import h5py
import numpy as np
import tensorflow as tf
import matplotlib.pyplot as plt
import math as math
import scipy

f = h5py.File('/Users/Krzysztof/PycharmProjects/untitled/venv/4studs.hdf5', 'r')

keys = list(f.keys())

def wejscie(k): #zwraca (1, 32, 6250, 1)
    x = f.get(keys[k])
    x1 = tf.slice(x,[0,0],[32,6250])
    x2 = tf.reshape(x1,[1,32,6250,1])
    x3 = tf.keras.utils.normalize(x2)
    return x3

def wyjscie(k): #zwraca (1, 1, 1, 6211)
    x = f.get(keys[180+k])
    x1 = tf.slice(x,[0],[6211])
    x2 = tf.reshape(x1,[1,1,1,6211])
    return x2

#MODEL
model = tf.keras.Sequential([
     tf.keras.layers.Conv2D(64, kernel_size=(32,40),padding='valid',input_shape=(32,6250,1)),
     tf.keras.layers.Dense(32,activation='tanh'),
     tf.keras.layers.Dense(1,activation='linear')
 ])
model.compile(optimizer='nadam',loss='mse',metrics=['accuracy'])

for n in range(1):
    model.fit(wejscie(n),wyjscie(n),epochs=3)
pred = model.predict(wejscie(179))
print(pred.shape)

pred1 = tf.reshape(pred,[-1,1,6211])
pred2 = tf.reshape(pred1,[-1,6211])
pred3 = tf.reshape(pred2,[6211])
plt.figure()
plt.plot(pred3)
plt.plot(f.get(keys[359]))
plt.show()

f.close()