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Nanopore-tools/ConcatenateEvents.py
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ConcatenateEvents.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)
baseline = 100
fitOn = 1
percentage = 1
plotPoints=0
filenames = {'/Users/migraf/SWITCHdrive/PhD/BPS/Poster Resources/Data/17B_1MKCl_IV_80mer_both_Ch25_8_OriginalDB.hdf5'}
#filenames = askopenfilenames()
for filename in filenames:
print(filename)
f = h5py.File(filename, 'r')
out = fu.OpenFile(f['General/FileName'].value)
file = os.sep + str(os.path.split(filename)[1][:-5])
# Graphene Or Not??
if not out['graphene']:
## Only need to plot i1 and i1_Up and v1
toPlot = ['i1', 'i1_Up']
for k in toPlot:
NumberOfEvents=f['LowPassSegmentation/' + k + '/NumberOfEvents'].value
if NumberOfEvents is not 0:
pp = PdfPages(pm.OutputFolder + file + '_Only_' + k + '_EventPlots.pdf')
fig = plt.figure()
ax1 = fig.add_subplot(211)
ax2 = fig.add_subplot(212, sharex=ax1)
ax1.set_title('Ionic Current Trace "' + k + '"')
ax2.set_title('Ionic Voltage')
ax1.set_ylabel('Current [nA]')
ax2.set_ylabel('Voltage [V]')
ax1.set_xlabel('Time [s]')
ax2.set_xlabel('Time [s]')
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')
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 < 0:
startp_i1 = 0
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(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(startp_i1, endp_i1)/out['samplerate'], fit1)
line1.set_data(np.arange(startp_i1, endp_i1)/out['samplerate'], out['i1'][startp_i1:endp_i1])
line2.set_data(np.arange(startp_i1, endp_i1)/out['samplerate'], out['v1'][startp_i1:endp_i1])
ax1.set_title('{} Event {}\n Ionic Current Trace'.format(filter, i))
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))
pp.close()
else:
toPlot = []
#Initialize all the cases
toPlot.append(['i1', '', np.uint64(f['LowPassSegmentation/i1/OnlyIndexes'].value), np.uint64(f['LowPassSegmentation/i1/OnlyIndexes'].value)])
#toPlot.append(['i1_Up', '', np.uint64(f['LowPassSegmentation/i1_Up/OnlyIndexes'].value), np.uint64(f['LowPassSegmentation/i1_Up/OnlyIndexes'].value)])
#toPlot.append(['', 'i2', np.uint64(f['LowPassSegmentation/i2/OnlyIndexes'].value), np.uint64(f['LowPassSegmentation/i2/OnlyIndexes'].value)])
#toPlot.append(['', 'i2_Up', np.uint64(f['LowPassSegmentation/i2_Up/OnlyIndexes'].value), np.uint64(f['LowPassSegmentation/i2_Up/OnlyIndexes'].value)])
#toPlot.append(['i1', 'i2', np.uint64(f['LowPassSegmentation/i1/CommonIndexesWithi2'].value), np.uint64(f['LowPassSegmentation/i2/CommonIndexesWithi1'].value)])
#toPlot.append(['i1_Up', 'i2', np.uint64(f['LowPassSegmentation/i1_Up/CommonIndexesWithi2'].value), np.uint64(f['LowPassSegmentation/i2/CommonIndexesWithi1_Up'].value)])
#toPlot.append(['i1_Up', 'i2_Up', np.uint64(f['LowPassSegmentation/i1_Up/CommonIndexesWithi2_Up'].value), np.uint64(f['LowPassSegmentation/i2_Up/CommonIndexesWithi1_Up'].value)])
#toPlot.append(['i1', 'i2_Up', np.uint64(f['LowPassSegmentation/i1/CommonIndexesWithi2_Up'].value), np.uint64(f['LowPassSegmentation/i2_Up/CommonIndexesWithi1'].value)])
line1data = np.array([])
line2data = np.array([])
line3data = np.array([])
line4data = np.array([])
line1fdata = np.array([])
line2fdata = np.array([])
for cases in toPlot:
NumberOfEvents = len(cases[2])
for i in np.linspace(0, NumberOfEvents-1, NumberOfEvents*percentage, dtype=np.uint64):
if not cases[0] == '':
startp_i1 = np.int64(
f['LowPassSegmentation/' + cases[0] + '/' + 'StartPoints'].value[cases[2][i]] - baseline)
endp_i1 = np.int64(
f['LowPassSegmentation/' + cases[0] + '/' + 'EndPoints'].value[cases[2][i]] + baseline)
else:
startp_i1 = np.int64(
f['LowPassSegmentation/' + cases[1] + '/' + 'StartPoints'].value[cases[3][i]] - baseline)
endp_i1 = np.int64(
f['LowPassSegmentation/' + cases[1] + '/' + 'EndPoints'].value[cases[3][i]] + baseline)
if not cases[1] == '':
startp_i2 = np.int64(
f['LowPassSegmentation/' + cases[1] + '/' + 'StartPoints'].value[cases[3][i]] - baseline)
endp_i2 = np.int64(
f['LowPassSegmentation/' + cases[1] + '/' + 'EndPoints'].value[cases[3][i]] + baseline)
else:
startp_i2 = np.int64(
f['LowPassSegmentation/' + cases[0] + '/' + 'StartPoints'].value[cases[2][i]] - baseline)
