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.. _routines.polynomial:
Polynomials
===========
Polynomials in NumPy can be *created*, *manipulated*, and even *fitted* using
the :doc:`convenience classes <routines.polynomials.classes>`
of the `numpy.polynomial` package, introduced in NumPy 1.4.
Prior to NumPy 1.4, `numpy.poly1d` was the class of choice and it is still
available in order to maintain backward compatibility.
However, the newer `polynomial package <numpy.polynomial>` is more complete
and its `convenience classes <routines.polynomials.classes>` provide a
more consistent, better-behaved interface for working with polynomial
expressions.
Therefore :mod:`numpy.polynomial` is recommended for new coding.
.. note:: **Terminology**
The term *polynomial module* refers to the old API defined in
``numpy.lib.polynomial``, which includes the :class:`numpy.poly1d` class and
the polynomial functions prefixed with *poly* accessible from the `numpy`
namespace (e.g. `numpy.polyadd`, `numpy.polyval`, `numpy.polyfit`, etc.).
The term *polynomial package* refers to the new API defined in
`numpy.polynomial`, which includes the convenience classes for the
different kinds of polynomials (:class:`~numpy.polynomial.polynomial.Polynomial`,
:class:`~numpy.polynomial.chebyshev.Chebyshev`, etc.).
Transitioning from `numpy.poly1d` to `numpy.polynomial`
-------------------------------------------------------
As noted above, the :class:`poly1d class <numpy.poly1d>` and associated
functions defined in ``numpy.lib.polynomial``, such as `numpy.polyfit`
and `numpy.poly`, are considered legacy and should **not** be used in new
code.
Since NumPy version 1.4, the `numpy.polynomial` package is preferred for
working with polynomials.
Quick Reference
~~~~~~~~~~~~~~~
The following table highlights some of the main differences between the
legacy polynomial module and the polynomial package for common tasks.
The `~numpy.polynomial.polynomial.Polynomial` class is imported for brevity::
from numpy.polynomial import Polynomial
+------------------------+------------------------------+---------------------------------------+
| **How to...** | Legacy (`numpy.poly1d`) | `numpy.polynomial` |
+------------------------+------------------------------+---------------------------------------+
| Create a | ``p = np.poly1d([1, 2, 3])`` | ``p = Polynomial([3, 2, 1])`` |
| polynomial object | | |
| from coefficients [1]_ | | |
+------------------------+------------------------------+---------------------------------------+
| Create a polynomial | ``r = np.poly([-1, 1])`` | ``p = Polynomial.fromroots([-1, 1])`` |
| object from roots | ``p = np.poly1d(r)`` | |
+------------------------+------------------------------+---------------------------------------+
| Fit a polynomial of | | |
| degree ``deg`` to data | ``np.polyfit(x, y, deg)`` | ``Polynomial.fit(x, y, deg)`` |
+------------------------+------------------------------+---------------------------------------+
.. [1] Note the reversed ordering of the coefficients
Transition Guide
~~~~~~~~~~~~~~~~
There are significant differences between ``numpy.lib.polynomial`` and
`numpy.polynomial`.
The most significant difference is the ordering of the coefficients for the
polynomial expressions.
The various routines in `numpy.polynomial` all
deal with series whose coefficients go from degree zero upward,
which is the *reverse order* of the poly1d convention.
The easy way to remember this is that indices
correspond to degree, i.e., ``coef[i]`` is the coefficient of the term of
degree *i*.
Though the difference in convention may be confusing, it is straightforward to
convert from the legacy polynomial API to the new.
For example, the following demonstrates how you would convert a `numpy.poly1d`
instance representing the expression :math:`x^{2} + 2x + 3` to a
`~numpy.polynomial.polynomial.Polynomial` instance representing the same
expression:
.. try_examples::
>>> import numpy as np
>>> p1d = np.poly1d([1, 2, 3])
>>> p = np.polynomial.Polynomial(p1d.coef[::-1])
In addition to the ``coef`` attribute, polynomials from the polynomial
package also have ``domain`` and ``window`` attributes.
These attributes are most relevant when fitting
polynomials to data, though it should be noted that polynomials with
different ``domain`` and ``window`` attributes are not considered equal, and
can't be mixed in arithmetic:
>>> p1 = np.polynomial.Polynomial([1, 2, 3])
>>> p1
Polynomial([1., 2., 3.], domain=[-1., 1.], window=[-1., 1.], symbol='x')
>>> p2 = np.polynomial.Polynomial([1, 2, 3], domain=[-2, 2])
>>> p1 == p2
False
>>> p1 + p2
Traceback (most recent call last):
...
TypeError: Domains differ
See the documentation for the
`convenience classes <routines.polynomials.classes>`_ for further details on
the ``domain`` and ``window`` attributes.
Another major difference between the legacy polynomial module and the
polynomial package is polynomial fitting. In the old module, fitting was
done via the `~numpy.polyfit` function. In the polynomial package, the
`~numpy.polynomial.polynomial.Polynomial.fit` class method is preferred. For
example, consider a simple linear fit to the following data:
.. ipython:: python
rng = np.random.default_rng()
x = np.arange(10)
y = np.arange(10) + rng.standard_normal(10)
With the legacy polynomial module, a linear fit (i.e. polynomial of degree 1)
could be applied to these data with `~numpy.polyfit`:
.. ipython:: python
np.polyfit(x, y, deg=1)
With the new polynomial API, the `~numpy.polynomial.polynomial.Polynomial.fit`
class method is preferred:
.. ipython:: python
p_fitted = np.polynomial.Polynomial.fit(x, y, deg=1)
p_fitted
Note that the coefficients are given *in the scaled domain* defined by the
linear mapping between the ``window`` and ``domain``.
`~numpy.polynomial.polynomial.Polynomial.convert` can be used to get the
coefficients in the unscaled data domain.
.. ipython:: python
p_fitted.convert()
Documentation for the `~numpy.polynomial` package
-------------------------------------------------
In addition to standard power series polynomials, the polynomial package
provides several additional kinds of polynomials including Chebyshev,
Hermite (two subtypes), Laguerre, and Legendre polynomials.
Each of these has an associated
`convenience class <routines.polynomials.classes>` available from the
`numpy.polynomial` namespace that provides a consistent interface for working
with polynomials regardless of their type.
.. toctree::
:maxdepth: 1
routines.polynomials.classes
Documentation pertaining to specific functions defined for each kind of
polynomial individually can be found in the corresponding module documentation:
.. toctree::
:maxdepth: 1
routines.polynomials.polynomial
routines.polynomials.chebyshev
routines.polynomials.hermite
routines.polynomials.hermite_e
routines.polynomials.laguerre
routines.polynomials.legendre
routines.polynomials.polyutils
Documentation for legacy polynomials
------------------------------------
.. toctree::
:maxdepth: 2
routines.polynomials.poly1d
|
