numpy.column_stack(tup) [source] 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. See also hstack, vstack, concatena

numpy.polymul(a1, a2) [source] Find the product of two polynomials. Finds the polynomial resulting from the multiplication of the two input polynomials. Each input must be either a poly1d object or a 1D sequence of polynomial coefficients, from highest to lowest degree. Parameters: a1, a2 : array_like or poly1d object Input polynomials. Returns: out : ndarray or poly1d object The polynomial resulting from the multiplication of the inputs. If either inputs is a poly1d object, then the

numpy.core.defchararray.rsplit(a, sep=None, maxsplit=None) [source] For each element in a, return a list of the words in the string, using sep as the delimiter string. Calls str.rsplit element-wise. Except for splitting from the right, rsplit behaves like split. Parameters: a : array_like of str or unicode sep : str or unicode, optional If sep is not specified or None, any whitespace string is a separator. maxsplit : int, optional If maxsplit is given, at most maxsplit splits are done,

numpy.rad2deg(x[, out]) = Convert angles from radians to degrees. Parameters: x : array_like Angle in radians. out : ndarray, optional Array into which the output is placed. Its type is preserved and it must be of the right shape to hold the output. See doc.ufuncs. Returns: y : ndarray The corresponding angle in degrees. See also deg2rad Convert angles from degrees to radians. unwrap Remove large jumps in angle by wrapping. Notes New in version 1.3.0. rad2deg(x) is 180

numpy.polynomial.laguerre.lagint(c, m=1, k=[], lbnd=0, scl=1, axis=0) [source] Integrate a Laguerre series. Returns the Laguerre series coefficients c integrated m times from lbnd along axis. At each iteration the resulting series is multiplied by scl and an integration constant, k, is added. The scaling factor is for use in a linear change of variable. (?Buyer beware?: note that, depending on what one is doing, one may want scl to be the reciprocal of what one might expect; for more inform

numpy.modf(x[, out1, out2]) = Return the fractional and integral parts of an array, element-wise. The fractional and integral parts are negative if the given number is negative. Parameters: x : array_like Input array. Returns: y1 : ndarray Fractional part of x. y2 : ndarray Integral part of x. Notes For integer input the return values are floats. Examples >>> np.modf([0, 3.5]) (array([ 0. , 0.5]), array([ 0., 3.])) >>> np.modf(-0.5) (-0.5, -0)

numpy.ones(shape, dtype=None, order='C') [source] Return a new array of given shape and type, filled with ones. Parameters: shape : int or sequence of ints Shape of the new array, e.g., (2, 3) or 2. dtype : data-type, optional The desired data-type for the array, e.g., numpy.int8. Default is numpy.float64. order : {?C?, ?F?}, optional Whether to store multidimensional data in C- or Fortran-contiguous (row- or column-wise) order in memory. Returns: out : ndarray Array of ones with

numpy.random.power(a, size=None) Draws samples in [0, 1] from a power distribution with positive exponent a - 1. Also known as the power function distribution. Parameters: a : float parameter, > 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. Returns: samples : ndarray or scalar The returned samples lie in [0, 1]. Raises: ValueError If a &

Note This page describes the numpy-specific API for accessing the contents of a numpy array from other C extensions. PEP 3118 ? The Revised Buffer Protocol introduces similar, standardized API to Python 2.6 and 3.0 for any extension module to use. Cython?s buffer array support uses the PEP 3118 API; see the Cython numpy tutorial. Cython provides a way to write code that supports the buffer protocol with Python versions older than 2.6 because it has a backward-compatible implementation utilizi

numpy.atleast_1d(*arys) [source] Convert inputs to arrays with at least one dimension. Scalar inputs are converted to 1-dimensional arrays, whilst higher-dimensional inputs are preserved. Parameters: arys1, arys2, ... : array_like One or more input arrays. Returns: ret : ndarray An array, or sequence of arrays, each with a.ndim >= 1. Copies are made only if necessary. See also atleast_2d, atleast_3d Examples >>> np.atleast_1d(1.0) array([ 1.]) >>> x = np.aran