGTe\nHnnrrKKVO2YsjFPGJx2vKkNEREREVwj3+xvt2sQH2ktZVRG3zzPgvf+p76eNjR8f5NsB/XtVGZ5x\nJyIiIqJLatjI4I6b6jGxhuZ1e10LYnzVRXNWsXt77v7SSx/5ZHAuwEREREREV5CRmqEXjZk3L/YC\n//v006PP91+RkadvTJiwAsCuXQ9MmLBi9uybdu16oGezBAC0Xk6X2WfiTkRERESX0HEcOXqs896x\nI4cx6hxX6rdY+tqc2tb2QlBQ0IQJK9pT9vY8vm8Noyv8W+POxJ2IiIiILhl16Dg43t7tntt+eczG\n16ubDZlj+uniQe1pelBQEID29L39dq/TdybuRERERHSFUE1MSUeWeVHlvy5MHLWrbP52IG9S5EWf\n1hc+TN+5ABMRERERXUHq1j2eVbK9/XZqQWV2XNhZAbGxsX0vlemuPV9HR+E7epi7BwUFacXBzbyO\nOxEREREpnTHlya23Od1eqNQhIZpLl452nnrvLHzv6al3lsoQERER0ZVFHaLz16K4Pi9876NTp04N\nGDBAEsnEnYiIiIiuOL1L332e2jc3NzscjjFjxkiCuXIqEREREV2h2tP0CRNWdC2e6SyF786HK6ce\nO3Zs3759kyZNWrNmjXC2TNyJiIiI6MrV1tbWmb7v2vVA17Pv5wgW/7sAj8fz7bffZmVl3XjjjR9+\n+KF8qiyVIaKA4m60rnt14ycNLRgxcuK0lGkxEf17mPPYsqZkRD9fMydK020q9dUvvvLmfseI8OiU\nmb80hp6jmNPTWPva7oG/SPzx7uq3WkffFmcI6fJf1tcszkm/mFi/ecvAidOjz/V0IiLyFWHlTB+b\nU7/77ruWlpY///nP8+fP78XTecadiAJH446lCWk55mpHSPhIOKqLcmfdmlXpOn+8x14ZG2uq8/Rl\nm94jQIvXe/bD7rqshFlFVQfCb4p0VJuzZqRsa+oWA+fStNxt7iFqqOpfLslb8nrXifx9eU5Z2Ucq\ntWbo0bU5M164wKsgIiJfaT/73lk5s2vXA2dVzvSlVMblcr311lujRo3qXdYOJu5EFDjc1vl5VYiZ\nt95iXrhgQaHZUpmfiLqyv1idHQFOu81ms9W72lNor+dQ01HA3vB5k8t9Omf2uprsNpvN3ni+ZN7r\nbrLZbDZ7o7vLg4NV8DjrbTZb4+mh4fzob3XQFteYF8zNNr9jjkfz8o22s4Zy1r5YDcMjv4gCNLf/\nOhH2yt2d6bnHtn47Yhak6ADjvQv1qFpTx9SdiOgSuUDhe+9KZb755puGhoakpKQ77rjjq6++6vXE\nWCpDRAHCvmWVA/riJ1J0HY+ETc4u/Hpk80AvgKYdS2fkVXX8j7F4/Z9Gf/nCrLwqAEWmGcvTSzfP\nNTbuWJrWGaNPXf1SdsSZ9SlnBMBYuP5PMRqMBMwZd5o7Hs18flNaVIgmIqmweGZ0e/mM6gc/1MJ+\n9nzdb/6pGomFUWoA0N2cZET1+h31MYkRAJy7366DvjAuAgA0UQ/GI//lXSbjVB6yiYguje5XfG9f\nsKn7l6cXEBQU5PF4VCrV7373u2XLlvV9VvwUIKIA0eo+Am3C6M5Sc6/XC3VMyhwAgHtn1RuG1IKS\n7DiNp75oyqz8v9o2z81eX4zknJ2F61+O0angqp2fVxWTWbwoLRpN1idm5GS/ELM5O/r7Dbhq5+dV\n6RPzynOmalx1f0jOyi1817JIdwKAIb2yxBSmOlQ5P61s0YbENXM0oVExoaefZ69+pqIZC6adY0Fv\nbfDQ07dUhnsTtbkvv+tOjNDAa11fBeOCcacr3lWhPzJgXYMbCOk+BBER9Zvuhe89MmHCitWrVz/4\n4IO+mg8TdyIKQG5beUJGRfttQ3qpea4xpXTzdFfTIbutofVEsAHDAADqIcMBDFWrALgb9jqApIgh\nn9tsGDpkrBG1b3zgyo7uzJXdDXsd0JdmTw1RATrj7zetfdCrAQ4ASH/43jCNCgiL/rkBlS1dZ+K0\nlpuKtsfMMyeGnXn23ntkjwN3jA/vfGBcUhqqy/6nac7kENuLtUgs/HnnEwYOGgIer4mI/KRr+t5T\nPszawQ8CIgoY3hag+Ygb0ADq8KTnn584dOCxMlNuAwC4tyw2FdQ4AABaAIZ/7/Z81WAAZbkZ7fe0\nWq3+xmtUZwd8vzC3KiQ0DIAHbqDzu9PWk25gcOcz3LZ1yTkV+tSCxSmGc8550MDvb6sNkxJRtv5/\n640j33TAmHSzrkuc8GdARET9pa2tLSgoSL66altb2/Hjx1955RUf5u5sTiWiAPGjCQlA9dpaJwCV\nJjQqKirihmsAaAar4P70xRpHfF6lxWKxWDbMM+Dw98/TqNqTcW8LoC3eamm3efM686K7zrjEo7cF\nOHyso2vVZbfusNZfYD7exi2/zCjRJxasyY47xzkS1cixery9u6HLQ6HTMg11y8tKVlRrE+81dHlO\nq/sEelhYSUREPifP2gEEBQVpNJrZs2d7vd6srCyfTICJOxEFCI0xJdOAqtzkpdXWJpez0bbt8btM\ntTBk3hUF1UAAjgZ7Y1NjbeUfSuy4FgCgGjipkc2UAAACoUlEQVQIsO/8e12j062JvDUGzTnzy22N\nzkbblqzYKQnPvnfG+FG3xqA5d26Rtb6p3lo915ST92bjebNpl/XXaQXN0N976zV1Vqu1trau3tk9\nyuFo7no3alKatrl2ux1zksZ1edjbdMCOG8O7XSieiIgud4MGDRowYMDixYsbGhpuueWWPo7GUhki\nChiatPK1gwrzSopyqooAAPr4grK86BAAUQsy43PK8tMqAH1Carx2JwBAfUNsqqGyIj9rU2rx5uzo\nJ1bn/3ZWfkZaBQBtjGn1I3FnDK8yLKoseCIzL2dWdZcA20hg8OmT9hg4aGT7DXfDB3UA4CjKOV17\nA226ZfPcrrOd9GB8Wf5LtkfiojqL2UN/lmZE2cHUeEOXgnj3nuXbkVgQzeM1EZFCDR8+fPjw4Rs2\nbLBarTNnzmxqaurdOD2o1CEiUgSP2+lye1UqjU535klqr8ftgUZz4SVIvR6PF1Cp1efLky8aIJ+o\nzTQl45oFqwsTIy4QZV+XZSoZuvqdwn5eA5aI6DISGxtrsVj8PQvf++677zweT3l5+SOPPNL54O9/\n//v8/HzJ01kqQ0SBRq3RhYaGnp21A1CpL5a1A1Cp1eoLJuUXDRBTR+Xnpx59/yP3hYJce+uQXvAI\ns3YiogBw1VVXDR06NDMz88SJE3PmzOnp03nGnYiIiIguL4F6xr2rY8eOHTp0aObMmdOmTROecec5\nHCIiIiKiSy04ODg4OHjr1q2HDh0SPoVn3ImIiIiI/ObUqVMDBgyQRDJxJyIiIiJSADanEhEREREp\nABN3IiIiIiIFYOJORERERKQATNyJiIiIiBSAiTsRERERkQIwcSciIiIiUgAm7kRERERECsDEnYiI\niIhIAZi4ExEREREpABN3IiIiIiIF+H8ZBrAG5Dwv/QAAAABJRU5ErkJggg==\n",
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Pxaklkxq9YdOBak06tgk2l/ehiIiIiEilYY9t8Vq40xL2rHj2xdlpgEfvoDbBLcr7cERE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\nZkrCpE1MJz64dAyAI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       "text/plain": [
-       ""
+       ""
       ]
      },
-     "execution_count": 16,
+     "execution_count": 21,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -672,31 +925,42 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 17,
+   "execution_count": 22,
    "metadata": {},
    "outputs": [
     {
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "Started at 2017-06-28 13:41:54\n",
+      "DEBUG\n",
+      "['dmm_voltage', 'dac_ch2_set', 'dac_ch1_set'] dmm_voltage\n",
+      "Considering the units V, V\n",
+      "Started at 2017-06-30 14:26:27\n",
       "DataSet:\n",
-      "   location = '/Users/william/sourcecodes/qdev-dk/docs/examples/PlotTesting/plottestsample/031'\n",
+      "   location = '/Users/william/sourcecodes/qdev-dk/docs/examples/PlotTesting/plottestsample/099'\n",
       "      |   |  | \n",
       "   Setpoint | dac_ch1_set | ch1          | (100,)\n",
       "   Setpoint | dac_ch2_set | ch2          | (100, 55)\n",
       "   Measured | dmm_voltage | voltage      | (100, 55)\n",
-      "Finished at 2017-06-28 13:43:13\n"
+      "Finished at 2017-06-30 14:27:46\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "/Users/william/.pyenv/versions/3.5.3/envs/qcodes-qdev/lib/python3.5/site-packages/matplotlib/pyplot.py:524: RuntimeWarning: More than 20 figures have been opened. Figures created through the pyplot interface (`matplotlib.pyplot.figure`) are retained until explicitly closed and may consume too much memory. (To control this warning, see the rcParam `figure.max_open_warning`).\n",
+      "  max_open_warning, RuntimeWarning)\n"
      ]
     },
     {
      "data": {
-      "image/png": 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JB6nWZBioJ5jlcAQTyztA7enQi5t0CPQ6ys1Y5Zpic6mByFhEpYCM1Uxq49/c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\n3VF3pRtjAAAAAElFTkSuQmCC\n",
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DU88RTGxC0FwmNjmTKnZM4YodG3Ll2gZnKuYx7RkmtpnrYA2AahMKnCYrLP2e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\nQn3zSeMXI7EGWPvvuWZQTEQkE+ueffhfJBURMxNaLR0amRQ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oJFUJK16fDx1SHm+B3sFa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\neOdN9ZhonWPjfvviKE910ZxV4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       "text/plain": [
-       ""
+       ""
       ]
      },
-     "execution_count": 17,
+     "execution_count": 22,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -706,7 +970,7 @@
     "#     X: Gate ch2 (V), Y: Gate ch1 (μV), Z: Gate voltage (MV)\n",
     "# Check pan/autozoom\n",
     "dmm.voltage.get = lambda: 1e6*random.randint(0, 100)\n",
-    "plot, data = do2d(dac.ch1, 0, 10e-7, 100, 0.1,\n",
+    "plot, data = do2d(dac.ch1, 0, 10e-7, 100, 0.01,\n",
     "                  dac.ch2, 0, 10, 55, 0.01,\n",
     "                  dmm.voltage)\n",
     "dmm.voltage.get = lambda: random.randint(0, 100)\n",
@@ -722,31 +986,42 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 18,
+   "execution_count": 23,
    "metadata": {},
    "outputs": [
     {
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "Started at 2017-06-28 13:43:15\n",
+      "DEBUG\n",
+      "['dmm_voltage', 'dac_ch2', 'dac_ch1_set'] dmm_voltage\n",
+      "Considering the units V, V\n",
+      "Started at 2017-06-30 14:27:48\n",
       "DataSet:\n",
-      "   location = '/Users/william/sourcecodes/qdev-dk/docs/examples/PlotTesting/plottestsample/032'\n",
+      "   location = '/Users/william/sourcecodes/qdev-dk/docs/examples/PlotTesting/plottestsample/100'\n",
       "      |   |  | \n",
       "   Setpoint | dac_ch1_set | ch1          | (50,)\n",
       "   Measured | dmm_voltage | voltage      | (50,)\n",
       "   Measured | dac_ch2     | ch2          | (50,)\n",
-      "Finished at 2017-06-28 13:43:16\n"
+      "Finished at 2017-06-30 14:27:51\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "/Users/william/.pyenv/versions/3.5.3/envs/qcodes-qdev/lib/python3.5/site-packages/matplotlib/pyplot.py:524: RuntimeWarning: More than 20 figures have been opened. Figures created through the pyplot interface (`matplotlib.pyplot.figure`) are retained until explicitly closed and may consume too much memory. (To control this warning, see the rcParam `figure.max_open_warning`).\n",
+      "  max_open_warning, RuntimeWarning)\n"
      ]
     },
     {
      "data": {
-      "image/png": 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CnSAIgiCIellIFQBs6gyM9ARlRR2dTVX8FcJ+EtmScKeKe0NCwp0gCIIgiHpZSOcBdIe9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\njM5wnQUJEu4EwQ/ZooIrpy8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C9AXYJdyZwX1VZyoj7LfP73BWv8XnrukC0mZIuFcgRB53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dLapsF5bkvEUAgNXKm6aGqwyctURDYt9UjzFP92qVMqrUuF+fzmzf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bg35c1wD11paoUEdwcmuDc6lOHuKyS4E0JRokND/pLtwSruhKtwJD37sP5nsuwYlaaGk5lC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\nAQAAAAAgC/5/CwUhioSoHVcAAAAASUVORK5CYII=\n",
       "text/plain": [
-       ""
+       ""
       ]
      },
-     "execution_count": 18,
+     "execution_count": 23,
      "metadata": {},
      "output_type": "execute_result"
     }
diff --git a/qcodes/utils/wrappers.py b/qcodes/utils/wrappers.py
index e945cee06954..f15cf1db543d 100644
--- a/qcodes/utils/wrappers.py
+++ b/qcodes/utils/wrappers.py
@@ -129,6 +129,17 @@ def _plot_setup(data, inst_meas, useQT=True, startranges=None):
         plot = MatPlot(subplots=(1, num_subplots))
 
     def _create_plot(plot, i, name, data, counter_two, j, k):
+        """
+        Args:
+            plot: The plot object, either QtPlot() or MatPlot()
+            i: The parameter to measure
+            name: -
+            data: The DataSet of the current measurement
+            counter_two: The sub-measurement counter. Each measurement has a
+                number and each sub-measurement has a counter.
+            j: The current sub-measurement
+            k: -
+        """
         color = 'C' + str(counter_two)
         counter_two += 1
         inst_meas_name = "{}_{}".format(i._instrument.name, name)
@@ -154,7 +165,10 @@ def _create_plot(plot, i, name, data, counter_two, j, k):
                                   pos})
             tdict = {'bottom': 'setXRange', 'left': 'setYRange'}
             # now find the data (not setpoint)
-            thedata = [d for d in data.arrays.values() if not d.is_setpoint][0]
+            checkstring = '{}_{}'.format(i._instrument.name, i.name)
+
+            thedata = [data.arrays[d] for d in data.arrays.keys()
+                       if d == checkstring][0]
 
             # Disable autoscale for the measured data
             if thedata.unit not in standardunits:
@@ -166,7 +180,7 @@ def _create_plot(plot, i, name, data, counter_two, j, k):
                 except KeyError:
                     # 2D measurement
                     # Then we should fetch the colorbar
-                    ax = plot.traces[0]['plot_object']['hist'].axis
+                    ax = plot.traces[j]['plot_object']['hist'].axis
                     ax.enableAutoSIPrefix(False)
                     ax.setLabel(text=thedata.label, unit=thedata.unit,
                                 unitPrefix='')
@@ -187,7 +201,7 @@ def _create_plot(plot, i, name, data, counter_two, j, k):
                                 unitPrefix='')
                 # set the axis ranges
                 if not(np.all(np.isnan(setarr))):
-                    # In this case the setpoints are "baked" into the parameter
+                    # In this case the setpoints are "baked" into the param
                     rangesetter = getattr(subplot.getViewBox(),
                                           tdict[whatwhere[setarr.label]])
                     rangesetter(setarr.min(), setarr.max())

From 30f1c0cbd1b32b06143d3d96a7008f4d81a96745 Mon Sep 17 00:00:00 2001
From: Jens Hedegaard Nielsen 
Date: Fri, 7 Jul 2017 15:51:03 +0200
Subject: [PATCH 235/328] Faster qdac (#53)

* add support for faster setting of qdac voltage

* SR830 fail early in buffered readout if butffer is not full as expected

* add option to qdac to ignore return read on voltage set

This ivery experimental and likely to break if you are not careful
---
 qcodes/instrument_drivers/QDev/QDac.py        | 32 +++++++++++++++----
 .../stanford_research/SR830.py                |  3 +-
 2 files changed, 27 insertions(+), 8 deletions(-)

diff --git a/qcodes/instrument_drivers/QDev/QDac.py b/qcodes/instrument_drivers/QDev/QDac.py
index f7d1221f2475..0432ca937543 100644
--- a/qcodes/instrument_drivers/QDev/QDac.py
+++ b/qcodes/instrument_drivers/QDev/QDac.py
@@ -156,6 +156,22 @@ def __init__(self, name, address, num_chans=48, update_currents=True):
                            set_cmd='ver {}',
                            val_mapping={True: 1, False: 0})
 
+        self.add_parameter(name='fast_voltage_set',
+                           label='fast voltage set',
+                           parameter_class=ManualParameter,
+                           vals=vals.Bool(),
+                           initial_value=False,
+                           docstring=""""Toggles if DC voltage set should unset any ramp attached to this channel.
+                                     If you enable this you should ensure thay any function generator is unset
+                                     from the channel before setting voltage""")
+        self.add_parameter(name='voltage_set_dont_wait',
+                           label='voltage set dont wait',
+                           parameter_class=ManualParameter,
+                           vals=vals.Bool(),
+                           initial_value=False,
+                           docstring=""""If enabled the voltage set does not wait for a reply from the 
+                           qdac. This is experimental and may break but has the potential to speed up
+                           stepping the qdac""")
         # Initialise the instrument, all channels DC (unbind func. generators)
         for chan in self.chan_range:
             # Note: this call does NOT change the voltage on the channel
@@ -208,18 +224,19 @@ def _set_voltage(self, chan, v_set):
             self._assigned_fgs[chan] = fg
             # We need .get and not get_latest in case a ramp was interrupted
             v_start = self.parameters['ch{:02}_v'.format(chan)].get()
-            time = abs(v_set-v_start)/slope
-            log.info('Slope: {}, time: {}'.format(slope, time))
+            mytime = abs(v_set-v_start)/slope
+            log.info('Slope: {}, time: {}'.format(slope, mytime))
             # Attenuation compensation and syncing
             # happen inside _rampvoltage
-            self._rampvoltage(chan, fg, v_start, v_set, time)
+            self._rampvoltage(chan, fg, v_start, v_set, mytime)
         else:
             # compensate for the 0.1 multiplier, if it's on
             if self.parameters['ch{:02}_vrange'.format(chan)].get_latest() == 1:
                 v_set = v_set*10
             # set the mode back to DC in case it had been changed
-            self.write('wav {} 0 0 0'.format(chan))
-            self.write('set {} {:.6f}'.format(chan, v_set))
+            if not self.fast_voltage_set():
+                self.write('wav {} 0 0 0'.format(chan))
+            self.write('set {} {:.6f}'.format(chan, v_set), fast=self.voltage_set_dont_wait())
 
     def _set_vrange(self, chan, switchint):
         """
@@ -535,7 +552,7 @@ def _rampvoltage(self, chan, fg, v_start, setvoltage, ramptime):
         self.write(funmssg)
 
 
-    def write(self, cmd):
+    def write(self, cmd, fast=False):
         """
         QDac always returns something even from set commands, even when
         verbose mode is off, so we'll override write to take this out
@@ -553,7 +570,8 @@ def write(self, cmd):
 
         nr_bytes_written, ret_code = self.visa_handle.write(cmd)
         self.check_error(ret_code)
-        self._write_response = self.visa_handle.read()
+        if not fast:
+            self._write_response = self.visa_handle.read()
 
     def read(self):
         return self.visa_handle.read()
diff --git a/qcodes/instrument_drivers/stanford_research/SR830.py b/qcodes/instrument_drivers/stanford_research/SR830.py
index b7d315cd3c8a..1b0827f6231d 100644
--- a/qcodes/instrument_drivers/stanford_research/SR830.py
+++ b/qcodes/instrument_drivers/stanford_research/SR830.py
@@ -108,7 +108,8 @@ def get(self):
         # parse it
         realdata = np.fromstring(rawdata, dtype='
Date: Wed, 19 Jul 2017 11:09:30 +0200
Subject: [PATCH 236/328] Resize qt window before saving (#54)

and reset histgram too
---
 qcodes/utils/wrappers.py | 16 ++++++++++++++++
 1 file changed, 16 insertions(+)

diff --git a/qcodes/utils/wrappers.py b/qcodes/utils/wrappers.py
index f15cf1db543d..31b8b797d946 100644
--- a/qcodes/utils/wrappers.py
+++ b/qcodes/utils/wrappers.py
@@ -388,6 +388,22 @@ def _do_measurement(loop: Loop, set_params: tuple, meas_params: tuple,
         for subplot in plot.subplots:
             vBox = subplot.getViewBox()
             vBox.enableAutoRange(vBox.XYAxes)
+        cmap = None
+        # resize histogram
+        for trace in plot.traces:
+            if 'plot_object' in trace.keys():
+                print(type(trace['plot_object']))
+                if (isinstance(trace['plot_object'], dict) and
+                            'hist' in trace['plot_object'].keys()):
+                    cmap = trace['plot_object']['cmap']
+                    max = trace['config']['z'].max()
+                    min = trace['config']['z'].min()
+                    trace['plot_object']['hist'].setLevels(min, max)
+                    trace['plot_object']['hist'].vb.autoRange()
+        if cmap:
+            plot.set_cmap(cmap)
+        # set window back to original size
+        plot.win.resize(1000, 600)
         plot.save()
         pdfplot, num_subplots = _plot_setup(data, meas_params, useQT=False)
         # pad a bit more to prevent overlap between

From 7b6c47f45f04c0c557eee80f2be0e06651456b49 Mon Sep 17 00:00:00 2001
From: Jens Hedegaard Nielsen 
Date: Wed, 19 Jul 2017 11:26:30 +0200
Subject: [PATCH 237/328] Fix multiparamplot (#55)

* correctly handle plotting of multiparameters

* update notebook

* remove print statement
---
 .../examples/T10 Plotting Test Examples.ipynb | 362 +++++++++++-------
 qcodes/utils/wrappers.py                      |   5 +-
 2 files changed, 220 insertions(+), 147 deletions(-)

