hermder(c, m=1, scl=1, axis=0)
Returns the Hermite series coefficients c differentiated m times along :None:None:`axis`. At each iteration the result is multiplied by :None:None:`scl` (the scaling factor is for use in a linear change of variable). The argument c is an array of coefficients from low to high degree along each axis, e.g., [1,2,3] represents the series 1*H_0 + 2*H_1 + 3*H_2
while [[1,2],[1,2]] represents 1*H_0(x)*H_0(y) + 1*H_1(x)*H_0(y) +
2*H_0(x)*H_1(y) + 2*H_1(x)*H_1(y)
if axis=0 is x
and axis=1 is y
.
In general, the result of differentiating a Hermite series does not resemble the same operation on a power series. Thus the result of this function may be "unintuitive," albeit correct; see Examples section below.
Array of Hermite series coefficients. If c is multidimensional the different axis correspond to different variables with the degree in each axis given by the corresponding index.
Number of derivatives taken, must be non-negative. (Default: 1)
Each differentiation is multiplied by :None:None:`scl`. The end result is multiplication by scl**m
. This is for use in a linear change of variable. (Default: 1)
Axis over which the derivative is taken. (Default: 0).
Hermite series of the derivative.
Differentiate a Hermite series.
>>> from numpy.polynomial.hermite import hermder
... hermder([ 1. , 0.5, 0.5, 0.5]) array([1., 2., 3.])
>>> hermder([-0.5, 1./2., 1./8., 1./12., 1./16.], m=2) array([1., 2., 3.])See :
The following pages refer to to this document either explicitly or contain code examples using this.
numpy.polynomial.hermite.hermint
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