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UsefulFunctions.py
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import matplotlib
matplotlib.use('Qt5Agg')
import numpy as np
import scipy
import scipy.signal as sig
import os
import pickle as pkl
from scipy import io
from scipy import signal
from PyQt5 import QtGui, QtWidgets
from numpy import linalg as lin #import linear algebra
import pyqtgraph as pg
import matplotlib.pyplot as plt
from matplotlib.backends.backend_pdf import PdfPages
import pandas as pd
import h5py
from timeit import default_timer as timer
import platform
from scipy.optimize import curve_fit #fitting of data to function
def CalculatePoreSize(G, L, s): # Basic formula for pore size calculation
return (G+np.sqrt(G*(G+16*L*s/np.pi)))/(2*s)
def ImportAxopatchData(datafilename):
x=np.fromfile(datafilename, np.dtype('>f4'))
f=open(datafilename, 'rb')
graphene=0
for i in range(0, 10):
a=str(f.readline())
#print(a)
if 'Acquisition' in a or 'Sample Rate' in a:
samplerate=int(''.join(i for i in a if i.isdigit()))/1000
if 'FEMTO preamp Bandwidth' in a:
femtoLP=int(''.join(i for i in a if i.isdigit()))
if 'I_Graphene' in a:
graphene=1
print('This File Has a Graphene Channel!')
end = len(x)
if graphene:
#pore current
i1 = x[250:end-3:4]
#graphene current
i2 = x[251:end-2:4]
#pore voltage
v1 = x[252:end-1:4]
#graphene voltage
v2 = x[253:end:4]
print('The femto was set to : {} Hz, if this value was correctly entered in the LabView!'.format(str(femtoLP)))
output={'FemtoLowPass': femtoLP, 'type': 'Axopatch', 'graphene': 1, 'samplerate': samplerate, 'i1': i1, 'v1': v1, 'i2': i2, 'v2': v2, 'filename': datafilename}
else:
i1 = np.array(x[250:end-1:2])
v1 = np.array(x[251:end:2])
output={'type': 'Axopatch', 'graphene': 0, 'samplerate': samplerate, 'i1': i1, 'v1': v1, 'filename': datafilename}
return output
def ImportChimeraRaw(datafilename):
matfile=io.loadmat(str(os.path.splitext(datafilename)[0]))
samplerate = np.float64(matfile['ADCSAMPLERATE'])
data = np.fromfile(datafilename, np.dtype('<u2'))
#buffersize=matfile['DisplayBuffer']
TIAgain = np.int32(matfile['SETUP_TIAgain'])
preADCgain = np.float64(matfile['SETUP_preADCgain'])
currentoffset = np.float64(matfile['SETUP_pAoffset'])
ADCvref = np.float64(matfile['SETUP_ADCVREF'])
ADCbits = np.int32(matfile['SETUP_ADCBITS'])
closedloop_gain = TIAgain * preADCgain
bitmask = (2 ** 16 - 1) - (2 ** (16 - ADCbits) - 1)
data = -ADCvref + (2 * ADCvref) * (data & bitmask) / 2 ** 16
data = (data / closedloop_gain + currentoffset)
data.shape = [data.shape[1], ]
output = {'matfilename': str(os.path.splitext(datafilename)[0]),'i1raw': data, 'v1': np.float64(matfile['SETUP_mVoffset']), 'samplerate': np.int64(samplerate), 'type': 'ChimeraRaw', 'filename': datafilename} #final data representation
return output
def ImportChimeraData(datafilename):
matfile = io.loadmat(str(os.path.splitext(datafilename)[0]))
samplerate = matfile['ADCSAMPLERATE']
if samplerate<4e6:
data = np.fromfile(datafilename, np.dtype('float64'))
buffersize = matfile['DisplayBuffer']
out = Reshape1DTo2D(data, buffersize)
output = {'i1': out['i1'], 'v1': out['v1'], 'samplerate':float(samplerate), 'type': 'ChimeraNotRaw', 'filename': datafilename} #final data representation
else:
output = ImportChimeraRaw(datafilename)
return output
def PlotSingle(self):
self.p1.clear() #clear signal plot
self.p3.clear() #clear event plot
self.voltagepl.clear() #clear voltage plot
self.transverseAxis.clear() #clear axis
self.transverseAxisVoltage.clear() #clear axis
self.p1.plotItem.hideAxis('right') #clear axis for transvers current (2nd channel)
self.voltagepl.plotItem.hideAxis('right') #clear axis for transvers voltage (2nd channel)
if self.ui.ndChannel.isChecked():
temp_i = self.out['i2'] #channel2 for current
temp_v = self.out['v2'] #channel2 for voltage
self.p1.setLabel('left', text='Transverse Current', units='A') #setting current as in-plane current
self.voltagepl.setLabel('left', text='Transverse Voltage', units='V') #setting voltage
else:
temp_i = np.array(self.out['i1']) #channel1 for current
temp_v = self.out['v1'] #channel1 for voltage
self.p1.setLabel('left', text='Ionic Current', units='A') #setting current as perpendicular to the plane current
self.voltagepl.setLabel('left', text='Ionic Voltage', units='V') #setting voltage
self.p1.setLabel('bottom', text='Time', units='s') #setting time axis for current
self.voltagepl.setLabel('bottom', text='Time', units='s') #setting time axis for voltage
self.p1.plot(self.t, temp_i, pen='b') #plotting current plot
aphy, aphx = np.histogram(temp_i, bins=int(np.round(len(temp_i) / 1000)))
aphhist = pg.PlotCurveItem(aphx, aphy, stepMode=True, fillLevel=0, brush='b')
self.p3.addItem(aphhist)
if self.out['type'] == 'ChimeraRaw':
self.voltagepl.addLine(y=self.out['v1'], pen='b')
