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
from jar_functions import avgNestedLists


identifier = ['2018lepto1',
              '2018lepto4',
              '2018lepto5',
              '2018lepto76',
              '2018lepto98',
              '2019lepto03',
              '2019lepto24',
              '2019lepto27',
              '2019lepto30',
              '2020lepto04',
              '2020lepto06',
              '2020lepto16',
              '2020lepto19',
              '2020lepto20'
              ]

amf = [0.001, 0.002, 0.005, 0.01, 0.02, 0.05, 0.1, 0.2, 0.5, 1]

custom_f = np.logspace(-2, -1, 10)
custom_alpha = np.logspace(1.5, 1, 10)
c_gain = []
custom_tau = abs(1 / (2 * np.pi * custom_f))
for t, a in zip(custom_tau, custom_alpha):
    custom_gain = []
    for am in amf:
        custom_g = gain_curve_fit(am, t, a)
        custom_gain.append(custom_g)
    c_gain.append(custom_gain)
fig = plt.figure()
ax = fig.add_subplot(111)
ax.set_xscale('log')
ax.set_yscale('log')
for cc, c in enumerate(c_gain): 
    ax.plot(amf, c)
    ax.axvline(x=custom_f[cc], ymin=0, ymax=5, alpha=0.8)  # colors_uniform[ff])

plt.show()

mean = avgNestedLists(c_gain)
fig = plt.figure()
ax = fig.add_subplot(111)
ax.set_xscale('log')
ax.set_yscale('log')
ax.plot(amf, mean)
plt.show()

all = []

for ident in identifier:
    data = np.load('gain_%s.npy' %ident)
    all.append(data)

av = avgNestedLists(all)
embed()

fig = plt.figure(figsize=(8.27,11.69/2))
ax = fig.add_subplot(111)
ax.plot(amf, av, 'o', c = 'C0', label = 'gain')

#plt.show()

tau = []
f_c = []
fit = []
fit_amf = []
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, [2, 3])
    #print('tau:', sinv[0])
    tau.append(sinv[0])
    f_cutoff = abs(1 / (2*np.pi*sinv[0]))
    print('f_cutoff:', f_cutoff)
    f_c.append(f_cutoff)
    fit.append(gain_curve_fit(amf, *sinv))
    fit_amf.append(amf)
col = plt.cm.magma(np.linspace(0,0.8,len(fit)))
for ff ,f in enumerate(fit):
    ax.plot(fit_amf[ff], fit[ff], c = col[ff])
    ax.axvline(x=f_c[ff], ymin=0, ymax=5, alpha=0.8, c = col[ff])  # colors_uniform[ff])

ax.set_xscale('log')
ax.set_yscale('log')
ax.set_title('gain average all fish')
ax.set_ylabel('gain [Hz/(mV/cm)]')
ax.set_xlabel('envelope_frequency [Hz]')
ax.set_ylim(0.0008, )
#ax.plot(f_c, np.full(len(identifier), 0.0015), 'o', alpha = 0.5, c = 'darkorange', label = 'cutoff frequencies')
ax.legend(loc = 'center left')

plt.show()

embed()