diff --git a/.gitattributes b/.gitattributes new file mode 100644 index 000000000000..176a458f94e0 --- /dev/null +++ b/.gitattributes @@ -0,0 +1 @@ +* text=auto diff --git a/.gitignore b/.gitignore new file mode 100644 index 000000000000..f798098a0095 --- /dev/null +++ b/.gitignore @@ -0,0 +1,81 @@ +# Editor temporary/working/backup files # +######################################### +.#* +[#]*# +*~ +*$ +*.bak +*.diff +*.org +.project +*.rej +.settings/ +.*.sw[nop] +.sw[nop] +*.tmp + +# Compiled source # +################### +*.a +*.com +*.class +*.dll +*.exe +*.o +*.py[ocd] +*.so + +# Packages # +############ +# it's better to unpack these files and commit the raw source +# git has its own built in compression methods +*.7z +*.bz2 +*.bzip2 +*.dmg +*.iso +*.jar +*.rar +*.tar +*.tbz2 +*.tgz +*.zip + +# Python files # +################ +# setup.py working directory +build +# sphinx build directory +_build +# setup.py dist directory +dist +doc/build +doc/cdoc/build +# Egg metadata +*.egg-info +# The shelf plugin uses this dir +./.shelf + +# Logs and databases # +###################### +*.log +*.sql +*.sqlite + +# Patches # +########### +*.patch +*.diff + +# OS generated files # +###################### +.gdb_history +.DS_Store? +ehthumbs.db +Icon? +Thumbs.db + +# Things specific to this project # +################################### +lib/matplotlib/mpl-data/matplotlib.conf +lib/matplotlib/mpl-data/matplotlibrc diff --git a/doc/devel/add_new_projection.rst b/doc/devel/add_new_projection.rst index 2a7943010800..5efbead7fc40 100644 --- a/doc/devel/add_new_projection.rst +++ b/doc/devel/add_new_projection.rst @@ -1,134 +1,134 @@ -.. _adding-new-scales: - -*********************************************** -Adding new scales and projections to matplotlib -*********************************************** - -.. ::author Michael Droettboom - -Matplotlib supports the addition of custom procedures that transform -the data before it is displayed. - -There is an important distinction between two kinds of -transformations. Separable transformations, working on a single -dimension, are called "scales", and non-separable transformations, -that handle data in two or more dimensions at a time, are called -"projections". - -From the user's perspective, the scale of a plot can be set with -:meth:`~matplotlib.axes.Axes.set_xscale` and -:meth:`~matplotlib.axes.Axes.set_xscale`. Projections can be chosen -using the ``projection`` keyword argument to the -:func:`~matplotlib.pylab.plot` or :func:`~matplotlib.pylab.subplot` -functions, e.g.:: - - plot(x, y, projection="custom") - -This document is intended for developers and advanced users who need -to create new scales and projections for matplotlib. The necessary -code for scales and projections can be included anywhere: directly -within a plot script, in third-party code, or in the matplotlib source -tree itself. - -.. _creating-new-scale: - -Creating a new scale -==================== - -Adding a new scale consists of defining a subclass of -:class:`matplotlib.scale.ScaleBase`, that includes the following -elements: - - - A transformation from data coordinates into display coordinates. - - - An inverse of that transformation. This is used, for example, to - convert mouse positions from screen space back into data space. - - - A function to limit the range of the axis to acceptable values - (``limit_range_for_scale()``). A log scale, for instance, would - prevent the range from including values less than or equal to - zero. - - - Locators (major and minor) that determine where to place ticks in - the plot, and optionally, how to adjust the limits of the plot to - some "good" values. Unlike ``limit_range_for_scale()``, which is - always enforced, the range setting here is only used when - automatically setting the range of the plot. - - - Formatters (major and minor) that specify how the tick labels - should be drawn. - -Once the class is defined, it must be registered with matplotlib so -that the user can select it. - -A full-fledged and heavily annotated example is in -:file:`examples/api/custom_scale_example.py`. There are also some classes -in :mod:`matplotlib.scale` that may be used as starting points. - - -.. _creating-new-projection: - -Creating a new projection -========================= - -Adding a new projection consists of defining a subclass of -:class:`matplotlib.axes.Axes`, that includes the following elements: - - - A transformation from data coordinates into display coordinates. - - - An inverse of that transformation. This is used, for example, to - convert mouse positions from screen space back into data space. - - - Transformations for the gridlines, ticks and ticklabels. Custom - projections will often need to place these elements in special - locations, and matplotlib has a facility to help with doing so. - - - Setting up default values (overriding - :meth:`~matplotlib.axes.Axes.cla`), since the defaults for a - rectilinear axes may not be appropriate. - - - Defining the shape of the axes, for example, an elliptical axes, - that will be used to draw the background of the plot and for - clipping any data elements. - - - Defining custom locators and formatters for the projection. For - example, in a geographic projection, it may be more convenient to - display the grid in degrees, even if the data is in radians. - - - Set up interactive panning and zooming. This is left as an - "advanced" feature left to the reader, but there is an example of - this for polar plots in :mod:`matplotlib.projections.polar`. - - - Any additional methods for additional convenience or features. - -Once the class is defined, it must be registered with matplotlib -so that the user can select it. - -A full-fledged and heavily annotated example is in -:file:`examples/api/custom_projection_example.py`. The polar plot -functionality in :mod:`matplotlib.projections.polar` may also be of -interest. - -API documentation -================= - -matplotlib.scale ----------------- - -.. automodule:: matplotlib.scale - :members: - :show-inheritance: - -matplotlib.projections ----------------------- - -.. automodule:: matplotlib.projections - :members: - :show-inheritance: - -matplotlib.projections.polar -~~~~~~~~~~~~~~~~~~~~~~~~~~~~ - -.. automodule:: matplotlib.projections.polar - :members: - :show-inheritance: +.. _adding-new-scales: + +*********************************************** +Adding new scales and projections to matplotlib +*********************************************** + +.. ::author Michael Droettboom + +Matplotlib supports the addition of custom procedures that transform +the data before it is displayed. + +There is an important distinction between two kinds of +transformations. Separable transformations, working on a single +dimension, are called "scales", and non-separable transformations, +that handle data in two or more dimensions at a time, are called +"projections". + +From the user's perspective, the scale of a plot can be set with +:meth:`~matplotlib.axes.Axes.set_xscale` and +:meth:`~matplotlib.axes.Axes.set_xscale`. Projections can be chosen +using the ``projection`` keyword argument to the +:func:`~matplotlib.pylab.plot` or :func:`~matplotlib.pylab.subplot` +functions, e.g.:: + + plot(x, y, projection="custom") + +This document is intended for developers and advanced users who need +to create new scales and projections for matplotlib. The necessary +code for scales and projections can be included anywhere: directly +within a plot script, in third-party code, or in the matplotlib source +tree itself. + +.. _creating-new-scale: + +Creating a new scale +==================== + +Adding a new scale consists of defining a subclass of +:class:`matplotlib.scale.ScaleBase`, that includes the following +elements: + + - A transformation from data coordinates into display coordinates. + + - An inverse of that transformation. This is used, for example, to + convert mouse positions from screen space back into data space. + + - A function to limit the range of the axis to acceptable values + (``limit_range_for_scale()``). A log scale, for instance, would + prevent the range from including values less than or equal to + zero. + + - Locators (major and minor) that determine where to place ticks in + the plot, and optionally, how to adjust the limits of the plot to + some "good" values. Unlike ``limit_range_for_scale()``, which is + always enforced, the range setting here is only used when + automatically setting the range of the plot. + + - Formatters (major and minor) that specify how the tick labels + should be drawn. + +Once the class is defined, it must be registered with matplotlib so +that the user can select it. + +A full-fledged and heavily annotated example is in +:file:`examples/api/custom_scale_example.py`. There are also some classes +in :mod:`matplotlib.scale` that may be used as starting points. + + +.. _creating-new-projection: + +Creating a new projection +========================= + +Adding a new projection consists of defining a subclass of +:class:`matplotlib.axes.Axes`, that includes the following elements: + + - A transformation from data coordinates into display coordinates. + + - An inverse of that transformation. This is used, for example, to + convert mouse positions from screen space back into data space. + + - Transformations for the gridlines, ticks and ticklabels. Custom + projections will often need to place these elements in special + locations, and matplotlib has a facility to help with doing so. + + - Setting up default values (overriding + :meth:`~matplotlib.axes.Axes.cla`), since the defaults for a + rectilinear axes may not be appropriate. + + - Defining the shape of the axes, for example, an elliptical axes, + that will be used to draw the background of the plot and for + clipping any data elements. + + - Defining custom locators and formatters for the projection. For + example, in a geographic projection, it may be more convenient to + display the grid in degrees, even if the data is in radians. + + - Set up interactive panning and zooming. This is left as an + "advanced" feature left to the reader, but there is an example of + this for polar plots in :mod:`matplotlib.projections.polar`. + + - Any additional methods for additional convenience or features. + +Once the class is defined, it must be registered with matplotlib +so that the user can select it. + +A full-fledged and heavily annotated example is in +:file:`examples/api/custom_projection_example.py`. The polar plot +functionality in :mod:`matplotlib.projections.polar` may also be of +interest. + +API documentation +================= + +matplotlib.scale +---------------- + +.. automodule:: matplotlib.scale + :members: + :show-inheritance: + +matplotlib.projections +---------------------- + +.. automodule:: matplotlib.projections + :members: + :show-inheritance: + +matplotlib.projections.polar +~~~~~~~~~~~~~~~~~~~~~~~~~~~~ + +.. automodule:: matplotlib.projections.polar + :members: + :show-inheritance: diff --git a/doc/users/artists.rst b/doc/users/artists.rst index b49c79a3f384..03c0067ade28 100644 --- a/doc/users/artists.rst +++ b/doc/users/artists.rst @@ -1,643 +1,643 @@ -.. _artist-tutorial: - -*************** -Artist tutorial -*************** - -There are three layers to the matplotlib API. The -:class:`matplotlib.backend_bases.FigureCanvas` is the area onto which -the figure is drawn, the :class:`matplotlib.backend_bases.Renderer` is -the object which knows how to draw on the -:class:`~matplotlib.backend_bases.FigureCanvas`, and the -:class:`matplotlib.artist.Artist` is the object that knows how to use -a renderer to paint onto the canvas. The -:class:`~matplotlib.backend_bases.FigureCanvas` and -:class:`~matplotlib.backend_bases.Renderer` handle all the details of -talking to user interface toolkits like `wxPython -`_ or drawing languages like PostScript®, and -the ``Artist`` handles all the high level constructs like representing -and laying out the figure, text, and lines. The typical user will -spend 95% of his time working with the ``Artists``. - -There are two types of ``Artists``: primitives and containers. The -primitives represent the standard graphical objects we want to paint -onto our canvas: :class:`~matplotlib.lines.Line2D`, -:class:`~matplotlib.patches.Rectangle`, -:class:`~matplotlib.text.Text`, :class:`~matplotlib.image.AxesImage`, -etc., and the containers are places to put them -(:class:`~matplotlib.axis.Axis`, :class:`~matplotlib.axes.Axes` and -:class:`~matplotlib.figure.Figure`). The standard use is to create a -:class:`~matplotlib.figure.Figure` instance, use the ``Figure`` to -create one or more :class:`~matplotlib.axes.Axes` or -:class:`~matplotlib.axes.Subplot` instances, and use the ``Axes`` -instance helper methods to create the primitives. In the example -below, we create a ``Figure`` instance using -:func:`matplotlib.pyplot.figure`, which is a convenience method for -instantiating ``Figure`` instances and connecting them with your user -interface or drawing toolkit ``FigureCanvas``. As we will discuss -below, this is not necessary, and you can work directly with -PostScript, PDF Gtk+, or wxPython ``FigureCanvas`` instances. For -example, instantiate your ``Figures`` directly and connect them -yourselves, but since we are focusing here on the ``Artist`` API we'll let -:mod:`~matplotlib.pyplot` handle some of those details for us:: - - import matplotlib.pyplot as plt - fig = plt.figure() - ax = fig.add_subplot(2,1,1) # two rows, one column, first plot - -The :class:`~matplotlib.axes.Axes` is probably the most important -class in the matplotlib API, and the one you will be working with most -of the time. This is because the ``Axes`` is the plotting area into -which most of the objects go, and the ``Axes`` has many special helper -methods (:meth:`~matplotlib.axes.Axes.plot`, -:meth:`~matplotlib.axes.Axes.text`, -:meth:`~matplotlib.axes.Axes.hist`, -:meth:`~matplotlib.axes.Axes.imshow`) to create the most common -graphics primitives (:class:`~matplotlib.lines.Line2D`, -:class:`~matplotlib.text.Text`, -:class:`~matplotlib.patches.Rectangle`, -:class:`~matplotlib.image.Image`, respectively). These helper methods -will take your data (eg. ``numpy`` arrays and strings) create -primitive ``Artist`` instances as needed (eg. ``Line2D``), add them to -the relevant containers, and draw them when requested. Most of you -are probably familiar with the :class:`~matplotlib.axes.Subplot`, -which is just a special case of an ``Axes`` that lives on a regular -rows by columns grid of ``Subplot`` instances. If you want to create -an ``Axes`` at an arbitrary location, simply use the -:meth:`~matplotlib.figure.Figure.add_axes` method which takes a list -of ``[left, bottom, width, height]`` values in 0-1 relative figure -coordinates:: - - fig2 = plt.figure() - ax2 = fig2.add_axes([0.15, 0.1, 0.7, 0.3]) - -Continuing with our example:: - - import numpy as np - t = np.arange(0.0, 1.0, 0.01) - s = np.sin(2*np.pi*t) - line, = ax.plot(t, s, color='blue', lw=2) - -In this example, ``ax`` is the ``Axes`` instance created by the -``fig.add_subplot`` call above (remember ``Subplot`` is just a -subclass of ``Axes``) and when you call ``ax.plot``, it creates a -``Line2D`` instance and adds it to the :attr:`Axes.lines -` list. In the interactive `ipython -`_ session below, you can see that the -``Axes.lines`` list is length one and contains the same line that was -returned by the ``line, = ax.plot(x, y, 'o')`` call: - -.. sourcecode:: ipython - - In [101]: ax.lines[0] - Out[101]: - - In [102]: line - Out[102]: - -If you make subsequent calls to ``ax.plot`` (and the hold state is "on" -which is the default) then additional lines will be added to the list. -You can remove lines later simply by calling the list methods; either -of these will work:: - - del ax.lines[0] - ax.lines.remove(line) # one or the other, not both! - -The Axes also has helper methods to configure and decorate the x-axis -and y-axis tick, tick labels and axis labels:: - - xtext = ax.set_xlabel('my xdata') # returns a Text instance - ytext = ax.set_ylabel('my xdata') - -When you call :meth:`ax.set_xlabel `, -it passes the information on the :class:`~matplotlib.text.Text` -instance of the :class:`~matplotlib.axis.XAxis`. Each ``Axes`` -instance contains an :class:`~matplotlib.axis.XAxis` and a -:class:`~matplotlib.axis.YAxis` instance, which handle the layout and -drawing of the ticks, tick labels and axis labels. - -.. I'm commenting this out, since the new Sphinx cross-references -.. sort of take care of this above - MGD - -.. Here are the most important matplotlib modules that contain the -.. classes referenced above - -.. =============== ================== -.. Artist Module -.. =============== ================== -.. Artist matplotlib.artist -.. Rectangle matplotlib.patches -.. Line2D matplotlib.lines -.. Axes matplotlib.axes -.. XAxis and YAxis matplotlib.axis -.. Figure matplotlib.figure -.. Text matplotlib.text -.. =============== ================== - -Try creating the figure below. - -.. plot:: pyplots/fig_axes_labels_simple.py - -.. _customizing-artists: - -Customizing your objects -======================== - -Every element in the figure is represented by a matplotlib -:class:`~matplotlib.artist.Artist`, and each has an extensive list of -properties to configure its appearance. The figure itself contains a -:class:`~matplotlib.patches.Rectangle` exactly the size of the figure, -which you can use to set the background color and transparency of the -figures. Likewise, each :class:`~matplotlib.axes.Axes` bounding box -(the standard white box with black edges in the typical matplotlib -plot, has a ``Rectangle`` instance that determines the color, -transparency, and other properties of the Axes. These instances are -stored as member variables :attr:`Figure.patch -` and :attr:`Axes.patch -` ("Patch" is a name inherited from -MATLAB™, and is a 2D "patch" of color on the figure, eg. rectangles, -circles and polygons). Every matplotlib ``Artist`` has the following -properties - -========== ====================================================================== -Property Description -========== ====================================================================== -alpha The transparency - a scalar from 0-1 -animated A boolean that is used to facilitate animated drawing -axes The axes that the Artist lives in, possibly None -clip_box The bounding box that clips the Artist -clip_on Whether clipping is enabled -clip_path The path the artist is clipped to -contains A picking function to test whether the artist contains the pick point -figure The figure instance the artist lives in, possibly None -label A text label (eg. for auto-labeling) -picker A python object that controls object picking -transform The transformation -visible A boolean whether the artist should be drawn -zorder A number which determines the drawing order -========== ====================================================================== - -Each of the properties is accessed with an old-fashioned setter or -getter (yes we know this irritates Pythonistas and we plan to support -direct access via properties or traits but it hasn't been done yet). -For example, to multiply the current alpha by a half:: - - a = o.get_alpha() - o.set_alpha(0.5*a) - -If you want to set a number of properties at once, you can also use -the ``set`` method with keyword arguments. For example:: - - o.set(alpha=0.5, zorder=2) - -If you are working interactively at the python shell, a handy way to -inspect the ``Artist`` properties is to use the -:func:`matplotlib.artist.getp` function (simply -:func:`~matplotlib.pylab.getp` in pylab), which lists the properties -and their values. This works for classes derived from ``Artist`` as -well, eg. ``Figure`` and ``Rectangle``. Here are the ``Figure`` rectangle -properties mentioned above: - -.. sourcecode:: ipython - - In [149]: matplotlib.artist.getp(fig.patch) - alpha = 1.0 - animated = False - antialiased or aa = True - axes = None - clip_box = None - clip_on = False - clip_path = None - contains = None - edgecolor or ec = w - facecolor or fc = 0.75 - figure = Figure(8.125x6.125) - fill = 1 - hatch = None - height = 1 - label = - linewidth or lw = 1.0 - picker = None - transform = - verts = ((0, 0), (0, 1), (1, 1), (1, 0)) - visible = True - width = 1 - window_extent = - x = 0 - y = 0 - zorder = 1 - -.. TODO: Update these URLs - -The docstrings for all of the classes also contain the ``Artist`` -properties, so you can consult the interactive "help" or the -:ref:`artist-api` for a listing of properties for a given object. - -.. _object-containers: - -Object containers -================= - - -Now that we know how to inspect and set the properties of a given -object we want to configure, we need to now how to get at that object. -As mentioned in the introduction, there are two kinds of objects: -primitives and containers. The primitives are usually the things you -want to configure (the font of a :class:`~matplotlib.text.Text` -instance, the width of a :class:`~matplotlib.lines.Line2D`) although -the containers also have some properties as well -- for example the -:class:`~matplotlib.axes.Axes` :class:`~matplotlib.artist.Artist` is a -container that contains many of the primitives in your plot, but it -also has properties like the ``xscale`` to control whether the xaxis -is 'linear' or 'log'. In this section we'll review where the various -container objects store the ``Artists`` that you want to get at. - -.. _figure-container: - -Figure container -================ - -The top level container ``Artist`` is the -:class:`matplotlib.figure.Figure`, and it contains everything in the -figure. The background of the figure is a -:class:`~matplotlib.patches.Rectangle` which is stored in -:attr:`Figure.patch `. As -you add subplots (:meth:`~matplotlib.figure.Figure.add_subplot`) and -axes (:meth:`~matplotlib.figure.Figure.add_axes`) to the figure -these will be appended to the :attr:`Figure.axes -`. These are also returned by the -methods that create them: - -.. sourcecode:: ipython - - In [156]: fig = plt.figure() - - In [157]: ax1 = fig.add_subplot(211) - - In [158]: ax2 = fig.add_axes([0.1, 0.1, 0.7, 0.3]) - - In [159]: ax1 - Out[159]: - - In [160]: print fig.axes - [, ] - -Because the figure maintains the concept of the "current axes" (see -:meth:`Figure.gca ` and -:meth:`Figure.sca `) to support the -pylab/pyplot state machine, you should not insert or remove axes -directly from the axes list, but rather use the -:meth:`~matplotlib.figure.Figure.add_subplot` and -:meth:`~matplotlib.figure.Figure.add_axes` methods to insert, and the -:meth:`~matplotlib.figure.Figure.delaxes` method to delete. You are -free however, to iterate over the list of axes or index into it to get -access to ``Axes`` instances you want to customize. Here is an -example which turns all the axes grids on:: - - for ax in fig.axes: - ax.grid(True) - - -The figure also has its own text, lines, patches and images, which you -can use to add primitives directly. The default coordinate system for -the ``Figure`` will simply be in pixels (which is not usually what you -want) but you can control this by setting the transform property of -the ``Artist`` you are adding to the figure. - -.. TODO: Is that still true? - -More useful is "figure coordinates" where (0, 0) is the bottom-left of -the figure and (1, 1) is the top-right of the figure which you can -obtain by setting the ``Artist`` transform to :attr:`fig.transFigure -`: - -.. sourcecode:: ipython - - In [191]: fig = plt.figure() - - In [192]: l1 = matplotlib.lines.Line2D([0, 1], [0, 1], - transform=fig.transFigure, figure=fig) - - In [193]: l2 = matplotlib.lines.Line2D([0, 1], [1, 0], - transform=fig.transFigure, figure=fig) - - In [194]: fig.lines.extend([l1, l2]) - - In [195]: fig.canvas.draw() - -.. plot:: pyplots/fig_x.py - - -Here is a summary of the Artists the figure contains - -.. TODO: Add xrefs to this table - -================ =============================================================== -Figure attribute Description -================ =============================================================== -axes A list of Axes instances (includes Subplot) -patch The Rectangle background -images A list of FigureImages patches - useful for raw pixel display -legends A list of Figure Legend instances (different from Axes.legends) -lines A list of Figure Line2D instances (rarely used, see Axes.lines) -patches A list of Figure patches (rarely used, see Axes.patches) -texts A list Figure Text