else:
self.voltagepl.plot(self.t, temp_v, pen='b')
self.psdplot.clear()
MakePSD(temp_i, self.out['samplerate'], self.psdplot)
siSamplerate = pg.siScale(self.out['samplerate'])
siSTD = pg.siScale(np.std(temp_i))
self.ui.SampleRateLabel.setText(
'Samplerate: ' + str(self.out['samplerate'] * siSamplerate[0]) + siSamplerate[1] + 'Hz')
self.ui.STDLabel.setText('STD: ' + str(siSTD[0] * np.std(temp_i)) + siSTD[1] + 'A')
self.voltagepl.enableAutoRange(axis='y')
self.p1.enableAutoRange(axis='y')
def MakePSD(input, samplerate, fig):
f, Pxx_den = scipy.signal.periodogram(input, samplerate)
#f, Pxx_den = scipy.signal.welch(input, samplerate, nperseg=10*256, scaling='spectrum')
fig.setLabel('left', 'PSD', units='pA^2/Hz')
fig.setLabel('bottom', 'Frequency', units='Hz')
f = np.delete(f,0)
Pxx_den = np.delete(Pxx_den,0)
fig.setLogMode(x=True, y=True)
fig.plot(f, Pxx_den*1e24, pen='k')
return (f,Pxx_den)
def CutDataIntoVoltageSegments(output, x, y, extractedSegments, delay=0.7, plotSegments = 1):
sweepedChannel = ''
if output['type'] == 'ChimeraNotRaw':
current = output['i1']
voltage = output['v1']
samplerate = output['samplerate']
elif output['type'] == 'Axopatch' and x == 'i1' and y == 'v1':
current = output['i1']
voltage = output['v1']
print('i1,v1')
samplerate = output['samplerate']
elif output['type'] == 'Axopatch' and output['graphene'] and x == 'i2' and y == 'v2':
current = output['i2']
voltage = output['v2']
samplerate = output['samplerate']
print('i2,v2')
elif output['type'] == 'Axopatch' and output['graphene'] and x == 'i2' and y == 'v1':
current = output['i2']
voltage = output['v1']
samplerate = output['samplerate']
print('i2,v1')
elif output['type'] == 'Axopatch' and output['graphene'] and x == 'i1' and y == 'v2':
current = output['i1']
voltage = output['v2']
samplerate = output['samplerate']
print('i1,v2')
else:
print('File doesn''t contain any IV data on the selected channel...')
return (0, 0)
time=np.float32(np.arange(0, len(current))/samplerate)
delayinpoints = int(delay * samplerate)
diffVoltages = np.diff(voltage)
VoltageChangeIndexes = diffVoltages
ChangePoints = np.where(diffVoltages)[0]
Values = voltage[ChangePoints]
Values = np.append(Values, voltage[-1])
print('Cutting into Segments\n{} change points detected...'.format(len(ChangePoints)))
if len(ChangePoints) is 0:
print('Can\'t segment the file. It doesn\'t contain any voltage switches')
return (0,0)
# Store All Data
AllDataList = []
# First
Item={}
Item['Voltage'] = Values[0]
Item['CurrentTrace'] = current[0:ChangePoints[0]]
AllDataList.append(Item)
for i in range(1, len(Values) - 1):
Item={}
Item['CurrentTrace'] = current[ChangePoints[i - 1] + delayinpoints:ChangePoints[i]]
Item['Voltage']=Values[i]
AllDataList.append(Item)
# Last
Item = {}
Item['CurrentTrace'] = current[ChangePoints[len(ChangePoints) - 1] + delayinpoints:len(current) - 1]
Item['Voltage']= Values[len(Values) - 1]
AllDataList.append(Item)
if plotSegments:
#extractedSegments = pg.PlotWidget(title="Extracted Parts")
extractedSegments.plot(time, current, pen='b', linewidth = 2000)
extractedSegments.setLabel('left', text='Current', units='A')
extractedSegments.setLabel('bottom', text='Time', units='s')
# First
extractedSegments.plot(np.arange(0,ChangePoints[0])/samplerate, current[0:ChangePoints[0]], pen='r')
#Loop
for i in range(1, len(Values) - 1):
extractedSegments.plot(np.arange(ChangePoints[i - 1] + delayinpoints, ChangePoints[i]) / samplerate, current[ChangePoints[i - 1] + delayinpoints:ChangePoints[i]], pen='r')
#Last
extractedSegments.plot(np.arange(ChangePoints[len(ChangePoints) - 1] + delayinpoints, len(current) - 1 )/samplerate, current[ChangePoints[len(ChangePoints) - 1] + delayinpoints:len(current) - 1], pen='r')
else:
extractedSegments=0
return (AllDataList, extractedSegments)
def MakeIV(CutData, plot=1):
l=len(CutData)
IVData={}
IVData['Voltage'] = np.zeros(l)
IVData['Mean'] = np.zeros(l)
IVData['STD'] = np.zeros(l)
count=0
for i in CutData:
#print('Voltage: ' + str(i['Voltage']) + ', length: ' + str(len(i['CurrentTrace'])))
IVData['Voltage'][count] = np.float32(i['Voltage'])
IVData['Mean'][count] = np.mean(i['CurrentTrace'])
IVData['STD'][count] = np.std(i['CurrentTrace'])
count+=1
if plot:
spacing=np.sort(IVData['Voltage'])
iv = pg.PlotWidget(title='Current-Voltage Plot')
err = pg.ErrorBarItem(x=IVData['Voltage'], y=IVData['Mean'], top=IVData['STD'], bottom=IVData['STD'], beam=((spacing[1]-spacing[0]))/2)
iv.addItem(err)
iv.plot(IVData['Voltage'], IVData['Mean'], symbol='o', pen=None)
iv.setLabel('left', text='Current', units='A')
iv.setLabel('bottom', text='Voltage', units='V')
print('raboraet')
else:
iv=0
return (IVData, iv)
def ExpFunc(x, a, b, c):
return a * np.exp(-b * x) + c
def MakeExponentialFit(xdata,ydata):
try:
popt, pcov = curve_fit(ExpFunc, xdata, ydata)
return (popt, pcov)
