Diffusion Nanopore Tools (smalladditions) Browse

Nanopore-tools/Functions.py
820e9a946792smalladditions

Functions.py
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			import matplotlib
			#matplotlib.use('Qt5Agg')
			import numpy as np
			import math
			import scipy
			import scipy.signal as sig
			import os
			import pickle as pkl
			from scipy import constants as cst
			from scipy import io
			from scipy import signal
			from PyQt5 import QtGui, QtWidgets
			from numpy import linalg as lin
			import pyqtgraph as pg
			import matplotlib.pyplot as plt
			from matplotlib.backends.backend_pdf import PdfPages
			import pandas as pd
			import h5py
			from timeit import default_timer as timer
			import platform
			from scipy.optimize import curve_fit
			from sys import platform
			import pyabf
			from matplotlib.ticker import EngFormatter

			def GetKClConductivity(Conc, Temp):
			p = pkl.load(open('KCl_ConductivityValues.p', 'rb'))
			return np.polyval(p[str(Conc)], Temp)

			def GetTempFromKClConductivity(Conc, Cond):
			p = pkl.load(open('KCl_ConductivityValues.p', 'rb'))
			return (Cond-p[str(Conc)][1])/p[str(Conc)][0]



			def RecursiveLowPassFast(signal, coeff, samplerate):
			padlen = np.uint64(samplerate)
			prepadded = np.ones(padlen)*np.mean(signal[0:1000])
			signaltofilter = np.concatenate((prepadded, signal))

			mltemp = scipy.signal.lfilter([1 - coeff['a'], 0], [1, -coeff['a']], signaltofilter)
			vltemp = scipy.signal.lfilter([1 - coeff['a'], 0], [1, -coeff['a']], np.square(signaltofilter - mltemp))

			ml = np.delete(mltemp, np.arange(padlen))
			vl = np.delete(vltemp, np.arange(padlen))

			sl = ml - coeff['S'] * np.sqrt(vl)
			Ni = len(signal)
			points = np.array(np.where(signal <= sl)[0])
			to_pop = np.array([])
			for i in range(1, len(points)):
			if points[i] - points[i - 1] == 1:
			to_pop = np.append(to_pop, i)
			points = np.unique(np.delete(points, to_pop))
			RoughEventLocations = []
			NumberOfEvents = 0

			for i in points:
			if NumberOfEvents is not 0:
			if i >= RoughEventLocations[NumberOfEvents-1][0] and i <= RoughEventLocations[NumberOfEvents-1][1]:
			continue
			NumberOfEvents += 1
			start = i
			El = ml[i] + coeff['E'] * np.sqrt(vl[i])
			Mm = ml[i]
			Vv = vl[i]
			duration = 0
			endp = start
			if (endp + 1) < len(signal):
			while signal[endp + 1] < El and endp < (Ni - 2):# and duration < coeff['eventlengthLimit']*samplerate:
			duration += 1
			endp += 1
			if duration >= coeff['eventlengthLimit'] * samplerate or endp > (Ni - 10):
			NumberOfEvents -= 1
			continue
			else:
			k = start
			while signal[k] < Mm and k > 1:
			k -= 1
			start = k - 1
			k2 = i + 1
			#while signal[k2] > Mm:
			# k2 -= 1
			#endp = k2
			if start < 0:
			start = 0
			RoughEventLocations.append((start, endp, ml[start], vl[start]))

			return np.array(RoughEventLocations)#, ml, vl, sl

			def RecursiveLowPassFastUp(signal, coeff, samplerate):
			ml = scipy.signal.lfilter([1 - coeff['a'], 0], [1, -coeff['a']], signal)
			vl = scipy.signal.lfilter([1 - coeff['a'], 0], [1, -coeff['a']], np.square(signal - ml))
			sl = ml + coeff['S'] * np.sqrt(vl)
			Ni = len(signal)
			points = np.array(np.where(signal>=sl)[0])
			to_pop=np.array([])
			for i in range(1,len(points)):
			if points[i] - points[i - 1] == 1:
			to_pop=np.append(to_pop, i)
			points = np.delete(points, to_pop)

			points =np.delete(points, np.array(np.where(points == 0)[0]))

			RoughEventLocations = []
			NumberOfEvents=0
			for i in points:
			if NumberOfEvents is not 0:
			if i >= RoughEventLocations[NumberOfEvents-1][0] and i <= RoughEventLocations[NumberOfEvents-1][1]:
			continue
			NumberOfEvents += 1
			start = i
			El = ml[i] + coeff['E'] * np.sqrt(vl[i])
			Mm = ml[i]
			duration = 0
			while signal[i + 1] > El and i < (Ni - 2) and duration < coeff['eventlengthLimit']*samplerate:
			duration += 1
			i += 1
			if duration >= coeff['eventlengthLimit']*samplerate or i > (Ni - 10):
			NumberOfEvents -= 1
			else:
			k = start
			while signal[k] > Mm and k > 2:
			k -= 1
			start = k - 1
			k2 = i + 1
			while signal[k2] > Mm:
			k2 -= 1
			endp = k2
			RoughEventLocations.append((start, endp, ml[start], vl[start]))

			return np.array(RoughEventLocations)

			def ImportABF(datafilename):
			abf = pyabf.ABF(datafilename)
			abf.info() # shows what is available
			output={'type': 'Clampfit', 'graphene': 0, 'samplerate': abf.pointsPerSec, 'i1': -20000./65536 * abf.dataY, 'v1': abf.dataC, 'filename': datafilename}
			return output

			def ImportAxopatchData(datafilename):
			x=np.fromfile(datafilename, np.dtype('>f4'))
			f=open(datafilename, 'rb')
			graphene=0
			for i in range(0, 10):
			a=str(f.readline())
			#print(a)
			if 'Acquisition' in a or 'Sample Rate' in a:
			samplerate=int(''.join(i for i in a if i.isdigit()))/1000
			if 'FEMTO preamp Bandwidth' in a:
			femtoLP=int(''.join(i for i in a if i.isdigit()))
			if 'I_Graphene' in a:
			graphene=1
			print('This File Has a Graphene Channel!')
			end = len(x)
			if graphene:
			#pore current
			i1 = x[250:end-3:4]
			#graphene current
			i2 = x[251:end-2:4]
			#pore voltage
			v1 = x[252:end-1:4]
			#graphene voltage
			v2 = x[253:end:4]
			print('The femto was set to : {} Hz, if this value was correctly entered in the LabView!'.format(str(femtoLP)))
			output={'FemtoLowPass': femtoLP, 'type': 'Axopatch', 'graphene': 1, 'samplerate': samplerate, 'i1': i1, 'v1': v1, 'i2': i2, 'v2': v2, 'filename': datafilename}
			else:
			i1 = np.array(x[250:end-1:2])
			v1 = np.array(x[251:end:2])
			output={'type': 'Axopatch', 'graphene': 0, 'samplerate': samplerate, 'i1': i1, 'v1': v1, 'filename': datafilename}
			return output

			def ImportChimeraRaw(datafilename):
			matfile=io.loadmat(str(os.path.splitext(datafilename)[0]))
			#buffersize=matfile['DisplayBuffer']
			data = np.fromfile(datafilename, np.dtype('<u2'))
			samplerate = np.float64(matfile['ADCSAMPLERATE'])
			TIAgain = np.int32(matfile['SETUP_TIAgain'])
			preADCgain = np.float64(matfile['SETUP_preADCgain'])
			currentoffset = np.float64(matfile['SETUP_pAoffset'])
			ADCvref = np.float64(matfile['SETUP_ADCVREF'])
			ADCbits = np.int32(matfile['SETUP_ADCBITS'])

			closedloop_gain = TIAgain * preADCgain
			bitmask = (2 16 - 1) - (2 (16 - ADCbits) - 1)
			data = -ADCvref + (2 * ADCvref) * (data & bitmask) / 2 ** 16
			data = (data / closedloop_gain + currentoffset)
			data.shape = [data.shape[1], ]
			output = {'matfilename': str(os.path.splitext(datafilename)[0]),'i1raw': data, 'v1': np.float64(matfile['SETUP_biasvoltage']), 'samplerateRaw': np.int64(samplerate), 'type': 'ChimeraRaw', 'filename': datafilename, 'graphene': 0}
			return output

			def ImportChimeraData(datafilename):
			matfile = io.loadmat(str(os.path.splitext(datafilename)[0]))
			samplerate = matfile['ADCSAMPLERATE']
			if samplerate<4e6:
			data = np.fromfile(datafilename, np.dtype('float64'))
			buffersize = matfile['DisplayBuffer']
			out = Reshape1DTo2D(data, buffersize)
			output = {'i1': out['i1'], 'v1': out['v1'], 'samplerate':float(samplerate), 'type': 'ChimeraNotRaw', 'filename': datafilename}
			else:
			output = ImportChimeraRaw(datafilename)
			return output

