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Nanopore-tools/SaveAllEventsSingleChannel.py
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SaveAllEventsSingleChannel.py
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## Cross-correlation of two channels
import AnalysisParameters as pm
import numpy as np
import scipy
import scipy.signal as sig
import Functions as fu
import os
import matplotlib.pyplot as plt
from matplotlib.backends.backend_pdf import PdfPages
from matplotlib.font_manager import FontProperties
import pandas as pd
import MiscParameters as pm
from matplotlib.ticker import EngFormatter
import h5py
from tkinter.filedialog import askopenfilenames
from matplotlib import rc
rc('mathtext', default='regular')
pm.init(LoadFiles = 0)
baselinetime = 10e-3
fitOn = 1
percentage = 1
plotPoints=0
AllInOneFile = 0
#filenames = {'/Volumes/lben/lben-commun/2018 User Data/Martina/Axonpatch/20180518_13A/Results/13A_1MKCl_events250mV3_OriginalDB.hdf5'}
filenames = askopenfilenames()
if AllInOneFile:
pp = PdfPages(pm.OutputFolder + 'All_EventPlots.pdf')
fig = plt.figure()
ax1 = fig.add_subplot(211)
ax2 = fig.add_subplot(212, sharex=ax1)
ax2.set_title('Ionic Voltage')
ax1.set_ylabel('Current')
ax2.set_ylabel('Voltage')
ax1.set_xlabel('Time')
ax2.set_xlabel('Time')
ax1.yaxis.set_major_formatter(EngFormatter(unit='A', places=1))
ax2.yaxis.set_major_formatter(EngFormatter(unit='V', places=1))
ax1.xaxis.set_major_formatter(EngFormatter(unit='s', places=1))
ax2.xaxis.set_major_formatter(EngFormatter(unit='s', places=1))
line1, = ax1.plot([])
line2, = ax2.plot([], 'c')
if fitOn:
line1f, = ax1.plot([], 'r', ls='dashed')
for filename in filenames:
print(filename)
f = h5py.File(filename, 'r')
out = fu.OpenFile(f['General/FileName'].value)
baseline = np.uint64(baselinetime * out['samplerate'])
file = os.sep + str(os.path.split(filename)[1][:-5])
toPlot = ['i1']
for k in toPlot:
NumberOfEvents=f['LowPassSegmentation/' + k + '/NumberOfEvents'].value
if NumberOfEvents is not 0:
if not AllInOneFile:
pp = PdfPages(pm.OutputFolder + file + '_Only_' + k + '_EventPlots.pdf')
fig = plt.figure()
ax1 = fig.add_subplot(211)
ax2 = fig.add_subplot(212, sharex=ax1)
ax2.set_title('Ionic Voltage')
ax1.set_ylabel('Current')
ax2.set_ylabel('Voltage')
ax1.set_xlabel('Time')
ax2.set_xlabel('Time')
ax1.yaxis.set_major_formatter(EngFormatter(unit='A', places=1))
ax2.yaxis.set_major_formatter(EngFormatter(unit='V', places=1))
ax1.xaxis.set_major_formatter(EngFormatter(unit='s', places=1))
ax2.xaxis.set_major_formatter(EngFormatter(unit='s', places=1))
line1, = ax1.plot([])
line2, = ax2.plot([], 'c')
if fitOn:
line1f, = ax1.plot([], 'r', ls='dashed')
for i in np.linspace(0, NumberOfEvents-1, NumberOfEvents*percentage, dtype=np.uint64):
startp_i1 = np.int64(
f['LowPassSegmentation/' + k + '/' + 'StartPoints'].value[i] - baseline)
endp_i1 = np.int64(
f['LowPassSegmentation/' + k + '/' + 'EndPoints'].value[i] + baseline)
if startp_i1 < 2*baseline:
continue
if endp_i1 > len(out['i1']):
endp_i1 = len(out['i1'])
if fitOn:
if plotPoints:
fit1 = np.concatenate(([np.ones(baseline) *
f['LowPassSegmentation/' + k + '/LocalBaseline'].value[i],
f['LowPassSegmentation/' + k + '/AllEvents/' + '{:09d}'.format(i)].value[i],
np.ones(baseline) *
f['LowPassSegmentation/' + k + '/LocalBaseline'].value[i]]))
else:
fit1 = np.concatenate([np.ones(baseline) *
f['LowPassSegmentation/' + k + '/LocalBaseline'].value[i],
np.ones(np.uint64(endp_i1 - startp_i1 - 2 * baseline)) *
f['LowPassSegmentation/' + k + '/FitLevel'].value[i],
np.ones(baseline) *
f['LowPassSegmentation/' + k + '/LocalBaseline'].value[i]])
line1f.set_data(np.arange(endp_i1-startp_i1)/out['samplerate'], fit1)
line1.set_data(np.arange(endp_i1-startp_i1)/out['samplerate'], out['i1'][startp_i1:endp_i1])
line2.set_data(np.arange(endp_i1-startp_i1)/out['samplerate'], out['v1'][startp_i1:endp_i1])
ax1.set_title('Event {}\n {}'.format(i, file[1:-11]))
ax1.relim()
ax2.relim()
ax1.autoscale_view(True, True, True)
ax2.autoscale_view(True, True, True)
fig.canvas.draw()
pp.savefig(fig)
print('{}: {} out of {} saved!'.format(toPlot, str(i), NumberOfEvents-1))
if not AllInOneFile:
pp.close()
if AllInOneFile:
pp.close()
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