import pandas as pd
import os
import plotly.graph_objects as go
from algorithmContract.confBusiness import *
from algorithmContract.contract import Contract
from behavior.analystNotFilter import AnalystNotFilter
from plotly.subplots import make_subplots

class FaultAnalyst(AnalystNotFilter):
    """
    风电机组故障分析
    """

    def typeAnalyst(self):
        return "fault"
    
    def selectColumns(self):
        # 这里的字段必须与数据库 _fault 表中的列名对应
        return [Field_DeviceName, Field_FaultTime, Field_FaultDetail]

    # 强制重写获取数据源类型的方法，防止因配置错误去查 _minute 表，强制代码去查 _fault 表
    def getTimeGranularitys(self, conf: Contract):
        return ["fault"] 

    def turbinesAnalysis(self, outputAnalysisDir, conf: Contract, turbineCodes):
        dictionary = self.processTurbineData(turbineCodes, conf, self.selectColumns())
        dataFrameMerge = self.userDataFrame(dictionary, conf.dataContract.configAnalysis, self)
        
        # 增加空数据保护
        if dataFrameMerge.empty:
            print("Warning: No fault data found for the selected turbines.")
            return pd.DataFrame()
            
        return self.get_result(dataFrameMerge, outputAnalysisDir, conf)

    def get_result(self, dataFrame: pd.DataFrame, outputAnalysisDir: str, conf: Contract):
        # 双重保险：如果数据为空直接返回
        if dataFrame.empty:
            return pd.DataFrame()

        #---------------整个风场维度统计故障时长与次数---------------------------
        # 统计各种类型故障出现的次数
        if Field_FaultDetail in dataFrame.columns:
            fault_detail_count = dataFrame[Field_FaultDetail].value_counts().reset_index()
            fault_detail_count.columns = [Field_FaultDetail, 'count']

            # 统计每个 fault_detail 的时长加和
            fault_time_sum = dataFrame.groupby(Field_FaultDetail)[Field_FaultTime].sum().reset_index()
            fault_time_sum.columns = [Field_FaultDetail, 'fault_time_sum']

            # 合并两个 DataFrame
            fault_summary = pd.merge(fault_detail_count, fault_time_sum, on=Field_FaultDetail, how='inner')
            fault_summary_sorted = fault_summary.sort_values(by='fault_time_sum', ascending=False)
        else:
            # 防御性代码：如果缺列
            fault_summary_sorted = pd.DataFrame()

        # -------------按风机分组统计故障情况------------------------------------------
        # 确保有设备名称列
        if Field_DeviceName not in dataFrame.columns:
             # 有时 Field_DeviceName 没取到，尝试用 DeviceCode 或其他
             groupby_col = dataFrame.columns[0] 
        else:
             groupby_col = Field_DeviceName

        grouped = dataFrame.groupby(groupby_col)
        results= []

        for name, group in grouped:
            turbine_fault_summary = pd.DataFrame({
                Field_DeviceName: [name],
                'count': [len(group)],
                'fault_time': [group[Field_FaultTime].sum()]
            })
            results.append(turbine_fault_summary)

        # 合并所有风机的故障统计结果
        if results:
            turbine_fault_summary = pd.concat(results, ignore_index=True)
            turbine_fault_sorted = turbine_fault_summary.sort_values(by='fault_time', ascending=False)
            # 故障类型前十名
            # draw_results=turbine_fault_sorted.head(10) # 暂时没用到
        else:
            turbine_fault_sorted = pd.DataFrame()

        # 保存结果
        result_rows = []

        if not turbine_fault_sorted.empty:
            filePathOfturbinefault = os.path.join(outputAnalysisDir, f"turbine_fault_result{CSVSuffix}")
            turbine_fault_sorted.to_csv(filePathOfturbinefault, index=False,encoding='utf-8-sig')

            result_rows.append({
                Field_Return_TypeAnalyst: self.typeAnalyst(),
                Field_PowerFarmCode: conf.dataContract.dataFilter.powerFarmID,
                Field_Return_BatchCode: conf.dataContract.dataFilter.dataBatchNum,
                Field_CodeOfTurbine: "total",
                Field_MillTypeCode:"turbine_fault_result",
                Field_Return_FilePath: filePathOfturbinefault,
                Field_Return_IsSaveDatabase: True
            })

        if not fault_summary_sorted.empty:
            filePathOftotalfault = os.path.join(outputAnalysisDir, f"total_fault_result{CSVSuffix}")
            fault_summary_sorted.to_csv(filePathOftotalfault, index=False,encoding='utf-8-sig')

            result_rows.append({
                Field_Return_TypeAnalyst: self.typeAnalyst(),
                Field_PowerFarmCode: conf.dataContract.dataFilter.powerFarmID,
                Field_Return_BatchCode: conf.dataContract.dataFilter.dataBatchNum,
                Field_CodeOfTurbine: "total",
                Field_MillTypeCode:"total_fault_result",
                Field_Return_FilePath: filePathOftotalfault,
                Field_Return_IsSaveDatabase: True
            })
            
        result_df = pd.DataFrame(result_rows)
        return result_df