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SingleChannelAnalysis.py
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Fri, May 3, 17:03

SingleChannelAnalysis.py
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#Input Stuff
import MiscParameters as pm
import numpy as np
import Functions as f
import os
from scipy import signal
import matplotlib.pyplot as plt
from tkinter import Tk
from tkinter.filedialog import askopenfilenames
from tkinter.filedialog import askopenfilename
from matplotlib.font_manager import FontProperties
fontP = FontProperties()
fontP.set_size('small')
pm.init(LoadFiles = 0)
root = Tk()
root.withdraw()
os.system('''/usr/bin/osascript -e 'tell app "Finder" to set frontmost of process "python" to true' ''')
# Load File, empty string prompts a pop.up window for file selection. Else a file-path can be given
root.update()
filenames = askopenfilenames()
root.destroy()
for filename in filenames:
print(filename)
inp = f.OpenFile(filename, ChimeraLowPass=pm.ChimeraLowPass)
if pm.AxopatchLowPassForDetection:
# Low-Pass and downsample
Wn = round(2 * pm.AxopatchLowPassForDetection / inp['samplerate'], 4) # [0,1] nyquist frequency
b, a = signal.bessel(4, Wn, btype='low', analog=False)
inp['i1'] = signal.filtfilt(b, a, inp['i1'])
folder = pm.OutputFolder
file = os.sep + str(os.path.split(filename)[1][:-4])
print('Number of samples in file: {}'.format(len(inp['i1'])))
if not os.path.exists(folder):
os.makedirs(folder)
#Low Pass Event Detection
AnalysisResults = {}
chan = 'i1'
AnalysisResults[chan] = {}
AnalysisResults[chan]['RoughEventLocations'] = f.RecursiveLowPassFast(inp[chan], pm.coefficients[chan], inp['samplerate'])
print('Found {} events'.format(len(AnalysisResults[chan]['RoughEventLocations'])))
##Event time limit -> deletes events that are too long
ind = np.array([])
for (k,i) in enumerate(AnalysisResults[chan]['RoughEventLocations'][:]):
if i[1] - i[0] <= pm.minmalEventLengthPoints:
ind = np.append(ind, k)
AnalysisResults[chan]['RoughEventLocations'] = np.delete(
AnalysisResults[chan]['RoughEventLocations'][:], ind, axis=0)
if pm.UpwardsOn: # Upwards detection can be turned on or off
AnalysisResults[chan + '_Up'] = {}
AnalysisResults[chan + '_Up']['RoughEventLocations'] = f.RecursiveLowPassFastUp(inp[chan], pm.coefficients[chan], inp['samplerate'])
ind = np.array([])
for (k, i) in enumerate(AnalysisResults[chan + '_Up']['RoughEventLocations'][:]):
if i[1] - i[0] <= pm.minmalEventLengthPoints:
ind = np.append(ind, k)
AnalysisResults[chan + '_Up']['RoughEventLocations'] = np.delete(
AnalysisResults[chan + '_Up']['RoughEventLocations'][:], ind, axis=0)
print('Deleted All Events shorter than {:0.2e}s'.format(pm.minmalEventLengthPoints*inp['samplerate']))
############Plot the Lowpass Detections
if pm.PlotTheLowPassDetection:
fig = plt.figure(1, figsize=(16,5))
ax = fig.add_subplot(111)
ax.plot(np.arange(0, len(inp['i1']), 1)/inp['samplerate'], inp['i1'], 'b')
for i in AnalysisResults['i1']['RoughEventLocations']:
ax.plot(np.arange(np.uint64(i[0]), np.uint64(i[1]), 1)/inp['samplerate'], inp['i1'][np.uint64(i[0]):np.uint64(i[1])], 'r')
if pm.UpwardsOn:
for i in AnalysisResults['i1_Up']['RoughEventLocations']:
ax.plot(y=inp['i1'][np.uint64(i[0]):np.uint64(i[1])], x=np.arange(np.uint64(i[0]), np.uint64(i[1]), 1)/inp['samplerate'], pen='r')
ax.set
ax.set_ylabel("Current [A]")
ax.set_xlabel("Time [s]")
# Refine the Rough Event Detection done by the LP filter and Add event infos
if inp['graphene']:
AnalysisResults = f.RefinedEventDetection(inp, AnalysisResults, signals=['i1', 'i2'], limit=pm.MinimalFittingLimit*inp['samplerate'])
else:
AnalysisResults = f.RefinedEventDetection(inp, AnalysisResults, signals=['i1'], limit=pm.MinimalFittingLimit*inp['samplerate'])
## Print Lenghts
f.SaveToHDF5(inp, AnalysisResults, pm.coefficients, folder)
if pm.PlotTheLowPassDetection:
plt.show()

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