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ScatterPlotsForSingleChannel.py
e1fb93801dfbDevelopment-of-Analysis…

ScatterPlotsForSingleChannel.py
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			import numpy as np
			import pandas as pd
			import scipy.signal as sig
			import Functions as f
			from sklearn.mixture import GaussianMixture as GMM
			import os
			import matplotlib.pyplot as plt
			import matplotlib
			import h5py
			import seaborn as sns
			from tkinter.filedialog import askopenfilenames
			matplotlib.rcParams['pdf.fonttype'] = 42
			matplotlib.rcParams['ps.fonttype'] = 42

			filenames = askopenfilenames() # show an "Open" dialog box and return the path to the selected file
			Name = '2kb, Roche Substrate, MoS2'
			cm = plt.cm.get_cmap('RdYlBu')
			voltageLimits = [0.11, 0.51]
			dwellrange = (0, 10)
			dIrange = (0, 7)
			FracDIRange = (0, 95)

			count = 0
			dt = {}
			dIF = {}
			count = {}

			dt = np.array([])
			dIF = np.array([])
			dI = np.array([])
			localBase = np.array([])
			v = np.array([])
			t = np.array([])
			t2 = np.array([])
			count = 0

			for file in filenames:
			print(file)
			f = h5py.File(file, 'r')
			dt = np.append(dt, f['LowPassSegmentation/' + 'i1' + '/DwellTime'].value*1e3)
			dIF = np.append(dIF, f['LowPassSegmentation/' + 'i1' + '/FractionalCurrentDrop'].value*100)
			dI = np.append(dI, f['LowPassSegmentation/' + 'i1' + '/DeltaI'].value*1e9)
			t = np.append(t, f['General/TimeFileWritten'].value*np.ones(len(f['LowPassSegmentation/' + 'i1' + '/DeltaI'].value)))
			t2 = np.append(t2, f['General/TimeFileLastModified'].value*np.ones(len(f['LowPassSegmentation/' + 'i1' + '/DeltaI'].value)))
			v = np.append(v, f['LowPassSegmentation/' + 'i1' + '/LocalVoltage'].value)
			count += len(f['LowPassSegmentation/' + 'i1' + '/DeltaI'].value)
			localBase = np.append(localBase, f['LowPassSegmentation/' + 'i1' + '/LocalBaseline'].value*1e9/f['LowPassSegmentation/' + 'i1' + '/LocalVoltage'].value)


			# Sort the voltages.
			availableVoltages = np.unique(v[np.where((v > voltageLimits[0]) & (v < voltageLimits[1]))])
			print(availableVoltages)
			data = []
			datadII0 = []
			datalBase = []
			dataDwell = []
			dataRate = []
			labels = []
			NEvents = []
			for vo in availableVoltages:
			ind1 = np.argwhere(v == vo)
			ind2 = np.intersect1d(ind1, np.argwhere((dt > dwellrange[0]) & (dt < dwellrange[1])))
			ind = np.intersect1d(ind2, np.argwhere((dI > dIrange[0]) & (dI < dIrange[1])))
			data.append(dI[ind])
			datadII0.append(dIF[ind])
			dataDwell.append(dt[ind])
			datalBase.append(localBase[ind])
			NEvents.append(len(ind))
			if f['General/Machine'].value == 'Axopatch':
			print('Modified-Written is applied')
			if (np.max(t2[ind])-np.min(t[ind])) == 0:
			dataRate.append(0)
			else:
			dataRate.append(len(ind)/(np.max(t2[ind])-np.min(t[ind])))
			else:
			if (np.max(t[ind])-np.min(t[ind])) == 0:
			dataRate.append(0)
			else:
			dataRate.append(len(ind)/(np.max(t[ind])-np.min(t[ind])))
			labels.append('{:0.0f}'.format(vo*1000))

			cond = np.array([])
			for i,dati in enumerate(data):
			cond = np.append(cond, dati/availableVoltages[i])

