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)