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UsefulFunctions.py
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Fri, May 10, 14:33

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 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
#import AnalysisParameters as pm
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, npieces+1):
if i % 2 == 1:
currents = np.append(currents, inputarray[(i-1)*buffersize:i*buffersize-1], axis=0)
#print('Length Currents: {}'.format(len(currents)))
else:
voltages = np.append(voltages, inputarray[(i-1)*buffersize: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 CalculatePoreSize(G, L, s):
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, LPfiltercutoff=0):
matfile=io.loadmat(str(os.path.splitext(datafilename)[0]))
#buffersize=matfile['DisplayBuffer']
data = np.fromfile(datafilename, np.dtype('<u2'))
samplerate = np.float64(matfile['ADCSAMPLERATE'])
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], ]
if LPfiltercutoff:
#Low-Pass and downsample
Wn = round(2*LPfiltercutoff/samplerate, 4) # [0,1] nyquist frequency
b, a = signal.bessel(4, Wn, btype='low', analog=False)
datalp = scipy.signal.resample(signal.filtfilt(b, a, data), np.int64(len(data)*(4*LPfiltercutoff/samplerate)))
else:
datalp=0
output = {'matfilename': str(os.path.splitext(datafilename)[0]), 'i1': datalp, 'i1raw': data, 'v1': np.float64(matfile['SETUP_mVoffset']), 'sampleratelp': np.int64(4*LPfiltercutoff), 'samplerate': np.int64(samplerate), 'type': 'ChimeraRaw', 'filename': datafilename}
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}
else:
output = ImportChimeraRaw(datafilename)
return output
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 CutDataIntoVoltageSegments(output):
sweepedChannel = ''
if output['type'] == 'ChimeraNotRaw' or (output['type'] == 'Axopatch' and not output['graphene']):
ChangePoints = np.where(np.diff(output['v1']))[0]
sweepedChannel = 'v1'
if len(ChangePoints) is 0:
print('No voltage sweeps in this file')
return (0,0)
elif (output['type'] == 'Axopatch' and output['graphene']):
ChangePoints = np.where(np.diff(output['v1']))[0]
sweepedChannel = 'v1'
if len(ChangePoints) is 0:
ChangePoints = np.where(np.diff(output['v2']))[0]
if len(ChangePoints) is 0:
print('No voltage sweeps in this file')
return (0,0)
else:
sweepedChannel = 'v2'
print('Cutting into Segments...\n{} change points detected in channel {}...'.format(len(ChangePoints), sweepedChannel))
return (ChangePoints, sweepedChannel)
def MakeIVData(output, approach = 'mean', delay = 0.7):
(ChangePoints, sweepedChannel) = CutDataIntoVoltageSegments(output)
if ChangePoints is 0:
return 0
if output['graphene']:
currents = ['i1', 'i2']
else:
currents = ['i1']
Values = output[sweepedChannel][ChangePoints]
Values = np.append(Values, output[sweepedChannel][::-1][0])
delayinpoints = np.int64(delay * output['samplerate'])
# Store All Data
All={}
for current in currents:
Item = {}
l = len(Values)
Item['Voltage'] = np.zeros(l)
Item['StartPoint'] = np.zeros(l,dtype=np.uint64)
Item['EndPoint'] = np.zeros(l, dtype=np.uint64)
Item['Mean'] = np.zeros(l)
Item['STD'] = np.zeros(l)
Item['SweepedChannel'] = sweepedChannel
Item['YorkFitValues'] = {}
Item['ExponentialFitValues'] = np.zeros((3, l))
Item['ExponentialFitSTD'] = np.zeros((3, l))
# First item
Item['Voltage'][0] = Values[0]
trace = output[current][0 + delayinpoints:ChangePoints[0]]
Item['StartPoint'][0] = 0 + delayinpoints
Item['EndPoint'][0] = ChangePoints[0]
Item['Mean'][0] = np.mean(trace)
Item['STD'][0] = np.std(trace)
(popt, pcov) = MakeExponentialFit(np.arange(len(trace))/output['samplerate'], trace)
if popt[0]:
Item['ExponentialFitValues'][:, 0] = popt
Item['ExponentialFitSTD'][:, 0] = np.sqrt(np.diag(pcov))
else:
print('Exponential Fit on for ' + current + ' failed at V=' + str(Item['Voltage'][0]))
Item['ExponentialFitValues'][:, 0] = np.mean(trace)
Item['ExponentialFitSTD'][:, 0] = np.std(trace)
for i in range(1, len(Values) - 1):
trace = output[current][ChangePoints[i - 1]+delayinpoints:ChangePoints[i]]
Item['Voltage'][i] = Values[i]
Item['StartPoint'][i] = ChangePoints[i - 1]+delayinpoints
Item['EndPoint'][i] = ChangePoints[i]
Item['Mean'][i] = np.mean(trace)
Item['STD'][i] = np.std(trace)
(popt, pcov) = MakeExponentialFit(np.arange(len(trace)) / output['samplerate'], trace)
if popt[0]:
Item['ExponentialFitValues'][:, i] = popt
Item['ExponentialFitSTD'][:, i] = np.sqrt(np.diag(pcov))
else:
print('Exponential Fit on for ' + current + ' failed at V=' + str(Item['Voltage'][i]))
Item['ExponentialFitValues'][:, 0] = np.mean(trace)
Item['ExponentialFitSTD'][:, 0] = np.std(trace)
# Last
if 1:
trace = output[current][ChangePoints[len(ChangePoints) - 1] + delayinpoints : len(output[current]) - 1]
Item['Voltage'][-1:] = Values[len(Values) - 1]
Item['StartPoint'][-1:] = ChangePoints[len(ChangePoints) - 1]+delayinpoints
Item['EndPoint'][-1:] = len(output[current]) - 1
Item['Mean'][-1:] = np.mean(trace)
Item['STD'][-1:] = np.std(trace)
(popt, pcov) = MakeExponentialFit(np.arange(len(trace)) / output['samplerate'], trace)
if popt[0]:
Item['ExponentialFitValues'][:, -1] = popt
Item['ExponentialFitSTD'][:, -1] = np.sqrt(np.diag(pcov))
