PlotFile.py
0e9cb3c37cedDevelopment-of-Analysis…

PlotFile.py
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			import numpy as np
			import scipy
			from scipy.optimize import curve_fit
			from scipy import constants as cst
			import Functions as uf
			import os
			import matplotlib.pyplot as plt
			from matplotlib.backends.backend_pdf import PdfPages
			import h5py
			import pandas as pd
			from matplotlib.ticker import EngFormatter
			from matplotlib import rc
			import LoadData
			#rc('text', usetex=True)
			formCurrent = EngFormatter(unit='A', places=3)
			formA = EngFormatter(unit='A', places=3)
			formB = EngFormatter(unit='s', places=3)

			file = '/Volumes/lben/lben-commun/2018 User Data/Michael/Axopatch/20180706/NorcadaCh1_100mMKCl_1mM_Cis_trans_ph74_640nm_100mW_IV_Later_Trace.dat'
			#file = '/Volumes/lben/lben-commun/2018 User Data/Michael/Axopatch/20180706/NorcadaCh1_100mMKCl_1mM_Cis_trans_ph74_640nm_150mmlens_50mW_IV_Later_Trace.dat'

			dat = LoadData.OpenFile(file)

			fig1 = plt.figure(1, figsize=(8, 4))
			ax_part1 = fig1.add_subplot(1, 1, 1)

			'''
			start2 = int(1.44e7)
			end2 = int(2.04e7)
			start1 = int(0)
			end1 = int(900000)
			i1 = np.array([])
			#i1 = np.append(i1, dat['i1'][start1:end1])
			#i1 = np.append(i1, dat['i1'][start2:end2]+(i1[-1:]-dat['i1'][start2]))
			'''
			t = np.arange(len(dat['i1']))/dat['samplerate']
			ax_part1.plot(t, dat['i1']*1e9)

			#ax_part1.plot(dat['i1'])

			plt.show()
			#fig1.savefig('/Users/migraf/SWITCHdrive/PhD/Generator Laser/Figures/Raw/640Temp' + '.eps')