import os
import numpy as np
import matplotlib.pyplot as plt
from IPython import embed
from jar_functions import mean_noise_cut
from matplotlib.mlab import specgram
import DataLoader as dl

#print(np.logspace(-3, 1, 10))
'''for idx, dataset in enumerate(datasets):
    datapath = os.path.join(base_path, dataset)
    for info, key, time, data in dl.iload_traces(datapath, repro='Beats', before=0.0, after=0.0):
'''

'''base_path = 'D:\\jar_project\\JAR'

#nicht: -5Hz delta f, 19-aa, 22-ae, 22-ad (?)
datasets = [#'2020-06-19-aa',   #-5Hz delta f, horrible fit
            #'2020-06-19-ab',   #-5Hz delta f, bad fit
            #'2020-06-22-aa',   #-5Hz delta f, bad fit
            #'2020-06-22-ab',   #-5Hz delta f, bad fit
            #'2020-06-22-ac',   #-15Hz delta f, good fit
            #'2020-06-22-ad',   #-15Hz delta f, horrible fit
            #'2020-06-22-ae',   #-15Hz delta f, horrible fit
            '2020-06-22-af',    #-15Hz delta f, good fit
            #'2020-07-21-ak'      #sin
            ]
for idx, dataset in enumerate(datasets):
    datapath = os.path.join(base_path, dataset)
    for info, key, time, data in dl.iload_traces(datapath, repro='Beats', before=0.0, after=0.0):
        print(data[0])

dat = np.arange(100)

for d in range(int(len(data)/10)):
    nfft = 2

    spec, freqs, times = specgram(data[0][d*10:(d+1)*10], NFFT=nfft, noverlap=nfft*0.5)

    #print(freqs)
    #print(times)
    embed()'''

g = [1.2917623576698833, -5.479055166593157, -2.689492238578325, -0.11604244418416806, -0.05353823781665627]
a = [0.2, 0.002, 0.02, 0.5, 1.0]

np.save('g.npy', g)
print(np.load('g.npy'))