8000 Merge remote-tracking branch 'matplotlib/v2.1.0-doc' into v2.1.x · matplotlib/matplotlib@58883c5 · GitHub
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Merge remote-tracking branch 'matplotlib/v2.1.0-doc' into v2.1.x
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‎doc/_templates/autofunctions.rst

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{{ fullname | escape | underline }}
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.. currentmodule:: {{ fullname }}
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.. automodule:: {{ fullname }}
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:no-members:
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{% block functions %}
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{% if functions %}
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Functions
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---------
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.. autosummary::
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.. autosummary::
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:template: autosummary.rst
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:toctree:
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{% for item in functions %}{% if item not in ['plotting', 'colormaps'] %}

‎doc/_templates/citing.html

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<h1>Citing Matplotlib</h1>
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<p>
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If Matplotlib contributes to a project that leads to a scientific publication,
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please acknowledge this fact by citing the project. You can use this
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BibTeX entry:
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please acknowledge this fact by citing
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<a href ='http://doi.org/10.1109/MCSE.2007.55'>Hunter et al (2007)</a> using this ready-made BibTeX entry:
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</p>
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<pre>
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@Article{Hunter:2007,

‎doc/users/index.rst

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intro.rst
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installing.rst
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../tutorials/index.rst
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interactive.rst
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whats_new.rst
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github_stats.rst

‎examples/animation/movie_demo_sgskip.py

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from __future__ import print_function
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import os
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import subprocess
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import matplotlib.pyplot as plt
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import numpy as np

‎examples/event_handling/close_event.py

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Close Event
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===========
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Example to show connecting events that occur when the figure closes.
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"""
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from __future__ import print_function
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import matplotlib.pyplot as plt

‎examples/event_handling/data_browser.py

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Data Browser
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============
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Connecting data between multiple canvases.
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This example covers how to interact data with multiple canvases. This
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let's you select and highlight a point on one axis, and generating the
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data of that point on the other axis.
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"""
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import numpy as np
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‎examples/event_handling/looking_glass.py

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Looking Glass
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=============
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Example using mouse events to simulate a looking glass for inspecting data.
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"""
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import numpy as np
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import matplotlib.pyplot as plt

‎examples/event_handling/path_editor.py

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Path Editor
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===========
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Sharing events across GUIs.
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This example demonstrates a cross-GUI application using Matplotlib event
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handling to interact with and modify objects on the canvas.
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"""
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import numpy as np
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import matplotlib.path as mpath

‎lib/matplotlib/legend_handler.py

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legend_handler(legend, orig_handle, fontsize, handlebox)
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Where *legend* is the legend itself, *orig_handle* is the original
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plot, *fontsize* is the fontsize in pixles, and *handlebox* is a
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plot, *fontsize* is the fontsize in pixels, and *handlebox* is a
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OffsetBox instance. Within the call, you should create relevant
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artists (using relevant properties from the *legend* and/or
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*orig_handle*) and add them into the handlebox. The artists needs to

‎lib/matplotlib/patches.py

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def _update_patch_transform(self):
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"""NOTE: This cannot be called until after this has been added
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to an Axes, otherwise unit conversion will fail. This
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maxes it very important to call the accessor method and
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makes it very important to call the accessor method and
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not directly access the transformation member variable.
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"""
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x = self.convert_xunits(self._x)
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def _recompute_transform(self):
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"""NOTE: This cannot be called until after this has been added
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to an Axes, otherwise unit conversion will fail. This
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maxes it very important to call the accessor method and
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makes it very important to call the accessor method and
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not directly access the transformation member variable.
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"""
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center = (self.convert_xunits(self.center[0]),

‎lib/matplotlib/spines.py

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def _recompute_transform(self):
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"""NOTE: This cannot be called until after this has been added
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to an Axes, otherwise unit conversion will fail. This
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maxes it very important to call the accessor method and
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makes it very important to call the accessor method and
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not directly access the transformation member variable.
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"""
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assert self._patch_type in ('arc', 'circle')

‎tutorials/advanced/README.txt

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.. _tutorials-advanced:
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Advanced
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========
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--------
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These tutorials cover advanced topics for experienced Matplotlib
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users and developers.

