# package np

Legend:
Library
Module
Module type
Parameter
Class
Class type
val get_py : string -> Py.Object.t

Get an attribute of this module as a Py.Object.t. This is useful to pass a Python function to another function.

val lag2poly : [> Ndarray ] Obj.t -> [ ArrayLike | Ndarray | Object ] Obj.t

Convert a Laguerre series to a polynomial.

Convert an array representing the coefficients of a Laguerre series, ordered from lowest degree to highest, to an array of the coefficients of the equivalent polynomial (relative to the 'standard' basis) ordered from lowest to highest degree.

Parameters ---------- c : array_like 1-D array containing the Laguerre series coefficients, ordered from lowest order term to highest.

Returns ------- pol : ndarray 1-D array containing the coefficients of the equivalent polynomial (relative to the 'standard' basis) ordered from lowest order term to highest.

Notes ----- The easy way to do conversions between polynomial basis sets is to use the convert method of a class instance.

Examples -------- >>> from numpy.polynomial.laguerre import lag2poly >>> lag2poly( 23., -63., 58., -18.) array(0., 1., 2., 3.)

val lagadd : c1:Py.Object.t -> c2:Py.Object.t -> unit -> [ ArrayLike | Ndarray | Object ] Obj.t

Add one Laguerre series to another.

Returns the sum of two Laguerre series c1 + c2. The arguments are sequences of coefficients ordered from lowest order term to highest, i.e., 1,2,3 represents the series P_0 + 2*P_1 + 3*P_2.

Parameters ---------- c1, c2 : array_like 1-D arrays of Laguerre series coefficients ordered from low to high.

Returns ------- out : ndarray Array representing the Laguerre series of their sum.

Notes ----- Unlike multiplication, division, etc., the sum of two Laguerre series is a Laguerre series (without having to 'reproject' the result onto the basis set) so addition, just like that of 'standard' polynomials, is simply 'component-wise.'

Examples -------- >>> from numpy.polynomial.laguerre import lagadd >>> lagadd(1, 2, 3, 1, 2, 3, 4) array(2., 4., 6., 4.)

val lagcompanion : [> Ndarray ] Obj.t -> [ ArrayLike | Ndarray | Object ] Obj.t

Return the companion matrix of c.

The usual companion matrix of the Laguerre polynomials is already symmetric when c is a basis Laguerre polynomial, so no scaling is applied.

Parameters ---------- c : array_like 1-D array of Laguerre series coefficients ordered from low to high degree.

Returns ------- mat : ndarray Companion matrix of dimensions (deg, deg).

Notes -----

val lagder : ?m:int -> ?scl:[ F of float | I of int | Bool of bool | S of string ] -> ?axis:int -> c:[> Ndarray ] Obj.t -> unit -> [ ArrayLike | Ndarray | Object ] Obj.t

Differentiate a Laguerre series.

Returns the Laguerre series coefficients c differentiated m times along axis. At each iteration the result is multiplied by 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*L_0 + 2*L_1 + 3*L_2 while [1,2],[1,2] represents 1*L_0(x)*L_0(y) + 1*L_1(x)*L_0(y) + 2*L_0(x)*L_1(y) + 2*L_1(x)*L_1(y) if axis=0 is x and axis=1 is y.

Parameters ---------- c : array_like Array of Laguerre series coefficients. If c is multidimensional the different axis correspond to different variables with the degree in each axis given by the corresponding index. m : int, optional Number of derivatives taken, must be non-negative. (Default: 1) scl : scalar, optional Each differentiation is multiplied by scl. The end result is multiplication by scl**m. This is for use in a linear change of variable. (Default: 1) axis : int, optional Axis over which the derivative is taken. (Default: 0).

Returns ------- der : ndarray Laguerre series of the derivative.

Notes ----- In general, the result of differentiating a Laguerre 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.

Examples -------- >>> from numpy.polynomial.laguerre import lagder >>> lagder( 1., 1., 1., -3.) array(1., 2., 3.) >>> lagder( 1., 0., 0., -4., 3., m=2) array(1., 2., 3.)

val lagdiv : c1:Py.Object.t -> c2:Py.Object.t -> unit -> Py.Object.t

Divide one Laguerre series by another.

Returns the quotient-with-remainder of two Laguerre series c1 / c2. The arguments are sequences of coefficients from lowest order 'term' to highest, e.g., 1,2,3 represents the series P_0 + 2*P_1 + 3*P_2.

Parameters ---------- c1, c2 : array_like 1-D arrays of Laguerre series coefficients ordered from low to high.