diff --git a/docs/examples/T10 Plotting Test Examples.ipynb b/docs/examples/T10 Plotting Test Examples.ipynb
index 928d882fa3cd..be1681583b2b 100644
--- a/docs/examples/T10 Plotting Test Examples.ipynb	
+++ b/docs/examples/T10 Plotting Test Examples.ipynb	
@@ -14,15 +14,21 @@
   {
    "cell_type": "code",
    "execution_count": 1,
-   "metadata": {
-    "collapsed": true
-   },
-   "outputs": [],
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "User schema at /Users/jhn/qcodesrc_schema.json not found.User settings won't be validated\n"
+     ]
+    }
+   ],
    "source": [
     "\n",
     "import numpy as np\n",
     "import qcodes as qc\n",
-    "from qcodes.instrument.parameter import ArrayParameter\n",
+    "from qcodes.instrument.parameter import ArrayParameter, MultiParameter\n",
     "from qcodes.tests.instrument_mocks import DummyInstrument\n",
     "from qcodes.utils.wrappers import do1d, do2d, do1dDiagonal, init"
    ]
@@ -37,7 +43,7 @@
      "output_type": "stream",
      "text": [
       "Activating auto-logging. Current session state plus future input saved.\n",
-      "Filename       : /Users/william/sourcecodes/qdev-dk/docs/examples/PlotTesting/plottestsample/commands.log\n",
+      "Filename       : /Users/jhn/src/Qcodes-dk/docs/examples/PlotTesting/plottestsample/commands.log\n",
       "Mode           : append\n",
       "Output logging : True\n",
       "Raw input log  : False\n",
@@ -93,10 +99,44 @@
     "        item = self.get_multiplier*np.random.randn(25)\n",
     "        self._save_val(item)\n",
     "        return item\n",
-    "\n",
+    "    \n",
     "dmm.add_parameter('myarray',\n",
     "                  parameter_class=MyArray)\n",
     "\n",
+    "# We add an arrayparameter as well\n",
+    "\n",
+    "class MyMultiParam(MultiParameter):\n",
+    "\n",
+    "    def __init__(self, instrument=None, name='testmultiparameter'):\n",
+    "        shapes = ((10,), (10,))\n",
+    "        names = ('this', 'that')\n",
+    "        labels = ('this label', 'that label')\n",
+    "        units = ('this unit', 'that unit')\n",
+    "        sp_base = tuple(np.linspace(7, 12, 10))\n",
+    "        setpoints = ((sp_base,),(sp_base,))\n",
+    "        setpoint_names = (('this_setpoint',), ('this_setpoint',))\n",
+    "        setpoint_labels = (('this setpoint',), ('this setpoint',))\n",
+    "        setpoint_units = (('this setpointunit',),('this setpointunit',))\n",
+    "        self.get_multiplier = 1  # used to check AutoSIPrefix\n",
+    "        super().__init__(name,\n",
+    "                         names,\n",
+    "                         shapes,\n",
+    "                         instrument,\n",
+    "                         labels=labels,\n",
+    "                         units=units,\n",
+    "                         setpoints=setpoints,\n",
+    "                         setpoint_labels=setpoint_labels,\n",
+    "                         setpoint_names=setpoint_names,\n",
+    "                         setpoint_units=setpoint_units)\n",
+    "\n",
+    "    def get(self):\n",
+    "        item = (self.get_multiplier*np.random.randn(10),self.get_multiplier*np.random.randn(10))\n",
+    "        self._save_val(item)\n",
+    "        return item\n",
+    "    \n",
+    "dmm.add_parameter('mymultiparameter',\n",
+    "                  parameter_class=MyMultiParam)\n",
+    "\n",
     "station = qc.Station(dac, dmm)\n",
     "\n",
     "init(mainfolder='PlotTesting',\n",
@@ -108,9 +148,7 @@
   {
    "cell_type": "code",
    "execution_count": 3,
-   "metadata": {
-    "collapsed": true
-   },
+   "metadata": {},
    "outputs": [],
    "source": [
     "station = qc.Station(dac, dmm)\n",
@@ -139,23 +177,20 @@
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "DEBUG\n",
-      "['dmm_voltage', 'dac_ch1_set'] dmm_voltage\n",
-      "Considering the units V, V\n",
-      "Started at 2017-06-30 14:24:02\n",
+      "Started at 2017-07-10 17:22:37\n",
       "DataSet:\n",
-      "   location = '/Users/william/sourcecodes/qdev-dk/docs/examples/PlotTesting/plottestsample/084'\n",
+      "   location = '/Users/jhn/src/Qcodes-dk/docs/examples/PlotTesting/plottestsample/375'\n",
       "      |   |  | \n",
       "   Setpoint | dac_ch1_set | ch1          | (50,)\n",
       "   Measured | dmm_voltage | voltage      | (50,)\n",
-      "Finished at 2017-06-30 14:24:04\n"
+      "Finished at 2017-07-10 17:22:41\n"
      ]
     },
     {
      "data": {
-      "image/png": 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HC3ciIiIi21hMZDLZXItX9rqc4pGteuGuqqYeGdUeC3ciIiIi29BHynjzj3T43a0+Vyyl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\nH6OKOyqO6TanOnpyKpLbV7fUEEpHadUnBBMNYJEUNZUvc5yBK8EimuJ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T2/E839vb+x//\n8R8ejwcArr322ptuumnz5s1XXnnlVVddlclkwmFKB3YOZPKUwe6JHJ1wrikk7fa4g54ICa4yuANA\n0CP6RD5XkvNl2V2RuswqQ4F5I2UQLREkRhZJv/2JTl9CtKNESAqKOzonNGOVoRS5mDQ4NhUqk1MJ\nK+4owX1lc5inmCaIFBkS8wp0xd0Wq4wP7FDcmcfdmdQ61SpjRnHfvXv38uXLUdUOADzPr1q1ateu\nXYIg1NfXx2IxrCt0GZOlqW7yMtV0VNV+jzvoiZDgqkgZ0PqTXNmfmmRWGfIgg/u81i9qM5jSVZwA\nWATNYKIQ5W5acQ/RmpyaMJ5WiRpnSTen0pyZWqGlBgXL4BenbfS423XiyjzuzkRX3BdE4d7U1LRr\n165UKoX+WigU3nzzzaamppGRkYGBgaamJqwrdBnHx3MAgJSP7nEbCveCJEuy6hX56sf7kaAjqhXu\n9Xbsv1ZA/amum8FUiYMsSkqhTLyIOTWpxioDhOdKTobo2FQEmsHUO5EjHW5tevpEgJbHXbPKGPO4\n02hOpZwFiWiNEHk6VVQ1YV+ouV0D+OLG2ycYFHCs4m5GqjnzzDM3bNhw7bXXnnfeeTzPv/3222vW\nrDn//PNvvfXWK6+8MhCwwRziHJDKfuHqphcPjnbTmjg4GduzIBGV7j0SeWFEabBv8LUVJqdAJvLl\nVlf5fNxClVYZasEyFFJl6gLesE/MFKVYtoQcwISw6HGnMoCpBAY97nQmp2pZkM10FXcyT6fpgqSo\nasgnkm7snhH74iANu7AYFNAU9wUTB3nrrbfu2rVr7969iqJs2bLl/PPPB4C///u/Z2nuPeNZALhw\nTdOLB0d77FDcbZ++VKEjGuqeyJ7b4bJLQutPcluwzEmFe67sLoeSWzBklaFSuJehigcJK3ActEeD\n+wZTvbEchcLdhF8fqdo0ctydGgepZUHSVdybyVzksaydbm/kcaev2sSZx92RLKg4SMSmTZs2bdo0\n+SOsagcApLJfuLoJAHpiOUVVaTYMgWMUdwB46eaL7F6CGZDHfcJtHndklakLehK5cpLZ3MmgWWXm\nL9yRGEkpVYb0m709Gto3mOqZyHa21ZH7KkjWMpXjTi0O0rAsGiKfMZ/Kl4dTBb9HwB7LODfasVIS\n80VuY6QM6KrNOF3VRlFVPbCIKe7OAukIDvS4myzcf/e73/3iF7+o2NwBYOvWrddddx2mVbmVbFEa\nzxT9HqEjGmqq8Y2li6PpImXt0wmdqa4m6marTHs0lMglWLAMIVAzaHh+j/vCscpApT+VcLBM0qzH\nPaRNJyWruEuymi1KAs8ZOt+oPFSoKhAScI6M2RApAwC1fo9P5LMlKVuUME7qsDFSBiqbP13VJpWX\nVBXCPlEUbJj6wpgDxyruZmxkvb2999xzz4c//OGzzjpr8+bNn/3sZ+vr6y+99FLsi3MdKHuhIxrk\nOK07k75bxgnTl1xN1IXNqaqqbS6oxkqwKHcyZKuMg0SpMklKVhmizakwqT+V6FdJmfe4a1YZou2z\nlQMBQ+WxyHM+kVdVyBPrFz9Ed2ZqBY6rPKDi1KftDUYMekWvyOfLMrnf13RYZ6pjqdU87o7LcTdT\nuB84cODss8/+yEc+snLlyoaGhksuueSyyy576aWXcK/NfaDOVDSyBOUf0+9P1cMZ7Pe4uxS9OdVN\nHvdsSZIVNegVkPE0zqwyZEAe93kLdyRGZooS6RxAfQATYcWdygwm09MnRIETBU5R1ZJMsA8ePQxb\nSKskdSXYkgWJIBEsE7MvCxIAOM4G0Z1lQTqWBaW4h0KhRCIBANFo9Pjx4wAgiuKRI0cwL82FVBR3\n0PMQ6Ue5a8oQU9zNEnWhx12vKrwo8oINTyVElVYZjtNqGhIp1yetR1PcyRbubVSsMkjWMlEZgy66\nE7W5W4yZJzeDqTJ9idDrz0FzDX5LWNzufJVomPYMJr09iRXujgNpNOlCWSGdhmsQM4X7eeedNzw8\n/Oyzz55++umvvfba/fff//DDD69fvx774lzH8fEsALQ3hgCgozEIdkS5m45DZiAabIrytUJCP2xF\n563M406IKhV3oGVzR82yRCenAsCiiF8UuLF0kZx/QFFVKw8hyOZONFim0vxt9B+GCPfOIsWdcqQM\ngkQiZDxbAoAG+6pYff+nd+Iaz9qWW8+YG4Hnavyiqmo7v3MwU7h7vd4HH3xw7dq1TU1N//iP/zg4\nOLht27YrrrgC++JcB9LXOzSrDFLcqVtlHBMH6VKQx91dk1MT+mgYrXBnVhkyVOlxhxNiJNnbf4r8\nACYAEHhuWX0QAHqJie6ZgtafJ/Bm+vMC5PtTTSdtB0kGy2SK0lCy4BN59AuiDDpWwq24lwCgPmRb\nFWuHVYZ53J2LM23uZsq7YrHI83xbWxsAXHDBBRdccEGhUMjlcjU1NtjsHAWyyixvPGGV6ZnIkssT\nmBHnxEG6lJBX9Ip8tiQVJcXe6bPVo1UVAU8k4AWmuBOjSqsMEJsrORlZUbNFieO0CUREaWsIHh/P\n9k7k1pDxUpuOlEFQsMpY8LgLAJAjE+WOEtxPaw6be+CxCInm1Ljdhm/6J656iDuzyjiRSMAzEM8n\n82XKcatzY6Yueeedd+65557JH3nmmWd++MMfYlqSW8mV5JFUwSvyaDur8YsNIW+uJI9nqLY5sjhI\ni5zoT3JPf2pSd0nqijsr3IlgxCpDZK7kZJD8H/KKFHIAUas9uf5UKwZ30IenErXK6EnbJkNvMoQK\n91HbOlMBoKXGB7jTk+JZm6tY+nHA+nkpK9ydCNJAnRblbkxxL5fLd9111+jo6MDAwB133IE+WCqV\n3nzzzb/6q78isDw3gc6R2xuClftoRzQUy5a6J7JNNT5qy2BxkNZpCHmHkoWJTGlRxEEP2XOAXJJ1\nAQ8qLFjhTghUK1fjw0ZP76MkC3dtGhSVd7oWLEPMKmNRcQ94kI+caOFeAoCIcT9DWEuVIbK2wzZl\nQSJakFUmTcIqcwo1pzKPu5NxZrCMscKd47jW1lZJkmKxWGtra+XjnZ2dW7duxb02l4HkqI7GUOUj\nHY3BXb3xnoncuR30ZsrqijvzuJtHG57qnv5UbRh70INkG1RkMLCT1kXueT+zlXyUu9bNQrgzFaEF\nyxDr2DEd4o4IkSyOEbobzbjH3SeA/siHnUP2ZUHCpFQZXHZQVa0o7rZVsZpVhuI5ue5xZ4q7E9E9\n7m4u3EVR/NSnPtXd3b1///4Pf/jDhNbkUlAfKpKmEO12JEKajkNmVGhEM5jc059a2fpRe1+uJJck\nxesSg75bUFRVc6dU05yKrDJpgrd/OlmQCDSDqY+04m72ewkQTm4BK3GQXpKKu31ZkAAQ9Ao1fjFd\nkOK5Epbk9WxJkhQ14BH8HuJtG7NBfwBf3NaZU4y5WQiKuyzLALBs2bJly5ahP1fgOI7nT+lCAQ1J\nRSHuCC3KfZxesIyqMo87Blw3g6liwOU4iAQ8sWwpmS/TNGidCqDaK+gVqmkErMRBkutNpzN9CYEU\n9754TlZUEn2Qlbmk5v550EvcKqOnypjwuKPEG/wPFdmiNJjIe0Ue/XZsobXWny5kRlIFLIU7cpbb\nqz03MI87YxJITXC3x/2iiy6a7X9dffXVX/7yl60uxxrd3d0kXnZgYKCaTzswMAEAfilTWYavlAeA\nQ0NxQgubTkFSJFn1CNxQfy+drwgAw8PD1L5BOvClLAAcGxzr7rb6LFrlxWOR4VgaAIqpie7uQsgD\nMYB9R7o76l1QuLvo4hnLlgEgIHJVLjjsEzJFec/BIxGztfXcF8/x/iQA8HKJzg8wGhQnctKf9h1p\nrcEvCvQOjwOAWswa+l4qF085lwGAwZFxcj+JsVQWALLx0W41aegfFrMpABgai2H/Nb07mgeAZRFv\nX2/P9P9LZ+ep9agAsOdwb6CIQfV/dywPAGGPSuGSnm3nyZYUABhPFajtS7F0EQDS40PdadvOGaZD\n5/pxPnI+DQD9oye9fxOJBLnLo6OjY97PMXZHefrpp2f7Xz6f/VVCNd8wuVcezhwDgPPXL1+m6x91\nzWV48thgqtze3kEnEXIkVQA4EAl4yf0ophOPx2l+OQqcNibAW6OyGMDyfVH44RSUHgBYs6Kto7Wm\nqXawL1EK1Td1UOysMI2LLh5pNANwqDboq3LBiyI9h0czvrqWjlbzFuQ5vpZ/pAegvzUaofMDXNE8\nNNEdkwP1HR2N2F+c250BgLZFTYa+l8rF03qkDDDhC9WQ+1Fky4cBYP3KjsawsTvd0lEeYFjwhbCv\n7c8T/QBw+tKG2V6ZwoXR0ZJ8eyCrBiIdHcusv1p3cQzgWEtdmMLKZ9t5VBU8wsG8pCxa2kYhDrgs\nK3lpH89x61evoBAPZQi37MxEWR73AAypnpOKgbq6Ont/OMauy8gkwuFwKpUaHx/3+XyRSMTv9xNa\noisolOWhZF4UuEV1J3JI6oKeSMCTKUpxWgNx2PQlLERDbvO450+c47NESEJkqo6UQaAod3LBMnpz\nKiVTHLK5EwqWMe0gR2hZ6cSsMqpqvn1WT5XBb5WxtzMV0YJ1BpPtkTJAPQ44rluwnFa1MxDIdZx0\n2P3UZIX3xhtv3HnnnalUyuPxlEql7du3f+Yzn8G7MnfRF88DwLL6oHiyAbQjGtrdn+ieyGKxAM4L\nm76EBb0/yR0ed1U9MYAJ9MLdac00C4B01SHuiOZasjOY0gVjDxIWaW8IAkAvmWAZiy31pD3uuZIk\nKWrIK3oEwxKsNjm1iH9tWhZkiz2dqQgU5Y6tcLc7UgbREPYOpwrjVOKA2dhUhxNxZKqMmZOg8fHx\n22677cYbb3z++eefeeaZhx566Nlnn33ppZdwr81NdGudqecnWSgAACAASURBVKEpH0eDS6j1p7LO\nVCzQ70+yQr4sl2XFJ/IoigEl1iVoHfKcOlQfKYMgMVdyMihVpvoHCYugDsheMoo7mj5hIiUdEfCQ\nnZyqjU01tTx0GkCiOfXQaBoAVjXbqrjX4lfc6Yhcc0BzBlPC7kmxjLmpdWSqjJnCfe/evZs2bbrw\nwgvRXzs6OrZv3/7mm29iXZjLQCHuyxunFu7oI+QmDk6BTV/CQhTluLvEKlMZm4r+isqLhMM2mgWA\nYasM1ppmhvUU6A1ggsoMJjJbmcU4SNKKuxUnj664Yy7csyVpIJ73CHxb1LZIGdD9YLieTmNZR+Sr\nNFC0SupZkKxwdyia4u6w+6mZwl0UxULhpLtRoVDweE7pYlEPcZ+6h1KOctesMszjbo2wTxQFLlOU\nSpJi91rmZ7JPpvIHNI2PgRGjVpmWWpwugtnWQ88qE9VmMKkq/hfHEgeZJ1a4T+4hMQoh//2R0QwA\nnNYUEgmkc1YPushxDRpLOERxD/uArsfd9FkTgzRITVgIivvGjRuPHDnyyCOPDAwMjI2Nvfjii488\n8sgll1yCfXEuQrPKTFPcOxptsMpEmOJuDY7TRXc3uGWmnOOz4amEQKJpuOo3F14XwXQ0Xxytwr0+\n6A35REKt9pribrpw94lAxo6C0IzIppaHvFXYFfcjmsHdTp8MADSF/RwHE9miJGN4nos5YxSRFk5A\nySrDFHdH4/cIXpEvSkrRSSqemcI9HA5/5zvfefvtt6+77rqrrrrqoYceuummmzZs2IB9cS4CaerT\nFfcOOxR3aiLcAgb1p7rC5j5lfgdLlSEEssqEvdVmLZP3uFO1ynCctr9ht7kXJaUkKR6B94smc6yD\nHjqKu5nqCk1OzeJeG5qZusqmmakVRIGLhnyqCmMZDNd53BmGb5pWGd3jzrQ254K8x45yy5is8Fas\nWHH33XcriiLL8ilukgGAkqQMJgoCzy2tm1q41we9YZ+YzJcTuTKFzvG0Fgd5qv9GrEMzEcwiU+RA\n9AfmcceOPqm02jdXU9jHcTCWLkqKSsLPoD1IUHxKb28I7h9M9UzkNi6rw/iyutwumg7ECzjY466v\nTcI7QxdlQdquuANAS61vPFMcSRUWRaxGQuupMnZbZSg2pzKPu/OJBDzjmWKq4KBh5GYU9z179nzn\nO9/Zu3cvz/OsageA/nheUdWl9QFRmLoxc5zmn6HTn8pSZXBBU3SxyBQDrtacylJlcJMpGktxQWKk\noqoTOMTI6aBUGZrHa4SCZUxHpFcg3pxqwYgs8pxP5FUV8mWcy0OK+2pbsyARuCxhquqIHHcAaAgj\nnyTzuDMA9KZBR9nczRTuS5cuDQaD3/zmN6+55pof//jHw8PD2JflLnSfzFSDO6IDJUKSyT+eQpKl\nymACedxdYZVJTm1ORXGQDtplFgYoittQoYw3c2MyqqpbZXz03uyEgmUshriD7iMnaJWx4HEHfXkY\n0ypzJbkvlhMFrr1h5psOTVoxWcJyZakkKV6RD3hMOqZwQTcOkinuTifivERIM4V7Q0PDF7/4xSee\neOKf//mf8/n83/3d391www27du3Cvji3gAr3jlliuWgGy+iKO/O4WwUp7uNuKNz1ER7a1l/jFzkO\nMkUJS7sYo0LaYI47kAyWyZdlWVEDHmH6KR852sh43C1GygAAKvVyZYlE4g1Y87jDif5UbM8VR8cy\nAHBaY5jmb382cA0aS2TLANAQ9No+QpTm5GzmcXc+usedVO+7CcwU7hXWrl370Y9+9PLLLz9+/Pie\nPXtwrcl19EzkYKbpS4gOLUaNSuHOJqdiQmtOJWNywAuqKiqHrQLPaRuNw4a9uZ2M8YFHLTWkgmXo\nG9xBH57ag/vwEB0ZWTH4CTznFXlVhYJERHS34nEHgJBuc8e1HifMTK2A6+kURcrU2e2TAYAav4da\nHHD8ZNmF4UAizhtGbnLfHxgYePHFF1988cVEInHZZZfdf//97e3teFfmIlAW5GxWGU1xp5IIyTzu\nuMCVCFaW1Y5//DUAvPLVDyxrIDIqZUqOOwDUBT2oH9r2ROSFhAmrTEuEVOFO3+AOAIvqAiLPjaQK\nhbLsx+dnSBUksKa4A0DQK5QkJV+SSRgtpr/FDBHEHSxzeCQNACttnZlaAfnBRq0r7ijE3QHaM8dB\nQ9A7mi5OZEvWO27noGLrpxBcwTCNA1NlzCjuu3bt+vSnP93d3f2FL3zhiSee+NznPncqV+1Qsco0\nzlyWoeZUClYZVa1MTmVWGas0hPF43I/GtPtZjFi36PRzfM3mzqLcsZIulsGwVQa5CPCf21DOgkSI\nPLe0Hr9bxmKIOyLgQT5yIoq7Vribra60GUz4otyd05kK+rGS9RlMaGyqQ9zeuPb/ucmVJElWfQ6w\n9TPmwIEedzMV3qpVq5566qlAIIB9NW5EktX+eJ7nuGX1MxfuTWFf0CvEsqVUvkxUCy9KcllWPALv\nMxuHzKiAqz/pwEge/YGcYzI5bWoJi3InQcbg5FQg6XFP052+VGFZQ7B7Itsby63GF0RoPVUGTgwo\nJeJDTeUtRX8gxT2Dr3B3ThYkVFJl0lafTh0SKYNopBIHnHBGbj1jblDZ5ijrqRnFvaamhlXtFfoT\nOVlRF9f5veLMP0yO09MYcDd1TSGlhbibj0NmVNCaUy173N8d1Qp3cuJNYlqgGCvcsSPJalFSeI4z\npI2hwA3rLoLppOxQ3EGfMdeL1eaup8pYegghlwhZlpVsSRJ5LugxucKwD+dpQL4s98VzIs8tn8Wc\nSZn6kEcUuFS+bDHvUg9xd4RphE4ccNwxtn7GHDhQcbfUnMqA+TpTEXT6U1lnKkZq/R6R59IFqSxb\n6k86oBfu1p8BZqQoKfmyLAonVRXINsOsMhipNIMaeipuwRS4McN6jMv/WECFO14NwmLrJyLgJWWV\nSepyu2lBJOgTACCLSXE/PpZVVVhcN8PYEFvgOa4ZRxO2oxR3OpOz49Z6Jxh0QFuT6z3ujMnM3ZmK\n6KDSn8o6UzHCcZroYmXvThek3oRWrxMSb/SE6ZMy1NCdIMkUd3ygwh2Nr6+euqBHFLhErlzEHU9h\nS3MqnAiWwalBYNm4gh5SVhm9M9V8QRnC+lCBngNXNDlCbkcgS9iotV4OZ3ncQz4gHwecmOZyZDgQ\ndBi4cBT3crlcKOAXk9yFprjP0pmKaKfSn5pi05ewYr0/aXd/ovJnQuLNlLGpCG14qpM2GreTMVUo\n8xzXQsYtY0tzKgC0aTOYHKe4I6sMiRlM1peH1pbF9FAxli4CQFMNwbQTo7TiOFnSUmUcoriHaMQB\nM4+7K9AU94L7c9x37959/fXXX3rppf/zP/9z+PDhO+64Q5ZJTa1zOPr0pWqsMjQU9wibvoQJ64mQ\nu/sSALAoEgBiVpnkTIet+vBUZpXBBpq+ZMKaomVukCnc6TentjUEAaAvnpMVbLOOsHj8glh95JOx\nGCkDJwYw4bnxj6aLANBc48PyaljQ+lOtXeQxJwUjIquM9TjguWEed1dQuzA87rFY7Bvf+MY111zz\nuc99DgDa2tr6+vp+/etf416bO0CFe/ssY1MRdIanMo87Xqz3J73TlwCAS9Y1A7F7QGKm+R3o5hdn\nVhl8oBB3EwOP9GAZzI9taTsGMAFA0Cs0hn2SrGKMysGouONStSeDekVwFO54HirG0gUAaHJU4a7N\nK7B0kcf1yal41mQN6z7JarA4H4BBBzSMPF0oK4QmMxvHTOG+e/fu888/f8uWLX6/HwB8Pt+2bdt2\n796Ne20uQFJUFGncNudsnZZan0/kx9JFXKLLjKRwxCEzKjRqoovJu5Gqnly4E/K4zxRUh4oM5nHH\nSKZoeGwqAtd4minoHncb3uztWM8PFVVF/QMW/frI407OKmPN447Tf+9ExR1HlLuzmlNDNHLc48zj\n7gZ4jgv7RFXVIgGcgJnCPRAIpNPpyR9Jp9ORSATTktzEUCIvyeqiiH/uOYI8x7UT8IZOIWXT6flC\npcHa3j2cKoyli0EPf25HAwBMZIskHtdn1GzYACbspM2muDSTCZbRPe42vNnxBsukC5KqQtgnCryl\njJQAsThI9ABsRRDRJqdiU9yRx91JhTtqTk2bv8jzZblQlkWBM9r/TQhcccBzo7uwHPGswpgDp9nc\nzRTuGzdu7O3tffDBB0dGRkZHR3/+858//PDDl112GfbFOZ/uiRzMFymDQHc7om4ZprjjRe9PMln+\nIoP7upZgyCv6PUJJUsic48+w9bMcd+xkzVpTWnAk5U3HrlQZAGhrQBoEnq0M165Frjl1xv5vQ6Dh\nULibUx1UuLdGrF7ker6K1yFDSGoDooAjDnhuHHXIwJgDp9nczRTufr//3//932Ox2EsvvfTCCy+8\n8sor//qv/7pmzRrsi3M+PVpn6lw+GUQHDcWdFe44abDWnIoK97XNgUqyJAm3jB4HedIvvTLpDWMH\n4SlOxmxzaisO++909OZU26wyuGYwYTG4g96cSuTZeKa3mCF0xR3D2lRVs8o4qnDX5hUkC6YPFR1l\ncAcAnuMo2NyZx90tOC3K3aRg09TU9PWvfx3vUtyIprg3zq+4o7xIoop7ksVBYkWvtk2WXO/0JwBg\nXZMfABrD3sFEfiJbnLuJ2QTJmSIvRJ4L+8RMUUoXJIekNLgd01YZvTmViFWG/gAmwG2V0ec9Wy7c\nPcSsMjMdahkCKe5Y1pYtSYWyHPKKDrGUIEJeMegVciU5VSibewaLOS9fJRryjqWLsWwJPZaQQPe4\nO+i7ZswIKqvcrbjv3bv3hz/84eSPvPbaa4888gimJbkJpLhXM3pam8FEQ3F30IbuahrDPjCruMuK\nuqcvCQBrmwOgtzoRUdxnOcfX3DLM5o6JjGmrjJ6Uh7fDwVarTBAAejEV7tgUd3JWmZzVFWKMg3Sg\nTwYAOM7qkGAtxN1JKoPFE9d5kRVVS3B20nfNmBHd4+6Uwt3Yvq8oyh//+MdDhw7t3bv39ddfRx8s\nlUqPP/74xo0bCSzP6aCxqQasMuPkPe5McceElaPSo2OZbElaFPE3hjxAMhUY3fAi0yIv6oLe/ng+\nmStDFPvXPBVBVVeNcYU77BNDXjFbkrIlCZdAXpaVoqSIAucT5+qJJ0Q05At6hVS+nMiVrZ/npDAV\n7gFtOil+q0zSuscdWWVwPFSgeKLmWmcV7gDQUus/Pp4dTRXWtNSY+OeOGpuKsBhOMC+pQllVocYv\nitbashkUcJrH3diNpFwu79y5M5vNplKpnTt3Vj4ejUa3bduGe21OR1FVdF7cVoXi3hrxewR+OFXI\nl+XAnBE0pmEed7yg/qRkvizJqigY21uRwX3Dsjr016g1180czKq4B9jwVJwga0rIVOXdXOs7Pi4N\nJwsrm8MYF1Pr99jSycdx0BYNvTuU6oll64J1Fl8tqckNVh9pMNpRpmD9TEBPvJFUFSz+ysYyRQBo\nCjuwcLc0r8CBbZpIbSEXLMMiZVyEuz3uPp/vRz/60a5du15++eWbbrqJ0JrcwnCyUJKU5hofOqWd\nG4Hn2hqCR8cyPRO5ta1mNIm5UVVI5VkcJE54jqsPesczxXiuZPRs+p2+JEwu3MPErDKztDexYBm8\nmG5OBV2MHElhLtxtMbgj2huC7w6leidyG5ZaLdz1ec8O9bgrqpqwHAcp8pxP5IuSki/L1dws5mA0\nVQRHKu6ttZai3ONZxyWaRwk3p7IQdxeByirnKO5mPO6bNm1iVTvohvWOKjpTEag/FVeM2hSKklyW\nFY/A23J6vlAxrZTv7k8AwMallcLd0iyn2ZBkNVuUBJ6bPogHCTnISMOwTsZCbjr2YBkbDe4IjDOY\nkpjiIAlZZbJFWVHVsM+qnwGd1VhfnuZxd6Li7geAEbNR7o5V3E3HAc8LU9xdhNNy3E1u/S+//PIv\nf/nLVCpV+ciWLVs+8YlPYFqVO0ARMdWEuCPaSfan6uEMokNycBcG0bAXRgx70wtl+d2hFMfBmUsj\n40NpINacWjnEn/5L15tTnaIQuJ1MybxVpqUGc7CMPn3JNq0OY7AMLo97kMwAJi0L0rIsGvKJsWwp\nW5QbrR26jGac2JwK+qAx00+njvW4k2tOZYq7i4g4bBi5GcW9v7//jjvueM973tPR0XHGGWdcccUV\nHo9n8+bN2BfncHqq7kxFEO1PZZ2pJDDXn7R/KCUp6qrmmoqZQbNL4r4HoNCYGYseZJ5xzkbjdjIW\n3CmVYBlci7FdcUfBMlgOD5OYNi5CqTLWsyARId3mbvF1dKsMqYBC02jHSmatMlqqjKMUd8JWGaa4\nuwi0Qbk1VQaxf//+TZs2ffzjH//5z39eKpUuv/xySZLeeOONZcuWYV+fk6l+bCqig+TwVNaZSgJz\naTDv9J7UmQoAjdqpK2arTGKmEHeEZpVhcZA4UFVrHnfLcyWnkC7aPLEBDU/tw6G4a6dGlnVHv0cA\ngHxZVlSVx3fsmMB2IIAnWMa5zanWjpVimE42MEK6OZUp7i4i4rBUGTOKeyAQyOVyAFBfX9/b2wsA\nwWDw4MGDmJfmeKofm4oga5Vh05cIYC4R8p2+BABsXBaZ9Dqacq9gTfPWO1Nn0GzQRsOaU7FQlGRZ\nUb0i7xXNbJgWI66nozWn2qe4L6kLCDw3nCoUJasD4XG11As8V6ndLb7UZHDNtkShN9aj3B0bB4kO\nAcYyRRPTmlUVBuJ5cJxVhinuDI1ah6XKmLkPnXvuud3d3b///e/Xr1//yiuv7Ny58z//8z9Xr16N\nfXFORlUNK+5L6gMizw0l89bvdtPRwxlYpAxO0GmpUdEFdaZODtzwiXzIJ0qKisoUXCBBfRbFnRXu\n2LAit8MJMRJjc6r5TlksiAK3pC6gqhhEd1wDmICMWyaJ3GiWZVFNcbdWuEuyGs+VUNqVxfVgxyfy\n9UGvrKgmTOEPv34cABpCXhsv6elEAp5KHDCJ10+wsanuYSEo7n6//wc/+EFHR0dra+uNN964d+/e\niy666GMf+xj2xTmZ0XShUJYN7TUizy2tD2K5202HedxJgGIcDYku8VypZyLnE/m1rbWTP665ZbDq\nN8n5rDJxliqDA4vxi0hxH00XcJ236B53O9/suIJlMHr8AgT6U5OYZNGwlipjaW0T2aKqQjTsFRw5\nskePcjd2stTVm7j91wcA4Pb/70yMHifr8ByHtlZCu2h89t2b4TR8Iu8V+aKkkFBdTWCmcAeA5ubm\nFStWAMCWLVvuuuuuv/mbv/F4Tq3rD81MXV51FiQCJUIeJ9CfmsKUqsaYjIn+pD39SQA4Y0lkysym\nBlPi/dzoBtwZqoo6hykErkZT3M3KgV6Rrw96JVmNZ/H8OqxkU+IC2dx7Ypa2skJZLkmKR+D9OEJs\nSUS5Y/O447DKjKaLANDsvEgZRLPxJux4rvTFn+ySFPWz71u+9YxWYkszSZRksEzcebZ+xhw4agaT\n4cJ9YmLi7rvvjsViyWTyk5/85I4dOx544IFslkjDpZPRQtyr9skgtGAZAv2pWhykk44aFwANWo67\ngY37nb6pnamIRuPi/bzMsfVXPO54XfWnJllrVhkwK0bOBnqz19g3gAkA2qJBAOi1prjjDbEN+kTA\nbZXB5nH3YlDctRB3pxburQYLd0VVb/rpO0PJfGdb3dc/tJbk0kyihROQ6U9FlxazyrgFZGdwiBZm\nrHBPJpN/+7d/OzQ0FAgEZFmOx+Mf/ehHjx079td//deFArbWKwAY3v3CY4/9bPfwpNfMHHrszlt3\n7Nhx+71PDTsgBV8Pca+2MxVBrj+VKe4kMNGftFvrTJ1auGuznLDOYJqjqvCKfNArKKqaLeIfAn+q\nYX1SqcXxNNPW4wCrTEMQAHqtuf4wGtxB97hnsc5g0uMg8aRVWlybXrg7LgsSoT+dVrvFff/Foy8d\nHKsPer//yU0eweThP1GIJkIyj7u70GcwubBwf+KJJ9asWfNv//ZvgUAAADwez5YtW+68884lS5Y8\n8cQT2BaVeOPGHf/8wAN3/2FEv89l9n31Q9c/8NTgmjPbX3/8zqs/ee8wti9mkh6DY1MR5IansjhI\nEtQFPTzHxXMlqbqoBFXVFfdpo+Abwj4AGMc6g2nuXAJkoWHDU61jsTkVKsEyZlOup2B7cypg8rjj\n7cwh0ZyKrDI4UmUwNKc63Cpj6CJ/4+jEXc8d4jj492s2LooECC/NJNqJK4HCvSQpuZIs8JyVXYVB\nk9qACC5V3Pfu3bt161b0Z0EQFi1ahP68ZcuW/fv3Y1pS5mf/96uDqz/03ghU6pF9T931Bqz+3q8e\nuuHzN//ikZth8PGdf7C5dDenuHcQU9yTLA6SADzH1YeQ56Sqvbs/notlS3VBD5pQM5lGAuJNcvZU\nGWDDU/GRsRy/aFSMnGc9RfsLd3SF98VzVrxYeBX3gAeDHWUKydysM84MoRfuFq0yBXCwVab6QWOj\n6eIN/9WlqOqOD6y8cHUT+aWZxNwAvmqI69eVk9pxGXOhe9wd4PcwWrgLglAua3VAJBL5wQ9+gP6s\nKNg6bYf/cM/du+G2O754Xgj0t0vmT788FNn21+fUAQCIyz9442p4/c3juL6iCVQVesbNeNyX1gd4\njhuI58sy5t5kXXFnj++YMdSfVAmCnL4do4AavHZJpLjPVlWwREhcZEpWFXc0V3IUk8fdCVaZkE+M\nhr0lSbFi3Mdr8Atimk46Ge0t5ozJqa5Q3EfS82xxkqLueGzXeKa4+bTo313q6BTpqPEepyqJM4O7\n26h1Ut6DscJ9/fr1zzzzjHqyxKKq6nPPPbdu3ToMyynsvuWWZ1Z/4Qfvb/RPsZOEmirhev51F6xO\nvrwngeHrmWQiW8yWpLqgx6gS4xH4JfUBRVX743m8S2JxkITQbO7V7d3v9CUBoLNtqk8GTqTKYLXK\nzGnA1YNlmFXGKhnLHvfmGpwzmFIOsMoAQDsKlrFwfohXcUdDjjAr7tg87lgUd0c3p2rHSvNZZb77\n7MG3jseaanz3/J9OZ+ZaVohqGb74m1OTbGyq23BUqoyxrf+aa665/vrrb7nllu3bty9fvlxV1WPH\njv3kJz8ZHh6++uqrra/mD3fdcgi2PXHt6QAFH0Bp0vKawvObUrq6uqyvYTrDw8PxeHzyRw6MlwCg\nKcCZ+IpRr9wH8MJbe85ehLPHKJbJA0Dv0YPpAdpdPocPH6b8FWkilHMA8Pa+g/7U/EbM1w6MA0C4\nNFG5MCoXz3iiDACDEylcV6miapvIsXf3zXj7k/NpAPjLwWNL5BEsX5EErrh4jvcnASAxPtzVlTH3\nColYGQC6R+KGfvvTdx4AUFTIFiWOg4P79tibe13DFwHgla53fUljjsEKB49nACCfipl7U0y5eFLx\nNAAc7envCuFRdcoK5MuyR+AO/GWPxZ/04GgRAEZiCStv//7xFACM9R3tivfM+8kzXjxEUVRAHUFv\n/XmXR5j55/XnweIDr0zwHHfjOeG+w/v7aK7vZKrZeWKjRQDoGTF5fc7Brv4CAPBSnlDRYh3614/D\nSU9kAOBwz0BXV3p4eJjcL66zs3PezzFWuIdCofvvv/9HP/rRDTfcUCqVAMDn833wgx+8+eabUbuq\nFaS+p255JrnhCx+Avr4+iI0BpHqODi85rbURIAtjE6nKZ6bGRqAjOr3sreYbNkF3d3dHR8fkjxx9\nux9gfP2yRhNf8Yyeve8M9/CR1s7O5bhWqKqQ+9kzALD5nI1o7jdlCP3kncBpvXtf6+upaVzc2dkx\n92dKinr8yd8BwMcuPBvp6zDp4lmSLsLvns9IHK6fVTxXAhisDXjO3jTzC542/C4cPVoTbe3sXInl\nKxLC+RdP4MhugOy60zo6O5eZe4Ul6SI893yqzBv6ZqfvPKB1pg6GfeLZmzaZWwwuNo4fern7MIQb\nOzvXmHuFXw8eAEit7lja2bnC3CtM/nm+Fj8C+w9Gok2dnXiyBUfTRYDBuqB30yxvseoR+pPw4quc\nx2/6aldVSD75WwC48PxOFC45NzNePKRp+m1sJFVYfNq6pfUz1AP98fx9T70CADdftmb7RadRXtt0\n5v1dhEcz8OLLRfBi36PeLfcCxNpaGzs7N+B9ZVzYcv04mUNSH+ze46+t7+w8q6ury97bluHD1mg0\n+rWvfe2rX/3q2NgYx3GNjY0cJtWnEBsCgN0P3HT1A/qH7tzxEmx/5pXrWzth8Pddmc9vCAMA9P3m\nqeTqL6yzMRMLdaYaNbgjOjBNHJxMUZLLsuIReB+OOSaMyVTfn3R4JF0oy8sagpWq/aTX0UeZyoqK\n5YBYjwGe9bC1LuQF1pyKA2SVCVmwykRDXp7jJrJFSVbFWcTIKnGCwR3RZnkr0w1+eDw/2FNlkpgi\nZQCHjSdbkgplOeQVq6na7aKl1jeSKgynCtML95KkfOknu5L58iXrmj9/ocnnNMqQi4NkIe6uw1Ee\nd5NbAMdxzc3NeJcSPv36Z575pCiKACCKiYeuuDp1yxM3v7cVAN531XbY8dC3Hjvjn7Z1/PGBf3gJ\n4JaLTWo8WOgez4Eeym4ULcod6/BUvHNMGJOJVp0INlsQJEIUuEjAk8yXk/nyjJW9UfSqYtaXqgsY\nyMNhzIH1FBeB55pqfCOpwlimYDH8zgnTlxBoK+u1MDwVc467D3OqTEIbcIbh3Wo9DnI0VQSA5lqH\nGtwRLbV+gOSM/cq3/ebA7v7EkvrAd6/eaK/Fq3pQ6ksij01tqZBgHne34WKPO1lEMRwO638J+wD8\nQe2v4Q2fv+/G/h133/SRBwAAPn7bY1tb7Vw5CmJfbjDEHYH+FV7FnXWmkqMhXK3ogkYvzdiZioiG\nvcl8eTxTxFK463kXsyvuQQcpBK5Gb0619P5qrfWPpAojqaLFwl1X3O3futEMJuvNqY5NlZk7tckQ\nSCbPWniocHgWJGK2RMin9wz+5+vdosB9/9pN1jt9qSHwXH3QG8uWErkyalTFBUqVqcNxI2DQIbIA\nFHfyhD/99CuT/77hqn957tLxjASiv64ubOeyVdVkKhHWWgAAIABJREFUiDtiWUOQ46A/nrN+aF6B\nTV8iR1RLg5k/WEBT3KfNTJ30Ur5jY9mJTAlaMCxMkwNn/6XrirsjNhpXow1gslYrI610OFkAkz55\nDX36kv1v9sawL+AR0CGSueoWbVz4ctwxp8poBwI4Cs2A/lChqmBObh7LFAGgKeyGwv3kYJljY9mv\n/ewvAHDr/14/x/boTKIhbyxbGs8WcRfu8+zeDKeBHH3owNN2nDhneDb8dY2NjY32Vu0AEM+V0gWp\nxi+aM6j5RH5RJCAp6kACWyJkik1fIgbKX59Xcc+WpEMjGYHnTl9cO9vnoK0f1xy+ubMgQQ+fZlYZ\n6+iTUy01kFQ/nqaaxThBcec4bQyTadE9SWByKvbCHUt1JfKcT+RVFfJlk8tzhVWmFSVCTopyz5fl\nLzz6drYkXX7Wor96b4dtKzMLmnhdZRxw9TCPu+twlOLupsLdIaC7VEc0ZNqnp/enYrO568KV/ffy\nhUeV/Un7BlKKqq5prQnMnuqjzXLCNIMJbf1zGHDZ5FRc4LLKQBXjaebFOc2poPen9sZMFu4pvB53\nrwhYm1MxetxBt7mbdvJoIe5uUNyHdcVdVeH//mLvwZH08sbQHVee5RJn+0lU3+NkCOZxdx1hv8hx\nkC6UrcyKxgUr3A2j+2TMGNwRKI6mG5/NnXncyYH6k1AazByfhnwyG2fpTEXgVdzRZKV5rTLxXMkB\n+4yLUVQ1W5JAF3RNU+V4mnlxyPQlhNafakqDkBU1jfV7Cfpwe9wxz4eyNINpNIMUdxvT1Oan+eRj\npcf/3Pfzt/v9HuGB7WdbCWWykQYywTLM4+46eI6r8XtUVdNxbF6M3QtwHygQpqPR5MwRAGhvxBws\ng3dyOGMyqD9JVec5I0OdqRtn70wF3AO0521O9XsEn8hLspor27/RuBdkvQh6BYuxErisMniLXYto\n/ammFHfdgCTiyusIegSw1gA6hWQOz9hURMha7yyyyji8ORUdK6GlHhhKfeOXewHgX684Y21rjc0r\nM0sjgeGpqjp/hxLDgTjH5s4Kd8NYCXFHIKtMN06rDPK4O+JevvBoqKI/9Z3+eTpTQbfLT2C6B2hW\nmdnjIEE/5U+y/lQLpDWfjNU3V0vEDwDD1j3uTrLKtFuIcscbKQMkrDL4PO6gL8/0c4UrmlMjAY9X\n5LMlaShZ+MKju4qS8vFzll119lK712UeNMcDr1UmW5IkRfV7BFumJTJM4xybOyvcDYMsLuYiZRBa\nlDvGwp0p7iSZtz91PFMciOeDXmFlU3i2zwHsijuyyswpB7JgGetgiZQBgJaaBai4W5nBhNfgDpOS\nW3C9YHK+Qy1DoBlMpqPcR1MFcHxzKsdpJ0uf2vlW90R27aLab330dLsXZYkGrJs2Yt7ZeQxnUuuY\nKHdWuBumx7LijqIYemO5uW3T1cPiIIkyb38SMrifubRu7kN/3eOOtzl1rl96hPWnWgZVWjXWOlNB\nFyPTBcli7AmuEwAsLKkL8Bw3nMqXJMXov8WuuPs9PAAUJQXXvoqejfH2zpor3CVZjedKPMc5P4cE\nuWUOjaRDPvGBT25yu6hMojk1jrXpmUENpri7lUSunMiVg16h0cKRZdArtNT6JVkdstyphkiyOEiS\naP1Js4sumsF9aWTu10HXDC7xZt7JqaDfG1gipBVQoRyylgUJABynB8tYE921p3RnKO4egV9c51dV\n6I8bDrdFBj+MijvPcaiBuGA2cnEK1bzFqidsYbDrRLaoqhANe/HO7yRB5Yd/51VnmZtR6Ci0AXyY\nosAQeloRu1+7DFRioR3YXljhboyemBYpYzHZqh2rzV1X3B1xL194VKG4J2E+gzvoATXJfFmSrSqC\niqpWM9ZRs8o4QCFwL7pVBsNdtkVr3bNUuOs57k656yPjH9oYDZHEbZUB3S2DpT9VUdVkvsxxmENv\nzCnuo+kiADQ7uzMVcdOW1T6R37Zh8YfPXGT3WjDQSMDjHmch7u6EKe5uBbk5rQsJ6BVwRbmzOEii\naDbHWSwuqgq7+xMA0DlnpAzoATUAELMsgWeLsqKqIZ849/BdJOqw5lQraM2gOKwpLTii3B3lcYdK\nsIxxm7u+a+H8RpAdBYvNPV2QVBVq/R5cInfIa15x10Lc3VC4X7y2+eC/fuie/9Np90LwUBf0cBwk\ncmVc/iuozuXIcCDM4+5WUIajlc5UhBblPo4nyp153IkSnXN4XvdENpUvN9X4WmsD876U7paxevAa\nr+6wVW9OZVYZ82SKMuBoTgU9yn3YmkHOUQOYoDKDyXjhTkJxR4mQWIJlsC8P2XgylhR3R4e4L0gE\nnqsLeNHxC67XjGvTl5ji7jKY4u5WrGdBIjBaZVQVUnkWB0mQua0ymsF9WV017ik9WdJqJa1nQc5X\nuCOPuwM2GveCKi0s42OsR7mrqrOaU8GCVYaE3KDPYMJQuGOXRa1MTnWR4r7waMDdn8o87i6F5bi7\nFaSRd2BT3DEU7kVJLsuKR+B9orv79x2L1p80W+GOEtznnJlaoXHOl6oeNDZ1Xs0G3RtYHKQVMFpl\nWiOocDd/3lKQZFlRAx5hbosUTVBG1gsHRo26ZZJVNGkYBaNVJqlFymCTRa1MTh1LF4AV7jYRxd2f\nyjzuLiXiGOspK9yNgTTydssed21wSSynWJ5Hr01fCogW+2UZsxGd0+OOsiDn7UzVXgqTVaZKOZAp\n7tbRrDJYFPcaH1hT3J1mcAeANS01axfVAsBH7nv1xYOj1f/DJIHOnKDXwYq7hZh5FzWnLjywJ0Iy\nxd2lIJWBpcq4jHRBimVLfo9gfQMN+cTGsK8kKdYHsrDOVNKg8jeeLU9/yirLyr7BFABsmC8LEqFZ\nZSzfA/RImfkUd+TJYx53C2SKZcBklWm2bJVxmsEdAESBe+Lz792yviWVL3/2x3+6+4XDVYoR6P6H\na7wRIuDBVrgT8LhbUdyZVcY20PBU68ekFZji7lJQlcU87i4DhcC0NwR5HOI28ttY709lnamkEXmu\nPuitJDBO5sBQuiQpK5pCVf78GzGdumrD2OdX3JlVxip6/CJOj7vpYzbN4O4kxR0Aavzig9ed/Q8f\nXAMA33vu0N888udqgheSRFJlsDWnEvC4o6hK8x531pxqC8gqY70xqQJT3F1KhKXKuJHuiRzg8Mkg\n0OtY709NselL5NFmME0TXSqdqVW+ThRTKnCVWz+bnGodjM2gQa9Q4xeLkmJas0k7afrSZHiO23Hx\nyv/8zHmRgOeFA6Pb7nvt4Eh67n+CNi7MHncLDaBT0J6N8SvuhtemqppVprGGabQ2oG/+zON+qlPL\nUmXcSI8WKWO1MxWB+lNN5B9PQTtxZtOXSBKdpan0HSOdqVAJKLCeKlNdVRH0iKLAFcoyrlmSpyDZ\nIk6RW49yN+mW0T3uDn1Kf//qpl/d8L71i2u7J7JX3Pfa03sGZ/tMVdUVd8zNqdgGMKFnY4zPFaYn\np2ZLUqEsh7wiSoJnUAYdk5qYDTwjsqKmCFz5DAr4RN4n8kVJkVSbGwpZ4W4ApLhbz4JEdGBKhGQe\ndwrMlghmVHHXctwtizdJ7Rx/Hs2G47SB7U4QCVyKNjkVU/xiq7XhqQ5sTp1CW0Pw51/Y/LFNS/Jl\necdjXbf9+oA00+SaShaWH2sWFvYc93nfYtVjenLqaKoIAM21zOBuD8vqgwAwaG38QoXK86qIabAX\ngybocasgs8LdPeCavoRox5QIyR7fKaAr5ScV3OmCdHQs4xH4dYtqq3wdXHbJ6uVAZKeJM5u7WfDm\npiPF3fQMJgc2p04n4BG+e/XGb247XeS5/3jl2PYfvTn9iEkvXzBnYQVwxkFibk5FermJ0wCWBWkv\nHY3andp6BBxUPYKD4UzQhlBQbK6cWeFuAKSOL8fkcdcV95zF3UCLg3SwCLcA0JXyk+qPvwwkVRXW\nL671itW+j2r9HpHnskWpKClW1lNlcyqwYBnLZLBaZZBuajrK3WnTl2aD4+BTmzv++/Pvbarx/fHY\nxOX3voLOpiqgXQuvwR2cHQcZ0OMgjW74Y5kiADSFWeFuD5GApyHkLZRlKxMYKrCxqa4GWRsKClPc\nXUK2JI2li16RR1NUrFOrbwejZg2vCKa4U2DG5lSjPhkA4Dg8rU6J6qwycEpGuRfKMg5pDACgLCsl\nSRF4DpejA1lljps1yKWLbnpKP6e9/tdfvuCc9vqhZOGqH7zxX2/1Vv4XiRB30JNbMFplMD5aiDzn\nE3lVhbzBhhNmlbEd5I89jmNgIvYHQgZN0IaQZ1YZt9A7kQOANkxZkAhtDJO1/lQWB0mB6ExNpV19\nxjpTtZcK+8CaW0ZVIZGv1ioTOcUSIYeS+bW3/nb5139ttDyaESS3h3zYHB0rmkIA8NbxmLl/7nyP\n+xSaa3z/9bn3fGpzR1lWvv7kX/7x53tKkgK63IBdcQ94kB0FR6oM7uZU0KcBGHXyaCHuTHG3j+VN\n2Cado+uqPsQUd1dSGxABoKjYPKWeFe7VgrczFYFezWJ/apLFQZJnRpnchOIOlSh3C4mQubIkyWrA\nI/iqsOggq8ypo7i/3aNZMkz7yCeTwW1NOX95NOQT+2I5c4/rrvC4T8Ej8N/cdvpdH9/oE/n//lPf\n1T94YyiZJxEpA/isMoWyXJQUv0fwe3DeoVHhbnQGkzY2tZaFuNvG8mgIAI7hKNyRVYZ53F0KU9xd\nBiqvcXWmIrT+VDyKu2tEODcyXXEfThVGUoXagKej0dgloQ1PtTCDKWnksFWzypwyHvddPXH0hyEc\nhTvKAKnBV7h7Rf7itc0A8Lt9wyb+uTMHMFXDxzYtefKL/2tpfWB3f+J/3/Mq+vYJedytW2Wwh7gj\nQrrN3dC/GmVjU+0Go+Ier9rlyHAgLFXGZfSMoxB3vIp7sPLKpmFxkBSITmtO3a35ZCJGrVPaS1mw\nyiDfS6S6rV9vTj1lFPdevXBPYMhdTutWGesvVWHrGa1gtnDPuM0qM5nTF9c+fcMF71/dFMuWfrt3\nGAgo7gFMijt2gzsiaCpYhjWn2o6uuGesv5TenMru166Epcq4DM0qY1BenZsOHMNTmcedAigEIJ4r\nVRLB3kGFu0GfDAA0zjKEtXoMyYF1BIanPr1n6J+e/MuBoRTG18RCoSzvG0iiP2NR3PFGyiAuWtPk\nFfldvXHkXTaE9mZ37VN6XdDz8KfPveHileivzbhV5BCmOMik9mxMpHfWaJQ7Sv1nzak20t4YBIDe\nWG7GoQSGQJcW87i7FIekyrhSubEFPcQdp+KOjDfd4zlVBXPdb6qqTQ53S9CESxEFLhLwJPPlVF5C\npfBuU52pANCgNaeat8og30vVVhkP4LbK7HhsFwAMJQsPf+ZcjC9rnb8MJCt31sEkBsVdU7ixKu4h\nr3jBqsYXDow+t3/k2vPbDP1b1zWnTkfgub//4Jqz2xuCXuHcjga8L45LcdffYpirK01xN1K4S7Ia\nz5V4jmMBgjYS8oottf6RVGEgnrdol2Ued1fDPO5uIl+Wh1MFkecW1wUwvmxdwFsb8GRLkulRmpUB\nhD6sAwgZ05mcCKmo6u7+JJhS3GcMqDEEks+rvJGTi4NEyTaOYldvAvTf1FDCoYo7AFx2eisA/Na4\nW8aNzakzctGapvOWN+CdvgT4mlOTZDzuYS1VxsDyJrJFVYXGsFdggzZtZTmO43FgHneXg6wNRWaV\ncQW9sRwALGsI4h1TzHEnxjCZewVt+hLuAYSM6UQnNZUeHctmi9KiSMDEWT+a5WTFKpM0MntPS5XB\n53GvjI6y8uxBiLd74gBw+VmLAGAoha1wx+txB4BL17XwHPf6kfGUkQeqsqwUJUUUOK/A9u2Z8YkC\nx0FZVixaGhKEPO7GrTKsM9UhoML92JjVwj3BPO5uhinubqJnHH+kDAJ5b0z3p7LOVGpEJxXcyCfT\n2WZYbodKqoyFAUxawrSRVBmMzamDetNnbyxn1K1LFFXVImUuP2sxYGpOJWGVAYCGkPe85Q2Sov7+\n3dHq/1Vam5HsYU/ps8Fxmh3FYrAMoSk5ugXfwNrGWOHuDHAp7gnmcXczrDnVTZAIcUfoirvZwp11\nptIiOskqY7ozFfQcdytytaHD1rBPFHguW5LKsmL6K06mP37idGi/k/pT++O58UyxPug9u71eFLhk\nvmzdMpEmY5UB3S1jKFtmARjcKYDcMhZnMBEq3NHaMsYV9+YaFuJuM1gU96Kk5MuyyHPoEY7hOlAz\nISvc3QEqrFEIDF70YBmzVhk2fYkWDajgnqS4b1waMfE6Qa/oE/lCWTZdVhpKleE4TSRIYrK598dP\nKNn7Bh1UuCOfzKb2OoHnWmv9gGMGU5aMVQYALju9BQBeOjhWqHrC64IxuBMFS5Q7oThIE5NTmeLu\nELAo7nH9sJQdmrmUsF/kOCgqXCVfzhZY4V4VPQQV9xAA9FhT3CNs+hJ5KsNTi5JyYCjFc9wZpgp3\njoOGEIpyN+mWMZQqA3r9gcvmjgp39NX3O6lw39UbB4Cz2+oBYFEkADiCZQhZZQBgcV3gzCWRfFl+\n5fB4lf8kjXuM64IkYNyOMp1kvgQAkQBmP4OJyalj6QKwwt0BtDUEeY4biOdLkvmjy0QWGdyZT8at\n8BxX4/eoqnZrsG0ZNn5tF0FibCoCFe7Hx7Pmnt+Yx50a0RCKcSztH0xJirqqOWz6uLNxknhvAkPN\nqYA7yn0gkQeAD65vBYB9g0ksr4kFXXFHhbsfcNjcyVllwLhbBlksmFVmboIeM9NJp0DM4254bbpV\nhhXuNuMV+SX1AUVVUVKFOQwFgjGcCRLCUqxwdzglWR1M5AWeW1qPMwsS0RDyhnxiuiDFTSVta4U7\n87iTJxrWPO7I4L7RVGfq5JcybXPXIi+q3v3rAl4AiFvIsZlMfywHAFvWtwDAwZE0Luu8RXIl+d3h\ntMBzZy2tAwAU22p9BhOyyhASudEI1ecPjFQZgeL26Ut0QEOOLFplULmMPQ5Sz3FnzamuBLlljluY\ndB4n80DIoAmyueOynpqDFe7zM5QqqSosqQt4CKSwVRIhe0zZ3LU4SCbCkUfLX8+Wdveb70zVX8oH\nAKbD+41aZdBn4tpokOK+trWmIxqSZPXwCIYx4NbZ05+QFXXdolpkcdYUd8uFe4akO2Vlc3hFUyiR\nK791PFbN57Pm1GpAVpmshcJdUlT03kRBUhjRJqeaUdxZc6r94CjciQz2YtAkEvAInOH5x3hhhfv8\nDKRKgHtm6mSQW8Zc1wtT3KmhedwzxXd6UWeqPYp7oSwXJcUr8v6qR25hHJ5alpWRVJHnuEWRwPrF\nteAYt4zmk9GPQVDhPojLKkPMVm7ILcMK92oIGrejTGEgnpdktbXWj14KI0Ynp6qqprg31rBSz360\n/lQLhbvucWf3axfzyGfPv2XVyHnLMU99NgQr3OdnIFkCgI5G/AZ3RHuj+f5UFgdJjf+/vXsPb6s+\n80X/LmnpLlvyLbGVOLEDOEDKpCkpbXoMDTClSQvpMBsyDKVMN+kU2A3DycwknUPa58lpG55TMmc4\nKcwOzN6hHUrTNuQciuk0UEpJ49mkBUMwYCCGxE58v0qyddeS1vnjt5biq7QkLWktyd/PX4mj2CuO\nLL169f29LyvcR6ejvRNBq8nYsrwi50/FOnm5ZdxTI2WUzyVgZ+xUybgP+SNJUVxeaeWN3DpPJelm\nIiQ7mfqpVVXstw1uG6kxVaZAm1NTtqyrJ6Lfdg0rOeISiMSJyImoTFos455PVKZwM8Sy3ZwajAmR\neMJh5jE9UA+kiZD5FO7IuJc+3qj9SCAU7pmxjnshRsow+SxP9WMcZLGYjIbUC6RPeCrz+emVUjc5\nTZXxZb8xW+64q1C4D3jDRMQOe6zzuEgfEyFFkd467yOiq1dLhbtHjakyoljYjDsRXbXS1eCyDvkj\n7wz4Mt4YHXclbFLHPffCnc3qXlOAwj3bzamjU1EiWlaJgLsu5N9xR8YdVFFWzwG9vb2F+LRnR6aJ\nyJYIFOjzW+MhIjozMJnD5x/zB4go6Bvr7c13o1vOhoeHC/Sd0ZtKMzcVJiJa4zYq/CcPDAzM/6AQ\nnCaivjF/Dt+3M4NBIrIaEsr/bjzgI6KBMV/+/01vf+QlIrc52dvbW5kQiOjdft+5nh5DrnOJVbnz\n9Ptj3lCsysYL/tHeKSKipCjyBm46Inzw0TmbKcf2RERIJpKiycgN9F3I8wrT+Gyj/Tl/5Jf/64zr\nM8vn/NGcO8/QhJ+IotPe3l5dHAjW3IJ3nng4QESDoxO9vTn+v7/TM0RELmNM9Ye1YCxJRNORuMLP\n/O5gkIgqTGIOV7LgIw+k5PDIIyRFo4GGpyI5P6oMjvuIKBbw6f9HGPefNHw+FZ5MF9PU1JTxNmVV\nuCv5B+dgIvoREV1zRXPTMmchPr+tOkLP9wwHEjlcfzTZS0Rrm1cV6NqU8Hq9BfrO681y92C/P0ZE\n161rbGryKPxb8785U7yf6EI4acjh+3YmOEzUu7yqQvnfvSw6RjQgGEz5/zdFP44R0dqVtU1NTU1E\ndRW9Y9NRQ+WynN+PUuXO8+ab/UR0zZra5uaLn8rj7r0wGTK5luX8ozE2HSX6oMKqwvctjdsTFc+9\n98c/9ocf/qsFvsrML500jhLRmkZPU1Nd4a6nhCx452k4nyAaM9udOf+vjb8ySkSfumxlU9Pcl1J5\nEpIi0QcRIbl6dZOSl7rvTQ0S9a6qc+X2b1kiD8u5ye2Rp6nmwtmxADlrmxoqc/iiUXGQiNY2rWhq\nqsnhrxcZ7j+Lcbvd2n5zEJXJIJ5IDk/HOY4aqwuVcV9WYbWZjN5QLIe5H8i4FxOLuRPR+jxOptLF\nqExOGXdNozJ93jARrXBLc1GvbKgkHaxhmjnBPaVBmgiZe1qmOHPTr2mudttN58aCH49mmM8TKHBu\npzzIc9zzzbg3FyAqwxs4C28QRQorW5fLojKYBakfecbcpRNKyLhDflC4Z9DvDSdF0eO2WfhCfa84\nTjqfmu1gGVGkqTDGQRZPajv9yqq8XsVVywuYcti6lTqcqvyvSJtT1TicKmfcpX/+uhW6iLnLJ1Nn\nvZqSB8vkfj6VFcqOAhfKvIG78YrlpGC2DDLuSrBRMDkfTo0JyQFv2MBxqwrTqXFI51MVxdylIe5q\nT6WEnOUZc2fjIDFVBvKEwj0DacJAwU6mMrmNco8KiXgiaTIaLIonA0I++r1S+zbXRLfEZjI6zHw8\nkQxkPws22yHupOo4yH5viOTDqUS0TgcTIQNR4czING/krlrhmvnx/Ee5F3SI+0xblA2FnI7EiagC\nJ9HTYnPccx4HeWEylBTFFVU2c2E6NaxwV7iDSRriXokh7nqRT8ddFOWl1+i4Q37QvMmgdzxERKtr\nCpWTYaRR7lk+HEjbl2x8nnUkKPQ/7t74Qufgn+WXk2GqnebgpDAeiGbbQGUP/VkNFKu0mjiOpiOC\nkBR5Q+73FSEpsgGLHvecwl3LjvvpCz5RpHUel9U06+UrGyyj/6gMEV17Wa3dbHyn3z/oC6e+t/Nh\n25oS8pKjHDvubL1OIXIyjCObMfOjWJuqM/l03ANRQUiKNpOxcO/ewxKBO1AG54vScV+dU8dd2r6E\nDlyxXLrMuesLLTdesSz/T8Vi7pPZj3JniRdXNh13o4Fjd5Kp/NIyo1MRISnWVVhSTzyrqu0OCz82\nHWXv6WuC5WSuXlU15+P1KkVlitBxt5qMn2+pI6KXukYWu00iKQajAsdJS3xgMXnOcS904W7PZrHr\nWABRGX1pymN5qg9rU0ElKNwzkKMyRem4Z5lxx8nU0lXLdjBlP8pdevTP8j+dpWVyOP08U/+MIe6M\ngeOk86narWFa8GQqyW8LDOfTcY8UI+POZFyhyk5bOi14ey2DPKMyBe+4ZzPKfXQqQpjjrifLKy02\nk3EymMskCTbEvcqB52vIF5o3GbAu+OqCPY4zbC3rm+e9WQ3o+OPZCULHvTSxATXjuXbcs23buG3m\n8xTKc7BMvzRSZtaL2HWeyjd6J7sG/KxnXGRJUTzNOu6r5+aXpMOp+WTcWVSmKIX7DZcv4w3c6z2T\nk8FYanjRTAi4K2TPbwETK9wLsX2JkTruCgp3ISF6QzEDx2HRpn4YOG51rePDoane8eD6xuwyk7n1\nXADmQ+GejpAU+yZDRFSgCQMpyyutFt4QFZJf+lF7tn/XhQeCElTjNBPRZPYTIaVxkFn+p7NojS+c\n1/nUAV+YiBqrZoWwtY25nx0LTkeEBpe1wTU3Gl5lN1t4QzAqTEeE3HLq0ywqU5RaudJm+tyltSe7\nx175YOT2jY3zb4CAu0KspZ1nVKapYIW7U5oqk/nyxoNRUaS6CrMxj3MpoLo1tY4Ph6Z6si/cvdkf\nTwJYEJ4G0pmOxDc2VfumQzZTYce2GDjusb/e8OjvPsr2L1ZY+f9j6+WFuCQoKCkqE8w6KuPPaWk2\nK/Tz7LgPeENEtGJu4a7lREgpJzMv4E5EHEcNLlvvRHDIH66wVuTwyYPFnZu+ZV39ye6xl7oWLtzR\ncVfIZmIh8lyiMsGYMDIV4Y3cisWPCOfJrjgqM4aTqbqUc8wdGXdQCwr3dKrs5l9887OF2207003r\n6m9aV1+ELwR6IEVlsuy4xxPJYEzgDVy2JxRZoe/NbyJk/+wh7sxly528keudCAajQnHi4DO9tUjA\nnWlwW3sngkP+SMvyXAp3eRxkkWatfuHK5Xt/9e7Jj8YW/E5i+5JC+cxxP89miFU7CtfkdpiVdtxR\nuOvTmlwLd2TcQS04nAqggVpnLlNl/PJImWxPKLI2jz/PjrsvTPM67iajgZXFmpxPlUbKLFK4s4mQ\ng74cz6cWMypDRHUVlk+tqooJyRPdYwtcDLYvKWMyGowGTkiK8UQy27/bMxEkojV1BTzRxF5XKFng\nIA1xr8AQd33Jt+OOaCvkDYU7gAZqHLlMlfFKAfes32x15708NSmK8uHUuSkCrdIyvlD849GAmTew\nnP18bCLkcK7nU4sclaHUbJn3Fpgtg6iMQhy8BKFXAAAgAElEQVRHLNmYw/nUnrGCD/9VvjkVHXd9\nSnXcs917La9NRVQG8oXCHUAD1c5cojI5rE1lXHkvTx0PxOKJZLXDzFqGM2l1PvXtPh8RXbXCZTIu\n/Djmcec1WKZom1NTWOH++w9H53eLcThVOYfiA6BzsI57cyE77so3p45NR4hoGQp3namymyusfCAq\nZHtCyYu1qaASFO4AGmALmLyhWDKbvo0vp5OpJDfp8zmc2s9Opi50aE8a5T7oz/mT5+bN85O0eE6G\niNiomaHcozJxInIWsVZeXWO/vKEyEBVeOzsx548CiMooZs9mO+lMrOPeXNCOu+Jrw9pUfeI4WlPr\npOzTMn5k3EElKNwBNGAyGiptpkRSzGqRB7txLlEZe75RmYF525dSrvRUEtGZkekcUsX5eOuCjxYZ\nKcOwUe5DpdNxJ6It65YT0Yvz0jIsKlO0wH1Js+U6yr238B13eY47DqeWMLZ3pTfLwt0rZdzRcYd8\noXAH0AZruk9kk5ZhWRdXDh13tjk1r447O5m6wEIDp4VvqnEICfGjkUDOnz9biaT4NivcM3bc/eFs\n06gMq66K3ORmaZnfvj+cSM66aBxOVc5uymWwjD8cnwzGbCbj8kKeB5U2pyruuONwqg411zqJ6Fxu\nhXv2j94Ac6BwB9AGK9yzGiwjrU3Nfi6BFJXJYwFT/+Idd7oYcy9eWqZ7ZDoYE1ZW2dKEgF02k81k\nDMUSU5GsX7EkkiKrruZn+gvq8vrKVdX2iUCMDcxJQeGunE3xyMWZeuXVS9mObMqKws2poih13Gsr\n0KDVneZaB2XZcReS4nRE4DgsTAQVoHAH0EaN00JE49kMlvHleryJPVv4w/GsIvUzpcm4k5yWKeZE\nSFbXpsnJEBHHSYNlckjLsLLPYeYNBa3j5uE4ebZM18jMj7PZlJWIyijAutrZZtxZZLm5YDtTGYWb\nU4MxIRJPOMy8I8uNDVAEzdlPhJwKx4mo0mrCHlzIHwp3AG3kFJXJ8XAqb+QcFl4UpcZtDtgQ98ZF\nO+7Fngj51nkfpT2ZynjcUlom288fKPrJ1JQvfoIV7sMzX2RJGXcsYFLAnlPGvTiFu8LNqaNTUSJa\nVomAux5JHfeJkPI+CHIyoCIU7gDaqHGaiWgim6iMP8wmAefy6F8lTYTMJeYuiuky7jRjImTOHf1s\nvZl2Z2qKdD7Vl3XHPRBNkEaF8qdWuWudlr7J0NmJi5eNqIxyNlMuUZniFO6sgx7MdG1sFiROpupT\nhZWvcZoj8cTIlNIHFsyCBBWhcAfQBovKZDUMmJXdrpzmErDnjNxi7t5QLBJPVNpMixWOdRWWugpL\nMCpcmAzl8PmzNRmM9U4ErSbjFfULr15KkQfLZN9x12KkDGPguJuuXE5E7T0X38HAAiblchsHWZzC\nXZ54I6R/hSufTEXhrlNsZmjPuNKHO28w954LwBwo3AG0kUtUJpxjVIbkI625DZbpSxtwZ4q5hom1\n29c3unljhsBog9tGOe1g0jAqQ0RbPlFPMwr3VMZJq+spLaxwz2qqjCgWqXDnDZyFN4gihePpLg+z\nIHWuuY6Nclc6R4tN8sXaVFAFCncAbcgd96zHQeYwVYbyG+WeZoh7ypUeFxG9X5TCXT6Z6s54S0+u\nO5g0jMoQ0aZLaiqs/LnJ6PmJEBFFhEQiKdrNRh4n2xSwWxTFUWaaCEYDUaHSZipCaSUvdk33hoBU\nuDtRuOtUc42dsuq4I+MO6kHhDqANKeOueKoMGyhm4Ljc2q6uPJanspOp6Qv3Yk6EZKuXMp5MJcp9\nqkxA08OgJqPhhsuXEdFLXcMkB9xxMlUhNsc9q6iM1G6vKewsSIYV7ul3MElRmUoMcdepbDvuyLiD\nilC4A2gj26gMGyjmsplyG1AoddxDuWTc5SHuC59MZYoWlRESYmdfhp2pKR65cM/20Cwbv6hhNEUe\nCskKdwTcs5BDVEYq3Au5MzXFoSCCP4qojL7JHXelEyF9UsYdhTuoAIU7gDbcdjPHkS8cE5KKikp/\nHgF3uli45x6VSZ9xX1Vtd1j4sekoe5e/cD4YnorEE001jmpH5mfBCqvJYeYj8US2p3I1PJzKfH5t\nncnIvXneOzodxUiZrNiyHwfJKrCmmmIU7nYFg2XGAjicqmurax1EdGEipPDR2ydl3PHaG1SAwh1A\nG7yBq7KbRVFpF1weKZNr4W5jGffcOu4hIlqRNipj4LgrG4qxhumt815SlpMhIo6jBreViIazTMsE\nte64O8z8NY1OInr5/WG5cMezviIOZUuOZmKF+5ridNwVjHIfncI4SF2zmYwNLpuQFFlTIyM5446O\nO6gAhTuAZlhaZlxZWobV3Hl03HPMuKeGuKfPuFMqLTNQ2Ji7PME988lUhk2EHMxylLsUldF0b+W1\nzZVE9OJ7I3JUBh13RWzZZ9x7i9hxlzPui16ekBC9oZiB45Cs0LPm2izSMt5cd+cBzIfCHUAz1U4L\nEU0qGyzjy+94E2vV51C4T0XigahgNxvdmebHFyfmLo+UUdRxJ6IGVy7LUwM6GL+4abXTaOBOnR1n\nh4NRuCuU7ebUpCgWZxYkw3Ywpbm88WBUFKnWaTZiiJCONdey86mKCndk3EFFKNwBNFPryGKwjFS4\n5xyVsecYlZFnQdoznold53FRgQv30elovzfsMPMtyysU/hWPO5fBMoGo9oNcXFb+muZqISk+//Yg\nISqjGAuRKz+cOjIViQrJGqe5OC+N7JmiMhjiXhLkjruiwTLIuIOKULgDaEaaCKms4+7XKCrTr2D7\nEnPZcidv5HongoG0+d18sID7J1e5lTcj63PruOugcCd5tsx7A35Cx10xW5abU8+NBYloTa2zgNc0\nQ8aOOwr3kiB33DOPco/EE5F4gjdydk2jd1A2ULgDaKbaYaEsO+6uTHmVxbCojD8cz3YwYj8b4l6d\nuXA3GQ2sEf5Bwc6nspyMwpOpjCenjHtA68OpzBfXLU/9GoW7Qo4sozK9E0EiaipKTobkJE+aF7fS\nEPcKDHHXNRasUtJxlwLuNnMRtgTAUoDCHUAztc4sRrn78hsHaeENNpMxkRSD2RzaI3mIu5KOOxU+\nLSOdTFUccCeiBreNsp8qo/k4SKbBZVu/UjqGW6H1xZQKu9zSVvgaVe64F6lwz7g5FR33ktBYbTMa\nuAFfOCYk09+SzQ1DTgbUgsIdQDM1Tgspjsp4g/kuzc5tlPuAgu1LKQU9nxpPJN8d8BPRhlVKR8qQ\nPFVmyB/O6q0G1hDVQ5M71XRHxl0h3sjxRi4pirFEhoqKKXLHPePm1NHpCGGIu+6ZjIaVVTZRpPOT\nGdIy7CG3SsHeCQAlULgDaKYmq8OpYen91py/nEuKuWd3PpVl3DPOgmTkwr0gEyG7BqdiQvLSZc6s\nhtk7LbzTwkeFpDebfzgr3B06aHJ/8RP17BcsYgFKyE13RW8usY57cUbKkILNqei4lwp2n+nNNFgG\nQ9xBXSjcATST3eHUvCcBV0mDZbLsuPsUDXFnrmioJKLukem4smZnVnLIyTAeNzufqjQtExOSMSFp\nNHBWXvta+ZI6p5k3GA1cY7WiNz2AiOwmIykbLCMkxb7JEBGtrinSt1fanLp4xx2Fe6mQY+4ZCvc8\nB4IBzKF9PwlgyapxZBGVyXMBE6WWp2YTlQlGBV8obuEN7FIzclr4phpH70Two5HAlZ7KHC90EW9J\nq5eyLtzrXdbukelBX3idsktKjZTRyWGy7h9s1foSSgwbuajkfGq/NyQkxQaXja1tKgJpc+riHXcc\nTi0VCke5e5FxB1Wh4w6gmUobbzRwU+F4xv50UhT94TgRVeYRdGbv1fqzGeXORsqsqLIpL2ELl5Zh\nHfesRsowHld2o9yD+hgpAzmTutoKojK94yGSZ3IXh9xxX/jaRBEd95KhcHmq1HFHxh1UgsIdQDMG\njqt2KErLTEcEUaRKmymfZYo5dNwHpJEyWZQ1BTqfOuQPD09FKm2mS+qyziI3uLMb5S513DF0uWSx\n8wBKojLnxgMkt06Lw2lJN8c9GBMi8YTDwuNIg/5l2XFH4Q7q0N8zU6Cn7cc//+2ZwarVG2/72h3r\n6+W3CwPdRw799LXzXs/am+65f1u9/i4cIAc1TsvYdHQyEKuvTPfOuCopSVf2U2X6vVkE3JkrPS4i\nel/tUe5vnvcR0YZGtyH7/EpDlh336Qg67qWN5V6URGXYycI12b8azFn6zamjUywng3Z7CWhwWU1G\nw8hUJBgTHIu/zkfGHdSls457oHPn1rsPHD27+qq1g22Hd95+2yvDAhFRoGvP1h2H2gbXXrX6taMH\nbv/qY8NaXymAKqTBMsEMg2XyD7hTanlqNodTB7IZKcOkOu7JbFc9pZVzwJ2IGlw2Ihr0Zdlx18FI\nGciNXfEOJtYubaopXuHukGI8C1/b2HSEkJMpEUYD11RjJ6LzafenIuMO6tJX4T7+7n90kuvR44d3\n3/vA4VcPbyb/vz3fRURdbf9yiloefeHwA/fu/tXTu2nw6FMnUbpDOWCF+3imHUz+/NamMnJUJpuM\nezbbl5i6CktdhSUYFS5kGm+clTcv5DhShog8bitls4MpiMK9xNkzLTlK6Sl6x90mj4Nc8IWtfDIV\nhXtpYOP/eybSpWWQcQd16atwdzZ/5ZFHD21kaUN+2UoX+3Dgjee7Xdu+sdFNRMQ33/RgC732px7N\nrhJAPbVOCxFNZsq457k2lclhARM7nLoyy0GEqsfcI/FE16Cf47JbvZRSL0dlFL4JMI3DqSVOYcc9\nJiQHfGGjgWtUtl9MFbyBs/AGUaRwfIHLw8nU0sIW7vaMpSvckXEHdemrcLfWr9u0sZH9urvt/37G\nT1/90lr2W0ddao6b9YprW/x/eMenxRUCqKta6rhnisrkPcSdcuy4hyjLjjvJMXcVC/d3B/xCQly7\nvCK3LrjDzFfaTPFEMuMLJCYQQce9tCmc435+MiSK1Fhl541FHfzpWPwNAalwd6JwLw0ZO+6iSP4w\nMu6gJp0+M413PLnjwIlNDx7e1mglChBRnTNzR+T06dOFuJjh4WGv11uIz1wePvroI60vQb8y3nmC\nk0Ei6j4/dPp0ugT2B2eniSjin8jnTj4eShDR2FRI4SeJJcSJQMxooMGzHwxnU9jYo2Ei+uOHfaeX\nZUjLKLzzvPBhgIhW2RM5//OrzOJUmE683nlJVeanz7Pnp4ko6Bsv0EOKQnjkSS/Nncc3MU1E5y70\nnz6d7tXj6wMRIqo2C0X+jzZxSSJ64/S79c65o2M+PO8jorB35PTp6Xy+BO486an1tJXwRonovd7R\nxe5CwVgykRStPNf1bqcqX7E4cP9JY3h4uHCPGBs2bMh4Gz0W7oGuY7fuesazff/Dt7VIHwrS2MTF\nx9+psRFqqpk/g0PJPzgHvb29TU1NhfjMZaNA3/kykPHOM24ZoY4OzlqR/nv4wsD7RNOXr1m1YUNz\nzhcTjifohReDcfrkJzcoGc1ydixANLTCbb/6U9n9/1atCv7zayf6AoruGEpuc+i9N4mmvnj1ZRs2\nrMzqSlLWvP3Gef9o5fJVG9bVZ7wx+25fsnrlhg1rcvtyqsAjT0aL3Xk6Aueo64PK6roNG65M89df\nnz5HNPnJNZ70N1Nd1R9OjgSmmy5tYZuGZxLeep0otPETLRta6vL5ErjzZKTK05ZnKvKdV18Ziyz6\n2c5PhIiGq53W0nqWxP0njdOnT2v7v6mvqAwRCX0v3nHfQc+2/b984Dr5VYW1fgMN/v50QPpt32/a\n/C2fuwJr5aAMKIzK+NUYKGbljWbeEE8kFwzXzjeQ/SxIZlW13WHhx6aj7H3/PIliXiNlGDYRclDZ\n+VREZUqdwow7mwXJ0g7FZF98sAybKoPDqaViWYXVbjZOBmP+RaZ1sYFgGCkDKtJZ4e7r+N/v3O8n\nz1evr+ns6Og4daqzZ5yIb73tLho8/L0jHb7A+IsH/vEE0e03rNX6WgFUUONUtICJPfq78nv05zip\n9Fe4PFUaKZP9uT0Dx6l4PrXfGxoPRKvs5nxm9rEdTAoHy7BxkBU4nFqybCZFU2XOjQeJqLnohbtj\n8VHuozicWlI4Tnrh17vIGib5eBJOpoJq9PXMFDj/ZicR0eCBXfdJH3Ld1f7re53r7338wf6dB3fd\ncoiIaPv+I1uwgQnKgjRVJtM4SLUe/d128+h01BeKs9Hm6UkjZbLvuBPRlQ2Vr/dMvj/o37w2r3f8\niehNqd3uzn7z0kUe1nFXNspdnuOOJlmpyqrjrkXhztNChbuQEL2hmIHjMIGkhKypdbw/OHVuPLi+\ncYGZV94gOu6gMn2Vv87197a337vgH62/7fsv//l4QCDe6nY79XXZADlzmHmT0RCMCeF4gq17XJBa\nu/eymgjZPxkiopVZjpRhVOy4v5XHBPeU+myWp7KoDGuLQilihXv6qTLBmDAyFTHzBhajKia2g2n+\n64rxYFQUqa7CbDQUdcoN5CN9x92LjjuoTWdRmbSs7tra2lpU7VBOOI5qnWbK1HRXZXMqEbnYREhl\ny1MH8ui4r1NvIuRbF3xEdHUeAXci8rhtRDTkz6LjXoGMe8mSFzClK9zZtsvV1fbiV8n2RaIyGOJe\nitg7NucWKdz9yLiD2kqpcAcoSzVOC6WNuYti6nBqvm0b9ha8wlHuA7lm3InosuVO3sj1TgQDCwV5\nlQvFEh8MTRkN3J+tzGX1UgrruA9PKdrBJEVlrHiuLVVsjnv6jLsUcK9zFumaZlis4z46xdamYvJC\nKWlGxx2KC4U7gMbYYJmJ4KIDWEIxQUiKDguf/5oYKSqjoOMeTyRHpiNGA1efU5DAZDS0LK8gog+G\n8mq6v9PvSyTFy+srWPghZzaT0W03CQlxPNNxApILd0RlSpdNQcZdCrjXFG9nagq7M89/TTsWQMe9\n9KQ67gv2BFjGPf83SwFSULgDaIxFZSYWLyhVWZvKSFNlFGTcB30RUaTllVY+1yCBKmkZdjI1z5wM\nw87jZkzLiCLGQZY8JYdTe7TruDsX2ZyKqEwpqrKbXTZTMCos2HxhHXecNgYVoXAH0FiNI0NUxqfe\nxmy34qhMvzdEuQbcGVXOp6pyMpXxuK1ENOTLcD41HE8kRdHCG0xGPDyWKjYoPf3h1B4NO+7SVJl5\nURkMcS9NUtN9bIG0jJxxR+EOqsEzE4DGqqWO+6JRGW+IvdmqwkO/S3FUJp+TqYxcuPtz/gyiSG+d\nV+FkKsM67oOZOu5BKeCOdnsJk6IycSHNiQYNO+4O88IRfHTcS5QUc59YoHD3qvd+KQCDwh1AY7WO\nDDuY1JoFmfokSsZBStuXcpoFybB17t0j0/FEMrfP0DsR9IZitU7LypwOyM7Bpv5l3MEkD3FH4V7C\neANn5g2iSBFh4aa7LxT3hmIOM1/n1KBKljanzuu4o3AvUaxw71mo446MO6gOhTuAxqSpMot33P1q\nrE1llEdl2EiZfCpmp4VvqnEICfGjkUBun+HU2Qki+tTqqnxWL6VIHfdMUZlpBNzLQvpR7qw52lRr\nV+WulS1pc+q8jjtbm4qpMiVHKtznddyFhBiIChxHlRhRBepB4Q6gsRqHwsOpKkRllHfc+7whIlqR\nR1SG8kvLjE1HH3ruXSL6dJMKORlKZdwzRWUwC7I82EzpRrn3aLQzlZE77rMKd1FEx71ULTYR0h+O\nE5HLZsJGLVARCncAjWWc465mVKaIGXfK43yqNxS763/+iYiu9FTe8elV+VxDisLlqUFsXyoLrKu9\n2Ch3bQt350L7oQJRIRJPOCx8npNPofikjvt4cM6aCOl4Ut77NwBmQuEOoDFpjnsguthBOmmqjBpR\nGbuZ5w1cJJ6IxNMN3BCSIsuCe1z5Fe4rcpkIGYgKf/PU62dGpi9d5nxmx2cqVDonyqIyI1ORRDLd\nDiYWlcEQ91KXfiIkK9ybtOq4L7Q5dUzKyaDdXnocFr6uwhIVkiNTs/oC8lwBvH0HakLhDqAxu9lo\nMxmjQnKx7qBPatuo8OjPcVJW3p+26T7ijySS4vJKq5nP6yHiyoZKInp/aErJvlImHE/c85M33un3\nr6q2P/ONz7BXNaqw8IZqhzmRFMcWP05AFw+n4rm2tNkW2U7KsMJ9Ta0GI2VI3pwanH1tY9MRQk6m\nZC04EdKHIe5QACjcAbRX40w3WMYfVnNpNnvfNn1ahg1xz2ekDFNXYamrsASjwoXJkJLbx4TkN59+\n8/WeyfpK65G//Wx9pcqn9JQMlpGiMhgHWeLspkWjMqKY6rhrMMSdLi52nTWtchQd91K24ERI1nOp\ncqALAGpC4Q6gPXmwzMKFO2vbqDJVhuT3bdMvT+1XI+DOKI+5Cwlx589Pt380VuM0H/nbz6ry1efw\nuNlgmXTnU6ejLCqDwr20pZkqMx6IBqOC227SqhXKGzgLbxBFCs9IrOFkaklbsOMuDXFHxh1UhcId\nQHtssMz4IhEOFaMylDqfmnYipDTEXZ3CXVHMPZEU/+HZt3/bNeyymX624zNr6goSPlbScQ9E4oRx\nkKXPvtABUEZqt9doE3BnHNLlXXxDALMgS9qCHXdk3KEQULgDaI913CcXisqIohRrcalVuCuIyrAh\n7o1qrD260lNJRO+nnQgpivSdX733/NuDDjP/9D3XXN5Qmf/XXZC8PDVt4Y6oTFlgHff5s9IpFXAv\nzItDhVjhPnMHEzruJQ0ZdygaFO4A2quVB8vM/6OIkIgJSavJaDWpM+fEZc88yp3NglSp454hKiOK\n9IP/eP/nr1+wmow//q+fXt/ozv+LLoZ13IfSRmUC0QSh4176WMZ9wahMrx467ua5EfxRFO6lbHWN\ng+OobzIkzBhahYw7FAIKdwDtVTvNRDS+UMdd7tmo9tDP2j9KDqeqkjJfVW13WPix6ShrKM73//yu\n+/B/9vBG7t++dvU1zdX5f8U0WMY9/Sh3FpVBxr3U2RYfB3lOBx13toMpMGMiJJsqg8OpJcrCGzxu\nm5AU2YMn41Vvdx5ACgp3AO3VOBaNyvhDMSJyqffQLy9PXTTjnhRF1nH35D1VhogMHJem6f7ch4GD\nr3xkNHD/euenrmupy//LpSfvYErfcUdUphykOZyqi467Ze7rCnTcS11zDduferFwV/d4EgCDwh1A\ne7XORaMy0vYl9R76M06VGZ2OCgmx2mG2qRTOYdPcu+bF3H966vzTnVMcR/98+/ovrqtX5Wulx+ZL\njkxFhcV3MLEFTIjKlDr7vNOfTFIU2QlCrdamMnLGXbo8ISF6QzEDxyEPXbqa6xxEdG48kPqIFxl3\nKAAU7gDaY4dTxxcaB+kLqbY2lZGmyiwelVHxZCqzTjqfOqvj/v++2f/d598jov23XnXrhhVqfa30\nzLyh1mlJiuJiuR2SaylEZUody7gH53Xch/2RqJBcVmHR9r/YMXs/1HgwKopU6zQbDZyGVwX5kDvu\nF8+nSh13ZNxBVSjcAbTH9oMuGJVRvePuYlNlFo/KqDgLkpk/EfI37w7tPvYOEd2zwXXnNavU+kJK\neNwZ0jKIypSHxaIy8uolLdvtRGS3GGlGxx0jZcoA67j3yIV7OJ6ICkmT0WA34cEE1ITCHUB7NfJU\nGXFegkPq2aiYcc/ccVftZCpz2XInb+R6J4KsJn71zOjf/eJ0UhT//gstt7QUu36qZxMhfQufT00k\nRdYEZWUflC6beeGojDQLUuvCfU7HfXQKQ9xLHjs1kSrc5YduE4c3UUBVKNwBtGfmDRVWXkiKU5G5\n9bRf1bWpdPFw6qKFu9RxV+NkKmMyGtYuryCi9wenTp2duO+nbwoJ8b7PX/LADZep9SWU80g7mBbu\nuLNCymHhDXiyLXH2RabK6KXjbjbSjKkyYwF03EteY5WdN3ADvnBUSBKRN4iAOxQECncAXVhssIzq\nURmnlTdwXDAqxBPJBW/Q7wsT0Ur1Mu5EdKXHRUQ/+9P5Hf/+RlRIfm3T6m9vuVyT2rjBnW4HUyCK\ntallwpG2cNe84+6cfXYWUZkywBu5xmq7KNKFyRClHrqxNhXUhsIdQBdq2Cj3eYNlVI/KGDiOLWH1\nL5KWYXOIVcy4k3w+9fm3B0OxxH+5euX/uW2dVh3t9DuYMFKmbKSPymjfcZ+9OXUUQ9zLAhtV1DMW\nICIv276EjjuoDYU7gC6wwTIT8wbLqN5xp1TMfaG0jChKU2VUzLiTXLgT0Zeuavjhf/kzDYMoUuG+\naMddICInTqaWvgWjMkJS7JsMcRyt1nSIO83bnIqOe3lgLwh7JkI0I+Ou8TVB2cHzE4Au1CwyWMar\n9jhIImId9wXPp04Eo1Eh6bKZ1O06b2iseuKuqz8Ymtp5w6W8pgPvPK50y1PZlI8KdNxL34JTZfq9\nISEprqiyWXiNm1Zsc+rFjjs7nFqJw6mlbc3Mjjsy7lAYeH4C0IXFojL+ArRt5I77AhMhC9FuJyLe\nyG35RP2WTxRjy1J6yyutHEej0xEhIfLGuS8hWFQGQ9zLgNVkJKJwPJEUxdQ7PDoJuJO8OTUYm304\n1YmOe2mb2XH3ouMOhYGoDIAusMOpE/MPp7KpMjY12zYsMb/g8lR2MnWFqidTdYU3cnVOiyhKqeI5\nEJUpG0YDl6rdUx/UScCdZm9OFUVEZcoEe03IdjCxtzTRcQfVoXAH0AXWcZ+TcY8KyXA8YTIabCY1\nx4q7F4/K9Bem464raQbLBCKIypSP+WkZVrg366FwnzHHPRAVIvGEw8Jje0Cpq3dZLbxhZCoSjAnI\nuEOBoHAH0AVpB1NwVlTGLw8UU/cwZ9qoTIiIVqo3xF2H0gyWYR13RGXKg23e+dRe3RTuMzensnY7\nRsqUAQPHsTVM58dDyLhDgaBwB9CFBafKSD0bVUfKkBy8SdNxV3cWpN6kOZ+KqEw5sZvmFu7ndFO4\ns457MJYgorHpCCEnUy5YEOvceBAZdygQFO4AurBgx50F3KscKvdsqhYfBykfTi3bjDsRNbjZRMiF\nOu6IypQRNis9FZWJCslBX5g3cHq4e+hxk/MAAB2NSURBVMvvBgiiSKPouJeRVMzdj4w7FAYKdwBd\nYNW5NxhPimLqg+yh36V2x50dTp1fuIui3HFfAlGZQd/iHXcU7mWBRcZTk1vOTwRFkRqr7doOJGV4\nA2fhDaJI4XgCJ1PLidxxD/gKMMkXgFC4A+gEb+Cq7OakKM6sp1Vfm8qw5xJ/eG7G3R+OB2OCw8Kr\n/lJBVxpcNiIaXjwqg4x7eZhzOFU/AXeG3c1CMUHuuGOIezlgd7DOPn9SFB1m3mRElQUqw10KQC+q\npbTMxXq6EGtTKbWAaV7HvV8+mardYtNi8LitRDS4YFSGLWBCxr0s2EwXJ7eQngLujDwREh33ssLu\nYGfHAkTkdpRzBwS0gsIdQC/kiZAXY+4FerNVmioz73DqgC9MRCuryzknQ0R1FVYDx40HovFEcs4f\nsQVMiMqUB7ucI2e/1V3HXb68URTuZaTWaUm9ZYeAOxQCCncAvah1zt3BVKDCvdJqIqKpcDyRFGd+\nfCkE3ImIN3DLKiyiSCNTc/fUBjFVpoyw7aRhvXbc7WaeiAJRgU2VweHU8sBxF+9jqr9ZCkAo3AH0\nQ4rKzJgIyWLo6q5NJSKjgau0mYhoKjKr6b4URsowbLDM4LxR7jicWk5sM0Yukg477hZpWiU67mXm\nYuGOjjsUQFk9P/X29hbi0w4MDBTi05aN4eHhAn3ny0BWdx5eCBHR2f6R3l6pET40MUVE0enJ3t65\nveE8OU3cVJi6Pupd6br41HJmYJyIzEKgaP+hWt15XHySiN75uG8ZNzXz41PhOBFNDA8EeO2bGnjk\nSS/jnScamCKiobGJ3l4+HE+OTkfNRi7iHe716eIMB5eIEdHHFwYngzGjgfOPDgbGVbsw3HnSK+gj\nTzUvNUT4ZKREnxxx/0nD5/MV7r+1qakp423KqnBX8g/W22cuA16vF9+fNJR/cy4Z4qhjLGGyp/5K\nlPqJaG3TyqYVLnWvqrayf3Aq5qha1rTKnfqgN9ZHRBtaVjWtdC/+V9Wk1Z3nEk/oxLkpweyc+dVj\nQlJIdvEGruWSNTo5noufrDQy3nlW9ItEo2abs6mpqWtwioiaa51rmpuLdH2Z1Ln9RFNhg4OIahzm\nS9aofGG486RR0EeeT3pN//7mGBGtWl5Tuv8LpXvlheZ2u7X95mjfVQIApnqBw6kFGQdJF5enzpoI\nyTLuK93lH5XxuBdYnpqaBamTqh3yJC85ShBRz3iAiJrr9JKTISK7xUhEvRNBQk6mvKxBxh0KCYU7\ngF7Uzh8HWbAVHu55y1MDUWEqHLeajNVqL2rVIXkH06yMewAnU8tLalA6EfWMh4iouUZHhbvDzJOc\nvMcQ93LShIw7FBIKdwC9qGFTZeTDqUJCDEQFo4FjT/Dqml+4p0bKLIV+84I7mAIRgYgqcDK1XNhM\n+u64m41E1IOOe9lJLbBLJOcOnAXIH56iAPRCXsAkRWXYyBeXzVSISpq9hztzeaq0famqzGdBMg0L\n7WDCSJkyY58VlQkSUZOeOu7snsZeqKNwLzM3ras/NxbYsKpK6wuBMoSnKAC9cNtNBo7zheJCQuSN\nXOFyMiS/hzuz485mQa5YGoV7ndNiNHATgVhMSJrlATKpjLumlwaqYYPSwzMK9zW66rjPuKdhiHuZ\n+bevXa31JUDZQlQGQC8MHMea7t5QjOSTo261h7gzrOM+c3mqfDJ1SRTuRgPHUsXDUxfTMqxwr0DG\nvVzYpUHpgi8U94XiDgtf49BRfcw2pzLouAOAQijcAXSkxnFxsIw3WMCOu0vKuF+Mygz4wkS0srr8\nR8owHreViIZmnE9lGXdEZcqG3WQkomAswSa3rKl16Or8hn3G2ZVllTicCgCKoHAH0JEap5mIxoMX\nO+5VhZlLUDUvKsMy7iuWRsed5POpg/65HXenFRPcykQqKnNuLEgzZn3oBNucytQ50XEHAEVQuAPo\nCBssMxmMEZE/FCe5Na461sj3z4/KLI2MO8kTIYfnF+4zyikoafIcdyHVcdf6imaZeZoCURkAUAiF\nO4COsKjMeCBKcgC9QCs8WHQ+1XEPxRKTwZjJaFg6BcT8wTKIypQZq8lARFEheXY0QDrsuMtRGYeF\nt5vxchEAFEHhDqAjM0e5F25tKsmThv3heFIUSV5FtMJtM+gqBVxIHpeNiIZ8iMqULQPHsYL4/aEp\n0l/H3S6/t4ORMgCgHAp3AB1hGXcWlSnoOEjeyDksfFIUWZu5fynNgmRYx31oZscdUZmyw9Iy0hB3\nnRXuqY770nmbCwDyh8IdQEeKFpWh1PLUcJyIBnxLaPsSww6nDs3IuE9LURl03MtHanJLtcPsKtiP\nUm5scjwGJ1MBQDkU7gA6MjMqU9DDqZQa5R6KE1H/pBSVKdDX0qFap5k3cpPBWCSeYB8JRONE5MQc\n9zLCJkISUbPO2u1ExBukWBrucgCgHAp3AB1hHffJYMEXMNHF5akxIupnQ9yrlsoQdyIycFx95awd\nTMFognA4tbykutp6y8nMhGW9AKAcCncAHZkVlSlkxp1mL08dWGKzIJmG2edTpzFVpuykamK9nUyd\nyYGRMgCgGAp3AB2psJp4IxeICuF4YioS5ziqKNjb6PLy1DjJ25eWXuE+ayKkFJVB4V5G7KXQcbfj\nLgcAiuHxAkBHOI5qHZbhqcj58aAokttuKtx8xlRUJiYkR6ejRgO31Paue9w2kncwiSKiMmXIJmfc\n9dlxf+yvN5wdC966YYXWFwIAJQNPUQD6Uu00D09FPh4LUiED7jQjKjPgCxNRvcuaOi23RNSzjrsv\nQkTheCIpilaTkTcurW9CebPKhfvqGj0W7res92h9CQBQYhCVAdCXGoeFiM6OBaiQI2VITs/7Q1Lh\nvqROpjIe18VR7myIuwND3MuLPyztBsZqUgAoDyjcAfSl1mkmonNjASrkEHe62HGPse1LK5fSLEim\nwX1xlDtbRFWBIe7lZWQqkvlGAAClA4U7gL5UO8xEdJZFZQrbcWcZ9/jAkjyZSvLh1Jkdd0zULjNs\nFTEAQNnAsxSAvtQ6LZTquNsLWHakpsqwjvuKpVe4VzvMJqPBF4qH4wk5KoOHxLJy+G8+rfUlAACo\nCR13AH1hPcJQLEHFisos2Yy7geNY033YHwlE4kRUgcIdAAB0DIU7gL6ww6lMQTvu7JP7Q/G+yTAR\nrVh6GXe6OFgmPI2oDAAA6B4KdwB9mZnKLWjG3cIbbCajkBSH/GGOI497aQ1xZzzy+VQMcQcAAP1D\n4Q6gLzWOIhXuMz//8gqrybgUHw3k86lSVAaFOwAA6NlSfKoG0LMa54yoTCEXMBGRS47iLMGRMozH\nZSOioVRUBoU7AADoGAp3AH2xm42pdY8F77jLh1+X4EgZRsq4+8MYBwkAAPqHwh1Ad1Ixd1chp8rQ\njBcGS3CkDMMy7sP+SBAddwAA0D0U7gC6Uy0nWCoLXbgv+Y57g9Rxj0xHULgDAIDeoXAH0B0LL/1g\n8gauoF8oNW6ycakW7lV2s4U3TIXjo1NRQlQGAAD0DYU7gO4YizXgxSVHZVa4l2hUhuOowWUjoo9H\nA4QFTAAAoG8o3AF0p9CN9pRKucG8NIe4Mw1uKxEFYwIROVC4AwCAjqFwB9AdA1ekwj0qJNkvUnNs\nliAWc2cQlQEAAD1D4Q6gO59priaiVdUFj680LtVhMjOxqAyDqAwAAOgZnqUAdGfnDZfuvOHSInyh\nL1y5vPf/+nIRvpCepWJCHEc289J95wEAAPQPHXcAWNLqK6WOu93MFy2kBAAAkAMU7gCwpKU67sjJ\nAACAzpXOE1Wg+8ihn7523utZe9M992+rL50LBwA9S2XccTIVAAB0rkQ67oGuPVt3HGobXHvV6teO\nHrj9q48Na31FAFAeXPL6WFHU9kIAAAAyKI3CvavtX05Ry6MvHH7g3t2/eno3DR596iRKdwBQQSrW\nHogKml4IAABABiVRuAfeeL7bte0bG91ERHzzTQ+20Gt/6tH6qgCgrExH4lpfAgAAQDolUbgTETnq\nKuVfWq+4tsX/h3d8Wl4OAJSbUCyh9SUAAACkUzKHseqcmTfFPPXUU4X40j6fz+12F+Izl4fh4eHT\np09rfRU6hTtPejq581SZar1xo9WQLNBjSG5w50lPJ3cefcKdJz3cedLD/SeN06dPF+6Z4p577sl4\nmxIp3IM0NjGV+t3U2Ag11Vjn3UrJPzgHvb29TU1NhfjM5eH06dMbNmzQ+ip0Cnee9HRy5ynIA0fe\ncOdJTyd3Hn3CnSc93HnSw/0njaeeeqpA1aZCJRGVsdZvoMHfnw5Iv+37TZu/5XNXzC/cAQAAAADK\nVUkU7nzrbXfR4OHvHenwBcZfPPCPJ4huv2Gt1lcFAAAAAFA8pRGVca6/9/EH+3ce3HXLISKi7fuP\nbMEGJgAAAABYSkqm/F1/2/df/vPxgEC81e12lsxlAwAAAACoopQqYKu7Frl2AAAAAFiaSiLjDgAA\nAACw1KFwBwAAAAAoASjcAQAAAABKAAp3AAAAAIASgMIdAAAAAKAEoHAHAAAAACgBKNwBAAAAAEoA\nCncAAAAAgBKAwh0AAAAAoASgcAcAAAAAKAEo3AEAAAAASgAKdwAAAACAEoDCHQAAAACgBKBwBwAA\nAAAoASjcAQAAAABKACeKotbXoI5rr71W60sAAAAAAMhFe3t7xtuUT+EOAAAAAFDGEJUBAAAAACgB\nvNYXoH+Bjhdf7KXLbt6y3qr1pehMpPPFXxz7fUfUsrr1K3ds29io9fXoi6/n1C9//pt3B6Oeq1r/\n+qvbmp1aX5AuDXe+8vsu77obbl5fjx+vFKHrld98ECQzERHFYrEVV2/ZhDvQDIGeUz9+6rkzXlq9\n8YY77tjSiPuOzNd18ncfjJrNZvkDMXJc8aUb1+GZPiUy3PmLnx7rOO+tWv252772l3jkmU3oeuXo\nL156O0ruT37xK3954zp8d5jhrpO//yB+w1/cWM9+lgLdRw799LXzXs/am+65f1t9cX/AEJVJR/B1\nPXrvfW2DRK67Xvj1vW6tr0dPhBe/e/3+E7R+23bP+ZeOd/o3PXj4kdtatL4qvQh0Hdl63yHybLrr\nhrrfP9M2SJseP/7IepRec/hO/dUtewaJtj/+wgPr8eOVMnzg2tvbyOVxUZCI/P6mHU88/vV1Wl+V\nXvg6j9yy8xB5Nm1vtRw9eoJo89Ovfr8ZlSkREXUf++6Og6ddLiIih4MGB/1E219ofwA/XYww/OL1\nt+8n1/q7brvq3WPPdPpd+5/91XVFLrv0S3jl4b/Yd9zv2byt1fbh0ePdrm37f737Oq2vSnO+k09+\nZ+8znUSeR4//cqOTKNC1Z+t9p6hl+12Xv/RMm9+z/dlfPlBfzCsSYTHh9+5pbW391iNPfOfLrdt/\nPK315ehL+O0vt7Z+53dD7Hd/eOTLrV9+wqvtJenJG4+0trb+SPqGjP3hy62tP3oD3545pp/9Vmvr\nPft3f7n1ibfxzZlh+u3tra3PnotrfR36NPajL7e27n5WekAe+l1rK+4/i4i/963W1t3Pn9P6OnTk\nvR/f09q6/4L0u3PfaW3d/sTbml6RjsSHjre2tu4/Lt1hxl77UWtr67Pnwtpelebee+LLra3bH9l/\nT2vrPW9Pi6Iovveze1pb72FP6fFzz7e2tu7/w1AxLwkZ98XxlVvv3/fy47uvv2I5BbW+GL3hV+/b\n/8i3/jfpRWZNnUPby9GbT97/wgvH75/V5TJpdS06NXzyRwc7af8P/9s1DoppfTH6whNRS1NFoK+r\nq7OrxydofT26Mv7+S366/xtb+OHujo6OrvjV7e3t9+LtmoV0PvMvnbT5/i81a30hOmJy2ojC0o+U\nEI8SrV5dre0l6Udk5DyR56ZW6Q5T++kbWoi6z41pe1WaM63++qMv/HLXX99IFCAiosAbz3e7tn1j\no5uIiG++6cEWeu1PPcW8JLxDtDi+8bY7G4koHgtofSn6w7s3XrdJ+vXwye8dHmzZ8Vk8eabwTreb\nIh1tR9+bnDh++Kh//f1fQ20xU6Rz797jLfc/cV2t9Um8Kp4tcuGjQeredest8gda9h/51+uQ4yYi\nosDAOT/RKz+841C3n33Es3Xvvz+0Bd+duQIdDx/u3rT7OwgRzdRy8z9sPXj33Tfv2Pw5z+BrJ7pd\n257ejNNZEr6yhmjw1LvjGzfVElHkTGc3UeDjSbpxSX+LWrbcRkSBgVn9J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02HI/BO5OZNd/3dVUimuGQ4TDYX5/sGz88jhQpicq6aamjSv/r7Ov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jc5pHrCvQV3SBFJ4ulIWdkMdXX7Shm4RxNUnC6tNtr1dldsDig3O7Yd9gsKkBi4s1IGwHOmhtKG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l0W55rjMK7CcfxsO8C+6mZdcNCwCiIUJxB8pDU3PleqluJCM6zfNpv2vcOzuWo55wr3g5vsJH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XAf3v30PG7MG3Mlp2ibXS9I0gykA2xoqq4HwOniRA2VMuRwnw7S0ImACu6EeirFpD2zR8E6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\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       "text/plain": [
-       ""
+       ""
       ]
      },
      "execution_count": 4,
@@ -191,55 +226,32 @@
     "data.arrays.keys()"
    ]
   },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {
-    "collapsed": true
-   },
-   "outputs": [],
-   "source": []
-  },
   {
    "cell_type": "code",
    "execution_count": 6,
-   "metadata": {
-    "collapsed": true
-   },
-   "outputs": [],
-   "source": [
-    "plot.win.close()"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 7,
    "metadata": {},
    "outputs": [
     {
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "DEBUG\n",
-      "['dmm_voltage', 'dac_ch1_set'] dmm_voltage\n",
-      "Considering the units V, V\n",
-      "Started at 2017-06-30 14:24:05\n",
+      "Started at 2017-07-10 17:22:57\n",
       "DataSet:\n",
-      "   location = '/Users/william/sourcecodes/qdev-dk/docs/examples/PlotTesting/plottestsample/085'\n",
+      "   location = '/Users/jhn/src/Qcodes-dk/docs/examples/PlotTesting/plottestsample/376'\n",
       "      |   |  | \n",
       "   Setpoint | dac_ch1_set | ch1          | (50,)\n",
       "   Measured | dmm_voltage | voltage      | (50,)\n",
-      "Finished at 2017-06-30 14:24:07\n"
+      "Finished at 2017-07-10 17:23:00\n"
      ]
     },
     {
      "data": {
-      "image/png": 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IFe5vzyVMXP5afmN5IZ4x7SAAMAMId1CdpXRBlikcdPOu1eBnWI3KWG1ZNwCA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\nAID2+M6+66Lar3+pRvlEJBKJ1Kxce++6lWai3rcOHqtd+9TLb3R2vrFrY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6JR3YGs8+GUNOZvrBe9dEIskyyw8AABaHwB0wrTkrZUpyM+22tB7P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\nI0IkeO5pUMqvDZypwki4U7mto5mQwL99ex4AnjrAZMhdxEtRNIFKGUvAg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E116oKUMxgJ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\nt176pksrAX93AMACmRmjkjctx+0O7SO/tNnqdwZ2tZQjZ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       "text/plain": [
-       ""
+       ""
       ]
      },
-     "execution_count": 7,
+     "execution_count": 6,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -254,33 +266,30 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 8,
+   "execution_count": 7,
    "metadata": {},
    "outputs": [
     {
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "DEBUG\n",
-      "['dmm_voltage', 'dac_ch1_set'] dmm_voltage\n",
-      "Considering the units V, V\n",
-      "Started at 2017-06-30 14:24:09\n",
+      "Started at 2017-07-10 17:23:13\n",
       "DataSet:\n",
-      "   location = '/Users/william/sourcecodes/qdev-dk/docs/examples/PlotTesting/plottestsample/086'\n",
+      "   location = '/Users/jhn/src/Qcodes-dk/docs/examples/PlotTesting/plottestsample/377'\n",
       "      |   |  | \n",
       "   Setpoint | dac_ch1_set | ch1          | (50,)\n",
       "   Measured | dmm_voltage | voltage      | (50,)\n",
-      "Finished at 2017-06-30 14:24:10\n"
+      "Finished at 2017-07-10 17:23:16\n"
      ]
     },
     {
      "data": {
-      "image/png": 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jDlA1yQwbwLQ8426j0+viq8uKNqzxUwUdIZXAvcmz5HFlIgmuZABgRZHU8hp3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uOQo9xVPPwok1OJbWjayCqTsK1VJqD7GQlFyqxrCqDJnSd1uGUWSKhcqxBqg2VKkTLYqp5VQ/Qw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\nfz9v47olS3Y0Okf8z47Go7v2vNMx4iL47nd27Wuy22v37TnaZB90yNa6X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3vX/B99t3u++tKb+s7wRYlpTzrvKt6QhhqJQBAG0RuAPANOJxae9V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1JI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vJZhJtw58CdZOXWuEe9N7U7tG5t13VN++2bTyTcr1cA/HKvaVrR996EwCtwZ58MyWWhDncO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\nP1HLCfdvHR0t6vrP7ehqobQa64/TbvO5HPmCn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       "text/plain": [
-       ""
+       ""
       ]
      },
-     "execution_count": 8,
+     "execution_count": 7,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -304,23 +313,20 @@
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "DEBUG\n",
-      "['dmm_voltage', 'dac_ch1_set'] dmm_voltage\n",
-      "Considering the units XX, XX\n",
-      "Started at 2017-06-30 14:24:12\n",
+      "Started at 2017-07-10 17:24:29\n",
       "DataSet:\n",
-      "   location = '/Users/william/sourcecodes/qdev-dk/docs/examples/PlotTesting/plottestsample/087'\n",
+      "   location = '/Users/jhn/src/Qcodes-dk/docs/examples/PlotTesting/plottestsample/378'\n",
       "      |   |  | \n",
       "   Setpoint | dac_ch1_set | ch1          | (50,)\n",
       "   Measured | dmm_voltage | voltage      | (50,)\n",
-      "Finished at 2017-06-30 14:24:13\n"
+      "Finished at 2017-07-10 17:24:32\n"
      ]
     },
     {
      "data": {
-      "image/png": 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DzdDcQ2pHV31qUtzpcSeEVIzhcW9Gq0wqp2SVQsjnnXHfwsLdWVi4NxRCUGwJ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ugpeCKLlW2BmWz8xDQ7x7qGOvtsTNwtZpXpG00CQG+XvX0yoJe4//73v6/0T263VZQzKwDPKjOl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\nA1BWTFTc2dVnHAsByJNxinvQeDJwQ7WLSlDcBwyTBcmQFPeIIAf4GOitB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JBO4AADjDWCIlyZhr01STwB3AjfLjM4PZrHZuaHz/e+ok9TLhXkrC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\n3OMvqurFebYWmqLBoanQI2XY4U5E7U8vuFfVelI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45veaXt2YKOsRNLsO3bDOaNb+fb+0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\nxAI+aClJ/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5UyrG/qUFqfs/Z2xISOqXBl09RsSToylhO9wpK2iPnPngj5LuhukxVl62vO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\nA7j2vHkAHn7lmCXPThCNoCXc3buqcWqvztNJ5bnuQ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WNxDkyUQQl3c2AnqHheWOX5Kbgn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ZNLhqGOXcOIy/z85T4AOHt1O+viNuPOk6bAnfT1GBK4AxjF24LZcLfeFPYHrUnfNdwnKgDQ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\noqyor5zNEtG+6yBwN4Z91/b/IdHh0xlVJfO+TaUaZgS5i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DchRURkyoQUq8Vy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       "text/plain": [
-       ""
+       ""
       ]
      },
      "execution_count": 9,
@@ -349,23 +355,20 @@
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "DEBUG\n",
-      "['dmm_voltage', 'dac_ch1_set'] dmm_voltage\n",
-      "Considering the units V, V\n",
-      "Started at 2017-06-30 14:24:15\n",
+      "Started at 2017-07-10 17:24:39\n",
       "DataSet:\n",
-      "   location = '/Users/william/sourcecodes/qdev-dk/docs/examples/PlotTesting/plottestsample/088'\n",
+      "   location = '/Users/jhn/src/Qcodes-dk/docs/examples/PlotTesting/plottestsample/379'\n",
       "      |   |  | \n",
       "   Setpoint | dac_ch1_set | ch1          | (50,)\n",
       "   Measured | dmm_voltage | voltage      | (50,)\n",
-      "Finished at 2017-06-30 14:24:17\n"
+      "Finished at 2017-07-10 17:24:42\n"
      ]
     },
     {
      "data": {
-      "image/png": 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sdE1frJ3nUWzu7utPheIOrGXV4lKD+c9Yv/8sFHe/0OFWGTXBvV97BHGQfmJt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AuaX1uFvSuvMrp91x9FxYCkzfvX1tWZH1vw+c/lFzx1k8wmHX4NZ/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9qK+1pgatWWRSO/Iy5kkHCHMSpNxR/R809tK8LcHIc1VsMZuPPxVNIVQ4LjgXsuFXy3ErfKVFr4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V25rWtVOSVvNN4L5SX+0OYYFrqVpH0yMiL4Wo3F0OLOT4XWACd/sd7gAwOzJUw90SuPt3p8pS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5eiT0YYbo17sd7+wYUln6b967vHh/IB5cTA3TGccCeShCjKq7VkvzWrcGMqEW2IgTsRbUdI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\nqhG4A8BtXQ/cP3bnQFc44OxhALS2fRu65TaBezpPpQyAq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       "text/plain": [
-       ""
+       ""
       ]
      },
      "execution_count": 10,
@@ -392,23 +395,20 @@
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "DEBUG\n",
-      "['dmm_voltage', 'dac_ch1_set'] dmm_voltage\n",
-      "Considering the units V, V\n",
-      "Started at 2017-06-30 14:24:18\n",
+      "Started at 2017-07-10 17:24:49\n",
       "DataSet:\n",
-      "   location = '/Users/william/sourcecodes/qdev-dk/docs/examples/PlotTesting/plottestsample/089'\n",
+      "   location = '/Users/jhn/src/Qcodes-dk/docs/examples/PlotTesting/plottestsample/380'\n",
       "      |   |  | \n",
       "   Setpoint | dac_ch1_set | ch1          | (10,)\n",
       "   Measured | dmm_voltage | voltage      | (10,)\n",
-      "Finished at 2017-06-30 14:24:28\n"
+      "Finished at 2017-07-10 17:25:00\n"
      ]
     },
     {
      "data": {
-      "image/png": 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RuHsRjnemEhJ3AIChdOhsvb7DmhITlBY3FCc6Y6ID/zhfTUTv7D/33cXmIXhG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\nilz1i4FBp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cAYDe55lXNs95oT4vMeBbx1omk1aYnzh7eEF7qnP5xpYMjgcAAED/JHAHAHqZ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       "text/plain": [
-       ""
+       ""
       ]
      },
      "execution_count": 11,
@@ -440,10 +440,7 @@
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "DEBUG\n",
-      "['dmm_voltage', 'dac_ch1_set'] dmm_voltage\n",
-      "Considering the units V, V\n",
-      "Started at 2017-06-30 14:24:30\n",
+      "Started at 2017-07-10 15:57:41\n",
       "Printing the number 1\n",
       "Printing the number 2\n",
       "Printing the number 3\n",
@@ -455,18 +452,18 @@
       "Printing the number 9\n",
       "Printing the number 10\n",
       "DataSet:\n",
-      "   location = '/Users/william/sourcecodes/qdev-dk/docs/examples/PlotTesting/plottestsample/090'\n",
+      "   location = '/Users/jhn/src/Qcodes-dk/docs/examples/PlotTesting/plottestsample/363'\n",
       "      |   |  | \n",
       "   Setpoint | dac_ch1_set | ch1          | (10,)\n",
       "   Measured | dmm_voltage | voltage      | (10,)\n",
-      "Finished at 2017-06-30 14:24:31\n"
+      "Finished at 2017-07-10 15:57:42\n"
      ]
     },
     {
      "data": {
