"""
Objects for dealing with polynomials.

This module provides a number of objects (mostly functions) useful for
dealing with polynomials, including a `Polynomial` class that
encapsulates the usual arithmetic operations.  (General information
on how this module represents and works with polynomial objects is in
the docstring for its "parent" sub-package, `numpy.polynomial`).

Constants
---------
- `polydomain` -- Polynomial default domain, [-1,1].
- `polyzero` -- (Coefficients of the) "zero polynomial."
- `polyone` -- (Coefficients of the) constant polynomial 1.
- `polyx` -- (Coefficients of the) identity map polynomial, ``f(x) = x``.

Arithmetic
----------
- `polyadd` -- add two polynomials.
- `polysub` -- subtract one polynomial from another.
- `polymul` -- multiply two polynomials.
- `polydiv` -- divide one polynomial by another.
- `polypow` -- raise a polynomial to an positive integer power
- `polyval` -- evaluate a polynomial at given points.

Calculus
--------
- `polyder` -- differentiate a polynomial.
- `polyint` -- integrate a polynomial.

Misc Functions
--------------
- `polyfromroots` -- create a polynomial with specified roots.
- `polyroots` -- find the roots of a polynomial.
- `polyvander` -- Vandermonde-like matrix for powers.
- `polyfit` -- least-squares fit returning a polynomial.
- `polytrim` -- trim leading coefficients from a polynomial.
- `polyline` -- polynomial representing given straight line.

Classes
-------
- `Polynomial` -- polynomial class.

See also
--------
`numpy.polynomial`

"""
from __future__ import division

__all__ = ['polyzero', 'polyone', 'polyx', 'polydomain', 'polyline',
    'polyadd', 'polysub', 'polymulx', 'polymul', 'polydiv', 'polypow',
    'polyval', 'polyder', 'polyint', 'polyfromroots', 'polyvander',
    'polyfit', 'polytrim', 'polyroots', 'Polynomial']

import numpy as np
import numpy.linalg as la
import polyutils as pu
import warnings
from polytemplate import polytemplate

polytrim = pu.trimcoef

#
# These are constant arrays are of integer type so as to be compatible
# with the widest range of other types, such as Decimal.
#

# Polynomial default domain.
polydomain = np.array([-1,1])

# Polynomial coefficients representing zero.
polyzero = np.array([0])

# Polynomial coefficients representing one.
polyone = np.array([1])

# Polynomial coefficients representing the identity x.
polyx = np.array([0,1])

#
# Polynomial series functions
#

def polyline(off, scl) :
    """
    Returns an array representing a linear polynomial.

    Parameters
    ----------
    off, scl : scalars
        The "y-intercept" and "slope" of the line, respectively.

    Returns
    -------
    y : ndarray
        This module's representation of the linear polynomial ``off +
        scl*x``.

    See Also
    --------
    chebline

    Examples
    --------
    >>> from numpy import polynomial as P
    >>> P.polyline(1,-1)
    array([ 1, -1])
    >>> P.polyval(1, P.polyline(1,-1)) # should be 0
    0.0

    """
    if scl != 0 :
        return np.array([off,scl])
    else :
        return np.array([off])

def polyfromroots(roots) :
    """
    Generate a polynomial with the given roots.

    Return the array of coefficients for the polynomial whose leading
    coefficient (i.e., that of the highest order term) is `1` and whose
    roots (a.k.a. "zeros") are given by *roots*.  The returned array of
    coefficients is ordered from lowest order term to highest, and zeros
    of multiplicity greater than one must be included in *roots* a number
    of times equal to their multiplicity (e.g., if `2` is a root of
    multiplicity three, then [2,2,2] must be in *roots*).

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

    Returns
    -------
    out : ndarray
        1-d array of the polynomial's coefficients, ordered from low to
        high.  If all roots are real, ``out.dtype`` is a float type;
        otherwise, ``out.dtype`` is a complex type, even if all the
        coefficients in the result are real (see Examples below).

