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@@ -0,0 +1,603 @@
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+# -*- coding: utf-8 -*-
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+"""
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+Created on Fri Oct 05 15:49:46 2018
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+
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+@author: aemdlabs
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+"""
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+
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+#!/usr/bin/env python2
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+# -*- coding: utf-8 -*-
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+"""
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+Created on Wed Sep 5 12:05:21 2018
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+
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+@author: aguimera
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+"""
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+
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+from PhyREC.NeoInterface import NeoSegment, NeoSignal#, ReadMCSFile
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+import PhyREC.SignalAnalysis as Ran
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+import PhyREC.PlotWaves as Rplt
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+import PhyREC.SignalAnalysis as Ran
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+import quantities as pq
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+import matplotlib.pyplot as plt
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+import numpy as np
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+import neo
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+import PhyREC.SignalProcess as RPro
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+import deepdish as dd
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+import csv
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+from datetime import datetime
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+import os
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+from scipy import integrate
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+from scipy.interpolate import interp1d
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+
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+import PyGFET.AnalyzeData as FETana
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+import PyGFET.PlotDataClass as FETplt
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+
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+def ReadMCSFile(McsFile, OutSeg=None, SigNamePrefix=''):
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+ import McsPy.McsData as McsData
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+
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+ Dat = McsData.RawData(McsFile)
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+ Rec = Dat.recordings[0]
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+ NSamps = Rec.duration
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+
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+ if OutSeg is None:
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+ OutSeg = NeoSegment()
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+
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+ for AnaStrn, AnaStr in Rec.analog_streams.iteritems():
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+ if len(AnaStr.channel_infos) == 1:
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+ continue
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+
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+ for Chn, Chinfo in AnaStr.channel_infos.iteritems():
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+ print 'Analog Stream ', Chinfo.label, Chinfo.sampling_frequency
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+ ChName = str(SigNamePrefix + Chinfo.label)
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+ print ChName
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+
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+ Fs = Chinfo.sampling_frequency
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+ Var, Unit = AnaStr.get_channel_in_range(Chn, 0, NSamps)
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+ sig = neo.AnalogSignal(pq.Quantity(Var, Chinfo.info['Unit']),
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+ t_start=0*pq.s,
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+ sampling_rate=Fs.magnitude*pq.Hz,
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+ name=ChName)
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+
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+ OutSeg.AddSignal(sig)
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+ return OutSeg
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+
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+def ReadLogFile(File):
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+ Fin = open(File)
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+
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+ reader = csv.reader(Fin, delimiter='\t')