-      "image/png": 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AZKa7NziKOwAAgIOq/uilGoU391wwsbe7i3bFgax/LE+2UN0bGMUdAADAQSXWdPRSjbq0\n9J4//jadk+abXWdmrjlMd29QFHcAAAAHlVTLLWWuq1do08/G9nDSqL/YcnLOL6n1FA3XQXEHAABw\nRPml5WculLpo1eHNPW/xpfqG+X88qptGrfrw52Of/XayXuLhWhR3AAAAR3Qwo1BEOgZ5aTWqW3+1\n+zsFvDsiSqWSWWsOL0o4fesviGtR3AEAABxRYkah3PI8mas93K3FzIcjReQfy5N/3J9ZXy+Lyyju\nAAAAjqhygnuX+ivuIjKqV6tXHoywWORv3yf+lJJdj68MobgDAAA4IItFDqQXiEhUcB33gryRx2Pa\nPHtfe5PZ8vTifb+l5tTvizs4ijsAAIDDydYbcoouerhoQ/3c6/3Fp98bNjmmjdFkeeLrvbtOXaj3\n13dYFHcAAACHUzlPJrKlt1pVDytT/0ClklceiBjVq5WhwjThy92Vxzzh1lHcAQAAHE7lytSu9TrB\n/Woqlbw5tPNDXYNKLhofi9919FxRA72RQ6G4AwAAOJyk9AK5tTNTa6RRq94b0bV/x+YFpRWjP084\nlVvScO/lICjuAAAAjsVikaTMyr0g63ll6h9oNapPRnWPae+XW3xx1OcJWQVlDfp2do/iDgAA4FjS\n8kr0ZRW+Hs6B3q4N/V7OWvWnY3veFtL0bGHZqM8TcoouNvQ72jGKOwAAgGNJunT0UgMsTL0ON2dN\n/PjbIlt4p+WVjPkiIb+0vDHe1R5R3AEAABxLYgMcvVQ9T532q4m92jfzOHqu6LH4XcUXjY321vaE\n4g4AAOBYkhrm6KXqNXV3XvT47a193ZIyCid+ubuswtSY724fKO4AAAAOxGi2JGfpRSSqEZ+4V2rm\n6bJ48u2B3rpdpy48sWBvudHcyAFsHcUdAADAgRw/V2SoMLVo4trU3bnx371FE9fFj9/u6+G85VjO\ntG/2G82Wxs9guyjuAAAADuTApZWpSgUI9XNfNKm3t6vTTynZM75PNFvo7jeL4g4AAOBAqo5eauAd\n3KvXIdBrwcRe7s7aH/dnvro8hep+kyjuAAAADqRyS5muDXlm6s2ICvaJH9/TRatelHD632sP091v\nBsUdAADAUVw0mo9mF6lUEtlCySfulXq38f10bE+tRvXZbyc/+vWY0nFsgFbpAH9UcGrHd9/8eDBL\nWkf2GTk6Ntjj0g+KUxfP+3r76fyg8AETp8QGWF1wAAAAa3coS280W9o183B3sYou1S/cf+6fuz+9\naN8HG1I9XLST+oQqnciqWdcT99w9nw4Z98LC7RIe7rN54exRg149Vbk9f3HKC4MmzVuZFR7ZevuS\n2SNGz81WOCkAAIDtqZwno+DK1GsN6hwwe3gXEXlz9aFvdp1ROo5Vs6riXrx+9kK5Y/ra1e9Mm/by\n6rX/vUM2vbUkRURSVr6/Q8I+WBU37ckZyxfMkKwl8b9R3QEAAGonKUP5lanXGtaj5ZsPdRaRl388\nuOJAltJxrJc1FXfD6e1ZEtWrS9XsGI/WHYMkdcXuAinevSLVO3ZyTx8REW3ogOlhsj3hlJJRAQAA\nbFBieqGIRCm9MvVaY+9o/eKgDhaLPL/kwPoUns9enzUVd12Lnt6SmJp+6fv8nBKRS9Pw3f29Lt8X\nERNWuDmpoNEDAgAA2K7ii8aTucVatSoi0KvmuxvdU3e1nXZPO5PZ8vTi/VuO5SodxxpZxbqES3zu\nmtovbubrgwsOje/luevLuB2FIs2rfubv4Vbj76elpTVErMzMzIZ4WbuRnZ3dQJ+8HWDwVI/BUw0G\nT/UYPNVg8FTPkQfP/swSi0Xa+OnOZtxwKrmy4+dP7V3O5vj+cDBv8le73x3cOjKg5vrXmAoKChpu\n8ISEhNR4j1UVdwkd+Ob3fj+8P+/7L7+Uu8bPfD3vq9dXXTSKSInk5Okv36bPOSchvrprfv1m/sB1\n03CvbAfy8/P5fKrBh1MNBk/1+HCqweCpHh9ONRx58Kw7fUJEerbxr/4TUPbzmR0SolmW9N3u9JfW\npn/zxO3WsG3lZT4+Psp+ONY0VUYMiSsX75J+78R9t3r1dzOG90r/NVWiI3xEF9BNsn7dX1x1W/qa\nlYVh0RHXFncAAADcSFJGoVjZljLXUqlk1sORQ6KCii8ax8XtSj1XpHQiK2JVxV1XcXrx7Oce/nJH\nakFB+spZj8VlyYw/9xLR9hk+RrLi3li8p6A4d93sv20SGXFPuNJpAQAAbMmlvSCt6Bn2dWnUqg8e\n6XpfRPP80vLRXySk5ZUonchaWNdUmZ5T5k1KnxL3wqQ4EREZM3NxbKhORDyinvx4esbUOc8NmSci\n8sjMxQM5gQkAAOCm5RWXZ+aXuTpp2jX3VDpLzbQa1Seju0/8cve247mjPk9YOuWOQG9XpUMpz8rq\nrzZ4/DurRxYUFBuNWg8/n6tmw0QNf3PDfbnFRtHqfHw8rCw2AACAdat83N65hbdWrVI6y01x0ao/\nG9djXNyuvafzR32e8P1Td/h5uCgdSmFWNVWmis7Hx8/vd6390nU/Pz8/WjsAAEBtWefRS9Vzd9bO\nH39bpyCvU7klY75IKCitUDqRwqyxuAMAAKB+We3RS9XzcnX6elLvds08jmQXPTZ/V8lFo9KJlERx\nBwAAsHMWS9VUGdt64l6pqbvzwsm9g5u6JaYXTPxqT1mFSelEiqG4AwAA2LmsgrILJeXerk6tm7or\nnaUuArx0iyf3DvDSJZzMe+rrveVGs9KJlEFxBwAAsHOXH7erbGNh6nUEN3Vb9Hjvpu7Om1Nznvl2\nv9FsUTqRAijuAAAAdq7y6KUu1n30Uo3a+nssnNTby9VpXXL2Cz8kmi0O190p7gAAAHbOVo5eqlHH\nIK+vJvRyc9Ys25f52ooUR6vuFHcAAAB7ZrZYDlY+cbe1LWWuq1srny8eu81Zq1648/Tb6444VHen\nuAMAANizU7klxReNzTxdAryuOSXHNkW39f3vmB5ateq/m098vPG40nEaD8UdAADAntnoDu7Vu6dD\nsw9HdlOrVO+tPxq/9ZTScRoJxR0AAMCeXZrgblfFXUQGdwl8e1ikiLyx+tB3u9OVjtMYKO4AAAD2\nLDG9QESigm1+Zeq1RvQMfj22k4i8uCxpVWKW0nEaHMUdAADAblWYzIfO6kUksoW9PXGvND46ZMb9\n4RaLPPfdgZ8Pn1M6TsOiuAMAANito9lF5UZza183HzcnpbM0lKfvbveXu9sZzZa/LNq37Xiu0nEa\nEMUdAADAbtnH0Us1mjEgfHx0SLnRPPmrPXtO5ysdp6FQ3AEAAOyW3Ry9VD2VSl4b0nF4j5ZlFabx\n8buSMwuVTtQgKO4AAAB2K9ExnriLiFqlentYlwcjA4svGsfG7Tp2vljpRPWP4g4AAGCfyipMx84V\nqVWqzi3s/Il7JY1a9eHIrvd0aJZfWj76852n80qVTlTPKO4AAAD2KSVLbzJb2jfzcHPWKJ2lkThp\n1P8Z3f2Otr7niy6O+mLn2UKD0onqE8UdAADAPiWlF4hIF/s6M7VGOifNF+N6dg32ycwvG/3Fzrzi\ncqUT1RuKOwAAgH2qXJna1R6PXqqeu4v2q4m9IgK9TuaUjIlLKCyrUDpR/aC4AwAA2CcH2Qvyurxd\nnRZO6t3G3/3wWf1j8btKLhqVTlQPKO4AAAB2qLCs4lRuiZNG3SHAU+ksyvD1cF40+faWTVwPpBdM\n+mqPocKkdKJbRXEHAACwQ5WP2zsGeTlpHLfvBXrrFk2+vZmny86TeVMW7qswmZVOdEsc9x8kAACA\nHUtyjKOXatTa123R47c3dXfeePT8s98eMJotSieqO4o7AACAHao8einKISe4/0H7Zh4LJvbycNH+\n7+DZF5cmmS222t0p7gAAAHbIMfeCvJHOLby/mtjL1Unzw96Mf606ZKPVneIOAABgb84XXczWG9yd\ntW383JXOYi16tG7y+WM9nTTqr7anvfPTEaXj1AXFHQAAwN4kpheISOeW3hq1SuksVqRPO7//jO6u\nUavmbTrxycbjSsepNYo7AACAvWFl6o3079j8g0e7qlQy+6ej87elKR2ndijuAAAA9ibRgY9eqlFs\nVNC//9RFRP61KmXJnnSl49QCxR0AAMCuWCxVT9y7sjL1BkbeFvza4I4i8uLSg6uTziod52ZR3AEA\nAOxKen5pQWlFU3fnFj6uSmexXhP7hD7fP8xssTz77f5fDp9XOs5NobgDAADYlcrH7V1aeqtYmFqt\nafe0f7JvG6PZMmXR3u0n8pSOUzOKOwAAgF05kM7RSzdFpZIXB0WMub11udE8+avd+87kK52oBhR3\nAAAAu3LpiTvFvWYqlbzxUKdh3VuWlpsei991KEuvdKLqUNwBAADsh8lsSc4sFJGoYPaCvClqlert\n4V0GdQ4oMhjHxCWcyClWOtENUdwBAADsx/Gc4tJyU6C3q5+Hi9JZbIZWrfroz93uCvO/UFI+6vOE\nMxdKlU50fRR3AAAA+5GUXiA8bq89J436v2N79Aptek5vGP1FQrbeoHSi66C4AwAA2I/Ko5dYmVoH\nrk6a+PG3RbX0Sb9QOvrzhLzicqUT/RHFHQAAwH5c3gtS6SA2ycNF+9XEXh0CPE/kFI+NT9CXVSid\n6Hco7gAAAHai3Gg+dFYvIpEtKO515OPmtHBy71A/90NZ+sfm7yopNyqd6AqrK+6G7MQvZ786derU\nV2cvTsy+anZRceri2a9OnTp11tyV2Vb0AQIAAFiLw9l6o8nSxt/dy9VJ6Sw2zM/DZdHk3kE+rvvP\nFDz+1Z6LRrPSiapYV3E3Zq/rP2Jq3Ob8yJ6R+ZvnTR0x/LfKkl6c8sKgSfNWZoVHtt6+ZPaI0XOz\nlY4KAABgbZI4eqmeBPm4Ln68t7+ny/YTeX9ZtNdosiidSMTaivvRdd+LDFq8+uMnxz/58eoF/aTw\nkxUpIpKy8v0dEvbBqrhpT85YvmCGZC2J/43qDgAA8DuJHL1Uf0J83RdO7u3j5vTL4fPPfnfAZFa+\nu1tXcXfycBUpq5oIY6y4KNK6dVOR4t0rUr1jJ/f0ERHRhg6YHibbE04pGRQAAMD6JGVw9FJ9Cm/u\nuWBib3cX7eqkrJeWHVS8uVtXcQ8b/NdBsmnc4Emvznp10tBJO7xjp/QLrvyRu7/Xpbt0ETFhhZuT\nCpRKCQAAYH1KLhqPnS/SqlUdA71qvhs3p0tL7/njb9M5aZbsSV+f42lRtLxrlXzzaxSfOXpCRERc\nK78vPHL0THFomIiIv4dbjb++f//+hkiVnZ2dn5/fEK9sH44dO6Z0BOvF4Kkeg6caDJ7qMXiqweCp\nnh0PnpTzFy0WCfbRHk5OqvOLMH6u5STy92jvmVvyE/Ld3v9x2z2hNZfSOujWrVuN91hVcS/+4dWZ\nqXfMWPtOrIeISMEPU4fMfHVln+9ipURy8vSX79PnnJMQX901v38zf+A6SEtLCwkJaYhXthsN9Mnb\nAQZPjRg8N8LgqRGD50YYPDWy18Gz+7eTInm3tw/s1i2yzi/C+LmubiItW5/74MdtU2PvcNEqNmPF\nuqbKiIj4B3lUfeXTpWeQlBQZRRfQTbJ+3V9cdT19zcrCsOiIa4s7AACAw+LopQbVv2Pz4YEFCrZ2\nsbLirm3aWmTllysTTxUUFJza88PbcVnSrZ2HaPsMHyNZcW8s3lNQnLtu9t82iYy4J1zptAAAAFYk\nMYO9IO2cVU2V0cW+seDs36bNnjputoiIeN8xZsHf79WKeEQ9+fH0jKlznhsyT0TkkZmLBwY0UvLk\nzEKTgQOfAACAVbtQUp5+odRFqw5r7ql0FjQUqyruIrrQJz9e/VhBbrGIiIefz5XpMFHD39xwX26x\nUbQ6Hx+PRop9oaR8wpe7LWbzR6OaRLf1bZw3BQAAqK2DmYUi0inIW6tRKZ0FDcWqpspU0fn4+fn4\nXd3ar1z382u01i4iF43mVk3dcksqRn+x8+11R6zk0CwAAIA/SEwvEHZwt3fWWNytR6C37rsn73is\nh79KVPM2nRg2b3taXonSoQAAAP4oiQnuDoDiXgOtWjXhtmbfPnF7oLdrYkbBg3O2Lt2Xoeze+wAA\nAFezWCQxo/KJO8XdnlHcb0qv0Kbrno15IDKwpNz41yWJz363v4gVqwAAwDpk66aWfPIAACAASURB\nVA05RRc9ddrWvg1yNhCsBMX9Znm7On0yqvvbw7q4OmlWHMh64KMt+88UKB0KAADg8g7uPmoVK1Pt\nGcW9FlQqefS24P89E9MpyCv9Qunw/26f++txk5l5MwAAQEmVO7hz9JLdo7jXWht/9x//cufjMW1M\nZst764/++fOdZwvLlA4FAAAcV9WWMqxMtXcU97pw1qpfeTBiwcRefh4uu05dGPjhlnXJ2UqHAgAA\njshssSRlsBekQ6C4113fMP+fnu17d3izwrKKpxbufWnZwdJyk9KhAACAYzmdV1pkMPp5uAR4uSqd\nBQ2L4n5LfD2c48ff9s8hnZw06m92nRkyd+uhLL3SoQAAgAO5fPQSC1PtHsX9VqlUMuHOkJVT72zX\nzONETvFDn2yL23rKzE7vAACgUSRVrUxlgrv9o7jXj4hAr1XT+ozu3brCZH5z9aEJ83fnFl9UOhQA\nALB/VUcvUdwdAMW93rg6aWY+3PnTsT183Jw2p+bc/+Fvm1NzlA4FAADsmdFsScnSC3tBOgaKez27\nv1PA2ul9b2/jm1dc/lj8rjdXHyo3mpUOBQAA7NOxc0WGClNwU7em7s5KZ0GDo7jXv0Bv3aLJvWfc\nH65Rq+K2nnrok23HzxcrHQoAANihyqOXonjc7hgo7g1Co1Y9fXe7pVOiWzV1O3xWP3ju1m92nWHB\nKgAAqF9J6QXCylSHQXFvQF2DfdZMj3m4WwtDhemlZQf/smhvQWmF0qEAAID9uLQylSfuDoHi3rA8\nXLQfPNr1g0e7urto1yZnD5rzW8LJPKVDAQAAe2CoMB3NLlKppHMLirtDoLg3hoe7tVjzTEzXYJ+z\nhYaRn+98b/1Ro4l5MwAA4JYcOqs3mi3t/D3cXbRKZ0FjoLg3kta+bj88FT31nnYiMvfX4yM+3Z5+\noVTpUAAAwIYlpheKSJdgJrg7Cop749FqVH8bEP7N47cHeOn2nykYNGfLigNZSocCAAC2KomjlxwM\nxb2x3d7Gd+2zMQM6BRRfNE7/dv9flySWXDQqHQoAANgeVqY6Goq7Apq4OX86psesP0XqnDRL92U8\n8NGWxPQCpUMBAABbUmQwnswp0WpUEYFeSmdBI6G4K0OlklG9Wq2a1qdDoNfpvNJh87bP23TCzE7v\nAADg5hzMLBSRjoFezlrqnKPgn7SS2jfzWPH0nRPvDDWaLW+vOzLmi4RsvUHpUAAAwAZUzpPh6CWH\nQnFXmItW/dqQjvMn3NbU3Xn7ibxBH27ZcOic0qEAAIC1qzwzlQnuDoXibhXuDm/207N9Y9r755eW\nP75gzz+WJxsqTEqHAgAA1isxg70gHQ7F3Vr4e7p8NfG2fzwYodWoFu48PWTu1iPZRUqHAgAA1iiv\nuDyroMzNWdPO30PpLGg8FHcrolapJse0WfF0nzb+7sfOF8d+vPWr7WksWAUAAH9QOcG9cwtvjVql\ndBY0Hoq71ekU5LV6WsyjtwWXG83/XJkyecHuCyXlSocCAABWJImVqQ6J4m6N3Jw1bw/r8sno7l6u\nTr8cPn//h79tOZardCgAAGAtEtMLhZWpjofibr0ejAxcNz2mV2jTnKKLY+MSZq05XGEyKx0KAAAo\nzGJhL0gHRXG3akE+rt88fvvz/cM0atVnv53803+2n8otUToUAABQUmZB2YWSch83p1ZN3ZTOgkZF\ncbd2GrXqmXvbL3nyjhZNXA9mFj740Zbv96SzYhUAAIdV+bg9soWPioWpDobibht6tG6y9pmYIVFB\npeWmGT8kTftmn76sQulQAABAAZVHL3UNZoK7w6G42wwvV6ePRnZ7b0SUm7NmddLZgXO27Dmdr3Qo\nAADQ2KqOXmKCu+OhuNsSlUqG9Wj5v2diurT0ziooe+S/Oz78+ZjRzLwZAAAchdliOZhZKCJRnJnq\neCjutifUz33plOin7mprtlg+/Dn1z5/tzMwvUzoUAABoDCdzSkouGgO8dM08XZTOgsZGcbdJThr1\ni4M6LJzcu5mny+60CwPn/LY66azSoQAAQIOr2giSx+0OieJuw/q081v3bN/7IpoXGYxTF+974Yek\nknKj0qEAAEADSsrg6CXHRXG3bU3dnT8f1/ONhzo7a9VL9qQP/mhrcmah0qEAAEBDSUzn6CXHRXG3\neSqVjLuj9appfcKae57KLRn6n22f/XbSzE7vAADYnQqT+dBZvYh04Ym7Q9IqHeB3ClJ++/nweWdn\n50sXysU94oF7O2lFpDh18byvt5/ODwofMHFKbIB1BVdeeHPPlVPvnLXm8IIdp2etObzlWM57j3Rl\n2QoAAPbkaHZRudEc4uvu7eqkdBYowLr67/nDG+bM2e/tLSLi7i5ZWYUij/S9t5NPccoLg57aIWGP\njOnw08LZa7ee/v67aQFKp7U2OifNGw917hvmP+P7pC3Hcgd++Nu7I6Lu6dBM6VwAAKB+VK1M5XG7\no7Ku4h42/M0twy99Y0yZevdTbjMG+4ikrHx/h4R9sCqup49MGRB+97jZ8b+NeLkv1f067otovu7Z\nmOeXJG47njvxy93jo0NeeiDCRcucKAAAbF5iOju4OzTr7XOJC99PlH5THggVKd69ItU7dnJPHxER\nbeiA6WGyPeGU0gGtV3Mv3deTer04qINWrfpye9pDH29NPVekdCgAAHCrkjIKhOLuwKy1uBfvmRWX\neseMiaGX/krA3d/r0s90ETFhhZuTChSKZhPUKtVTd7Vd+pfoEF/3I9lFQ+ZuXbjzNAtWAQCwXaXl\nptRzxRq1qlOQV813wx5Z11SZyxIXzc6Sfv/3QOjlK/4ebjX+VlpaWkOEyczMbIiXbQTeIvOGtpqz\n9ey6owX/WJ68LvHMjLsCvXX1/A89Ozu7gT55O2C7g6dxMHiqweCpHoOnGgye6tnu4DmYXWq2WNo0\n1Z3LTG+4d2H8VKOgoKDhBk9ISEiN91hlcS/YM2th1h0z/u/y43YpkZw8/eWf63POSYiv7prfu5k/\ncN003Cs3gv+2b7MqMeulZQe3ntIfyyv/8NGud7T1rcfXz8/Pt+nPp6Hx4VSDwVM9PpxqMHiqx4dT\nDdsdPL9knBKR29r4N3R+G/18GoGPj4+yH441TpXZ8/XsrKrZ7ZV0Ad0k69f9xVXfpq9ZWRgWHXFt\ncceNDIkKWvds3+6tmpzTG0Z9sfOdn44aTcybAQDAllQevRTF0UsOzPqKe+6O15dk9XvliSuP20Xb\nZ/gYyYp7Y/GeguLcdbP/tklkxD3hykW0SS2buC556o7p97ZXieo/G48Pm7f9dF6p0qEAAMDNSsoo\nFPaCdGxWV9xTVn9RKIOeuC/46oseUU9+PL3fjnnPDRn08MyVWY/MXDyQE5hqT6tWPdc/7Nsnbg/0\ndk3MKHhgzpZl+5jHBgCADSgsq0jLK3HWqjsEsDLVcVld/e00Pm7L+Otcjxr+5ob7couNotX5+HhY\nXWwb0iu06bpnY15adnDNwbPPLznw27Gct4Z29nDhIwUAwHpVPm7vGOil1aiUzgLFWN0T92rofPz8\n/Pxo7bfO29Xpk1Hd3x7WxdVJs3x/5qA5W/afYXdNAACsFzu4Q2yruKMeqVTy6G3Bq5/p0ynIK/1C\n6fD/bv/41+MmMytWAQCwRolMcAfF3cG19ff48S93Ph7TxmS2vLv+6KgvEs4WlikdCgAA/BFbykAo\n7nDWql95MGLBxF5+Hi4JJ/MGfrhlXXK20qEAAMAV5/SGc3qDu4u2jb+70lmgJIo7RET6hvn/9Gzf\nfuH+hWUVTy3c+/Kyg6XlJqVDAQAAkas2glSrWJnq0CjuqOLr4Tx/fK9/DunkpFEv3nVmyNyth7L0\nNf8aAABoYIkZzJOBCMUdV1OpZMKdISun3tmumceJnOKHPtkWv/WUhQWrAAAoKjGdlakQobjjWhGB\nXqum9RnVu1WFyfzG6kMTvtyVW3xR6VAAADgoi0UOZvLEHSIUd1yXq5Nm1sOR/x3Tw9vVadPRnIEf\nbtmcmqN0KAAAHNGZC6UFpRVN3Z2DfFyVzgKFUdxxQwM7B6x7tu/tbXxziy8+Fr/rzdWHyo1mpUMB\nAOBYki5NcGdhKijuqE6gt27R5N4z7g/XqFVxW08N/c+2EznFSocCAMCBVB69FBXMBHdQ3FETjVr1\n9N3tlk6JDm7qdihLP/ijrd/uTmfFKgAAjaPyiXsXJriD4o6b1DXYZ+30mKHdWpRVmF5cmvSXRXsL\nSiuUDgUAgJ0zmS3JmYXCylSICMUdN8/DRfvho10/eLSru4t2bXL2oDm/7Tp1QelQAADYs+M5xaXl\npiAfV18PZ6WzQHkUd9TOw91arHkmJirY52yhYeRnO99bf9TEtBkAABpGUnqBiHQN5nE7RCjuqIPW\nvm5Ln4p++u52FrHM/fX4Vwc4YBUAgAZxgKOXcBWKO+pCq1HNuD/8w0e7icjqY8WVRzEDAID6dXkv\nSKWDwCpQ3FF3D3UNmhzTxmKRl5cdNJqZMQMAQH0qN5oPZ+tVKonkiTtEhOKOW/Rc//a+rpqULP2C\n7WlKZwEAwK4cPqs3mixt/Dw8XLRKZ4FVoLjjlrg7a5/o4S0i761PPVtoUDoOAAD2g6OX8AcUd9yq\nXi10AzoFlJQbX1+ZonQWAADsRyJHL+H3KO6oB/+K7ejurP0pJXvDoXNKZwEAwE5U7gXJylRcVsfi\nbjabs7OzU1NTT58+XVJSUr+ZYHMCvV2fHxAmIq+tSCkpNyodBwAAm1dy0Xg8p1irVnUM8lI6C6xF\nrdc6XLhwIT4+/ueffy4tLXVzcysrK7NYLK1atbr33nuHDh3apEmThkgJ6/dYdMjSfRmHsvQfbjj2\nyoMRSscBAMC2JWcWWiwSHujpomV+BKrUrrivX79+2bJl99xzz8cffxwaGqrRaCoqKs6dO3fmzJk1\na9aMHTv2/fffDwsLa6CssGZaterff4oc+sm2+G2nHu7WgscDAADciqqVqcyTwVVqV9zbtWv3n//8\nR62+8m9+Tk5OLVu2bNmyZXR0dHZ2tsXCZt6OK6qlz7g7Qr7anvbSjweXTYnWqFVKJwIAwFZVHb0U\nTHHHFbX7yxeNRmM03nAGc0BAQGBg4C1Hgg3724DwZp4uiekFixPOKJ0FAAAbdumJO3tB4oraFfdV\nq1bFxsa+8847SUlJDRQINs1Tp/1nbCcReXvdkfNFF5WOAwCATbpQUp5+oVTnpGnX3FPpLLAitSvu\nU6dO/eCDD1xdXV977bWRI0fOnz//7NmzDZQMNuqBzoF3hzcrvmh8Y9UhpbMAAGCTkjIKRaRzkJeW\neae4Sq3XKUdEREybNm3ZsmUzZsw4f/78pEmTnn766VWrVrEpJCqpVPLm0M46J83qpKzNqTlKxwEA\nwPZUHb3EBHf8Xh03GFKr1T169Pj73/++YsWKnj17vvvuu3FxcfWbDLarZRPX6fe1F5F/LE8uqzAp\nHQcAABtTtTKVLWXwe7Xex/2yrKysDRs2/PzzzwaDYcyYMUOGDKnHWLB1j/dps3xf5tFzRXN/Pf7C\n/eFKxwEAwGZYLJKYXigiXViZit+rdXHPz8//9ddf169fn5aW1q9fv+eff75r164qFROw8DtajWrW\nnyKHzdv+2eYTQ7sGhbG2BgCAm5OtL8stvujl6hTi6650FliX2hX3hQsXxsfHd+3addiwYX379tXp\ndA0UC3agR+smo3q1WrzrzCs/Jn/35O1q/u0OAICbUPW4vYU3/8+JP6hdcY+Kivruu+/8/f0bKA3s\nzN8HdfjpUPbutAtL9mSMvC1Y6TgAANgAVqbiRmq3ODUoKKia1l5WVpafn3/LkWA/vF2dXhvcSUT+\nveZwXnG50nEAALABSRy9hBuoXXHfuHHj3//+961bt169+WNFRcXJkyfnzp37yCOPnDnDeZn4ndio\noD7t/ArLKmauYVt3AABqYLZYqraU4Yk7rlG7qTLDhw/v3r37J5988sorrzRr1szT07OwsDA3N9fF\nxaVfv35z584NCQlpmJywVSqVvPVw5wEf/LZsX+bwHsHRbX2VTgQAgPU6nVdaZDA283QJ8GIlIf6o\n1rvKtGnT5r333tPr9SdOnNDr9c7Ozn5+fm3btlWr67glPOxeiK/71Lvbvb8h9ZUfD657tq+LlqEC\nAMD1JabzuB03VMd93L28vLp161a/UWDHnrqr7YoDWSdyiudtOv7sfWFKxwEAwEpVrUzl6CVcD88+\n0RictepZD3cWkU82njiZU1Lj/QAAOKbKvSBZmYrrorijkfRu4zu8R8sKk/mV5QctFqXTAABgfYwm\nS0pWoYhEUtxxPXWcKtOADNkrv4pffzCrSVDkA39+9I7QS39VVJy6eN7X20/nB4UPmDglNsD6gqNG\nLz8Q8cvh8ztO5C3bnzGse0ul4wAAYF1SzxVdNJpbNXVr4uasdBZYo7o/cS8oKNi9e/eZM2cMBkNF\nRUX9xDGkzuo/YvbC7UHh4Re3L3xh3JAvU4pFRIpTXhg0ad7KrPDI1tuXzB4xem52/bwfGlVTd+dX\nHowQkZn/O5xfyrbuAAD8DhPcUb06FvclS5aMHDly5syZ27ZtS09PnzBhgl6vv/U0qcs+With7/y4\n+uVp095ZvWpMkMR9u9UokrLy/R0S9sGquGlPzli+YIZkLYn/jepuk4Z1b9m7je+FkvL/W3tE6SwA\nAFiXqqOXgpkng+urS3E/ffr0d999N3/+/LFjx4pI+/btO3XqtGzZslsOU7x9RWLQmGfu8MlN3LMn\nMSX/se+2bHlzoFaKd69I9Y6d3NNHREQbOmB6mGxPOHXLbwcFqFQy6+HOWo3qu93pu05dUDoOAABW\npPKJexRP3HEDdSnuR48ejY6ODgwMvHwlOjr69OnTtxzGKCJZC1+Jufvhqc89N/Wpcf1jXk0sqPqZ\nu7/Xpdt0ETFhhZuTCq7/IrB2bf09ptzVVkRe/vFghcmsdBwAAKyCocJ0NLtIrVJ1auFV891wSHVZ\n4xkYGPj999+bzVcqV0pKSosWLW41iyHzUJaIyJQPvh/VM6A4/bc3Rr0ydda6je/0ERF/D7caX2D/\n/v23muF6srOz8/PzG+KV7cOxY8dq+yt9mlp+8NAeP1/8+rdbh3f0bIhUVoLBU706DB7HweCpHoOn\nGgye6lnt4DmaV24yW1p5O6WmHFQwBuOnGtnZ2Q3UNkXkZo5Iqktx79y5s6+v79SpU729vU0mU0pK\nSnJycnx8fB1e6nd0rbuGyY6gqaN6BoiIR3DfxyYF7fjhdLH0kRLJybsyh16fc05CfK89CLiBzoRK\nS0sLCQlpiFe2G3X45Gf75I75IuGHwyVPDOzZ2rfmfyuzUQyeGnGU240weGrE4LkRBk+NrHPwHNiW\nJpLbu31At25dFIzB+KnG/v37lR08dZkqo1KpZs2aFRsb6+Xl5erq2r59+wULFjRt2rR+EmUVGy59\naSyq/E9dQDfJ+nV/cdXl9DUrC8OiI64t7rAhfdr5PdQ16KLR/OqKZLZ1BwAgqWqCOytTcUN13A5d\nrVYPHDhw4MCB9RrGI/aZSfOmzpm5uNnTD3TM27t06pKsoEd6+Ii2z/AxMjXujcWdX44N2Tnvb5tE\nXrknvF7fGgp4dXDHjUdzfkvNWZ2UNSQqSOk4AAAoib0gUaO6FPfjx48vXLjwDxfVanVoaOijjz7q\n7Fz3IwM8osZ/PD1r6pxXNs0TEQkaNOPTaT1FxCPqyY+nZ0yd89yQeSIij8xcPJATmGyfn4fLi4M6\nvLzs4L9WHborzN/L1UnpRAAAKENfVnEyp8RJo44ItOelX7hFdam/3t7eGo3mxIkT99xzj1qt3rJl\nS5MmTbp3775r166jR4++9dZbtxIoavjLWwY/k1tsEK2Hn4/uqutvbrgvt9goWp2Pjwet3U6MvC14\n6d6Mvafz3/np6FtDOysdBwAAZRzMLBSRjoFeTpq6H44Ju1eXwaFWq8+cOfP555+PGzduzJgx8+bN\nKysri46Ofvvtt1NSUoqLi2t+ierpPPz8/K5u7VWXffz8/Pxo7fZErVLNfDhSq1YtSjh9IJ0dPgEA\nDqry6KUuHL2EatWluCcmJoaGhjo5VU1sUKvV7du337dvn0ajadKkyYULnKqDWugQ4Pl4TBuLRV5c\ndtBoYpkqAMARcfQSbkZdiru/v/++ffv0+qr9GQ0GQ0JCgr+//7lz5zIzM/39/es1IezfM/e1b9nE\n9chZffw2DsQFADiixPRCEenCljKoVl2mnURGRkZFRY0aNapXr15qtXrv3r3h4eG9e/d+9dVXhw0b\n5urqWu8pYd9cnTRvDu08Yf7uDzakPhgZ2KIJQwgA4EByiy+eLSxzc9a09fdQOgusWh3ni7/66qv7\n9u1LTk42m839+/fv3bu3iPz1r3+tt93c4WDuDm/2QGTgmoNn/7ky5fNxPVUqpQMBANBYKh+3d27h\nrVHz/3+oTt0Xenbv3r179+5XX6G141b8c0jHzak5Px8+t/5Q9v2dApSOAwBAI0ligjtuTh2L+08/\n/bR8+fLL09xFZODAgWPHjq2nVHBEzb10L9wf/s+VKf9ckdKnnZ+7C9sHAQAcQtXK1GCKO2pQl8Wp\nZ86c+eijjx544IEuXbpER0dPnDixSZMm9913X72Hg6MZc3vrLi29s/WG99anKp0FAIDGYLFU7QUZ\nxcpU1KQuxf3w4cM9evQYMmRIu3btmjZteu+9995///2bNm2q72xwOBq1atbDkWqV6svtacmZhUrH\nAQCgwWUWlF0oKW/i5tyyiZvSWWDt6lLc3d3dCwoKRMTX1/fUqVMiotVqjx8/Xs/R4JA6t/CecGeI\n2WJ5adlBk5lt3QEAdq5ynkyXlt5szIAa1aW49+rVKzs7e/369Z06ddq2bdsnn3wyf/78jh071ns4\nOKbnB4QFeusOZhYu2HFa6SwAADSspHQmuONm1aW4Ozs7f/rppx06dPD393/xxRezsrJiY2OHDh1a\n7+HgmNydtf+K7SQi764/mq03KB0HAIAGdCCDo5dws+qyccfFixfVanWrVq1EJCYmJiYmxmAwlJaW\nenp61nc8OKgBnQL6d2y+4dC5f61MmTemh9JxAABoECazJblqZSpP3FGzujxxP3DgwEcffXT1lbVr\n13722Wf1FAkQEflXbCc3Z83a5OxfDp9XOgsAAA3iZG5JSbkx0Fvn7+midBbYgNo9ca+oqHj//ffP\nnz+fmZn59ttvV14sLy9PSEgYN25cA8SD4wrycX2+f9hb/zv86orkO9re5easUToRAAD1rHKCexce\nt+Pm1K64q1SqgIAAo9F44cKFgIArZ1t269Zt4MCB9Z0Njm78naHL9mceytLP+Tn1pQcilI4DAEA9\nqzp6iQnuuDm1K+5arfaxxx5LS0s7dOjQAw880ECZgEpaterfD0cO/c+2L7aeerhbiw6BXkonAgCg\nPiVWrkxlSxncnNrNcTeZTCaTKTg4+P777zf9ntlsbqCIcGRRwT5jb29tMlte+vGg2cK27gAA+1Fh\nMh/K0otIlxY8ccdNqd0T9379+t3oRyNGjHjmmWduNQ5wjRn3d1iXnL3/TME3u86M7t1a6TgAANSP\nI9lFFSZzqJ+7l6uT0llgG2pX3FevXn2jH7m4sBoaDcJTp31tSKepi/f939ojAzoGsO4eAGAfkjI4\negm1U7upMt5X8fDw0Ov1ubm5Li4u3t7eOp2ugSICD0YG9gv3LzIY31x9SOksAADUj8R0jl5C7dRl\nH3cR2bFjx4gRIyZMmDB16tQHH3xw/vz59RsLuJpKJW8+1FnnpFmZmLXlWI7ScQAAqAdVT9zZCxI3\nrS7FPTc3d+bMmdOnT//555/Xrl0bFxe3fv36TZs21Xc24Irgpm7T720vIv9YnmyoMCkdBwCAW1Ja\nbko9V6xRqzoGsWcablZdintycnL37t3vuuuuym9DQkLGjBmTkJBQr8GAP3o8pk1Yc8/TeaUfbzyu\ndBYAAG5Jcmah2WIJa+7p6sQJg7hZdSnuWq3WYDBcfcVgMDg5sSAaDUurUc36U6SI/HfziePni5WO\nAwBA3SVx9BJqry7FvWvXrsePH1+wYEFmZmZOTs7GjRsXLFhw77331ns44A96tm7y516tjCbLyz8e\nZFd3AIDt4ugl1EFdiruHh8e77767d+/esWPHDh8+PC4u7rnnnouKiqr3cMC1/j6wQ1N3512nLvyw\nN13pLAAA1BErU1EHtdvH/bI2bdrMmTPHbDabTCYmyaAx+bg5vTq443PfHZi55vC9Ec2bujsrnQgA\ngNopKK04nVfqolWHN/dUOgtsSV2euCclJb377rvJyclqtZrWjsY3tGuLO9v5FZRWzFxzWOksAADU\n2sHMAhHpFOSt1aiUzgJbUpfi3rJlSzc3t3/9618jR4788ssvs7Oz6z0WUA2VSt4a2tlZq166N2Pn\nyTyl4wAAUDuVRy9FBbMyFbVTl+LetGnTv/zlL99///3rr79eVlb27LPPTps2bd++ffUeDriRUD/3\np+9uJyIv/3iw3GhWOg4AALWQmFEgIl2Y4I5aquPJqZU6dOjw0EMPDR48+NSpU0lJSfWVCbgZU+5q\n28bf/WROybzNJ5TOAgBALSRlFAorU1F7dVycmpmZuXHjxo0bNxYUFNx///2ffPJJ69at6zcZUD1n\nrXrWw5EjP9v5ycbjsVFBoX7uSicCAKBm5/SGc3qDh4s2xM9N6SywMXV54r5v377x48enpaVNmTLl\n+++/f+KJJ2jtUMTtbXyH9WhZbjT/Y3ky27oDAGxC5eP2Li291SpWpqJ26vLEvX379itXrnR1da33\nNEBtvfJAxC+Hz207nrviQObQbi2UjgMAQA0S2cEddVWXJ+6enp60dliJpu7OrzwQISJvrD5UUFqh\ndBwAAGqQmF4gnJmKOrmlxamANRjeI7hXaNMLJeVvrzuidBYAAKpjsVxemcpekKg1ijtsnkolsx6O\n1GpU3+w6s+d0vtJxAAC4odMXSgrLKnw9nAO9mbyAWrul4l5RUWEwGOoriEcmdwAAIABJREFUClBn\n7Zp5PHVXWxF5ZdlBo4llqgAAK3V5I0gWpqIO6ljcExMTJ02adN999/3444/Hjh17++23TSZT/SYD\namXq3e1a+7odPVf0+ZaTSmcBAOD6Kie4RzHBHXVSl+J+4cKF1157beTIkU888YSItGrVKj09/X//\n+199ZwNqQeekeWtopIjM+eVY+oVSpeMAAHAdHL2EW1GX4p6YmNi7d+/+/fvrdDoRcXFxiY2NTUxM\nrO9sQO3EtPeLjQoyVJheXcG27gAAq2M0W5IzqzZxVzoLbFJd9nF3dXUtKiq6+kpRUZG3d30MQUP6\nutUJ5c7Old+Vl7v3GXpvQGXG4tTF877efjo/KHzAxCmxAXU88hV27tXBHTcePb/paM6a5LMPRgYq\nHQcAgCuOny8uqzC1bOLa1N1Z6SywSXXpv127dp07d+6nn35qMpkqKiqWLl06f/78995779bTFB9d\nM3POQm/vIJESESks7Nh+4L0BHiLFKS8MemqHhD0ypsNPC2ev3Xr6+++mBdz6+8Hu+Hu6vDQo4uUf\nD76+MqVve39PHf+GBwCwFkkcvYRbU5dao9PpPvzwwy+++GL//v0Gg6FNmzZvvfVWeHh4PaTRisiY\nH1Y/qfv99ZSV7++QsA9WxfX0kSkDwu8eNzv+txEv96W64zpG9gr+YW/GvjP5s3868sZDnZWOAwBA\nlcT0QuHoJdyCOj6P9Pf3f+mll+o3ioicPXFCvNvmFGTrT58TrxadQv1ERKR494pU79h3evqIiGhD\nB0wPm/1lwimhuON61CrVrIc7Pzh369c7Tw/r3pKV+wAAK3HpiTsT3FFHdVmcmpyc/Nlnn119Zdu2\nbQsWLLj1NKUXSqVw4aghI56aOvWpcQ/HvLA499KP3P29Ln2pi4gJK9ycVHDr7wc71SHQa3KfUItF\nXvrxoNHMMlUAgPLKjebD2XqVSiJbUNxRR7V74m42m3fu3JmampqcnLx9+/bKi+Xl5UuWLOnatWt9\n5CkTueOdxa/dEaxL37Fw1Avz3ll5xzux/iLi7+FW4y+npaXVR4Y/yszMbIiXtRvZ2dkN9MnfiqHt\nXZbvdzqUpf9g9b4RXXyVisHgqZ51Dh4rweCpHoOnGgye6ik1eI6cLzOaLK2buOSczchp/Le/aYyf\nahQUFDTc4AkJCanxntoV94qKivj4+JKSEr1eHx8ff/m6r69vbGxsbfNdq9P4uC3jq74OvmPkJO+4\nH87qRfylRHLy9Jdv0+eckxBf3TW/fjN/4LppuFe2A/n5+db5+fx7mMfEL3fP35Mzum/HIB/FTpa2\nzg/HSljt4LESfDjVYPBUjw+nGkoNns1ZaSLSM9Tf+v/pWH9Cpfj4+Cj74dSuuLu4uHzxxRf79u3b\nvHnzc889V99hDOtmPf1ryPPvjOokIiJGEZHyChFdQDfJ+nV/8ZNRHiIi6WtWFoZNibi2uANXu6dD\ns0GdA9YmZ/9zZcrn43oqHQcA4NAqj15iB3fcirrMce/evXsDtHYR0QUHyY55f/9yR2pxce6OHz6K\nK5S7olqKaPsMHyNZcW8s3lNQnLtu9t82iYy4px42sYHd+2dsJ3cX7YZD5zYcOqd0FgCAQ0vMKBCR\nrmyZgFtQx11lNm/evGLFCr3+yvSV/v37P/roo7eYptPI1yed+FvcC5PiRESk35QPnusbICIeUU9+\nPD1j6pznhswTEXlk5uKBnMCEmxDgpZtxf/jrK1NeW5Ec3dbX3YVhA9tgNFtYVw3Yk5KLxhM5xVqN\nKiLQq+a7gRuoS4/JyMh4++23x48fn5qa6uHh0a5du//973/R0dH1EEcXPP7N70YW5BYbRevh43PV\n6TlRw9/ccF9usVG0Oh8fD+oXbtbY21sv25eRlFH4/obUVwd3VDoOUAN9WcU3u9O/3HbqbKFhyl0X\nnx8Q5qSpy1+NArAqBzMLLRaJCPBy1vLfaNRdXRrwoUOHunfv/sgjjyxdurS8vHzw4MFGo3HHjh3B\nwcH1kknn43fd+es3ug5UQ6NWzXo4MvbjbfO3/X979x7XdL3/Afw9GDBgsHGTi3LzMgTEgbdSgbyk\nSSmmCV4yK/WklualtI5Ux1NhJZWS9lM7YRcN85IXNNG8g4opCuMiMi/cx5DbBgMGDPb7Y0Be8bbx\n3eD1fPQHbF+2t+u77bXP3p/PJ3fygB6+LhjqAD2VV17709mcnckFtQ1Nmks2nr5x9kbZd9MDPOws\nma0NAJ6SqKXBHX0y8FSe5GOfubl5bW0tEdnY2OTn5xORhYVFdna2lksD0JJ+3XlvDPdoVqtX7klv\nQvsB6Bm1mi7kVLy19dKIr0/+fC63tqEpsLf9T28O/m6ipwvfPK1Q/lL0mT8uFapx5gIYsrQCGREJ\nXTEzFZ7KkwT3wYMH5+bmnjhxwsfHJzExccuWLb/88otAINB6cQDa8t4YgTOPIyqU/fZ3PtO1ALRQ\nNan3pRSFbjgTvjnpr0wp28hoysAehxcHbZv7zEivbv2dLeIXB73k51zToHpvl2jx7ynVShXTJQPA\nE9LMTMWIOzylJ2mV4XA4mzZtUigUTk5Oixcvjo+PHzFixOTJk7VeHIC2WJqxV4X6ztt66avDV1/w\ndXS0RtcVMElW27j9Qv4v53KlVUoisrU0nfms+2vPujtYmd1+GM/cZMOMASMuFfwnLjNOJLmcX/nd\n9IABbjYMVQ0AT6iipqGwss7cxLh3Ny7TtYBhe8JZnt26devWrRsRjRkzZsyYMVotCUAnxvo4Pe/t\neCyr5NMDV75/dQDT5UAXlVNW89PZnF3JhXWNTUTUuxt3blDPl/1dOCbG9z2exaKwQa6DPGwXbU/J\nKJKHbUpaPLrPOyN7GxuxOrZwAHhymhXc+3XnsfHMhafz2MG9vLx827Ztr732mrGx8dtvv21jY+Pr\n6ztr1ixLS8ydAr3GYtGnE33P3Sj7M704LLt0hJcD0xVBF6JW09855T8m5hy/WqLpVg/q4zA3yDO4\njwPrEd7HPe0t9749LOpI9g8JN789Kj5zvWzdVH8G9wMGgMfS2ieDBnd4Wo/X4y6Xy+fPn19cXGxu\nbt7U1FRZWTlx4sSbN2/OnTtXqVTqqEQAbXHhmy8dIyCij/ala8Y7AXStsal5z+Wil9YnTvvh/LGs\nEhNjo6mDXY8sDd46Z8hzgkdK7RomxkYrX/TeOucZByuzCzkV46IT4zOkuiwcALQmrVAzMxUN7vC0\nHi+479q1y8vL68svvzQ3NyciExOTMWPGREVFde/efdeuXbqpEECb3hzu6e1sXVhZ992xa0zXAp1c\nZW3D9yevD//yxLKdqVckVbaWpkueFyR9OPqrV/p7OVo92W0G9bE/siR4VN9uVXWNC7Zd+vee9La1\nIwFAP6nVlFqAEXfQjsdrlcnIyJgyZYrmZ2NjY2dnZ83PY8aMOXXqlHYrA9AFthHri8l+k/7v7P8S\nb748oPsT5yeAdtwsrYk5k/PH5UJlYxMRCRyt5gZ5TvTvbqaNjVdsLU1jXh/8S1Lu6kNZ2y/k/51T\nvn76AGxQAKC3iuV15YoGnrmJuy2aiuFpPV5wNzY2bmxs1PzM4/E2bdqk+bm5uVnLdQHojL8rf+az\n7luT8lbuSd81f6jRozcrALRLraakm+U/Jt48cfWW5pLnBA5zg3oG9rbX7lnGYtEbwzye9bRdtD3l\n2i3Fy9+f/TCk75vDPXAyA+ihtq2X8ASFp/d4wz8+Pj7x8fHqOzcCUavVR48e9fb21mphADq04oW+\nDlZml/Iqd1wsYLoW6AwaVM1/XCoM+S5xxv/On7h6y4xtNH2I29Flz/0ye0hQHy2n9jZ9na3jFgXO\nfNa9san5s4NX3vzpYpmiXif3BABPAVsvgRY9XnCfNm1afn5+RETElStX6urqamtrMzIyVq5cKZVK\nw8LCdFQigNZZcdj/meBDRF/EX0XWgadRUdOw/sT14V+deG+X6GpxlT3XbNkYQdK/R38x2a+P7hds\nNjcx/vzlfv+bNYhvYXJaXPrCuoRT2aW6vlMAeCyaJWWE2HoJtOHxWmUsLS2///77H3/8cdGiRQ0N\nDURkZmY2duzY5cuXa6arAhiKl/xcdgoKE8Sln/+ZtW6qP9PlgOG5fkux5UzOH5cL61XNRNTXyWpu\nUM9QoYupNhrZH8sYH8fDS4KX7khNulH+xk8XZg/3/DCkb8eXAQD3alar01paZTDiDlrw2Ou429nZ\nffDBBytWrCgtLWWxWPb29iw0bYEBYrHo85f7jfn29L6UoikDewT2tme6IjAMajWduV4Wc+Zm29j2\nSK9uc4M8h/XSVUvMo3Cy5myb88wPCTe/+St7y9mcpJvl66cHYI9GAMblltUq6lWO1hzs2A1a8YQ7\np7JYLM3OqQCGy83WYvHoPmuOZH+8L+PwkmCtrPgBnVi9qjkutSjmTM5VaTURcUyMXxnQY3agRy8H\nvcjHxkasBSN6Detl9+7vKVnFVePXn/lkgs/0wW4YWgFgELZeAu16wuAO0Dn8K7jn3pSia7cU/3fy\numZvJoB7lSsatp7P23o+t1zRQEQOVmavD/WY8YybraUp06XdTejKP/Ru0Cf7M/+4XLhyT3qCuPTL\nyf35FiZM1wXQRaWhwR20CsEdujQTY6PVk/3CNiV9f+p6qL+Lngydgv4Ql1THnMnZm1LUoGomIh8X\n6zmBnhP6M9DI/ugszdjfhAuDBQ4Re9MPZ0hT82Xrpvk/29OO6boAuiJRgZywpAxoD4I7dHWDPWyn\nDXb9/WLByr0Zv//rWfQVABGp1ZR4rfTHMzkJ4pZG9ue9HecGeT7jaWcoZ8hEf5cBbvx3f09JyZdN\n/9/5d0b2XjJawDY2kOoBOgVVkzpTIiciv+4YcQftQHAHoA9DvP+6UvL3zfI9lwtfGdiD6XKAScrG\npn2pki1ncsQlLY3sYYN6zB7u6WlveFseutpa7Jo3LPq4eMPJ6xtOXD97vSx6WoCbrQXTdQF0FeKS\n6npVs7udBdrVQFsQ3AGIb2Hy0Us+y3amfv5n1si+3fSwcRk6QJmifmtS3tbzeRU1DUTkaM15Y5jH\n9CFuBv2OyzZmvTfWK7C3/ZIdqSn5spDoxNWT/Cb6uzBdF0CXkIoGd9A2BHcAIqJJAd13Xyo4d6P8\ni/irUVP6M10OdKir0uqYMzn7Uooam5qJqF933txAz5f6O5sY628j+2N5pqdd/OLgD/ekHc6QLv49\n5bT41qcT+3HN8PoPoFute6YiuIPW4IUbgIiIxaLISX5j1ybsSi4IG9hjiKct0xWBzjWr1Qnish8T\nb565XkZELBaN8XH8V1DPwR62htLI/uj4FiYbXx34+8X8/x64sudyUXJu5frpAcgTADolwtZLoG0I\n7gAtPO0t3xnZe90x8cq96fGLgzrNaCvcS9nYtCelKCYx50apgojMTYzDB7u+OdzDw87wGtkfHYtF\n04e4DfG0XbQ95Yqk6pWN55aNEcx7rpexUaf7mAKgB+oam8Ql1UYslq8LgjtoDYI7wD/eHtFrf2rR\n9VuKzadvLhzVm+lyQPtKq+t/Tcrddj6/sraBiJx5nNeHeUwf4sYzN+BG9sfSy4G77+3hXx2+GnMm\nZ82R7MTrZWun+jthT0cAbbsiqWpqVvd1srIwNWa6Fug8ENwB/mHKNlo9yW/6/85/d+LaeKFz5x5/\n7Wqyiqt+PJOzP7VI1aQmov49eHODer7Yz7kLrpBoyjb6eLxPsMBh2c7UpBvl49YlrHml/1hfJ6br\nAuhUWvdMRUMaaBOaAQDuMLSX3SsDejSomj/el6FWM10NPLVmtfp41q0Z/zsfEp34x6XCpmb1C75O\nu+YP3f9OYKjQpQum9jbPCRyOLAl+TuAgq218a+uliL0ZdY1NTBcF0HmkFWLrJdA+jLgD3C3iJe/j\nV0sSr5UdSJOECrFwnqGqa2z641JhzJmcnLIaIrI0ZYcP7vHGME93Oyxk3sKea/bTm4N/Ppv7RfzV\n3/7Ou5BTvn56QF9na6brAugMRAUYcQftQ3AHuJutpem/Q7w/+CPt0wNXnhM4dJ3u506jpEr5a1Le\nb3/nyWobiciZZ/7mcI9pg12t8b/yHkYs1uxAz2d72i3annLtliL0+7MrX/R+fahH51tXB6AjVdU1\n5pTVmBgb9XWyYroW6FQQ3AHuI2xQjz8uF17Iqfjq8NXVk/yYLgceVaakKubMzTiRRNPILnTl/yvI\nc1w/ZzYWTmmXj4v1gUWBnx+8Enshf1VcZoK49OswIXYiA3hi6UVyIvJxscYCZaBdOJ8A7sOIxYqc\n5Mc2ZsX+nX85v5LpcuAhmtXqo1dKpv1w/qXvEvdcLmpuphf9nP9YMGzf28PH93dBan8UFqbGqyf7\nbZw5kGducuLqrRfWJSReK2W6KABD1dLgjhXcQdsw4g5wf326cecF9/r+5PWVe9IPLgrqyrMY9Vlt\nQ9PuS4U/nW1tZDdjTxvs+sYwD1dbNLI/iZB+Tv6uvCU7RH/fLH8t5sJbwT2Xv+CFIUOAx6VZUgZ7\nnIHWIbgDPNCiUb0PiCRXpdUxZ3PmBfdkuhy4Q7Fc+eu53NgL+fK6RiLqbmM+e7jn1MGuXDO8rD0V\nZ5557NxnNp66sfaY+IeEm+dulK+fHuBpj6VRAR6DqEAz4o7gDlqGdziAB+KYGH/+cr9ZWy6sPSp+\nyc+5h4050xUBEVFaoTzmzM0/04pVzWoiGuBmMzfIc6yvE1pitMXYiLVwVO/hve3f/T0lo0j+YnTi\nfyf6hg10xYxVgEdRWl1fLK+zNGP3dMAnXtAyfAEK0J5ggcMEoYuysemT/VjWnWFNzeq/MqXhm5NC\nN5zZnypRE43v77z37eF73h72oh+mn2pfgBv/0LtBE/1d6hqbVuxOW7T9clVdI9NFARgATZ+MX3ee\nET7sgrZhxB3gIT4Z73Mq+9aJq7cOZ0pD+mF3SQbUNKh2JRf+dDYnr7yWiLhm7OlD3N4Y5tEd34Ho\nmBWHHT0t4DlBt4/3ZRxMK76cL4ue5j/Yw5bpugD0Gmamgu4guAM8hIOV2YchfSP2ZqyKywzqY48W\n6o5ULK/7+Wxu7IX8aqWKiFxtLd4c7jF1kKsl/i90oMkDug90t3n39xRRgWzq5vPvju69cFQffMUB\n8CAtWy9hZiroAN78AB5u+hC33ZcKU/Jl3/yV/Z8JvkyX0yWICmQ/nsk5lF7c1KwmokHuNnODeo7x\ncTRGXmSCu53FH/OHfXtMvPHU9XXHrp25VhY9LQDfeADcS61uG3FHcAftQ487wMMZsVhfTPIzNmL9\nfC5X84oMOtLUrD6cIZ2y8dzE788eEEmIaILQZf87w3cvGDaunxNSO4PYxqwVL3j9NvdZR2tOcl7l\nuOiEg2kSposC0DuFlbWVtQ22lqbd+fhkC9qH4A7wSPo6W88J9FSraeXedM1iJqBdNfWqLWdzRnx9\nav62S8l5lVYc9rzgnmc+GLl+egDWQtYfw3rZHV4SNMbHsVqpWhibsnx3Wk2DiumiAPSIqFBORP17\n8DAxFXQBrTIAj2rJ84KDacUZRfJfk3JnD/dkupzOo6iy7udzudsv5CvqVUTkbmfx5nDPsEE9LE3x\nAqWPbCxMf3htUOyFvE8PXNmVXJCcW/Hd9AC/7piHB0BElKbZegl9MqAbeF8EeFQWpsafTew355eL\n3xwRh/RzduZxmK7I4InLG2NiL8dnSDWN7EM8becGeo72RiO7vmOx6NVn3Id42i2KvXxVWj3p/84u\nf6Hvv4I8sfgdQOuIO4I76ASCO8BjGO3dbVw/p8MZ0lVxmZtfG8h0OQapXNFwOb/ycn7l3stF0iol\nEbGNWC8HdJ8T6IlRW8PSpxt3/8LAL+Ozfjqb+8WhrERx6bdT/btZmTFdFwBjmprVGZqZqa54NQOd\n0NPgLhUdP5FZ6TtqvNCpdVBTIY7duPVcXqWL19jZC0Kd9LRw6Pz+M8E3UVx2JFN6LKvkeW9Hpssx\nAKomdZa06nJeZUqB7HJeZX5FbdtVlqZGs4Z5zhrqga8vDJQZ2+g/E3yD+ji8v0t05nrZuHUJUVOE\no727MV0XADNultXUNKhc+Ob2XHyCBZ3Qy/wrS1q8cJWEKNz3+ZbgrshcETI/iQThM/se2RYVfyZv\n145F2AgHGOHM47z/gtd/D2R+sj9zWC97C1NjpivSR2WK+st5lZfzZZfzK9MK5crGprarLEyNha78\nAW42AW58S0XR0MF9GawTtGJU326HlwS/tzM18VrZnF8uvj7MY+WL3mZsLH4AXU5agabBHcPtoCt6\nGNwVuz9aIRGEDC2JN229KDPu2yQSrD0QM4hPC8Z6jZwVtSUhbGUwojswY9ZQ9z2XC9OL5OuOiVe+\n6M10OXpB1aS+Ulx1Ob/ycl7l5fzKwsq626/1tLcc4GYzwJ0f4GojcLJq27snJQXrCXYS3azMfpk9\nJOZMzleHr/5yLvf8jfL1MwIEjlZM1wXQoVILsfUS6JbeBXdpwnfRIorc+/atd+KLWy5TXNwv5oWu\nGcQnImJ7jl0siPr57xxCcAeGGBuxVk/2m7jhbMyZnEkB3b2drZmuiBm3qutTWpK6LK1QVq9qbrvK\n0pQtdOUNcLcZ4Gbj78q3tTRt53agczBisf4V1HNoT7tF21OyS6onrD/z8XifV59xx4RV6DrSCrD1\nEuiWngV3pSgiIl6wYFOwPWdzzR3XWDq0ZSOOd5BAvjtNtnwonhnAFL/uvNeHuf90Nvffe9L/WDCs\ni6yC0tjUfEVSpWmAuZxfWXTvsLq7zUA3mwFu/D6OVl3kMYG79OvOO/hu4KcHruy4WPDRvozT4tKv\nXumPT27QFTQ2NV8priIizLMH3dGv4J7wbYSYQnfN8CVSmhE13FaeA9fioX+ekpKii6qkUmllZaUu\nbrlzuHbtGtMlMGOMkzrO3Di1QLbmj7Pjelve95hOcPJU1DVllzeKyxuyyxquVzQ0/jOqTuYmRn1s\nTbzsTLzsTQV2plamRkTNROV10vI06SPdeJc9eR6FQZ8803qSq4nt/12sPHql5FLOiSXP2vh103J2\nx8nTDoM+eTqAjk6e6xWNjU3N3a3Z17PSdXH7HQbnTzukUqmO0iYRBQQEPPQYPQruqoK4iHi5cMFI\nKigooIpSoqq8G9LuvZzsiWqotLyq7ciq0hLysLt3EYpH+Qc/gdzcXA8PD13ccqeho0de/0WaS+dv\nuxSbWTNn3OD7roJniCdPY1NzpqSqbWqpRHbHsHovB26AG1/TA9OnG/fph9W77MnzUIZ48twuIIAm\nBdUt2ZF6MbfiP6fK5gf3em+sF9tYm9/D4OR5EEM/eTqALk6ezPN5RKVDejkGBPhr/cY7Es6fdqSk\npDD7yqNHwV1ZUUxEoo1Lwza2XhS18BTNjE+c4xRAkhMpinlCLhFRwaE4uWCBN1aPA8a94Os02rvb\n8axbnx64smGGAWcIaZVSk9RT8ivTi+QNt3Wrc83YAW78ALeWbnW+hQmDdYJh6W5jvv2tZ78/eT36\n2LWNp2+cu1EePd3fw+7+X08BGDpsvQQdQI+CO9d3Tnz8q2w2m4jYbFnMy2FVEbuWD3UiosApM2lh\nzKex/VaGepzf+P4poohRXgyXC0DEYtGnof3OXT99ME0SPqhHsMCB6YoeVYOqOVPyzyIwxXLl7df2\n7sYd4GYzwN0mwI3f20ELw+rQZbGNWItH9xne237x7ymiQtlL0Wc+fdl3ckAPzFiFzqdlLUhsvQS6\npEfBndhsLpfb+gvXjIhj0fIrVzhvw+LChdFLJ2wkIgqPjB2HHZhAP3S3MV86RrD6UNZH+zL+WhrM\nMdHfZd2L5XWX82WapJ5RVNXY9M+wuhWH7e9qM9CdH+Bm4+/K55ljWB20aZC7Tfy7QSv3ZhxMk7y3\nU3Q6uzRykp8VBy/j0HnUNjRdu6VgG7F8uuo6Y9Ax9PZ1k/vGwcTbfxdO+ezo82UKFbE5fD5Xb8uG\nrmj2cM89KUVXi6vWn7i+/AU9+i6oXtWcUSRPya/U5HVp1R3D6n26cTWt6gFu/N7duEYYAgVdsjY3\nWT89YISXwyf7M+JEksv5ld9NDxjgZsN0XQDakVEkb1ar+zpb6/PwDXQChpSAOXx79LWDHmIbs76Y\n5Dd549nNp29M9HdhcNMZtVozrN6S1DMldw+ra1rVB7rzhT341hhWh47FYtGUgT0Gutu8uz0lvUge\ntilpyfOCt0f0Qi8WdAJphTIi8keDO+iYIQV3AL0V4MZ/9Rn3befzIvZm7Jj3bEeOXtermtOL5Jfz\nKjUj6yW3DauzWCRwtBrQughMTwdLDKsD4zztLfe8PezrI9mbE25+81d24rXS6Gn+zjxzpusCeCot\nM1OxZyroGII7gHaseMHrSKb0Ym7FruTCqYNddXdHajVJZJph9crL+bJMiVzVpG671trcJMCV37Zl\nKdqIQQ+ZGBv9+0XvIIHDsh2pF3IqXliX+NUr/UP6YTNsMGCaEXdhD8xMBd3CmzqAdlibm3wy3mfR\n9pQv4rPG+Dhqd6tIZWNTepFc0wCTkl95q7q+7SoWi/o6WWla1Qe423jaY1gdDENgb/vDS4JX7E47\nllWyYNul6UPcPh7vY2GK/mAwPJW1DXnltRwT4z7MtUpCF4HgDqA14/u77EwuTLxWGvln1jfhwqe5\nKbWaCitrUwpaFoG5IqlSNf8zrM63MAlwbUnq/q58rhmeyGCQbC1N/zdr0NbzeZ//eWX7hfwLORXr\npwf4uGBRjkelalLfqlYWy5XF8rpiubJY1vJDQ0P9Z5N5A90x97eDpBfKicjXxZqNCRugY3i/B9Aa\nFos+f7nf2LWn/7hc+MrAHsN62T3Wn9c1NqUXytumlpYp/hlWN2Kx+jpbD3DjD3CzGeBm42lviVF1\n6BxYLJo11H2Ip+2721PEJdUTvz/7YUjfN4d74IujNqomdUmVsrhKKZXXSWRKqVwpkdcVy5VSubK0\nur5Zrb7vX7392+UjS4KxY1rH0DS4CzEzFXQPwR1Am9ztLN4d3SeNmHtTAAAgAElEQVTqSHbE3vQj\nS4LbP1itpoLK2st5lZqR9aziO4bVbSxMA9z4mh4Yf1e+JYbVofPq62QVt3D46kNZvyblfXbwSoK4\n9JtwoT3XjOm6Oo4mnUvkdVL53SPopYr6B4RzYrHI0ZrjzNP8Z+7M5zjzON2sOMt+v1QoV368P2P9\ndAPe0dmAaBrc+6PBHXQPUQBAy94K7rkvpejaLcX/nbrxcu+7h7tqG5rSC2WX82Wa2aXlioa2q4xY\nLG9n6wFuNgPc+QPcbDzsMKwOXQjHxPjTif2C+jis2J12Wlz6wrqEb8P9nzOc3YgfRWNTc0lVfUso\nl98xgl72iOmcb96S0XktGZ1tfJ+Xia9edP/XHzcPiCRjfBxDhS66/VcBkahlz1SMuIPOIbgDaJmJ\nsVHkJL/wzUnfn7w+yKGXu5ryK2o1MT0lX5ZVXNV027C6rWXLsPoAN5v+PXgYVocuboyP4+ElQct2\nis5eL3t9y4XZgZ4fjutryjZiuq7H0NjUrBk1l1YpJbI7RtDbSedGLJajtZkTj+PCN3dqG0HncVz4\nHAfu/dN5O7rzTD8e7/PvPekf7csY7GHrzMMmKDokrVLeqq634rDd7SyYrgU6P6QEAO0b4mk7dbDr\njosFM7dfs43Lq6j5Z1jd2Ijl62Kt2QhpgDvf3RbD6gB3cLTmbJ0z5IeEm18fyd5yJifpRvn66QG9\nu3GZrusODarmkipNIlfeNYJ+++yUu9yVzl14HKenSOftmzbY7VhWyfGsWyt2i36ZPQRzBnQnrUDT\nJ8PHgwwdAMEdQCc+DOl79EpJRU1DRU2DraXpADcbzUZIfj14lqZ43gG0x4jFmv9cr6G97BZvT80q\nrhq//swnE3ymD3br4FzUoGqWVimlcqVEVlfc+oOms+X2Jre7GLFY3azMnPkcZ94/6dyFz3HmcRys\nOB226giLRV9O7v/CuoTEa2W/JuW9McyjY+63C2rZegkN7tAhECAAdMLGwvT4e88dOH/1OWFvN1sL\nDMQAPC5hD/6f7wb+Jy5z96XClXvSE8SlX07ur/VlUjTpvFhWd9fwefEjp/M7u887NJ23z8HK7IvJ\nfvO2XvriUFZgb3t9+9ai09DMTPVHgzt0CAR3AF2xsTAN7mmNrkeAJ2Zpxv46TPicwOHfe9IPZ0hF\nBbJ1U/2f6fl4C60SUb1K03fesopi2+ItElnd7Z1sd/mns4XX2nfeOjfUwcpMT9J5+17wdZoysMfu\nS4VLd6TufXu4drtxgIjUakprGXFHcIeOgOAOAAB6bYLQJcDN5t3tKZfzK6f97/zCkb0Xjxbce1i9\nqrm4bTlFWV1x1T8bEj16Or99bqihpPP2rQr1TbpZnl4k/+7EtWVj7vO4wdPIq6iR1zU6WJk5WWMG\nMHQEBHcAANB3PWzMd84f+t3xaxtOXF9/4vqhdOkzTkZJsuuS20bQH5rONW0tt80NNXficTpHOm8H\n14y9Ntx/6g9J35+8PtKrW4AbBoa1Ka116yX0Q0LHQHAHAAADwDZiLRsjCOxtv/j31BulihulROlV\ntx9wezpv6zh37hrpvH1DPG3fCuq5OeHm0h2phxYHWZgaM11R55FagK2XoEMhuAMAgMEY4ml7eElQ\n9LFr2flSbw/n2zcNted26XTevvfGep0Wl16VVkf+mRU5qR/T5XQeadh6CToWgjsAABgSnrnJJxN8\nUlLqAwK8ma7FYJiyjdZN9Z+w4exvf+eN8XEc4dWptqRliqpZnSGpIiK/7hhxhw5iSNvRAQAAwJPp\n62z9/gteRLR8t6id+QDw6K6XVCsbm1xtLWwtTZmuBboKBHcAAIAuYW6g5xBP29Lq+oi96Wo109UY\nPlHLzFQMt0PHQXAHAADoEoyNWN+G+1uaseMzpHtTipgux+CJCjUzU9HgDh0HwR0AAKCr6GFj/t9Q\nXyL6ZH+GRFbHdDmGLQ0j7tDhENwBAAC6kFcG9Bjr66SoVy3bKWpGx8yTqlc1Xy2uMmKx+iG4QwdC\ncAcAAOhCWCz6crKfHdf0/M3yLWdymC7HUGUVV6ma1b27cS1NsUAfdBwEdwAAgK7F1tJ0zStCIlpz\nJDu7pJrpcgySCFsvARMQ3AEAALqc0d7dpg9xa1A1L/k9tUHVzHQ5hqe1wR0zU6FDIbgDAAB0RR+N\n93aztcgqrlp7TMx0LYanZUkZV4y4Q4dCcAcAAOiKLE3Za6f6G7FYm0/fvJhbwXQ5hkRRr7pRqmAb\ns7ydrJmuBboWBHcAAIAuaqC7zYIRvZrV6mU7RTX1KqbLMRjphXK1mnycrU3ZyFHQoXDCAQAAdF1L\nnu/j62JdUFH76cErTNdiMLD1EjAFwR0AAKDrMjE2WjctwJRttONiwdErJUyXYxiw9RIwBcEdAACg\nS+vTjfvhuL5E9MEfaeWKBqbLMQCtM1Mx4g4dDcEdAACgq3tjuMewXnYVNQ0f7knDbqrtq6hpKKqs\nszA17u3AZboW6HIQ3AEAALo6Ixbr6zChFYd99ErJrksFTJej1zTD7f2684yNWEzXAl0OgjsAAACQ\nC9/8s4n9iOi/cVfyK2qZLkd/iQqw9RIwBsEdAAAAiIgm+nd/yc+5pkH13k5RUzM6Zu4vrVBGREJs\nvQRMQHAHAAAAIiIWiz6f1K+bldnF3IofEm8yXY4+UquxFiQwCcEdAAAAWthYmEaFCYnom7+ys4qr\nmC5H7xTL68oVDTYWpq42FkzXAl0RgjsAAAD84zmBw2tD3VVN6iW/p9armpkuR7+ICuVE5NeDx8LE\nVGACgjsAAADc4d8h3p72ltkl1V8fyWa6Fv2SViAjbL0EzEFwBwAAgDtYmBqvm+pvbMT68czN8zfL\nmS5Hj6SiwR0YheAOAAAAdxO68heN6q1W07Kdomqliuly9EKzWp1eKCciIfZMBYawmS7gbrKcpB3b\nD6VL6l38Aqe/GurZtiuZQhy7ceu5vEoXr7GzF4Q66V3hAAAAncrCkX1OXL2VVihfdSDzmzAh0+Uw\nL6esRlGvcrLmdLMyY7oW6KL0a8RdkRk7YdaKbaJ6Pz8H0baoWSErRIqWK1aEzNkYJ/Hycz+3Myrs\n1fVShisFAADo5NjGrHVTAzgmxn9cKozPwBtvy9ZL/THcDszRr+B+9dBGovADO9bMm7d8x95IHiUl\nXJURUWbct0kkWHsgZtG85ft+XU6SnVsS8AoCAACgWz0dLFe+6E1EK/ek36quZ7ochmm2XvLHzFRg\njn4Fd/8FBw7EL7jjk6wJESku7hfzQucO4hMRsT3HLhbQub9zGKkQAACgS3ntWfegPg6VtQ0f7E5T\nd+3dVFu2XsKIOzBHv4I7m8vnc1XJcbE//7x+6qQIuXDBa8KWp4elg3XrURzvIIH8dJqMqSoBAAC6\nDBaLosL688xNTmbf2n4hn+lyGKNqUl+RVBFR/+4YcQfG6OEcT5Uk41yipE5CRLlZWVLlUCciIgfu\nw7coy83N1UVBRUVFurjZTkMqleroke8EcPK0DydPO3DytA8nTzt0cfIsCXT879HCTw9kupsru1ub\nav32O9KTnTzXypT1quYePNOKkqIKHVSlP/Di0w6ZTKa7Vx4PD4+HHqOHwZ0bunJDKBEpc6LGzFqx\nZUziygFUQ6Xl/2y8XFVaQh52nHv+8lH+wU9Gd7fcCVRWVuLxaQcenHbg5GkfHpx24ORpn9YfnDc9\nKLW0eX+q5OszpbvmD2MbGfDGoU928py7lU9EAz3tu8KJ1xX+jU+Gz+cz++DoVauMIm71wqjDrc3r\nHM8BI4jOZcmI4xRAkhMpipYrCg7FyQXDvO8N7gAAAKAjn07s58zjpOTLNp66wXQtDGjdMxUN7sAk\nvQruXD6J4iI/j0vOkSlkmcc3rzpFghmBfGIHTplJkphPY5NlirLDUe+fIgob5cV0tQAAAF0Iz9zk\n6zAhEUUfE6cXyZkup6OJCrEWJDBPv1plgt+NmSlbFrV0VhQREQlCl381w5eIuMJ5GxYXLoxeOmEj\nEVF4ZOw47MAEAADQsYb3tp893HPL2Zwlv6f++W4gx8SY6Yo6SF1jk7ik2tiI5eti/fCjAXRGz+Iv\nVzBvzcHXZWUypYrDtedz/ylPOOWzo8+XKVTE5vBvvxwAAAA6zIpxXgnXSq/fUqw5nP3JBB+my+kg\nmZKqpmZ1X2dr8y7zWQX0k161yrTg8O2dnJzuTeccvr29vT1SOwAAAFM4JsZrp/qzjVhbzuacuV7G\ndDkdpLXBHQtBAsP0MbgDAACA3vLrzlvyvICI3t8pktc1Ml1OR9BsvYSZqcA4BHcAAAB4PPNH9Apw\n40urlJ/sz2C6lo6QVignIiFmpgLTENwBAADg8bCNWGun+pubGO9PlRxMkzBdjm5V1TXmlNWYsY28\nHK2YrgW6OgR3AAAAeGwedpYfT/Ahooi9GdIqJdPl6FBakZyIfFys2cYGvO0UdA4I7gAAAPAkpg92\nG9W3m7yucfkuUbNazXQ5uoKtl0B/ILgDAADAk2Cx6KtX+ttYmCZeK9ualMd0ObrSsvUSgjvoAQR3\nAAAAeEIOVmZfTPYjoi/ir94oVTBdjk6kaZaUccVakMA8BHcAAAB4cuP6Ob0ysIeysWnpjlRVU2dr\nmCmtri+WK7lmbE97S6ZrAUBwBwAAgKezaoJvdxvztEL5+hPXmK5FyzQruPv14BmxMDMVmIfgDgAA\nAE/FisP+NkzIYtGGk9dTC2RMl6NNLSu4o8Ed9AOCOwAAADytZ3ra/SuoZ1OzeumO1NqGJqbL0RpR\ngYyI+vdAgzvoBQR3AAAA0IL3xnp5OVrllNV8EZ/FdC3aoVa3tMpgxB30BII7AAAAaIEZ22jdNH+2\nMWtrUt6p7FKmy9GCgspaWW2jHdfUhW/OdC0ARAjuAAAAoC3eztbvj/UiouW7RZW1DUyX87TSWofb\nMTEV9ASCOwAAAGjNv4J6DvG0La2uj9ibYei7qYoKsPUS6BcEdwAAANAaYyPWN2FCS1P2ofTifalF\nTJfzVETYegn0DII7AAAAaJOrrcV/Qn2I6JP9GRJZHdPlPKGmZnVGEdaCBP2C4A4AAABaFjbQdYyP\nY7VS9d4uUbNhdszcKFXUNjR1tzG3tTRluhaAFgjuAAAAoGUsFn05ub8d1zTpRvlPZ3OZLudJYOsl\n0EMI7gAAAKB9dlzTr17pT0RfHb4qLqlmupzHpmlwx9ZLoFcQ3AEAAEAnnvd2nDbYtUHVvGRHamNT\nM9PlPJ60Aoy4g95BcAcAAABd+Xi8j6utxRVJ1bpj15iu5TE0NjVfKa5iscgPI+6gTxDcAQAAQFcs\nzdhrp/obsVgbT91IzqtkupxHlVVc3djU3NOeyzVjM10LwD8Q3AEAAECHBrnbzB/Rq1mtXrYjtaZe\nxXQ5j0RUICMif1f0yYB+QXAHAAAA3Vr6fB8fF+v8itrP/8xiupZHgpmpoJ8Q3AEAAEC3TIyN1k71\nN2Ubbb+QfyyrhOlyHq5lLUiMuIOeQXAHAAAAnfNytFrxghcRffBHWkVNA9PltKemQXX9loJtxPJ2\ntma6FoA7ILgDAABAR5gd6Dm0l125ouHDPen6vJtqZlFVs1rd19najI2YBPoFZyQAAAB0BCMW65sw\nIdeM/VemdPelAqbLeSA0uIPeQnAHAACADuLCN/90Yj8iWnXgSkFFLdPl3J8IWy+BvkJwBwAAgI4z\nKaD7i37ONfWq93aJmpr1sWMmrVBGREKMuIP+QXAHAACAjsNiUeSkfg5WZhdyKn48k8N0OXerrG3I\nr6jlmBj3drRiuhaAuyG4AwAAQIeysTCNmiIkoqgjV7OKq5gu5w7phXIi6udizTZiMV0LwN0Q3AEA\nAKCjjfBymPmsu6pJvXRHaoOqmely/iEqlBNRf6zgDnoJwR0AAAAYsPJFb097y6vS6m/+yma6ln+0\nNrgjuIM+QnAHAAAABliYGq+d6m9sxPoh8ebfN8uZLoeISK2m1AIZEQldMTMV9BGCOwAAADDD35W/\ncGRvtZqW7RIp6lVMl0PSKmVpdT3P3MTd1pLpWgDuA8EdAAAAGLNoVJ/+PXhFlXWr4jKZrqWlT6Z/\nDx4LE1NBLyG4AwAAAGPYxqy1U/3N2Ea7LxUeyZQyW0zLzFQ0uIO+QnAHAAAAJvVy4P77RW8i+vee\n9NLqegYrSSvA1kug1xDcAQAAgGGzhroH9bGvqGn44I80NUO7qarVlFaEtSBBr+lfcFfkxK1fvXDh\nwo+jfhZJlbddLo6N+njhwoWr18dJmZ++AgAAAFpjxGJFhQl55iYnrt7afjGfkRpyy2uq6hq7WZk5\nWXMYKQDgofQsuCtEC0NmRe284e7nJYmLWRg25bgmpCsyV4TM2Rgn8fJzP7czKuzV9Qw3wQEAAIBW\nOVlzPn+5HxF9fvBKbnlNxxeQVignIiGG20GP6VdwL0v/U0S8tfExy+ctijkZM4LkP+zPJKLMuG+T\nSLD2QMyiecv3/bqcJDu3JCC6AwAAdCoThC6hQpfahqZlO0Sq5o7umBG1LCmD4A76S7+CO9dz4pq1\nGwdxiYiI3a11coji4n4xL3TuID4REdtz7GIBnfs7h7EqAQAAQDc+e7mfkzXncn7l5tM3OviuMTMV\n9J9+BXeOk+/QQa6an8Vx32yT06sveml+tXSwbjvKO0ggP50mY6JCAAAA0B2euUlUmJCI1h4VZxTJ\nO+x+Vc3qDEkVEfkhuIMeYzNdwP2VJW+eE3Vq6OKYUFcOkYKIHLgWD/2rlJQUXRQjlUorKyt1ccud\nw7Vr15guQX/h5GkfTp524ORpH06edhj6ycMlGi+wPCiumf/L+W/GOpgaa3kzpPuePHmyRmVjkxPX\nODc7M1e792doDP380SmpVKqjtElEAQEBDz1GH4O7InP3pKXbXMIjV08RtFxUQ6XlVW0HVJWWkIfd\nvVO+H+Uf/ARyc3M9PDx0ccudho4e+U4AJ89D4eR5EJw8D4WT50E6wcnzdb+mrO/O3ChVHJFyPhnv\no/Xbv/fkyb5YQFQ6uJcjzqtOcP7oTkpKCrNniH61yhCRquDwtPnRLqGROxYFt36q4DgFkOREiqLl\n14JDcXLBMG+s1QQAANApcUyM1071ZxuxtpzJOXu9rAPuEQ3uYBD0LLjLkpfMiJSTy6sj7UTJyclJ\nSaKcMiJ24JSZJIn5NDZZpig7HPX+KaKwUV5M1woAAAC60r8H793RfYjo/V2iqrpGXd8dlpQBg6Bf\nrTKKvEsiIiJJ1NL5LRfxZiYenMcVztuwuHBh9NIJG4mIwiNjxznpV+UAAACgXW+P7H3i6q3UAtkn\ncZnrpvrr7o7qVc3Z0mojFqtfd4y4g17Tr/jLFc5LTJx336uEUz47+nyZQkVsDp/P1a+yAQAAQOvY\nRqy1U/1fjE7cl1L0vLfj+P7OOrqjK5IqVbPay9HKwtRYR3cBoBV61irTLg7f3t7eHqkdAACgi/C0\nt/xovDcRRexNl1YpdXQvLX0y2DMV9J4hBXcAAADoamYMcR/h5SCva1yxO02tm91U0woxMxUMA4I7\nAAAA6C8Wi9ZMEdpYmCaIS7edz9PFXYgK5ISZqWAIENwBAABAr3WzMls92Y+IIg9l3Syt0e6NK+pV\nN8sUJsZG3s5W2r1lAK1DcAcAAAB9F9LP6ZUBPZSNTUt3pKqatNkxk14oV6vJx9naxBihCPQdzlEA\nAAAwAKtCfV345qJC2YaT17V4s5qZqUJXNLiDAUBwBwAAAANgxWF/Gy5ksWj9iWuiApm2bjatUE5E\nQjS4gyFAcAcAAADD8GxPuzmBPZua1Ut2pNY1NmnlNrEWJBgQBHcAAAAwGMtf8BI4WuWU1XxxKOvp\nb61c0VBUWWdpyu5pb/n0twagawjuAAAAYDDM2EbrpvqzjVm/JuUliEuf8tY0w+39evCMjVjaqA5A\ntxDcAQAAwJD4uFi/N8aLiN7fJaqsbXiam8LWS2BYENwBAADAwLwV3HOQu82t6vqP92U8zXaq2HoJ\nDAuCOwAAABgYYyPWt1P9LU3ZB9OK40SSJ7sRtbp1LUiMuIOBQHAHAAAAw+Nma/HJBB8i+mhferG8\n7gluQSKrq6hpsLEw7WFjoe3qAHQCwR0AAAAMUvgg1zE+jtVK1Xs7Rc2P3zHTshBkDx4LE1PBQCC4\nAwAAgEFisejLyf1tLU3P3Sj/+Vzu4/55y9ZLWMEdDAeCOwAAABgqO67pV6/0J6Iv469eu6V4rL9t\nbXBHcAeDgeAOAAAABmyMj+PUwa4NqualO1Ibm5of8a+a1er0lhF3zEwFg4HgDgAAAIbtk/E+rrYW\nGUXy6OPXHvFPcspqFPUqZ565PddMp7UBaBGCOwAAABg2SzP2t+FCFov+7+SNS3mVj/InmhXcMdwO\nhgXBHQAAAAzeYA/b+cG9mtXqZTtTaxpUDz0eDe5giBDcAQAAoDNYOkbg7WydV14beTDroQeLClrW\ngtR9XQBag+AOAAAAnYEp22jdNH8TY6PYC/nHs261c2RjU/OV4ioi8uuO4A6GBMEdAAAAOgkvR6sV\n47yI6IM/0ipqGh50WLa0ukHV7GlvaW1u0oHVATwtBHcAAADoPOYEej7b065MUf/vPekP2k0VWy+B\ngUJwBwAAgM7DiMX6JkzINWMfyZTuuVx432M0M1PR4A4GB8EdAAAAOpXuNub/nehLRJ/EZRZW1t17\ngEgz4o4lZcDQILgDAABAZzM5oEdIP6eaetWynalNzXd0zNQ3qa+VVBsbsXxdrJkqD+DJILgDAABA\nZ8NiUeQkP3uu2YWcipgzObdfdbOysalZ7eVkxTExZqo8gCeD4A4AAACdkK2laVRYfyKKOpJ9tbiq\n7fIbFY2EPhkwTAjuAAAA0DmN9Oo24xm3xqbmJTtFDapmzYXXKhoIM1PBMCG4AwAAQKcV8ZK3h53l\n1eKqb4+KNZdcx4g7GCwEdwAAAOi0LE3Za6f6G7FYmxNuXMipkNc1SqpVZmwjgaMV06UBPDYEdwAA\nAOjMAtz474zspVbTsp2pSTfKicjXhcc2ZjFdF8BjQ3AHAACATm7xaIFfd15hZd38bZeISOiKBncw\nSAjuAAAA0MmxjVlrp/qbsVtiT380uINhQnAHAACAzq93N+6HId6anzEzFQwUm+kCAAAAADrC68Pc\nbSxMmiuLPO0tma4F4ElgxB0AAAC6BCMW6+WA7p42JixMTAXDhOAOAAAAAGAAENwBAAAAAAwAgjsA\nAAAAgAHQ28mpiuTDh3Opz/hxQk7LBeLYjVvP5VW6eI2dvSDUSW8LBwAAAADQAX3MvypZ5tp58+Mk\nRLyZz2uCuyJzRcj8JBKEz+x7ZFtU/Jm8XTsWOTFdJwAAAABAh9G/Vhll5rwJ8+McQmeO4JGlmeaD\nRWbct0kkWHsgZtG85ft+XU6SnVsSpAzXCQAAAADQgfQvuLOtQxasOrph+UhvR6rRXKS4uF/MC507\niE9ExPYcu1hA5/7OYbJIAAAAAICOpYfB3XXKjNEcosYGxe0XWzpYt/7I8Q4SyE+nyTq+NgAAAAAA\nhuhjj/t9OXAtHnpMbm6uLu66qKhIFzfbaUilUh098p0ATp724eRpB06e9uHkaQdOnvbh5Gkfzp92\nyGQy3Z08Hh4eDz3GQIJ7DZWWV7X9VlVaQh52nHuOepR/8JPR3S13ApWVlXh82oEHpx04edqHB6cd\nOHnahwenHTh5HgqPz4Pw+XxmHxz9a5W5D45TAElOpLS2zhQcipMLhnnfG9wBAAAAADorPQ/u9URE\nxA6cMpMkMZ/GJssUZYej3j9FFDbKi9nKAAAAAAA6kv62ypiYcsnSSvMzVzhvw+LChdFLJ2wkIgqP\njB2HHZgAAAAAoCvR3/grmBGTOOOfX4VTPjv6fJlCRWwOn8/V37IBAAAAAHTBkBIwh2+PvnYAAAAA\n6Jr0vMcdAAAAAACIENwBAAAAAAwCgjsAAAAAgAFAcAcAAAAAMAAstVrNdA3aERQUxHQJAAAAAABP\nIjEx8aHHdJ7gDgAAAADQiaFVBgAAAADAABjSOu4MUSQfPpxLfcaPE2IV+TspRYd/330iud7MPXDi\ntNBBrkzXo19kOUk7th9Kl9S7+AVOfzXUk8t0QXpJKjp+IrPSd9R4oROeXm1UmccPZdWQKRERNTQ0\ndB84bihOoNsocpJ+2rI3u5LcB42aNm2cK86dVrLMhGNZt0xNTVsvaCBL7xdH++Kdvo1SKvp96+7k\nvEob92FTXpuMV547qTKP7/z9SGo98f1fmDh5tC8eHQ1pZsKJrMZRL4920jyXFOLYjVvP5VW6eI2d\nvSDUqWOfYGiVaY9Klrl23vw4CRFv5oGD8/hM16NPVIc/Hhl5ioSh4S55R+JF8qGLY9ZMETBdlb5Q\nZMaGzN9ILkNnjnI4sS1OQkM3xK8RInrdRZY0dcIKCVH4hgOLhHh6tZFGBYXFEc+FRzVEJJd7zNm0\n4Q1fpqvSFzJR7ISFG8llaHig2c6dp4hG/HryM08kUyIiEu/+eE50Co9HRGRpSRKJnCj8QOIiPLs0\nVNLDI8MiiSecOcUvffc2kZwXuWtfcAfHLv2lOr765VXxcpcRoYHmV3fGi3mhkQeXBzNdFeNkCZs/\nitgmInJZG79jEJdIkbkiZH4SCcJn9j2yLU7uEr5rxyKnjqxIDQ9SlzE7MDDwnTWbPnopMPynaqbL\n0S91qS8FBn50rFjz2+k1LwW+tKmS2ZL0ycU1gYGB37U8IKWnXwoM/O4iHp67VO96JzBwduTylwI3\npeLBuU11anhg4K6bjUzXoZ9Kv3spMHD5rpYX5OJjgYE4fx6gMeOdwMDl+28yXYceyfhpdmBgZH7L\nbzc/CgwM35TKaEV6pLE4PjAwMDK+5YQpPfddYGDgrpt1zFbFuIxNLwUGhq+JnB0YODu1Wq1WqzN+\nmx0YOFvzlt54c39gYGDk6eKOLAk97g/Gtg5ZsOrohuUjvR2phuli9A3bfVXkmneGt3zItHOwZLYc\nfeO/4MCB+AV3jHKZMFWLnpImfBctosiv3h5iSQ1MF6Nf2P66OMwAAAxgSURBVEQk8LBSFGR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AAHBA/eKXGw79en3NS6mNO4v7dBWHZpdWlkVENNQsbji0e0qyYfGyhoiIon5FB45FRHS1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\npton7ps25XtLtx09JLHu11M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mtdNqMf/1kWuz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YI0Oeu3OiiDy35Uh1kw8NlnG53dsZ4A4A6BddmHa0IaLL6So+3aJ0Fr9H4Q6/tOrTY9VNndkm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\nTkiICqJlB+B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       "text/plain": [
-       ""
+       ""
       ]
      },
      "execution_count": 12,
@@ -498,27 +495,21 @@
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "DEBUG\n",
-      "['dmm_voltage', 'dmm_current', 'dac_ch1_set'] dmm_voltage\n",
-      "Considering the units V, V\n",
-      "DEBUG\n",
-      "['dmm_voltage', 'dmm_current', 'dac_ch1_set'] dmm_current\n",
-      "Considering the units A, A\n",
-      "Started at 2017-06-30 14:24:33\n",
+      "Started at 2017-07-10 15:57:48\n",
       "DataSet:\n",
-      "   location = '/Users/william/sourcecodes/qdev-dk/docs/examples/PlotTesting/plottestsample/091'\n",
+      "   location = '/Users/jhn/src/Qcodes-dk/docs/examples/PlotTesting/plottestsample/364'\n",
       "      |   |  | \n",
       "   Setpoint | dac_ch1_set | ch1          | (100,)\n",
       "   Measured | dmm_voltage | voltage      | (100,)\n",
       "   Measured | dmm_current | current      | (100,)\n",
-      "Finished at 2017-06-30 14:24:40\n"
+      "Finished at 2017-07-10 15:58:04\n"
      ]
     },
     {
      "data": {
-      "image/png": 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V5LfuBNQPTHSSwgAA1MjjThAEkTtIuCdj2fbo5783+vnv9baBgVV5ZY87j4Ok6m/b6e0r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\nuKDae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573i9I57JdbhHJE2lLDeS3MKiqb7Lia1MRlJ45oS7y4umUioDAACA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\nbA3c41hkYaIprNHhrj7dU0WmUmbNd5Fc31AfUn/4bVTKhGH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jX8r6o0TmHVkz9NbhOBgd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T4R7wGi86WPq2Yoa7KE11SBmnlVZqoDS8rACxRVYn2O9l9QL2UmWFkylsqDS1I4Y7HXNTR9xK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vtlIDz4pU/McVZxhvu3vvWtW2+9Vf72F1100Z/8yZ/s3r07v10qFRWxPnjeQc/xwV9Tv2kd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Uge8cG5Ge7DQhH6QMxIXc2STDR8oQ5HeqoRESJNwJw52dGJVjuNu2Gx/X1dXj+TLB+aDG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BfjsUVE/IUCKRPHKVKGknCnjlWjmHANoiNxbKt7nZY6ysYI4rIYvntgWBIpk3kh5Y+o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Gsl/qdwhtl0zWDfcGSplBPX2OTEau4+wu/CCMSo683lUjTvyUDIkOx55NZxyCp+Nh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\nTFs0lc0HAAAgu7QKu7FoqosTZAC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       "text/plain": [
-       ""
+       ""
       ]
      },
      "execution_count": 13,
@@ -545,20 +536,14 @@
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "DEBUG\n",
-      "['dmm_voltage', 'dmm_current', 'dac_ch1_set'] dmm_voltage\n",
-      "Considering the units V, V\n",
-      "DEBUG\n",
-      "['dmm_voltage', 'dmm_current', 'dac_ch1_set'] dmm_current\n",
-      "Considering the units electrons/second, electrons/second\n",
-      "Started at 2017-06-30 14:24:43\n",
+      "Started at 2017-07-10 15:58:14\n",
       "DataSet:\n",
-      "   location = '/Users/william/sourcecodes/qdev-dk/docs/examples/PlotTesting/plottestsample/092'\n",
+      "   location = '/Users/jhn/src/Qcodes-dk/docs/examples/PlotTesting/plottestsample/365'\n",
       "      |   |  | \n",
       "   Setpoint | dac_ch1_set | ch1          | (100,)\n",
       "   Measured | dmm_voltage | voltage      | (100,)\n",
       "   Measured | dmm_current | current      | (100,)\n",
-      "Finished at 2017-06-30 14:24:52\n"
+      "Finished at 2017-07-10 15:58:33\n"
      ]
     }
    ],
@@ -597,23 +582,20 @@
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "DEBUG\n",
-      "['this_setpoint_set', 'dmm_myarray', 'dac_ch1_set'] dmm_myarray\n",
-      "Considering the units this unit, this unit\n",
-      "Started at 2017-06-30 14:24:54\n",
+      "Started at 2017-07-10 15:58:43\n",
       "DataSet:\n",
-      "   location = '/Users/william/sourcecodes/qdev-dk/docs/examples/PlotTesting/plottestsample/093'\n",
+      "   location = '/Users/jhn/src/Qcodes-dk/docs/examples/PlotTesting/plottestsample/366'\n",
       "      |   |  | \n",
       "   Setpoint | dac_ch1_set | ch1          | (50,)\n",
       "   Measured | dmm_myarray | myarray      | (50, 25)\n",
-      "Finished at 2017-06-30 14:24:59\n"
+      "Finished at 2017-07-10 15:58:49\n"
      ]
     },
     {
      "data": {
-      "image/png": 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HoHAHAAAAPACFOwAAAOABOA4SAAAAcH/cnAoAAID6I/fY9g/e3/xzZnHMDb0eGDssLtjV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WdJSUn62enTp69bt+6UU04ZPXp01Ji0X3X1psUyeV3pXlD9eNYp3NN6tPlsef+PhbAioxea9/AN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\nJ0hihy/ET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       "text/plain": [
-       ""
+       ""
       ]
      },
      "execution_count": 15,
@@ -640,23 +622,20 @@
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "DEBUG\n",
-      "['this_setpoint_set', 'dmm_myarray', 'dac_ch1_set'] dmm_myarray\n",
-      "Considering the units Hz, Hz\n",
-      "Started at 2017-06-30 14:25:01\n",
+      "Started at 2017-07-10 15:58:59\n",
       "DataSet:\n",
-      "   location = '/Users/william/sourcecodes/qdev-dk/docs/examples/PlotTesting/plottestsample/094'\n",
+      "   location = '/Users/jhn/src/Qcodes-dk/docs/examples/PlotTesting/plottestsample/367'\n",
       "      |   |  | \n",
       "   Setpoint | dac_ch1_set | ch1          | (50,)\n",
       "   Measured | dmm_myarray | myarray      | (50, 25)\n",
-      "Finished at 2017-06-30 14:25:06\n"
+      "Finished at 2017-07-10 15:59:05\n"
      ]
     },
     {
      "data": {
-      "image/png": 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YJtegcAcAAAB+3XpnABMAAADgvhISEmy77zUOYCpz+MN47LgDAAAAF3bca2uS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ACUpe67YvLSs3dQuAMAAAAAUOqy38NOnijcAQAAAABKVZeSXb0el8IdAAAAAKBUpVKp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\nR0hg8B6xEyRx8PjYC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       "text/plain": [
-       ""
+       ""
       ]
      },
      "execution_count": 16,
@@ -694,23 +673,20 @@
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "DEBUG\n",
-      "['this_setpoint_set', 'dmm_myarray', 'dac_ch1_set'] dmm_myarray\n",
-      "Considering the units this unit, this unit\n",
-      "Started at 2017-06-30 14:25:08\n",
+      "Started at 2017-07-10 15:59:18\n",
       "DataSet:\n",
-      "   location = '/Users/william/sourcecodes/qdev-dk/docs/examples/PlotTesting/plottestsample/095'\n",
+      "   location = '/Users/jhn/src/Qcodes-dk/docs/examples/PlotTesting/plottestsample/368'\n",
       "      |   |  | \n",
       "   Setpoint | dac_ch1_set | ch1          | (3,)\n",
       "   Measured | dmm_myarray | myarray      | (3, 25)\n",
-      "Finished at 2017-06-30 14:25:24\n"
+      "Finished at 2017-07-10 15:59:34\n"
      ]
     },
     {
      "data": {
-      "image/png": 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4kJgQI2Jo3/BLc+aazWJc9PHKJx5d9HzGyhQ5mrbvpE26BwAAANxIzeDuQa0y7bvNXr4j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\nReLal/70xd5lz9lV2Aakrlk2XQKMm7ckFQBMgCxICiCw+33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       "text/plain": [
-       ""
+       ""
       ]
      },
      "execution_count": 17,
@@ -739,27 +715,21 @@
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "DEBUG\n",
-      "['dmm_voltage', 'this_setpoint_set', 'dmm_myarray', 'dac_ch1_set'] dmm_voltage\n",
-      "Considering the units V, V\n",
-      "DEBUG\n",
-      "['dmm_voltage', 'this_setpoint_set', 'dmm_myarray', 'dac_ch1_set'] dmm_myarray\n",
-      "Considering the units this unit, this unit\n",
-      "Started at 2017-06-30 14:25:26\n",
+      "Started at 2017-07-10 16:00:09\n",
       "DataSet:\n",
-      "   location = '/Users/william/sourcecodes/qdev-dk/docs/examples/PlotTesting/plottestsample/096'\n",
+      "   location = '/Users/jhn/src/Qcodes-dk/docs/examples/PlotTesting/plottestsample/369'\n",
       "      |   |  | \n",
       "   Setpoint | dac_ch1_set | ch1          | (50,)\n",
       "   Measured | dmm_voltage | voltage      | (50,)\n",
       "   Measured | dmm_myarray | myarray      | (50, 25)\n",
-      "Finished at 2017-06-30 14:25:32\n"
+      "Finished at 2017-07-10 16:00:16\n"
      ]
     },
     {
      "data": {
-      "image/png": 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CAAAAbOKkkBiJuzGxZMU9nqugnk9zqpkRuA5L3JmxoKGWuuDE5ImXU1KmKCUI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wp3rVUm7k5CUOXP0bXKWLr0StbjHl9xRxdFre1xl5tT7bw1hEN2dnavXr0OHz7ctWvXtJ0UkySm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nl8xu5w+a/oka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PgnE/AT0/pvumtlczvc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\n9BCJ2hOorq7+xS9+8cMPP8ycOdP7jSEELZ/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NLUgBPutnTDOsFOo4LmrqiUUfellqYGun7jh3rpVr/k3I2p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\n+gasvuJFgTsAAAAAxMYc2fHAF+rXxVgnXjwkIiKjl160Js7H1MGWZ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       "text/plain": [
-       ""
+       ""
       ]
      },
      "execution_count": 18,
@@ -865,6 +835,110 @@
     "dmm.myarray.label"
    ]
   },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### do1d with multiparameter"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 4,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Started at 2017-07-10 17:15:03\n",
+      "DataSet:\n",
+      "   location = '/Users/jhn/src/Qcodes-dk/docs/examples/PlotTesting/plottestsample/371'\n",
+      "      |         |   | \n",
+      "   Setpoint | dac_ch1_set       | ch1           | (50,)\n",
+      "   Setpoint | this_setpoint_set | this_setpoint | (50, 10)\n",
+      "   Measured | dmm_this          | this          | (50, 10)\n",
+      "   Measured | dmm_that          | that          | (50, 10)\n",
+      "Finished at 2017-07-10 17:15:13\n"
+     ]
+    },
+    {
+     "data": {
+      "image/png": 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rvrqxj6dyzFK9eAAAgAKkumcWuAMADFG7Hv5mW55+3dfX3vzJRTMrJ02qnDH/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\nuaNuW8DTk8AdAKBkNNz28WVrczdnLPv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BqutxKaapFG4GrTcCdwAAAOid+di7tffqeloH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\n0aNHbGxs+/btfXx8qr3zwoULR44cOXDgQGpq6sWLF1esWLF58+ann36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vzxTJnvHCyvWL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4W9ZPuhAa7zwAAADAr3Rk5o6p7kFBvl/RROOZmQF3AAAA+KmOaTv+04XQ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\nsvPgsYs2vd4km86K3H7XAOXhuiN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+      "text/plain": [
+       ""
+      ]
+     },
+     "execution_count": 4,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "# Exceptional Units, small set range\n",
+    "# Expected labels: \n",
+    "#     X: This setpoint (this setpointunit), Y: Gate ch1 (μV)\n",
+    "#     Two plots next to each other\n",
+    "\n",
+    "plot, data = do1d(dac.ch1, 0, 10e-7, 50, 0.01, dmm.mymultiparameter)\n",
+    "\n",
+    "plot"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 8,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Started at 2017-07-10 17:20:31\n",
+      "DataSet:\n",
+      "   location = '/Users/jhn/src/Qcodes-dk/docs/examples/PlotTesting/plottestsample/374'\n",
+      "      |         |   | \n",
+      "   Setpoint | dac_ch1_set       | ch1           | (50,)\n",
+      "   Setpoint | this_setpoint_set | this_setpoint | (50, 10)\n",
+      "   Measured | dmm_this          | this          | (50, 10)\n",
+      "   Measured | dmm_that          | that          | (50, 10)\n",
+      "Finished at 2017-07-10 17:20:41\n"
+     ]
+    },
+    {
+     "data": {
+      "image/png": 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gok1TeuwUY7/q9JrqlSKZ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RfjAiZuZu27puRWq0h/ssmTllr7w1J1G9udxhCat3\nVeTPS3W/Jyt3w5bVGUpl47DRjpHQKA9/45ywaMMr+fPc/vwQnV60Y2dGgumPU9i48dEi/V/azJd9\nTMLLWyXi6us8/Xzpa+IVkf4pCgawNdWvz7v3zntmL8xZsmTJkiU5OXPvybw7Z23NcVMsSgzAL2wH\n39bWuY7Of9jcc2LUyt4JGasrNuZ7eLGWrA0VW8yRFRWMyv2+5EHsE+RItgBUNqK7W/E/OQC6aWk8\n/uHHjTJ6fHjHua7R46+ffMOEqFCNR3qxtzd/dOzEubCx4eeaWsPH35KUOCFUg7F/2ZqP7/97Y/j4\n8V2fN4WPt9yWyJ+zAEW1H96auaTU271ZBVtzZw68TsKMGTNKSvbqWlcIO3ToUFJSkse7pk4dISJx\nfnhSbbUDYjbgVyplb3tz40cnPj43dnx4U1PH+IRvJMZPMLqkgCIqAwgGZGxnZOz+8XsK0E1MfEJK\nPMsI9hEWNcGSrI0HLAaXEtwiJiSkOq4im2hwKQAMZGt4orfbHpeeu/JH30oYf+7j93cWF1ZZRUQq\nC3767eRtaWq1WQDAM5Wyd9iEeMuEeBERi5KZmqgMAAgtLCkDAAAQFJr3v+ZYxzR5RdW2NVmpibGx\nsZaUjNXbXlnq+Dy99bevHjauQHPq9sN/AAAAgMoUz9g03AEAAILCJ3VvaxvzFn2v72ULYrIWzXNs\nXrAHtijzU3wwAAAAAOhO8YzNkjIAAABBIXHBrzaknpXwsOvcrvhnF5shJakgtLI7AAAAEPwUz9g0\n3AEAAIJCWEx8corHa6La//Z6nbYVd/XYQJakl6lTR/ztb4FO3YcOHfJlN8UHAwAAAAhRgc/YPgZs\nUT5j03AHAAAIXraWxtefKyjWrpoq6Q/+IFSvEOjv8YB7+q+ocGwkJfnvaQEAAADDBDhj9wZsIWP3\ni4Y7AABAkLHVL7tzWb3LjXFZG8tyE3zLbl1d/65/VUM1bdrzIrJv30NTp47QNvzxLDfe6HrLz37m\n2Ojq8scTAgAAQC1k7N6ALWTsftFwBwAACDL2rjMebu363NoiFtfl3T3q6Jjf/w533HHHEOoamu7u\n50aMGDFt2vPaMEAbG3R3B8PHTJ8X5T/uCgAAAB99+9vPGV3CJWTsYDYiOM4EAACAidl25s0pqW31\ndFfqxjeKEiNc9z9c83r9xxdHjpSLnx/fU17Z0HNHVsHW3Jke13m/ZMaMGXv37h12zfobMcIx+0Yb\nD4jRQwKtngl+eORmETH61QEAAEBHZOxB1aN4xmaGOwAAgL/Z20967LaLyMlOu/uNEYlpWYk9X8xf\nvLxu6y9WldaKSGXBk3d9e0NylH/K9LPu7m6XaTgjRhg/+SMEAjsAAADgBRk7CNFwBwAA8LewGzLn\nZZ2RCLeZ7Da58fqBu+cRKfPX5h+ZVVjdKlK/74OW5BSfFpYJQlr0752G07tt4JBA8cEAAAAAQh0Z\nO9jQcAcAAPC3iLRFy9MG2Mfe1HD0dKddIiclWmLdIlrYhLFjRFpFRML9UmIgBeGQAAAAAAhpZOzg\nQcMdAAAgGDRvzllSJSKSuWPv6li3u8PHhuY6Mt4FyZCA8QcAAABMwyVjayvMCBk7sC4zugAAAACI\nSIwlWduoenprvct9tuO788sdV069bpypOu9a9J827fnedSe1IQEAAACAoenN2Pv2PUTGDjxmuAMA\nAASDiO8tyi5ZVS4itaXL7n0/e+VDP7g57kppP/O3N39XUFrl2Cs1Nz3ebPmtdxqOS889MNNwFJ99\nAwAAAFMy9uOkimdssw3YAAAAQlRMyuKi7HfzyhtExFpbnldb7rpHdNZLT2S5XnjVLIwaEig+GAAA\nAICJkbENQcMdAAAgWKQuLts6deeTBSX1rS73RGcuzX9kfqqpVpPxJPBDAsUHAwAAADC9wC/srnjG\nHsGVagEAAIJNS2PDhx9/3iESLjJ6/Ncm3xAf5fM0iRkzZuzdu9ef1QVC7yqT2pBA/DAe0J7iCn0f\nVERE2kQk4FemAgAAgP+YKWNr81rI2P7DDHcAAICgExNvSYk3ughDGbuwOwAAAGA+xi7srg4a7gAA\nAAhSDAlc2Gy28PDwyy+/3OhCAAAAEKrI2C46OzsjIyN1fEAa7gAAAAhqfh0ShMqo4ssvv+zq6vrz\nn/88ffp0Gu4AAAAYJr8u7B4qGdtut4vIvn377rjjDh0f9jIdHwsAAADwEy36T5v2vMsiM8N9WD/8\np7uWlpbXXnvta1/72iuvvOKHhwcAAICiejP2vn0PqZaxW1tbd+3aNXbs2D/96U/6PjIz3AEAABAa\n/LGwe5DPvjl37lxzc/OCBQveeecdo2sBAACACfnj46RBnrFbW1s//vjjBQsW7N+/3x+PT8MdAAAA\noUTfIUHQDgZsNtvll1+em5v7m9/8xuhaAAAAYHKKZOzOzs7u7u6HH354y5Yt/nsWGu4AAAAIPSa+\n1lN3d/f58+dfeumlhx9+2OhaAAAAoBC/LuxurK+++urixYsbNmzIy8vz93PRcAcAAECo6u7u1laY\nEachQUiPB1paWg4cOLBgwYJTp04ZXQsAAABU1JuxtXkt5sjY+/btW7BgQXNzcwCejoY70Je9s7Mr\nPDKSfxqemPLgmPJF6aezszP0jg7nFFDMMBd2D55xQ3t7e1tb2/333//GG28YXQswGPzm7YcpD44p\nX5R+yM8AzGGYHycNnox97ty5s2fPLliw4O233w7Yk14WsGcCDHHmbzvzV+Wvyn/hlH3gnds/2Hlr\n+OjRo8Nn/3qv/0sLMSFzcOynXngiPz9/1cY3B54YGDIvyhjtr+TfMXr06PARC/ae8eHfT3DgnALK\n6u7u7u7unjbtea3tvm/fQyNGjNBGBQN8ox/+G6yuri4RKSgoiIuLo9sOw5Gf9RIyB4f8rBvyMwCz\nCemMfeHCBbvd/uijjyYkJASy2y403GF6p/68Ze0za59Z+5LVNvDOR1/fclBERHat2PJBp58rCzUG\nHpzOD3bOvvXWW0fcsfOYD09sO1P52Nq1a5/ZdujMgPtyxvvTfujptdUiIlK+5Y0PDS1lEAZ3Tjs/\nWHXriFtvvTV/5wd+rwxAQAxhSGD4YODcuXO/+93vLrvssqeeemo4rx3QC/lZL+Rn5ZCfAZiUS8YW\nkSDP2N3d3e3t7S+88EJ4ePjGjRuH9+qHgoY7zMBufXPJ7Nmz71iw9YN2l7tGjowSEZGYcB8eZ2Lc\n9Y6tKbdMitSxQH3Z3/z1ktmzZy/I3+n6av1p+Aenn9PUr1O/vHnuroMHD2YvnDm594m9H4TLxXHK\nfXjoEDnjQz50wxMx8baezRtumDSkhzDgverxnHo9gJE3Zf/v7IMHD66dO+8VX2bxAQgR2gddXYYE\nXnc2bjDQ0tLy3nvvTZs27f777w/pNTERmrz+mjZjfjYmTZGfDUR+9h35GYCPejO2Nq9Fgjhjv/32\n20lJSUuXtTgytgAAHL9JREFULtXhZQ8JS3TBDGyfHtq0a5eI3HH+18N5nGvnrKnbc0/rRYmbmnal\nTrX5ga2hYtOuapGW7w/r1Q7S8A/O0E7TB1vXrhURkR0FC5yeV5+DECJnXLd3+OCETV57su77H7aO\nDI9LmzbEcx7496rHc9rPAZz6UP7ipeWb5GBW/vaOl+cH8bARwOD4vrC7IX3ujo6OCxcu5OTkVFRU\nGPH8gJCmAoD8bCDys+/IzwB85/vC7oZk7Pb29vPnz99///2vvfaaEc9/CQ13mMHlI0dqGyNH+jIR\npx9RU7/73eHX42eXj9Imn8TIMF/tIA334AzlNNk/eOa+TSIiszZnTHb+eaXXQQiJM67jO3xwIq+d\nmnHtcB7AkPeqh3Pa3wEMu+nh0uxNS8ul/L6KgtnzJzNkAExlmNd68ocvv/zyq6++Kioqevzxx42q\nARAR0lRAkJ8NQ34eDPIzgMEJwozd1dUVFhb2xBNPFBcXG1WDMxruCG32zjPWL7pajzZqXzYePXpm\n/MROu1wx8dorXX7vXy4i9lMH6/5+4kz7hQujoibd8u2Uya47dZ45daZTJHJc3MSoPv862s8c23/w\n76fPXhAZNW7S9VNunzIxyvcy248d3P/3E6cvXJBRUeOu/8aUKZMnentB1g/q/nbo4wsiIqMm3Zic\nMuVapxLtX1itnXLmw2MiInKs8bjVGm23S9gV18ZpcxE6rafO2EWuiLv2yjCxt1vr3j3QET7m4unT\nIxNuT53qNQrZ26317x44frZdRGTUuBuTb59yrfuMDPeDYz9zynpOZFLctVFhcubUwYP1J85euCCj\nohJumTrV6TUO4jT1Zf1/L28SEZHVy7/fc7wHPAh9X9oXp+r2//3M2fYLMmpSwi0prgfB6xkfzFnz\nwPc3jL3dWvfu3z4+e0FERkVNSv52ivNBGfDQdX5hPdNml8hx106M0t4/B050jBl58fT5MbdPS5k8\n0es5t35Qf+Afx9sviIiM+9qNt6dMudLtF4K9/Yz1bKdIZNy1E7U7v7CearOLhI27Ni5KOs8crDt4\n4vTZCyJRkxKmpkx1eraBTpO9/ZT1rIiMi7vW9cDb263Ws3YJm3htXO9HWn18p4mIyzn15b03Jeu+\nKUvLD4r838218wtDYAAJYLC8DQkCr7W19c0331y4cGF7eyCXhQNc+JymzJCfB0xTQ8zPvqUp8jP5\nmfwMwLRcMva0ac8bmLFfffXV+++//8svvzSkAA+6gVB2oCTd4xv7ydqzjh1Ks0VEZHHNgZqVU1x3\nW7m5tsv54dpqtYeb8mSd040fbl45y/0pVpbuafOhwg/3bPbwzekr93zo+t0dn9SsdH81UxbvOdnR\ns0ed51c7pUR7tW0HSrUbSg98UrdlpftD1fQ+lNPT1mxe7eEhs0uOuBTodnA6Ptyi7VxSU7d5sevB\nnbK49GTPwR3wNHnR1nPsZtX2FjPQQejuqNNOefbmmprN7gdhZe0nTufc4xkfzFnzVLXvb5gODxWK\nLC7d03ueBjp0baXa/embP/uszv0dvri0xsMp9/hOkylPVh1x2bPO8ezpPce/rUR7ilmlB2o3uz3b\nlNKakz6epra6kr4vxOn49dxVesDxrL6/07q7Xc+pb++9sz17ZR9wP15ACJo+fbrRJQSp3h8CWttd\nc7kf/tM4P3VLS8uRI0dSUlI8/lDy3YoVK2w2m1EHECYx0K9pU+XngcLAkPKzz2mK/Ex+Jj8DJkLG\n9kb7cdC7sLsmYBn7vffeS0xM9PiDy3ePPfaYzsdE34cDAuxAqYdkJiIldS4Nd68W7/jw0sP1ZM1Z\nJb3x8bMnnfPJlCnOX6U/WdN/eZ/VPClev3tWzWeXYk7bkS39FLlFS04dBzy/WreGu3dTKvuMGc5u\nWdzPzrP2OGdrt4Mz8NNl7+jw7TR51nHAcfLSS8863dj/Qeit07uVR3pfloczPoiz5vGc+/yGadvh\nHvB7Ld7S5tOha/M0suljyupK51PedqC/d9qsJ2ucX17PP59ZdT0DhgGfbsuRDl9OU++bx/094H6X\n7+8093Pq43uvrsSx2+YDvvQBgGDHYKB/2r93lyGBn3R3d3d2dnZ0dCxcuFCXB6ThDh0M9GvaVPl5\noDAw+Pw8mDRFfhYhP5OfAdMgY/dP+5kQsIx9/vz5lpaWf/3Xf9XlAWm4A311dbR1dJzc40h4j+85\n2dXR1tbWdmmCg9OAYUp2Sd0nZ7u6uzvOfrj50hSF/uLjZ7U92TF9dc1JLWF0fFi7ueeb02v6S7yf\n9U4OWL255mxXd3ffp57yZG3Pnid7J5lPWVx65LO2rq6uts+OlPRORpjy+Cfac7e1dXScfFJ7gPQn\nT3Z0tLW1tbU5XoBLrlpcuueztq7u7q5PDlReOgrpJb0ln6y8ND1k9Zbasx2OnS814WeVXkpPAwwY\nsnfUnuzo6u7q+Mx51smWDzt8OU0edfQMotIvHaiBD0LfAcOUksq6sx1d3V0dH9aU9p7yS6NET0NE\nn8+ap1Pu8xvmk6pL57yk6khbV1dXV9uRqpLeMcTjez7z4dD1TfBTVu458llXd3dX2yeVT146DCWX\nZqN8eOncpD9ee/KstnNVyaVzXlp36Zz3P2CY9fiOk2c7uru7PvvQacrPYkd29/G9OvgBQ7/vNPdz\n6tt7r3eUuNK5gwCELAYDvtD+1WtDAj/9JyIdHR1PPfWU6IeGO3TR/69pk+Xn/sPAYPPz4NIU+dn1\nrHk65eRn8jMQIsjYvtB+Mvg7Y1+4cOGJJ54Q/ejecGcNd4S4sMioMLl84ljtq5jocWGRUZ7X+sve\n/M7LD2h3hV05+YF128+8f9Wj1SJyoqVNxMs15M99/Km28fh//Z8Z12rfHTl52gPPV7YVv3lc5OqY\nfq5lYz93vFrbevL/PDDD8c1XTn6g+Pk2KT4ucvUNY7S7z+x9ca22tXjHXzbO0VbGi5p40yMb/zTy\n9Lilu0QOPla+b2netInai5vouJDO2HGRkd5Wwly548N1cyZr23FT7nm5rW7MFSmbRKR6xa6DDz4w\nJUrE+mL+M9oOJTWfPTJjYu/OGzsORI6+9RkR2bV0x8HsB6YMuNxm+p7PXv6u9gBhE2c8sK72zInU\nR3eJSHPLlyKDOU1Ovuw6p23ckdLnwkO+H4TNB97pKT5s8owl/1NzblzaoyJy1HpGZLLn7/H5rHnk\n8xvmTHme45xvOfKX+Tc5zvlNGY+8UzfyipSlIvLYmt8t/e4jE30/dFNWH3mv8Cbth3pU3D15Lx+Y\nOObWBzeJyIpnqx6cNj9K5MybWxynfFbJZxWPOE55VFzGIxuPTIi8+b5nRGTpM5XZL88f+JQ/XlPx\nixna9sTJM9b9T83749KqReRos7Zkmu+naTAGeqe58+0AXnndDdrGCWuLDmUCCAXd3d0jRoyYNu15\nvz5LQkLC6dOn/foUwBD4+mvaFPnZ9yDqQ37WMU2Rnx3Iz+RnAGYSmIw9ceLEc+fO+fUphukyowsA\ndOCUFLq87VOaN7dvSpiYea822WDXux994e27ui52ahv7X3/rjP3S7ZPveWTjunUb1+X114vu6nJ8\ns9TuPnjm0u1hkx9Zt3Hduo15c7TZGPZ9/7NTRETS9/zXnL7X6Lnyof/arG1VvXvM9fH7CTZTnlw9\np28gjpqau8MxC+Olt46KiP3UO48dFBGRWZsfnNH3sjmRU1b1TH7Xdu5f+pNPfLfvAyRnfl/b+Otf\nj/fe6MtpctbZ4vjpeVX0WK879XMQZm2e2/f0XJkyUzvl1RXvez/lPp41L9/t2xvGfupPjx4UEUl/\nvKZntOAQNfWhLdoMk+q3jvVcUc+XQ/f4sytv6vsn1CkLch1nsXzHoXYR6dz3ivZOk9InHnS5UtJN\n81f13bl/6U8sn9HnhitvX+g4uH8+2um2u04h3Md3mrsBD2DYxBsdM5pGDqM+AKFG32ksLlpaWl5/\n/XWjXyLgA++/ps2UnwdOUz7kZx3TFPn50neTn8nPAMzFrxm7ra1t165dRr/EgdFwhyJmfTPBNdqP\nv/6WAb/t69O/p23seizrqvBb///27j+o6TvP4/hbQ9DEb4C4Rm+xS+vRbem1cWq1N+1ZHFg7u/1F\nB6/SzlSKxZ4VetOidiuOnlKd0yvOtSvd3QG2nXqe4OxW5mTwOnLeqqk/ClegP0h7lRmpW3dlRukB\nI9GgULk/vkk0yTchwS8E4fn4x/j9fr7f7yefzyfwyofvj4KNO2rqj7d3dA+E30xluvNJT0SvzZk7\nc+6Sgh0VNced7d3ugK37zv1Jnfl2HPjDXn81Hx76H7VQkoQ5FyhQ9j8stgUtTF28LEPdVXy8iPR1\nfu8p/IsFwd96UjI9X6eSIshPSVPNAUt8+cx19WpENdZy9vMG9cXVyL5gBMj+xdzA92WyzVPTfpjW\njLTXtEU4YHyN76j5j701gX3e9KWnWBRdLtk/nxfU53Gpf++5JFUxGkTkB+9Iy33wTo0+z/Xch1GJ\n4LhJ5pCFXP0j9lTwERppIiLi2bz9z50R9TQADCUxMXHx4sVnzpx55513Yl0XYHjIz4H5Wcc0RX72\nIT+TnwEgcoqiPPXUU52dnfreUkZ33FIGE5d19k+GLBOXsvSbui33PF0iIiLOyu3FlZ41GVuqt659\nPj3sVX5xWf/6zZa2e0ocIiLO2sriWs/WGblbtm5b671kUqZ4N9hZuCzUvmr3N7lemx/hRYV3JCdp\nLJ1qvk3d1RdtbrH7zkTIfHCORmGDUT1W7X+1ugoiPe7YohEgp/8kVcQZ/oSRSHtNe+MIB4zvzxjO\nnctyQu2s9mRb9/z5Ia7WDnSHbarG0qQEtc+r2jp+Nz/VO9KyF95t0igcH6/WrqrpTPn8oe8jFFI0\n33PGHOf/ufr51QhAJ5MnTzaZTIWFhYWFhQUFBbt37451jYCbNcHzs0lGKU3FDPmZ/Bw98jOAUWYw\nGAwGw+uvv15UVPTSSy/V1NTEukYa+KmIiSvC0wjSsjb3X3j24H/WfVR3sLLW4V3sKFm2qOTgB117\n8sMFOiVt89HLzx7/Y139Rwe3V17fuKpkUVVJeXNXgX8czM5dNUvrDoeXLp2f9uBdWoFQ2/e9l7UW\ne1JqRvJ0k4jvqse+y26R4HToKWy/Y4Yh4uPq68f3zRNRLxQa3QgaZa8FiG7A2LNX/d0szf1cOm+6\nT/NLgDb3Zc2c6/mKkDHdZLw+5Hsuana5r04zzLHq89j5oV/9RGTfr/llCgCGb+rUqSJSVla2fv36\nvLy8pqamWNcIGL4Jnp+vG6tpivxMfh495GcAMWU2m0Xkvffe27hx4wsvvPDVV1/FukZ+mHDHODMi\nyTLOlpaVn5aVv+437u6O79oOf/jbFSVVIiJVK/6wLqdgiHMZTGnpWWnpWeu2/aa7s6Ot5fBv162o\ncoqIFP6yNvdoviL9VzwnjORu3VVhj/xDqXUWjuovnRqnoAx0tlWp2yUlioh4LyE82NC+Lj3oasru\nc+p1mal3J49Afoqom6y2meqLHu0/CYhIuEa4OUP2WjhDDxhv42ev+ZeK/LRoKhaq6dp6+kQCu2rg\nT61qNybZEuLEN9IcDe0usQW+h4HOP/9FRERS58zSu89Dd1N8fOA7GuEvK9oN6Gr7wnMTuJu9tBYA\ntCUmJiYmJh46dMjhcOTl5fX29sa6RoDXyKSpMZqfRUKFgYjycwzTFPmZ/OxFfgYAn6SkpPvvv//E\niRMfffTR8uXLBwbGyj2uuIc7xpW+y8GPm7k5AwNut9t3+8E4kzUl7aH8zXu+q9ugLjl5MtyjZgbc\nfltbbSkPPZa/p/W0Z2PHkTa3iFjvzcwQEZGqgw2dwTtxu1wul0vjjfVcDPVuHcXVp4J+yDT/vtrz\nKj5eRJSU+9SjOqoOdQTtoaWm0uktHeIgwxdhN/lOoTraEPTAWJ/QjTBskfVaqI0jGjC+xq/990Ma\nXT7gdrlcLq27XoZuOsfvDp4KXOZqfmen2o2KGOSGkVZ74ERQn7u+/PV2xw2FdRW6m9razgcs+d+T\nJ/U+/HWhGtAQ7zmXSbFonSMHADpJSkrKysr6/vvvx/hNJzGx6J6mxnJ+Dh0GIsnPMUxT5Gfysw/5\nGQACJCYm5uTkXLlyZd26dbGuiwcT7hgXvH9TP/j5WV3369qVYzSbzWZjZn3wnLSIiKTc9aOQGzsr\njGaz2Wx+YOMR/zVGUR89ZP/pj00iIvOWPKeuKF60qaXbr2jLrgKzxWKxWNbUXA/NV9R/HA3tIZNr\n5bMlB1w3/L+7ZdfLxeopCPZXn7GLiNj+Nk99xo+zpPjd4zeGU5dzb36RWjjDU1gXUXaTMudBtYKO\nhi9cQWsjaIThiLzXNLeOdMD4Gt9RtGlXi1+J7pYCo9lisVjMb7T7eiWCpqtaVlhzyq/P9/7yZYda\n5Q358z0jLU9dt/3x4uMdN/Z5d82mfM/42OIprItQ3WQwJqgvdm6tvrGpBs7W/3NhlW6H9xmqATva\nWtUXC+fepv/RAeAGBoMhPj5+7dq1PT09zzzzTKyrgwltZNLUGM3PEaSpCPLz6Kcp8jP52Yv8DABh\nGI3GyZMnb9q06fz580888USsq8OEO8aFqbYZ6gtHUf7GXfWNjY0dwQFzOJTb56kh0fH47CW76ls6\nOrs7z5468O6a25/eLiIi9vS0mSE3/qu71I2d2xcv2birpb2ju7vzVOOBNZm3b3eKiNizHklWS9qf\nK1fjo1QumJ5ZcaDx7Nmzp1qOvLsmc8EK9aFB2SsfS/Xu2DB7lrrj2pef31p//HijUyOcOrc/bVmy\nsb6xxelsOVCxZvqCFZ4z1lfvWOS5f4ySs7VcfVVVtOiBF3YcaXGqhS1zl6mF7Rs2/SzoZjPDFnU3\nKSkPZ4iISO3JM4HfCiJqhGGIvNc0t454wCg5/+Rp/MoVCzLXVDQ6nU6n80jNu5nTF3i6vDw31Xt9\ndGRN58i5x7JxV32L09nSeGBN5vRl3qsUtq1a5KmfPcc70qoWzX5gR80Rp7dwjudcHvuOwkXRNVk4\nIbvJlPaI55wlZ8nszDUHGp3t7c4DFRuNtz9eq9/hfYZqQPfnfzwsIiLZ9//1Lfh8MwC3oGnTpiUm\nJr7//vufffbZvffeG+vqYGIaoTQ1NvNzRGkqgvw82mmK/Ex+Jj8DQOQURZk5c2Z1dfWJEyfmzJkT\ny6oMAuPBhVL/87BLG7rUFa2efJTd0Bu4TW+rJ7GVNXd5FzV7Spc1+wqtCv3xyS1vDtypv9bqcFt7\n6zg4ODg42NW8OvSp5BsOfuf3bo+V+q22l3X5v6OQMracvuxfw30bwry9c37tFdg4vc2ew11vrqC2\n9V8VsptCaSjL9pa8ELAqVCNodOIN9fIMh4zyMIWj6LVg0QyY1urVIYvat3zX7/d2QzRd7wfZoY8n\nIiJb6k4H1HBD6JFWfsyvz4M+Pt4G1PhAaa8K2U2Dg77LhEPxfTCjG2naAyDs2Ltw2LPSNzCAW9wj\njzwS6yogCj09PXv27DEYIrofQVFRUV9fX6yrjHEi1K/pcZmfQ4WBYeTnKNIU+VmE/Ex+BsYNMvYt\n5Nq1a729vZWVlUP8xPcqKSnRtwKc4Y7xwbb249bSVRm+/0/1PtnFPOMOERFRjBpfYz13nbNM85ae\nOs1T2uK9cblir7h8bl9ZYJi3Z2+oaz63p2B++GrZn68417xvdXbg1qvL6s5d3vOQ9YZl1vm/ar1Q\nVxaYNrNXlR473bXtsZQbF9rS17bWlWb4/v+jwOfm5H7Q/F1zda7/YXNL6y4c3Zzqf7Wjfem2rm8O\nBtUwo3Rf8+U9BX6nogQ1jsE2W63DrBmBt+0zmGfYNVaF7KZQFjyZo74o/rfGgDsyhmwEXz3jNe4+\nP0U9/eK2hMDCluuFo+i1YNEMGPvzv7rQWndDe6iyS6uPdbVuTvF7/NeQTbeq+dzp6g25/kddVdfa\ntTkr1W+hYt/W2nW4PPC7Ssaq0uZzlwvS/frc+/GZNc3z8THcMS/bf4mPYYbnZBy/VWHGakrWttOH\ny6+vEhGxry4/fO6bfeqOfF0S3UjT6tPwDdj+3x+qpyetXvtE+O4FgJGQmJj43HPP9ff3v/HGG7Gu\nCyaWUL+mx2V+HjJNRZ6fo0hT5GfyM/kZAGJh0qRJiqKoj1F95ZVXYlCBwcHB0T8qMFIGBtzqY9Tj\n4uKGLBzdnt2dF7rFZBK3WxKsNiW6u/S5XZ3dF9Wt46zJ1nAbu10dnV1Gi6Xf7U6wzlRM4d7HgHtA\nTNI/IKa4OBFxOSsscwtFJLusef9r80XE1dnpjovr7+012ZKtYXfl6u7ochstxv7efqMt2abfTQg1\n6x15N3VXZE4vdIhIxrGuo+laaS6gEXQURa9pVSvyAePq7OjqN1mMbrckzLQpYbs8oOlcu5ZYVtSK\nSHZz7/75ilppd5yxv7fflJxsDbsrV0dHl9Fi6e/tNU63RTukoxW6m9ydZzvFYul3Dwz15nWpR/DY\n69g6d3aJU0Rym3v3zOeKWIwL6enpx48fj3UtEDWXy3Xp0qUXX3yxvr4+VJmioqLS0tIpU6aMZsUw\n7o1UmhqT+TkgDNxMfh7VNEV+9kd+Jj8Do4+MfYu6ePFid3f38uXLP/7441BlSkpK3nzzTR0POtI/\nm4HRFRc3UqknzmRLNomIWIfzJ3yTYjMpkW1tUpJTIiwqcaY4EQmTkxWbTYlsV4o1WbGKiIzGGQpR\ndJP1ma2lhYuKRRzv7GlJf03jlKghG2HYoui1YNEMGMWWHHGXD9V03kpHsKsoRtrNC91NJltKisjo\njDyNBuxurClxiohklP0j3xYAxJaiKIqi7N279+uvv87Lyztz5kysa4SJYqTS1JjMz0Omqcjz86im\nKfKzP/KzCPkZACKSkJCQkJCwf//+pqamvLy88+fPj8JBuaUMgDHNlr5SvblhbVH+kc5Y1wbjTcev\nHy4SEZHcd1c+FOO6AICIiFit1oULF3755Zfl5UPdWhoAtJCfMZLIzwBuSVar9dFHH/3222/ffvvt\nUTgcZ7gD48jVq+q/7X39sa2IrqwFH3wze9lXV0S5c2Sv2rwl9fao//aMpy4fRQk/P7jvb1wya95i\nO6MLwJgxadIki8WSn5+/cuXKV199lZl3YASRnyce8vPNIT8DuFVNnjzZbDYXeu3evXvkjsWEOzB+\nKHc/ue8DS6/I7Q/fHeu66MqalrU0LdaVGJuUJ7fus5zulSl33s3lnMOhPPTY0ljXAQC0qTdqf+ut\nt4qLi/Py8o4dOxbrGgHjEPl54iE/3yTyM4Bbm8lkEpGdO3euX78+Ly+vqalpJI7ChDswjiipS/NT\nY10JjKrU9KWp6bGuBABgxKg3naytrf3000/z8vJiXR1g3CE/TzzkZwBAUlJSUlLSoUOHjh49unz5\nct33P2lwcFD3nQIAAADQ0Q8//HD16tWGhoaFCxeqJ78DAAAAuBkDAwPXrl375JNPMjIydNwtE+4A\nAADAraGvr89oNBoMhlhXBAAAABgn3G63eqsZvTDhDgAAAAAAAACADibHugIAAAAAAAAAAIwHTLgD\nAAAAAAAAAKADJtwBAAAAAAAAANABE+4AAAAAAAAAAOiACXcAAAAAAAAAAHTAhDsAAAAAAAAAADpg\nwh0AAAAAAAAAAB0w4Q4AAAAAAAAAgA6YcAcAAAAAAAAAQAdMuAMAAAAAAAAAoAMm3AEAAAAAAAAA\n0AET7gAAAAAAAAAA6IAJdwAAAAAAAAAAdMCEOwAAAAAAAAAAOmDCHQAAAAAAAAAAHTDhDgAAAAAA\nAACADphwBwAAAAAAAABAB0y4AwAAAAAAAACgAybcAQAAAAAAAADQARPuAAAAAAAAAADogAl3AAAA\nAAAAAAB0wIQ7AAAAAAAAAAA6+H+M3zMraYjDnwAAAABJRU5ErkJggg==\n",
+      "text/plain": [
+       ""
+      ]
+     },
+     "execution_count": 8,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "# Exceptional Units, small set range, large value range\n",
+    "# Expected labels: \n",
+    "#     X: This setpoint (this setpointunit), Y: Gate ch1 (μV), Z: this label (GHz)\n",
+    "#     Two plots next to each other\n",
+    "\n",
+    "dmm.mymultiparameter.get_multiplier = 1e9\n",
+    "dmm.mymultiparameter.units = ('Hz', 'HZ')\n",
+    "\n",
+    "plot, data = do1d(dac.ch1, 0, 10e-7, 50, 0.01, dmm.mymultiparameter)\n",
+    "\n",
+    "def get(self):\n",
+    "    item = np.random.randn(25)\n",
+    "    self._save_val(item)\n",
+    "    return item\n",
+    "\n",
+    "dmm.mymultiparameter.get_multiplier = 1\n",
+    "dmm.mymultiparameter.units = ('this unit', 'that unit')\n",
+    "\n",
+    "plot"
+   ]
+  },
   {
    "cell_type": "markdown",
    "metadata": {},
diff --git a/qcodes/utils/wrappers.py b/qcodes/utils/wrappers.py
index 31b8b797d946..c68522721ded 100644
--- a/qcodes/utils/wrappers.py
+++ b/qcodes/utils/wrappers.py
@@ -165,7 +165,7 @@ def _create_plot(plot, i, name, data, counter_two, j, k):
                                   pos})
             tdict = {'bottom': 'setXRange', 'left': 'setYRange'}
             # now find the data (not setpoint)
-            checkstring = '{}_{}'.format(i._instrument.name, i.name)
+            checkstring = '{}_{}'.format(i._instrument.name, name)
 
