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


class Weather(object):
  def __init__(self, weather_values):
    self._weather_values = pd.concat([mv.MonthlyValues().month_hour, weather_values], axis=1)
    self._temperatures_hourly = None

  @property
  def external_and_sky_temperatures(self):
    if self._temperatures_hourly is None:
      self._temperatures_hourly = self._weather_values[['month', 'temperature']]
      sky_temperature = self.sky_temperature(self._temperatures_hourly)
      self._temperatures_hourly = pd.concat([self._temperatures_hourly, sky_temperature], axis=1)
    return self._temperatures_hourly

  @staticmethod
  def 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.celsius_to_kelvin), 1.5) \
              + 0.32 * (temperature + cte.celsius_to_kelvin) - cte.celsius_to_kelvin
      values.append(value)
    return pd.DataFrame(values, columns=['sky temperature'])