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Stanford_Mod_VX_X.py
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Stanford_Mod_VX_X.py
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"""
To be filled later
"""
import os, sys, pylab, math, warnings, datetime, time, shutil, string, gc, inspect, warnings, glob, pickle
import numpy as np
import scipy as sp
from scipy.io import loadmat
from scipy.optimize import curve_fit as cfit
from scipy.odr import ODR, Model, Data, RealData
from scipy.misc import factorial as fac
from scipy.signal import periodogram
from scipy.stats import skew
from scipy.linalg import toeplitz
from scipy.interpolate import interp1d
from matplotlib import pyplot as plt
from matplotlib.colors import LogNorm
from matplotlib import patches as patches
from mpl_toolkits.mplot3d import Axes3D
#from scipy import ndimage as nd
#import fitFunctions
#import fitVoigt
def analyzeData(noiseStr, pulseStr, templateStr = None, dpath = None, spath = None, fullpath = False,
voltage = 'N/A', pw = 'N/a', **kwords):
"""
Fill Later
"""
# Keywords to set the input parameters for processTemplate()
if 'tIter' in kwords.keys():
tIter = kwords['tIter']
else:
tIter = 4
if 'tendBin' in kwords.keys():
tendBin = kwords['tendBin']
else:
tendBin = 1000
if 'ttrigBin' in kwords.keys():
ttrigBin = kwords['ttrigBin']
else:
ttrigBin = 150
if 'Full' in kwords.keys():
Full = kwords['Full']
else:
Full = True
if templateStr is None:
templateStr = pulseStr
if dpath is None:
dpath = '/Applications/Data-Processed/*/trace_data/{0}/{0}_*.mat'
if spath is None:
spath = '/Applications/Data-Processed/Pickles/{0}'
print('Noise Series: ',noiseStr)
print('Template Series: ',templateStr)
if isinstance(pulseStr, str):
print('Pulse Series: ',pulseStr)
else:
print('Pulse Series Starting at: ', '_'.join(pulseStr[0].split('/')[-1].split('_')[:2]))
# Data file names
if not fullpath:
if isinstance(pulseStr, str):
pulsefiles = glob.glob(dpath.format(pulseStr))
pulsefiles.sort(key = lambda line: int(line.split('_')[-1].split('.')[0]))
elif isinstance(pulseStr, list):
pulsefiles = list()
for line in pulseStr:
pulsefiles = (pulsefiles
+ sorted(glob.glob(dpath.format(line)),
key = lambda line: int(line.split('_')[-1].split('.')[0])))
if templateStr == pulseStr:
templateStr = pulseStr[0]
pulseStr = pulseStr[0]
else:
pulsefiles = pulseStr
pulseStr = '_'.join(pulseStr[0].split('/')[-1].split('_')[:2])
print(pulseStr)
#Think these two lines are obsolete...
#noisefiles = glob.glob(dpath.format(noiseStr))
#templatefiles = glob.glob(dpath.format(templateStr))
# Data save names
noiseSaveFile = 'PSDs_'+noiseStr+'.pkl'
templateSaveFile = 'Templates_'+templateStr+'.pkl'
OFSaveFile = 'OFResults_'+pulseStr+'.pkl'
rawDataSaveFile = 'Pulses_'+pulseStr+'.pkl'
# Save settings
saveNoise = True
saveTemplate = True
scaleTrig=False
psInt=int(pulseStr[0:6])
if(psInt > 180208):
scaleTrig = False #True
print('Loading Data...')
