diff options
Diffstat (limited to 'doc/source')
| -rw-r--r-- | doc/source/reference/routines.polynomials.classes.rst | 4 | ||||
| -rw-r--r-- | doc/source/user/absolute_beginners.rst | 3 | ||||
| -rw-r--r-- | doc/source/user/quickstart.rst | 1 |
3 files changed, 0 insertions, 8 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 diff --git a/doc/source/user/absolute_beginners.rst b/doc/source/user/absolute_beginners.rst index ecbc37bfa..f74a28af4 100644 --- a/doc/source/user/absolute_beginners.rst +++ b/doc/source/user/absolute_beginners.rst @@ -1654,7 +1654,6 @@ If you already have Matplotlib installed, you can import it with:: All you need to do to plot your values is run:: >>> plt.plot(a) - [<matplotlib.lines.Line2D object at 0x...>] # If you are running from a command line, you may need to do this: # >>> plt.show() @@ -1668,9 +1667,7 @@ For example, you can plot a 1D array like this:: >>> x = np.linspace(0, 5, 20) >>> y = np.linspace(0, 10, 20) >>> plt.plot(x, y, 'purple') # line - [<matplotlib.lines.Line2D object at 0x...>] >>> plt.plot(x, y, 'o') # dots - [<matplotlib.lines.Line2D object at 0x...>] .. plot:: user/plots/matplotlib2.py :align: center diff --git a/doc/source/user/quickstart.rst b/doc/source/user/quickstart.rst index b1a5a8b16..8e0e3b6ba 100644 --- a/doc/source/user/quickstart.rst +++ b/doc/source/user/quickstart.rst @@ -1274,7 +1274,6 @@ set <https://en.wikipedia.org/wiki/Mandelbrot_set>`__: ... return divtime >>> plt.clf() >>> plt.imshow(mandelbrot(400, 400)) - <matplotlib.image.AxesImage object at 0x...> The second way of indexing with booleans is more similar to integer indexing; for each dimension of the array we give a 1D boolean array |