except RuntimeError:
popt = (0,0,0)
pcov = 0
return (popt, pcov)
def YorkFit(X, Y, sigma_X, sigma_Y, r=0):
N_itermax=10 #maximum number of interations
tol=1e-15 #relative tolerance to stop at
N = len(X)
temp = np.matrix([X, np.ones(N)])
#make initial guess at b using linear squares
tmp = np.matrix(Y)*lin.pinv(temp)
b_lse = np.array(tmp)[0][0]
#a_lse=tmp(2);
b = b_lse #initial guess
omega_X = np.true_divide(1,np.power(sigma_X,2))
omega_Y = np.true_divide(1, np.power(sigma_Y,2))
alpha=np.sqrt(omega_X*omega_Y)
b_save = np.zeros(N_itermax+1) #vector to save b iterations in
b_save[0]=b
for i in np.arange(N_itermax):
W=omega_X*omega_Y/(omega_X+b*b*omega_Y-2*b*r*alpha)
X_bar=np.sum(W*X)/np.sum(W)
Y_bar=np.sum(W*Y)/np.sum(W)
U=X-X_bar
V=Y-Y_bar
beta=W*(U/omega_Y+b*V/omega_X-(b*U+V)*r/alpha)
b=sum(W*beta*V)/sum(W*beta*U)
b_save[i+1]=b
if np.abs((b_save[i+1]-b_save[i])/b_save[i+1]) < tol:
break
a=Y_bar-b*X_bar
x=X_bar+beta
y=Y_bar+b*beta
x_bar=sum(W*x)/sum(W)
y_bar=sum(W*y)/sum(W)
u=x-x_bar
#%v=y-y_bar
sigma_b=np.sqrt(1/sum(W*u*u))
sigma_a=np.sqrt(1./sum(W)+x_bar*x_bar*sigma_b*sigma_b)
return (a, b, sigma_a, sigma_b, b_save)
def FitIV(IVData, plot=1, x='i1', y='v1', iv=0):
sigma_v = 1e-12*np.ones(len(IVData['Voltage']))
(a, b, sigma_a, sigma_b, b_save) = YorkFit(IVData['Voltage'], IVData['Mean'], sigma_v, IVData['STD'])
x_fit = np.linspace(min(IVData['Voltage']), max(IVData['Voltage']), 1000)
y_fit = scipy.polyval([b,a], x_fit)
if plot:
spacing = np.sort(IVData['Voltage'])
#iv = pg.PlotWidget(title='Current-Voltage Plot', background=None)
err = pg.ErrorBarItem(x=IVData['Voltage'], y=IVData['Mean'], top=IVData['STD'],
bottom=IVData['STD'], pen='b', beam=((spacing[1]-spacing[0]))/2)
iv.addItem(err)
iv.plot(IVData['Voltage'], IVData['Mean'], symbol='o', pen=None)
iv.setLabel('left', text=x + ', Current', units='A')
iv.setLabel('bottom', text=y + ', Voltage', units='V')
iv.plot(x_fit, y_fit, pen='r')
textval = pg.siFormat(1/b, precision=5, suffix='Ohm', space=True, error=None, minVal=1e-25, allowUnicode=True)
textit = pg.TextItem(text=textval, color=(0, 0, 0))
textit.setPos(min(IVData['Voltage']), max(IVData['Mean']))
iv.addItem(textit)
else:
iv=0
YorkFitValues={'x_fit': x_fit, 'y_fit': y_fit, 'Yintercept':a, 'Slope':b, 'Sigma_Yintercept':sigma_a, 'Sigma_Slope':sigma_b, 'Parameter':b_save}
return (YorkFitValues, iv)
def PlotIV(output, AllData, current = 'i1', unit=1e9, axis = '', WithFit = 1):
axis.errorbar(AllData[current]['Voltage'], AllData[current]['Mean']*unit, yerr=AllData[current]['STD']*unit, fmt='o', label=str(os.path.split(output['filename'])[1])[:-4])
axis.set_ylabel('Current ' + current + ' [nA]')
axis.set_xlabel('Voltage ' + AllData[current]['SweepedChannel'] +' [V]')
if WithFit:
axis.set_title('IV Plot with Fit: G={:.2f}nS'.format(AllData[current]['YorkFitValues']['Slope']*unit))
axis.plot(AllData[current]['YorkFitValues']['x_fit'], AllData[current]['YorkFitValues']['y_fit']*unit, 'r--', label='Linear Fit')
else:
axis.set_title('IV Plot')
return axis
def Reshape1DTo2D(inputarray, buffersize):
npieces = np.uint16(len(inputarray)/buffersize)
voltages = np.array([], dtype=np.float64)
currents = np.array([], dtype=np.float64)
#print(npieces)
for i in range(1, int(npieces+1)):
if i % 2 == 1:
currents = np.append(currents, inputarray[int((i-1)*buffersize):int(i*buffersize-1)], axis=0)
#print('Length Currents: {}'.format(len(currents)))
else:
voltages = np.append(voltages, inputarray[int((i-1)*buffersize):int(i*buffersize-1)], axis=0)
#print('Length Voltages: {}'.format(len(voltages)))
v1 = np.ones((len(voltages)), dtype=np.float64)
i1 = np.ones((len(currents)), dtype=np.float64)
v1[:]=voltages
i1[:]=currents
out = {'v1': v1, 'i1': i1}
print('Currents:' + str(v1.shape))
print('Voltages:' + str(i1.shape))
return out
def OpenFile(filename = ''):
if filename == '':
datafilename = QtGui.QFileDialog.getOpenFileName()
datafilename=datafilename[0]
print(datafilename)
else:
datafilename=filename
if datafilename[-3::] == 'dat':
isdat = 1
output = ImportAxopatchData(datafilename)
else:
isdat = 0
output = ImportChimeraData(datafilename)
return output
def zoom_factory(ax, base_scale = 2.):
def zoom_fun(event):
# get the current x and y limits
cur_xlim = ax.get_xlim()
cur_ylim = ax.get_ylim()
cur_xrange = (cur_xlim[1] - cur_xlim[0])*.5
cur_yrange = (cur_ylim[1] - cur_ylim[0])*.5
xdata = event.xdata # get event x location
ydata = event.ydata # get event y location
if event.button == 'up':
# deal with zoom in
scale_factor = 1/base_scale
elif event.button == 'down':
# deal with zoom out
scale_factor = base_scale
else:
# deal with something that should never happen
scale_factor = 1
print(event.button)
# set new limits
ax.set_xlim([xdata - cur_xrange*scale_factor,
xdata + cur_xrange*scale_factor])
ax.set_ylim([ydata - cur_yrange*scale_factor,