Returns ------- quo, rem : ndarrays Of Laguerre series coefficients representing the quotient and remainder.

Notes ----- In general, the (polynomial) division of one Laguerre series by another results in quotient and remainder terms that are not in the Laguerre polynomial basis set. Thus, to express these results as a Laguerre series, it is necessary to 'reproject' the results onto the Laguerre basis set, which may produce 'unintuitive' (but correct) results; see Examples section below.

Examples -------- >>> from numpy.polynomial.laguerre import lagdiv >>> lagdiv( 8., -13., 38., -51., 36., 0, 1, 2) (array(1., 2., 3.), array(0.)) >>> lagdiv( 9., -12., 38., -51., 36., 0, 1, 2) (array(1., 2., 3.), array(1., 1.))

val lagfit : ?rcond:float -> ?full:bool -> ?w:[> Ndarray ] Obj.t -> y:[> Ndarray ] Obj.t -> deg:[ T1_D_array_like of Py.Object.t | I of int ] -> [> Ndarray ] Obj.t -> [ ArrayLike | Ndarray | Object ] Obj.t

Least squares fit of Laguerre series to data.

Return the coefficients of a Laguerre series of degree deg that is the least squares fit to the data values y given at points x. If y is 1-D the returned coefficients will also be 1-D. If y is 2-D multiple fits are done, one for each column of y, and the resulting coefficients are stored in the corresponding columns of a 2-D return. The fitted polynomial(s) are in the form

.. math:: p(x) = c_0 + c_1 * L_1(x) + ... + c_n * L_n(x),

where n is deg.

Parameters ---------- x : array_like, shape (M,) x-coordinates of the M sample points (xi, yi). 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 or 1-D array_like Degree(s) of the fitting polynomials. If deg is a single integer all terms up to and including the deg'th term are included in the fit. For NumPy versions >= 1.11.0 a list of integers specifying the degrees of the terms to include may be used instead. 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 contribution of each point (xi,yi) to the fit is weighted by wi. Ideally the weights are chosen so that the errors of the products wi*yi all have the same variance. The default value is None.

Returns ------- coef : ndarray, shape (M,) or (M, K) Laguerre coefficients ordered from low to high. If y was 2-D, the coefficients for the data in column k of y are in column k.

residuals, rank, singular_values, rcond : list These values are only returned if full = True

resid -- sum of squared residuals of the least squares fit rank -- the numerical rank of the scaled Vandermonde matrix sv -- singular values of the scaled Vandermonde matrix rcond -- value of rcond.

For more details, see linalg.lstsq.

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)

See Also -------- chebfit, legfit, polyfit, hermfit, hermefit lagval : Evaluates a Laguerre series. lagvander : pseudo Vandermonde matrix of Laguerre series. lagweight : Laguerre weight function. linalg.lstsq : Computes a least-squares fit from the matrix. scipy.interpolate.UnivariateSpline : Computes spline fits.

Notes ----- The solution is the coefficients of the Laguerre series p that minimizes the sum of the weighted squared errors

.. math:: E = \sum_j w_j^2 * |y_j - p(x_j)|^2,

where the :math:w_j are the weights. This problem is solved by setting up as the (typically) overdetermined matrix equation

.. math:: V(x) * c = w * y,

where V is the weighted pseudo Vandermonde matrix of x, c are the coefficients to be solved for, w are the weights, and y are the observed values. This equation is then solved using the singular value decomposition of V.

If some of the singular values of V are so small that they are neglected, then a RankWarning will be issued. This means that the coefficient values may be poorly determined. Using a lower order fit will usually get rid of the warning. The rcond parameter can also be set to a value smaller than its default, but the resulting fit may be spurious and have large contributions from roundoff error.

Fits using Laguerre series are probably most useful when the data can be approximated by sqrt(w(x)) * p(x), where w(x) is the Laguerre weight. In that case the weight sqrt(w(xi) should be used together with data values yi/sqrt(w(xi). The weight function is available as lagweight.

References ---------- .. 1 Wikipedia, 'Curve fitting', https://en.wikipedia.org/wiki/Curve_fitting

Examples -------- >>> from numpy.polynomial.laguerre import lagfit, lagval >>> x = np.linspace(0, 10) >>> err = np.random.randn(len(x))/10 >>> y = lagval(x, 1, 2, 3) + err >>> lagfit(x, y, 2) array( 0.96971004, 2.00193749, 3.00288744) # may vary

val lagfromroots : [> Ndarray ] Obj.t -> [ ArrayLike | Ndarray | Object ] Obj.t

Generate a Laguerre series with given roots.

