EventPlots.py
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Created: Sun, Apr 28, 03:32

EventPlots.py
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	import numpy as np
	import matplotlib
	matplotlib.use('TkAgg')
	import matplotlib.pyplot as plt
	import matplotlib.patches as patches
	from matplotlib.ticker import EngFormatter
	from matplotlib.widgets import CheckButtons, Button
	import argparse
	from tkinter import Tk
	import platform
	from tkinter.filedialog import askopenfilenames,askdirectory
	import shelve
	import os
	from pprint import pprint
	import Functions
	import LoadData
	import NanoporeClasses as NC

	from bokeh.models import Legend,LegendItem,Range1d
	from bokeh.palettes import Spectral4
	from bokeh.plotting import figure, output_file, show, output_notebook
	from bokeh.layouts import row, column, gridplot

	#needed for plotting in jupyter
	from bokeh.resources import INLINE
	import bokeh.io
	bokeh.io.output_notebook(INLINE)


	if (platform.system()=='Darwin'):
	root = Tk()
	root.withdraw()

	if (platform.system()=='Darwin'):
	os.system('''/usr/bin/osascript -e 'tell app "Finder" to set frontmost of process "python" to true' ''')

	Amp = EngFormatter(unit='A', places=2)
	Time = EngFormatter(unit='s', places=2)
	Volt = EngFormatter(unit='V', places=2)
	Cond = EngFormatter(unit='S', places=2)


	def PlotG_tau(events, savefile = None, showCurrent=False, normalized=False, showCUSUM=True, showLog=False):
	"""
	Function used to produce scatter plots and histograms of the events.
	The figure produced has 3 subplots:
	Up: Histogram with the number of events per event length
	Right: Histogram with the number of events per conductance drop [nS]
	Center: Scatter-plot of all the events wiht in on x-coordinates the event length [s]
	and in y-coordinates the conductance drop [nS].

	In the plots, the events were distributed into the 3 types of events:
	CUSUM-fitted in red ('CUSUM' type)
	Non-fitted in blue ('Rough' type)
	Impulse in green ('Impulse' type)

	Parameters
	----------
	events : AllEvents object
	All the events to be plotted.
	savefile : str, optional
	Full path to file where the plots will be saved.
	showCurrent : bool, optional
	False by default. If True, it will change the SI unit in the y-axis from siemens [S] to ampers [A].
	So instead of units in conductance drop, it will have current drop.
	normalized : bool, optional
	False by default. If True, it will change in the y-axis the unit from siemens [S] to normalized current drop without unit.
	showCUSUM : bool, optional
	True by default.

	"""

	# Clean up events
	events = [event for event in events if event.voltage is not 0]

	# categorize events in three types
	# CUSUM fitted events
	if any(hasattr(event, 'changeTimes') and len(event.changeTimes)>2 for event in events):
	CUSUMIndices, CUSUMEdevents = zip(*[(ind, event) for ind, event in enumerate(events) if
	hasattr(event, 'changeTimes') and len(event.changeTimes) > 2])
	else:
	CUSUMIndices = CUSUMEdevents = []

	# Non-fitted events
	if any(event.type =='Rough' for event in events):
	nonFittedIndices, nonFittedEvents = zip(*[(ind, event) for ind, event in enumerate(events) if
	event.type == 'Rough'])
	else:
	nonFittedIndices = nonFittedEvents = []

	# Impulse events
	if any(event.type == 'Impulse' for event in events):
	impulseIndices, impulseEvents = zip(*[(ind, event) for ind, event in enumerate(events) if
	event.type == 'Impulse'])
	else:
	impulseIndices = impulseEvents = []

	catEvents=(CUSUMEdevents, nonFittedEvents, impulseEvents)
	catIndices=(CUSUMIndices,nonFittedIndices,impulseIndices)