else:
print('Exponential Fit on for ' + current + ' failed at V=' + str(Item['Voltage'][-1:]))
Item['ExponentialFitValues'][:, 0] = np.mean(trace)
Item['ExponentialFitSTD'][:, 0] = np.std(trace)
sigma_v = 1e-12 * np.ones(len(Item['Voltage']))
(a, b, sigma_a, sigma_b, b_save) = YorkFit(Item['Voltage'], Item['Mean'], sigma_v, Item['STD'])
x_fit = np.linspace(min(Item['Voltage']), max(Item['Voltage']), 1000)
y_fit = scipy.polyval([b, a], x_fit)
Item['YorkFitValues'] = {'x_fit': x_fit, 'y_fit': y_fit, 'Yintercept': a, 'Slope': b, 'Sigma_Yintercept': sigma_a,
'Sigma_Slope': sigma_b, 'Parameter': b_save}
(a, b, sigma_a, sigma_b, b_save) = YorkFit(Item['Voltage'], Item['ExponentialFitValues'][2,:], sigma_v, Item['ExponentialFitSTD'][2,:])
y_fit = scipy.polyval([b, a], x_fit)
Item['YorkFitValuesExponential'] = {'x_fit': x_fit, 'y_fit': y_fit, 'Yintercept': a, 'Slope': b, 'Sigma_Yintercept': sigma_a,
'Sigma_Slope': sigma_b, 'Parameter': b_save}
All[current] = Item
All['Currents'] = currents
return All
def PlotIV(output, AllData, current = 'i1', unit=1e9, axis = '', WithFit = 1, PoreSize = [0,0], title = ''):
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(title + ' : G={}S, R={}Ohm'.format(pg.siFormat(AllData[current]['YorkFitValues']['Slope']), pg.siFormat(1/(AllData[current]['YorkFitValues']['Slope']))))
axis.plot(AllData[current]['YorkFitValues']['x_fit'], AllData[current]['YorkFitValues']['y_fit']*unit, 'r--', label='Linear Fit')
else:
axis.set_title(title + 'IV Plot')
if PoreSize[0]:
textstr = 'Pore Size\nConductance: {}S/m\nLenght: {}m:\ndiameter: {}m'.format(pg.siFormat(PoreSize[0]), pg.siFormat(PoreSize[1]), pg.siFormat(CalculatePoreSize(AllData[current]['YorkFitValues']['Slope'], PoreSize[1], PoreSize[0])))
axis.text(0.05, 0.95, textstr, transform=axis.transAxes, fontsize=12,
verticalalignment='top', bbox=dict(boxstyle='round', facecolor='wheat', alpha=0.5))
return axis
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 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 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 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')
fig.setLogMode(x=True, y=True)
fig.plot(f, Pxx_den*1e24, pen='k')
return (f,Pxx_den)
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 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):
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 PlotSingle(self):
self.p1.clear()
self.transverseAxis.clear()
self.p1.plotItem.hideAxis('right')
self.voltagepl.plotItem.hideAxis('right')
self.transverseAxisVoltage.clear()
self.p3.clear()
self.voltagepl.clear()
if self.ui.ndChannel.isChecked():
temp_i = self.out['i2']
temp_v = self.out['v2']
self.p1.setLabel('left', text='Transverse Current', units='A')
self.voltagepl.setLabel('left', text='Transverse Voltage', units='V')
else:
temp_i = np.array(self.out['i1'])
temp_v = self.out['v1']
self.p1.setLabel('left', text='Ionic Current', units='A')
self.voltagepl.setLabel('left', text='Ionic Voltage', units='V')
self.p1.setLabel('bottom', text='Time', units='s')
print('self.t:' + str(self.t.shape) + ', temp_i:'+str(temp_i.shape))
self.voltagepl.setLabel('bottom', text='Time', units='s')
self.p1.plot(self.t, temp_i, pen='b')
aphy, aphx = np.histogram(temp_i, bins=np.int64(np.round(len(temp_i) / 1000)))
aphx = aphx
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()
print('Samplerate: ' + str(self.out['samplerate']))
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 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))
vl = np.zeros((Ni, 1))
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['maxEventLength']:
duration += 1
i += 1
if duration >= coeff['maxEventLength'] 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)
vl = scipy.signal.lfilter([1 - coeff['a'], 0], [1, -coeff['a']], np.square(signal - ml))
sl = ml - coeff['S'] * np.sqrt(vl)
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['maxEventLength']:
duration += 1
i += 1
if duration >= coeff['maxEventLength'] 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):
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)
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['maxEventLength']:
duration += 1
i += 1
if duration >= coeff['maxEventLength'] 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):
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]
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][startpoints[i]+1: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][startpoints[i]+5: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
AnalysisResults[self.sig]['FractionalCurrentDrop'] = frac
AnalysisResults[self.sig]['DeltaI'] = deli
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)
self.w4.addItem(hist)
# hist = pg.PlotCurveItem(dtx, dty , stepMode = True, fillLevel=0, brush = x, pen = 'k')
# self.w5.addItem(hist)
hist = pg.BarGraphItem(height=dty, x0=dtx[:-1], x1=dtx[1:], brush=x)
self.w5.addItem(hist)
def save(self):
np.savetxt(self.matfilename + 'DB.txt', np.column_stack((self.deli, self.frac, self.dwell, self.dt)),
delimiter='\t')
def PlotEventSingle(self, clicked=[]):
f = h5py.File(self.matfilename + '_OriginalDB.hdf5', "r")
sig='i1'