The function returns the coefficients of the polynomial

.. math:: p(x) = (x - r_0) * (x - r_1) * ... * (x - r_n),

in Laguerre form, where the r_n are the roots specified in roots. If a zero has multiplicity n, then it must appear in roots n times. For instance, if 2 is a root of multiplicity three and 3 is a root of multiplicity 2, then roots looks something like 2, 2, 2, 3, 3. The roots can appear in any order.

If the returned coefficients are c, then

.. math:: p(x) = c_0 + c_1 * L_1(x) + ... + c_n * L_n(x)

The coefficient of the last term is not generally 1 for monic polynomials in Laguerre form.

Parameters ---------- roots : array_like Sequence containing the roots.

Returns ------- out : ndarray 1-D array of coefficients. If all roots are real then out is a real array, if some of the roots are complex, then out is complex even if all the coefficients in the result are real (see Examples below).

Examples -------- >>> from numpy.polynomial.laguerre import lagfromroots, lagval >>> coef = lagfromroots((-1, 0, 1)) >>> lagval((-1, 0, 1), coef) array(0., 0., 0.) >>> coef = lagfromroots((-1j, 1j)) >>> lagval((-1j, 1j), coef) array(0.+0.j, 0.+0.j)

val laggauss : int -> [ ArrayLike | Ndarray | Object ] Obj.t * [ ArrayLike | Ndarray | Object ] Obj.t

Computes the sample points and weights for Gauss-Laguerre quadrature. These sample points and weights will correctly integrate polynomials of degree :math:2*deg - 1 or less over the interval :math:0, \inf with the weight function :math:f(x) = \exp(-x).

Parameters ---------- deg : int Number of sample points and weights. It must be >= 1.

Returns ------- x : ndarray 1-D ndarray containing the sample points. y : ndarray 1-D ndarray containing the weights.

Notes -----

The results have only been tested up to degree 100 higher degrees may be problematic. The weights are determined by using the fact that

.. math:: w_k = c / (L'_n(x_k) * L_n-1(x_k))

where :math:c is a constant independent of :math:k and :math:x_k is the k'th root of :math:L_n, and then scaling the results to get the right value when integrating 1.

val laggrid2d : y:Py.Object.t -> c:[> Ndarray ] Obj.t -> Py.Object.t -> Py.Object.t

Evaluate a 2-D Laguerre series on the Cartesian product of x and y.

This function returns the values:

.. math:: p(a,b) = \sum_,j c_,j * L_i(a) * L_j(b)

where the points (a, b) consist of all pairs formed by taking a from x and b from y. The resulting points form a grid with x in the first dimension and y in the second.

The parameters x and y are converted to arrays only if they are tuples or a lists, otherwise they are treated as a scalars. In either case, either x and y or their elements must support multiplication and addition both with themselves and with the elements of c.

If c has fewer than two dimensions, ones are implicitly appended to its shape to make it 2-D. The shape of the result will be c.shape2: + x.shape + y.shape.

Parameters ---------- x, y : array_like, compatible objects The two dimensional series is evaluated at the points in the Cartesian product of x and y. If x or y is a list or tuple, it is first converted to an ndarray, otherwise it is left unchanged and, if it isn't an ndarray, it is treated as a scalar. c : array_like Array of coefficients ordered so that the coefficient of the term of multi-degree i,j is contained in ci,j. If c has dimension greater than two the remaining indices enumerate multiple sets of coefficients.

Returns ------- values : ndarray, compatible object The values of the two dimensional Chebyshev series at points in the Cartesian product of x and y.

Notes -----

val laggrid3d : y:Py.Object.t -> z:Py.Object.t -> c:[> Ndarray ] Obj.t -> Py.Object.t -> Py.Object.t

Evaluate a 3-D Laguerre series on the Cartesian product of x, y, and z.

This function returns the values:

.. math:: p(a,b,c) = \sum_,j,k c_,j,k * L_i(a) * L_j(b) * L_k(c)

where the points (a, b, c) consist of all triples formed by taking a from x, b from y, and c from z. The resulting points form a grid with x in the first dimension, y in the second, and z in the third.

The parameters x, y, and z are converted to arrays only if they are tuples or a lists, otherwise they are treated as a scalars. In either case, either x, y, and z or their elements must support multiplication and addition both with themselves and with the elements of c.

