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Nanopore-tools/SaveAllEventsToPDF.py
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SaveAllEventsToPDF.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
import h5py
from tkinter.filedialog import askopenfilenames
from matplotlib import rc
rc('mathtext', default='regular')
pm.init(LoadFiles = 0)
baseline = 250
fitOn = 1
filenames = {'/Users/migraf/Desktop/Chan/Data/R43_KCl gradient_100mMcis_1mM trans_4_20180405_182358_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])
Combinations={}
categories1 = ['i1', 'i1_Up']
if out['graphene']:
filters = ['CommonIndexes', 'OnlyIndexesi1', 'OnlyIndexesi2']
categories2 = ['i2', 'i2_Up']
else:
categories2 = ['']
filters = ['OnlyIndexesi1']
for cat1 in categories1:
for cat2 in categories2:
for filter in filters:
print(filter)
pp = PdfPages(pm.OutputFolder + file + '_' + cat1 + '_vs_' + cat2 + '_' + filter + '_EventPlots.pdf')
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()
ax1.set_title('Ionic Current Trace "' + cat1 + '"')
ax2.set_title('Transverse Current Trace "' + cat2 + '"')
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 [ms]')
ax1.set_xlabel('Time [ms]')
ax2.set_xlabel('Time [ms]')
ax4.set_ylabel('Tr. Voltage [V]')
line1, = ax1.plot([])
line2, = ax2.plot([])
line3, = ax3.plot([], 'c')
line4, = ax4.plot([], 'y')
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')
if fitOn:
line1f, = ax1.plot([], 'r')
line2f, = ax2.plot([], 'r')
# Generate Event List
if filter is 'CommonIndexes':
i1_ActualEventList = np.uint64(f['LowPassSegmentation/' + cat1 + '/CommonIndexesWith' + cat2].value)
i2_ActualEventList = np.uint64(f['LowPassSegmentation/' + cat2 + '/CommonIndexesWith' + cat1].value)
NumberOfEvents = len(i2_ActualEventList)
if filter is 'OnlyIndexesi1' and out['graphene']:
i1_ActualEventList = np.uint64(f['LowPassSegmentation/' + cat1 + '/OnlyIndexesWith' + cat2].value)
i2_ActualEventList = np.uint64(f['LowPassSegmentation/' + cat1 + '/OnlyIndexesWith' + cat2].value)
NumberOfEvents = len(i1_ActualEventList)
if filter is 'OnlyIndexesi1' and not out['graphene']:
i1_ActualEventList = np.arange(f['LowPassSegmentation/' + cat1 + '/NumberOfEvents'].value)
NumberOfEvents = len(i1_ActualEventList)
if filter is 'OnlyIndexesi2':
i1_ActualEventList = np.uint64(f['LowPassSegmentation/' + cat2 + '/OnlyIndexesWith' + cat1].value)
i2_ActualEventList = np.uint64(f['LowPassSegmentation/' + cat2 + '/OnlyIndexesWith' + cat1].value)
NumberOfEvents = len(i1_ActualEventList)
for i in np.arange(0, NumberOfEvents):
if filter is 'OnlyIndexesi2':
startp_i2 = np.int64(
f['LowPassSegmentation/'+ cat2 + '/' + 'StartPoints'].value[i2_ActualEventList[i]] - baseline)
endp_i2 = np.int64(f['LowPassSegmentation/' + cat2 + '/' + 'EndPoints'].value[i2_ActualEventList[i]] + baseline)
startp_i1 = startp_i2
endp_i1 = endp_i2
if filter is 'OnlyIndexesi1':
startp_i1 = np.int64(
f['LowPassSegmentation/' + cat1 + '/' + 'StartPoints'].value[i1_ActualEventList[i]] - baseline)