             thedata = [data.arrays[d] for d in data.arrays.keys()
                        if d == checkstring][0]
@@ -180,7 +180,7 @@ def _create_plot(plot, i, name, data, counter_two, j, k):
                 except KeyError:
                     # 2D measurement
                     # Then we should fetch the colorbar
-                    ax = plot.traces[j]['plot_object']['hist'].axis
+                    ax = plot.traces[j+k]['plot_object']['hist'].axis
                     ax.enableAutoSIPrefix(False)
                     ax.setLabel(text=thedata.label, unit=thedata.unit,
                                 unitPrefix='')
@@ -392,7 +392,6 @@ def _do_measurement(loop: Loop, set_params: tuple, meas_params: tuple,
         # resize histogram
         for trace in plot.traces:
             if 'plot_object' in trace.keys():
-                print(type(trace['plot_object']))
                 if (isinstance(trace['plot_object'], dict) and
                             'hist' in trace['plot_object'].keys()):
                     cmap = trace['plot_object']['cmap']

From ca4ed183ae96e4650fb7cca8e9a248add4123876 Mon Sep 17 00:00:00 2001
From: Jens Hedegaard Nielsen 
Date: Wed, 2 Aug 2017 17:06:07 +0200
Subject: [PATCH 238/328] make it configurable if the matplot(pdf) plots should
 be shown (#56)

---
 qcodes/utils/wrappers.py | 30 +++++++++++++++++++++---------
 1 file changed, 21 insertions(+), 9 deletions(-)

diff --git a/qcodes/utils/wrappers.py b/qcodes/utils/wrappers.py
index c68522721ded..7a37ee3d6081 100644
--- a/qcodes/utils/wrappers.py
+++ b/qcodes/utils/wrappers.py
@@ -6,6 +6,7 @@
 from copy import deepcopy
 import numpy as np
 from typing import Optional, Tuple
+import matplotlib.pyplot as plt
 
 import qcodes as qc
 from qcodes.loops import Loop
@@ -20,10 +21,10 @@
 log = logging.getLogger(__name__)
 CURRENT_EXPERIMENT = {}
 CURRENT_EXPERIMENT["logging_enabled"] = False
-
+pdfdisplay = {}
 
 def init(mainfolder: str, sample_name: str, station, plot_x_position=0.66,
-         annotate_image=True):
+         annotate_image=True, display_pdf=True, display_individual_pdf=False):
     """
 
     Args:
@@ -34,6 +35,9 @@ def init(mainfolder: str, sample_name: str, station, plot_x_position=0.66,
                          1 is all the way to the right.
 
     """
+    plt.ioff()
+    pdfdisplay['individual'] = display_individual_pdf
+    pdfdisplay['combined'] = display_pdf
     if sep in sample_name:
         raise TypeError("Use Relative names. That is without {}".format(sep))
     # always remove trailing sep in the main folder
@@ -259,9 +263,9 @@ def __get_plot_type(data, plot):
     return metadata
 
 
-def _save_individual_plots(data, inst_meas):
+def _save_individual_plots(data, inst_meas, display_plot=True):
 
-    def _create_plot(i, name, data, counter_two):
+    def _create_plot(i, name, data, counter_two, display_plot=True):
         # Step the color on all subplots no just on plots
         # within the same axis/subplot
         # this is to match the qcodes-pyqtplot behaviour.
@@ -288,17 +292,21 @@ def _create_plot(i, name, data, counter_two):
             plot.subplots[0].set_title(title)
         plot.save("{}_{:03d}.pdf".format(plot.get_default_title(),
                                          counter_two))
-        plot.fig.canvas.draw()
+        if display_plot:
+            plot.fig.canvas.draw()
+            plt.show()
+        else:
+            plt.close(plot.fig)
 
     counter_two = 0
     for j, i in enumerate(inst_meas):
         if getattr(i, "names", False):
             # deal with multidimensional parameter
             for k, name in enumerate(i.names):
-                _create_plot(i, name, data, counter_two)
+                _create_plot(i, name, data, counter_two, display_plot)
                 counter_two += 1
         else:
-            _create_plot(i, i.name, data, counter_two)
+            _create_plot(i, i.name, data, counter_two, display_plot)
             counter_two += 1
 
 
@@ -409,9 +417,13 @@ def _do_measurement(loop: Loop, set_params: tuple, meas_params: tuple,
         # suptitle and title
         pdfplot.fig.tight_layout(pad=3)
         pdfplot.save("{}.pdf".format(plot.get_default_title()))
-        pdfplot.fig.canvas.draw()
+        if pdfdisplay['combined'] or (num_subplots == 1 and pdfdisplay['individual']):
+            pdfplot.fig.canvas.draw()
+            plt.show()
+        else:
+            plt.close(pdfplot.fig)
         if num_subplots > 1:
-            _save_individual_plots(data, meas_params)
+            _save_individual_plots(data, meas_params, pdfdisplay['individual'])
     if CURRENT_EXPERIMENT.get('device_image'):
         log.debug('Saving device image')
         save_device_image(tuple(sp[0] for sp in set_params))

From e4dcbc109a0c1026ab720419bbe539b777f22f26 Mon Sep 17 00:00:00 2001
From: Jens Hedegaard Nielsen 
Date: Tue, 8 Aug 2017 10:49:54 +0200
Subject: [PATCH 239/328] Crosssection viewer [WIP] (#57)

* Add simple click widget, still very much wip

* Fix add datacursor to viewer gui

* Fix: Clickwidget read labels from dataset

* Fix: more advanced widget

* Add titles to plots

* Needs mplcursors too

* WIP save func

* fix: correct event for button

* fix: disable buttons, calc bbox right

* fix: make save subplots more consistent and improve layout

* fix: improve savenames

* fix: better titles for saved subplots

* Trying to improve extend alg

* Fix don't include ticks. wrong ticks are includes when zooming

* fix: Move widget to its own module and partially fix for new labels

* Fix: change titles to use units correctly

* fix: remove unused import

* restore missing import

* improve non interactive logic for not showing plots
---
 qcodes/plots/qcmatplotlib_viewer_widget.py | 246 +++++++++++++++++++++
 qcodes/utils/wrappers.py                   |   3 +-
 requirements.txt                           |   1 +
 3 files changed, 249 insertions(+), 1 deletion(-)
 create mode 100644 qcodes/plots/qcmatplotlib_viewer_widget.py

diff --git a/qcodes/plots/qcmatplotlib_viewer_widget.py b/qcodes/plots/qcmatplotlib_viewer_widget.py
new file mode 100644
index 000000000000..968633dc8763
--- /dev/null
+++ b/qcodes/plots/qcmatplotlib_viewer_widget.py
@@ -0,0 +1,246 @@
+from matplotlib.widgets import Cursor
+import mplcursors
+import numpy as np
+import matplotlib.pyplot as plt
+
+from PyQt5 import QtWidgets
+from .base import BasePlot
+
+class ClickWidget(BasePlot):
+
+    def __init__(self, dataset):
+        super().__init__()
+        data = {}
+        self.expand_trace(args=[dataset], kwargs=data)
+        self.traces = []
+
+        data['xlabel'] = self.get_label(data['x'])
+        data['ylabel'] = self.get_label(data['y'])
+        data['zlabel'] = self.get_label(data['z'])
+        data['xaxis'] = data['x'].ndarray[0, :]
+        data['yaxis'] = data['y'].ndarray
+        self.traces.append({
+            'config': data,
+        })
+        self.fig = plt.figure()
+
+        self._lines = []
+        self._datacursor = []
+        self._cid = 0
+
+        hbox = QtWidgets.QHBoxLayout()
+        self.fig.canvas.setLayout(hbox)
+        hspace = QtWidgets.QSpacerItem(0,
+                                       0,
+                                       QtWidgets.QSizePolicy.Expanding,
+                                       QtWidgets.QSizePolicy.Expanding)
+        vspace = QtWidgets.QSpacerItem(0,
+                                       0,
+                                       QtWidgets.QSizePolicy.Minimum,
+                                       QtWidgets.QSizePolicy.Expanding)
+        hbox.addItem(hspace)
+
+        vbox = QtWidgets.QVBoxLayout()
+        self.crossbtn = QtWidgets.QCheckBox('Cross section')
+        self.crossbtn.setToolTip("Display extra subplots with selectable cross sections "
+                                 "or sums along axis.")
+        self.sumbtn = QtWidgets.QCheckBox('Sum')
+        self.sumbtn.setToolTip("Display sums or cross sections.")
+
+        self.savehmbtn = QtWidgets.QPushButton('Save Heatmap')
+        self.savehmbtn.setToolTip("Save heatmap as a file (PDF)")
+        self.savexbtn = QtWidgets.QPushButton('Save Vert')
+        self.savexbtn.setToolTip("Save vertical cross section or sum as a file (PDF)")
+        self.saveybtn = QtWidgets.QPushButton('Save Horz')
+        self.savexbtn.setToolTip("Save horizontal cross section or sum as a file (PDF)")
+
+        self.crossbtn.toggled.connect(self.toggle_cross)
+        self.sumbtn.toggled.connect(self.toggle_sum)
+
+        self.savehmbtn.pressed.connect(self.save_heatmap)
+        self.savexbtn.pressed.connect(self.save_subplot_x)
+        self.saveybtn.pressed.connect(self.save_subplot_y)
+
+        self.toggle_cross()
+        self.toggle_sum()
+
+        vbox.addItem(vspace)
+        vbox.addWidget(self.crossbtn)
+        vbox.addWidget(self.sumbtn)
+        vbox.addWidget(self.savehmbtn)
+        vbox.addWidget(self.savexbtn)
+        vbox.addWidget(self.saveybtn)
+
+        hbox.addLayout(vbox)
+
+    @staticmethod
+    def full_extent(ax, pad=0.0):
+        """Get the full extent of an axes, including axes labels, tick labels, and
+        titles."""
+        # for text objects we only include them if they are non empty.
+        # empty ticks may be rendered outside the figure
+        from matplotlib.transforms import Bbox
+        items = []
+        items += [ax.xaxis.label, ax.yaxis.label, ax.title]
+        items = [item for item in items if item.get_text()]
+        items.append(ax)
+        bbox = Bbox.union([item.get_window_extent() for item in items])
+
+        return bbox.expanded(1.0 + pad, 1.0 + pad)
+
+    def save_subplot(self, axnumber, savename, saveformat='pdf'):
+        extent = self.full_extent(self.ax[axnumber]).transformed(self.fig.dpi_scale_trans.inverted())
+        full_title = "{}.{}".format(savename, saveformat)
+        self.fig.savefig(full_title, bbox_inches=extent)
+
+    def save_subplot_x(self):
+        title = self.get_default_title()
+        label, unit = self._get_label_and_unit(self.traces[0]['config']['xlabel'])
+        if self.sumbtn.isChecked():
+            title += " sum over {}".format(label)
+        else:
+            title += " cross section {} = {} {}".format(label,
+                                                        self.traces[0]['config']['xpos'],
+                                                        unit)
+        self.save_subplot(axnumber=(0, 1), savename=title)
+
+    def save_subplot_y(self):
+        title = self.get_default_title()
+        label, unit = self._get_label_and_unit(self.traces[0]['config']['ylabel'])
+        if self.sumbtn.isChecked():
+            title += " sum over {}".format(label)
+        else:
+            title += " cross section {} = {} {}".format(label,
+                                                        self.traces[0]['config']['xpos'],
+                                                        unit)
+        self.save_subplot(axnumber=(1, 0), savename=title)
+
+    def save_heatmap(self):
+        title = self.get_default_title() + " heatmap"
+        self.save_subplot(axnumber=(0, 0), savename=title)
+
+    def _update_label(self, ax, axletter, label, extra=None):
+
+        if type(label) == tuple and len(label) == 2:
+            label, unit = label
+        else:
+            unit = ""
+        axsetter = getattr(ax, "set_{}label".format(axletter))
+        if extra:
+            axsetter(extra + "{} ({})".format(label, unit))
+        else:
+            axsetter("{} ({})".format(label, unit))
+
+    @staticmethod
+    def _get_label_and_unit(config):
+        if type(config) == tuple and len(config) == 2:
+            label, unit = config
+        else:
+            unit = ""
+            label = config
+        return label, unit
+
+    def toggle_cross(self):
+        self.remove_plots()
+        self.fig.clear()
+        if self._cid:
+            self.fig.canvas.mpl_disconnect(self._cid)
+        if self.crossbtn.isChecked():
+            self.sumbtn.setEnabled(True)
+            self.savexbtn.setEnabled(True)
+            self.saveybtn.setEnabled(True)
+            self.ax = np.empty((2, 2), dtype='O')
+            self.ax[0, 0] = self.fig.add_subplot(2, 2, 1)
+            self.ax[0, 1] = self.fig.add_subplot(2, 2, 2)
+            self.ax[1, 0] = self.fig.add_subplot(2, 2, 3)
+            self._cid = self.fig.canvas.mpl_connect('button_press_event', self._click)
+            self._cursor = Cursor(self.ax[0, 0], useblit=True, color='black')
+            self.toggle_sum()
+            figure_rect = (0, 0, 1, 1)
+        else:
+            self.sumbtn.setEnabled(False)
+            self.savexbtn.setEnabled(False)
+            self.saveybtn.setEnabled(False)
+            self.ax = np.empty((1, 1), dtype='O')
+            self.ax[0, 0] = self.fig.add_subplot(1, 1, 1)
+            figure_rect = (0, 0.0, 0.75, 1)
+        self.ax[0, 0].pcolormesh(self.traces[0]['config']['x'],
+                                 self.traces[0]['config']['y'],
+                                 self.traces[0]['config']['z'],
+                                 edgecolor='face')
+        self._update_label(self.ax[0, 0], 'x', self.traces[0]['config']['xlabel'])
+        self._update_label(self.ax[0, 0], 'y', self.traces[0]['config']['ylabel'])
+        self.fig.tight_layout(rect=figure_rect)
+        self.fig.canvas.draw_idle()
+
+    def toggle_sum(self):
+        self.remove_plots()
+        if not self.crossbtn.isChecked():
+            return
+        self.ax[1, 0].clear()
+        self.ax[0, 1].clear()
+        if self.sumbtn.isChecked():
+            self._cursor.set_active(False)
+            self.ax[1, 0].set_ylim(0, self.traces[0]['config']['z'].sum(axis=0).max() * 1.05)
+            self.ax[0, 1].set_xlim(0, self.traces[0]['config']['z'].sum(axis=1).max() * 1.05)
+            self._update_label(self.ax[1, 0], 'x', self.traces[0]['config']['xlabel'])
+            self._update_label(self.ax[1, 0], 'y', self.traces[0]['config']['zlabel'], extra='sum of ')
+
+            self._update_label(self.ax[0, 1], 'x', self.traces[0]['config']['zlabel'], extra='sum of ')
+            self._update_label(self.ax[0, 1], 'y', self.traces[0]['config']['ylabel'])
+
+            self._lines.append(self.ax[0, 1].plot(self.traces[0]['config']['z'].sum(axis=1),
+                                                  self.traces[0]['config']['yaxis'],
+                                                  color='C0',
+                                                  marker='.')[0])
+            self.ax[0, 1].set_title("")
+            self._lines.append(self.ax[1, 0].plot(self.traces[0]['config']['xaxis'],
+                                                  self.traces[0]['config']['z'].sum(axis=0),
+                                                  color='C0',
+                                                  marker='.')[0])
+            self.ax[1, 0].set_title("")
+            self._datacursor = mplcursors.cursor(self._lines, multiple=False)
+        else:
+            self._cursor.set_active(True)
+            self._update_label(self.ax[1, 0], 'x', self.traces[0]['config']['xlabel'])
+            self._update_label(self.ax[1, 0], 'y', self.traces[0]['config']['zlabel'])
+
+            self._update_label(self.ax[0, 1], 'x', self.traces[0]['config']['zlabel'])
+            self._update_label(self.ax[0, 1], 'y', self.traces[0]['config']['ylabel'])
+
+            self.ax[1, 0].set_ylim(0, self.traces[0]['config']['z'].max() * 1.05)
+            self.ax[0, 1].set_xlim(0, self.traces[0]['config']['z'].max() * 1.05)
+        self.fig.canvas.draw_idle()
+
+    def remove_plots(self):
+        for line in self._lines:
+            line.remove()
+        self._lines = []
+        if self._datacursor:
+            self._datacursor.remove()
+
+    def _click(self, event):
+
+        if event.inaxes == self.ax[0, 0] and not self.sumbtn.isChecked():
+            xpos = (abs(self.traces[0]['config']['xaxis'] - event.xdata)).argmin()
+            ypos = (abs(self.traces[0]['config']['yaxis'] - event.ydata)).argmin()
+            self.remove_plots()
+
+            self._lines.append(self.ax[0, 1].plot(self.traces[0]['config']['z'][:, xpos],
+                                                  self.traces[0]['config']['yaxis'],
+                                                  color='C0',
+                                                  marker='.')[0])
+            xlabel, xunit = self._get_label_and_unit(self.traces[0]['config']['xlabel'])
+            self.ax[0, 1].set_title("{} = {} {} ".format(xlabel, self.traces[0]['config']['xaxis'][xpos], xunit),
+                                    fontsize='small')
+            self.traces[0]['config']['xpos'] = self.traces[0]['config']['xaxis'][xpos]
+            self._lines.append(self.ax[1, 0].plot(self.traces[0]['config']['xaxis'],
+                                                  self.traces[0]['config']['z'][ypos, :],
+                                                  color='C0',
+                                                  marker='.')[0])
+            ylabel, yunit = self._get_label_and_unit(self.traces[0]['config']['ylabel'])
+            self.ax[1, 0].set_title("{} = {} {}".format(ylabel, self.traces[0]['config']['yaxis'][ypos], yunit),
+                                    fontsize='small')
+            self.traces[0]['config']['ypos'] = self.traces[0]['config']['yaxis'][ypos]
+            self._datacursor = mplcursors.cursor(self._lines, multiple=False)
+            self.fig.canvas.draw_idle()
diff --git a/qcodes/utils/wrappers.py b/qcodes/utils/wrappers.py
index 7a37ee3d6081..99a8c7a541ad 100644
--- a/qcodes/utils/wrappers.py
+++ b/qcodes/utils/wrappers.py
@@ -35,7 +35,6 @@ def init(mainfolder: str, sample_name: str, station, plot_x_position=0.66,
                          1 is all the way to the right.
 
