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import numpy as np

import h5py

import tensorflow as tf

from tensorflow.keras.models import Sequential

from tensorflow.keras.layers import Dense

from tensorflow.keras.optimizers import Adam

from tensorflow.keras import metrics

from tensorflow.keras.layers import Dropout

from tensorflow.keras.layers import BatchNormalization

from data_loader import *

n_in = 5

n_out = 1

infilename = "/m100_work/IscrC_CD-DLS/simulations/"

rho, T, vx, vy, vz, Bx, By, Bz, B2 = load_data(infilename, [500, 500, 500], [1000, 1000, 501])

n_cells = rho.size

n_train = n_cells

Xtrain = np.zeros((n_train, n_in),dtype=np.float64)

Ytrain = np.zeros((n_train, n_out),dtype=np.float64)

xxx = rho.flatten()

Xtrain[0:n_train,0] = xxx[0:n_train]

xxx = T.flatten()

Xtrain[0:n_train,1] = xxx[0:n_train]

xxx = vx.flatten()

Xtrain[0:n_train,2] = xxx[0:n_train]

xxx = vy.flatten()

Xtrain[0:n_train,3] = xxx[0:n_train]

xxx = vz.flatten()

Xtrain[0:n_train,4] = xxx[0:n_train]

Bxx = Bx.flatten()

Bxy = By.flatten()

Bxz = Bz.flatten()

Bx2 = B2.flatten()

# save the trained network

ckptfile = 'models/trained_networ.ckpt'

model = tf.keras.models.load_model(ckptfile)

model.summary()

Ytrain = model.predict(Xtrain)

print(Ytrain.shape)

Bx2.tofile('B.bin')

Xtrain[:,0].tofile('rho.bin')

Xtrain[:,1].tofile('T.bin')

#B2 = Ytrain[:,0]

#B2.tofile('eval.bin')

Ytrain[:,0].tofile('eval.bin')