"""Reporting service layer aggregating deterministic and simulation metrics."""

from __future__ import annotations

from dataclasses import dataclass, field
from datetime import date
import math
from typing import Mapping, Sequence
from urllib.parse import urlencode

import plotly.graph_objects as go
import plotly.io as pio

from fastapi import Request

from models import FinancialCategory, Project, Scenario
from services.financial import (
    CashFlow,
    ConvergenceError,
    PaybackNotReachedError,
    internal_rate_of_return,
    net_present_value,
    payback_period,
)
from services.simulation import (
    CashFlowSpec,
    SimulationConfig,
    SimulationMetric,
    SimulationResult,
    run_monte_carlo,
)
from services.unit_of_work import UnitOfWork

DEFAULT_DISCOUNT_RATE = 0.1
DEFAULT_ITERATIONS = 500
DEFAULT_PERCENTILES: tuple[float, float, float] = (5.0, 50.0, 95.0)

_COST_CATEGORY_SIGNS: Mapping[FinancialCategory, float] = {
    FinancialCategory.REVENUE: 1.0,
    FinancialCategory.CAPITAL_EXPENDITURE: -1.0,
    FinancialCategory.OPERATING_EXPENDITURE: -1.0,
    FinancialCategory.CONTINGENCY: -1.0,
    FinancialCategory.OTHER: -1.0,
}


@dataclass(frozen=True)
class IncludeOptions:
    """Flags controlling optional sections in report payloads."""

    distribution: bool = False
    samples: bool = False


@dataclass(slots=True)
class ReportFilters:
    """Filter parameters applied when selecting scenarios for a report."""

    scenario_ids: set[int] | None = None
    start_date: date | None = None
    end_date: date | None = None

    def matches(self, scenario: Scenario) -> bool:
        if self.scenario_ids is not None and scenario.id not in self.scenario_ids:
            return False
        if self.start_date and scenario.start_date and scenario.start_date < self.start_date:
            return False
        if self.end_date and scenario.end_date and scenario.end_date > self.end_date:
            return False
        return True

    def to_dict(self) -> dict[str, object]:
        payload: dict[str, object] = {}
        if self.scenario_ids is not None:
            payload["scenario_ids"] = sorted(self.scenario_ids)
        if self.start_date is not None:
            payload["start_date"] = self.start_date
        if self.end_date is not None:
            payload["end_date"] = self.end_date
        return payload


@dataclass(slots=True)
class ScenarioFinancialTotals:
    currency: str | None
    inflows: float
    outflows: float
    net: float
    by_category: dict[str, float]

    def to_dict(self) -> dict[str, object]:
        return {
            "currency": self.currency,
            "inflows": _round_optional(self.inflows),
            "outflows": _round_optional(self.outflows),
            "net": _round_optional(self.net),
            "by_category": {
                key: _round_optional(value) for key, value in sorted(self.by_category.items())
            },
        }


@dataclass(slots=True)
class ScenarioDeterministicMetrics:
    currency: str | None
    discount_rate: float
    compounds_per_year: int
    npv: float | None
    irr: float | None
    payback_period: float | None
    notes: list[str] = field(default_factory=list)

    def to_dict(self) -> dict[str, object]:
        return {
            "currency": self.currency,
            "discount_rate": _round_optional(self.discount_rate, digits=4),
            "compounds_per_year": self.compounds_per_year,
            "npv": _round_optional(self.npv),
            "irr": _round_optional(self.irr, digits=6),
            "payback_period": _round_optional(self.payback_period, digits=4),
            "notes": self.notes,
        }


@dataclass(slots=True)
class ScenarioMonteCarloResult:
    available: bool
    notes: list[str] = field(default_factory=list)
    result: SimulationResult | None = None
    include_samples: bool = False

    def to_dict(self) -> dict[str, object]:
        if not self.available or self.result is None:
            return {
                "available": False,
                "notes": self.notes,
            }

        metrics: dict[str, dict[str, object]] = {}
        for metric, summary in self.result.summaries.items():
            metrics[metric.value] = {
                "mean": _round_optional(summary.mean),
                "std_dev": _round_optional(summary.std_dev),
                "minimum": _round_optional(summary.minimum),
                "maximum": _round_optional(summary.maximum),
                "percentiles": {
                    f"{percentile:g}": _round_optional(value)
                    for percentile, value in sorted(summary.percentiles.items())
                },
                "sample_size": summary.sample_size,
                "failed_runs": summary.failed_runs,
            }