    See Also
    --------
    chebfromroots

    Notes
    -----
    What is returned are the :math:`a_i` such that:

    .. math::

        \\sum_{i=0}^{n} a_ix^i = \\prod_{i=0}^{n} (x - roots[i])

    where ``n == len(roots)``; note that this implies that `1` is always
    returned for :math:`a_n`.

    Examples
    --------
    >>> import numpy.polynomial as P
    >>> P.polyfromroots((-1,0,1)) # x(x - 1)(x + 1) = x^3 - x
    array([ 0., -1.,  0.,  1.])
    >>> j = complex(0,1)
    >>> P.polyfromroots((-j,j)) # complex returned, though values are real
    array([ 1.+0.j,  0.+0.j,  1.+0.j])

    """
    if len(roots) == 0 :
        return np.ones(1)
    else :
        [roots] = pu.as_series([roots], trim=False)
        roots.sort()
        p = [polyline(-r, 1) for r in roots]
        n = len(p)
        while n > 1:
            m, r = divmod(n, 2)
            tmp = [polymul(p[i], p[i+m]) for i in range(m)]
            if r:
                tmp[0] = polymul(tmp[0], p[-1])
            p = tmp
            n = m
        return p[0]


def polyadd(c1, c2):
    """
    Add one polynomial to another.

    Returns the sum of two polynomials `c1` + `c2`.  The arguments are
    sequences of coefficients from lowest order term to highest, i.e.,
    [1,2,3] represents the polynomial ``1 + 2*x + 3*x**2"``.

    Parameters
    ----------
    c1, c2 : array_like
        1-d arrays of polynomial coefficients ordered from low to high.

    Returns
    -------
    out : ndarray
        The coefficient array representing their sum.

    See Also
    --------
    polysub, polymul, polydiv, polypow

    Examples
    --------
    >>> from numpy import polynomial as P
    >>> c1 = (1,2,3)
    >>> c2 = (3,2,1)
    >>> sum = P.polyadd(c1,c2); sum
    array([ 4.,  4.,  4.])
    >>> P.polyval(2, sum) # 4 + 4(2) + 4(2**2)
    28.0

    """
    # c1, c2 are trimmed copies
    [c1, c2] = pu.as_series([c1, c2])
    if len(c1) > len(c2) :
        c1[:c2.size] += c2
        ret = c1
    else :
        c2[:c1.size] += c1
        ret = c2
    return pu.trimseq(ret)


def polysub(c1, c2):
    """
    Subtract one polynomial from another.

    Returns the difference of two polynomials `c1` - `c2`.  The arguments
    are sequences of coefficients from lowest order term to highest, i.e.,
    [1,2,3] represents the polynomial ``1 + 2*x + 3*x**2``.

    Parameters
    ----------
    c1, c2 : array_like
        1-d arrays of polynomial coefficients ordered from low to
        high.

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

    See Also
    --------
    polyadd, polymul, polydiv, polypow

    Examples
    --------
    >>> from numpy import polynomial as P
    >>> c1 = (1,2,3)
    >>> c2 = (3,2,1)
    >>> P.polysub(c1,c2)
    array([-2.,  0.,  2.])
    >>> P.polysub(c2,c1) # -P.polysub(c1,c2)
    array([ 2.,  0., -2.])

    """
    # c1, c2 are trimmed copies
    [c1, c2] = pu.as_series([c1, c2])
    if len(c1) > len(c2) :
        c1[:c2.size] -= c2
        ret = c1
    else :
        c2 = -c2
        c2[:c1.size] += c1
        ret = c2
    return pu.trimseq(ret)


def polymulx(cs):
    """Multiply a polynomial by x.

    Multiply the polynomial `cs` by x, where x is the independent
    variable.


    Parameters
    ----------
    cs : array_like
        1-d array of polynomial coefficients ordered from low to
        high.

