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Move remaining examples from api/
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‎doc/api/pyplot_summary.rst

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we recommend using the object-oriented approach for more control
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and customization of your plots. See the methods in the
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:meth:`matplotlib.axes.Axes` class for many of the same plotting functions.
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For examples of the OO approach to Matplotlib, see the
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:ref:`API Examples<api_examples>`.
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Colors in Matplotlib
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--------------------

‎doc/faq/howto_faq.rst

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pp.close()
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The same can be done using the pgf backend::
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from matplotlib.backends.backend_pgf import PdfPages
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.. htmlonly::
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See :ref:`sphx_glr_gallery_api_two_scales.py` for a complete example
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See :ref:`sphx_glr_gallery_subplots_axes_and_figures_two_scales.py`
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for a complete example
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.. _howto-batch:
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Alternatively, you can avoid pylab/pyplot altogether, which will give
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you a little more control, by calling the API directly as shown in
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:ref:`sphx_glr_gallery_api_agg_oo_sgskip.py`.
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:ref:`sphx_glr_gallery_misc_agg_oo_sgskip.py`.
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You can either generate hardcopy on the filesystem by calling savefig::
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‎doc/users/prev_whats_new/whats_new_1.1.rst

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Kevin Davies has extended Yannick Copin's original Sankey example into a module
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(:mod:`~matplotlib.sankey`) and provided new examples
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(:ref:`sphx_glr_gallery_api_sankey_basics.py`, :ref:`sphx_glr_gallery_api_sankey_links.py`,
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:ref:`sphx_glr_gallery_api_sankey_rankine.py`).
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(:ref:`sphx_glr_gallery_specialty_plots_sankey_basics.py`, :ref:`sphx_glr_gallery_specialty_plots_sankey_links.py`,
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:ref:`sphx_glr_gallery_specialty_plots_sankey_rankine.py`).
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.. figure:: ../../gallery/api/images/sphx_glr_sankey_rankine_001.png
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:target: ../../gallery/api/sankey_rankine.html
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.. figure:: ../../gallery/specialty_plots/images/sphx_glr_sankey_rankine_001.png
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:target: ../../gallery/specialty_plots/sankey_rankine.html
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:align: center
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:scale: 50
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‎doc/users/prev_whats_new/whats_new_1.4.rst

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:class:`~matplotlib.Axes`. This transform is important for some plot types,
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specifically the Skew-T used in meteorology.
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.. figure:: ../../gallery/api/images/sphx_glr_skewt_001.png
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:target: ../../gallery/api/skewt.html
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.. figure:: ../../gallery/specialty_plots/images/sphx_glr_skewt_001.png
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:target: ../../gallery/specialty_plots/skewt.html
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:align: center
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:scale: 50
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‎doc/users/prev_whats_new/whats_new_1.5.rst

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This is particularly useful for plotting pre-binned histograms.
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.. figure:: ../../gallery/api/images/sphx_glr_filled_step_001.png
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:target: ../../gallery/api/filled_step.html
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.. figure:: ../../gallery/lines_bars_and_markers/images/sphx_glr_filled_step_001.png
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:target: ../../gallery/lines_bars_and_markers/filled_step.html
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:align: center
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‎examples/api/README.txt

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‎examples/api/image_zcoord.py renamed to ‎examples/images_contours_and_fields/image_zcoord.py

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‎examples/api/watermark_image.py renamed to ‎examples/images_contours_and_fields/watermark_image.py

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Watermark image
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===============
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Use a PNG file as a watermark
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Using a PNG file as a watermark.
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"""
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import numpy as np
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import matplotlib.cbook as cbook

‎examples/api/filled_step.py renamed to ‎examples/lines_bars_and_markers/filled_step.py

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‎examples/api/joinstyle.py renamed to ‎examples/lines_bars_and_markers/joinstyle.py

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‎examples/api/scatter_piecharts.py renamed to ‎examples/lines_bars_and_markers/scatter_piecharts.py

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‎examples/api/span_regions.py renamed to ‎examples/lines_bars_and_markers/span_regions.py

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================
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Illustrate some helper functions for shading regions where a logical
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mask is True
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mask is True.
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See :meth:`matplotlib.collections.BrokenBarHCollection.span_where`
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"""

‎examples/api/agg_oo_sgskip.py renamed to ‎examples/misc/agg_oo_sgskip.py

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The object-oriented interface
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=============================
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A pure OO (look Ma, no pyplot!) example using the agg backend.
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A pure object-oriented example using the agg backend.
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"""
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from matplotlib.backends.backend_agg import FigureCanvasAgg as FigureCanvas