        samples_payload: dict[str, list[float | None]] | None = None
        if self.include_samples and self.result.samples:
            samples_payload = {}
            for metric, samples in self.result.samples.items():
                samples_payload[metric.value] = [
                    _sanitize_float(sample) for sample in samples.tolist()
                ]

        payload: dict[str, object] = {
            "available": True,
            "iterations": self.result.iterations,
            "metrics": metrics,
            "notes": self.notes,
        }
        if samples_payload:
            payload["samples"] = samples_payload
        return payload


@dataclass(slots=True)
class ScenarioReport:
    scenario: Scenario
    totals: ScenarioFinancialTotals
    deterministic: ScenarioDeterministicMetrics
    monte_carlo: ScenarioMonteCarloResult | None

    def to_dict(self) -> dict[str, object]:
        scenario_info = {
            "id": self.scenario.id,
            "project_id": self.scenario.project_id,
            "name": self.scenario.name,
            "description": self.scenario.description,
            "status": self.scenario.status.value if hasattr(self.scenario.status, 'value') else self.scenario.status,
            "start_date": self.scenario.start_date,
            "end_date": self.scenario.end_date,
            "currency": self.scenario.currency,
            "primary_resource": self.scenario.primary_resource.value
            if self.scenario.primary_resource and hasattr(self.scenario.primary_resource, 'value')
            else self.scenario.primary_resource,
            "discount_rate": _round_optional(self.deterministic.discount_rate, digits=4),
            "created_at": self.scenario.created_at,
            "updated_at": self.scenario.updated_at,
            "simulation_parameter_count": len(self.scenario.simulation_parameters or []),
        }
        payload: dict[str, object] = {
            "scenario": scenario_info,
            "financials": self.totals.to_dict(),
            "metrics": self.deterministic.to_dict(),
        }
        if self.monte_carlo is not None:
            payload["monte_carlo"] = self.monte_carlo.to_dict()
        return payload


@dataclass(slots=True)
class AggregatedMetric:
    average: float | None
    minimum: float | None
    maximum: float | None

    def to_dict(self) -> dict[str, object]:
        return {
            "average": _round_optional(self.average),
            "minimum": _round_optional(self.minimum),
            "maximum": _round_optional(self.maximum),
        }


@dataclass(slots=True)
class ProjectAggregates:
    total_inflows: float
    total_outflows: float
    total_net: float
    deterministic_metrics: dict[str, AggregatedMetric]

    def to_dict(self) -> dict[str, object]:
        return {
            "financials": {
                "total_inflows": _round_optional(self.total_inflows),
                "total_outflows": _round_optional(self.total_outflows),
                "total_net": _round_optional(self.total_net),
            },
            "deterministic_metrics": {
                metric: data.to_dict()
                for metric, data in sorted(self.deterministic_metrics.items())
            },
        }


@dataclass(slots=True)
class MetricComparison:
    metric: str
    direction: str
    best: tuple[int, str, float] | None
    worst: tuple[int, str, float] | None
    average: float | None

    def to_dict(self) -> dict[str, object]:
        return {
            "metric": self.metric,
            "direction": self.direction,
            "best": _comparison_entry(self.best),
            "worst": _comparison_entry(self.worst),
            "average": _round_optional(self.average),
        }


def parse_include_tokens(raw: str | None) -> IncludeOptions:
    tokens: set[str] = set()
    if raw:
        for part in raw.split(","):
            token = part.strip().lower()
            if token:
                tokens.add(token)
    if "all" in tokens:
        return IncludeOptions(distribution=True, samples=True)
    return IncludeOptions(
        distribution=bool({"distribution", "monte_carlo", "mc"} & tokens),
        samples="samples" in tokens,
    )


def validate_percentiles(values: Sequence[float] | None) -> tuple[float, ...]:
    if not values:
        return DEFAULT_PERCENTILES
    seen: set[float] = set()
    cleaned: list[float] = []
    for value in values:
        percentile = float(value)
        if percentile < 0.0 or percentile > 100.0:
            raise ValueError("Percentiles must be between 0 and 100.")
        if percentile not in seen:
            seen.add(percentile)
            cleaned.append(percentile)
    if not cleaned:
        return DEFAULT_PERCENTILES
    return tuple(cleaned)


class ReportingService:
    """Coordinates project and scenario reporting aggregation."""