			# Fractional Current Drop Scatter Plot
			fig1 = plt.figure(1, figsize=(9, 6))
			ax = fig1.add_subplot(111)
			sc = ax.scatter(dt[np.where(v > 0)]*1e3, dIF[np.where(v > 0)], c=v[np.where(v > 0)], vmin=min(v[np.where(v > 0)]), vmax=max(v[np.where(v > 0)]), s=35, cmap=cm)
			cbar = plt.colorbar(sc, ticks=availableVoltages)
			cbar.ax.set_yticklabels(labels) # vertically oriented colorbar
			ax.set_xlabel('Time (s)')
			ax.set_ylabel('Current Drop dI/I0 (%)')
			ax.set_title('{}\nScatter Plot, {} events'.format(Name, count))
			#plt.show()
			fig1.savefig(file[:-24] + 'ScatterFrac.pdf', transparent=True)
			ax.set_xlim(dwellrange)
			ax.set_ylim(FracDIRange)
			fig1.savefig(file[:-24] + 'ScatterFracZoomed.pdf', transparent=True)

			#fig1.clear()
			#fig1.close()

			# Fractional Current Drop Scatter Plot
			fig6 = plt.figure(6, figsize=(9, 6))
			ax6 = fig6.add_subplot(111)
			sc6 = ax6.scatter(dt[np.where(v > 0)], dI[np.where(v > 0)], c=v[np.where(v > 0)], vmin=min(v[np.where(v > 0)]), vmax=max(v[np.where(v > 0)]), s=35, cmap=cm)
			cbar6 = plt.colorbar(sc6, ticks=availableVoltages)
			cbar6.ax.set_yticklabels(labels) # vertically oriented colorbar
			ax6.set_xlabel('Time (s)')
			ax6.set_ylabel('Current Drop dI (nA)')
			ax6.set_title('{}\nScatter Plot, {} events'.format(Name, count))
			fig6.savefig(file[:-24] + 'ScatterdI.pdf', transparent=True)
			ax6.set_xlim(dwellrange)
			ax6.set_ylim(dIrange)
			fig6.savefig(file[:-24] + 'ScatterdIZoomed.pdf', transparent=True)

			#fig1.clear()
			#fig1.close()

			# BoxPlot Delta I vs Voltages
			fig2 = plt.figure(2, figsize=(9, 6))
			ax2 = fig2.add_subplot(111)
			bp = ax2.boxplot(data, notch=True, autorange=True, sym='', whis=1)
			ax2.set_xlabel('Voltage (mV)')
			ax2.set_ylabel('Current Drop dI (nA)')
			ax2.set_title('{}\nBoxplot (outliers removed)\n{} events'.format(Name, count))
			ax2.set_xticklabels(labels)
			ax2.get_xaxis().tick_bottom()
			ax2.get_yaxis().tick_left()
			fig2.savefig(file[:-24] + 'BoxplotdI.pdf', transparent=True)

			# BoxPlot DwellTime vs Voltages
			fig3 = plt.figure(3, figsize=(9, 6))
			ax3 = fig3.add_subplot(111)
			bp = ax3.boxplot(dataDwell, notch=True, autorange=True, sym='', whis=1)
			ax3.set_xlabel('Voltage (mV)')
			ax3.set_ylabel('Dwell Time dt (ms)')
			ax3.set_title('{}\nBoxplot (outliers removed)\n{} events'.format(Name, count))
			ax3.set_xticklabels(labels)
			ax3.get_xaxis().tick_bottom()
			ax3.get_yaxis().tick_left()
			fig3.savefig(file[:-24] + 'BoxplotDwellTime.pdf', transparent=True)

			# BoxPlot Event Rate vs Voltages
			fig4 = plt.figure(4, figsize=(9, 6))
			ax4 = fig4.add_subplot(111)
			bp = ax4.bar(np.arange(len(dataRate)), dataRate)
			ax4.set_xlabel('Voltage (mV)')
			ax4.set_ylabel('Event Rate (Hz)')
			ax4.set_title('{}\Barplot\n{} events'.format(Name, count))
			ax4.set_xticks(np.arange(len(dataRate)))
			ax4.set_xticklabels(labels)
			ax4.get_xaxis().tick_bottom()
			ax4.get_yaxis().tick_left()
			fig4.savefig(file[:-24] + 'BarplotEventRate.pdf', transparent=True)

			# Bar Plot Event Number vs Voltages
			fig5 = plt.figure(5, figsize=(9, 6))
			ax5 = fig5.add_subplot(111)
			print(NEvents)
			bp = ax5.bar(np.arange(len(NEvents)), NEvents)
			ax5.set_xlabel('Voltage (mV)')
			ax5.set_ylabel('Number Of Events')
			ax5.set_title('{}\nBarplot\n{} events'.format(Name, count))
			ax5.set_xticks(np.arange(len(NEvents)))
			ax5.set_xticklabels(labels)
			ax5.get_xaxis().tick_bottom()
			ax5.get_yaxis().tick_left()
			fig5.savefig(file[:-24] + 'BarplotEventNumber.pdf', transparent=True)