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

    Notes
    -----
    .. versionadded:: 1.5.0

    """
    # cs is a trimmed copy
    [cs] = pu.as_series([cs])
    # The zero series needs special treatment
    if len(cs) == 1 and cs[0] == 0:
        return cs

    prd = np.empty(len(cs) + 1, dtype=cs.dtype)
    prd[0] = cs[0]*0
    prd[1:] = cs
    return prd


def polymul(c1, c2):
    """
    Multiply one polynomial by another.

    Returns the product of two polynomials `c1` * `c2`.  The arguments are
    sequences of coefficients, from lowest order term to highest, e.g.,
    [1,2,3] represents the polynomial ``1 + 2*x + 3*x**2.``

    Parameters
    ----------
    c1, c2 : array_like
        1-d arrays of coefficients representing a polynomial, relative to the
        "standard" basis, and ordered from lowest order term to highest.

    Returns
    -------
    out : ndarray
        Of the coefficients of their product.

    See Also
    --------
    polyadd, polysub, polydiv, polypow

    Examples
    --------
    >>> import numpy.polynomial as P
    >>> c1 = (1,2,3)
    >>> c2 = (3,2,1)
    >>> P.polymul(c1,c2)
    array([  3.,   8.,  14.,   8.,   3.])

    """
    # c1, c2 are trimmed copies
    [c1, c2] = pu.as_series([c1, c2])
    ret = np.convolve(c1, c2)
    return pu.trimseq(ret)


def polydiv(c1, c2):
    """
    Divide one polynomial by another.

    Returns the quotient-with-remainder of two polynomials `c1` / `c2`.
    The arguments are sequences of coefficients, from lowest order term
    to highest, e.g., [1,2,3] represents ``1 + 2*x + 3*x**2``.

    Parameters
    ----------
    c1, c2 : array_like
        1-d arrays of polynomial coefficients ordered from low to high.

    Returns
    -------
    [quo, rem] : ndarrays
        Of coefficient series representing the quotient and remainder.

    See Also
    --------
    polyadd, polysub, polymul, polypow

    Examples
    --------
    >>> import numpy.polynomial as P
    >>> c1 = (1,2,3)
    >>> c2 = (3,2,1)
    >>> P.polydiv(c1,c2)
    (array([ 3.]), array([-8., -4.]))
    >>> P.polydiv(c2,c1)
    (array([ 0.33333333]), array([ 2.66666667,  1.33333333]))

    """
    # c1, c2 are trimmed copies
    [c1, c2] = pu.as_series([c1, c2])
    if c2[-1] == 0 :
        raise ZeroDivisionError()

    len1 = len(c1)
    len2 = len(c2)
    if len2 == 1 :
        return c1/c2[-1], c1[:1]*0
    elif len1 < len2 :
        return c1[:1]*0, c1
    else :
        dlen = len1 - len2
        scl = c2[-1]
        c2  = c2[:-1]/scl
        i = dlen
        j = len1 - 1
        while i >= 0 :
            c1[i:j] -= c2*c1[j]
            i -= 1
            j -= 1
        return c1[j+1:]/scl, pu.trimseq(c1[:j+1])

def polypow(cs, pow, maxpower=None) :
    """Raise a polynomial to a power.

    Returns the polynomial `cs` raised to the power `pow`. The argument
    `cs` is a sequence of coefficients ordered from low to high. i.e.,
    [1,2,3] is the series  ``1 + 2*x + 3*x**2.``

    Parameters
    ----------
    cs : array_like
        1d array of chebyshev 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 umanageable size. Default is 16

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

    See Also
    --------
    chebadd, chebsub, chebmul, chebdiv

    Examples
    --------

    """
    # cs is a trimmed copy
    [cs] = pu.as_series([cs])
    power = int(pow)
    if power != pow or power < 0 :
        raise ValueError("Power must be a non-negative integer.")
    elif maxpower is not None and power > maxpower :
        raise ValueError("Power is too large")
    elif power == 0 :
        return np.array([1], dtype=cs.dtype)
    elif power == 1 :
        return cs
    else :
        # This can be made more efficient by using powers of two
        # in the usual way.
        prd = cs
        for i in range(2, power + 1) :
            prd = np.convolve(prd, cs)
        return prd

def polyder(cs, m=1, scl=1):
    """
    Differentiate a polynomial.

