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 sin_response

def take_second(elem):
    return elem[1]

predict = []

gain = []
mgain = []
phaseshift = []
mphaseshift = []
amfreq = []
amf = [0.001, 0.002, 0.005, 0.01, 0.02, 0.05, 0.1, 0.2, 0.5, 1]

currf = None
idxlist = []

data = sorted(np.load('files.npy'), key = take_second)

for i, d in enumerate(data):
    dd = list(d)
    jar = np.load('%s.npy' %dd)
    jm = jar - np.mean(jar)
    print(dd)

    time = np.load('time: %s.npy' %dd)

    b, a = signal.butter(4, (float(d[1]) / 2) / 10000, 'high', analog=True)
    y = signal.filtfilt(b, a, jm)
    #plt.plot(time, y)
    #plt.plot(time, jar)

    sinv, sinc = curve_fit(sin_response, time, y, [float(d[1]), 2, 0.5])
    print('frequency, phaseshift, amplitude:', sinv)

    phaseshift.append(np.sqrt(sinv[1]**2))
    gain.append(np.sqrt(sinv[2]**2))
    amfreq.append(d[1])

    Rs = []
    for ix, t in enumerate(time):
        R = (jm[ix] - sin_response(t, float(d[1]), np.sqrt(sinv[1]**2), np.sqrt(sinv[2]**2)))**2
        Rs.append(R)

    sigma = sum(Rs)
    rms = np.sqrt((1/len(time)) * sigma)
    #plt.plot(time, sin_response(time, *sinv), label='fit: f=%f, p=%.2f, A=%.2f' % tuple(sinv))

    #mean over same amfreqs for phase and gain
    if currf is None or currf == d[1]:
        currf = d[1]
        idxlist.append(i)

    else:  # currf != f
        meanf = []  # lists to make mean of
        meanp = []
        for x in idxlist:
            meanf.append(gain[x])
            meanp.append(phaseshift[x])
        meanedf = np.mean(meanf)
        meanedp = np.mean(meanp)
        mgain.append(meanedf)
        mphaseshift.append(meanedp)
        currf = d[1]    # set back for next loop
        idxlist = [i]

meanf = []
meanp = []
for y in idxlist:
    meanf.append(gain[y])
    meanp.append(phaseshift[y])
meanedf = np.mean(meanf)
meanedp = np.mean(meanp)
mgain.append(meanedf)
mphaseshift.append(meanedp)

for f in amf:
    G = np.max(mgain) / np.sqrt(1 + (2*((np.pi*f*3.14)**2)))
    predict.append(G)



fig = plt.figure()
ax = fig.add_subplot(1, 1, 1)
ax.plot(amf, mgain, 'o')
ax.plot(amf, predict)
ax.set_yscale('log')
ax.set_xscale('log')
ax.set_title('2018lepto98')
ax.set_ylabel('gain [Hz/(mV/cm)]')
ax.set_xlabel('AM-frequency [Hz]')
#plt.savefig('2018lepto98_gain')
pylab.show()
embed()

#phase in degree
# Q10 / conductivity
# AM-frequency / envelope-frequency scale title?