climate_tools.py

"""Climate analysis tools for EcoTrack agents.

Provides async tool functions for querying climate data, running forecasts,
detecting anomalies, and computing long-term trends.
"""
from __future__ import annotations

from datetime import datetime, timedelta
from typing import Any

import structlog

from ecotrack_agents.base import AgentRole, ToolDefinition

logger = structlog.get_logger(__name__)


# ---------------------------------------------------------------------------
# Tool implementations
# ---------------------------------------------------------------------------


async def query_climate_data(
    variable: str,
    bbox: list[float],
    start_date: str,
    end_date: str,
) -> dict[str, Any]:
    """Query climate observations for a variable within a bounding box.

    Args:
        variable: Climate variable name (e.g. ``temperature``, ``precipitation``).
        bbox: Bounding box as ``[min_lon, min_lat, max_lon, max_lat]``.
        start_date: ISO-8601 start date string.
        end_date: ISO-8601 end date string.

    Returns:
        Dictionary with ``variable``, ``bbox``, ``time_range``, ``data_points``
        count, and a ``summary`` sub-dict containing mean / min / max /
        std_dev placeholders.
    """
    logger.info(
        "query_climate_data",
        variable=variable,
        bbox=bbox,
        start_date=start_date,
        end_date=end_date,
    )
    # Stub implementation — in production this would call ERA5 / CDS APIs
    return {
        "variable": variable,
        "bbox": bbox,
        "time_range": {"start": start_date, "end": end_date},
        "data_points": 365,
        "summary": {
            "mean": 15.2,
            "min": -5.0,
            "max": 38.7,
            "std_dev": 8.4,
            "unit": "°C" if "temp" in variable.lower() else "mm",
        },
        "source": "ERA5 Reanalysis",
        "status": "success",
    }


async def run_climate_forecast(
    variable: str,
    bbox: list[float],
    horizon_hours: int = 168,
) -> dict[str, Any]:
    """Run a climate forecast model for a given variable and region.

    Args:
        variable: Climate variable to forecast.
        bbox: Bounding box ``[min_lon, min_lat, max_lon, max_lat]``.
        horizon_hours: Forecast horizon in hours (default 168 = 7 days).

    Returns:
        Dictionary with forecast metadata, timesteps, predicted values,
        and confidence intervals.
    """
    logger.info(
        "run_climate_forecast",
        variable=variable,
        bbox=bbox,
        horizon_hours=horizon_hours,
    )
    now = datetime.utcnow()
    timesteps = [
        (now + timedelta(hours=h)).isoformat()
        for h in range(0, horizon_hours, 24)
    ]
    return {
        "variable": variable,
        "bbox": bbox,
        "horizon_hours": horizon_hours,
        "model": "EcoTrack-ClimateNet-v1",
        "timesteps": timesteps,
        "predictions": [15.0 + i * 0.3 for i in range(len(timesteps))],
        "confidence_intervals": {
            "lower": [14.0 + i * 0.2 for i in range(len(timesteps))],
            "upper": [16.0 + i * 0.4 for i in range(len(timesteps))],
        },
        "status": "success",
    }


async def detect_climate_anomalies(
    variable: str,
    bbox: list[float],
    lookback_days: int = 30,
) -> dict[str, Any]:
    """Detect climate anomalies by comparing recent data against climatology.

    Args:
        variable: Climate variable to analyse.
        bbox: Bounding box ``[min_lon, min_lat, max_lon, max_lat]``.
        lookback_days: Number of days to look back for anomaly detection.

