import pandas as pd

from .model import SystemModel

class ModelDiag:
    def __init__(
        self,
        model: SystemModel,
        data : pd.DataFrame,
        c_var: list,
        d_var: list,
    ) -> None:
        self.model = model 
        self.data = data.iloc[[0],:]
        self.c_var = c_var
        self.d_var = d_var
    
    def summary_diag(self):
        print('-'*20+'系统模型诊断'+'-'*20)
        with pd.option_context('display.max_rows', None,'display.max_columns', None,'display.width',500):
            derivate_c_info = self.diag_c_var_derivate()
            print('-'*20+'系统连续变量偏导'+'-'*20)
            print(derivate_c_info)
    
    def diag_c_var_derivate(self) -> pd.DataFrame:
            
        derivate_1 = self.model.predict_derivate_1(data=self.data,vars=self.c_var)
        derivate_2 = self.model.predict_derivate_2(data=self.data,vars=self.c_var)
        
        derivate_1 = process_derivate(derivate_1,'D1')
        derivate_2 = process_derivate(derivate_2,'D2')
        derivate   = derivate_1.join(derivate_2).round(4)
        
        return derivate
        
    
    def diag_d_var_change(self) -> pd.DataFrame:
        ...
        

def process_derivate(df:pd.DataFrame,D_name):
    df = (
        df
        .reset_index()
        .assign(__VAR__SAMPLE=lambda dt:dt.__VAR__SAMPLE.str.extract('__(.+)__'))
        .melt(id_vars='__VAR__SAMPLE',var_name='SYS_OUTPUT',value_name=D_name)
        .rename(columns={'__VAR__SAMPLE':'OPT_VAR'})
        .set_index(['SYS_OUTPUT','OPT_VAR'])
    )
    return df