			def OpenFile(filename = '', ChimeraLowPass = 10e3):
			if filename == '':
			datafilename = QtGui.QFileDialog.getOpenFileName()
			datafilename=datafilename[0]
			print(datafilename)
			else:
			datafilename=filename
			if datafilename[-3::] == 'dat':
			isdat = 1
			output = ImportAxopatchData(datafilename)
			elif datafilename[-3::] == 'log':
			isdat = 0
			output = ImportChimeraData(datafilename)
			print(len(output['i1raw']))
			if output['type'] is 'ChimeraRaw': # Lowpass and downsample
			Wn = round(2 * ChimeraLowPass / output['samplerateRaw'], 4) # [0,1] nyquist frequency
			b, a = signal.bessel(4, Wn, btype='low', analog=False) # 4-th order digital filter
			Filt_sig = signal.filtfilt(b, a, output['i1raw'], method = 'gust')
			ds_factor = output['samplerateRaw'] / (5 * ChimeraLowPass)
			output['i1'] = scipy.signal.resample(Filt_sig, int(len(output['i1raw']) / ds_factor))
			output['samplerate'] = output['samplerateRaw'] / ds_factor
			output['v1'] = output['v1']*np.ones(len(output['i1']))
			print('Samplerate after filtering:' + str(output['samplerate']))
			print(len(output['i1']))
			elif datafilename[-3::] == 'abf':
			output = ImportABF(datafilename)
			st = os.stat(datafilename)
			if platform == 'darwin':
			print('Platform Is Darwin')
			output['TimeFileWritten'] = st.st_birthtime
			output['TimeFileLastModified'] = st.st_mtime
			output['ExperimentDuration'] = st.st_mtime - st.st_birthtime
			else:
			print('Platform Is WinShit')
			output['TimeFileWritten'] = st.st_ctime
			output['TimeFileLastModified'] = st.st_mtime
			output['ExperimentDuration'] = st.st_mtime - st.st_ctime
			return output

			def MakeIVData(output, approach = 'mean', delay = 0.7, UseVoltageRange=0):
			(ChangePoints, sweepedChannel) = CutDataIntoVoltageSegments(output)
			if ChangePoints is 0:
			return 0

			if output['graphene']:
			currents = ['i1', 'i2']
			else:
			currents = ['i1']

			Values = output[sweepedChannel][ChangePoints]
			Values = np.append(Values, output[sweepedChannel][::-1][0])
			delayinpoints = np.int64(delay * output['samplerate'])
			# Store All Data
			All={}
			for current in currents:
			Item = {}
			ItemExp = {}
			l = len(Values)
			Item['Voltage'] = np.zeros(l)
			Item['StartPoint'] = np.zeros(l,dtype=np.uint64)
			Item['EndPoint'] = np.zeros(l, dtype=np.uint64)
			Item['Mean'] = np.zeros(l)
			Item['STD'] = np.zeros(l)
			Item['SweepedChannel'] = sweepedChannel
			ItemExp['SweepedChannel'] = sweepedChannel
			Item['YorkFitValues'] = {}
			ItemExp['YorkFitValues'] = {}
			### MEAN METHOD###
			# First item
			Item['Voltage'][0] = Values[0]
			trace = output[current][0 + delayinpoints:ChangePoints[0]]
			Item['StartPoint'][0] = 0 + delayinpoints
			Item['EndPoint'][0] = ChangePoints[0]
			Item['Mean'][0] = np.mean(trace)
			isProblematic = math.isclose(Values[0], 0, rel_tol=1e-3) or math.isclose(Values[0], np.min(Values), rel_tol=1e-3) or math.isclose(Values[0], np.max(Values), rel_tol=1e-3)
			if sweepedChannel is 'v2' and isProblematic:
			print('In Problematic If for Values: {}, index {}'.format(Values[0],0))
			Item['STD'][0] = np.std(trace[-np.int64(output['samplerate']):])
			else:
			Item['STD'][0] = np.std(trace)
			for i in range(1, len(Values) - 1):
			trace = output[current][ChangePoints[i - 1] + delayinpoints : ChangePoints[i]]
			Item['Voltage'][i] = Values[i]
			Item['StartPoint'][i] = ChangePoints[i - 1]+ delayinpoints
			Item['EndPoint'][i] = ChangePoints[i]
			Item['Mean'][i] = np.mean(trace)
			isProblematic = math.isclose(Values[i], 0, rel_tol=1e-3) or math.isclose(Values[i], np.min(Values),
			rel_tol=1e-3) or math.isclose(Values[i], np.max(Values), rel_tol=1e-3)
			if sweepedChannel is 'v2' and isProblematic:
			print('In Problematic If for Values: {}, index {}'.format(Values[i], i))
			Item['STD'][i] = np.std(trace[-np.int64(output['samplerate']):])
			else:
			Item['STD'][i] = np.std(trace)
			print('{}, {},{}'.format(i, ChangePoints[i - 1] + delayinpoints, ChangePoints[i]))
			# Last
			if 1:
			trace = output[current][ChangePoints[len(ChangePoints) - 1] + delayinpoints : len(output[current]) - 1]
			Item['Voltage'][-1:] = Values[len(Values) - 1]
			Item['StartPoint'][-1:] = ChangePoints[len(ChangePoints) - 1]+delayinpoints
			Item['EndPoint'][-1:] = len(output[current]) - 1
			Item['Mean'][-1:] = np.mean(trace)
			isProblematic = math.isclose(Values[-1:], 0, rel_tol=1e-3) or math.isclose(Values[-1:], np.min(Values),
			rel_tol=1e-3) or math.isclose(
			Values[-1:], np.max(Values), rel_tol=1e-3)
			if sweepedChannel is 'v2' and isProblematic:
			print('In Problematic If for Values: {}, index {}'.format(Values[-1:], i))
			Item['STD'][-1:] = np.std(trace[-np.int64(output['samplerate']):])
			else:
			Item['STD'][-1:] = np.std(trace)

			## GET RECTIFICATION FACTOR
			parts = {'pos': Item['Voltage'] > 0, 'neg': Item['Voltage'] < 0}
			a = {}
			b = {}
			sigma_a = {}
			sigma_b = {}
			b_save = {}
			x_values = {}

			for part in parts:
			(a[part], b[part], sigma_a[part], sigma_b[part], b_save[part]) = YorkFit(
			Item['Voltage'][parts[part]].flatten(), Item['Mean'][parts[part]].flatten(),
			1e-12 * np.ones(len(Item['Voltage'][parts[part]].flatten())),
			Item['STD'][parts[part]].flatten())
			factor = b['neg'] / b['pos']
			Item['Rectification'] = factor
			ItemExp['Rectification'] = factor

			###EXPONENTIAL METHOD###
			if approach is not 'mean':
			l = 1000
			ItemExp['StartPoint'] = np.zeros(l, dtype=np.uint64)
			ItemExp['EndPoint'] = np.zeros(l, dtype=np.uint64)
			baselinestd = np.std(output[current][0:np.int64(1 * output['samplerate'])])
			movingstd = pd.rolling_std(output[current], 10)

			# Extract The Desired Parts
			ind = (np.abs(movingstd - baselinestd) < 1e-9) & (np.abs(output[current]) < np.max(output[current]) / 2)

			# How Many Parts?
			restart = True
			counter = 0
			for i, value in enumerate(ind):
			if value and restart:
			ItemExp['StartPoint'][counter] = i
			print('Start: {}\t'.format(i))
			restart = False
			elif not value and not restart:
			if ((i - 1) - ItemExp['StartPoint'][counter]) > 1 * output['samplerate']:
			ItemExp['EndPoint'][counter] = i - 1
			print('End: {}'.format(i - 1))
			restart = True
			counter += 1
			else:
			restart = True

			if not restart:
			counter += 1
			ItemExp['EndPoint'][counter] = len(ind)

			ItemExp['StartPoint'] = ItemExp['StartPoint'][np.where(ItemExp['StartPoint'])]
			ItemExp['EndPoint'] = ItemExp['EndPoint'][np.where(ItemExp['EndPoint'])]
			NumberOfPieces = len(ItemExp['StartPoint'])
			ItemExp['Voltage'] = np.zeros(NumberOfPieces)
			ItemExp['ExpValues'] = np.zeros((3, NumberOfPieces))
			ItemExp['STD'] = np.zeros(NumberOfPieces)
			ItemExp['Mean'] = np.zeros(NumberOfPieces)

			# Make It Piecewise
			for i in np.arange(0, NumberOfPieces):
			trace = output[current][ItemExp['StartPoint'][i]:ItemExp['EndPoint'][i]]
			popt, pcov = MakeExponentialFit(np.arange(len(trace)) / output['samplerate'], trace)

			ItemExp['Voltage'][i] = output[sweepedChannel][ItemExp['StartPoint'][i]]
			if popt[0]:
			ItemExp['ExpValues'][:, i] = popt
			ItemExp['Mean'][i] = popt[2]
			try:
			ItemExp['STD'][i] = np.sqrt(np.diag(pcov))[2]
			except RuntimeWarning:
			ItemExp['STD'][i] = np.sqrt(np.std(trace))
			print('ExpFit: STD of voltage {} failed calculating...'.format(ItemExp['Voltage'][i]))
			else:
			print('Exponential Fit on for ' + current + ' failed at V=' + str(ItemExp['Voltage'][0]))
			ItemExp['Mean'][i] = np.mean(trace)
			ItemExp['STD'][i] = np.std(trace)

