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@@ -1,495 +0,0 @@
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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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-
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-from PhyREC.NeoInterface import NeoSegment#, ReadMCSFile
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-import PhyREC.SignalAnalysis as Ran
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-import PhyREC.PlotWaves as Rplt
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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 deepdish as dd
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-import csv
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-from datetime import datetime
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-from scipy import integrate
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-
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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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-
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- Path = 'C:/Users/RGarcia1/Dropbox (ICN2 AEMD - GAB GBIO)/TeamFolderLMU/Wireless/Characterization/'
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-
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-
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- Files = [
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- '28062019/B12142O30-T4/2019-06-29T17-07-11B12142O30-T4-1mVVgsSweep-ICN2-2',
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- '28062019/B12142O37-T2/2019-06-29T18-00-36B12142O37-T2-1mVVgsSweep-ICN2-PostEthx2-Pt',
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- '03072019/B12142O37-T5/2019-07-03T09-55-56B12142O37-T5-ACDC-1mVsine-Pt-PostEth',
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- '03072019/B12142O37-T6/2019-07-03T10-18-36B12142O37-T6-ACDC-1mVsine-Pt-PostEth',
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- '03072019/B12142O30-T6/2019-07-03T08-31-00B12142O30-T6-ACDC-1mVsine-Pt-PostEth',
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- '03072019/B12142O30-T9/2019-07-03T10-43-12B12142O30-T9-ACDC-1mVsine-Pt-PostEth',
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- '04072019/B12784O18-T1/2019-07-05T08-04-13B12784O18-T1-1mVVgsSweep-PostEth-ICN2',
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- '23072019/B12784O18-T2/2019-07-23T16-18-20B12784O18-T2-ACDC-PostEth-1mV_2',
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- '23072019/B12784O18-T3/2019-07-23T18-55-56B12784O18-T3-ACDC-PostEth-PostLong',]############
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- LogFiles = [
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- '28062019/B12142O30-T4/B12142O30-T4-PreEth-ACDC1mV-AgAgCl-2.txt',
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- '28062019/B12142O37-T2/B12142O37-T2-ACDC-PostEthx2-Pt.txt',
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- '03072019/B12142O37-T5/B12142O37-T5-ACDC-Pt-PostEth.txt',
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- '03072019/B12142O37-T6/B12142O37-T6-ACDC-Pt-PostEth.txt',
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- '03072019/B12142O30-T6/B12142O30-T6_Pt_PostEth-ACDC.txt',
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- '03072019/B12142O30-T9/B12142o30-T9-ACDC-Pt-PostEth.txt',
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- '04072019/B12784O18-T1/B12784O18-T1-ACdc-postTH.txt',
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- '23072019/B12784O18-T2/B12784O18-T2-ACDC-PostEth_Cy2.txt',
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- '23072019/B12784O18-T3/B12784O18-T3-ACDC-PostEth-PostLong.txt']
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-
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- UrmsTot = {}
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- IrmsTot = {}
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- GmTot = {}
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- IdsTot = {}
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- VGS = {}
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- for iFile, File in enumerate(Files):
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- StartCycle = -1
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-
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- LogFile = Path + LogFiles[iFile]
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- InFileM = Path + File + '.h5'
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- InFileS = Path + File + '_2.h5'
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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 = 'SE29'
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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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- VGS[File] = Vgs
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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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-
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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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- # %% Detect Vgs switch time and arrange DC signals into SlotsDC
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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=False)
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-
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- SlotsDC = []
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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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- SlotsDC.append(Rplt.WaveSlot(sig,))
