# -*- coding: utf-8 -*-
"""
Created on Wed Aug 31 13:24:08 2016

@author: jbrody
"""

import numpy as np
import matplotlib.pyplot as plt, pandas as pd
from scipy.optimize import curve_fit
from sklearn.metrics import r2_score
print ('Your x data and y data should appear as columns in Excel\n')
input("Copy your x data to clipboard (CTRL-C) and press Enter.")
x=pd.read_clipboard(header=None)
x=np.array(x).T[0]
input("Copy your y data to clipboard (CTRL-C) and press Enter.")
y=pd.read_clipboard(header=None)
y=np.array(y).T[0]
def func(x,k,R,a):
    return 
popt, pcov = curve_fit(func, x, y,bounds=([0,0,0],[np.inf,np.inf,np.inf]))
plt.plot(x,y,'o',label="measured data")
yfit=func(x,*popt)
yplot=func(np.linspace(np.min(x),np.max(x),100),*popt)
plt.plot(np.linspace(np.min(x),np.max(x),100),yplot,label="theoretical fit")
perr = np.sqrt(np.diag(pcov))
r2=r2_score(y, yfit)
print("The fitting parameters are",popt)
print("The uncertainties in the fitting parameters are",perr)
print("The coefficient of determination is", r2)
plt.legend()