‎tutorials/advanced/transforms_tutorial.py

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Transformations Tutorial
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========================
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Transforming visuals in Matplotlib.
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Like any graphics packages, matplotlib is built on top of a
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Like any graphics packages, Matplotlib is built on top of a
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transformation framework to easily move between coordinate systems,
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the userland `data` coordinate system, the `axes` coordinate system,
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the `figure` coordinate system, and the `display` coordinate system.
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In 95% of your plotting, you won't need to think about this, as it
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happens under the hood, but as you push the limits of custom figure
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generation, it helps to have an understanding of these objects so you
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can reuse the existing transformations matplotlib makes available to
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can reuse the existing transformations Matplotlib makes available to
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you, or create your own (see :mod:`matplotlib.transforms`). The table
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below summarizes the existing coordinate systems, the transformation
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below summarizes the some useful coordinate systems, the transformation
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object you should use to work in that coordinate system, and the
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description of that system. In the `Transformation Object` column,
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``ax`` is a :class:`~matplotlib.axes.Axes` instance, and ``fig`` is a
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:class:`~matplotlib.figure.Figure` instance.
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========== ===================== ====================================================================================
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Coordinate Transformation Object Description
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========== ===================== ====================================================================================
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`data` ``ax.transData`` The userland data coordinate system, controlled by the xlim and ylim
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`axes` ``ax.transAxes`` The coordinate system of the :class:`~matplotlib.axes.Axes`; (0, 0) is
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bottom left of the axes, and (1, 1) is top right of the axes.
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`figure` ``fig.transFigure`` The coordinate system of the :class:`~matplotlib.figure.Figure`; (0, 0)
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is bottom left of the figure, and (1, 1) is top right of the figure.
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`display` `None` This is the pixel coordinate system of the display; (0, 0) is the bottom
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left of the display, and (width, height) is the top right of the display in pixels.
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Alternatively, the identity transform
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(:class:`matplotlib.transforms.IdentityTransform()`) may be used instead of None.
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========== ===================== ====================================================================================
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=========== ============================= =====================================
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Coordinates Transformation object Description
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=========== ============================= =====================================
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"data" ``ax.transData```` The coordinate system for the data,
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controlled by xlim and ylim.
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"axes" ``ax.transAxes`` The coordinate system of the
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`~.Axes`; (0, 0) is bottom left of
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the axes, and (1, 1) is top right of
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the axes.
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"figure" ``fig.transFigure`` The coordinate system of the
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`~.Figure`; (0, 0) is bottom left of
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the figure, and (1, 1) is top right
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of the figure.
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"display" ``None``, or The pixel coordinate system of the
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``IdentityTransform()`` display; (0, 0) is bottom left of the
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display, and (width, height) is top
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right of the display in pixels.
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"xaxis", ``ax.get_xaxis_transform()``, Blended coordinate systems; use data
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"yaxis" ``ax.get_yaxis_transform()`` coordinates on one of the axis and
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axes coordinates on the other.
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=========== ============================= =====================================
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All of the transformation objects in the table above take inputs in
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their coordinate system, and transform the input to the `display`
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================
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Let's start with the most commonly used coordinate, the `data`
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coordinate system. Whenever you add data to the axes, matplotlib
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coordinate system. Whenever you add data to the axes, Matplotlib
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updates the datalimits, most commonly updated with the
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:meth:`~matplotlib.axes.Axes.set_xlim` and
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:meth:`~matplotlib.axes.Axes.set_ylim` methods. For example, in the
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# The final piece is the ``self.transScale`` attribute, which is
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# responsible for the optional non-linear scaling of the data, e.g., for
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# logarithmic axes. When an Axes is initially setup, this is just set to
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# the identity transform, since the basic matplotlib axes has linear
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# the identity transform, since the basic Matplotlib axes has linear
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# scale, but when you call a logarithmic scaling function like
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# :meth:`~matplotlib.axes.Axes.semilogx` or explicitly set the scale to
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# logarithmic with :meth:`~matplotlib.axes.Axes.set_xscale`, then the
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# data, to a separable Cartesian coordinate system. There are several
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# projection examples in the ``matplotlib.projections`` package, and the
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# best way to learn more is to open the source for those packages and
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# see how to make your own, since matplotlib supports extensible axes
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# see how to make your own, since Matplotlib supports extensible axes
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# and projections. Michael Droettboom has provided a nice tutorial
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# example of creating a hammer projection axes; see
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# example of creating a Hammer projection axes; see
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# :ref:`sphx_glr_gallery_api_custom_projection_example.py`.

‎tutorials/colors/README.txt

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.. _tutorials-colors:
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Colors
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======
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------
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Matplotlib has support for visualizing information with a wide array
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of colors and colormaps. These tutorials cover the basics of how

‎tutorials/intermediate/README.txt

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.. _tutorials-intermediate:
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Intermediate
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============
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------------
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These tutorials cover some of the more complicated classes and functions
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in Matplotlib. They can be useful for particular custom and complex

‎tutorials/introductory/README.txt

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.. _tutorials-introductory:
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Introductory
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============
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-------------
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These tutorials cover the basics of creating visualizations with
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Matplotlib, as well as some best-practices in using the package

‎tutorials/introductory/usage.py

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# ------------------------------
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#
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# This is what you think of as 'a plot', it is the region of the image
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# with the data space (marked as the inner blue box). A given figure
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# with the data space. A given figure
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# can contain many Axes, but a given :class:`~matplotlib.axes.Axes`
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# object can only be in one :class:`~matplotlib.figure.Figure`. The
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# Axes contains two (or three in the case of 3D)
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# :class:`~matplotlib.axis.Axis`
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# ------------------------------
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#
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# These are the number-line-like objects (circled in green). They take
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# These are the number-line-like objects. They take
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# care of setting the graph limits and generating the ticks (the marks
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# on the axis) and ticklabels (strings labeling the ticks). The
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# location of the ticks is determined by a

‎tutorials/text/README.txt

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.. _tutorials-text:
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Text
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----
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matplotlib has extensive text support, including support for
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mathematical expressions, truetype support for raster and

‎tutorials/toolkits/README.txt

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.. _tutorials-toolkits:
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Toolkits
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These tutorials cover toolkits designed to extend the functionality
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of Matplotlib in order to accomplish specific goals.

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