     """
-    plt.ioff()
     pdfdisplay['individual'] = display_individual_pdf
     pdfdisplay['combined'] = display_pdf
     if sep in sample_name:
@@ -412,6 +411,7 @@ def _do_measurement(loop: Loop, set_params: tuple, meas_params: tuple,
         # set window back to original size
         plot.win.resize(1000, 600)
         plot.save()
+        plt.ioff()
         pdfplot, num_subplots = _plot_setup(data, meas_params, useQT=False)
         # pad a bit more to prevent overlap between
         # suptitle and title
@@ -424,6 +424,7 @@ def _do_measurement(loop: Loop, set_params: tuple, meas_params: tuple,
             plt.close(pdfplot.fig)
         if num_subplots > 1:
             _save_individual_plots(data, meas_params, pdfdisplay['individual'])
+        plt.ion()
     if CURRENT_EXPERIMENT.get('device_image'):
         log.debug('Saving device image')
         save_device_image(tuple(sp[0] for sp in set_params))
diff --git a/requirements.txt b/requirements.txt
index 52bffd4da99b..2dbd9d4c59d7 100644
--- a/requirements.txt
+++ b/requirements.txt
@@ -1,6 +1,7 @@
 jupyter==1.0.0
 numpy==1.12.1
 matplotlib==2.0.2
+mplcursors==0.1
 pyqtgraph==0.10.0
 PyVISA==1.8
 PyQt5==5.7.1

From 40ad3e140859af64fcda50dfeecfbe6e881dd701 Mon Sep 17 00:00:00 2001
From: Jens Hedegaard Nielsen 
Date: Wed, 9 Aug 2017 11:50:58 +0200
Subject: [PATCH 240/328] fix default config reader

---
 qcodes/utils/configreader.py | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

diff --git a/qcodes/utils/configreader.py b/qcodes/utils/configreader.py
index e42603321cbb..3987c5d730e7 100644
--- a/qcodes/utils/configreader.py
+++ b/qcodes/utils/configreader.py
@@ -26,7 +26,7 @@ def __init__(self, filename, isdefault=True):
         # If this is the default config object, we make it available as a
         # class method.
         if isdefault:
-            default = self
+            Config.default = self
 
         self._filename = filename
         self._cfg = ConfigParser()

From ff65ef36398c97a93813a76b0231caef4f431cd2 Mon Sep 17 00:00:00 2001
From: Jens Hedegaard Nielsen 
Date: Wed, 9 Aug 2017 17:14:39 +0200
Subject: [PATCH 241/328] Save pdfs in subfolder (#60)

* Save pdfs in subfolder

* rescale pdf(matplotlib) plots with mv scale
---
 qcodes/utils/wrappers.py | 35 +++++++++++++++++++++++++++++++----
 1 file changed, 31 insertions(+), 4 deletions(-)

diff --git a/qcodes/utils/wrappers.py b/qcodes/utils/wrappers.py
index 99a8c7a541ad..e8775257b48b 100644
--- a/qcodes/utils/wrappers.py
+++ b/qcodes/utils/wrappers.py
@@ -16,6 +16,7 @@
 from qcodes.instrument.visa import VisaInstrument
 from qcodes.utils.qcodes_device_annotator import DeviceImage
 
+from matplotlib import ticker
 from IPython import get_ipython
 
 log = logging.getLogger(__name__)
@@ -53,12 +54,16 @@ def init(mainfolder: str, sample_name: str, station, plot_x_position=0.66,
 
     path_to_experiment_folder = sep.join([mainfolder, sample_name, ""])
     CURRENT_EXPERIMENT["exp_folder"] = path_to_experiment_folder
-
+    CURRENT_EXPERIMENT['pdf_subfolder'] = 'pdf'
     try:
         makedirs(path_to_experiment_folder)
     except FileExistsError:
         pass
-
+    try:
+        makedirs(sep.join([mainfolder, sample_name,
+                           CURRENT_EXPERIMENT['pdf_subfolder']]))
+    except FileExistsError:
+        pass
     log.info("experiment started at {}".format(path_to_experiment_folder))
 
     loc_provider = qc.FormatLocation(
@@ -289,7 +294,12 @@ def _create_plot(i, name, data, counter_two, display_plot=True):
             plot.subplots[0].set_title(title + rasterized_note)
         else:
             plot.subplots[0].set_title(title)
-        plot.save("{}_{:03d}.pdf".format(plot.get_default_title(),
+        title_list = plot.get_default_title().split(sep)
+        title_list.insert(-1 , CURRENT_EXPERIMENT['pdf_subfolder'])
+        title = sep.join(title_list)
+        _rescale_mpl_axes(plot)
+        plot.tight_layout()
+        plot.save("{}_{:03d}.pdf".format(title,
                                          counter_two))
         if display_plot:
             plot.fig.canvas.draw()
@@ -415,8 +425,14 @@ def _do_measurement(loop: Loop, set_params: tuple, meas_params: tuple,
         pdfplot, num_subplots = _plot_setup(data, meas_params, useQT=False)
         # pad a bit more to prevent overlap between
         # suptitle and title
+        _rescale_mpl_axes(pdfplot)
         pdfplot.fig.tight_layout(pad=3)
-        pdfplot.save("{}.pdf".format(plot.get_default_title()))
+        title_list = plot.get_default_title().split(sep)
+        title_list.insert(-1 , CURRENT_EXPERIMENT['pdf_subfolder'])
+        title = sep.join(title_list)
+
+
+        pdfplot.save("{}.pdf".format(title))
         if pdfdisplay['combined'] or (num_subplots == 1 and pdfdisplay['individual']):
             pdfplot.fig.canvas.draw()
             plt.show()
@@ -437,6 +453,17 @@ def _do_measurement(loop: Loop, set_params: tuple, meas_params: tuple,
         raise KeyboardInterrupt
     return plot, data
 
+def _rescale_mpl_axes(plot):
+    for i, subplot in enumerate(plot.subplots):
+        for axis in 'x', 'y':
+            unit = plot.traces[i]['config'][axis].unit
+            label = plot.traces[i]['config'][axis].label
+            maxval = abs(plot.traces[0]['config'][axis].ndarray).max()
+            if unit == 'V' and maxval < 0.1:
+                unit = 'mV'
+                tx = ticker.FuncFormatter(lambda i, pos: '{0:g}'.format(i*1e3))
+                getattr(subplot, "{}axis".format(axis)).set_major_formatter(tx)
+                getattr(subplot, "set_{}label".format(axis))("{} ({})".format(label, unit))
 
 def do1d(inst_set, start, stop, num_points, delay, *inst_meas, do_plots=True):
     """

From 10d56c4f82844be12df09b0ee04255e0bc8fb6d8 Mon Sep 17 00:00:00 2001
From: Jens Hedegaard Nielsen 
Date: Wed, 16 Aug 2017 13:27:15 +0200
Subject: [PATCH 242/328] Fix monitor for channel parameters (#61)

---
 qcodes/monitor/monitor.py | 14 +++++++++-----
 1 file changed, 9 insertions(+), 5 deletions(-)

diff --git a/qcodes/monitor/monitor.py b/qcodes/monitor/monitor.py
index 04dc9f91a888..df0bfc560d81 100644
--- a/qcodes/monitor/monitor.py
+++ b/qcodes/monitor/monitor.py
@@ -34,7 +34,7 @@
 
 def _get_metadata(*parameters) -> Dict[float, list]:
     """
-    Return a dict that contains the paraemter metadata grouped by the
+    Return a dict that contains the parameter metadata grouped by the
     instrument it belongs to.
     """
     ts = time.time()
@@ -45,16 +45,20 @@ def _get_metadata(*parameters) -> Dict[float, list]:
         if _meta:
             meta = _meta()
         else:
-            raise ValueError("Input is not a paraemter; Refusing to proceed")
+            raise ValueError("Input is not a parameter; Refusing to proceed")
         # convert to string
         meta['value'] = str(meta['value'])
         if meta["ts"] is not None:
             meta["ts"] = time.mktime(meta["ts"].timetuple())
         meta["name"] = parameter.label or parameter.name
         meta["unit"] = parameter.unit
-        accumulator = metas.get(str(parameter._instrument), [])
+        # find the base instrument in case this is a channel parameter
+        baseinst = parameter._instrument
+        while hasattr(baseinst, '_parent'):
+            baseinst = baseinst._parent
+        accumulator = metas.get(str(baseinst), [])
         accumulator.append(meta)
-        metas[str(parameter._instrument)] = accumulator
+        metas[str(baseinst)] = accumulator
     parameters = []
     for instrument in metas:
         temp = {"instrument": instrument, "parameters": metas[instrument]}
@@ -117,7 +121,7 @@ def run(self):
 
     def stop(self):
         """
-        Shutodwn the server, close the event loop and join the thread
+        Shutdown the server, close the event loop and join the thread
         """
         # this contains the server
         # or any exception

From a99e7c9315475bde8a1c11d94da63621aef2fd56 Mon Sep 17 00:00:00 2001
From: Jens Hedegaard Nielsen 
Date: Wed, 24 May 2017 10:03:20 +0200
Subject: [PATCH 243/328] Update Znb20 notebook

---
 ...es example with Rohde Schwarz ZNB 20.ipynb | 2741 +++++++++++++++++
 1 file changed, 2741 insertions(+)
 create mode 100644 docs/examples/driver_examples/Qcodes example with Rohde Schwarz ZNB 20.ipynb

diff --git a/docs/examples/driver_examples/Qcodes example with Rohde Schwarz ZNB 20.ipynb b/docs/examples/driver_examples/Qcodes example with Rohde Schwarz ZNB 20.ipynb
new file mode 100644
index 000000000000..77e040d78557
--- /dev/null
+++ b/docs/examples/driver_examples/Qcodes example with Rohde Schwarz ZNB 20.ipynb	
@@ -0,0 +1,2741 @@
+{
+ "cells": [
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "# Qcodes example with Rohde Schwarz ZN20"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 1,
+   "metadata": {
+    "collapsed": true
+   },
+   "outputs": [],
+   "source": [
+    "%matplotlib nbagg\n",
+    "import matplotlib.pyplot as plt\n",
+    "\n",
+    "import qcodes as qc"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 2,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "import qcodes.instrument_drivers.rohde_schwarz.ZNB20 as vna"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 3,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Connected to: Rohde-Schwarz ZNB20-2Port (serial:1311601062101551, firmware:2.82) in 0.28s\n"
+     ]
+    }
+   ],
+   "source": [
+    "vna = vna.ZNB20('VNA', 'TCPIP0::192.168.15.100::inst0::INSTR', server_name=None)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 4,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "station = qc.Station(vna)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "The QCoDes driver for the Rohde Schwarz ZNB 20 is setup with 4 channels each containing one trace and reprecenting the 4 standars S parameters (S11, S12, S21 and S22). For each S parameter you can define a frequency sweep as and the power of the rf source i.e for s11 sweep from 100 KHz to 6 MHz in 100 steps:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 5,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "vna.start11(100e3)\n",
+    "vna.stop11(6e6)\n",
+    "vna.npts11(100)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "With a power of -30 dBm"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 6,
+   "metadata": {
+    "collapsed": true
+   },
+   "outputs": [],
+   "source": [
+    "vna.power11(-30)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Now we can meassure a frequency trace, first remembering to turn on the rf source."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 7,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "DataSet:\n",
+      "   location = 'data/2017-05-24/#009_{name}_09-59-42'\n",
+      "      |         |   | \n",
+      "   Setpoint | frequency_set     | frequency     | (100,)\n",
+      "   Measured | VNA_s11_magnitude | s11_magnitude | (100,)\n",
+      "   Measured | VNA_s11_phase     | s11_phase     | (100,)\n",
+      "acquired at 2017-05-24 09:59:43\n"
+     ]
+    }
+   ],
+   "source": [
+    "vna.rf_on()\n",
+    "data = qc.Measure(vna.trace11).run()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 8,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "application/javascript": [
+       "/* Put everything inside the global mpl namespace */\n",
+       "window.mpl = {};\n",
+       "\n",
+       "\n",
+       "mpl.get_websocket_type = function() {\n",
+       "    if (typeof(WebSocket) !== 'undefined') {\n",
+       "        return WebSocket;\n",
+       "    } else if (typeof(MozWebSocket) !== 'undefined') {\n",
+       "        return MozWebSocket;\n",
+       "    } else {\n",
+       "        alert('Your browser does not have WebSocket support.' +\n",
+       "              'Please try Chrome, Safari or Firefox ≥ 6. ' +\n",
+       "              'Firefox 4 and 5 are also supported but you ' +\n",
+       "              'have to enable WebSockets in about:config.');\n",
+       "    };\n",
+       "}\n",
+       "\n",
+       "mpl.figure = function(figure_id, websocket, ondownload, parent_element) {\n",
+       "    this.id = figure_id;\n",
+       "\n",
+       "    this.ws = websocket;\n",
+       "\n",
+       "    this.supports_binary = (this.ws.binaryType != undefined);\n",
+       "\n",
+       "    if (!this.supports_binary) {\n",
+       "        var warnings = document.getElementById(\"mpl-warnings\");\n",
+       "        if (warnings) {\n",
+       "            warnings.style.display = 'block';\n",
+       "            warnings.textContent = (\n",
+       "                \"This browser does not support binary websocket messages. \" +\n",
+       "                    \"Performance may be slow.\");\n",
+       "        }\n",
+       "    }\n",
+       "\n",
+       "    this.imageObj = new Image();\n",
+       "\n",
+       "    this.context = undefined;\n",
+       "    this.message = undefined;\n",
+       "    this.canvas = undefined;\n",
+       "    this.rubberband_canvas = undefined;\n",
+       "    this.rubberband_context = undefined;\n",
+       "    this.format_dropdown = undefined;\n",
+       "\n",
+       "    this.image_mode = 'full';\n",
+       "\n",
+       "    this.root = $('
');\n",
+       "    this._root_extra_style(this.root)\n",
+       "    this.root.attr('style', 'display: inline-block');\n",
+       "\n",
+       "    $(parent_element).append(this.root);\n",
+       "\n",
+       "    this._init_header(this);\n",
+       "    this._init_canvas(this);\n",
+       "    this._init_toolbar(this);\n",
+       "\n",
+       "    var fig = this;\n",
+       "\n",
+       "    this.waiting = false;\n",
+       "\n",
+       "    this.ws.onopen =  function () {\n",
+       "            fig.send_message(\"supports_binary\", {value: fig.supports_binary});\n",
+       "            fig.send_message(\"send_image_mode\", {});\n",
+       "            if (mpl.ratio != 1) {\n",
+       "                fig.send_message(\"set_dpi_ratio\", {'dpi_ratio': mpl.ratio});\n",
+       "            }\n",
+       "            fig.send_message(\"refresh\", {});\n",
+       "        }\n",
+       "\n",
+       "    this.imageObj.onload = function() {\n",
+       "            if (fig.image_mode == 'full') {\n",
+       "                // Full images could contain transparency (where diff images\n",
+       "                // almost always do), so we need to clear the canvas so that\n",
+       "                // there is no ghosting.\n",
+       "                fig.context.clearRect(0, 0, fig.canvas.width, fig.canvas.height);\n",
+       "            }\n",
+       "            fig.context.drawImage(fig.imageObj, 0, 0);\n",
+       "        };\n",
+       "\n",
+       "    this.imageObj.onunload = function() {\n",
+       "        this.ws.close();\n",
+       "    }\n",
+       "\n",
+       "    this.ws.onmessage = this._make_on_message_function(this);\n",
+       "\n",
+       "    this.ondownload = ondownload;\n",
+       "}\n",
+       "\n",
+       "mpl.figure.prototype._init_header = function() {\n",
+       "    var titlebar = $(\n",
+       "        '
');\n",
+       "    var titletext = $(\n",
+       "        '
');\n",
+       "    titlebar.append(titletext)\n",
+       "    this.root.append(titlebar);\n",
+       "    this.header = titletext[0];\n",
+       "}\n",
+       "\n",
+       "\n",
+       "\n",
+       "mpl.figure.prototype._canvas_extra_style = function(canvas_div) {\n",
+       "\n",
+       "}\n",
+       "\n",
+       "\n",
+       "mpl.figure.prototype._root_extra_style = function(canvas_div) {\n",
+       "\n",
+       "}\n",
+       "\n",
+       "mpl.figure.prototype._init_canvas = function() {\n",
+       "    var fig = this;\n",
+       "\n",
+       "    var canvas_div = $('
');\n",
+       "\n",
+       "    canvas_div.attr('style', 'position: relative; clear: both; outline: 0');\n",
+       "\n",
+       "    function canvas_keyboard_event(event) {\n",
+       "        return fig.key_event(event, event['data']);\n",
+       "    }\n",
+       "\n",
+       "    canvas_div.keydown('key_press', canvas_keyboard_event);\n",
+       "    canvas_div.keyup('key_release', canvas_keyboard_event);\n",
+       "    this.canvas_div = canvas_div\n",
+       "    this._canvas_extra_style(canvas_div)\n",
+       "    this.root.append(canvas_div);\n",
+       "\n",
+       "    var canvas = $('');\n",
+       "    canvas.addClass('mpl-canvas');\n",
+       "    canvas.attr('style', \"left: 0; top: 0; z-index: 0; outline: 0\")\n",
+       "\n",
+       "    this.canvas = canvas[0];\n",
+       "    this.context = canvas[0].getContext(\"2d\");\n",
+       "\n",
+       "    var backingStore = this.context.backingStorePixelRatio ||\n",
+       "\tthis.context.webkitBackingStorePixelRatio ||\n",
+       "\tthis.context.mozBackingStorePixelRatio ||\n",
+       "\tthis.context.msBackingStorePixelRatio ||\n",
+       "\tthis.context.oBackingStorePixelRatio ||\n",
+       "\tthis.context.backingStorePixelRatio || 1;\n",
+       "\n",
+       "    mpl.ratio = (window.devicePixelRatio || 1) / backingStore;\n",
+       "\n",
+       "    var rubberband = $('');\n",
+       "    rubberband.attr('style', \"position: absolute; left: 0; top: 0; z-index: 1;\")\n",
+       "\n",
+       "    var pass_mouse_events = true;\n",
+       "\n",
+       "    canvas_div.resizable({\n",
+       "        start: function(event, ui) {\n",
+       "            pass_mouse_events = false;\n",
+       "        },\n",
+       "        resize: function(event, ui) {\n",
+       "            fig.request_resize(ui.size.width, ui.size.height);\n",
+       "        },\n",
+       "        stop: function(event, ui) {\n",
+       "            pass_mouse_events = true;\n",
+       "            fig.request_resize(ui.size.width, ui.size.height);\n",
+       "        },\n",
+       "    });\n",
+       "\n",
+       "    function mouse_event_fn(event) {\n",
+       "        if (pass_mouse_events)\n",
+       "            return fig.mouse_event(event, event['data']);\n",
+       "    }\n",
+       "\n",
+       "    rubberband.mousedown('button_press', mouse_event_fn);\n",
+       "    rubberband.mouseup('button_release', mouse_event_fn);\n",
+       "    // Throttle sequential mouse events to 1 every 20ms.\n",
+       "    rubberband.mousemove('motion_notify', mouse_event_fn);\n",
+       "\n",
+       "    rubberband.mouseenter('figure_enter', mouse_event_fn);\n",
+       "    rubberband.mouseleave('figure_leave', mouse_event_fn);\n",
+       "\n",
+       "    canvas_div.on(\"wheel\", function (event) {\n",
+       "        event = event.originalEvent;\n",
+       "        event['data'] = 'scroll'\n",
+       "        if (event.deltaY < 0) {\n",
+       "            event.step = 1;\n",
+       "        } else {\n",
+       "            event.step = -1;\n",
+       "        }\n",
+       "        mouse_event_fn(event);\n",
+       "    });\n",
+       "\n",
+       "    canvas_div.append(canvas);\n",
+       "    canvas_div.append(rubberband);\n",
+       "\n",
+       "    this.rubberband = rubberband;\n",
+       "    this.rubberband_canvas = rubberband[0];\n",
+       "    this.rubberband_context = rubberband[0].getContext(\"2d\");\n",
+       "    this.rubberband_context.strokeStyle = \"#000000\";\n",
+       "\n",
+       "    this._resize_canvas = function(width, height) {\n",
+       "        // Keep the size of the canvas, canvas container, and rubber band\n",
+       "        // canvas in synch.\n",
+       "        canvas_div.css('width', width)\n",
+       "        canvas_div.css('height', height)\n",
+       "\n",
+       "        canvas.attr('width', width * mpl.ratio);\n",
+       "        canvas.attr('height', height * mpl.ratio);\n",
+       "        canvas.attr('style', 'width: ' + width + 'px; height: ' + height + 'px;');\n",
+       "\n",
+       "        rubberband.attr('width', width);\n",
+       "        rubberband.attr('height', height);\n",
+       "    }\n",
+       "\n",
+       "    // Set the figure to an initial 600x600px, this will subsequently be updated\n",
+       "    // upon first draw.\n",
+       "    this._resize_canvas(600, 600);\n",
+       "\n",
+       "    // Disable right mouse context menu.\n",
+       "    $(this.rubberband_canvas).bind(\"contextmenu\",function(e){\n",
+       "        return false;\n",
+       "    });\n",
+       "\n",
+       "    function set_focus () {\n",
+       "        canvas.focus();\n",
+       "        canvas_div.focus();\n",
+       "    }\n",
+       "\n",
+       "    window.setTimeout(set_focus, 100);\n",
+       "}\n",
+       "\n",
+       "mpl.figure.prototype._init_toolbar = function() {\n",
+       "    var fig = this;\n",
+       "\n",
+       "    var nav_element = $('
')\n",
+       "    nav_element.attr('style', 'width: 100%');\n",
+       "    this.root.append(nav_element);\n",
+       "\n",
+       "    // Define a callback function for later on.\n",
+       "    function toolbar_event(event) {\n",
+       "        return fig.toolbar_button_onclick(event['data']);\n",
+       "    }\n",
+       "    function toolbar_mouse_event(event) {\n",
+       "        return fig.toolbar_button_onmouseover(event['data']);\n",
+       "    }\n",
+       "\n",
+       "    for(var toolbar_ind in mpl.toolbar_items) {\n",
+       "        var name = mpl.toolbar_items[toolbar_ind][0];\n",
+       "        var tooltip = mpl.toolbar_items[toolbar_ind][1];\n",
+       "        var image = mpl.toolbar_items[toolbar_ind][2];\n",
+       "        var method_name = mpl.toolbar_items[toolbar_ind][3];\n",
+       "\n",
+       "        if (!name) {\n",
+       "            // put a spacer in here.\n",
+       "            continue;\n",
+       "        }\n",
+       "        var button = $('');\n",
+       "        button.click(method_name, toolbar_event);\n",
+       "        button.mouseover(tooltip, toolbar_mouse_event);\n",
+       "        nav_element.append(button);\n",
+       "    }\n",
+       "\n",
+       "    // Add the status bar.\n",
+       "    var status_bar = $('');\n",
+       "    nav_element.append(status_bar);\n",
+       "    this.message = status_bar[0];\n",
+       "\n",
+       "    // Add the close button to the window.\n",
+       "    var buttongrp = $('
');\n",
+       "    var button = $('');\n",
+       "    button.click(function (evt) { fig.handle_close(fig, {}); } );\n",
+       "    button.mouseover('Stop Interaction', toolbar_mouse_event);\n",
+       "    buttongrp.append(button);\n",
+       "    var titlebar = this.root.find($('.ui-dialog-titlebar'));\n",
+       "    titlebar.prepend(buttongrp);\n",
+       "}\n",
+       "\n",
+       "mpl.figure.prototype._root_extra_style = function(el){\n",
+       "    var fig = this\n",
+       "    el.on(\"remove\", function(){\n",
+       "\tfig.close_ws(fig, {});\n",
+       "    });\n",
+       "}\n",
+       "\n",
+       "mpl.figure.prototype._canvas_extra_style = function(el){\n",
+       "    // this is important to make the div 'focusable\n",
+       "    el.attr('tabindex', 0)\n",
+       "    // reach out to IPython and tell the keyboard manager to turn it's self\n",
+       "    // off when our div gets focus\n",
+       "\n",
+       "    // location in version 3\n",
+       "    if (IPython.notebook.keyboard_manager) {\n",
+       "        IPython.notebook.keyboard_manager.register_events(el);\n",
+       "    }\n",
+       "    else {\n",
+       "        // location in version 2\n",
+       "        IPython.keyboard_manager.register_events(el);\n",
+       "    }\n",
+       "\n",
+       "}\n",
+       "\n",
+       "mpl.figure.prototype._key_event_extra = function(event, name) {\n",
+       "    var manager = IPython.notebook.keyboard_manager;\n",
+       "    if (!manager)\n",
+       "        manager = IPython.keyboard_manager;\n",
+       "\n",
+       "    // Check for shift+enter\n",
+       "    if (event.shiftKey && event.which == 13) {\n",
+       "        this.canvas_div.blur();\n",
+       "        // select the cell after this one\n",
+       "        var index = IPython.notebook.find_cell_index(this.cell_info[0]);\n",
+       "        IPython.notebook.select(index + 1);\n",
+       "    }\n",
+       "}\n",
+       "\n",
+       "mpl.figure.prototype.handle_save = function(fig, msg) {\n",
+       "    fig.ondownload(fig, null);\n",
+       "}\n",
+       "\n",
+       "\n",
+       "mpl.find_output_cell = function(html_output) {\n",
+       "    // Return the cell and output element which can be found *uniquely* in the notebook.\n",
+       "    // Note - this is a bit hacky, but it is done because the \"notebook_saving.Notebook\"\n",
+       "    // IPython event is triggered only after the cells have been serialised, which for\n",
+       "    // our purposes (turning an active figure into a static one), is too late.\n",
+       "    var cells = IPython.notebook.get_cells();\n",
+       "    var ncells = cells.length;\n",
+       "    for (var i=0; i= 3 moved mimebundle to data attribute of output\n",
+       "                    data = data.data;\n",
+       "                }\n",
+       "                if (data['text/html'] == html_output) {\n",
+       "                    return [cell, data, j];\n",
+       "                }\n",
+       "            }\n",
+       "        }\n",
+       "    }\n",
+       "}\n",
+       "\n",
+       "// Register the function which deals with the matplotlib target/channel.\n",
+       "// The kernel may be null if the page has been refreshed.\n",
+       "if (IPython.notebook.kernel != null) {\n",
+       "    IPython.notebook.kernel.comm_manager.register_target('matplotlib', mpl.mpl_figure_comm);\n",
+       "}\n"
+      ],
+      "text/plain": [
+       ""
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "text/html": [
+       ""
+      ],
+      "text/plain": [
+       ""
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "data = qc.Measure(vna.trace11).run()\n",
+    "plot = qc.MatPlot(subplots=(1,2))\n",
+    "plot.add(data.VNA_s11_magnitude, subplot=1)\n",
+    "plot.add(data.VNA_s11_phase, subplot=2)\n",
+    "plot.fig.tight_layout(rect=(0, 0, 1, 0.95))"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "We can display all 4 S parameters in a split view on the VNA display."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 11,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "vna.display_sij_split()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Or we can display all 4 parameters in one view."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 12,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "vna.display_sij_overlay()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "We can autoscale the scale the y axis"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 13,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "vna.autoscale_all()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "It is possible to switch the display update on and off"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 14,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "vna.update_display_on()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 15,
+   "metadata": {
+    "collapsed": true
+   },
+   "outputs": [],
+   "source": [
+    "vna.update_display_off()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "And switch the rf output on and off"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 16,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "vna.rf_on()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 17,
+   "metadata": {
+    "collapsed": true
+   },
+   "outputs": [],
+   "source": [
+    "vna.rf_off()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Doing a 2D sweep is supported too"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 18,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Started at 2017-05-24 09:59:47\n",
+      "DataSet:\n",
+      "   location = 'data/2017-05-24/#011_{name}_09-59-47'\n",
+      "      |         |   | \n",
+      "   Setpoint | VNA_power11_set   | power11       | (15,)\n",
+      "   Setpoint | frequency_set     | frequency     | (15, 100)\n",
+      "   Measured | VNA_s11_magnitude | s11_magnitude | (15, 100)\n",
+      "   Measured | VNA_s11_phase     | s11_phase     | (15, 100)\n",
+      "Finished at 2017-05-24 09:59:49\n"
+     ]
+    }
+   ],
+   "source": [
+    "vna.rf_on()\n",
+    "data1 = qc.Loop(vna.power11.sweep(-15,-1,1)).each(vna.trace11).run()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 19,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "application/javascript": [
+       "/* Put everything inside the global mpl namespace */\n",
+       "window.mpl = {};\n",
+       "\n",
+       "\n",
+       "mpl.get_websocket_type = function() {\n",
+       "    if (typeof(WebSocket) !== 'undefined') {\n",
+       "        return WebSocket;\n",
+       "    } else if (typeof(MozWebSocket) !== 'undefined') {\n",
+       "        return MozWebSocket;\n",
+       "    } else {\n",
+       "        alert('Your browser does not have WebSocket support.' +\n",
+       "              'Please try Chrome, Safari or Firefox ≥ 6. ' +\n",
+       "              'Firefox 4 and 5 are also supported but you ' +\n",
+       "              'have to enable WebSockets in about:config.');\n",
+       "    };\n",
+       "}\n",
+       "\n",
+       "mpl.figure = function(figure_id, websocket, ondownload, parent_element) {\n",
+       "    this.id = figure_id;\n",
+       "\n",
+       "    this.ws = websocket;\n",
+       "\n",
+       "    this.supports_binary = (this.ws.binaryType != undefined);\n",
+       "\n",
+       "    if (!this.supports_binary) {\n",
+       "        var warnings = document.getElementById(\"mpl-warnings\");\n",
+       "        if (warnings) {\n",
+       "            warnings.style.display = 'block';\n",
+       "            warnings.textContent = (\n",
+       "                \"This browser does not support binary websocket messages. \" +\n",
+       "                    \"Performance may be slow.\");\n",
+       "        }\n",
+       "    }\n",
+       "\n",
+       "    this.imageObj = new Image();\n",
+       "\n",
+       "    this.context = undefined;\n",
+       "    this.message = undefined;\n",
+       "    this.canvas = undefined;\n",
+       "    this.rubberband_canvas = undefined;\n",
+       "    this.rubberband_context = undefined;\n",
+       "    this.format_dropdown = undefined;\n",
+       "\n",
+       "    this.image_mode = 'full';\n",
+       "\n",
+       "    this.root = $('
');\n",
+       "    this._root_extra_style(this.root)\n",
+       "    this.root.attr('style', 'display: inline-block');\n",
+       "\n",
+       "    $(parent_element).append(this.root);\n",
+       "\n",
+       "    this._init_header(this);\n",
+       "    this._init_canvas(this);\n",
+       "    this._init_toolbar(this);\n",
+       "\n",
+       "    var fig = this;\n",
+       "\n",
+       "    this.waiting = false;\n",
+       "\n",
+       "    this.ws.onopen =  function () {\n",
+       "            fig.send_message(\"supports_binary\", {value: fig.supports_binary});\n",
+       "            fig.send_message(\"send_image_mode\", {});\n",
+       "            if (mpl.ratio != 1) {\n",
+       "                fig.send_message(\"set_dpi_ratio\", {'dpi_ratio': mpl.ratio});\n",
+       "            }\n",
+       "            fig.send_message(\"refresh\", {});\n",
+       "        }\n",
+       "\n",
+       "    this.imageObj.onload = function() {\n",
+       "            if (fig.image_mode == 'full') {\n",
+       "                // Full images could contain transparency (where diff images\n",
+       "                // almost always do), so we need to clear the canvas so that\n",
+       "                // there is no ghosting.\n",
+       "                fig.context.clearRect(0, 0, fig.canvas.width, fig.canvas.height);\n",
+       "            }\n",
+       "            fig.context.drawImage(fig.imageObj, 0, 0);\n",
+       "        };\n",
+       "\n",
+       "    this.imageObj.onunload = function() {\n",
+       "        this.ws.close();\n",
+       "    }\n",
+       "\n",
+       "    this.ws.onmessage = this._make_on_message_function(this);\n",
+       "\n",
+       "    this.ondownload = ondownload;\n",
+       "}\n",
+       "\n",
+       "mpl.figure.prototype._init_header = function() {\n",
+       "    var titlebar = $(\n",
+       "        '
');\n",
+       "    var titletext = $(\n",
+       "        '
');\n",
+       "    titlebar.append(titletext)\n",
+       "    this.root.append(titlebar);\n",
+       "    this.header = titletext[0];\n",
+       "}\n",
+       "\n",
+       "\n",
+       "\n",
+       "mpl.figure.prototype._canvas_extra_style = function(canvas_div) {\n",
+       "\n",
+       "}\n",
+       "\n",
+       "\n",
+       "mpl.figure.prototype._root_extra_style = function(canvas_div) {\n",
+       "\n",
+       "}\n",
+       "\n",
+       "mpl.figure.prototype._init_canvas = function() {\n",
+       "    var fig = this;\n",
+       "\n",
+       "    var canvas_div = $('
');\n",
+       "\n",
+       "    canvas_div.attr('style', 'position: relative; clear: both; outline: 0');\n",
+       "\n",
+       "    function canvas_keyboard_event(event) {\n",
+       "        return fig.key_event(event, event['data']);\n",
+       "    }\n",
+       "\n",
+       "    canvas_div.keydown('key_press', canvas_keyboard_event);\n",
+       "    canvas_div.keyup('key_release', canvas_keyboard_event);\n",
+       "    this.canvas_div = canvas_div\n",
+       "    this._canvas_extra_style(canvas_div)\n",
+       "    this.root.append(canvas_div);\n",
+       "\n",
+       "    var canvas = $('');\n",
+       "    canvas.addClass('mpl-canvas');\n",
+       "    canvas.attr('style', \"left: 0; top: 0; z-index: 0; outline: 0\")\n",
+       "\n",
+       "    this.canvas = canvas[0];\n",
+       "    this.context = canvas[0].getContext(\"2d\");\n",
+       "\n",
+       "    var backingStore = this.context.backingStorePixelRatio ||\n",
+       "\tthis.context.webkitBackingStorePixelRatio ||\n",
+       "\tthis.context.mozBackingStorePixelRatio ||\n",
+       "\tthis.context.msBackingStorePixelRatio ||\n",
+       "\tthis.context.oBackingStorePixelRatio ||\n",
+       "\tthis.context.backingStorePixelRatio || 1;\n",
+       "\n",
+       "    mpl.ratio = (window.devicePixelRatio || 1) / backingStore;\n",
+       "\n",
+       "    var rubberband = $('');\n",
+       "    rubberband.attr('style', \"position: absolute; left: 0; top: 0; z-index: 1;\")\n",
+       "\n",
+       "    var pass_mouse_events = true;\n",
+       "\n",
+       "    canvas_div.resizable({\n",
+       "        start: function(event, ui) {\n",
+       "            pass_mouse_events = false;\n",
+       "        },\n",
+       "        resize: function(event, ui) {\n",
+       "            fig.request_resize(ui.size.width, ui.size.height);\n",
+       "        },\n",
+       "        stop: function(event, ui) {\n",
+       "            pass_mouse_events = true;\n",
+       "            fig.request_resize(ui.size.width, ui.size.height);\n",
+       "        },\n",
+       "    });\n",
+       "\n",
+       "    function mouse_event_fn(event) {\n",
+       "        if (pass_mouse_events)\n",
+       "            return fig.mouse_event(event, event['data']);\n",
+       "    }\n",
+       "\n",
+       "    rubberband.mousedown('button_press', mouse_event_fn);\n",
+       "    rubberband.mouseup('button_release', mouse_event_fn);\n",
+       "    // Throttle sequential mouse events to 1 every 20ms.\n",
+       "    rubberband.mousemove('motion_notify', mouse_event_fn);\n",
+       "\n",
+       "    rubberband.mouseenter('figure_enter', mouse_event_fn);\n",
+       "    rubberband.mouseleave('figure_leave', mouse_event_fn);\n",
+       "\n",
+       "    canvas_div.on(\"wheel\", function (event) {\n",
+       "        event = event.originalEvent;\n",
+       "        event['data'] = 'scroll'\n",
+       "        if (event.deltaY < 0) {\n",
+       "            event.step = 1;\n",
+       "        } else {\n",
+       "            event.step = -1;\n",
+       "        }\n",
+       "        mouse_event_fn(event);\n",
+       "    });\n",
+       "\n",
+       "    canvas_div.append(canvas);\n",
+       "    canvas_div.append(rubberband);\n",
+       "\n",
+       "    this.rubberband = rubberband;\n",
+       "    this.rubberband_canvas = rubberband[0];\n",
+       "    this.rubberband_context = rubberband[0].getContext(\"2d\");\n",
+       "    this.rubberband_context.strokeStyle = \"#000000\";\n",
+       "\n",
+       "    this._resize_canvas = function(width, height) {\n",
+       "        // Keep the size of the canvas, canvas container, and rubber band\n",
+       "        // canvas in synch.\n",
+       "        canvas_div.css('width', width)\n",
+       "        canvas_div.css('height', height)\n",
+       "\n",
+       "        canvas.attr('width', width * mpl.ratio);\n",
+       "        canvas.attr('height', height * mpl.ratio);\n",
+       "        canvas.attr('style', 'width: ' + width + 'px; height: ' + height + 'px;');\n",
+       "\n",
+       "        rubberband.attr('width', width);\n",
+       "        rubberband.attr('height', height);\n",
+       "    }\n",
+       "\n",
+       "    // Set the figure to an initial 600x600px, this will subsequently be updated\n",
+       "    // upon first draw.\n",
+       "    this._resize_canvas(600, 600);\n",
+       "\n",
+       "    // Disable right mouse context menu.\n",
+       "    $(this.rubberband_canvas).bind(\"contextmenu\",function(e){\n",
+       "        return false;\n",
+       "    });\n",
+       "\n",
+       "    function set_focus () {\n",
+       "        canvas.focus();\n",
+       "        canvas_div.focus();\n",
+       "    }\n",
+       "\n",
+       "    window.setTimeout(set_focus, 100);\n",
+       "}\n",
+       "\n",
+       "mpl.figure.prototype._init_toolbar = function() {\n",
+       "    var fig = this;\n",
+       "\n",
+       "    var nav_element = $('
')\n",
+       "    nav_element.attr('style', 'width: 100%');\n",
+       "    this.root.append(nav_element);\n",
+       "\n",
+       "    // Define a callback function for later on.\n",
+       "    function toolbar_event(event) {\n",
+       "        return fig.toolbar_button_onclick(event['data']);\n",
+       "    }\n",
+       "    function toolbar_mouse_event(event) {\n",
+       "        return fig.toolbar_button_onmouseover(event['data']);\n",
+       "    }\n",
+       "\n",
+       "    for(var toolbar_ind in mpl.toolbar_items) {\n",
+       "        var name = mpl.toolbar_items[toolbar_ind][0];\n",
+       "        var tooltip = mpl.toolbar_items[toolbar_ind][1];\n",
+       "        var image = mpl.toolbar_items[toolbar_ind][2];\n",
+       "        var method_name = mpl.toolbar_items[toolbar_ind][3];\n",
+       "\n",
+       "        if (!name) {\n",
+       "            // put a spacer in here.\n",
+       "            continue;\n",
+       "        }\n",
+       "        var button = $('');\n",
+       "        button.click(method_name, toolbar_event);\n",
+       "        button.mouseover(tooltip, toolbar_mouse_event);\n",
+       "        nav_element.append(button);\n",
+       "    }\n",
+       "\n",
+       "    // Add the status bar.\n",
+       "    var status_bar = $('');\n",
+       "    nav_element.append(status_bar);\n",
+       "    this.message = status_bar[0];\n",
+       "\n",
+       "    // Add the close button to the window.\n",
+       "    var buttongrp = $('
');\n",
+       "    var button = $('');\n",
+       "    button.click(function (evt) { fig.handle_close(fig, {}); } );\n",
+       "    button.mouseover('Stop Interaction', toolbar_mouse_event);\n",
+       "    buttongrp.append(button);\n",
+       "    var titlebar = this.root.find($('.ui-dialog-titlebar'));\n",
+       "    titlebar.prepend(buttongrp);\n",
+       "}\n",
+       "\n",
+       "mpl.figure.prototype._root_extra_style = function(el){\n",
+       "    var fig = this\n",
+       "    el.on(\"remove\", function(){\n",
+       "\tfig.close_ws(fig, {});\n",
+       "    });\n",
+       "}\n",
+       "\n",
+       "mpl.figure.prototype._canvas_extra_style = function(el){\n",
+       "    // this is important to make the div 'focusable\n",
+       "    el.attr('tabindex', 0)\n",
+       "    // reach out to IPython and tell the keyboard manager to turn it's self\n",
+       "    // off when our div gets focus\n",
+       "\n",
+       "    // location in version 3\n",
+       "    if (IPython.notebook.keyboard_manager) {\n",
+       "        IPython.notebook.keyboard_manager.register_events(el);\n",
+       "    }\n",
+       "    else {\n",
+       "        // location in version 2\n",
+       "        IPython.keyboard_manager.register_events(el);\n",
+       "    }\n",
+       "\n",
+       "}\n",
+       "\n",
+       "mpl.figure.prototype._key_event_extra = function(event, name) {\n",
+       "    var manager = IPython.notebook.keyboard_manager;\n",
+       "    if (!manager)\n",
+       "        manager = IPython.keyboard_manager;\n",
+       "\n",
+       "    // Check for shift+enter\n",
+       "    if (event.shiftKey && event.which == 13) {\n",
+       "        this.canvas_div.blur();\n",
+       "        // select the cell after this one\n",
+       "        var index = IPython.notebook.find_cell_index(this.cell_info[0]);\n",
+       "        IPython.notebook.select(index + 1);\n",
+       "    }\n",
+       "}\n",
+       "\n",
+       "mpl.figure.prototype.handle_save = function(fig, msg) {\n",
+       "    fig.ondownload(fig, null);\n",
+       "}\n",
+       "\n",
+       "\n",
+       "mpl.find_output_cell = function(html_output) {\n",
+       "    // Return the cell and output element which can be found *uniquely* in the notebook.\n",
+       "    // Note - this is a bit hacky, but it is done because the \"notebook_saving.Notebook\"\n",
+       "    // IPython event is triggered only after the cells have been serialised, which for\n",
+       "    // our purposes (turning an active figure into a static one), is too late.\n",
+       "    var cells = IPython.notebook.get_cells();\n",
+       "    var ncells = cells.length;\n",
+       "    for (var i=0; i= 3 moved mimebundle to data attribute of output\n",
+       "                    data = data.data;\n",
+       "                }\n",
+       "                if (data['text/html'] == html_output) {\n",
+       "                    return [cell, data, j];\n",
+       "                }\n",
+       "            }\n",
+       "        }\n",
+       "    }\n",
+       "}\n",
+       "\n",
+       "// Register the function which deals with the matplotlib target/channel.\n",
+       "// The kernel may be null if the page has been refreshed.\n",
+       "if (IPython.notebook.kernel != null) {\n",
+       "    IPython.notebook.kernel.comm_manager.register_target('matplotlib', mpl.mpl_figure_comm);\n",
+       "}\n"
+      ],
+      "text/plain": [
+       ""
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "text/html": [
+       ""
+      ],
+      "text/plain": [
+       ""
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "data = qc.Measure(vna.trace11).run()\n",
+    "plot = qc.MatPlot(subplots=(1,2))\n",
+    "plot.add(data.VNA_s11_magnitude, subplot=1)\n",
+    "plot.add(data.VNA_s11_phase, subplot=2)\n",
+    "plot.fig.tight_layout(rect=(0, 0, 1, 0.95))"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "We can also measure the magniture in dB."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 11,
    "metadata": {},
    "outputs": [
     {
@@ -955,12 +1780,10 @@
      "output_type": "stream",
      "text": [
       "DataSet:\n",
-      "   location = 'data/2017-05-24/#010_{name}_09-59-44'\n",
-      "      |         |   | \n",
-      "   Setpoint | frequency_set     | frequency     | (100,)\n",
-      "   Measured | VNA_s11_magnitude | s11_magnitude | (100,)\n",
-      "   Measured | VNA_s11_phase     | s11_phase     | (100,)\n",
-      "acquired at 2017-05-24 09:59:44\n"
+      "   location = 'data/2017-05-24/#049_{name}_14-35-58'\n",
+      "      |     |  | \n",
+      "   Measured | VNA_tracedb11 | tracedb11    | (100,)\n",
+      "acquired at 2017-05-24 14:35:58\n"
      ]
     },
     {
@@ -1743,7 +2566,7 @@
     {
      "data": {
       "text/html": [
-       ""
+       ""
       ],
       "text/plain": [
        ""
@@ -1754,11 +2577,9 @@
     }
    ],
    "source": [
-    "data = qc.Measure(vna.trace11).run()\n",
-    "plot = qc.MatPlot(subplots=(1,2))\n",
-    "plot.add(data.VNA_s11_magnitude, subplot=1)\n",
-    "plot.add(data.VNA_s11_phase, subplot=2)\n",
-    "plot.fig.tight_layout(rect=(0, 0, 1, 0.95))"
+    "vna.rf_on()\n",
+    "data = qc.Measure(vna.tracedb11).run()\n",
+    "plot = qc.MatPlot(data.VNA_tracedb11)"
    ]
   },
   {
@@ -1770,7 +2591,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 11,
+   "execution_count": 12,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -1786,7 +2607,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 12,
+   "execution_count": 13,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -1802,7 +2623,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 13,
+   "execution_count": 14,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -1818,7 +2639,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 14,
+   "execution_count": 15,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -1827,7 +2648,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 15,
+   "execution_count": 16,
    "metadata": {
     "collapsed": true
    },
@@ -1845,7 +2666,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 16,
+   "execution_count": 17,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -1854,7 +2675,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 17,
+   "execution_count": 18,
    "metadata": {
     "collapsed": true
    },
@@ -1872,22 +2693,22 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 18,
+   "execution_count": 19,
    "metadata": {},
    "outputs": [
     {
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "Started at 2017-05-24 09:59:47\n",
+      "Started at 2017-05-24 14:36:07\n",
       "DataSet:\n",
-      "   location = 'data/2017-05-24/#011_{name}_09-59-47'\n",
+      "   location = 'data/2017-05-24/#050_{name}_14-36-07'\n",
       "      |         |   | \n",
       "   Setpoint | VNA_power11_set   | power11       | (15,)\n",
       "   Setpoint | frequency_set     | frequency     | (15, 100)\n",
       "   Measured | VNA_s11_magnitude | s11_magnitude | (15, 100)\n",
       "   Measured | VNA_s11_phase     | s11_phase     | (15, 100)\n",
-      "Finished at 2017-05-24 09:59:49\n"
+      "Finished at 2017-05-24 14:36:10\n"
      ]
     }
    ],
@@ -1898,7 +2719,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 19,
+   "execution_count": 20,
    "metadata": {},
    "outputs": [
     {
@@ -2681,7 +3502,7 @@
     {
      "data": {
       "text/html": [
-       ""
+       ""
       ],
       "text/plain": [
        ""
@@ -2698,15 +3519,6 @@
     "plot.fig.tight_layout(rect=(0, 0, 1, 0.95))"
    ]
   },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {
-    "collapsed": true
-   },
-   "outputs": [],
-   "source": []
-  },
   {
    "cell_type": "code",
    "execution_count": null,

From b309c5f6dc847948cc750f98aebd58f769597f01 Mon Sep 17 00:00:00 2001
From: giulio ungaretti 
Date: Mon, 16 Jan 2017 11:27:16 +0100
Subject: [PATCH 245/328] feature: Add wrappers

this covers some of the request made years ago.
Because of the design of many pieces of qcodes the code
is really hacky and anti-cool.
---
 qcodes/__init__.py       |   1 +
 qcodes/data/io.py        |   1 -
 qcodes/data/location.py  |   6 +-
 qcodes/utils/wrappers.py | 235 +++++++++++++++++++++++++++++++++++++++
 4 files changed, 240 insertions(+), 3 deletions(-)
 create mode 100644 qcodes/utils/wrappers.py

diff --git a/qcodes/__init__.py b/qcodes/__init__.py
index 542d2c103597..c08b890547eb 100644
--- a/qcodes/__init__.py
+++ b/qcodes/__init__.py
@@ -27,6 +27,7 @@
               'try "from qcodes.plots.qcmatplotlib import MatPlot" '
               'to see the full error')
 
+from qcodes.utils.wrappers import  do1d, do2d, do1dDiagonal, show_num, init
 
 from qcodes.station import Station
 from qcodes.loops import Loop, active_loop, active_data_set
diff --git a/qcodes/data/io.py b/qcodes/data/io.py
index 4b7a82c8588b..4b2fd45381c3 100644
--- a/qcodes/data/io.py
+++ b/qcodes/data/io.py
@@ -91,7 +91,6 @@ def open(self, filename, mode, encoding=None):
             raise ValueError('mode {} not allowed in IO managers'.format(mode))
 
         filepath = self.to_path(filename)
-
         # make directories if needed
         dirpath = os.path.dirname(filepath)
         if not os.path.exists(dirpath):
diff --git a/qcodes/data/location.py b/qcodes/data/location.py
index 91af8594cf1d..b342b0bfbf0b 100644
--- a/qcodes/data/location.py
+++ b/qcodes/data/location.py
@@ -85,6 +85,7 @@ class FormatLocation:
 
     default_fmt = config['core']['default_fmt']
 
+
     def __init__(self, fmt=None, fmt_date=None, fmt_time=None,
                  fmt_counter=None, record=None):
         #TODO(giulioungaretti) this should be
@@ -95,7 +96,7 @@ def __init__(self, fmt=None, fmt_date=None, fmt_time=None,
         self.fmt_counter = fmt_counter or '{:03}'
         self.base_record = record
         self.formatter = SafeFormatter()
-
+        self.counter = 0
         for testval in (1, 23, 456, 7890):
             if self._findint(self.fmt_counter.format(testval)) != testval:
                 raise ValueError('fmt_counter must produce a correct integer '
@@ -163,7 +164,8 @@ def __call__(self, io, record=None):
             cnt = self._findint(f[len(head):])
             existing_count = max(existing_count, cnt)
 
-        format_record['counter'] = self.fmt_counter.format(existing_count + 1)
+        self.counter = existing_count +1
+        format_record['counter'] = self.fmt_counter.format(self.counter)
         location = self.formatter.format(loc_fmt, **format_record)
 
         return location
diff --git a/qcodes/utils/wrappers.py b/qcodes/utils/wrappers.py
new file mode 100644
index 000000000000..49e68501079f
--- /dev/null
+++ b/qcodes/utils/wrappers.py
@@ -0,0 +1,235 @@
+import qcodes as qc
+from os.path import abspath
+from os.path import sep
+from os import makedirs
+import logging
+from qcodes.plots.pyqtgraph import QtPlot
+from IPython import get_ipython
+CURRENT_EXPERIMENT  = {}
+
+def init(mainfolder:str, sample_name: str):
+    """
+
+    Args:
+        mainfolder:  base loacation for the data
+        sample_name:  name of the sample
+
+    """
+    if sep in sample_name:
+        raise  TypeError("Use Relative names. That is wihtout {}".format(sep))
+    # always remove trailing sep in the main folder
+    if mainfolder[-1] ==  sep:
+        mainfolder = mainfolder[:-1]
+
+    mainfolder = abspath(mainfolder)
+
+    CURRENT_EXPERIMENT["mainfolder"]  =  mainfolder
+    CURRENT_EXPERIMENT["subfolder"] = sample_name
+    CURRENT_EXPERIMENT['init']  = True
+
+    path_to_experiment_folder = sep.join([mainfolder, sample_name, ""])
+    CURRENT_EXPERIMENT["exp_folder"] = path_to_experiment_folder
+
+
+    try:
+        makedirs(path_to_experiment_folder)
+    except FileExistsError:
+        pass
+
+    logging.info("experiment started at {}".format(path_to_experiment_folder))
+
+
+    loc_provider = qc.FormatLocation(
+        fmt= path_to_experiment_folder + '{counter}')
+    qc.data.data_set.DataSet.location_provider = loc_provider
+    CURRENT_EXPERIMENT["provider"] = loc_provider
+
+    ipython = get_ipython()
+    # turn on logging only if in ipython
+    # else crash and burn
+    if ipython is None:
+        raise RuntimeWarning("History can't be saved refusing to proceed (use IPython/jupyter)")
+    else:
+        logfile = "{}{}".format(path_to_experiment_folder, "commands.log")
+        logging.debug("Logging commands to: t{}".format(logfile))
+        ipython.magic("%logstart -t {} {}".format(logfile, "append"))
+
+def do1d(inst_set, start, stop, division, delay, *inst_meas):
+    """
+
+    Args:
+        inst_set:  Instrument to sweep over
+        start:  Start of sweep
+        stop:  End of sweep
+        division:  Spacing between values
+        delay:  Delay at every step
+        *inst_meas:  any number of instrument to measure
+
+    Returns:
+        plot, data : returns the plot and the dataset
+
+    """
+    loop = qc.Loop(inst_set.sweep(start, stop, division), delay).each(*inst_meas)
+    data = loop.get_data_set()
+    title = "#{0:03d}".format(data.location_provider.counter)
+    plot = QtPlot()
+
+    for j, i in enumerate(inst_meas):
+        if getattr(i, "names", False):
+            # deal with multi dimenstional parameter
+            for k, name in enumerate(i.names):
+                inst_meas_name = "{}_{}".format(i._instrument.name, name)
+                plot.add(getattr(data, inst_meas_name), subplot=j + k + 1)
+                plot.subplots[j+k].showGrid(True, True)
+                if j == 0:
+                    plot.subplots[0].setTitle(title)
+                else:
+                    plot.subplots[j+k].setTitle("")
+        else:
+            # simple_parameters
+            inst_meas_name = "{}_{}".format(i._instrument.name, i.name)
+            plot.add(getattr(data, inst_meas_name), subplot=j + 1)
+            plot.subplots[j].showGrid(True, True)
+            if j == 0:
+                plot.subplots[0].setTitle(title)
+            else:
+                plot.subplots[j].setTitle("")
+    try:
+        _ = loop.with_bg_task(plot.update, plot.save).run()
+    except KeyboardInterrupt:
+        print("Measurement Interrupted")
+    return plot, data
+
+
+def do1dDiagonal(inst_set, inst2_set, start, stop, division, delay, start2, slope, *inst_meas):
+    """
+    Perform diagonal sweep in 1 dimension, given two insturments
+
+    Args:
+        inst_set:  Instrument to sweep over
+        inst2_set: Second instrument to sweep over
+        start:  Start of sweep
+        stop:  End of sweep
+        division:  Spacing between values
+        delay:  Delay at every step
+        start2:  Second start point
+        slope:  slope of the diagonal cut
+        *inst_meas:  any number of instrument to measure
+
+    Returns:
+        plot, data : returns the plot and the dataset
+
+    """
+    loop = qc.Loop(inst_set.sweep(start, stop, division), delay).each(
+        qc.Task(inst2_set, (inst_set) * slope + (slope * start - start2)), *inst_meas, inst2_set)
+    data = loop.get_data_set()
+    title = "#{0:03d}".format(data.location_provider.counter)
+    plot = QtPlot()
+    for j, i in enumerate(inst_meas):
+        if getattr(i, "names", False):
+            # deal with multi dimenstional parameter
+            for k, name in enumerate(i.names):
+                inst_meas_name = "{}_{}".format(i._instrument.name, name)
+                plot.add(getattr(data, inst_meas_name), subplot=j + k + 1)
+                plot.subplots[j+k].showGrid(True, True)
+                if j == 0:
+                    plot.subplots[0].setTitle(title)
+                else:
+                    plot.subplots[j+k].setTitle("")
+        else:
+            inst_meas_name = "{}_{}".format(i._instrument.name, i.name)
+            plot.add(getattr(data, inst_meas_name), subplot=j + 1 )
+            plot.subplots[j].showGrid(True, True)
+            if j == 0:
+                plot.subplots[0].setTitle(title)
+            else:
+                plot.subplots[j].setTitle("")
+    try:
+        _ = loop.with_bg_task(plot.update, plot.save).run()
+    except KeyboardInterrupt:
+        print("Measurement Interrupted")
+    return data
+
+
+def do2d(inst_set, start, stop, division, delay, inst_set2, start2, stop2, division2, delay2, *inst_meas):
+    """
+
+    Args:
+        inst_set:  Instrument to sweep over
+        start:  Start of sweep
+        stop:  End of sweep
+        division:  Spacing between values
+        delay:  Delay at every step
+        inst_set_2:  Second instrument to sweep over
+        start_2:  Start of sweep for second intrument
+        stop_2:  End of sweep for second intrument
+        division_2:  Spacing between values for second intrument
+        delay_2:  Delay at every step for second intrument
+        *inst_meas:
+
+    Returns:
+        plot, data : returns the plot and the dataset
+
+    """
+    for inst in inst_meas:
+        if getattr(inst, "setpoints", False):
+            raise ValueError("3d plotting is not supported")
+
+    loop = qc.Loop(inst_set.sweep(start, stop, division), delay).loop(inst_set2.sweep(start2,stop2,division2), delay2).each(
+        *inst_meas)
+    data = loop.get_data_set()
+    title = "#{0:03d}".format(data.location_provider.counter)
+    plot = QtPlot()
+    for j, i in enumerate(inst_meas):
+        if getattr(i, "names", False):
+            # deal with multi dimenstional parameter
+            for k, name in enumerate(i.names):
+                inst_meas_name = "{}_{}".format(i._instrument.name, name)
+                plot.add(getattr(data, inst_meas_name), subplot=j + k + 1)
+                plot.subplots[j+k].showGrid(True, True)
+                if j == 0:
+                    plot.subplots[0].setTitle(title)
+                else:
+                    plot.subplots[j+k].setTitle("")
+        else:
+            inst_meas_name = "{}_{}".format(i._instrument.name, i.name)
+            plot.add(getattr(data, inst_meas_name), subplot=j + 1)
+            plot.subplots[j].showGrid(True, True)
+            if j == 0:
+                plot.subplots[0].setTitle(title)
+            else:
+                plot.subplots[j].setTitle("")
+    try:
+        _ = loop.with_bg_task(plot.update, plot.save).run()
+    except KeyboardInterrupt:
+        print("Measurement Interrupted")
+    return plot, data
+
+
+def show_num(id):
+    """
+    Show  and return plot and data for id in current instrument.
+    Args:
+        id(number): id of intrumetn
+
+    Returns:
+        plot, data : returns the plot and the dataset
+
+    """
+    if not getattr(CURRENT_EXPERIMENT, "init", True):
+        raise RuntimeError("Experiment not initalized. use qc.Init(mainfolder, samplename)")
+
+    str_id = '{0:03d}'.format(id)
+
+    t = qc.DataSet.location_provider.fmt.format(counter=str_id)
+    data = qc.load_data(t)
+
+    plots = []
+    for value in data.arrays.keys():
+        if "set" not in value:
+            plot = QtPlot(getattr(data, value))
+            title = "#{}".format(str_id)
+            plot.subplots[0].setTitle(title)
+            plot.subplots[0].showGrid(True, True)
+            plots.append(plot)
+    return data, plots

From a11fc61de40ee3f95aad54544fa6403f672d8570 Mon Sep 17 00:00:00 2001
From: Giulio Ungaretti 
Date: Mon, 20 Feb 2017 16:16:07 +0100
Subject: [PATCH 246/328] Clean up wrappers

---
 qcodes/utils/wrappers.py | 98 ++++++++++++++--------------------------
 1 file changed, 33 insertions(+), 65 deletions(-)

diff --git a/qcodes/utils/wrappers.py b/qcodes/utils/wrappers.py
index 49e68501079f..3d33ac849396 100644
--- a/qcodes/utils/wrappers.py
+++ b/qcodes/utils/wrappers.py
@@ -6,6 +6,7 @@
 from qcodes.plots.pyqtgraph import QtPlot
 from IPython import get_ipython
 CURRENT_EXPERIMENT  = {}
+CURRENT_EXPERIMENT["logging_enabled"] = False
 
 def init(mainfolder:str, sample_name: str):
     """
@@ -24,7 +25,7 @@ def init(mainfolder:str, sample_name: str):
     mainfolder = abspath(mainfolder)
 
     CURRENT_EXPERIMENT["mainfolder"]  =  mainfolder
-    CURRENT_EXPERIMENT["subfolder"] = sample_name
+    CURRENT_EXPERIMENT["sample_name"] = sample_name
     CURRENT_EXPERIMENT['init']  = True
 
     path_to_experiment_folder = sep.join([mainfolder, sample_name, ""])
@@ -38,7 +39,6 @@ def init(mainfolder:str, sample_name: str):
 
     logging.info("experiment started at {}".format(path_to_experiment_folder))
 
-
     loc_provider = qc.FormatLocation(
         fmt= path_to_experiment_folder + '{counter}')
     qc.data.data_set.DataSet.location_provider = loc_provider
@@ -51,29 +51,17 @@ def init(mainfolder:str, sample_name: str):
         raise RuntimeWarning("History can't be saved refusing to proceed (use IPython/jupyter)")
     else:
         logfile = "{}{}".format(path_to_experiment_folder, "commands.log")
-        logging.debug("Logging commands to: t{}".format(logfile))
-        ipython.magic("%logstart -t {} {}".format(logfile, "append"))
-
-def do1d(inst_set, start, stop, division, delay, *inst_meas):
-    """
-
-    Args:
-        inst_set:  Instrument to sweep over
-        start:  Start of sweep
-        stop:  End of sweep
-        division:  Spacing between values
-        delay:  Delay at every step
-        *inst_meas:  any number of instrument to measure
+        if not CURRENT_EXPERIMENT["logging_enabled"]:
+            logging.debug("Logging commands to: t{}".format(logfile))
+            ipython.magic("%logstart -t {} {}".format(logfile, "append"))
+            CURRENT_EXPERIMENT["logging_enabled"] = True
+        else:
+            logging.debug("Logging already started at {}".format(logfile))
 
-    Returns:
-        plot, data : returns the plot and the dataset
 
-    """
-    loop = qc.Loop(inst_set.sweep(start, stop, division), delay).each(*inst_meas)
-    data = loop.get_data_set()
-    title = "#{0:03d}".format(data.location_provider.counter)
+def _plot_setup(data, inst_meas):
+    title = "{} #{:03d}".format(CURRENT_EXPERIMENT["sample_name"], data.location_provider.counter)
     plot = QtPlot()
-
     for j, i in enumerate(inst_meas):
         if getattr(i, "names", False):
             # deal with multi dimenstional parameter
@@ -94,6 +82,26 @@ def do1d(inst_set, start, stop, division, delay, *inst_meas):
                 plot.subplots[0].setTitle(title)
             else:
                 plot.subplots[j].setTitle("")
+    return plot
+
+def do1d(inst_set, start, stop, division, delay, *inst_meas):
+    """
+
+    Args:
+        inst_set:  Instrument to sweep over
+        start:  Start of sweep
+        stop:  End of sweep
+        division:  Spacing between values
+        delay:  Delay at every step
+        *inst_meas:  any number of instrument to measure
+
+    Returns:
+        plot, data : returns the plot and the dataset
+
+    """
+    loop = qc.Loop(inst_set.sweep(start, stop, division), delay).each(*inst_meas)
+    data = loop.get_data_set()
+    plot = _plot_setup(data, inst_meas)
     try:
         _ = loop.with_bg_task(plot.update, plot.save).run()
     except KeyboardInterrupt:
@@ -123,27 +131,7 @@ def do1dDiagonal(inst_set, inst2_set, start, stop, division, delay, start2, slop
     loop = qc.Loop(inst_set.sweep(start, stop, division), delay).each(
         qc.Task(inst2_set, (inst_set) * slope + (slope * start - start2)), *inst_meas, inst2_set)
     data = loop.get_data_set()
-    title = "#{0:03d}".format(data.location_provider.counter)
-    plot = QtPlot()
-    for j, i in enumerate(inst_meas):
-        if getattr(i, "names", False):
-            # deal with multi dimenstional parameter
-            for k, name in enumerate(i.names):
-                inst_meas_name = "{}_{}".format(i._instrument.name, name)
-                plot.add(getattr(data, inst_meas_name), subplot=j + k + 1)
-                plot.subplots[j+k].showGrid(True, True)
-                if j == 0:
-                    plot.subplots[0].setTitle(title)
-                else:
-                    plot.subplots[j+k].setTitle("")
-        else:
-            inst_meas_name = "{}_{}".format(i._instrument.name, i.name)
-            plot.add(getattr(data, inst_meas_name), subplot=j + 1 )
-            plot.subplots[j].showGrid(True, True)
-            if j == 0:
-                plot.subplots[0].setTitle(title)
-            else:
-                plot.subplots[j].setTitle("")
+    plot = _plot_setup(data, inst_meas)
     try:
         _ = loop.with_bg_task(plot.update, plot.save).run()
     except KeyboardInterrupt:
@@ -178,27 +166,7 @@ def do2d(inst_set, start, stop, division, delay, inst_set2, start2, stop2, divis
     loop = qc.Loop(inst_set.sweep(start, stop, division), delay).loop(inst_set2.sweep(start2,stop2,division2), delay2).each(
         *inst_meas)
     data = loop.get_data_set()
-    title = "#{0:03d}".format(data.location_provider.counter)
-    plot = QtPlot()
-    for j, i in enumerate(inst_meas):
-        if getattr(i, "names", False):
-            # deal with multi dimenstional parameter
-            for k, name in enumerate(i.names):
-                inst_meas_name = "{}_{}".format(i._instrument.name, name)
-                plot.add(getattr(data, inst_meas_name), subplot=j + k + 1)
-                plot.subplots[j+k].showGrid(True, True)
-                if j == 0:
-                    plot.subplots[0].setTitle(title)
-                else:
-                    plot.subplots[j+k].setTitle("")
-        else:
-            inst_meas_name = "{}_{}".format(i._instrument.name, i.name)
-            plot.add(getattr(data, inst_meas_name), subplot=j + 1)
-            plot.subplots[j].showGrid(True, True)
-            if j == 0:
-                plot.subplots[0].setTitle(title)
-            else:
-                plot.subplots[j].setTitle("")
+    plot = _plot_setup(data, inst_meas)
     try:
         _ = loop.with_bg_task(plot.update, plot.save).run()
     except KeyboardInterrupt:
@@ -228,7 +196,7 @@ def show_num(id):
     for value in data.arrays.keys():
         if "set" not in value:
             plot = QtPlot(getattr(data, value))
-            title = "#{}".format(str_id)
+            title = "{} #{}".format(CURRENT_EXPERIMENT["sample_name"], str_id)
             plot.subplots[0].setTitle(title)
             plot.subplots[0].showGrid(True, True)
             plots.append(plot)