			# BoxPlot Delta I vs Voltages
			fig7 = plt.figure(7, figsize=(9, 6))
			ax7 = fig7.add_subplot(111)
			bp = ax7.boxplot(datadII0, notch=True, autorange=True, sym='', whis=1)
			ax7.set_xlabel('Voltage (mV)')
			ax7.set_ylabel('Current Drop dI/I0 (%)')
			ax7.set_title('{}\nBoxplot (outliers removed)\n{} events'.format(Name, count))
			ax7.set_xticklabels(labels)
			ax7.get_xaxis().tick_bottom()
			ax7.get_yaxis().tick_left()
			fig7.savefig(file[:-24] + 'BoxplotdII0.pdf', transparent=True)


			# Histogram DwellTime vs Voltages
			fig8 = plt.figure(8, figsize=(6, 9))
			for i,dati in enumerate(dataDwell):
			ax8 = fig8.add_subplot(len(dataDwell), 1, i+1)
			n, bins, patches = ax8.hist(x=dati, bins = 'auto', alpha=0.7, rwidth=0.85)
			ax8.grid(axis='y', alpha=0.75)
			ax8.set_xlabel('Dwell Time (ms)')
			ax8.set_ylabel('Frequency')
			ax8.set_title('{}mV'.format(int(availableVoltages[i]*1e3)))
			ax8.set_xlim(left=np.min(np.concatenate(dataDwell).ravel()), right=np.max(np.concatenate(dataDwell).ravel()))
			fig8.savefig(file[:-24] + 'HistogramDwellTime.pdf', transparent=True)

			# Histogram DwellTime vs Voltages
			fig9 = plt.figure(9, figsize=(6, 9))
			for i,dati in enumerate(data):
			ax9 = fig9.add_subplot(len(data), 1, i+1)
			n, bins, patches = ax9.hist(x=dati, bins = 'auto', alpha=0.7, rwidth=0.85)
			ax9.grid(axis='y', alpha=0.75)
			ax9.set_xlabel('Current Drop (nA)')
			ax9.set_ylabel('Frequency')
			ax9.set_title('{}mV'.format(int(availableVoltages[i]*1e3)))
			ax9.set_xlim(left=np.min(np.concatenate(data).ravel()), right=np.max(np.concatenate(data).ravel()))
			fig9.savefig(file[:-24] + 'HistogramCurrentDrop.pdf', transparent=True)

			plt.style.use("seaborn-darkgrid")

			# KDE DwellTime vs Voltages
			fig10 = plt.figure(10, figsize=(6, 9))
			for i, dati in enumerate(dataDwell):
			ax10 = fig10.add_subplot(len(dataDwell), 1, i+1)
			ax10 = sns.distplot(dati)
			ax10.set_xlabel('Dwell Time (ms)')
			ax10.set_ylabel('Frequency')
			ax10.set_title('{}mV'.format(int(availableVoltages[i]*1e3)))
			ax10.set_xlim(left=np.min(np.concatenate(dataDwell).ravel()), right=np.max(np.concatenate(dataDwell).ravel()))
			fig10.savefig(file[:-24] + 'KDEDwellTime.pdf')#, transparent=True)

			# KDE DwellTime vs Voltages
			fig11 = plt.figure(11, figsize=(6, 9))
			for i,dati in enumerate(data):
			ax11 = fig11.add_subplot(len(data), 1, i+1)
			ax11 = sns.distplot(dati)
			ax11.set_xlabel('Current Drop (nA)')
			ax11.set_ylabel('Frequency')
			ax11.set_title('{}mV'.format(int(availableVoltages[i]*1e3)))
			ax11.set_xlim(left=np.min(np.concatenate(data).ravel()), right=np.max(np.concatenate(data).ravel()))
			fig11.savefig(file[:-24] + 'KDECurrentDrop.pdf')#, transparent=True)

			# KDE DwellTime vs Voltages
			fig17 = plt.figure(17, figsize=(6, 9))
			for i,dati in enumerate(datadII0):
			ax17 = fig17.add_subplot(len(datadII0), 1, i+1)
			ax17 = sns.distplot(dati)
			ax17.set_xlabel('Current Drop (nA)')
			ax17.set_ylabel('Frequency')
			ax17.set_title('{}mV'.format(int(availableVoltages[i]*1e3)))
			ax17.set_xlim(left=0, right=100)
			fig17.savefig(file[:-24] + 'KDECurrentDropdII0.pdf')#, transparent=True)