endp_i1 = np.int64(f['LowPassSegmentation/' + cat1 + '/' + 'EndPoints'].value[i1_ActualEventList[i]] + baseline)
startp_i2 = startp_i1
endp_i2 = endp_i1
if filter is 'CommonIndexes':
startp_i1 = np.int64(f['LowPassSegmentation/' + cat1 + '/' + 'StartPoints'].value[i1_ActualEventList[i]] - baseline)
endp_i1 = np.int64(f['LowPassSegmentation/' + cat1 + '/' + 'EndPoints'].value[i1_ActualEventList[i]] + baseline)
startp_i2 = np.int64(f['LowPassSegmentation/' + cat2 + '/' + 'StartPoints'].value[i2_ActualEventList[i]] - baseline)
endp_i2 = np.int64(f['LowPassSegmentation/' + cat2 + '/' + 'EndPoints'].value[i2_ActualEventList[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 out['graphene']:
if endp_i2 > len(out['i2']):
endp_i2 = len(out['i2'])
i2 = out['i2'][startp_i2:endp_i2]
v2 = out['v2'][startp_i1:endp_i1]
t2 = np.arange(startp_i2, endp_i2) / out['samplerate']
i1 = out['i1'][startp_i1:endp_i1]
v1 = out['v1'][startp_i1:endp_i1]
t1 = np.arange(startp_i1, endp_i1)/out['samplerate']
if fitOn:
if filter is 'OnlyIndexesi1' and (endp_i2 - startp_i2 - 2 * baseline)>0:
if len(cat1) > 2:
fit1 = np.concatenate([np.ones(baseline) * f['LowPassSegmentation/' + cat1 + '/LocalBaseline'].value[i1_ActualEventList[i]],
np.ones(endp_i1-startp_i1-2*baseline) * (
f['LowPassSegmentation/' + cat1 + '/LocalBaseline'].value[i1_ActualEventList[i]] +
f['LowPassSegmentation/' + cat1 + '/DeltaI'].value[i1_ActualEventList[i]]),
np.ones(baseline) * f['LowPassSegmentation/' + cat1 + '/LocalBaseline'].value[i1_ActualEventList[i]]])
else:
fit1 = np.concatenate([np.ones(baseline) *
f['LowPassSegmentation/' + cat1 + '/LocalBaseline'].value[
i1_ActualEventList[i]],
np.ones(endp_i1 - startp_i1 - 2 * baseline) * (
f['LowPassSegmentation/' + cat1 + '/LocalBaseline'].value[
i1_ActualEventList[i]] -
f['LowPassSegmentation/' + cat1 + '/DeltaI'].value[
i1_ActualEventList[i]]),
np.ones(baseline) *
f['LowPassSegmentation/' + cat1 + '/LocalBaseline'].value[
i1_ActualEventList[i]]])
fit2=0
line1f.set_data(t1, fit1)
line2f.set_data([], [])
elif filter is 'OnlyIndexesi2' and (endp_i2 - startp_i2 - 2 * baseline)>0:
if len(cat1) > 2:
fit2 = np.concatenate([np.ones(baseline) * f['LowPassSegmentation/' + cat2 + '/LocalBaseline'].value[i2_ActualEventList[i]],
np.ones(endp_i2-startp_i2-2*baseline) * (
f['LowPassSegmentation/' + cat2 + '/LocalBaseline'].value[i2_ActualEventList[i]] +
f['LowPassSegmentation/' + cat2 + '/DeltaI'].value[i2_ActualEventList[i]]),
np.ones(baseline) * f['LowPassSegmentation/' + cat2 + '/LocalBaseline'].value[i2_ActualEventList[i]]])
else:
fit2 = np.concatenate([np.ones(baseline) *
f['LowPassSegmentation/' + cat2 + '/LocalBaseline'].value[
i2_ActualEventList[i]],
np.ones(endp_i2 - startp_i2 - 2 * baseline) * (
f['LowPassSegmentation/' + cat2 + '/LocalBaseline'].value[
i2_ActualEventList[i]] -
f['LowPassSegmentation/' + cat2 + '/DeltaI'].value[
i2_ActualEventList[i]]),
np.ones(baseline) *
f['LowPassSegmentation/' + cat2 + '/LocalBaseline'].value[