startpoints=self.AnalysisResults[sig]['StartPoints']
endpoints=self.AnalysisResults[sig]['EndPoints']
localBaseline=self.AnalysisResults[sig]['LocalBaseline']
# Reset plot
self.p3.setLabel('bottom', text='Time', units='s')
self.p3.setLabel('left', text='Current', units='A')
self.p3.clear()
eventnumber = np.int(self.ui.eventnumberentry.text())
eventbuffer = np.int(self.ui.eventbufferentry.value())
# plot event trace
self.p3.plot(self.t[startpoints[eventnumber] - eventbuffer:endpoints[eventnumber] + eventbuffer],
self.out[sig][startpoints[eventnumber] - eventbuffer:endpoints[eventnumber] + eventbuffer],
pen='b')
# plot event fit
self.p3.plot(self.t[startpoints[eventnumber] - eventbuffer:endpoints[eventnumber] + eventbuffer], np.concatenate((
np.repeat(np.array([localBaseline[eventnumber]]), eventbuffer),
np.repeat(np.array([localBaseline[eventnumber] - self.AnalysisResults['i1']['DeltaI'][eventnumber
]]), endpoints[eventnumber] - startpoints[eventnumber]),
np.repeat(np.array([localBaseline[eventnumber]]), eventbuffer)), 0),
pen=pg.mkPen(color=(173, 27, 183), width=3))
self.p3.autoRange()
def PlotEventDouble(self, clicked=[]):
f = h5py.File(self.matfilename + '_OriginalDB.hdf5', "r")
if self.ui.actionPlot_i1_detected_only.isChecked():
indexes = f['LowPassSegmentation/i1/OnlyIndex']
i = f['LowPassSegmentation/i1/']
sig = 'i1'
sig2 = 'i2'
leftlabel = "Ionic Current"
rightlabel = "Transverse Current"
if self.ui.actionPlot_i2_detected_only.isChecked():
indexes = f['LowPassSegmentation/i2/OnlyIndex']
i = f['LowPassSegmentation/i2/']
sig = 'i2'
sig2 = 'i1'
rightlabel = "Ionic Current"
leftlabel = "Transverse Current"
self.p3.clear()
self.transverseAxisEvent.clear()
p1 = self.p3.plotItem
p1.getAxis('left').setLabel(text=leftlabel, color='#0000FF', units='A')
## create a new ViewBox, link the right axis to its coordinate system
p1.showAxis('right')
p1.scene().addItem(self.transverseAxisEvent)
p1.getAxis('right').linkToView(self.transverseAxisEvent)
self.transverseAxisEvent.setXLink(p1)
self.transverseAxisEvent.show()
p1.getAxis('right').setLabel(text=rightlabel, color='#FF0000', units='A')
def updateViews():
## view has resized; update auxiliary views to match
self.transverseAxisEvent.setGeometry(p1.vb.sceneBoundingRect())
self.transverseAxisEvent.linkedViewChanged(p1.vb, self.transverseAxisEvent.XAxis)
updateViews()
p1.vb.sigResized.connect(updateViews)
# Correct for user error if non-extistent number is entered
eventbuffer = np.int(self.ui.eventbufferentry.value())
maxEvents=self.NumberOfEvents
eventnumber = np.int(self.ui.eventnumberentry.text())
if eventnumber >= maxEvents:
eventnumber=0
self.ui.eventnumberentry.setText(str(eventnumber))
elif eventnumber < 0:
eventnumber=maxEvents
self.ui.eventnumberentry.setText(str(eventnumber))
# plot event trace
parttoplot=np.arange(i['StartPoints'][indexes[eventnumber]] - eventbuffer, i['EndPoints'][indexes[eventnumber]] + eventbuffer,1, dtype=np.uint64)
p1.plot(self.t[parttoplot], self.out[sig][parttoplot], pen='b')
# plot event fit
p1.plot(self.t[parttoplot],
np.concatenate((
np.repeat(np.array([i['LocalBaseline'][indexes[eventnumber]]]), eventbuffer),
np.repeat(np.array([i['LocalBaseline'][indexes[eventnumber]] - i['DeltaI'][indexes[eventnumber]
]]), i['EndPoints'][indexes[eventnumber]] - i['StartPoints'][indexes[eventnumber]]),
np.repeat(np.array([i['LocalBaseline'][indexes[eventnumber]]]), eventbuffer)), 0),
pen=pg.mkPen(color=(173, 27, 183), width=3))
# plot 2nd Channel
if self.Derivative == 'i2':
self.transverseAxisEvent.addItem(pg.PlotCurveItem(self.t[parttoplot][:-1], np.diff(self.out[sig][parttoplot]), pen='r'))
p1.getAxis('right').setLabel(text='Derivative of i2', color='#FF0000', units='A')
print('In if...')
#plt.plot(t[:-1], np.diff(i1part), 'y')
else:
self.transverseAxisEvent.addItem(pg.PlotCurveItem(self.t[parttoplot], self.out[sig2][parttoplot], pen='r'))
min1 = np.min(self.out[sig][parttoplot])
max1 = np.max(self.out[sig][parttoplot])
self.p3.setYRange(min1-(max1-min1), max1)
self.p3.enableAutoRange(axis='x')
min2 = np.min(self.out[sig2][parttoplot])
max2 = np.max(self.out[sig2][parttoplot])
self.transverseAxisEvent.setYRange(min2, max2+(max2-min2))
# Mark event start and end points
p1.plot([self.t[i['StartPoints'][indexes[eventnumber]]], self.t[i['StartPoints'][indexes[eventnumber]]]],
[self.out[sig][i['StartPoints'][indexes[eventnumber]]], self.out[sig][i['StartPoints'][indexes[eventnumber]]]],
pen=None,
symbol='o', symbolBrush='g', symbolSize=12)
p1.plot([self.t[i['EndPoints'][indexes[eventnumber]]], self.t[i['EndPoints'][indexes[eventnumber]]]],
[self.out[sig][i['EndPoints'][indexes[eventnumber]]], self.out[sig][i['EndPoints'][indexes[eventnumber]]]],
pen=None,
symbol='o', symbolBrush='r', symbolSize=12)
dtime=pg.siFormat(i['DwellTime'][indexes[eventnumber]], precision=5, suffix='s', space=True, error=None, minVal=1e-25, allowUnicode=True)
dI=pg.siFormat(i['DwellTime'][indexes[eventnumber]], precision=5, suffix='A', space=True, error=None, minVal=1e-25, allowUnicode=True)
self.ui.eventinfolabel.setText(leftlabel + ': Dwell Time=' + dtime + ', Deli=' + dI)
def PlotEventDoubleFit(self, clicked=[]):
