"""Tests for src.analyzer — core aggregation and analysis logic."""
import sys
from pathlib import Path

import pandas as pd
import numpy as np

sys.path.insert(0, str(Path(__file__).resolve().parent.parent))

from src.analyzer import _agg_group, _safe_pct_change, compute_wow


def _make_df(rows: list[dict]) -> pd.DataFrame:
    return pd.DataFrame(rows)


class TestSafePctChange:
    def test_normal(self):
        assert _safe_pct_change(150, 100) == 50.0

    def test_decrease(self):
        assert _safe_pct_change(80, 100) == -20.0

    def test_zero_prev(self):
        assert _safe_pct_change(100, 0) is None

    def test_nan_prev(self):
        assert _safe_pct_change(100, float("nan")) is None


class TestAggGroup:
    def test_single_row(self):
        df = _make_df([{
            "Cost": 100, "Ads.Sales": 500, "Impressions": 10000,
            "CTR": 0.02, "GA4.Sessions": 50, "CVR": 0.05,
        }])
        result = _agg_group(df, ["Cost", "Ads.Sales", "ROAS", "CTR", "CVR", "CPC"])
        assert result["Cost"] == 100
        assert result["Ads.Sales"] == 500
        assert result["ROAS"] == 5.0
        assert abs(result["CTR"] - 0.02) < 1e-6
        assert abs(result["CVR"] - 0.05) < 1e-6

    def test_multi_row_weighted_ctr(self):
        df = _make_df([
            {"Cost": 100, "Ads.Sales": 300, "Impressions": 10000, "CTR": 0.01, "GA4.Sessions": 20, "CVR": 0.1},
            {"Cost": 200, "Ads.Sales": 400, "Impressions": 20000, "CTR": 0.03, "GA4.Sessions": 80, "CVR": 0.05},
        ])
        result = _agg_group(df, ["Cost", "Ads.Sales", "ROAS", "CTR", "CVR", "CPC"])
        assert result["Cost"] == 300
        assert result["Ads.Sales"] == 700

        expected_clicks = 10000 * 0.01 + 20000 * 0.03  # 100 + 600 = 700
        expected_ctr = expected_clicks / 30000
        assert abs(result["CTR"] - expected_ctr) < 1e-6
        assert abs(result["CPC"] - 300 / expected_clicks) < 1e-6

    def test_empty_df(self):
        df = _make_df([])
        df = pd.DataFrame(columns=["Cost", "Ads.Sales", "Impressions", "CTR"])
        result = _agg_group(df, ["Cost", "Ads.Sales"])
        assert result.get("Cost", 0) == 0
        assert result.get("Ads.Sales", 0) == 0


class TestComputeWow:
    def test_normal_change(self):
        curr = {"Cost": 200, "ROAS": 3.0}
        prev = {"Cost": 100, "ROAS": 2.0}
        result = compute_wow(curr, prev, ["Cost", "ROAS"])
        assert result["Cost"] == 100.0
        assert result["ROAS"] == 50.0

    def test_zero_prev(self):
        curr = {"Cost": 100}
        prev = {"Cost": 0}
        result = compute_wow(curr, prev, ["Cost"])
        assert result["Cost"] is None