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Commit 9c48e4b

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QuLogicQuLogic
authored
Merge pull request #25021 from tacaswell/doc/sg_splitter
Doc: sg section separator
2 parents be7ea76 + e5f0066 commit 9c48e4b
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‎doc/devel/documenting_mpl.rst

Copy file name to clipboardExpand all lines: doc/devel/documenting_mpl.rst
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@@ -812,9 +812,15 @@ to create a gallery of images in the :file:`/doc/gallery` and
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:file:`/doc/tutorials` directories respectively. To exclude an example
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from having an plot generated insert "sgskip" somewhere in the filename.
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Formatting the example
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----------------------
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The format of these files is relatively straightforward. Properly
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formatted comment blocks are treated as ReST_ text, the code is
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displayed, and figures are put into the built page.
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displayed, and figures are put into the built page. Matplotlib uses the
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``# %%`` section separator so that IDEs will identify "code cells" to make
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it easy to re-run sub-sections of the example.
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For instance the example :doc:`/gallery/lines_bars_and_markers/simple_plot`
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example is generated from
@@ -853,7 +859,7 @@ Tutorials are made with the exact same mechanism, except they are longer, and
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typically have more than one comment block (i.e.
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:doc:`/tutorials/introductory/quick_start`). The first comment block
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can be the same as the example above. Subsequent blocks of ReST text
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are delimited by a line of ``###`` characters:
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are delimited by the line ``# %%`` :
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.. code-block:: python
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@@ -868,7 +874,7 @@ are delimited by a line of ``###`` characters:
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ax.grid()
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plt.show()
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##########################################################################
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# %%
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# Second plot
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# ===========
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#
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.. code-block:: python
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###############################################################################
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# %%
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#
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# .. admonition:: References
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#

‎examples/animation/animated_histogram.py

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data = np.random.randn(1000)
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n, _ = np.histogram(data, HIST_BINS)
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###############################################################################
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# %%
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# To animate the histogram, we need an ``animate`` function, which generates
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# a random set of numbers and updates the heights of rectangles. We utilize a
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# python closure to track an instance of `.BarContainer` whose `.Rectangle`
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return bar_container.patches
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return animate
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###############################################################################
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# %%
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# Using :func:`~matplotlib.pyplot.hist` allows us to get an instance of
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# `.BarContainer`, which is a collection of `.Rectangle` instances. Calling
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# ``prepare_animation`` will define ``animate`` function working with supplied

‎examples/animation/multiple_axes.py

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plt.show()
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#############################################################################
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# %%
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#
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# .. admonition:: References
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#

‎examples/axes_grid1/demo_axes_hbox_divider.py

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plt.show()
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###############################################################################
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# %%
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# Using a `.VBoxDivider` to arrange subplots.
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#
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# Note that both axes' location are adjusted so that they have

‎examples/axes_grid1/demo_fixed_size_axes.py

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from mpl_toolkits.axes_grid1 import Divider, Size
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###############################################################################
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# %%
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fig = plt.figure(figsize=(6, 6))
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ax.plot([1, 2, 3])
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###############################################################################
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# %%
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fig = plt.figure(figsize=(6, 6))

‎examples/axes_grid1/inset_locator_demo.py

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"""
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###############################################################################
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# %%
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# The `.inset_locator`'s `~.inset_locator.inset_axes` allows
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# easily placing insets in the corners of the axes by specifying a width and
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# height and optionally a location (loc) that accepts locations as codes,
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plt.show()
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###############################################################################
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# %%
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# The parameters *bbox_to_anchor* and *bbox_transform* can be used for a more
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# fine-grained control over the inset position and size or even to position
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# the inset at completely arbitrary positions.
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plt.show()
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###############################################################################
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# %%
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# In the above the axes transform together with 4-tuple bounding boxes has been
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# used as it mostly is useful to specify an inset relative to the axes it is
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# an inset to. However, other use cases are equally possible. The following