ydata + cur_yrange*scale_factor])
plt.draw() # force re-draw
fig = ax.get_figure() # get the figure of interest
# attach the call back
fig.canvas.mpl_connect('scroll_event',zoom_fun)
#return the function
return zoom_fun
def PlotData(output):
if output['type'] == 'Axopatch':
time=np.float32(np.arange(0, len(output['i1']))/output['samplerate'])
#plot channel 1
ch1_current = pg.PlotWidget(title="Current vs time Channel 1")
ch1_current.plot(time, output['i1'])
ch1_current.setLabel('left', text='Current', units='A')
ch1_current.setLabel('bottom', text='Time', units='s')
ch1_voltage = pg.PlotWidget(title="Voltage vs time Channel 1")
ch1_voltage.plot(time, output['v1'])
ch1_voltage.setLabel('left', text='Voltage', units='V')
ch1_voltage.setLabel('bottom', text='Time', units='s')
#ch1_voltage.setYLink(ch1_current)
ch1_voltage.setXLink(ch1_current)
if output['graphene']:
# plot channel 1
ch2_current = pg.PlotWidget(title="Current vs time Channel 2")
ch2_current.plot(time, output['i2'])
ch2_current.setLabel('left', text='Current', units='A')
ch2_current.setLabel('bottom', text='Time', units='s')
ch2_voltage = pg.PlotWidget(title="Voltage vs time Channel 2")
ch2_voltage.plot(time, output['v2'])
ch2_voltage.setLabel('left', text='Voltage', units='V')
ch2_voltage.setLabel('bottom', text='Time', units='s')
#ch2_voltage.setYLink(ch2_current)
ch2_voltage.setXLink(ch2_current)
fig_handles={'Ch1_Voltage': ch1_voltage, 'Ch2_Voltage': ch2_voltage, 'Ch2_Current': ch2_current, 'Ch1_Current': ch1_current}
return fig_handles
else:
fig_handles = {'Ch1_Voltage': ch1_voltage, 'Ch1_Current': ch1_current, 'Ch2_Voltage': 0, 'Ch2_Current': 0}
return fig_handles
if output['type'] == 'ChimeraRaw':
time = np.float32(np.arange(0, len(output['current']))/output['samplerate'])
figure = plt.figure('Chimera Raw Current @ {} mV'.format(output['voltage']*1e3))
plt.plot(time, output['current']*1e9)
plt.ylabel('Current [nA]')
plt.xlabel('Time [s]')
figure.show()
fig_handles = {'Fig1': figure, 'Fig2': 0, 'Zoom1': 0, 'Zoom2': 0}
return fig_handles
if output['type'] == 'ChimeraNotRaw':
time = np.float32(np.arange(0, len(output['current']))/output['samplerate'])
figure2 = plt.figure('Chimera Not Raw (Display Save Mode)')
ax3 = plt.subplot(211)
ax3.plot(time, output['current'] * 1e9)
plt.ylabel('Current [nA]')
ax4 = plt.subplot(212, sharex=ax3)
ax4.plot(time, output['voltage'] * 1e3)
plt.xlabel('Time [s]')
plt.ylabel('Voltage [mV]')
f2 = zoom_factory(ax3, 1.5)
figure2.show()
fig_handles = {'Fig1': 0, 'Fig2': figure2, 'Zoom1': 0, 'Zoom2': f2}
return fig_handles
def PlotIV(output, AllData, current = 'i1', unit=1e9, axis = '', WithFit = 1):
axis.errorbar(AllData[current]['Voltage'], AllData[current]['Mean']*unit, yerr=AllData[current]['STD']*unit, fmt='o', label=str(os.path.split(output['filename'])[1])[:-4])
axis.set_ylabel('Current ' + current + ' [nA]')
axis.set_xlabel('Voltage ' + AllData[current]['SweepedChannel'] +' [V]')
if WithFit:
axis.set_title('IV Plot with Fit: G={:.2f}nS'.format(AllData[current]['YorkFitValues']['Slope']*unit))
axis.plot(AllData[current]['YorkFitValues']['x_fit'], AllData[current]['YorkFitValues']['y_fit']*unit, 'r--', label='Linear Fit')
else:
axis.set_title('IV Plot')
return axis
def PlotExtractedPart(output, AllData, current = 'i1', unit=1e9, axis = '', axis2 = ''):
time = np.arange(0, len(output[current])) / output['samplerate']
axis.plot(time, output[current] * unit, 'b', label=str(os.path.split(output['filename'])[1])[:-4])
axis.set_ylabel('Current ' + current + ' [nA]')
axis.set_title('Time Trace')
for i in range(0, len(AllData[current]['StartPoint'])):
axis.plot(time[AllData[current]['StartPoint'][i]:AllData[current]['EndPoint'][i]],
output[current][AllData[current]['StartPoint'][i]:AllData[current]['EndPoint'][i]] * unit, 'r')
axis2.plot(time, output[AllData[current]['SweepedChannel']], 'b', label=str(os.path.split(output['filename'])[1])[:-4])
axis2.set_ylabel('Voltage ' + AllData[current]['SweepedChannel'] + ' [V]')
axis2.set_xlabel('Time')
return (axis, axis2)
def ExportIVData(self):
f = h5py.File(self.matfilename + '_IVData.hdf5', "w")
ivdata = f.create_group("IVData")
for k, l in self.IVData.items():
ivdata.create_dataset(k, data=l)
fitdata = f.create_group("FitData")
for k, l in self.FitValues.items():
fitdata.create_dataset(k, data=l)
Data={}
Data['IVFit']=self.FitValues
Data['IVData']=self.IVData
scipy.io.savemat(self.matfilename + '_IVData.mat', Data, appendmat=True)
def SaveFigureList(folder, list):
filename=os.path.splitext(os.path.basename(folder))[0]
dirname=os.path.dirname(folder)
for i in list:
if list[i]:
list[i].savefig(dirname+os.sep+filename+'_'+i+'.png', format='png')
return 0
def DoublePlot(self):
self.p1.clear()
p1 = self.p1.plotItem
p1_v = self.voltagepl.plotItem
p1.getAxis('left').setLabel(text='Ionic Current', color='#0000FF', units='A')
