numpy.lib.polynomial

def _binary_op_dispatcher(a1, a2):

Undocumented

def _poly_dispatcher(seq_of_zeros):

Undocumented

def _polyder_dispatcher(p, m=None):

Undocumented

def _polydiv_dispatcher(u, v):

Undocumented

def _polyfit_dispatcher(x, y, deg, rcond=None, full=None, w=None, cov=None):

Undocumented

def _polyint_dispatcher(p, m=None, k=None):

Undocumented

def _polyval_dispatcher(p, x):

Undocumented

def _raise_power(astr, wrap=70):

Undocumented

def _roots_dispatcher(p):

Undocumented

@array_function_dispatch(_poly_dispatcher)
def poly(seq_of_zeros):

Find the coefficients of a polynomial with the given sequence of roots.

Note

This forms part of the old polynomial API. Since version 1.4, the new polynomial API defined in numpy.polynomial is preferred. A summary of the differences can be found in the :doc:`transition guide </reference/routines.polynomials>`.

Returns the coefficients of the polynomial whose leading coefficient is one for the given sequence of zeros (multiple roots must be included in the sequence as many times as their multiplicity; see Examples). A square matrix (or array, which will be treated as a matrix) can also be given, in which case the coefficients of the characteristic polynomial of the matrix are returned.

Parameters

seq_of_zeros : array_like, shape (N,) or (N, N): A sequence of polynomial roots, or a square array or matrix object.

Returns

c : ndarray

1D array of polynomial coefficients from highest to lowest degree:

c[0] * x**(N) + c[1] * x**(N-1) + ... + c[N-1] * x + c[N] where c[0] always equals 1.

Raises

ValueError: If input is the wrong shape (the input must be a 1-D or square 2-D array).

Notes

Specifying the roots of a polynomial still leaves one degree of freedom, typically represented by an undetermined leading coefficient. [1] In the case of this function, that coefficient - the first one in the returned array - is always taken as one. (If for some reason you have one other point, the only automatic way presently to leverage that information is to use polyfit.)

The characteristic polynomial, p_a(t), of an n-by-n matrix A is given by

p_a(t) = det(t I − A),

where I is the n-by-n identity matrix. [2]

References

[1]	M. Sullivan and M. Sullivan, III, "Algebra and Trignometry, Enhanced With Graphing Utilities," Prentice-Hall, pg. 318, 1996.

[2]	G. Strang, "Linear Algebra and Its Applications, 2nd Edition," Academic Press, pg. 182, 1980.

Examples

Given a sequence of a polynomial's zeros:

>>> np.poly((0, 0, 0)) # Multiple root example
array([1., 0., 0., 0.])

The line above represents z**3 + 0*z**2 + 0*z + 0.

>>> np.poly((-1./2, 0, 1./2))
array([ 1.  ,  0.  , -0.25,  0.  ])

The line above represents z**3 - z/4

>>> np.poly((np.random.random(1)[0], 0, np.random.random(1)[0]))
array([ 1.        , -0.77086955,  0.08618131,  0.        ]) # random

Given a square array object:

>>> P = np.array([[0, 1./3], [-1./2, 0]])
>>> np.poly(P)
array([1.        , 0.        , 0.16666667])

Note how in all cases the leading coefficient is always 1.

@array_function_dispatch(_binary_op_dispatcher)
def polyadd(a1, a2):

Find the sum of two polynomials.

Note

This forms part of the old polynomial API. Since version 1.4, the new polynomial API defined in numpy.polynomial is preferred. A summary of the differences can be found in the :doc:`transition guide </reference/routines.polynomials>`.

Returns the polynomial resulting from the sum of two input polynomials. Each input must be either a poly1d object or a 1D sequence of polynomial coefficients, from highest to lowest degree.

Parameters

a1, a2 : array_like or poly1d object: Input polynomials.

Returns

out : ndarray or poly1d object: The sum of the inputs. If either input is a poly1d object, then the output is also a poly1d object. Otherwise, it is a 1D array of polynomial coefficients from highest to lowest degree.

