fitting data with numpy

Unfortunately, np.polynomial.polynomial.polyfit returns the coefficients in the opposite order of that for np.polyfit and np.polyval (or, as you used np.poly1d). To illustrate:

In [40]: np.polynomial.polynomial.polyfit(x, y, 4)Out[40]: array([  84.29340848, -100.53595376,   44.83281408,   -8.85931101,          0.65459882])In [41]: np.polyfit(x, y, 4)Out[41]: array([   0.65459882,   -8.859311  ,   44.83281407, -100.53595375,         84.29340846])

In general: np.polynomial.polynomial.polyfit returns coefficients [A, B, C] to A + Bx + Cx^2 + ..., while np.polyfit returns: ... + Ax^2 + Bx + C.

So if you want to use this combination of functions, you must reverse the order of coefficients, as in:

ffit = np.polyval(coefs[::-1], x_new)

However, the documentation states clearly to avoid np.polyfit, np.polyval, and np.poly1d, and instead to use only the new(er) package.

You're safest to use only the polynomial package:

import numpy.polynomial.polynomial as polycoefs = poly.polyfit(x, y, 4)ffit = poly.polyval(x_new, coefs)plt.plot(x_new, ffit)

Or, to create the polynomial function:

ffit = poly.Polynomial(coefs)    # instead of np.poly1dplt.plot(x_new, ffit(x_new))

Note that you can use the Polynomial class directly to do the fitting and return a Polynomial instance.

from numpy.polynomial import Polynomialp = Polynomial.fit(x, y, 4)plt.plot(*p.linspace())

p uses scaled and shifted x values for numerical stability. If you need the usual form of the coefficients, you will need to follow with

pnormal = p.convert(domain=(-1, 1))