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Nanopore-tools/Plotting/NanoporePlots.py
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NanoporePlots.py
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import numpy as np
import matplotlib.pyplot as plt
from matplotlib.ticker import EngFormatter
Amp = EngFormatter(unit='A', places=2)
Time = EngFormatter(unit='s', places=2)
Volt = EngFormatter(unit='V', places=2)
def PlotI_tau(current,tau, normalized=False):
# 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
plt.figure(1, figsize=(10, 8))
axScatter = plt.axes(rect_scatter)
axHistx = plt.axes(rect_histx, sharex=axScatter)
axHisty = plt.axes(rect_histy, sharey=axScatter)
# the scatter plot:
axScatter.scatter(tau, current, color='tomato', marker='o', s=30, linewidths=0.1, edgecolors='red') # added some stuff here to improve aesthetics
# now determine nice limits by hand:
extra = 0.1 # 0.1 = 10%
tauRange = np.max(tau) - np.min(tau)
currentClean = [x for x in current if str(x) != 'nan']
currentRange = np.max(currentClean) - np.min(currentClean)
#
# tauLim = (int(tauMax/binwidth) + 1) * binwidth
# currentLim = (int(currentMax/binwidth) + 1) * binwidth
axScatter.set_xlim((np.min(tau) - extra * tauRange, np.max(tau) + extra * tauRange))
axScatter.set_ylim((np.min(currentClean) - extra * currentRange, np.max(currentClean) + extra * currentRange))
# tauBins = np.arange(-tauLim, tauLim + binwidth, binwidth)
# currentBins = np.arange(-currentLim, currentLim + binwidth, binwidth)
axHistx.hist(tau, bins=50, color='tomato')
plt.setp(axHistx.get_xticklabels(), visible=False)
axHisty.hist(currentClean, bins=50, orientation='horizontal', color='tomato') #increased binning here to avoid overlapping bars in the histogram, added color option
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:
axScatter.set_ylabel('current drop (A)')
axScatter.yaxis.set_major_formatter(Amp)
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()
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