Examples

>>> np.polyadd([1, 2], [9, 5, 4])
array([9, 6, 6])

Using poly1d objects:

>>> p1 = np.poly1d([1, 2])
>>> p2 = np.poly1d([9, 5, 4])
>>> print(p1)
1 x + 2
>>> print(p2)
   2
9 x + 5 x + 4
>>> print(np.polyadd(p1, p2))
   2
9 x + 6 x + 6

@array_function_dispatch(_polyder_dispatcher)
def polyder(p, m=1):

Return the derivative of the specified order of a polynomial.

Note

This forms part of the old polynomial API. Since version 1.4, the new polynomial API defined in numpy.polynomial is preferred. A summary of the differences can be found in the :doc:`transition guide </reference/routines.polynomials>`.

Parameters

p : poly1d or sequence: Polynomial to differentiate. A sequence is interpreted as polynomial coefficients, see poly1d.
m : int, optional: Order of differentiation (default: 1)

Returns

der : poly1d: A new polynomial representing the derivative.

Examples

The derivative of the polynomial x³ + x² + x¹ + 1 is:

>>> p = np.poly1d([1,1,1,1])
>>> p2 = np.polyder(p)
>>> p2
poly1d([3, 2, 1])

which evaluates to:

>>> p2(2.)
17.0

We can verify this, approximating the derivative with (f(x + h) - f(x))/h:

>>> (p(2. + 0.001) - p(2.)) / 0.001
17.007000999997857

The fourth-order derivative of a 3rd-order polynomial is zero:

>>> np.polyder(p, 2)
poly1d([6, 2])
>>> np.polyder(p, 3)
poly1d([6])
>>> np.polyder(p, 4)
poly1d([0])

@array_function_dispatch(_polydiv_dispatcher)
def polydiv(u, v):

Returns the quotient and remainder of polynomial division.

Note

This forms part of the old polynomial API. Since version 1.4, the new polynomial API defined in numpy.polynomial is preferred. A summary of the differences can be found in the :doc:`transition guide </reference/routines.polynomials>`.

The input arrays are the coefficients (including any coefficients equal to zero) of the "numerator" (dividend) and "denominator" (divisor) polynomials, respectively.

Parameters

u : array_like or poly1d: Dividend polynomial's coefficients.
v : array_like or poly1d: Divisor polynomial's coefficients.

Returns

q : ndarray: Coefficients, including those equal to zero, of the quotient.
r : ndarray: Coefficients, including those equal to zero, of the remainder.

Notes

Both u and v must be 0-d or 1-d (ndim = 0 or 1), but u.ndim need not equal v.ndim. In other words, all four possible combinations - u.ndim = v.ndim = 0, u.ndim = v.ndim = 1, u.ndim = 1, v.ndim = 0, and u.ndim = 0, v.ndim = 1 - work.

Examples

(3x² + 5x + 2)/(2x + 1) = 1.5x + 1.75, remainder0.25

>>> x = np.array([3.0, 5.0, 2.0])
>>> y = np.array([2.0, 1.0])
>>> np.polydiv(x, y)
(array([1.5 , 1.75]), array([0.25]))

@array_function_dispatch(_polyfit_dispatcher)
def polyfit(x, y, deg, rcond=None, full=False, w=None, cov=False):

Least squares polynomial fit.

Note

This forms part of the old polynomial API. Since version 1.4, the new polynomial API defined in numpy.polynomial is preferred. A summary of the differences can be found in the :doc:`transition guide </reference/routines.polynomials>`.

Fit a polynomial p(x) = p[0] * x**deg + ... + p[deg] of degree deg to points (x, y). Returns a vector of coefficients p that minimises the squared error in the order deg, deg-1, ... 0.

The Polynomial.fit class method is recommended for new code as it is more stable numerically. See the documentation of the method for more information.