			## FIT THE EXP CALCULATED VALUES ###
			(a, b, sigma_a, sigma_b, b_save) = YorkFit(ItemExp['Voltage'], ItemExp['Mean'],
			1e-12 * np.ones(len(ItemExp['Voltage'])), ItemExp['STD'])
			x_fit = np.linspace(min(Item['Voltage']), max(Item['Voltage']), 1000)
			y_fit = scipy.polyval([b, a], x_fit)
			ItemExp['YorkFitValues'] = {'x_fit': x_fit, 'y_fit': y_fit, 'Yintercept': a, 'Slope': b,
			'Sigma_Yintercept': sigma_a,
			'Sigma_Slope': sigma_b, 'Parameter': b_save}
			All[current + '_Exp'] = ItemExp

			##Remove NaNs
			nans = np.isnan(Item['Mean'][:])
			nans = np.logical_not(nans)
			Item['Voltage'] = Item['Voltage'][nans]
			Item['StartPoint'] = Item['StartPoint'][nans]
			Item['EndPoint'] = Item['EndPoint'][nans]
			Item['Mean'] = Item['Mean'][nans]
			Item['STD'] = Item['STD'][nans]

			## Restrict to set voltage Range
			if UseVoltageRange is not 0:
			print('Voltages Cut')
			ind = np.argwhere((Item['Voltage'] >= UseVoltageRange[0]) & (Item['Voltage'] <= UseVoltageRange[1]))
			Item['Voltage'] = Item['Voltage'][ind].flatten()
			Item['StartPoint'] = Item['StartPoint'][ind].flatten()
			Item['EndPoint'] = Item['EndPoint'][ind].flatten()
			Item['Mean'] = Item['Mean'][ind].flatten()
			Item['STD'] = Item['STD'][ind].flatten()

			## FIT THE MEAN CALCULATED VALUES ###
			(a, b, sigma_a, sigma_b, b_save) = YorkFit(Item['Voltage'].flatten(), Item['Mean'].flatten(), 1e-9 * np.ones(len(Item['Voltage'])), Item['STD'].flatten())
			x_fit = np.linspace(min(Item['Voltage']), max(Item['Voltage']), 1000)
			y_fit = scipy.polyval([b, a], x_fit)
			Item['YorkFitValues'] = {'x_fit': x_fit, 'y_fit': y_fit, 'Yintercept': a, 'Slope': b, 'Sigma_Yintercept': sigma_a,
			'Sigma_Slope': sigma_b, 'Parameter': b_save}

			All[current] = Item
			All['Currents'] = currents

			return All

			def ExpFunc(x, a, b, c):
			return a * np.exp(-b * x) + c

			def YorkFit(X, Y, sigma_X, sigma_Y, r=0):
			N_itermax=10 #maximum number of interations
			tol=1e-15 #relative tolerance to stop at
			N = len(X)
			temp = np.matrix([X, np.ones(N)])
			#make initial guess at b using linear squares

			tmp = np.matrix(Y)*lin.pinv(temp)
			b_lse = np.array(tmp)[0][0]
			#a_lse=tmp(2);
			b = b_lse #initial guess
			omega_X = np.true_divide(1,np.power(sigma_X,2))
			omega_Y = np.true_divide(1, np.power(sigma_Y,2))
			alpha=np.sqrt(omega_X*omega_Y)
			b_save = np.zeros(N_itermax+1) #vector to save b iterations in
			b_save[0]=b

			for i in np.arange(N_itermax):
			W=omega_Xomega_Y/(omega_X+bbomega_Y-2bralpha)

			X_bar=np.sum(W*X)/np.sum(W)
			Y_bar=np.sum(W*Y)/np.sum(W)

			U=X-X_bar
			V=Y-Y_bar

			beta=W(U/omega_Y+bV/omega_X-(bU+V)r/alpha)

			b=sum(WbetaV)/sum(WbetaU)
			b_save[i+1]=b
			if np.abs((b_save[i+1]-b_save[i])/b_save[i+1]) < tol:
			break

			a=Y_bar-b*X_bar
			x=X_bar+beta
			y=Y_bar+b*beta
			x_bar=sum(W*x)/sum(W)
			y_bar=sum(W*y)/sum(W)
			u=x-x_bar
			#%v=y-y_bar
			sigma_b=np.sqrt(1/sum(Wuu))
			sigma_a=np.sqrt(1./sum(W)+x_barx_barsigma_b*sigma_b)
			return (a, b, sigma_a, sigma_b, b_save)

			def DoubleExpFunc(x, Y0, percentFast, plateau, Kfast, Kslow):
			SpanFast = (Y0-plateau) * percentFast * 0.01
			SpanSlow = (Y0-plateau) (100-percentFast) 0.01
			return plateau + ((Y0-plateau) * percentFast * 0.01)np.exp(-Kfastx) + ((Y0-plateau) (100-percentFast) 0.01)np.exp(-Kslowx)

			def MakeExponentialFit(xdata, ydata):
			try:
			popt, pcov = curve_fit(ExpFunc, xdata, ydata, method='lm', xtol = 1e-20, ftol = 1e-12, gtol = 1e-20)
			#popt, pcov = curve_fit(DoubleExpFunc, xdata, ydata, p0 = (ydata[0], 50, ydata[-1], 1 / 15, 1 / 60))
			return (popt, pcov)
			except RuntimeError:
			popt = (0,0,0)
			pcov = 0
			return (popt, pcov)

			def CutDataIntoVoltageSegments(output):
			sweepedChannel = ''
			if output['type'] == 'ChimeraNotRaw' or (output['type'] == 'Axopatch' and not output['graphene']):
			ChangePoints = np.where(np.diff(output['v1']))[0]
			sweepedChannel = 'v1'
			if len(ChangePoints) is 0:
			print('No voltage sweeps in this file:\n{}'.format(output['filename']))
			return (0,0)
			elif (output['type'] == 'Axopatch' and output['graphene']):
			ChangePoints = np.where(np.diff(output['v1']))[0]
			sweepedChannel = 'v1'
			if len(ChangePoints) is 0:
			ChangePoints = np.where(np.diff(output['v2']))[0]
			if len(ChangePoints) is 0:
			print('No voltage sweeps in this file')
			return (0,0)
			else:
			sweepedChannel = 'v2'
			print('Cutting into Segments...\n{} change points detected in channel {}...'.format(len(ChangePoints), sweepedChannel))
			return (ChangePoints, sweepedChannel)

			def CalculatePoreSize(G, L, s):
			return (G+np.sqrt(G(G+16Ls/np.pi)))/(2s)

			def CalculateConductance(L, s, d):
			return s1/(4L/(np.pid*2)+1/d)

			def GetSurfaceChargeFromLeeEquation(s, c, D, G, L, A, B, version=1):
			lDu = 1/(8np.piBDs)*(
			version * np.sqrt((4np.piAD2s+np.piBD*2s - 4BGL - 8np.piDG)**2
			- 16np.piBDs(np.piAD3s - 4ADGL - 2np.piD*2G))
			- 4np.piAD2s - np.piBD*2s + 4BGL + 8np.piDG
			)
			e = cst.elementary_charge
			Na = cst.Avogadro
			return lDu(2 c * Na * 1e3 * e)

			def ConductivityFromConductanceModel(L, d, G):
			return G * (4 * L / (np.pi * d ** 2) + 1 / d)

			def PlotIV(output, AllData, current = 'i1', unit=1e9, axis = '', WithFit = 1, PoreSize = [0,0], title = '', labeltxt = '', Polynomial=0, color='b', RectificationFactor=0, useEXP=0):
			if useEXP:
			current=current+'_Exp'

			if labeltxt is '':
			labeltxt = str(os.path.split(output['filename'])[1])[:-4]

			ind = np.argsort(AllData[current]['Voltage'])

			#Calculate Rectification Factor
			if RectificationFactor:
			parts = {'pos': AllData[current]['Voltage'] > 0, 'neg': AllData[current]['Voltage'] < 0}
			a={}; b={}; sigma_a={}; sigma_b={}; b_save={};x_values={}

			for part in parts:
			(a[part], b[part], sigma_a[part], sigma_b[part], b_save[part]) = YorkFit(AllData[current]['Voltage'][parts[part]], AllData[current]['Mean'][parts[part]], 1e-12 * np.ones(len(AllData[current]['Voltage'][parts[part]])), AllData[current]['STD'][parts[part]])
			x_values[part] = np.linspace(min(AllData[current]['Voltage'][parts[part]]), max(AllData[current]['Voltage'][parts[part]]), 1000)
			axis.plot(x_values[part], scipy.polyval([b[part], a[part]], x_values[part])*unit, color=color, ls='--')
			factor = b['neg'] / b['pos']
			labeltxt = labeltxt + ', R:{:4.2f}'.format(factor)

