kata#5.py

#!/usr/bin/env python
# Encoding: utf-8
# -----------------------------------------------------------------------------
# Project : KATA #5
# -----------------------------------------------------------------------------
# Author : Edouard Richard                                  <edou4rd@gmail.com>
# -----------------------------------------------------------------------------
# Creation : 17-Apr-2014
# Last mod : 17-Apr-2014
# -----------------------------------------------------------------------------

import requests
from collections import OrderedDict
import sys

country = sys.argv[1]

URL = "http://climatedataapi.worldbank.org/climateweb/rest/v1/country/annualavg/inmcm3_0/a2/{var}/2060/2079/{country}.json"

DATA = {
	"Potatoes"   : {"pr":1400, "tas" :8},
	"White Wine" : {"pr":1300, "tas" :11.5},
	"Red Wine"   : {"pr":1200, "tas" :13},
	"Olives"     : {"pr":800,  "tas" :14.5},
	"Oranges"    : {"pr":900,  "tas" :16},
	"Mango"      : {"pr":410,  "tas" :20},
}

pr  = requests.get(URL.format(country=country, var='pr')).json()[0]['annualData'][0]
tas = requests.get(URL.format(country=country, var='tas')).json()[0]['annualData'][0]

results = {}

for item, info in DATA.items():
	delta_pr  = abs(info['pr']  - pr)
	delta_tas = abs(info['tas'] - tas)
	results[item] = {"delta_pr": delta_pr, 'delta_tas':delta_tas}

moyennes = {'tas':0, 'pr': 0}
for item, info in results.items():
	moyennes['tas'] += info["delta_tas"]
	moyennes['pr'] += info["delta_pr"]
moyennes['tas'] = moyennes['tas'] / len(results.keys())
moyennes['pr'] = moyennes['pr'] / len(results.keys())

for item, info in results.items():
	results[item]["score_tas"] = info["delta_tas"] / moyennes['tas']
	results[item]["score_pr"]  = info["delta_pr"] / moyennes['pr']
	results[item]["score"]     = results[item]["score_tas"] + results[item]["score_pr"]

r = OrderedDict(sorted(results.items(), key=lambda t: t[1]['score']))

for i, (item, info) in enumerate(r.items()):
	print "#%d" % (i+1), item, info['score']

# EOF


#  python kata5.py deu 
#  #1 Red Wine 0.218520281791
#  #2 White Wine 0.683275003277
#  #3 Olives 1.93571175662
#  #4 Oranges 2.07202077903
#  #5 Potatoes 2.11864782465


#  python kata5.py fra
#  #1 Oranges 0.447508577521
#  #2 Olives 0.633856575672
#  #3 Red Wine 1.50405455813
#  #4 White Wine 2.31223836059
#  #5 Mango 3.35126873604
#  #6 Potatoes 3.75107319204


#  python kata5.py che
#  #1 Red Wine 0.583767891527
#  #2 Olives 1.25701423138
#  #3 White Wine 1.41896342225
#  #4 Oranges 1.42181870065
#  #5 Potatoes 2.92082561965
#  #6 Mango 4.39761013454