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+
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+ LogVals = {}
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+ ValsPos = {}
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+ for il, e in enumerate(reader):
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+ if il == 0:
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+ for ih, v in enumerate(e):
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+ ValsPos[ih] = v
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+ LogVals[v] = []
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+ else:
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+ for ih, v in enumerate(e):
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+ par = ValsPos[ih]
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+ if (par=='Vgs') or (par=='Vds') or (par=='Vref'):
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+ LogVals[par].append(float(v.replace(',','.')))
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+ elif par == 'Date/Time':
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+ LogVals[par].append(datetime.strptime(v, '%d/%m/%Y %H:%M:%S'))
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+ else:
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+ LogVals[par].append(v)
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+
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+ deltas = np.array(LogVals['Date/Time'])[:]-LogVals['Date/Time'][0]
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+ LogVals['Time'] = []
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+ for d in deltas:
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+ LogVals['Time'].append(d.total_seconds())
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+
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+ Fin.close()
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+
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+ return LogVals
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+
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+def GetSwitchTimes(Sig, Thres=-1e-4, Plot=True):
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+ s = Sig.GetSignal(None)
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+ ds = np.abs(np.diff(np.array(s), axis=0))
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+
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+ if Plot:
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+ plt.figure()
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+ plt.plot(s.times, s)
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+ plt.plot(s.times[1:], ds)
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+
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+ ds = Sig.duplicate_with_new_array(signal=ds)
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+ Times = Ran.threshold_detection(ds,
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+ threshold=Thres,
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+ RelaxTime=5*pq.s)
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+ return Times
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+
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+def MeanStd(Data, var):
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+ Arr = np.zeros([len(Data.keys()),len(Data[Data.keys()[0]][var])])
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+ for iT,TrtName in enumerate(Data.keys()):
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+ Arr[iT,:] = Data[TrtName][var][:,0]
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+
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+ return np.mean(Arr,0), np.std(Arr,0)
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+
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+def MeanStdGM(Data):
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+ Arr = np.zeros([len(Data.keys()),len(Data[Data.keys()[0]])])
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+ for iT,TrtName in enumerate(Data.keys()):
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+ Arr[iT,:] = Data[TrtName]
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+
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+ return np.mean(Arr,0), np.std(Arr,0)
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+
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+def Integrate(PSD, Freqs, Fmin, Fmax):
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+ indices = np.where((Freqs >= Fmin) & (Freqs<=Fmax))
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+ print( Freqs[indices])
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+ Irms = np.sqrt(integrate.trapz(PSD[indices], Freqs[indices]))
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+ return Irms
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+
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+
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+MCSMapI={'SE1':'Ch03',
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+ 'SE2':'Ch05',
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+ 'SE3':'Ch01',
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+ 'SE4':'Ch02',
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+ 'SE5':'Ch22',
