"""Some useful utilities when dealing with neural nets w/ tensorflow.

Parag K. Mital, Jan. 2016
"""
import tensorflow as tf
import numpy as np


def montage_batch(images):
    """Draws all filters (n_input * n_output filters) as a
    montage image separated by 1 pixel borders.

    Parameters
    ----------
    batch : numpy.ndarray
        Input array to create montage of.

    Returns
    -------
    m : numpy.ndarray
        Montage image.
    """
    img_h = images.shape[1]
    img_w = images.shape[2]
    n_plots = int(np.ceil(np.sqrt(images.shape[0])))
    m = np.ones(
        (images.shape[1] * n_plots + n_plots + 1,
         images.shape[2] * n_plots + n_plots + 1, 3)) * 0.5

    for i in range(n_plots):
        for j in range(n_plots):
            this_filter = i * n_plots + j
            if this_filter < images.shape[0]:
                this_img = images[this_filter, ...]
                m[1 + i + i * img_h:1 + i + (i + 1) * img_h,
                  1 + j + j * img_w:1 + j + (j + 1) * img_w, :] = this_img
    return m


# %%
def montage(W):
    """Draws all filters (n_input * n_output filters) as a
    montage image separated by 1 pixel borders.

    Parameters
    ----------
    W : numpy.ndarray
        Input array to create montage of.

    Returns
    -------
    m : numpy.ndarray
        Montage image.
    """
    W = np.reshape(W, [W.shape[0], W.shape[1], 1, W.shape[2] * W.shape[3]])
    n_plots = int(np.ceil(np.sqrt(W.shape[-1])))
    m = np.ones(
        (W.shape[0] * n_plots + n_plots + 1,
         W.shape[1] * n_plots + n_plots + 1)) * 0.5
    for i in range(n_plots):
        for j in range(n_plots):
            this_filter = i * n_plots + j
            if this_filter < W.shape[-1]:
                m[1 + i + i * W.shape[0]:1 + i + (i + 1) * W.shape[0],
                  1 + j + j * W.shape[1]:1 + j + (j + 1) * W.shape[1]] = (
                    np.squeeze(W[:, :, :, this_filter]))
    return m




# %%
def corrupt(x):
    """Take an input tensor and add uniform masking.

    Parameters
    ----------
    x : Tensor/Placeholder
        Input to corrupt.

    Returns
    -------
    x_corrupted : Tensor
        50 pct of values corrupted.
    """
    return tf.multiply(x, tf.cast(tf.random_uniform(shape=tf.shape(x),
                                               minval=0,
                                               maxval=2,
                                               dtype=tf.int32), tf.float32))


# %%
def weight_variable(shape):
    '''Helper function to create a weight variable initialized with
    a normal distribution

    Parameters
    ----------
    shape : list
        Size of weight variable
    '''
    initial = tf.random_normal(shape, mean=0.0, stddev=0.01)
    return tf.Variable(initial)


# %%
def bias_variable(shape):
    '''Helper function to create a bias variable initialized with
    a constant value.

    Parameters
    ----------
    shape : list
        Size of weight variable
    '''
    initial = tf.random_normal(shape, mean=0.0, stddev=0.01)
    return tf.Variable(initial)