‎examples/api/histogram_path.py renamed to ‎examples/misc/histogram_path.py

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‎examples/api/logos2.py renamed to ‎examples/misc/logos2.py

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‎examples/api/power_norm.py renamed to ‎examples/scales/power_norm.py

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‎examples/api/patch_collection.py renamed to ‎examples/shapes_and_collections/patch_collection.py

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‎examples/api/quad_bezier.py renamed to ‎examples/shapes_and_collections/quad_bezier.py

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‎examples/api/radar_chart.py renamed to ‎examples/specialty_plots/radar_chart.py

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‎examples/api/sankey_basics.py renamed to ‎examples/specialty_plots/sankey_basics.py

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‎examples/api/sankey_links.py renamed to ‎examples/specialty_plots/sankey_links.py

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‎examples/api/sankey_rankine.py renamed to ‎examples/specialty_plots/sankey_rankine.py

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‎examples/api/skewt.py renamed to ‎examples/specialty_plots/skewt.py

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‎examples/api/two_scales.py renamed to ‎examples/subplots_axes_and_figures/two_scales.py

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Plots with different scales
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===========================
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Demonstrate how to do two plots on the same axes with different left and
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right scales.
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Two plots on the same axes with different left and right scales.
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The trick is to use *two different axes* that share the same *x* axis.
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You can use separate `matplotlib.ticker` formatters and locators as

‎examples/test2.png

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‎examples/api/font_family_rc_sgskip.py renamed to ‎examples/text_labels_and_annotations/font_family_rc_sgskip.py

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‎examples/api/font_file.py renamed to ‎examples/text_labels_and_annotations/font_file.py

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‎examples/api/legend.py renamed to ‎examples/text_labels_and_annotations/legend.py

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legend = ax.legend(loc='upper center', shadow=True, fontsize='x-large')
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# Put a nicer background color on the legend.
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legend.get_frame().set_facecolor('#00FFCC')
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legend.get_frame().set_facecolor('C0')
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plt.show()

‎examples/api/line_with_text.py renamed to ‎examples/text_labels_and_annotations/line_with_text.py

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‎examples/api/watermark_text.py renamed to ‎examples/text_labels_and_annotations/watermark_text.py

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Text watermark
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==============
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Use a Text as a watermark
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Adding a text watermark.
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"""
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import numpy as np
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#import matplotlib
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#matplotlib.use('Agg')
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# Fixing random state for reproducibility
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ax.plot(np.random.rand(20), '-o', ms=20, lw=2, alpha=0.7, mfc='orange')
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ax.grid()
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# position bottom right
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fig.text(0.95, 0.05, 'Property of MPL',
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fontsize=50, color='gray',
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ha='right', va='bottom', alpha=0.5)

‎lib/matplotlib/axes/_axes.py

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Examples
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--------
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.. plot:: gallery/api/legend.py
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.. plot:: gallery/text_labels_and_annotations/legend.py
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"""
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handles, labels, extra_args, kwargs = mlegend._parse_legend_args(

‎lib/matplotlib/path.py

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numsides x 2) numpy array of vertices. Return object is a
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:class:`Path`
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.. plot:: gallery/api/histogram_path.py
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.. plot:: gallery/misc/histogram_path.py
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"""
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‎lib/matplotlib/sankey.py

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**Examples:**
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.. plot:: gallery/api/sankey_basics.py
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.. plot:: gallery/specialty_plots/sankey_basics.py
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"""
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# Check the arguments.
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if gap < 0:

‎tutorials/introductory/lifecycle.py

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"axis", which refers to the x/y axis of a plot).
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We call methods that do the plotting directly from the Axes, which gives
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us much more flexibility and power in customizing our plot. See the
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:ref:`object-oriented examples <api_examples>` for many examples of how
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this approach is used.
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us much more flexibility and power in customizing our plot.
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.. note::
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‎tutorials/introductory/sample_plots.py

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generates figure legends, with MATLAB-compatible legend-placement
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functions.
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.. figure:: ../../gallery/api/images/sphx_glr_legend_001.png
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:target: ../../gallery/api/legend.html
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.. figure:: ../../gallery/text_labels_and_annotations/images/sphx_glr_legend_001.png
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:target: ../../gallery/text_labels_and_annotations/legend.html
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:align: center
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:scale: 50
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