numpy.polynomial.hermite.hermmulx(c) [source] Multiply a Hermite series by x. Multiply the Hermite series c by x, where x is the independent variable. Parameters: c : array_like 1-D array of Hermite series coefficients ordered from low to high. Returns: out : ndarray Array representing the result of the multiplication. Notes The multiplication uses the recursion relationship for Hermite polynomials in the form xP_i(x) = (P_{i + 1}(x)/2 + i*P_{i - 1}(x)) Examples >>> from

recarray.prod(axis=None, dtype=None, out=None, keepdims=False) Return the product of the array elements over the given axis Refer to numpy.prod for full documentation. See also numpy.prod equivalent function

numpy.random.standard_normal(size=None) Draw samples from a standard Normal distribution (mean=0, stdev=1). 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: out : float or ndarray Drawn samples. Examples >>> s = np.random.standard_normal(8000) >>> s array([ 0.6888893 , 0.78096262, -0.89086505, ..., 0.49876311

MaskedArray.argsort(axis=None, kind='quicksort', order=None, fill_value=None) [source] Return an ndarray of indices that sort the array along the specified axis. Masked values are filled beforehand to fill_value. Parameters: axis : int, optional Axis along which to sort. The default is -1 (last axis). If None, the flattened array is used. fill_value : var, optional Value used to fill the array before sorting. The default is the fill_value attribute of the input array. kind : {?quicksor

matrix.getfield(dtype, offset=0) Returns a field of the given array as a certain type. A field is a view of the array data with a given data-type. The values in the view are determined by the given type and the offset into the current array in bytes. The offset needs to be such that the view dtype fits in the array dtype; for example an array of dtype complex128 has 16-byte elements. If taking a view with a 32-bit integer (4 bytes), the offset needs to be between 0 and 12 bytes. Parameters:

numpy.bitwise_and(x1, x2[, out]) = Compute the bit-wise AND of two arrays element-wise. Computes the bit-wise AND of the underlying binary representation of the integers in the input arrays. This ufunc implements the C/Python operator &. Parameters: x1, x2 : array_like Only integer and boolean types are handled. Returns: out : array_like Result. See also logical_and, bitwise_or, bitwise_xor binary_repr Return the binary representation of the input number as a string. Examp

numpy.polynomial.legendre.poly2leg(pol) [source] Convert a polynomial to a Legendre series. Convert an array representing the coefficients of a polynomial (relative to the ?standard? basis) ordered from lowest degree to highest, to an array of the coefficients of the equivalent Legendre series, ordered from lowest to highest degree. Parameters: pol : array_like 1-D array containing the polynomial coefficients Returns: c : ndarray 1-D array containing the coefficients of the equivalent

Laguerre.linspace(n=100, domain=None) [source] Return x, y values at equally spaced points in domain. Returns the x, y values at n linearly spaced points across the domain. Here y is the value of the polynomial at the points x. By default the domain is the same as that of the series instance. This method is intended mostly as a plotting aid. New in version 1.5.0. Parameters: n : int, optional Number of point pairs to return. The default value is 100. domain : {None, array_like}, option

numpy.polynomial.hermite_e.hermex = array([0, 1])

numpy.polynomial.chebyshev.chebvander(x, deg) [source] Pseudo-Vandermonde matrix of given degree. Returns the pseudo-Vandermonde matrix of degree deg and sample points x. The pseudo-Vandermonde matrix is defined by where 0 <= i <= deg. The leading indices of V index the elements of x and the last index is the degree of the Chebyshev polynomial. If c is a 1-D array of coefficients of length n + 1 and V is the matrix V = chebvander(x, n), then np.dot(V, c) and chebval(x, c) are the s