i2_ActualEventList[i]]])
fit1=0
line1f.set_data([], [])
line2f.set_data(t2, fit2)
elif filter is 'CommonIndexes' and (endp_i2 - startp_i2 - 2 * baseline)>0:
if len(cat2) > 2:
fit2 = np.concatenate(
[np.ones(baseline) * f['LowPassSegmentation/' + cat2 + '/LocalBaseline'].value[i2_ActualEventList[i]],
np.ones(endp_i2 - startp_i2 - 2 * baseline) * (
f['LowPassSegmentation/' + cat2 + '/LocalBaseline'].value[i2_ActualEventList[i]] +
f['LowPassSegmentation/' + cat2 + '/DeltaI'].value[i2_ActualEventList[i]]),
np.ones(baseline) * f['LowPassSegmentation/' + cat2 + '/LocalBaseline'].value[i2_ActualEventList[i]]])
else:
fit2 = np.concatenate(
[np.ones(baseline) * f['LowPassSegmentation/' + cat2 + '/LocalBaseline'].value[
i2_ActualEventList[i]],
np.ones(endp_i2 - startp_i2 - 2 * baseline) * (
f['LowPassSegmentation/' + cat2 + '/LocalBaseline'].value[i2_ActualEventList[i]] -
f['LowPassSegmentation/' + cat2 + '/DeltaI'].value[i2_ActualEventList[i]]),
np.ones(baseline) * f['LowPassSegmentation/' + cat2 + '/LocalBaseline'].value[
i2_ActualEventList[i]]])
if len(cat1) > 2:
fit1 = np.concatenate(
[np.ones(baseline) * f['LowPassSegmentation/' + cat1 + '/LocalBaseline'].value[i1_ActualEventList[i]],
np.ones(endp_i1 - startp_i1 - 2 * baseline) * (
f['LowPassSegmentation/' + cat1 + '/LocalBaseline'].value[i1_ActualEventList[i]] +
f['LowPassSegmentation/' + cat1 + '/DeltaI'].value[i1_ActualEventList[i]]),
np.ones(baseline) * f['LowPassSegmentation/' + cat1 + '/LocalBaseline'].value[i1_ActualEventList[i]]])
else:
fit1 = np.concatenate(
[np.ones(baseline) * f['LowPassSegmentation/' + cat1 + '/LocalBaseline'].value[i1_ActualEventList[i]],
np.ones(endp_i1 - startp_i1 - 2 * baseline) * (
f['LowPassSegmentation/' + cat1 + '/LocalBaseline'].value[i1_ActualEventList[i]] -
f['LowPassSegmentation/' + cat1 + '/DeltaI'].value[i1_ActualEventList[i]]),
np.ones(baseline) * f['LowPassSegmentation/' + cat1 + '/LocalBaseline'].value[i1_ActualEventList[i]]])
line1f.set_data(t1, fit1)
line2f.set_data(t2, fit2)
else:
line1f.set_data([], [])
line2f.set_data([], [])
#print('Values\t i1: {}, t1:{}, i2:{}, t2:{}\nFit1:{}, Fit2:{}'.format(len(i1),len(t1), len(i2),len(t2), len(fit1),len(fit2)))
line1.set_data(t1, i1)
line3.set_data(t1, v1)
if out['graphene']:
line4.set_data(t1, v2)
line2.set_data(t2, i2)
ax1.set_title('{} Event {}\n Ionic Current Trace'.format(filter, i))
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(v1)-np.mean(v1)*5/10, np.max(v1)+np.mean(v1)*1/10)
if out['graphene']:
ax4.set_ylim([np.min(v2)-np.mean(v2)*2/10, np.max(v2)+np.mean(v2)*5/10])
fig.tight_layout()
fig.canvas.draw()
pp.savefig(fig)
print('{} out of {} saved!'.format(str(i), NumberOfEvents-1))
pp.close()
if NumberOfEvents == 0:
print('Empty PDF deleted: {}'.format(pm.OutputFolder + file + '_' + cat1 + '_vs_' + cat2 + '_' + filter + '_EventPlots.pdf'))
os.remove(pm.OutputFolder + file + '_' + cat1 + '_vs_' + cat2 + '_' + filter + '_EventPlots.pdf')
plt.close()
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