Homec4science
Diffusion Nanopore Tools (Development-of-Analysis-GUI) Browse

Nanopore-tools/Plotting/EventPlots.py
3b21f3fa5b7aDevelopment-of-Analysis…

Last Change
Raw File

EventPlots.py
View Options

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, showCurrentInstead=False, normalized=False, showCUSUM=True):
"""
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 ('Real' 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.
showCurrentInstead : 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 showCurrentInstead:
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 showCurrentInstead:
fig.savefig(directory + os.sep + 'PlotITau.pdf', transparent=True)
else:
fig.savefig(directory + os.sep + 'PlotGTau.pdf', transparent=True)
# 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
#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 showCurrentInstead:
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)
# Determine nice limits by hand:
def limits(tau, yVals, axScatter):
extra = 0.1 # 0.1 = 10%
tauRange = np.max(tau) - np.min(tau)
yClean = [y for y in yVals if str(y) != 'nan']
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))
#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
axScatter.set_xscale('log')
axHistx.hist(tau, bins=100, color=colors[i], visible=visBool[i])
axHisty.hist(yVals, bins=50, orientation='horizontal', color=colors[i], visible=visBool[i])
alltau.extend(tau)
allyVals.extend(yVals)
#set limits
if len(alltau)>0:
limits(alltau,allyVals,axScatter)
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, showCurrentInstead = False):
#categorize events in three types
#CUSUM fitted events
CUSUMEvents = eventClass.GetEventTypes('CUSUM')
#Non-fitted events
nonFittedEvents = eventClass.GetEventTypes('Rough')
#Impulse events
impulseEvents = eventClass.GetEventTypes('Impulse')
#Extract y and tau out of events
def extractytau(filteredevents):
if showCurrentInstead:
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 showCurrentInstead 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
False by default. If True, it will plot the CUSUM-fit overlayed in yellow.
"""
#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), Cond.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, showCurrentInstead = False):
#sort the voltages
# categorize events in three types
# CUSUM fitted events
voltageLimits = [0.01, 0.91]
voltagesList = eventClass.GetAllVoltages()
#Extract y and tau out of events
def extractytau(filteredevents):
if showCurrentInstead:
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 showCurrentInstead 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)
c4science · Help