if pulsefiles == []:
print('No files in list')
print(dpath.format(pulseStr))
raise
data = loadDataset(pulsefiles, scaleTrig = scaleTrig)
Fs=data['Info']['Fs']
voltage=str(data['Info']['Voltage'])
pw=str(data['Info']['LaserPulseWidth'])
lp=str(data['Info']['LaserPower'])
atten=str(data['Info']['LaserAttenuation'])
#forcing template to be fixed if not triggered on the laser
if(not data['Info']['LaserTrigger']):
templateSaveFile='Templates_180119_2256.pkl'
print('Template: '+templateSaveFile)
if os.path.exists(os.path.join(spath.format('PSDs/'), noiseSaveFile)):
print('Using existing PSDs from '+noiseSaveFile)
saveNoise=False
with open(os.path.join(spath.format('PSDs/'), noiseSaveFile), 'rb') as output:
res=pickle.load(output)
PSD=res['PSD']
else:
print('No existing PSDs from '+os.path.join(spath.format('PSDs/'), noiseSaveFile))
#get noise PSDs
PSD=dict()
fInterp=np.fft.rfftfreq(len(data['Pulses']['A'][0]),d=1/Fs)
#Processing PSDs
print('Processing PSDs')
for chan in ['A','B','Total']:
#get PSD for channel from noise traces
noiseResults = processNoise(data['Noise'][chan], makePlots = False, autoCut = True,
fs = Fs, fileStr = pulseStr+'_'+chan, saveResults = False,
slopeCut = None)
os.system('mkdir -p plots')
#os.system('mv Noise*.png plots/')
freq=noiseResults['f']
#re-sample PSD to match PSD binning of signal
fPSD=interp1d(freq,noiseResults['psdMean'])
PSD[chan]=fPSD(fInterp)
if(saveNoise):
print('...Saving Noise: ' + os.path.join(spath.format('PSDs/'), noiseSaveFile))
noiseResults={'freq':fInterp,'PSD':PSD}
with open(os.path.join(spath.format('PSDs/'), noiseSaveFile), 'wb') as output:
pickle.dump(noiseResults, output)
if(os.path.exists(os.path.join(spath.format('Templates/'), templateSaveFile))):
print('Using existing templates from '+templateSaveFile)
saveTemplate=False
with open(os.path.join(spath.format('Templates/'), templateSaveFile), 'rb') as output:
template=pickle.load(output)
else:
print('No existing template from '+templateSaveFile)
print('Creating Templates')
#get pulses and create templates
template=dict()
for chan in ['A','B','Total']:
template[chan] = processTemplate(data['Pulses'][chan],
trigBin = ttrigBin,
endBin = tendBin,
cutIters = tIter)
#normalize template amplitude to 1
template[chan]-=np.mean(template[chan][0:100])
template[chan]/=np.max(np.abs(template[chan]))
if(saveTemplate):
print('...Saving Templates')
with open(os.path.join(spath.format('Templates/'), templateSaveFile), 'wb') as output:
pickle.dump(template, output)
for traceType in ['Noise','Pulses']:
print('Running Optimal Filter for '+traceType)
#process OF with templates and noise generated above
resultsOF=dict()
resultsOF['Info']=dict()
for k,v in data['Info'].items():
resultsOF['Info'][k]=v
if(traceType == 'Noise'):
extraStr='_Randoms'
OFSaveFile='OFResults_'+pulseStr+'_Randoms.pkl'
else:
extraStr=''
OFSaveFile='OFResults_'+pulseStr+'.pkl'
if Full:
resultsOF['TrigAmp']=np.max(data[traceType]['T'],axis=1)-np.min(data[traceType]['T'],axis=1)
resultsOF['TrigTime']=np.argmax(data[traceType]['T'],axis=1)*(1.0/Fs)
resultsOF['Triggered']=(resultsOF['TrigAmp']>1e-5)
resultsOF['time']=data['Info']['time']
resultsOF['StartTime']=min(resultsOF['time'])
resultsOF['time']-=resultsOF['StartTime']
tLen=len(template['Total'])
trigpt=np.argmax(np.abs(template['Total']))
ptsAfterTrig=tLen-trigpt
time=np.arange(0,tLen)/Fs
for chan in ['A','B','Total']:
print('...',chan)
chanPSD=PSD[chan]*1e-12
#estimate noise variance from PSD
varPSD=(np.fft.irfft(chanPSD**2)*Fs)[0]
if Full:
resultsOF['NoiseVariance_'+chan]=varPSD
resultsOF['Sum_I_'+chan]=list()
resultsOF['Sum_I2_'+chan]=list()
resultsOF['OFP_A1_'+chan]=list()
resultsOF['OFP_t1_'+chan]=list()
resultsOF['OFP_A2_'+chan]=list()
resultsOF['OFP_t2_'+chan]=list()
resultsOF['OFP_chisq_'+chan]=list()
resultsOF['OFP_chisqTime_'+chan]=list()
resultsOF['Slope_'+chan]=list()
