import numpy as np
from sklearn.preprocessing import MinMaxScaler

from app.services.forecaster import forecast_recursive


class IncrementModel:
    def predict(self, model_input, verbose=0):
        return np.asarray([[model_input[0, -1, 0] + 0.1]])


def test_recursive_forecast_uses_scaled_feedback_and_keeps_prefix():
    scaler = MinMaxScaler().fit(np.asarray([[0.0], [100.0]]))
    window = np.asarray([10.0, 20.0, 30.0])
    prices, out_of_range = forecast_recursive(IncrementModel(), scaler, window, 5)

    assert prices == [40, 50, 60, 70, 80]
    assert out_of_range is False


def test_forecast_never_returns_negative_prices():
    class NegativeModel:
        def predict(self, model_input, verbose=0):
            return np.asarray([[-2.0]])

    scaler = MinMaxScaler().fit(np.asarray([[0.0], [100.0]]))
    prices, _ = forecast_recursive(NegativeModel(), scaler, np.asarray([10.0, 20.0, 30.0]), 1)
    assert prices == [0]