Functions.py
No OneTemporary
Actions

Subscribers

None

File Metadata

Created: Fri, May 3, 18:58

Functions.py
View Options

	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
	from pprint import pprint
	import shelve
	from tkinter import filedialog

	# if not 'verbose' in globals():
	# verbose = False
	#
	# verboseprint = print if verbose else lambda a, *k: None
	#

	def GetKClConductivity(Conc, Temp):
	p = pkl.load(open('KCl_ConductivityValues.p', 'rb'))
	if Conc==1.0:
	Conc = np.uint(1)
	return np.polyval(p[str(Conc)], Temp)

	def SaveVariables(savename, **kwargs):
	if os.path.isdir(savename):
	savefile=os.path.join(savename,os.path.basename(savename)+'_Events')
	else:
	if os.path.isfile(savename + '.dat'):
	savename = filedialog.asksaveasfile(mode='w', defaultextension=".dat")
	if savename is None: # asksaveasfile return `None` if dialog closed with "cancel".
	return
	savefile=base=os.path.splitext(savename.name)[0]
	# raise IOError('File ' + savename + '.dat already exists.')
	else:
	savefile = savename

	#Check if directory exists
	directory = os.path.dirname(savefile)
	if not os.path.exists(directory):
	os.makedirs(directory)

	shelfFile=shelve.open(savefile)
	for arg_name in kwargs:
	shelfFile[arg_name]=kwargs[arg_name]
	shelfFile.close()
	print('saved as: ' + savefile + '.dat')


	def GetTempFromKClConductivity(Conc, Cond):
	p = pkl.load(open('KCl_ConductivityValues.p', 'rb'))
	if Conc==1.0:
	Conc = np.uint(1)
	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): # or duration <= coeff['minEventLength'] * samplerate:
	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'])
	if 'blockLength' in matfile:
	blockLength=np.int32(matfile['blockLength'])
	else:
	blockLength=1048576

	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, 'blockLength':blockLength}
	return output

	def Reshape1DTo2D(inputarray, buffersize):
	npieces = int(len(inputarray)/buffersize)
	voltages = np.array([], dtype=np.float64)
	currents = np.array([], dtype=np.float64)

	for i in range(1, npieces+1):
	if i % 2 == 1:
	currents = np.append(currents, inputarray[(i-1)buffersize:ibuffersize-1], axis=0)
	#print('Length Currents: {}'.format(len(currents)))
	else:
	voltages = np.append(voltages, inputarray[(i-1)buffersize:ibuffersize-1], axis=0)
	#print('Length Voltages: {}'.format(len(voltages)))

	v1 = np.ones((len(voltages)), dtype=np.float64)
	i1 = np.ones((len(currents)), dtype=np.float64)
	v1[:]=voltages
	i1[:]=currents

	out = {'v1': v1, 'i1': i1}
	print('Currents:' + str(v1.shape))
	print('Voltages:' + str(i1.shape))
	return out

	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 = int(matfile['DisplayBuffer'])
	out = Reshape1DTo2D(data, buffersize)
	output = {'i1': out['i1'], 'v1': out['v1'], 'samplerate':float(samplerate), 'type': 'ChimeraNotRaw', 'filename': datafilename,'graphene': 0}
	else:
	output = ImportChimeraRaw(datafilename)
	return output

	def OpenFile(filename = '', ChimeraLowPass = 10e3,approxImpulseResponse=False,Split=False,verbose=False):
	if ChimeraLowPass==None:
	ChimeraLowPass=10e3
	if filename == '':
	datafilename = QtGui.QFileDialog.getOpenFileName()
	datafilename=datafilename[0]
	print(datafilename)
	else:
	datafilename=filename

	if verbose:
	print('Loading file... ' +filename)

	if datafilename[-3::] == 'dat':
	isdat = 1
	output = ImportAxopatchData(datafilename)
	elif datafilename[-3::] == 'log':
	isdat = 0
	output = ImportChimeraData(datafilename)
	if output['type'] is 'ChimeraRaw': # Lowpass and downsample
	if verbose:
	print('length: ' + str(len(output['i1raw'])))
	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


	if approxImpulseResponse:
	z, p, k = signal.tf2zpk(b, a)
	eps = 1e-9
	r = np.max(np.abs(p))
	approx_impulse_len = int(np.ceil(np.log(eps) / np.log(r)))
	Filt_sig=(signal.filtfilt(b, a, output['i1raw'], method='gust', irlen=approx_impulse_len))
	else:
	Filt_sig=(signal.filtfilt(b, a, output['i1raw'], method='gust'))

	ds_factor = np.ceil(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']))
	if verbose:
	print('Samplerate after filtering:' + str(output['samplerate']))
	print('new length: ' + str(len(output['i1'])))

