"""Identification of sensor difference outliers between 2 series.

<p>This check identifies periods where for redundant sensors the typical
difference is significantly altered compared to normal.</p>
<p><img src='../static/images/reporting/sensor_outlier.svg'></p>
<p class="scoring-explanation">The score of this check is calculated based on the count of all
points where sensor drift is identified. Imagine that 100 points are analyzed in a given time-frame
and that drift is detected for 10 (consecutive) points. The score for this check in that case would be
90% = 1 - 10 / 100. Which means that for 90% of all points no drift occurs.</p>
<div class="ts-check-impact">
<p>Changes in a physical relation between a set of series could indicate process or instrumentation issues.</p>
</div>
"""

from datetime import timedelta

import pandas as pd
import numpy as np

from timeseer import (
    AnalysisResult,
    BivariateCheckResult,
    DataType,
    MultivariateAnalysisInput,
    ModuleParameterType,
)
from timeseer.analysis.utils import (
    event_frames_from_dataframe,
    get_percentile_based_on_sensitivity,
)


_CHECK_NAME = "Redundant sensor outlier"
_EVENT_FRAME_NAME = "Sensor outlier"

META = {
    "checks": [
        {
            "name": _CHECK_NAME,
            "event_frames": [_EVENT_FRAME_NAME],
        },
    ],
    "conditions": [
        {
            "min_series": 2,
            "max_series": 2,
        },
        {
            "min_weeks": 1,
            "min_data_points": 300,
            "data_type": [DataType.FLOAT32, DataType.FLOAT64],
        },
    ],
    "parameters": [
        {
            "name": "sensitivity",
            "type": ModuleParameterType.HIDDEN,
            "range": {
                "lower": 0,
                "upper": 1,
            },
            "default": 0.75,
            "helpText": "Sensitivity influences the IQR cutoff for redundant sensor outlier.",
        },
    ],
    "signature": "bivariate",
}


def _is_frame_long_enough(frame):
    return (frame.end_date - frame.start_date) >= timedelta(hours=12)


def _filter_stale_event_frames(all_frames):
    filter_iterator = filter(_is_frame_long_enough, all_frames)
    return filter_iterator


def _get_anomalies(lower, upper, arr):
    a_lower = arr < lower
    a_upper = arr > upper
    return a_lower | a_upper


def _find_anomalous_times(analysis_input, diff_matrix):
    pct_low = 0.25
    pct_high = 0.75

    if "sensitivity" in analysis_input.parameters:
        pct_high = get_percentile_based_on_sensitivity(
            analysis_input.parameters["sensitivity"]
        )
        pct_low = 1 - pct_high

    q25, q75 = np.nanquantile(
        np.array(diff_matrix, dtype=float), [pct_low, pct_high], axis=0
    )
    iqr = q75 - q25
    upper = q75 + 1.5 * iqr
    lower = q25 - 1.5 * iqr
    return [_get_anomalies(lower, upper, x) for x in np.array(diff_matrix, dtype=float)]


def _make_diff_matrix(df):
    diff_matrix = np.zeros((df.shape[0], df.shape[1], df.shape[1]))
    for i in range(df.shape[1] - 1):
        for j in range(i + 1, df.shape[1]):
            diff_matrix[:, i, j] = df.iloc[:, i] - df.iloc[:, j]
    return diff_matrix


def _anomalous_times_per_series(anomalous_times_matrix, series):
    results = []
    for i in range(len(series) - 1):
        for j in range(i + 1, len(series)):
            results.append(
                {
                    "series_x": series[i],
                    "series_y": series[j],
                    "drifts": [time[i, j] for time in anomalous_times_matrix],
                }
            )
    return results


def _get_intervals(anomalies, df):
    anomalies = pd.Series(data=anomalies, index=df.index)
    interval_grp = (anomalies != anomalies.shift().bfill()).cumsum()

    intervals = (
        df.assign(interval_grp=interval_grp)[anomalies]
        .reset_index()
        .groupby(["interval_grp"])
        .agg(start_date=("ts", "first"), end_date=("ts", "last"))
    )
    intervals["type"] = _EVENT_FRAME_NAME
    return intervals


def _get_results_per_series(series_anomaly, df) -> BivariateCheckResult:
    anomalies = series_anomaly["drifts"]
    intervals = _get_intervals(anomalies, df)

    frames = event_frames_from_dataframe(intervals)

    return BivariateCheckResult(
        _CHECK_NAME,
        series_anomaly["series_x"],
        series_anomaly["series_y"],
        _filter_stale_event_frames(frames),
    )


def _clean_input(inputs):
    concatenated_df = (
        pd.concat(
            [
                series.data["value"][
                    ~series.data["value"].index.duplicated(keep="first")
                ]
                for series in inputs
            ],
            axis=1,
            sort=False,
        )
        .interpolate("time")
        .dropna()
    )
    concatenated_df = concatenated_df[(concatenated_df != 0).all(1)]
    return concatenated_df


def _run_detect_redundant_sensor_outlier(
    analysis_input: MultivariateAnalysisInput,
) -> list[BivariateCheckResult]:
    inputs = analysis_input.inputs
    concatenated_df = _clean_input(inputs)

    diff_matrix = _make_diff_matrix(concatenated_df)
    anomalous_times_matrix = _find_anomalous_times(analysis_input, diff_matrix)

    series = [series.metadata.series for series in inputs]
    anomalous_times_per_series = _anomalous_times_per_series(
        anomalous_times_matrix, series
    )

    return [
        _get_results_per_series(series_anomaly, concatenated_df)
        for series_anomaly in anomalous_times_per_series
    ]


def run(
    analysis_input: MultivariateAnalysisInput,
):  # pylint: disable=missing-function-docstring
    results = _run_detect_redundant_sensor_outlier(analysis_input)

    return AnalysisResult(bivariate_check_results=results)
