"""
PDF-rapportgeneratie voor geselecteerde rapportageonderdelen (FR10).

Gebruikt fpdf2 (puur Python, geen systeemdependencies) voor de PDF-opbouw en
matplotlib voor een eenvoudige statische AGP-grafiek specifiek voor de PDF —
bewust niet dezelfde Plotly-figuren als op de pagina's zelf (die zouden
kaleido vereisen, met een ingebakken Chromium-build: een groter image en meer
Trivy-oppervlak, voor dit doel niet nodig).
"""

from __future__ import annotations

import io

import matplotlib

matplotlib.use("Agg")
import matplotlib.pyplot as plt
import pandas as pd
from fpdf import FPDF
from fpdf.enums import XPos, YPos

from reporting import (
    HIGH,
    LOW,
    VERY_HIGH,
    VERY_LOW,
    agp_percentile_bands,
    completeness_warning,
    detect_events,
    gmi_percent,
    percent_cv,
    sensor_active_percent,
    tir_breakdown_5band,
)

SECTION_SUMMARY = "Samenvatting"
SECTION_AGP = "AGP profiel"
SECTION_DAGOVERZICHT = "Dagoverzicht"
SECTION_EVENTS = "Hypo-/hyperevents"

AVAILABLE_SECTIONS = [SECTION_SUMMARY, SECTION_AGP, SECTION_DAGOVERZICHT, SECTION_EVENTS]

_LINE_KWARGS = {"new_x": XPos.LMARGIN, "new_y": YPos.NEXT}

# Zelfde kleuren als het PALETTE["light"] in ui_common.py — bewust hier
# gedupliceerd (5 hex-waarden) in plaats van ui_common.py te importeren, want
# dat zou een streamlit-dependency in deze module trekken die verder bewust
# vrij van UI-framework-imports is (los te unit-testen, zie moduledocstring).
_MUTED = "#898781"
_TIR_BAND_ORDER = ["very_low", "low", "in_range", "high", "very_high"]
_TIR_BAND_LABELS = {
    "very_low": "Zeer laag",
    "low": "Laag",
    "in_range": "In bereik",
    "high": "Hoog",
    "very_high": "Zeer hoog",
}
_TIR_COLORS = {
    "very_low": "#8b1a1a",
    "low": "#d03b3b",
    "in_range": "#0ca30c",
    "high": "#fab219",
    "very_high": "#c76f0a",
}
_THRESHOLD_LINES = [VERY_LOW, LOW, HIGH, VERY_HIGH]
DAGOVERZICHT_MAX_DAYS = 14


def _add_threshold_lines(ax) -> None:
    """Teken de 4 klinische drempellijnen (3.0/3.9/10.0/13.9 mmol/L) op een matplotlib-as.

    PDF-tegenhanger van ui_common.add_threshold_lines() voor de Plotly-versie
    op de dashboardpagina's (gitea #15) — zelfde 4 waarden, zelfde grijstint.
    """
    for value in _THRESHOLD_LINES:
        ax.axhline(value, color=_MUTED, linewidth=0.7, linestyle=":")


def _agp_figure_png(glucose: pd.DataFrame) -> bytes:
    bands = agp_percentile_bands(glucose)
    fig, ax = plt.subplots(figsize=(7, 3))
    hours = bands["minute_of_day"] / 60
    ax.fill_between(hours, bands["p5"], bands["p95"], alpha=0.15, color="#2a78d6", label="5e-95e pct")
    ax.fill_between(hours, bands["p25"], bands["p75"], alpha=0.3, color="#2a78d6", label="25e-75e pct")
    ax.plot(hours, bands["p50"], color="#2a78d6", linewidth=2, label="Mediaan")
    _add_threshold_lines(ax)
    ax.set_xlabel("Uur van de dag")
    ax.set_ylabel("mmol/L")
    ax.set_xlim(0, 24)
    ax.legend(loc="upper right", fontsize=8)
    fig.tight_layout()

    buf = io.BytesIO()
    fig.savefig(buf, format="png", dpi=150)
    plt.close(fig)
    buf.seek(0)
    return buf.getvalue()


def _tir_bar_figure_png(tir: dict) -> bytes:
    """Gestapelde staafgrafiek van de 5 TIR-banden — PDF-tegenhanger van
    ui_common.build_tir_stacked_bar_figure() (gitea #14)."""
    fig, ax = plt.subplots(figsize=(7, 1.1))
    left = 0.0
    for band in _TIR_BAND_ORDER:
        value = tir[band]
        ax.barh(0, value, left=left, color=_TIR_COLORS[band], label=_TIR_BAND_LABELS[band])
        left += value
    ax.set_xlim(0, 100)
    ax.set_yticks([])
    ax.set_xlabel("%")
    ax.legend(loc="upper center", bbox_to_anchor=(0.5, -0.75), ncol=5, fontsize=7, frameon=False)

