generic.dumps() 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.ma.flatnotmasked_edges(a) [source] Find the indices of the first and last unmasked values. Expects a 1-D MaskedArray, returns None if all values are masked. Parameters: a : array_like Input 1-D MaskedArray Returns: edges : ndarray or None The indices of first and last non-masked value in the array. Returns None if all values are masked. See also flatnotmasked_contiguous, notmasked_contiguous, notmasked_edges, clump_masked, clump_unmasked Notes Only accepts 1-D arrays. Exampl

numpy.ma.notmasked_contiguous(a, axis=None) [source] Find contiguous unmasked data in a masked array along the given axis. Parameters: a : array_like The input array. axis : int, optional Axis along which to perform the operation. If None (default), applies to a flattened version of the array. Returns: endpoints : list A list of slices (start and end indexes) of unmasked indexes in the array. See also flatnotmasked_edges, flatnotmasked_contiguous, notmasked_edges, clump_masked, c

numpy.ma.count_masked(arr, axis=None) [source] Count the number of masked elements along the given axis. Parameters: arr : array_like An array with (possibly) masked elements. axis : int, optional Axis along which to count. If None (default), a flattened version of the array is used. Returns: count : int, ndarray The total number of masked elements (axis=None) or the number of masked elements along each slice of the given axis. See also MaskedArray.count Count non-masked eleme

numpy.ma.column_stack(tup) = Stack 1-D arrays as columns into a 2-D array. Take a sequence of 1-D arrays and stack them as columns to make a single 2-D array. 2-D arrays are stacked as-is, just like with hstack. 1-D arrays are turned into 2-D columns first. Parameters: tup : sequence of 1-D or 2-D arrays. Arrays to stack. All of them must have the same first dimension. Returns: stacked : 2-D array The array formed by stacking the given arrays. Notes The function is applied to both

RandomState.beta(a, b, size=None) Draw samples from a Beta distribution. The Beta distribution is a special case of the Dirichlet distribution, and is related to the Gamma distribution. It has the probability distribution function where the normalisation, B, is the beta function, It is often seen in Bayesian inference and order statistics. Parameters: a : float Alpha, non-negative. b : float Beta, non-negative. size : int or tuple of ints, optional Output shape. If the given sha

chararray.transpose(*axes) Returns a view of the array with axes transposed. For a 1-D array, this has no effect. (To change between column and row vectors, first cast the 1-D array into a matrix object.) For a 2-D array, this is the usual matrix transpose. For an n-D array, if axes are given, their order indicates how the axes are permuted (see Examples). If axes are not provided and a.shape = (i[0], i[1], ... i[n-2], i[n-1]), then a.transpose().shape = (i[n-1], i[n-2], ... i[1], i[0]). Pa

numpy.fft.fftshift(x, axes=None) [source] Shift the zero-frequency component to the center of the spectrum. This function swaps half-spaces for all axes listed (defaults to all). Note that y[0] is the Nyquist component only if len(x) is even. Parameters: x : array_like Input array. axes : int or shape tuple, optional Axes over which to shift. Default is None, which shifts all axes. Returns: y : ndarray The shifted array. See also ifftshift The inverse of fftshift. Examples &g

generic.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

numpy.poly(seq_of_zeros) [source] Find the coefficients of a polynomial with the given sequence of roots. Returns the coefficients of the polynomial whose leading coefficient is one for the given sequence of zeros (multiple roots must be included in the sequence as many times as their multiplicity; see Examples). A square matrix (or array, which will be treated as a matrix) can also be given, in which case the coefficients of the characteristic polynomial of the matrix are returned. Paramet