record.view() Not implemented (virtual attribute) Class generic exists solely to derive numpy scalars from, and possesses, albeit unimplemented, all the attributes of the ndarray class so as to provide a uniform API. See also The

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,

record.choose() Not implemented (virtual attribute) Class generic exists solely to derive numpy scalars from, and possesses, albeit unimplemented, all the attributes of the ndarray class so as to provide a uniform API. See also The

MaskedArray.__imod__ x.__imod__(y) <==> x%=y

ndarray.conjugate() Return the complex conjugate, element-wise. Refer to numpy.conjugate for full documentation. See also numpy.conjugate equivalent function

numpy.diagonal(a, offset=0, axis1=0, axis2=1) [source] Return specified diagonals. If a is 2-D, returns the diagonal of a with the given offset, i.e., the collection of elements of the form a[i, i+offset]. If a has more than two dimensions, then the axes specified by axis1 and axis2 are used to determine the 2-D sub-array whose diagonal is returned. The shape of the resulting array can be determined by removing axis1 and axis2 and appending an index to the right equal to the size of the res

MaskedArray.prod(axis=None, dtype=None, out=None) [source] Return the product of the array elements over the given axis. Masked elements are set to 1 internally for computation. Parameters: axis : {None, int}, optional Axis over which the product is taken. If None is used, then the product is over all the array elements. dtype : {None, dtype}, optional Determines the type of the returned array and of the accumulator where the elements are multiplied. If dtype has the value None and the

MaskedArray.mean(axis=None, dtype=None, out=None) [source] Returns the average of the array elements. Masked entries are ignored. The average is taken over the flattened array by default, otherwise over the specified axis. Refer to numpy.mean for the full documentation. Parameters: a : array_like Array containing numbers whose mean is desired. If a is not an array, a conversion is attempted. axis : int, optional Axis along which the means are computed. The default is to compute the mean

MaskedArray.anom(axis=None, dtype=None) [source] Compute the anomalies (deviations from the arithmetic mean) along the given axis. Returns an array of anomalies, with the same shape as the input and where the arithmetic mean is computed along the given axis. Parameters: axis : int, optional Axis over which the anomalies are taken. The default is to use the mean of the flattened array as reference. dtype : dtype, optional Type to use in computing the variance. For arrays of integer type

MaskedArray.clip(min=None, max=None, out=None) [source] Return an array whose values are limited to [min, max]. One of max or min must be given. Refer to numpy.clip for full documentation. See also numpy.clip equivalent function