	# Extract y and tau out of events
	def extractytau(filteredevents):
	if showCurrent:
	yVals = [event.currentDrop for event in filteredevents]
	tau = [event.eventLength for event in filteredevents]
	else:
	yVals = [event.currentDrop / event.voltage for event in filteredevents if event.voltage > 0]
	tau = [event.eventLength for event in filteredevents if event.voltage > 0]
	return tau, yVals

	# Save figure
	def SavePlot(event):
	# Check if directory exists
	directory = os.path.dirname(savefile)
	if showCurrent:
	fig.savefig(directory + os.sep + 'PlotITau.pdf', transparent=True)
	else:
	fig.savefig(directory + os.sep + 'PlotGTau.pdf', transparent=True)

	def ShowLog():
	bshowlog.label.set_text = 'Show ' + axScatter.xscale
	axScatter.set_xscale('linear') if axScatter.xscale == 'log' else axScatter.set_xscale('log')

	# definitions for the axes
	left, width = 0.15, 0.55
	bottom, height = 0.1, 0.6
	left_h = left + width + 0.015
	bottom_h = bottom + height + 0.015

	rect_scatter = [left, bottom, width, height]
	rect_histx = [left, bottom_h, width, 0.2]
	rect_histy = [left_h, bottom, 0.2, height]

	# start with a rectangular Figure
	fig = plt.figure(1, figsize=(10, 8))

	# define axes and link histogram to scatterplot
	axScatter = plt.axes(rect_scatter)

	axHistx = plt.axes(rect_histx, sharex=axScatter)
	axHisty = plt.axes(rect_histy, sharey=axScatter)

	# Checkboxes to turn on or off events
	rax = plt.axes([0.75, 0.73, 0.14, 0.15])
	visBool=[True,True,True]
	labelsCheckBox=('CUSUM-fitted', 'Not fitted', 'impulse')
	check = CheckButtons(rax,labelsCheckBox , visBool)
	# Link button to axes to preserve function
	rax._check = check


	bax = plt.axes([0.77, 0.9, 0.1, 0.03])
	bnext = Button(bax, 'Save figure')
	bnext.on_clicked(SavePlot)
	# Link button to axes to preserve function
	bax._bnext = bnext

	baxl = plt.axes([0.77, 0.95, 0.1, 0.03])
	txt = 'log' if showLog else 'linear'
	bshowlog = Button(baxl, 'Show ' + txt)
	bshowlog.on_clicked(ShowLog)
	# Link button to axes to preserve function
	bax._bshowlog = bshowlog

	# Show labels
	def setlabels():
	plt.setp(axHistx.get_xticklabels(), visible=False)
	plt.setp(axHisty.get_yticklabels(), visible=False)

	axScatter.set_xlabel('Event length (s)')
	axScatter.xaxis.set_major_formatter(Time)
	if normalized:
	axScatter.set_ylabel('current drop (normalized)')
	else:
	if showCurrent:
	axScatter.set_ylabel('current drop (A)')
	axScatter.yaxis.set_major_formatter(Amp)
	else:
	axScatter.set_ylabel('Conductance drop (G)')
	axScatter.yaxis.set_major_formatter(Cond)

	# Set limits
	bins = 100
	extra = 0.1 # 0.1 = 10%
	allTau, allYVals = extractytau(events)
	yClean = [y for y in allYVals if str(y) != 'nan']
	taurangeHist = np.linspace(min(allTau), max(allTau), num=bins)
	yValsrangeHist = np.linspace(min(yClean), max(yClean), num=bins)

	# define colors of the 3 classes
	colors = ['tomato', 'lightgreen','skyblue']
	linecolors = ['red','green', 'blue']

	# the scatter plot:
	scatters = [None]*3

	def PlotEvents(visBool):
	# clear axes
	axScatter.clear()
	axHistx.clear()
	axHisty.clear()

	# lists for setting the limits
	alltau = []
	allyVals = []
	for i in range(len(catEvents)):
	# If checkbox is True, plot events
	if visBool[i]:

	# Extract Tau and Y
	events = catEvents[i]
	tau, yVals = extractytau(events)
	scatters[i] = axScatter.scatter(tau, yVals, color=colors[i], marker='o', s=30,
	linewidths=0.1,edgecolors=linecolors[i], picker=5,visible=visBool[i]) # added some stuff here to improve aesthetics
	if showLog:
	axScatter.set_xscale('log')

	axHistx.hist(tau, bins=taurangeHist, color=colors[i], visible=visBool[i])
	axHisty.hist(yVals, bins=yValsrangeHist, orientation='horizontal', color=colors[i], visible=visBool[i])

	# set limits
	if len(alltau) > 0:
	tauRange = np.max(tau) - np.min(tau)
	yRange = np.max(yClean) - np.min(yClean)
	axScatter.set_xlim((np.min(tau) - extra * tauRange, np.max(tau) + extra * tauRange))
	axScatter.set_ylim((np.min(yClean) - extra * yRange, np.max(yClean) + extra * yRange))

	setlabels()
	plt.title('{} number of events'.format(len(alltau)))

	PlotEvents(visBool)

	# If click on checkbox, switch Boolean and replot events
	def func(label):
	for i in range(len(labelsCheckBox)):
	if label == labelsCheckBox[i]:
	visBool[i] = not visBool[i]
	PlotEvents(visBool)
	plt.draw()

	# WHen clicking on event
	def onpick(event):
	for i in range(len(catEvents)):
	if event.artist == scatters[i]:

	N = len(event.ind)
	if not N: return True
	figi = plt.figure(figsize=(10, 6))
	for subplotnum, dataind in enumerate(event.ind):
	ax = figi.add_subplot(N, 1, subplotnum + 1)
	PlotEvent(catEvents[i][dataind], ax, savefile, showCUSUM)
	figi.show()
	return True

	fig.canvas.mpl_connect('pick_event', onpick)
	check.on_clicked(func)

	plt.show()


	def PlotGTau(eventClass, xLim=None, yLim=None, showCurrent=False, showRough=False):

	# categorize events in three types:
	# CUSUM fitted events
	CUSUMEvents = eventClass.GetEventTypes('CUSUM')

	# Non-fitted events
	nonFittedEvents = eventClass.GetEventTypes('Rough') if showRough else []

	# Impulse events
	impulseEvents = eventClass.GetEventTypes('Impulse')

	# Extract y and tau out of events
	def extractytau(filteredevents):
	if showCurrent:
	yVals = [event.currentDrop for event in filteredevents]
	tau = [event.eventLength for event in filteredevents]
	else:
	yVals = [event.currentDrop / event.voltage for event in filteredevents if event.voltage > 0]
	tau = [event.eventLength for event in filteredevents if event.voltage > 0]
	return tau,yVals

	# define of variables
	TOOLS = "box_zoom,pan,wheel_zoom,reset"
	colors = ['tomato', 'lightgreen','skyblue']
	linecolors = ['red','green', 'blue']
	backgroundColor = "#fafafa"
	bins = 50

	output_notebook()
	p = figure(plot_width=500, plot_height=500, min_border=10, min_border_left=50, tools=TOOLS,
	x_axis_location=None, y_axis_location=None,title="Linked Histograms")

	p.background_fill_color = "#fafafa"

	# select min max
	events = [event for event in eventClass.events if showCurrent or event.voltage is not 0]

	allTau, allYVals = extractytau(events)

	# Set limits
	if xLim is None:
	taurange = np.linspace(min(allTau), max(allTau), num=bins)
	else:
	assert(len(xLim) is 2)
	p.x_range=Range1d(xLim[0], xLim[1])
	taurange = np.linspace(xLim[0], xLim[1], num=bins)

	if yLim is None:
	yValsrange = np.linspace(min(allYVals), max(allYVals), num=bins)
	else:
	assert(len(yLim) is 2)
	p.y_range=Range1d(yLim[0], yLim[1])
	yValsrange = np.linspace(yLim[0], yLim[1], num=bins)