    def __init__(self, uow: UnitOfWork) -> None:
        self._uow = uow

    def project_summary(
        self,
        project: Project,
        *,
        filters: ReportFilters,
        include: IncludeOptions,
        iterations: int,
        percentiles: tuple[float, ...],
    ) -> dict[str, object]:
        scenarios = self._load_scenarios(project.id, filters)
        reports = [
            self._build_scenario_report(
                scenario,
                include_distribution=include.distribution,
                include_samples=include.samples,
                iterations=iterations,
                percentiles=percentiles,
            )
            for scenario in scenarios
        ]
        aggregates = self._aggregate_project(reports)
        return {
            "project": _project_payload(project),
            "scenario_count": len(reports),
            "filters": filters.to_dict(),
            "aggregates": aggregates.to_dict(),
            "scenarios": [report.to_dict() for report in reports],
        }

    def scenario_comparison(
        self,
        project: Project,
        scenarios: Sequence[Scenario],
        *,
        include: IncludeOptions,
        iterations: int,
        percentiles: tuple[float, ...],
    ) -> dict[str, object]:
        reports = [
            self._build_scenario_report(
                self._reload_scenario(scenario.id),
                include_distribution=include.distribution,
                include_samples=include.samples,
                iterations=iterations,
                percentiles=percentiles,
            )
            for scenario in scenarios
        ]
        comparison = {
            metric: data.to_dict()
            for metric, data in self._build_comparisons(reports).items()
        }
        return {
            "project": _project_payload(project),
            "scenarios": [report.to_dict() for report in reports],
            "comparison": comparison,
        }

    def scenario_distribution(
        self,
        scenario: Scenario,
        *,
        include: IncludeOptions,
        iterations: int,
        percentiles: tuple[float, ...],
    ) -> dict[str, object]:
        report = self._build_scenario_report(
            self._reload_scenario(scenario.id),
            include_distribution=True,
            include_samples=include.samples,
            iterations=iterations,
            percentiles=percentiles,
        )
        return {
            "scenario": report.to_dict()["scenario"],
            "summary": report.totals.to_dict(),
            "metrics": report.deterministic.to_dict(),
            "monte_carlo": (
                report.monte_carlo.to_dict() if report.monte_carlo else {
                    "available": False}
            ),
        }

    def _load_scenarios(self, project_id: int, filters: ReportFilters) -> list[Scenario]:
        scenarios = self._uow.scenarios.list_for_project(
            project_id, with_children=True)
        return [scenario for scenario in scenarios if filters.matches(scenario)]

    def _reload_scenario(self, scenario_id: int) -> Scenario:
        return self._uow.scenarios.get(scenario_id, with_children=True)

    def _build_scenario_report(
        self,
        scenario: Scenario,
        *,
        include_distribution: bool,
        include_samples: bool,
        iterations: int,
        percentiles: tuple[float, ...],
    ) -> ScenarioReport:
        cash_flows, totals = _build_cash_flows(scenario)
        deterministic = _calculate_deterministic_metrics(
            scenario, cash_flows, totals)
        monte_carlo: ScenarioMonteCarloResult | None = None
        if include_distribution:
            monte_carlo = _run_monte_carlo(
                scenario,
                cash_flows,
                include_samples=include_samples,
                iterations=iterations,
                percentiles=percentiles,
            )
        return ScenarioReport(
            scenario=scenario,
            totals=totals,
            deterministic=deterministic,
            monte_carlo=monte_carlo,
        )

    def _aggregate_project(self, reports: Sequence[ScenarioReport]) -> ProjectAggregates:
        total_inflows = sum(report.totals.inflows for report in reports)
        total_outflows = sum(report.totals.outflows for report in reports)
        total_net = sum(report.totals.net for report in reports)