resultsOF['Mean_'+chan]=list()
resultsOF['MeanBase_'+chan]=list()
resultsOF['Range_'+chan]=list()
resultsOF['OF0_'+chan]=list()
resultsOF['OF0_chisq_'+chan]=list()
resultsOF['OF0_chisqTime_'+chan]=list()
resultsOF['OF_'+chan]=list()
resultsOF['OF_time_'+chan]=list()
resultsOF['OF_chisq_'+chan]=list()
resultsOF['OF_chisqTime_'+chan]=list()
chanTemplate=template[chan]
trigpt=np.argmax(np.abs(chanTemplate))
count=0
for trace in data[traceType][chan]:
if Full:
resultsOF['Slope_'+chan].append(slope(time,trace,removeMeans=True))
resultsOF['Mean_'+chan].append(np.mean(trace))
resultsOF['MeanBase_'+chan].append(np.mean(trace[0:(trigpt-20)]))
resultsOF['Range_'+chan].append(np.ptp(trace))
resultsOF['Sum_I_'+chan].append(np.sum((trace-resultsOF['MeanBase_'+chan][-1])))
resultsOF['Sum_I2_'+chan].append(np.sum((trace-resultsOF['MeanBase_'+chan][-1])**2.0))
count=count+1
#calculate optimum filter amplitude in microamps
a,t0,x = OptimumFilterAmplitude(trace, chanTemplate, chanPSD, Fs,normalize=False,withDelay=False)
resultsOF['OF0_'+chan].append(a*1e6) #store amplitude in micro-amps
resultsOF['OF0_chisq_'+chan].append(x)
#compute no-delay time domain chi-square
xt = np.var(trace - a*chanTemplate)/varPSD
resultsOF['OF0_chisqTime_'+chan].append(xt)
a,t,x = OptimumFilterAmplitude(trace, chanTemplate, chanPSD, Fs,normalize=False,withDelay=True)
resultsOF['OF_'+chan].append(a*1e6)
resultsOF['OF_time_'+chan].append(t)
resultsOF['OF_chisq_'+chan].append(x)
offset = int(t*Fs)
if(offset > ptsAfterTrig):
offset-=tLen
fit=chanTemplate.copy()
fit=np.roll(fit,offset)
fit*=a
xt=np.var(trace-fit)/varPSD
resultsOF['OF_chisqTime_'+chan].append(xt)
if Full:
#calculate pileup OF
a1,a2,t1,t2,x = OptimumFilterAmplitude_Pileup(trace, chanTemplate,
chanPSD, Fs, downSample=8)
resultsOF['OFP_A1_'+chan].append(a1*1e6)
resultsOF['OFP_A2_'+chan].append(a2*1e6)
resultsOF['OFP_t1_'+chan].append(t1)
resultsOF['OFP_t2_'+chan].append(t2)
resultsOF['OFP_chisq_'+chan].append(x)
offset1=int(t1*Fs)
offset2=int(t2*Fs)
if(offset1 > ptsAfterTrig):
offset1-=tLen
if(offset2 > ptsAfterTrig):
offset2-=tLen
fit1=np.roll(chanTemplate.copy(),offset1)
fit1*=a1
fit2=np.roll(chanTemplate.copy(),offset2)
fit2*=a2
fit=fit1+fit2
xt=np.var(trace-fit)/varPSD
resultsOF['OFP_chisqTime_'+chan].append(xt)
if Full:
resultsOF['Slope_'+chan]=np.array(resultsOF['Slope_'+chan])
resultsOF['Mean_'+chan]=np.array(resultsOF['Mean_'+chan])
resultsOF['MeanBase_'+chan]=np.array(resultsOF['MeanBase_'+chan])
resultsOF['Range_'+chan]=np.array(resultsOF['Range_'+chan])
resultsOF['OFP_A1_'+chan]=np.array(resultsOF['OFP_A1_'+chan])
resultsOF['OFP_t1_'+chan]=np.array(resultsOF['OFP_t1_'+chan])
resultsOF['OFP_A2_'+chan]=np.array(resultsOF['OFP_A2_'+chan])
resultsOF['OFP_t2_'+chan]=np.array(resultsOF['OFP_t2_'+chan])
resultsOF['OFP_'+chan]=resultsOF['OFP_A1_'+chan]+resultsOF['OFP_A2_'+chan]
resultsOF['OFP_chisq_'+chan]=np.array(resultsOF['OFP_chisq_'+chan])
resultsOF['OFP_chisqTime_'+chan]=np.array(resultsOF['OFP_chisqTime_'+chan])
resultsOF['OF0_'+chan]=np.array(resultsOF['OF0_'+chan])
resultsOF['OF0_chisq_'+chan]=np.array(resultsOF['OF0_chisq_'+chan])
resultsOF['OF0_chisqTime_'+chan]=np.array(resultsOF['OF0_chisqTime_'+chan])
resultsOF['OF_'+chan]=np.array(resultsOF['OF_'+chan])
resultsOF['OF_time_'+chan]=np.array(resultsOF['OF_time_'+chan])
resultsOF['OF_chisq_'+chan]=np.array(resultsOF['OF_chisq_'+chan])
resultsOF['OF_chisqTime_'+chan]=np.array(resultsOF['OF_chisqTime_'+chan])
resultsOF['OF0_Sum']=resultsOF['OF0_A']+resultsOF['OF0_B']
resultsOF['OF_Sum']=resultsOF['OF_A']+resultsOF['OF_B']
if Full:
resultsOF['OFP_A1_Sum']=resultsOF['OFP_A1_A']+resultsOF['OFP_A1_B']
resultsOF['OFP_A2_Sum']=resultsOF['OFP_A2_A']+resultsOF['OFP_A2_B']
resultsOF['OFP_Sum']=resultsOF['OFP_A']+resultsOF['OFP_B']
del data # Clear memory space so help with saving maybe(?)
