numpy.binary_repr(num, width=None) [source] Return the binary representation of the input number as a string. For negative numbers, if width is not given, a minus sign is added to the front. If width is given, the two?s complement of the number is returned, with respect to that width. In a two?s-complement system negative numbers are represented by the two?s complement of the absolute value. This is the most common method of representing signed integers on computers [R16]. A N-bit two?s-com

MaskedArray.std(axis=None, dtype=None, out=None, ddof=0) [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 standard dev

numpy.random.poisson(lam=1.0, size=None) Draw samples from a Poisson distribution. The Poisson distribution is the limit of the binomial distribution for large N. Parameters: lam : float or sequence of float Expectation of interval, should be >= 0. A sequence of expectation intervals must be broadcastable over the requested size. 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

RandomState.noncentral_f(dfnum, dfden, nonc, size=None) Draw samples from the noncentral F distribution. Samples are drawn from an F distribution with specified parameters, dfnum (degrees of freedom in numerator) and dfden (degrees of freedom in denominator), where both parameters > 1. nonc is the non-centrality parameter. Parameters: dfnum : int Parameter, should be > 1. dfden : int Parameter, should be > 1. nonc : float Parameter, should be >= 0. size : int or tuple of

numpy.polynomial.laguerre.laggrid3d(x, y, z, c) [source] Evaluate a 3-D Laguerre series on the Cartesian product of x, y, and z. This function returns the values: where the points (a, b, c) consist of all triples formed by taking a from x, b from y, and c from z. The resulting points form a grid with x in the first dimension, y in the second, and z in the third. 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.

numpy.piecewise(x, condlist, funclist, *args, **kw) [source] Evaluate a piecewise-defined function. Given a set of conditions and corresponding functions, evaluate each function on the input data wherever its condition is true. Parameters: x : ndarray The input domain. condlist : list of bool arrays Each boolean array corresponds to a function in funclist. Wherever condlist[i] is True, funclist[i](x) is used as the output value. Each boolean array in condlist selects a piece of x, and s

ndarray.__ne__ x.__ne__(y) <==> x!=y

matrix.data Python buffer object pointing to the start of the array?s data.

numpy.ldexp(x1, x2[, out]) = Returns x1 * 2**x2, element-wise. The mantissas x1 and twos exponents x2 are used to construct floating point numbers x1 * 2**x2. Parameters: x1 : array_like Array of multipliers. x2 : array_like, int Array of twos exponents. out : ndarray, optional Output array for the result. Returns: y : ndarray or scalar The result of x1 * 2**x2. See also frexp Return (y1, y2) from x = y1 * 2**y2, inverse to ldexp. Notes Complex dtypes are not supported, th

record.conj()