p1_v.getAxis('left').setLabel(text='Ionic Voltage', color='#0000FF', units='V')
## create a new ViewBox, link the right axis to its coordinate system
p1.showAxis('right')
p1_v.showAxis('right')
p1.scene().addItem(self.transverseAxis)
p1_v.scene().addItem(self.transverseAxisVoltage)
p1.getAxis('right').linkToView(self.transverseAxis)
p1_v.getAxis('right').linkToView(self.transverseAxisVoltage)
self.transverseAxis.setXLink(p1)
self.transverseAxisVoltage.setXLink(p1_v)
self.transverseAxis.show()
self.transverseAxisVoltage.show()
p1.getAxis('right').setLabel(text='Transverse Current', color='#FF0000', units='A')
p1_v.getAxis('right').setLabel(text='Transverse Voltage', color='#FF0000', units='V')
def updateViews():
## view has resized; update auxiliary views to match
self.transverseAxis.setGeometry(p1.vb.sceneBoundingRect())
self.transverseAxisVoltage.linkedViewChanged(p1_v.vb, self.transverseAxisVoltage.XAxis)
self.transverseAxisVoltage.setGeometry(p1_v.vb.sceneBoundingRect())
self.transverseAxis.linkedViewChanged(p1.vb, self.transverseAxis.XAxis)
updateViews()
p1.vb.sigResized.connect(updateViews)
p1_v.vb.sigResized.connect(updateViews)
p1.plot(self.t, self.out['i1'], pen='b')
p1_v.plot(self.t, self.out['v1'], pen='b')
self.transverseAxis.addItem(pg.PlotCurveItem(self.t, self.out['i2'], pen='r'))
self.transverseAxisVoltage.addItem(pg.PlotCurveItem(self.t, self.out['v2'], pen='r'))
#Set Ranges
self.transverseAxis.setYRange(np.min(self.out['i2'])-10*np.std(self.out['i2']), np.max(self.out['i2'])+1*np.std(self.out['i2']))
self.p1.setYRange(np.min(self.out['i1'])-1*np.std(self.out['i1']), np.max(self.out['i1'])+ 10*np.std(self.out['i1']))
self.p1.enableAutoRange(axis='x')
self.transverseAxisVoltage.setYRange(np.min(self.out['v2']) - 10/100 * np.mean(self.out['v2']),
np.max(self.out['v2']) + 1/100 * np.mean(self.out['v2']))
self.voltagepl.setYRange(np.min(self.out['v1']) - 1/100 * np.mean(self.out['v1']),
np.max(self.out['v1']) + 10/100 * np.mean(self.out['v1']))
def MatplotLibIV(self):
#IV
if not hasattr(self, 'IVData'):
return
x_fit = np.linspace(min(self.IVData['Voltage']), max(self.IVData['Voltage']), 1000)
y_fit = scipy.polyval([self.FitValues['Slope'], self.FitValues['Yintercept']], x_fit)
x_si_params=pg.siScale(max(x_fit))
y_si_params=pg.siScale(max(y_fit))
plt.figure(1)
plt.clf()
plt.errorbar(self.IVData['Voltage']*x_si_params[0], self.IVData['Mean']*y_si_params[0], fmt='ob', yerr=self.IVData['STD']*y_si_params[0], linestyle='None')
plt.hold(True)
textval = pg.siFormat(1 / self.FitValues['Slope'], precision=5, suffix='Ohm', space=True, error=None, minVal=1e-25, allowUnicode=True)
plt.annotate(textval, [min(self.IVData['Voltage']*x_si_params[0]), max(self.IVData['Mean']*y_si_params[0])])
plt.plot(x_fit*x_si_params[0], y_fit*y_si_params[0], '-r')
plt.xlabel('Voltage Channel {} [{}V]'.format(self.xaxisIV+1, x_si_params[1]))
plt.ylabel('Current Channel {} [{}A]'.format(self.yaxisIV+1, y_si_params[1]))
filename=os.path.splitext(os.path.basename(self.datafilename))[0]
dirname=os.path.dirname(self.datafilename)
plt.savefig(dirname+os.sep+filename+'_IV_' + str(self.xaxisIV) + str(self.yaxisIV) + '.eps')
plt.savefig(dirname+os.sep+filename+'_IV_' + str(self.xaxisIV) + str(self.yaxisIV) + '.png')
#plt.show()
def SaveDerivatives(self):
PartToConsider=np.array(self.eventplot.viewRange()[0])
partinsamples = np.int64(np.round(self.out['samplerate'] * PartToConsider))
t = self.t[partinsamples[0]:partinsamples[1]]
i1part = self.out['i1'][partinsamples[0]:partinsamples[1]]
i2part = self.out['i2'][partinsamples[0]:partinsamples[1]]
plt.figure(1, figsize=(20,7))
plt.subplot(2, 1, 1)
plt.plot(t, i1part, 'b')
plt.title('i1 vs. i2')
plt.ylabel('Ionic Current [A]')
ax = plt.gca()
ax.set_xticklabels([])
plt.subplot(2, 1, 2)
plt.plot(t, i2part, 'r')
plt.xlabel('time (s)')
plt.ylabel('Transverse Current [A]')
self.pp.savefig()
plt.figure(2, figsize=(20,7))
plt.subplot(2, 1, 1)
plt.plot(t, i1part, 'b')
plt.title('i1 vs. its derivative')
plt.ylabel('Ionic Current [A]')
ax = plt.gca()
ax.set_xticklabels([])
plt.subplot(2, 1, 2)
plt.plot(t[:-1], np.diff(i1part), 'y')
plt.xlabel('time (s)')
plt.ylabel('d(Ionic Current [A])/dt')
self.pp.savefig()
plt.figure(3, figsize=(20,7))
plt.subplot(2, 1, 1)
plt.plot(t, i2part, 'r')
plt.title('i2 vs. its derivative')
plt.ylabel('Transverse Current [A]')
ax = plt.gca()
ax.set_xticklabels([])
plt.subplot(2, 1, 2)
plt.plot(t[:-1], np.diff(i2part), 'y')
plt.xlabel('time (s)')
plt.ylabel('d(Transverse Current [A])/dt')
self.pp.savefig()
def MatplotLibCurrentSignal(self):
if not hasattr(self, 'out'):
return
fig, ax1 = plt.subplots(figsize=(20, 7))
if self.ui.plotBoth.isChecked():
ax1.plot(self.t, self.out['i1'], 'b-')
ax1.set_xlim(self.p1.viewRange()[0])
ax1.set_xlabel('time [s]')
