numpy.ma.sort(a, axis=-1, kind='quicksort', order=None, endwith=True, fill_value=None) [source] Sort the array, in-place Parameters: a : array_like Array to be sorted. axis : int, optional Axis along which to sort. If None, the array is flattened before sorting. The default is -1, which sorts along the last axis. kind : {?quicksort?, ?mergesort?, ?heapsort?}, optional Sorting algorithm. Default is ?quicksort?. order : list, optional When a is a structured array, this argument specif

numpy.random.rayleigh(scale=1.0, size=None) Draw samples from a Rayleigh distribution. The and Weibull distributions are generalizations of the Rayleigh. Parameters: scale : scalar Scale, also equals the mode. Should be >= 0. 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. Notes The probability density function for the Rayleigh distribution is

generic.min() 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

ndarray.__len__() <==> len(x)

numpy.array_split(ary, indices_or_sections, axis=0) [source] Split an array into multiple sub-arrays. Please refer to the split documentation. The only difference between these functions is that array_split allows indices_or_sections to be an integer that does not equally divide the axis. See also split Split array into multiple sub-arrays of equal size. Examples >>> x = np.arange(8.0) >>> np.array_split(x, 3) [array([ 0., 1., 2.]), array([ 3., 4., 5.]), array

numpy.random.bytes(length) Return random bytes. Parameters: length : int Number of random bytes. Returns: out : str String of length length. Examples >>> np.random.bytes(10) ' eh\x85\x022SZ\xbf\xa4' #random

numpy.logical_and(x1, x2[, out]) = Compute the truth value of x1 AND x2 element-wise. Parameters: x1, x2 : array_like Input arrays. x1 and x2 must be of the same shape. Returns: y : ndarray or bool Boolean result with the same shape as x1 and x2 of the logical AND operation on corresponding elements of x1 and x2. See also logical_or, logical_not, logical_xor, bitwise_and Examples >>> np.logical_and(True, False) False >>> np.logical_and([True, False], [False, Fal

numpy.polynomial.hermite.hermval2d(x, y, c) [source] Evaluate a 2-D Hermite series at points (x, y). This function returns the values: The parameters x and y 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 and y or their elements must support multiplication and addition both with themselves and with the elements of c. If c is a 1-D array a one is implicitly app

RandomState.triangular(left, mode, right, size=None) Draw samples from the triangular distribution. The triangular distribution is a continuous probability distribution with lower limit left, peak at mode, and upper limit right. Unlike the other distributions, these parameters directly define the shape of the pdf. Parameters: left : scalar Lower limit. mode : scalar The value where the peak of the distribution occurs. The value should fulfill the condition left <= mode <= right.

matrix.ctypes An object to simplify the interaction of the array with the ctypes module. This attribute creates an object that makes it easier to use arrays when calling shared libraries with the ctypes module. The returned object has, among others, data, shape, and strides attributes (see Notes below) which themselves return ctypes objects that can be used as arguments to a shared library. Parameters: None Returns: c : Python object Possessing attributes data, shape, strides, etc. S