Integrate a Hermite series.
Returns the Hermite 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 information, see the Notes section below.) The argument c is an array of coefficients from low to high degree along each axis, e.g., [1,2,3] represents the series 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.
Parameters :  c : array_like
m : int, optional
k : {[], list, scalar}, optional
lbnd : scalar, optional
scl : scalar, optional
axis : int, optional


Returns :  S : ndarray

Raises :  ValueError :

See also
Notes
Note that the result of each integration is multiplied by scl. Why is this important to note? Say one is making a linear change of variable in an integral relative to x. Then .. math::dx = du/a, so one will need to set scl equal to  perhaps not what one would have first thought.
Also note that, in general, the result of integrating a Cseries needs to be “reprojected” onto the Cseries basis set. Thus, typically, the result of this function is “unintuitive,” albeit correct; see Examples section below.
Examples
>>> from numpy.polynomial.hermite import hermint
>>> hermint([1,2,3]) # integrate once, value 0 at 0.
array([ 1. , 0.5, 0.5, 0.5])
>>> hermint([1,2,3], m=2) # integrate twice, value & deriv 0 at 0
array([0.5 , 0.5 , 0.125 , 0.08333333, 0.0625 ])
>>> hermint([1,2,3], k=1) # integrate once, value 1 at 0.
array([ 2. , 0.5, 0.5, 0.5])
>>> hermint([1,2,3], lbnd=1) # integrate once, value 0 at 1
array([2. , 0.5, 0.5, 0.5])
>>> hermint([1,2,3], m=2, k=[1,2], lbnd=1)
array([ 1.66666667, 0.5 , 0.125 , 0.08333333, 0.0625 ])