from scipy import signal
import matplotlib.pyplot as plt
import numpy as np
import pylab
from IPython import embed
from scipy.optimize import curve_fit
from jar_functions import gain_curve_fit

identifier = ['2018lepto1',
              '2018lepto4',
              '2018lepto5',
              '2018lepto76',
              '2018lepto98',
              '2019lepto03',
              '2019lepto24',
              '2019lepto27',
              '2019lepto30',
              '2020lepto04',
              '2020lepto06',
              '2020lepto16',
              '2020lepto19',
              '2020lepto20'
              ]
tau = []
f_c = []
for ID in identifier:
    print(ID)
    amf = np.load('amf_%s.npy' %ID)
    gain = np.load('gain_%s.npy' %ID)

    sinv, sinc = curve_fit(gain_curve_fit, amf, gain)
    print('tau:', sinv[0])
    tau.append(sinv[0])
    f_cutoff = 1 / (2*np.pi*sinv[0])
    print('f_cutoff:', f_cutoff)
    f_c.append(f_cutoff)

    fig = plt.figure()
    ax = fig.add_subplot(111)
    ax.plot(amf, gain, 'o')
    amff = np.logspace(-3, 0, 200)
    ax.plot(amff, gain_curve_fit(amff, *sinv))
    ax.set_yscale('log')
    ax.set_xscale('log')
    plt.show()

#welche zeitkonstante ist das? was ist mit der zweiten? --> eher zweite zeitkonstante obwohl werte so klein?