			# KDE DwellTime vs Voltages
			fig18 = plt.figure(18, figsize=(6, 9))
			for i, dati in enumerate(data):
			ax18 = fig18.add_subplot(len(data), 1, i+1)
			ax18 = sns.distplot(dati/availableVoltages[i])
			ax18.set_xlabel('Conductance Drop (nS)')
			ax18.set_ylabel('Frequency')
			ax18.set_title('{}mV'.format(int(availableVoltages[i]*1e3)))
			ax18.set_xlim(left=np.min(cond), right=np.max(cond))
			fig18.savefig(file[:-24] + 'KDEConductanceDrop.pdf')#, transparent=True)

			# BiVariate KDE plot Individual
			fig15 = plt.figure(15, figsize=(9, 9))
			for i,dati in enumerate(data):
			ax15 = fig15.add_subplot(2, 2, i + 1)
			ax15 = sns.kdeplot(dataDwell[i], dati, shade=True, shade_lowest=False, label='{}mV'.format(str(int(availableVoltages[i]*1e3))))
			ax15.set_ylabel('Current Drop (nA)')
			ax15.set_xlabel('Dwell Time (ms)')
			ax15.set_title('{}mV'.format(int(availableVoltages[i]*1e3)))
			ax15.set_xlim(left=np.min(np.concatenate(dataDwell).ravel())-1, right=np.max(np.concatenate(dataDwell).ravel())+1)
			ax15.set_ylim(bottom=np.min(np.concatenate(data).ravel())-1, top=np.max(np.concatenate(data).ravel()))
			fig15.savefig(file[:-24] + 'KDEScatterIndividualdI.pdf')#, transparent=True)

			# BiVariate KDE plot Individual Fractional
			fig16 = plt.figure(16, figsize=(9, 9))
			for i,dati in enumerate(datadII0):
			ax16 = fig16.add_subplot(2, 2, i + 1)
			ax16 = sns.kdeplot(dataDwell[i], dati, shade=True, shade_lowest=False, label='{}mV'.format(str(int(availableVoltages[i]*1e3))))
			ax16.set_ylabel('Fractional Current Drop (dI/I0)')
			ax16.set_xlabel('Dwell Time (ms)')
			ax16.set_title('{}mV'.format(int(availableVoltages[i]*1e3)))
			ax16.set_xlim(left=np.min(np.concatenate(dataDwell).ravel())-1, right=np.max(np.concatenate(dataDwell).ravel())+1)
			ax16.set_ylim(bottom=-5, top=100)
			fig16.savefig(file[:-24] + 'KDEScatterIndividualdIdI0.pdf')#, transparent=True)

			# BiVariate KDE plot Individual
			fig19 = plt.figure(19, figsize=(9, 9))
			for i,dati in enumerate(data):
			ax19 = fig19.add_subplot(2, 2, i + 1)
			ax19 = sns.kdeplot(dataDwell[i], dati/availableVoltages[i], shade=True, shade_lowest=False, label='{}mV'.format(str(int(availableVoltages[i]*1e3))))
			ax19.set_ylabel('Conductance Drop (nS)')
			ax19.set_xlabel('Dwell Time (ms)')
			ax19.set_title('{}mV'.format(int(availableVoltages[i]*1e3)))
			ax19.set_xlim(left=np.min(np.concatenate(dataDwell).ravel())-1, right=np.max(np.concatenate(dataDwell).ravel())+1)
			ax19.set_ylim(bottom=np.min(cond)-1, top=np.max(cond))
			fig19.savefig(file[:-24] + 'KDEScatterConductanceDrop.pdf')#, transparent=True)

			matplotlib.rcParams['font.size'] = 22
			# KDE DwellTime vs Voltages ALL on One
			fig12 = plt.figure(12, figsize=(16, 9))
			ax12 = fig12.add_subplot(111)
			for i, dati in enumerate(dataDwell):
			ax12 = sns.distplot(dati, label='{}mV'.format(str(int(availableVoltages[i]*1e3))))
			ax12.legend()
			ax12.set_xlabel('Dwell Time (ms)')
			ax12.set_ylabel('Frequency')
			ax12.set_title('Dwell Time Histograms')
			ax12.set_xlim(left=np.min(np.concatenate(dataDwell).ravel()), right=np.max(np.concatenate(dataDwell).ravel()))
			fig12.savefig(file[:-24] + 'KDESingleDwellTime.pdf')#, transparent=True)

