In addition to the MaskedArray class, the numpy.ma module defines several constants. numpy.ma.masked The masked constant is a special case of MaskedArray, with a float datatype and a null shape. It is used to test whether a specific entry of a masked array is masked, or to mask one or several entries of a masked array: >>> x = ma.array([1, 2, 3], mask=[0, 1, 0]) >>> x[1] is ma.masked True >>> x[-1] = ma.masked >>> x masked_array(data = [1 -- --],

numpy.ma.around = Round an array to the given number of decimals. Refer to around for full documentation. See also around equivalent function

numpy.copyto(dst, src, casting='same_kind', where=None) Copies values from one array to another, broadcasting as necessary. Raises a TypeError if the casting rule is violated, and if where is provided, it selects which elements to copy. New in version 1.7.0. Parameters: dst : ndarray The array into which values are copied. src : array_like The array from which values are copied. casting : {?no?, ?equiv?, ?safe?, ?same_kind?, ?unsafe?}, optional Controls what kind of data casting may

Aliases for functions which may be accelerated by Scipy. Scipy can be built to use accelerated or otherwise improved libraries for FFTs, linear algebra, and special functions. This module allows developers to transparently support these accelerated functions when scipy is available but still support users who have only installed Numpy. Linear algebra cholesky(a) Cholesky decomposition. det(a) Compute the determinant of an array. eig(a) Compute the eigenvalues and right eigenvectors of a sq

numpy.blackman(M) [source] Return the Blackman window. The Blackman window is a taper formed by using the first three terms of a summation of cosines. It was designed to have close to the minimal leakage possible. It is close to optimal, only slightly worse than a Kaiser window. Parameters: M : int Number of points in the output window. If zero or less, an empty array is returned. Returns: out : ndarray The window, with the maximum value normalized to one (the value one appears only i

ndarray.__lshift__ x.__lshift__(y) <==> x<<y

MaskedArray.__gt__ x.__gt__(y) <==> x>y

numpy.nanstd(a, axis=None, dtype=None, out=None, ddof=0, keepdims=False) [source] Compute the standard deviation along the specified axis, while ignoring NaNs. Returns the standard deviation, a measure of the spread of a distribution, of the non-NaN array elements. The standard deviation is computed for the flattened array by default, otherwise over the specified axis. For all-NaN slices or slices with zero degrees of freedom, NaN is returned and a RuntimeWarning is raised. New in version

generic.any() 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

numpy.nanpercentile(a, q, axis=None, out=None, overwrite_input=False, interpolation='linear', keepdims=False) [source] Compute the qth percentile of the data along the specified axis, while ignoring nan values. Returns the qth percentile(s) of the array elements. New in version 1.9.0. Parameters: a : array_like Input array or object that can be converted to an array. q : float in range of [0,100] (or sequence of floats) Percentile to compute, which must be between 0 and 100 inclusive.