instances -================ =============================================================== - -.. _axes-container: - -Axes container -============== - -The :class:`matplotlib.axes.Axes` is the center of the matplotlib -universe -- it contains the vast majority of all the ``Artists`` used -in a figure with many helper methods to create and add these -``Artists`` to itself, as well as helper methods to access and -customize the ``Artists`` it contains. Like the -:class:`~matplotlib.figure.Figure`, it contains a -:class:`~matplotlib.patches.Patch` -:attr:`~matplotlib.axes.Axes.patch` which is a -:class:`~matplotlib.patches.Rectangle` for Cartesian coordinates and a -:class:`~matplotlib.patches.Circle` for polar coordinates; this patch -determines the shape, background and border of the plotting region:: - - ax = fig.add_subplot(111) - rect = ax.patch # a Rectangle instance - rect.set_facecolor('green') - -When you call a plotting method, eg. the canonical -:meth:`~matplotlib.axes.Axes.plot` and pass in arrays or lists of -values, the method will create a :meth:`matplotlib.lines.Line2D` -instance, update the line with all the ``Line2D`` properties passed as -keyword arguments, add the line to the :attr:`Axes.lines -` container, and returns it to you: - -.. sourcecode:: ipython - - In [213]: x, y = np.random.rand(2, 100) - - In [214]: line, = ax.plot(x, y, '-', color='blue', linewidth=2) - -``plot`` returns a list of lines because you can pass in multiple x, y -pairs to plot, and we are unpacking the first element of the length -one list into the line variable. The line has been added to the -``Axes.lines`` list: - -.. sourcecode:: ipython - - In [229]: print ax.lines - [] - -Similarly, methods that create patches, like -:meth:`~matplotlib.axes.Axes.bar` creates a list of rectangles, will -add the patches to the :attr:`Axes.patches -` list: - -.. sourcecode:: ipython - - In [233]: n, bins, rectangles = ax.hist(np.random.randn(1000), 50, facecolor='yellow') - - In [234]: rectangles - Out[234]: - - In [235]: print len(ax.patches) - -You should not add objects directly to the ``Axes.lines`` or -``Axes.patches`` lists unless you know exactly what you are doing, -because the ``Axes`` needs to do a few things when it creates and adds -an object. It sets the figure and axes property of the ``Artist``, as -well as the default ``Axes`` transformation (unless a transformation -is set). It also inspects the data contained in the ``Artist`` to -update the data structures controlling auto-scaling, so that the view -limits can be adjusted to contain the plotted data. You can, -nonetheless, create objects yourself and add them directly to the -``Axes`` using helper methods like -:meth:`~matplotlib.axes.Axes.add_line` and -:meth:`~matplotlib.axes.Axes.add_patch`. Here is an annotated -interactive session illustrating what is going on: - -.. sourcecode:: ipython - - In [261]: fig = plt.figure() - - In [262]: ax = fig.add_subplot(111) - - # create a rectangle instance - In [263]: rect = matplotlib.patches.Rectangle( (1,1), width=5, height=12) - - # by default the axes instance is None - In [264]: print rect.get_axes() - None - - # and the transformation instance is set to the "identity transform" - In [265]: print rect.get_transform() - - - # now we add the Rectangle to the Axes - In [266]: ax.add_patch(rect) - - # and notice that the ax.add_patch method has set the axes - # instance - In [267]: print rect.get_axes() - Subplot(49,81.25) - - # and the transformation has been set too - In [268]: print rect.get_transform() - - - # the default axes transformation is ax.transData - In [269]: print ax.transData - - - # notice that the xlimits of the Axes have not been changed - In [270]: print ax.get_xlim() - (0.0, 1.0) - - # but the data limits have been updated to encompass the rectangle - In [271]: print ax.dataLim.get_bounds() - (1.0, 1.0, 5.0, 12.0) - - # we can manually invoke the auto-scaling machinery - In [272]: ax.autoscale_view() - - # and now the xlim are updated to encompass the rectangle - In [273]: print ax.get_xlim() - (1.0, 6.0) - - # we have to manually force a figure draw - In [274]: ax.figure.canvas.draw() - - -There are many, many ``Axes`` helper methods for creating primitive -``Artists`` and adding them to their respective containers. The table -below summarizes a small sampling of them, the kinds of ``Artist`` they -create, and where they store them - -============================== ==================== ======================= -Helper method Artist Container -============================== ==================== ======================= -ax.annotate - text annotations Annotate ax.texts -ax.bar - bar charts Rectangle ax.patches -ax.errorbar - error bar plots Line2D and Rectangle ax.lines and ax.patches -ax.fill - shared area Polygon ax.patches -ax.hist - histograms Rectangle ax.patches -ax.imshow - image data AxesImage ax.images -ax.legend - axes legends Legend ax.legends -ax.plot - xy plots Line2D ax.lines -ax.scatter - scatter charts PolygonCollection ax.collections -ax.text - text Text ax.texts -============================== ==================== ======================= - - -In addition to all of these ``Artists``, the ``Axes`` contains two -important ``Artist`` containers: the :class:`~matplotlib.axis.XAxis` -and :class:`~matplotlib.axis.YAxis`, which handle the drawing of the -ticks and labels. These are stored as instance variables -:attr:`~matplotlib.axes.Axes.xaxis` and -:attr:`~matplotlib.axes.Axes.yaxis`. The ``XAxis`` and ``YAxis`` -containers will be detailed below, but note that the ``Axes`` contains -many helper methods which forward calls on to the -:class:`~matplotlib.axis.Axis` instances so you often do not need to -work with them directly unless you want to. For example, you can set -the font size of the ``XAxis`` ticklabels using the ``Axes`` helper -method:: - - for label in ax.get_xticklabels(): - label.set_color('orange') - -Below is a summary of the Artists that the Axes contains - -============== ====================================== -Axes attribute Description -============== ====================================== -artists A list of Artist instances -patch Rectangle instance for Axes background -collections A list of Collection instances -images A list of AxesImage -legends A list of Legend instances -lines A list of Line2D instances -patches A list of Patch instances -texts A list of Text instances -xaxis matplotlib.axis.XAxis instance -yaxis matplotlib.axis.YAxis instance -============== ====================================== - -.. _axis-container: - -Axis containers -=============== - -The :class:`matplotlib.axis.Axis` instances handle the drawing of the -tick lines, the grid lines, the tick labels and the axis label. You -can configure the left and right ticks separately for the y-axis, and -the upper and lower ticks separately for the x-axis. The ``Axis`` -also stores the data and view intervals used in auto-scaling, panning -and zooming, as well as the :class:`~matplotlib.ticker.Locator` and -:class:`~matplotlib.ticker.Formatter` instances which control where -the ticks are placed and how they are represented as strings. - -Each ``Axis`` object contains a :attr:`~matplotlib.axis.Axis.label` -attribute (this is what the :mod:`~matplotlib.pylab` calls to -:func:`~matplotlib.pylab.xlabel` and :func:`~matplotlib.pylab.ylabel` -set) as well as a list of major and minor ticks. The ticks are -:class:`~matplotlib.axis.XTick` and :class:`~matplotlib.axis.YTick` -instances, which contain the actual line and text primitives that -render the ticks and ticklabels. Because the ticks are dynamically -created as needed (eg. when panning and zooming), you should access -the lists of major and minor ticks through their accessor methods -:meth:`~matplotlib.axis.Axis.get_major_ticks` and -:meth:`~matplotlib.axis.Axis.get_minor_ticks`. Although the ticks -contain all the primitives and will be covered below, the ``Axis`` methods -contain accessor methods to return the tick lines, tick labels, tick -locations etc.: - -.. sourcecode:: ipython - - In [285]: axis = ax.xaxis - - In [286]: axis.get_ticklocs() - Out[286]: array([ 0., 1., 2., 3., 4., 5., 6., 7., 8., 9.]) - - In [287]: axis.get_ticklabels() - Out[287]: - - # note there are twice as many ticklines as labels because by - # default there are tick lines at the top and bottom but only tick - # labels below the xaxis; this can be customized - In [288]: axis.get_ticklines() - Out[288]: - - # by default you get the major ticks back - In [291]: axis.get_ticklines() - Out[291]: - - # but you can also ask for the minor ticks - In [292]: axis.get_ticklines(minor=True) - Out[292]: - -Here is a summary of some of the useful accessor methods of the ``Axis`` -(these have corresponding setters where useful, such as -set_major_formatter) - -====================== ========================================================= -Accessor method Description -====================== ========================================================= -get_scale The scale of the axis, eg 'log' or 'linear' -get_view_interval The interval instance of the axis view limits -get_data_interval The interval instance of the axis data limits -get_gridlines A list of grid lines for the Axis -get_label The axis label - a Text instance -get_ticklabels A list of Text instances - keyword minor=True|False -get_ticklines A list of Line2D instances - keyword minor=True|False -get_ticklocs A list of Tick locations - keyword minor=True|False -get_major_locator The matplotlib.ticker.Locator instance for major ticks -get_major_formatter The matplotlib.ticker.Formatter instance for major ticks -get_minor_locator The matplotlib.ticker.Locator instance for minor ticks -get_minor_formatter The matplotlib.ticker.Formatter instance for minor ticks -get_major_ticks A list of Tick instances for major ticks -get_minor_ticks A list of Tick instances for minor ticks -grid Turn the grid on or off for the major or minor ticks -====================== ========================================================= - -Here is an example, not recommended for its beauty, which customizes -the axes and tick properties - -.. plot:: pyplots/fig_axes_customize_simple.py - :include-source: - - -.. _tick-container: - -Tick containers -=============== - -The :class:`matplotlib.axis.Tick` is the final container object in our -descent from the :class:`~matplotlib.figure.Figure` to the -:class:`~matplotlib.axes.Axes` to the :class:`~matplotlib.axis.Axis` -to the :class:`~matplotlib.axis.Tick`. The ``Tick`` contains the tick -and grid line instances, as well as the label instances for the upper -and lower ticks. Each of these is accessible directly as an attribute -of the ``Tick``. In addition, there are boolean variables that determine -whether the upper labels and ticks are on for the x-axis and whether -the right labels and ticks are on for the y-axis. - -============== ========================================================== -Tick attribute Description -============== ========================================================== -tick1line Line2D instance -tick2line Line2D instance -gridline Line2D instance -label1 Text instance -label2 Text instance -gridOn boolean which determines whether to draw the tickline -tick1On boolean which determines whether to draw the 1st tickline -tick2On boolean which determines whether to draw the 2nd tickline -label1On boolean which determines whether to draw tick label -label2On boolean which determines whether to draw tick label -============== ========================================================== - -Here is an example which sets the formatter for the upper ticks with -dollar signs and colors them green on the right side of the yaxis - -.. plot:: pyplots/dollar_ticks.py - :include-source: +.. _artist-tutorial: + +*************** +Artist tutorial +*************** + +There are three layers to the matplotlib API. The +:class:`matplotlib.backend_bases.FigureCanvas` is the area onto which +the figure is drawn, the :class:`matplotlib.backend_bases.Renderer` is +the object which knows how to draw on the +:class:`~matplotlib.backend_bases.FigureCanvas`, and the +:class:`matplotlib.artist.Artist` is the object that knows how to use +a renderer to paint onto the canvas. The +:class:`~matplotlib.backend_bases.FigureCanvas` and +:class:`~matplotlib.backend_bases.Renderer` handle all the details of +talking to user interface toolkits like `wxPython +`_ or drawing languages like PostScript®, and +the ``Artist`` handles all the high level constructs like representing +and laying out the figure, text, and lines. The typical user will +spend 95% of his time working with the ``Artists``. + +There are two types of ``Artists``: primitives and containers. The +primitives represent the standard graphical objects we want to paint +onto our canvas: :class:`~matplotlib.lines.Line2D`, +:class:`~matplotlib.patches.Rectangle`, +:class:`~matplotlib.text.Text`, :class:`~matplotlib.image.AxesImage`, +etc., and the containers are places to put them +(:class:`~matplotlib.axis.Axis`, :class:`~matplotlib.axes.Axes` and +:class:`~matplotlib.figure.Figure`). The standard use is to create a +:class:`~matplotlib.figure.Figure` instance, use the ``Figure`` to +create one or more :class:`~matplotlib.axes.Axes` or +:class:`~matplotlib.axes.Subplot` instances, and use the ``Axes`` +instance helper methods to create the primitives. In the example +below, we create a ``Figure`` instance using +:func:`matplotlib.pyplot.figure`, which is a convenience method for +instantiating ``Figure`` instances and connecting them with your user +interface or drawing toolkit ``FigureCanvas``. As we will discuss +below, this is not necessary, and you can work directly with +PostScript, PDF Gtk+, or wxPython ``FigureCanvas`` instances. For +example, instantiate your ``Figures`` directly and connect them +yourselves, but since we are focusing here on the ``Artist`` API we'll let +:mod:`~matplotlib.pyplot` handle some of those details for us:: + + import matplotlib.pyplot as plt + fig = plt.figure() + ax = fig.add_subplot(2,1,1) # two rows, one column, first plot + +The :class:`~matplotlib.axes.Axes` is probably the most important +class in the matplotlib API, and the one you will be working with most +of the time. This is because the ``Axes`` is the plotting area into +which most of the objects go, and the ``Axes`` has many special helper +methods (:meth:`~matplotlib.axes.Axes.plot`, +:meth:`~matplotlib.axes.Axes.text`, +:meth:`~matplotlib.axes.Axes.hist`, +:meth:`~matplotlib.axes.Axes.imshow`) to create the most common +graphics primitives (:class:`~matplotlib.lines.Line2D`, +:class:`~matplotlib.text.Text`, +:class:`~matplotlib.patches.Rectangle`, +:class:`~matplotlib.image.Image`, respectively). These helper methods +will take your data (eg. ``numpy`` arrays and strings) create +primitive ``Artist`` instances as needed (eg. ``Line2D``), add them to +the relevant containers, and draw them when requested. Most of you +are probably familiar with the :class:`~matplotlib.axes.Subplot`, +which is just a special case of an ``Axes`` that lives on a regular +rows by columns grid of ``Subplot`` instances. If you want to create +an ``Axes`` at an arbitrary location, simply use the +:meth:`~matplotlib.figure.Figure.add_axes` method which takes a list +of ``[left, bottom, width, height]`` values in 0-1 relative figure +coordinates:: + + fig2 = plt.figure() + ax2 = fig2.add_axes([0.15, 0.1, 0.7, 0.3]) + +Continuing with our example:: + + import numpy as np + t = np.arange(0.0, 1.0, 0.01) + s = np.sin(2*np.pi*t) + line, = ax.plot(t, s, color='blue', lw=2) + +In this example, ``ax`` is the ``Axes`` instance created by the +``fig.add_subplot`` call above (remember ``Subplot`` is just a +subclass of ``Axes``) and when you call ``ax.plot``, it creates a +``Line2D`` instance and adds it to the :attr:`Axes.lines +` list. In the interactive `ipython +`_ session below, you can see that the +``Axes.lines`` list is length one and contains the same line that was +returned by the ``line, = ax.plot(x, y, 'o')`` call: + +.. sourcecode:: ipython + + In [101]: ax.lines[0] + Out[101]: + + In [102]: line + Out[102]: + +If you make subsequent calls to ``ax.plot`` (and the hold state is "on" +which is the default) then additional lines will be added to the list. +You can remove lines later simply by calling the list methods; either +of these will work:: + + del ax.lines[0] + ax.lines.remove(line) # one or the other, not both! + +The Axes also has helper methods to configure and decorate the x-axis +and y-axis tick, tick labels and axis labels:: + + xtext = ax.set_xlabel('my xdata') # returns a Text instance + ytext = ax.set_ylabel('my xdata') + +When you call :meth:`ax.set_xlabel `, +it passes the information on the :class:`~matplotlib.text.Text` +instance of the :class:`~matplotlib.axis.XAxis`. Each ``Axes`` +instance contains an :class:`~matplotlib.axis.XAxis` and a +:class:`~matplotlib.axis.YAxis` instance, which handle the layout and +drawing of the ticks, tick labels and axis labels. + +.. I'm commenting this out, since the new Sphinx cross-references +.. sort of take care of this above - MGD + +.. Here are the most important matplotlib modules that contain the +.. classes referenced above + +.. =============== ================== +.. Artist Module +.. =============== ================== +.. Artist matplotlib.artist +.. Rectangle matplotlib.patches +.. Line2D matplotlib.lines +.. Axes matplotlib.axes +.. XAxis and YAxis matplotlib.axis +.. Figure matplotlib.figure +.. Text matplotlib.text +.. =============== ================== + +Try creating the figure below. + +.. plot:: pyplots/fig_axes_labels_simple.py + +.. _customizing-artists: + +Customizing your objects +======================== + +Every element in the figure is represented by a matplotlib +:class:`~matplotlib.artist.Artist`, and each has an extensive list of +properties to configure its appearance. The figure itself contains a +:class:`~matplotlib.patches.Rectangle` exactly the size of the figure, +which you can use to set the background color and transparency of the +figures. Likewise, each :class:`~matplotlib.axes.Axes` bounding box +(the standard white box with black edges in the typical matplotlib +plot, has a ``Rectangle`` instance that determines the color, +transparency, and other properties of the Axes. These instances are +stored as member variables :attr:`Figure.patch +` and :attr:`Axes.patch +` ("Patch" is a name inherited from +MATLAB™, and is a 2D "patch" of color on the figure, eg. rectangles, +circles and polygons). Every matplotlib ``Artist`` has the following +properties + +========== ====================================================================== +Property Description +========== ====================================================================== +alpha The transparency - a scalar from 0-1 +animated A boolean that is used to facilitate animated drawing +axes The axes that the Artist lives in, possibly None +clip_box The bounding box that clips the Artist +clip_on Whether clipping is enabled +clip_path The path the artist is clipped to +contains A picking function to test whether the artist contains the pick point +figure The figure instance the artist lives in, possibly None +label A text label (eg. for auto-labeling) +picker A python object that controls object picking +transform The transformation +visible A boolean whether the artist should be drawn +zorder A number which determines the drawing order +========== ====================================================================== + +Each of the properties is accessed with an old-fashioned setter or +getter (yes we know this irritates Pythonistas and we plan to support +direct access via properties or traits but it hasn't been done yet). +For example, to multiply the current alpha by a half:: + + a = o.get_alpha() + o.set_alpha(0.5*a) + +If you want to set a number of properties at once, you can also use +the ``set`` method with keyword arguments. For example:: + + o.set(alpha=0.5, zorder=2) + +If you are working interactively at the python shell, a handy way to +inspect the ``Artist`` properties is to use the +:func:`matplotlib.artist.getp` function (simply +:func:`~matplotlib.pylab.getp` in pylab), which lists the properties +and their values. This works for classes derived from ``Artist`` as +well, eg. ``Figure`` and ``Rectangle``. Here are the ``Figure`` rectangle +properties mentioned above: + +.. sourcecode:: ipython + + In [149]: matplotlib.artist.getp(fig.patch) + alpha = 1.0 + animated = False + antialiased or aa = True + axes = None + clip_box = None + clip_on = False + clip_path = None + contains = None + edgecolor or ec = w + facecolor or fc = 0.75 + figure = Figure(8.125x6.125) + fill = 1 + hatch = None + height = 1 + label = + linewidth or lw = 1.0 + picker = None + transform = + verts = ((0, 0), (0, 1), (1, 1), (1, 0)) + visible = True + width = 1 + window_extent = + x = 0 + y = 0 + zorder = 1 + +.. TODO: Update these URLs + +The docstrings for all of the classes also contain the ``Artist`` +properties, so you can consult the interactive "help" or the +:ref:`artist-api` for a listing of properties for a given object. + +.. _object-containers: + +Object containers +================= + + +Now that we know how to inspect and set the properties of a given +object we want to configure, we need to now how to get at that object. +As mentioned in the introduction, there are two kinds of objects: +primitives and containers. The primitives are usually the things you +want to configure (the font of a :class:`~matplotlib.text.Text` +instance, the width of a :class:`~matplotlib.lines.Line2D`) although +the containers also have some properties as well -- for example the +:class:`~matplotlib.axes.Axes` :class:`~matplotlib.artist.Artist` is a +container that contains many of the primitives in your plot, but it +also has properties like the ``xscale`` to control whether the xaxis +is 'linear' or 'log'. In this section we'll review where the various +container objects store the ``Artists`` that you want to get at. + +.. _figure-container: + +Figure container +================ + +The top level container ``Artist`` is the +:class:`matplotlib.figure.Figure`, and it contains everything in the +figure. The background of the figure is a +:class:`~matplotlib.patches.Rectangle` which is stored in +:attr:`Figure.patch `. As +you add subplots (:meth:`~matplotlib.figure.Figure.add_subplot`) and +axes (:meth:`~matplotlib.figure.Figure.add_axes`) to the figure +these will be appended to the :attr:`Figure.axes +`. These are also returned by the +methods that create them: + +.. sourcecode:: ipython + + In [156]: fig = plt.figure() + + In [157]: ax1 = fig.add_subplot(211) + + In [158]: ax2 = fig.add_axes([0.1, 0.1, 0.7, 0.3]) + + In [159]: ax1 + Out[159]: + + In [160]: print fig.axes + [, ] + +Because the figure maintains the concept of the "current axes" (see +:meth:`Figure.gca ` and +:meth:`Figure.sca `) to support the +pylab/pyplot state machine, you should not insert or remove axes +directly from the axes list, but rather use the +:meth:`~matplotlib.figure.Figure.add_subplot` and +:meth:`~matplotlib.figure.Figure.add_axes` methods to insert, and the +:meth:`~matplotlib.figure.Figure.delaxes` method to delete. You are +free however, to iterate over the list of axes or index into it to get +access to ``Axes`` instances you want to customize. Here is an +example which turns all the axes grids on:: + + for ax in fig.axes: + ax.grid(True) + + +The figure also has its own text, lines, patches and images, which you +can use to add primitives directly. The default coordinate system for +the ``Figure`` will simply be in pixels (which is not usually what you +want) but you can control this by setting the transform property of +the ``Artist`` you are adding to the figure. + +.. TODO: Is that still true? + +More useful is "figure coordinates" where (0, 0) is the bottom-left of +the figure and (1, 1) is the top-right of the figure which you can +obtain by setting the ``Artist`` transform to :attr:`fig.transFigure +`: + +.. sourcecode:: ipython + + In [191]: fig = plt.figure() + + In [192]: l1 = matplotlib.lines.Line2D([0, 1], [0, 1], + transform=fig.transFigure, figure=fig) + + In [193]: l2 = matplotlib.lines.Line2D([0, 1], [1, 0], + transform=fig.transFigure, figure=fig) + + In [194]: fig.lines.extend([l1, l2]) + + In [195]: fig.canvas.draw() + +.. plot:: pyplots/fig_x.py + + +Here is a summary of the Artists the