			# KDE DwellTime vs Voltages ALL on One
			fig13 = plt.figure(13, figsize=(16, 9))
			ax13 = fig13.add_subplot(111)
			for i,dati in enumerate(data):
			ax13 = sns.distplot(dati, label='{}mV'.format(str(int(availableVoltages[i]*1e3))))
			ax13.legend()
			ax13.set_xlabel('Current Drop (nA)')
			ax13.set_ylabel('Frequency')
			ax13.set_title('Current Drop Histograms')
			ax13.set_xlim(left=np.min(np.concatenate(data).ravel()), right=np.max(np.concatenate(data).ravel()))
			fig13.savefig(file[:-24] + 'KDESingleCurrentDrop.pdf')#, transparent=True)

			# KDE DwellTime vs Voltages ALL on One
			fig20 = plt.figure(20, figsize=(16, 9))
			ax20 = fig20.add_subplot(111)
			for i,dati in enumerate(data):
			ax20 = sns.distplot(dati/availableVoltages[i], label='{}mV'.format(str(int(availableVoltages[i]*1e3))))
			ax20.legend()
			ax20.set_xlabel('Conductance Drop (nS)')
			ax20.set_ylabel('Frequency')
			ax20.set_title('Conductance Drop Histograms')
			ax20.set_xlim(left=np.min(cond), right=np.max(cond))
			fig20.savefig(file[:-24] + 'KDESingleConductanceDrop.pdf')#, transparent=True)

			# BiVariate KDE plot All In One
			fig14 = plt.figure(14, figsize=(9, 9))
			ax14 = fig14.add_subplot(111)
			for i,dati in enumerate(data):
			ax14 = sns.kdeplot(dataDwell[i], dati, shade=True, shade_lowest=False, label='{}mV'.format(str(int(availableVoltages[i]*1e3))))
			ax14.legend()
			ax14.set_ylabel('Current Drop (nA)')
			ax14.set_xlabel('Dwell Time (ms)')
			ax14.set_title('KDE Scatter')
			#ax14.set_xlim(left=np.min(np.concatenate(data).ravel()), right=np.max(np.concatenate(data).ravel()))
			fig14.savefig(file[:-24] + 'KDEScatterdI.pdf')#, transparent=True)

			# BiVariate KDE plot All In One
			fig21 = plt.figure(21, figsize=(9, 9))
			ax21 = fig21.add_subplot(111)
			for i,dati in enumerate(data):
			ax21 = sns.kdeplot(dataDwell[i], dati/availableVoltages[i], shade=True, shade_lowest=False, label='{}mV'.format(str(int(availableVoltages[i]*1e3))))
			ax21.legend()
			ax21.set_ylabel('Conductance Drop (nA)')
			ax21.set_xlabel('Dwell Time (ms)')
			ax21.set_title('KDE Scatter')
			#ax21.set_xlim(left=np.min(np.concatenate(data).ravel()), right=np.max(np.concatenate(data).ravel()))
			fig21.savefig(file[:-24] + 'KDEScatterConductance.pdf')#, transparent=True)

			# KDE DwellTime vs Voltages
			fig22 = plt.figure(22, figsize=(6, 9))
			for i, dati in enumerate(datalBase):
			ax22 = fig22.add_subplot(len(data), 1, i+1)
			ax22 = sns.distplot(dati)
			ax22.set_xlabel('Conductance (nS)')
			ax22.set_ylabel('Frequency')
			ax22.set_title('{}mV'.format(int(availableVoltages[i]*1e3)))
			ax22.set_xlim(left=-10, right=50)#np.min(np.concatenate(datalBase).ravel()), right=np.max(np.concatenate(datalBase).ravel()))
			fig22.savefig(file[:-24] + 'LocalBaseline.pdf')#, transparent=True)

Nanopore-tools/ObsoleteFunctions/ScatterPlotsForSingleChannel.pye1fb93801dfbDevelopment-of-Analysis…

ScatterPlotsForSingleChannel.pyView Options

Nanopore-tools/ObsoleteFunctions/ScatterPlotsForSingleChannel.py
e1fb93801dfbDevelopment-of-Analysis…

ScatterPlotsForSingleChannel.py
View Options