from scipy.optimize import curve_fit
from numpy import linspace
import numpy as np
from numpy import tanh
from scipy.constants import e, k
from numpy import sqrt
from numpy import array
import pandas as pd


# Global variables

new_U = linspace(-22, 22, 1000)


# Function for approximation


def function_IU(U, I_n, T_e, A, B):
    return I_n * tanh(e*U/2/k/T_e) + A*U + B

# Find the best fit params of In and Te


def find_FitParams(data_I, data_U):
    popt, pcov = curve_fit(function_IU, data_U, data_I, p0=[0, 50000, 0, 0])
    return popt, pcov


# Returns disperancy of given value


def discrepancy(data, popt):
    I = data[0]
    U = data[1]
    tmp = []

    for i in range(len(U)):
        temp = sqrt((function_IU(
            U[i], *popt)-I[i])**2/len(U)/(len(U)-1))
        tmp.append(temp)

    return tmp


# Returns pandas.DataFrame with approximated Current and Voltage values


def getApproxValues(data):
    data = data.sort_values(by=['U'])
    popt, pcov = find_FitParams(data['I'], data['U'])
    df = pd.DataFrame()
    df['U'] = new_U
    df['I'] = function_IU(df['U'], *popt)
    error = np.sqrt(np.diag(pcov))
    return [df, popt, error]