f = h5py.File(self.matfilename + '_OriginalDB.hdf5', "r")
i1_indexes=f['LowPassSegmentation/i1/CommonIndex']
i2_indexes=f['LowPassSegmentation/i2/CommonIndex']
i1=f['LowPassSegmentation/i1/']
i2=f['LowPassSegmentation/i2/']
self.p3.clear()
self.transverseAxisEvent.clear()
leftlabel="Ionic Current"
rightlabel="Transverse Current"
p1 = self.p3.plotItem
p1.getAxis('left').setLabel(text=leftlabel, color='#0000FF', units='A')
## create a new ViewBox, link the right axis to its coordinate system
p1.showAxis('right')
p1.scene().addItem(self.transverseAxisEvent)
p1.getAxis('right').linkToView(self.transverseAxisEvent)
self.transverseAxisEvent.setXLink(p1)
self.transverseAxisEvent.show()
p1.getAxis('right').setLabel(text=rightlabel, color='#FF0000', units='A')
def updateViews():
## view has resized; update auxiliary views to match
self.transverseAxisEvent.setGeometry(p1.vb.sceneBoundingRect())
self.transverseAxisEvent.linkedViewChanged(p1.vb, self.transverseAxisEvent.XAxis)
updateViews()
p1.vb.sigResized.connect(updateViews)
# Correct for user error if non-extistent number is entered
eventbuffer = np.int(self.ui.eventbufferentry.value())
maxEvents=len(i1_indexes)
eventnumber = np.int(self.ui.eventnumberentry.text())
if eventnumber >= maxEvents:
eventnumber=0
self.ui.eventnumberentry.setText(str(eventnumber))
elif eventnumber < 0:
eventnumber=maxEvents
self.ui.eventnumberentry.setText(str(eventnumber))
# plot event trace
parttoplot=np.arange(i1['StartPoints'][i1_indexes[eventnumber]] - eventbuffer, i1['EndPoints'][i1_indexes[eventnumber]] + eventbuffer,1, dtype=np.uint64)
parttoplot2=np.arange(i2['StartPoints'][i2_indexes[eventnumber]] - eventbuffer, i2['EndPoints'][i2_indexes[eventnumber]] + eventbuffer,1, dtype=np.uint64)
p1.plot(self.t[parttoplot], self.out['i1'][parttoplot], pen='b')
# plot event fit
p1.plot(self.t[parttoplot],
np.concatenate((
np.repeat(np.array([i1['LocalBaseline'][i1_indexes[eventnumber]]]), eventbuffer),
np.repeat(np.array([i1['LocalBaseline'][i1_indexes[eventnumber]] - i1['DeltaI'][i1_indexes[eventnumber]
]]), i1['EndPoints'][i1_indexes[eventnumber]] - i1['StartPoints'][i1_indexes[eventnumber]]),
np.repeat(np.array([i1['LocalBaseline'][i1_indexes[eventnumber]]]), eventbuffer)), 0),
pen=pg.mkPen(color=(173, 27, 183), width=3))
# plot 2nd Channel
self.transverseAxisEvent.addItem(pg.PlotCurveItem(self.t[parttoplot2], self.out['i2'][parttoplot2], pen='r'))
self.transverseAxisEvent.addItem(pg.PlotCurveItem(self.t[parttoplot2], np.concatenate((np.repeat(
np.array([i2['LocalBaseline'][i2_indexes[eventnumber]]]), eventbuffer), np.repeat(
np.array([i2['LocalBaseline'][i2_indexes[eventnumber]] - i2['DeltaI'][i2_indexes[eventnumber]]]),
i2['EndPoints'][i2_indexes[eventnumber]] - i2['StartPoints'][i2_indexes[eventnumber]]), np.repeat(
np.array([i2['LocalBaseline'][i2_indexes[eventnumber]]]), eventbuffer)), 0), pen=pg.mkPen(color=(173, 27, 183), width=3)))
min1 = np.min(self.out['i1'][parttoplot])
max1 = np.max(self.out['i1'][parttoplot])
self.p3.setYRange(min1-(max1-min1), max1)
self.p3.enableAutoRange(axis='x')
min2 = np.min(self.out['i2'][parttoplot2])
max2 = np.max(self.out['i2'][parttoplot2])
self.transverseAxisEvent.setYRange(min2, max2+(max2-min2))
# Mark event start and end points
p1.plot([self.t[i1['StartPoints'][i1_indexes[eventnumber]]], self.t[i1['StartPoints'][i1_indexes[eventnumber]]]],
[self.out['i1'][i1['StartPoints'][i1_indexes[eventnumber]]], self.out['i1'][i1['StartPoints'][i1_indexes[eventnumber]]]],
pen=None,
symbol='o', symbolBrush='g', symbolSize=12)
p1.plot([self.t[i1['EndPoints'][i1_indexes[eventnumber]]], self.t[i1['EndPoints'][i1_indexes[eventnumber]]]],
[self.out['i1'][i1['EndPoints'][i1_indexes[eventnumber]]], self.out['i1'][i1['EndPoints'][i1_indexes[eventnumber]]]],
pen=None,
symbol='o', symbolBrush='r', symbolSize=12)
self.transverseAxisEvent.addItem(pg.PlotCurveItem([self.t[i2['StartPoints'][i2_indexes[eventnumber]]], self.t[i2['StartPoints'][i2_indexes[eventnumber]]]],
[self.out['i2'][i2['StartPoints'][i2_indexes[eventnumber]]], self.out['i2'][i1['StartPoints'][i2_indexes[eventnumber]]]],
pen=None,
symbol='o', symbolBrush='g', symbolSize=12))
self.transverseAxisEvent.addItem(pg.PlotCurveItem([self.t[i2['EndPoints'][i2_indexes[eventnumber]]], self.t[i2['EndPoints'][i2_indexes[eventnumber]]]],
[self.out['i2'][i1['EndPoints'][i2_indexes[eventnumber]]], self.out['i2'][i1['EndPoints'][i2_indexes[eventnumber]]]],
pen=None,
symbol='o', symbolBrush='r', symbolSize=12))
dtime=pg.siFormat(i1['DwellTime'][i1_indexes[eventnumber]], precision=5, suffix='s', space=True, error=None, minVal=1e-25, allowUnicode=True)
dI=pg.siFormat(i1['DwellTime'][i1_indexes[eventnumber]], precision=5, suffix='A', space=True, error=None, minVal=1e-25, allowUnicode=True)
dtime2=pg.siFormat(i2['DwellTime'][i2_indexes[eventnumber]], precision=5, suffix='s', space=True, error=None, minVal=1e-25, allowUnicode=True)
dI2=pg.siFormat(i2['DwellTime'][i2_indexes[eventnumber]], precision=5, suffix='A', space=True, error=None, minVal=1e-25, allowUnicode=True)
self.ui.eventinfolabel.setText('Ionic Dwell Time=' + dtime + ', Ionic Deli=' + dI + ', ' 'Trans Dwell Time=' + dtime2 + ', Trans Deli=' + dI2)