If c has fewer than three dimensions, ones are implicitly appended to its shape to make it 3-D. The shape of the result will be c.shape3: + x.shape + y.shape + z.shape.

Parameters ---------- x, y, z : array_like, compatible objects The three dimensional series is evaluated at the points in the Cartesian product of x, y, and z. If x,y, or z is a list or tuple, it is first converted to an ndarray, otherwise it is left unchanged and, if it isn't an ndarray, it is treated as a scalar. c : array_like Array of coefficients ordered so that the coefficients for terms of degree i,j are contained in ci,j. If c has dimension greater than two the remaining indices enumerate multiple sets of coefficients.

Returns ------- values : ndarray, compatible object The values of the two dimensional polynomial at points in the Cartesian product of x and y.

Notes -----

val lagint : ?m:int -> ?k: [ I of int | Ndarray of [> Ndarray ] Obj.t | T_ of Py.Object.t | Bool of bool | F of float | S of string ] -> ?lbnd:[ F of float | I of int | Bool of bool | S of string ] -> ?scl:[ F of float | I of int | Bool of bool | S of string ] -> ?axis:int -> c:[> Ndarray ] Obj.t -> unit -> [ ArrayLike | Ndarray | Object ] Obj.t

Integrate a Laguerre series.

Returns the Laguerre 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 L_0 + 2*L_1 + 3*L_2 while [1,2],[1,2] represents 1*L_0(x)*L_0(y) + 1*L_1(x)*L_0(y) + 2*L_0(x)*L_1(y) + 2*L_1(x)*L_1(y) if axis=0 is x and axis=1 is y.

Parameters ---------- c : array_like Array of Laguerre series coefficients. If c is multidimensional the different axis correspond to different variables with the degree in each axis given by the corresponding index. m : int, optional Order of integration, must be positive. (Default: 1) k :

], list, scalar}, optional
Integration constant(s).  The value of the first integral at
lbnd is the first value in the list, the value of the second
integral at lbnd is the second value, etc.  If k == [] (the
default), all constants are set to zero.  If m == 1, a single
scalar can be given instead of a list.
lbnd : scalar, optional
The lower bound of the integral. (Default: 0)
scl : scalar, optional
Following each integration the result is *multiplied* by scl
before the integration constant is added. (Default: 1)
axis : int, optional
Axis over which the integral is taken. (Default: 0).

Returns
-------
S : ndarray
Laguerre series coefficients of the integral.

Raises
------
ValueError
If m < 0, len(k) > m, np.ndim(lbnd) != 0, or
np.ndim(scl) != 0.

--------
lagder

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 :math:u = ax + b in an integral relative to x.  Then
:math:dx = du/a, so one will need to set scl equal to
:math:1/a - perhaps not what one would have first thought.

Also note that, in general, the result of integrating a C-series needs
to be 'reprojected' onto the C-series basis set.  Thus, typically,
the result of this function is 'unintuitive,' albeit correct; see
Examples section below.

Examples
--------
>>> from numpy.polynomial.laguerre import lagint
>>> lagint([1,2,3])
array([ 1.,  1.,  1., -3.])
>>> lagint([1,2,3], m=2)
array([ 1.,  0.,  0., -4.,  3.])
>>> lagint([1,2,3], k=1)
array([ 2.,  1.,  1., -3.])
>>> lagint([1,2,3], lbnd=-1)
array([11.5,  1. ,  1. , -3. ])
>>> lagint([1,2], m=2, k=[1,2], lbnd=-1)
array([ 11.16666667,  -5.        ,  -3.        ,   2.        ]) # may vary
val lagline : off:Py.Object.t -> scl:Py.Object.t -> unit -> [ ArrayLike | Ndarray | Object ] Obj.t

Laguerre series whose graph is a straight line.

Parameters ---------- off, scl : scalars The specified line is given by off + scl*x.

Returns ------- y : ndarray This module's representation of the Laguerre series for off + scl*x.

Examples -------- >>> from numpy.polynomial.laguerre import lagline, lagval >>> lagval(0,lagline(3, 2)) 3.0 >>> lagval(1,lagline(3, 2)) 5.0

val lagmul : c1:Py.Object.t -> c2:Py.Object.t -> unit -> [ ArrayLike | Ndarray | Object ] Obj.t

Multiply one Laguerre series by another.

Returns the product of two Laguerre series c1 * c2. The arguments are sequences of coefficients, from lowest order 'term' to highest, e.g., 1,2,3 represents the series P_0 + 2*P_1 + 3*P_2.