# Make the y-axis label and tick labels match the line color.
ax1.set_ylabel('Ionic Current [A]', color='b')
for tl in ax1.get_yticklabels():
tl.set_color('b')
ax2 = ax1.twinx()
ax2.plot(self.t, self.out['i2'], 'r-')
ax1.set_xlim(self.p1.viewRange()[0])
ax1.set_ylim(self.p1.viewRange()[1])
ax2.set_ylabel('Transverse Current [A]', color='r')
ax2.set_ylim(self.transverseAxis.viewRange()[1])
for tl in ax2.get_yticklabels():
tl.set_color('r')
elif self.ui.ndChannel.isChecked():
ax1.plot(self.t, self.out['i2'], 'r-')
ax1.set_xlim(self.p1.viewRange()[0])
ax1.set_ylim(self.p1.viewRange()[1])
ax1.set_xlabel('time [s]')
ax1.set_ylabel('Transverse Current [A]')
for tl in ax1.get_yticklabels():
tl.set_color('r')
else:
ax1.plot(self.t, self.out['i1'], 'b-')
ax1.set_xlim(self.p1.viewRange()[0])
ax1.set_ylim(self.p1.viewRange()[1])
ax1.set_xlabel('time [s]')
ax1.set_ylabel('Ionic Current [A]')
#self.pp.savefig()
#SaveDerivatives(self)
#plt.savefig(dirname+os.sep+filename+'_IV_' + str(self.xaxisIV) + str(self.yaxisIV) + '.eps')
plt.savefig(self.matfilename + str(self.p1.viewRange()[0][0]) + '_Figure.eps')
#plt.show()
def SaveToHDF5(self):
f = h5py.File(self.matfilename + '_OriginalDB.hdf5', "w")
general = f.create_group("General")
general.create_dataset('FileName', data=self.out['filename'])
general.create_dataset('Samplerate', data=self.out['samplerate'])
general.create_dataset('Machine', data=self.out['type'])
general.create_dataset('TransverseRecorded', data=self.out['graphene'])
segmentation_LP = f.create_group("LowPassSegmentation")
for k,l in self.AnalysisResults.items():
set1 = segmentation_LP.create_group(k)
lpset1 = set1.create_group('LowPassSettings')
for o, p in self.coefficients[k].items():
lpset1.create_dataset(o, data=p)
for m, l in self.AnalysisResults[k].items():
set1.create_dataset(m, data=l)
# #
# segmentation_LP.create_dataset('RoughEventLocations', data=self.RoughEventLocations)
# segmentation_LP.create_dataset('StartPoints', data=self.startpoints)
# segmentation_LP.create_dataset('EndPoints', data=endpoints)
# segmentation_LP.create_dataset('LocalBaseline', data=self.localBaseline)
# segmentation_LP.create_dataset('LocalVariance', data=self.localVariance)
# segmentation_LP.create_dataset('DeltaI', data=self.deli)
# segmentation_LP.create_dataset('DwellTime', data=self.dwell)
# segmentation_LP.create_dataset('FractionalCurrentDrop', data=self.frac)
# segmentation_LP.create_dataset('Frequency', data=self.dt)
# segmentation_LP.create_dataset('NumberOfEvents', data=self.numberofevents)
def RecursiveLowPass(signal, coeff):
Ni = len(signal)
RoughEventLocations = []
ml = np.zeros((Ni, 1))#mean
vl = np.zeros((Ni, 1))#sqrt(STD)
ml[0] = np.mean(signal)
vl[0] = np.var(signal)
i = 0
NumberOfEvents = 0
while i < (Ni - 2):
i += 1
# local mean low pass filtering
ml[i] = coeff['a'] * ml[i - 1] + (1 - coeff['a']) * signal[i]
vl[i] = coeff['a'] * vl[i - 1] + (1 - coeff['a']) * (signal[i] - ml[i])**2
Sl = ml[i] - coeff['S'] * np.sqrt(vl[i])
if signal[i + 1] <= Sl:
NumberOfEvents += 1
start = 1 + i
El = ml[i] - coeff['E'] * np.sqrt(vl[i])
Mm = ml[i]
Vv = vl[i]
duration = 0
while signal[i + 1] < El and i < (Ni - 2) and duration < coeff['eventlengthLimit']:
duration += 1
i += 1
if duration >= coeff['eventlengthLimit'] or i > (Ni - 10):
NumberOfEvents -= 1
else:
k = start
while signal[k] < Mm and k > 1:
k -= 1
start = k - 1
k2 = i + 1
while signal[k2] > Mm:
k2 -= 1
endp = k2 + 1
RoughEventLocations.append((start,endp,Mm, Vv))
ml[i] = Mm
vl[i] = Vv
return np.array(RoughEventLocations)
def RecursiveLowPassFast(signal, coeff):
ml = scipy.signal.lfilter([1 - coeff['a'], 0], [1, -coeff['a']], signal) #mean
vl = scipy.signal.lfilter([1 - coeff['a'], 0], [1, -coeff['a']], np.square(signal - ml))
sl = ml - coeff['S'] * np.sqrt(vl)
#self.p1.plot(np.arange(int(zero_points -2* Delay_back_points), int(zero_points),1), sl)
#self.p1.plot(np.arange(int(zero_points -2* Delay_back_points), int(zero_points),1), sl, pen=pg.mkPen(color=(173, 27, 183), width=3))
Ni = len(signal)
points = np.array(np.where(signal<=sl)[0])
to_pop=np.array([])
for i in range(1,len(points)):
if points[i] - points[i - 1] == 1:
to_pop=np.append(to_pop, i)
points = np.delete(points, to_pop)
RoughEventLocations = []
NumberOfEvents=0
for i in points:
if NumberOfEvents is not 0:
