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


import argparse
import logging
import subprocess
import h5py
import numpy
    import keras.callbacks
    import keras.models
from .config import config
    from keras.preprocessing.image import ImageDataGenerator

PATCH_RADIUS = config['patch_radius']
PATCH_DIAMETER = PATCH_RADIUS * 2


def train(training_h5, model_json, weights_path, epochs, batch_size, s3=False,
          s3_bucket=None, using_dataset=False, test_set_index=0):
    """Trains a CNN.

    training_h5: Training HDF5 file.
    model_json: JSON model file.
    weights_path: Output weights HDF5 file.
    epochs: Number of training epochs.
    batch_size: Batch size.
    s3: Whether to periodically dump to Amazon S3. Default False.
    s3_bucket: Name of the bucket to dump to. Must be specified iff s3 is True.
    using_dataset: Whether the given training file is the Zenodo crowdastro
        dataset. Default False (i.e. we are using the generated training file).
    test_set_index: Index of the test set to not use for training. Default 0.
    """
    if s3 and not s3_bucket:
        raise ValueError('Must specify s3_bucket to dump to S3.')
    if not s3 and s3_bucket:
        raise ValueError('s3_bucket was specified but s3 is False.')

    model = keras.models.model_from_json(model_json.read())
    model.compile(loss='binary_crossentropy', optimizer='adadelta')

    if not using_dataset:
        ir_survey = training_h5.attrs['ir_survey']

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


# Path: crowdastro/train_cnn.py
import argparse
import logging
import subprocess
import h5py
import numpy
    import keras.callbacks
    import keras.models
from .config import config
    from keras.preprocessing.image import ImageDataGenerator


PATCH_RADIUS = config['patch_radius']
PATCH_DIAMETER = PATCH_RADIUS * 2


def train(training_h5, model_json, weights_path, epochs, batch_size, s3=False,
          s3_bucket=None, using_dataset=False, test_set_index=0):
    """Trains a CNN.

    training_h5: Training HDF5 file.
    model_json: JSON model file.
    weights_path: Output weights HDF5 file.
    epochs: Number of training epochs.
    batch_size: Batch size.
    s3: Whether to periodically dump to Amazon S3. Default False.
    s3_bucket: Name of the bucket to dump to. Must be specified iff s3 is True.
    using_dataset: Whether the given training file is the Zenodo crowdastro
        dataset. Default False (i.e. we are using the generated training file).
    test_set_index: Index of the test set to not use for training. Default 0.
    """
    if s3 and not s3_bucket:
        raise ValueError('Must specify s3_bucket to dump to S3.')
    if not s3 and s3_bucket:
        raise ValueError('s3_bucket was specified but s3 is False.')

    model = keras.models.model_from_json(model_json.read())
    model.compile(loss='binary_crossentropy', optimizer='adadelta')

    if not using_dataset:
        ir_survey = training_h5.attrs['ir_survey']

        n_nonimage_features = config['surveys'][ir_survey]['n_features']