Parameters ---------- c1, c2 : array_like 1-D arrays of Laguerre series coefficients ordered from low to high.

Returns ------- out : ndarray Of Laguerre series coefficients representing their product.

Notes ----- In general, the (polynomial) product of two C-series results in terms that are not in the Laguerre polynomial basis set. Thus, to express the product as a Laguerre series, it is necessary to 'reproject' the product onto said basis set, which may produce 'unintuitive' (but correct) results; see Examples section below.

Examples -------- >>> from numpy.polynomial.laguerre import lagmul >>> lagmul(1, 2, 3, 0, 1, 2) array( 8., -13., 38., -51., 36.)

val lagmulx : [> Ndarray ] Obj.t -> [ ArrayLike | Ndarray | Object ] Obj.t

Multiply a Laguerre series by x.

Multiply the Laguerre series c by x, where x is the independent variable.

Parameters ---------- c : array_like 1-D array of Laguerre series coefficients ordered from low to high.

Returns ------- out : ndarray Array representing the result of the multiplication.

Notes ----- The multiplication uses the recursion relationship for Laguerre polynomials in the form

.. math::

xP_i(x) = (-(i + 1)*P_+ 1(x) + (2i + 1)P_(x) - iP_- 1(x))

Examples -------- >>> from numpy.polynomial.laguerre import lagmulx >>> lagmulx(1, 2, 3) array(-1., -1., 11., -9.)

val lagpow : ?maxpower:int -> c:[> Ndarray ] Obj.t -> pow:int -> unit -> [ ArrayLike | Ndarray | Object ] Obj.t

Raise a Laguerre series to a power.

Returns the Laguerre series c raised to the power pow. The argument c is a sequence of coefficients ordered from low to high. i.e., 1,2,3 is the series P_0 + 2*P_1 + 3*P_2.

Parameters ---------- c : array_like 1-D array of Laguerre series coefficients ordered from low to high. pow : integer Power to which the series will be raised maxpower : integer, optional Maximum power allowed. This is mainly to limit growth of the series to unmanageable size. Default is 16

Returns ------- coef : ndarray Laguerre series of power.

Examples -------- >>> from numpy.polynomial.laguerre import lagpow >>> lagpow(1, 2, 3, 2) array( 14., -16., 56., -72., 54.)

val lagroots : Py.Object.t -> [ ArrayLike | Ndarray | Object ] Obj.t

Compute the roots of a Laguerre series.

Return the roots (a.k.a. 'zeros') of the polynomial

.. math:: p(x) = \sum_i ci * L_i(x).

Parameters ---------- c : 1-D array_like 1-D array of coefficients.

Returns ------- out : ndarray Array of the roots of the series. If all the roots are real, then out is also real, otherwise it is complex.

Notes ----- The root estimates are obtained as the eigenvalues of the companion matrix, Roots far from the origin of the complex plane may have large errors due to the numerical instability of the series for such values. Roots with multiplicity greater than 1 will also show larger errors as the value of the series near such points is relatively insensitive to errors in the roots. Isolated roots near the origin can be improved by a few iterations of Newton's method.

The Laguerre series basis polynomials aren't powers of x so the results of this function may seem unintuitive.

Examples -------- >>> from numpy.polynomial.laguerre import lagroots, lagfromroots >>> coef = lagfromroots(0, 1, 2) >>> coef array( 2., -8., 12., -6.) >>> lagroots(coef) array(-4.4408921e-16, 1.0000000e+00, 2.0000000e+00)

val lagsub : c1:Py.Object.t -> c2:Py.Object.t -> unit -> [ ArrayLike | Ndarray | Object ] Obj.t

Subtract one Laguerre series from another.

Returns the difference of two Laguerre series c1 - c2. The sequences of coefficients are from lowest order term to highest, i.e., 1,2,3 represents the series P_0 + 2*P_1 + 3*P_2.

Parameters ---------- c1, c2 : array_like 1-D arrays of Laguerre series coefficients ordered from low to high.

Returns ------- out : ndarray Of Laguerre series coefficients representing their difference.

Notes ----- Unlike multiplication, division, etc., the difference of two Laguerre series is a Laguerre series (without having to 'reproject' the result onto the basis set) so subtraction, just like that of 'standard' polynomials, is simply 'component-wise.'

Examples -------- >>> from numpy.polynomial.laguerre import lagsub >>> lagsub(1, 2, 3, 4, 1, 2, 3) array(0., 0., 0., 4.)

val lagtrim : ?tol:[ F of float | I of int ] -> c:[> Ndarray ] Obj.t -> unit -> [ ArrayLike | Ndarray | Object ] Obj.t

Remove 'small' 'trailing' coefficients from a polynomial.