def SaveEventPlotMatplot(self):
eventbuffer = np.int(self.ui.eventbufferentry.value())
eventnumber = np.int(self.ui.eventnumberentry.text())
parttoplot = np.arange(i['StartPoints'][indexes[eventnumber]][eventnumber] - eventbuffer, i['EndPoints'][indexes[eventnumber]][eventnumber] + eventbuffer, 1,
dtype=np.uint64)
t=np.arange(0,len(parttoplot))
t=t/self.out['samplerate']*1e3
fig1=plt.figure(1, figsize=(20,7))
plt.subplot(2, 1, 1)
plt.cla
plt.plot(t, self.out['i1'][parttoplot]*1e9, 'b')
plt.ylabel('Ionic Current [nA]')
ax = plt.gca()
ax.set_xticklabels([])
plt.subplot(2, 1, 2)
plt.cla
plt.plot(t, self.out['i2'][parttoplot]*1e9, 'r')
plt.ylabel('Transverse Current [nA]')
plt.xlabel('time (ms)')
self.pp.savefig()
fig1.clear()
def CombineTheTwoChannels(file):
f = h5py.File(file, 'a')
i1 = f['LowPassSegmentation/i1/']
i2 = f['LowPassSegmentation/i2/']
i1StartP = i1['StartPoints'][:]
i2StartP = i2['StartPoints'][:]
# Common Events
# Take Longer
CommonEventsi1Index = np.array([], dtype=np.uint64)
CommonEventsi2Index = np.array([], dtype=np.uint64)
DelayLimit = 10e-3*f['General/Samplerate/'].value
for k in i1StartP:
val = i2StartP[(i2StartP > k - DelayLimit) & (i2StartP < k + DelayLimit)]
if len(val)==1:
CommonEventsi2Index = np.append(CommonEventsi2Index, np.where(i2StartP == val)[0])
CommonEventsi1Index = np.append(CommonEventsi1Index, np.where(i1StartP == k)[0])
if len(val) > 1:
diff=np.absolute(val-k)
minIndex=np.where(diff == np.min(diff))
CommonEventsi2Index = np.append(CommonEventsi2Index, np.where(i2StartP == val[minIndex])[0])
CommonEventsi1Index = np.append(CommonEventsi1Index, np.where(i1StartP == k)[0])
# for i in range(len(i1StartP)):
# for j in range(len(i2StartP)):
# if np.absolute(i1StartP[i] - i2StartP[j]) < DelayLimit:
# CommonEventsi1Index = np.append(CommonEventsi1Index, i)
# CommonEventsi2Index = np.append(CommonEventsi2Index, j)
# Only i1
Onlyi1Indexes = np.delete(range(len(i1StartP)), CommonEventsi1Index)
# Only i2
Onlyi2Indexes = np.delete(range(len(i2StartP)), CommonEventsi2Index)
e = "CommonIndex" in i1
if e:
del i1['CommonIndex']
i1.create_dataset('CommonIndex', data=CommonEventsi1Index)
del i2['CommonIndex']
i2.create_dataset('CommonIndex', data=CommonEventsi2Index)
del i1['OnlyIndex']
i1.create_dataset('OnlyIndex', data=Onlyi1Indexes)
del i2['OnlyIndex']
i2.create_dataset('OnlyIndex', data=Onlyi2Indexes)
else:
i1.create_dataset('CommonIndex', data=CommonEventsi1Index)
i2.create_dataset('CommonIndex', data=CommonEventsi2Index)
i1.create_dataset('OnlyIndex', data=Onlyi1Indexes)
i2.create_dataset('OnlyIndex', data=Onlyi2Indexes)
CommonIndexes={}
CommonIndexes['i1']=CommonEventsi1Index
CommonIndexes['i2']=CommonEventsi2Index
OnlyIndexes={}
OnlyIndexes['i1'] = Onlyi1Indexes
OnlyIndexes['i2'] = Onlyi2Indexes
return (CommonIndexes, OnlyIndexes)
def PlotEvent(t1, t2, i1, i2, fit1 = np.array([]), fit2 = np.array([])):
fig1 = plt.figure(1, figsize=(20, 7))
ax1 = fig1.add_subplot(211)
ax2 = fig1.add_subplot(212, sharex=ax1)
ax1.plot(t1, i1*1e9, 'b')
if len(fit1) is not 0:
ax1.plot(t1, fit1*1e9, 'y')
ax2.plot(t2, i2*1e9, 'r')
if len(fit2) is not 0:
ax2.plot(t2, fit2*1e9, 'y')
ax1.set_ylabel('Ionic Current [nA]')
#ax1.set_xticklabels([])
ax2.set_ylabel('Transverse Current [nA]')
ax2.set_xlabel('Time [ms]')
ax2.ticklabel_format(useOffset=False)
ax2.ticklabel_format(useOffset=False)
ax1.ticklabel_format(useOffset=False)
ax1.ticklabel_format(useOffset=False)
return fig1
def EditInfoText(self):
text2='ionic: {} events, trans: {} events\n'.format(str(self.AnalysisResults['i1']['RoughEventLocations'].shape[0]), str(self.AnalysisResults['i2']['RoughEventLocations'].shape[0]))
text1='The file contains:\n{} Common Events\n{} Ionic Only Events\n{} Transverse Only Events'.format(len(self.CommonIndexes['i1']), len(self.OnlyIndexes['i1']), len(self.OnlyIndexes['i2']))
self.ui.InfoTexts.setText(text2+text1)
def creation_date(path_to_file):
if platform.system() == 'Windows':
return os.path.getctime(path_to_file)
else:
stat = os.stat(path_to_file)
try:
return stat.st_mtime
except AttributeError:
# We're probably on Linux. No easy way to get creation dates here,
# so we'll settle for when its content was last modified.
return stat.st_mtime
def CUSUM(input, delta, h):
Nd = 0
kd = len(input)
krmv = len(input)
k0 = 0
k = 0
l = len(input)
m = np.zeros(l)
m[k0] = input[k0]
v = np.zeros(l)
sp = np.zeros(l)
Sp = np.zeros(l)
gp = np.zeros(l)
sn = np.zeros(l)
Sn = np.zeros(l)
gn = np.zeros(l)
while k < l:
m[k] = np.mean(input[k0:k])
v[k] = np.var(input[k0:k])
sp[k] = delta / v[k] * (input[k] - m[k] - delta / 2)
sn[k] = -delta / v[k] * (input[k] - m[k] + delta / 2)
Sp[k] = Sp[k - 1] + sp[k]
Sn[k] = Sn[k - 1] + sn[k]
gp[k] = np.max(gp[k - 1] + sp[k], 0)
gn[k] = np.max(gn[k - 1] + sn[k], 0)
if gp[k] > h or gn[k] > h:
kd[Nd] = k
kmin = int(np.where(Sn == np.min(Sn[k0:k]))[0])
krmv[Nd] = kmin + k0 - 1
if gp(k) > h:
kmin = int(np.where(Sn == np.min(Sn[k0:k]))[0])
krmv[Nd] = kmin + k0 - 1
k0 = k
m[k0] = input[k0]
v[k0] = 0
sp[k0] = 0
Sp[k0] = 0
gp[k0] = 0
sn[k0] = 0
Sn[k0] = 0
gn[k0] = 0
Nd = Nd + 1
k += 1
if Nd == 0:
mc = np.mean(input) * np.ones(k)
elif Nd == 1:
mc = [m[krmv[0]] * np.ones(krmv[0]), m[k] * np.ones(k - krmv[0])]
else:
mc = m[krmv[0]] * np.ones(krmv[0])
for ii in range(1, Nd):
mc = [mc, m[krmv[ii]] * np.ones(krmv[ii] - krmv[ii - 1])]
mc = [mc, m(k) * np.ones(k - krmv[Nd])]
return (mc, kd, krmv)
def CUSUM2(input, delta, h, startp):
Nd = 0
kd = []
krmv = []
krmvdown = []
both = []
k0 = 0
k = 0
l = len(input)
m = np.zeros(l)
m[k0] = input[k0]
v = np.zeros(l)
sp = np.zeros(l)
Sp = np.zeros(l)
gp = np.zeros(l)
sn = np.zeros(l)
Sn = np.zeros(l)
gn = np.zeros(l)
mc = []
while k < l-1:
k += 1
m[k] = np.mean(input[k0:k+1])
v[k] = np.var(input[k0:k+1])
sp[k] = delta / v[k] * (input[k] - m[k] - delta / 2)
sn[k] = -delta / v[k] * (input[k] - m[k] + delta / 2)
Sp[k] = Sp[k - 1] + sp[k]
Sn[k] = Sn[k - 1] + sn[k]
gp[k] = np.max([gp[k - 1] + sp[k], 0])
gn[k] = np.max([gn[k - 1] + sn[k], 0])
if gp[k] > h or gn[k] > h:
if gp[k] > h:
kmin = np.argmin(Sp[k0:k])
krmv.append(kmin + k0 - 1)
both.append(kmin + k0 - 1)
else:
kd.append(k)
kmin = np.argmin(Sn[k0:k])
krmvdown.append(kmin + k0 - 1)
both.append(kmin + k0 - 1)
k0 = k
m[k0] = input[k0]
v[k0] = 0
sp[k0] = 0
Sp[k0] = 0
gp[k0] = 0
sn[k0] = 0
Sn[k0] = 0
gn[k0] = 0
Nd = Nd + 1
'''
if Nd == 0:
mc = np.mean(input) * np.ones(k)
elif Nd == 1:
mc = [m[krmv[0]] * np.ones(krmv[0]), m[k] * np.ones(k - krmv[0])]
else:
mc.append(m[krmv[0]] * np.ones(krmv[0]))
for ii in range(1, Nd-1):
mc.append(m[krmv[ii]] * np.ones(krmv[ii] - krmv[ii - 1]))
mc.append(m[k] * np.ones(k - krmv[Nd-1]))
'''
## Calculate Inner Levels
if len(both) >= 2:
levels = [np.zeros(len(both)-1), np.zeros(len(both)-1),
np.zeros(len(both)-1), np.zeros(len(both)-1)]
# 0: level number, 1: current, 2: length, 3: std
fit = np.array([])
for ind, g in enumerate(both[:-1]):
levels[0][ind] = ind
levels[1][ind] = np.mean(input[g:both[ind + 1]])
levels[2][ind] = both[ind + 1] - g
levels[3][ind] = np.std(input[g:both[ind + 1]])
np.append(fit, levels[1][ind] * np.ones(np.uint64(levels[2][ind])))
else:
levels = []
fit = []
out = {'up': krmv+startp, 'down': krmvdown+startp, 'both': both+startp, 'levels':levels, 'fit': fit}
return out
def cusum_detection(data, threshhold, minlength, maxstates):
print(threshhold)
s = timer()
logp = 0 # instantaneous log-likelihood for positive jumps
logn = 0 # instantaneous log-likelihood for negative jumps
cpos = np.zeros(len(data), dtype='float64') # cumulative log-likelihood function for positive jumps
cneg = np.zeros(len(data), dtype='float64') # cumulative log-likelihood function for negative jumps
gpos = np.zeros(2, dtype='float64') # decision function for positive jumps
gneg = np.zeros(2, dtype='float64') # decision function for negative jumps
edges = np.array([0],
dtype='int64') # initialize an array with the position of the first subevent - the start of the event
real_start = np.array([],
dtype='int64') # initialize an array with the position of the first subevent - the start of the event
real_end = np.array([],
dtype='int64') # initialize an array with the position of the first subevent - the start of the event
real_Depth = np.array([],
dtype='int64') # initialize an array with the position of the first subevent - the start of the event
anchor = 0 # the last detected change
length = len(data)
data_std = np.std(data)
h = threshhold / data_std
k = 0
nStates = np.uint64(0)
varM = data[0]
varS = 0
mean = data[0]
print('length data =' + str(length))
v = np.zeros(length, dtype='float64')
while k < length - 100:
k += 1
if nStates == 0:
variance = np.var(data[anchor:k]) # initial params for pattern region
mean = np.mean(data[anchor:k])
if variance == 0: break
logp = threshhold / variance * (data[
k] - mean - threshhold / 2.) # instantaneous log-likelihood for current sample assuming local baseline has jumped in the positive direction
logn = -threshhold / variance * (data[
k] - mean + threshhold / 2.) # instantaneous log-likelihood for current sample assuming local baseline has jumped in the negative direction
cpos[k] = cpos[k - 1] + logp # accumulate positive log-likelihoods
cneg[k] = cneg[k - 1] + logn # accumulate negative log-likelihoods
gpos[1] = max(gpos[0] + logp, 0) # accumulate or reset positive decision function
gneg[1] = max(gneg[0] + logn, 0) # accumulate or reset negative decision function
if (gpos[1] > h or gneg[1] > h):
if (gpos[1] > h): # significant positive jump detected
jump = np.uint64(anchor + np.argmin(cpos[anchor:k + 1])) # find the location of the start of the jump
if jump - edges[np.uint64(nStates)] > minlength and np.abs(data[np.uint64(jump + minlength)] - data[jump]) > threshhold / 4:
edges = np.append(edges, jump)
nStates += 1
# print('EVENT!!!!! at ='+str(self.t[jump]))
anchor = k # no data meaning at bad points!
# away from bad point more!
cpos[0:len(cpos)] = 0 # reset all decision arrays
cneg[0:len(cneg)] = 0
gpos[0:len(gpos)] = 0
gneg[0:len(gneg)] = 0
if (gneg[1] > h): # significant negative jump detected
jump = anchor + np.argmin(cneg[anchor:k + 1])
if jump - edges[np.uint64(nStates)] > minlength and np.abs(data[np.uint64(jump + minlength)] - data[jump]) > threshhold / 4:
edges = np.append(edges, jump)
nStates += 1
# print('EVENT!!!!! at ='+str(self.t[jump] ))
anchor = k # no data meaning at bad points!