Parameters

x : array_like, shape (M,): x-coordinates of the M sample points (x[i], y[i]).
y : array_like, shape (M,) or (M, K): y-coordinates of the sample points. Several data sets of sample points sharing the same x-coordinates can be fitted at once by passing in a 2D-array that contains one dataset per column.
deg : int: Degree of the fitting polynomial
rcond : float, optional: Relative condition number of the fit. Singular values smaller than this relative to the largest singular value will be ignored. The default value is len(x)*eps, where eps is the relative precision of the float type, about 2e-16 in most cases.
full : bool, optional: Switch determining nature of return value. When it is False (the default) just the coefficients are returned, when True diagnostic information from the singular value decomposition is also returned.
w : array_like, shape (M,), optional: Weights. If not None, the weight w[i] applies to the unsquared residual y[i] - y_hat[i] at x[i]. Ideally the weights are chosen so that the errors of the products w[i]*y[i] all have the same variance. When using inverse-variance weighting, use w[i] = 1/sigma(y[i]). The default value is None.
cov : bool or str, optional: If given and not False, return not just the estimate but also its covariance matrix. By default, the covariance are scaled by chi2/dof, where dof = M - (deg + 1), i.e., the weights are presumed to be unreliable except in a relative sense and everything is scaled such that the reduced chi2 is unity. This scaling is omitted if cov='unscaled', as is relevant for the case that the weights are w = 1/sigma, with sigma known to be a reliable estimate of the uncertainty.

Returns

p : ndarray, shape (deg + 1,) or (deg + 1, K)

Polynomial coefficients, highest power first. If y was 2-D, the coefficients for k-th data set are in p[:,k].

residuals, rank, singular_values, rcond

These values are only returned if full == True

residuals -- sum of squared residuals of the least squares fit
rank -- the effective rank of the scaled Vandermonde

coefficient matrix
singular_values -- singular values of the scaled Vandermonde

coefficient matrix
rcond -- value of rcond.

For more details, see numpy.linalg.lstsq.

V : ndarray, shape (M,M) or (M,M,K)

Present only if full == False and cov == True. The covariance matrix of the polynomial coefficient estimates. The diagonal of this matrix are the variance estimates for each coefficient. If y is a 2-D array, then the covariance matrix for the k-th data set are in V[:,:,k]

Warns

RankWarning

The rank of the coefficient matrix in the least-squares fit is deficient. The warning is only raised if full == False.

The warnings can be turned off by

>>> import warnings
>>> warnings.simplefilter('ignore', np.RankWarning)

Notes

The solution minimizes the squared error

E = ^k∑_j = 0|p(x_j) − y_j|²

in the equations:

x[0]**n * p[0] + ... + x[0] * p[n-1] + p[n] = y[0]
x[1]**n * p[0] + ... + x[1] * p[n-1] + p[n] = y[1]
...
x[k]**n * p[0] + ... + x[k] * p[n-1] + p[n] = y[k]

The coefficient matrix of the coefficients p is a Vandermonde matrix.

polyfit issues a RankWarning when the least-squares fit is badly conditioned. This implies that the best fit is not well-defined due to numerical error. The results may be improved by lowering the polynomial degree or by replacing x by x - x.mean(). The rcond parameter can also be set to a value smaller than its default, but the resulting fit may be spurious: including contributions from the small singular values can add numerical noise to the result.

Note that fitting polynomial coefficients is inherently badly conditioned when the degree of the polynomial is large or the interval of sample points is badly centered. The quality of the fit should always be checked in these cases. When polynomial fits are not satisfactory, splines may be a good alternative.

References

[1]	Wikipedia, "Curve fitting", https://en.wikipedia.org/wiki/Curve_fitting

[2]	Wikipedia, "Polynomial interpolation", https://en.wikipedia.org/wiki/Polynomial_interpolation

Examples

>>> import warnings
>>> x = np.array([0.0, 1.0, 2.0, 3.0,  4.0,  5.0])
>>> y = np.array([0.0, 0.8, 0.9, 0.1, -0.8, -1.0])
>>> z = np.polyfit(x, y, 3)
>>> z
array([ 0.08703704, -0.81349206,  1.69312169, -0.03968254]) # may vary

It is convenient to use poly1d objects for dealing with polynomials:

>>> p = np.poly1d(z)
>>> p(0.5)
0.6143849206349179 # may vary
>>> p(3.5)
-0.34732142857143039 # may vary
>>> p(10)
22.579365079365115 # may vary

High-order polynomials may oscillate wildly:

>>> with warnings.catch_warnings():
...     warnings.simplefilter('ignore', np.RankWarning)
...     p30 = np.poly1d(np.polyfit(x, y, 30))
...
>>> p30(4)
-0.80000000000000204 # may vary
>>> p30(5)
-0.99999999999999445 # may vary
>>> p30(4.5)
-0.10547061179440398 # may vary

Illustration:

>>> import matplotlib.pyplot as plt
>>> xp = np.linspace(-2, 6, 100)
>>> _ = plt.plot(x, y, '.', xp, p(xp), '-', xp, p30(xp), '--')
>>> plt.ylim(-2,2)
(-2, 2)
>>> plt.show()

@array_function_dispatch(_polyint_dispatcher)
def polyint(p, m=1, k=None):

Return an antiderivative (indefinite integral) of a polynomial.

Note

This forms part of the old polynomial API. Since version 1.4, the new polynomial API defined in numpy.polynomial is preferred. A summary of the differences can be found in the :doc:`transition guide </reference/routines.polynomials>`.

The returned order m antiderivative P of polynomial p satisfies (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

(k_m − 1)/(0!)x⁰ + … + (k₀)/((m − 1)!)x^m − 1

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

Parameters

p : array_like or poly1d

Polynomial to integrate. 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.        ]) # may vary
>>> 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. ]) # may vary

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

@array_function_dispatch(_binary_op_dispatcher)
def polymul(a1, a2):

Find the product of two polynomials.

Note

This forms part of the old polynomial API. Since version 1.4, the new polynomial API defined in numpy.polynomial is preferred. A summary of the differences can be found in the :doc:`transition guide </reference/routines.polynomials>`.

Finds the polynomial resulting from the multiplication of the two input polynomials. Each input must be either a poly1d object or a 1D sequence of polynomial coefficients, from highest to lowest degree.

Parameters

a1, a2 : array_like or poly1d object: Input polynomials.

Returns

out : ndarray or poly1d object: The polynomial resulting from the multiplication of the inputs. If either inputs is a poly1d object, then the output is also a poly1d object. Otherwise, it is a 1D array of polynomial coefficients from highest to lowest degree.

Examples

>>> np.polymul([1, 2, 3], [9, 5, 1])
array([ 9, 23, 38, 17,  3])

Using poly1d objects:

>>> p1 = np.poly1d([1, 2, 3])
>>> p2 = np.poly1d([9, 5, 1])
>>> print(p1)
   2
1 x + 2 x + 3
>>> print(p2)
   2
9 x + 5 x + 1
>>> print(np.polymul(p1, p2))
   4      3      2
9 x + 23 x + 38 x + 17 x + 3

@array_function_dispatch(_binary_op_dispatcher)
def polysub(a1, a2):

Difference (subtraction) of two polynomials.

Note

This forms part of the old polynomial API. Since version 1.4, the new polynomial API defined in numpy.polynomial is preferred. A summary of the differences can be found in the :doc:`transition guide </reference/routines.polynomials>`.

Given two polynomials a1 and a2, returns a1 - a2. a1 and a2 can be either array_like sequences of the polynomials' coefficients (including coefficients equal to zero), or poly1d objects.

Parameters

a1, a2 : array_like or poly1d: Minuend and subtrahend polynomials, respectively.

Returns

out : ndarray or poly1d: Array or poly1d object of the difference polynomial's coefficients.

Examples

(2x² + 10x − 2) − (3x² + 10x − 4) = ( − x² + 2)

>>> np.polysub([2, 10, -2], [3, 10, -4])
array([-1,  0,  2])

@array_function_dispatch(_polyval_dispatcher)
def polyval(p, x):

Evaluate a polynomial at specific values.

Note

This forms part of the old polynomial API. Since version 1.4, the new polynomial API defined in numpy.polynomial is preferred. A summary of the differences can be found in the :doc:`transition guide </reference/routines.polynomials>`.

If p is of length N, this function returns the value:

p[0]*x**(N-1) + p[1]*x**(N-2) + ... + p[N-2]*x + p[N-1]

If x is a sequence, then p(x) is returned for each element of x. If x is another polynomial then the composite polynomial p(x(t)) is returned.