From b38f0d1dceb9c05f4adc7c52587da7069d5bca5a Mon Sep 17 00:00:00 2001
From: Jens Hedegaard Nielsen 
Date: Tue, 14 Mar 2017 16:51:15 +0100
Subject: [PATCH 247/328] Fix: set default matplotlib colormap to hot to match
 pyqtgraph

---
 qcodes/plots/qcmatplotlib.py | 6 +++++-
 1 file changed, 5 insertions(+), 1 deletion(-)

diff --git a/qcodes/plots/qcmatplotlib.py b/qcodes/plots/qcmatplotlib.py
index 2dd93448b88c..0a3a9ed9bfbb 100644
--- a/qcodes/plots/qcmatplotlib.py
+++ b/qcodes/plots/qcmatplotlib.py
@@ -7,6 +7,7 @@
 
 import matplotlib.pyplot as plt
 from matplotlib.transforms import Bbox
+from matplotlib import cm
 import numpy as np
 from numpy.ma import masked_invalid, getmask
 from collections import Sequence
@@ -303,6 +304,9 @@ def _draw_pcolormesh(self, ax, z, x=None, y=None, subplot=1,
             # to check for them.
             return False
 
+        if 'cmap' not in kwargs:
+            kwargs['cmap'] = cm.hot
+
         if x is not None and y is not None:
             # If x and y are provided, modify the arrays such that they
             # correspond to grid corners instead of grid centers.
@@ -400,4 +404,4 @@ def tight_layout(self):
         Perform a tight layout on the figure. A bit of additional spacing at
         the top is also added for the title.
         """
-        self.fig.tight_layout(rect=[0, 0, 1, 0.95])
\ No newline at end of file
+        self.fig.tight_layout(rect=[0, 0, 1, 0.95])

From 2c4e3cdb8f070968f532b9ece9f425222726c173 Mon Sep 17 00:00:00 2001
From: Jens Hedegaard Nielsen 
Date: Tue, 14 Mar 2017 16:57:18 +0100
Subject: [PATCH 248/328] Fix: improve quality of saved pdf images in
 matplotlib by drawing edges

---
 qcodes/plots/qcmatplotlib.py | 6 ++++++
 1 file changed, 6 insertions(+)

diff --git a/qcodes/plots/qcmatplotlib.py b/qcodes/plots/qcmatplotlib.py
index 0a3a9ed9bfbb..797f99525279 100644
--- a/qcodes/plots/qcmatplotlib.py
+++ b/qcodes/plots/qcmatplotlib.py
@@ -345,6 +345,12 @@ def _draw_pcolormesh(self, ax, z, x=None, y=None, subplot=1,
             # Only the masked value of z is used as a mask
             args = args_masked[-1:]
 
+        if 'edgecolors' not in kwargs:
+            # Matplotlib pcolormesh per default are drawn as individual patches lined up next to each other
+            # due to rounding this produces visible gaps in some pdf viewers. To prevent this we draw each
+            # mesh with a visible edge (slightly overlapping) this assumes alpha=1 or it will produce artifacts
+            # at the overlaps
+            kwargs['edgecolors'] = 'face'
         pc = ax.pcolormesh(*args, **kwargs)
 
         # Set x and y limits if arrays are provided

From d7ea83eea8e7f299e8f9a049459e03e6d3341860 Mon Sep 17 00:00:00 2001
From: Jens Hedegaard Nielsen 
Date: Tue, 14 Mar 2017 16:58:51 +0100
Subject: [PATCH 249/328] Fix: add support to wrappers for plotting pdf via
 matplotlib

---
 qcodes/utils/wrappers.py | 63 ++++++++++++++++++++++++++++------------
 1 file changed, 44 insertions(+), 19 deletions(-)

diff --git a/qcodes/utils/wrappers.py b/qcodes/utils/wrappers.py
index 3d33ac849396..562120476666 100644
--- a/qcodes/utils/wrappers.py
+++ b/qcodes/utils/wrappers.py
@@ -4,7 +4,9 @@
 from os import makedirs
 import logging
 from qcodes.plots.pyqtgraph import QtPlot
+from qcodes.plots.qcmatplotlib import MatPlot
 from IPython import get_ipython
+from matplotlib import cm
 CURRENT_EXPERIMENT  = {}
 CURRENT_EXPERIMENT["logging_enabled"] = False
 
@@ -12,7 +14,7 @@ def init(mainfolder:str, sample_name: str):
     """
 
     Args:
-        mainfolder:  base loacation for the data
+        mainfolder:  base location for the data
         sample_name:  name of the sample
 
     """
@@ -59,29 +61,46 @@ def init(mainfolder:str, sample_name: str):
             logging.debug("Logging already started at {}".format(logfile))
 
 
-def _plot_setup(data, inst_meas):
+def _plot_setup(data, inst_meas, useQT=True):
     title = "{} #{:03d}".format(CURRENT_EXPERIMENT["sample_name"], data.location_provider.counter)
-    plot = QtPlot()
+    if useQT:
+        plot = QtPlot()
+    else:
+        plot = MatPlot()
     for j, i in enumerate(inst_meas):
         if getattr(i, "names", False):
-            # deal with multi dimenstional parameter
+            # deal with multidimensional parameter
             for k, name in enumerate(i.names):
                 inst_meas_name = "{}_{}".format(i._instrument.name, name)
                 plot.add(getattr(data, inst_meas_name), subplot=j + k + 1)
-                plot.subplots[j+k].showGrid(True, True)
-                if j == 0:
-                    plot.subplots[0].setTitle(title)
+                if useQT:
+                    plot.subplots[j+k].showGrid(True, True)
+                    if j == 0:
+                        plot.subplots[0].setTitle(title)
+                    else:
+                        plot.subplots[j+k].setTitle("")
                 else:
-                    plot.subplots[j+k].setTitle("")
+                    plot.subplots[j+k].grid()
+                    if j == 0:
+                        plot.subplots[0].set_title(title)
+                    else:
+                        plot.subplots[j+k].set_title("")
         else:
             # simple_parameters
             inst_meas_name = "{}_{}".format(i._instrument.name, i.name)
             plot.add(getattr(data, inst_meas_name), subplot=j + 1)
-            plot.subplots[j].showGrid(True, True)
-            if j == 0:
-                plot.subplots[0].setTitle(title)
+            if useQT:
+                plot.subplots[j].showGrid(True, True)
+                if j == 0:
+                    plot.subplots[0].setTitle(title)
+                else:
+                    plot.subplots[j].setTitle("")
             else:
-                plot.subplots[j].setTitle("")
+                plot.subplots[j].grid()
+                if j == 0:
+                    plot.subplots[0].set_title(title)
+                else:
+                    plot.subplots[j].set_title("")
     return plot
 
 def do1d(inst_set, start, stop, division, delay, *inst_meas):
@@ -106,12 +125,14 @@ def do1d(inst_set, start, stop, division, delay, *inst_meas):
         _ = loop.with_bg_task(plot.update, plot.save).run()
     except KeyboardInterrupt:
         print("Measurement Interrupted")
+    staticplot = _plot_setup(data, inst_meas, useQT=False)
+    staticplot.save(staticplot.get_default_title()+'.pdf')
     return plot, data
 
 
 def do1dDiagonal(inst_set, inst2_set, start, stop, division, delay, start2, slope, *inst_meas):
     """
-    Perform diagonal sweep in 1 dimension, given two insturments
+    Perform diagonal sweep in 1 dimension, given two instruments
 
     Args:
         inst_set:  Instrument to sweep over
@@ -136,7 +157,9 @@ def do1dDiagonal(inst_set, inst2_set, start, stop, division, delay, start2, slop
         _ = loop.with_bg_task(plot.update, plot.save).run()
     except KeyboardInterrupt:
         print("Measurement Interrupted")
-    return data
+    staticplot = _plot_setup(data, inst_meas, useQT=False)
+    staticplot.save(staticplot.get_default_title()+'.pdf')
+    return plot, data
 
 
 def do2d(inst_set, start, stop, division, delay, inst_set2, start2, stop2, division2, delay2, *inst_meas):
@@ -149,10 +172,10 @@ def do2d(inst_set, start, stop, division, delay, inst_set2, start2, stop2, divis
         division:  Spacing between values
         delay:  Delay at every step
         inst_set_2:  Second instrument to sweep over
-        start_2:  Start of sweep for second intrument
-        stop_2:  End of sweep for second intrument
-        division_2:  Spacing between values for second intrument
-        delay_2:  Delay at every step for second intrument
+        start_2:  Start of sweep for second instrument
+        stop_2:  End of sweep for second instrument
+        division_2:  Spacing between values for second instrument
+        delay_2:  Delay at every step for second instrument
         *inst_meas:
 
     Returns:
@@ -171,6 +194,8 @@ def do2d(inst_set, start, stop, division, delay, inst_set2, start2, stop2, divis
         _ = loop.with_bg_task(plot.update, plot.save).run()
     except KeyboardInterrupt:
         print("Measurement Interrupted")
+    staticplot = _plot_setup(data, inst_meas, useQT=False)
+    staticplot.save(staticplot.get_default_title()+'.pdf')
     return plot, data
 
 
@@ -178,7 +203,7 @@ def show_num(id):
     """
     Show  and return plot and data for id in current instrument.
     Args:
-        id(number): id of intrumetn
+        id(number): id of instrument
 
     Returns:
         plot, data : returns the plot and the dataset

From 2e95d30841536ff5c3649f04ff6bc871068e22f6 Mon Sep 17 00:00:00 2001
From: Jens Hedegaard Nielsen 
Date: Wed, 15 Mar 2017 16:29:24 +0100
Subject: [PATCH 250/328] fix: make show_num default to plot with matplotlib

---
 qcodes/utils/wrappers.py | 16 +++++++++++-----
 1 file changed, 11 insertions(+), 5 deletions(-)

diff --git a/qcodes/utils/wrappers.py b/qcodes/utils/wrappers.py
index 562120476666..d3627d76b783 100644
--- a/qcodes/utils/wrappers.py
+++ b/qcodes/utils/wrappers.py
@@ -199,7 +199,7 @@ def do2d(inst_set, start, stop, division, delay, inst_set2, start2, stop2, divis
     return plot, data
 
 
-def show_num(id):
+def show_num(id, useQT=False):
     """
     Show  and return plot and data for id in current instrument.
     Args:
@@ -220,9 +220,15 @@ def show_num(id):
     plots = []
     for value in data.arrays.keys():
         if "set" not in value:
-            plot = QtPlot(getattr(data, value))
-            title = "{} #{}".format(CURRENT_EXPERIMENT["sample_name"], str_id)
-            plot.subplots[0].setTitle(title)
-            plot.subplots[0].showGrid(True, True)
+            if useQT:
+                plot = QtPlot(getattr(data, value))
+                title = "{} #{}".format(CURRENT_EXPERIMENT["sample_name"], str_id)
+                plot.subplots[0].setTitle(title)
+                plot.subplots[0].showGrid(True, True)
+            else:
+                plot = MatPlot(getattr(data, value))
+                title = "{} #{}".format(CURRENT_EXPERIMENT["sample_name"], str_id)
+                plot.subplots[0].set_title(title)
+                plot.subplots[0].grid()
             plots.append(plot)
     return data, plots

From 6cf4ccc5f04e1e707ab32f9f3145f12fa05555c7 Mon Sep 17 00:00:00 2001
From: Jens Hedegaard Nielsen 
Date: Thu, 16 Mar 2017 10:04:05 +0100
Subject: [PATCH 251/328] fix save individual plots as pdf

---
 qcodes/utils/wrappers.py | 36 ++++++++++++++++++++++++++++++------
 1 file changed, 30 insertions(+), 6 deletions(-)

diff --git a/qcodes/utils/wrappers.py b/qcodes/utils/wrappers.py
index d3627d76b783..8c5a915e73f2 100644
--- a/qcodes/utils/wrappers.py
+++ b/qcodes/utils/wrappers.py
@@ -103,6 +103,33 @@ def _plot_setup(data, inst_meas, useQT=True):
                     plot.subplots[j].set_title("")
     return plot
 
+
+def _save_individual_plots(data, inst_meas):
+    title = "{} #{:03d}".format(CURRENT_EXPERIMENT["sample_name"], data.location_provider.counter)
+    counter_two = 0
+    for j, i in enumerate(inst_meas):
+        if getattr(i, "names", False):
+            # deal with multidimensional parameter
+            for k, name in enumerate(i.names):
+                counter_two += 1
+                plot = MatPlot()
+                inst_meas_name = "{}_{}".format(i._instrument.name, name)
+                plot.add(getattr(data, inst_meas_name))
+                plot.subplots[0].set_title(title)
+                plot.subplots[0].grid()
+                plot.save("{}_{:03d}.pdf".format(plot.get_default_title(), counter_two))
+        else:
+            counter_two += 1
+            plot = MatPlot()
+            # simple_parameters
+            inst_meas_name = "{}_{}".format(i._instrument.name, i.name)
+            plot.add(getattr(data, inst_meas_name))
+            plot.subplots[0].set_title(title)
+            plot.subplots[0].grid()
+            plot.save("{}_{:03d}.pdf".format(plot.get_default_title(), counter_two))
+
+
+
 def do1d(inst_set, start, stop, division, delay, *inst_meas):
     """
 
@@ -125,8 +152,7 @@ def do1d(inst_set, start, stop, division, delay, *inst_meas):
         _ = loop.with_bg_task(plot.update, plot.save).run()
     except KeyboardInterrupt:
         print("Measurement Interrupted")
-    staticplot = _plot_setup(data, inst_meas, useQT=False)
-    staticplot.save(staticplot.get_default_title()+'.pdf')
+    _save_individual_plots(data, inst_meas)
     return plot, data
 
 
@@ -157,8 +183,7 @@ def do1dDiagonal(inst_set, inst2_set, start, stop, division, delay, start2, slop
         _ = loop.with_bg_task(plot.update, plot.save).run()
     except KeyboardInterrupt:
         print("Measurement Interrupted")
-    staticplot = _plot_setup(data, inst_meas, useQT=False)
-    staticplot.save(staticplot.get_default_title()+'.pdf')
+    _save_individual_plots(data, inst_meas)
     return plot, data
 
 
@@ -194,8 +219,7 @@ def do2d(inst_set, start, stop, division, delay, inst_set2, start2, stop2, divis
         _ = loop.with_bg_task(plot.update, plot.save).run()
     except KeyboardInterrupt:
         print("Measurement Interrupted")
-    staticplot = _plot_setup(data, inst_meas, useQT=False)
-    staticplot.save(staticplot.get_default_title()+'.pdf')
+    _save_individual_plots(data, inst_meas)
     return plot, data
 
 

From 60c305da2dcc069f3a6419a00a34b9e417bcd82d Mon Sep 17 00:00:00 2001
From: Jens Hedegaard Nielsen 
Date: Fri, 17 Mar 2017 16:45:03 +0100
Subject: [PATCH 252/328] Fix add basic support for moving qt windows to a
 specific position

---
 qcodes/plots/pyqtgraph.py | 12 +++++++++++-
 qcodes/utils/wrappers.py  | 16 +++++++++++-----
 2 files changed, 22 insertions(+), 6 deletions(-)

diff --git a/qcodes/plots/pyqtgraph.py b/qcodes/plots/pyqtgraph.py
index 97b585178771..1e9bcd134428 100644
--- a/qcodes/plots/pyqtgraph.py
+++ b/qcodes/plots/pyqtgraph.py
@@ -7,6 +7,7 @@
 from pyqtgraph.multiprocess.remoteproxy import ClosedError
 import qcodes.utils.helpers
 
+from qtpy import QtWidgets
 import warnings
 from collections import namedtuple, deque
 
@@ -30,6 +31,10 @@ class QtPlot(BasePlot):
 
         figsize: (width, height) tuple in pixels to pass to GraphicsWindow
             default (1000, 600)
+        fig_x_pos: fraction of screen width to place the figure at
+            0 is all the way to the left and
+            1 is all the way to the right.
+            default None let qt decide.
         interval: period in seconds between update checks
             default 0.25
         theme: tuple of (foreground_color, background_color), where each is
@@ -51,7 +56,8 @@ class QtPlot(BasePlot):
     plots = deque(maxlen=qcodes.config['gui']['pyqtmaxplots'])
 
     def __init__(self, *args, figsize=(1000, 600), interval=0.25,
-                 window_title='', theme=((60, 60, 60), 'w'), show_window=True, remote=True, **kwargs):
+                 window_title='', theme=((60, 60, 60), 'w'), show_window=True, remote=True, fig_x_position=None,
+                 **kwargs):
         super().__init__(interval)
 
         if 'windowTitle' in kwargs.keys():
@@ -80,6 +86,10 @@ def __init__(self, *args, figsize=(1000, 600), interval=0.25,
                 raise err
         self.win.setBackground(theme[1])
         self.win.resize(*figsize)
+        if fig_x_position:
+            _, _, width, height = QtWidgets.QDesktopWidget().screenGeometry().getCoords()
+            y_pos = self.win.y()
+            self.win.move(width * fig_x_position, y_pos)
         self.subplots = [self.add_subplot()]
 
         if args or kwargs:
diff --git a/qcodes/utils/wrappers.py b/qcodes/utils/wrappers.py
index 8c5a915e73f2..f52bf102a03d 100644
--- a/qcodes/utils/wrappers.py
+++ b/qcodes/utils/wrappers.py
@@ -3,19 +3,24 @@
 from os.path import sep
 from os import makedirs
 import logging
+from qtpy import QtWidgets
+
 from qcodes.plots.pyqtgraph import QtPlot
 from qcodes.plots.qcmatplotlib import MatPlot
 from IPython import get_ipython
-from matplotlib import cm
+
 CURRENT_EXPERIMENT  = {}
 CURRENT_EXPERIMENT["logging_enabled"] = False
 
-def init(mainfolder:str, sample_name: str):
+def init(mainfolder:str, sample_name: str, plot_x_position=0.66):
     """
 
     Args:
         mainfolder:  base location for the data
         sample_name:  name of the sample
+        plot_x_position: fractional of screen position to put QT plots.
+                         0 is all the way to the left and
+                         1 is all the way to the right.
 
     """
     if sep in sample_name:
@@ -30,10 +35,11 @@ def init(mainfolder:str, sample_name: str):
     CURRENT_EXPERIMENT["sample_name"] = sample_name
     CURRENT_EXPERIMENT['init']  = True
 
+    CURRENT_EXPERIMENT['plot_x_position'] = plot_x_position
+
     path_to_experiment_folder = sep.join([mainfolder, sample_name, ""])
     CURRENT_EXPERIMENT["exp_folder"] = path_to_experiment_folder
 
-
     try:
         makedirs(path_to_experiment_folder)
     except FileExistsError:
@@ -64,7 +70,7 @@ def init(mainfolder:str, sample_name: str):
 def _plot_setup(data, inst_meas, useQT=True):
     title = "{} #{:03d}".format(CURRENT_EXPERIMENT["sample_name"], data.location_provider.counter)
     if useQT:
-        plot = QtPlot()
+        plot = QtPlot(fig_x_position=CURRENT_EXPERIMENT['plot_x_position'])
     else:
         plot = MatPlot()
     for j, i in enumerate(inst_meas):
@@ -245,7 +251,7 @@ def show_num(id, useQT=False):
     for value in data.arrays.keys():
         if "set" not in value:
             if useQT:
-                plot = QtPlot(getattr(data, value))
+                plot = QtPlot(getattr(data, value), fig_x_position=CURRENT_EXPERIMENT['plot_x_position'])
                 title = "{} #{}".format(CURRENT_EXPERIMENT["sample_name"], str_id)
                 plot.subplots[0].setTitle(title)
                 plot.subplots[0].showGrid(True, True)

From a5962d584c95c8367a8c6ba2b8879e54aa52f9d9 Mon Sep 17 00:00:00 2001
From: Jens Hedegaard Nielsen 
Date: Tue, 21 Mar 2017 10:01:36 +0100
Subject: [PATCH 253/328] fix: dont depend on qtpy

---
 qcodes/plots/pyqtgraph.py | 5 +++--
 qcodes/utils/wrappers.py  | 1 -
 2 files changed, 3 insertions(+), 3 deletions(-)

diff --git a/qcodes/plots/pyqtgraph.py b/qcodes/plots/pyqtgraph.py
index 1e9bcd134428..0abd0ee94424 100644
--- a/qcodes/plots/pyqtgraph.py
+++ b/qcodes/plots/pyqtgraph.py
@@ -7,7 +7,8 @@
 from pyqtgraph.multiprocess.remoteproxy import ClosedError
 import qcodes.utils.helpers
 
-from qtpy import QtWidgets
+from pyqtgraph import QtGui # note that pyqtgraph still uses the old pyqt4 layout
+
 import warnings
 from collections import namedtuple, deque
 
@@ -87,7 +88,7 @@ def __init__(self, *args, figsize=(1000, 600), interval=0.25,
         self.win.setBackground(theme[1])
         self.win.resize(*figsize)
         if fig_x_position:
-            _, _, width, height = QtWidgets.QDesktopWidget().screenGeometry().getCoords()
+            _, _, width, height = QtGui.QDesktopWidget().screenGeometry().getCoords()
             y_pos = self.win.y()
             self.win.move(width * fig_x_position, y_pos)
         self.subplots = [self.add_subplot()]
diff --git a/qcodes/utils/wrappers.py b/qcodes/utils/wrappers.py
index f52bf102a03d..5c43f4a672a5 100644
--- a/qcodes/utils/wrappers.py
+++ b/qcodes/utils/wrappers.py
@@ -3,7 +3,6 @@
 from os.path import sep
 from os import makedirs
 import logging
-from qtpy import QtWidgets
 
 from qcodes.plots.pyqtgraph import QtPlot
 from qcodes.plots.qcmatplotlib import MatPlot

From 192ca92f145700cadc27a33ae2441b97a79a79cc Mon Sep 17 00:00:00 2001
From: William Nielsen 
Date: Tue, 28 Mar 2017 14:25:02 +0200
Subject: [PATCH 254/328] fix: Allow execution of tasks in doNd[_diagonal]

Check whether stuff to do at each step in a loop is plottable.
---
 qcodes/utils/wrappers.py | 62 ++++++++++++++++++++++++++++------------
 1 file changed, 44 insertions(+), 18 deletions(-)

diff --git a/qcodes/utils/wrappers.py b/qcodes/utils/wrappers.py
index 5c43f4a672a5..3e40d3ddd686 100644
--- a/qcodes/utils/wrappers.py
+++ b/qcodes/utils/wrappers.py
@@ -8,9 +8,10 @@
 from qcodes.plots.qcmatplotlib import MatPlot
 from IPython import get_ipython
 
-CURRENT_EXPERIMENT  = {}
+CURRENT_EXPERIMENT = {}
 CURRENT_EXPERIMENT["logging_enabled"] = False
 
+
 def init(mainfolder:str, sample_name: str, plot_x_position=0.66):
     """
 
@@ -23,14 +24,14 @@ def init(mainfolder:str, sample_name: str, plot_x_position=0.66):
 
     """
     if sep in sample_name:
-        raise  TypeError("Use Relative names. That is wihtout {}".format(sep))
+        raise TypeError("Use Relative names. That is wihtout {}".format(sep))
     # always remove trailing sep in the main folder
-    if mainfolder[-1] ==  sep:
+    if mainfolder[-1] == sep:
         mainfolder = mainfolder[:-1]
 
     mainfolder = abspath(mainfolder)
 
-    CURRENT_EXPERIMENT["mainfolder"]  =  mainfolder
+    CURRENT_EXPERIMENT["mainfolder"] = mainfolder
     CURRENT_EXPERIMENT["sample_name"] = sample_name
     CURRENT_EXPERIMENT['init']  = True
 
@@ -66,8 +67,25 @@ def init(mainfolder:str, sample_name: str, plot_x_position=0.66):
             logging.debug("Logging already started at {}".format(logfile))
 
 
+def _select_plottables(tasks):
+    """
+    Helper function to select plottable tasks. Used inside the doNd functions.
+
+    A task is here understood to be anything that the qc.Loop 'each' can eat.
+    """
+    # allow passing a single task
+    if not isinstance(tasks, tuple):
+        tasks = (tasks,)
+
+    # is the following check necessary AND sufficient?
+    plottables = [task for task in tasks if hasattr(task, '_instrument')]
+
+    return tuple(plottables)
+
+
 def _plot_setup(data, inst_meas, useQT=True):
-    title = "{} #{:03d}".format(CURRENT_EXPERIMENT["sample_name"], data.location_provider.counter)
+    title = "{} #{:03d}".format(CURRENT_EXPERIMENT["sample_name"],
+                                data.location_provider.counter)
     if useQT:
         plot = QtPlot(fig_x_position=CURRENT_EXPERIMENT['plot_x_position'])
     else:
@@ -134,7 +152,6 @@ def _save_individual_plots(data, inst_meas):
             plot.save("{}_{:03d}.pdf".format(plot.get_default_title(), counter_two))
 
 
-
 def do1d(inst_set, start, stop, division, delay, *inst_meas):
     """
 
@@ -144,20 +161,23 @@ def do1d(inst_set, start, stop, division, delay, *inst_meas):
         stop:  End of sweep
         division:  Spacing between values
         delay:  Delay at every step
-        *inst_meas:  any number of instrument to measure
+        *inst_meas:  any number of instrument to measure and/or tasks to
+            perform at each step of the sweep
 
     Returns:
         plot, data : returns the plot and the dataset
 
     """
-    loop = qc.Loop(inst_set.sweep(start, stop, division), delay).each(*inst_meas)
+    loop = qc.Loop(inst_set.sweep(start,
+                                  stop, division), delay).each(*inst_meas)
     data = loop.get_data_set()
-    plot = _plot_setup(data, inst_meas)
+    plottables = _select_plottables(inst_meas)
+    plot = _plot_setup(data, plottables)
     try:
         _ = loop.with_bg_task(plot.update, plot.save).run()
     except KeyboardInterrupt:
         print("Measurement Interrupted")
-    _save_individual_plots(data, inst_meas)
+    _save_individual_plots(data, plottables)
     return plot, data
 
 
@@ -183,12 +203,13 @@ def do1dDiagonal(inst_set, inst2_set, start, stop, division, delay, start2, slop
     loop = qc.Loop(inst_set.sweep(start, stop, division), delay).each(
         qc.Task(inst2_set, (inst_set) * slope + (slope * start - start2)), *inst_meas, inst2_set)
     data = loop.get_data_set()
-    plot = _plot_setup(data, inst_meas)
+    plottables = _select_plottables(inst_meas)
+    plot = _plot_setup(data, plottables)
     try:
         _ = loop.with_bg_task(plot.update, plot.save).run()
     except KeyboardInterrupt:
         print("Measurement Interrupted")
-    _save_individual_plots(data, inst_meas)
+    _save_individual_plots(data, plottables)
     return plot, data
 
 
@@ -219,12 +240,13 @@ def do2d(inst_set, start, stop, division, delay, inst_set2, start2, stop2, divis
     loop = qc.Loop(inst_set.sweep(start, stop, division), delay).loop(inst_set2.sweep(start2,stop2,division2), delay2).each(
         *inst_meas)
     data = loop.get_data_set()
-    plot = _plot_setup(data, inst_meas)
+    plottables = _select_plottables(inst_meas)
+    plot = _plot_setup(data, plottables)
     try:
         _ = loop.with_bg_task(plot.update, plot.save).run()
     except KeyboardInterrupt:
         print("Measurement Interrupted")
-    _save_individual_plots(data, inst_meas)
+    _save_individual_plots(data, plottables)
     return plot, data
 
 
@@ -239,7 +261,8 @@ def show_num(id, useQT=False):
 
     """
     if not getattr(CURRENT_EXPERIMENT, "init", True):
-        raise RuntimeError("Experiment not initalized. use qc.Init(mainfolder, samplename)")
+        raise RuntimeError("Experiment not initalized. "
+                           "use qc.Init(mainfolder, samplename)")
 
     str_id = '{0:03d}'.format(id)
 
@@ -250,13 +273,16 @@ def show_num(id, useQT=False):
     for value in data.arrays.keys():
         if "set" not in value:
             if useQT:
-                plot = QtPlot(getattr(data, value), fig_x_position=CURRENT_EXPERIMENT['plot_x_position'])
-                title = "{} #{}".format(CURRENT_EXPERIMENT["sample_name"], str_id)
+                plot = QtPlot(getattr(data, value),
+                              fig_x_position=CURRENT_EXPERIMENT['plot_x_position'])
+                title = "{} #{}".format(CURRENT_EXPERIMENT["sample_name"],
+                                        str_id)
                 plot.subplots[0].setTitle(title)
                 plot.subplots[0].showGrid(True, True)
             else:
                 plot = MatPlot(getattr(data, value))
-                title = "{} #{}".format(CURRENT_EXPERIMENT["sample_name"], str_id)
+                title = "{} #{}".format(CURRENT_EXPERIMENT["sample_name"],
+                                        str_id)
                 plot.subplots[0].set_title(title)
                 plot.subplots[0].grid()
             plots.append(plot)

From 2d55bafa12a77f55717c8e9ecaf683055c8671e6 Mon Sep 17 00:00:00 2001
From: William Nielsen 
Date: Tue, 28 Mar 2017 15:05:29 +0200
Subject: [PATCH 255/328] feat: Add device annotator

Add a device annotator gui

fix: Change import path for device_annotator

Wip integration with device annotation

Fix: save device image in do2d and diag
---
 qcodes/utils/qcodes_device_annotator.py | 290 ++++++++++++++++++++++++
 qcodes/utils/wrappers.py                |  27 ++-
 2 files changed, 314 insertions(+), 3 deletions(-)
 create mode 100644 qcodes/utils/qcodes_device_annotator.py

diff --git a/qcodes/utils/qcodes_device_annotator.py b/qcodes/utils/qcodes_device_annotator.py
new file mode 100644
index 000000000000..130e535a3749
--- /dev/null
+++ b/qcodes/utils/qcodes_device_annotator.py
@@ -0,0 +1,290 @@
+# A device image plotter
+
+import sys
+import os
+import json
+import glob
+import qtpy.QtWidgets as qt
+import qtpy.QtGui as gui
+import qtpy.QtCore as core
+
+from shutil import copyfile
+
+class MakeDeviceImage(qt.QWidget):
+    """
+    Class for clicking and adding labels
+    """
+
+    def __init__(self, folder, station):
+
+        super().__init__()
+
+        self.folder = folder
+        self.station = station
+
+        # BACKEND
+        self._data = {}
+        self.filename = None
+
+        # FRONTEND
+
+        grid = qt.QGridLayout()
+        self.setLayout(grid)
+
+        self.imageCanvas = qt.QLabel()
+        self.loadButton = qt.QPushButton('Load image')
+        self.labelButton = qt.QRadioButton("Insert Label")
+        self.labelButton.setChecked(True)
+        self.annotButton = qt.QRadioButton('Place annotation')
+
+        self.okButton = qt.QPushButton('Save and close')
+
+        self.loadButton.clicked.connect(self.loadimage)
+        self.imageCanvas.mousePressEvent = self.set_label_or_annotation
+        self.imageCanvas.setStyleSheet('background-color: white')
+        self.okButton.clicked.connect(self.saveAndClose)
+
+        self.treeView = qt.QTreeView()
+        self.model = gui.QStandardItemModel()
+        self.model.setHorizontalHeaderLabels([self.tr("Instruments")])
+        self.addStation(self.model, station)
+        self.treeView.setModel(self.model)
+
+        grid.addWidget(self.imageCanvas, 0, 0, 4, 6)
+        grid.addWidget(self.loadButton, 4, 0)
+        grid.addWidget(self.labelButton, 4, 1)
+        grid.addWidget(self.annotButton, 4, 2)
+        grid.addWidget(self.okButton, 4, 9)
+        grid.addWidget(self.treeView, 0, 6, 4, 4)
+
+        self.resize(600, 400)
+        self.move(100, 100)
+        self.setWindowTitle('Generate annotated device image')
+        self.show()
+
+    def addStation(self, parent, station):
+
+        for inst in station.components:
+            item = gui.QStandardItem(inst)
+            item.setEditable(False)
+            item.setSelectable(False)
+            parent.appendRow(item)
+            for param in station[inst].parameters:
+                paramitem = gui.QStandardItem(param)
+                paramitem.setEditable(False)
+                item.appendRow(paramitem)
+
+    def loadimage(self):
+        """
+        Select an image from disk.
+        """
+        fd = qt.QFileDialog()
+        filename = fd.getOpenFileName(self, 'Select device image',
+                                      os.getcwd(),
+                                      "Image files(*.jpg *.png *.jpeg)")
+        self.filename = filename[0]
+        self.pixmap = gui.QPixmap(filename[0])
+        width = self.pixmap.width()
+        height = self.pixmap.height()
+
+        self.imageCanvas.setPixmap(self.pixmap)
+
+        # fix the image scale, so that the pixel values of the mouse are
+        # unambiguous
+        self.imageCanvas.setMaximumWidth(width)
+        self.imageCanvas.setMaximumHeight(height)
+
+    def set_label_or_annotation(self, event):
+
+
+        # verify valid
+        if not self.treeView.selectedIndexes():
+            return
+        selected = self.treeView.selectedIndexes()[0]
+        selected_instrument = selected.parent().data()
+        selected_parameter = selected.data()
+        self.click_x = event.pos().x()
+        self.click_y = event.pos().y()
+
+        # update the data
+        if selected_instrument not in self._data.keys():
+            self._data[selected_instrument] = {}
+        if selected_parameter not in self._data[selected_instrument].keys():
+            self._data[selected_instrument][selected_parameter] = {}
+
+        if self.labelButton.isChecked():
+            self._data[selected_instrument][selected_parameter]['labelpos'] = (self.click_x, self.click_y)
+        elif self.annotButton.isChecked():
+            self._data[selected_instrument][selected_parameter]['annotationpos'] = (self.click_x, self.click_y)
+        self._data[selected_instrument][selected_parameter]['value'] = 'NaN'
+
+        # draw it
+        self.imageCanvas, _ = self._renderImage(self._data,
+                                                self.imageCanvas,
+                                                self.filename)
+
+    def saveAndClose(self):
+        """
+        Save and close
+        """
+        if self.filename is None:
+            return
+
+        fileformat = self.filename.split('.')[-1]
+        rawpath = os.path.join(self.folder, 'deviceimage_raw.'+fileformat)
+        copyfile(self.filename, rawpath)
+
+        # Now forget about the original
+        self.filename = rawpath
+
+        self.close()
+
+    @staticmethod
+    def _renderImage(data, canvas, filename):
+        """
+        Render an image
+        """
+
+        pixmap = gui.QPixmap(filename)
+        width = pixmap.width()
+        height = pixmap.height()
+
+        label_size = min(height/30, width/30)
+        spacing = int(label_size * 0.2)
+
+        painter = gui.QPainter(pixmap)
+        for instrument, parameters in data.items():
+            for parameter, positions in parameters.items():
+
+                if 'labelpos' in positions:
+                    label_string = "{}_{} ".format(instrument, parameter)
+                    (lx, ly) = positions['labelpos']
+                    painter.setBrush(gui.QColor(255, 255, 255, 100))
+
+                    textfont = gui.QFont('Decorative', label_size)
+                    textwidth = gui.QFontMetrics(textfont).width(label_string)
+                    rectangle_start_x = lx - spacing
+                    rectangle_start_y = ly - spacing
+                    rectangle_width = textwidth+2*spacing
+                    rectangle_height = label_size+2*spacing
+                    painter.drawRect(rectangle_start_x,
+                                     rectangle_start_y,
+                                     rectangle_width,
+                                     rectangle_height)
+                    painter.setBrush(gui.QColor(25, 25, 25))
+
+                    painter.setFont(textfont)
+                    painter.drawText(core.QRectF(rectangle_start_x, rectangle_start_y,
+                                                 rectangle_width, rectangle_height),
+                                     core.Qt.AlignCenter,
+                                     label_string)
+
+                if 'annotationpos' in positions:
+                    (ax, ay) = positions['annotationpos']
+                    annotationstring = data[instrument][parameter]['value']
+
+                    textfont = gui.QFont('Decorative', label_size)
+                    textwidth = gui.QFontMetrics(textfont).width(annotationstring)
+                    rectangle_start_x = ax - spacing
+                    rectangle_start_y = ay - spacing
+                    rectangle_width = textwidth + 2 * spacing
+                    rectangle_height = label_size + 2 * spacing
+                    painter.setBrush(gui.QColor(255, 255, 255, 100))
+                    painter.drawRect(rectangle_start_x,
+                                     rectangle_start_y,
+                                     rectangle_width,
+                                     rectangle_height)
+                    painter.setBrush(gui.QColor(50, 50, 50))
+                    painter.setFont(textfont)
+                    painter.drawText(core.QRectF(rectangle_start_x, rectangle_start_y,
+                                                 rectangle_width, rectangle_height),
+                                     core.Qt.AlignCenter,
+                                     annotationstring)
+
+            canvas.setPixmap(pixmap)
+
+        return canvas, pixmap
+
+
+class DeviceImage:
+
+    """
+    Manage an image of a device
+    """
+
+    def __init__(self, folder, station):
+
+        self._data = {}
+        self.filename = None
+        self.folder = folder
+        self.station = station
+
+    def annotateImage(self):
+        """
+        Launch a Qt Widget to click
+        """
+        if not qt.QApplication.instance():
+            app = qt.QApplication(sys.argv)
+        else:
+            app = qt.QApplication.instance()
+        imagedrawer = MakeDeviceImage(self.folder, self.station)
+        app.exec_()
+        imagedrawer.close()
+        self._data = imagedrawer._data
+        self.filename = imagedrawer.filename
+        self.saveAnnotations()
+
+    def saveAnnotations(self):
+        """
+        Save annotated image to disk (image+instructions)
+        """
+        filename = os.path.join(self.folder, 'deviceimage_annotations.json')
+        with open(filename, 'w') as fid:
+            json.dump(self._data, fid)
+
+    def loadAnnotations(self):
+        """
+        Get the annotations. Only call this if the files exist
+        Need to load png/jpeg too
+        """
+
+        json_filename = os.path.join(self.folder, 'deviceimage_annotations.json')
+        self.filename = glob.glob(os.path.join(self.folder, 'deviceimage_raw.*'))[0]
+        # this assumes there is only on of deviceimage_raw.*
+        with open(json_filename, 'r') as fid:
+            self._data = json.load(fid)
+
+    def updateValues(self, station):
+        """
+        Update the data with actual voltages from the QDac
+        """
+
+        for instrument, parameters in self._data.items():
+            for parameter in parameters.keys():
+                self._data[instrument][parameter]['value'] = str(station.components[instrument][parameter].get_latest())
+
+    def makePNG(self, counter, path=None):
+        """
+        Render the image with new voltage values and save it to disk
+
+        Args:
+            counter (int): A counter for the experimental run number
+        """
+        if self.filename is None:
+            raise ValueError('No image selected!')
+        if not qt.QApplication.instance():
+            app = qt.QApplication(sys.argv)
+        else:
+            app = qt.QApplication.instance()
+        win = qt.QWidget()
+        grid = qt.QGridLayout()
+        win.setLayout(grid)
+        win.imageCanvas = qt.QLabel()
+        grid.addWidget(win.imageCanvas)
+        win.imageCanvas, pixmap = MakeDeviceImage._renderImage(self._data,
+                                                               win.imageCanvas,
+                                                               self.filename)
+        filename = '{:03d}_deviceimage.png'.format(counter)
+        if path:
+            filename = os.path.join(path, filename)
+        pixmap.save(filename, 'png')
diff --git a/qcodes/utils/wrappers.py b/qcodes/utils/wrappers.py
index 3e40d3ddd686..b7ac472593ad 100644
--- a/qcodes/utils/wrappers.py
+++ b/qcodes/utils/wrappers.py
@@ -6,6 +6,8 @@
 
 from qcodes.plots.pyqtgraph import QtPlot
 from qcodes.plots.qcmatplotlib import MatPlot
+from qcodes.utils.qcodes_device_annotator import DeviceImage
+
 from IPython import get_ipython
 
 CURRENT_EXPERIMENT = {}
@@ -66,6 +68,15 @@ def init(mainfolder:str, sample_name: str, plot_x_position=0.66):
         else:
             logging.debug("Logging already started at {}".format(logfile))
 
+def init_device_image(station):
+    # TODO handle default station
+    di = DeviceImage(CURRENT_EXPERIMENT["exp_folder"], station)
+    try:
+        di.loadAnnotations()
+    except:
+        di.annotateImage()
+    CURRENT_EXPERIMENT['device_image'] = di
+    CURRENT_EXPERIMENT['station'] = station
 
 def _select_plottables(tasks):
     """
@@ -151,6 +162,10 @@ def _save_individual_plots(data, inst_meas):
             plot.subplots[0].grid()
             plot.save("{}_{:03d}.pdf".format(plot.get_default_title(), counter_two))
 
+def save_device_image():
+    CURRENT_EXPERIMENT['device_image'].updateValues(CURRENT_EXPERIMENT['station'])
+    CURRENT_EXPERIMENT['device_image'].makePNG(CURRENT_EXPERIMENT["provider"].counter,
+                                               CURRENT_EXPERIMENT["exp_folder"])
 
 def do1d(inst_set, start, stop, division, delay, *inst_meas):
     """
@@ -177,7 +192,9 @@ def do1d(inst_set, start, stop, division, delay, *inst_meas):
         _ = loop.with_bg_task(plot.update, plot.save).run()
     except KeyboardInterrupt:
         print("Measurement Interrupted")
-    _save_individual_plots(data, plottables)
+    _save_individual_plots(data, inst_meas)
+    if CURRENT_EXPERIMENT.get('device_image'):
+        save_device_image()
     return plot, data
 
 
@@ -209,7 +226,9 @@ def do1dDiagonal(inst_set, inst2_set, start, stop, division, delay, start2, slop
         _ = loop.with_bg_task(plot.update, plot.save).run()
     except KeyboardInterrupt:
         print("Measurement Interrupted")
-    _save_individual_plots(data, plottables)
+    _save_individual_plots(data, inst_meas)
+    if CURRENT_EXPERIMENT.get('device_image'):
+        save_device_image()
     return plot, data
 
 
@@ -246,7 +265,9 @@ def do2d(inst_set, start, stop, division, delay, inst_set2, start2, stop2, divis
         _ = loop.with_bg_task(plot.update, plot.save).run()
     except KeyboardInterrupt:
         print("Measurement Interrupted")
-    _save_individual_plots(data, plottables)
+    _save_individual_plots(data, inst_meas)
+    if CURRENT_EXPERIMENT.get('device_image'):
+        save_device_image()
     return plot, data
 
 

From b6b41ce3ad6d1b81c56867b6b8fa4725b92da332 Mon Sep 17 00:00:00 2001
From: Giulio Ungaretti 
Date: Wed, 29 Mar 2017 11:56:29 +0200
Subject: [PATCH 256/328] Feat/majorana features (#13)

* feat: Add station to init

Add a station to init and make a device image if it does not exist.

Breaking changes: init now requires a station

* fix: Don't save ann. img. in main folder

Save annotated device images in the data subfolders.
---
 qcodes/utils/wrappers.py | 35 +++++++++++++++++++++++++++--------
 1 file changed, 27 insertions(+), 8 deletions(-)

diff --git a/qcodes/utils/wrappers.py b/qcodes/utils/wrappers.py
index b7ac472593ad..2e6b4a382673 100644
--- a/qcodes/utils/wrappers.py
+++ b/qcodes/utils/wrappers.py
@@ -2,6 +2,7 @@
 from os.path import abspath
 from os.path import sep
 from os import makedirs
+import os
 import logging
 
 from qcodes.plots.pyqtgraph import QtPlot
@@ -14,7 +15,8 @@
 CURRENT_EXPERIMENT["logging_enabled"] = False
 
 
-def init(mainfolder:str, sample_name: str, plot_x_position=0.66):
+def init(mainfolder:str, sample_name: str, station, plot_x_position=0.66,
+         annotate_image=True):
     """
 
     Args:
@@ -54,6 +56,8 @@ def init(mainfolder:str, sample_name: str, plot_x_position=0.66):
     qc.data.data_set.DataSet.location_provider = loc_provider
     CURRENT_EXPERIMENT["provider"] = loc_provider
 
+    CURRENT_EXPERIMENT['station'] = station
+
     ipython = get_ipython()
     # turn on logging only if in ipython
     # else crash and burn
@@ -68,15 +72,20 @@ def init(mainfolder:str, sample_name: str, plot_x_position=0.66):
         else:
             logging.debug("Logging already started at {}".format(logfile))
 
-def init_device_image(station):
-    # TODO handle default station
+    # Annotate image if wanted and necessary
+    if annotate_image:
+        _init_device_image(station)
+
+
+def _init_device_image(station):
+
     di = DeviceImage(CURRENT_EXPERIMENT["exp_folder"], station)
     try:
         di.loadAnnotations()
     except:
         di.annotateImage()
     CURRENT_EXPERIMENT['device_image'] = di
-    CURRENT_EXPERIMENT['station'] = station
+
 
 def _select_plottables(tasks):
     """
@@ -162,10 +171,19 @@ def _save_individual_plots(data, inst_meas):
             plot.subplots[0].grid()
             plot.save("{}_{:03d}.pdf".format(plot.get_default_title(), counter_two))
 
+
 def save_device_image():
-    CURRENT_EXPERIMENT['device_image'].updateValues(CURRENT_EXPERIMENT['station'])
-    CURRENT_EXPERIMENT['device_image'].makePNG(CURRENT_EXPERIMENT["provider"].counter,
-                                               CURRENT_EXPERIMENT["exp_folder"])
+    counter = CURRENT_EXPERIMENT['provider'].counter
+    di = CURRENT_EXPERIMENT['device_image']
+    di.updateValues(CURRENT_EXPERIMENT['station'])
+
+    print(os.path.join(CURRENT_EXPERIMENT["exp_folder"],
+                       '{:03d}'.format(counter)))
+
+    di.makePNG(CURRENT_EXPERIMENT["provider"].counter,
+               os.path.join(CURRENT_EXPERIMENT["exp_folder"],
+                            '{:03d}'.format(counter)))
+
 
 def do1d(inst_set, start, stop, division, delay, *inst_meas):
     """
@@ -192,8 +210,9 @@ def do1d(inst_set, start, stop, division, delay, *inst_meas):
         _ = loop.with_bg_task(plot.update, plot.save).run()
     except KeyboardInterrupt:
         print("Measurement Interrupted")
-    _save_individual_plots(data, inst_meas)
+    _save_individual_plots(data, plottables)
     if CURRENT_EXPERIMENT.get('device_image'):
+        print('Saving an image')
         save_device_image()
     return plot, data
 

From 323587d010eb07d758dd3e2b73eba04d6641472e Mon Sep 17 00:00:00 2001
From: Giulio Ungaretti 
Date: Wed, 29 Mar 2017 11:59:46 +0200
Subject: [PATCH 257/328] fix: Use logging instead of print

---
 qcodes/utils/wrappers.py | 11 ++++++-----
 1 file changed, 6 insertions(+), 5 deletions(-)

diff --git a/qcodes/utils/wrappers.py b/qcodes/utils/wrappers.py
index 2e6b4a382673..6594696d137f 100644
--- a/qcodes/utils/wrappers.py
+++ b/qcodes/utils/wrappers.py
@@ -11,6 +11,7 @@
 
 from IPython import get_ipython
 
+log = logging.getLogger(__name__)
 CURRENT_EXPERIMENT = {}
 CURRENT_EXPERIMENT["logging_enabled"] = False
 
@@ -49,7 +50,7 @@ def init(mainfolder:str, sample_name: str, station, plot_x_position=0.66,
     except FileExistsError:
         pass
 
-    logging.info("experiment started at {}".format(path_to_experiment_folder))
+    log.info("experiment started at {}".format(path_to_experiment_folder))
 
     loc_provider = qc.FormatLocation(
         fmt= path_to_experiment_folder + '{counter}')
@@ -66,11 +67,11 @@ def init(mainfolder:str, sample_name: str, station, plot_x_position=0.66,
     else:
         logfile = "{}{}".format(path_to_experiment_folder, "commands.log")
         if not CURRENT_EXPERIMENT["logging_enabled"]:
-            logging.debug("Logging commands to: t{}".format(logfile))
+            log.debug("Logging commands to: t{}".format(logfile))
             ipython.magic("%logstart -t {} {}".format(logfile, "append"))
             CURRENT_EXPERIMENT["logging_enabled"] = True
         else:
-            logging.debug("Logging already started at {}".format(logfile))
+            log.debug("Logging already started at {}".format(logfile))
 
     # Annotate image if wanted and necessary
     if annotate_image:
@@ -177,7 +178,7 @@ def save_device_image():
     di = CURRENT_EXPERIMENT['device_image']
     di.updateValues(CURRENT_EXPERIMENT['station'])
 
-    print(os.path.join(CURRENT_EXPERIMENT["exp_folder"],
+    log.debug(os.path.join(CURRENT_EXPERIMENT["exp_folder"],
                        '{:03d}'.format(counter)))
 
     di.makePNG(CURRENT_EXPERIMENT["provider"].counter,
@@ -212,7 +213,7 @@ def do1d(inst_set, start, stop, division, delay, *inst_meas):
         print("Measurement Interrupted")
     _save_individual_plots(data, plottables)
     if CURRENT_EXPERIMENT.get('device_image'):
-        print('Saving an image')
+        log.debug('Saving device image')
         save_device_image()
     return plot, data
 

From 2aa67bd93dc4f78c3efc9974aafb95c7134bb49c Mon Sep 17 00:00:00 2001
From: Giulio Ungaretti 
Date: Wed, 29 Mar 2017 12:27:24 +0200
Subject: [PATCH 258/328] fix: Fixup git-fus

---
 qcodes/utils/wrappers.py | 4 ++--
 1 file changed, 2 insertions(+), 2 deletions(-)

diff --git a/qcodes/utils/wrappers.py b/qcodes/utils/wrappers.py
index 6594696d137f..5e80235d1530 100644
--- a/qcodes/utils/wrappers.py
+++ b/qcodes/utils/wrappers.py
@@ -246,7 +246,7 @@ def do1dDiagonal(inst_set, inst2_set, start, stop, division, delay, start2, slop
         _ = loop.with_bg_task(plot.update, plot.save).run()
     except KeyboardInterrupt:
         print("Measurement Interrupted")
-    _save_individual_plots(data, inst_meas)
+    _save_individual_plots(data, plottables)
     if CURRENT_EXPERIMENT.get('device_image'):
         save_device_image()
     return plot, data
@@ -285,7 +285,7 @@ def do2d(inst_set, start, stop, division, delay, inst_set2, start2, stop2, divis
         _ = loop.with_bg_task(plot.update, plot.save).run()
     except KeyboardInterrupt:
         print("Measurement Interrupted")
-    _save_individual_plots(data, inst_meas)
+    _save_individual_plots(data, plottables)
     if CURRENT_EXPERIMENT.get('device_image'):
         save_device_image()
     return plot, data

From 17475f28a1e2568b50759f582fc322552a2ce3ee Mon Sep 17 00:00:00 2001
From: Jens Hedegaard Nielsen 
Date: Thu, 30 Mar 2017 13:16:07 +0200
Subject: [PATCH 259/328] Fix: sort parameters in device annotator

---
 qcodes/utils/qcodes_device_annotator.py | 1 +
 1 file changed, 1 insertion(+)

diff --git a/qcodes/utils/qcodes_device_annotator.py b/qcodes/utils/qcodes_device_annotator.py
index 130e535a3749..51827ac6fa2d 100644
--- a/qcodes/utils/qcodes_device_annotator.py
+++ b/qcodes/utils/qcodes_device_annotator.py
@@ -73,6 +73,7 @@ def addStation(self, parent, station):
                 paramitem = gui.QStandardItem(param)
                 paramitem.setEditable(False)
                 item.appendRow(paramitem)
+            item.sortChildren(0)
 
     def loadimage(self):
         """

From 98e3b0a7d1f087b0399facf8669eb236f85adb9b Mon Sep 17 00:00:00 2001
From: Jens Hedegaard Nielsen 
Date: Fri, 31 Mar 2017 16:44:00 +0200
Subject: [PATCH 260/328] Fix: correctly sort instruments in device annotator
 (#16)

* Fix: correctly sort instruments in device annotator

* Fix: device annotator improve formatting

* Fix: use pyqt5 directly
---
 qcodes/utils/qcodes_device_annotator.py | 21 +++++++++++++++------
 1 file changed, 15 insertions(+), 6 deletions(-)

diff --git a/qcodes/utils/qcodes_device_annotator.py b/qcodes/utils/qcodes_device_annotator.py
index 51827ac6fa2d..b7e4a8384ccb 100644
--- a/qcodes/utils/qcodes_device_annotator.py
+++ b/qcodes/utils/qcodes_device_annotator.py
@@ -4,9 +4,9 @@
 import os
 import json
 import glob
-import qtpy.QtWidgets as qt
-import qtpy.QtGui as gui
-import qtpy.QtCore as core
+import PyQt5.QtWidgets as qt
+import PyQt5.QtGui as gui
+import PyQt5.QtCore as core
 
 from shutil import copyfile
 
@@ -49,7 +49,8 @@ def __init__(self, folder, station):
         self.model.setHorizontalHeaderLabels([self.tr("Instruments")])
         self.addStation(self.model, station)
         self.treeView.setModel(self.model)
-
+        self.treeView.sortByColumn(0, core.Qt.AscendingOrder)
+        self.treeView.setSortingEnabled(True)
         grid.addWidget(self.imageCanvas, 0, 0, 4, 6)
         grid.addWidget(self.loadButton, 4, 0)
         grid.addWidget(self.labelButton, 4, 1)
@@ -73,7 +74,6 @@ def addStation(self, parent, station):
                 paramitem = gui.QStandardItem(param)
                 paramitem.setEditable(False)
                 item.appendRow(paramitem)
-            item.sortChildren(0)
 
     def loadimage(self):
         """
@@ -262,7 +262,16 @@ def updateValues(self, station):
 
         for instrument, parameters in self._data.items():
             for parameter in parameters.keys():
-                self._data[instrument][parameter]['value'] = str(station.components[instrument][parameter].get_latest())
+                value = station.components[instrument][parameter].get_latest()
+                try:
+                    floatvalue = float(station.components[instrument][parameter].get_latest())
+                    if floatvalue > 1000 or floatvalue < 0.1:
+                        valuestr = "{:.2e}".format(floatvalue)
+                    else:
+                        valuestr = "{:.2f}".format(floatvalue)
+                except ValueError:
+                    valuestr = str(value)
+                self._data[instrument][parameter]['value'] = valuestr
 
     def makePNG(self, counter, path=None):
         """

From 36836b47311f9c7ac29c54afd80d2a3ab14d72a7 Mon Sep 17 00:00:00 2001
From: Giulio Ungaretti 
Date: Fri, 31 Mar 2017 16:44:12 +0200
Subject: [PATCH 261/328] Feature/monitor (#15)
MIME-Version: 1.0
Content-Type: text/plain; charset=UTF-8
Content-Transfer-Encoding: 8bit

* First monitor commit, now 🍔 !

* polish things up

* types for the masses

* chore: Refactor monitor

* fix: Add todo remove space

* refactor

* silm api

* better monitor

* fix: Add static files to pacakge setup

* fix: Add websocket dependency

* fix: Use http with webbrowser open
---
 qcodes/__init__.py                        |   1 +
 qcodes/monitor/dist/css/main.3cd7fa7f.css |   1 +
 qcodes/monitor/dist/favicon.ico           | Bin 0 -> 103614 bytes
 qcodes/monitor/dist/index.html            |   1 +
 qcodes/monitor/dist/js/main.6641e1a4.js   |   3 +
 qcodes/monitor/dist/webpack-assets.json   |   1 +
 qcodes/monitor/monitor.py                 | 211 ++++++++++++++++++++++
 setup.py                                  |   7 +-
 8 files changed, 222 insertions(+), 3 deletions(-)
 create mode 100644 qcodes/monitor/dist/css/main.3cd7fa7f.css
 create mode 100644 qcodes/monitor/dist/favicon.ico
 create mode 100644 qcodes/monitor/dist/index.html
 create mode 100644 qcodes/monitor/dist/js/main.6641e1a4.js
 create mode 100644 qcodes/monitor/dist/webpack-assets.json
 create mode 100644 qcodes/monitor/monitor.py

diff --git a/qcodes/__init__.py b/qcodes/__init__.py
index c08b890547eb..89e3c34289ed 100644
--- a/qcodes/__init__.py
+++ b/qcodes/__init__.py
@@ -33,6 +33,7 @@
 from qcodes.loops import Loop, active_loop, active_data_set
 from qcodes.measure import Measure
 from qcodes.actions import Task, Wait, BreakIf
+from qcodes.monitor.monitor import Monitor
 
 from qcodes.data.data_set import DataSet, new_data, load_data
 from qcodes.data.location import FormatLocation
diff --git a/qcodes/monitor/dist/css/main.3cd7fa7f.css b/qcodes/monitor/dist/css/main.3cd7fa7f.css
new file mode 100644
index 000000000000..248fe4f5913e
--- /dev/null
+++ b/qcodes/monitor/dist/css/main.3cd7fa7f.css
@@ -0,0 +1 @@
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index 000000000000..8e74faa7b767
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\ No newline at end of file
diff --git a/qcodes/monitor/dist/webpack-assets.json b/qcodes/monitor/dist/webpack-assets.json
new file mode 100644
index 000000000000..dac0b1cb6428
--- /dev/null
+++ b/qcodes/monitor/dist/webpack-assets.json
@@ -0,0 +1 @@
+{"main":{"js":"/js/main.6641e1a4.js","css":"/css/main.3cd7fa7f.css"}}
\ No newline at end of file
diff --git a/qcodes/monitor/monitor.py b/qcodes/monitor/monitor.py
new file mode 100644
index 000000000000..d82ba0cabf4b
--- /dev/null
+++ b/qcodes/monitor/monitor.py
@@ -0,0 +1,211 @@
+#! /usr/bin/env python
+# -*- coding: utf-8 -*-
+# vim:fenc=utf-8
+#
+# Copyright © 2017 unga 
+#
+# Distributed under terms of the MIT license.
+"""
+Monitor a set of parameter in a background thread
+stream opuput over websocket
+"""
+
+import asyncio
+import logging
+import os
+import time
+import json
+import http.server
+import socketserver
+import webbrowser
+
+from threading import Thread
+from typing import Dict
+from concurrent.futures import Future
+from concurrent.futures import CancelledError
+import functools
+
+import websockets
+
+SERVER_PORT = 3000
+
+log = logging.getLogger(__name__)
+
+
+def _get_metadata(*parameters) -> Dict[float, list]:
+    """
+    Return a dict that contains the paraemter metadata grouped by the
+    instrument it belongs to.
+    """
+    ts = time.time()
+    # group meta data by instrument if any
+    metas = {}
+    for parameter in parameters:
+        _meta = getattr(parameter, "_latest", None)
+        if _meta:
+            meta = _meta()
+        else:
+            raise ValueError("Input is not a paraemter; Refusing to proceed")
+        # convert to string
+        meta['value'] = str(meta['value'])
+        if meta["ts"] is not None:
+            meta["ts"] = time.mktime(meta["ts"].timetuple())
+        meta["name"] = parameter.label or parameter.name
+        meta["unit"] = parameter.unit
+        accumulator = metas.get(str(parameter._instrument), [])
+        accumulator.append(meta)
+        metas[str(parameter._instrument)] = accumulator
+    parameters = []
+    for instrument in metas:
+        temp = {"instrument": instrument, "parameters": metas[instrument]}
+        parameters.append(temp)
+    state = {"ts": ts, "parameters": parameters}
+    return state
+
+
+def _handler(parameters, interval: int):
+
+    async def serverFunc(websocket, path):
+        while True:
+            try:
+                try:
+                    meta = _get_metadata(*parameters)
+                except ValueError as e:
+                    log.exception(e)
+                    break
+                log.debug("sending..")
+                try:
+                    await websocket.send(json.dumps(meta))
+                # mute browser discconects
+                except websockets.exceptions.ConnectionClosed as e:
+                    log.debug(e)
+                    pass
+                await asyncio.sleep(interval)
+            except CancelledError:
+                break
+        log.debug("closing sever")
+
+    return serverFunc
+
+
+class Monitor(Thread):
+    running = None
+    server = None
+
+    def __init__(self, *parameters, interval=1):
+        """
+        Monitor qcodes parameters.
+
+        Args:
+            *parameters: Parameters to monitor
+            interval: How often one wants to refresh the values
+        """
+        super().__init__()
+        self.loop = None
+        # start the server to server monitor http/files
+        if Monitor.server:
+            self.show()
+        else:
+            Monitor.server = Server(port=SERVER_PORT)
+            Monitor.server.start()
+        self._monitor(*parameters, interval=1)
+
+    def run(self):
+        """
+        Start the event loop and run forever
+        """
+        self.loop = asyncio.new_event_loop()
+        asyncio.set_event_loop(self.loop)
+        Monitor.running = self
+        self.show()
+        self.loop.run_forever()
+
+    def stop(self):
+        """
+        Shutodwn the server, close the event loop and join the thread
+        """
+        # this contains the server
+        # or any exception
+        server = self.future_restult.result()
+        # server.close()
+        self.loop.call_soon_threadsafe(server.close)
+        self.loop.call_soon_threadsafe(self.loop.stop)
+        self.join()
+        Monitor.running = None
+
+    @staticmethod
+    def show():
+        """
+        Overwrite this method to show/raise your monitor GUI
+        F.ex.
+
+        ::
+
+            import webbrowser
+            url = "localhost:3000"
+            # Open URL in new window, raising the window if possible.
+            webbrowser.open_new(url)
+
+        """
+        webbrowser.open("http://localhost:{}".format(SERVER_PORT))
+
+    def _monitor(self, *parameters, interval=1):
+        handler = _handler(parameters, interval=interval)
+        # TODO (giulioungaretti) read from config
+        server = websockets.serve(handler, '127.0.0.1', 5678)
+
+        log.debug("Start monitoring thread")
+
+        if Monitor.running:
+            # stop the old server
+            log.debug("Stoppging and restarting server")
+            Monitor.running.stop()
+
+        self.start()
+
+        # let the thread start
+        time.sleep(0.001)
+
+        log.debug("Start monitoring server")
+        self._add_task(server)
+
+    def _create_task(self, future, coro):
+        task = self.loop.create_task(coro)
+        future.set_result(task)
+
+    def _add_task(self, coro):
+        future = Future()
+        self.task = coro
+        p = functools.partial(self._create_task, future, coro)
+        self.loop.call_soon_threadsafe(p)
+        # this stores the result of the future
+        self.future_restult = future.result()
+        self.future_restult.add_done_callback(_log_result)
+
+
+def _log_result(future):
+    try:
+        future.result()
+        log.debug("Started server loop")
+    except:
+        log.exception("Could not start server loop")
+
+
+class Server(Thread):
+
+    def __init__(self, port=3000):
+        self.port = port
+        self.handler = http.server.SimpleHTTPRequestHandler
+        self.httpd = socketserver.TCPServer(("", self.port), self.handler)
+        self.static_dir = os.path.join(os.path.dirname(__file__), 'dist')
+        super().__init__()
+
+    def run(self):
+        os.chdir(self.static_dir)
+        log.debug("serving directory %s", self.static_dir)
+        log.debug("serving at port", self.port)
+        self.httpd.serve_forever()
+
+    def stop(self):
+        self.httpd.shutdown()
+        self.join()
diff --git a/setup.py b/setup.py
index 4bc1c18289b5..3e148dad9120 100644
--- a/setup.py
+++ b/setup.py
@@ -54,14 +54,15 @@ def readme():
       # if we want to install without tests:
       # packages=find_packages(exclude=["*.tests", "tests"]),
       packages=find_packages(),
-      package_data={'qcodes': ['widgets/*.js', 'widgets/*.css', 'config/*.json']},
-      install_requires= [
+      package_data={'qcodes': ['monitor/dist/*', 'config/*.json']},
+      install_requires=[
           'numpy>=1.10',
           'pyvisa>=1.8',
           'ipython>=4.1.0',
           'ipykernel!=4.6.0', # https://github.com/ipython/ipykernel/issues/240 in 4.6
           'jupyter>=1.0.0',
-          'h5py>=2.6'
+          'h5py>=2.6',
+          'websockets>=3.2'
       ],
 
       test_suite='qcodes.tests',

From b3d04d833f877b9f3d25c50cab38efd45c2815ae Mon Sep 17 00:00:00 2001
From: Giulio Ungaretti 
Date: Mon, 3 Apr 2017 13:59:26 +0200
Subject: [PATCH 262/328] fix: Include static, add init

---
 qcodes/monitor/__init__.py | 0
 setup.py                   | 3 ++-
 2 files changed, 2 insertions(+), 1 deletion(-)
 create mode 100644 qcodes/monitor/__init__.py

diff --git a/qcodes/monitor/__init__.py b/qcodes/monitor/__init__.py
new file mode 100644
index 000000000000..e69de29bb2d1
diff --git a/setup.py b/setup.py
index 3e148dad9120..0c3354e2e103 100644
--- a/setup.py
+++ b/setup.py
@@ -54,7 +54,8 @@ def readme():
       # if we want to install without tests:
       # packages=find_packages(exclude=["*.tests", "tests"]),
       packages=find_packages(),
-      package_data={'qcodes': ['monitor/dist/*', 'config/*.json']},
+      package_data={'qcodes': ['monitor/dist/*', 'monitor/dist/js/*',
+                               'monitor/dist/css/*', 'config/*.json']},
       install_requires=[
           'numpy>=1.10',
           'pyvisa>=1.8',

From 01637b044addb110862a7eb917e0e8a5e6a26c23 Mon Sep 17 00:00:00 2001
From: Jens Hedegaard Nielsen 
Date: Mon, 3 Apr 2017 17:25:07 +0200
Subject: [PATCH 263/328] Fix: also catch TypeError (#18)

May happen if the value is None
---
 qcodes/utils/qcodes_device_annotator.py | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

diff --git a/qcodes/utils/qcodes_device_annotator.py b/qcodes/utils/qcodes_device_annotator.py
index b7e4a8384ccb..8e04754b3611 100644
--- a/qcodes/utils/qcodes_device_annotator.py
+++ b/qcodes/utils/qcodes_device_annotator.py
@@ -269,7 +269,7 @@ def updateValues(self, station):
                         valuestr = "{:.2e}".format(floatvalue)
                     else:
                         valuestr = "{:.2f}".format(floatvalue)
-                except ValueError:
+                except (ValueError, TypeError):
                     valuestr = str(value)
                 self._data[instrument][parameter]['value'] = valuestr
 

From 8e0a0fb64660eb8f880c929a9e466d27e176acaa Mon Sep 17 00:00:00 2001
From: Jens Hedegaard Nielsen 
Date: Mon, 3 Apr 2017 17:29:53 +0200
Subject: [PATCH 264/328] Fix: change do1d/do2d wrappers to use num points

---
 qcodes/utils/wrappers.py | 28 ++++++++++++++--------------
 1 file changed, 14 insertions(+), 14 deletions(-)

diff --git a/qcodes/utils/wrappers.py b/qcodes/utils/wrappers.py
index 5e80235d1530..40d358921f46 100644
--- a/qcodes/utils/wrappers.py
+++ b/qcodes/utils/wrappers.py
@@ -186,14 +186,14 @@ def save_device_image():
                             '{:03d}'.format(counter)))
 
 
-def do1d(inst_set, start, stop, division, delay, *inst_meas):
+def do1d(inst_set, start, stop, num_points, delay, *inst_meas):
     """
 
     Args:
         inst_set:  Instrument to sweep over
         start:  Start of sweep
         stop:  End of sweep
-        division:  Spacing between values
+        num_points:  Number of steps to perform
         delay:  Delay at every step
         *inst_meas:  any number of instrument to measure and/or tasks to
             perform at each step of the sweep
@@ -203,7 +203,7 @@ def do1d(inst_set, start, stop, division, delay, *inst_meas):
 
     """
     loop = qc.Loop(inst_set.sweep(start,
-                                  stop, division), delay).each(*inst_meas)
+                                  stop, num=num_points), delay).each(*inst_meas)
     data = loop.get_data_set()
     plottables = _select_plottables(inst_meas)
     plot = _plot_setup(data, plottables)
@@ -218,7 +218,7 @@ def do1d(inst_set, start, stop, division, delay, *inst_meas):
     return plot, data
 
 
-def do1dDiagonal(inst_set, inst2_set, start, stop, division, delay, start2, slope, *inst_meas):
+def do1dDiagonal(inst_set, inst2_set, start, stop, num_points, delay, start2, slope, *inst_meas):
     """
     Perform diagonal sweep in 1 dimension, given two instruments
 
@@ -227,7 +227,7 @@ def do1dDiagonal(inst_set, inst2_set, start, stop, division, delay, start2, slop
         inst2_set: Second instrument to sweep over
         start:  Start of sweep
         stop:  End of sweep
-        division:  Spacing between values
+        num_points:  Number of steps to perform
         delay:  Delay at every step
         start2:  Second start point
         slope:  slope of the diagonal cut
@@ -237,7 +237,7 @@ def do1dDiagonal(inst_set, inst2_set, start, stop, division, delay, start2, slop
         plot, data : returns the plot and the dataset
 
     """
-    loop = qc.Loop(inst_set.sweep(start, stop, division), delay).each(
+    loop = qc.Loop(inst_set.sweep(start, stop, num=num_points), delay).each(
         qc.Task(inst2_set, (inst_set) * slope + (slope * start - start2)), *inst_meas, inst2_set)
     data = loop.get_data_set()
     plottables = _select_plottables(inst_meas)
@@ -252,20 +252,20 @@ def do1dDiagonal(inst_set, inst2_set, start, stop, division, delay, start2, slop
     return plot, data
 
 
-def do2d(inst_set, start, stop, division, delay, inst_set2, start2, stop2, division2, delay2, *inst_meas):
+def do2d(inst_set, start, stop, num_points, delay, inst_set2, start2, stop2, num_points2, delay2, *inst_meas):
     """
 
     Args:
         inst_set:  Instrument to sweep over
         start:  Start of sweep
         stop:  End of sweep
-        division:  Spacing between values
+        num_points:  Number of steps to perform
         delay:  Delay at every step
-        inst_set_2:  Second instrument to sweep over
-        start_2:  Start of sweep for second instrument
-        stop_2:  End of sweep for second instrument
-        division_2:  Spacing between values for second instrument
-        delay_2:  Delay at every step for second instrument
+        inst_set2:  Second instrument to sweep over
+        start2:  Start of sweep for second instrument
+        stop2:  End of sweep for second instrument
+        num_points2:  Number of steps to perform
+        delay2:  Delay at every step for second instrument
         *inst_meas:
 