# away from bad point more!
cpos[0:len(cpos)] = 0 # reset all decision arrays
cneg[0:len(cneg)] = 0
gpos[0:len(gpos)] = 0
gneg[0:len(gneg)] = 0
gpos[0] = gpos[1]
gneg[0] = gneg[1]
if maxstates > 0:
if nStates > maxstates:
print('too sensitive')
nStates = 0
k = 0
threshhold = threshhold * 1.1
h = h * 1.1
logp = 0 # instantaneous log-likelihood for positive jumps
logn = 0 # instantaneous log-likelihood for negative jumps
cpos = np.zeros(len(data), dtype='float64') # cumulative log-likelihood function for positive jumps
cneg = np.zeros(len(data), dtype='float64') # cumulative log-likelihood function for negative jumps
gpos = np.zeros(2, dtype='float64') # decision function for positive jumps
gneg = np.zeros(2, dtype='float64') # decision function for negative jumps
edges = np.array([0],
dtype='int64') # initialize an array with the position of the first subevent - the start of the event
anchor = 0 # the last detected change
length = len(data)
mean = data[0]
nStates = 0
mean = data[0]
edges = np.append(edges, len(data) - 1) # mark the end of the event as an edge
nStates += 1
cusum = dict()
print('Events = ' + str([edges]))
for i in range(len(edges) - 1):
real_start = np.append(real_start, edges[i])
real_end = np.append(real_end, edges[i + 1])
real_Depth = np.append(real_Depth, np.mean(data[np.uint64(edges[i]):np.uint64(edges[i + 1])]))
cusum['Real_Start'] = real_start
cusum['Real_End'] = real_end
cusum['Real_Depth'] = real_Depth
print('Real Start =' + str(cusum['Real_Start']))
print('Real End =' + str(cusum['Real_End']))
cusum['CurrentLevels'] = [np.average(data[np.uint64(edges[i]) + minlength:np.uint64(edges[i + 1])]) for i in
range(np.uint64(nStates))] # detect current levels during detected sub-event
print('Edges[-1] = ' + str(edges[-1]))
cusum['EventDelay'] = edges # locations of sub-events in the data
cusum['Threshold'] = threshhold # record the threshold used
print('Event = ' + str(cusum['EventDelay']))
cusum['jumps'] = np.diff(cusum['CurrentLevels'])
e = timer()
print('cusum took = ' + str(e - s) + 's')
return cusum
def detect_cusum(data, var, threshhold, minlength, maxstates):
logp = 0 # instantaneous log-likelihood for positive jumps
logn = 0 # instantaneous log-likelihood for negative jumps
cpos = np.zeros(len(data), dtype='float64') # cumulative log-likelihood function for positive jumps
cneg = np.zeros(len(data), dtype='float64') # cumulative log-likelihood function for negative jumps
gpos = np.zeros(len(data), dtype='float64') # decision function for positive jumps
gneg = np.zeros(len(data), dtype='float64') # decision function for negative jumps
edges = np.array([0],
dtype='int64') # initialize an array with the position of the first subevent - the start of the event
anchor = 0 # the last detected change
length = len(data)
mean = data[0]
h = threshhold / var
k = self.safety_reg - self.control1
nStates = 0
varM = data[0]
varS = 0
mean = data[0]
print('length data =' + str(length))
v = np.zeros(length, dtype='float64')
while k < length - 100:
k += 1
if nStates == 0: variance = np.var(data[anchor:k])
mean = np.mean(data[anchor:k])
logp = threshhold / variance * (data[
k] - mean - threshhold / 2.) # instantaneous log-likelihood for current sample assuming local baseline has jumped in the positive direction
logn = -threshhold / variance * (data[
k] - mean + threshhold / 2.) # instantaneous log-likelihood for current sample assuming local baseline has jumped in the negative direction
cpos[k] = cpos[k - 1] + logp # accumulate positive log-likelihoods
cneg[k] = cneg[k - 1] + logn # accumulate negative log-likelihoods
gpos[k] = max(gpos[k - 1] + logp, 0) # accumulate or reset positive decision function
gneg[k] = max(gneg[k - 1] + logn, 0) # accumulate or reset negative decision function
if (gpos[k] > h or gneg[k] > h):
if (gpos[k] > h): # significant positive jump detected
jump = anchor + np.argmin(cpos[anchor:k + 1]) # find the location of the start of the jump
if jump - edges[nStates] > minlength and np.abs(data[jump + minlength] - data[jump]) > threshhold / 4:
edges = np.append(edges, jump)
nStates += 1
print('EVENT!!!!! at =' + str((self.control1 + jump) / self.out['samplerate']))
anchor = k # no data meaning at bad points!
# away from bad point more!
cpos[0:len(cpos)] = 0 # reset all decision arrays
cneg[0:len(cneg)] = 0
gpos[0:len(gpos)] = 0
gneg[0:len(gneg)] = 0
if (gneg[k] > h): # significant negative jump detected
jump = anchor + np.argmin(cneg[anchor:k + 1])
if jump - edges[nStates] > minlength and np.abs(data[jump + minlength] - data[jump]) > threshhold / 4:
edges = np.append(edges, jump)
nStates += 1
print('EVENT!!!!! at =' + str((self.control1 + jump) / self.out['samplerate']))
anchor = k # no data meaning at bad points!
# away from bad point more!
cpos[0:len(cpos)] = 0 # reset all decision arrays
cneg[0:len(cneg)] = 0
gpos[0:len(gpos)] = 0
gneg[0:len(gneg)] = 0
if maxstates > 0:
if nStates > 10:
print('too sensitive')
nStates = 0
k = 0
threshhold = threshhold * 1.1
h = h * 1.1
logp = 0 # instantaneous log-likelihood for positive jumps
logn = 0 # instantaneous log-likelihood for negative jumps
cpos = np.zeros(len(data), dtype='float64') # cumulative log-likelihood function for positive jumps
cneg = np.zeros(len(data), dtype='float64') # cumulative log-likelihood function for negative jumps
gpos = np.zeros(len(data), dtype='float64') # decision function for positive jumps
gneg = np.zeros(len(data), dtype='float64') # decision function for negative jumps
edges = np.array([0],
dtype='int64') # initialize an array with the position of the first subevent - the start of the event
anchor = 0 # the last detected change
length = len(data)
mean = data[0]
nStates = 0
mean = data[0]
edges = np.append(edges, len(data)) # mark the end of the event as an edge
nStates += 1
cusum = dict()
cusum['CurrentLevels'] = [np.average(data[edges[i] + minlength:edges[i + 1]]) for i in
range(nStates)] # detect current levels during detected sub-events
cusum['EventDelay'] = edges # locations of sub-events in the data
cusum['Threshold'] = threshhold # record the threshold used
cusum['jumps'] = np.diff(cusum['CurrentLevels'])
# self.__recordevent(cusum)
return cusum
