MaskedArray.__reduce__() [source] Return a 3-tuple for pickling a MaskedArray.

numpy.ma.asarray(a, dtype=None, order=None) [source] Convert the input to a masked array of the given data-type. No copy is performed if the input is already an ndarray. If a is a subclass of MaskedArray, a base class MaskedArray is returned. Parameters: a : array_like Input data, in any form that can be converted to a masked array. This includes lists, lists of tuples, tuples, tuples of tuples, tuples of lists, ndarrays and masked arrays. dtype : dtype, optional By default, the data-ty

numpy.polynomial.chebyshev.chebdiv(c1, c2) [source] Divide one Chebyshev series by another. Returns the quotient-with-remainder of two Chebyshev series c1 / c2. The arguments are sequences of coefficients from lowest order ?term? to highest, e.g., [1,2,3] represents the series T_0 + 2*T_1 + 3*T_2. Parameters: c1, c2 : array_like 1-D arrays of Chebyshev series coefficients ordered from low to high. Returns: [quo, rem] : ndarrays Of Chebyshev series coefficients representing the quotien

MaskedArray.iscontiguous() [source] Return a boolean indicating whether the data is contiguous. Parameters: None Examples >>> x = np.ma.array([1, 2, 3]) >>> x.iscontiguous() True iscontiguous returns one of the flags of the masked array: >>> x.flags C_CONTIGUOUS : True F_CONTIGUOUS : True OWNDATA : False WRITEABLE : True ALIGNED : True UPDATEIFCOPY : False

MaskedArray.compress(condition, axis=None, out=None) [source] Return a where condition is True. If condition is a MaskedArray, missing values are considered as False. Parameters: condition : var Boolean 1-d array selecting which entries to return. If len(condition) is less than the size of a along the axis, then output is truncated to length of condition array. axis : {None, int}, optional Axis along which the operation must be performed. out : {None, ndarray}, optional Alternative ou

numpy.nonzero(a) [source] Return the indices of the elements that are non-zero. Returns a tuple of arrays, one for each dimension of a, containing the indices of the non-zero elements in that dimension. The values in a are always tested and returned in row-major, C-style order. The corresponding non-zero values can be obtained with: a[nonzero(a)] To group the indices by element, rather than dimension, use: transpose(nonzero(a)) The result of this is always a 2-D array, with a row for each

ndarray.__pos__ x.__pos__() <==> +x

ndarray.prod(axis=None, dtype=None, out=None, keepdims=False) Return the product of the array elements over the given axis Refer to numpy.prod for full documentation. See also numpy.prod equivalent function

ndarray.byteswap(inplace) Swap the bytes of the array elements Toggle between low-endian and big-endian data representation by returning a byteswapped array, optionally swapped in-place. Parameters: inplace : bool, optional If True, swap bytes in-place, default is False. Returns: out : ndarray The byteswapped array. If inplace is True, this is a view to self. Examples >>> A = np.array([1, 256, 8755], dtype=np.int16) >>> map(hex, A) ['0x1', '0x100', '0x2233'] >&g

recarray.all(axis=None, out=None, keepdims=False) Returns True if all elements evaluate to True. Refer to numpy.all for full documentation. See also numpy.all equivalent function