			#Polynomial is guide to the eye
			if not Polynomial:
			axis.errorbar(AllData[current]['Voltage'], AllData[current]['Mean']unit, yerr=AllData[current]['STD']unit, fmt='o', label=labeltxt, color=color)
			else:
			p = np.polyfit(AllData[current]['Voltage'][ind], AllData[current]['Mean'][ind]*unit, Polynomial)
			axis.errorbar(AllData[current]['Voltage'][ind], AllData[current]['Mean'][ind]unit, yerr=AllData[current]['STD'][ind]unit, fmt='o', label=labeltxt, color=color)
			axis.plot(AllData[current]['Voltage'][ind], np.polyval(p, AllData[current]['Voltage'][ind]), color=color)

			axis.set_ylabel('Current ' + current + ' [nA]')
			axis.set_xlabel('Voltage ' + AllData[current]['SweepedChannel'] +' [V]')

			if WithFit:
			axis.set_title(title + '\nG={}S, R={}Ohm'.format(pg.siFormat(AllData[current]['YorkFitValues']['Slope']), pg.siFormat(1/(AllData[current]['YorkFitValues']['Slope']))))
			axis.plot(AllData[current]['YorkFitValues']['x_fit'], AllData[current]['YorkFitValues']['y_fit']*unit, 'r--', label='Linear Fit of '+labeltxt)
			else:
			axis.set_title(title + 'IV Plot')

			if PoreSize[0]:
			textstr = 'Pore Size\nConductance: {}S/m\nLenght: {}m:\ndiameter: {}m'.format(pg.siFormat(PoreSize[0]), pg.siFormat(PoreSize[1]), pg.siFormat(CalculatePoreSize(AllData[current]['YorkFitValues']['Slope'], PoreSize[1], PoreSize[0])))
			axis.text(0.05, 0.95, textstr, transform=axis.transAxes, fontsize=12,
			verticalalignment='top', bbox=dict(boxstyle='round', facecolor='wheat', alpha=0.5))
			return axis

			def RefinedEventDetection(out, AnalysisResults, signals, limit):
			for sig in signals:
			#Choose correct reference
			if sig is 'i1_Up':
			sig1 = 'i1'
			elif sig is 'i2_Up':
			sig1 = 'i2'
			else:
			sig1 = sig
			if sig is 'i1' or 'i1_Up':
			volt='v1'
			elif sig is 'i2' or 'i2_Up':
			volt='v2'

			if len(AnalysisResults[sig]['RoughEventLocations']) > 1:
			startpoints = np.uint64(AnalysisResults[sig]['RoughEventLocations'][:, 0])
			endpoints = np.uint64(AnalysisResults[sig]['RoughEventLocations'][:, 1])
			localBaseline = AnalysisResults[sig]['RoughEventLocations'][:, 2]
			localVariance = AnalysisResults[sig]['RoughEventLocations'][:, 3]

			CusumBaseline=500
			numberofevents = len(startpoints)
			AnalysisResults[sig]['StartPoints'] = startpoints
			AnalysisResults[sig]['EndPoints'] = endpoints
			AnalysisResults[sig]['LocalBaseline'] = localBaseline
			AnalysisResults[sig]['LocalVariance'] = localVariance
			AnalysisResults[sig]['NumberOfEvents'] = len(startpoints)

			deli = np.zeros(numberofevents)
			dwell = np.zeros(numberofevents)
			fitvalue = np.zeros(numberofevents)
			AllEvents = []

			#####FIX THE UP AND DOWN STORY!! ALL THE PROBLEMS ARE IN HERE...
			for i in range(numberofevents):
			length = endpoints[i] - startpoints[i]
			if out[sig1][startpoints[i]] < 0 and (sig == 'i1_Up' or sig == 'i2_Up'):
			if length <= limit and length > 3:
			# Impulsion Fit to minimal value
			fitvalue[i] = np.max(out[sig1][startpoints[i]+np.uint(1):endpoints[i]-np.uint(1)])
			elif length > limit:
			fitvalue[i] = np.mean(out[sig1][startpoints[i]+np.uint(5):endpoints[i]-np.uint(5)])
			else:
			fitvalue[i] = np.max(out[sig1][startpoints[i]:endpoints[i]])
			deli[i] = -(localBaseline[i] - fitvalue[i])

			elif out[sig1][startpoints[i]] < 0 and (sig == 'i1' or sig == 'i2'):
			if length <= limit and length > 3:
			# Impulsion Fit to minimal value
			fitvalue[i] = np.min(out[sig1][startpoints[i]+np.uint(1):endpoints[i]-np.uint(1)])
			elif length > limit:
			fitvalue[i] = np.mean(out[sig1][startpoints[i]+np.uint(5):endpoints[i]-np.uint(5)])
			else:
			fitvalue[i] = np.min(out[sig1][startpoints[i]:endpoints[i]])
			deli[i] = -(localBaseline[i] - fitvalue[i])

			elif out[sig1][startpoints[i]] > 0 and (sig == 'i1_Up' or sig == 'i2_Up'):
			if length <= limit and length > 3:
			# Impulsion Fit to minimal value
			fitvalue[i] = np.max(out[sig1][startpoints[i]+np.uint(1):endpoints[i]-np.uint(1)])
			elif length > limit:
			fitvalue[i] = np.mean(out[sig1][startpoints[i]+np.uint(5):endpoints[i]-np.uint(5)])
			else:
			fitvalue[i] = np.max(out[sig1][startpoints[i]:endpoints[i]])
			deli[i] = (localBaseline[i] - fitvalue[i])

			elif out[sig1][startpoints[i]] > 0 and (sig == 'i1' or sig == 'i2'):
			if length <= limit and length > 3:
			# Impulsion Fit to minimal value
			fitvalue[i] = np.min(out[sig1][startpoints[i]+np.uint(1):endpoints[i]-np.uint(1)])
			elif length > limit:
			fitvalue[i] = np.mean(out[sig1][startpoints[i]+np.uint(5):endpoints[i]-np.uint(5)])
			else:
			fitvalue[i] = np.min(out[sig1][startpoints[i]:endpoints[i]])
			deli[i] = (localBaseline[i] - fitvalue[i])
			else:
			print('Strange event that has to come from a neighboring universe...Computer will self-destruct in 3s!')
			dwell[i] = (endpoints[i] - startpoints[i]) / out['samplerate']
			AllEvents.append(out[sig1][startpoints[i]:endpoints[i]])
			frac = deli / localBaseline
			dt = np.array(0)
			dt = np.append(dt, np.diff(startpoints) / out['samplerate'])
			numberofevents = len(dt)
			#AnalysisResults[sig]['CusumFits'] = AllFits
			AnalysisResults[sig]['FractionalCurrentDrop'] = frac
			AnalysisResults[sig]['DeltaI'] = deli
			AnalysisResults[sig]['DwellTime'] = dwell
			AnalysisResults[sig]['Frequency'] = dt
			AnalysisResults[sig]['LocalVoltage'] = out[volt][startpoints]
			AnalysisResults[sig]['AllEvents'] = AllEvents
			AnalysisResults[sig]['FitLevel'] = fitvalue
			else:
			AnalysisResults[sig]['NumberOfEvents'] = 0

			return AnalysisResults

			def CorrelateTheTwoChannels(AnalysisResults, DelayLimit, Ch1 = 'i1', Ch2 = 'i2'):
			if len(AnalysisResults[Ch1]['RoughEventLocations']) is not 0:
			i1StartP = np.int64(AnalysisResults[Ch1]['StartPoints'][:])
			else:
			i1StartP = []
			if len(AnalysisResults[Ch2]['RoughEventLocations']) is not 0:
			i2StartP = np.int64(AnalysisResults[Ch2]['StartPoints'][:])
			else:
			i2StartP = []

			# Common Events, # Take Longer
			CommonEventsi1Index = np.array([], dtype=np.uint64)
			CommonEventsi2Index = np.array([], dtype=np.uint64)

			for k in i1StartP:
			val = i2StartP[(i2StartP > k - DelayLimit) & (i2StartP < k + DelayLimit)]
			if len(val) == 1:
			CommonEventsi2Index = np.append(CommonEventsi2Index, np.uint64(np.where(i2StartP == val)[0][0]))
			CommonEventsi1Index = np.append(CommonEventsi1Index, np.uint64(np.where(i1StartP == k)[0][0]))
			if len(val) > 1:
			diff = np.absolute(val-k)
			CommonEventsi2Index = np.append(CommonEventsi2Index, np.uint64(np.where(i2StartP == val[np.argmin(diff)]))[0][0])
			CommonEventsi1Index = np.append(CommonEventsi1Index, np.uint64(np.where(i1StartP == k)[0][0]))
			# Only i1
			Onlyi1Indexes = np.delete(range(len(i1StartP)), CommonEventsi1Index)
			#Onlyi1Indexes=[]
			# Only i2
			Onlyi2Indexes = np.delete(range(len(i2StartP)), CommonEventsi2Index)
			#Onlyi2Indexes=[]