     Returns:
@@ -276,7 +276,7 @@ def do2d(inst_set, start, stop, division, delay, inst_set2, start2, stop2, divis
         if getattr(inst, "setpoints", False):
             raise ValueError("3d plotting is not supported")
 
-    loop = qc.Loop(inst_set.sweep(start, stop, division), delay).loop(inst_set2.sweep(start2,stop2,division2), delay2).each(
+    loop = qc.Loop(inst_set.sweep(start, stop, num=num_points), delay).loop(inst_set2.sweep(start2,stop2,num=num_points2), delay2).each(
         *inst_meas)
     data = loop.get_data_set()
     plottables = _select_plottables(inst_meas)

From c7cb6502286d25af05d51dcbdd1ffc5346a6e2c7 Mon Sep 17 00:00:00 2001
From: Jens Hedegaard Nielsen 
Date: Tue, 4 Apr 2017 15:58:16 +0200
Subject: [PATCH 265/328] wrappers plotting changes (#20)

* Fix: always save png

Also when interrupted as requested

* Fix: Matplot always step color on new trace to match qtplot
---
 qcodes/utils/wrappers.py | 19 ++++++++++++++-----
 1 file changed, 14 insertions(+), 5 deletions(-)

diff --git a/qcodes/utils/wrappers.py b/qcodes/utils/wrappers.py
index 40d358921f46..6a2506172185 100644
--- a/qcodes/utils/wrappers.py
+++ b/qcodes/utils/wrappers.py
@@ -155,6 +155,9 @@ def _save_individual_plots(data, inst_meas):
         if getattr(i, "names", False):
             # deal with multidimensional parameter
             for k, name in enumerate(i.names):
+                # Step the color on all subplots no just on plots within the same axis/subplot
+                # this is to match the qcodes-pyqtplot behaviour.
+                color = 'C' + str(counter_two)
                 counter_two += 1
                 plot = MatPlot()
                 inst_meas_name = "{}_{}".format(i._instrument.name, name)
@@ -163,11 +166,14 @@ def _save_individual_plots(data, inst_meas):
                 plot.subplots[0].grid()
                 plot.save("{}_{:03d}.pdf".format(plot.get_default_title(), counter_two))
         else:
+            # Step the color on all subplots no just on plots within the same axis/subplot
+            # this is to match the qcodes-pyqtplot behaviour.
+            color = 'C'+str(counter_two)
             counter_two += 1
             plot = MatPlot()
-            # simple_parameters
+            # simple_parameter
             inst_meas_name = "{}_{}".format(i._instrument.name, i.name)
-            plot.add(getattr(data, inst_meas_name))
+            plot.add(getattr(data, inst_meas_name), color=color)
             plot.subplots[0].set_title(title)
             plot.subplots[0].grid()
             plot.save("{}_{:03d}.pdf".format(plot.get_default_title(), counter_two))
@@ -208,9 +214,10 @@ def do1d(inst_set, start, stop, num_points, delay, *inst_meas):
     plottables = _select_plottables(inst_meas)
     plot = _plot_setup(data, plottables)
     try:
-        _ = loop.with_bg_task(plot.update, plot.save).run()
+        _ = loop.with_bg_task(plot.update).run()
     except KeyboardInterrupt:
         print("Measurement Interrupted")
+    plot.save()
     _save_individual_plots(data, plottables)
     if CURRENT_EXPERIMENT.get('device_image'):
         log.debug('Saving device image')
@@ -243,9 +250,10 @@ def do1dDiagonal(inst_set, inst2_set, start, stop, num_points, delay, start2, sl
     plottables = _select_plottables(inst_meas)
     plot = _plot_setup(data, plottables)
     try:
-        _ = loop.with_bg_task(plot.update, plot.save).run()
+        _ = loop.with_bg_task(plot.update).run()
     except KeyboardInterrupt:
         print("Measurement Interrupted")
+    plot.save()
     _save_individual_plots(data, plottables)
     if CURRENT_EXPERIMENT.get('device_image'):
         save_device_image()
@@ -282,9 +290,10 @@ def do2d(inst_set, start, stop, num_points, delay, inst_set2, start2, stop2, num
     plottables = _select_plottables(inst_meas)
     plot = _plot_setup(data, plottables)
     try:
-        _ = loop.with_bg_task(plot.update, plot.save).run()
+        _ = loop.with_bg_task(plot.update).run()
     except KeyboardInterrupt:
         print("Measurement Interrupted")
+    plot.save()
     _save_individual_plots(data, plottables)
     if CURRENT_EXPERIMENT.get('device_image'):
         save_device_image()

From d19b16bbea6a22de498942f00c97e3e0f3c38dfc Mon Sep 17 00:00:00 2001
From: Jens Hedegaard Nielsen 
Date: Wed, 5 Apr 2017 10:16:51 +0200
Subject: [PATCH 266/328] Remove color for now it breaks 2d plots (#21)

---
 qcodes/utils/wrappers.py | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

diff --git a/qcodes/utils/wrappers.py b/qcodes/utils/wrappers.py
index 6a2506172185..20ad3c274869 100644
--- a/qcodes/utils/wrappers.py
+++ b/qcodes/utils/wrappers.py
@@ -173,7 +173,7 @@ def _save_individual_plots(data, inst_meas):
             plot = MatPlot()
             # simple_parameter
             inst_meas_name = "{}_{}".format(i._instrument.name, i.name)
-            plot.add(getattr(data, inst_meas_name), color=color)
+            plot.add(getattr(data, inst_meas_name))
             plot.subplots[0].set_title(title)
             plot.subplots[0].grid()
             plot.save("{}_{:03d}.pdf".format(plot.get_default_title(), counter_two))

From ab44131f55a62a5c8ffe8eb1f6601db2a9a7e61c Mon Sep 17 00:00:00 2001
From: Jens Hedegaard Nielsen 
Date: Wed, 5 Apr 2017 14:28:00 +0200
Subject: [PATCH 267/328] Device title (#24)

* make it possible to add a title to device annotator

* Fix: add default title to device annotator
---
 qcodes/utils/qcodes_device_annotator.py | 22 +++++++++++++++++++---
 qcodes/utils/wrappers.py                |  3 ++-
 2 files changed, 21 insertions(+), 4 deletions(-)

diff --git a/qcodes/utils/qcodes_device_annotator.py b/qcodes/utils/qcodes_device_annotator.py
index 8e04754b3611..997652e652bf 100644
--- a/qcodes/utils/qcodes_device_annotator.py
+++ b/qcodes/utils/qcodes_device_annotator.py
@@ -141,7 +141,7 @@ def saveAndClose(self):
         self.close()
 
     @staticmethod
-    def _renderImage(data, canvas, filename):
+    def _renderImage(data, canvas, filename, title=None):
         """
         Render an image
         """
@@ -154,6 +154,21 @@ def _renderImage(data, canvas, filename):
         spacing = int(label_size * 0.2)
 
         painter = gui.QPainter(pixmap)
+        if title:
+            painter.setBrush(gui.QColor(255, 255, 255, 100))
+            textfont = gui.QFont('Decorative', label_size)
+            textwidth = gui.QFontMetrics(textfont).width(title)
+            rectangle_width = textwidth + 2 * spacing
+            rectangle_height = label_size + 2 * spacing
+            painter.drawRect(0,
+                             0,
+                             rectangle_width,
+                             rectangle_height)
+            painter.drawText(core.QRectF(spacing, spacing,
+                                         rectangle_width, rectangle_height),
+                             core.Qt.AlignTop + core.Qt.AlignLeft,
+                             title)
+
         for instrument, parameters in data.items():
             for parameter, positions in parameters.items():
 
@@ -273,7 +288,7 @@ def updateValues(self, station):
                     valuestr = str(value)
                 self._data[instrument][parameter]['value'] = valuestr
 
-    def makePNG(self, counter, path=None):
+    def makePNG(self, counter, path=None, title=None):
         """
         Render the image with new voltage values and save it to disk
 
@@ -293,7 +308,8 @@ def makePNG(self, counter, path=None):
         grid.addWidget(win.imageCanvas)
         win.imageCanvas, pixmap = MakeDeviceImage._renderImage(self._data,
                                                                win.imageCanvas,
-                                                               self.filename)
+                                                               self.filename,
+                                                               title)
         filename = '{:03d}_deviceimage.png'.format(counter)
         if path:
             filename = os.path.join(path, filename)
diff --git a/qcodes/utils/wrappers.py b/qcodes/utils/wrappers.py
index 20ad3c274869..767ff178e721 100644
--- a/qcodes/utils/wrappers.py
+++ b/qcodes/utils/wrappers.py
@@ -181,6 +181,7 @@ def _save_individual_plots(data, inst_meas):
 
 def save_device_image():
     counter = CURRENT_EXPERIMENT['provider'].counter
+    title = "{} #{:03d}".format(CURRENT_EXPERIMENT["sample_name"], counter)
     di = CURRENT_EXPERIMENT['device_image']
     di.updateValues(CURRENT_EXPERIMENT['station'])
 
@@ -189,7 +190,7 @@ def save_device_image():
 
     di.makePNG(CURRENT_EXPERIMENT["provider"].counter,
                os.path.join(CURRENT_EXPERIMENT["exp_folder"],
-                            '{:03d}'.format(counter)))
+                            '{:03d}'.format(counter)), title)
 
 
 def do1d(inst_set, start, stop, num_points, delay, *inst_meas):

From ed94ce9a9ae553a840dce9c10f0f6b72eeff7e28 Mon Sep 17 00:00:00 2001
From: "William H.P. Nielsen" 
Date: Wed, 5 Apr 2017 14:29:07 +0200
Subject: [PATCH 268/328] fix: Make do1d log its dataset ID (#23)

second part of commit
---
 qcodes/utils/wrappers.py | 21 ++++++++++++++++++++-
 1 file changed, 20 insertions(+), 1 deletion(-)

diff --git a/qcodes/utils/wrappers.py b/qcodes/utils/wrappers.py
index 767ff178e721..81c469bf5184 100644
--- a/qcodes/utils/wrappers.py
+++ b/qcodes/utils/wrappers.py
@@ -66,9 +66,10 @@ def init(mainfolder:str, sample_name: str, station, plot_x_position=0.66,
         raise RuntimeWarning("History can't be saved refusing to proceed (use IPython/jupyter)")
     else:
         logfile = "{}{}".format(path_to_experiment_folder, "commands.log")
+        CURRENT_EXPERIMENT['logfile'] = logfile
         if not CURRENT_EXPERIMENT["logging_enabled"]:
             log.debug("Logging commands to: t{}".format(logfile))
-            ipython.magic("%logstart -t {} {}".format(logfile, "append"))
+            ipython.magic("%logstart -t -o {} {}".format(logfile, "append"))
             CURRENT_EXPERIMENT["logging_enabled"] = True
         else:
             log.debug("Logging already started at {}".format(logfile))
@@ -223,6 +224,12 @@ def do1d(inst_set, start, stop, num_points, delay, *inst_meas):
     if CURRENT_EXPERIMENT.get('device_image'):
         log.debug('Saving device image')
         save_device_image()
+
+    # add the measurement ID to the logfile
+    with open(CURRENT_EXPERIMENT['logfile'], 'a') as fid:
+        print("#[QCoDeS]# Saved dataset to: {}".format(data.location),
+              file=fid)
+
     return plot, data
 
 
@@ -258,6 +265,12 @@ def do1dDiagonal(inst_set, inst2_set, start, stop, num_points, delay, start2, sl
     _save_individual_plots(data, plottables)
     if CURRENT_EXPERIMENT.get('device_image'):
         save_device_image()
+
+    # add the measurement ID to the logfile
+    with open(CURRENT_EXPERIMENT['logfile'], 'a') as fid:
+        print("#[QCoDeS]# Saved dataset to: {}".format(data.location),
+              file=fid)
+
     return plot, data
 
 
@@ -298,6 +311,12 @@ def do2d(inst_set, start, stop, num_points, delay, inst_set2, start2, stop2, num
     _save_individual_plots(data, plottables)
     if CURRENT_EXPERIMENT.get('device_image'):
         save_device_image()
+
+    # add the measurement ID to the logfile
+    with open(CURRENT_EXPERIMENT['logfile'], 'a') as fid:
+        print("#[QCoDeS]# Saved dataset to: {}".format(data.location),
+              file=fid)
+
     return plot, data
 
 

From e2d15e21f6c85fefc64cf26a768fc112b023efb3 Mon Sep 17 00:00:00 2001
From: Jens Hedegaard Nielsen 
Date: Wed, 5 Apr 2017 14:29:44 +0200
Subject: [PATCH 269/328] Fix: only pass color to plot.add when doing a 1d plot
 and raster pcolormesh in pdf (#22)

* Fix: only pass color to plot.add when doing a 1d plot

Otherwise artifacts will be added to 2d plot this requires a hack

* Fix: rasterize pcolormesh plots in pdf

Significantly speeds up saving from 1m47s to 0.7s on my machine (100,5000) array. and makes pdfs that can be opened quickkly

* fix: only add grid to 1d plots in pdf
---
 qcodes/utils/wrappers.py | 26 ++++++++++++++++++++++----
 1 file changed, 22 insertions(+), 4 deletions(-)

diff --git a/qcodes/utils/wrappers.py b/qcodes/utils/wrappers.py
index 81c469bf5184..45707ff2994b 100644
--- a/qcodes/utils/wrappers.py
+++ b/qcodes/utils/wrappers.py
@@ -4,6 +4,7 @@
 from os import makedirs
 import os
 import logging
+from copy import deepcopy
 
 from qcodes.plots.pyqtgraph import QtPlot
 from qcodes.plots.qcmatplotlib import MatPlot
@@ -162,9 +163,19 @@ def _save_individual_plots(data, inst_meas):
                 counter_two += 1
                 plot = MatPlot()
                 inst_meas_name = "{}_{}".format(i._instrument.name, name)
-                plot.add(getattr(data, inst_meas_name))
+                inst_meas_data = getattr(data, inst_meas_name)
+                # this is a hack because expand_trace works
+                # in place. Also it should probably * expand its args and kwargs. N
+                # Same below
+                inst_meas_data_copy = deepcopy(inst_meas_data)
+                inst_meta_data = {}
+                plot.expand_trace((inst_meas_data_copy,), kwargs=inst_meta_data)
+                if 'z' in inst_meta_data:
+                    plot.add(inst_meas_data, rasterized=True)
+                else:
+                    plot.add(inst_meas_data, color=color)
+                    plot.subplots[0].grid()
                 plot.subplots[0].set_title(title)
-                plot.subplots[0].grid()
                 plot.save("{}_{:03d}.pdf".format(plot.get_default_title(), counter_two))
         else:
             # Step the color on all subplots no just on plots within the same axis/subplot
@@ -174,9 +185,16 @@ def _save_individual_plots(data, inst_meas):
             plot = MatPlot()
             # simple_parameter
             inst_meas_name = "{}_{}".format(i._instrument.name, i.name)
-            plot.add(getattr(data, inst_meas_name))
+            inst_meas_data = getattr(data, inst_meas_name)
+            inst_meas_data_copy = deepcopy(inst_meas_data)
+            inst_meta_data = {}
+            plot.expand_trace((inst_meas_data_copy,), kwargs=inst_meta_data)
+            if 'z' in inst_meta_data:
+                plot.add(inst_meas_data, rasterized=True)
+            else:
+                plot.add(inst_meas_data, color=color)
+                plot.subplots[0].grid()
             plot.subplots[0].set_title(title)
-            plot.subplots[0].grid()
             plot.save("{}_{:03d}.pdf".format(plot.get_default_title(), counter_two))
 
 

From 65833db8ce12eb39c65bfc04e8df871224a28faf Mon Sep 17 00:00:00 2001
From: Jens Hedegaard Nielsen 
Date: Wed, 5 Apr 2017 16:53:51 +0200
Subject: [PATCH 270/328] Fix: save pdf with all subplots (#25)

Only generate individual pdfs if more than one subplot
---
 qcodes/utils/wrappers.py | 87 ++++++++++++++++++++++++++++++----------
 1 file changed, 65 insertions(+), 22 deletions(-)

diff --git a/qcodes/utils/wrappers.py b/qcodes/utils/wrappers.py
index 45707ff2994b..72161674ee4f 100644
--- a/qcodes/utils/wrappers.py
+++ b/qcodes/utils/wrappers.py
@@ -109,46 +109,77 @@ def _select_plottables(tasks):
 def _plot_setup(data, inst_meas, useQT=True):
     title = "{} #{:03d}".format(CURRENT_EXPERIMENT["sample_name"],
                                 data.location_provider.counter)
+    num_subplots = 0
+    counter_two = 0
+    for j, i in enumerate(inst_meas):
+        if getattr(i, "names", False):
+            num_subplots += len(i.names)
+        else:
+            num_subplots += 1
     if useQT:
         plot = QtPlot(fig_x_position=CURRENT_EXPERIMENT['plot_x_position'])
     else:
-        plot = MatPlot()
+        plot = MatPlot(subplots=(1,num_subplots))
     for j, i in enumerate(inst_meas):
         if getattr(i, "names", False):
             # deal with multidimensional parameter
             for k, name in enumerate(i.names):
+                color = 'C' + str(counter_two)
+                counter_two += 1
                 inst_meas_name = "{}_{}".format(i._instrument.name, name)
-                plot.add(getattr(data, inst_meas_name), subplot=j + k + 1)
+                inst_meas_data = getattr(data, inst_meas_name)
+                inst_meta_data = __get_plot_type(inst_meas_data, plot)
                 if useQT:
+                    plot.add(inst_meas_data, subplot=j + k + 1)
                     plot.subplots[j+k].showGrid(True, True)
                     if j == 0:
                         plot.subplots[0].setTitle(title)
                     else:
                         plot.subplots[j+k].setTitle("")
                 else:
-                    plot.subplots[j+k].grid()
+                    if 'z' in inst_meta_data:
+                        plot.add(inst_meas_data, subplot=j + k + 1, rasterized=True)
+                    else:
+                        plot.add(inst_meas_data, subplot=j + k + 1, color=color)
+                        plot.subplots[j + k].grid()
                     if j == 0:
                         plot.subplots[0].set_title(title)
                     else:
                         plot.subplots[j+k].set_title("")
         else:
+            color = 'C' + str(counter_two)
+            counter_two += 1
             # simple_parameters
             inst_meas_name = "{}_{}".format(i._instrument.name, i.name)
-            plot.add(getattr(data, inst_meas_name), subplot=j + 1)
+            inst_meas_data = getattr(data, inst_meas_name)
+            inst_meta_data = __get_plot_type(inst_meas_data, plot)
             if useQT:
+                plot.add(inst_meas_data, subplot=j + 1)
                 plot.subplots[j].showGrid(True, True)
                 if j == 0:
                     plot.subplots[0].setTitle(title)
                 else:
                     plot.subplots[j].setTitle("")
             else:
-                plot.subplots[j].grid()
+                if 'z' in inst_meta_data:
+                    plot.add(inst_meas_data, subplot=j + 1, rasterized=True)
+                else:
+                    plot.add(inst_meas_data, subplot=j + 1, color=color)
+                    plot.subplots[j].grid()
                 if j == 0:
                     plot.subplots[0].set_title(title)
                 else:
                     plot.subplots[j].set_title("")
-    return plot
+    return plot, num_subplots
 
+def __get_plot_type(data, plot):
+    # this is a hack because expand_trace works
+    # in place. Also it should probably * expand its args and kwargs. N
+    # Same below
+    data_copy = deepcopy(data)
+    metadata = {}
+    plot.expand_trace((data_copy,), kwargs=metadata)
+    return metadata
 
 def _save_individual_plots(data, inst_meas):
     title = "{} #{:03d}".format(CURRENT_EXPERIMENT["sample_name"], data.location_provider.counter)
@@ -164,12 +195,7 @@ def _save_individual_plots(data, inst_meas):
                 plot = MatPlot()
                 inst_meas_name = "{}_{}".format(i._instrument.name, name)
                 inst_meas_data = getattr(data, inst_meas_name)
-                # this is a hack because expand_trace works
-                # in place. Also it should probably * expand its args and kwargs. N
-                # Same below
-                inst_meas_data_copy = deepcopy(inst_meas_data)
-                inst_meta_data = {}
-                plot.expand_trace((inst_meas_data_copy,), kwargs=inst_meta_data)
+                inst_meta_data = __get_plot_type(inst_meas_data, plot)
                 if 'z' in inst_meta_data:
                     plot.add(inst_meas_data, rasterized=True)
                 else:
@@ -186,9 +212,7 @@ def _save_individual_plots(data, inst_meas):
             # simple_parameter
             inst_meas_name = "{}_{}".format(i._instrument.name, i.name)
             inst_meas_data = getattr(data, inst_meas_name)
-            inst_meas_data_copy = deepcopy(inst_meas_data)
-            inst_meta_data = {}
-            plot.expand_trace((inst_meas_data_copy,), kwargs=inst_meta_data)
+            inst_meta_data = __get_plot_type(inst_meas_data, plot)
             if 'z' in inst_meta_data:
                 plot.add(inst_meas_data, rasterized=True)
             else:
@@ -232,13 +256,19 @@ def do1d(inst_set, start, stop, num_points, delay, *inst_meas):
                                   stop, num=num_points), delay).each(*inst_meas)
     data = loop.get_data_set()
     plottables = _select_plottables(inst_meas)
-    plot = _plot_setup(data, plottables)
+    plot, _ = _plot_setup(data, plottables)
     try:
         _ = loop.with_bg_task(plot.update).run()
     except KeyboardInterrupt:
         print("Measurement Interrupted")
     plot.save()
-    _save_individual_plots(data, plottables)
+    pdfplot, num_subplots = _plot_setup(data, plottables, useQT=False)
+    # pad a bit more to prevent overlap between
+    # suptitle and title
+    pdfplot.fig.tight_layout(pad=3)
+    pdfplot.save("{}.pdf".format(plot.get_default_title()))
+    if num_subplots > 1:
+        _save_individual_plots(data, plottables)
     if CURRENT_EXPERIMENT.get('device_image'):
         log.debug('Saving device image')
         save_device_image()
@@ -247,7 +277,6 @@ def do1d(inst_set, start, stop, num_points, delay, *inst_meas):
     with open(CURRENT_EXPERIMENT['logfile'], 'a') as fid:
         print("#[QCoDeS]# Saved dataset to: {}".format(data.location),
               file=fid)
-
     return plot, data
 
 
@@ -274,13 +303,20 @@ def do1dDiagonal(inst_set, inst2_set, start, stop, num_points, delay, start2, sl
         qc.Task(inst2_set, (inst_set) * slope + (slope * start - start2)), *inst_meas, inst2_set)
     data = loop.get_data_set()
     plottables = _select_plottables(inst_meas)
-    plot = _plot_setup(data, plottables)
+    plot, _ = _plot_setup(data, plottables)
     try:
         _ = loop.with_bg_task(plot.update).run()
     except KeyboardInterrupt:
         print("Measurement Interrupted")
     plot.save()
-    _save_individual_plots(data, plottables)
+    pdfplot, num_subplots = _plot_setup(data, plottables, useQT=False)
+    # pad a bit more to prevent overlap between
+    # suptitle and title
+    pdfplot.fig.tight_layout(pad=3)
+    pdfplot.save("{}.pdf".format(plot.get_default_title()))
+    if num_subplots > 1:
+        _save_individual_plots(data, plottables)
+    pdfplot.save("{}.pdf".format(plot.get_default_title()))
     if CURRENT_EXPERIMENT.get('device_image'):
         save_device_image()
 
@@ -320,13 +356,20 @@ def do2d(inst_set, start, stop, num_points, delay, inst_set2, start2, stop2, num
         *inst_meas)
     data = loop.get_data_set()
     plottables = _select_plottables(inst_meas)
-    plot = _plot_setup(data, plottables)
+    plot, _ = _plot_setup(data, plottables)
     try:
         _ = loop.with_bg_task(plot.update).run()
     except KeyboardInterrupt:
         print("Measurement Interrupted")
     plot.save()
-    _save_individual_plots(data, plottables)
+    pdfplot, num_subplots = _plot_setup(data, plottables, useQT=False)
+    # pad a bit more to prevent overlap between
+    # suptitle and title
+    pdfplot.fig.tight_layout(pad=3)
+    pdfplot.save("{}.pdf".format(plot.get_default_title()))
+    if num_subplots > 1:
+        _save_individual_plots(data, plottables)
+    pdfplot.save("{}.pdf".format(plot.get_default_title()))
     if CURRENT_EXPERIMENT.get('device_image'):
         save_device_image()
 

From c181899e565a9e44b58c45767513b4005f0f90a1 Mon Sep 17 00:00:00 2001
From: Giulio Ungaretti 
Date: Wed, 5 Apr 2017 14:57:13 +0200
Subject: [PATCH 271/328] wip: fix for monitor

---
 qcodes/monitor/monitor.py | 19 +++++++++++++------
 1 file changed, 13 insertions(+), 6 deletions(-)

diff --git a/qcodes/monitor/monitor.py b/qcodes/monitor/monitor.py
index d82ba0cabf4b..540688663b18 100644
--- a/qcodes/monitor/monitor.py
+++ b/qcodes/monitor/monitor.py
@@ -1,4 +1,4 @@
-#! /usr/bin/env python
+# ! /usr/bin/env python
 # -*- coding: utf-8 -*-
 # vim:fenc=utf-8
 #
@@ -19,10 +19,10 @@
 import socketserver
 import webbrowser
 
+from multiprocessing import Process
 from threading import Thread
 from typing import Dict
 from concurrent.futures import Future
-from concurrent.futures import CancelledError
 import functools
 
 import websockets
@@ -81,7 +81,7 @@ async def serverFunc(websocket, path):
                     log.debug(e)
                     pass
                 await asyncio.sleep(interval)
-            except CancelledError:
+            except asyncio.CancelledError:
                 break
         log.debug("closing sever")
 
@@ -103,11 +103,18 @@ def __init__(self, *parameters, interval=1):
         super().__init__()
         self.loop = None
         # start the server to server monitor http/files
+        # the server may have been already started in this
+        # session or it could be a zombie process
         if Monitor.server:
             self.show()
         else:
-            Monitor.server = Server(port=SERVER_PORT)
-            Monitor.server.start()
+            try:
+                Monitor.server = Server(port=SERVER_PORT)
+                Monitor.server.start()
+            except socketserver.socket.error as exc:
+                if exc.args[0] != 48:
+                    raise
+                self.show()
         self._monitor(*parameters, interval=1)
 
     def run(self):
@@ -191,7 +198,7 @@ def _log_result(future):
         log.exception("Could not start server loop")
 
 
-class Server(Thread):
+class Server(Process):
 
     def __init__(self, port=3000):
         self.port = port

From beb896d516d8a1738e6ec6d4a83bd41e516d47b0 Mon Sep 17 00:00:00 2001
From: Giulio Ungaretti 
Date: Thu, 6 Apr 2017 14:36:25 +0200
Subject: [PATCH 272/328] Revert "wip: fix for monitor"

This reverts commit 88e988fddb6cef33ed999795a705ce5b463a2712.
---
 qcodes/monitor/monitor.py | 19 ++++++-------------
 1 file changed, 6 insertions(+), 13 deletions(-)

diff --git a/qcodes/monitor/monitor.py b/qcodes/monitor/monitor.py
index 540688663b18..d82ba0cabf4b 100644
--- a/qcodes/monitor/monitor.py
+++ b/qcodes/monitor/monitor.py
@@ -1,4 +1,4 @@
-# ! /usr/bin/env python
+#! /usr/bin/env python
 # -*- coding: utf-8 -*-
 # vim:fenc=utf-8
 #
@@ -19,10 +19,10 @@
 import socketserver
 import webbrowser
 
-from multiprocessing import Process
 from threading import Thread
 from typing import Dict
 from concurrent.futures import Future
+from concurrent.futures import CancelledError
 import functools
 
 import websockets
@@ -81,7 +81,7 @@ async def serverFunc(websocket, path):
                     log.debug(e)
                     pass
                 await asyncio.sleep(interval)
-            except asyncio.CancelledError:
+            except CancelledError:
                 break
         log.debug("closing sever")
 
@@ -103,18 +103,11 @@ def __init__(self, *parameters, interval=1):
         super().__init__()
         self.loop = None
         # start the server to server monitor http/files
-        # the server may have been already started in this
-        # session or it could be a zombie process
         if Monitor.server:
             self.show()
         else:
-            try:
-                Monitor.server = Server(port=SERVER_PORT)
-                Monitor.server.start()
-            except socketserver.socket.error as exc:
-                if exc.args[0] != 48:
-                    raise
-                self.show()
+            Monitor.server = Server(port=SERVER_PORT)
+            Monitor.server.start()
         self._monitor(*parameters, interval=1)
 
     def run(self):
@@ -198,7 +191,7 @@ def _log_result(future):
         log.exception("Could not start server loop")
 
 
-class Server(Process):
+class Server(Thread):
 
     def __init__(self, port=3000):
         self.port = port

From ca1a1798c517c1eec9bbb84d93064500e226d6c3 Mon Sep 17 00:00:00 2001
From: Giulio Ungaretti 
Date: Thu, 6 Apr 2017 14:52:06 +0200
Subject: [PATCH 273/328] move server to onw  scripts

---
 qcodes/monitor/monitor.py | 14 ++++++--------
 1 file changed, 6 insertions(+), 8 deletions(-)

diff --git a/qcodes/monitor/monitor.py b/qcodes/monitor/monitor.py
index d82ba0cabf4b..0eb3a66302a5 100644
--- a/qcodes/monitor/monitor.py
+++ b/qcodes/monitor/monitor.py
@@ -102,12 +102,6 @@ def __init__(self, *parameters, interval=1):
         """
         super().__init__()
         self.loop = None
-        # start the server to server monitor http/files
-        if Monitor.server:
-            self.show()
-        else:
-            Monitor.server = Server(port=SERVER_PORT)
-            Monitor.server.start()
         self._monitor(*parameters, interval=1)
 
     def run(self):
@@ -191,14 +185,13 @@ def _log_result(future):
         log.exception("Could not start server loop")
 
 
-class Server(Thread):
+class Server():
 
     def __init__(self, port=3000):
         self.port = port
         self.handler = http.server.SimpleHTTPRequestHandler
         self.httpd = socketserver.TCPServer(("", self.port), self.handler)
         self.static_dir = os.path.join(os.path.dirname(__file__), 'dist')
-        super().__init__()
 
     def run(self):
         os.chdir(self.static_dir)
@@ -209,3 +202,8 @@ def run(self):
     def stop(self):
         self.httpd.shutdown()
         self.join()
+
+
+if __name__ == "__main__":
+    server = Server()
+    server.run()

From a4acea5d5709216f06a4621f6102f0f01f40eb2b Mon Sep 17 00:00:00 2001
From: unga 
Date: Thu, 6 Apr 2017 15:17:45 +0200
Subject: [PATCH 274/328] feat: make monitor server standalone

---
 qcodes/monitor/monitor.py | 12 ++++++++----
 1 file changed, 8 insertions(+), 4 deletions(-)

diff --git a/qcodes/monitor/monitor.py b/qcodes/monitor/monitor.py
index 0eb3a66302a5..9808083a0c6a 100644
--- a/qcodes/monitor/monitor.py
+++ b/qcodes/monitor/monitor.py
@@ -111,7 +111,6 @@ def run(self):
         self.loop = asyncio.new_event_loop()
         asyncio.set_event_loop(self.loop)
         Monitor.running = self
-        self.show()
         self.loop.run_forever()
 
     def stop(self):
@@ -196,7 +195,7 @@ def __init__(self, port=3000):
     def run(self):
         os.chdir(self.static_dir)
         log.debug("serving directory %s", self.static_dir)
-        log.debug("serving at port", self.port)
+        log.info("Open broswer at http://localhost::{}".format(self.port))
         self.httpd.serve_forever()
 
     def stop(self):
@@ -205,5 +204,10 @@ def stop(self):
 
 
 if __name__ == "__main__":
-    server = Server()
-    server.run()
+    server = Server(SERVER_PORT)
+    print("Open broswer at http://localhost:{}".format(server.port))
+    try:
+        webbrowser.open("http://localhost:{}".format(server.port))
+        server.run()
+    except KeyboardInterrupt:
+        exit()

From 27195894e814ee238d725f4dcb9f56c6d7999a69 Mon Sep 17 00:00:00 2001
From: unga 
Date: Thu, 6 Apr 2017 16:21:09 +0200
Subject: [PATCH 275/328] fix: Sleep before thread starts!

---
 qcodes/monitor/monitor.py | 4 +++-
 1 file changed, 3 insertions(+), 1 deletion(-)

diff --git a/qcodes/monitor/monitor.py b/qcodes/monitor/monitor.py
index 9808083a0c6a..04dc9f91a888 100644
--- a/qcodes/monitor/monitor.py
+++ b/qcodes/monitor/monitor.py
@@ -100,6 +100,8 @@ def __init__(self, *parameters, interval=1):
             *parameters: Parameters to monitor
             interval: How often one wants to refresh the values
         """
+        # let the thread start
+        time.sleep(0.01)
         super().__init__()
         self.loop = None
         self._monitor(*parameters, interval=1)
@@ -157,7 +159,7 @@ def _monitor(self, *parameters, interval=1):
         self.start()
 
         # let the thread start
-        time.sleep(0.001)
+        time.sleep(0.01)
 
         log.debug("Start monitoring server")
         self._add_task(server)

From c737f3e0ff05bc3bb614e2a624fd9e8b2ee9e876 Mon Sep 17 00:00:00 2001
From: Jens Hedegaard Nielsen 
Date: Sat, 8 Apr 2017 15:28:41 +0200
Subject: [PATCH 276/328] Fix: only raster plots if larger than 5000 datapoints
 (#28)

And add a note to pdf that this has happened
---
 qcodes/utils/wrappers.py | 142 +++++++++++++++++++--------------------
 1 file changed, 69 insertions(+), 73 deletions(-)

diff --git a/qcodes/utils/wrappers.py b/qcodes/utils/wrappers.py
index 72161674ee4f..02eeca6906b5 100644
--- a/qcodes/utils/wrappers.py
+++ b/qcodes/utils/wrappers.py
@@ -109,6 +109,7 @@ def _select_plottables(tasks):
 def _plot_setup(data, inst_meas, useQT=True):
     title = "{} #{:03d}".format(CURRENT_EXPERIMENT["sample_name"],
                                 data.location_provider.counter)
+    rasterized_note = " rasterized plot"
     num_subplots = 0
     counter_two = 0
     for j, i in enumerate(inst_meas):
@@ -120,56 +121,52 @@ def _plot_setup(data, inst_meas, useQT=True):
         plot = QtPlot(fig_x_position=CURRENT_EXPERIMENT['plot_x_position'])
     else:
         plot = MatPlot(subplots=(1,num_subplots))
+
+
+    def _create_plot(plot, i, name, data, counter_two, j, k):
+        color = 'C' + str(counter_two)
+        counter_two += 1
+        inst_meas_name = "{}_{}".format(i._instrument.name, name)
+        inst_meas_data = getattr(data, inst_meas_name)
+        inst_meta_data = __get_plot_type(inst_meas_data, plot)
+        if useQT:
+            plot.add(inst_meas_data, subplot=j + k + 1)
+            plot.subplots[j + k].showGrid(True, True)
+            if j == 0:
+                plot.subplots[0].setTitle(title)
+            else:
+                plot.subplots[j + k].setTitle("")
+        else:
+            if 'z' in inst_meta_data:
+                xlen, ylen = inst_meta_data['z'].shape
+                rasterized = xlen*ylen > 5000
+                plot.add(inst_meas_data, subplot=j + k + 1, rasterized=rasterized)
+            else:
+                plot.add(inst_meas_data, subplot=j + k + 1, color=color)
+                plot.subplots[j + k].grid()
+            if j == 0:
+                if rasterized:
+                    fulltitle = title + rasterized_note
+                else:
+                    fulltitle = title
+                plot.subplots[0].set_title(fulltitle)
+            else:
+                if rasterized:
+                    fulltitle = rasterized_note
+                else:
+                    fulltitle = ""
+                plot.subplots[j + k].set_title(fulltitle)
+
     for j, i in enumerate(inst_meas):
         if getattr(i, "names", False):
             # deal with multidimensional parameter
             for k, name in enumerate(i.names):
-                color = 'C' + str(counter_two)
+                _create_plot(plot, i, name, data, counter_two, j, k)
                 counter_two += 1
-                inst_meas_name = "{}_{}".format(i._instrument.name, name)
-                inst_meas_data = getattr(data, inst_meas_name)
-                inst_meta_data = __get_plot_type(inst_meas_data, plot)
-                if useQT:
-                    plot.add(inst_meas_data, subplot=j + k + 1)
-                    plot.subplots[j+k].showGrid(True, True)
-                    if j == 0:
-                        plot.subplots[0].setTitle(title)
-                    else:
-                        plot.subplots[j+k].setTitle("")
-                else:
-                    if 'z' in inst_meta_data:
-                        plot.add(inst_meas_data, subplot=j + k + 1, rasterized=True)
-                    else:
-                        plot.add(inst_meas_data, subplot=j + k + 1, color=color)
-                        plot.subplots[j + k].grid()
-                    if j == 0:
-                        plot.subplots[0].set_title(title)
-                    else:
-                        plot.subplots[j+k].set_title("")
         else:
-            color = 'C' + str(counter_two)
-            counter_two += 1
             # simple_parameters
-            inst_meas_name = "{}_{}".format(i._instrument.name, i.name)
-            inst_meas_data = getattr(data, inst_meas_name)
-            inst_meta_data = __get_plot_type(inst_meas_data, plot)
-            if useQT:
-                plot.add(inst_meas_data, subplot=j + 1)
-                plot.subplots[j].showGrid(True, True)
-                if j == 0:
-                    plot.subplots[0].setTitle(title)
-                else:
-                    plot.subplots[j].setTitle("")
-            else:
-                if 'z' in inst_meta_data:
-                    plot.add(inst_meas_data, subplot=j + 1, rasterized=True)
-                else:
-                    plot.add(inst_meas_data, subplot=j + 1, color=color)
-                    plot.subplots[j].grid()
-                if j == 0:
-                    plot.subplots[0].set_title(title)
-                else:
-                    plot.subplots[j].set_title("")
+            _create_plot(plot, i, i.name, data, counter_two, j, 0)
+            counter_two += 1
     return plot, num_subplots
 
 def __get_plot_type(data, plot):
@@ -182,44 +179,43 @@ def __get_plot_type(data, plot):
     return metadata
 
 def _save_individual_plots(data, inst_meas):
-    title = "{} #{:03d}".format(CURRENT_EXPERIMENT["sample_name"], data.location_provider.counter)
+
+    def _create_plot(i, name, data, counter_two):
+        # Step the color on all subplots no just on plots within the same axis/subplot
+        # this is to match the qcodes-pyqtplot behaviour.
+        title = "{} #{:03d}".format(CURRENT_EXPERIMENT["sample_name"], data.location_provider.counter)
+        rasterized_note = " rasterized plot full data available in datafile"
+        color = 'C' + str(counter_two)
+        counter_two += 1
+        plot = MatPlot()
+        inst_meas_name = "{}_{}".format(i._instrument.name, name)
+        inst_meas_data = getattr(data, inst_meas_name)
+        inst_meta_data = __get_plot_type(inst_meas_data, plot)
+        if 'z' in inst_meta_data:
+            xlen, ylen = inst_meta_data['z'].shape
+            rasterized = xlen * ylen > 5000
+            plot.add(inst_meas_data, rasterized=rasterized)
+        else:
+            plot.add(inst_meas_data, color=color)
+            plot.subplots[0].grid()
+        if rasterized:
+            plot.subplots[0].set_title(title + rasterized_note)
+        else:
+            plot.subplots[0].set_title(title)
+        plot.save("{}_{:03d}.pdf".format(plot.get_default_title(), counter_two))
+
+
     counter_two = 0
     for j, i in enumerate(inst_meas):
         if getattr(i, "names", False):
             # deal with multidimensional parameter
             for k, name in enumerate(i.names):
-                # Step the color on all subplots no just on plots within the same axis/subplot
-                # this is to match the qcodes-pyqtplot behaviour.
-                color = 'C' + str(counter_two)
+                _create_plot(i, name, data, counter_two)
                 counter_two += 1
-                plot = MatPlot()
-                inst_meas_name = "{}_{}".format(i._instrument.name, name)
-                inst_meas_data = getattr(data, inst_meas_name)
-                inst_meta_data = __get_plot_type(inst_meas_data, plot)
-                if 'z' in inst_meta_data:
-                    plot.add(inst_meas_data, rasterized=True)
-                else:
-                    plot.add(inst_meas_data, color=color)
-                    plot.subplots[0].grid()
-                plot.subplots[0].set_title(title)
-                plot.save("{}_{:03d}.pdf".format(plot.get_default_title(), counter_two))
         else:
-            # Step the color on all subplots no just on plots within the same axis/subplot
-            # this is to match the qcodes-pyqtplot behaviour.
-            color = 'C'+str(counter_two)
+            _create_plot(i, i.name, data, counter_two)
             counter_two += 1
-            plot = MatPlot()
-            # simple_parameter
-            inst_meas_name = "{}_{}".format(i._instrument.name, i.name)
-            inst_meas_data = getattr(data, inst_meas_name)
-            inst_meta_data = __get_plot_type(inst_meas_data, plot)
-            if 'z' in inst_meta_data:
-                plot.add(inst_meas_data, rasterized=True)
-            else:
-                plot.add(inst_meas_data, color=color)
-                plot.subplots[0].grid()
-            plot.subplots[0].set_title(title)
-            plot.save("{}_{:03d}.pdf".format(plot.get_default_title(), counter_two))
+
 
 
 def save_device_image():

From d55f1a2b0f477121f132e0a3eaa38b8f32c1c0e2 Mon Sep 17 00:00:00 2001
From: Jens Hedegaard Nielsen 
Date: Sat, 8 Apr 2017 15:29:03 +0200
Subject: [PATCH 277/328] Annotator improvements (#27)

* Fix: device annotator use left and right button to insert label and ano

* Fix: add device annotator
Fix: add tooltip explaining left and right click

* Fix: make device annotator label and formatter configurable

* Fix: use formatter in placeholder string

* Fix: rename loop parameter

* fix: make it possible to remove annotation/label

* Fix: mark parameters red in device annotator if sweept
---
 qcodes/utils/qcodes_device_annotator.py | 102 +++++++++++++++++++-----
 qcodes/utils/wrappers.py                |  10 +--
 2 files changed, 85 insertions(+), 27 deletions(-)

diff --git a/qcodes/utils/qcodes_device_annotator.py b/qcodes/utils/qcodes_device_annotator.py
index 997652e652bf..7031cf3f9711 100644
--- a/qcodes/utils/qcodes_device_annotator.py
+++ b/qcodes/utils/qcodes_device_annotator.py
@@ -32,17 +32,30 @@ def __init__(self, folder, station):
         self.setLayout(grid)
 
         self.imageCanvas = qt.QLabel()
+        self.imageCanvas.setToolTip("Left click to insert a label and right click to insert an annotation.")
         self.loadButton = qt.QPushButton('Load image')
-        self.labelButton = qt.QRadioButton("Insert Label")
-        self.labelButton.setChecked(True)
-        self.annotButton = qt.QRadioButton('Place annotation')
 
         self.okButton = qt.QPushButton('Save and close')
-
+        self.removeButton = qt.QPushButton('Remove')
+        self.removeButton.setToolTip("Remove annotation and label for this parameter")
+        self.labeltitle = qt.QLabel('Label:')
+        self.labelfield = qt.QLineEdit()
+        self.labelfield.setText('')
+        self.labelfield.setToolTip("String to be used as label. Defaults to instrument_parameter")
+        self.formattertitle = qt.QLabel('Formatter:')
+        self.formatterfield = qt.QLineEdit()
+        self.formatterfield.setText('')
+        self.formatterfield.setToolTip("Formatter to be used for this parameter:\n"
+                                       "Uses new style python formatters. I.e.\n"
+                                       "':.4f' standard formatter with 4 digits after the decimal point\n"
+                                       "':.2e' exponential formatter with 2 digits after the decimal point\n"
+                                       "Leave blank for default. \n"
+                                       "See www.pyformat.info for more examples")
         self.loadButton.clicked.connect(self.loadimage)
         self.imageCanvas.mousePressEvent = self.set_label_or_annotation
         self.imageCanvas.setStyleSheet('background-color: white')
         self.okButton.clicked.connect(self.saveAndClose)
+        self.removeButton.clicked.connect(self.remove_label_and_annotation)
 
         self.treeView = qt.QTreeView()
         self.model = gui.QStandardItemModel()
@@ -51,10 +64,14 @@ def __init__(self, folder, station):
         self.treeView.setModel(self.model)
         self.treeView.sortByColumn(0, core.Qt.AscendingOrder)
         self.treeView.setSortingEnabled(True)
+        self.treeView.clicked.connect(self.selection_changed)
         grid.addWidget(self.imageCanvas, 0, 0, 4, 6)
         grid.addWidget(self.loadButton, 4, 0)
-        grid.addWidget(self.labelButton, 4, 1)
-        grid.addWidget(self.annotButton, 4, 2)
+        grid.addWidget(self.labeltitle, 4, 1)
+        grid.addWidget(self.labelfield, 4, 2)
+        grid.addWidget(self.formattertitle, 4, 4)
+        grid.addWidget(self.formatterfield, 4, 5)
+        grid.addWidget(self.removeButton, 4, 8)
         grid.addWidget(self.okButton, 4, 9)
         grid.addWidget(self.treeView, 0, 6, 4, 4)
 
@@ -95,9 +112,21 @@ def loadimage(self):
         self.imageCanvas.setMaximumWidth(width)
         self.imageCanvas.setMaximumHeight(height)
 
-    def set_label_or_annotation(self, event):
-
+    def selection_changed(self):
+        if not self.treeView.selectedIndexes():
+            return
+        selected = self.treeView.selectedIndexes()[0]
+        selected_instrument = selected.parent().data()
+        selected_parameter = selected.data()
+        self.labelfield.setText("{}_{} ".format(selected_instrument, selected_parameter))
 
+    def set_label_or_annotation(self, event):
+        insertlabel = False
+        insertannotation = False
+        if event.button() == core.Qt.LeftButton:
+            insertlabel = True
+        elif event.button() == core.Qt.RightButton:
+            insertannotation = True
         # verify valid
         if not self.treeView.selectedIndexes():
             return
@@ -113,12 +142,29 @@ def set_label_or_annotation(self, event):
         if selected_parameter not in self._data[selected_instrument].keys():
             self._data[selected_instrument][selected_parameter] = {}
 
-        if self.labelButton.isChecked():
+        if insertlabel:
             self._data[selected_instrument][selected_parameter]['labelpos'] = (self.click_x, self.click_y)
-        elif self.annotButton.isChecked():
+            self._data[selected_instrument][selected_parameter]['labelstring'] = self.labelfield.text()
+        elif insertannotation:
             self._data[selected_instrument][selected_parameter]['annotationpos'] = (self.click_x, self.click_y)
-        self._data[selected_instrument][selected_parameter]['value'] = 'NaN'
+            if self.formatterfield.text():
+                formatstring  = '{' + self.formatterfield.text() + "}"
+                self._data[selected_instrument][selected_parameter]['annotationformatter'] = formatstring
+                self._data[selected_instrument][selected_parameter]['value'] = formatstring
+            else:
+                self._data[selected_instrument][selected_parameter]['value'] = 'NaN'
+
+        # draw it
+        self.imageCanvas, _ = self._renderImage(self._data,
+                                                self.imageCanvas,
+                                                self.filename)
 
+    def remove_label_and_annotation(self):
+        selected = self.treeView.selectedIndexes()[0]
+        selected_instrument = selected.parent().data()
+        selected_parameter = selected.data()
+        if selected_parameter in self._data[selected_instrument].keys():
+            self._data[selected_instrument][selected_parameter] = {}
         # draw it
         self.imageCanvas, _ = self._renderImage(self._data,
                                                 self.imageCanvas,
@@ -170,13 +216,19 @@ def _renderImage(data, canvas, filename, title=None):
                              title)
 
         for instrument, parameters in data.items():
-            for parameter, positions in parameters.items():
-
-                if 'labelpos' in positions:
-                    label_string = "{}_{} ".format(instrument, parameter)
-                    (lx, ly) = positions['labelpos']
-                    painter.setBrush(gui.QColor(255, 255, 255, 100))
+            for parameter, paramsettings in parameters.items():
 
+                if 'labelpos' in paramsettings:
+                    if paramsettings.get('labelstring'):
+                        label_string = paramsettings.get('labelstring')
+                    else:
+                        label_string = "{}_{} ".format(instrument, parameter)
+                    if paramsettings.get('update'):
+                        #parameters that are sweeped should be red.
+                        painter.setBrush(gui.QColor(255, 0, 0, 100))
+                    else:
+                        painter.setBrush(gui.QColor(255, 255, 255, 100))
+                    (lx, ly) = paramsettings['labelpos']
                     textfont = gui.QFont('Decorative', label_size)
                     textwidth = gui.QFontMetrics(textfont).width(label_string)
                     rectangle_start_x = lx - spacing
@@ -195,9 +247,9 @@ def _renderImage(data, canvas, filename, title=None):
                                      core.Qt.AlignCenter,
                                      label_string)
 
-                if 'annotationpos' in positions:
-                    (ax, ay) = positions['annotationpos']
-                    annotationstring = data[instrument][parameter]['value']
+                if 'annotationpos' in paramsettings:
+                    (ax, ay) = paramsettings['annotationpos']
+                    annotationstring = paramsettings['value']
 
                     textfont = gui.QFont('Decorative', label_size)
                     textwidth = gui.QFontMetrics(textfont).width(annotationstring)
@@ -270,7 +322,7 @@ def loadAnnotations(self):
         with open(json_filename, 'r') as fid:
             self._data = json.load(fid)
 
-    def updateValues(self, station):
+    def updateValues(self, station, sweeptparameters=None):
         """
         Update the data with actual voltages from the QDac
         """
@@ -280,13 +332,19 @@ def updateValues(self, station):
                 value = station.components[instrument][parameter].get_latest()
                 try:
                     floatvalue = float(station.components[instrument][parameter].get_latest())
-                    if floatvalue > 1000 or floatvalue < 0.1:
+                    if self._data[instrument][parameter].get('annotationformatter'):
+                        valuestr = self._data[instrument][parameter].get('annotationformatter').format(floatvalue)
+                    elif floatvalue > 1000 or floatvalue < 0.1:
                         valuestr = "{:.2e}".format(floatvalue)
                     else:
                         valuestr = "{:.2f}".format(floatvalue)
                 except (ValueError, TypeError):
                     valuestr = str(value)
                 self._data[instrument][parameter]['value'] = valuestr
+                if sweeptparameters:
+                    for sweeptparameter in sweeptparameters:
+                        if sweeptparameter._instrument.name == instrument and sweeptparameter.name == parameter:
+                            self._data[instrument][parameter]['update'] = True
 
     def makePNG(self, counter, path=None, title=None):
         """
diff --git a/qcodes/utils/wrappers.py b/qcodes/utils/wrappers.py
index 02eeca6906b5..11c62f241959 100644
--- a/qcodes/utils/wrappers.py
+++ b/qcodes/utils/wrappers.py
@@ -218,11 +218,11 @@ def _create_plot(i, name, data, counter_two):
 
 
 
-def save_device_image():
+def save_device_image(sweeptparameters):
     counter = CURRENT_EXPERIMENT['provider'].counter
     title = "{} #{:03d}".format(CURRENT_EXPERIMENT["sample_name"], counter)
     di = CURRENT_EXPERIMENT['device_image']
-    di.updateValues(CURRENT_EXPERIMENT['station'])
+    di.updateValues(CURRENT_EXPERIMENT['station'], sweeptparameters)
 
     log.debug(os.path.join(CURRENT_EXPERIMENT["exp_folder"],
                        '{:03d}'.format(counter)))
@@ -267,7 +267,7 @@ def do1d(inst_set, start, stop, num_points, delay, *inst_meas):
         _save_individual_plots(data, plottables)
     if CURRENT_EXPERIMENT.get('device_image'):
         log.debug('Saving device image')
-        save_device_image()
+        save_device_image((inst_set,))
 
     # add the measurement ID to the logfile
     with open(CURRENT_EXPERIMENT['logfile'], 'a') as fid:
@@ -314,7 +314,7 @@ def do1dDiagonal(inst_set, inst2_set, start, stop, num_points, delay, start2, sl
         _save_individual_plots(data, plottables)
     pdfplot.save("{}.pdf".format(plot.get_default_title()))
     if CURRENT_EXPERIMENT.get('device_image'):
-        save_device_image()
+        save_device_image((inst_set, inst2_set))
 
     # add the measurement ID to the logfile
     with open(CURRENT_EXPERIMENT['logfile'], 'a') as fid:
@@ -367,7 +367,7 @@ def do2d(inst_set, start, stop, num_points, delay, inst_set2, start2, stop2, num
         _save_individual_plots(data, plottables)
     pdfplot.save("{}.pdf".format(plot.get_default_title()))
     if CURRENT_EXPERIMENT.get('device_image'):
-        save_device_image()
+        save_device_image((inst_set, inst_set2))
 
     # add the measurement ID to the logfile
     with open(CURRENT_EXPERIMENT['logfile'], 'a') as fid:

From 4b2560125e8d288842045f5c73d0433ec057c8fd Mon Sep 17 00:00:00 2001
From: Jens Hedegaard Nielsen 
Date: Tue, 2 May 2017 11:16:17 +0200
Subject: [PATCH 278/328] Fix stupid error in wrappers (#31)

---
 qcodes/utils/wrappers.py | 2 ++
 1 file changed, 2 insertions(+)

diff --git a/qcodes/utils/wrappers.py b/qcodes/utils/wrappers.py
index 11c62f241959..3ac7f6d1c151 100644
--- a/qcodes/utils/wrappers.py
+++ b/qcodes/utils/wrappers.py
@@ -142,6 +142,7 @@ def _create_plot(plot, i, name, data, counter_two, j, k):
                 rasterized = xlen*ylen > 5000
                 plot.add(inst_meas_data, subplot=j + k + 1, rasterized=rasterized)
             else:
+                rasterized = False
                 plot.add(inst_meas_data, subplot=j + k + 1, color=color)
                 plot.subplots[j + k].grid()
             if j == 0:
@@ -196,6 +197,7 @@ def _create_plot(i, name, data, counter_two):
             rasterized = xlen * ylen > 5000
             plot.add(inst_meas_data, rasterized=rasterized)
         else:
+            rasterized = False
             plot.add(inst_meas_data, color=color)
             plot.subplots[0].grid()
         if rasterized:

From d9d17043c4f4516241a9aaabbf968ce65ecd85e5 Mon Sep 17 00:00:00 2001
From: Giulio Ungaretti 
Date: Wed, 10 May 2017 17:38:26 +0200
Subject: [PATCH 279/328] fix: Update monitor GUI

---
 qcodes/monitor/dist/css/main.0963d6b9.css                     | 1 +
 qcodes/monitor/dist/css/main.3cd7fa7f.css                     | 1 -
 qcodes/monitor/dist/index.html                                | 2 +-
 qcodes/monitor/dist/js/{main.6641e1a4.js => main.b221ee88.js} | 2 +-
 qcodes/monitor/dist/webpack-assets.json                       | 2 +-
 5 files changed, 4 insertions(+), 4 deletions(-)
 create mode 100644 qcodes/monitor/dist/css/main.0963d6b9.css
 delete mode 100644 qcodes/monitor/dist/css/main.3cd7fa7f.css
 rename qcodes/monitor/dist/js/{main.6641e1a4.js => main.b221ee88.js} (88%)

diff --git a/qcodes/monitor/dist/css/main.0963d6b9.css b/qcodes/monitor/dist/css/main.0963d6b9.css
new file mode 100644
index 000000000000..911f710db2c7
--- /dev/null
+++ b/qcodes/monitor/dist/css/main.0963d6b9.css
@@ -0,0 +1 @@
+body{font-family:Source Sans Pro,Trebuchet MS,Lucida Grande,Bitstream Vera Sans,Helvetica Neue,sans-serif;font-size:13px;margin:0;text-align:center;color:#293c4b}.header{display:-webkit-box;display:-webkit-flex;display:-ms-flexbox;display:flex;-webkit-flex-wrap:wrap;-ms-flex-wrap:wrap;flex-wrap:wrap}.parameters{margin:1em}.table-fill{background:#fff;border-radius:8px;border-collapse:collapse;height:320px;margin:auto;max-width:600px;padding:5px;width:100%;box-shadow:0 5px 10px rgba(0,0,0,.1)}.parameters th{color:#d5dde5;background:#1b1e24;border-bottom:4px solid #9ea7af;border-right:1px solid #343a45;font-size:13px;font-weight:100;padding:5px;text-align:left;text-shadow:0 1px 1px rgba(0,0,0,.1);vertical-align:middle}.parameters th:first-child{border-top-left-radius:3px}.parameters th:last-child{border-top-right-radius:3px;border-right:none}.parameters tr{border-top:1px solid #c1c3d1;border-bottom-:1px solid #c1c3d1;color:#666b85;font-size:13px;font-weight:400;text-shadow:0 1px 1px hsla(0,0%,100%,.1)}.parameters tr:hover td{background:#4e5066;color:#fff;border-top:1px solid #22262e;border-bottom:1px solid #22262e}.parameters tr:first-child{border-top:none}.parameters tr:last-child{border-bottom:none}.parameters tr:nth-child(odd) td{background:#ebebeb}.parameters tr:nth-child(odd):hover td{background:#4e5066}.parameters tr:last-child td:first-child{border-bottom-left-radius:3px}.parameters tr:last-child td:last-child{border-bottom-right-radius:3px}.parameters td{background:#fff;padding:3px;text-align:left;vertical-align:middle;font-weight:300;font-size:13px;text-shadow:-1px -1px 1px rgba(0,0,0,.1);border-right:1px solid #c1c3d1}.parameters td:last-child{border-right:0}.instrument{margin:5px;text-align:left}.instrument :hover{background:#4e5066;color:#fff}.parametersContainer{display:-webkit-box;display:-webkit-flex;display:-ms-flexbox;display:flex;-webkit-flex-wrap:wrap;-ms-flex-wrap:wrap;flex-wrap:wrap}
\ No newline at end of file
diff --git a/qcodes/monitor/dist/css/main.3cd7fa7f.css b/qcodes/monitor/dist/css/main.3cd7fa7f.css
deleted file mode 100644
index 248fe4f5913e..000000000000
--- a/qcodes/monitor/dist/css/main.3cd7fa7f.css
+++ /dev/null
@@ -1 +0,0 @@
-body{font-family:Source Sans Pro,Trebuchet MS,Lucida Grande,Bitstream Vera Sans,Helvetica Neue,sans-serif;font-size:20px;margin:0;text-align:center;color:#293c4b}.header{display:-webkit-box;display:-webkit-flex;display:-ms-flexbox;display:flex;-webkit-flex-wrap:wrap;-ms-flex-wrap:wrap;flex-wrap:wrap}.parameters{margin:1em}.table-fill{background:#fff;border-radius:8px;border-collapse:collapse;height:320px;margin:auto;max-width:600px;padding:5px;width:100%;box-shadow:0 5px 10px rgba(0,0,0,.1)}.parameters th{color:#d5dde5;background:#1b1e24;border-bottom:4px solid #9ea7af;border-right:1px solid #343a45;font-size:20px;font-weight:100;padding:10px;text-align:left;text-shadow:0 1px 1px rgba(0,0,0,.1);vertical-align:middle}.parameters th:first-child{border-top-left-radius:3px}.parameters th:last-child{border-top-right-radius:3px;border-right:none}.parameters tr{border-top:1px solid #c1c3d1;border-bottom-:1px solid #c1c3d1;color:#666b85;font-size:13px;font-weight:400;text-shadow:0 1px 1px hsla(0,0%,100%,.1)}.parameters tr:hover td{background:#4e5066;color:#fff;border-top:1px solid #22262e;border-bottom:1px solid #22262e}.parameters tr:first-child{border-top:none}.parameters tr:last-child{border-bottom:none}.parameters tr:nth-child(odd) td{background:#ebebeb}.parameters tr:nth-child(odd):hover td{background:#4e5066}.parameters tr:last-child td:first-child{border-bottom-left-radius:3px}.parameters tr:last-child td:last-child{border-bottom-right-radius:3px}.parameters td{background:#fff;padding:10px;text-align:left;vertical-align:middle;font-weight:300;font-size:18px;text-shadow:-1px -1px 1px rgba(0,0,0,.1);border-right:1px solid #c1c3d1}.parameters td:last-child{border-right:0}.instrument{margin:5px;text-align:left}
\ No newline at end of file
diff --git a/qcodes/monitor/dist/index.html b/qcodes/monitor/dist/index.html
index ef5bea7d990a..12db717023cc 100644
--- a/qcodes/monitor/dist/index.html
+++ b/qcodes/monitor/dist/index.html
@@ -1 +1 @@
-QCoDeS montior

\ No newline at end of file
+QCoDeS montior

\ No newline at end of file
diff --git a/qcodes/monitor/dist/js/main.6641e1a4.js b/qcodes/monitor/dist/js/main.b221ee88.js
similarity index 88%
rename from qcodes/monitor/dist/js/main.6641e1a4.js
rename to qcodes/monitor/dist/js/main.b221ee88.js
index 8e74faa7b767..08c84d652c7f 100644
--- a/qcodes/monitor/dist/js/main.6641e1a4.js
+++ b/qcodes/monitor/dist/js/main.b221ee88.js
@@ -1,3 +1,3 @@
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define&&define.amd)return void define([],function(){return $n});if("object"==typeof t)return void(t.exports=$n);var Gn=this.Elm;if("undefined"==typeof Gn)return void(this.Elm=$n);for(var Hn in $n){if(Hn in Gn)throw new Error("There are two Elm modules called `"+Hn+"` on this page! Rename one of them.");Gn[Hn]=$n[Hn]}}).call(this)}]);
\ No newline at end of file
diff --git a/qcodes/monitor/dist/webpack-assets.json b/qcodes/monitor/dist/webpack-assets.json
index dac0b1cb6428..397e5d7b63aa 100644
--- a/qcodes/monitor/dist/webpack-assets.json
+++ b/qcodes/monitor/dist/webpack-assets.json
@@ -1 +1 @@
-{"main":{"js":"/js/main.6641e1a4.js","css":"/css/main.3cd7fa7f.css"}}
\ No newline at end of file
+{"main":{"js":"/js/main.b221ee88.js","css":"/css/main.0963d6b9.css"}}
\ No newline at end of file

From d6e46d34a21ccb34ba00e051562c7b497837b069 Mon Sep 17 00:00:00 2001
From: "William H.P. Nielsen" 
Date: Thu, 11 May 2017 14:00:10 +0200
Subject: [PATCH 280/328] fix: Make do1d pre-flush VISA buffers (#32)

Make do1d flush the buffers of the involved instruments. This is to avoid instruments getting out of sync with their messages and consequently delivering garbage data.
---
 qcodes/utils/wrappers.py | 15 +++++++++++++++
 1 file changed, 15 insertions(+)

diff --git a/qcodes/utils/wrappers.py b/qcodes/utils/wrappers.py
index 3ac7f6d1c151..9e3929171b36 100644
--- a/qcodes/utils/wrappers.py
+++ b/qcodes/utils/wrappers.py
@@ -219,6 +219,17 @@ def _create_plot(i, name, data, counter_two):
             counter_two += 1
 
 
+def _flush_buffers(*insts):
+    """
+    Flush the VISA buffer of the provided insts.
+
+    Supposed to be called inside doNd like so:
+    _flush_buffers(inst_set, *inst_meas)
+    """
+    for inst in insts:
+        if hasattr(inst, 'visa_handle'):
+            inst.visa_handle.clear()
+
 
 def save_device_image(sweeptparameters):
     counter = CURRENT_EXPERIMENT['provider'].counter
@@ -250,6 +261,10 @@ def do1d(inst_set, start, stop, num_points, delay, *inst_meas):
         plot, data : returns the plot and the dataset
 
     """
+
+    # try to flush VISA buffers at the beginning of a measurement
+    _flush_buffers(inst_set, *inst_meas)
+
     loop = qc.Loop(inst_set.sweep(start,
                                   stop, num=num_points), delay).each(*inst_meas)
     data = loop.get_data_set()

From 54041f79cb011023dfaa5aa8d0411ffca511c247 Mon Sep 17 00:00:00 2001
From: "William H.P. Nielsen" 
Date: Thu, 1 Jun 2017 13:49:31 +0200
Subject: [PATCH 281/328] fix: preflush VISA buffers (#33)

* fix: preflush VISA buffers

Preflush VISA instrument buffers in doNd measurements

* fix: small errors in flushing

* Fix: also flush second instutrument in do1ddiag
---
 qcodes/utils/wrappers.py | 32 ++++++++++++++++++++++++--------
 1 file changed, 24 insertions(+), 8 deletions(-)

diff --git a/qcodes/utils/wrappers.py b/qcodes/utils/wrappers.py
index 9e3929171b36..9e508095071b 100644
--- a/qcodes/utils/wrappers.py
+++ b/qcodes/utils/wrappers.py
@@ -204,8 +204,8 @@ def _create_plot(i, name, data, counter_two):
             plot.subplots[0].set_title(title + rasterized_note)
         else:
             plot.subplots[0].set_title(title)
-        plot.save("{}_{:03d}.pdf".format(plot.get_default_title(), counter_two))
-
+        plot.save("{}_{:03d}.pdf".format(plot.get_default_title(),
+                                         counter_two))
 
     counter_two = 0
     for j, i in enumerate(inst_meas):
@@ -219,16 +219,24 @@ def _create_plot(i, name, data, counter_two):
             counter_two += 1
 
 
-def _flush_buffers(*insts):
+def _flush_buffers(*params):
     """
-    Flush the VISA buffer of the provided insts.
+    If possible, flush the VISA buffer of the instrument of the
+    provided parameters.
 