	# initialize values
	zerostau = np.zeros(len(taurange) - 1)
	previoustau = np.zeros(len(taurange) - 1)
	zerosy = np.zeros(len(yValsrange)-1)
	previousy = np.zeros(len(yValsrange) - 1)


	# Define histogram bottom
	ph = figure(plot_width=p.plot_width, plot_height=100, x_range=p.x_range,
	y_range=(0, 1), min_border=10, min_border_left=50, y_axis_location="right")

	ph.background_fill_color = backgroundColor
	ph.xgrid.grid_line_color = None
	ph.yaxis.major_label_orientation = np.pi / 4

	# Define the second histogram
	pv = figure(plot_width=300, plot_height=p.plot_height, x_range=(0, 1),
	y_range=p.y_range, min_border=10, y_axis_location="right")

	pv.ygrid.grid_line_color = None
	pv.xaxis.major_label_orientation = np.pi / 4
	pv.background_fill_color = backgroundColor

	legend_it = []

	for selectEvents, name, color, linecolor in zip([CUSUMEvents, nonFittedEvents, impulseEvents],
	["CUSUM", "Non-fitted", "Impulse"], colors, linecolors):
	tau, yVals = extractytau(selectEvents)

	c = p.scatter(tau, yVals, size=3, line_width=2, color=color, alpha=0.8)

	hhist, hedges = np.histogram(tau, bins=taurange)

	hh = ph.quad(bottom=zerostau +previoustau, left=hedges[:-1], right=hedges[1:], top=hhist+previoustau,
	fill_color=color, line_color = linecolor)

	previoustau +=hhist

	vhist, vedges = np.histogram(yVals, bins=yValsrange)
	vh = pv.quad(left=zerosy + previousy, bottom=vedges[:-1], top=vedges[1:], right=vhist+previousy,
	color=color, line_color=linecolor)
	previousy +=vhist

	# Sharing legend is broke
	# legend_it.append((name, [c, hh, vh]))
	legend_it.append(LegendItem(label=name, renderers=[vh]))

	# p.legend.location = "top_right"
	# legend = Legend(items=legend_it, location=(0, -30))
	legend = Legend(items=legend_it)
	pv.add_layout(legend, 'right')

	pv.legend.click_policy = "hide"

	ph.y_range.end = max(previoustau)*1.1
	pv.x_range.end = max(previousy) * 1.1

	fig = gridplot([[p, pv],[ph, None]], toolbar_location="left")

	# show the results
	show(fig)


	# for selectEvents, name, color in zip([CUSUMEvents, nonFittedEvents, impulseEvents], ["CUSUM", "Non-fitted", "Impulse"], colors):
	# tau, yVals = extractytau(selectEvents)
	#
	# c = p.scatter(tau, yVals, line_width=2, color=color, alpha=0.8)
	#
	# # create the horizontal histogram
	# hhist, hedges = np.histogram(tau, bins=20)
	# hzeros = np.zeros(len(hedges) - 1)
	# hmax = max(hhist) * 1.1
	#
	# ph.quad(bottom=0, left=hedges[:-1], right=hedges[1:], top=hhist, color="white", line_color="#3A5785")
	# hh1 = ph.quad(bottom=0, left=hedges[:-1], right=hedges[1:], top=hzeros, alpha=0.5,color=color, line_color=None)
	# hh2 = ph.quad(bottom=0, left=hedges[:-1], right=hedges[1:], top=hzeros, alpha=0.1, color=color, line_color=None)
	#
	# legend_it.append((name, [c]))

