init python:
    class Weather():
        weather_types = ("clear", "cloudy", "overcast", "blizzard", "snow", "storm", "rain")
        weather_weights = {
            "Janemar": (8, 9, 10, 3, 4, 7, 7),
            "Aprimju": (9, 10, 9, 2, 3, 8, 8),
            "Juliasep": (10, 10, 8, 1, 2, 6, 9),
            "Octobrinde": (6, 7, 9, 9, 10, 6, 6),
        }

        @staticmethod
        def _interpolate_weights(day, period):
            current_weights = Weather.weather_weights[period]
            current_index = Calendar.periods.index(period)

            next_index = (current_index + 1) % len(Calendar.periods)
            next_weights = Weather.weather_weights[Calendar.periods[next_index]]

            interpolation_factor = (day / 30) * 0.25

            # Interpolate between current season and next season
            adjusted_weights = tuple(
                max(0.01, (1 - interpolation_factor) * current_weight + interpolation_factor * next_weights[i])
                for i, current_weight in enumerate(current_weights)
            )

            return adjusted_weights

        @staticmethod
        def get_weather():
            # Proxy
            return states.env.weather

        @staticmethod
        def set_weather(value=None):
            if value is None or value == "random":
                period = Calendar.get_period()
                weights = Weather._interpolate_weights(states.env.day, period)
                value = states.env.random.choices(Weather.weather_types, weights=weights)[0]

            elif value not in Weather.weather_types:
                raise ValueError(f"Unsupported weather type: {value!r}")

            Weather._weather = value
            return value

        @staticmethod
        def forecast(span=35):
            forecast = {}
            start_day = states.env.day - (states.env.day % 30)
            period = Calendar.get_period()

            for i in range(span):
                day = start_day + i
                weights = Weather._interpolate_weights(day, period)
                rand = states.env._random(day)
                value = rand.choices(Weather.weather_types, weights=weights)[0]

                forecast[i] = value

            return forecast