# get the folders in "saved" and select most recent

base_path = os.path.dirname(__file__)

folder_path = os.path.join(base_path, "saved")

folders = os.listdir(folder_path)

folder = folders[-1]

# load in activation data

file_path = os.path.join(folder_path, folder, "weights.mat")

data = loadmat(file_path)

# visualize the receptive fields of the first layer

visualize.drawplots(data['layer0'].T, color='gray', convolution='y',

pad=0, examples=None, channels=1)

# visualize the distribution of weights

for l in xrange(3):

layer = 'layer' + str(l)

activations = data[layer]

pl.subplot(2, 2, l + 1)

pl.hist(activations.flatten(), bins=50)

pl.xlabel("Weights")

pl.ylabel("Count")

pl.title("Feature Weight Histogram for layer " + str(layer))

pl.show()

# # visualize the distribution of lifetime and population sparseness

# for l in xrange(len(data)):

# layer = 'layer' + str(l)

# visualize.dispSparseHist(activations_shuffled[layer].reshape(args.dimensions[l][0],

# data.shape[0] *

# activations_shuffled[layer].shape[2] *

# activations_shuffled[layer].shape[3]),

# layer=l)

#

# # visualize the distribution of activity across the "cortical sheet" and reconstruction

# if args.filename == 'patches_video.mat':

# f_hat = activations_norm['layer0'].T.reshape(video.shape[0], video.shape[1], args.dimensions[0][0])

# visualize.videoCortex(f_hat[0:100, :, :], 'y', args.convolution, 1)

# else:

# visualize.drawplots(activations_norm['layer0'], color='gray', convolution=args.convolution,