figure contains + +.. TODO: Add xrefs to this table + +================ =============================================================== +Figure attribute Description +================ =============================================================== +axes A list of Axes instances (includes Subplot) +patch The Rectangle background +images A list of FigureImages patches - useful for raw pixel display +legends A list of Figure Legend instances (different from Axes.legends) +lines A list of Figure Line2D instances (rarely used, see Axes.lines) +patches A list of Figure patches (rarely used, see Axes.patches) +texts A list Figure Text instances +================ =============================================================== + +.. _axes-container: + +Axes container +============== + +The :class:`matplotlib.axes.Axes` is the center of the matplotlib +universe -- it contains the vast majority of all the ``Artists`` used +in a figure with many helper methods to create and add these +``Artists`` to itself, as well as helper methods to access and +customize the ``Artists`` it contains. Like the +:class:`~matplotlib.figure.Figure`, it contains a +:class:`~matplotlib.patches.Patch` +:attr:`~matplotlib.axes.Axes.patch` which is a +:class:`~matplotlib.patches.Rectangle` for Cartesian coordinates and a +:class:`~matplotlib.patches.Circle` for polar coordinates; this patch +determines the shape, background and border of the plotting region:: + + ax = fig.add_subplot(111) + rect = ax.patch # a Rectangle instance + rect.set_facecolor('green') + +When you call a plotting method, eg. the canonical +:meth:`~matplotlib.axes.Axes.plot` and pass in arrays or lists of +values, the method will create a :meth:`matplotlib.lines.Line2D` +instance, update the line with all the ``Line2D`` properties passed as +keyword arguments, add the line to the :attr:`Axes.lines +` container, and returns it to you: + +.. sourcecode:: ipython + + In [213]: x, y = np.random.rand(2, 100) + + In [214]: line, = ax.plot(x, y, '-', color='blue', linewidth=2) + +``plot`` returns a list of lines because you can pass in multiple x, y +pairs to plot, and we are unpacking the first element of the length +one list into the line variable. The line has been added to the +``Axes.lines`` list: + +.. sourcecode:: ipython + + In [229]: print ax.lines + [] + +Similarly, methods that create patches, like +:meth:`~matplotlib.axes.Axes.bar` creates a list of rectangles, will +add the patches to the :attr:`Axes.patches +` list: + +.. sourcecode:: ipython + + In [233]: n, bins, rectangles = ax.hist(np.random.randn(1000), 50, facecolor='yellow') + + In [234]: rectangles + Out[234]: + + In [235]: print len(ax.patches) + +You should not add objects directly to the ``Axes.lines`` or +``Axes.patches`` lists unless you know exactly what you are doing, +because the ``Axes`` needs to do a few things when it creates and adds +an object. It sets the figure and axes property of the ``Artist``, as +well as the default ``Axes`` transformation (unless a transformation +is set). It also inspects the data contained in the ``Artist`` to +update the data structures controlling auto-scaling, so that the view +limits can be adjusted to contain the plotted data. You can, +nonetheless, create objects yourself and add them directly to the +``Axes`` using helper methods like +:meth:`~matplotlib.axes.Axes.add_line` and +:meth:`~matplotlib.axes.Axes.add_patch`. Here is an annotated +interactive session illustrating what is going on: + +.. sourcecode:: ipython + + In [261]: fig = plt.figure() + + In [262]: ax = fig.add_subplot(111) + + # create a rectangle instance + In [263]: rect = matplotlib.patches.Rectangle( (1,1), width=5, height=12) + + # by default the axes instance is None + In [264]: print rect.get_axes() + None + + # and the transformation instance is set to the "identity transform" + In [265]: print rect.get_transform() + + + # now we add the Rectangle to the Axes + In [266]: ax.add_patch(rect) + + # and notice that the ax.add_patch method has set the axes + # instance + In [267]: print rect.get_axes() + Subplot(49,81.25) + + # and the transformation has been set too + In [268]: print rect.get_transform() + + + # the default axes transformation is ax.transData + In [269]: print ax.transData + + + # notice that the xlimits of the Axes have not been changed + In [270]: print ax.get_xlim() + (0.0, 1.0) + + # but the data limits have been updated to encompass the rectangle + In [271]: print ax.dataLim.get_bounds() + (1.0, 1.0, 5.0, 12.0) + + # we can manually invoke the auto-scaling machinery + In [272]: ax.autoscale_view() + + # and now the xlim are updated to encompass the rectangle + In [273]: print ax.get_xlim() + (1.0, 6.0) + + # we have to manually force a figure draw + In [274]: ax.figure.canvas.draw() + + +There are many, many ``Axes`` helper methods for creating primitive +``Artists`` and adding them to their respective containers. The table +below summarizes a small sampling of them, the kinds of ``Artist`` they +create, and where they store them + +============================== ==================== ======================= +Helper method Artist Container +============================== ==================== ======================= +ax.annotate - text annotations Annotate ax.texts +ax.bar - bar charts Rectangle ax.patches +ax.errorbar - error bar plots Line2D and Rectangle ax.lines and ax.patches +ax.fill - shared area Polygon ax.patches +ax.hist - histograms Rectangle ax.patches +ax.imshow - image data AxesImage ax.images +ax.legend - axes legends Legend ax.legends +ax.plot - xy plots Line2D ax.lines +ax.scatter - scatter charts PolygonCollection ax.collections +ax.text - text Text ax.texts +============================== ==================== ======================= + + +In addition to all of these ``Artists``, the ``Axes`` contains two +important ``Artist`` containers: the :class:`~matplotlib.axis.XAxis` +and :class:`~matplotlib.axis.YAxis`, which handle the drawing of the +ticks and labels. These are stored as instance variables +:attr:`~matplotlib.axes.Axes.xaxis` and +:attr:`~matplotlib.axes.Axes.yaxis`. The ``XAxis`` and ``YAxis`` +containers will be detailed below, but note that the ``Axes`` contains +many helper methods which forward calls on to the +:class:`~matplotlib.axis.Axis` instances so you often do not need to +work with them directly unless you want to. For example, you can set +the font size of the ``XAxis`` ticklabels using the ``Axes`` helper +method:: + + for label in ax.get_xticklabels(): + label.set_color('orange') + +Below is a summary of the Artists that the Axes contains + +============== ====================================== +Axes attribute Description +============== ====================================== +artists A list of Artist instances +patch Rectangle instance for Axes background +collections A list of Collection instances +images A list of AxesImage +legends A list of Legend instances +lines A list of Line2D instances +patches A list of Patch instances +texts A list of Text instances +xaxis matplotlib.axis.XAxis instance +yaxis matplotlib.axis.YAxis instance +============== ====================================== + +.. _axis-container: + +Axis containers +=============== + +The :class:`matplotlib.axis.Axis` instances handle the drawing of the +tick lines, the grid lines, the tick labels and the axis label. You +can configure the left and right ticks separately for the y-axis, and +the upper and lower ticks separately for the x-axis. The ``Axis`` +also stores the data and view intervals used in auto-scaling, panning +and zooming, as well as the :class:`~matplotlib.ticker.Locator` and +:class:`~matplotlib.ticker.Formatter` instances which control where +the ticks are placed and how they are represented as strings. + +Each ``Axis`` object contains a :attr:`~matplotlib.axis.Axis.label` +attribute (this is what the :mod:`~matplotlib.pylab` calls to +:func:`~matplotlib.pylab.xlabel` and :func:`~matplotlib.pylab.ylabel` +set) as well as a list of major and minor ticks. The ticks are +:class:`~matplotlib.axis.XTick` and :class:`~matplotlib.axis.YTick` +instances, which contain the actual line and text primitives that +render the ticks and ticklabels. Because the ticks are dynamically +created as needed (eg. when panning and zooming), you should access +the lists of major and minor ticks through their accessor methods +:meth:`~matplotlib.axis.Axis.get_major_ticks` and +:meth:`~matplotlib.axis.Axis.get_minor_ticks`. Although the ticks +contain all the primitives and will be covered below, the ``Axis`` methods +contain accessor methods to return the tick lines, tick labels, tick +locations etc.: + +.. sourcecode:: ipython + + In [285]: axis = ax.xaxis + + In [286]: axis.get_ticklocs() + Out[286]: array([ 0., 1., 2., 3., 4., 5., 6., 7., 8., 9.]) + + In [287]: axis.get_ticklabels() + Out[287]: + + # note there are twice as many ticklines as labels because by + # default there are tick lines at the top and bottom but only tick + # labels below the xaxis; this can be customized + In [288]: axis.get_ticklines() + Out[288]: + + # by default you get the major ticks back + In [291]: axis.get_ticklines() + Out[291]: + + # but you can also ask for the minor ticks + In [292]: axis.get_ticklines(minor=True) + Out[292]: + +Here is a summary of some of the useful accessor methods of the ``Axis`` +(these have corresponding setters where useful, such as +set_major_formatter) + +====================== ========================================================= +Accessor method Description +====================== ========================================================= +get_scale The scale of the axis, eg 'log' or 'linear' +get_view_interval The interval instance of the axis view limits +get_data_interval The interval instance of the axis data limits +get_gridlines A list of grid lines for the Axis +get_label The axis label - a Text instance +get_ticklabels A list of Text instances - keyword minor=True|False +get_ticklines A list of Line2D instances - keyword minor=True|False +get_ticklocs A list of Tick locations - keyword minor=True|False +get_major_locator The matplotlib.ticker.Locator instance for major ticks +get_major_formatter The matplotlib.ticker.Formatter instance for major ticks +get_minor_locator The matplotlib.ticker.Locator instance for minor ticks +get_minor_formatter The matplotlib.ticker.Formatter instance for minor ticks +get_major_ticks A list of Tick instances for major ticks +get_minor_ticks A list of Tick instances for minor ticks +grid Turn the grid on or off for the major or minor ticks +====================== ========================================================= + +Here is an example, not recommended for its beauty, which customizes +the axes and tick properties + +.. plot:: pyplots/fig_axes_customize_simple.py + :include-source: + + +.. _tick-container: + +Tick containers +=============== + +The :class:`matplotlib.axis.Tick` is the final container object in our +descent from the :class:`~matplotlib.figure.Figure` to the +:class:`~matplotlib.axes.Axes` to the :class:`~matplotlib.axis.Axis` +to the :class:`~matplotlib.axis.Tick`. The ``Tick`` contains the tick +and grid line instances, as well as the label instances for the upper +and lower ticks. Each of these is accessible directly as an attribute +of the ``Tick``. In addition, there are boolean variables that determine +whether the upper labels and ticks are on for the x-axis and whether +the right labels and ticks are on for the y-axis. + +============== ========================================================== +Tick attribute Description +============== ========================================================== +tick1line Line2D instance +tick2line Line2D instance +gridline Line2D instance +label1 Text instance +label2 Text instance +gridOn boolean which determines whether to draw the tickline +tick1On boolean which determines whether to draw the 1st tickline +tick2On boolean which determines whether to draw the 2nd tickline +label1On boolean which determines whether to draw tick label +label2On boolean which determines whether to draw tick label +============== ========================================================== + +Here is an example which sets the formatter for