128 lines
3.4 KiB
Python
128 lines
3.4 KiB
Python
from scipy import signal
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import matplotlib.pyplot as plt
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import numpy as np
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import pylab
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from IPython import embed
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from scipy.optimize import curve_fit
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from jar_functions import sin_response
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from jar_functions import mean_noise_cut
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def take_second(elem): # function for taking the names out of files
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return elem[1]
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predict = []
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rootmeansquare = []
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threshold = []
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gain = []
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mgain = []
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phaseshift = []
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mphaseshift = []
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amfreq = []
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amf = [0.001, 0.002, 0.005, 0.01, 0.02, 0.05, 0.1, 0.2, 0.5, 1]
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currf = None
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idxlist = []
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identifier = '2020lepto06'
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data = sorted(np.load('%s files.npy' %identifier), key = take_second) # list with filenames in it
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for i, d in enumerate(data):
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dd = list(d)
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jar = np.load('%s.npy' %dd) # load data for every file name
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jm = jar - np.mean(jar) # low-pass filtering by subtracting mean
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print(dd)
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time = np.load('%s time.npy' %dd) # time file
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dt = time[1] - time[0]
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n = int(1/float(d[1])/dt)
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cutf = mean_noise_cut(jm, time, n = n)
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cutt = time
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plt.plot(time, jm-cutf, label='cut amfreq')
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plt.plot(time, jm, label='spec')
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#plt.legend()
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#plt.show()
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b, a = signal.butter(4, (float(d[1])) / (0.5/dt), 'high', analog=True) # high pass filtering so our fit gets a bit better
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y = signal.filtfilt(b, a, jm)
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#plt.plot(time, y)
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#plt.plot(time, jm)
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sinv, sinc = curve_fit(sin_response, time, y, [float(d[1]), 2, 0.5]) # fitting
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print('frequency, phaseshift, amplitude:', sinv)
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p = np.sqrt(sinv[1]**2)
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A = np.sqrt(sinv[2] ** 2)
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f = float(d[1])
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phaseshift.append(p)
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gain.append(A)
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amfreq.append(f)
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'''
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# root mean square
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RMS = np.sqrt(np.mean((cutf_arr - sin_response(cutt_arr, sinv[0], sinv[1], sinv[2]))**2))
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rootmeansquare.append(RMS)
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thresh = A / np.sqrt(2)
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threshold.append(thresh)
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'''
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plt.plot(time, sin_response(time, *sinv), label='fit: f=%f, p=%.2f, A=%.2f' % tuple(sinv))
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plt.legend()
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plt.show()
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# mean over same amfreqs for phase and gain
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if currf is None or currf == d[1]:
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currf = d[1]
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idxlist.append(i)
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else: # currf != f
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meanf = [] # lists to make mean of
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meanp = []
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for x in idxlist:
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meanf.append(gain[x])
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meanp.append(phaseshift[x])
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meanedf = np.mean(meanf)
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meanedp = np.mean(meanp)
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mgain.append(meanedf)
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mphaseshift.append(meanedp)
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currf = d[1] # set back for next loop
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idxlist = [i]
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meanf = []
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meanp = []
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for y in idxlist:
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meanf.append(gain[y])
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meanp.append(phaseshift[y])
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meanedf = np.mean(meanf)
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meanedp = np.mean(meanp)
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mgain.append(meanedf)
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mphaseshift.append(meanedp)
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# predict of gain
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for f in amf:
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G = np.max(mgain) / np.sqrt(1 + (2*((np.pi*f*3.14)**2)))
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predict.append(G)
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fig = plt.figure()
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ax = fig.add_subplot(1, 1, 1)
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ax.plot(amf, mgain, 'o')
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#ax.plot(amf, predict)
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ax.set_yscale('log')
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ax.set_xscale('log')
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ax.set_title('%s' % data[0][0])
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ax.set_ylabel('gain [Hz/(mV/cm)]')
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ax.set_xlabel('envelope_frequency [Hz]')
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#plt.savefig('%s gain' % data[0][0])
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pylab.show()
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plt.plot(threshold, label = 'threshold')
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plt.plot(rootmeansquare, label = 'RMS')
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plt.legend()
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plt.show()
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embed()
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# phase in degree
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# Q10 / conductivity
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# AM-frequency / envelope-frequency scale title?
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# einfach passendes n verwenden um AM-beats rauszufiltern? ...Menge Datenpunkte zu gering
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