    Returns:
        Dictionary with detected anomalies, severity scores, and baseline
        statistics used for comparison.
    """
    logger.info(
        "detect_climate_anomalies",
        variable=variable,
        bbox=bbox,
        lookback_days=lookback_days,
    )
    return {
        "variable": variable,
        "bbox": bbox,
        "lookback_days": lookback_days,
        "anomalies_detected": 2,
        "anomalies": [
            {
                "date": (datetime.utcnow() - timedelta(days=5)).isoformat(),
                "value": 42.1,
                "expected_range": [28.0, 35.0],
                "severity": "high",
                "z_score": 3.2,
            },
            {
                "date": (datetime.utcnow() - timedelta(days=12)).isoformat(),
                "value": -8.3,
                "expected_range": [-2.0, 5.0],
                "severity": "moderate",
                "z_score": -2.1,
            },
        ],
        "baseline": {
            "climatology_mean": 18.5,
            "climatology_std": 6.2,
            "reference_period": "1991-2020",
        },
        "status": "success",
    }


async def compute_climate_trends(
    variable: str,
    bbox: list[float],
    period_years: int = 30,
) -> dict[str, Any]:
    """Compute long-term climate trends over a specified period.

    Args:
        variable: Climate variable to analyse.
        bbox: Bounding box ``[min_lon, min_lat, max_lon, max_lat]``.
        period_years: Number of years to analyse.

    Returns:
        Dictionary with trend slope, statistical significance, and
        decadal change rate.
    """
    logger.info(
        "compute_climate_trends",
        variable=variable,
        bbox=bbox,
        period_years=period_years,
    )
    return {
        "variable": variable,
        "bbox": bbox,
        "period_years": period_years,
        "trend": {
            "slope_per_decade": 0.24,
            "unit": "°C/decade" if "temp" in variable.lower() else "mm/decade",
            "p_value": 0.003,
            "r_squared": 0.67,
            "significant": True,
        },
        "decadal_values": [14.2, 14.5, 14.9],
        "projection_2050": {
            "low_scenario": 16.1,
            "mid_scenario": 17.3,
            "high_scenario": 19.2,
        },
        "status": "success",
    }


# ---------------------------------------------------------------------------
# Tool definitions for registry
# ---------------------------------------------------------------------------

CLIMATE_TOOLS: list[ToolDefinition] = [
    ToolDefinition(
        name="query_climate_data",
        description="Query climate observations for a variable within a bounding box and date range.",
        parameters={
            "type": "object",
            "properties": {
                "variable": {"type": "string", "description": "Climate variable name"},
                "bbox": {
                    "type": "array",
                    "items": {"type": "number"},
                    "description": "Bounding box [min_lon, min_lat, max_lon, max_lat]",
                },
                "start_date": {"type": "string", "format": "date"},
                "end_date": {"type": "string", "format": "date"},
            },
            "required": ["variable", "bbox", "start_date", "end_date"],
        },
        handler=query_climate_data,
        required_role=AgentRole.CLIMATE_ANALYST,
    ),
    ToolDefinition(
        name="run_climate_forecast",
        description="Run a climate forecast model for a given variable and region.",
        parameters={
            "type": "object",
            "properties": {
                "variable": {"type": "string"},
                "bbox": {"type": "array", "items": {"type": "number"}},
                "horizon_hours": {"type": "integer", "default": 168},
            },
            "required": ["variable", "bbox"],
        },
        handler=run_climate_forecast,
        required_role=AgentRole.CLIMATE_ANALYST,
    ),
    ToolDefinition(
        name="detect_climate_anomalies",
        description="Detect climate anomalies by comparing recent data against long-term climatology.",
        parameters={
            "type": "object",
            "properties": {
                "variable": {"type": "string"},
                "bbox": {"type": "array", "items": {"type": "number"}},
                "lookback_days": {"type": "integer", "default": 30},
            },
            "required": ["variable", "bbox"],
        },
        handler=detect_climate_anomalies,
        required_role=AgentRole.CLIMATE_ANALYST,
    ),
    ToolDefinition(
        name="compute_climate_trends",
        description="Compute long-term climate trends over a specified multi-year period.",
        parameters={
            "type": "object",
            "properties": {
                "variable": {"type": "string"},
                "bbox": {"type": "array", "items": {"type": "number"}},
                "period_years": {"type": "integer", "default": 30},
            },
            "required": ["variable", "bbox"],
        },
        handler=compute_climate_trends,
        required_role=AgentRole.CLIMATE_ANALYST,
    ),
]

__all__ = [
    "query_climate_data",
    "run_climate_forecast",
    "detect_climate_anomalies",
    "compute_climate_trends",
    "CLIMATE_TOOLS",
]