    buf = io.BytesIO()
    # bbox_inches="tight" i.p.v. fig.tight_layout(): de legend zit buiten de
    # axes (bbox_to_anchor met negatieve y), waar tight_layout() niet voor
    # kan compenseren — savefig's eigen bbox-berekening kan dat wel.
    fig.savefig(buf, format="png", dpi=150, bbox_inches="tight")
    plt.close(fig)
    buf.seek(0)
    return buf.getvalue()


def _daily_figure_png(day_df: pd.DataFrame, day_label: str) -> bytes:
    """Eén dag als kleine lijngrafiek — PDF-tegenhanger van de Dagoverzicht-pagina (gitea #16)."""
    hours = day_df["timestamp"].dt.hour + day_df["timestamp"].dt.minute / 60
    fig, ax = plt.subplots(figsize=(7, 1.4))
    ax.plot(hours, day_df["mmol"], color="#2a78d6", linewidth=1.2)
    _add_threshold_lines(ax)
    ax.set_xlim(0, 24)
    ax.set_xticks(range(0, 25, 4))
    ax.set_ylabel("mmol/L", fontsize=8)
    ax.set_title(day_label, fontsize=9, loc="left")
    ax.tick_params(labelsize=7)
    fig.tight_layout()

    buf = io.BytesIO()
    fig.savefig(buf, format="png", dpi=150)
    plt.close(fig)
    buf.seek(0)
    return buf.getvalue()


def _event_hour_histogram_png(events: pd.DataFrame, color: str) -> bytes:
    """Aantal events per uur-van-de-dag — PDF-tegenhanger van render_event_level()'s
    histogram op de Hypo/Hyper Events-pagina's (gitea #17, #18)."""
    hour_counts = events["start"].dt.hour.value_counts().reindex(range(24), fill_value=0).sort_index()
    fig, ax = plt.subplots(figsize=(7, 2))
    ax.bar(hour_counts.index, hour_counts.to_numpy(), color=color)
    ax.set_xlabel("Uur van de dag")
    ax.set_ylabel("Aantal events")
    ax.set_xlim(-0.5, 23.5)
    ax.set_xticks(range(0, 24, 2))
    fig.tight_layout()

    buf = io.BytesIO()
    fig.savefig(buf, format="png", dpi=150)
    plt.close(fig)
    buf.seek(0)
    return buf.getvalue()


def _add_summary_section(pdf: FPDF, glucose: pd.DataFrame, start, end) -> None:
    mean_mmol = glucose["mmol"].mean()
    tir = tir_breakdown_5band(glucose)
    active_pct = sensor_active_percent(glucose, start, end)

    pdf.set_font("Helvetica", "B", 13)
    pdf.cell(0, 8, "Samenvatting", **_LINE_KWARGS)

    warning = completeness_warning(glucose, start, end)
    if warning:
        pdf.set_font("Helvetica", "BI", 10)
        pdf.multi_cell(0, 5, warning)
        pdf.ln(2)

    pdf.set_font("Helvetica", "", 11)
    pdf.cell(0, 6, f"Sensordekking: {active_pct:.0f}%", **_LINE_KWARGS)
    pdf.cell(0, 6, f"Gemiddelde glucose: {mean_mmol:.1f} mmol/L", **_LINE_KWARGS)
    pdf.cell(0, 6, f"Geschat HbA1c (GMI): {gmi_percent(mean_mmol):.1f}%", **_LINE_KWARGS)
    pdf.cell(0, 6, f"Variabiliteit (%CV): {percent_cv(glucose):.0f}%", **_LINE_KWARGS)
    pdf.ln(2)
    pdf.cell(0, 6, f"Zeer laag (<3.0 mmol/L): {tir['very_low']:.0f}%", **_LINE_KWARGS)
    pdf.cell(0, 6, f"Laag (3.0-3.9 mmol/L): {tir['low']:.0f}%", **_LINE_KWARGS)
    pdf.cell(0, 6, f"In bereik (3.9-10.0 mmol/L): {tir['in_range']:.0f}%", **_LINE_KWARGS)
    pdf.cell(0, 6, f"Hoog (10.0-13.9 mmol/L): {tir['high']:.0f}%", **_LINE_KWARGS)
    pdf.cell(0, 6, f"Zeer hoog (>13.9 mmol/L): {tir['very_high']:.0f}%", **_LINE_KWARGS)
    pdf.ln(2)
    png_bytes = _tir_bar_figure_png(tir)
    pdf.image(io.BytesIO(png_bytes), w=160)
    pdf.ln(4)


