# Path: crowdastro/config.py
# SCRIPT_PATH = os.path.dirname(os.path.realpath(__file__))
# def get(*args, **kwargs):


import argparse
import h5py
import matplotlib.pyplot as plt
import numpy
    import keras.models
from crowdastro.config import config
    # plt.figure(figsize=[8, 8])
    # for i, filts in enumerate(conv_layer_1):
    #     for j, filt in enumerate(filts):
    #         plt.subplot(32, 32, i * 32 + j + 1)
    #         plt.axis('off')
    #         plt.pcolor(filt, cmap='viridis')
    # plt.subplots_adjust(hspace=0.05, wspace=0.05, left=0.00, bottom=0.00,
    #                     top=1.00, right=1.00)
    # plt.show()

    # plt.figure(figsize=[8, 8])
    # for i, filts in enumerate(conv_layer_2):
    #     for j, filt in enumerate(filts):
    #         plt.subplot(32, 32, i * 32 + j + 1)
    #         plt.axis('off')
    #         plt.pcolor(filt, cmap='viridis')
    # plt.subplots_adjust(hspace=0.05, wspace=0.05, left=0.00, bottom=0.00,
    #                     top=1.00, right=1.00)
    # plt.show()

    encoded_0 = keras.backend.function([model.layers[0].input],
                                       [model.layers[0].output])
    encoded_1 = keras.backend.function([model.layers[0].input],
                                       [model.layers[3].output])
    encoded_2 = keras.backend.function([model.layers[0].input],
                                       [model.layers[6].output])
    print('Weights shapes:', [i.shape for i in model.get_weights()])
    print([(i, l) for i, l in enumerate(model.layers)])

    with h5py.File(training_h5_path, 'r') as f_h5:

# Path: crowdastro/config.py
# SCRIPT_PATH = os.path.dirname(os.path.realpath(__file__))
# def get(*args, **kwargs):


# Path: crowdastro/show_filters.py
import argparse
import h5py
import matplotlib.pyplot as plt
import numpy
    import keras.models
from crowdastro.config import config

    # plt.figure(figsize=[8, 8])
    # for i, filts in enumerate(conv_layer_1):
    #     for j, filt in enumerate(filts):
    #         plt.subplot(32, 32, i * 32 + j + 1)
    #         plt.axis('off')
    #         plt.pcolor(filt, cmap='viridis')
    # plt.subplots_adjust(hspace=0.05, wspace=0.05, left=0.00, bottom=0.00,
    #                     top=1.00, right=1.00)
    # plt.show()

    # plt.figure(figsize=[8, 8])
    # for i, filts in enumerate(conv_layer_2):
    #     for j, filt in enumerate(filts):
    #         plt.subplot(32, 32, i * 32 + j + 1)
    #         plt.axis('off')
    #         plt.pcolor(filt, cmap='viridis')
    # plt.subplots_adjust(hspace=0.05, wspace=0.05, left=0.00, bottom=0.00,
    #                     top=1.00, right=1.00)
    # plt.show()

    encoded_0 = keras.backend.function([model.layers[0].input],
                                       [model.layers[0].output])
    encoded_1 = keras.backend.function([model.layers[0].input],
                                       [model.layers[3].output])
    encoded_2 = keras.backend.function([model.layers[0].input],
                                       [model.layers[6].output])
    print('Weights shapes:', [i.shape for i in model.get_weights()])
    print([(i, l) for i, l in enumerate(model.layers)])

    with h5py.File(training_h5_path, 'r') as f_h5:

        n_raw_features = config['surveys'][