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]:

    

l1_2 = pd.read_csv(os.path.join(dir_res_, 'l1_nu_eq_2.csv'), header=0, index_col=0)[r'$\nu = 2$ bounds']
l1_6 = pd.read_csv(os.path.join(dir_res_, 'l1_nu_eq_6.csv'), header=0, index_col=0)[r'$\nu = 6$ bounds']
l1_50 = pd.read_csv(os.path.join(dir_res_, 'l1_nu_eq_50.csv'), header=0, index_col=0)[r'$\nu = 50$ bounds']


    

In [9]:

    

l1 = pd.DataFrame()
l1[r'$\nu = 2$'] = l1_2
l1[r'$\nu = 6$'] = l1_6
l1[r'$\nu = 50$'] = l1_50


    

In [10]:

    

l1_weights = l1

for col in l1_weights.columns:
    l1_weights[col] = l1_weights[col] / l1_weights[col].sum() * 100.

l1_weights.head()


    

    
Out[10]:






  

    

      

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

    

      
PB1
      
0.003754
      
0.003747
      
0.003709
    
    

      
PB2
      
0.001058
      
0.001029
      
0.001020
    
    

      
PB3
      
0.007419
      
0.007165
      
0.007096
    
    

      
PB4
      
1.456499
      
1.421488
      
1.413740
    
    

      
PB5
      
3.155080
      
3.177689
      
3.217428
    
  

In [11]:

    

f, ax = plt.subplots()

l1_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 [12]:

    

f, ax = plt.subplots()

l1_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()


    

In [13]:

    

f, ax = plt.subplots()

l1_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()

l1_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()


    

In [15]:

    

l3_2 = pd.read_csv(os.path.join(dir_res_, 'l3_nu_eq_2.csv'), header=0, index_col=0)[r'$\nu = 2$ bounds']
l3_6 = pd.read_csv(os.path.join(dir_res_, 'l3_nu_eq_6.csv'), header=0, index_col=0)[r'$\nu = 6$ bounds']
l3_50 = pd.read_csv(os.path.join(dir_res_, 'l3_nu_eq_50.csv'), header=0, index_col=0)[r'$\nu = 50$ bounds']


    

In [16]:

    

l3 = pd.DataFrame()
l3[r'$\nu = 2$'] = l3_2
l3[r'$\nu = 6$'] = l3_6
l3[r'$\nu = 50$'] = l3_50


    

In [17]:

    

l3_weights = l3

for col in l3_weights.columns:
    l3_weights[col] = l3_weights[col] / l3_weights[col].sum() * 100.

l3_weights.head()


    

    
Out[17]:






  

    

      

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

    

      
PB1
      
0.004589
      
0.004562
      
0.004513
    
    

      
PB2
      
0.002125
      
0.002084
      
0.002068
    
    

      
PB3
      
0.006878
      
0.006599
      
0.006478
    
    

      
PB4
      
1.339278
      
1.304330
      
1.299244
    
    

      
PB5
      
2.981591
      
2.987940
      
3.023878
    
  

In [18]:

    

f, ax = plt.subplots()

l3_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 [19]:

    

f, ax = plt.subplots()

l3_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()