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Nanopore-tools/ObsoleteFunctions/CapacitanceCalc.py
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CapacitanceCalc.py
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from ObsoleteFunctions import UsefulFunctions as f
import numpy as np
from scipy import signal
import matplotlib.pyplot as plt
from peakutils.peak import indexes
import pyqtgraph as pg
import os
plots=1
lowpass=1000
filenames = ['/Volumes/lben/lben-commun/2018 User Data/Martina/Axonpatch/20180920_31A_Michaels/31A_1MKClpH8_30Hz_1kHzLP.dat']
#filenames = askopenfilenames() # show an "Open" dialog box and return the path to the selected file
for file in filenames:
fi = open(str(os.path.split(file)[0]) + os.sep + 'Capacitances.txt', 'a')
inputData = f.ImportAxopatchData(file)
trace = inputData['i2']
Wn = round(2 * lowpass / (inputData['samplerate']), 4) # [0,1] nyquist frequency
b, a = signal.bessel(4, Wn, btype='low', analog=False)
trace = signal.filtfilt(b, a, trace)
derivative = np.gradient(trace)
time = np.float32(np.arange(0, len(inputData['i2'])) / inputData['samplerate'])
dertime=time[:-1]
#peakind = signal.find_peaks_cwt(derivative, np.arange(1,10))
#peakind, dertime[peakind], derivative[peakind]
print('Detect peaks with minimum height and distance filters.')
indexesPos = indexes(np.array(derivative), thres=0.75, min_dist=1000)
indexesNeg = indexes(np.array(np.negative(derivative)), thres=0.75, min_dist=1000)
#Get Local Minimas
AllData=np.zeros((4,len(indexesPos)),dtype=np.uint32)
NewData=np.zeros(len(indexesPos))
for l,i in enumerate(indexesPos):
k=i
while derivative[k]>derivative[k-1] and k>0:
k-=1
minimaLeft=k
k=i
if k<len(derivative)-2:
while derivative[k]>derivative[k+1] and k<len(derivative)-2:
k+=1
minimaRigth=k
AllData[0,l]=np.uint32(i)
AllData[1,l]=np.uint32(minimaLeft)
AllData[2,l]=np.uint32(minimaRigth)
NewData[l]=np.abs(trace[np.uint32(minimaLeft)]-trace[np.uint32(minimaRigth)])
fi.write('{}\nThe mean is: {}F, std is: {}F\n\n'.format(str(os.path.split(file)[1]), pg.siFormat(np.mean(NewData)/24), pg.siFormat(np.std(NewData)/24)))
#fi.write('{}\nThe mean of dI: {}A, std of dI: {}A\n\n'.format(str(os.path.split(file)[1]), pg.siFormat(np.mean(NewData)), pg.siFormat(np.std(NewData))))
fi.close
if plots:
both=np.concatenate((indexesPos,indexesNeg))
fig, ax1 = plt.subplots()
ax1.plot(time, derivative, 'b')
plt.hold
ax1.plot(time[AllData[0, :]], derivative[AllData[0, :]], 'r.')
ax1.plot(time[AllData[1, :]], derivative[AllData[1, :]], 'g.')
ax1.plot(time[AllData[2, :]], derivative[AllData[2, :]], 'y.')
fig.tight_layout()
fig, ax1 = plt.subplots()
ax1.plot(time, trace, 'b')
plt.hold
ax1.plot(time[AllData[0, :]], trace[AllData[0, :]], 'r.')
ax1.plot(time[AllData[1, :]], trace[AllData[1, :]], 'g.')
ax1.plot(time[AllData[2, :]], trace[AllData[2, :]], 'y.')
fig.tight_layout()
plt.show()
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