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.linalg.norm(x, ord=None, axis=None, keepdims=False) [source] Matrix or vector norm. This function is able to return one of eight different matrix norms, or one of an infinite number of vector norms (described below), depending on the value of the ord parameter. Parameters: x : array_like Input array. If axis is None, x must be 1-D or 2-D. ord : {non-zero int, inf, -inf, ?fro?, ?nuc?}, optional Order of the norm (see table under Notes). inf means numpy?s inf object. axis : {int, 2

chararray.isspace() [source] Returns true for each element if there are only whitespace characters in the string and there is at least one character, false otherwise. See also char.isspace

MaskedArray.reshape(*s, **kwargs) [source] Give a new shape to the array without changing its data. Returns a masked array containing the same data, but with a new shape. The result is a view on the original array; if this is not possible, a ValueError is raised. Parameters: shape : int or tuple of ints The new shape should be compatible with the original shape. If an integer is supplied, then the result will be a 1-D array of that length. order : {?C?, ?F?}, optional Determines whether

numpy.imag(val) [source] Return the imaginary part of the elements of the array. Parameters: val : array_like Input array. Returns: out : ndarray Output array. If val is real, the type of val is used for the output. If val has complex elements, the returned type is float. See also real, angle, real_if_close Examples >>> a = np.array([1+2j, 3+4j, 5+6j]) >>> a.imag array([ 2., 4., 6.]) >>> a.imag = np.array([8, 10, 12]) >>> a array([ 1. +8.j, 3

record.transpose() 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.random.vonmises(mu, kappa, size=None) Draw samples from a von Mises distribution. Samples are drawn from a von Mises distribution with specified mode (mu) and dispersion (kappa), on the interval [-pi, pi]. The von Mises distribution (also known as the circular normal distribution) is a continuous probability distribution on the unit circle. It may be thought of as the circular analogue of the normal distribution. Parameters: mu : float Mode (?center?) of the distribution. kappa : f

MaskedArray.__setstate__(state) [source] Restore the internal state of the masked array, for pickling purposes. state is typically the output of the __getstate__ output, and is a 5-tuple: class name a tuple giving the shape of the data a typecode for the data a binary string for the data a binary string for the mask.

numpy.random.hypergeometric(ngood, nbad, nsample, size=None) Draw samples from a Hypergeometric distribution. Samples are drawn from a hypergeometric distribution with specified parameters, ngood (ways to make a good selection), nbad (ways to make a bad selection), and nsample = number of items sampled, which is less than or equal to the sum ngood + nbad. Parameters: ngood : int or array_like Number of ways to make a good selection. Must be nonnegative. nbad : int or array_like Number o