‎examples/axes_grid1/make_room_for_ylabel_using_axesgrid.py

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make_axes_area_auto_adjustable(ax)
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###############################################################################
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# %%
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fig = plt.figure()
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ax1 = fig.add_axes([0, 0, 1, 0.5])
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make_axes_area_auto_adjustable(ax1, pad=0.1, use_axes=[ax1, ax2])
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make_axes_area_auto_adjustable(ax2, pad=0.1, use_axes=[ax1, ax2])
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###############################################################################
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# %%
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fig = plt.figure()
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ax1 = fig.add_axes([0, 0, 1, 1])

‎examples/axes_grid1/scatter_hist_locatable_axes.py

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plt.show()
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#############################################################################
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# %%
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#
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# .. admonition:: References
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#

‎examples/axes_grid1/simple_axes_divider1.py

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left=False, labelleft=False)
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# %%
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# Fixed axes sizes; fixed paddings.
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fig = plt.figure(figsize=(6, 6))
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ax4 = fig.add_axes(rect, axes_locator=div.new_locator(nx=2, nx1=4, ny=0))
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label_axes(ax4, "nx=2, nx1=4, ny=0")
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##############################################################################
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# %%
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# Axes sizes that scale with the figure size; fixed paddings.
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fig = plt.figure(figsize=(6, 6))

‎examples/axisartist/demo_floating_axes.py

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return ax1, aux_ax
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##########################################################
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# %%
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fig = plt.figure(figsize=(8, 4))
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fig.subplots_adjust(wspace=0.3, left=0.05, right=0.95)
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‎examples/color/color_by_yvalue.py

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ax.plot(t, smiddle, t, slower, t, supper)
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plt.show()
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# %%
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#
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# .. admonition:: References
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#

‎examples/color/color_cycle_default.py

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plt.show()
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# %%
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#
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# .. admonition:: References
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#

‎examples/color/color_demo.py

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plt.show()
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# %%
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#
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# .. admonition:: References
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#

‎examples/color/colorbar_basics.py

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cbar.minorticks_on()
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plt.show()
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# %%
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#
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# .. admonition:: References
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#

‎examples/color/colormap_reference.py

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# %%
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# .. _reverse-cmap:
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# Reversed colormaps
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# The built-in reversed colormaps are generated using `.Colormap.reversed`.
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# For an example, see :ref:`reversing-colormap`
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# %%
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#
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# .. admonition:: References
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#

‎examples/color/custom_cmap.py

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Z = np.cos(X) * np.sin(Y) * 10
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# %%
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# Colormaps from a list
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# ---------------------
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# %%
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# Custom colormaps
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# ----------------
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# %%
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# Now we will use this example to illustrate 2 ways of
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# handling custom colormaps.
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# First, the most direct and explicit:
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blue_red1 = LinearSegmentedColormap('BlueRed1', cdict1)
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# Second, create the map explicitly and register it.
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mpl.colormaps.register(LinearSegmentedColormap('BlueRed3', cdict3))
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# %%
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# %%
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‎examples/color/named_colors.py

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# %%
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# Base colors
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plot_colortable(mcolors.BASE_COLORS, ncols=3, sort_colors=False)
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# %%
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# ---------------
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# Tableau Palette
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plot_colortable(mcolors.TABLEAU_COLORS, ncols=2, sort_colors=False)
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# %%
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# %%
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# -----------
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# XKCD Colors
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# -----------

‎examples/event_handling/cursor_demo.py

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# %%
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# Faster redrawing using blitting
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# """""""""""""""""""""""""""""""
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# This technique stores the rendered plot as a background image. Only the
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# %%
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# Snapping to data points
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# """""""""""""""""""""""
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# The following cursor snaps its position to the data points of a `.Line2D`

‎examples/event_handling/ginput_manual_clabel_sgskip.py

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# %%
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# Define a triangle by clicking three points
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# %%
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# Now contour according to distance from triangle
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# corners - just an example
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tellme('Use mouse to select contour label locations, middle button to finish')
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# %%
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# Now do a zoom
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tellme('Now do a nested zoom, click to begin')

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