77 lines
1.9 KiB
Python
77 lines
1.9 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 jar_functions import gain_curve_fit
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from jar_functions import avgNestedLists
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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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amf = [0.001, 0.002, 0.005, 0.01, 0.02, 0.05, 0.1, 0.2, 0.5, 1]
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all = []
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for ident in identifier:
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data = np.load('5Hz_gain_%s.npy' %ident)
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all.append(data)
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av = avgNestedLists(all)
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fig = plt.figure()
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ax = fig.add_subplot(111)
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ax.plot(amf, av, 'o', c = 'C0', label = 'gain')
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#plt.show()
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tau = []
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f_c = []
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fit = []
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fit_amf = []
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for ID in identifier:
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print(ID)
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amf = np.load('5Hz_amf_%s.npy' %ID)
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gain = np.load('5Hz_gain_%s.npy' %ID)
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sinv, sinc = curve_fit(gain_curve_fit, amf, gain)
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#print('tau:', sinv[0])
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tau.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_c.append(f_cutoff)
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fit.append(gain_curve_fit(amf, *sinv))
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fit_amf.append(amf)
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col = plt.cm.magma(np.linspace(0,0.8,len(fit)))
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for ff ,f in enumerate(fit):
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ax.plot(fit_amf[ff], fit[ff], c = col[ff])
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ax.axvline(x=f_c[ff], ymin=0, ymax=5, alpha=0.8, c = col[ff]) # colors_uniform[ff])
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ax.set_xscale('log')
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ax.set_yscale('log')
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ax.set_title('gain average all fish, deltaf: -5Hz')
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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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ax.set_ylim(0.0008, )
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#ax.plot(f_c, np.full(len(identifier), 0.0015), 'o', alpha = 0.5, c = 'darkorange', label = 'cutoff frequencies')
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ax.legend(loc = 'center left')
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plt.show()
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embed()
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