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+ 'SE6':'Ch06',
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+ 'SE7':'Ch16',
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+ 'SE8':'Ch37',
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+ 'SE9':'Ch20',
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+ 'SE10':'Ch10',
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+ 'SE11':'Ch24',
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+ 'SE12':'Ch08',
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+ 'SE13':'Ch14',
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+ 'SE14':'Ch04',
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+ 'SE15':'Ch18',
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+ 'SE16':'Ch33',
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+ 'SE17':'Ch34',
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+ 'SE18':'Ch60',
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+ 'SE19':'Ch38',
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+ 'SE20':'Ch64',
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+ 'SE21':'Ch40',
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+ 'SE22':'Ch56',
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+ 'SE23':'Ch42',
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+ 'SE24':'Ch70',
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+ 'SE25':'Ch66',
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+ 'SE26':'Ch65',
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+ 'SE27':'Ch68',
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+ 'SE28':'Ch67',
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+ 'SE29':'Ch55',
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+ 'SE30':'Ch62',
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+ 'SE31':'Ch58',
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+ 'SE32':'Ch69',
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+ 'ME1':'Ch57',
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+ 'ME2':'Ch61',
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+ 'ME3':'Ch53',
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+ 'ME4':'Ch63',
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+ 'ME5':'Ch52',
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+ 'ME6':'Ch41',
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+ 'ME7':'Ch49',
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+ 'ME8':'Ch51',
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+ 'ME9':'Ch46',
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+ 'ME10':'Ch45',
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+ 'ME11':'Ch44',
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+ 'ME12':'Ch39',
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+ 'ME13':'Ch54',
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+ 'ME14':'Ch43',
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+ 'ME15':'Ch50',
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+ 'ME16':'Ch47',
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+ 'ME17':'Ch32',
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+ 'ME18':'Ch27',
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+ 'ME19':'Ch30',
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+ 'ME20':'Ch29',
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+ 'ME21':'Ch28',
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+ 'ME22':'Ch25',
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+ 'ME23':'Ch26',
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+ 'ME24':'Ch07',
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+ 'ME25':'Ch21',
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+ 'ME26':'Ch11',
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+ 'ME27':'Ch17',
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+ 'ME28':'Ch15',
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+ 'ME29':'Ch13',
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+ 'ME30':'Ch31',
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+ 'ME31':'Ch19',
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+ 'ME32':'Ch09'}
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+
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+ #Col, Row
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+MCSMapFacingDown={'Ch58':(0,1),
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+ 'Ch57':(0,2),
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+ 'Ch56':(0,3),
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+ 'Ch55':(0,4),
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+ 'Ch54':(0,5),
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+ 'Ch53':(0,6),
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+ 'Ch52':(0,7),
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+ 'Ch51':(0,8),
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+ 'Ch50':(0,9),
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+ 'Ch49':(0,10),
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+ 'Ch60':(1,0),
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+ 'Ch61':(1,1),
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+ 'Ch62':(1,2),
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+ 'Ch63':(1,3),
