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updating cross-refs and adding sphinx-gallery version dependency
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‎doc-requirements.txt

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numpydoc
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pillow
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scipy
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sphinx-gallery
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sphinx-gallery>=0.1.12

‎doc/_templates/index.html

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@@ -55,7 +55,7 @@ <h1>Introduction</h1>
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interface toolkits.</p>
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<div class="responsive_screenshots">
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<a a href="{{ pathto('tutorials/01_introductory/sample_plots') }}">
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<a a href="{{ pathto('tutorials/introductory/sample_plots') }}">
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<div class="responsive_subfig">
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<img align="middle", src="{{ pathto('_images/sphx_glr_membrane_thumb.png', 1) }}", border="0", alt="screenshots"/>
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</div>
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<p>Matplotlib tries to make easy things easy and hard things possible.
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You can generate plots, histograms, power spectra, bar charts,
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errorcharts, scatterplots, etc., with just a few lines of code.
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For examples, see the <a href="{{ pathto('tutorials/01_introductory/sample_plots')
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For examples, see the <a href="{{ pathto('tutorials/introductory/sample_plots')
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}}">sample plots</a> and <a href="{{ pathto('gallery/index') }}">thumbnail</a> gallery.</p>
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<p>For simple plotting the <tt>pyplot</tt> module provides a

‎doc/api/pyplot_summary.rst

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The :mod:`matplotlib.pyplot` module contains functions that allow you to generate
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many kinds of plots quickly. For examples that showcase the use
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of the :mod:`matplotlib.pyplot` module, see the
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:ref:`sphx_glr_tutorials_01_introductory_pyplot.py`
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:ref:`sphx_glr_tutorials_introductory_pyplot.py`
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or the :ref:`pyplots_examples`. We also recommend that you look into
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the object-oriented approach to plotting, described below.
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‎doc/conf.py

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@@ -105,7 +105,7 @@ def _check_deps():
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'../tutorials/introductory',
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'../tutorials/intermediate',
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'../tutorials/advanced']
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for folder in glob('../examples/*') + glob('../tutorials/*'):
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for folder in sorted(glob('../examples/*') + glob('../tutorials/*')):
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if not os.path.isdir(folder) or folder in explicit_order_folders:
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continue
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explicit_order_folders.append(folder)

‎doc/users/intro.rst

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@@ -62,10 +62,10 @@ The Matplotlib code is conceptually divided into three parts: the
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*pylab interface* is the set of functions provided by
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:mod:`matplotlib.pylab` which allow the user to create plots with code
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quite similar to MATLAB figure generating code
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(:ref:`sphx_glr_tutorials_01_introductory_pyplot.py`). The *Matplotlib frontend* or *Matplotlib
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(:ref:`sphx_glr_tutorials_introductory_pyplot.py`). The *Matplotlib frontend* or *Matplotlib
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API* is the set of classes that do the heavy lifting, creating and
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managing figures, text, lines, plots and so on
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(:ref:`sphx_glr_tutorials_02_intermediate_artists.py`). This is an abstract interface that knows
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(:ref:`sphx_glr_tutorials_intermediate_artists.py`). This is an abstract interface that knows
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nothing about output. The *backends* are device-dependent drawing
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devices, aka renderers, that transform the frontend representation to
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hardcopy or a display device (:ref:`what-is-a-backend`). Example

‎doc/users/prev_whats_new/whats_new_0.99.rst

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New documentation
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-----------------
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Jae-Joon Lee has written two new guides :ref:`sphx_glr_tutorials_02_intermediate_legend_guide.py`
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Jae-Joon Lee has written two new guides :ref:`sphx_glr_tutorials_intermediate_legend_guide.py`
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and :ref:`plotting-guide-annotation`. Michael Sarahan has written
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:ref:`sphx_glr_tutorials_01_introductory_images.py`. John Hunter has written two new tutorials on
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working with paths and transformations: :ref:`sphx_glr_tutorials_03_advanced_path_tutorial.py` and
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:ref:`sphx_glr_tutorials_03_advanced_transforms_tutorial.py`.
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:ref:`sphx_glr_tutorials_introductory_images.py`. John Hunter has written two new tutorials on
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working with paths and transformations: :ref:`sphx_glr_tutorials_advanced_path_tutorial.py` and
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:ref:`sphx_glr_tutorials_advanced_transforms_tutorial.py`.
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.. _whats-new-mplot3d:
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:scale: 50
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Whats New 99 Spines
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‎doc/users/prev_whats_new/whats_new_1.0.rst

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Jae-Joon Lee has written :mod:`~matplotlib.gridspec`, a new module for
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doing complex subplot layouts, featuring row and column spans and
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more. See :ref:`sphx_glr_tutorials_02_intermediate_gridspec.py` for a tutorial overview.
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more. See :ref:`sphx_glr_tutorials_intermediate_gridspec.py` for a tutorial overview.
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.. figure:: ../../gallery/userdemo/images/sphx_glr_demo_gridspec01_000.png
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:target: ../../gallery/userdemo/demo_gridspec01.html

‎doc/users/prev_whats_new/whats_new_1.1.rst

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and it will adjust the spacing between subplots
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so that the axis labels do not overlap with neighboring subplots. A
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:ref:`sphx_glr_tutorials_02_intermediate_tight_layout_guide.py` has been created to show how to use
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:ref:`sphx_glr_tutorials_intermediate_tight_layout_guide.py` has been created to show how to use
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this new tool.
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PyQT4, PySide, and IPython
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will now display correctly. Second, the 'best' placement of a legend has
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been improved in the presence of NANs.
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See the :ref:`sphx_glr_tutorials_02_intermediate_legend_guide.py` for more detailed explanation and
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See the :ref:`sphx_glr_tutorials_intermediate_legend_guide.py` for more detailed explanation and
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examples.
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.. figure:: ../../gallery/text_labels_and_annotations/images/sphx_glr_legend_demo_004.png
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other colormaps :ref:`here <color-colormaps_reference>`.
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* Many bug fixes and documentation improvements.
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‎doc/users/prev_whats_new/whats_new_1.4.rst

