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""" This file contains different utility functions that are not connected

in anyway to the networks presented in the tutorials, but rather help in

processing the outputs into a more understandable way.

For example ``tile_raster_images`` helps in generating a easy to grasp

image from a set of samples or weights.

"""

import numpy

def scale_to_unit_interval(ndar,eps=1e-8):

""" Scales all values in the ndarray ndar to be between 0 and 1 """

ndar = ndar.copy()

ndar -= ndar.min()

ndar *= 1.0 / (ndar.max()+eps)

return ndar

def tile_raster_images(X, img_shape, tile_shape,tile_spacing = (0,0),

scale_rows_to_unit_interval = True, output_pixel_vals = True):

"""

Transform an array with one flattened image per row, into an array in

which images are reshaped and layed out like tiles on a floor.

This function is useful for visualizing datasets whose rows are images,

and also columns of matrices for transforming those rows

(such as the first layer of a neural net).

:type X: a 2-D ndarray or a tuple of 4 channels, elements of which can

be 2-D ndarrays or None;

:param X: a 2-D array in which every row is a flattened image.

:type img_shape: tuple; (height, width)

:param img_shape: the original shape of each image

:type tile_shape: tuple; (rows, cols)

:param tile_shape: the number of images to tile (rows, cols)

:param output_pixel_vals: if output should be pixel values (i.e. int8

values) or floats

:param scale_rows_to_unit_interval: if the values need to be scaled before

being plotted to [0,1] or not

:returns: array suitable for viewing as an image.

(See:`PIL.Image.fromarray`.)

:rtype: a 2-d array with same dtype as X.

"""

assert len(img_shape) == 2

assert len(tile_shape) == 2

assert len(tile_spacing) == 2

# The expression below can be re-written in a more C style as

# follows :

#

# out_shape = [0,0]

# out_shape[0] = (img_shape[0]+tile_spacing[0])*tile_shape[0] -

# tile_spacing[0]

# out_shape[1] = (img_shape[1]+tile_spacing[1])*tile_shape[1] -

# tile_spacing[1]

out_shape = [(ishp + tsp) * tshp - tsp for ishp, tshp, tsp

in zip(img_shape, tile_shape, tile_spacing)]

if isinstance(X, tuple):

assert len(X) == 4

# Create an output numpy ndarray to store the image

if output_pixel_vals:

out_array = numpy.zeros((out_shape[0], out_shape[1], 4), dtype='uint8')

else:

out_array = numpy.zeros((out_shape[0], out_shape[1], 4), dtype=X.dtype)

#colors default to 0, alpha defaults to 1 (opaque)

if output_pixel_vals:

channel_defaults = [0,0,0,255]

else:

channel_defaults = [0.,0.,0.,1.]

for i in xrange(4):

if X[i] is None:

# if channel is None, fill it with zeros of the correct

# dtype

out_array[:,:,i] = numpy.zeros(out_shape,

dtype='uint8' if output_pixel_vals else out_array.dtype

)+channel_defaults[i]

else:

# use a recurrent call to compute the channel and store it

# in the output

out_array[:,:,i] = tile_raster_images(X[i], img_shape, tile_shape, tile_spacing, scale_rows_to_unit_interval, output_pixel_vals)

return out_array

else:

# if we are dealing with only one channel

H, W = img_shape

Hs, Ws = tile_spacing

# generate a matrix to store the output

out_array = numpy.zeros(out_shape, dtype='uint8' if output_pixel_vals else X.dtype)

for tile_row in xrange(tile_shape[0]):

for tile_col in xrange(tile_shape[1]):

if tile_row * tile_shape[1] + tile_col < X.shape[0]:

if scale_rows_to_unit_interval:

# if we should scale values to be between 0 and 1

# do this by calling the `scale_to_unit_interval`

# function

this_img = scale_to_unit_interval(X[tile_row * tile_shape[1] + tile_col].reshape(img_shape))

else:

this_img = X[tile_row * tile_shape[1] + tile_col].reshape(img_shape)

# add the slice to the corresponding position in the

# output array

out_array[

tile_row * (H+Hs):tile_row*(H+Hs)+H,

tile_col * (W+Ws):tile_col*(W+Ws)+W

] \

= this_img * (255 if output_pixel_vals else 1)

return out_array