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+ 'Ch64':(1,4),
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+ 'Ch65':(1,5),
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+ 'Ch43':(1,6),
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+ 'Ch44':(1,7),
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+ 'Ch45':(1,8),
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+ 'Ch46':(1,9),
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+ 'Ch47':(1,10),
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+ 'Ch70':(2,0),
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+ 'Ch69':(2,1),
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+ 'Ch68':(2,2),
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+ 'Ch67':(2,3),
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+ 'Ch66':(2,4),
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+ 'Ch42':(2,5),
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+ 'Ch41':(2,6),
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+ 'Ch40':(2,7),
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+ 'Ch39':(2,8),
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+ 'Ch38':(2,9),
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+ 'Ch37':(2,10),
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+ 'Ch01':(3,0),
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+ 'Ch02':(3,1),
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+ 'Ch03':(3,2),
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+ 'Ch04':(3,3),
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+ 'Ch05':(3,4),
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+ 'Ch06':(3,5),
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+ 'Ch30':(3,6),
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+ 'Ch31':(3,7),
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+ 'Ch32':(3,8),
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+ 'Ch33':(3,9),
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+ 'Ch34':(3,10),
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+ 'Ch11':(4,0),
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+ 'Ch10':(4,1),
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+ 'Ch09':(4,2),
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+ 'Ch08':(4,3),
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+ 'Ch07':(4,4),
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+ 'Ch29':(4,5),
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+ 'Ch28':(4,6),
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+ 'Ch27':(4,7),
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+ 'Ch26':(4,8),
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+ 'Ch25':(4,9),
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+ 'Ch24':(4,10),
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+ 'Ch12':None,
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+ 'Ch59':None,
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+ 'Ch13':(5,1),
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+ 'Ch14':(5,2),
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+ 'Ch15':(5,3),
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+ 'Ch16':(5,4),
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+ 'Ch17':(5,5),
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+ 'Ch18':(5,6),
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+ 'Ch19':(5,7),
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+ 'Ch20':(5,8),
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+ 'Ch21':(5,9),
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+ 'Ch22':(5,10)}
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+
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+if __name__ == '__main__':
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+
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+# Path = '../Characterization/28062019/B12142O37-T2/'
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+# InFileM = Path + '2019-06-29T18-00-36B12142O37-T2-1mVVgsSweep-ICN2-PostEthx2-Pt.h5' ############
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+# InFileS = Path + '2019-06-29T18-00-36B12142O37-T2-1mVVgsSweep-ICN2-PostEthx2-Pt_2.h5' ##########
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+# LogFile = Path + 'B12142O37-T2-ACDC-PostEthx2-Pt.txt'
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+# IVFigFile = 'Figs/InVivo-B12142O37-T2-2.png'
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+# GMFigFile = 'Figs/InVivo-B12142O37-T2-2.png'
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+# FoutDCvals = 'InVivo-B12142O37-T2-2.h5'
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+
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+
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+#
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+# Path = '../Characterization/28062019/B12142O30-T4/'
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+# InFileM = Path + '2019-06-29T17-31-17B12142O30-T4-1mVVgsSweep-ICN2-Pt-2.h5' ############
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+# InFileS = Path + '2019-06-29T17-31-17B12142O30-T4-1mVVgsSweep-ICN2-Pt-2_2.h5' ##########
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+# LogFile = Path + 'B12142O30-T4-PreEth-ACDC-1mV-Pt-2.txt'