    Returns the polynomial `cs` differentiated `m` times.  At each
    iteration the result is multiplied by `scl` (the scaling factor is for
    use in a linear change of variable).  The argument `cs` is the sequence
    of coefficients from lowest order term to highest, e.g., [1,2,3]
    represents the polynomial ``1 + 2*x + 3*x**2``.

    Parameters
    ----------
    cs: array_like
        1-d array of polynomial coefficients ordered from low to high.
    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)

    Returns
    -------
    der : ndarray
        Polynomial of the derivative.

    See Also
    --------
    polyint

    Examples
    --------
    >>> from numpy import polynomial as P
    >>> cs = (1,2,3,4) # 1 + 2x + 3x**2 + 4x**3
    >>> P.polyder(cs) # (d/dx)(cs) = 2 + 6x + 12x**2
    array([  2.,   6.,  12.])
    >>> P.polyder(cs,3) # (d**3/dx**3)(cs) = 24
    array([ 24.])
    >>> P.polyder(cs,scl=-1) # (d/d(-x))(cs) = -2 - 6x - 12x**2
    array([ -2.,  -6., -12.])
    >>> P.polyder(cs,2,-1) # (d**2/d(-x)**2)(cs) = 6 + 24x
    array([  6.,  24.])

    """
    cnt = int(m)

    if cnt != m:
        raise ValueError, "The order of derivation must be integer"
    if cnt < 0:
        raise ValueError, "The order of derivation must be non-negative"

    # cs is a trimmed copy
    [cs] = pu.as_series([cs])
    if cnt == 0:
        return cs
    elif cnt >= len(cs):
        return cs[:1]*0
    else :
        n = len(cs)
        d = np.arange(n)*scl
        for i in range(cnt):
            cs[i:] *= d[:n-i]
        return cs[i+1:].copy()

def polyint(cs, m=1, k=[], lbnd=0, scl=1):
    """
    Integrate a polynomial.

    Returns the polynomial `cs`, integrated `m` times from `lbnd` to `x`.
    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 `cs` is a sequence of coefficients, from lowest order
    term to highest, e.g., [1,2,3] represents the polynomial
    ``1 + 2*x + 3*x**2``.

    Parameters
    ----------
    cs : array_like
        1-d array of polynomial coefficients, ordered from low to high.
    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 zero
        is the first value in the list, the value of the second integral
        at zero 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)

    Returns
    -------
    S : ndarray
        Coefficients of the integral.

    Raises
    ------
    ValueError
        If ``m < 1``, ``len(k) > m``.

    See Also
    --------
    polyder

    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.

    Examples
    --------
    >>> from numpy import polynomial as P
    >>> cs = (1,2,3)
    >>> P.polyint(cs) # should return array([0, 1, 1, 1])
    array([ 0.,  1.,  1.,  1.])
    >>> P.polyint(cs,3) # should return array([0, 0, 0, 1/6, 1/12, 1/20])
    array([ 0.        ,  0.        ,  0.        ,  0.16666667,  0.08333333,
            0.05      ])
    >>> P.polyint(cs,k=3) # should return array([3, 1, 1, 1])
    array([ 3.,  1.,  1.,  1.])
    >>> P.polyint(cs,lbnd=-2) # should return array([6, 1, 1, 1])
    array([ 6.,  1.,  1.,  1.])
    >>> P.polyint(cs,scl=-2) # should return array([0, -2, -2, -2])
    array([ 0., -2., -2., -2.])

