import dataclasses

import numpy


@dataclasses.dataclass
class BaseInfo:
    id: str
    specification: str
    database_version: str
    version: str
    extract_log_rule_version: str
    extract_metrics_rule_version: str


@dataclasses.dataclass
class BaseQuantileStatistics:
    min: float
    max: float
    median: float
    q1: float
    q3: float


@dataclasses.dataclass
class BaseDescriptiveStatistics:
    mean: float
    std: float
    variance: float


@dataclasses.dataclass
class BaseItemStatisticsInfo:
    name: str  # metric name + metrics label
    data: numpy.array(float)
    quantile_statistics: BaseQuantileStatistics
    descriptive_statistics: BaseDescriptiveStatistics
    status: str  # normal, anomaly, unknown
    trend: str  # up, down, unknown


@dataclasses.dataclass
class LogStatisticsInfo:
    slow_query_count: BaseItemStatisticsInfo  # slow query count statistics
    new_slow_query_count: list[BaseItemStatisticsInfo]
    topn_slow_query_log: list[BaseItemStatisticsInfo]  # slow query log


@dataclasses.dataclass
class MetricsTrendDescription:
    stats: BaseItemStatisticsInfo
    highly_correlated: list[BaseItemStatisticsInfo]  # highly correlated metrics or slow query logs


class DiagnosisReportTemplate:
    base_info: BaseInfo
    log_statistics_info: LogStatisticsInfo
    metrics_trend_description: MetricsTrendDescription

    def __init__(self, base_info, log_statistics_info, metrics_trend_description):
        self.base_info = base_info
        self.log_statistics_info = log_statistics_info
        self.metrics_trend_description = metrics_trend_description

    def to_html(self):
        pass