        metrics: dict[str, AggregatedMetric] = {}
        for metric_name in ("npv", "irr", "payback_period"):
            values = [
                getattr(report.deterministic, metric_name)
                for report in reports
                if getattr(report.deterministic, metric_name) is not None
            ]
            if values:
                metrics[metric_name] = AggregatedMetric(
                    average=sum(values) / len(values),
                    minimum=min(values),
                    maximum=max(values),
                )
        return ProjectAggregates(
            total_inflows=total_inflows,
            total_outflows=total_outflows,
            total_net=total_net,
            deterministic_metrics=metrics,
        )

    def _build_comparisons(
        self, reports: Sequence[ScenarioReport]
    ) -> Mapping[str, MetricComparison]:
        comparisons: dict[str, MetricComparison] = {}
        for metric_name, direction in (
            ("npv", "higher_is_better"),
            ("irr", "higher_is_better"),
            ("payback_period", "lower_is_better"),
        ):
            entries: list[tuple[int, str, float]] = []
            for report in reports:
                value = getattr(report.deterministic, metric_name)
                if value is None:
                    continue
                entries.append(
                    (report.scenario.id, report.scenario.name, value))
            if not entries:
                continue
            if direction == "higher_is_better":
                best = max(entries, key=lambda item: item[2])
                worst = min(entries, key=lambda item: item[2])
            else:
                best = min(entries, key=lambda item: item[2])
                worst = max(entries, key=lambda item: item[2])
            average = sum(item[2] for item in entries) / len(entries)
            comparisons[metric_name] = MetricComparison(
                metric=metric_name,
                direction=direction,
                best=best,
                worst=worst,
                average=average,
            )
        return comparisons

    def build_project_summary_context(
        self,
        project: Project,
        filters: ReportFilters,
        include: IncludeOptions,
        iterations: int,
        percentiles: tuple[float, ...],
        request: Request,
    ) -> dict[str, object]:
        """Build template context for project summary page."""
        scenarios = self._load_scenarios(project.id, filters)
        reports = [
            self._build_scenario_report(
                scenario,
                include_distribution=include.distribution,
                include_samples=include.samples,
                iterations=iterations,
                percentiles=percentiles,
            )
            for scenario in scenarios
        ]
        aggregates = self._aggregate_project(reports)

        return {
            "request": request,
            "project": _project_payload(project),
            "scenario_count": len(reports),
            "aggregates": aggregates.to_dict(),
            "scenarios": [report.to_dict() for report in reports],
            "filters": filters.to_dict(),
            "include_options": include,
            "iterations": iterations,
            "percentiles": percentiles,
            "title": f"Project Summary · {project.name}",
            "subtitle": "Aggregated financial and simulation insights across scenarios.",
            "actions": [
                {
                    "href": request.url_for(
                        "reports.project_summary",
                        project_id=project.id,
                    ),
                    "label": "Download JSON",
                }
            ],
            "chart_data": self._generate_npv_comparison_chart(reports),
        }

    def build_scenario_comparison_context(
        self,
        project: Project,
        scenarios: Sequence[Scenario],
        include: IncludeOptions,
        iterations: int,
        percentiles: tuple[float, ...],
        request: Request,
    ) -> dict[str, object]:
        """Build template context for scenario comparison page."""
        reports = [
            self._build_scenario_report(
                self._reload_scenario(scenario.id),
                include_distribution=include.distribution,
                include_samples=include.samples,
                iterations=iterations,
                percentiles=percentiles,
            )
            for scenario in scenarios
        ]
        comparison = {
            metric: data.to_dict()
            for metric, data in self._build_comparisons(reports).items()
        }

        comparison_json_url = request.url_for(
            "reports.project_scenario_comparison",
            project_id=project.id,
        )
        scenario_ids = [str(s.id) for s in scenarios]
        comparison_query = urlencode(
            [("scenario_ids", str(identifier)) for identifier in scenario_ids]
        )
        if comparison_query:
            comparison_json_url = f"{comparison_json_url}?{comparison_query}"

        return {
            "request": request,
            "project": _project_payload(project),
            "scenarios": [report.to_dict() for report in reports],
            "comparison": comparison,
            "include_options": include,
            "iterations": iterations,
            "percentiles": percentiles,
            "title": f"Scenario Comparison · {project.name}",
            "subtitle": "Evaluate deterministic metrics and Monte Carlo trends side by side.",
            "actions": [
                {
                    "href": comparison_json_url,
                    "label": "Download JSON",
                }
            ],
        }