	if Split:
	splitNr=len(output['i1raw'])/output['blockLength']
	assert(splitNr.is_integer())
	rawSplit=np.array_split(output['i1raw'], splitNr)
	Filt_sigSplit=[]
	for raw in rawSplit:
	if approxImpulseResponse:
	z, p, k = signal.tf2zpk(b, a)
	eps = 1e-9
	r = np.max(np.abs(p))
	approx_impulse_len = int(np.ceil(np.log(eps) / np.log(r)))
	signalSplit=signal.filtfilt(b, a, raw, method='gust', irlen=approx_impulse_len)
	else:
	signalSplit=signal.filtfilt(b, a, raw, method = 'gust')
	Filt_sigSplit.append(scipy.signal.resample(signalSplit, int(len(raw) / ds_factor)))
	output['i1Cut']=Filt_sigSplit

	if max(abs(output['i1']))>1e-6:
	if verbose:
	print('converting to SI units')
	output['i1']=1e-9*output['i1']
	output['v1']=1e-3*output['v1']
	elif datafilename[-3::] == 'abf':
	output = ImportABF(datafilename)
	st = os.stat(datafilename)
	if platform.system() == 'Darwin':
	if verbose:
	print('Platform is ' + platform.system())
	output['TimeFileWritten'] = st.st_birthtime
	output['TimeFileLastModified'] = st.st_mtime
	output['ExperimentDuration'] = st.st_mtime - st.st_birthtime
	elif platform.system() == 'Windows':
	if verbose:
	print('Platform is WinShit')
	output['TimeFileWritten'] = st.st_ctime
	output['TimeFileLastModified'] = st.st_mtime
	output['ExperimentDuration'] = st.st_mtime - st.st_ctime
	else:
	if verbose:
	print('Platform is ' + platform.system() +
	', might not get accurate results.')
	try:
	output['TimeFileWritten'] = st.st_ctime
	output['TimeFileLastModified'] = st.st_mtime
	output['ExperimentDuration'] = st.st_mtime - st.st_ctime
	except:
	raise Exception('Platform not detected')

	return output

	def MakeIVData(output, approach = 'mean', delay = 0.7, UseVoltageRange=0,verbose=False):
	(ChangePoints, sweepedChannel) = CutDataIntoVoltageSegments(output,verbose)
	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'])
	if delayinpoints>ChangePoints[0]:
	raise ValueError("Delay is longer than Changepoint")

	# 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['SE'] = 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)
	Item['SE'][0]=Item['STD'][0]/np.sqrt(len(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)
	Item['SE'][i] = Item['STD'][i] / np.sqrt(len(trace))
	if verbose:
	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)
	Item['SE'][-1:] = Item['STD'][-1:] / np.sqrt(len(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.std(trace)/np.sqrt(len(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]
	Item['SE'] = Item['SE'][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()
	Item['SE'] = Item['SE'][ind].flatten()

	## FIT THE MEAN CALCULATED VALUES ###
	#Yorkfit
	(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['YorkFit'] = {'x_fit': x_fit, 'y_fit': y_fit, 'Yintercept': a, 'Slope': b, 'Sigma_Yintercept': sigma_a,
	'Sigma_Slope': sigma_b, 'Parameter': b_save}
	# Yorkfit
	p = np.polyfit(Item['Voltage'].flatten(), Item['Mean'].flatten(), 1)
	x_fit2 = np.linspace(min(Item['Voltage']), max(Item['Voltage']), 1000)
	y_fit2 = scipy.polyval(p, x_fit)
	Item['PolyFit'] = {'x_fit': x_fit2, 'y_fit': y_fit2, 'Yintercept': p[1], 'Slope': p[0]}


	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,verbose=False):
	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'
	if verbose:
	print('Cutting into Segments...\n{} change points detected in channel {}...'.format(len(ChangePoints), sweepedChannel))
	return (ChangePoints, sweepedChannel)

	def CalculatePoreSize(G, L, s):
	#https://doi.org/10.1088/0957-4484/22/31/315101
	return (G+np.sqrt(G(G+16Ls/np.pi)))/(2s)

	def CalculateCapillarySize(G, D=0.3e-3, t=3.3e-3, s=10.5):
	#DOI: 10.1021/nn405029j
	#s=conductance (10.5 S/m at 1M KCl)
	#t=taper length (3.3 mm)
	#D=diameter nanocapillary at the shaft (0.3 mm)
	return G(4t/np.pi+0.5D)/(sD-0.5*G)

	def CalculateShrunkenCapillarySize(G, D=0.3e-3, t=3.3e-3, s=10.5, ts=500e-9, Ds=500e-9):
	#DOI: 10.1021/nn405029j
	#s=conductance (10.5 S/m at 1M KCl)
	#t=taper length (3.3 mm)
	#D=diameter nanocapillary at the shaft (0.3 mm)
	#ts=taper length of the shrunken part (543nm fit)
	#Ds=diameter nanocapillary at the shrunken part (514nm fit)
	return G * D * (8 * ts / np.pi + Ds)/((2 * s * D * Ds) - (G * (8 * t / np.pi + Ds)))

