import pandas as pd

from routes.sales_routes import _build_top_product_chart_data
from services.sales_analysis_chart_service import (
    build_analysis_scope,
    build_bcg_chart_data,
    build_heatmap_chart_data,
    build_marketing_chart_data,
    build_sales_filter_options,
    build_seasonality_chart_data,
    build_treemap_chart_data,
    resolve_yoy_year_options,
)


def test_top_product_chart_aggregates_sku_rows_before_ranking():
    rows = pd.DataFrame(
        [
            {'pid': 'P1', 'name': '同名商品', 'amount': 100},
            {'pid': 'P1', 'name': '同名商品', 'amount': 50},
            {'pid': 'P2', 'name': '同名商品', 'amount': 90},
            {'pid': 'P3', 'name': '獨立商品', 'amount': 200},
            {'pid': 'P4', 'name': '零業績商品', 'amount': 0},
        ]
    )

    result = _build_top_product_chart_data(rows, 'pid', 'name', 'amount', 'amount')

    assert result['chart_values'] == [200.0, 150.0, 90.0]
    assert result['labels'] == ['獨立商品', '同名商品 [P1]', '同名商品 [P2]']
    assert result['metric_label'] == '銷售金額 ($)'


def test_top_product_chart_returns_explicit_empty_contract():
    result = _build_top_product_chart_data(
        pd.DataFrame(columns=['name', 'amount']),
        None,
        'name',
        'amount',
        'qty',
    )

    assert result == {
        'labels': [],
        'chart_values': [],
        'metric_label': '銷售數量',
        'is_money': False,
    }


def test_sales_chart_builders_emit_the_contract_consumed_by_frontend():
    frame = pd.DataFrame([
        {
            'pid': 'P1', 'name': '商品 A', 'category': '美妝', 'amount': 100,
            'qty': 2, 'calculated_profit': 30, '_month_str': '2026-04',
            '_dow': 2, '_hour': 10,
        },
        {
            'pid': 'P1', 'name': '商品 A', 'category': '美妝', 'amount': 50,
            'qty': 1, 'calculated_profit': 10, '_month_str': '2026-04',
            '_dow': 2, '_hour': 10,
        },
        {
            'pid': 'P2', 'name': '商品 B', 'category': '3C', 'amount': 300,
            'qty': 3, 'calculated_profit': 60, '_month_str': '2026-05',
            '_dow': 4, '_hour': 21,
        },
    ])

    treemap = build_treemap_chart_data(frame, 'category', 'name', 'amount')
    bcg = build_bcg_chart_data(frame, 'pid', 'name', 'qty', 'amount')
    heatmap = build_heatmap_chart_data(frame, 'amount')
    seasonality = build_seasonality_chart_data(frame, 'category', 'amount')

    assert treemap[0].keys() == {'category', 'name', 'value'}
    assert {item['name'] for item in treemap} == {'商品 A', '商品 B'}
    assert bcg['points'][0]['name'] == '商品 B'
    assert bcg['points'][1]['x'] == 3
    assert bcg['x_median'] == 3
    assert len(heatmap['matrix']) == 7
    assert all(len(row) == 24 for row in heatmap['matrix'])
    assert heatmap['matrix'][2][10] == 150
    assert heatmap['matrix'][4][21] == 300
    assert seasonality == {
        'categories': ['3C', '美妝'],
        'months': ['2026-04', '2026-05'],
        'matrix': [[0.0, 300.0], [150.0, 0.0]],
    }


def test_sales_chart_builders_return_explicit_empty_contracts():
    empty = pd.DataFrame()

    assert build_treemap_chart_data(empty, 'category', 'name', 'amount') == []
    assert build_bcg_chart_data(empty, 'pid', 'name', 'qty', 'amount') == {
        'points': [], 'x_median': 0.0, 'y_median': 0.0,
    }
    assert build_heatmap_chart_data(empty, 'amount')['matrix'] == []
    assert build_seasonality_chart_data(empty, 'category', 'amount')['matrix'] == []


def test_marketing_and_yoy_controls_follow_active_metric_and_period():
    marketing = build_marketing_chart_data({
        'discount': [{'name': '母親節', 'revenue': 500, 'qty': 5, 'profit': 120}],
        'coupon': [{'name': '折價券', 'revenue': 300, 'qty': 3, 'profit': 80}],
    }, 'profit')
    years = resolve_yoy_year_options(
        ['2024-12', '2026-04', float('nan')],
        {'end_date': '2026-04-30'},
    )

    assert marketing['metric'] == 'profit'
    assert marketing['discount'] == {'labels': ['母親節'], 'values': [120.0]}
    assert marketing['coupon'] == {'labels': ['折價券'], 'values': [80.0]}
    assert years == {'years': [2026, 2025, 2024], 'year1': 2025, 'year2': 2026}


def test_filter_options_are_scoped_to_the_loaded_period_frame():
    frame = pd.DataFrame([
        {'category': '美妝', 'brand': 'A', '_month_str': '2026-04'},
        {'category': '3C', 'brand': 'B', '_month_str': '2026-04'},
        {'category': '美妝', 'brand': 'A', '_month_str': None},
    ])

    options = build_sales_filter_options(frame, {
        'category': 'category',
        'brand': 'brand',
        'vendor': None,
        'activity': None,
        'payment': None,
    })

    assert options['categories'] == ['3C', '美妝']
    assert options['brands'] == ['A', 'B']
    assert options['months'] == ['2026-04']


def test_analysis_scope_names_every_active_time_filter():
    scope = build_analysis_scope(
        {
            'label': '最近 6 個月',
            'start_date': '2026-01-23',
            'end_date': '2026-07-22',
        },
        month='2026-04',
        dow='2',
        hour='9',
    )

    assert scope == {
        'label': '最近 6 個月 · 月份 2026-04 · 週三 · 09:00',
        'start_date': '2026-01-23',
        'end_date': '2026-07-22',
    }