A veces los dias son nublados.

requirements.txt

requests
#Optional for process_cloudy
#numpy

sunshine.py

import datetime
import cPickle as pickle
import time
import csv

import requests


API_KEY = "e6b7b4cc5f23b40f"

def generateTimeStamp(daysago, base_time = datetime.datetime.now()):
    delta = datetime.timedelta(daysago)
    date = base_time - delta
    return date.strftime("%Y%m%d")

def getWeatherData():
    li = []
    baseurl = "http://api.wunderground.com/api/"+API_KEY+"/history_{0}/q/MGGT.json"
    for i in range(63):
        print("Getting day {0}:".format(i))
        timestamp = generateTimeStamp(i)
        r = requests.get(baseurl.format(timestamp))
        j = r.json()
        li.append(j)
        time.sleep(10)
        pickle.dump(li, open("tmp_weather.p", "wb"))

    pickle.dump(li, open("weather.p", "wb"))
    return li

  

def process_cloudy():
  import numpy

  try:
      with open("weather.p","r") as f:
          data = pickle.load(f)
  except:
      data = getWeatherData()

  vism_array = map(lambda x: float(x["history"]["dailysummary"][0]["minvism"]),data)
  t_without = numpy.mean(vism_array[30:])
  t_with = numpy.mean(vism_array[:30])
  try:
      ratio = (t_without / t_with) - 1
      ratio_per = int(ratio*100)
      print("Ha sido {0}% mas nublado desde los ultimos 30 dias.".format(ratio_per))
  except:
      print("Nunca llovio (o nunca paro de llover).")


def write_daily_observations_average(daily_property = "tempm", filename = 'temps.csv'):
  '''Writes the aritmetic mean of property contained in the daily observations.'''

  try:
      with open("weather.p","r") as f:
          data = pickle.load(f)
  except:
      data = getWeatherData()

  observations_array = map(lambda x: x["history"]["observations"], data)    
  temperature_values_tuple_array = map(lambda individual_observations_array: map(lambda h: (int(h["date"]["hour"]), h[daily_property]), individual_observations_array), observations_array)

  dict_hour = {}

  for day in temperature_values_tuple_array:
      for hour_tuple in day:
          if hour_tuple[1] != '':
            temp = float(hour_tuple[1])  
            hour = hour_tuple[0]
            if not dict_hour.has_key(hour):
                dict_hour[hour]=[]
            if temp > -9999:
              dict_hour[hour].append(temp)

  with open(filename, 'wb') as csvfile:
    csvwriter = csv.writer(csvfile, delimiter=',', quotechar='|', quoting=csv.QUOTE_MINIMAL)
    for k in dict_hour:
      v = dict_hour[k]
      #manual average to avoid numpy
      csvwriter.writerow([k, sum(v)/float(len(v))])
  print("Saved daily variable: {0} to file: {1}".format(daily_property, filename))

if __name__ == '__main__':
  write_daily_observations_average()

temps.csv