			CommonIndexes={}
			CommonIndexes[Ch1]=CommonEventsi1Index
			CommonIndexes[Ch2]=CommonEventsi2Index
			OnlyIndexes={}
			OnlyIndexes[Ch1] = Onlyi1Indexes
			OnlyIndexes[Ch2] = Onlyi2Indexes
			return (CommonIndexes, OnlyIndexes)

			def PlotEvent(fig1, t1, i1, t2 = [], i2 = [], fit1 = np.array([]), fit2 = np.array([]), channel = 'i1'):
			if len(t2)==0:
			ax1 = fig1.add_subplot(111)
			ax1.plot(t1, i1*1e9, 'b')
			if len(fit1) is not 0:
			ax1.plot(t1, fit1*1e9, 'y')
			ax1.set_ylabel(channel + ' Current [nA]')
			ax1.set_xlabel(channel + ' Time [s]')
			ax1.ticklabel_format(useOffset=False)
			ax1.ticklabel_format(useOffset=False)
			return ax1
			else:
			ax1 = fig1.add_subplot(211)
			ax2 = fig1.add_subplot(212, sharex=ax1)
			ax1.plot(t1, i1*1e9, 'b')
			if len(fit1) is not 0:
			ax1.plot(t1, fit1*1e9, 'y')
			ax2.plot(t2, i2*1e9, 'r')
			if len(fit2) is not 0:
			ax2.plot(t2, fit2*1e9, 'y')
			ax1.set_ylabel('Ionic Current [nA]')
			#ax1.set_xticklabels([])
			ax2.set_ylabel('Transverse Current [nA]')
			ax2.set_xlabel('Time [s]')
			ax2.ticklabel_format(useOffset=False)
			ax2.ticklabel_format(useOffset=False)
			ax1.ticklabel_format(useOffset=False)
			ax1.ticklabel_format(useOffset=False)
			return ax1, ax2

			def SaveAllPlots(CommonIndexes, OnlyIndexes, AnalysisResults, directory, out, buffer, withFit = 1):
			if len(CommonIndexes['i1']) is not 0:
			# Plot All Common Events
			pp = PdfPages(directory + '_SavedEventsCommon.pdf')
			ind1 = np.uint64(CommonIndexes['i1'])
			ind2 = np.uint64(CommonIndexes['i2'])

			t = np.arange(0, len(out['i1']))
			t = t / out['samplerate'] * 1e3
			count=1
			for eventnumber in range(len(ind1)):
			parttoplot = np.arange(AnalysisResults['i1']['StartPoints'][ind1[eventnumber]] - buffer,
			AnalysisResults['i1']['EndPoints'][ind1[eventnumber]] + buffer, 1, dtype=np.uint64)
			parttoplot2 = np.arange(AnalysisResults['i2']['StartPoints'][ind2[eventnumber]] - buffer,
			AnalysisResults['i2']['EndPoints'][ind2[eventnumber]] + buffer, 1, dtype=np.uint64)

			fit1 = np.concatenate([np.ones(buffer) * AnalysisResults['i1']['LocalBaseline'][ind1[eventnumber]],
			np.ones(AnalysisResults['i1']['EndPoints'][ind1[eventnumber]] - AnalysisResults['i1']['StartPoints'][
			ind1[eventnumber]]) * (
			AnalysisResults['i1']['LocalBaseline'][ind1[eventnumber]] - AnalysisResults['i1']['DeltaI'][ind1[eventnumber]]),
			np.ones(buffer) * AnalysisResults['i1']['LocalBaseline'][ind1[eventnumber]]])

			fit2 = np.concatenate([np.ones(buffer) * AnalysisResults['i2']['LocalBaseline'][ind2[eventnumber]],
			np.ones(AnalysisResults['i2']['EndPoints'][ind2[eventnumber]] - AnalysisResults['i2']['StartPoints'][
			ind2[eventnumber]]) * (
			AnalysisResults['i2']['LocalBaseline'][ind2[eventnumber]] - AnalysisResults['i2']['DeltaI'][ind2[eventnumber]]),
			np.ones(buffer) * AnalysisResults['i2']['LocalBaseline'][ind2[eventnumber]]])
			if withFit:
			fig = PlotEvent(t[parttoplot], out['i1'][parttoplot], t[parttoplot2], out['i2'][parttoplot2],
			fit1=fit1, fit2=fit2)
			else:
			fig = PlotEvent(t[parttoplot], out['i1'][parttoplot], t[parttoplot2], out['i2'][parttoplot2])

			if not np.mod(eventnumber+1,200):
			pp.close()
			pp = PdfPages(directory + '_SavedEventsCommon_' + str(count) + '.pdf')
			count+=1
			pp.savefig(fig)
			print('{} out of {} saved!'.format(str(eventnumber), str(len(ind1))))
			print('Length i1: {}, Fit i1: {}'.format(len(out['i1'][parttoplot]), len(fit1)))
			print('Length i2: {}, Fit i2: {}'.format(len(out['i2'][parttoplot2]), len(fit2)))
			fig.clear()
			plt.close(fig)
			pp.close()

			if len(OnlyIndexes['i1']) is not 0:
			# Plot All i1
			pp = PdfPages(directory + '_SavedEventsOnlyi1.pdf')
			ind1 = np.uint64(OnlyIndexes['i1'])

			t = np.arange(0, len(out['i1']))
			t = t / out['samplerate'] * 1e3
			count=1
			for eventnumber in range(len(ind1)):
			parttoplot = np.arange(AnalysisResults['i1']['StartPoints'][ind1[eventnumber]] - buffer,
			AnalysisResults['i1']['EndPoints'][ind1[eventnumber]] + buffer, 1, dtype=np.uint64)

			fit1 = np.concatenate([np.ones(buffer) * AnalysisResults['i1']['LocalBaseline'][ind1[eventnumber]],
			np.ones(AnalysisResults['i1']['EndPoints'][ind1[eventnumber]] - AnalysisResults['i1']['StartPoints'][
			ind1[eventnumber]]) * (
			AnalysisResults['i1']['LocalBaseline'][ind1[eventnumber]] - AnalysisResults['i1']['DeltaI'][ind1[eventnumber]]),
			np.ones(buffer) * AnalysisResults['i1']['LocalBaseline'][ind1[eventnumber]]])

			fig = PlotEvent(t[parttoplot], out['i1'][parttoplot], t[parttoplot], out['i2'][parttoplot], fit1=fit1)
			if not np.mod(eventnumber+1,200):
			pp.close()
			pp = PdfPages(directory + '_SavedEventsCommon_' + str(count) + '.pdf')
			count+=1
			pp.savefig(fig)
			print('{} out of {} saved!'.format(str(eventnumber), str(len(ind1))))
			fig.clear()
			plt.close(fig)
			pp.close()

			if len(OnlyIndexes['i2']) is not 0:
			# Plot All i2
			pp = PdfPages(directory + '_SavedEventsOnlyi2.pdf')
			ind1 = np.uint64(OnlyIndexes['i2'])

			t = np.arange(0, len(out['i2']))
			t = t / out['samplerate'] * 1e3
			count=1
			for eventnumber in range(len(ind1)):
			parttoplot = np.arange(AnalysisResults['i2']['StartPoints'][ind1[eventnumber]] - buffer,
			AnalysisResults['i2']['EndPoints'][ind1[eventnumber]] + buffer, 1, dtype=np.uint64)

			fit1 = np.concatenate([np.ones(buffer) * AnalysisResults['i2']['LocalBaseline'][ind1[eventnumber]],
			np.ones(AnalysisResults['i2']['EndPoints'][ind1[eventnumber]] - AnalysisResults['i2']['StartPoints'][
			ind1[eventnumber]]) * (
			AnalysisResults['i2']['LocalBaseline'][ind1[eventnumber]] - AnalysisResults['i2']['DeltaI'][ind1[eventnumber]]),
			np.ones(buffer) * AnalysisResults['i2']['LocalBaseline'][ind1[eventnumber]]])

			fig = PlotEvent(t[parttoplot], out['i1'][parttoplot], t[parttoplot], out['i2'][parttoplot], fit2=fit1)
			if not np.mod(eventnumber+1,200):
			pp.close()
			pp = PdfPages(directory + '_SavedEventsCommon_' + str(count) + '.pdf')
			count+=1
			pp.savefig(fig)
			print('{} out of {} saved!'.format(str(eventnumber), str(len(ind1))))
			fig.clear()
			plt.close(fig)
			pp.close()

			# Derivative
			if len(CommonIndexes['i1']) is not 0:
			# Plot All i1
			pp = PdfPages(directory + '_i1vsderivi2.pdf')
			ind1 = np.uint64(CommonIndexes['i1'])
			ind2 = np.uint64(CommonIndexes['i2'])

			t = np.arange(0, len(out['i1']))
			t = t / out['samplerate'] * 1e3
			count=1
			for eventnumber in range(len(ind1)):
			parttoplot = np.arange(AnalysisResults['i1']['StartPoints'][ind1[eventnumber]] - buffer,
			AnalysisResults['i1']['EndPoints'][ind1[eventnumber]] + buffer, 1, dtype=np.uint64)
			parttoplot2 = np.arange(AnalysisResults['i2']['StartPoints'][ind2[eventnumber]] - buffer,
			AnalysisResults['i2']['EndPoints'][ind2[eventnumber]] + buffer, 1, dtype=np.uint64)

			fig = PlotEvent(t[parttoplot], out['i1'][parttoplot], t[parttoplot2][:-1],
			np.diff(out['i2'][parttoplot2]))

			if not np.mod(eventnumber+1,200):
			pp.close()
			pp = PdfPages(directory + '_SavedEventsCommon_' + str(count) + '.pdf')
			count+=1
			pp.savefig(fig)
			print('{} out of {} saved!'.format(str(eventnumber), str(len(ind1))))
			fig.clear()
			plt.close(fig)
			pp.close()

