#!/usr/bin/env python
# coding: utf-8

# In[1]:


import numpy as np, requests, pandas as pd


# In[2]:


url='http://hdr.undp.org/en/content/table-1-human-development-index-and-its-components'
r=requests.get(url)
hdi=pd.read_html(r.content,header=0,infer_types=False)[0].drop('Change in rank, 2012-2013',axis=1).set_index(['Country'],drop=True)
hdi.head()


# In[3]:


ids=pd.read_csv('http://bl.ocks.org/d/4090846/world-country-names.tsv',sep='\t').set_index(['name'],drop=True)
ids.head()


# In[4]:


def country_name_converter(country):
    if country=="Venezuela, Bolivarian Republic of": return "Venezuela (Bolivarian Republic of)"
    elif country=="Tanzania, United Republic of": return "Tanzania (United Republic of)"
    elif country=="Moldova, Republic of": return "Moldova (Republic of)"
    elif country=="Micronesia, Federated States of": return "Micronesia (Federated States of)"
    elif country=="Macedonia, the former Yugoslav Republic of": return "The former Yugoslav Republic of Macedonia"
    elif country=="Korea, Republic of": return "Korea (Republic of)"
    elif country=="Korea, Democratic People's Republic of": return "Korea (Democratic People's Rep. of)"
    elif country=="Côte d'Ivoire": return "C\xc3\xb4te d'Ivoire"
    elif country=="Iran, Islamic Republic of": return "Iran (Islamic Republic of)"
    elif country=="Hong Kong": return "Hong Kong, China (SAR)"
    elif country=="Palestinian Territory, Occupied": return "Palestine, State of"
    elif country=="Congo, the Democratic Republic of the": return "Congo (Democratic Republic of the)"
    elif country=="Bolivia, Plurinational State of": return "Bolivia (Plurinational State of)"
    else: return country


# In[94]:


import re
codes={}
for i in ids.index:
    try: 
        a=[i]
        a.append(round(float(re.sub(r'[^\d.]+', '',hdi.loc[country_name_converter(i)]\
                                    ["Human Development Index (HDI) Value, 2013"])),3))
        a.append(round((float(re.sub(r'[^\d.]+', '',hdi.loc[country_name_converter(i)]\
                                     ["Life expectancy at birth (years), 2013"]))-20)/(85-20),3))
        a.append(round((float(re.sub(r'[^\d.]+', '',hdi.loc[country_name_converter(i)]\
                                     ["Mean years of schooling (years), 2012 a"]))/15+\
                  float(re.sub(r'[^\d.]+', '',hdi.loc[country_name_converter(i)]\
                               ["Expected years of schooling (years), 2012 a"]))/18)/2,3))
        a.append(round((np.log(float(re.sub(r'[^\d.]+', '',hdi.loc[country_name_converter(i)]\
                        ["Gross national income (GNI) per capita (2011 PPP $), 2013"])))-np.log(100))\
                        /(np.log(75000)-np.log(100)),3))
        a.append(round((a[2]*a[3]*a[4])**(1.0/3.0),3))
        codes[repr(ids.loc[i][0])]=a
    except: pass


# In[96]:


import json
file('../hdi.json','w').write(json.dumps(codes))