import numpy as np


def stiffness_tp26(T, f, Emax, Emin, phi, z0, z1, T0=20.0):

    alphaT = np.exp(phi * ((1 / (T + 273.15)) - (1 / (T0 + 273.15))))
    x = np.log(f * alphaT) / np.log(10)
    E = Emin + (Emax - Emin) / (1 + np.exp(z0 * x + z1))

    return E


def calc_nu(T):
    #TODO: Prüfen ob Formel stimmt!
    nu = 0.15 + (0.35) / (1 + np.exp(3.1849 - 0.04233 * (9 / 5 * T + 32)))
    return nu

def calc_E(data, metadata, columns_analyse):

    data.index = data.index - data.index[0]
    
    res_temp = {}

    x = data.index.values

    freq = np.round(float(data['f'].unique()), 2)
    sigma = float(data['sigma'].unique())
    temperature = float(data['T'].unique())
    
    for idxcol, col in enumerate(columns_analyse):

        if not col in data.columns: continue

        y = data[col].values
        res = fit_cos(x, y, freq=freq)

        for key, value in res.items():
            res_temp[f'fit_{col}_{key}'] = value
            

        # analyse cycle data
        
        cycle_min = []
        cycle_max = []
        cycle_mean = []
        cycle_diff = []
        
        for N, data_cycle in data.groupby('N'):
            y = data_cycle[col].values
            
            cycle_min.append(y.min())
            cycle_max.append(y.max())
            cycle_mean.append(y.mean())
            cycle_diff.append(cycle_max[-1] - cycle_min[-1])
            
        res_temp[f'fit_{col}_cycle_min'] = cycle_min
        res_temp[f'fit_{col}_min'] = np.mean(cycle_min)
        res_temp[f'fit_{col}_min_std'] = np.std(cycle_min)
        res_temp[f'fit_{col}_min_diff_rel'] = (np.max(cycle_min) - np.min(cycle_min))/np.mean(cycle_min)
        res_temp[f'fit_{col}_cycle_max'] = cycle_max
        res_temp[f'fit_{col}_max'] = np.mean(cycle_max)
        res_temp[f'fit_{col}_max_std'] = np.std(cycle_max)
        res_temp[f'fit_{col}_max_diff_rel'] = (np.max(cycle_max) - np.min(cycle_max))/np.mean(cycle_max)
        res_temp[f'fit_{col}_cycle_mean'] = cycle_mean
        res_temp[f'fit_{col}_mean'] = np.mean(cycle_mean)
        res_temp[f'fit_{col}_mean_std'] = np.std(cycle_mean)
        res_temp[f'fit_{col}_mean_diff_rel'] = (np.max(cycle_mean) - np.min(cycle_mean))/np.mean(cycle_mean)
        res_temp[f'fit_{col}_cycle_diff'] = cycle_diff
        res_temp[f'fit_{col}_diff'] = np.mean(cycle_diff)
        res_temp[f'fit_{col}_diff_std'] = np.std(cycle_diff)
        res_temp[f'fit_{col}_diff_diff_rel'] = (np.max(cycle_diff) - np.min(cycle_diff))/np.mean(cycle_diff)

    # add more metadata
    res_temp['f_set'] = freq
    res_temp['sigma_set'] = sigma
    res_temp['T_set'] = temperature

    res_temp['N_from'] = data['N'].min()
    res_temp['N_to'] = data['N'].max()

    res_temp['n_samples_per_cycle'] = int(
        len(data) / (res_temp['N_to'] - res_temp['N_from'] + 1))

    ## Stiffness
    deltaF = res_temp['fit_F_amp']
    deltaU = res_temp['fit_s_hor_sum_amp']
    
    h = float(metadata['speciment_height'])
    d = float(metadata['speciment_diameter'])
    
    nu = calc_nu(temperature)
    res_temp['nu'] = nu
    
    #nach TP Asphalt 26
    res_temp['stiffness'] = deltaF /(h * deltaU) * (4.0/np.pi -1 + nu)
    
    ## Elastische hori. Dehnung                
    res_temp['el_strains'] = 2*2*deltaU/d * (1+3*nu)/(4 + np.pi*nu - np.pi) * 1000.0 # 2*2 daher, da deltaU nur Ampl. nicht Gesamtkraft ist
    
    # TODO: Überarbeiten und erweitern (ISSUE #2)
    res_temp['phase'] = res_temp['fit_F_phase'] - res_temp['fit_s_hor_sum_phase']
    
    return res_temp