	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 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(input, delta, h):

	#initialization
	Nd = k0 = 0
	kd = []
	krmv = []
	k = 1
	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)

	while k < l:
	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:
	kd.append(k)
	if gp[k] > h:
	kmin = np.argmin(Sp[k0:k + 1])
	krmv.append(kmin + k0)
	else:
	kmin=np.argmin(Sn[k0:k+1])
	krmv.append(kmin + k0)

	#Re-initialize
	k0 = k
	m[k0] = input[k0]
	v[k0] = sp[k0] = Sp[k0] = gp[k0] = sn[k0] = Sn[k0] = gn[k0] = 0

	Nd = Nd + 1
	k += 1

	if Nd == 0:
	mc = np.mean(input) * np.ones(k)
	elif Nd == 1:
	mc = np.append(m[krmv[0]] * np.ones(krmv[0]), m[k-1] * np.ones(k - krmv[0]))
	else:
	mc = m[krmv[0]] * np.ones(krmv[0])
	for ii in range(1, Nd):
	mc=np.append(mc , m[krmv[ii]] * np.ones(krmv[ii] - krmv[ii - 1]))
	mc=np.append(mc , m[k-1] * np.ones(k - krmv[Nd-1]))
	return (mc, kd, krmv)

	def SetCusum2ARL0(deltax,sigmax,Arl0_2=1000,thresholdlevels=[1000,0.1,10]):
	h0min=thresholdlevels[0];
	h0max=thresholdlevels[1]; #detection threshold interval

	ARL0=2*ARL0_2; #for two-sided algo
	# Optimization on h "hopt"
	change=0;
	mu=deltax(change-deltax/2)/sigmax*2;
	sigma=abs(deltax)/sigmax;
	mun=mu/sigma;
	def f(h):
	(exp(-2mun(h/sigma+1.166))-1+2mun(h/sigma+1.166))/(2*mun^2)-ARL0

	if(f(h0min)*f(h0max)<0):
	print('test')
	#hopt=fzero(f,[h0min h0max])
	elif(f(h0min) <0 and f(h0max) <0):
	hopt=h0max
	elif(f(h0min) >0 and f(h0max) >0):
	hopt=h0min

	hbook=sigmax*hopt/deltax;
	return hbook, hopt


	def event_detection(RawSignal, CusumParameters, RoughEventLocations, cutoff):
	for i in range(len(RoughEventLocations)):
	CusumReferencedEndPoint = RoughEventLocations[i][1] + CusumParameters['BaselineLength'];
	CusumReferencedStartPoint = RoughEventLocations[i][0] - CusumParameters['BaselineLength'];

	if CusumReferencedEndPoint > len(RawSignal):
	CusumReferencedEndPoint = len(RawSignal)
	if CusumReferencedStartPoint < 0:
	CusumReferencedStartPoint = 0

	if RoughEventLocations[i][2] < CusumParameters['ImpulsionLimit'] * CusumParameters['SamplingFrequency']:
	trace = RawSignal[CusumReferencedStartPoint:CusumReferencedEndPoint]
	#[mc,krmv]=ImpulsionFitting(trace,BaselineLength, cutoff, SamplingFrequency);
	#EventType='Impulsion';
	#krmv=[BaselineLength, BaselineLength+RoughEventLocations(i,3)];
	#mc=[ones(1,BaselineLength+1)mean(trace(1:BaselineLength)), ones(1,krmv(2)-krmv(1)-1)min(trace), ones(1,BaselineLength-2)*mean(trace(krmv(2):end))];
	else:
	mc, kd, krmv = cusum(RawSignal[CusumReferencedStartPoint:CusumReferencedEndPoint],CusumParameters['delta'],CusumParameters['h'])
	EventType='Standard Event';

	Startpoint = CusumReferencedStartPoint + krmv[0]
	EndPoint = CusumReferencedStartPoint + krm[-1]

	BaselinePart = RawSignal[CusumReferencedStartPoint:CusumReferencedStartPoint]


	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)

	def GetRedoxError(cmax, cmin, ErrorPercent = 0.1, T = 293.15):
	cmaxErr = cmax*ErrorPercent
	cminErr = cmin*ErrorPercent
	return cst.R * T / cst.physical_constants['Faraday constant'][0] * np.sqrt((1/cmax*2 cmaxErr2 + 1/cmin2 * cminErr**2))

	##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))

	def GetRedox(cmax, cmin, T = 295.15):
	return cst.R * T / cst.physical_constants['Faraday constant'][0] * np.log(cmax/cmin)

Functions.pyNo OneTemporaryActions

File Metadata

Functions.pyView Options

Event Timeline

Functions.py
No OneTemporary
Actions

Functions.py
View Options