'Small' means 'small in absolute value' and is controlled by the parameter tol; 'trailing' means highest order coefficient(s), e.g., in 0, 1, 1, 0, 0 (which represents 0 + x + x**2 + 0*x**3 + 0*x**4) both the 3-rd and 4-th order coefficients would be 'trimmed.'

Parameters ---------- c : array_like 1-d array of coefficients, ordered from lowest order to highest. tol : number, optional Trailing (i.e., highest order) elements with absolute value less than or equal to tol (default value is zero) are removed.

Returns ------- trimmed : ndarray 1-d array with trailing zeros removed. If the resulting series would be empty, a series containing a single zero is returned.

Raises ------ ValueError If tol < 0

Examples -------- >>> from numpy.polynomial import polyutils as pu >>> pu.trimcoef((0,0,3,0,5,0,0)) array(0., 0., 3., 0., 5.) >>> pu.trimcoef((0,0,1e-3,0,1e-5,0,0),1e-3) # item == tol is trimmed array(0.) >>> i = complex(0,1) # works for complex >>> pu.trimcoef((3e-4,1e-3*(1-i),5e-4,2e-5*(1+i)), 1e-3) array(0.0003+0.j , 0.001 -0.001j)

val lagval : ?tensor:bool -> c:[> Ndarray ] Obj.t -> [ Compatible_object of Py.Object.t | Ndarray of [> Ndarray ] Obj.t ] -> Py.Object.t

Evaluate a Laguerre series at points x.

If c is of length n + 1, this function returns the value:

.. math:: p(x) = c_0 * L_0(x) + c_1 * L_1(x) + ... + c_n * L_n(x)

The parameter x is converted to an array only if it is a tuple or a list, otherwise it is treated as a scalar. In either case, either x or its elements must support multiplication and addition both with themselves and with the elements of c.

If c is a 1-D array, then p(x) will have the same shape as x. If c is multidimensional, then the shape of the result depends on the value of tensor. If tensor is true the shape will be c.shape1: + x.shape. If tensor is false the shape will be c.shape1:. Note that scalars have shape (,).

Trailing zeros in the coefficients will be used in the evaluation, so they should be avoided if efficiency is a concern.

Parameters ---------- x : array_like, compatible object If x is a list or tuple, it is converted to an ndarray, otherwise it is left unchanged and treated as a scalar. In either case, x or its elements must support addition and multiplication with with themselves and with the elements of c. c : array_like Array of coefficients ordered so that the coefficients for terms of degree n are contained in cn. If c is multidimensional the remaining indices enumerate multiple polynomials. In the two dimensional case the coefficients may be thought of as stored in the columns of c. tensor : boolean, optional If True, the shape of the coefficient array is extended with ones on the right, one for each dimension of x. Scalars have dimension 0 for this action. The result is that every column of coefficients in c is evaluated for every element of x. If False, x is broadcast over the columns of c for the evaluation. This keyword is useful when c is multidimensional. The default value is True.

Returns ------- values : ndarray, algebra_like The shape of the return value is described above.

Notes ----- The evaluation uses Clenshaw recursion, aka synthetic division.

Examples -------- >>> from numpy.polynomial.laguerre import lagval >>> coef = 1,2,3 >>> lagval(1, coef) -0.5 >>> lagval([1,2],[3,4], coef) array([-0.5, -4. ], [-4.5, -2. ])

val lagval2d : y:Py.Object.t -> c:[> Ndarray ] Obj.t -> Py.Object.t -> Py.Object.t

Evaluate a 2-D Laguerre series at points (x, y).

This function returns the values:

.. math:: p(x,y) = \sum_,j c_,j * L_i(x) * L_j(y)

The parameters x and y are converted to arrays only if they are tuples or a lists, otherwise they are treated as a scalars and they must have the same shape after conversion. In either case, either x and y or their elements must support multiplication and addition both with themselves and with the elements of c.

If c is a 1-D array a one is implicitly appended to its shape to make it 2-D. The shape of the result will be c.shape2: + x.shape.

Parameters ---------- x, y : array_like, compatible objects The two dimensional series is evaluated at the points (x, y), where x and y must have the same shape. If x or y is a list or tuple, it is first converted to an ndarray, otherwise it is left unchanged and if it isn't an ndarray it is treated as a scalar. c : array_like Array of coefficients ordered so that the coefficient of the term of multi-degree i,j is contained in ci,j. If c has dimension greater than two the remaining indices enumerate multiple sets of coefficients.

