numpy.polyint()

numpy.polyint(p, m=1, k=None) [source]

Return an antiderivative (indefinite integral) of a polynomial.

The returned order m antiderivative P of polynomial p satisfies $\frac{d^m}{dx^m}P(x) = p(x)$ and is defined up to m - 1 integration constants k. The constants determine the low-order polynomial part

$\frac{k_{m-1}}{0!} x^0 + \ldots + \frac{k_0}{(m-1)!}x^{m-1}$

of P so that $P^{(j)}(0) = k_{m-j-1}$ .

Parameters:

Parameters:	p : array_like or poly1d Polynomial to differentiate. A sequence is interpreted as polynomial coefficients, see `poly1d`. m : int, optional Order of the antiderivative. (Default: 1) k : list of `m` scalars or scalar, optional Integration constants. They are given in the order of integration: those corresponding to highest-order terms come first. If `None` (default), all constants are assumed to be zero. If `m = 1`, a single scalar can be given instead of a list.

p : array_like or poly1d

Polynomial to differentiate. A sequence is interpreted as polynomial coefficients, see poly1d.

m : int, optional

Order of the antiderivative. (Default: 1)

k : list of m scalars or scalar, optional

Integration constants. They are given in the order of integration: those corresponding to highest-order terms come first.

If None (default), all constants are assumed to be zero. If m = 1, a single scalar can be given instead of a list.

Examples

The defining property of the antiderivative:

>>> p = np.poly1d([1,1,1])
>>> P = np.polyint(p)
>>> P
poly1d([ 0.33333333,  0.5       ,  1.        ,  0.        ])
>>> np.polyder(P) == p
True

The integration constants default to zero, but can be specified:

>>> P = np.polyint(p, 3)
>>> P(0)
0.0
>>> np.polyder(P)(0)
0.0
>>> np.polyder(P, 2)(0)
0.0
>>> P = np.polyint(p, 3, k=[6,5,3])
>>> P
poly1d([ 0.01666667,  0.04166667,  0.16666667,  3. ,  5. ,  3. ])

Note that 3 = 6 / 2!, and that the constants are given in the order of integrations. Constant of the highest-order polynomial term comes first:

>>> np.polyder(P, 2)(0)
6.0
>>> np.polyder(P, 1)(0)
5.0
>>> P(0)
3.0

Links:

https://docs.scipy.org/doc/numpy-1.11.0/reference/generated/numpy.polyint.html

doc_NumPy

2025-01-10 15:47:30

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