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Diffstat (limited to 'doc/source/reference')
| -rw-r--r-- | doc/source/reference/routines.polynomials.classes.rst | 4 |
1 files changed, 0 insertions, 4 deletions
diff --git a/doc/source/reference/routines.polynomials.classes.rst b/doc/source/reference/routines.polynomials.classes.rst index fd5b0a7e3..2ce29d9d0 100644 --- a/doc/source/reference/routines.polynomials.classes.rst +++ b/doc/source/reference/routines.polynomials.classes.rst @@ -294,7 +294,6 @@ polynomials up to degree 5 are plotted below. ... ax = plt.plot(x, T.basis(i)(x), lw=2, label=f"$T_{i}$") ... >>> plt.legend(loc="upper left") - <matplotlib.legend.Legend object at 0x...> >>> plt.show() In the range -1 <= `x` <= 1 they are nice, equiripple functions lying between +/- 1. @@ -309,7 +308,6 @@ The same plots over the range -2 <= `x` <= 2 look very different: ... ax = plt.plot(x, T.basis(i)(x), lw=2, label=f"$T_{i}$") ... >>> plt.legend(loc="lower right") - <matplotlib.legend.Legend object at 0x...> >>> plt.show() As can be seen, the "good" parts have shrunk to insignificance. In using @@ -335,10 +333,8 @@ illustrated below for a fit to a noisy sine curve. >>> y = np.sin(x) + np.random.normal(scale=.1, size=x.shape) >>> p = T.fit(x, y, 5) >>> plt.plot(x, y, 'o') - [<matplotlib.lines.Line2D object at 0x...>] >>> xx, yy = p.linspace() >>> plt.plot(xx, yy, lw=2) - [<matplotlib.lines.Line2D object at 0x...>] >>> p.domain array([0. , 6.28318531]) >>> p.window |