def _add_agp_section(pdf: FPDF, glucose: pd.DataFrame) -> None:
    pdf.set_font("Helvetica", "B", 13)
    pdf.cell(0, 8, "AGP profiel", **_LINE_KWARGS)
    png_bytes = _agp_figure_png(glucose)
    pdf.image(io.BytesIO(png_bytes), w=180)
    pdf.ln(4)


def _add_dagoverzicht_section(pdf: FPDF, glucose: pd.DataFrame) -> None:
    """Eén dagelijkse mini-grafiek per kalenderdag (gitea #16), max. de meest
    recente DAGOVERZICHT_MAX_DAYS dagen — voor langere periodes zou de PDF
    anders onwerkbaar lang worden."""
    pdf.set_font("Helvetica", "B", 13)
    pdf.cell(0, 8, "Dagoverzicht", **_LINE_KWARGS)

    df = glucose.copy()
    df["date"] = df["timestamp"].dt.date
    dates = sorted(df["date"].unique())
    if len(dates) > DAGOVERZICHT_MAX_DAYS:
        dates = dates[-DAGOVERZICHT_MAX_DAYS:]
        pdf.set_font("Helvetica", "I", 9)
        pdf.cell(0, 5, f"Beperkt tot de meest recente {DAGOVERZICHT_MAX_DAYS} dagen.", **_LINE_KWARGS)

    for day in dates:
        day_df = df[df["date"] == day]
        png_bytes = _daily_figure_png(day_df, day.strftime("%A %d-%m-%Y"))
        pdf.image(io.BytesIO(png_bytes), w=180)
    pdf.ln(4)


def _add_events_section(pdf: FPDF, glucose: pd.DataFrame, events_summary: dict[str, dict]) -> None:
    pdf.set_font("Helvetica", "B", 13)
    pdf.cell(0, 8, "Hypo-/hyperevents", **_LINE_KWARGS)
    pdf.set_font("Helvetica", "", 11)
    for label, summary in events_summary.items():
        pdf.cell(
            0,
            6,
            f"{label}: {summary['count']} events, gemiddeld {summary['avg_duration_minutes']:.0f} min, "
            f"{summary['events_per_week']:.1f}/week",
            **_LINE_KWARGS,
        )
    pdf.ln(2)

    # Alleen Level 1 krijgt een grafiek in het PDF-rapport (gitea #17, #18) —
    # geen tabel met losse events, de samenvattende cijfers staan al hierboven.
    if not glucose.empty:
        hypo_events = detect_events(glucose, LOW, "below")
        if not hypo_events.empty:
            pdf.image(io.BytesIO(_event_hour_histogram_png(hypo_events, _TIR_COLORS["low"])), w=180)
        hyper_events = detect_events(glucose, HIGH, "above")
        if not hyper_events.empty:
            pdf.image(io.BytesIO(_event_hour_histogram_png(hyper_events, _TIR_COLORS["high"])), w=180)

    pdf.ln(4)


def build_report_pdf(
    sections: list[str],
    glucose: pd.DataFrame,
    events_summary: dict[str, dict],
    start,
    end,
) -> bytes:
    """Bouw een PDF met de geselecteerde onderdelen.

    `events_summary` bevat vooraf berekende hypo/hyper-samenvattingen (label ->
    resultaat van reporting.summarize_events), zodat deze module geen
    event-detectie hoeft te herhalen.
    """
    pdf = FPDF()
    pdf.add_page()
    pdf.set_font("Helvetica", "B", 16)
    pdf.cell(0, 10, "Glucoserapport", **_LINE_KWARGS)
    pdf.set_font("Helvetica", "", 11)
    pdf.cell(0, 8, f"Periode: {start.strftime('%d-%m-%Y')} t/m {end.strftime('%d-%m-%Y')}", **_LINE_KWARGS)
    pdf.ln(4)

    if not glucose.empty:
        if SECTION_SUMMARY in sections:
            _add_summary_section(pdf, glucose, start, end)
        if SECTION_AGP in sections:
            _add_agp_section(pdf, glucose)
        if SECTION_DAGOVERZICHT in sections:
            _add_dagoverzicht_section(pdf, glucose)

    if SECTION_EVENTS in sections and events_summary:
        _add_events_section(pdf, glucose, events_summary)

    return bytes(pdf.output())