    def build_scenario_distribution_context(
        self,
        scenario: Scenario,
        include: IncludeOptions,
        iterations: int,
        percentiles: tuple[float, ...],
        request: Request,
    ) -> dict[str, object]:
        """Build template context for scenario distribution page."""
        report = self._build_scenario_report(
            self._reload_scenario(scenario.id),
            include_distribution=True,
            include_samples=include.samples,
            iterations=iterations,
            percentiles=percentiles,
        )

        return {
            "request": request,
            "scenario": report.to_dict()["scenario"],
            "summary": report.totals.to_dict(),
            "metrics": report.deterministic.to_dict(),
            "monte_carlo": (
                report.monte_carlo.to_dict() if report.monte_carlo else {
                    "available": False}
            ),
            "include_options": include,
            "iterations": iterations,
            "percentiles": percentiles,
            "title": f"Scenario Distribution · {scenario.name}",
            "subtitle": "Deterministic and simulated distributions for a single scenario.",
            "actions": [
                {
                    "href": request.url_for(
                        "reports.scenario_distribution",
                        scenario_id=scenario.id,
                    ),
                    "label": "Download JSON",
                }
            ],
            "chart_data": self._generate_distribution_histogram(report.monte_carlo) if report.monte_carlo else "{}",
        }

    def _generate_npv_comparison_chart(self, reports: Sequence[ScenarioReport]) -> str:
        """Generate Plotly chart JSON for NPV comparison across scenarios."""
        scenario_names = []
        npv_values = []

        for report in reports:
            scenario_names.append(report.scenario.name)
            npv_values.append(report.deterministic.npv or 0)

        fig = go.Figure(data=[
            go.Bar(
                x=scenario_names,
                y=npv_values,
                name='NPV',
                marker_color='lightblue'
            )
        ])

        fig.update_layout(
            title="NPV Comparison Across Scenarios",
            xaxis_title="Scenario",
            yaxis_title="NPV",
            showlegend=False
        )

        return pio.to_json(fig) or "{}"

    def _generate_distribution_histogram(self, monte_carlo: ScenarioMonteCarloResult) -> str:
        """Generate Plotly histogram for Monte Carlo distribution."""
        if not monte_carlo.available or not monte_carlo.result or not monte_carlo.result.samples:
            return "{}"

        # Get NPV samples
        npv_samples = monte_carlo.result.samples.get(SimulationMetric.NPV, [])
        if len(npv_samples) == 0:
            return "{}"

        fig = go.Figure(data=[
            go.Histogram(
                x=npv_samples,
                nbinsx=50,
                name='NPV Distribution',
                marker_color='lightgreen'
            )
        ])

        fig.update_layout(
            title="Monte Carlo NPV Distribution",
            xaxis_title="NPV",
            yaxis_title="Frequency",
            showlegend=False
        )

        return pio.to_json(fig) or "{}"


def _build_cash_flows(scenario: Scenario) -> tuple[list[CashFlow], ScenarioFinancialTotals]:
    cash_flows: list[CashFlow] = []
    by_category: dict[str, float] = {}
    inflows = 0.0
    outflows = 0.0
    net = 0.0
    period_index = 0

    for financial_input in scenario.financial_inputs or []:
        sign = _COST_CATEGORY_SIGNS.get(financial_input.category, -1.0)
        amount = float(financial_input.amount) * sign
        net += amount
        if amount >= 0:
            inflows += amount
        else:
            outflows += -amount
        by_category.setdefault(financial_input.category.value, 0.0)
        by_category[financial_input.category.value] += amount

        if financial_input.effective_date is not None:
            cash_flows.append(
                CashFlow(amount=amount, date=financial_input.effective_date)
            )
        else:
            cash_flows.append(
                CashFlow(amount=amount, period_index=period_index))
            period_index += 1

    currency = scenario.currency
    if currency is None and scenario.financial_inputs:
        currency = scenario.financial_inputs[0].currency

    totals = ScenarioFinancialTotals(
        currency=currency,
        inflows=inflows,
        outflows=outflows,
        net=net,
        by_category=by_category,
    )
    return cash_flows, totals