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Legend and PathEffects documentation
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------------------------------------
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The :ref:`sphx_glr_tutorials_02_intermediate_legend_guide.py` and :ref:`sphx_glr_tutorials_03_advanced_patheffects_guide.py` have both been
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The :ref:`sphx_glr_tutorials_intermediate_legend_guide.py` and :ref:`sphx_glr_tutorials_advanced_patheffects_guide.py` have both been
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updated to better reflect the full potential of each of these powerful
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features.
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GAE integration
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---------------
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Matplotlib will now run on google app engine.
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‎doc/users/shell.rst

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With the TkAgg backend, which uses the Tkinter user interface toolkit,
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you can use matplotlib from an arbitrary non-gui python shell. Just set your
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``backend : TkAgg`` and ``interactive : True`` in your
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:file:`matplotlibrc` file (see :ref:`sphx_glr_tutorials_01_introductory_customizing.py`) and fire
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:file:`matplotlibrc` file (see :ref:`sphx_glr_tutorials_introductory_customizing.py`) and fire
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up python. Then::
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>>> from pylab import *

‎lib/matplotlib/legend.py

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Most users would normally create a legend via the
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:meth:`~matplotlib.axes.Axes.legend` function. For more details on legends
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there is also a :ref:`legend guide
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<sphx_glr_tutorials_02_intermediate_legend_guide.py>`.
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<sphx_glr_tutorials_intermediate_legend_guide.py>`.
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The Legend class can be considered as a container of legend handles
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and legend texts. Creation of corresponding legend handles from the
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Note that not all kinds of artist are supported by the legend yet by default
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but it is possible to extend the legend handler's capabilities to support
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arbitrary objects. See the :ref:`legend guide
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<sphx_glr_tutorials_02_intermediate_legend_guide.py>` for more information.
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<sphx_glr_tutorials_intermediate_legend_guide.py>` for more information.
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"""
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from __future__ import (absolute_import, division, print_function,

‎lib/matplotlib/legend_handler.py

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This module defines default legend handlers.
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It is strongly encouraged to have read the :ref:`legend guide
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<sphx_glr_tutorials_02_intermediate_legend_guide.py>` before this documentation.
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<sphx_glr_tutorials_intermediate_legend_guide.py>` before this documentation.
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Legend handlers are expected to be a callable object with a following
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signature. ::

‎tutorials/introductory/lifecycle.py

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The second is based on MATLAB and uses
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a state-based interface. This is encapsulated in the :mod:`pyplot`
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module. See the :ref:`pyplot tutorials
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<sphx_glr_tutorials_01_introductory_pyplot.py>`
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<sphx_glr_tutorials_introductory_pyplot.py>`
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for a more in-depth look at the pyplot interface.
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Most of the terms are straightforward but the main thing to remember
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# that we create. To do this we'll set the ``autolayout`` value of our
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# rcParams. For more information on controlling the style, layout, and
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# other features of plots with rcParams, see
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# :ref:`sphx_glr_tutorials_01_introductory_customizing.py`.
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# :ref:`sphx_glr_tutorials_introductory_customizing.py`.
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plt.rcParams.update({'figure.autolayout': True})
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‎tutorials/introductory/usage.py

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#
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#
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# #. The ``backend`` parameter in your ``matplotlibrc`` file (see
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# :ref:`sphx_glr_tutorials_01_introductory_customizing.py`)::
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# :ref:`sphx_glr_tutorials_introductory_customizing.py`)::
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#
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# backend : WXAgg # use wxpython with antigrain (agg) rendering
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#
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# that are called, and on a state variable that determines whether
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# matplotlib is in "interactive mode". The default Boolean value is set
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# by the :file:`matplotlibrc` file, and may be customized like any other
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# configuration parameter (see :ref:`sphx_glr_tutorials_01_introductory_customizing.py`). It
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# configuration parameter (see :ref:`sphx_glr_tutorials_introductory_customizing.py`). It
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# may also be set via :func:`matplotlib.interactive`, and its
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# value may be queried via :func:`matplotlib.is_interactive`. Turning
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# interactive mode on and off in the middle of a stream of plotting

‎tutorials/text/mathtext.py

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# (from (La)TeX), `STIX <http://www.stixfonts.org/>`_ fonts (with are designed
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# to blend well with Times), or a Unicode font that you provide. The mathtext
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# font can be selected with the customization variable ``mathtext.fontset`` (see
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# :ref:`sphx_glr_tutorials_01_introductory_customizing.py`)
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# :ref:`sphx_glr_tutorials_introductory_customizing.py`)
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#
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# .. note::
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# On `"narrow" <http://wordaligned.org/articles/narrow-python>`_ builds

‎tutorials/text/usetex.py

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external dependencies must all be located on your :envvar:`PATH`.
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There are a couple of options to mention, which can be changed using :ref:`rc
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settings <sphx_glr_tutorials_01_introductory_customizing.py>`. Here is an example matplotlibrc file::
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settings <sphx_glr_tutorials_introductory_customizing.py>`. Here is an example matplotlibrc file::
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font.family : serif
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font.serif : Times, Palatino, New Century Schoolbook, Bookman, Computer Modern Roman

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