matrix.I Returns the (multiplicative) inverse of invertible self. Parameters: None Returns: ret : matrix object If self is non-singular, ret is such that ret * self == self * ret == np.matrix(np.eye(self[0,:].size) all return True. Raises: numpy.linalg.LinAlgError: Singular matrix If self is singular. See also linalg.inv Examples >>> m = np.matrix('[1, 2; 3, 4]'); m matrix([[1, 2], [3, 4]]) >>> m.getI() matrix([[-2. , 1. ], [ 1.5, -0.5]]) >&

matrix.H Returns the (complex) conjugate transpose of self. Equivalent to np.transpose(self) if self is real-valued. Parameters: None Returns: ret : matrix object complex conjugate transpose of self Examples >>> x = np.matrix(np.arange(12).reshape((3,4))) >>> z = x - 1j*x; z matrix([[ 0. +0.j, 1. -1.j, 2. -2.j, 3. -3.j], [ 4. -4.j, 5. -5.j, 6. -6.j, 7. -7.j], [ 8. -8.j, 9. -9.j, 10.-10.j, 11.-11.j]]) >>> z.getH() matrix([[

matrix.getT() [source] Returns the transpose of the matrix. Does not conjugate! For the complex conjugate transpose, use .H. Parameters: None Returns: ret : matrix object The (non-conjugated) transpose of the matrix. See also transpose, getH Examples >>> m = np.matrix('[1, 2; 3, 4]') >>> m matrix([[1, 2], [3, 4]]) >>> m.getT() matrix([[1, 3], [2, 4]])

matrix.getI() [source] Returns the (multiplicative) inverse of invertible self. Parameters: None Returns: ret : matrix object If self is non-singular, ret is such that ret * self == self * ret == np.matrix(np.eye(self[0,:].size) all return True. Raises: numpy.linalg.LinAlgError: Singular matrix If self is singular. See also linalg.inv Examples >>> m = np.matrix('[1, 2; 3, 4]'); m matrix([[1, 2], [3, 4]]) >>> m.getI() matrix([[-2. , 1. ], [ 1.5,

matrix.getH() [source] Returns the (complex) conjugate transpose of self. Equivalent to np.transpose(self) if self is real-valued. Parameters: None Returns: ret : matrix object complex conjugate transpose of self Examples >>> x = np.matrix(np.arange(12).reshape((3,4))) >>> z = x - 1j*x; z matrix([[ 0. +0.j, 1. -1.j, 2. -2.j, 3. -3.j], [ 4. -4.j, 5. -5.j, 6. -6.j, 7. -7.j], [ 8. -8.j, 9. -9.j, 10.-10.j, 11.-11.j]]) >>> z.get

matrix.getfield(dtype, offset=0) Returns a field of the given array as a certain type. A field is a view of the array data with a given data-type. The values in the view are determined by the given type and the offset into the current array in bytes. The offset needs to be such that the view dtype fits in the array dtype; for example an array of dtype complex128 has 16-byte elements. If taking a view with a 32-bit integer (4 bytes), the offset needs to be between 0 and 12 bytes. Parameters:

matrix.getA1() [source] Return self as a flattened ndarray. Equivalent to np.asarray(x).ravel() Parameters: None Returns: ret : ndarray self, 1-D, as an ndarray Examples >>> x = np.matrix(np.arange(12).reshape((3,4))); x matrix([[ 0, 1, 2, 3], [ 4, 5, 6, 7], [ 8, 9, 10, 11]]) >>> x.getA1() array([ 0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11])

matrix.getA() [source] Return self as an ndarray object. Equivalent to np.asarray(self). Parameters: None Returns: ret : ndarray self as an ndarray Examples >>> x = np.matrix(np.arange(12).reshape((3,4))); x matrix([[ 0, 1, 2, 3], [ 4, 5, 6, 7], [ 8, 9, 10, 11]]) >>> x.getA() array([[ 0, 1, 2, 3], [ 4, 5, 6, 7], [ 8, 9, 10, 11]])

matrix.flatten(order='C') [source] Return a flattened copy of the matrix. All N elements of the matrix are placed into a single row. Parameters: order : {?C?, ?F?, ?A?, ?K?}, optional ?C? means to flatten in row-major (C-style) order. ?F? means to flatten in column-major (Fortran-style) order. ?A? means to flatten in column-major order if m is Fortran contiguous in memory, row-major order otherwise. ?K? means to flatten m in the order the elements occur in memory. The default is ?C?. Re

matrix.flat A 1-D iterator over the array. This is a numpy.flatiter instance, which acts similarly to, but is not a subclass of, Python?s built-in iterator object. See also flatten Return a copy of the array collapsed into one dimension. flatiter Examples >>> x = np.arange(1, 7).reshape(2, 3) >>> x array([[1, 2, 3], [4, 5, 6]]) >>> x.flat[3] 4 >>> x.T array([[1, 4], [2, 5], [3, 6]]) >>> x.T.flat[3] 5 >>> type(x.