scripts.plot.py Maven / Gradle / Ivy

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from numpy import tanh, fabs, mean, ones,loadtxt,fromfile,zeros,product
import sys
import math
from PIL import Image
from matplotlib.pyplot import hist, title, subplot,scatter
import matplotlib.pyplot as plot

from mpltools import style, layout
style.use('ggplot')

def sigmoid(xx):
    return .5 * (1 + tanh(xx / 2.))



def from_file(path):
    return loadtxt(path,delimiter=',')
    
def hist_matrix(values,show = True,chart_title = ''):
    if product(values.shape) < 2:
      values = zeros((3,3))
      chart_title += '-fake'

    hist(values)
    magnitude = ' mm %g ' % mean(fabs(values))
    chart_title += ' ' + magnitude
    title(chart_title)
    
def scatter_matrix(values,show = True,chart_title = ''):
    if product(values.shape) < 2:
      values = zeros((3,3))
      chart_title += '-fake'

    scatter(values)
    magnitude = ' mm %g ' % mean(fabs(values))
    chart_title += ' ' + magnitude
    title(chart_title)

def render_hbias(path):
    hMean = from_file(path)
    image = Image.fromarray(hMean * 256).show()



def plot_multiple_scatters(paths):
    graph_count = 1
    print paths
    for i  in xrange(len(paths) - 1):
        if i % 2 == 0:
            path = paths[i]
            title = paths[i + 1]
            print 'Loading matrix ' + path + '\n'
            matrix = from_file(path)
            subplot(2,len(paths) / 3,graph_count)
            plot.tight_layout()
            scatter_matrix(matrix,False,chart_title=title)
            graph_count+= 1


    plot.show()


def plot_multiple_matrices(paths):
    graph_count = 1
    print paths
    for i  in xrange(len(paths) - 1):
        if i % 2 == 0:
            path = paths[i]
            title = paths[i + 1]
            print 'Loading matrix ' + path + '\n'
            matrix = from_file(path)
            subplot(2,len(paths) / 3,graph_count)
            plot.tight_layout()
            hist_matrix(matrix,False,chart_title=title)
            graph_count+= 1


    plot.show()    

def render_filter(path,nRows,nCols,data_length):
    X = from_file(path)
    data = math.sqrt(nRows)
    print 'data ' + str(data)
    # Initialize background to dark gray
    tiled = ones((data*nRows, data*nCols), dtype='uint8') * 51
    for row in xrange(nRows):
         for col in xrange(nCols):
            curr_neuron = col
            patch = X[:,curr_neuron].reshape((data,data))
            normPatch = ((patch - patch.min()) /
            (patch.max()-patch.min()+1e-6))
            tiled[row*data:row*data+data, col*data:col*data+data] = normPatch * 255
    Image.fromarray(tiled).show()

if __name__ == '__main__':
    if len(sys.argv) < 3:
       print 'Please specify a command: One of hbias,weights,plot and a file path'
       sys.exit(1)
    
    if sys.argv[1] == 'hbias':
        render_hbias(sys.argv[2])
    elif sys.argv[1] == 'weights':
        hist_matrix(sys.argv[2])
    elif sys.argv[1] == 'filter':
        nRows = int(sys.argv[3])
        nCols = int(sys.argv[4])
        length = int(sys.argv[5])
        print 'Rendering ' + sys.argv[3] + ' x ' + sys.argv[4] + ' matrix'
        render_filter(sys.argv[2],nRows,nCols,length)
    elif sys.argv[1] == 'multi':
        paths = sys.argv[2].split(',')
        plot_multiple_matrices(paths)
    elif sys.argv[1] == 'scatter':
        paths = sys.argv[2].split(',')
        plot_multiple_matrices(paths)