84 lines
1.9 KiB
Python
84 lines
1.9 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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def take_second(elem):
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return elem[1]
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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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data = sorted(np.load('files.npy'), key = take_second)
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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)
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print(dd)
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time = np.load('time: %s.npy' %dd)
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b, a = signal.butter(4, (float(d[1]) / 2) / 10000, 'high', analog=True)
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y = signal.filtfilt(b, a, jar - np.mean(jar))
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#plt.plot(time, y)
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#plt.plot(time, jar)
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sinv, sinc = curve_fit(sin_response, time, y, [float(d[1]), 2, 0.5])
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print('frequency, phaseshift, amplitude:', sinv)
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phaseshift.append(np.sqrt(sinv[1]**2))
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gain.append(np.sqrt(sinv[2]**2))
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amfreq.append(d[1])
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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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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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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.set_yscale('log')
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pylab.show()
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#betrag von A
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