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')