In [1]:

    

import os
import matplotlib.pyplot as plt
import matplotlib.cm as cm
import numpy as np
import pandas as pd
%matplotlib inline


    

In [43]:

    

path = os.path.join(os.getcwd(), "..", "param", "dummy", "beta.dat")
beta = pd.read_table(path, sep=" ", header = None)


    

In [44]:

    

num_topics = beta.shape[0]
num_words = beta.shape[1]

plt.figure(1, figsize=(10,10))
colors = iter(cm.rainbow(np.linspace(0, 1, num_topics+1)))
word_ids = xrange(num_words)

for k in xrange(num_topics):
    plt.bar(word_ids, beta.loc[k].values,  color=next(colors), alpha=0.4, edgecolor = "none")
    
plt.xlim(0, num_words)
plt.show()


    

In [42]:

    

num_topics = beta.shape[0]
num_words = beta.shape[1]

plt.figure(1, figsize=(10,10))
colors = iter(cm.rainbow(np.linspace(0, 1, num_topics+1)))
word_ids = xrange(num_words)

plt.bar(word_ids, beta.loc[1].values,  color=next(colors), alpha=0.4, edgecolor = "none")
    
plt.xlim(0, num_words)
plt.show()


    

In [ ]:

    

for i in xrange(100):
    path = os.path.join(os.getcwd(), "..", "param", "dummy", str(i)+".dat")
    beta = pd.read_table(path, sep=" ", header = None)

    num_topics = beta.shape[0]
    num_words = beta.shape[1]

    plt.figure(1, figsize=(10,10))
    colors = iter(cm.rainbow(np.linspace(0, 1, num_topics+1)))
    word_ids = xrange(num_words)

    for k in xrange(num_topics):
        plt.bar(word_ids, beta.loc[k].values,  color=next(colors), alpha=0.4, edgecolor = "none")

    plt.xlim(0, num_words)
    path = os.path.join(os.getcwd(), "img", str(i)+".png")
    plt.savefig(path)