the upper ticks with +dollar signs and colors them green on the right side of the yaxis + +.. plot:: pyplots/dollar_ticks.py + :include-source: diff --git a/doc/users/event_handling.rst b/doc/users/event_handling.rst index 1ef07ae5485f..8bdb5a781ee9 100644 --- a/doc/users/event_handling.rst +++ b/doc/users/event_handling.rst @@ -1,555 +1,555 @@ -.. _event-handling-tutorial: - -************************** -Event handling and picking -************************** - -matplotlib works with 5 user interface toolkits (wxpython, tkinter, -qt, gtk and fltk) and in order to support features like interactive -panning and zooming of figures, it is helpful to the developers to -have an API for interacting with the figure via key presses and mouse -movements that is "GUI neutral" so we don't have to repeat a lot of -code across the different user interfaces. Although the event -handling API is GUI neutral, it is based on the GTK model, which was -the first user interface matplotlib supported. The events that are -triggered are also a bit richer vis-a-vis matplotlib than standard GUI -events, including information like which :class:`matplotlib.axes.Axes` -the event occurred in. The events also understand the matplotlib -coordinate system, and report event locations in both pixel and data -coordinates. - -.. _event-connections: - -Event connections -================= - -To receive events, you need to write a callback function and then -connect your function to the event manager, which is part of the -:class:`~matplotlib.backend_bases.FigureCanvasBase`. Here is a simple -example that prints the location of the mouse click and which button -was pressed:: - - fig = plt.figure() - ax = fig.add_subplot(111) - ax.plot(np.random.rand(10)) - - def onclick(event): - print 'button=%d, x=%d, y=%d, xdata=%f, ydata=%f'%( - event.button, event.x, event.y, event.xdata, event.ydata) - - cid = fig.canvas.mpl_connect('button_press_event', onclick) - -The ``FigureCanvas`` method -:meth:`~matplotlib.backend_bases.FigureCanvasBase.mpl_connect` returns -a connection id which is simply an integer. When you want to -disconnect the callback, just call:: - - fig.canvas.mpl_disconnect(cid) - -Here are the events that you can connect to, the class instances that -are sent back to you when the event occurs, and the event descriptions - - -======================= ====================================================================================== -Event name Class and description -======================= ====================================================================================== -'button_press_event' :class:`~matplotlib.backend_bases.MouseEvent` - mouse button is pressed -'button_release_event' :class:`~matplotlib.backend_bases.MouseEvent` - mouse button is released -'draw_event' :class:`~matplotlib.backend_bases.DrawEvent` - canvas draw -'key_press_event' :class:`~matplotlib.backend_bases.KeyEvent` - key is pressed -'key_release_event' :class:`~matplotlib.backend_bases.KeyEvent` - key is released -'motion_notify_event' :class:`~matplotlib.backend_bases.MouseEvent` - mouse motion -'pick_event' :class:`~matplotlib.backend_bases.PickEvent` - an object in the canvas is selected -'resize_event' :class:`~matplotlib.backend_bases.ResizeEvent` - figure canvas is resized -'scroll_event' :class:`~matplotlib.backend_bases.MouseEvent` - mouse scroll wheel is rolled -'figure_enter_event' :class:`~matplotlib.backend_bases.LocationEvent` - mouse enters a new figure -'figure_leave_event' :class:`~matplotlib.backend_bases.LocationEvent` - mouse leaves a figure -'axes_enter_event' :class:`~matplotlib.backend_bases.LocationEvent` - mouse enters a new axes -'axes_leave_event' :class:`~matplotlib.backend_bases.LocationEvent` - mouse leaves an axes -======================= ====================================================================================== - -.. _event-attributes: - -Event attributes -================ - -All matplotlib events inherit from the base class -:class:`matplotlib.backend_bases.Event`, which store the attributes: - - ``name`` - the event name - - ``canvas`` - the FigureCanvas instance generating the event - - ``guiEvent`` - the GUI event that triggered the matplotlib event - - -The most common events that are the bread and butter of event handling -are key press/release events and mouse press/release and movement -events. The :class:`~matplotlib.backend_bases.KeyEvent` and -:class:`~matplotlib.backend_bases.MouseEvent` classes that handle -these events are both derived from the LocationEvent, which has the -following attributes - - ``x`` - x position - pixels from left of canvas - - ``y`` - y position - pixels from bottom of canvas - - ``inaxes`` - the :class:`~matplotlib.axes.Axes` instance if mouse is over axes - - ``xdata`` - x coord of mouse in data coords - - ``ydata`` - y coord of mouse in data coords - -Let's look a simple example of a canvas, where a simple line segment -is created every time a mouse is pressed:: - - class LineBuilder: - def __init__(self, line): - self.line = line - self.xs = list(line.get_xdata()) - self.ys = list(line.get_ydata()) - self.cid = line.figure.canvas.mpl_connect('button_press_event', self) - - def __call__(self, event): - print 'click', event - if event.inaxes!=self.line.axes: return - self.xs.append(event.xdata) - self.ys.append(event.ydata) - self.line.set_data(self.xs, self.ys) - self.line.figure.canvas.draw() - - fig = plt.figure() - ax = fig.add_subplot(111) - ax.set_title('click to build line segments') - line, = ax.plot([0], [0]) # empty line - linebuilder = LineBuilder(line) - - - -The :class:`~matplotlib.backend_bases.MouseEvent` that we just used is a -:class:`~matplotlib.backend_bases.LocationEvent`, so we have access to -the data and pixel coordinates in event.x and event.xdata. In -addition to the ``LocationEvent`` attributes, it has - - ``button`` - button pressed None, 1, 2, 3, 'up', 'down' (up and down are used for scroll events) - - ``key`` - the key pressed: None, chr(range(255), 'shift', 'win', or 'control' - -Draggable rectangle exercise ----------------------------- - -Write draggable rectangle class that is initialized with a -:class:`~matplotlib.patches.Rectangle` instance but will move its x,y -location when dragged. Hint: you will need to store the orginal -``xy`` location of the rectangle which is stored as rect.xy and -connect to the press, motion and release mouse events. When the mouse -is pressed, check to see if the click occurs over your rectangle (see -:meth:`matplotlib.patches.Rectangle.contains`) and if it does, store -the rectangle xy and the location of the mouse click in data coords. -In the motion event callback, compute the deltax and deltay of the -mouse movement, and add those deltas to the origin of the rectangle -you stored. The redraw the figure. On the button release event, just -reset all the button press data you stored as None. - -Here is the solution:: - - import numpy as np - import matplotlib.pyplot as plt - - class DraggableRectangle: - def __init__(self, rect): - self.rect = rect - self.press = None - - def connect(self): - 'connect to all the events we need' - self.cidpress = self.rect.figure.canvas.mpl_connect( - 'button_press_event', self.on_press) - self.cidrelease = self.rect.figure.canvas.mpl_connect( - 'button_release_event', self.on_release) - self.cidmotion = self.rect.figure.canvas.mpl_connect( - 'motion_notify_event', self.on_motion) - - def on_press(self, event): - 'on button press we will see if the mouse is over us and store some data' - if event.inaxes != self.rect.axes: return - - contains, attrd = self.rect.contains(event) - if not contains: return - print 'event contains', self.rect.xy - x0, y0 = self.rect.xy - self.press = x0, y0, event.xdata, event.ydata - - def on_motion(self, event): - 'on motion we will move the rect if the mouse is over us' - if self.press is None: return - if event.inaxes != self.rect.axes: return - x0, y0, xpress, ypress = self.press - dx = event.xdata - xpress - dy = event.ydata - ypress - #print 'x0=%f, xpress=%f, event.xdata=%f, dx=%f, x0+dx=%f'%(x0, xpress, event.xdata, dx, x0+dx) - self.rect.set_x(x0+dx) - self.rect.set_y(y0+dy) - - self.rect.figure.canvas.draw() - - - def on_release(self, event): - 'on release we reset the press data' - self.press = None - self.rect.figure.canvas.draw() - - def disconnect(self): - 'disconnect all the stored connection ids' - self.rect.figure.canvas.mpl_disconnect(self.cidpress) - self.rect.figure.canvas.mpl_disconnect(self.cidrelease) - self.rect.figure.canvas.mpl_disconnect(self.cidmotion) - - fig = plt.figure() - ax = fig.add_subplot(111) - rects = ax.bar(range(10), 20*np.random.rand(10)) - drs = [] - for rect in rects: - dr = DraggableRectangle(rect) - dr.connect() - drs.append(dr) - - plt.show() - - -**Extra credit**: use the animation blit techniques discussed in the -`animations recipe -`_ to make the -animated drawing faster and smoother. - -Extra credit solution:: - - # draggable rectangle with the animation blit techniques; see - # http://www.scipy.org/Cookbook/Matplotlib/Animations - import numpy as np - import matplotlib.pyplot as plt - - class DraggableRectangle: - lock = None # only one can be animated at a time - def __init__(self, rect): - self.rect = rect - self.press = None - self.background = None - - def connect(self): - 'connect to all the events we need' - self.cidpress = self.rect.figure.canvas.mpl_connect( - 'button_press_event', self.on_press) - self.cidrelease = self.rect.figure.canvas.mpl_connect( - 'button_release_event', self.on_release) - self.cidmotion = self.rect.figure.canvas.mpl_connect( - 'motion_notify_event', self.on_motion) - - def on_press(self, event): - 'on button press we will see if the mouse is over us and store some data' - if event.inaxes != self.rect.axes: return - if DraggableRectangle.lock is not None: return - contains, attrd = self.rect.contains(event) - if not contains: return - print 'event contains', self.rect.xy - x0, y0 = self.rect.xy - self.press = x0, y0, event.xdata, event.ydata - DraggableRectangle.lock = self - - # draw everything but the selected rectangle and store the pixel buffer - canvas = self.rect.figure.canvas - axes = self.rect.axes - self.rect.set_animated(True) - canvas.draw() - self.background = canvas.copy_from_bbox(self.rect.axes.bbox) - - # now redraw just the rectangle - axes.draw_artist(self.rect) - - # and blit just the redrawn area - canvas.blit(axes.bbox) - - def on_motion(self, event): - 'on motion we will move the rect if the mouse is over us' - if DraggableRectangle.lock is not self: - return - if event.inaxes != self.rect.axes: return - x0, y0, xpress, ypress = self.press - dx = event.xdata - xpress - dy = event.ydata - ypress - self.rect.set_x(x0+dx) - self.rect.set_y(y0+dy) - - canvas = self.rect.figure.canvas - axes = self.rect.axes - # restore the background region - canvas.restore_region(self.background) - - # redraw just the current rectangle - axes.draw_artist(self.rect) - - # blit just the redrawn area - canvas.blit(axes.bbox) - - def on_release(self, event): - 'on release we reset the press data' - if DraggableRectangle.lock is not self: - return - - self.press = None - DraggableRectangle.lock = None - - # turn off the rect animation property and reset the background - self.rect.set_animated(False) - self.background = None - - # redraw the full figure - self.rect.figure.canvas.draw() - - def disconnect(self): - 'disconnect all the stored connection ids' - self.rect.figure.canvas.mpl_disconnect(self.cidpress) - self.rect.figure.canvas.mpl_disconnect(self.cidrelease) - self.rect.figure.canvas.mpl_disconnect(self.cidmotion) - - fig = plt.figure() - ax = fig.add_subplot(111) - rects = ax.bar(range(10), 20*np.random.rand(10)) - drs = [] - for rect in rects: - dr = DraggableRectangle(rect) - dr.connect() - drs.append(dr) - - plt.show() - - -.. _enter-leave-events: - -Mouse enter and leave -====================== - -If you want to be notified when the mouse enters or leaves a figure or -axes, you can connect to the figure/axes enter/leave events. Here is -a simple example that changes the colors of the axes and figure -background that the mouse is over:: - - """ - Illustrate the figure and axes enter and leave events by changing the - frame