In [6]:

    

%matplotlib inline

import matplotlib
import matplotlib.pyplot as plt

import pandas as pd

import random
random.seed(int("54e22d", 16))

import analysis.Stats as Stats

stats = Stats.Stats()

cols = ['energy', 'sqEnergy', 'mag', 'sqMag']
data = pd.read_csv('./data/small-world.random.10000.4.3.csv', header=None)
data2 = pd.read_csv('./data/small-world.random.10000.4.8.csv', header=None)
data.columns = cols
data2.columns = cols


    

In [2]:

    

plt.hist(data2['energy'])


    

    
Out[2]:





(array([  1.00000000e+02,   3.89200000e+03,   3.13870000e+04,
          1.26497000e+05,   6.95730000e+04,   5.70100000e+03,
          4.51110000e+04,   4.64077000e+05,   2.28566000e+05,
          2.50960000e+04]),
 array([-25442. , -23665.6, -21889.2, -20112.8, -18336.4, -16560. ,
        -14783.6, -13007.2, -11230.8,  -9454.4,  -7678. ]),
 <a list of 10 Patch objects>)






    

In [12]:

    

plt.hist(stats.bootstrapping(stats.mean, data2['energy'], 10000))


    

    
Out[12]:





(array([  858.,  1636.,  2425.,  1659.,   930.,   767.,   663.,   440.,
          254.,   368.]),
 array([-13304.816692, -13304.729094, -13304.641496, -13304.553898,
        -13304.4663  , -13304.378702, -13304.291104, -13304.203506,
        -13304.115908, -13304.02831 , -13303.940712]),
 <a list of 10 Patch objects>)






    

In [10]:

    

plt.plot(data2['mag'][200000:200200])


    

    
Out[10]:





[<matplotlib.lines.Line2D at 0x109a21438>]






    

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