import numpy as np
from scipy.optimize import curve_fit



def covar2d(k):
	x=[]
	cov=[]
	nx=k.shape[0]
	for h in range(nx):
	
		x.append(h)
		kx,kh=k[:nx-h,:].reshape(-1),k[h:,:].reshape(-1)
		cov.append(np.mean((kx*kh)-(np.mean(kx)*np.mean(kh))))
	
	return cov,x

def vario2d(k):
	x=[]
	vario=[]
	nx=k.shape[0]
	for h in range(nx):
	
		x.append(h)
		kx,kh=k[:nx-h,:].reshape(-1),k[h:,:].reshape(-1)
		vario.append(np.mean((kh-kx)**2))
	
	return vario,x

def vario3d(k):
	x=[]
	vario=[]
	nx=k.shape[0]
	for h in range(nx):
	
		x.append(h)
		kx,kh=k[:nx-h,:,:].reshape(-1),k[h:,:,:].reshape(-1)
		vario.append(np.mean((kh-kx)**2))
	
	return vario,x


def modelcovexp(h,a,c):
        return c*(np.exp(-h/a))



def modelcovexpLin(h,a,c):
        return c-h/a


def modelvarioexp(h,a,c):
        return c*(1-np.exp(-h/a))

def modelcovgauss(h,a,c):
        return c*(np.exp(-(h/a)**2))

def modelvariogauss(h,a,c):
        return c*(1-np.exp(-(h/a)**2))


def get_CovPar2d(k,model):

	cov,x=vario2d(k)
	popt, pcov = curve_fit(model, x, cov)

	return np.abs(popt[0])  	#Ic,varianza

def get_varPar3d(k,model):
	
	vario,x=vario3d(k)
	popt, pcov = curve_fit(model, x, vario)
	return  np.abs(popt[0])	#Ic,varianza


def get_lc(k,vario):


	if vario==2:
		model=modelvariogauss
		mult=np.sqrt(3)
	else:
		model=modelvarioexp
		mult=3
	if k.shape[2]==1:
		lc=get_CovPar2d(k,model)*mult
	else:
		lc=get_varPar3d(k,model)*mult
	return lc