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Nanopore-tools/Plotting/EventPlots.py
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EventPlots.py
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import numpy as np
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.filedialog import askopenfilenames,askdirectory
import shelve
import os
from pprint import pprint
import Functions
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, showCurrentInstead=False, normalized=False,showCUSUM=True):
#Clean up events
events = [event for event in events if event.voltage > 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,False,False]
labelsCheckBox=('CUSUM-fitted', 'Not fitted', 'impulse')
check = CheckButtons(rax,labelsCheckBox , visBool)
bax = plt.axes([0.77, 0.9, 0.1, 0.03])
bnext = Button(bax, 'Save figure')
bnext.on_clicked(SavePlot)
#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
axHistx.hist(tau, bins=50, 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 PlotEvent(event,ax=None, savefile=os.getcwd(), showCUSUM=False):
#Link event to axes to keep it around
ax._event=event
if ax is None:
#plt.figure(figsize=(10, 6))
fig, ax = plt.subplots(figsize=(10, 6))
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)
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):
filename=event.filename
loadedData = Functions.OpenFile(filename,10e3,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):
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=''):
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', 'Data*.dat')])
if inputData:
inputData=os.path.splitext(inputData[0])[0]
if inputData:
shelfFile=shelve.open(inputData)
translocationEvents=shelfFile['TranslocationEvents']
shelfFile.close()
PlotG_tau(translocationEvents.events,inputData)
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