endp_i2 = np.int64(
f['LowPassSegmentation/' + cases[0] + '/' + 'EndPoints'].value[cases[2][i]] + baseline)
if startp_i1 < 0:
startp_i1 = 0
if startp_i2 < 0:
startp_i2 = 0
if endp_i1 > len(out['i1']):
endp_i1 = len(out['i1'])
if endp_i2 > len(out['i2']):
endp_i2 = len(out['i2'])
i1 = out['i1'][startp_i1:endp_i1]
v1 = out['v1'][startp_i1:endp_i1]
i2 = out['i2'][startp_i2:endp_i2]
v2 = out['v2'][startp_i2:endp_i2]
diff = len(i1)-len(i2)
if diff>0: #i1 is longer
i1 = i1[0:len(i2)]
v1 = v1[0:len(i2)]
if diff < 0: # i2 is longer
i2 = i2[0:len(i1)]
v2 = v2[0:len(i1)]
line1data = np.append(line1data, i1)
line2data = np.append(line2data, i2)
line3data = np.append(line3data, v1)
line4data = np.append(line4data, v2)
if fitOn:
if not cases[0] == '':
if plotPoints:
fit1 = np.concatenate([np.ones(baseline) *
f['LowPassSegmentation/' + cases[0] + '/LocalBaseline'].value[cases[2][i]],
f['LowPassSegmentation/' + cases[0] + '/AllEvents/' + '{:09d}'.format(cases[2][i])].value,
np.ones(baseline) *
f['LowPassSegmentation/' + cases[0] + '/LocalBaseline'].value[cases[2][i]]])
else:
fit1 = np.concatenate([np.ones(baseline) *
f['LowPassSegmentation/' + cases[0] + '/LocalBaseline'].value[cases[2][i]],
np.ones(endp_i1 - startp_i1 - 2 * baseline) * f[
'LowPassSegmentation/' + cases[0] + '/FitLevel'].value[cases[2][i]],
np.ones(baseline) *
f['LowPassSegmentation/' + cases[0] + '/LocalBaseline'].value[cases[2][i]]])
if diff > 0: # i1 is longer
fit1 = fit1[0:len(i2)]
line1fdata = np.append(line1fdata, fit1)
if not cases[1] == '':
if plotPoints:
fit2 = np.concatenate([np.ones(baseline) *
f['LowPassSegmentation/' + cases[1] + '/LocalBaseline'].value[cases[3][i]],
f['LowPassSegmentation/' + cases[1] + '/AllEvents/' + '{:09d}'.format(
cases[3][i])].value,
np.ones(baseline) *
f['LowPassSegmentation/' + cases[1] + '/LocalBaseline'].value[cases[3][i]]])
else:
fit2 = np.concatenate([np.ones(baseline) *
f['LowPassSegmentation/' + cases[1] + '/LocalBaseline'].value[cases[3][i]],
np.ones(endp_i2 - startp_i2 - 2 * baseline) * f['LowPassSegmentation/' + cases[1] + '/FitLevel'].value[cases[3][i]],
np.ones(baseline) *
f['LowPassSegmentation/' + cases[1] + '/LocalBaseline'].value[cases[3][i]]])
if diff < 0: # i2 is longer
fit2 = fit2[0:len(i1)]
line2fdata = np.append(line2fdata, fit2)
fig = plt.figure()
ax1 = fig.add_subplot(311)
ax2 = fig.add_subplot(312, sharex=ax1)
ax3 = fig.add_subplot(313, sharex=ax1)
ax4 = ax3.twinx()
ax4.set_title('Voltage Trace')
ax1.set_ylabel('Current [nA]')
ax2.set_ylabel('Current [nA]')
ax3.set_ylabel('Ionic Voltage [V]')
ax3.set_xlabel('Time [s]')
ax1.set_xlabel('Time [s]')
ax2.set_xlabel('Time [s]')
ax1.yaxis.set_major_formatter(EngFormatter(unit='A', places=1))
ax2.yaxis.set_major_formatter(EngFormatter(unit='A', places=1))
ax3.yaxis.set_major_formatter(EngFormatter(unit='V', places=1))
ax4.yaxis.set_major_formatter(EngFormatter(unit='V', places=1))
ax3.xaxis.set_major_formatter(EngFormatter(unit='s', places=1))
ax1.xaxis.set_major_formatter(EngFormatter(unit='s', places=1))
ax2.xaxis.set_major_formatter(EngFormatter(unit='s', places=1))
ax4.set_ylabel('Tr. Voltage [V]')
t = np.arange(len(line1data))/out['samplerate']
t2 = np.arange(len(line2data))/out['samplerate']
ax1.plot(t, line1data)
ax2.plot(t2, line2data)
ax3.plot(t, line3data, 'c')
ax4.plot(t2, line4data, 'y')
if fitOn:
ax1.plot(t, line1fdata, 'r')
#ax2.plot(t2, line2fdata, 'r')
ax4.tick_params(axis='y', colors='y')
ax4.yaxis.label.set_color('y')
ax3.tick_params(axis='y', colors='c')
ax3.yaxis.label.set_color('c')
ax1.set_title('Event {} of {}\n Ionic Current Trace "{}"'.format(i, NumberOfEvents, cases[0]))
ax1.relim()
ax2.relim()
ax3.relim()
ax4.relim()
ax1.autoscale_view(True, True, True)
ax2.autoscale_view(True, True, True)
# voltage plots
ax3.autoscale_view(True, True, False)
ax4.autoscale_view(True, True, False)
ax3.set_ylim(np.min(line3data) - np.mean(line3data) * 5 / 10, np.max(line3data) + np.mean(line3data) * 1 / 10)
ax4.set_ylim([np.min(line4data) - np.mean(line4data) * 2 / 10, np.max(line4data) + np.mean(line4data) * 5 / 10])
fig.tight_layout()
fig.canvas.draw()
plt.show()
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