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In [1]:



    


import matplotlib.pyplot as plt
%matplotlib inline

import seaborn as sns
sns.set_context('talk')

from matplotlib.ticker import FuncFormatter



    











In [2]:



    


labelsize = 15



    











In [3]:



    


def scientific_tex_formatter(x, pos):
    if x == 0.:
        return r'$0$'
    
    str_ = "%.1e" % x
    
    #str_ = str_.replace('1.0', '')
    str_ = str_.replace('e-0', '\,\,10^{-')
    str_ = str_.replace('e+0', '\,\,10^{')
    
    str_ = str_ + '}'
    str_ = r'$%s$' % str_
    
    return str_

f_scientific_tex_formatter = FuncFormatter(scientific_tex_formatter)

def scientific_tex_formatter2(x, pos):
    if x == 1.:
        return r'$1$'
    
    str_ = "%.1e" % x
    
    str_ = str_.replace('1.0', '')
    str_ = str_.replace('e-0', '\,\,10^{-')
    str_ = str_.replace('e+0', '\,\,10^{')
    
    str_ = str_ + '}'
    str_ = r'$%s$' % str_
    
    return str_

f_scientific_tex_formatter2 = FuncFormatter(scientific_tex_formatter2)



    











In [4]:



    


def tex_foramtter(x, pos):
    return r'$\rm{%s}$' % x

f_tex_foramtter = FuncFormatter(tex_foramtter)

def tex_transf(x):
    return r'$\rm{%s}$' % x



    











In [5]:



    


def tex_pct_formatter(x, pos):
    return r'$%.0f \%%$' % x

f_tex_pct_formatter = FuncFormatter(tex_pct_formatter)



    











In [6]:



    


import pandas as pd
import numpy as np

import os



    











In [7]:



    


dir_res_ = './results'



    











In [8]:



    


im_2 = pd.read_csv(os.path.join(dir_res_, 'IM_nu_eq_2.csv'), header=0, index_col=0)
im_6 = pd.read_csv(os.path.join(dir_res_, 'IM_nu_eq_6.csv'), header=0, index_col=0)
im_50 = pd.read_csv(os.path.join(dir_res_, 'IM_nu_eq_50.csv'), header=0, index_col=0)

Monetary Allocation

IM 99 %





In [9]:



    


im_99pct = pd.DataFrame()

im_99pct[r'$\nu = 2$'] = im_2['99.0']
im_99pct[r'$\nu = 6$'] = im_6['99.0']
im_99pct[r'$\nu = 50$'] = im_50['99.0']



    











In [10]:



    


f, ax = plt.subplots()

im_99pct.sort_values(r'$\nu = 2$')[::-1].plot.bar(ax=ax)

labels = ['' for item in ax.get_xticklabels()]
ax.set_xticklabels(labels)

ax.yaxis.set_major_formatter(f_scientific_tex_formatter)

plt.tick_params(axis='both', which='major', labelsize=labelsize)
plt.legend(fontsize=labelsize)

plt.show()



    


















    
























In [11]:



    


f, ax = plt.subplots()

im_99pct.sort_values(r'$\nu = 2$')[::-1][:10].plot.bar(ax=ax)

labels = [tex_transf(item.get_text()) for item in ax.get_xticklabels()]
ax.set_xticklabels(labels)

for label in ax.get_xticklabels():
    label.set_rotation(0) 

ax.yaxis.set_major_formatter(f_scientific_tex_formatter)

plt.tick_params(axis='both', which='major', labelsize=labelsize)
plt.legend(fontsize=labelsize)

plt.show()

IM 99.7 %





In [12]:



    


im_997pct = pd.DataFrame()

im_997pct[r'$\nu = 2$'] = im_2['99.7']
im_997pct[r'$\nu = 6$'] = im_6['99.7']
im_997pct[r'$\nu = 50$'] = im_50['99.7']



    











In [13]:



    


f, ax = plt.subplots()

im_997pct.sort_values(r'$\nu = 2$')[::-1].plot.bar(ax=ax)

labels = ['' for item in ax.get_xticklabels()]
ax.set_xticklabels(labels)

ax.yaxis.set_major_formatter(f_scientific_tex_formatter)

plt.tick_params(axis='both', which='major', labelsize=labelsize)
plt.legend(fontsize=labelsize)

plt.show()



    


















    
























In [14]:



    


f, ax = plt.subplots()

im_997pct.sort_values(r'$\nu = 2$')[::-1][:10].plot.bar(ax=ax)

labels = [tex_transf(item.get_text()) for item in ax.get_xticklabels()]
ax.set_xticklabels(labels)

for label in ax.get_xticklabels():
    label.set_rotation(0) 

ax.yaxis.set_major_formatter(f_scientific_tex_formatter)

plt.tick_params(axis='both', which='major', labelsize=labelsize)
plt.legend(fontsize=labelsize)

plt.show()

