114 lines
3.2 KiB
Python
114 lines
3.2 KiB
Python
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 matplotlib.mlab import specgram
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import os
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from jar_functions import gain_curve_fit
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plt.rcParams.update({'font.size': 12})
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identifier = [#'2018lepto1',
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#'2018lepto4',
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#'2018lepto5',
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#'2018lepto76',
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'2018lepto98',
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#'2019lepto03',
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#'2019lepto24',
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#'2019lepto27',
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#'2019lepto30',
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#'2020lepto04',
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#'2020lepto06',
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'2020lepto16',
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'2020lepto19',
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'2020lepto20'
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]
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amfs = []
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gains = []
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taus = []
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f_cs = []
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predicts = []
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for ID in identifier:
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predict = []
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print(ID)
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amf = np.load('5Hz_amf_%s.npy' %ID)
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amfs.append(amf)
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gain = np.load('5Hz_gain_%s.npy' %ID)
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gains.append(gain)
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sinv, sinc = curve_fit(gain_curve_fit, amf, gain, [2, 3])
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#print('tau:', sinv[0])
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taus.append(sinv[0])
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f_cutoff = abs(1 / (2*np.pi*sinv[0]))
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print('f_cutoff:', f_cutoff)
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f_cs.append(f_cutoff)
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# predict of gain
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for f in amf:
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G = np.max(gain) / np.sqrt(1 + (2 * ((np.pi * f * sinv[0]) ** 2)))
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predict.append(G)
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predicts.append(predict)
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sort = sorted(zip(f_cs, identifier))
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print(sort)
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# order of plotting: 2018lepto98, 2020lepto16, 2020lepto19, 2020lepto19, 2020lepto20
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# order of f_c: 2020lepto20, 2020lepto16, 2018lepto98, 2020lepto19
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fig = plt.figure(figsize=(8.27,11.69))
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ax0 = fig.add_subplot(221)
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fig.text(0.05, 0.5, 'gain [Hz/(mV/cm)]', ha='center', va='center', rotation='vertical')
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fig.text(0.5, 0.04, 'envelope frequency [Hz]', ha='center', va='center')
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ax0.set_xlim(0.0007, 1.5)
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ax0.set_ylim(0.001, 10)
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ax0.plot(amfs[0], gains[0],'o' , label = 'gain')
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ax0.plot(amfs[0], predicts[0], label = 'fit')
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ax0.axvline(x=f_cs[0], ymin=0, ymax=5, ls='-', alpha=0.5, label = 'cutoff frequency')
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ax0.set_xscale('log')
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ax0.set_yscale('log')
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ax0.axes.xaxis.set_ticklabels([])
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print('max[0]:', np.max(gain[0]))
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ax1 = fig.add_subplot(222)
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ax1.set_xlim(0.0007, 1.5)
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ax1.set_ylim(0.001, 10)
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ax1.plot(amfs[1], gains[1],'o' , label = 'gain')
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ax1.plot(amfs[1], predicts[1], label = 'fit')
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ax1.axvline(x=f_cs[1], ymin=0, ymax=5, ls='-', alpha=0.5, label = 'cutoff frequency')
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ax1.set_xscale('log')
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ax1.set_yscale('log')
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ax1.axes.yaxis.set_ticklabels([])
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ax1.axes.xaxis.set_ticklabels([])
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print('max[1]:', np.max(gain[1]))
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ax2 = fig.add_subplot(223)
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ax2.set_xlim(0.0007, 1.5)
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ax2.set_ylim(0.001, 10)
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ax2.plot(amfs[0], gains[0],'o' , label = 'gain')
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ax2.plot(amfs[0], predicts[0], label = 'fit')
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ax2.axvline(x=f_cs[0], ymin=0, ymax=5, ls='-', alpha=0.5, label = 'cutoff frequency')
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ax2.set_xscale('log')
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ax2.set_yscale('log')
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print('max[2]:', np.max(gain[2]))
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ax3 = fig.add_subplot(224)
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ax3.set_xlim(0.0007, 1.5)
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ax3.set_ylim(0.001, 10)
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ax3.plot(amfs[2], gains[2],'o' , label = 'gain')
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ax3.plot(amfs[2], predicts[2], label = 'fit')
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ax3.axvline(x=f_cs[2], ymin=0, ymax=5, ls='-', alpha=0.5, label = 'cutoff frequency')
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ax3.set_xscale('log')
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ax3.set_yscale('log')
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ax3.axes.yaxis.set_ticklabels([])
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print('max[3]:', np.max(gain[3]))
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#plt.legend(loc = 'lower left')
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plt.show()
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#np.save('f_c', f_c)
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#np.save('tau', tau) |