"""测试新增的时间序列函数和智能分发逻辑。"""

import pytest
import polars as pl
import numpy as np
from src.factors.dsl import Symbol, FunctionNode
from src.factors.translator import PolarsTranslator


def test_ts_sma_translate():
    """测试 ts_sma 翻译正确。"""
    close = Symbol("close")
    expr = FunctionNode("ts_sma", close, 10, 5)
    translator = PolarsTranslator()
    result = translator.translate(expr)
    assert isinstance(result, pl.Expr)


def test_ts_wma_translate():
    """测试 ts_wma 翻译正确。"""
    close = Symbol("close")
    expr = FunctionNode("ts_wma", close, 20)
    translator = PolarsTranslator()
    result = translator.translate(expr)
    assert isinstance(result, pl.Expr)


def test_ts_sumac_translate():
    """测试 ts_sumac 翻译正确。"""
    close = Symbol("close")
    expr = FunctionNode("ts_sumac", close)
    translator = PolarsTranslator()
    result = translator.translate(expr)
    assert isinstance(result, pl.Expr)


def test_max_intelligent_dispatch():
    """测试 max_ 智能分发: int -> ts_max，其他 -> element-wise max。"""
    from src.factors.api import max_, close

    # 正整数 -> ts_max
    result = max_(close, 20)
    assert result.func_name == "ts_max"

    # 零或负数 -> element-wise max
    result = max_(close, 0)
    assert result.func_name == "max"

    result = max_(close, -1)
    assert result.func_name == "max"

    # 浮点数 -> element-wise max
    result = max_(close, 10.5)
    assert result.func_name == "max"


def test_min_intelligent_dispatch():
    """测试 min_ 智能分发: int -> ts_min，其他 -> element-wise min。"""
    from src.factors.api import min_, close

    # 正整数 -> ts_min
    result = min_(close, 20)
    assert result.func_name == "ts_min"

    # 零或负数 -> element-wise min
    result = min_(close, 0)
    assert result.func_name == "min"


def create_test_data() -> pl.DataFrame:
    """创建测试数据。"""
    np.random.seed(42)
    n = 100
    return pl.DataFrame(
        {
            "ts_code": ["000001.SZ"] * n,
            "trade_date": list(range(20240101, 20240101 + n)),
            "close": np.random.randn(n).cumsum() + 100,
        }
    )


def test_ts_sma_computation():
    """测试 ts_sma 计算与原生 Polars 一致。"""
    df = create_test_data()
    translator = PolarsTranslator()

    # 翻译因子
    close = Symbol("close")
    expr_node = FunctionNode("ts_sma", close, 10, 5)
    expr = translator.translate(expr_node)

    # 使用翻译后的表达式计算
    result = df.select(["ts_code", "trade_date", "close", expr.alias("ts_sma_result")])

    # 原生 Polars 计算
    native = df.with_columns(
        [pl.col("close").ewm_mean(alpha=5 / 10, adjust=False).alias("native_sma")]
    )

    # 对比结果
    assert np.allclose(
        result["ts_sma_result"].to_numpy()[9:],
        native["native_sma"].to_numpy()[9:],
        equal_nan=True,
    )


def test_ts_sumac_computation():
    """测试 ts_sumac 计算与原生 Polars 一致。"""
    df = create_test_data()
    translator = PolarsTranslator()

    close = Symbol("close")
    expr_node = FunctionNode("ts_sumac", close)
    expr = translator.translate(expr_node)

    result = df.select(
        ["ts_code", "trade_date", "close", expr.alias("ts_sumac_result")]
    )

    native = df.with_columns([pl.col("close").cum_sum().alias("native_sumac")])

    assert np.allclose(
        result["ts_sumac_result"].to_numpy(), native["native_sumac"].to_numpy()
    )


if __name__ == "__main__":
    pytest.main([__file__, "-v"])