	#p.legend.location = "top_right"
	# legend = Legend(items=legend_it, location=(0, -30))
	# p.add_layout(legend, 'right')
	#
	# p.legend.click_policy = "hide"
	#
	# def update(attr, old, new):
	# inds = new
	# if len(inds) == 0 or len(inds) == len(x):
	# hhist1, hhist2 = hzeros, hzeros
	# #vhist1, vhist2 = vzeros, vzeros
	# else:
	# neg_inds = np.ones_like(x, dtype=np.bool)
	# neg_inds[inds] = False
	# hhist1, _ = np.histogram(x[inds], bins=hedges)
	# #vhist1, _ = np.histogram(y[inds], bins=vedges)
	# hhist2, _ = np.histogram(x[neg_inds], bins=hedges)
	# #vhist2, _ = np.histogram(y[neg_inds], bins=vedges)
	#
	# hh1.data_source.data["top"] = hhist1
	# hh2.data_source.data["top"] = -hhist2
	# #vh1.data_source.data["right"] = vhist1
	# #vh2.data_source.data["right"] = -vhist2
	#
	# c.data_source.selected.on_change('indices', update)
	# show(p)


	def PlotEvent(event, ax=None, savefile=os.getcwd(), showCUSUM=True, showCurrent=False):
	"""
	Function used to plot a single event passed in argument. The event will be represented
	in a blue trace and the baseline in a red trace.

	Parameters
	----------
	event : TranslocationEvent object
	Event to be plotted.
	ax :axes.Axes object or array of Axes object
	Axes of the plot.
	savefile : str, optional
	By default, the current working directory. Full path to the directory to save the plot.
	showCUSUM : bool, optional
	True by default. If True, it will plot the CUSUM-fit overlayed in yellow.
	showCurrent : bool, optional
	False by default. If True, it will plot the current drop instead of conductance drop.

	"""

	#Link event to axes to keep it around

	if ax is None:
	#plt.figure(figsize=(10, 6))
	fig, ax = plt.subplots(figsize=(10, 6))
	ax._event=event

	def SavePlot(eventMouse):
	# Check if directory exists
	directory = os.path.dirname(savefile)
	savename=directory + os.sep + 'event.pdf'
	i=1

	while os.path.exists(directory + os.sep + 'event_{}.pdf'.format(i)):
	i += 1
	savename = directory + os.sep + 'event_{}.pdf'.format(i)

	eventMouse.inaxes.figure.savefig(savename, transparent=True)

	def ShowFullTrace(eventMouse):
	event = eventMouse.inaxes.figure.axes[0]._event
	ShowEventInTrace(event)

	if showCUSUM and hasattr(event, 'changeTimes') and len(event.changeTimes) > 2:
	eventLength = event.eventLengthCUSUM
	currentDrop = event.currentDropCUSUM
	else:
	showCUSUM = False
	eventLength = event.eventLength
	currentDrop = event.currentDrop


	fn=filename_w_ext = os.path.basename(event.filename)
	if showCurrent:
	plotTitle = fn + '\n' + 'Event length: {}\nCurrent drop: {} with voltage {}'.format(
	Time.format_data(eventLength), Amp.format_data(currentDrop),
	Volt.format_data(event.voltage))
	else:
	plotTitle = fn + '\n' + 'Event length: {}\nConductance drop: {} with voltage {}'.\
	format(Time.format_data(eventLength), Cond.format_data(currentDrop/event.voltage),Volt.format_data(event.voltage))

	ax.set_xlabel('time (s)')
	ax.set_ylabel('current (A)')
	ax.xaxis.set_major_formatter(Time)
	ax.yaxis.set_major_formatter(Amp)

	if plotTitle:
	plt.title(plotTitle)


	# Add buttons

	# Save button
	bax = plt.axes([0.77, 0.95, 0.15, 0.03])
	bsave = Button(bax, 'Save figure')
	bsave.on_clicked(SavePlot)
	# Link button to axes to preserve function
	ax._bsave = bsave

	# Show original trace button
	bax2 = plt.axes([0.77, 0.9, 0.15, 0.03])
	bfull = Button(bax2, 'Show original Trace')
	# Link button to axes to preserve function
	ax._bfull = bfull
	bfull.on_clicked(ShowFullTrace)

