def targ(D,GT,x,y,cmin,cmax,symm,inc=inc,ecc=ecc,amp=amp,scale=scale,kwds=kwds):
# Spatial distance
aniso_geo_rad(D, x[:,:-1], y[:,:-1], inc, ecc,cmin=cmin,cmax=cmax,symm=symm)
imul(D,1./scale,cmin=cmin,cmax=cmax,symm=symm)
# Temporal variogram
origin_val = t_gam_fun(GT, x[:,-1], y[:,-1],cmin=cmin,cmax=cmax,symm=symm,**kwds)
# Covariance
stein_spatiotemporal(D,GT,origin_val,cmin=cmin,cmax=cmax,symm=symm)
imul(D,amp*amp,cmin=cmin,cmax=cmax,symm=symm)
def targ(D,GT,x,y,cmin,cmax,symm,inc=inc,ecc=ecc,amp=amp,scale=scale,diff_degree=diff_degree,h=h,geometry=geometry,kwds=kwds):
# Spatial distance
if geometry=='aniso_geo_rad':
aniso_geo_rad(D, x[:,:-1], y[:,:-1], inc, ecc,cmin=cmin,cmax=cmax,symm=symm)
else:
euclidean(D, x[:,:-1], y[:,:-1], cmin=cmin,cmax=cmax,symm=symm)
imul(D,1./scale,cmin=cmin,cmax=cmax,symm=symm)
# Temporal variogram
ddx, ddy = diff_degree(x), diff_degree(y)
origin_val = t_gam_fun(GT, x[:,-1], y[:,-1], ddx, ddy, cmin=cmin,cmax=cmax,symm=False,**kwds)
if np.any(GT<0):
raise pm.ZeroProbability, 'GT < 0.'
# GT = np.add.outer(ddx*.5,ddy*.5)
# Local properties
hx, hy = h(x), h(y)
# Covariance
nsst(D,GT,origin_val,hx,hy,cmin=cmin,cmax=cmax,symm=symm)
imul(D,amp*amp,cmin=cmin,cmax=cmax,symm=symm)