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+
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+
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+# Path = '../Characterization/03072019/B12142O37-T4/'
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+# InFileM = Path + '2019-07-03T09-23-45B12142O37-T4-ACDC-1mVsine-Pt-PostEthx2.h5' ############
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+# InFileS = Path + '2019-07-03T09-23-45B12142O37-T4-ACDC-1mVsine-Pt-PostEthx2_2.h5' ##########
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+# LogFile = Path + 'B12142O37-T4-ACDC-Pt-PostEthx2.txt'
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+#
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+# Path = '../Characterization/03072019/B12142O30-T9/'
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+# InFileM = Path + '2019-07-03T10-43-12B12142O30-T9-ACDC-1mVsine-Pt-PostEth.h5' ############
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+# InFileS = Path + '2019-07-03T10-43-12B12142O30-T9-ACDC-1mVsine-Pt-PostEth_2.h5' ##########
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+# LogFile = Path + 'B12142o30-T9-ACDC-Pt-PostEth.txt'
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+
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+
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+ Path = ''
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+ InFileM = Path + '2019-07-23T16-18-20B12784O18-T2-ACDC-PostEth-1mV_2.h5' ############
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+ InFileS = Path + '2019-07-23T16-18-20B12784O18-T2-ACDC-PostEth-1mV_2_2.h5' ##########
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+ LogFile = Path + 'B12784O18-T2-ACDC-PostEth_Cy2.txt'
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+
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+ StartCycle = -1
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+
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+ LogVals = ReadLogFile(LogFile)
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+ delta = np.mean([t-LogVals['Time'][it] for it, t in enumerate(LogVals['Time'][1:])])*pq.s
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+ Delay = delta * StartCycle
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+
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+ DCch = ('ME5', 'ME7', 'ME29', 'ME31', 'SE5', 'SE7', 'SE29', 'SE31')
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+
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+ TrigChannel = 'SE31'
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+ TrigThres = 5e-5
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+ Vgs = np.array(LogVals['Vgs'])
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+ Vds = LogVals['Vds'][0]
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+
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+ ivgain1 = 12e3*pq.V
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+ ivgain2 = 101
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+ ACgain = 10*1
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+ DCgain = 1*pq.V
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+ Fsig = 10
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+ StabTime = 10*pq.s
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+ GuardTime = 1*pq.s
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+ BW = 100
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+ ivgainDC = 118.8*pq.V #the gain (1e6) is already applied to the saved signal ## Check this gain
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+ ivgainAC = 1188*pq.V
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+
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+#
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+# SigProAC = [{'function': RPro.Gain, 'args': {'Gain': pq.A/ACgain}},
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+# {'function': RPro.Filter, 'args': {'Type':'highpass',
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+# 'Order':2,
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+# 'Freqs':(1)}},
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+# ]
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+#
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+# SigProDC = [{'function': RPro.Gain, 'args': {'Gain': pq.A/DCgain}},
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+# {'function': RPro.Filter, 'args': {'Type':'highpass',
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+# 'Order':2,
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+# 'Freqs':(1)}},
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+# ]
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+
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+
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+ Rec = ReadMCSFile(InFileM,
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+ OutSeg=None,
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+ SigNamePrefix='M')
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+
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+ Rec = ReadMCSFile(InFileS,
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+ OutSeg=Rec,
