132 lines
3.9 KiB
Python
132 lines
3.9 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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import matplotlib as mpl
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from matplotlib import cm
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#plt.rcParams.update({'font.size': 18})
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identifier_uniform = ['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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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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custom_amf = [0.001, 0.002, 0.005, 0.01, 0.02, 0.05, 0.1, 0.2, 0.5, 1]
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#colors = ['dimgray', 'dimgrey', 'gray', 'grey', 'darkgray', 'darkgrey', 'silver', 'lightgray', 'lightgrey', 'gainsboro', 'whitesmoke']
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colorss = ['g', 'b', 'r', 'y', 'c', 'm', 'k']
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all = []
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new_all = []
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for ident in identifier:
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data = np.load('gain_%s.npy' %ident)
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all.append(data)
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for ident in identifier_uniform:
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data = np.load('gain_%s.npy' % ident)
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new_all.append(data)
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av = avgNestedLists(all)
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new_av = avgNestedLists(new_all)
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fig = plt.figure(figsize=(8.27, 11.69/2))
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ax = fig.add_subplot(111)
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ax.plot(custom_amf, av, 'o', label = 'normal')
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#ax.plot(amf, new_av, 'o', label = 'uniform')
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sinv, sinc = curve_fit(gain_curve_fit, custom_amf, av, [2, 3])
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predict = []
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for f in custom_amf:
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G = np.max(av) / np.sqrt(1 + (2 * ((np.pi * f * sinv[0]) ** 2)))
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predict.append(G)
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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('amf_%s.npy' %ID)
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gain = np.load('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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ax.axvline(x=f_cutoff, ymin=0, ymax=5, color='C0', ls='-', alpha=0.5, label='cutoff frequency')
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# uniformed: 2018lepto1, 2018lepto5, 2018lepto76, 2018lepto98, 2020lepto06, 2019lepto24, 2020lepto06
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tau_uniform = []
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f_c_uniform = []
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fit_uniform = []
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fit_amf_uniform = []
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for ID in identifier_uniform:
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print(ID)
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amf = np.load('amf_%s.npy' %ID)
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gain = np.load('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_uniform.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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print('alpha:', sinv[1])
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f_c_uniform.append(f_cutoff)
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fit_uniform.append(gain_curve_fit(amf, *sinv))
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fit_amf_uniform.append(amf)
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colors = plt.cm.flag(np.linspace(0,1,len(fit_uniform)))
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#for ff ,f in enumerate(fit_uniform):
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# ax.plot(fit_amf_uniform[ff], fit_uniform[ff],color = colorss[ff])
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# ax.axvline(x=f_c_uniform[ff], ymin=0, ymax=5, ls = '-', alpha = 0.5, color= colorss[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_ylabel('gain [Hz/(mV/cm)]')
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ax.set_xlabel('envelope frequency [Hz]')
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ax.set_xlim(0.0007, 1.5)
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ax.set_ylim(0.001, 10)
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ax.plot(custom_amf, predict)
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#ax.plot(f_c, np.full(len(identifier), 0.0015), 'o', color = 'C0', alpha = 0.5, label = 'normal cutoff frequencies')
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#ax.plot(f_c_uniform, np.full(len(identifier_uniform), 0.002), 'o', alpha = 0.5, c = 'C0', label = 'uniform 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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