    """
    cnt = int(m)
    if not np.iterable(k):
        k = [k]

    if cnt != m:
        raise ValueError, "The order of integration must be integer"
    if cnt < 0 :
        raise ValueError, "The order of integration must be non-negative"
    if len(k) > cnt :
        raise ValueError, "Too many integration constants"

    # cs is a trimmed copy
    [cs] = pu.as_series([cs])
    if cnt == 0:
        return cs

    k = list(k) + [0]*(cnt - len(k))
    for i in range(cnt):
        n = len(cs)
        cs *= scl
        if n == 1 and cs[0] == 0:
            cs[0] += k[i]
        else:
            tmp = np.empty(n + 1, dtype=cs.dtype)
            tmp[0] = cs[0]*0
            tmp[1:] = cs/np.arange(1, n + 1)
            tmp[0] += k[i] - polyval(lbnd, tmp) 
            cs = tmp
    return cs

def polyval(x, cs):
    """
    Evaluate a polynomial.

    If `cs` is of length `n`, this function returns :

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

    If x is a sequence or array then p(x) will have the same shape as x.
    If r is a ring_like object that supports multiplication and addition
    by the values in `cs`, then an object of the same type is returned.

    Parameters
    ----------
    x : array_like, ring_like
        If x is a list or tuple, it is converted to an ndarray. Otherwise
        it must support addition and multiplication with itself and the
        elements of `cs`.
    cs : array_like
        1-d array of Chebyshev coefficients ordered from low to high.

    Returns
    -------
    values : ndarray
        The return array has the same shape as `x`.

    See Also
    --------
    polyfit

    Notes
    -----
    The evaluation uses Horner's method.

    """
    # cs is a trimmed copy
    [cs] = pu.as_series([cs])
    if isinstance(x, tuple) or isinstance(x, list) :
        x = np.asarray(x)

    c0 = cs[-1] + x*0
    for i in range(2, len(cs) + 1) :
        c0 = cs[-i] + c0*x
    return c0

def polyvander(x, deg) :
    """Vandermonde matrix of given degree.

    Returns the Vandermonde matrix of degree `deg` and sample points `x`.
    This isn't a true Vandermonde matrix because `x` can be an arbitrary
    ndarray. If ``V`` is the returned matrix and `x` is a 2d array, then
    the elements of ``V`` are ``V[i,j,k] = x[i,j]**k``

    Parameters
    ----------
    x : array_like
        Array of points. The values are converted to double or complex
        doubles. If x is scalar it is converted to a 1D array.
    deg : integer
        Degree of the resulting matrix.

    Returns
    -------
    vander : Vandermonde matrix.
        The shape of the returned matrix is ``x.shape + (deg+1,)``. The last
        index is the degree.

    """
    ideg = int(deg)
    if ideg != deg:
        raise ValueError("deg must be integer")
    if ideg < 0:
        raise ValueError("deg must be non-negative")

    x = np.array(x, copy=0, ndmin=1) + 0.0
    v = np.empty((ideg + 1,) + x.shape, dtype=x.dtype)
    v[0] = x*0 + 1
    if ideg > 0 :
        v[1] = x
        for i in range(2, ideg + 1) :
            v[i] = v[i-1]*x
    return np.rollaxis(v, 0, v.ndim)

def polyfit(x, y, deg, rcond=None, full=False, w=None):
    """
    Least-squares fit of a polynomial to data.

    Return the coefficients of a polynomial 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 * x + ... + c_n * x^n,

    where `n` is `deg`.