def PlotIV(output, AllData, current = 'i1', unit=1e9, axis = '', WithFit = 1, PoreSize = [0,0], title = '', labeltxt = '', Polynomial=0, color='b', RectificationFactor=0, useEXP=0):
if useEXP:
current=current+'_Exp'
if labeltxt is '':
labeltxt = str(os.path.split(output['filename'])[1])[:-4]
ind = np.argsort(AllData[current]['Voltage'])
#Calculate Rectification Factor
if RectificationFactor:
parts = {'pos': AllData[current]['Voltage'] > 0, 'neg': AllData[current]['Voltage'] < 0}
a={}; b={}; sigma_a={}; sigma_b={}; b_save={};x_values={}
for part in parts:
(a[part], b[part], sigma_a[part], sigma_b[part], b_save[part]) = YorkFit(AllData[current]['Voltage'][parts[part]], AllData[current]['Mean'][parts[part]], 1e-12 * np.ones(len(AllData[current]['Voltage'][parts[part]])), AllData[current]['STD'][parts[part]])
x_values[part] = np.linspace(min(AllData[current]['Voltage'][parts[part]]), max(AllData[current]['Voltage'][parts[part]]), 1000)
axis.plot(x_values[part], scipy.polyval([b[part], a[part]], x_values[part])*unit, color=color, ls='--')
factor = b['neg'] / b['pos']
labeltxt = labeltxt + ', R:{:4.2f}'.format(factor)
#Polynomial is guide to the eye
if not Polynomial:
axis.errorbar(AllData[current]['Voltage'], AllData[current]['Mean']*unit, yerr=AllData[current]['STD']*unit, fmt='o', label=labeltxt, color=color)
else:
p = np.polyfit(AllData[current]['Voltage'][ind], AllData[current]['Mean'][ind]*unit, Polynomial)
axis.errorbar(AllData[current]['Voltage'][ind], AllData[current]['Mean'][ind]*unit, yerr=AllData[current]['STD'][ind]*unit, fmt='o', label=labeltxt, color=color)
axis.plot(AllData[current]['Voltage'][ind], np.polyval(p, AllData[current]['Voltage'][ind]), color=color)
axis.set_ylabel('Current ' + current + ' [nA]')
axis.set_xlabel('Voltage ' + AllData[current]['SweepedChannel'] +' [V]')
if WithFit:
axis.set_title(title + '\nG={}S, R={}Ohm'.format(pg.siFormat(AllData[current]['YorkFitValues']['Slope']), pg.siFormat(1/(AllData[current]['YorkFitValues']['Slope']))))
axis.plot(AllData[current]['YorkFitValues']['x_fit'], AllData[current]['YorkFitValues']['y_fit']*unit, 'r--', label='Linear Fit of '+labeltxt)
else:
axis.set_title(title + 'IV Plot')
if PoreSize[0]:
textstr = 'Pore Size\nConductance: {}S/m\nLenght: {}m:\ndiameter: {}m'.format(pg.siFormat(PoreSize[0]), pg.siFormat(PoreSize[1]), pg.siFormat(CalculatePoreSize(AllData[current]['YorkFitValues']['Slope'], PoreSize[1], PoreSize[0])))
axis.text(0.05, 0.95, textstr, transform=axis.transAxes, fontsize=12,
verticalalignment='top', bbox=dict(boxstyle='round', facecolor='wheat', alpha=0.5))
return axis
def CombineEventDatabases(filename, DBfiles):
f = h5py.File(filename, "w")
general = f.create_group("RawDB")
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)
def MakeIV(filenames, directory, conductance=10, title=''):
if len(conductance) is 1:
conductance=np.ones(len(filenames))*conductance
for idx,filename in enumerate(filenames):
print(filename)
# Make Dir to save images
output = OpenFile(filename)
if not os.path.exists(directory):
os.makedirs(directory)
AllData = MakeIVData(output, delay=2)
if AllData == 0:
print('!!!! No Sweep in: ' + filename)
continue
# Plot Considered Part
# figExtracteParts = plt.figure(1)
# ax1 = figExtracteParts.add_subplot(211)
# ax2 = figExtracteParts.add_subplot(212, sharex=ax1)
# (ax1, ax2) = uf.PlotExtractedPart(output, AllData, current = 'i1', unit=1e9, axis = ax1, axis2=ax2)
# plt.show()
# figExtracteParts.savefig(directory + os.sep + 'PlotExtracted_' + str(os.path.split(filename)[1])[:-4] + '.eps')
# figExtracteParts.savefig(directory + os.sep + 'PlotExtracted_' + str(os.path.split(filename)[1])[:-4] + '.png', dpi=150)
# Plot IV
if output['graphene']:
figIV2 = plt.figure(3)
ax2IV = figIV2.add_subplot(111)
ax2IV = PlotIV(output, AllData, current='i2', unit=1e9, axis=ax2IV, WithFit=1, title=title[idx])
figIV2.tight_layout()
figIV2.savefig(directory + os.sep + str(os.path.split(filename)[1]) + '_IV_i2.png', dpi=300)
figIV2.savefig(directory + os.sep + str(os.path.split(filename)[1]) + '_IV_i2.eps')
figIV2.clear()
figIV = plt.figure(2)
ax1IV = figIV.add_subplot(111)
ax1IV = PlotIV(output, AllData, current='i1', unit=1e9, axis=ax1IV, WithFit=1, PoreSize=[conductance[idx], 20e-9], title=title[idx])
figIV.tight_layout()
# Save Figures
figIV.savefig(directory + os.sep + str(os.path.split(filename)[1]) + 'IV_i1.png', dpi=300)
figIV.savefig(directory + os.sep + str(os.path.split(filename)[1]) + 'IV_i1.eps')
figIV.clear()
def TwoChannelAnalysis(filenames, outdir, UpwardsOn=0):
for filename in filenames:
print(filename)
inp = OpenFile(filename)
file = os.sep + str(os.path.split(filename)[1][:-4])
if not os.path.exists(outdir):
os.makedirs(outdir)
# Low Pass Event Detection
AnalysisResults = {}
if inp['graphene']:
chan = ['i1', 'i2'] # For looping over the channels
else:
chan = ['i1']
for sig in chan:
AnalysisResults[sig] = {}
AnalysisResults[sig]['RoughEventLocations'] = RecursiveLowPassFast(inp[sig], pm.coefficients[sig],
inp['samplerate'])
if UpwardsOn: # Upwards detection can be turned on or off
AnalysisResults[sig + '_Up'] = {}
AnalysisResults[sig + '_Up']['RoughEventLocations'] = RecursiveLowPassFastUp(inp[sig],
pm.coefficients[sig],
inp['samplerate'])
# f.PlotRecursiveLPResults(AnalysisResults['i2'], inp, directory, 1e-3, channel='i2')
# f.PlotRecursiveLPResults(AnalysisResults['i2_Up'], inp, directory+'UP_', 1e-3, channel='i2')
# Refine the Rough Event Detection done by the LP filter and Add event infos
AnalysisResults = RefinedEventDetection(inp, AnalysisResults, signals=chan,
limit=pm.MinimalFittingLimit * inp['samplerate'])
# Correlate the two channels
if inp['graphene']:
(CommonIndexes, OnlyIndexes) = f.CorrelateTheTwoChannels(AnalysisResults, 10e-3 * inp['samplerate'])
print('\n\nAnalysis Done...\nThere are {} common events\n{} Events on i1 only\n{} Events on i2 only'.format(
len(CommonIndexes['i1']), len(OnlyIndexes['i1']), len(OnlyIndexes['i2'])))
# Plot The Events
f.SaveAllPlots(CommonIndexes, OnlyIndexes, AnalysisResults, directory, inp, pm.PlotBuffer)
else:
# Plot The Events
f.SaveAllAxopatchEvents(AnalysisResults, directory, inp, pm.PlotBuffer)

Event Timeline

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