show_data.py

import glob
import numpy as np
import matplotlib.pyplot as plt

fig = plt.figure()
ax = plt.subplot(111)



def savitzky_golay(y, window_size, order, deriv=0, rate=1):
    """
    http://scipy.github.io/old-wiki/pages/Cookbook/SavitzkyGolay
    """
    import numpy as np
    from math import factorial
    
    try:
        window_size = np.abs(np.int(window_size))
        order = np.abs(np.int(order))
    except ValueError, msg:
        raise ValueError("window_size and order have to be of type int")
    if window_size % 2 != 1 or window_size < 1:
        raise TypeError("window_size size must be a positive odd number")
    if window_size < order + 2:
        raise TypeError("window_size is too small for the polynomials order")
    order_range = range(order+1)
    half_window = (window_size -1) // 2
    # precompute coefficients
    b = np.mat([[k**i for i in order_range] for k in range(-half_window, half_window+1)])
    m = np.linalg.pinv(b).A[deriv] * rate**deriv * factorial(deriv)
    # pad the signal at the extremes with
    # values taken from the signal itself
    firstvals = y[0] - np.abs( y[1:half_window+1][::-1] - y[0] )
    lastvals = y[-1] + np.abs(y[-half_window-1:-1][::-1] - y[-1])
    y = np.concatenate((firstvals, y, lastvals))
    return np.convolve( m[::-1], y, mode='valid')
    

for filename in glob.iglob('*.npy'):
  if 'aliens' in filename:
    print(filename)
    data = np.load(filename)
    data = data.item()['tests']
    x = [] ; y = [] ; y_err = []

    for d in data:
        x.append(d['step'])
        y.append(np.mean(d['scores']))
        y_err.append(np.std(d['scores'])/7.07)
        
    if len(y) > 4:
      pass#y = savitzky_golay(y, window_size=51, order=4)
    ax.errorbar(x, y, yerr=y_err, label=filename)
    
#ax.xlabel('some numbers')

# Shrink current axis's height by 10% on the bottom
box = ax.get_position()
ax.set_position([box.x0, box.y0 + box.height * 0.1,
                 box.width* 0.8, box.height ])

# Put a legend below current axis
ax.legend(loc='right', bbox_to_anchor=(1.4, 0.5),
          fancybox=True, shadow=True, ncol=1)
          
plt.show()