def __mul__(self, other):
"""interpret other and call one of the following
self._mul_scalar()
self._mul_vector()
self._mul_multivector()
self._mul_sparse_matrix()
"""
M,N = self.shape
if isscalarlike(other):
# scalar value
return self._mul_scalar(other)
if issparse(other):
if self.shape[1] != other.shape[0]:
raise ValueError('dimension mismatch')
return self._mul_sparse_matrix(other)
try:
other.shape
except AttributeError:
# If it's a list or whatever, treat it like a matrix
other = np.asanyarray(other)
other = np.asanyarray(other)
if other.ndim == 1 or other.ndim == 2 and other.shape[1] == 1:
# dense row or column vector
if other.shape != (N,) and other.shape != (N,1):
raise ValueError('dimension mismatch')
result = self._mul_vector(np.ravel(other))
if isinstance(other, np.matrix):
result = np.asmatrix(result)
if other.ndim == 2 and other.shape[1] == 1:
# If 'other' was an (nx1) column vector, reshape the result
result = result.reshape(-1,1)
return result
elif other.ndim == 2:
##
# dense 2D array or matrix ("multivector")
if other.shape[0] != self.shape[1]:
raise ValueError('dimension mismatch')
result = self._mul_multivector(np.asarray(other))
if isinstance(other, np.matrix):
result = np.asmatrix(result)
return result
else:
raise ValueError('could not interpret dimensions')
def __rmul__(self, other): # other * self
if isscalarlike(other):
return self.__mul__(other)
else:
# Don't use asarray unless we have to
try:
tr = other.transpose()
except AttributeError:
tr = np.asarray(other).transpose()
return (self.transpose() * tr).transpose()
def makeshapes(x, y, type=None, z=None, m=None):
"""
Make shapes by x and y coordinates.
:param x: (*array_like*) X coordinates.
:param y: (*array_like*) Y coordinates.
:param type: (*string*) Shape type [point | line | polygon].
:param z: (*array_like*) Z coordinates.
:param m: (*array_like*) M coordinates.
:returns: Shapes
"""
shapes = []
if isinstance(x, (int, float)):
shape = PointShape()
shape.setPoint(PointD(x, y))
shapes.append(shape)
else:
x = np.asarray(x).array
y = np.asarray(y).array
if not z is None:
if m is None:
m = np.zeros(len(z)).array
else:
m = np.asarray(m).array
z = np.asarray(z).array
if type == 'point':
if z is None:
shapes = ShapeUtil.createPointShapes(x, y)
else:
shapes = ShapeUtil.createPointShapes(x, y, z, m)
elif type == 'line':
if z is None:
shapes = ShapeUtil.createPolylineShapes(x, y)
else:
shapes = ShapeUtil.createPolylineShapes(x, y, z, m)
elif type == 'polygon':
if z is None:
shapes = ShapeUtil.createPolygonShapes(x, y)
else:
shapes = ShapeUtil.createPolygonShape(x, y, z, m)
return shapes
def to_native(A):
return np.asarray(A,dtype=A.dtype.newbyteorder('native'))