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Diffstat (limited to 'doc/source/reference/routines.polynomials.classes.rst')
| -rw-r--r-- | doc/source/reference/routines.polynomials.classes.rst | 28 |
1 files changed, 0 insertions, 28 deletions
diff --git a/doc/source/reference/routines.polynomials.classes.rst b/doc/source/reference/routines.polynomials.classes.rst index 2cfbec5d9..9294728c8 100644 --- a/doc/source/reference/routines.polynomials.classes.rst +++ b/doc/source/reference/routines.polynomials.classes.rst @@ -322,31 +322,3 @@ illustrated below for a fit to a noisy sin curve. >>> p.window array([-1., 1.]) >>> plt.show() - -The fit will ignore data points masked with NA. We demonstrate this with -the previous example, but add an outlier that messes up the fit, then mask -it out. - -.. plot:: - - >>> import numpy as np - >>> import matplotlib.pyplot as plt - >>> from numpy.polynomial import Chebyshev as T - >>> np.random.seed(11) - >>> x = np.linspace(0, 2*np.pi, 20) - >>> y = np.sin(x) + np.random.normal(scale=.1, size=x.shape) - >>> y[10] = 2 - >>> p = T.fit(x, y, 5) - >>> plt.plot(x, y, 'o') - [<matplotlib.lines.Line2D object at 0x2136c10>] - >>> xx, yy = p.linspace() - >>> plt.plot(xx, yy, lw=2, label="unmasked") - [<matplotlib.lines.Line2D object at 0x1cf2890>] - >>> ym = y.view(maskna=1) - >>> ym[10] = np.NA - >>> p = T.fit(x, ym, 5) - >>> xx, yy = p.linspace() - >>> plt.plot(xx, yy, lw=2, label="masked") - >>> plt.legend(loc="upper right") - <matplotlib.legend.Legend object at 0x3b3ee10> - >>> plt.show() |