print('Saving Results')
#save results in pkl files for later use
print(os.path.join(spath.format('OF_Results/'), OFSaveFile))
with open(os.path.join(spath.format('OF_Results/'), OFSaveFile), 'wb') as output:
pickle.dump(resultsOF, output)
return None
def applyCut(inds, array):
"""
Fill Later
"""
for a, line in enumerate(array):
array[a] = line[inds]
return array
def getChannels(filelist, verbose = False):
"""
Fill Later
"""
if filelist == []:
print('There are no files in list')
raise
if(type(filelist) == str):
return getChannelsSingleFile(filelist,verbose=verbose)
else:
res1 = getChannelsSingleFile(filelist[0],verbose=verbose)
combined = dict()
combined['A'] = [res1['A']]
combined['B'] = [res1['B']]
combined['Total'] = [res1['Total']]
combined['T'] = [res1['T']]
combined['dVdI'] = res1['dVdI']
combined['Fs'] = res1['Fs']
combined['prop'] = res1['prop']
combined['time'] = [res1['time']]
combined['ai0'] = [res1['ai0']]
combined['ai1'] = [res1['ai1']]
combined['ai2'] = [res1['ai2']]
#combined['pulsenum'] = [res1['pulsenum']]
#combined['filename'] = [res1['filename']]
combined['file'] = dict(zip(list(range(len(filelist))), filelist))
if 'ai3' in res1.keys():
combined['ai3'] = [res1['ai3']]
for i in range(1,len(filelist)):
try:
res = getChannelsSingleFile(filelist[i],verbose=verbose)
combined['A'].append(res['A'])
combined['B'].append(res['B'])
combined['Total'].append(res['Total'])
combined['T'].append(res['T'])
combined['time'].append(res['time'])
combined['ai0'].append(res['ai0'])
combined['ai1'].append(res['ai1'])
combined['ai2'].append(res['ai2'])
#combined['pulsenum'].append(res['pulsenum'])
#combined['filename'].append(res['filename'])
if 'ai3' in res.keys():
combined['ai3'].append(res['ai3'])
except:
print('Skipping '+filelist[i])
combined['A'] = np.concatenate(combined['A'])
combined['B'] = np.concatenate(combined['B'])
combined['Total'] = np.concatenate(combined['Total'])
combined['T'] = np.concatenate(combined['T'])
combined['time'] = np.concatenate(combined['time'])
combined['ai0'] = np.concatenate(combined['ai0'])
combined['ai1'] = np.concatenate(combined['ai1'])
combined['ai2'] = np.concatenate(combined['ai2'])
#combined['pulsenum'] = np.concatenate(combined['pulsenum'])
#combined['filename'] = np.concatenate(combined['filename'])
if 'ai3' in combined.keys():
combined['ai3'] = np.concatenate(combined['ai3'])
else:
print('ai3 is unavailable', end = '\r')
#print(combined['T'].shape)
combined['filenum'] = len(filelist)
return combined
def getChannelsSingleFile(filename, verbose = False):
"""
Function for opening a .mat file from the Stanford DAQ and returns a dictionary
that contains the data.
Parameters
----------
filename : str
The filename that will be opened. Should be a Stanford DAQ .mat file.
Returns
-------
res : dict
A dictionary that has all of the needed data taken from a Stanford DAQ
.mat file.