Returns ------- values : ndarray, compatible object The values of the two dimensional polynomial at points formed with pairs of corresponding values from x and y.

Notes -----

val lagval3d : y:Py.Object.t -> z:Py.Object.t -> c:[> Ndarray ] Obj.t -> Py.Object.t -> Py.Object.t

Evaluate a 3-D Laguerre series at points (x, y, z).

This function returns the values:

.. math:: p(x,y,z) = \sum_,j,k c_,j,k * L_i(x) * L_j(y) * L_k(z)

The parameters x, y, and z are converted to arrays only if they are tuples or a lists, otherwise they are treated as a scalars and they must have the same shape after conversion. In either case, either x, y, and z or their elements must support multiplication and addition both with themselves and with the elements of c.

If c has fewer than 3 dimensions, ones are implicitly appended to its shape to make it 3-D. The shape of the result will be c.shape3: + x.shape.

Parameters ---------- x, y, z : array_like, compatible object The three dimensional series is evaluated at the points (x, y, z), where x, y, and z must have the same shape. If any of x, y, or z is a list or tuple, it is first converted to an ndarray, otherwise it is left unchanged and if it isn't an ndarray it is treated as a scalar. c : array_like Array of coefficients ordered so that the coefficient of the term of multi-degree i,j,k is contained in ci,j,k. If c has dimension greater than 3 the remaining indices enumerate multiple sets of coefficients.

Returns ------- values : ndarray, compatible object The values of the multidimension polynomial on points formed with triples of corresponding values from x, y, and z.

Notes -----

val lagvander : deg:int -> [> Ndarray ] Obj.t -> [ ArrayLike | Ndarray | Object ] Obj.t

Pseudo-Vandermonde matrix of given degree.

Returns the pseudo-Vandermonde matrix of degree deg and sample points x. The pseudo-Vandermonde matrix is defined by

.. math:: V..., i = L_i(x)

where 0 <= i <= deg. The leading indices of V index the elements of x and the last index is the degree of the Laguerre polynomial.

If c is a 1-D array of coefficients of length n + 1 and V is the array V = lagvander(x, n), then np.dot(V, c) and lagval(x, c) are the same up to roundoff. This equivalence is useful both for least squares fitting and for the evaluation of a large number of Laguerre series of the same degree and sample points.

Parameters ---------- x : array_like Array of points. The dtype is converted to float64 or complex128 depending on whether any of the elements are complex. If x is scalar it is converted to a 1-D array. deg : int Degree of the resulting matrix.

Returns ------- vander : ndarray The pseudo-Vandermonde matrix. The shape of the returned matrix is x.shape + (deg + 1,), where The last index is the degree of the corresponding Laguerre polynomial. The dtype will be the same as the converted x.

Examples -------- >>> from numpy.polynomial.laguerre import lagvander >>> x = np.array(0, 1, 2) >>> lagvander(x, 3) array([ 1. , 1. , 1. , 1. ], [ 1. , 0. , -0.5 , -0.66666667], [ 1. , -1. , -1. , -0.33333333])

val lagvander2d : y:Py.Object.t -> deg:Py.Object.t -> Py.Object.t -> [ ArrayLike | Ndarray | Object ] Obj.t

Pseudo-Vandermonde matrix of given degrees.

Returns the pseudo-Vandermonde matrix of degrees deg and sample points (x, y). The pseudo-Vandermonde matrix is defined by

.. math:: V..., (deg[1] + 1)*i + j = L_i(x) * L_j(y),

where 0 <= i <= deg0 and 0 <= j <= deg1. The leading indices of V index the points (x, y) and the last index encodes the degrees of the Laguerre polynomials.

If V = lagvander2d(x, y, xdeg, ydeg), then the columns of V correspond to the elements of a 2-D coefficient array c of shape (xdeg + 1, ydeg + 1) in the order

.. math:: c_

, c_

, c_

... , c_

, c_

, c_

...

and np.dot(V, c.flat) and lagval2d(x, y, c) will be the same up to roundoff. This equivalence is useful both for least squares fitting and for the evaluation of a large number of 2-D Laguerre series of the same degrees and sample points.

Parameters ---------- x, y : array_like Arrays of point coordinates, all of the same shape. The dtypes will be converted to either float64 or complex128 depending on whether any of the elements are complex. Scalars are converted to 1-D arrays. deg : list of ints List of maximum degrees of the form x_deg, y_deg.