colors on enter and leave - """ - import matplotlib.pyplot as plt - - def enter_axes(event): - print 'enter_axes', event.inaxes - event.inaxes.patch.set_facecolor('yellow') - event.canvas.draw() - - def leave_axes(event): - print 'leave_axes', event.inaxes - event.inaxes.patch.set_facecolor('white') - event.canvas.draw() - - def enter_figure(event): - print 'enter_figure', event.canvas.figure - event.canvas.figure.patch.set_facecolor('red') - event.canvas.draw() - - def leave_figure(event): - print 'leave_figure', event.canvas.figure - event.canvas.figure.patch.set_facecolor('grey') - event.canvas.draw() - - fig1 = plt.figure() - fig1.suptitle('mouse hover over figure or axes to trigger events') - ax1 = fig1.add_subplot(211) - ax2 = fig1.add_subplot(212) - - fig1.canvas.mpl_connect('figure_enter_event', enter_figure) - fig1.canvas.mpl_connect('figure_leave_event', leave_figure) - fig1.canvas.mpl_connect('axes_enter_event', enter_axes) - fig1.canvas.mpl_connect('axes_leave_event', leave_axes) - - fig2 = plt.figure() - fig2.suptitle('mouse hover over figure or axes to trigger events') - ax1 = fig2.add_subplot(211) - ax2 = fig2.add_subplot(212) - - fig2.canvas.mpl_connect('figure_enter_event', enter_figure) - fig2.canvas.mpl_connect('figure_leave_event', leave_figure) - fig2.canvas.mpl_connect('axes_enter_event', enter_axes) - fig2.canvas.mpl_connect('axes_leave_event', leave_axes) - - plt.show() - - - -.. _object-picking: - -Object picking -============== - -You can enable picking by setting the ``picker`` property of an -:class:`~matplotlib.artist.Artist` (eg a matplotlib -:class:`~matplotlib.lines.Line2D`, :class:`~matplotlib.text.Text`, -:class:`~matplotlib.patches.Patch`, :class:`~matplotlib.patches.Polygon`, -:class:`~matplotlib.patches.AxesImage`, etc...) - -There are a variety of meanings of the ``picker`` property: - - ``None`` - picking is disabled for this artist (default) - - ``boolean`` - if True then picking will be enabled and the artist will fire a - pick event if the mouse event is over the artist - - ``float`` - if picker is a number it is interpreted as an epsilon tolerance in - points and the the artist will fire off an event if its data is - within epsilon of the mouse event. For some artists like lines - and patch collections, the artist may provide additional data to - the pick event that is generated, eg the indices of the data - within epsilon of the pick event. - - ``function`` - if picker is callable, it is a user supplied function which - determines whether the artist is hit by the mouse event. The - signature is ``hit, props = picker(artist, mouseevent)`` to - determine the hit test. If the mouse event is over the artist, - return ``hit=True`` and props is a dictionary of properties you - want added to the :class:`~matplotlib.backend_bases.PickEvent` - attributes - - -After you have enabled an artist for picking by setting the ``picker`` -property, you need to connect to the figure canvas pick_event to get -pick callbacks on mouse press events. Eg:: - - def pick_handler(event): - mouseevent = event.mouseevent - artist = event.artist - # now do something with this... - - -The :class:`~matplotlib.backend_bases.PickEvent` which is passed to -your callback is always fired with two attributes: - - ``mouseevent`` the mouse event that generate the pick event. The - mouse event in turn has attributes like ``x`` and ``y`` (the - coords in display space, eg pixels from left, bottom) and xdata, - ydata (the coords in data space). Additionally, you can get - information about which buttons were pressed, which keys were - pressed, which :class:`~matplotlib.axes.Axes` the mouse is over, - etc. See :class:`matplotlib.backend_bases.MouseEvent` for - details. - - ``artist`` - the :class:`~matplotlib.artist.Artist` that generated the pick - event. - -Additionally, certain artists like :class:`~matplotlib.lines.Line2D` -and :class:`~matplotlib.collections.PatchCollection` may attach -additional meta data like the indices into the data that meet the -picker criteria (eg all the points in the line that are within the -specified epsilon tolerance) - -Simple picking example ----------------------- - -In the example below, we set the line picker property to a scalar, so -it represents a tolerance in points (72 points per inch). The onpick -callback function will be called when the pick event it within the -tolerance distance from the line, and has the indices of the data -vertices that are within the pick distance tolerance. Our onpick -callback function simply prints the data that are under the pick -location. Different matplotlib Artists can attach different data to -the PickEvent. For example, ``Line2D`` attaches the ind property, -which are the indices into the line data under the pick point. See -:meth:`~matplotlib.lines.Line2D.pick` for details on the ``PickEvent`` -properties of the line. Here is the code:: - - import numpy as np - import matplotlib.pyplot as plt - - fig = plt.figure() - ax = fig.add_subplot(111) - ax.set_title('click on points') - - line, = ax.plot(np.random.rand(100), 'o', picker=5) # 5 points tolerance - - def onpick(event): - thisline = event.artist - xdata = thisline.get_xdata() - ydata = thisline.get_ydata() - ind = event.ind - print 'onpick points:', zip(xdata[ind], ydata[ind]) - - fig.canvas.mpl_connect('pick_event', onpick) - - plt.show() - - -Picking exercise ----------------- - -Create a data set of 100 arrays of 1000 Gaussian random numbers and -compute the sample mean and standard deviation of each of them (hint: -numpy arrays have a mean and std method) and make a xy marker plot of -the 100 means vs the 100 standard deviations. Connect the line -created by the plot command to the pick event, and plot the original -time series of the data that generated the clicked on points. If more -than one point is within the tolerance of the clicked on point, you -can use multiple subplots to plot the multiple time series. - -Exercise solution:: - - """ - compute the mean and stddev of 100 data sets and plot mean vs stddev. - When you click on one of the mu, sigma points, plot the raw data from - the dataset that generated the mean and stddev - """ - import numpy as np - import matplotlib.pyplot as plt - - X = np.random.rand(100, 1000) - xs = np.mean(X, axis=1) - ys = np.std(X, axis=1) - - fig = plt.figure() - ax = fig.add_subplot(111) - ax.set_title('click on point to plot time series') - line, = ax.plot(xs, ys, 'o', picker=5) # 5 points tolerance - - - def onpick(event): - - if event.artist!=line: return True - - N = len(event.ind) - if not N: return True - - - figi = plt.figure() - for subplotnum, dataind in enumerate(event.ind): - ax = figi.add_subplot(N,1,subplotnum+1) - ax.plot(X[dataind]) - ax.text(0.05, 0.9, 'mu=%1.3f\nsigma=%1.3f'%(xs[dataind], ys[dataind]), - transform=ax.transAxes, va='top') - ax.set_ylim(-0.5, 1.5) - figi.show() - return True - - fig.canvas.mpl_connect('pick_event', onpick) - +.. _event-handling-tutorial: + +************************** +Event handling and picking +************************** + +matplotlib works with 5 user interface toolkits (wxpython, tkinter, +qt, gtk and fltk) and in order to support features like interactive +panning and zooming of figures, it is helpful to the developers to +have an API for interacting with the figure via key presses and mouse +movements that is "GUI neutral" so we don't have to repeat a lot of +code across the different user interfaces. Although the event +handling API is GUI neutral, it is based on the GTK model, which was +the first user interface matplotlib supported. The events that are +triggered are also a bit richer vis-a-vis matplotlib than standard GUI +events, including information like which :class:`matplotlib.axes.Axes` +the event occurred in. The events also understand the matplotlib +coordinate system, and report event locations in both pixel and data +coordinates. + +.. _event-connections: + +Event connections +================= + +To receive events, you need to write a callback function and then +connect your function to the event manager, which is part of the +:class:`~matplotlib.backend_bases.FigureCanvasBase`. Here is a simple +example that prints the location of the mouse click and which button +was pressed:: + + fig = plt.figure() + ax = fig.add_subplot(111) + ax.plot(np.random.rand(10)) + + def onclick(event): + print 'button=%d, x=%d, y=%d, xdata=%f, ydata=%f'%( + event.button, event.x, event.y, event.xdata, event.ydata) + + cid = fig.canvas.mpl_connect('button_press_event', onclick) + +The ``FigureCanvas`` method +:meth:`~matplotlib.backend_bases.FigureCanvasBase.mpl_connect` returns +a connection id which is simply an integer. When you want to +disconnect the callback, just call:: + + fig.canvas.mpl_disconnect(cid) + +Here are the events that you can connect to, the class instances that +are sent back to you when the event occurs, and the event descriptions + + +======================= ====================================================================================== +Event name Class and description +======================= ====================================================================================== +'button_press_event' :class:`~matplotlib.backend_bases.MouseEvent` - mouse button is pressed +'button_release_event' :class:`~matplotlib.backend_bases.MouseEvent` - mouse button is released +'draw_event' :class:`~matplotlib.backend_bases.DrawEvent` - canvas draw +'key_press_event' :class:`~matplotlib.backend_bases.KeyEvent` - key is pressed +'key_release_event' :class:`~matplotlib.backend_bases.KeyEvent` - key is released +'motion_notify_event' :class:`~matplotlib.backend_bases.MouseEvent` - mouse motion +'pick_event' :class:`~matplotlib.backend_bases.PickEvent` - an object in the canvas is selected +'resize_event' :class:`~matplotlib.backend_bases.ResizeEvent` - figure canvas is resized +'scroll_event' :class:`~matplotlib.backend_bases.MouseEvent` - mouse scroll wheel is rolled +'figure_enter_event' :class:`~matplotlib.backend_bases.LocationEvent` - mouse enters a new figure +'figure_leave_event' :class:`~matplotlib.backend_bases.LocationEvent` - mouse leaves a figure +'axes_enter_event' :class:`~matplotlib.backend_bases.LocationEvent` - mouse enters a new axes +'axes_leave_event' :class:`~matplotlib.backend_bases.LocationEvent` - mouse leaves an axes +======================= ====================================================================================== + +.. _event-attributes: + +Event attributes +================ + +All matplotlib events inherit from the base class +:class:`matplotlib.backend_bases.Event`, which store the attributes: + + ``name`` + the event name + + ``canvas`` + the FigureCanvas instance generating the event + + ``guiEvent`` + the GUI event that triggered the matplotlib event + + +The most common events that are the bread and butter of event handling +are key press/release events and mouse press/release and movement +events. The :class:`~matplotlib.backend_bases.KeyEvent` and +:class:`~matplotlib.backend_bases.MouseEvent` classes that handle +these events are both derived from the LocationEvent, which has the +following attributes + + ``x`` + x position - pixels from left of canvas + + ``y`` + y position - pixels from bottom of canvas + + ``inaxes`` + the :class:`~matplotlib.axes.Axes` instance if mouse is over axes + + ``xdata`` + x coord of mouse in data coords + + ``ydata`` + y coord of mouse in data coords + +Let's look a simple example of a canvas, where a simple line segment +is created every time a mouse is pressed:: + + class LineBuilder: + def __init__(self, line): + self.line = line + self.xs = list(line.get_xdata()) + self.ys = list(line.get_ydata()) + self.cid = line.figure.canvas.mpl_connect('button_press_event', self) + + def __call__(self, event): + print 'click', event + if event.inaxes!