"""
pvsim/statistics.py

Modulo per il calcolo delle statistiche e dei KPI
della simulazione fotovoltaica.

Livelli supportati:

    Plant
        |
        +-- Inverter
        |
        +-- Combiner
        |
        +-- Panel

KPI principali:

- energia prodotta;
- potenza media;
- potenza massima;
- potenza minima;
- efficienza;
- performance ratio;
- availability;
- fault count;
- fault duration;
- clipping;
- yield specifico.

Il modulo riceve un SimulationResult e non modifica
i dati della simulazione.
"""

from __future__ import annotations

from dataclasses import dataclass

from typing import Any, Dict, Optional

import pandas as pd

from .simulator import SimulationResult

from .utils import (
    normalize_dataframe,
    safe_divide,
)


# ======================================================================
# KPI RESULT
# ======================================================================


@dataclass
class KPIResult:
    """
    Contenitore dei KPI principali.
    """

    energy_Wh: float = 0.0

    energy_kWh: float = 0.0

    peak_power_W: float = 0.0

    average_power_W: float = 0.0

    minimum_power_W: float = 0.0

    nominal_power_W: float = 0.0

    efficiency: float = 0.0

    performance_ratio: float = 0.0

    availability: float = 0.0

    fault_count: int = 0

    clipping_loss_Wh: float = 0.0

    specific_yield_kWh_kWp: float = 0.0

    def to_dict(
        self,
    ) -> Dict[str, Any]:

        return {

            "energy_Wh":
                self.energy_Wh,

            "energy_kWh":
                self.energy_kWh,

            "peak_power_W":
                self.peak_power_W,

            "average_power_W":
                self.average_power_W,

            "minimum_power_W":
                self.minimum_power_W,

            "nominal_power_W":
                self.nominal_power_W,

            "efficiency":
                self.efficiency,

            "performance_ratio":
                self.performance_ratio,

            "availability":
                self.availability,

            "fault_count":
                self.fault_count,

            "clipping_loss_Wh":
                self.clipping_loss_Wh,

            "specific_yield_kWh_kWp":
                self.specific_yield_kWh_kWp,
        }


# ======================================================================
# STATISTICS
# ======================================================================


class SimulationStatistics:
    """
    Calcola statistiche e KPI su SimulationResult.
    """

    def __init__(
        self,
        result: SimulationResult,
    ) -> None:

        self.result = result

    # ==================================================================
    # DATAFRAME
    # ==================================================================

    def plant_dataframe(
        self,
    ) -> pd.DataFrame:

        return normalize_dataframe(

            self.result.plant_dataframe()
        )

    # ------------------------------------------------------------------

    def inverter_dataframe(
        self,
    ) -> pd.DataFrame:

        return normalize_dataframe(

            self.result.inverter_dataframe()
        )

    # ------------------------------------------------------------------

    def combiner_dataframe(
        self,
    ) -> pd.DataFrame:

        return normalize_dataframe(

            self.result.combiner_dataframe()
        )

    # ------------------------------------------------------------------

    def panel_dataframe(
        self,
    ) -> pd.DataFrame:

        return normalize_dataframe(

            self.result.panel_dataframe()
        )

    # ------------------------------------------------------------------

    def faults_dataframe(
        self,
    ) -> pd.DataFrame:

        return normalize_dataframe(

            self.result.faults_dataframe()
        )

    # ==================================================================
    # PLANT KPI
    # ==================================================================

    def plant_kpi(
        self,
    ) -> KPIResult:
        """
        Calcola i KPI dell'intero impianto.
        """

        df = self.plant_dataframe()

        if df.empty:

            return KPIResult()

        # --------------------------------------------------------------
        # Energia
        # --------------------------------------------------------------

        if "energy_Wh" in df.columns:

            energy_Wh = (

                df[
                    "energy_Wh"
                ].sum()
            )

        else:

            energy_Wh = 0.0

        # --------------------------------------------------------------
        # Potenza
        # --------------------------------------------------------------

        power_column = (

            "effective_ac_power_W"

            if "effective_ac_power_W"
            in df.columns

            else "ac_power_W"
        )

        power = df[
            power_column
        ]

        peak_power = (

            power.max()
        )

        average_power = (

            power.mean()
        )

        minimum_power = (

            power.min()
        )

        # --------------------------------------------------------------
        # Nominale
        # --------------------------------------------------------------

        nominal_power = 0.0

        if "nominal_power_W" in df.columns:

            nominal_power = (

                df[
                    "nominal_power_W"
                ].max()
            )

        # --------------------------------------------------------------
        # Efficienza
        # --------------------------------------------------------------

        efficiency = 0.0

        if (

            "efficiency"

            in df.columns
        ):

            efficiency = (

                df[
                    "efficiency"
                ].mean()
            )

        # --------------------------------------------------------------
        # Performance Ratio
        # --------------------------------------------------------------

        performance_ratio = 0.0

        if (

            "performance_ratio"

            in df.columns
        ):

            performance_ratio = (

                df[
                    "performance_ratio"
                ].mean()
            )

        # --------------------------------------------------------------
        # Availability
        # --------------------------------------------------------------

        availability = 0.0

        if (

            "availability"

            in df.columns
        ):

            availability = (

                df[
                    "availability"
                ].mean()
            )

        # --------------------------------------------------------------
        # Faults
        # --------------------------------------------------------------

        faults = self.faults_dataframe()

        fault_count = len(

            faults
        )

        # --------------------------------------------------------------
        # Clipping
        # --------------------------------------------------------------

        clipping_loss_Wh = 0.0

        if (

            "clipping_loss_W"

            in df.columns
        ):

            clipping_loss_Wh = (

                df[
                    "clipping_loss_W"
                ].sum()
            )