"""
res = loadmat(filename, squeeze_me = False)
prop = res['exp_prop']
data = res['data_post']
exp_prop = dict()
for line in prop.dtype.names:
try:
val = prop[line][0][0][0]
except IndexError:
val = 'Nothing'
if type(val) is str:
exp_prop[line] = val
elif val.size == 1:
exp_prop[line] = val[0]
else:
exp_prop[line] = np.array(val, dtype = 'f')
gains = np.array(prop['SRS'][0][0][0], dtype = 'f')
rfbs = np.array(prop['Rfb'][0][0][0], dtype = 'f')
turns = np.array(prop['turn_ratio'][0][0][0], dtype = 'f')
fs = float(prop['sample_rate'][0][0][0])
minnum = min(len(gains), len(rfbs), len(turns))
if 'daqrange' in prop.dtype.names:
#The factor of 2 is because the range is +/- (added on 190905)
convert = 2*prop['daqrange'][0][0][0][0]/2**12
else:
convert = 1
ch1 = data[:,:,0]*convert
ch2 = data[:,:,1]*convert
try:
trig = data[:,:,2]*convert
except IndexError:
trig = np.array([])
ai0 = ch1[:]
ai1 = ch2[:]
ai2 = trig[:]
try:
ai3 = data[:, :, 3]*convert
except:
pass
try:
ttable = np.array([24*3600.0, 3600.0, 60.0, 1.0])
reltime = res['t_rel_trig'].squeeze()
abstime = res['t_abs_trig'].squeeze()
timestamp = abstime[:,2:].dot(ttable)+reltime
except:
timestamp = np.arange(0,len(ch1))
dvdi = turns[:minnum]*rfbs[:minnum]*gains[:minnum]
didv = 1.0/dvdi
res = dict()
res['A'] = ch1*didv[0]
res['B'] = ch2*didv[1]
res['Total'] = res['A']+res['B']
res['T'] = trig
res['dVdI'] = dvdi
res['Fs'] = fs
res['prop'] = prop
res['filenum'] = 1
res['time'] = timestamp
res['exp_prop'] = exp_prop
res['ai0'] = ai0
res['ai1'] = ai1
res['ai2'] = ai2
try:
res['ai3'] = ai3
except:
pass
return res
"""
def getChannelsSingleFile(filename, verbose = False):
if(verbose):
print('Loading',filename, end = '\r')
res = loadmat(filename, squeeze_me = False)
prop = res['exp_prop']
data = res['data_post']
exp_prop = dict()
for line in prop.dtype.names:
try:
val = prop[line][0][0][0]
except IndexError:
val = 'Nothing'
if type(val) is str:
exp_prop[line] = val
elif val.size == 1:
exp_prop[line] = val[0]
else:
exp_prop[line] = np.array(val, dtype = 'f')
Gains = np.array(prop['SRS'][0][0][0], dtype = 'f')
Rfbs = np.array(prop['Rfb'][0][0][0], dtype = 'f')
Turns = np.array(prop['turn_ratio'][0][0][0], dtype = 'f')
Fs = float(prop['sample_rate'][0][0][0])
minnum = min(len(Gains), len(Rfbs), len(Turns))
if 'daqrange' in prop.dtype.names:
convert = prop['daqrange'][0][0][0][0]/2**12
else:
convert = 1
ch1 = data[:,:,0]*convert
ch2 = data[:,:,1]*convert
try:
trig = data[:,:,2]*convert
except IndexError:
trig = np.array([])
ai0 = ch1[:]
ai1 = ch2[:]
ai2 = trig[:]
try:
ai3 = data[:, :, 3]*convert
except:
pass
try:
ttable = np.array([24*3600.0,3600.0,60.0,1.0])
reltime = res['t_rel_trig'].squeeze()
abstime = res['t_abs_trig'].squeeze()
timestamp = abstime[:,2:].dot(ttable)+reltime
except:
timestamp = np.arange(0,len(ch1))
if(verbose):
print('No Timestamps Found')
dVdI = Turns[:minnum]*Rfbs[:minnum]*Gains[:minnum]
dIdV = 1.0/dVdI
res = dict()
res['A'] = ch1*dIdV[0]
res['B'] = ch2*dIdV[1]
res['Total'] = res['A']+res['B']
res['T'] = trig
res['dVdI'] = dVdI
res['Fs'] = Fs
res['prop'] = prop
res['filenum'] = 1
res['time'] = timestamp
res['exp_prop'] = exp_prop
res['ai0'] = ai0
res['ai1'] = ai1
res['ai2'] = ai2
res['pulsenum'] = np.arange(len(ai0))
res['filename'] = np.array([filename.split('/')[-1]]*len(ai0))
try:
res['ai3'] = ai3
except:
pass
return res
"""
def loadDataset(filelist, scaleTrig = True, **kwords):
# keywords for tailoring the division of a trace into 'noise' and 'pulse'
traces = getChannels(filelist, verbose=True)
tlen=len(traces['A'][0])
dataset=dict()
