MaskedArray.__rfloordiv__(other) [source] Divide self into other, and return a new masked array.

MaskedArray.__setmask__(mask, copy=False) [source] Set the mask.

numpy.choose(a, choices, out=None, mode='raise') [source] Construct an array from an index array and a set of arrays to choose from. First of all, if confused or uncertain, definitely look at the Examples - in its full generality, this function is less simple than it might seem from the following code description (below ndi = numpy.lib.index_tricks): np.choose(a,c) == np.array([c[a[I]][I] for I in ndi.ndindex(a.shape)]). But this omits some subtleties. Here is a fully general summary: Given

Numpy binary files (NPY, NPZ) load(file[, mmap_mode, allow_pickle, ...]) Load arrays or pickled objects from .npy, .npz or pickled files. save(file, arr[, allow_pickle, fix_imports]) Save an array to a binary file in NumPy .npy format. savez(file, *args, **kwds) Save several arrays into a single file in uncompressed .npz format. savez_compressed(file, *args, **kwds) Save several arrays into a single file in compressed .npz format. The format of these binary file types is documented in h

generic.ravel() 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.__ilshift__ x.__ilshift__(y) <==> x<<=y

numpy.triu_indices(n, k=0, m=None) [source] Return the indices for the upper-triangle of an (n, m) array. Parameters: n : int The size of the arrays for which the returned indices will be valid. k : int, optional Diagonal offset (see triu for details). m : int, optional New in version 1.9.0. The column dimension of the arrays for which the returned arrays will be valid. By default m is taken equal to n. Returns: inds : tuple, shape(2) of ndarrays, shape(n) The indices for the tr

numpy.ma.sum(self, axis=None, dtype=None, out=None) = Return the sum of the array elements over the given axis. Masked elements are set to 0 internally. Parameters: axis : {None, -1, int}, optional Axis along which the sum is computed. The default (axis = None) is to compute over the flattened array. dtype : {None, dtype}, optional Determines the type of the returned array and of the accumulator where the elements are summed. If dtype has the value None and the type of a is an integer

chararray.size Number of elements in the array. Equivalent to np.prod(a.shape), i.e., the product of the array?s dimensions. Examples >>> x = np.zeros((3, 5, 2), dtype=np.complex128) >>> x.size 30 >>> np.prod(x.shape) 30

generic.round() 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