        # --------------------------------------------------------------
        # Specific Yield
        # --------------------------------------------------------------

        energy_kWh = (

            energy_Wh

            / 1000.0
        )

        nominal_kWp = (

            nominal_power

            / 1000.0
        )

        specific_yield = (

            safe_divide(

                energy_kWh,

                nominal_kWp
            )
        )

        return KPIResult(

            energy_Wh=

                energy_Wh,

            energy_kWh=

                energy_kWh,

            peak_power_W=

                peak_power,

            average_power_W=

                average_power,

            minimum_power_W=

                minimum_power,

            nominal_power_W=

                nominal_power,

            efficiency=

                efficiency,

            performance_ratio=

                performance_ratio,

            availability=

                availability,

            fault_count=

                fault_count,

            clipping_loss_Wh=

                clipping_loss_Wh,

            specific_yield_kWh_kWp=

                specific_yield,
        )

    # ==================================================================
    # GENERIC GROUP KPI
    # ==================================================================

    def _group_kpi(
        self,
        dataframe: pd.DataFrame,
        group_column: str,
        power_column: str,
    ) -> pd.DataFrame:
        """
        Calcola KPI aggregati per componente.
        """

        if dataframe.empty:

            return pd.DataFrame()

        if group_column not in dataframe.columns:

            return pd.DataFrame()

        if power_column not in dataframe.columns:

            return pd.DataFrame()

        grouped = (

            dataframe

            .groupby(

                group_column
            )

            .agg(

                power_mean_W=(

                    power_column,

                    "mean"
                ),

                power_peak_W=(

                    power_column,

                    "max"
                ),

                power_min_W=(

                    power_column,

                    "min"
                ),

                records=(

                    power_column,

                    "count"
                ),
            )

            .reset_index()
        )

        return grouped

    # ==================================================================
    # PANEL KPI
    # ==================================================================

    def panel_kpi(
        self,
    ) -> pd.DataFrame:
        """
        KPI per ogni pannello.
        """

        df = self.panel_dataframe()

        return self._group_kpi(

            df,

            "panel_id",

            "dc_power_W",
        )

    # ==================================================================
    # COMBINER KPI
    # ==================================================================

    def combiner_kpi(
        self,
    ) -> pd.DataFrame:
        """
        KPI per ogni Combiner Box.
        """

        df = self.combiner_dataframe()

        return self._group_kpi(

            df,

            "combiner_id",

            "dc_power_W",
        )

    # ==================================================================
    # INVERTER KPI
    # ==================================================================

    def inverter_kpi(
        self,
    ) -> pd.DataFrame:
        """
        KPI per ogni inverter.
        """

        df = self.inverter_dataframe()

        return self._group_kpi(

            df,

            "inverter_id",

            "ac_power_W",
        )

    # ==================================================================
    # DAILY
    # ==================================================================

    def daily_statistics(
        self,
    ) -> pd.DataFrame:
        """
        Calcola statistiche giornaliere.
        """

        df = self.plant_dataframe()

        if df.empty:

            return pd.DataFrame()

        df = df.copy()

        df[
            "date"
        ] = df[
            "timestamp"
        ].dt.date

        aggregation = {

            "ac_power_W":
                [
                    "mean",
                    "max",
                ],

            "energy_Wh":
                "sum",
        }

        valid_columns = {

            key: value

            for key, value
            in aggregation.items()

            if key in df.columns
        }

        if not valid_columns:

            return pd.DataFrame()

        return (

            df

            .groupby(

                "date"
            )

            .agg(

                valid_columns
            )

            .reset_index()
        )

    # ==================================================================
    # MONTHLY
    # ==================================================================

    def monthly_statistics(
        self,
    ) -> pd.DataFrame:
        """
        Calcola statistiche mensili.
        """

        df = self.plant_dataframe()

        if df.empty:

            return pd.DataFrame()

        df = df.set_index(

            "timestamp"
        )

        result = (

            df.resample(

                "ME"
            )
            .agg(

                {

                    "ac_power_W":

                        [
                            "mean",
                            "max",
                        ],

                    "energy_Wh":

                        "sum",
                }
            )
        )

        return result.reset_index()

    # ==================================================================
    # FAULT SUMMARY
    # ==================================================================

    def fault_summary(
        self,
    ) -> pd.DataFrame:
        """
        Aggrega i fault per livello e componente.
        """

        df = self.faults_dataframe()

        if df.empty:

            return pd.DataFrame()

        columns = [

            "component_level",

            "component_id",

            "fault_type",
        ]

        columns = [

            column

            for column
            in columns

            if column in df.columns
        ]

        if not columns:

            return pd.DataFrame()

        return (

            df

            .groupby(

                columns
            )

            .size()

            .reset_index(

                name="fault_count"
            )
        )

    # ==================================================================
    # FULL REPORT
    # ==================================================================

    def report(
        self,
    ) -> Dict[str, Any]:
        """
        Genera un report completo.
        """

        plant = self.plant_kpi()

        return {

            "plant": plant.to_dict(),

            "panel_kpi":

                self.panel_kpi(),

            "combiner_kpi":

                self.combiner_kpi(),

            "inverter_kpi":

                self.inverter_kpi(),

            "daily":

                self.daily_statistics(),

            "monthly":

                self.monthly_statistics(),

            "faults":

                self.fault_summary(),
        }


# ======================================================================
# HELPER
# ======================================================================


def calculate_statistics(
    result: SimulationResult,
) -> Dict[str, Any]:
    """
    Helper function.
    """

    statistics = SimulationStatistics(

        result
    )

    return statistics.report()


# ======================================================================


__all__ = [

    "KPIResult",

    "SimulationStatistics",

    "calculate_statistics",
]
```