    Parameters
    ----------
    x : array_like, shape (`M`,)
        x-coordinates of the `M` sample (data) points ``(x[i], y[i])``.
    y : array_like, shape (`M`,) or (`M`, `K`)
        y-coordinates of the sample points.  Several sets of sample points
        sharing the same x-coordinates can be (independently) fit with one
        call to `polyfit` by passing in for `y` a 2-D array that contains
        one data set per column.
    deg : int
        Degree of the polynomial(s) to be fit.
    rcond : float, optional
        Relative condition number of the fit.  Singular values smaller
        than `rcond`, relative to the largest singular value, will be
        ignored.  The default value is ``len(x)*eps``, where `eps` is the
        relative precision of the platform's float type, about 2e-16 in
        most cases.
    full : bool, optional
        Switch determining the nature of the return value.  When ``False``
        (the default) just the coefficients are returned; when ``True``,
        diagnostic information from the singular value decomposition (used
        to solve the fit's matrix equation) is also returned.
    w : array_like, shape (`M`,), optional
        Weights. If not None, the contribution of each point
        ``(x[i],y[i])`` to the fit is weighted by `w[i]`. Ideally the
        weights are chosen so that the errors of the products ``w[i]*y[i]``
        all have the same variance.  The default value is None.

        .. versionadded:: 1.5.0

    Returns
    -------
    coef : ndarray, shape (`deg` + 1,) or (`deg` + 1, `K`)
        Polynomial coefficients ordered from low to high.  If `y` was 2-D,
        the coefficients in column `k` of `coef` represent the polynomial
        fit to the data in `y`'s `k`-th column.

    [residuals, rank, singular_values, rcond] : present when `full` == True
        Sum of the squared residuals (SSR) of the least-squares fit; the
        effective rank of the scaled Vandermonde matrix; its singular
        values; and the specified value of `rcond`.  For more information,
        see `linalg.lstsq`.

    Raises
    ------
    RankWarning
        Raised if the matrix in the least-squares fit is rank deficient.
        The warning is only raised if `full` == False.  The warnings can
        be turned off by:

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

    See Also
    --------
    chebfit, legfit, lagfit, hermfit, hermefit
    polyval : Evaluates a polynomial.
    polyvander : Vandermonde matrix for powers.
    linalg.lstsq : Computes a least-squares fit from the matrix.
    scipy.interpolate.UnivariateSpline : Computes spline fits.

    Notes
    -----
    The solution is the coefficients of the polynomial `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 the (typically) over-determined 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 (and `full` == ``False``), a `RankWarning` will be raised.
    This means that the coefficient values may be poorly determined.
    Fitting to a lower order polynomial will usually get rid of the warning
    (but may not be what you want, of course; if you have independent
    reason(s) for choosing the degree which isn't working, you may have to:
    a) reconsider those reasons, and/or b) reconsider the quality of your
    data).  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.

    Polynomial fits using double precision tend to "fail" at about
    (polynomial) degree 20. Fits using Chebyshev or Legendre series are
    generally better conditioned, but much can still depend on the
    distribution of the sample points and the smoothness of the data.  If
    the quality of the fit is inadequate, splines may be a good
    alternative.

    Examples
    --------
    >>> from numpy import polynomial as P
    >>> x = np.linspace(-1,1,51) # x "data": [-1, -0.96, ..., 0.96, 1]
    >>> y = x**3 - x + np.random.randn(len(x)) # x^3 - x + N(0,1) "noise"
    >>> c, stats = P.polyfit(x,y,3,full=True)
    >>> c # c[0], c[2] should be approx. 0, c[1] approx. -1, c[3] approx. 1
    array([ 0.01909725, -1.30598256, -0.00577963,  1.02644286])
    >>> stats # note the large SSR, explaining the rather poor results
    [array([ 38.06116253]), 4, array([ 1.38446749,  1.32119158,  0.50443316,
    0.28853036]), 1.1324274851176597e-014]

    Same thing without the added noise

    >>> y = x**3 - x
    >>> c, stats = P.polyfit(x,y,3,full=True)
    >>> c # c[0], c[2] should be "very close to 0", c[1] ~= -1, c[3] ~= 1
    array([ -1.73362882e-17,  -1.00000000e+00,  -2.67471909e-16,
             1.00000000e+00])
    >>> stats # note the minuscule SSR
    [array([  7.46346754e-31]), 4, array([ 1.38446749,  1.32119158,
    0.50443316,  0.28853036]), 1.1324274851176597e-014]

    """
    order = int(deg) + 1
    x = np.asarray(x) + 0.0
    y = np.asarray(y) + 0.0

