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Nanopore-tools/SingleChannelEventSaver.py
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SingleChannelEventSaver.py
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## Cross-correlation of two channels
import MiscParameters as pm
import numpy as np
import scipy
import scipy.signal as sig
import Functions as fu
import os
import matplotlib
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)
matplotlib.rcParams['pdf.fonttype'] = 42
matplotlib.rcParams['ps.fonttype'] = 42
baselinetime = 10e-3
fitOn = 0
percentage = 1
plotPoints=0
AllInOneFile = 0
timeconvert = 1000 #ms
#filenames = {'/Users/migraf/Desktop/Temporary Analysis/Roche_Pdms53_1MKCl_2kb_500mV_1_OriginalDB.hdf5'}
filenames = askopenfilenames()
if AllInOneFile:
pp = PdfPages(pm.OutputFolder + 'All_EventPlots.pdf')
fig = plt.figure(1, figsize=(8, 4))
ax1 = fig.add_subplot(111)
#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')
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(1, figsize=(5, 5))
ax1 = fig.add_subplot(111)
#ax2 = fig.add_subplot(212, sharex=ax1)
#ax2.set_title('Ionic Voltage')
ax1.set_ylabel('Current (nA)')
#ax2.set_ylabel('Voltage')
ax1.set_xlabel('Time (ms)')
#ax2.set_xlabel('Time')
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']*timeconvert, fit1*1e9)
#line1.set_data(np.arange(startp_i1, endp_i1)/out['samplerate']*timeconvert, out['i1'][startp_i1:endp_i1])
line1.set_data(np.arange(endp_i1-startp_i1)/out['samplerate']*timeconvert, out['i1'][startp_i1:endp_i1]*1e9)
#line2.set_data(np.arange(endp_i1-startp_i1)/out['samplerate'], out['v1'][startp_i1:endp_i1])
ax1.set_title('Event {}\n {}\nVoltage: {}V'.format(i, file[1:-11], np.mean(out['v1'][startp_i1:endp_i1])))
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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