			def PlotRecursiveLPResults(RoughEventLocations, inp, directory, buffer, channel='i2'):
			pp = PdfPages(directory + '_' + channel + '_DetectedEventsFromLPFilter.pdf')
			a=1
			for i in RoughEventLocations['RoughEventLocations']:
			startp = np.uint64(i[0]-buffer*inp['samplerate'])
			endp = np.uint64(i[1]+buffer*inp['samplerate'])
			t = np.arange(startp, endp)
			t = t / inp['samplerate'] * 1e3
			fig = PlotEvent(t, inp[channel][startp:endp], channel=channel)
			pp.savefig(fig)
			print('{} out of {} saved!'.format(str(a), str(len(RoughEventLocations['RoughEventLocations']))))
			a+=1
			fig.clear()
			plt.close(fig)
			pp.close()

			def SaveAllAxopatchEvents(AnalysisResults, directory, out, buffer, withFit = 1):
			# Plot All Common Events
			pp = PdfPages(directory + '_SavedEventsAxopatch.pdf')
			t = np.arange(0, len(out['i1']))
			t = t / out['samplerate'] * 1e3

			for eventnumber in range(AnalysisResults['i1']['NumberOfEvents']):
			parttoplot = np.arange(AnalysisResults['i1']['StartPoints'][eventnumber] - buffer,
			AnalysisResults['i1']['EndPoints'][eventnumber] + buffer, 1, dtype=np.uint64)

			fit1 = np.concatenate([np.ones(buffer) * AnalysisResults['i1']['LocalBaseline'][eventnumber],
			np.ones(AnalysisResults['i1']['EndPoints'][eventnumber] -
			AnalysisResults['i1']['StartPoints'][
			eventnumber]) * (
			AnalysisResults['i1']['LocalBaseline'][eventnumber] -
			AnalysisResults['i1']['DeltaI'][eventnumber]),
			np.ones(buffer) * AnalysisResults['i1']['LocalBaseline'][eventnumber]])

			if withFit:
			fig = PlotEvent(t[parttoplot], out['i1'][parttoplot], fit1=fit1)
			else:
			fig = PlotEvent(t[parttoplot], out['i1'][parttoplot])
			try:
			pp.savefig(fig)
			except:
			print('Problem at {} !'.format(str(eventnumber)))

			print('{} out of {} saved!'.format(str(eventnumber), str(AnalysisResults['i1']['NumberOfEvents'])))
			#print('Length i1: {}, Fit i1: {}'.format(len(out['i1'][parttoplot]), len(fit1)))
			fig.clear()
			plt.close(fig)
			pp.close()

			def MakeIV(filenames, directory, conductance=10, title=''):
			if len(conductance) is 1:
			conductance=np.ones(len(filenames))*conductance
			for idx,filename in enumerate(filenames):
			print(filename)

			# Make Dir to save images
			output = OpenFile(filename)
			if not os.path.exists(directory):
			os.makedirs(directory)

			AllData = MakeIVData(output, delay=2)
			if AllData == 0:
			print('!!!! No Sweep in: ' + filename)
			continue

			# Plot Considered Part
			# figExtracteParts = plt.figure(1)
			# ax1 = figExtracteParts.add_subplot(211)
			# ax2 = figExtracteParts.add_subplot(212, sharex=ax1)
			# (ax1, ax2) = uf.PlotExtractedPart(output, AllData, current = 'i1', unit=1e9, axis = ax1, axis2=ax2)
			# plt.show()
			# figExtracteParts.savefig(directory + os.sep + 'PlotExtracted_' + str(os.path.split(filename)[1])[:-4] + '.eps')
			# figExtracteParts.savefig(directory + os.sep + 'PlotExtracted_' + str(os.path.split(filename)[1])[:-4] + '.png', dpi=150)


			# Plot IV
			if output['graphene']:
			figIV2 = plt.figure(3)
			ax2IV = figIV2.add_subplot(111)
			ax2IV = PlotIV(output, AllData, current='i2', unit=1e9, axis=ax2IV, WithFit=1, title=title[idx])
			figIV2.tight_layout()
			figIV2.savefig(directory + os.sep + str(os.path.split(filename)[1]) + '_IV_i2.png', dpi=300)
			figIV2.savefig(directory + os.sep + str(os.path.split(filename)[1]) + '_IV_i2.eps')
			figIV2.clear()

			figIV = plt.figure(2)
			ax1IV = figIV.add_subplot(111)
			ax1IV = PlotIV(output, AllData, current='i1', unit=1e9, axis=ax1IV, WithFit=1, PoreSize=[conductance[idx], 20e-9], title=title[idx])
			figIV.tight_layout()

			# Save Figures
			figIV.savefig(directory + os.sep + str(os.path.split(filename)[1]) + 'IV_i1.png', dpi=300)
			figIV.savefig(directory + os.sep + str(os.path.split(filename)[1]) + 'IV_i1.eps')
			figIV.clear()

			def TwoChannelAnalysis(filenames, outdir, UpwardsOn=0):

			for filename in filenames:
			print(filename)
			inp = OpenFile(filename)
			file = os.sep + str(os.path.split(filename)[1][:-4])

			if not os.path.exists(outdir):
			os.makedirs(outdir)

			# Low Pass Event Detection
			AnalysisResults = {}

			if inp['graphene']:
			chan = ['i1', 'i2'] # For looping over the channels
			else:
			chan = ['i1']

			for sig in chan:
			AnalysisResults[sig] = {}
			AnalysisResults[sig]['RoughEventLocations'] = RecursiveLowPassFast(inp[sig], pm.coefficients[sig],
			inp['samplerate'])
			if UpwardsOn: # Upwards detection can be turned on or off
			AnalysisResults[sig + '_Up'] = {}
			AnalysisResults[sig + '_Up']['RoughEventLocations'] = RecursiveLowPassFastUp(inp[sig],
			pm.coefficients[sig],
			inp['samplerate'])

			# f.PlotRecursiveLPResults(AnalysisResults['i2'], inp, directory, 1e-3, channel='i2')
			# f.PlotRecursiveLPResults(AnalysisResults['i2_Up'], inp, directory+'UP_', 1e-3, channel='i2')

			# Refine the Rough Event Detection done by the LP filter and Add event infos
			AnalysisResults = RefinedEventDetection(inp, AnalysisResults, signals=chan,
			limit=pm.MinimalFittingLimit * inp['samplerate'])

			# Correlate the two channels
			if inp['graphene']:
			(CommonIndexes, OnlyIndexes) = f.CorrelateTheTwoChannels(AnalysisResults, 10e-3 * inp['samplerate'])
			print('\n\nAnalysis Done...\nThere are {} common events\n{} Events on i1 only\n{} Events on i2 only'.format(
			len(CommonIndexes['i1']), len(OnlyIndexes['i1']), len(OnlyIndexes['i2'])))
			# Plot The Events
			f.SaveAllPlots(CommonIndexes, OnlyIndexes, AnalysisResults, directory, inp, pm.PlotBuffer)
			else:
			# Plot The Events
			f.SaveAllAxopatchEvents(AnalysisResults, directory, inp, pm.PlotBuffer)

			def SaveToHDF5(inp_file, AnalysisResults, coefficients, outdir):
			file = str(os.path.split(inp_file['filename'])[1][:-4])
			f = h5py.File(outdir + file + '_OriginalDB.hdf5', "w")
			general = f.create_group("General")
			general.create_dataset('FileName', data=inp_file['filename'])
			general.create_dataset('Samplerate', data=inp_file['samplerate'])
			general.create_dataset('Machine', data=inp_file['type'])
			general.create_dataset('TransverseRecorded', data=inp_file['graphene'])
			general.create_dataset('TimeFileWritten', data=inp_file['TimeFileWritten'])
			general.create_dataset('TimeFileLastModified', data=inp_file['TimeFileLastModified'])
			general.create_dataset('ExperimentDuration', data=inp_file['ExperimentDuration'])

			segmentation_LP = f.create_group("LowPassSegmentation")
			for k,l in AnalysisResults.items():
			set1 = segmentation_LP.create_group(k)
			lpset1 = set1.create_group('LowPassSettings')
			for o, p in coefficients[k].items():
			lpset1.create_dataset(o, data=p)
			for m, l in AnalysisResults[k].items():
			if m is 'AllEvents':
			eventgroup = set1.create_group(m)
			for i, val in enumerate(l):
			eventgroup.create_dataset('{:09d}'.format(i), data=val)
			elif m is 'Cusum':
			eventgroup = set1.create_group(m)
			for i1, val1 in enumerate(AnalysisResults[k]['Cusum']):
			cusevent = eventgroup.create_group('{:09d}'.format(i1))
			cusevent.create_dataset('NumberLevels', data=np.uint64(len(AnalysisResults[k]['Cusum'][i1]['levels'])))
			if len(AnalysisResults[k]['Cusum'][i1]['levels']):
			cusevent.create_dataset('up', data=AnalysisResults[k]['Cusum'][i1]['up'])
			cusevent.create_dataset('down', data=AnalysisResults[k]['Cusum'][i1]['down'])
			cusevent.create_dataset('both', data=AnalysisResults[k]['Cusum'][i1]['both'])
			cusevent.create_dataset('fit', data=AnalysisResults[k]['Cusum'][i1]['fit'])
			# 0: level number, 1: current, 2: length, 3: std
			cusevent.create_dataset('levels_current', data=AnalysisResults[k]['Cusum'][i1]['levels'][1])
			cusevent.create_dataset('levels_length', data=AnalysisResults[k]['Cusum'][i1]['levels'][2])
			cusevent.create_dataset('levels_std', data=AnalysisResults[k]['Cusum'][i1]['levels'][3])
			else:
			set1.create_dataset(m, data=l)

			def PlotExtractedPart(output, AllData, current = 'i1', unit=1e9, axis = '', axis2 = ''):
			time = np.arange(0, len(output[current])) / output['samplerate']
			axis.plot(time, output[current] * unit, 'b', label=str(os.path.split(output['filename'])[1])[:-4])
			axis.set_ylabel('Current ' + current + ' [nA]')
			axis.set_title('Time Trace')
			for i in range(0, len(AllData[current]['StartPoint'])):
			axis.plot(time[AllData[current]['StartPoint'][i]:AllData[current]['EndPoint'][i]],
			output[current][AllData[current]['StartPoint'][i]:AllData[current]['EndPoint'][i]] * unit, 'r')
			axis2.plot(time, output[AllData[current]['SweepedChannel']], 'b', label=str(os.path.split(output['filename'])[1])[:-4])
			axis2.set_ylabel('Voltage ' + AllData[current]['SweepedChannel'] + ' [V]')
			axis2.set_xlabel('Time')
			return (axis, axis2)

			def xcorr(x, y, k, normalize=True):
			n = x.shape[0]