Weights Allocation

IM 99 %





In [15]:



    


im_99pct_weights = im_99pct

for col in im_99pct_weights.columns:
    im_99pct_weights[col] = im_99pct_weights[col] / im_99pct_weights[col].sum() * 100.

im_99pct_weights.head()



    


















    
Out[15]:










  
    

      
      $\nu = 2$
      $\nu = 6$
      $\nu = 50$
    

  
  
    

      PB1
      0.003680
      0.003678
      0.003703
    

    

      PB2
      0.000961
      0.000979
      0.001014
    

    

      PB3
      0.006884
      0.006852
      0.006954
    

    

      PB4
      1.411597
      1.411941
      1.412033
    

    

      PB5
      3.217966
      3.201827
      3.180822
    

  




















In [16]:



    


f, ax = plt.subplots()

im_99pct_weights.sort_values(r'$\nu = 2$')[::-1].plot.bar(ax=ax)

labels = ['' for item in ax.get_xticklabels()]
ax.set_xticklabels(labels)

ax.yaxis.set_major_formatter(f_tex_pct_formatter)

plt.tick_params(axis='both', which='major', labelsize=labelsize)
plt.legend(fontsize=labelsize)

plt.show()



    


















    
























In [17]:



    


f, ax = plt.subplots()

im_99pct_weights.sort_values(r'$\nu = 2$')[::-1][:10].plot.bar(ax=ax)

labels = [tex_transf(item.get_text()) for item in ax.get_xticklabels()]
ax.set_xticklabels(labels)

for label in ax.get_xticklabels():
    label.set_rotation(0) 

ax.yaxis.set_major_formatter(f_tex_pct_formatter)

plt.tick_params(axis='both', which='major', labelsize=labelsize)
plt.legend(fontsize=labelsize)

plt.show()

$\nu = 6$





In [18]:



    


f, ax = plt.subplots()

im_99pct_weights[r'$\nu = 6$'].sort_values()[::-1].plot.bar(ax=ax, logy=True)

labels = ['' for item in ax.get_xticklabels()]
ax.set_xticklabels(labels)

ax.yaxis.set_major_formatter(f_scientific_tex_formatter2)

plt.tick_params(axis='both', which='major', labelsize=labelsize)
plt.legend(fontsize=labelsize)

plt.show()



    


















    
























In [22]:



    


f, ax = plt.subplots()

im_99pct_weights[r'$\nu = 6$'].sort_values()[::-1][:12].plot.bar(ax=ax)

labels = [tex_transf(item.get_text()) for item in ax.get_xticklabels()]
ax.set_xticklabels(labels)

for label in ax.get_xticklabels():
    label.set_rotation(0) 

ax.yaxis.set_major_formatter(f_tex_pct_formatter)

plt.tick_params(axis='both', which='major', labelsize=labelsize)
plt.legend(fontsize=labelsize)

plt.show()

IM 99.7 %





In [19]:



    


im_997pct_weights = im_997pct

for col in im_997pct_weights.columns:
    im_997pct_weights[col] = im_997pct_weights[col] / im_997pct_weights[col].sum() * 100.

im_997pct_weights.head()



    


















    
Out[19]:










  
    

      
      $\nu = 2$
      $\nu = 6$
      $\nu = 50$
    

  
  
    

      PB1
      0.003633
      0.003616
      0.003712
    

    

      PB2
      0.000940
      0.000930
      0.001006
    

    

      PB3
      0.006697
      0.006740
      0.006965
    

    

      PB4
      1.402566
      1.407083
      1.405505
    

    

      PB5
      3.240997
      3.218709
      3.155724
    

  




















In [20]:



    


f, ax = plt.subplots()

im_997pct_weights.sort_values(r'$\nu = 2$')[::-1].plot.bar(ax=ax)

labels = ['' for item in ax.get_xticklabels()]
ax.set_xticklabels(labels)

ax.yaxis.set_major_formatter(f_tex_pct_formatter)

plt.tick_params(axis='both', which='major', labelsize=labelsize)
plt.legend(fontsize=labelsize)

plt.show()



    


















    
























In [21]:



    


f, ax = plt.subplots()

im_997pct_weights.sort_values(r'$\nu = 2$')[::-1][:10].plot.bar(ax=ax)

labels = [tex_transf(item.get_text()) for item in ax.get_xticklabels()]
ax.set_xticklabels(labels)

for label in ax.get_xticklabels():
    label.set_rotation(0) 

ax.yaxis.set_major_formatter(f_tex_pct_formatter)

plt.tick_params(axis='both', which='major', labelsize=labelsize)
plt.legend(fontsize=labelsize)

plt.show()

Content source: yarmenti/MSRA

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