ndarray.__deepcopy__() ? Deep copy of array. Used if copy.deepcopy is called on an array.

numpy.testing.assert_array_equal(x, y, err_msg='', verbose=True) [source] Raises an AssertionError if two array_like objects are not equal. Given two array_like objects, check that the shape is equal and all elements of these objects are equal. An exception is raised at shape mismatch or conflicting values. In contrast to the standard usage in numpy, NaNs are compared like numbers, no assertion is raised if both objects have NaNs in the same positions. The usual caution for verifying equali

record.conjugate() 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

dtype.shape Shape tuple of the sub-array if this data type describes a sub-array, and () otherwise.

MaskedArray.__float__() [source] Convert to float.

record.strides tuple of bytes steps in each dimension

MaskedArray.nonzero() [source] Return the indices of unmasked elements that are not zero. Returns a tuple of arrays, one for each dimension, containing the indices of the non-zero elements in that dimension. The corresponding non-zero values can be obtained with: a[a.nonzero()] To group the indices by element, rather than dimension, use instead: np.transpose(a.nonzero()) The result of this is always a 2d array, with a row for each non-zero element. Parameters: None Returns: tuple_of_ar

recarray.flags Information about the memory layout of the array. Notes The flags object can be accessed dictionary-like (as in a.flags['WRITEABLE']), or by using lowercased attribute names (as in a.flags.writeable). Short flag names are only supported in dictionary access. Only the UPDATEIFCOPY, WRITEABLE, and ALIGNED flags can be changed by the user, via direct assignment to the attribute or dictionary entry, or by calling ndarray.setflags. The array flags cannot be set arbitrarily: UPDATE

generic.T transpose

numpy.linalg.eigvals(a) [source] Compute the eigenvalues of a general matrix. Main difference between eigvals and eig: the eigenvectors aren?t returned. Parameters: a : (..., M, M) array_like A complex- or real-valued matrix whose eigenvalues will be computed. Returns: w : (..., M,) ndarray The eigenvalues, each repeated according to its multiplicity. They are not necessarily ordered, nor are they necessarily real for real matrices. Raises: LinAlgError If the eigenvalue computatio