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.polynomial.polynomial.polysub(c1, c2) [source] Subtract one polynomial from another. Returns the difference of two polynomials c1 - c2. The arguments are sequences of coefficients from lowest order term to highest, i.e., [1,2,3] represents the polynomial 1 + 2*x + 3*x**2. Parameters: c1, c2 : array_like 1-D arrays of polynomial coefficients ordered from low to high. Returns: out : ndarray Of coefficients representing their difference. See also polyadd, polymul, polydiv, polyp

matrix.swapaxes(axis1, axis2) Return a view of the array with axis1 and axis2 interchanged. Refer to numpy.swapaxes for full documentation. See also numpy.swapaxes equivalent function

Legendre.cutdeg(deg) [source] Truncate series to the given degree. Reduce the degree of the series to deg by discarding the high order terms. If deg is greater than the current degree a copy of the current series is returned. This can be useful in least squares where the coefficients of the high degree terms may be very small. New in version 1.5.0. Parameters: deg : non-negative int The series is reduced to degree deg by discarding the high order terms. The value of deg must be a non-ne

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.searchsorted(v, side='left', sorter=None) Find indices where elements of v should be inserted in a to maintain order. For full documentation, see numpy.searchsorted See also numpy.searchsorted equivalent function

numpy.concatenate((a1, a2, ...), axis=0) Join a sequence of arrays along an existing axis. Parameters: a1, a2, ... : sequence of array_like The arrays must have the same shape, except in the dimension corresponding to axis (the first, by default). axis : int, optional The axis along which the arrays will be joined. Default is 0. Returns: res : ndarray The concatenated array. See also ma.concatenate Concatenate function that preserves input masks. array_split Split an array

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

numpy.polynomial.hermite.hermpow(c, pow, maxpower=16) [source] Raise a Hermite series to a power. Returns the Hermite series c raised to the power pow. The argument c is a sequence of coefficients ordered from low to high. i.e., [1,2,3] is the series P_0 + 2*P_1 + 3*P_2. Parameters: c : array_like 1-D array of Hermite series coefficients ordered from low to high. pow : integer Power to which the series will be raised maxpower : integer, optional Maximum power allowed. This is mainly t