	# Plotting
	timeVals1 = np.linspace(0, len(event.before) / event.samplerate, num=len(event.before))
	timeVals2 = np.linspace(0 + max(timeVals1), len(event.eventTrace) / event.samplerate + max(timeVals1),
	num=len(event.eventTrace))
	timeVals3 = np.linspace(0 + max(timeVals2), len(event.after) / event.samplerate + max(timeVals2), num=len(event.after))




	ax.plot(np.append(timeVals1,timeVals2[0]), np.append(event.before,event.eventTrace[0]), color='tomato')
	ax.plot(timeVals2, event.eventTrace, color='mediumslateblue')
	ax.plot(np.append(timeVals2[-1],timeVals3), np.append(event.eventTrace[-1],event.after), color='tomato')

	if showCUSUM:
	timeVals = np.linspace(0, len(event.segmentedSignal) / event.samplerate, num=len(event.segmentedSignal))

	if hasattr(event,'mcbefore') and hasattr(event,'mcafter') and hasattr(event,'mctrace'):
	ax.plot(timeVals1, event.mcbefore, '--', color='tomato')
	x = np.append(np.append(timeVals1[-1], timeVals2), timeVals3[0])
	y = np.append(np.append(event.mcbefore[-1], event.mctrace), event.mcafter[0])
	ax.plot(x, y, color='yellow')
	ax.plot(timeVals3, event.mcafter, '--', color='tomato')
	else:
	ax.plot(timeVals, event.segmentedSignal, color='yellow') #,timeVals3[0],event.mcafter[0]
	else:
	beforeBaseline=np.full(len(event.before), event.baseline)
	ax.plot(timeVals1,beforeBaseline, '--', color='tomato')
	afterBaseline = np.full(len(event.after), event.baseline)
	ax.plot(timeVals3,afterBaseline, '--', color='tomato')

	meanTrace = np.full(len(event.eventTrace), event.baseline-event.currentDrop)
	ax.plot(timeVals2,meanTrace, '--', color='mediumslateblue')

	if 'fig' in locals():
	plt.show()

	def ShowEventInTrace(event):
	"""
	Function used to show the event with it's location framed in red in the original full signal trace in blue.

	Parameters
	----------
	event : TranslocationEvent object
	Event to be plotted.

	"""

	filename=event.filename
	loadedData = LoadData.OpenFile(filename,1e3,True) #, ChimeraLowPass, True, CutTraces)

	fig, ax = plt.subplots(figsize=(10, 6))

	FullTrace = loadedData['i1']

	times=np.linspace(0, len(FullTrace) / event.samplerate, num=len(FullTrace))
	ax.plot(times,FullTrace,zorder=1)

	ax.set_xlabel('time (s)')
	ax.set_ylabel('current (A)')
	ax.xaxis.set_major_formatter(Time)
	ax.yaxis.set_major_formatter(Amp)


	# Create a Rectangle patch
	if hasattr(event,'changeTimes') and len(event.changeTimes)>2:
	start_i = (event.beginEventCUSUM - len(event.before))/event.samplerate
	end_i = (event.endEventCUSUM + len(event.after))/event.samplerate
	else:
	start_i = (event.beginEvent - len(event.before))/event.samplerate
	end_i = (event.endEvent + len(event.after))/event.samplerate
	minE=np.min(np.append(np.append(event.eventTrace,event.before),event.after))
	maxE=np.max(np.append(np.append(event.eventTrace,event.before),event.after))
	rect = patches.Rectangle((start_i, minE-0.1(maxE-minE)), end_i-start_i, maxE+0.2(maxE-minE)-minE, linestyle='--', linewidth=1, edgecolor='r', facecolor='none',zorder=10)

	# Add the patch to the Axes
	ax.add_patch(rect)

	plt.title(os.path.basename(filename))

	plt.show()


	def PlotCurrentTrace(currentTrace, samplerate):
	"""
	Function used in TranslocationEvent class methods in the NanoporeClasses module to plot events.
	It will plot the TranslocationEvent's currrentTrace passed in argument.