     Supposed to be called inside doNd like so:
     _flush_buffers(inst_set, *inst_meas)
     """
-    for inst in insts:
-        if hasattr(inst, 'visa_handle'):
-            inst.visa_handle.clear()
+
+    for param in params:
+        if hasattr(param, '_instrument'):
+            inst = param._instrument
+            if hasattr(inst, 'visa_handle'):
+                status_code = inst.visa_handle.clear()
+                if status_code is not None:
+                    log.warning("Cleared visa buffer on "
+                                "{} with status code {}".format(inst.name,
+                                                                status_code))
 
 
 def save_device_image(sweeptparameters):
@@ -238,7 +246,7 @@ def save_device_image(sweeptparameters):
     di.updateValues(CURRENT_EXPERIMENT['station'], sweeptparameters)
 
     log.debug(os.path.join(CURRENT_EXPERIMENT["exp_folder"],
-                       '{:03d}'.format(counter)))
+                           '{:03d}'.format(counter)))
 
     di.makePNG(CURRENT_EXPERIMENT["provider"].counter,
                os.path.join(CURRENT_EXPERIMENT["exp_folder"],
@@ -312,6 +320,10 @@ def do1dDiagonal(inst_set, inst2_set, start, stop, num_points, delay, start2, sl
         plot, data : returns the plot and the dataset
 
     """
+
+    # try to flush VISA buffers at the beginning of a measurement
+    _flush_buffers(inst_set, inst2_set, *inst_meas)
+
     loop = qc.Loop(inst_set.sweep(start, stop, num=num_points), delay).each(
         qc.Task(inst2_set, (inst_set) * slope + (slope * start - start2)), *inst_meas, inst2_set)
     data = loop.get_data_set()
@@ -361,6 +373,10 @@ def do2d(inst_set, start, stop, num_points, delay, inst_set2, start2, stop2, num
         plot, data : returns the plot and the dataset
 
     """
+
+    # try to flush VISA buffers at the beginning of a measurement
+    _flush_buffers(inst_set, inst_set2, *inst_meas)
+
     for inst in inst_meas:
         if getattr(inst, "setpoints", False):
             raise ValueError("3d plotting is not supported")

From 9d40eb354ef58621f9d7c4bd019e906f1aa5f4e6 Mon Sep 17 00:00:00 2001
From: Jens Hedegaard Nielsen 
Date: Thu, 1 Jun 2017 11:16:04 +0200
Subject: [PATCH 282/328] DoND reraise interupts so that loops of measurements
 can be safely stopped

---
 qcodes/utils/wrappers.py | 14 ++++++++++++--
 1 file changed, 12 insertions(+), 2 deletions(-)

diff --git a/qcodes/utils/wrappers.py b/qcodes/utils/wrappers.py
index 9e508095071b..4c6401c5131a 100644
--- a/qcodes/utils/wrappers.py
+++ b/qcodes/utils/wrappers.py
@@ -273,6 +273,7 @@ def do1d(inst_set, start, stop, num_points, delay, *inst_meas):
     # try to flush VISA buffers at the beginning of a measurement
     _flush_buffers(inst_set, *inst_meas)
 
+    interrupted = False
     loop = qc.Loop(inst_set.sweep(start,
                                   stop, num=num_points), delay).each(*inst_meas)
     data = loop.get_data_set()
@@ -281,6 +282,7 @@ def do1d(inst_set, start, stop, num_points, delay, *inst_meas):
     try:
         _ = loop.with_bg_task(plot.update).run()
     except KeyboardInterrupt:
+        interrupted = True
         print("Measurement Interrupted")
     plot.save()
     pdfplot, num_subplots = _plot_setup(data, plottables, useQT=False)
@@ -298,6 +300,8 @@ def do1d(inst_set, start, stop, num_points, delay, *inst_meas):
     with open(CURRENT_EXPERIMENT['logfile'], 'a') as fid:
         print("#[QCoDeS]# Saved dataset to: {}".format(data.location),
               file=fid)
+    if interrupted:
+        raise KeyboardInterrupt
     return plot, data
 
 
@@ -324,6 +328,7 @@ def do1dDiagonal(inst_set, inst2_set, start, stop, num_points, delay, start2, sl
     # try to flush VISA buffers at the beginning of a measurement
     _flush_buffers(inst_set, inst2_set, *inst_meas)
 
+    interrupted = False
     loop = qc.Loop(inst_set.sweep(start, stop, num=num_points), delay).each(
         qc.Task(inst2_set, (inst_set) * slope + (slope * start - start2)), *inst_meas, inst2_set)
     data = loop.get_data_set()
@@ -333,6 +338,7 @@ def do1dDiagonal(inst_set, inst2_set, start, stop, num_points, delay, start2, sl
         _ = loop.with_bg_task(plot.update).run()
     except KeyboardInterrupt:
         print("Measurement Interrupted")
+        interrupted = True
     plot.save()
     pdfplot, num_subplots = _plot_setup(data, plottables, useQT=False)
     # pad a bit more to prevent overlap between
@@ -349,7 +355,8 @@ def do1dDiagonal(inst_set, inst2_set, start, stop, num_points, delay, start2, sl
     with open(CURRENT_EXPERIMENT['logfile'], 'a') as fid:
         print("#[QCoDeS]# Saved dataset to: {}".format(data.location),
               file=fid)
-
+    if interrupted:
+        raise KeyboardInterrupt
     return plot, data
 
 
@@ -377,6 +384,7 @@ def do2d(inst_set, start, stop, num_points, delay, inst_set2, start2, stop2, num
     # try to flush VISA buffers at the beginning of a measurement
     _flush_buffers(inst_set, inst_set2, *inst_meas)
 
+    interrupted = False
     for inst in inst_meas:
         if getattr(inst, "setpoints", False):
             raise ValueError("3d plotting is not supported")
@@ -389,6 +397,7 @@ def do2d(inst_set, start, stop, num_points, delay, inst_set2, start2, stop2, num
     try:
         _ = loop.with_bg_task(plot.update).run()
     except KeyboardInterrupt:
+        interrupted = True
         print("Measurement Interrupted")
     plot.save()
     pdfplot, num_subplots = _plot_setup(data, plottables, useQT=False)
@@ -406,7 +415,8 @@ def do2d(inst_set, start, stop, num_points, delay, inst_set2, start2, stop2, num
     with open(CURRENT_EXPERIMENT['logfile'], 'a') as fid:
         print("#[QCoDeS]# Saved dataset to: {}".format(data.location),
               file=fid)
-
+    if interrupted:
+        raise KeyboardInterrupt
     return plot, data
 
 

From 34d410c5935994efa80fd93e2de422ec5f9ca94d Mon Sep 17 00:00:00 2001
From: "William H.P. Nielsen" 
Date: Tue, 13 Jun 2017 10:35:45 +0200
Subject: [PATCH 283/328] feat: Add a configreader (#35)

Add a file containing the configreader class. The config object is heavily used at T10.
---
 qcodes/utils/configreader.py | 84 ++++++++++++++++++++++++++++++++++++
 1 file changed, 84 insertions(+)
 create mode 100644 qcodes/utils/configreader.py

diff --git a/qcodes/utils/configreader.py b/qcodes/utils/configreader.py
new file mode 100644
index 000000000000..c4adc636e1da
--- /dev/null
+++ b/qcodes/utils/configreader.py
@@ -0,0 +1,84 @@
+# Module containing the config file reader class
+
+
+class Config:
+    """
+    Object to be used for interacting with the config file.
+
+    The ConfigFile is constantly synced with the config file on disk
+    (provided that only this object was used to change the file).
+
+    Args:
+        filename (str): The path to the configuration file on disk
+        isdefault (Optional[bool]): Whether this is the default Config object.
+            Default: True.
+
+    Attributes:
+        default (Union[None, Config]): A reference to the default Config
+            object, if it exists. Else None.
+    """
+
+    default = None
+
+    def __init__(self, filename, isdefault=True):
+
+        # If this is the default config object, we make it available as a
+        # class method.
+        if isdefault:
+            default = self
+
+        self._filename = filename
+        self._cfg = ConfigParser()
+        self._load()
+
+    def _load(self):
+        self._cfg.read(self._filename)
+
+    def reload(self):
+        """
+        Reload the file from disk.
+        """
+        self._load()
+
+    def get(self, section, field=None):
+        """
+        Gets the value of the specified section/field.
+        If no field is specified, the entire section is returned
+        as a dict.
+
+        Example: Config.get('QDac Channel Labels', '2')
+
+        Args:
+            section (str): Name of the section
+            field (Optional[str]): The field to return. If omitted, the entire
+                section is returned as a dict
+        """
+        # Try to be really clever about the input
+        if not isinstance(field, str) and (field is not None):
+            field = '{}'.format(field)
+
+        if field is None:
+            output = dict(zip(self._cfg[section].keys(),
+                              self._cfg[section].values()))
+        else:
+            output = self._cfg[section][field]
+
+        return output
+
+    def set(self, section, field, value):
+        """
+        Set a value in the config file.
+        Immediately writes to disk.
+
+        Args:
+            section (str): The name of the section to write to
+            field (str): The name of the field to write
+            value (Union[str, float, int]): The value to write
+        """
+        if not isinstance(value, str):
+            value = '{}'.format(value)
+
+        self._cfg[section][field] = value
+
+        with open(self._filename, 'w') as configfile:
+            self._cfg.write(configfile)

From ffc6cd208465583670753634e5cc68308a1a7323 Mon Sep 17 00:00:00 2001
From: "William H.P. Nielsen" 
Date: Tue, 13 Jun 2017 15:22:25 +0200
Subject: [PATCH 284/328] plot: Disable autoSIprefix for non-standard units
 (#36)

Make the Qt live plot disable autoSIprefix for non-standard units, such that we
avoid things like 'me^2/h' on graph axes.
---
 qcodes/utils/wrappers.py | 17 +++++++++++++++--
 1 file changed, 15 insertions(+), 2 deletions(-)

diff --git a/qcodes/utils/wrappers.py b/qcodes/utils/wrappers.py
index 4c6401c5131a..9498c6d7e722 100644
--- a/qcodes/utils/wrappers.py
+++ b/qcodes/utils/wrappers.py
@@ -54,7 +54,7 @@ def init(mainfolder:str, sample_name: str, station, plot_x_position=0.66,
     log.info("experiment started at {}".format(path_to_experiment_folder))
 
     loc_provider = qc.FormatLocation(
-        fmt= path_to_experiment_folder + '{counter}')
+        fmt=path_to_experiment_folder + '{counter}')
     qc.data.data_set.DataSet.location_provider = loc_provider
     CURRENT_EXPERIMENT["provider"] = loc_provider
 
@@ -122,7 +122,6 @@ def _plot_setup(data, inst_meas, useQT=True):
     else:
         plot = MatPlot(subplots=(1,num_subplots))
 
-
     def _create_plot(plot, i, name, data, counter_two, j, k):
         color = 'C' + str(counter_two)
         counter_two += 1
@@ -136,6 +135,18 @@ def _create_plot(plot, i, name, data, counter_two, j, k):
                 plot.subplots[0].setTitle(title)
             else:
                 plot.subplots[j + k].setTitle("")
+
+            # Avoid SI rescaling if units are not standard units
+            standardunits = ['V', 's', 'J', 'W', 'm', 'eV', 'A', 'K', 'g',
+                             'Hz', 'rad', 'T', 'H', 'F', 'Pa', 'C', 'Ω', 'Ohm',
+                             'S']
+            for arr in data.arrays.values():
+                if arr.unit not in standardunits:
+                    subplot = plot.subplots[j + k]
+                    if arr.is_setpoint:  # setpoints on bottom axis
+                        subplot.getAxis('bottom').enableAutoSIPrefix(False)
+                    else:  # measured values to the left
+                        subplot.getAxis('left').enableAutoSIPrefix(False)
         else:
             if 'z' in inst_meta_data:
                 xlen, ylen = inst_meta_data['z'].shape
@@ -170,6 +181,7 @@ def _create_plot(plot, i, name, data, counter_two, j, k):
             counter_two += 1
     return plot, num_subplots
 
+
 def __get_plot_type(data, plot):
     # this is a hack because expand_trace works
     # in place. Also it should probably * expand its args and kwargs. N
@@ -179,6 +191,7 @@ def __get_plot_type(data, plot):
     plot.expand_trace((data_copy,), kwargs=metadata)
     return metadata
 
+
 def _save_individual_plots(data, inst_meas):
 
     def _create_plot(i, name, data, counter_two):

From 4d95e67b2a2290922fd10d2129009a50728e4fa4 Mon Sep 17 00:00:00 2001
From: Jens Hedegaard Nielsen 
Date: Wed, 14 Jun 2017 10:36:05 +0200
Subject: [PATCH 285/328] add support for dond without plotting

---
 qcodes/utils/wrappers.py | 99 +++++++++++++++++++++++++---------------
 1 file changed, 63 insertions(+), 36 deletions(-)

diff --git a/qcodes/utils/wrappers.py b/qcodes/utils/wrappers.py
index 9498c6d7e722..9f9fbc04b310 100644
--- a/qcodes/utils/wrappers.py
+++ b/qcodes/utils/wrappers.py
@@ -5,6 +5,7 @@
 import os
 import logging
 from copy import deepcopy
+import matplotlib.pyplot as plt
 
 from qcodes.plots.pyqtgraph import QtPlot
 from qcodes.plots.qcmatplotlib import MatPlot
@@ -266,7 +267,7 @@ def save_device_image(sweeptparameters):
                             '{:03d}'.format(counter)), title)
 
 
-def do1d(inst_set, start, stop, num_points, delay, *inst_meas):
+def do1d(inst_set, start, stop, num_points, delay, *inst_meas, do_plots=True):
     """
 
     Args:
@@ -276,7 +277,9 @@ def do1d(inst_set, start, stop, num_points, delay, *inst_meas):
         num_points:  Number of steps to perform
         delay:  Delay at every step
         *inst_meas:  any number of instrument to measure and/or tasks to
-            perform at each step of the sweep
+          perform at each step of the sweep
+        do_plots: Default True: If False no plots are produced. Data is still saved
+          and can be displayed with show_num.
 
     Returns:
         plot, data : returns the plot and the dataset
@@ -291,20 +294,28 @@ def do1d(inst_set, start, stop, num_points, delay, *inst_meas):
                                   stop, num=num_points), delay).each(*inst_meas)
     data = loop.get_data_set()
     plottables = _select_plottables(inst_meas)
-    plot, _ = _plot_setup(data, plottables)
+    if do_plots:
+        plot, _ = _plot_setup(data, plottables)
+    else:
+        plot = None
     try:
-        _ = loop.with_bg_task(plot.update).run()
+        if do_plots:
+            _ = loop.with_bg_task(plot.update).run()
+        else:
+            _ = loop.run()
     except KeyboardInterrupt:
         interrupted = True
         print("Measurement Interrupted")
-    plot.save()
-    pdfplot, num_subplots = _plot_setup(data, plottables, useQT=False)
-    # pad a bit more to prevent overlap between
-    # suptitle and title
-    pdfplot.fig.tight_layout(pad=3)
-    pdfplot.save("{}.pdf".format(plot.get_default_title()))
-    if num_subplots > 1:
-        _save_individual_plots(data, plottables)
+    if do_plots:
+        plot.save()
+        pdfplot, num_subplots = _plot_setup(data, plottables, useQT=False)
+        # pad a bit more to prevent overlap between
+        # suptitle and title
+        pdfplot.fig.tight_layout(pad=3)
+        pdfplot.save("{}.pdf".format(plot.get_default_title()))
+
+        if num_subplots > 1:
+            _save_individual_plots(data, plottables)
     if CURRENT_EXPERIMENT.get('device_image'):
         log.debug('Saving device image')
         save_device_image((inst_set,))
@@ -318,7 +329,7 @@ def do1d(inst_set, start, stop, num_points, delay, *inst_meas):
     return plot, data
 
 
-def do1dDiagonal(inst_set, inst2_set, start, stop, num_points, delay, start2, slope, *inst_meas):
+def do1dDiagonal(inst_set, inst2_set, start, stop, num_points, delay, start2, slope, *inst_meas, do_plots=True):
     """
     Perform diagonal sweep in 1 dimension, given two instruments
 
@@ -332,6 +343,8 @@ def do1dDiagonal(inst_set, inst2_set, start, stop, num_points, delay, start2, sl
         start2:  Second start point
         slope:  slope of the diagonal cut
         *inst_meas:  any number of instrument to measure
+        do_plots: Default True: If False no plots are produced. Data is still saved
+          and can be displayed with show_num.
 
     Returns:
         plot, data : returns the plot and the dataset
@@ -346,21 +359,28 @@ def do1dDiagonal(inst_set, inst2_set, start, stop, num_points, delay, start2, sl
         qc.Task(inst2_set, (inst_set) * slope + (slope * start - start2)), *inst_meas, inst2_set)
     data = loop.get_data_set()
     plottables = _select_plottables(inst_meas)
-    plot, _ = _plot_setup(data, plottables)
+    if do_plots:
+        plot, _ = _plot_setup(data, plottables)
+    else:
+        plot = None
     try:
-        _ = loop.with_bg_task(plot.update).run()
+        if do_plots:
+            _ = loop.with_bg_task(plot.update).run()
+        else:
+            _ = loop.run()
     except KeyboardInterrupt:
         print("Measurement Interrupted")
         interrupted = True
-    plot.save()
-    pdfplot, num_subplots = _plot_setup(data, plottables, useQT=False)
-    # pad a bit more to prevent overlap between
-    # suptitle and title
-    pdfplot.fig.tight_layout(pad=3)
-    pdfplot.save("{}.pdf".format(plot.get_default_title()))
-    if num_subplots > 1:
-        _save_individual_plots(data, plottables)
-    pdfplot.save("{}.pdf".format(plot.get_default_title()))
+    if do_plots:
+        plot.save()
+        pdfplot, num_subplots = _plot_setup(data, plottables, useQT=False)
+        # pad a bit more to prevent overlap between
+        # suptitle and title
+        pdfplot.fig.tight_layout(pad=3)
+        pdfplot.save("{}.pdf".format(plot.get_default_title()))
+        if num_subplots > 1:
+            _save_individual_plots(data, plottables)
+        pdfplot.save("{}.pdf".format(plot.get_default_title()))
     if CURRENT_EXPERIMENT.get('device_image'):
         save_device_image((inst_set, inst2_set))
 
@@ -373,7 +393,8 @@ def do1dDiagonal(inst_set, inst2_set, start, stop, num_points, delay, start2, sl
     return plot, data
 
 
-def do2d(inst_set, start, stop, num_points, delay, inst_set2, start2, stop2, num_points2, delay2, *inst_meas):
+def do2d(inst_set, start, stop, num_points, delay, inst_set2, start2, stop2, num_points2, delay2,
+         *inst_meas, do_plots=True):
     """
 
     Args:
@@ -388,6 +409,8 @@ def do2d(inst_set, start, stop, num_points, delay, inst_set2, start2, stop2, num
         num_points2:  Number of steps to perform
         delay2:  Delay at every step for second instrument
         *inst_meas:
+        do_plots: Default True: If False no plots are produced. Data is still saved
+          and can be displayed with show_num.
 
     Returns:
         plot, data : returns the plot and the dataset
@@ -406,21 +429,25 @@ def do2d(inst_set, start, stop, num_points, delay, inst_set2, start2, stop2, num
         *inst_meas)
     data = loop.get_data_set()
     plottables = _select_plottables(inst_meas)
-    plot, _ = _plot_setup(data, plottables)
+    if do_plots:
+        plot, _ = _plot_setup(data, plottables)
+    else:
+        plot = None
     try:
-        _ = loop.with_bg_task(plot.update).run()
+        _ = loop.run()
     except KeyboardInterrupt:
         interrupted = True
         print("Measurement Interrupted")
-    plot.save()
-    pdfplot, num_subplots = _plot_setup(data, plottables, useQT=False)
-    # pad a bit more to prevent overlap between
-    # suptitle and title
-    pdfplot.fig.tight_layout(pad=3)
-    pdfplot.save("{}.pdf".format(plot.get_default_title()))
-    if num_subplots > 1:
-        _save_individual_plots(data, plottables)
-    pdfplot.save("{}.pdf".format(plot.get_default_title()))
+    if do_plots:
+        plot.save()
+        pdfplot, num_subplots = _plot_setup(data, plottables, useQT=False)
+        # pad a bit more to prevent overlap between
+        # suptitle and title
+        pdfplot.fig.tight_layout(pad=3)
+        pdfplot.save("{}.pdf".format(plot.get_default_title()))
+        if num_subplots > 1:
+            _save_individual_plots(data, plottables)
+        pdfplot.save("{}.pdf".format(plot.get_default_title()))
     if CURRENT_EXPERIMENT.get('device_image'):
         save_device_image((inst_set, inst_set2))
 

From 1dbd91e2512eee4282e360ef7910696aac699fd2 Mon Sep 17 00:00:00 2001
From: Jens Hedegaard Nielsen 
Date: Wed, 14 Jun 2017 10:36:58 +0200
Subject: [PATCH 286/328] Revert "add support for dond without plotting"

This reverts commit 74bbb2087bf8814de04fd02ebaa91ae1dbd5c837.
---
 qcodes/utils/wrappers.py | 99 +++++++++++++++-------------------------
 1 file changed, 36 insertions(+), 63 deletions(-)

diff --git a/qcodes/utils/wrappers.py b/qcodes/utils/wrappers.py
index 9f9fbc04b310..9498c6d7e722 100644
--- a/qcodes/utils/wrappers.py
+++ b/qcodes/utils/wrappers.py
@@ -5,7 +5,6 @@
 import os
 import logging
 from copy import deepcopy
-import matplotlib.pyplot as plt
 
 from qcodes.plots.pyqtgraph import QtPlot
 from qcodes.plots.qcmatplotlib import MatPlot
@@ -267,7 +266,7 @@ def save_device_image(sweeptparameters):
                             '{:03d}'.format(counter)), title)
 
 
-def do1d(inst_set, start, stop, num_points, delay, *inst_meas, do_plots=True):
+def do1d(inst_set, start, stop, num_points, delay, *inst_meas):
     """
 
     Args:
@@ -277,9 +276,7 @@ def do1d(inst_set, start, stop, num_points, delay, *inst_meas, do_plots=True):
         num_points:  Number of steps to perform
         delay:  Delay at every step
         *inst_meas:  any number of instrument to measure and/or tasks to
-          perform at each step of the sweep
-        do_plots: Default True: If False no plots are produced. Data is still saved
-          and can be displayed with show_num.
+            perform at each step of the sweep
 
     Returns:
         plot, data : returns the plot and the dataset
@@ -294,28 +291,20 @@ def do1d(inst_set, start, stop, num_points, delay, *inst_meas, do_plots=True):
                                   stop, num=num_points), delay).each(*inst_meas)
     data = loop.get_data_set()
     plottables = _select_plottables(inst_meas)
-    if do_plots:
-        plot, _ = _plot_setup(data, plottables)
-    else:
-        plot = None
+    plot, _ = _plot_setup(data, plottables)
     try:
-        if do_plots:
-            _ = loop.with_bg_task(plot.update).run()
-        else:
-            _ = loop.run()
+        _ = loop.with_bg_task(plot.update).run()
     except KeyboardInterrupt:
         interrupted = True
         print("Measurement Interrupted")
-    if do_plots:
-        plot.save()
-        pdfplot, num_subplots = _plot_setup(data, plottables, useQT=False)
-        # pad a bit more to prevent overlap between
-        # suptitle and title
-        pdfplot.fig.tight_layout(pad=3)
-        pdfplot.save("{}.pdf".format(plot.get_default_title()))
-
-        if num_subplots > 1:
-            _save_individual_plots(data, plottables)
+    plot.save()
+    pdfplot, num_subplots = _plot_setup(data, plottables, useQT=False)
+    # pad a bit more to prevent overlap between
+    # suptitle and title
+    pdfplot.fig.tight_layout(pad=3)
+    pdfplot.save("{}.pdf".format(plot.get_default_title()))
+    if num_subplots > 1:
+        _save_individual_plots(data, plottables)
     if CURRENT_EXPERIMENT.get('device_image'):
         log.debug('Saving device image')
         save_device_image((inst_set,))
@@ -329,7 +318,7 @@ def do1d(inst_set, start, stop, num_points, delay, *inst_meas, do_plots=True):
     return plot, data
 
 
-def do1dDiagonal(inst_set, inst2_set, start, stop, num_points, delay, start2, slope, *inst_meas, do_plots=True):
+def do1dDiagonal(inst_set, inst2_set, start, stop, num_points, delay, start2, slope, *inst_meas):
     """
     Perform diagonal sweep in 1 dimension, given two instruments
 
@@ -343,8 +332,6 @@ def do1dDiagonal(inst_set, inst2_set, start, stop, num_points, delay, start2, sl
         start2:  Second start point
         slope:  slope of the diagonal cut
         *inst_meas:  any number of instrument to measure
-        do_plots: Default True: If False no plots are produced. Data is still saved
-          and can be displayed with show_num.
 
     Returns:
         plot, data : returns the plot and the dataset
@@ -359,28 +346,21 @@ def do1dDiagonal(inst_set, inst2_set, start, stop, num_points, delay, start2, sl
         qc.Task(inst2_set, (inst_set) * slope + (slope * start - start2)), *inst_meas, inst2_set)
     data = loop.get_data_set()
     plottables = _select_plottables(inst_meas)
-    if do_plots:
-        plot, _ = _plot_setup(data, plottables)
-    else:
-        plot = None
+    plot, _ = _plot_setup(data, plottables)
     try:
-        if do_plots:
-            _ = loop.with_bg_task(plot.update).run()
-        else:
-            _ = loop.run()
+        _ = loop.with_bg_task(plot.update).run()
     except KeyboardInterrupt:
         print("Measurement Interrupted")
         interrupted = True
-    if do_plots:
-        plot.save()
-        pdfplot, num_subplots = _plot_setup(data, plottables, useQT=False)
-        # pad a bit more to prevent overlap between
-        # suptitle and title
-        pdfplot.fig.tight_layout(pad=3)
-        pdfplot.save("{}.pdf".format(plot.get_default_title()))
-        if num_subplots > 1:
-            _save_individual_plots(data, plottables)
-        pdfplot.save("{}.pdf".format(plot.get_default_title()))
+    plot.save()
+    pdfplot, num_subplots = _plot_setup(data, plottables, useQT=False)
+    # pad a bit more to prevent overlap between
+    # suptitle and title
+    pdfplot.fig.tight_layout(pad=3)
+    pdfplot.save("{}.pdf".format(plot.get_default_title()))
+    if num_subplots > 1:
+        _save_individual_plots(data, plottables)
+    pdfplot.save("{}.pdf".format(plot.get_default_title()))
     if CURRENT_EXPERIMENT.get('device_image'):
         save_device_image((inst_set, inst2_set))
 
@@ -393,8 +373,7 @@ def do1dDiagonal(inst_set, inst2_set, start, stop, num_points, delay, start2, sl
     return plot, data
 
 
-def do2d(inst_set, start, stop, num_points, delay, inst_set2, start2, stop2, num_points2, delay2,
-         *inst_meas, do_plots=True):
+def do2d(inst_set, start, stop, num_points, delay, inst_set2, start2, stop2, num_points2, delay2, *inst_meas):
     """
 
     Args:
@@ -409,8 +388,6 @@ def do2d(inst_set, start, stop, num_points, delay, inst_set2, start2, stop2, num
         num_points2:  Number of steps to perform
         delay2:  Delay at every step for second instrument
         *inst_meas:
-        do_plots: Default True: If False no plots are produced. Data is still saved
-          and can be displayed with show_num.
 
     Returns:
         plot, data : returns the plot and the dataset
@@ -429,25 +406,21 @@ def do2d(inst_set, start, stop, num_points, delay, inst_set2, start2, stop2, num
         *inst_meas)
     data = loop.get_data_set()
     plottables = _select_plottables(inst_meas)
-    if do_plots:
-        plot, _ = _plot_setup(data, plottables)
-    else:
-        plot = None
+    plot, _ = _plot_setup(data, plottables)
     try:
-        _ = loop.run()
+        _ = loop.with_bg_task(plot.update).run()
     except KeyboardInterrupt:
         interrupted = True
         print("Measurement Interrupted")
-    if do_plots:
-        plot.save()
-        pdfplot, num_subplots = _plot_setup(data, plottables, useQT=False)
-        # pad a bit more to prevent overlap between
-        # suptitle and title
-        pdfplot.fig.tight_layout(pad=3)
-        pdfplot.save("{}.pdf".format(plot.get_default_title()))
-        if num_subplots > 1:
-            _save_individual_plots(data, plottables)
-        pdfplot.save("{}.pdf".format(plot.get_default_title()))
+    plot.save()
+    pdfplot, num_subplots = _plot_setup(data, plottables, useQT=False)
+    # pad a bit more to prevent overlap between
+    # suptitle and title
+    pdfplot.fig.tight_layout(pad=3)
+    pdfplot.save("{}.pdf".format(plot.get_default_title()))
+    if num_subplots > 1:
+        _save_individual_plots(data, plottables)
+    pdfplot.save("{}.pdf".format(plot.get_default_title()))
     if CURRENT_EXPERIMENT.get('device_image'):
         save_device_image((inst_set, inst_set2))
 

From b249222af3cd50845ad572b52b64e72e5f4f0029 Mon Sep 17 00:00:00 2001
From: Jens Hedegaard Nielsen 
Date: Wed, 14 Jun 2017 10:38:58 +0200
Subject: [PATCH 287/328] Fix: add support for dond without plotting""

It's now possible to pass do_plots=False to doND to prevent plots from being saved
---
 qcodes/utils/wrappers.py | 99 +++++++++++++++++++++++++---------------
 1 file changed, 63 insertions(+), 36 deletions(-)

diff --git a/qcodes/utils/wrappers.py b/qcodes/utils/wrappers.py
index 9498c6d7e722..9f9fbc04b310 100644
--- a/qcodes/utils/wrappers.py
+++ b/qcodes/utils/wrappers.py
@@ -5,6 +5,7 @@
 import os
 import logging
 from copy import deepcopy
+import matplotlib.pyplot as plt
 
 from qcodes.plots.pyqtgraph import QtPlot
 from qcodes.plots.qcmatplotlib import MatPlot
@@ -266,7 +267,7 @@ def save_device_image(sweeptparameters):
                             '{:03d}'.format(counter)), title)
 
 
-def do1d(inst_set, start, stop, num_points, delay, *inst_meas):
+def do1d(inst_set, start, stop, num_points, delay, *inst_meas, do_plots=True):
     """
 
     Args:
@@ -276,7 +277,9 @@ def do1d(inst_set, start, stop, num_points, delay, *inst_meas):
         num_points:  Number of steps to perform
         delay:  Delay at every step
         *inst_meas:  any number of instrument to measure and/or tasks to
-            perform at each step of the sweep
+          perform at each step of the sweep
+        do_plots: Default True: If False no plots are produced. Data is still saved
+          and can be displayed with show_num.
 
     Returns:
         plot, data : returns the plot and the dataset
@@ -291,20 +294,28 @@ def do1d(inst_set, start, stop, num_points, delay, *inst_meas):
                                   stop, num=num_points), delay).each(*inst_meas)
     data = loop.get_data_set()
     plottables = _select_plottables(inst_meas)
-    plot, _ = _plot_setup(data, plottables)
+    if do_plots:
+        plot, _ = _plot_setup(data, plottables)
+    else:
+        plot = None
     try:
-        _ = loop.with_bg_task(plot.update).run()
+        if do_plots:
+            _ = loop.with_bg_task(plot.update).run()
+        else:
+            _ = loop.run()
     except KeyboardInterrupt:
         interrupted = True
         print("Measurement Interrupted")
-    plot.save()
-    pdfplot, num_subplots = _plot_setup(data, plottables, useQT=False)
-    # pad a bit more to prevent overlap between
-    # suptitle and title
-    pdfplot.fig.tight_layout(pad=3)
-    pdfplot.save("{}.pdf".format(plot.get_default_title()))
-    if num_subplots > 1:
-        _save_individual_plots(data, plottables)
+    if do_plots:
+        plot.save()
+        pdfplot, num_subplots = _plot_setup(data, plottables, useQT=False)
+        # pad a bit more to prevent overlap between
+        # suptitle and title
+        pdfplot.fig.tight_layout(pad=3)
+        pdfplot.save("{}.pdf".format(plot.get_default_title()))
+
+        if num_subplots > 1:
+            _save_individual_plots(data, plottables)
     if CURRENT_EXPERIMENT.get('device_image'):
         log.debug('Saving device image')
         save_device_image((inst_set,))
@@ -318,7 +329,7 @@ def do1d(inst_set, start, stop, num_points, delay, *inst_meas):
     return plot, data
 
 
-def do1dDiagonal(inst_set, inst2_set, start, stop, num_points, delay, start2, slope, *inst_meas):
+def do1dDiagonal(inst_set, inst2_set, start, stop, num_points, delay, start2, slope, *inst_meas, do_plots=True):
     """
     Perform diagonal sweep in 1 dimension, given two instruments
 
@@ -332,6 +343,8 @@ def do1dDiagonal(inst_set, inst2_set, start, stop, num_points, delay, start2, sl
         start2:  Second start point
         slope:  slope of the diagonal cut
         *inst_meas:  any number of instrument to measure
+        do_plots: Default True: If False no plots are produced. Data is still saved
+          and can be displayed with show_num.
 
     Returns:
         plot, data : returns the plot and the dataset
@@ -346,21 +359,28 @@ def do1dDiagonal(inst_set, inst2_set, start, stop, num_points, delay, start2, sl
         qc.Task(inst2_set, (inst_set) * slope + (slope * start - start2)), *inst_meas, inst2_set)
     data = loop.get_data_set()
     plottables = _select_plottables(inst_meas)
-    plot, _ = _plot_setup(data, plottables)
+    if do_plots:
+        plot, _ = _plot_setup(data, plottables)
+    else:
+        plot = None
     try:
-        _ = loop.with_bg_task(plot.update).run()
+        if do_plots:
+            _ = loop.with_bg_task(plot.update).run()
+        else:
+            _ = loop.run()
     except KeyboardInterrupt:
         print("Measurement Interrupted")
         interrupted = True
-    plot.save()
-    pdfplot, num_subplots = _plot_setup(data, plottables, useQT=False)
-    # pad a bit more to prevent overlap between
-    # suptitle and title
-    pdfplot.fig.tight_layout(pad=3)
-    pdfplot.save("{}.pdf".format(plot.get_default_title()))
-    if num_subplots > 1:
-        _save_individual_plots(data, plottables)
-    pdfplot.save("{}.pdf".format(plot.get_default_title()))
+    if do_plots:
+        plot.save()
+        pdfplot, num_subplots = _plot_setup(data, plottables, useQT=False)
+        # pad a bit more to prevent overlap between
+        # suptitle and title
+        pdfplot.fig.tight_layout(pad=3)
+        pdfplot.save("{}.pdf".format(plot.get_default_title()))
+        if num_subplots > 1:
+            _save_individual_plots(data, plottables)
+        pdfplot.save("{}.pdf".format(plot.get_default_title()))
     if CURRENT_EXPERIMENT.get('device_image'):
         save_device_image((inst_set, inst2_set))
 
@@ -373,7 +393,8 @@ def do1dDiagonal(inst_set, inst2_set, start, stop, num_points, delay, start2, sl
     return plot, data
 
 
-def do2d(inst_set, start, stop, num_points, delay, inst_set2, start2, stop2, num_points2, delay2, *inst_meas):
+def do2d(inst_set, start, stop, num_points, delay, inst_set2, start2, stop2, num_points2, delay2,
+         *inst_meas, do_plots=True):
     """
 
     Args:
@@ -388,6 +409,8 @@ def do2d(inst_set, start, stop, num_points, delay, inst_set2, start2, stop2, num
         num_points2:  Number of steps to perform
         delay2:  Delay at every step for second instrument
         *inst_meas:
+        do_plots: Default True: If False no plots are produced. Data is still saved
+          and can be displayed with show_num.
 
     Returns:
         plot, data : returns the plot and the dataset
@@ -406,21 +429,25 @@ def do2d(inst_set, start, stop, num_points, delay, inst_set2, start2, stop2, num
         *inst_meas)
     data = loop.get_data_set()
     plottables = _select_plottables(inst_meas)
-    plot, _ = _plot_setup(data, plottables)
+    if do_plots:
+        plot, _ = _plot_setup(data, plottables)
+    else:
+        plot = None
     try:
-        _ = loop.with_bg_task(plot.update).run()
+        _ = loop.run()
     except KeyboardInterrupt:
         interrupted = True
         print("Measurement Interrupted")
-    plot.save()
-    pdfplot, num_subplots = _plot_setup(data, plottables, useQT=False)
-    # pad a bit more to prevent overlap between
-    # suptitle and title
-    pdfplot.fig.tight_layout(pad=3)
-    pdfplot.save("{}.pdf".format(plot.get_default_title()))
-    if num_subplots > 1:
-        _save_individual_plots(data, plottables)
-    pdfplot.save("{}.pdf".format(plot.get_default_title()))
+    if do_plots:
+        plot.save()
+        pdfplot, num_subplots = _plot_setup(data, plottables, useQT=False)
+        # pad a bit more to prevent overlap between
+        # suptitle and title
+        pdfplot.fig.tight_layout(pad=3)
+        pdfplot.save("{}.pdf".format(plot.get_default_title()))
+        if num_subplots > 1:
+            _save_individual_plots(data, plottables)
+        pdfplot.save("{}.pdf".format(plot.get_default_title()))
     if CURRENT_EXPERIMENT.get('device_image'):
         save_device_image((inst_set, inst_set2))
 

From 9f455289c62c43aaf337478fea0e7516b05bd5e9 Mon Sep 17 00:00:00 2001
From: Jens Hedegaard Nielsen 
Date: Wed, 14 Jun 2017 13:24:15 +0200
Subject: [PATCH 288/328] Fix mistake in plot update of do2d:

---
 qcodes/utils/wrappers.py | 5 ++++-
 1 file changed, 4 insertions(+), 1 deletion(-)

diff --git a/qcodes/utils/wrappers.py b/qcodes/utils/wrappers.py
index 9f9fbc04b310..ff364c886a7a 100644
--- a/qcodes/utils/wrappers.py
+++ b/qcodes/utils/wrappers.py
@@ -434,7 +434,10 @@ def do2d(inst_set, start, stop, num_points, delay, inst_set2, start2, stop2, num
     else:
         plot = None
     try:
-        _ = loop.run()
+        if do_plots:
+            _ = loop.with_bg_task(plot.update).run()
+        else:
+            _ = loop.run()
     except KeyboardInterrupt:
         interrupted = True
         print("Measurement Interrupted")

From f2fbe4231cd289303888c74bd37e8745a399a44f Mon Sep 17 00:00:00 2001
From: "William H.P. Nielsen" 
Date: Wed, 14 Jun 2017 14:44:26 +0200
Subject: [PATCH 289/328] docs: Example notebook for QDev specifics (#39)

* docs: Example notebook for QDev specifics

* docs: reader-friendly linewraps
---
 docs/examples/Tutorial for QDevvies.ipynb | 2987 +++++++++++++++++++++
 1 file changed, 2987 insertions(+)
 create mode 100644 docs/examples/Tutorial for QDevvies.ipynb

diff --git a/docs/examples/Tutorial for QDevvies.ipynb b/docs/examples/Tutorial for QDevvies.ipynb
new file mode 100644
index 000000000000..64095a3824f2
--- /dev/null
+++ b/docs/examples/Tutorial for QDevvies.ipynb	
@@ -0,0 +1,2987 @@
+{
+ "cells": [
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "# QCoDeS tutorial for QDevvies\n",
+    "\n",
+    "Basic overview of the QDev flavour of QCoDeS. The QDev version shares most of the backbone structure with normal (international) QCoDeS, but differs in the exact function call used to acquire data. In particalur, QDev QCoDeS contains the following 4 extra functions:\n",
+    "\n",
+    "`init`, `do1d`, `do2d`, `show_num`.\n",
+    "\n",
+    "## Table of Contents \n",
+    "  * [Workflow](#workflow)\n",
+    "  * [Basic instrument interaction](#inst_io)\n",
+    "  * [Measuring](#measurement)\n",
+    "    * [1D loop with `do1d`](#do1d)\n",
+    "    * [2D loop with `do2d`](#do2d)\n"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Typical QCodes workflow \n",
+    "(back to [ToC](#toc))\n",
+    "\n",
+    "1. Start up an interactive python session (e.g. using jupyter) \n",
+    "2. Import desired modules \n",
+    "3. Instantiate/resume the experiment \n",
+    "4. Get data or die trying!"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### Importing"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 1,
+   "metadata": {
+    "collapsed": true,
+    "scrolled": false
+   },
+   "outputs": [],
+   "source": [
+    "%matplotlib notebook\n",
+    "\n",
+    "# usually, one imports QCoDeS and some instruments\n",
+    "import qcodes as qc\n",
+    "\n",
+    "# In QDev land, one imports a handful of helper functions\n",
+    "from qcodes.utils.wrappers import init, do1d, do2d, show_num\n",
+    "\n",
+    "# In this tutorial, we import the dummy instrument\n",
+    "from qcodes.tests.instrument_mocks import DummyInstrument\n",
+    "# real instruments are imported in a similar way, e.g.\n",
+    "# from qcodes.instrument_drivers.Keysight.Keysight_33500B import Keysight_33500B"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### Instantiation of Experiment\n",
+    "\n",
+    "The special QDev function `init` is the function that sets up the entire experiment.\n",
+    "It\n",
+    "  * Makes folders for the data\n",
+    "  * Keeps track of how many measurements have been performed (across Python sessions)\n",
+    "  * Annotates a device image for each measurement\n",
+    "  * Logs the jupyter notebook execution"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 2,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Activating auto-logging. Current session state plus future input saved.\n",
+      "Filename       : /Users/william/sourcecodes/qcodes-jenshnielsen/docs/examples/TutorialExperiment/TutorialSample/commands.log\n",
+      "Mode           : append\n",
+      "Output logging : True\n",
+      "Raw input log  : False\n",
+      "Timestamping   : True\n",
+      "State          : active\n"
+     ]
+    }
+   ],
+   "source": [
+    "# It is not enough to import the instruments, they must also be instantiated\n",
+    "# Note that this can only be done once. If you try to \n",
+    "# re-instantiate an existing instrument, QCoDeS will \n",
+    "# complain that 'Another instrument has the name'.\n",
+    "\n",
+    "\n",
+    "# In this turotial, we consider a simple situation: \n",
+    "# A single DAC outputting voltages to a Digital Multi Meter\n",
+    "\n",
+    "dac = DummyInstrument(name=\"dac\", gates=['ch1', 'ch2'])  # The DAC voltage source\n",
+    "dmm = DummyInstrument(name=\"dmm\", gates=['voltage'])  # The DMM voltage reader\n",
+    "\n",
+    "# For pedagical purposes, we \"pimp\" the dummy instruments a bit\n",
+    "\n",
+    "# the default dummy instrument returns always a constant value, \n",
+    "# in the following line we make it random \n",
+    "# just for the looks 💅\n",
+    "import random\n",
+    "dmm.voltage.get =  lambda: random.randint(0, 100)\n",
+    "\n",
+    "# We add a parameter that loudly prints what it is being set to\n",
+    "# (It is used below)\n",
+    "\n",
+    "chX = 0\n",
+    "def myget():\n",
+    "    return chX\n",
+    "    \n",
+    "def myset(x):\n",
+    "    global chX\n",
+    "    chX = x\n",
+    "    print('Setting to {}'.format(x))\n",
+    "    return None\n",
+    "    \n",
+    "dac.add_parameter('verbose_channel',\n",
+    "                  label='Verbose Channel',\n",
+    "                  unit='V',\n",
+    "                  get_cmd=myget,\n",
+    "                  set_cmd=myset)\n",
+    "\n",
+    "# Next, the instruments should be bound to a Station. \n",
+    "# Only instruments bound to the Station get recorded in the\n",
+    "# measurement metadata, so your metadata is blind to \n",
+    "# any instrument not in the Station.\n",
+    "\n",
+    "station = qc.Station(dac, dmm)\n",
+    "\n",
+    "# Finally, we instantiate the experiment. \n",
+    "init(mainfolder='TutorialExperiment',\n",
+    "     sample_name='TutorialSample',\n",
+    "     station=station,\n",
+    "     annotate_image=False)\n",
+    "# NB: In the tutorial we don't annotate an image, but change False to True to do so\n"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "By all means, have a look at the `commands.log` file. From this, you can find out EXACTLY what you did when and thus retrace your whole experiment. "
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "We are now ready to play with the instruments!"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Basic instrument interaction \n",
+    "(back to [ToC](#toc))\n",
+    "\n",
+    "The interaction with instruments mainly consists of `setting` and `getting` the instruments' `parameters`. A parameter can be anything from the frequency of a signal generator over the output impedance of an AWG to the traces from a lock-in amplifier. In this tutorial we --for didactical reasons-- only consider scalar parameters. "
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 3,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "8"
+      ]
+     },
+     "execution_count": 3,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "# The voltages output by the dac can be set like so\n",
+    "dac.ch1.set(8)  \n",
+    "# Now the output is 8 V. We can read this value back\n",
+    "dac.ch1.get()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Setting IMMEDIATELY changes a value. For voltages, that is sometimes undesired. The value can instead be ramped by stepping and waiting."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 4,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Setting to 0\n",
+      "Setting to 9\n",
+      "Setting to 8.9\n",
+      "Setting to 8.8\n",
+      "Setting to 8.7\n",
+      "Setting to 8.6\n",
+      "Setting to 8.5\n",
+      "Setting to 8.4\n",
+      "Setting to 8.3\n",
+      "Setting to 8.2\n",
+      "Setting to 8.1\n",
+      "Setting to 8.0\n",
+      "Setting to 7.9\n",
+      "Setting to 7.8\n",
+      "Setting to 7.7\n",
+      "Setting to 7.6\n",
+      "Setting to 7.5\n",
+      "Setting to 7.4\n",
+      "Setting to 7.3\n",
+      "Setting to 7.2\n",
+      "Setting to 7.1\n",
+      "Setting to 7.0\n",
+      "Setting to 6.9\n",
+      "Setting to 6.8\n",
+      "Setting to 6.699999999999999\n",
+      "Setting to 6.6\n",
+      "Setting to 6.5\n",
+      "Setting to 6.4\n",
+      "Setting to 6.3\n",
+      "Setting to 6.199999999999999\n",
+      "Setting to 6.1\n",
+      "Setting to 6.0\n",
+      "Setting to 5.9\n",
+      "Setting to 5.8\n",
+      "Setting to 5.699999999999999\n",
+      "Setting to 5.6\n",
+      "Setting to 5.5\n",
+      "Setting to 5.4\n",
+      "Setting to 5.3\n",
+      "Setting to 5.199999999999999\n",
+      "Setting to 5.1\n",
+      "Setting to 5\n"
+     ]
+    }
+   ],
+   "source": [
+    "dac.verbose_channel.set(0) \n",
+    "dac.verbose_channel.set(9)  # immediate voltage jump of 9 Volts (!)\n",
+    "\n",
+    "# first set a step size\n",
+    "dac.verbose_channel.set_step(0.1)\n",
+    "# and a wait time\n",
+    "dac.verbose_channel.set_delay(0.25)  # in seconds\n",
+    "\n",
+    "# now a \"staircase ramp\" is performed by setting\n",
+    "dac.verbose_channel.set(5)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "**NOTE**: that is ramp is blocking and has a low resolution since each `set` on a real instrument has a latency on the order of ms. Some instrument drivers support native-resolution asynchronous ramping. Always refer to your instrument driver if you need high performance of an instrument."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 5,
+   "metadata": {
+    "collapsed": true
+   },
+   "outputs": [],
+   "source": [
+    "# after such a ramp, it is a good idea to reset the step and delay\n",
+    "dac.verbose_channel.set_step(0)\n",
+    "# and a wait time\n",
+    "dac.verbose_channel.set_delay(0)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Measuring \n",
+    "(back to [ToC](#toc))\n",
+    "\n",
+    "The above foolin' about with instruments did not count as measurements; no data sets were produced or saved. To do\n",
+    "that, utilise the `do1d` or `do2d` function.\n",
+    "\n",
+    "### 1D Loop example \n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 6,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Started at 2017-06-14 13:31:37\n",
+      "DataSet:\n",
+      "   location = '/Users/william/sourcecodes/qcodes-jenshnielsen/docs/examples/TutorialExperiment/TutorialSample/011'\n",
+      "      |   |  | \n",
+      "   Setpoint | dac_ch1_set | ch1          | (25,)\n",
+      "   Measured | dmm_voltage | voltage      | (25,)\n",
+      "Finished at 2017-06-14 13:31:40\n"
+     ]
+    },
+    {
+     "data": {
+      "application/javascript": [
+       "/* Put everything inside the global mpl namespace */\n",
+       "window.mpl = {};\n",
+       "\n",
+       "\n",
+       "mpl.get_websocket_type = function() {\n",
+       "    if (typeof(WebSocket) !== 'undefined') {\n",
+       "        return WebSocket;\n",
+       "    } else if (typeof(MozWebSocket) !== 'undefined') {\n",
+       "        return MozWebSocket;\n",
+       "    } else {\n",
+       "        alert('Your browser does not have WebSocket support.' +\n",
+       "              'Please try Chrome, Safari or Firefox ≥ 6. ' +\n",
+       "              'Firefox 4 and 5 are also supported but you ' +\n",
+       "              'have to enable WebSockets in about:config.');\n",
+       "    };\n",
+       "}\n",
+       "\n",
+       "mpl.figure = function(figure_id, websocket, ondownload, parent_element) {\n",
+       "    this.id = figure_id;\n",
+       "\n",
+       "    this.ws = websocket;\n",
+       "\n",
+       "    this.supports_binary = (this.ws.binaryType != undefined);\n",
+       "\n",
+       "    if (!this.supports_binary) {\n",
+       "        var warnings = document.getElementById(\"mpl-warnings\");\n",
+       "        if (warnings) {\n",
+       "            warnings.style.display = 'block';\n",
+       "            warnings.textContent = (\n",
+       "                \"This browser does not support binary websocket messages. \" +\n",
+       "                    \"Performance may be slow.\");\n",
+       "        }\n",
+       "    }\n",
+       "\n",
+       "    this.imageObj = new Image();\n",
+       "\n",
+       "    this.context = undefined;\n",
+       "    this.message = undefined;\n",
+       "    this.canvas = undefined;\n",
+       "    this.rubberband_canvas = undefined;\n",
+       "    this.rubberband_context = undefined;\n",
+       "    this.format_dropdown = undefined;\n",
+       "\n",
+       "    this.image_mode = 'full';\n",
+       "\n",
+       "    this.root = $('
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+       "    this._root_extra_style(this.root)\n",
+       "    this.root.attr('style', 'display: inline-block');\n",
+       "\n",
+       "    $(parent_element).append(this.root);\n",
+       "\n",
+       "    this._init_header(this);\n",
+       "    this._init_canvas(this);\n",
+       "    this._init_toolbar(this);\n",
+       "\n",
+       "    var fig = this;\n",
+       "\n",
+       "    this.waiting = false;\n",
+       "\n",
+       "    this.ws.onopen =  function () {\n",
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+       "            fig.send_message(\"send_image_mode\", {});\n",
+       "            if (mpl.ratio != 1) {\n",
+       "                fig.send_message(\"set_dpi_ratio\", {'dpi_ratio': mpl.ratio});\n",
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+       "            fig.send_message(\"refresh\", {});\n",
+       "        }\n",
+       "\n",
+       "    this.imageObj.onload = function() {\n",
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+       "                // Full images could contain transparency (where diff images\n",
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+       "                // there is no ghosting.\n",
+       "                fig.context.clearRect(0, 0, fig.canvas.width, fig.canvas.height);\n",
+       "            }\n",
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+       "        };\n",
+       "\n",
+       "    this.imageObj.onunload = function() {\n",
+       "        this.ws.close();\n",
+       "    }\n",
+       "\n",
+       "    this.ws.onmessage = this._make_on_message_function(this);\n",
+       "\n",
+       "    this.ondownload = ondownload;\n",
+       "}\n",
+       "\n",
+       "mpl.figure.prototype._init_header = function() {\n",
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+       "        '
');\n",
+       "    var titletext = $(\n",
+       "        '
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+       "    titlebar.append(titletext)\n",
+       "    this.root.append(titlebar);\n",
+       "    this.header = titletext[0];\n",
+       "}\n",
+       "\n",
+       "\n",
+       "\n",
+       "mpl.figure.prototype._canvas_extra_style = function(canvas_div) {\n",
+       "\n",
+       "}\n",
+       "\n",
+       "\n",
+       "mpl.figure.prototype._root_extra_style = function(canvas_div) {\n",
+       "\n",
+       "}\n",
+       "\n",
+       "mpl.figure.prototype._init_canvas = function() {\n",
+       "    var fig = this;\n",
+       "\n",
+       "    var canvas_div = $('
');\n",
+       "\n",
+       "    canvas_div.attr('style', 'position: relative; clear: both; outline: 0');\n",
+       "\n",
+       "    function canvas_keyboard_event(event) {\n",
+       "        return fig.key_event(event, event['data']);\n",
+       "    }\n",
+       "\n",
+       "    canvas_div.keydown('key_press', canvas_keyboard_event);\n",
+       "    canvas_div.keyup('key_release', canvas_keyboard_event);\n",
+       "    this.canvas_div = canvas_div\n",
+       "    this._canvas_extra_style(canvas_div)\n",
+       "    this.root.append(canvas_div);\n",
+       "\n",
+       "    var canvas = $('');\n",
+       "    canvas.addClass('mpl-canvas');\n",
+       "    canvas.attr('style', \"left: 0; top: 0; z-index: 0; outline: 0\")\n",
+       "\n",
+       "    this.canvas = canvas[0];\n",
+       "    this.context = canvas[0].getContext(\"2d\");\n",
+       "\n",
+       "    var backingStore = this.context.backingStorePixelRatio ||\n",
+       "\tthis.context.webkitBackingStorePixelRatio ||\n",
+       "\tthis.context.mozBackingStorePixelRatio ||\n",
+       "\tthis.context.msBackingStorePixelRatio ||\n",
+       "\tthis.context.oBackingStorePixelRatio ||\n",
+       "\tthis.context.backingStorePixelRatio || 1;\n",
+       "\n",
+       "    mpl.ratio = (window.devicePixelRatio || 1) / backingStore;\n",
+       "\n",
+       "    var rubberband = $('');\n",
+       "    rubberband.attr('style', \"position: absolute; left: 0; top: 0; z-index: 1;\")\n",
+       "\n",
+       "    var pass_mouse_events = true;\n",
+       "\n",
+       "    canvas_div.resizable({\n",
+       "        start: function(event, ui) {\n",
+       "            pass_mouse_events = false;\n",
+       "        },\n",
+       "        resize: function(event, ui) {\n",
+       "            fig.request_resize(ui.size.width, ui.size.height);\n",
+       "        },\n",
+       "        stop: function(event, ui) {\n",
+       "            pass_mouse_events = true;\n",
+       "            fig.request_resize(ui.size.width, ui.size.height);\n",
+       "        },\n",
+       "    });\n",
+       "\n",
+       "    function mouse_event_fn(event) {\n",
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+       "    }\n",
+       "\n",
+       "    rubberband.mousedown('button_press', mouse_event_fn);\n",
+       "    rubberband.mouseup('button_release', mouse_event_fn);\n",
+       "    // Throttle sequential mouse events to 1 every 20ms.\n",
+       "    rubberband.mousemove('motion_notify', mouse_event_fn);\n",
+       "\n",
+       "    rubberband.mouseenter('figure_enter', mouse_event_fn);\n",
+       "    rubberband.mouseleave('figure_leave', mouse_event_fn);\n",
+       "\n",
+       "    canvas_div.on(\"wheel\", function (event) {\n",
+       "        event = event.originalEvent;\n",
+       "        event['data'] = 'scroll'\n",
+       "        if (event.deltaY < 0) {\n",
+       "            event.step = 1;\n",
+       "        } else {\n",
+       "            event.step = -1;\n",
+       "        }\n",
+       "        mouse_event_fn(event);\n",
+       "    });\n",
+       "\n",
+       "    canvas_div.append(canvas);\n",
+       "    canvas_div.append(rubberband);\n",
+       "\n",
+       "    this.rubberband = rubberband;\n",
+       "    this.rubberband_canvas = rubberband[0];\n",
+       "    this.rubberband_context = rubberband[0].getContext(\"2d\");\n",
+       "    this.rubberband_context.strokeStyle = \"#000000\";\n",
+       "\n",
+       "    this._resize_canvas = function(width, height) {\n",
+       "        // Keep the size of the canvas, canvas container, and rubber band\n",
+       "        // canvas in synch.\n",
+       "        canvas_div.css('width', width)\n",
+       "        canvas_div.css('height', height)\n",
+       "\n",
+       "        canvas.attr('width', width * mpl.ratio);\n",
+       "        canvas.attr('height', height * mpl.ratio);\n",
+       "        canvas.attr('style', 'width: ' + width + 'px; height: ' + height + 'px;');\n",
+       "\n",
+       "        rubberband.attr('width', width);\n",
+       "        rubberband.attr('height', height);\n",
+       "    }\n",
+       "\n",
+       "    // Set the figure to an initial 600x600px, this will subsequently be updated\n",
+       "    // upon first draw.\n",
+       "    this._resize_canvas(600, 600);\n",
+       "\n",
+       "    // Disable right mouse context menu.\n",
+       "    $(this.rubberband_canvas).bind(\"contextmenu\",function(e){\n",
+       "        return false;\n",
+       "    });\n",
+       "\n",
+       "    function set_focus () {\n",
+       "        canvas.focus();\n",
+       "        canvas_div.focus();\n",
+       "    }\n",
+       "\n",
+       "    window.setTimeout(set_focus, 100);\n",
+       "}\n",
+       "\n",
+       "mpl.figure.prototype._init_toolbar = function() {\n",
+       "    var fig = this;\n",
+       "\n",
+       "    var nav_element = $('
')\n",
+       "    nav_element.attr('style', 'width: 100%');\n",
+       "    this.root.append(nav_element);\n",
+       "\n",
+       "    // Define a callback function for later on.\n",
+       "    function toolbar_event(event) {\n",
+       "        return fig.toolbar_button_onclick(event['data']);\n",
+       "    }\n",
+       "    function toolbar_mouse_event(event) {\n",
+       "        return fig.toolbar_button_onmouseover(event['data']);\n",
+       "    }\n",
+       "\n",
+       "    for(var toolbar_ind in mpl.toolbar_items) {\n",
+       "        var name = mpl.toolbar_items[toolbar_ind][0];\n",
+       "        var tooltip = mpl.toolbar_items[toolbar_ind][1];\n",
+       "        var image = mpl.toolbar_items[toolbar_ind][2];\n",
+       "        var method_name = mpl.toolbar_items[toolbar_ind][3];\n",
+       "\n",
+       "        if (!name) {\n",
+       "            // put a spacer in here.\n",
+       "            continue;\n",
+       "        }\n",
+       "        var button = $('');\n",
+       "        button.click(method_name, toolbar_event);\n",
+       "        button.mouseover(tooltip, toolbar_mouse_event);\n",
+       "        nav_element.append(button);\n",
+       "    }\n",
+       "\n",
+       "    // Add the status bar.\n",
+       "    var status_bar = $('');\n",
+       "    nav_element.append(status_bar);\n",
+       "    this.message = status_bar[0];\n",
+       "\n",
+       "    // Add the close button to the window.\n",
+       "    var buttongrp = $('
');\n",
+       "    var button = $('');\n",
+       "    button.click(function (evt) { fig.handle_close(fig, {}); } );\n",
+       "    button.mouseover('Stop Interaction', toolbar_mouse_event);\n",
+       "    buttongrp.append(button);\n",
+       "    var titlebar = this.root.find($('.ui-dialog-titlebar'));\n",
+       "    titlebar.prepend(buttongrp);\n",
+       "}\n",
+       "\n",
+       "mpl.figure.prototype._root_extra_style = function(el){\n",
+       "    var fig = this\n",
+       "    el.on(\"remove\", function(){\n",
+       "\tfig.close_ws(fig, {});\n",
+       "    });\n",
+       "}\n",
+       "\n",
+       "mpl.figure.prototype._canvas_extra_style = function(el){\n",
+       "    // this is important to make the div 'focusable\n",
+       "    el.attr('tabindex', 0)\n",
+       "    // reach out to IPython and tell the keyboard manager to turn it's self\n",
+       "    // off when our div gets focus\n",
+       "\n",
+       "    // location in version 3\n",
+       "    if (IPython.notebook.keyboard_manager) {\n",
+       "        IPython.notebook.keyboard_manager.register_events(el);\n",
+       "    }\n",
+       "    else {\n",
+       "        // location in version 2\n",
+       "        IPython.keyboard_manager.register_events(el);\n",
+       "    }\n",
+       "\n",
+       "}\n",
+       "\n",
+       "mpl.figure.prototype._key_event_extra = function(event, name) {\n",
+       "    var manager = IPython.notebook.keyboard_manager;\n",
+       "    if (!manager)\n",
+       "        manager = IPython.keyboard_manager;\n",
+       "\n",
+       "    // Check for shift+enter\n",
+       "    if (event.shiftKey && event.which == 13) {\n",
+       "        this.canvas_div.blur();\n",
+       "        // select the cell after this one\n",
+       "        var index = IPython.notebook.find_cell_index(this.cell_info[0]);\n",
+       "        IPython.notebook.select(index + 1);\n",
+       "    }\n",
+       "}\n",
+       "\n",
+       "mpl.figure.prototype.handle_save = function(fig, msg) {\n",
+       "    fig.ondownload(fig, null);\n",
+       "}\n",
+       "\n",
+       "\n",
+       "mpl.find_output_cell = function(html_output) {\n",
+       "    // Return the cell and output element which can be found *uniquely* in the notebook.\n",
+       "    // Note - this is a bit hacky, but it is done because the \"notebook_saving.Notebook\"\n",
+       "    // IPython event is triggered only after the cells have been serialised, which for\n",
+       "    // our purposes (turning an active figure into a static one), is too late.\n",
+       "    var cells = IPython.notebook.get_cells();\n",
+       "    var ncells = cells.length;\n",
+       "    for (var i=0; i= 3 moved mimebundle to data attribute of output\n",
+       "                    data = data.data;\n",
+       "                }\n",
+       "                if (data['text/html'] == html_output) {\n",
+       "                    return [cell, data, j];\n",
+       "                }\n",
+       "            }\n",
+       "        }\n",
+       "    }\n",
+       "}\n",
+       "\n",
+       "// Register the function which deals with the matplotlib target/channel.\n",
+       "// The kernel may be null if the page has been refreshed.\n",
+       "if (IPython.notebook.kernel != null) {\n",
+       "    IPython.notebook.kernel.comm_manager.register_target('matplotlib', mpl.mpl_figure_comm);\n",
+       "}\n"
+      ],
+      "text/plain": [
+       ""
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "text/html": [
+       ""
+      ],
+      "text/plain": [
+       ""
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "text/plain": [
+       "(DataSet:\n",
+       "    location = '/Users/william/sourcecodes/qcodes-jenshnielsen/docs/examples/TutorialExperiment/TutorialSample/002'\n",
+       "       |   |  | \n",
+       "    Setpoint | dac_ch1_set | ch1          | (25,)\n",
+       "    Measured | dmm_voltage | voltage      | (25,),\n",
+       " [])"
+      ]
+     },
+     "execution_count": 8,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "# The data may be brought up easily by referecing the unique ID number\n",
+    "show_num(2)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "#### Products of `do1d`\n",
+    "\n",
+    "The `do1d` function creates a DataSet. \n",
+    "The representation of the dataset shows what arrays it contains and where it is saved.   \n",
+    "The dataset initially starts out empty (filled with NAN's) and gets filled while the `do1d` is being executed. "
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Once the measurement is done, take a look at the file in finder/explorer (the dataset.location should give you the relative path). \n",
+    "Note also the snapshot that captures the settings of all instruments at the start of the Loop. \n",
+    "This metadata is also accessible from the dataset and captures a snapshot of each instrument listed in the station. "
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 9,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "{'__class__': 'qcodes.tests.instrument_mocks.DummyInstrument',\n",
+       " 'functions': {},\n",
+       " 'name': 'dac',\n",
+       " 'parameters': {'IDN': {'__class__': 'qcodes.instrument.parameter.StandardParameter',\n",
+       "   'instrument': 'qcodes.tests.instrument_mocks.DummyInstrument',\n",
+       "   'instrument_name': 'dac',\n",
+       "   'label': 'IDN',\n",
+       "   'name': 'IDN',\n",
+       "   'ts': '2017-06-14 13:31:25',\n",
+       "   'unit': '',\n",
+       "   'vals': '',\n",
+       "   'value': {'firmware': None,\n",
+       "    'model': 'dac',\n",
+       "    'serial': None,\n",
+       "    'vendor': None}},\n",
+       "  'ch1': {'__class__': 'qcodes.instrument.parameter.ManualParameter',\n",
+       "   'instrument': 'qcodes.tests.instrument_mocks.DummyInstrument',\n",
+       "   'instrument_name': 'dac',\n",
+       "   'label': 'Gate ch1',\n",
+       "   'name': 'ch1',\n",
+       "   'ts': '2017-06-14 13:31:43',\n",
+       "   'unit': 'V',\n",
+       "   'vals': '',\n",
+       "   'value': 15.0},\n",
+       "  'ch2': {'__class__': 'qcodes.instrument.parameter.ManualParameter',\n",
+       "   'instrument': 'qcodes.tests.instrument_mocks.DummyInstrument',\n",
+       "   'instrument_name': 'dac',\n",
+       "   'label': 'Gate ch2',\n",
+       "   'name': 'ch2',\n",
+       "   'ts': '2017-06-14 13:31:25',\n",
+       "   'unit': 'V',\n",
+       "   'vals': '',\n",
+       "   'value': 0},\n",
+       "  'verbose_channel': {'__class__': 'qcodes.instrument.parameter.StandardParameter',\n",
+       "   'instrument': 'qcodes.tests.instrument_mocks.DummyInstrument',\n",
+       "   'instrument_name': 'dac',\n",
+       "   'label': 'Verbose Channel',\n",
+       "   'name': 'verbose_channel',\n",
+       "   'ts': '2017-06-14 13:31:35',\n",
+       "   'unit': 'V',\n",
+       "   'vals': '',\n",
+       "   'value': 5}}}"
+      ]
+     },
+     "execution_count": 9,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "dac.snapshot()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "There is also a more human-readable version of the essential information."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 10,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "dac:\n",
+      "\tparameter      value\n",
+      "--------------------------------------------------------------------------------\n",
+      "IDN             :\t{'firmware': None, 'serial': None, 'vendor': None, 'model':...\n",
+      "ch1             :\t15 (V)\n",
+      "ch2             :\t0 (V)\n",
+      "verbose_channel :\t5 (V)\n"
+     ]
+    }
+   ],
+   "source": [
+    "dac.print_readable_snapshot()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "\n",
+    "### 2D Loop example \n",
+    "\n",
+    "The `do2d` function works just the way you would expect."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 11,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Started at 2017-06-14 13:31:44\n",
+      "DataSet:\n",
+      "   location = '/Users/william/sourcecodes/qcodes-jenshnielsen/docs/examples/TutorialExperiment/TutorialSample/013'\n",
+      "      |   |  | \n",
+      "   Setpoint | dac_ch1_set | ch1          | (25,)\n",
+      "   Setpoint | dac_ch2_set | ch2          | (25, 10)\n",
+      "   Measured | dmm_voltage | voltage      | (25, 10)\n",
+      "Finished at 2017-06-14 13:31:47\n"
+     ]
+    },
+    {
+     "data": {
+      "application/javascript": [
+       "/* Put everything inside the global mpl namespace */\n",
+       "window.mpl = {};\n",
+       "\n",
+       "\n",
+       "mpl.get_websocket_type = function() {\n",
+       "    if (typeof(WebSocket) !== 'undefined') {\n",
+       "        return WebSocket;\n",
+       "    } else if (typeof(MozWebSocket) !== 'undefined') {\n",
+       "        return MozWebSocket;\n",
+       "    } else {\n",
+       "        alert('Your browser does not have WebSocket support.' +\n",
+       "              'Please try Chrome, Safari or Firefox ≥ 6. ' +\n",
+       "              'Firefox 4 and 5 are also supported but you ' +\n",
+       "              'have to enable WebSockets in about:config.');\n",
+       "    };\n",
+       "}\n",
+       "\n",
+       "mpl.figure = function(figure_id, websocket, ondownload, parent_element) {\n",
+       "    this.id = figure_id;\n",
+       "\n",
+       "    this.ws = websocket;\n",
+       "\n",
+       "    this.supports_binary = (this.ws.binaryType != undefined);\n",
+       "\n",
+       "    if (!this.supports_binary) {\n",
+       "        var warnings = document.getElementById(\"mpl-warnings\");\n",
+       "        if (warnings) {\n",
+       "            warnings.style.display = 'block';\n",
+       "            warnings.textContent = (\n",
+       "                \"This browser does not support binary websocket messages. \" +\n",
+       "                    \"Performance may be slow.\");\n",
+       "        }\n",
+       "    }\n",
+       "\n",
+       "    this.imageObj = new Image();\n",
+       "\n",
+       "    this.context = undefined;\n",
+       "    this.message = undefined;\n",
+       "    this.canvas = undefined;\n",
+       "    this.rubberband_canvas = undefined;\n",
+       "    this.rubberband_context = undefined;\n",
+       "    this.format_dropdown = undefined;\n",
+       "\n",
+       "    this.image_mode = 'full';\n",
+       "\n",
+       "    this.root = $('
');\n",
+       "    this._root_extra_style(this.root)\n",
+       "    this.root.attr('style', 'display: inline-block');\n",
+       "\n",
+       "    $(parent_element).append(this.root);\n",
+       "\n",
+       "    this._init_header(this);\n",
+       "    this._init_canvas(this);\n",
+       "    this._init_toolbar(this);\n",
+       "\n",
+       "    var fig = this;\n",
+       "\n",
+       "    this.waiting = false;\n",
+       "\n",
+       "    this.ws.onopen =  function () {\n",
+       "            fig.send_message(\"supports_binary\", {value: fig.supports_binary});\n",
+       "            fig.send_message(\"send_image_mode\", {});\n",
+       "            if (mpl.ratio != 1) {\n",
+       "                fig.send_message(\"set_dpi_ratio\", {'dpi_ratio': mpl.ratio});\n",
+       "            }\n",
+       "            fig.send_message(\"refresh\", {});\n",
+       "        }\n",
+       "\n",
+       "    this.imageObj.onload = function() {\n",
+       "            if (fig.image_mode == 'full') {\n",
+       "                // Full images could contain transparency (where diff images\n",
+       "                // almost always do), so we need to clear the canvas so that\n",
+       "                // there is no ghosting.\n",
+       "                fig.context.clearRect(0, 0, fig.canvas.width, fig.canvas.height);\n",
+       "            }\n",
+       "            fig.context.drawImage(fig.imageObj, 0, 0);\n",
+       "        };\n",
+       "\n",
+       "    this.imageObj.onunload = function() {\n",
+       "        this.ws.close();\n",
+       "    }\n",
+       "\n",
+       "    this.ws.onmessage = this._make_on_message_function(this);\n",
+       "\n",
+       "    this.ondownload = ondownload;\n",
+       "}\n",
+       "\n",
+       "mpl.figure.prototype._init_header = function() {\n",
+       "    var titlebar = $(\n",
+       "        '
');\n",
+       "    var titletext = $(\n",
+       "        '
');\n",
+       "    titlebar.append(titletext)\n",
+       "    this.root.append(titlebar);\n",
+       "    this.header = titletext[0];\n",
+       "}\n",
+       "\n",
+       "\n",
+       "\n",
+       "mpl.figure.prototype._canvas_extra_style = function(canvas_div) {\n",
+       "\n",
+       "}\n",
+       "\n",
+       "\n",
+       "mpl.figure.prototype._root_extra_style = function(canvas_div) {\n",
+       "\n",
+       "}\n",
+       "\n",
+       "mpl.figure.prototype._init_canvas = function() {\n",
+       "    var fig = this;\n",
+       "\n",
+       "    var canvas_div = $('
');\n",
+       "\n",
+       "    canvas_div.attr('style', 'position: relative; clear: both; outline: 0');\n",
+       "\n",
+       "    function canvas_keyboard_event(event) {\n",
+       "        return fig.key_event(event, event['data']);\n",
+       "    }\n",
+       "\n",
+       "    canvas_div.keydown('key_press', canvas_keyboard_event);\n",
+       "    canvas_div.keyup('key_release', canvas_keyboard_event);\n",
+       "    this.canvas_div = canvas_div\n",
+       "    this._canvas_extra_style(canvas_div)\n",
+       "    this.root.append(canvas_div);\n",
+       "\n",
+       "    var canvas = $('');\n",
+       "    canvas.addClass('mpl-canvas');\n",
+       "    canvas.attr('style', \"left: 0; top: 0; z-index: 0; outline: 0\")\n",
+       "\n",
+       "    this.canvas = canvas[0];\n",
+       "    this.context = canvas[0].getContext(\"2d\");\n",
+       "\n",
+       "    var backingStore = this.context.backingStorePixelRatio ||\n",
+       "\tthis.context.webkitBackingStorePixelRatio ||\n",
+       "\tthis.context.mozBackingStorePixelRatio ||\n",
+       "\tthis.context.msBackingStorePixelRatio ||\n",
+       "\tthis.context.oBackingStorePixelRatio ||\n",
+       "\tthis.context.backingStorePixelRatio || 1;\n",
+       "\n",
+       "    mpl.ratio = (window.devicePixelRatio || 1) / backingStore;\n",
+       "\n",
+       "    var rubberband = $('');\n",
+       "    rubberband.attr('style', \"position: absolute; left: 0; top: 0; z-index: 1;\")\n",
+       "\n",
+       "    var pass_mouse_events = true;\n",
+       "\n",
+       "    canvas_div.resizable({\n",
+       "        start: function(event, ui) {\n",
+       "            pass_mouse_events = false;\n",
+       "        },\n",
+       "        resize: function(event, ui) {\n",
+       "            fig.request_resize(ui.size.width, ui.size.height);\n",
+       "        },\n",
+       "        stop: function(event, ui) {\n",
+       "            pass_mouse_events = true;\n",
+       "            fig.request_resize(ui.size.width, ui.size.height);\n",
+       "        },\n",
+       "    });\n",
+       "\n",
+       "    function mouse_event_fn(event) {\n",
+       "        if (pass_mouse_events)\n",
+       "            return fig.mouse_event(event, event['data']);\n",
+       "    }\n",
+       "\n",
+       "    rubberband.mousedown('button_press', mouse_event_fn);\n",
+       "    rubberband.mouseup('button_release', mouse_event_fn);\n",
+       "    // Throttle sequential mouse events to 1 every 20ms.\n",
+       "    rubberband.mousemove('motion_notify', mouse_event_fn);\n",
+       "\n",
+       "    rubberband.mouseenter('figure_enter', mouse_event_fn);\n",
+       "    rubberband.mouseleave('figure_leave', mouse_event_fn);\n",
+       "\n",
+       "    canvas_div.on(\"wheel\", function (event) {\n",
+       "        event = event.originalEvent;\n",
+       "        event['data'] = 'scroll'\n",
+       "        if (event.deltaY < 0) {\n",
+       "            event.step = 1;\n",
+       "        } else {\n",
+       "            event.step = -1;\n",
+       "        }\n",
+       "        mouse_event_fn(event);\n",
+       "    });\n",
+       "\n",
+       "    canvas_div.append(canvas);\n",
+       "    canvas_div.append(rubberband);\n",
+       "\n",
+       "    this.rubberband = rubberband;\n",
+       "    this.rubberband_canvas = rubberband[0];\n",
+       "    this.rubberband_context = rubberband[0].getContext(\"2d\");\n",
+       "    this.rubberband_context.strokeStyle = \"#000000\";\n",
+       "\n",
+       "    this._resize_canvas = function(width, height) {\n",
+       "        // Keep the size of the canvas, canvas container, and rubber band\n",
+       "        // canvas in synch.\n",
+       "        canvas_div.css('width', width)\n",
+       "        canvas_div.css('height', height)\n",
+       "\n",
+       "        canvas.attr('width', width * mpl.ratio);\n",
+       "        canvas.attr('height', height * mpl.ratio);\n",
+       "        canvas.attr('style', 'width: ' + width + 'px; height: ' + height + 'px;');\n",
+       "\n",
+       "        rubberband.attr('width', width);\n",
+       "        rubberband.attr('height', height);\n",
+       "    }\n",
+       "\n",
+       "    // Set the figure to an initial 600x600px, this will subsequently be updated\n",
+       "    // upon first draw.\n",
+       "    this._resize_canvas(600, 600);\n",
+       "\n",
+       "    // Disable right mouse context menu.\n",
+       "    $(this.rubberband_canvas).bind(\"contextmenu\",function(e){\n",
+       "        return false;\n",
+       "    });\n",
+       "\n",
+       "    function set_focus () {\n",
+       "        canvas.focus();\n",
+       "        canvas_div.focus();\n",
+       "    }\n",
+       "\n",
+       "    window.setTimeout(set_focus, 100);\n",
+       "}\n",
+       "\n",
+       "mpl.figure.prototype._init_toolbar = function() {\n",
+       "    var fig = this;\n",
+       "\n",
+       "    var nav_element = $('
')\n",
+       "    nav_element.attr('style', 'width: 100%');\n",
+       "    this.root.append(nav_element);\n",
+       "\n",
+       "    // Define a callback function for later on.\n",
+       "    function toolbar_event(event) {\n",
+       "        return fig.toolbar_button_onclick(event['data']);\n",
+       "    }\n",
+       "    function toolbar_mouse_event(event) {\n",
+       "        return fig.toolbar_button_onmouseover(event['data']);\n",
+       "    }\n",
+       "\n",
+       "    for(var toolbar_ind in mpl.toolbar_items) {\n",
+       "        var name = mpl.toolbar_items[toolbar_ind][0];\n",
+       "        var tooltip = mpl.toolbar_items[toolbar_ind][1];\n",
+       "        var image = mpl.toolbar_items[toolbar_ind][2];\n",
+       "        var method_name = mpl.toolbar_items[toolbar_ind][3];\n",
+       "\n",
+       "        if (!name) {\n",
+       "            // put a spacer in here.\n",
+       "            continue;\n",
+       "        }\n",
+       "        var button = $('