			# initialize the output array
			out = np.empty((2 * k) + 1, dtype=np.double)
			lags = np.arange(-k, k + 1)

			# pre-compute E(x), E(y)
			mu_x = x.mean()
			mu_y = y.mean()

			# loop over lags
			for ii, lag in enumerate(lags):

			# use slice indexing to get 'shifted' views of the two input signals
			if lag < 0:
			xi = x[:lag]
			yi = y[-lag:]
			elif lag > 0:
			xi = x[:-lag]
			yi = y[lag:]
			else:
			xi = x
			yi = y

			# x - mu_x; y - mu_y
			xdiff = xi - mu_x
			ydiff = yi - mu_y

			# E[(x - mu_x) * (y - mu_y)]
			out[ii] = xdiff.dot(ydiff) / n

			# NB: xdiff.dot(ydiff) == (xdiff * ydiff).sum()

			if normalize:
			# E[(x - mu_x) * (y - mu_y)] / (sigma_x * sigma_y)
			out /= np.std(x) * np.std(y)

			return lags, out

			def cusum_detection(data, threshhold, minlength, maxstates):
			print(threshhold)
			s = timer()
			logp = 0 # instantaneous log-likelihood for positive jumps
			logn = 0 # instantaneous log-likelihood for negative jumps
			cpos = np.zeros(len(data), dtype='float64') # cumulative log-likelihood function for positive jumps
			cneg = np.zeros(len(data), dtype='float64') # cumulative log-likelihood function for negative jumps
			gpos = np.zeros(2, dtype='float64') # decision function for positive jumps
			gneg = np.zeros(2, dtype='float64') # decision function for negative jumps
			edges = np.array([0],
			dtype='int64') # initialize an array with the position of the first subevent - the start of the event
			real_start = np.array([],
			dtype='int64') # initialize an array with the position of the first subevent - the start of the event
			real_end = np.array([],
			dtype='int64') # initialize an array with the position of the first subevent - the start of the event
			real_Depth = np.array([],
			dtype='int64') # initialize an array with the position of the first subevent - the start of the event

			anchor = 0 # the last detected change
			length = len(data)
			data_std = np.std(data)
			h = threshhold / data_std
			k = 0
			nStates = np.uint64(0)
			varM = data[0]
			varS = 0
			mean = data[0]
			print('length data =' + str(length))
			v = np.zeros(length, dtype='float64')
			while k < length - 100:
			k += 1
			if nStates == 0:
			variance = np.var(data[anchor:k]) # initial params for pattern region
			mean = np.mean(data[anchor:k])
			if variance == 0: break
			logp = threshhold / variance * (data[
			k] - mean - threshhold / 2.) # instantaneous log-likelihood for current sample assuming local baseline has jumped in the positive direction
			logn = -threshhold / variance * (data[
			k] - mean + threshhold / 2.) # instantaneous log-likelihood for current sample assuming local baseline has jumped in the negative direction
			cpos[k] = cpos[k - 1] + logp # accumulate positive log-likelihoods
			cneg[k] = cneg[k - 1] + logn # accumulate negative log-likelihoods
			gpos[1] = max(gpos[0] + logp, 0) # accumulate or reset positive decision function
			gneg[1] = max(gneg[0] + logn, 0) # accumulate or reset negative decision function
			if (gpos[1] > h or gneg[1] > h):

			if (gpos[1] > h): # significant positive jump detected
			jump = np.uint64(anchor + np.argmin(cpos[anchor:k + 1])) # find the location of the start of the jump
			if jump - edges[np.uint64(nStates)] > minlength and np.abs(data[np.uint64(jump + minlength)] - data[jump]) > threshhold / 4:
			edges = np.append(edges, jump)
			nStates += 1
			# print('EVENT!!!!! at ='+str(self.t[jump]))
			anchor = k # no data meaning at bad points!
			# away from bad point more!
			cpos[0:len(cpos)] = 0 # reset all decision arrays
			cneg[0:len(cneg)] = 0
			gpos[0:len(gpos)] = 0
			gneg[0:len(gneg)] = 0
			if (gneg[1] > h): # significant negative jump detected
			jump = anchor + np.argmin(cneg[anchor:k + 1])
			if jump - edges[np.uint64(nStates)] > minlength and np.abs(data[np.uint64(jump + minlength)] - data[jump]) > threshhold / 4:
			edges = np.append(edges, jump)
			nStates += 1
			# print('EVENT!!!!! at ='+str(self.t[jump] ))
			anchor = k # no data meaning at bad points!
			# away from bad point more!
			cpos[0:len(cpos)] = 0 # reset all decision arrays
			cneg[0:len(cneg)] = 0
			gpos[0:len(gpos)] = 0
			gneg[0:len(gneg)] = 0

			gpos[0] = gpos[1]
			gneg[0] = gneg[1]
			if maxstates > 0:
			if nStates > maxstates:
			print('too sensitive')
			nStates = 0
			k = 0
			threshhold = threshhold * 1.1
			h = h * 1.1
			logp = 0 # instantaneous log-likelihood for positive jumps
			logn = 0 # instantaneous log-likelihood for negative jumps
			cpos = np.zeros(len(data), dtype='float64') # cumulative log-likelihood function for positive jumps
			cneg = np.zeros(len(data), dtype='float64') # cumulative log-likelihood function for negative jumps
			gpos = np.zeros(2, dtype='float64') # decision function for positive jumps
			gneg = np.zeros(2, dtype='float64') # decision function for negative jumps
			edges = np.array([0],
			dtype='int64') # initialize an array with the position of the first subevent - the start of the event
			anchor = 0 # the last detected change
			length = len(data)
			mean = data[0]
			nStates = 0
			mean = data[0]
			edges = np.append(edges, len(data) - 1) # mark the end of the event as an edge
			nStates += 1

			cusum = dict()
			print('Events = ' + str([edges]))
			for i in range(len(edges) - 1):
			real_start = np.append(real_start, edges[i])
			real_end = np.append(real_end, edges[i + 1])
			real_Depth = np.append(real_Depth, np.mean(data[np.uint64(edges[i]):np.uint64(edges[i + 1])]))

			cusum['Real_Start'] = real_start
			cusum['Real_End'] = real_end
			cusum['Real_Depth'] = real_Depth
			print('Real Start =' + str(cusum['Real_Start']))
			print('Real End =' + str(cusum['Real_End']))
			cusum['CurrentLevels'] = [np.average(data[np.uint64(edges[i]) + minlength:np.uint64(edges[i + 1])]) for i in
			range(np.uint64(nStates))] # detect current levels during detected sub-event
			print('Edges[-1] = ' + str(edges[-1]))
			cusum['EventDelay'] = edges # locations of sub-events in the data
			cusum['Threshold'] = threshhold # record the threshold used
			print('Event = ' + str(cusum['EventDelay']))
			cusum['jumps'] = np.diff(cusum['CurrentLevels'])

			e = timer()
			print('cusum took = ' + str(e - s) + 's')
			return cusum

			def detect_cusum(data, var, threshhold, minlength, maxstates):
			logp = 0 # instantaneous log-likelihood for positive jumps
			logn = 0 # instantaneous log-likelihood for negative jumps
			cpos = np.zeros(len(data), dtype='float64') # cumulative log-likelihood function for positive jumps
			cneg = np.zeros(len(data), dtype='float64') # cumulative log-likelihood function for negative jumps
			gpos = np.zeros(len(data), dtype='float64') # decision function for positive jumps
			gneg = np.zeros(len(data), dtype='float64') # decision function for negative jumps
			edges = np.array([0],
			dtype='int64') # initialize an array with the position of the first subevent - the start of the event
			anchor = 0 # the last detected change
			length = len(data)
			mean = data[0]
			h = threshhold / var
			k = self.safety_reg - self.control1
			nStates = 0
			varM = data[0]
			varS = 0
			mean = data[0]
			print('length data =' + str(length))
			v = np.zeros(length, dtype='float64')
			while k < length - 100:
			k += 1
			if nStates == 0: variance = np.var(data[anchor:k])
			mean = np.mean(data[anchor:k])
			logp = threshhold / variance * (data[
			k] - mean - threshhold / 2.) # instantaneous log-likelihood for current sample assuming local baseline has jumped in the positive direction
			logn = -threshhold / variance * (data[
			k] - mean + threshhold / 2.) # instantaneous log-likelihood for current sample assuming local baseline has jumped in the negative direction
			cpos[k] = cpos[k - 1] + logp # accumulate positive log-likelihoods
			cneg[k] = cneg[k - 1] + logn # accumulate negative log-likelihoods
			gpos[k] = max(gpos[k - 1] + logp, 0) # accumulate or reset positive decision function
			gneg[k] = max(gneg[k - 1] + logn, 0) # accumulate or reset negative decision function
			if (gpos[k] > h or gneg[k] > h):