dataset['Pulses']=dict()
dataset['Noise']=dict()
dataset['Info']=dict()
chans=['A','B','T','Total']
exclude = ['ai0', 'ai1', 'ai2', 'ai3']
for chan in chans:
if(chan == 'T'):
#need to remove offset which tracks MC temperature
trigTrace=traces['T']
dataset['Info']['R_MC']=-1.0*np.mean(trigTrace,axis=1)
trigChan=np.abs(np.add(trigTrace.transpose(),dataset['Info']['R_MC'])).transpose()
print(np.shape(trigChan))
#if offset present, need to scale back up due to attenuation
if(scaleTrig):
trigChan=trigChan*6e2
dataset['Noise'][chan]=trigChan[:,0:int(tlen/2)]
dataset['Pulses'][chan]=trigChan[:,int(tlen/2):]
else:
dataset['Noise'][chan]=traces[chan][:,0:int(tlen/2)]
#dataset['Pulses'][chan]=traces[chan][:,int(tlen/2) - 100:-100]
dataset['Pulses'][chan]=traces[chan][:,int(tlen/2):]
for k in traces.keys():
if k not in chans and k not in exclude:
dataset['Info'][k]=traces[k]
#if('R_MC' in dataset['Info'].keys()):
# dataset['Info']['Temperature']=temperature(1.0*dataset['Info']['R_MC']*dataset['Info']['dVdI'][2]*-1e3)
try:
dataset['Info']['V_S1']=dataset['Info']['prop']['hvs1'][0][0][0][0]
dataset['Info']['V_S2']=dataset['Info']['prop']['hvs2'][0][0][0][0]
dataset['Info']['Voltage']=dataset['Info']['V_S2']+dataset['Info']['V_S1']
except:
print('No Voltage Fields')
dataset['Info']['V_S1']='NA'
dataset['Info']['V_S2']='NA'
dataset['Info']['Voltage']='NA'
try:
dataset['Info']['LaserRate']=dataset['Info']['prop']['ltr'][0][0][0]
except:
print('No Laser Rate Field')
dataset['Info']['LaserRate']='NA'
try:
dataset['Info']['LaserPulseWidth']=dataset['Info']['prop']['lwd'][0][0][0]
except:
print('No Laser PW Field')
dataset['Info']['LaserPulseWidth']='NA'
try:
dataset['Info']['LaserPower']=dataset['Info']['prop']['lpk'][0][0][0]
except:
print('No Laser Power Field')
dataset['Info']['LaserPower']='NA'
try:
dataset['Info']['LaserAttenuation']=dataset['Info']['prop']['laseratten'][0][0][0][0]
except:
print('No Attenuation Field')
dataset['Info']['LaserAttenuation']='NA'
try:
dataset['Info']['TriggerThreshold']=dataset['Info']['prop']['threshtrig'][0][0][0][0]
except:
print('No Trigger Threshold Field')
dataset['Info']['TriggerThreshold']='NA'
try:
dataset['Info']['TriggerChannel']=dataset['Info']['prop']['trigchan'][0][0][0][0]
except:
print('No Trigger Channel Field')
dataset['Info']['TriggerChannel']=0
if(dataset['Info']['TriggerChannel'] == 3):
dataset['Info']['LaserTrigger']=True
else:
dataset['Info']['LaserTrigger']=False
try:
dataset['Info']['Temp']=dataset['Info']['prop']['T_MC'][0][0][0][0]
except:
print('No Temperature Field')
dataset['Info']['Temp']='NA'
return dataset
def massOFA(Signal, Template, NoisePSD, Fs, withDelay = True, normalize = False, coupling = 'AC'):
"""
Takes a signal trace and calculates the amplitude assuming the profile of template and noise from NoisePSD
"""
dt = 1.0/Fs
Ns = float(len(Signal))
T = Ns*dt
dnu = 1.0/T
if(normalize):
tRange = max(Template) - min(Template)
Template = Template*1.0/tRange
#Template /= tRange
#take one-sided fft of Signal and Template
Sf = np.fft.rfft(Signal, axis = 1)
Tf = np.fft.rfft(Template)
#check for compatibility between PSD and fft
if(len(NoisePSD) != len(Sf[0])):
raise ValueError("PSD length incompatible with signal size")
#take squared noise PSD
J = NoisePSD**2.0*dnu
#If AC coupled, the 0 component of the PSD is non-sensical
#If DC coupled, ignoring the DC component will still give the correct amplitude
if(coupling == 'AC'):
J[0] = np.inf
#find optimum filter and norm
OF = Tf.conjugate()/J
#Norm = np.real(OF.dot(Tf)) # As written this is not normalized...
