PSD_Analysis.py
0e9cb3c37cedsmalladditions

PSD_Analysis.py
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			import numpy as np
			import scipy
			import scipy.signal as sig
			import Functions as uf
			import pyqtgraph as pg
			import os
			import matplotlib.pyplot as plt
			import matplotlib
			from tkinter import Tk
			from tkinter.filedialog import askopenfilenames
			from matplotlib.font_manager import FontProperties
			import platform
			import csv
			fontP = FontProperties()
			fontP.set_size('small')
			props = dict(boxstyle='round', facecolor='wheat', alpha=0.5)
			matplotlib.rcParams['pdf.fonttype'] = 42
			matplotlib.rcParams['ps.fonttype'] = 42
			from matplotlib.ticker import EngFormatter

			fig = plt.figure(1, figsize=(10, 8))
			ax = fig.add_subplot(111)

			#file = '/Volumes/lben/lben-commun/2018 User Data/Michael/Axopatch/20181003/NIPm10_5nm_1MKCl_pH75_Noise_100kHz_0mV_1.dat'
			filenames = askopenfilenames() # show an "Open" dialog box and return the path to the selected file
			#colors=['k','r','b']
			for i,file in enumerate(filenames):
			dat = uf.OpenFile(file)
			filename = str(os.path.split(file)[1][:-4])
			os.chdir(os.path.dirname(file))
			directory = (str(os.path.split(file)[0]) + os.sep + 'PSD' + '_SavedImages')
			if not os.path.exists(directory):
			os.makedirs(directory)
			f, Pxx = sig.welch(dat['i1'], dat['samplerate'], nperseg=2**18)
			ax.plot(f, Pxx*1e24, label = filename)#, color = colors[i])

			ax.set_xlabel('Frequency (Hz)')
			ax.set_ylabel(r'PSD ($\frac{pA^2}{Hz}$)')
			ax.set_yscale('log')
			ax.set_xscale('log')
			ax.legend()

			fig.savefig(directory + os.sep + filename + '_PSD.pdf', transparent=True)
			fig.savefig(directory + os.sep + filename + '_PSD.png',dpi=300)

			#plt.show()