def _calculate_deterministic_metrics(
    scenario: Scenario,
    cash_flows: Sequence[CashFlow],
    totals: ScenarioFinancialTotals,
) -> ScenarioDeterministicMetrics:
    notes: list[str] = []
    discount_rate = _normalise_discount_rate(scenario.discount_rate)
    if scenario.discount_rate is None:
        notes.append(
            f"Discount rate not set; defaulted to {discount_rate:.2%}."
        )

    if not cash_flows:
        notes.append(
            "No financial inputs available for deterministic metrics.")
        return ScenarioDeterministicMetrics(
            currency=totals.currency,
            discount_rate=discount_rate,
            compounds_per_year=1,
            npv=None,
            irr=None,
            payback_period=None,
            notes=notes,
        )

    npv_value: float | None
    try:
        npv_value = net_present_value(
            discount_rate,
            cash_flows,
            compounds_per_year=1,
        )
    except ValueError as exc:
        npv_value = None
        notes.append(f"NPV unavailable: {exc}.")

    irr_value: float | None
    try:
        irr_value = internal_rate_of_return(
            cash_flows,
            compounds_per_year=1,
        )
    except (ValueError, ConvergenceError) as exc:
        irr_value = None
        notes.append(f"IRR unavailable: {exc}.")

    payback_value: float | None
    try:
        payback_value = payback_period(
            cash_flows,
            compounds_per_year=1,
        )
    except (ValueError, PaybackNotReachedError) as exc:
        payback_value = None
        notes.append(f"Payback period unavailable: {exc}.")

    return ScenarioDeterministicMetrics(
        currency=totals.currency,
        discount_rate=discount_rate,
        compounds_per_year=1,
        npv=npv_value,
        irr=irr_value,
        payback_period=payback_value,
        notes=notes,
    )


def _run_monte_carlo(
    scenario: Scenario,
    cash_flows: Sequence[CashFlow],
    *,
    include_samples: bool,
    iterations: int,
    percentiles: tuple[float, ...],
) -> ScenarioMonteCarloResult:
    if not cash_flows:
        return ScenarioMonteCarloResult(
            available=False,
            notes=["No financial inputs available for Monte Carlo simulation."],
        )

    discount_rate = _normalise_discount_rate(scenario.discount_rate)
    specs = [CashFlowSpec(cash_flow=flow) for flow in cash_flows]
    notes: list[str] = []
    if not scenario.simulation_parameters:
        notes.append(
            "Scenario has no stochastic parameters; simulation mirrors deterministic cash flows."
        )
    config = SimulationConfig(
        iterations=iterations,
        discount_rate=discount_rate,
        metrics=(
            SimulationMetric.NPV,
            SimulationMetric.IRR,
            SimulationMetric.PAYBACK,
        ),
        percentiles=percentiles,
        return_samples=include_samples,
    )
    try:
        result = run_monte_carlo(specs, config)
    except Exception as exc:  # pragma: no cover - safeguard for unexpected failures
        notes.append(f"Simulation failed: {exc}.")
        return ScenarioMonteCarloResult(available=False, notes=notes)
    return ScenarioMonteCarloResult(
        available=True,
        notes=notes,
        result=result,
        include_samples=include_samples,
    )


def _normalise_discount_rate(value: float | None) -> float:
    if value is None:
        return DEFAULT_DISCOUNT_RATE
    rate = float(value)
    if rate > 1.0:
        return rate / 100.0
    return rate


def _sanitize_float(value: float | None) -> float | None:
    if value is None:
        return None
    if math.isnan(value) or math.isinf(value):
        return None
    return float(value)


def _round_optional(value: float | None, *, digits: int = 2) -> float | None:
    clean = _sanitize_float(value)
    if clean is None:
        return None
    return round(clean, digits)


def _comparison_entry(entry: tuple[int, str, float] | None) -> dict[str, object] | None:
    if entry is None:
        return None
    scenario_id, name, value = entry
    return {
        "scenario_id": scenario_id,
        "name": name,
        "value": _round_optional(value),
    }


def _project_payload(project: Project) -> dict[str, object]:
    return {
        "id": project.id,
        "name": project.name,
        "location": project.location,
        "operation_type": project.operation_type.value,
        "description": project.description,
        "created_at": project.created_at,
        "updated_at": project.updated_at,
    }