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]


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)
    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, jar - np.mean(jar))
    #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])


    #plt.plot(time, sin_response(time, *sinv), label='fit: f=%f, p=%.2f, A=%.2f' % tuple(sinv))

    # plt.legend()
    #plt.show()

    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)

fig = plt.figure()
ax = fig.add_subplot(1, 1, 1)
ax.plot(amf, mgain, 'o')
ax.set_yscale('log')
pylab.show()

#betrag von A