def gradient(self,x=None,y=None):
"""
Computes the gradient of the components of the spin1 field at each point
:param x: optional, x positions at which to evaluate the gradient
:type x: array with units
:param y: optional, y positions at which to evaluate the gradient
:type y: array with units
:returns: the gradient of the spin1 field in array form, of shape (4,:,:) where the four components are, respectively, 1x,1y,2x,2y; the units for the finite difference are pixels
"""
if self.data.shape[0] > 2:
raise ValueError("Gradients are nor defined yet for spin>1 fields!!")
if (x is not None) and (y is not None):
assert x.shape==y.shape,"x and y must have the same shape!"
#x coordinates
if type(x)==quantity.Quantity:
assert x.unit.physical_type=="angle"
j = np.mod(((x / self.resolution).decompose().value).astype(np.int32),self.data.shape[1])
else:
j = np.mod((x / self.resolution.to(rad).value).astype(np.int32),self.data.shape[1])
#y coordinates
if type(y)==quantity.Quantity:
assert y.unit.physical_type=="angle"
i = np.mod(((y / self.resolution).decompose().value).astype(np.int32),self.data.shape[1])
else:
i = np.mod((y / self.resolution.to(rad).value).astype(np.int32),self.data.shape[1])
else:
i = None
j = None
#Call the C backend
grad1x,grad1y = _topology.gradient(self.data[0],j,i)
grad2x,grad2y = _topology.gradient(self.data[1],j,i)
#Return
if (x is not None) and (y is not None):
return np.array([grad1x.reshape(x.shape),grad1y.reshape(y.shape),grad2x.reshape(x.shape),grad2y.reshape(y.shape)])
else:
return np.array([grad1x,grad1y,grad2x,grad2y])
def gradient(self, x=None, y=None):
"""
Computes the gradient of the components of the spin1 field at each point
:param x: optional, x positions at which to evaluate the gradient
:type x: array with units
:param y: optional, y positions at which to evaluate the gradient
:type y: array with units
:returns: the gradient of the spin1 field in array form, of shape (4,:,:) where the four components are, respectively, 1x,1y,2x,2y; the units for the finite difference are pixels
"""
if self.data.shape[0] > 2:
raise ValueError(
"Gradients are nor defined yet for spin>1 fields!!")
if (x is not None) and (y is not None):
assert x.shape == y.shape, "x and y must have the same shape!"
#x coordinates
if type(x) == quantity.Quantity:
assert x.unit.physical_type == "angle"
j = np.mod(
((x / self.resolution).decompose().value).astype(np.int32),
self.data.shape[1])
else:
j = np.mod(
(x / self.resolution.to(rad).value).astype(np.int32),
self.data.shape[1])
#y coordinates
if type(y) == quantity.Quantity:
assert y.unit.physical_type == "angle"
i = np.mod(
((y / self.resolution).decompose().value).astype(np.int32),
self.data.shape[1])
else:
i = np.mod(
(y / self.resolution.to(rad).value).astype(np.int32),
self.data.shape[1])
else:
i = None
j = None
#Call the C backend
grad1x, grad1y = _topology.gradient(self.data[0], j, i)
grad2x, grad2y = _topology.gradient(self.data[1], j, i)
#Return
if (x is not None) and (y is not None):
return np.array([
grad1x.reshape(x.shape),
grad1y.reshape(y.shape),
grad2x.reshape(x.shape),
grad2y.reshape(y.shape)
])
else:
return np.array([grad1x, grad1y, grad2x, grad2y])
def gradient(self, x=None, y=None, save=True):
"""
Computes the gradient of the map and sets the gradient_x,gradient_y attributes accordingly
:param x: optional, x positions at which to evaluate the gradient
:type x: array with units
:param y: optional, y positions at which to evaluate the gradient
:type y: array with units
:param save: if True saves the gradient as attrubutes
:type save: bool.
:returns: tuple -- (gradient_x,gradient_y)
>>> test_map = ConvergenceMap.load("map.fit")
>>> gx,gy = test_map.gradient()
"""
if (x is not None) and (y is not None):
assert x.shape == y.shape, "x and y must have the same shape!"
#x coordinates
if type(x) == quantity.Quantity:
assert x.unit.physical_type == self.side_angle.unit.physical_type
j = np.mod(
((x / self.resolution).decompose().value).astype(np.int32),
self.data.shape[1])
else:
j = np.mod(
(x / self.resolution.to(rad).value).astype(np.int32),
self.data.shape[1])
#y coordinates
if type(y) == quantity.Quantity:
assert y.unit.physical_type == self.side_angle.unit.physical_type
i = np.mod(
((y / self.resolution).decompose().value).astype(np.int32),
self.data.shape[0])
else:
i = np.mod(
(y / self.resolution.to(rad).value).astype(np.int32),
self.data.shape[0])
else:
i = None
j = None
#Call the C backend
gradient_x, gradient_y = _topology.gradient(self.data, j, i)
#Return the gradients
if (x is not None) and (y is not None):
return gradient_x.reshape(x.shape), gradient_y.reshape(x.shape)
else:
if save:
self.gradient_x = gradient_x
self.gradient_y = gradient_y
return gradient_x, gradient_y