Norm = np.sqrt(np.real(OF.dot(OF.conjugate()))) # FP Replacement
OFp = OF/Norm
Sfilt = OFp*Sf
#compute OF with delay
if(withDelay):
#have to correct for numpy rfft convention by multiplying by N/2
At = np.fft.irfft(Sfilt, axis = 1)*Ns/2.0
#signal pary of chi-square
chi0 = np.real(np.sum(Sf*Sf.conjugate()/J, axis = 1))*dt/Ns
#fitting part of chi-square
chit = (At**2)*Norm*dt/Ns
#sum parts of chi-square
chi = (np.ones((len(chit[0]), len(chit)))*chi0).transpose() - chit
#find time of best-fit
bInd = np.argmin(chi, axis = 1)
A = At[np.arange(len(bInd)),bInd]
Xr = chi[np.arange(len(bInd)),bInd]
t0 = bInd*dt - T*(bInd*dt - T == 0).astype(int)
#compute OF amplitude no delay
else:
A = np.real(np.sum(Sfilt, axis = 1))
t0 = 0.0
#compute reduced chi-square, normal chi-square divided by number of points
delta = Sf - Tf*(np.ones((len(Sf[0]), len(Sf)))*A).transpose()
Xr = np.real(np.sum((delta.conjugate()*(delta/J)), axis = 1))*dt/Ns
return A,t0,Xr
def OptimumFilterAmplitude(Signal, Template, NoisePSD, Fs, withDelay = True, normalize = False, coupling = 'AC'):
"""
Takes a signal trace and calculates the amplitude assuming the profile of template and noise from NoisePSD
"""
dt = 1.0/Fs
Ns = float(len(Signal))
T = Ns*dt
dnu = 1.0/T
if(normalize):
tRange = max(Template) - min(Template)
Template = Template*1.0/tRange
#Template /= tRange
#take one-sided fft of Signal and Template
Sf = np.fft.rfft(Signal)
Tf = np.fft.rfft(Template)
#check for compatibility between PSD and fft
if(len(NoisePSD) != len(Sf)):
raise ValueError("PSD length incompatible with signal size")
#take squared noise PSD
J = NoisePSD**2.0*dnu
#If AC coupled, the 0 component of the PSD is non-sensical
#If DC coupled, ignoring the DC component will still give the correct amplitude
if(coupling == 'AC'):
J[0] = np.inf
#find optimum filter and norm
OF = Tf.conjugate()/J
#Norm = np.real(OF.dot(Tf)) # As written this is not normalized...
Norm = np.sqrt(np.real(OF.dot(OF.conjugate()))) # FP Replacement
OFp = OF/Norm
Sfilt = OFp*Sf
#global trouble
#trouble = [Sf, Tf, NoisePSD, Fs, J, OF, Norm, OFp]
#raise ValueError
#compute OF with delay
if(withDelay):
#have to correct for numpy rfft convention by multiplying by N/2
At = np.fft.irfft(Sfilt)*Ns/2.0
#signal pary of chi-square
chi0 = np.real(np.dot(Sf.conjugate()/J, Sf))*dt/Ns
#fitting part of chi-square
chit = (At**2)*Norm*dt/Ns
#sum parts of chi-square
chi = chi0 - chit
#find time of best-fit
bInd = np.argmin(chi)
A = At[bInd]
Xr = chi[bInd]
t0 = bInd*dt
if(t0 == T):
t0 -= T
#compute OF amplitude no delay
else:
A = np.real(np.sum(Sfilt))
t0 = 0.0
#compute reduced chi-square, normal chi-square divided by number of points
delta = Sf - Tf*A
Xr = np.real(np.dot(delta/J,delta.conjugate()))*dt/Ns
return A,t0,Xr
def OptimumFilterAmplitude_Pileup(Signal, Template, NoisePSD, Fs, downSample = 10):
dt = 1.0/Fs
Ns = float(len(Signal))
T = Ns*dt
dnu = 1.0/T
#take one-sided fft of Signal and Template
Sf = np.fft.rfft(Signal)
Tf = np.fft.rfft(Template)
#check for compatibility between PSD and fft
if(len(NoisePSD) != len(Sf)):
raise ValueError("PSD length incompatible with signal size")
#take squared noise PSD
J = NoisePSD**2.0*dnu
#TEMPORARY: NEED TO SWITCH TO FULL FFT
J[0] = np.inf
#find optimum filter and norm
OF = Tf.conjugate()/J
Norm = np.real(OF.dot(Tf))
OFp = OF/Norm
#filter template and trace
Sfilt = OFp*Sf
Tfilt = OFp*Tf
#compute OF with delay
#have to correct for numpy rfft convention by multiplying by N/2
At = np.fft.irfft(Sfilt)*Ns/2.0
Gt = np.real(np.fft.irfft(Tfilt))*Ns/2.0
#signal part of chi-square
chi0 = np.real(np.dot(Sf.conjugate()/J,Sf))*dt/Ns
#construct matrices for t0 in the row and delta t in the column