    # check arguments.
    if deg < 0 :
        raise ValueError, "expected deg >= 0"
    if x.ndim != 1:
        raise TypeError, "expected 1D vector for x"
    if x.size == 0:
        raise TypeError, "expected non-empty vector for x"
    if y.ndim < 1 or y.ndim > 2 :
        raise TypeError, "expected 1D or 2D array for y"
    if len(x) != len(y):
        raise TypeError, "expected x and y to have same length"

    # set up the least squares matrices
    lhs = polyvander(x, deg)
    rhs = y
    if w is not None:
        w = np.asarray(w) + 0.0
        if w.ndim != 1:
            raise TypeError, "expected 1D vector for w"
        if len(x) != len(w):
            raise TypeError, "expected x and w to have same length"
        # apply weights
        if rhs.ndim == 2:
            lhs *= w[:, np.newaxis]
            rhs *= w[:, np.newaxis]
        else:
            lhs *= w[:, np.newaxis]
            rhs *= w

    # set rcond
    if rcond is None :
        rcond = len(x)*np.finfo(x.dtype).eps

    # scale the design matrix and solve the least squares equation
    scl = np.sqrt((lhs*lhs).sum(0))
    c, resids, rank, s = la.lstsq(lhs/scl, rhs, rcond)
    c = (c.T/scl).T

    # warn on rank reduction
    if rank != order and not full:
        msg = "The fit may be poorly conditioned"
        warnings.warn(msg, pu.RankWarning)

    if full :
        return c, [resids, rank, s, rcond]
    else :
        return c


def polycompanion(cs):
    """Return the companion matrix of cs.


    Parameters
    ----------
    cs : array_like
        1-d array of series coefficients ordered from low to high degree.

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

    """
    # cs is a trimmed copy
    [cs] = pu.as_series([cs])
    if len(cs) < 2 :
        raise ValueError('Series must have maximum degree of at least 1.')
    if len(cs) == 2:
        return np.array(-cs[0]/cs[1])

    n = len(cs) - 1
    mat = np.zeros((n, n), dtype=cs.dtype)
    bot = mat.reshape(-1)[n::n+1]
    bot[...] = 1
    mat[:,-1] -= cs[:-1]/cs[-1]
    return mat


def polyroots(cs):
    """
    Compute the roots of a polynomial.

    Return the roots (a.k.a. "zeros") of the "polynomial" `cs`, the
    polynomial's coefficients from lowest order term to highest
    (e.g., [1,2,3] represents the polynomial ``1 + 2*x + 3*x**2``).

    Parameters
    ----------
    cs : array_like of shape (M,)
        1-d array of polynomial coefficients ordered from low to high.

    Returns
    -------
    out : ndarray
        Array of the roots of the polynomial.  If all the roots are real,
        then so is the dtype of ``out``; otherwise, ``out``'s dtype is
        complex.

    See Also
    --------
    chebroots

    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 power 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.

    Examples
    --------
    >>> import numpy.polynomial.polynomial as poly
    >>> poly.polyroots(poly.polyfromroots((-1,0,1)))
    array([-1.,  0.,  1.])
    >>> poly.polyroots(poly.polyfromroots((-1,0,1))).dtype
    dtype('float64')
    >>> j = complex(0,1)
    >>> poly.polyroots(poly.polyfromroots((-j,0,j)))
    array([  0.00000000e+00+0.j,   0.00000000e+00+1.j,   2.77555756e-17-1.j])

    """
    # cs is a trimmed copy
    [cs] = pu.as_series([cs])
    if len(cs) < 2:
        return np.array([], dtype=cs.dtype)
    if len(cs) == 2:
        return np.array([-cs[0]/cs[1]])

    m = polycompanion(cs)
    r = la.eigvals(m)
    r.sort()
    return r


#
# polynomial class
#

exec polytemplate.substitute(name='Polynomial', nick='poly', domain='[-1,1]')