if i >= RoughEventLocations[NumberOfEvents-1][0] and i <= RoughEventLocations[NumberOfEvents-1][1]:
continue
NumberOfEvents += 1
start = i
El = ml[i] - coeff['E'] * np.sqrt(vl[i])
Mm = ml[i]
Vv = vl[i]
duration = 0
while signal[i + 1] < El and i < (Ni - 2) and duration < coeff['eventlengthLimit']:
duration += 1
i += 1
if duration >= coeff['eventlengthLimit'] or i > (Ni - 10):
NumberOfEvents -= 1
else:
k = start
while signal[k] < Mm and k > 1:
k -= 1
start = k - 1
k2 = i + 1
while signal[k2] > Mm:
k2 -= 1
endp = k2
if start<0:
start=0
RoughEventLocations.append((start, endp, ml[start], vl[start]))
return np.array(RoughEventLocations)
def RecursiveLowPassFastUp(signal, coeff, self, zero_points, Delay_back_points, backPoint_coef):
ml = scipy.signal.lfilter([1 - coeff['a'], 0], [1, -coeff['a']], self.out['i1'])
vl = scipy.signal.lfilter([1 - coeff['a'], 0], [1, -coeff['a']], np.square(self.out['i1'] - ml))
ml = ml[int(zero_points -backPoint_coef* Delay_back_points):int(zero_points)]
vl = vl[int(zero_points -backPoint_coef* Delay_back_points):int(zero_points)]
#ml = scipy.signal.lfilter([1 - coeff['a'], 0], [1, -coeff['a']], signal)
#vl = scipy.signal.lfilter([1 - coeff['a'], 0], [1, -coeff['a']], np.square(signal - ml))
sl = ml + coeff['S'] * np.sqrt(vl)
self.p1.plot(np.arange(int(zero_points -backPoint_coef* Delay_back_points), int(zero_points),1)/self.out['samplerate'], sl, pen=pg.mkPen(color=(173, 27, 183), width=3))
#self.p1.plot(np.arange(int(zero_points -2* Delay_back_points), int(zero_points),1)/self.out['samplerate'], sl, pen=pg.mkPen(color=(173, 27, 183), width=3))
Ni = len(signal)
points = np.array(np.where(signal>=sl)[0])
to_pop=np.array([])
for i in range(1,len(points)):
if points[i] - points[i - 1] == 1:
to_pop=np.append(to_pop, i)
points = np.delete(points, to_pop)
points =np.delete(points, np.array(np.where(points == 0)[0]))
RoughEventLocations = []
NumberOfEvents=0
for i in points:
if NumberOfEvents is not 0:
if i >= RoughEventLocations[NumberOfEvents-1][0] and i <= RoughEventLocations[NumberOfEvents-1][1]:
continue
NumberOfEvents += 1
start = i
El = ml[i] + coeff['E'] * np.sqrt(vl[i])
Mm = ml[i]
duration = 0
while signal[i + 1] > El and i < (Ni - 2) and duration < coeff['eventlengthLimit']:
duration += 1
i += 1
if duration >= coeff['eventlengthLimit'] or i > (Ni - 10):
NumberOfEvents -= 1
else:
k = start
while signal[k] > Mm and k > 2:
k -= 1
start = k - 1
k2 = i + 1
while signal[k2] > Mm:
k2 -= 1
endp = k2
RoughEventLocations.append((start, endp, ml[start], vl[start]))
return np.array(RoughEventLocations)
def AddInfoAfterRecursive(self):
print('Mistake was here')
#self.p1.plot(self.t, np.sin(self.t), pen='b')
#self.p1.plt.axvline(10, color='green')
print(self.AnalysisResults[self.sig]['RoughEventLocations'])
startpoints = np.uint64(self.AnalysisResults[self.sig]['RoughEventLocations'][:, 0])
endpoints = np.uint64(self.AnalysisResults[self.sig]['RoughEventLocations'][:, 1])
localBaseline = self.AnalysisResults[self.sig]['RoughEventLocations'][:, 2]
localVariance = self.AnalysisResults[self.sig]['RoughEventLocations'][:, 3]
print('Start points=')
print('type='+ str(type(startpoints[0])))
for (j,k) in enumerate(startpoints): print("%10.7f"% float(startpoints[j]/self.out['samplerate']))
CusumBaseline=500
numberofevents = len(startpoints)
self.AnalysisResults[self.sig]['StartPoints'] = startpoints
self.AnalysisResults[self.sig]['EndPoints'] = endpoints
self.AnalysisResults[self.sig]['LocalBaseline'] = localBaseline
self.AnalysisResults[self.sig]['LocalVariance'] = localVariance
self.AnalysisResults[self.sig]['NumberOfEvents'] = len(startpoints)
#### Now we want to move the endpoints to be the last minimum for each ####
#### event so we find all minimas for each event, and set endpoint to last ####
deli = np.zeros(numberofevents)
dwell = np.zeros(numberofevents)
limit=500e-6*self.out['samplerate']
AllFits={}
for i in range(numberofevents):
length = endpoints[i] - startpoints[i]
if length <= limit and length>3:
# Impulsion Fit to minimal value
deli[i] = localBaseline[i] - np.min(self.out[self.sig][int(startpoints[i]+1):int(endpoints[i]-1)])
dwell[i] = (endpoints[i] - startpoints[i]) / self.out['samplerate']
elif length > limit:
deli[i] = localBaseline[i] - np.mean(self.out[self.sig][int(startpoints[i]+5):int(endpoints[i]-5)])