ds = int(downSample)
At0 = timeMatrix(At[::ds])
Atd = timeShiftMatrix(At[::ds])
GM = (timeMatrix(Gt[::ds])).transpose()
#compute full solution
A2 = (Atd - At0*GM)/(1.0 - GM**2 + 1e-10)
A1 = At0-A2*GM
#compute chi-square
chit = (A1**2 + A2**2 + 2*A1*A2*GM)*Norm*dt/Ns
#sum parts of chi-square
chi = chi0 - chit
#find time of best-fit
dti, ti = np.unravel_index(np.argmin(chi), np.shape(chi))
A1s = A1[dti,ti]
A2s = A2[dti,ti]
Xr = chi[dti,ti]
dtS = float(downSample)*dt
t1 = float(ti)*dtS
t2 = t1 + float(dti)*dtS
#keep times in domain
if(t1 >= T):
t1 -= T
if(t2 >= T):
t2 -= T
#first time is first amplitude
if(t2 < t1):
t1t = t1
t1 = t2
t2 = t1t
A1t = A1
A1 = A2
A2 = A1t
return A1s, A2s, t1, t2, Xr
def processNoise(rawTraces, traceGain = 1.0, fs = 625e3, fit = False, autoCut = False,
cutIters = 2, fileStr = '', makePlots = False, saveResults = True,
slopeCut = None, meanCutHigh = None, meanCutLow = None):
#converting sampling rate to time step
dt = (1/fs)*1e6 #time in microseconds
#get trace x values, scale to microseconds
xvals = np.arange(0, len(rawTraces[0]))
xvals_scaled = xvals*dt
#for storing results
tt = 1e6*rawTraces/traceGain
ranges = tt.max(1) - tt.min(1)
means = np.mean(tt, axis = 1)
slopes = slope(xvals, tt)
freq, ps = periodogram(tt, fs = fs, axis = 1)
ps = np.sqrt(ps)*1e6
traces = tt - (np.ones((len(tt[0]), len(tt)))*slopes).transpose()*xvals
skewnesses = np.abs(skew(traces, axis = 1))
freq, psc = periodogram(traces, fs = fs, axis = 1)
psc = np.sqrt(psc)*1e6
if(slopeCut != None):
inds = np.abs(slopes) < slopeCut/1e6
[means, ranges, slopes, skewnesses, traces, ps, psc] = applyCut(inds, [means, ranges, slopes, skewnesses, traces, ps, psc])
if(meanCutHigh != None):
inds = means < meanCutHigh
[means, ranges, slopes, skewnesses, traces, ps, psc] = applyCut(inds, [means, ranges, slopes, skewnesses, traces, ps, psc])
if(meanCutLow != None):
inds = means > meanCutLow
[means, ranges, slopes, skewnesses, traces, ps, psc] = applyCut(inds, [means, ranges, slopes, skewnesses, traces, ps, psc])
if(autoCut):
for i in range(0, cutIters):
#make cuts
inds = (removeOutliers(means) & removeOutliers(ranges) &
removeOutliers(slopes) & removeOutliers(skewnesses))
[means, ranges, slopes, skewnesses, traces, ps, psc] = applyCut(inds, [means, ranges, slopes, skewnesses, traces, ps, psc])
print('Acceptance: ', float(len(slopes))/float(len(rawTraces)))
#store results
psd_corr_mean = np.mean(psc, axis = 0)
psd_corr_err = stdComplex(psc, axis = 0)
tmean = np.mean(traces, axis = 0)
f2, psd_correlated = periodogram(tmean, fs = fs)
psd_correlated = np.sqrt(psd_correlated)*1e6
offset = np.mean(means)
saveData = {'f':freq, 'psdMean':psd_corr_mean,
'psdErr':psd_corr_err, 'psdCorr':psd_correlated,
't':xvals_scaled, 'traceMean':tmean, 'Is0':offset}
if(saveResults):
pkl_file = open('Noise' + fileStr + '.pkl','wb')
pickle.dump(saveData, pkl_file)
pkl_file.close()
return saveData
def processTemplate(rawTraces, autocut = True, cutIters = 2,trigBin = 450, endBin = 2000):
"""
Inputs:
rawTraces - np.array of traces used to calculate the template only upto 10K will be used
autocut - True, default,
"""
x = np.arange(0,len(rawTraces[0]))
nonTrace = np.logical_or(x < trigBin, x > endBin)
xNT = x[nonTrace]
xSlope= xNT - np.mean(xNT)
#for storing results
tt = 1e6*rawTraces[:10000,nonTrace] # Current in microAmps, use only up to 10000 traces
ranges = tt.max(1) - tt.min(1)
means = np.mean(tt, axis = 1)
slopes = slope(xSlope, tt - (np.ones((len(tt[0]), len(tt)))*means).transpose(), removeMeans = False)
skewnesses = np.abs(skew(tt, axis = 1))
traces = rawTraces[:10000]
if(autocut):
for i in range(0,cutIters):