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-
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-
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- SwTimes = LogVals['Time']*pq.s+SwTimes[0]+Delay
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-
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- #%% calc IV DC
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- DevDCVals = {}
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-
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- Ids = {}
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- for sl in SlotsDC:
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- Ids[sl.name] = []
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- for isw, (t, vg) in enumerate(zip(SwTimes, Vgs)):
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- ts = SwTimes[isw]+delta
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- TWind = (t+StabTime, ts-GuardTime)
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- s = sl.GetSignal(TWind, Units='V')
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-
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- vio = np.mean(s).magnitude
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- ids = (vio*101-(-vg+Vds))/12e3 #apply hardware gain calibration
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- Ids[sl.name].append(ids)
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-
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- IdsTot[File.split('/')[1]] = Ids
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-
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-
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-
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- #%% Calc GM and integrated noise Irms
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-
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- GM = {}
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- Irms = {}
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- Urms = {}
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-
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- fig, (AxPsd, Axt) = plt.subplots(2,1)
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- fig2, (AxPs, Axt) = plt.subplots(2,1)
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- for sig in Rec.Signals():
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- if sig.name[0:3] == 'SEn': #discard encoder channels
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- continue
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-
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-
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- GM[sig.name] = []
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- Irms[sig.name] = []
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- Urms[sig.name] = []
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-
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-
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- for isw, (t, vg) in enumerate(zip(SwTimes, Vgs)):
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- if isw == len(SwTimes)-1:
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- ts = sl.Signal.t_stop
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- else:
|
|
|
- ts = SwTimes[isw+1]
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|
|
- 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)
|
|
|
-
|
|
|
- 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*3.3)
|
|
|
- Irms[sig.name] = np.append(Irms[sig.name],irms*2) #
|
|
|
- # square root of 2 adjusts the 1/f integrated noise to a 1 order of magnitude frequency range
|
|
|
- Irms[sig.name] = Irms[sig.name]
|
|
|
- Urms[sig.name] = Irms[sig.name]/GM[sig.name]
|
|
|
-
|
|
|
- plt.close('all') #close psd figures created
|
|
|
-
|
|
|
-
|
|
|
- UrmsTot[File.split('/')[1]] = Urms
|
|
|
- IrmsTot[File.split('/')[1]] = Irms
|
|
|
- GmTot[File.split('/')[1]] = GM
|
|
|
-
|
|
|
-
|
|
|
- plt.figure()
|
|
|
- GMmean, GMstd = MeanStdGM(GM)
|
|
|
- plt.plot(Vgs-0.70, 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()
|
|
|
- IrmsMean, IrmsStd = MeanStdGM(Irms)
|
|
|
- plt.semilogy(Vgs-0.70, IrmsMean,'k',label = 'rms')
|
|
|
- plt.fill_between(Vgs-0.70, IrmsMean-IrmsStd, IrmsMean+IrmsStd ,color = 'k',alpha =0.3)
|
|
|
-
|
|
|
-
|
|
|
- plt.figure()
|
|
|
- UrmsMean, UrmsStd = MeanStdGM(Urms)
|
|
|
- plt.semilogy(Vgs-0.70, UrmsMean,'k',label = '1 metal layer')
|
|
|
- plt.fill_between(Vgs-0.70, UrmsMean-UrmsStd, UrmsMean+UrmsStd ,color = 'k',alpha =0.3)
|
|
|
- plt.xlabel('V$_{gs}$ - V$_{CNP}$ (V)')
|
|
|
- plt.ylabel('U$_{rms}$ (A)')
|
|
|
- plt.legend()
|
|
|
-
|
|
|
-
|
|
|
-
|
|
|
- dd.io.save('GmIrmsUrmsIds10Probe_2.h5',(GmTot, IrmsTot, UrmsTot, IdsTot))
|
|
|
-
|
|
|
- #%% plot map Urms
|
|
|
-
|
|
|
- plt.figure()
|
|
|
- A=np.log10(np.ones((11,6))*5e-17)
|
|
|
-
|
|
|
- import matplotlib.colors as colors
|
|
|
- for Trt in Urms.keys():
|
|
|
- ch = MCSMapI[Trt]
|
|
|
-
|
|
|
- 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)
|
|
|
-
|
|
|
- #%% plot map Gm
|
|
|
-
|
|
|
- plt.figure()
|
|
|
- A=np.log10(np.ones((11,6))*5e-17)
|
|
|
-
|
|
|
- import matplotlib.colors as colors
|
|
|
- for Trt in Urms.keys():
|
|
|
- ch = MCSMapI[Trt]
|
|
|
-
|
|
|
- A[MCSMapFacingDown[ch][1], MCSMapFacingDown[ch][0]] = (GM[Trt][9]*1000/0.1)
|
|
|
-
|
|
|
- plt.imshow(A, interpolation='nearest', vmin=1, vmax=3, norm=colors.LogNorm(vmin=1, vmax=3))
|
|
|
- plt.grid(True)
|
|
|
- cbar=plt.colorbar()
|
|
|
- plt.xlabel('column',fontsize=12)
|
|
|
- plt.ylabel('row',fontsize=12)
|
|
|
- cbar.set_label('G$_{m}$ (mS/V)', rotation=270, labelpad=15,fontsize=13)
|
