def covariance(self,p1,p2,epsilon=1.0e-5):
h2 = sqdistance(p1,p2,self.get_default_rotmat())
ret = np.zeros_like(h2)
for st in self.structures:
covariance = st.covariance(p1,p2)
ret += covariance
if isinstance(h2, np.ndarray):
idx = np.where(h2 < epsilon)[0]
if len(idx) > 0:
ret[idx] = self.max_covariance()
return ret
else:
if h2 < epsilon:
return self.max_covariance()
return ret
def covariance(self,p1,p2,epsilon=1.0e-5):
h2 = sqdistance(p1,p2,self.get_default_rotmat())
ret = np.zeros_like(h2)
for st in self.structures:
covariance = st.covariance(p1,p2)
ret += covariance
if isinstance(h2, np.ndarray):
idx = np.where(h2 < epsilon)[0]
if len(idx) > 0:
ret[idx] = self.max_covariance()
return ret
else:
if h2 < epsilon:
return self.max_covariance()
return ret
def covariance(self,p1,p2):
h2 = sqdistance(p1,p2,self._rotmat)
h = np.sqrt(h2)
#print('covariance',p1,p2,h2,h)
return self._function(h,self.sill,self._range)
def covariance(self,p1,p2):
h2 = sqdistance(p1,p2,self._rotmat)
h = np.sqrt(h2)
#print('covariance',p1,p2,h2,h)
return self._function(h,self.sill,self._range)
