RandomState.standard_cauchy(size=None) Draw samples from a standard Cauchy distribution with mode = 0. Also known as the Lorentz distribution. Parameters: size : int or tuple of ints, optional Output shape. If the given shape is, e.g., (m, n, k), then m * n * k samples are drawn. Default is None, in which case a single value is returned. Returns: samples : ndarray or scalar The drawn samples. Notes The probability density function for the full Cauchy distribution is and the Stand

numpy.fft.fft2(a, s=None, axes=(-2, -1), norm=None) [source] Compute the 2-dimensional discrete Fourier Transform This function computes the n-dimensional discrete Fourier Transform over any axes in an M-dimensional array by means of the Fast Fourier Transform (FFT). By default, the transform is computed over the last two axes of the input array, i.e., a 2-dimensional FFT. Parameters: a : array_like Input array, can be complex s : sequence of ints, optional Shape (length of each transfo

numpy.insert(arr, obj, values, axis=None) [source] Insert values along the given axis before the given indices. Parameters: arr : array_like Input array. obj : int, slice or sequence of ints Object that defines the index or indices before which values is inserted. New in version 1.8.0. Support for multiple insertions when obj is a single scalar or a sequence with one element (similar to calling insert multiple times). values : array_like Values to insert into arr. If the type of val

ndarray.real The real part of the array. See also numpy.real equivalent function Examples >>> x = np.sqrt([1+0j, 0+1j]) >>> x.real array([ 1. , 0.70710678]) >>> x.real.dtype dtype('float64')

MaskedArray.__ror__ x.__ror__(y) <==> y|x

numpy.std(a, axis=None, dtype=None, out=None, ddof=0, keepdims=False) [source] Compute the standard deviation along the specified axis. Returns the standard deviation, a measure of the spread of a distribution, of the array elements. The standard deviation is computed for the flattened array by default, otherwise over the specified axis. Parameters: a : array_like Calculate the standard deviation of these values. axis : None or int or tuple of ints, optional Axis or axes along which the

numpy.polynomial.laguerre.lagval3d(x, y, z, c) [source] Evaluate a 3-D Laguerre series at points (x, y, z). This function returns the values: The parameters x, y, and z are converted to arrays only if they are tuples or a lists, otherwise they are treated as a scalars and they must have the same shape after conversion. In either case, either x, y, and z or their elements must support multiplication and addition both with themselves and with the elements of c. If c has fewer than 3 dimens

numpy.iscomplexobj(x) [source] Check for a complex type or an array of complex numbers. The type of the input is checked, not the value. Even if the input has an imaginary part equal to zero, iscomplexobj evaluates to True. Parameters: x : any The input can be of any type and shape. Returns: iscomplexobj : bool The return value, True if x is of a complex type or has at least one complex element. See also isrealobj, iscomplex Examples >>> np.iscomplexobj(1) False >>&

RandomState.multivariate_normal(mean, cov[, size]) Draw random samples from a multivariate normal distribution. The multivariate normal, multinormal or Gaussian distribution is a generalization of the one-dimensional normal distribution to higher dimensions. Such a distribution is specified by its mean and covariance matrix. These parameters are analogous to the mean (average or ?center?) and variance (standard deviation, or ?width,? squared) of the one-dimensional normal distribution. Para

generic.fill() 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