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+ SigNamePrefix='S')
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+
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+# %%
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+ plt.close('all')
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+ plt.ion()
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+
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+ SwTimes = GetSwitchTimes(Sig=Rec.GetSignal(TrigChannel),
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+ Thres=TrigThres,
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+ Plot=True)
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+
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+ SlotsDC = []
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+ SlotsAC = []
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+ for sig in Rec.Signals():
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+ if sig.name not in DCch:
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+ continue
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+
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+ if sig.name.startswith('M'):
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+ col = 'r'
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+ else:
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+ col = 'g'
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+ SlotsDC.append(Rplt.WaveSlot(sig,
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+ Position=0,
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+ Color=col,
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+ Alpha=0.5))
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+
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+ Splots = Rplt.PlotSlots(SlotsDC)
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+ Splots.PlotChannels(Time=None,
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+ Units='mV')
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+ Splots.PlotEvents(SwTimes,
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+ color='k')
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+ Splots.PlotEvents(LogVals['Time']*pq.s+SwTimes[0]+Delay,
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+ color='r')
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+
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+ SwTimes = LogVals['Time']*pq.s+SwTimes[0]+Delay
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+#%% calc IV DC
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+ DevDCVals = {}
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+
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+ fig, Axt = plt.subplots()
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+ Ids = {}
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+ for sl in SlotsDC:
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+ Ids[sl.name] = []
|
|
|
+ for isw, (t, vg) in enumerate(zip(SwTimes, Vgs)):
|
|
|
+ ts = SwTimes[isw]+delta
|
|
|
+ TWind = (t+StabTime, ts-GuardTime)
|
|
|
+ s = sl.GetSignal(TWind, Units='V')
|
|
|
+ Axt.plot(s)
|
|
|
+ vio = np.mean(s).magnitude
|
|
|
+ ids = (vio*101-(-vg+Vds))/12e3
|
|
|
+ Ids[sl.name].append(ids)
|
|
|
+
|
|
|
+ DCVals = {'Ids': np.array((Ids[sl.name],)).transpose(),
|
|
|
+ 'Vds': np.array((Vds,)),
|
|
|
+ 'Vgs': np.array(Vgs),
|
|
|
+ 'ChName': sl.name,
|
|
|
+ 'Name': sl.name,
|
|
|
+ 'DateTime': LogVals['Date/Time'][0]}
|
|
|
+ DevDCVals[sl.name] = DCVals
|
|
|
+
|
|
|
+ FETana.CheckIsOK(DevDCVals, RdsRange=[400, 40e3])
|
|
|
+ FETana.CalcGM(DevDCVals)
|
|
|
+ pltDC = FETplt.PyFETPlot()
|
|
|
+ pltDC.AddAxes(('Ids', 'Gm', 'Rds'))
|
|
|
+ pltDC.PlotDataCh(DevDCVals, PltIsOK=True)
|
|
|
+ pltDC.AddLegend()
|
|
|
+# if os.path.exists(IVFigFile[0:-7]) == False:
|
|
|
+# os.mkdir((IVFigFile[0:-7]))
|
|
|
+# pltDC.Fig.savefig(IVFigFile[0:-7]+'/'+IVFigFile[5:])
|
|
|
+
|
|
|
+#%% Calc GM
|
|
|
+ GM = {}
|
|
|
+ Irms = {}
|
|
|
+ Urms = {}
|
|
|
+ UrmsDrift = {}
|
|
|
+ SNR = {}
|
|
|
+ Irms2 = {}
|
|
|
+ figgm, axgm = plt.subplots()
|
|
|
+ fig, (AxPsd, Axt) = plt.subplots(2,1)
|
|
|
+ fig2, (AxPs, Axt) = plt.subplots(2,1)
|
|
|
+ for sig in Rec.Signals():
|
|
|
+ if sig.name[0:3] == 'SEn':
|
|
|
+ continue
|
|
|
+# if sig.name in DCch:
|
|
|
+# sig.SignalProcess = SigProDC
|
|
|
+# else:
|
|
|
+# sig.SignalProcess = SigProAC
|
|
|
+#
|
|
|
+ GM[sig.name] = []
|
|
|
+ Irms[sig.name] = []
|
|
|
+ Irms2[sig.name] = []
|
|
|
+ Urms[sig.name] = []
|
|
|
+ SNR[sig.name] = []
|
|
|
+ UrmsDrift[sig.name] = []
|
|
|
+ for isw, (t, vg) in enumerate(zip(SwTimes, Vgs)):
|
|
|
+ if isw == len(SwTimes)-1:
|
|
|
+ ts = sl.Signal.t_stop
|
|
|
+ else:
|
|
|
+ ts = SwTimes[isw+1]
|
|
|
+ TWind = (t+StabTime, ts-GuardTime)
|
|
|
+ s = sig.GetSignal(TWind, Units ='V')
|
|
|
+
|
|
|
+
|
|
|
+ if s.name in DCch:
|
|
|
+ s = (s*ivgain2-(-vg+Vds)*pq.V)/ivgain1
|
|
|
+ else:
|
|
|
+ s = s/(ivgain1*ACgain/ivgain2)
|
|
|
+
|
|
|
+
|
|
|
+ Axt.plot(s.times, s, label=vg, alpha=0.5)
|
|
|
+
|
|
|
+ PS = Ran.PlotPSD((s,),
|
|
|
+ Time = TWind,
|
|
|
+ Ax=AxPs,
|
|
|
+ FMin=1,
|
|
|
+ Label=str(vg),
|
|
|
+ scaling='spectrum')
|
|
|
+
|
|
|
+ ps = PS[sig.name]['psd']
|
|
|
+ Fps = PS[sig.name]['ff']
|
