import datetime

import lmfit as lm
import numpy as np
import pandas as pd
import scipy.special as sf
from bson import ObjectId
from scipy.optimize import curve_fit

from paveit.datamodels import CITTSiffnessResults, RegCITT


def temp_freq_equivalence(T, f, phi, T0=20.0):
    
    alphaT = np.exp(phi * ((1 / (T + 273.15)) - (1 / (T0 + 273.15))))
    x = np.log(f * alphaT) / np.log(10)
    
    return x

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

    x = temp_freq_equivalence(T, f, phi, T0)    
    
    E = Emin + (Emax - Emin) / (1 + np.exp(z1 * x + z0))

    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 citt(task_id: str):
    """
    Postprocessing task
    """
    
    print('postprocessing')
    
    task_id = ObjectId(task_id)
    
    # read all data
    data = []
    parlist = ['f_set', 'T_set', 'stiffness', 'phase']
    
    for obj in CITTSiffnessResults.objects(task_id=task_id).only(*parlist):
        data.append(dict((k, obj[k]) for k in parlist ))
        
    data = pd.DataFrame.from_records(data)
    
    #Emax/Emin
    line_mod = lm.models.LinearModel()
    out = line_mod.fit(data.stiffness, x=data.phase)
    
    Emax = line_mod.eval(out.params, x=0.0)
    
    Emin = 0
    
    assert Emin < Emax
    
    # Fit data        
    mod = lm.models.Model(stiffness_tp26, independent_vars=['f','T'])
    
    mod.set_param_hint(
        'Emin',
        value=Emin,
        min=0,
        max=0.9*Emax,
        vary=True,
    )
    
    mod.set_param_hint(
        'Emax',
        value=Emax,
        min=0.9*Emax,
        max=1.1*Emax,
        vary=True,
    )
    
    mod.set_param_hint(
        'T0',
        value=20.0,
        vary=False,
    )
    
    mod.set_param_hint('phi', value=25000, min=15000, max=35000, vary=True)
    mod.set_param_hint('z0', value=1,min=1e-10, max=1000., vary=True)
    mod.set_param_hint('z1', value=-1, min=-1000., max=-1e-10, vary=True)
    
    parms_fit = [
        mod.param_hints['Emin']['value'], mod.param_hints['Emax']['value'],
        mod.param_hints['phi']['value'], mod.param_hints['z0']['value'],
        mod.param_hints['z1']['value']
    ]
    
    
    ## run fit
    results = []
    r2 = []
    
    
    try:
        methods = ['leastsq', 'powell']

        for method in methods:
            result = mod.fit(data.stiffness, T=data.T_set, f=data.f_set, method=method, verbose=False)
            
            r2temp =  1.0 - result.redchi / np.var(data.stiffness.values, ddof=2)
            r2.append(r2temp)

            results.append(result)

        best = np.nanargmax(r2)
        
        res = results[best].best_values
        res['stat_r2'] = r2[best]
        
    except:
        print('error regression, send default values')
        
        res = mod.valuesdict()
        
    #add metadata
    res['nsamples'] = len(data)
    res['task_id'] = task_id        
    res['date'] = datetime.datetime.now()

    print(res)
    
    # save results to db
    doc = RegCITT.objects(task_id=task_id).modify(upsert=True, **res)
    
    return True