	Parameters
	----------
	currentTrace : list of float
	Data points in current to be plotted.
	samplerate : float
	Sampling frequency of the data acquisition.

	"""

	timeVals = np.linspace(0, len(currentTrace) / samplerate, num=len(currentTrace))
	fig,ax=plt.subplots(figsize=(10, 6))

	ax.plot(timeVals, currentTrace)
	ax.set_xlabel('time (s)')
	ax.set_ylabel('current (A)')
	ax.xaxis.set_major_formatter(Time)
	ax.yaxis.set_major_formatter(Amp)

	plt.show()


	def PlotCurrentTraceBaseline(before, currentTrace, after, samplerate, plotTitle=''):
	"""
	Function used in TranslocationEvent class methods in the NanoporeClasses module to plot events.
	It will plot TranslocationEvent's currentTrace and the surrounding baseline (after and before)
	passed in argument.

	Parameters
	----------
	before : list of float
	Data points in current of the baseline trace before the event.
	currentTrace : list of float
	Data points in current of the event trace.
	after : list of float
	Data points in current of the baseline trace after the event.
	samplerate : float
	Sampling frequency of the data aquisition.
	plotTitle : str, optional
	Plot title.

	"""

	timeVals1 = np.linspace(0, len(before) / samplerate, num=len(before))
	timeVals2 = np.linspace(0 + max(timeVals1), len(currentTrace) / samplerate + max(timeVals1), num=len(currentTrace))
	timeVals3 = np.linspace(0 + max(timeVals2), len(after) / samplerate + max(timeVals2), num=len(after))

	#plt.figure(figsize=(10, 6))
	fig,ax=plt.subplots(figsize=(10, 6))

	ax.plot(timeVals1, before, color='red')
	ax.plot(timeVals2, currentTrace)
	ax.plot(timeVals3, after, color='red')
	ax.set_xlabel('time (s)')
	ax.set_ylabel('current (A)')
	ax.xaxis.set_major_formatter(Time)
	ax.yaxis.set_major_formatter(Amp)

	if plotTitle:
	plt.title(plotTitle)

	plt.show()

	if __name__=='__main__':
	parser = argparse.ArgumentParser()
	parser.add_argument('-i', '--input', help='Input file')

	args = parser.parse_args()
	inputData = args.input
	if inputData == None:
	inputData = askopenfilenames(filetypes=[('data files', '.dat')]) #for Mac systems, replace 'Data.dat' with >> '*.dat'
	#if inputData:
	# inputData=os.path.splitext(inputData[0])[0]
	if (platform.system() == 'Darwin'):
	root.update()

	translocationEvents = NC.AllEvents()
	if inputData:
	for filename in inputData:
	shelfFile = shelve.open(os.path.splitext(filename)[0])
	translocationEventstemp = shelfFile['TranslocationEvents']
	shelfFile.close()
	translocationEvents.AddEvent(translocationEventstemp)
	PlotG_tau(translocationEvents.events, inputData)


	def PlotGTauVoltage (eventClass, xLim=None, yLim=None, showCurrent=False, showRough=False):
	# sort the voltages
	# categorize events in three types
	# CUSUM fitted events
	voltageLimits = [0.01, 0.91]
	voltagesList = eventClass.GetAllVoltages()
	voltagesList.sort()

	# Extract y and tau out of events
	def extractytau(filteredevents):
	filteredevents = [event for event in filteredevents if showRough or not event.type == 'Rough']
	if showCurrent:
	yVals = [event.currentDrop for event in filteredevents]
	tau = [event.eventLength for event in filteredevents]
	else:
	yVals = [event.currentDrop / event.voltage for event in filteredevents if event.voltage > 0]
	tau = [event.eventLength for event in filteredevents if event.voltage > 0]
	return tau,yVals