Parameters

p : array_like or poly1d object: 1D array of polynomial coefficients (including coefficients equal to zero) from highest degree to the constant term, or an instance of poly1d.
x : array_like or poly1d object: A number, an array of numbers, or an instance of poly1d, at which to evaluate p.

Returns

values : ndarray or poly1d: If x is a poly1d instance, the result is the composition of the two polynomials, i.e., x is "substituted" in p and the simplified result is returned. In addition, the type of x - array_like or poly1d - governs the type of the output: x array_like => values array_like, x a poly1d object => values is also.

Notes

Horner's scheme [1] is used to evaluate the polynomial. Even so, for polynomials of high degree the values may be inaccurate due to rounding errors. Use carefully.

If x is a subtype of ndarray the return value will be of the same type.

References

[1]	I. N. Bronshtein, K. A. Semendyayev, and K. A. Hirsch (Eng. trans. Ed.), Handbook of Mathematics, New York, Van Nostrand Reinhold Co., 1985, pg. 720.

Examples

>>> np.polyval([3,0,1], 5)  # 3 * 5**2 + 0 * 5**1 + 1
76
>>> np.polyval([3,0,1], np.poly1d(5))
poly1d([76])
>>> np.polyval(np.poly1d([3,0,1]), 5)
76
>>> np.polyval(np.poly1d([3,0,1]), np.poly1d(5))
poly1d([76])

@array_function_dispatch(_roots_dispatcher)
def roots(p):

Return the roots of a polynomial with coefficients given in p.

Note

This forms part of the old polynomial API. Since version 1.4, the new polynomial API defined in numpy.polynomial is preferred. A summary of the differences can be found in the :doc:`transition guide </reference/routines.polynomials>`.

The values in the rank-1 array p are coefficients of a polynomial. If the length of p is n+1 then the polynomial is described by:

p[0] * x**n + p[1] * x**(n-1) + ... + p[n-1]*x + p[n]

Parameters

p : array_like: Rank-1 array of polynomial coefficients.

Returns

out : ndarray: An array containing the roots of the polynomial.

Raises

ValueError: When p cannot be converted to a rank-1 array.

Notes

The algorithm relies on computing the eigenvalues of the companion matrix [1].

References

[1]	R. A. Horn & C. R. Johnson, Matrix Analysis. Cambridge, UK: Cambridge University Press, 1999, pp. 146-7.

Examples

>>> coeff = [3.2, 2, 1]
>>> np.roots(coeff)
array([-0.3125+0.46351241j, -0.3125-0.46351241j])

Variable	`array_function_dispatch`	Undocumented
Class	`poly1d`	A one-dimensional polynomial class.
Class	`RankWarning`	Issued by `polyfit` when the Vandermonde matrix is rank deficient.
Function	`_binary_op_dispatcher`	Undocumented
Function	`_poly_dispatcher`	Undocumented
Function	`_polyder_dispatcher`	Undocumented
Function	`_polydiv_dispatcher`	Undocumented
Function	`_polyfit_dispatcher`	Undocumented
Function	`_polyint_dispatcher`	Undocumented
Function	`_polyval_dispatcher`	Undocumented
Function	`_raise_power`	Undocumented
Function	`_roots_dispatcher`	Undocumented
Function	`poly`	Find the coefficients of a polynomial with the given sequence of roots.
Function	`polyadd`	Find the sum of two polynomials.
Function	`polyder`	Return the derivative of the specified order of a polynomial.
Function	`polydiv`	Returns the quotient and remainder of polynomial division.
Function	`polyfit`	Least squares polynomial fit.
Function	`polyint`	Return an antiderivative (indefinite integral) of a polynomial.
Function	`polymul`	Find the product of two polynomials.
Function	`polysub`	Difference (subtraction) of two polynomials.
Function	`polyval`	Evaluate a polynomial at specific values.
Function	`roots`	Return the roots of a polynomial with coefficients given in p.
Variable	`_poly_mat`	Undocumented

numpy.lib.polynomial

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`numpy.lib.polynomial`