#### Previous topic

numpy.ma.vander

#### Next topic

numpy.ma.around

# numpy.ma.polyfit

`numpy.ma.``polyfit`(x, y, deg, rcond=None, full=False, w=None, cov=False)

  Least squares polynomial fit.

  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.

  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 to apply to the y-coordinates of the sample points. For gaussian uncertainties, use 1/sigma (not 1/sigma**2). cov : bool, optional Return the estimate and the covariance matrix of the estimate If full is True, then cov is not returned.
  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 Present only if full = True. Residuals of the least-squares fit, the effective rank of the scaled Vandermonde coefficient matrix, its singular values, and the specified value of rcond. For more details, see 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)

  See also

  `polyval`
    Compute polynomial values.
  `linalg.lstsq`
    Computes a least-squares fit.
  `scipy.interpolate.UnivariateSpline`
    Computes spline fits.

  Notes

  Any masked values in x is propagated in y, and vice-versa.

  References

  [R57] | Wikipedia, “Curve fitting”, http://en.wikipedia.org/wiki/Curve_fitting

  [R58] | Wikipedia, “Polynomial interpolation”, http://en.wikipedia.org/wiki/Polynomial_interpolation

  Examples

      >>> 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])

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

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

  High-order polynomials may oscillate wildly:

      >>> p30 = np.poly1d(np.polyfit(x, y, 30))
      /... RankWarning: Polyfit may be poorly conditioned...
      >>> p30(4)
      -0.80000000000000204
      >>> p30(5)
      -0.99999999999999445
      >>> p30(4.5)
      -0.10547061179440398

  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()

  (Source code, png, pdf)