	#define of variables
	TOOLS = "box_zoom,pan,wheel_zoom,reset"
	colors = ['tomato', 'lightgreen','skyblue', 'magenta','black']
	linecolors = ['red', 'green', 'blue', 'magenta', 'black']

	assert(len(voltagesList) <= len(colors))
	backgroundColor = "#fafafa"
	bins = 50

	output_notebook()
	p = figure(plot_width=500, plot_height=500, min_border=10, min_border_left=50, tools=TOOLS,
	x_axis_location=None, y_axis_location=None,title="Linked Histograms")

	p.background_fill_color = "#fafafa"

	# select min max
	events = [event for event in eventClass.events if showCurrent or event.voltage is not 0]

	allTau, allYVals = extractytau(events)

	# Set limits
	if xLim is None:
	taurange = np.linspace(min(allTau), max(allTau), num=bins)
	else:
	assert(len(xLim) is 2)
	p.x_range=Range1d(xLim[0], xLim[1])
	taurange = np.linspace(xLim[0], xLim[1], num=bins)

	if yLim is None:
	yValsrange = np.linspace(min(allYVals), max(allYVals), num=bins)
	else:
	assert(len(yLim) is 2)
	p.y_range=Range1d(yLim[0], yLim[1])
	yValsrange = np.linspace(yLim[0], yLim[1], num=bins)

	# initialize values
	zerostau = np.zeros(len(taurange) - 1)
	previoustau = np.zeros(len(taurange) - 1)
	zerosy = np.zeros(len(yValsrange)-1)
	previousy = np.zeros(len(yValsrange) - 1)


	# Define histogram bottom
	ph = figure(plot_width=p.plot_width, plot_height=100, x_range=p.x_range,
	y_range=(0, 1), min_border=10, min_border_left=50, y_axis_location="right")

	ph.background_fill_color = backgroundColor
	ph.xgrid.grid_line_color = None
	ph.yaxis.major_label_orientation = np.pi / 4

	# Define the second histogram
	pv = figure(plot_width=300, plot_height=p.plot_height, x_range=(0, 1),
	y_range=p.y_range, min_border=10, y_axis_location="right")

	pv.ygrid.grid_line_color = None
	pv.xaxis.major_label_orientation = np.pi / 4
	pv.background_fill_color = backgroundColor

	legend_it = []

	i = 0
	# for i in range(len(voltagesList)):
	for voltage in voltagesList:
	color = colors[i]
	linecolor = linecolors[i]
	i += 1
	# for voltage, color, linecolor in zip(voltagesList, colors, linecolors):
	selectEvents = eventClass.GetEventsforVoltages(voltage)
	tau, yVals = extractytau(selectEvents)

	c = p.scatter(tau, yVals, size=3, line_width=2, color=color, alpha=0.8)

	hhist, hedges = np.histogram(tau, bins=taurange)

	hh = ph.quad(bottom=zerostau +previoustau, left=hedges[:-1], right=hedges[1:], top=hhist+previoustau,
	fill_color=color, line_color=linecolor)

	previoustau +=hhist

	vhist, vedges = np.histogram(yVals, bins=yValsrange)
	vh = pv.quad(left=zerosy + previousy, bottom=vedges[:-1], top=vedges[1:], right=vhist+previousy,
	color=color, line_color=linecolor)
	previousy +=vhist

	# Sharing legend is broke
	#legend_it.append((name, [c, hh, vh]))
	name = str(Volt.format_data(voltage))
	legend_it.append(LegendItem(label=name, renderers=[vh]))

	#p.legend.location = "top_right"
	#legend = Legend(items=legend_it, location=(0, -30))
	legend = Legend(items=legend_it)
	pv.add_layout(legend, 'right')

	pv.legend.click_policy = "hide"

	ph.y_range.end = max(previoustau)*1.1
	pv.x_range.end = max(previousy) * 1.1

	fig = gridplot([[p, pv],[ph, None]], toolbar_location="left")

	# show the results
	show(fig)

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