import math
import pandas as pd
import helpers.constants as cte
from helpers import monthly_values as mv


class Weather(object):
  def __init__(self, full_path_weather):
    self._full_path_weather = full_path_weather
    if self._full_path_weather is not None:
      # TODO: catch error if file does not exist
      self._weather_values = pd.read_csv(self._full_path_weather, sep='\s+', header=None,
                                         names=['hour', 'global_horiz', 'temperature', 'diffuse', 'beam', 'empty'])
      self._weather_values = pd.concat([mv.MonthlyValues().month_hour, self._weather_values], axis=1)

  @property
  def weather_temperatures(self):
    temperatures_hourly = self._weather_values[['month', 'temperature']]
    sky_temperature = self.calculate_sky_temperature(temperatures_hourly)
    temperatures_hourly = pd.concat([temperatures_hourly, sky_temperature], axis=1)
    return temperatures_hourly

  @staticmethod
  def calculate_sky_temperature(ambient_temperature):
    # Swinbank - Fuentes sky model approximation(1963) based on cloudiness statistics(32 %) in United States
    # ambient temperatures( in °C)
    # sky temperatures( in °C)
    _ambient_temperature = ambient_temperature[['temperature']].to_numpy()
    values = []
    for temperature in _ambient_temperature:
      value = 0.037536 * math.pow((temperature + cte.celsius2kelvin), 1.5) \
                              + 0.32 * (temperature + cte.celsius2kelvin) - cte.celsius2kelvin
      values.append(value)
    return pd.DataFrame(values, columns=['sky temperature'])