dwell[i] = (endpoints[i] - startpoints[i]) / self.out['samplerate']
# # Cusum Fit
# sigma = np.sqrt(localVariance[i])
# delta = 2e-9
# h = 1 * delta / sigma
# (mc, kd, krmv) = CUSUM(self.out[self.sig][startpoints[i]-CusumBaseline:endpoints[i]+CusumBaseline], delta, h)
# zeroPoint = startpoints[i]-CusumBaseline
# krmv = krmv+zeroPoint+1
# AllFits['Event' + str(i)] = {}
# AllFits['Event' + str(i)]['mc'] = mc
# AllFits['Event' + str(i)]['krmv'] = krmv
else:
deli[i] = localBaseline[i] - np.min(self.out[self.sig][startpoints[i]:endpoints[i]])
dwell[i] = (endpoints[i] - startpoints[i]) / self.out['samplerate']
frac = deli / localBaseline
dt = np.array(0)
dt = np.append(dt, np.diff(startpoints) / self.out['samplerate'])
numberofevents = len(dt)
#self.AnalysisResults[self.sig]['CusumFits'] = AllFits
self.AnalysisResults[self.sig]['FractionalCurrentDrop'] = frac
self.AnalysisResults[self.sig]['DeltaI'] = deli
self.AnalysisResults[self.sig]['DwellTime'] = dwell
self.AnalysisResults[self.sig]['Frequency'] = dt
def SavingAndPlottingAfterRecursive(self):
startpoints=self.AnalysisResults[self.sig]['StartPoints']
endpoints=self.AnalysisResults[self.sig]['EndPoints']
numberofevents=self.AnalysisResults[self.sig]['NumberOfEvents']
deli=self.AnalysisResults[self.sig]['DeltaI']
dwell=self.AnalysisResults[self.sig]['DwellTime']
frac=self.AnalysisResults[self.sig]['FractionalCurrentDrop']
dt=self.AnalysisResults[self.sig]['Frequency']
localBaseline=self.AnalysisResults[self.sig]['LocalBaseline']
if not self.ui.actionDon_t_Plot_if_slow.isChecked():
self.p1.clear()
# Event detection plot, Main Window
self.p1.plot(self.t, self.out[self.sig], pen='b')
self.p1.plot(self.t[startpoints], self.out[self.sig][startpoints], pen=None, symbol='o', symbolBrush='g', symbolSize=10)
self.p1.plot(self.t[endpoints], self.out[self.sig][endpoints], pen=None, symbol='o', symbolBrush='r', symbolSize=10)
#self.p1.plot(self.t[startpoints-10], localBaseline, pen=None, symbol='x', symbolBrush='y', symbolSize=10)
try:
self.p2.data = self.p2.data[np.where(np.array(self.sdf.fn) != self.matfilename)]
except:
IndexError
self.sdf = self.sdf[self.sdf.fn != self.matfilename]
fn = pd.Series([self.matfilename, ] * numberofevents)
color = pd.Series([self.cb.color(), ] * numberofevents)
self.sdf = self.sdf.append(pd.DataFrame({'fn': fn, 'color': color, 'deli': deli,
'frac': frac, 'dwell': dwell,
'dt': dt, 'startpoints': startpoints,
'endpoints': endpoints, 'baseline': localBaseline}), ignore_index=True)
self.p2.addPoints(x=np.log10(dwell), y=frac,
symbol='o', brush=(self.cb.color()), pen=None, size=10)
self.w1.addItem(self.p2)
self.w1.setLogMode(x=True, y=False)
self.p1.autoRange()
self.w1.autoRange()
self.ui.scatterplot.update()
self.w1.setRange(yRange=[0, 1])
colors = self.sdf.color
for i, x in enumerate(colors):
fracy, fracx = np.histogram(self.sdf.frac[self.sdf.color == x],
bins=np.linspace(0, 1, int(self.ui.fracbins.text())))
deliy, delix = np.histogram(self.sdf.deli[self.sdf.color == x],
bins=np.linspace(float(self.ui.delirange0.text()) * 10 ** -9,
float(self.ui.delirange1.text()) * 10 ** -9,
int(self.ui.delibins.text())))
bins_dwell=np.linspace(float(self.ui.dwellrange0.text()), float(self.ui.dwellrange1.text()), int(self.ui.dwellbins.text()))
dwelly, dwellx = np.histogram(np.log10(self.sdf.dwell[self.sdf.color == x]),
bins=bins_dwell,range=(bins_dwell.min(),bins_dwell.max()))
dty, dtx = np.histogram(self.sdf.dt[self.sdf.color == x],
bins=np.linspace(float(self.ui.dtrange0.text()), float(self.ui.dtrange1.text()),
int(self.ui.dtbins.text())))
# hist = pg.PlotCurveItem(fracy, fracx , stepMode = True, fillLevel=0, brush = x, pen = 'k')
# self.w2.addItem(hist)
hist = pg.BarGraphItem(height=fracy, x0=fracx[:-1], x1=fracx[1:], brush=x)
self.w2.addItem(hist)
# hist = pg.PlotCurveItem(delix, deliy , stepMode = True, fillLevel=0, brush = x, pen = 'k')
# self.w3.addItem(hist)
hist = pg.BarGraphItem(height=deliy, x0=delix[:-1], x1=delix[1:], brush=x)
self.w3.addItem(hist)
# self.w3.autoRange()
self.w3.setRange(
xRange=[float(self.ui.delirange0.text()) * 10 ** -9, float(self.ui.delirange1.text()) * 10 ** -9])
# hist = pg.PlotCurveItem(dwellx, dwelly , stepMode = True, fillLevel=0, brush = x, pen = 'k')
# self.w4.addItem(hist)
hist = pg.BarGraphItem(height=dwelly, x0=dwellx[:-1], x1=dwellx[1:], brush=x)