from pathlib import Path

import numpy as np
import pandas as pd
import pytest

from app.algorithms.cleaning import pressure as pressure_cleaning


DATA_DIR = Path(__file__).resolve().parents[3] / "data"
RAW_DATA_PATH = DATA_DIR / "node_simulation.csv"
NOISY_DATA_PATH = DATA_DIR / "node_simulation_noisy.csv"
REQUIRES_PRESSURE_SAMPLES = pytest.mark.skipif(
    not RAW_DATA_PATH.exists() or not NOISY_DATA_PATH.exists(),
    reason="pressure cleaning sample CSV files are not available",
)

@REQUIRES_PRESSURE_SAMPLES
def test_clean_pressure_data_df_km_repairs_long_form_pressure_series():
    raw_df = pd.read_csv(RAW_DATA_PATH)
    noisy_df = pd.read_csv(NOISY_DATA_PATH)
    cleaned_df = pressure_cleaning.clean_pressure_data_df_km(noisy_df)

    for df in (raw_df, noisy_df, cleaned_df):
        df["time"] = pd.to_datetime(df["time"])

    assert len(cleaned_df) == len(raw_df)
    assert set(cleaned_df.columns) == {"time", "id", "pressure"}
    assert cleaned_df["pressure"].isna().sum() == 0

    noisy_joined = raw_df.merge(
        noisy_df,
        on=["time", "id"],
        how="inner",
        suffixes=("_raw", "_noisy"),
    )
    cleaned_joined = raw_df.merge(
        cleaned_df,
        on=["time", "id"],
        how="inner",
        suffixes=("_raw", "_clean"),
    )

    noisy_rmse = float(
        np.sqrt(
            np.mean(
                (noisy_joined["pressure_raw"] - noisy_joined["pressure_noisy"])
                ** 2
            )
        )
    )
    cleaned_rmse = float(
        np.sqrt(
            np.mean(
                (cleaned_joined["pressure_raw"] - cleaned_joined["pressure_clean"])
                ** 2
            )
        )
    )
    noisy_mae = float(
        np.mean(np.abs(noisy_joined["pressure_raw"] - noisy_joined["pressure_noisy"]))
    )
    cleaned_mae = float(
        np.mean(np.abs(cleaned_joined["pressure_raw"] - cleaned_joined["pressure_clean"]))
    )

    assert cleaned_rmse < 0.35
    assert cleaned_rmse < noisy_rmse * 0.5
    assert cleaned_mae < noisy_mae

    repaired_gap = cleaned_df[
        (cleaned_df["id"] == 170490)
        & (cleaned_df["time"] == pd.Timestamp("2026-01-01T05:00:00+08:00"))
    ]["pressure"].iloc[0]
    assert abs(repaired_gap - 30.62433433532715) < 1.0

    spike_row = cleaned_df[
        (cleaned_df["id"] == 42563)
        & (cleaned_df["time"] == pd.Timestamp("2026-01-01T03:45:00+08:00"))
    ]["pressure"].iloc[0]
    assert abs(spike_row - 28.018701553344727) < 2.0


@REQUIRES_PRESSURE_SAMPLES
def test_clean_pressure_data_df_km_accepts_single_sensor_wide_frame_with_utc_strings():
    noisy_df = pd.read_csv(NOISY_DATA_PATH)
    single_sensor = (
        noisy_df[noisy_df["id"] == 170490][["time", "pressure"]]
        .rename(columns={"pressure": "170490"})
        .copy()
    )
    single_sensor["time"] = (
        pd.to_datetime(single_sensor["time"], utc=True).dt.strftime("%Y-%m-%dT%H:%M:%SZ")
    )

    cleaned_df = pressure_cleaning.clean_pressure_data_df_km(single_sensor)

    assert len(cleaned_df) == 192
    assert cleaned_df["170490"].isna().sum() == 0