			if (gpos[k] > h): # significant positive jump detected
			jump = anchor + np.argmin(cpos[anchor:k + 1]) # find the location of the start of the jump
			if jump - edges[nStates] > minlength and np.abs(data[jump + minlength] - data[jump]) > threshhold / 4:
			edges = np.append(edges, jump)
			nStates += 1
			print('EVENT!!!!! at =' + str((self.control1 + jump) / self.out['samplerate']))
			anchor = k # no data meaning at bad points!
			# away from bad point more!
			cpos[0:len(cpos)] = 0 # reset all decision arrays
			cneg[0:len(cneg)] = 0
			gpos[0:len(gpos)] = 0
			gneg[0:len(gneg)] = 0
			if (gneg[k] > h): # significant negative jump detected
			jump = anchor + np.argmin(cneg[anchor:k + 1])
			if jump - edges[nStates] > minlength and np.abs(data[jump + minlength] - data[jump]) > threshhold / 4:
			edges = np.append(edges, jump)
			nStates += 1
			print('EVENT!!!!! at =' + str((self.control1 + jump) / self.out['samplerate']))
			anchor = k # no data meaning at bad points!
			# away from bad point more!
			cpos[0:len(cpos)] = 0 # reset all decision arrays
			cneg[0:len(cneg)] = 0
			gpos[0:len(gpos)] = 0
			gneg[0:len(gneg)] = 0

			if maxstates > 0:
			if nStates > 10:
			print('too sensitive')
			nStates = 0
			k = 0
			threshhold = threshhold * 1.1
			h = h * 1.1
			logp = 0 # instantaneous log-likelihood for positive jumps
			logn = 0 # instantaneous log-likelihood for negative jumps
			cpos = np.zeros(len(data), dtype='float64') # cumulative log-likelihood function for positive jumps
			cneg = np.zeros(len(data), dtype='float64') # cumulative log-likelihood function for negative jumps
			gpos = np.zeros(len(data), dtype='float64') # decision function for positive jumps
			gneg = np.zeros(len(data), dtype='float64') # decision function for negative jumps
			edges = np.array([0],
			dtype='int64') # initialize an array with the position of the first subevent - the start of the event
			anchor = 0 # the last detected change
			length = len(data)
			mean = data[0]
			nStates = 0
			mean = data[0]
			edges = np.append(edges, len(data)) # mark the end of the event as an edge
			nStates += 1

			cusum = dict()
			cusum['CurrentLevels'] = [np.average(data[edges[i] + minlength:edges[i + 1]]) for i in
			range(nStates)] # detect current levels during detected sub-events
			cusum['EventDelay'] = edges # locations of sub-events in the data
			cusum['Threshold'] = threshhold # record the threshold used
			cusum['jumps'] = np.diff(cusum['CurrentLevels'])
			# self.__recordevent(cusum)

			return cusum

			def CUSUM(input, delta, h, startp):
			Nd = 0
			kd = []
			krmv = []
			krmvdown = []
			both = []
			k0 = 0
			k = 0
			l = len(input)
			m = np.zeros(l)
			m[k0] = input[k0]
			v = np.zeros(l)
			sp = np.zeros(l)
			Sp = np.zeros(l)
			gp = np.zeros(l)
			sn = np.zeros(l)
			Sn = np.zeros(l)
			gn = np.zeros(l)
			mc = []
			while k < l-1:
			k += 1
			m[k] = np.mean(input[k0:k+1])
			v[k] = np.var(input[k0:k+1])

			sp[k] = delta / v[k] * (input[k] - m[k] - delta / 2)
			sn[k] = -delta / v[k] * (input[k] - m[k] + delta / 2)

			Sp[k] = Sp[k - 1] + sp[k]
			Sn[k] = Sn[k - 1] + sn[k]

			gp[k] = np.max([gp[k - 1] + sp[k], 0])
			gn[k] = np.max([gn[k - 1] + sn[k], 0])

			if gp[k] > h or gn[k] > h:
			if gp[k] > h:
			kmin = np.argmin(Sp[k0:k])
			krmv.append(kmin + k0 - 1)
			both.append(kmin + k0 - 1)
			else:
			kd.append(k)
			kmin = np.argmin(Sn[k0:k])
			krmvdown.append(kmin + k0 - 1)
			both.append(kmin + k0 - 1)
			k0 = k
			m[k0] = input[k0]
			v[k0] = 0
			sp[k0] = 0
			Sp[k0] = 0
			gp[k0] = 0
			sn[k0] = 0
			Sn[k0] = 0
			gn[k0] = 0
			Nd = Nd + 1
			'''
			if Nd == 0:
			mc = np.mean(input) * np.ones(k)
			elif Nd == 1:
			mc = [m[krmv[0]] * np.ones(krmv[0]), m[k] * np.ones(k - krmv[0])]
			else:
			mc.append(m[krmv[0]] * np.ones(krmv[0]))
			for ii in range(1, Nd-1):
			mc.append(m[krmv[ii]] * np.ones(krmv[ii] - krmv[ii - 1]))
			mc.append(m[k] * np.ones(k - krmv[Nd-1]))
			'''
			## Calculate Inner Levels
			if len(both) >= 2:
			levels = [np.zeros(len(both)-1), np.zeros(len(both)-1),
			np.zeros(len(both)-1), np.zeros(len(both)-1)]
			# 0: level number, 1: current, 2: length, 3: std
			fit = np.array([])
			for ind, g in enumerate(both[:-1]):
			levels[0][ind] = ind
			levels[1][ind] = np.mean(input[g:both[ind + 1]])
			levels[2][ind] = both[ind + 1] - g
			levels[3][ind] = np.std(input[g:both[ind + 1]])
			np.append(fit, levels[1][ind] * np.ones(np.uint64(levels[2][ind])))
			else:
			levels = []
			fit = []
			out = {'up': krmv+startp, 'down': krmvdown+startp, 'both': both+startp, 'levels':levels, 'fit': fit}
			return out

			def CondPoreSizeTick(x, pos):
			formCond = EngFormatter(unit='S')
			formPoreSize = EngFormatter(unit='m')
			outstring = '{}, {}'.format(formCond.format_eng(x), formPoreSize.format_eng(CalculatePoreSize(x, 1e-9, 10)))
			return outstring

			def EredoxBefore14062018():
			Eredox = np.array([31.7, 82.9, 135, 185], dtype=float)
			Eredox = Eredox * 1e-3
			return (Eredox-Eredox[0])

			def DebyeHuckelParam(c, T = 20):
			## Valid Only for same valency and c1 = c2
			epsilon_r = 87.740-0.40008T+9.398e-4T*2-1.410e-6T**3
			print('Dielectric of Water is {}'.format(epsilon_r))
			n = c1e3cst.Avogadro
			formCond = EngFormatter(unit='m')
			k = np.sqrt((cst.e*22n)/(cst.k(T+273.15)cst.epsilon_0epsilon_r))
			return [k, 1/k]#, '{}'.format(formCond.format_eng(1/k))]

			def ElectrostaticPotential(T, sigma, Tau=5e18, pK=7.9, pH=11):
			return cst.k * T / cst.e * (np.log(-sigma / (cst.e * Tau + sigma)) + (pK - pH) * np.log(10))

			def GetTempRedoxPotential(Cdiff=1e-3/1e-1, T=293.15):
			return cst.R * T / cst.physical_constants['Faraday constant'][0] * np.log(Cdiff)

			##Result: Negative ion divided by positive ion
			#Goldman–Hodgkin–Katz voltage equation
			def GetIonSelectivityWithoutPotential(c_trans, c_cis, Erev, T):
			n_trans = c_trans#1e3#cst.Avogadro
			n_cis = c_cis#1e3#cst.Avogadro
			A = Erev * cst.physical_constants['Faraday constant'][0] / (cst.R*T)
			return -(n_trans-n_cisnp.exp(-A))(1-np.exp(A))/((n_trans-n_cisnp.exp(A))(1-np.exp(-A)))

			def GetIonSelectivityWithPotential(c_trans, c_cis, Erev, T, phi):
			sel1 = GetIonSelectivityWithoutPotential(c_trans, c_cis, Erev, T)
			return sel1np.exp((-2phicst.physical_constants['Faraday constant'][0])/(cst.R T))

			def Selectivity(ConcGradient, V):
			return V * cst.physical_constants['Faraday constant'][0]/((cst.R293.15) np.log(ConcGradient))

Nanopore-tools/Functions.py820e9a946792smalladditions

Functions.pyView Options

Nanopore-tools/Functions.py
820e9a946792smalladditions

Functions.py
View Options