Returns ------- vander2d : ndarray The shape of the returned matrix is x.shape + (order,), where :math:order = (deg0+1)*(deg(1+1). The dtype will be the same as the converted x and y.

Notes -----

val lagvander3d : y:Py.Object.t -> z:Py.Object.t -> deg:Py.Object.t -> Py.Object.t -> [ ArrayLike | Ndarray | Object ] Obj.t

Pseudo-Vandermonde matrix of given degrees.

Returns the pseudo-Vandermonde matrix of degrees deg and sample points (x, y, z). If l, m, n are the given degrees in x, y, z, then The pseudo-Vandermonde matrix is defined by

.. math:: V..., (m+1)(n+1)i + (n+1)j + k = L_i(x)*L_j(y)*L_k(z),

where 0 <= i <= l, 0 <= j <= m, and 0 <= j <= n. The leading indices of V index the points (x, y, z) and the last index encodes the degrees of the Laguerre polynomials.

If V = lagvander3d(x, y, z, xdeg, ydeg, zdeg), then the columns of V correspond to the elements of a 3-D coefficient array c of shape (xdeg + 1, ydeg + 1, zdeg + 1) in the order

.. math:: c_

, c_

, c_

,... , c_

, c_

, c_

,...

and np.dot(V, c.flat) and lagval3d(x, y, z, c) will be the same up to roundoff. This equivalence is useful both for least squares fitting and for the evaluation of a large number of 3-D Laguerre series of the same degrees and sample points.

Parameters ---------- x, y, z : array_like Arrays of point coordinates, all of the same shape. The dtypes will be converted to either float64 or complex128 depending on whether any of the elements are complex. Scalars are converted to 1-D arrays. deg : list of ints List of maximum degrees of the form x_deg, y_deg, z_deg.

Returns ------- vander3d : ndarray The shape of the returned matrix is x.shape + (order,), where :math:order = (deg0+1)*(deg(1+1)*(deg2+1). The dtype will be the same as the converted x, y, and z.

Notes -----

val lagweight : [> Ndarray ] Obj.t -> [ ArrayLike | Ndarray | Object ] Obj.t

Weight function of the Laguerre polynomials.

The weight function is :math:exp(-x) and the interval of integration is :math:0, \inf. The Laguerre polynomials are orthogonal, but not normalized, with respect to this weight function.

Parameters ---------- x : array_like Values at which the weight function will be computed.

Returns ------- w : ndarray The weight function at x.

Notes -----

val normalize_axis_index : ?msg_prefix:string -> axis:int -> ndim:int -> unit -> int

normalize_axis_index(axis, ndim, msg_prefix=None)

Normalizes an axis index, axis, such that is a valid positive index into the shape of array with ndim dimensions. Raises an AxisError with an appropriate message if this is not possible.

Used internally by all axis-checking logic.

Parameters ---------- axis : int The un-normalized index of the axis. Can be negative ndim : int The number of dimensions of the array that axis should be normalized against msg_prefix : str A prefix to put before the message, typically the name of the argument

Returns ------- normalized_axis : int The normalized axis index, such that 0 <= normalized_axis < ndim

Raises ------ AxisError If the axis index is invalid, when -ndim <= axis < ndim is false.

Examples -------- >>> normalize_axis_index(0, ndim=3) 0 >>> normalize_axis_index(1, ndim=3) 1 >>> normalize_axis_index(-1, ndim=3) 2

>>> normalize_axis_index(3, ndim=3) Traceback (most recent call last): ... AxisError: axis 3 is out of bounds for array of dimension 3 >>> normalize_axis_index(-4, ndim=3, msg_prefix='axes_arg') Traceback (most recent call last): ... AxisError: axes_arg: axis -4 is out of bounds for array of dimension 3

val poly2lag : [> Ndarray ] Obj.t -> [ ArrayLike | Ndarray | Object ] Obj.t

poly2lag(pol)

Convert a polynomial to a Laguerre series.

Convert an array representing the coefficients of a polynomial (relative to the 'standard' basis) ordered from lowest degree to highest, to an array of the coefficients of the equivalent Laguerre series, ordered from lowest to highest degree.

Parameters ---------- pol : array_like 1-D array containing the polynomial coefficients

Returns ------- c : ndarray 1-D array containing the coefficients of the equivalent Laguerre series.

Examples -------- >>> from numpy.polynomial.laguerre import poly2lag >>> poly2lag(np.arange(4)) array( 23., -63., 58., -18.)