|
|
+
|
|
|
+ indicesPeak = np.where( ((Fps >= Fsig-4) & (Fps<=Fsig+4)))
|
|
|
+
|
|
|
+ IDSpeak = np.sqrt(ps[np.argmax(ps[indicesPeak])+indicesPeak[0][0]]+
|
|
|
+ ps[np.argmax(ps[indicesPeak])+indicesPeak[0][0]+1]+
|
|
|
+ ps[np.argmax(ps[indicesPeak])+indicesPeak[0][0]-1])
|
|
|
+#
|
|
|
+ gm = IDSpeak*1000/0.707
|
|
|
+ GM[sig.name] = np.append(GM[sig.name],gm)
|
|
|
+
|
|
|
+ PSD = Ran.PlotPSD((s,),
|
|
|
+ Time = TWind,
|
|
|
+ Ax=AxPsd,
|
|
|
+ FMin=1,
|
|
|
+ Label=str(vg),
|
|
|
+ scaling='density')
|
|
|
+
|
|
|
+ psd = PSD[sig.name]['psd'][:,0]
|
|
|
+ Fpsd = PSD[sig.name]['ff']
|
|
|
+
|
|
|
+ irms = Integrate(psd, Fpsd, 1.9, 1.9*3.2)
|
|
|
+ Irms[sig.name] = np.append(Irms[sig.name],irms*2)
|
|
|
+ Irms[sig.name] = Irms[sig.name]
|
|
|
+
|
|
|
+ irms2 = np.sqrt(psd[2]*Fpsd[2]*np.log(100))
|
|
|
+ Irms2[sig.name] = np.append(Irms2[sig.name],irms2)
|
|
|
+ Irms2[sig.name] = Irms2[sig.name]
|
|
|
+
|
|
|
+
|
|
|
+ SNR[sig.name] = 20*np.log10(GM[sig.name]*(0.707/1000)/Irms[sig.name])
|
|
|
+# if float(SNR[sig.name][0]) <=15:
|
|
|
+# continue
|
|
|
+ Urms[sig.name] = Irms[sig.name]/GM[sig.name]
|
|
|
+ Polate = interp1d(Vgs-0.34,Urms[sig.name])
|
|
|
+ VgsInt=np.linspace(-0.2,-0.01,10)
|
|
|
+ UrmsDrift[sig.name] = Polate(VgsInt)
|
|
|
+ plt.figure(8)
|
|
|
+ plt.plot(Vgs-0.34, Urms[sig.name],'k',alpha=0.1)
|
|
|
+# axgm.plot(Vgs, GM[sig.name], label=sig.name)
|
|
|
+
|
|
|
+ plt.figure(9)
|
|
|
+ plt.plot(Vgs, SNR[sig.name])
|
|
|
+ axgm.plot(Vgs, GM[sig.name], label=sig.name)
|
|
|
+
|
|
|
+
|
|
|
+# plt.figure(8)
|
|
|
+# plt.plot(Vgs-0.7, Urms[sig.name])
|
|
|
+
|
|
|
+ plt.figure(5)
|
|
|
+ plt.xlabel('Ugs(V)')
|
|
|
+ plt.ylabel('Gm(S)')
|
|
|
+# fig.savefig(IVFigFile[0:-7]+'/'+sig.name+'psd'+'.png')
|
|
|
+# plt.close(fig)
|
|
|
+# plt.close()
|
|
|
+ plt.figure(6)
|
|
|
+ GMmean, GMstd = MeanStdGM(GM)
|
|
|
+ plt.plot(Vgs, GMmean*1000/0.1,'k',label = '1 metal layer')
|
|
|
+ plt.fill_between(Vgs-0.70, GMmean*1000/0.1-GMstd*1000/0.1, GMmean*1000/0.1+GMstd*1000/0.1,color = 'k',alpha =0.3)
|
|
|
+ plt.xlabel('V$_{gs}$ - V$_{CNP}$ (V)')
|
|
|
+ plt.ylabel('G$_m$ (mS/V)')
|
|
|
+ plt.legend()
|
|
|
+
|
|
|
+ plt.figure(7)
|
|
|
+ IrmsMean, IrmsStd = MeanStdGM(Irms)
|
|
|
+ plt.semilogy(Vgs, IrmsMean,'k',label = 'rms')
|
|
|
+ plt.fill_between(Vgs, IrmsMean-IrmsStd, IrmsMean+IrmsStd ,color = 'k',alpha =0.3)
|
|
|
+
|
|
|
+ IrmsMean2, IrmsStd2 = MeanStdGM(Irms2)
|
|
|
+ plt.semilogy(Vgs, IrmsMean2,'b',label = 'a param')
|
|
|
+ plt.fill_between(Vgs, IrmsMean2-IrmsStd2, IrmsMean2+IrmsStd2 ,color = 'b',alpha =0.3)
|
|
|
+
|
|
|
+ plt.xlabel('V$_{gs}$ - V$_{CNP}$ (V)')
|
|
|
+ plt.ylabel('I$_{rms}$ (A)')
|
|
|
+ plt.legend()
|
|
|
+
|
|
|
+ plt.figure(8)
|
|
|
+ UrmsMean, UrmsStd = MeanStdGM(Urms)
|
|
|
+ plt.semilogy(Vgs-0.34, UrmsMean,'k')
|
|
|
+ plt.fill_between(Vgs-0.34, UrmsMean-UrmsStd, UrmsMean+UrmsStd ,color = 'k',alpha =0.3)
|
|
|
+ plt.xlabel('V$_{gs}$ - V$_{CNP}$ (V)')
|
|
|
+ plt.ylabel('V$_{gs-rms}$ ($\mu$V)')
|
|
|
+ plt.legend()
|
|
|
+
|
|
|
+ plt.figure(9)
|
|
|
+ SNRMean, SNRStd = MeanStdGM(SNR)
|
|
|
+ plt.plot(Vgs-0.70, SNRMean,'k',label = '1 metal layer')
|
|
|
+ plt.fill_between(Vgs-0.70, SNRMean-SNRStd, SNRMean+SNRStd ,color = 'k',alpha =0.3)
|
|
|
+ plt.xlabel('V$_{gs}$ - V$_{CNP}$ (V)')
|
|
|
+ plt.ylabel('SNR (dB)')
|
|
|
+ plt.legend()
|
|
|
+
|
|
|
+ fig, ax = plt.subplots()
|
|
|
+ UrmsDriftArray = np.zeros((len(UrmsDrift.keys()),len(UrmsDrift[UrmsDrift.keys()[0]])))
|
|
|
+ for ikeyTrt,keyTrt in enumerate(UrmsDrift.keys()):
|
|
|
+ for ikeyVgs in np.arange(len(UrmsDrift[UrmsDrift.keys()[0]])):
|
|
|
+ UrmsDriftArray[ikeyTrt, ikeyVgs] = UrmsDrift[keyTrt][ikeyVgs]*1e6
|
|
|
+# ax.plot(np.random.normal(VgsInt[ikeyVgs], 0.003)-0.0015, UrmsDrift[keyTrt][ikeyVgs]*1e6,'*k')
|
|
|
+ bpr = ax.boxplot(UrmsDriftArray, positions=VgsInt, widths=0.006)
|
|
|
+
|
|
|
+# axgm.legend()
|
|
|
+# figgm.savefig(IVFigFile[0:-7]+'/'+'Gm'+'.png')
|
|
|
+# dd.io.save(FoutDCvals, (DevDCVals,GM ), ('zlib', 1))
|
|
|
+
|
|
|
+ dd.io.save(Path+'GM-B12784O18-T2', GM)
|
|
|
+
|
|
|
+#%% plot map SNR
|
|
|
+
|
|
|
+plt.figure()
|
|
|
+A=np.log10(np.ones((11,6))*5e-17)
|
|
|
+
|
|
|
+
|
|
|
+for Trt in SNR.keys():
|
|
|
+ ch = MCSMapI[Trt]
|
|
|
+# if Trt in DCch:
|
|
|
+# continue
|
|
|
+
|
|
|
+ A[MCSMapFacingDown[ch][1],MCSMapFacingDown[ch][0]] = SNR[Trt][7]
|
|
|
+
|
|
|
+plt.imshow(A, interpolation='nearest', vmin=10, vmax=38)
|
|
|
+plt.grid(True)
|
|
|
+cbar=plt.colorbar()
|
|
|
+plt.xlabel('column')
|
|
|
+plt.ylabel('row')
|
|
|
+cbar.set_label('SNR [dB]', rotation=270, labelpad=15)
|
|
|
+
|
|
|
+#%% plot map Urms
|
|
|
+
|
|
|
+plt.figure()
|
|
|
+A=np.log10(np.ones((11,6))*5e-17)
|
|
|
+
|
|
|
+import matplotlib.colors as colors
|
|
|
+for Trt in SNR.keys():
|
|
|
+ ch = MCSMapI[Trt]
|
|
|
+# if Trt in DCch:
|
|
|
+# continue
|
|
|
+
|
|
|
+ A[MCSMapFacingDown[ch][1],MCSMapFacingDown[ch][0]] = (Urms[Trt][9])*1e6
|
|
|
+
|
|
|
+plt.imshow(A, interpolation='nearest', vmin=3, vmax=30, norm=colors.LogNorm(vmin=3, vmax=30))
|
|
|
+plt.grid(True)
|
|
|
+cbar=plt.colorbar()
|
|
|
+plt.xlabel('column',fontsize=12)
|
|
|
+plt.ylabel('row',fontsize=12)
|
|
|
+cbar.set_label('U$_{gs-rms}$ ($\mu$V)', rotation=270, labelpad=15,fontsize=13)
|