import numpy as np
import pandas as pd

from paveit.labtest.citt import calc_E


class CittAnalyseFatigue():
    
    def _fit_split_data(self):
        
        data_exp = []
        
        N = self.data['N'].unique()
        N = np.array(N)
        gaps = N[1:][np.diff(N)>1]

        for i,gap in enumerate(gaps):
            print(i, gap)
            if i == 0:
                f = self.data['N']<gap
            elif i == len(gaps):
                f = self.data['N']>=gap
            else:
                f = (self.data['N']>=gaps[i-1]) & (self.data['N']<gap)
        
            # filter data by geps
            d = self.data[f]
            
            # get 5 cycles
            if i == 0:
                f = (d['N']>=98) & (d['N']<=102)
            else:
                Nsel = d['N'].unique()
                f = (d['N']>=Nsel[-5]) & (d['N']<=Nsel[-1])
            
            d = d[f]
            
            
            
            data_exp.append(d)
        
        
        self.data = data_exp
        
    
    def _fit_select_data(self):
        
        ''' analyse data

        '''
        pass 
        
    def _calc(self):
        print('calc fatigue')
        
        print(self.metadata)
        
        fit = []
        
        # Je Aufzeichnungsintervall
        
        
        for i, d in enumerate(self.data):
            
            try:
                res = calc_E(d, metadata=self.metadata, columns_analyse=['F', 's_hor_sum'])
            
                res['idx'] = i
                
                res['energy_ratio'] = res['stiffness']*np.round(res['N_from'] + (res['N_to'] - res['N_from'])/2, 0)
            
                fit.append(res)
            except:
                raise
            
        self.fit_single_results = pd.DataFrame.from_records(fit)
        
        EN_max = self.fit_single_results['energy_ratio'].max()
        
        sel_f = self.fit_single_results[(self.fit_single_results['energy_ratio']>=0.8*EN_max) & (self.fit_single_results['energy_ratio']<=1.2*EN_max)]
        par = np.polyfit(sel_f['N_from'], sel_f['energy_ratio'], 4)
        
        x = np.arange(sel_f['N_from'].min(),sel_f['N_from'].max(), 1)
        y = np.polyval(par, x)

        Nmakro = x[y.argmax()]

        self.fit = {'Nmakro': Nmakro, 
                        'energy_ratio_max': y.max(),
                        'par_fit': par,
                        'epislon_elast_98': self.fit_single_results.iloc[0]['el_strains']}