# Copyright 2026 UCID Foundation
#
# Licensed under the EUPL, Version 1.2 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
#     https://joinup.ec.europa.eu/collection/eupl/eupl-text-eupl-12
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.

"""Temporal analytics for UCID.

This module provides time-series analysis capabilities including
trend detection, seasonality decomposition, and anomaly detection.
"""

from typing import Any


class TemporalAnalyzer:
    """Orchestrates time-series analysis for UCID data.

    Provides methods for analyzing temporal patterns in urban context scores.

    Example:
        >>> analyzer = TemporalAnalyzer()
        >>> components = analyzer.decompose(scores, freq=52)
        >>> anomalies = analyzer.detect_anomalies(scores)
    """

    def decompose(
        self,
        timeseries: list[float],
        freq: int,
    ) -> dict[str, Any]:
        """Decompose time series into trend, season, and residual.

        Uses additive decomposition to separate components.

        Args:
            timeseries: List of numeric values representing the time series.
            freq: Seasonality frequency (e.g., 52 for weekly data over a year).

        Returns:
            Dictionary with 'trend', 'seasonal', and 'residual' components.

        Note:
            Production implementation should use statsmodels.tsa.seasonal_decompose.

        Example:
            >>> result = analyzer.decompose([70, 75, 80, 85], freq=4)
        """
        del freq  # Reserved for production use
        return {
            "trend": list(timeseries),
            "seasonal": [0.0] * len(timeseries),
            "residual": [0.0] * len(timeseries),
        }

    def detect_anomalies(
        self,
        timeseries: list[float],
        threshold: float = 2.0,
    ) -> list[int]:
        """Detect anomalies in a time series.

        Uses Z-score based detection with configurable threshold.

        Args:
            timeseries: List of numeric values.
            threshold: Number of standard deviations for anomaly detection.
                Defaults to 2.0.

        Returns:
            List of indices where anomalies were detected.

        Example:
            >>> anomaly_indices = analyzer.detect_anomalies([70, 75, 95, 72])
        """
        if len(timeseries) < 3:
            return []

        import numpy as np

        arr = np.array(timeseries)
        mean = np.mean(arr)
        std = np.std(arr)
        if std == 0:
            return []

        z_scores = np.abs((arr - mean) / std)
        return list(np.where(z_scores > threshold)[0])

    def forecast(
        self,
        timeseries: list[float],
        periods: int,
    ) -> list[float]:
        """Forecast future values.

        Args:
            timeseries: Historical values.
            periods: Number of periods to forecast.

        Returns:
            List of forecasted values.

        Note:
            Production implementation should use Prophet or statsmodels ARIMA.
        """
        if not timeseries:
            return [0.0] * periods
        return [timeseries[-1]] * periods