=self.line.axes: return + self.xs.append(event.xdata) + self.ys.append(event.ydata) + self.line.set_data(self.xs, self.ys) + self.line.figure.canvas.draw() + + fig = plt.figure() + ax = fig.add_subplot(111) + ax.set_title('click to build line segments') + line, = ax.plot([0], [0]) # empty line + linebuilder = LineBuilder(line) + + + +The :class:`~matplotlib.backend_bases.MouseEvent` that we just used is a +:class:`~matplotlib.backend_bases.LocationEvent`, so we have access to +the data and pixel coordinates in event.x and event.xdata. In +addition to the ``LocationEvent`` attributes, it has + + ``button`` + button pressed None, 1, 2, 3, 'up', 'down' (up and down are used for scroll events) + + ``key`` + the key pressed: None, chr(range(255), 'shift', 'win', or 'control' + +Draggable rectangle exercise +---------------------------- + +Write draggable rectangle class that is initialized with a +:class:`~matplotlib.patches.Rectangle` instance but will move its x,y +location when dragged. Hint: you will need to store the orginal +``xy`` location of the rectangle which is stored as rect.xy and +connect to the press, motion and release mouse events. When the mouse +is pressed, check to see if the click occurs over your rectangle (see +:meth:`matplotlib.patches.Rectangle.contains`) and if it does, store +the rectangle xy and the location of the mouse click in data coords. +In the motion event callback, compute the deltax and deltay of the +mouse movement, and add those deltas to the origin of the rectangle +you stored. The redraw the figure. On the button release event, just +reset all the button press data you stored as None. + +Here is the solution:: + + import numpy as np + import matplotlib.pyplot as plt + + class DraggableRectangle: + def __init__(self, rect): + self.rect = rect + self.press = None + + def connect(self): + 'connect to all the events we need' + self.cidpress = self.rect.figure.canvas.mpl_connect( + 'button_press_event', self.on_press) + self.cidrelease = self.rect.figure.canvas.mpl_connect( + 'button_release_event', self.on_release) + self.cidmotion = self.rect.figure.canvas.mpl_connect( + 'motion_notify_event', self.on_motion) + + def on_press(self, event): + 'on button press we will see if the mouse is over us and store some data' + if event.inaxes != self.rect.axes: return + + contains, attrd = self.rect.contains(event) + if not contains: return + print 'event contains', self.rect.xy + x0, y0 = self.rect.xy + self.press = x0, y0, event.xdata, event.ydata + + def on_motion(self, event): + 'on motion we will move the rect if the mouse is over us' + if self.press is None: return + if event.inaxes != self.rect.axes: return + x0, y0, xpress, ypress = self.press + dx = event.xdata - xpress + dy = event.ydata - ypress + #print 'x0=%f, xpress=%f, event.xdata=%f, dx=%f, x0+dx=%f'%(x0, xpress, event.xdata, dx, x0+dx) + self.rect.set_x(x0+dx) + self.rect.set_y(y0+dy) + + self.rect.figure.canvas.draw() + + + def on_release(self, event): + 'on release we reset the press data' + self.press = None + self.rect.figure.canvas.draw() + + def disconnect(self): + 'disconnect all the stored connection ids' + self.rect.figure.canvas.mpl_disconnect(self.cidpress) + self.rect.figure.canvas.mpl_disconnect(self.cidrelease) + self.rect.figure.canvas.mpl_disconnect(self.cidmotion) + + fig = plt.figure() + ax = fig.add_subplot(111) + rects = ax.bar(range(10), 20*np.random.rand(10)) + drs = [] + for rect in rects: + dr = DraggableRectangle(rect) + dr.connect() + drs.append(dr) + + plt.show() + + +**Extra credit**: use the animation blit techniques discussed in the +`animations recipe +`_ to make the +animated drawing faster and smoother. + +Extra credit solution:: + + # draggable rectangle with the animation blit techniques; see + # http://www.scipy.org/Cookbook/Matplotlib/Animations + import numpy as np + import matplotlib.pyplot as plt + + class DraggableRectangle: + lock = None # only one can be animated at a time + def __init__(self, rect): + self.rect = rect + self.press = None + self.background = None + + def connect(self): + 'connect to all the events we need' + self.cidpress = self.rect.figure.canvas.mpl_connect( + 'button_press_event', self.on_press) + self.cidrelease = self.rect.figure.canvas.mpl_connect( + 'button_release_event', self.on_release) + self.cidmotion = self.rect.figure.canvas.mpl_connect( + 'motion_notify_event', self.on_motion) + + def on_press(self, event): + 'on button press we will see if the mouse is over us and store some data' + if event.inaxes != self.rect.axes: return + if DraggableRectangle.lock is not None: return + contains, attrd = self.rect.contains(event) + if not contains: return + print 'event contains', self.rect.xy + x0, y0 = self.rect.xy + self.press = x0, y0, event.xdata, event.ydata + DraggableRectangle.lock = self + + # draw everything but the selected rectangle and store the pixel buffer + canvas = self.rect.figure.canvas + axes = self.rect.axes + self.rect.set_animated(True) + canvas.draw() + self.background = canvas.copy_from_bbox(self.rect.axes.bbox) + + # now redraw just the rectangle + axes.draw_artist(self.rect) + + # and blit just the redrawn area + canvas.blit(axes.bbox) + + def on_motion(self, event): + 'on motion we will move the rect if the mouse is over us' + if DraggableRectangle.lock is not self: + return + if event.inaxes != self.rect.axes: return + x0, y0, xpress, ypress = self.press + dx = event.xdata - xpress + dy = event.ydata - ypress + self.rect.set_x(x0+dx) + self.rect.set_y(y0+dy) + + canvas = self.rect.figure.canvas + axes = self.rect.axes + # restore the background region + canvas.restore_region(self.background) + + # redraw just the current rectangle + axes.draw_artist(self.rect) + + # blit just the redrawn area + canvas.blit(axes.bbox) + + def on_release(self, event): + 'on release we reset the press data' + if DraggableRectangle.lock is not self: + return + + self.press = None + DraggableRectangle.lock = None + + # turn off the rect animation property and reset the background + self.rect.set_animated(False) + self.background = None + + # redraw the full figure + self.rect.figure.canvas.draw() + + def disconnect(self): + 'disconnect all the stored connection ids' + self.rect.figure.canvas.mpl_disconnect(self.cidpress) + self.rect.figure.canvas.mpl_disconnect(self.cidrelease) + self.rect.figure.canvas.mpl_disconnect(self.cidmotion) + + fig = plt.figure() + ax = fig.add_subplot(111) + rects = ax.bar(range(10), 20*np.random.rand(10)) + drs = [] + for rect in rects: + dr = DraggableRectangle(rect) + dr.connect() + drs.append(dr) + + plt.show() + + +.. _enter-leave-events: + +Mouse enter and leave +====================== + +If you want to be notified when the mouse enters or leaves a figure or +axes, you can connect to the figure/axes enter/leave events. Here is +a simple example that changes the colors of the axes and figure +background that the mouse is over:: + + """ + Illustrate the figure and axes enter and leave events by changing the + frame colors on enter and leave + """ + import matplotlib.pyplot as plt + + def enter_axes(event): + print 'enter_axes', event.inaxes + event.inaxes.patch.set_facecolor('yellow') + event.canvas.draw() + + def leave_axes(event): + print 'leave_axes', event.inaxes + event.inaxes.patch.set_facecolor('white') + event.canvas.draw() + + def enter_figure(event): + print 'enter_figure', event.canvas.figure + event.canvas.figure.patch.set_facecolor('red') + event.canvas.draw() + + def leave_figure(event): + print 'leave_figure', event.canvas.figure + event.canvas.figure.patch.set_facecolor('grey') + event.canvas.draw() + + fig1 = plt.figure() + fig1.suptitle('mouse hover over figure or axes to trigger events') + ax1 = fig1.add_subplot(211) + ax2 = fig1.add_subplot(212) + + fig1.canvas.mpl_connect('figure_enter_event', enter_figure) + fig1.canvas.mpl_connect('figure_leave_event', leave_figure) + fig1.canvas.mpl_connect('axes_enter_event', enter_axes) + fig1.canvas.mpl_connect('axes_leave_event', leave_axes) + + fig2 = plt.figure() + fig2.suptitle('mouse hover over figure or axes to trigger events') + ax1 = fig2.add_subplot(211) + ax2 = fig2.add_subplot(212) + + fig2.canvas.mpl_connect('figure_enter_event', enter_figure) + fig2.canvas.mpl_connect('figure_leave_event', leave_figure) + fig2.canvas.mpl_connect('axes_enter_event', enter_axes) + fig2.canvas.mpl_connect('axes_leave_event', leave_axes) + + plt.show() + + + +.. _object-picking: + +Object picking +============== + +You can enable picking by setting the ``picker`` property of an +:class:`~matplotlib.artist.Artist` (eg a matplotlib +:class:`~matplotlib.lines.Line2D`, :class:`~matplotlib.text.Text`, +:class:`~matplotlib.patches.Patch`, :class:`~matplotlib.patches.Polygon`, +:class:`~matplotlib.patches.AxesImage`, etc...) + +There are a variety of meanings of the ``picker`` property: + + ``None`` + picking is disabled for this artist (default) + + ``boolean`` + if True then picking will be enabled and the artist will fire a + pick event if the mouse event is over the artist + + ``float`` + if picker is a number it is interpreted as an epsilon tolerance in + points and the the artist will fire off an event if its data is + within epsilon of the mouse event. For some artists like lines + and patch collections, the artist may provide additional data to + the pick event that is generated, eg the indices of the data + within epsilon of the pick event. + + ``function`` + if picker is callable, it is a user supplied function which + determines whether the artist is hit by the mouse event. The + signature is ``hit, props = picker(artist, mouseevent)`` to + determine the hit test. If the mouse event is over the artist, + return ``hit=True`` and props is a dictionary of properties you + want added to the :class:`~matplotlib.backend_bases.PickEvent` + attributes + + +After you have enabled an artist for picking by setting the ``picker`` +property, you need to connect to the figure canvas pick_event to get +pick callbacks on mouse press events. Eg:: + + def pick_handler(event): + mouseevent = event.mouseevent + artist = event.artist + # now do something with this... + + +The :class:`~matplotlib.backend_bases.PickEvent` which is passed to +your callback is always fired with two attributes: + + ``mouseevent`` the mouse event that generate the pick event. The + mouse event in turn has attributes like ``x`` and ``y`` (the + coords in display space, eg pixels from left, bottom) and xdata, + ydata (the coords in data space). Additionally, you can get + information about which buttons were pressed, which keys were + pressed, which :class:`~matplotlib.axes.Axes` the mouse is over, + etc. See :class:`matplotlib.backend_bases.MouseEvent` for + details. + + ``artist`` + the :class:`~matplotlib.artist.Artist` that generated the pick + event. + +Additionally, certain artists like :class:`~matplotlib.lines.Line2D` +and :class:`~matplotlib.collections.PatchCollection` may attach +additional meta data like the indices into the data that meet the +picker criteria (eg all the points in the line that are within the +specified epsilon tolerance) + +Simple picking example +---------------------- + +In the example below, we set the line picker property to a scalar, so +it represents a tolerance in points (72 points per inch). The onpick +callback function will be called when the pick event it within the +tolerance distance from the line, and has the indices of the data +vertices that are within the pick distance tolerance. Our onpick +callback function simply prints the data that are under the pick +location. Different matplotlib Artists can attach different data to +the PickEvent. For example, ``Line2D`` attaches the ind property, +which are the indices into the line data under the pick point. See +:meth:`~matplotlib.lines.Line2D.pick` for details on the ``PickEvent`` +properties of the line. Here is the code:: + + import numpy as np + import matplotlib.pyplot as plt + + fig = plt.figure() + ax = fig.add_subplot(111) + ax.set_title('click on points') + + line, = ax.plot(np.random.rand(100), 'o', picker=5) # 5 points tolerance + + def onpick(event): + thisline = event.artist + xdata = thisline.get_xdata() + ydata = thisline.get_ydata() + ind = event.ind + print 'onpick points:', zip(xdata[ind], ydata[ind]) + + fig.canvas.mpl_connect('pick_event', onpick) + + plt.show() + + +Picking exercise +---------------- + +Create a data set of 100 arrays of 1000 Gaussian random numbers and +compute the sample mean and standard deviation of each of them (hint: +numpy arrays have a mean and std method) and make a xy marker plot of +the 100 means vs the 100 standard deviations. Connect the line +created by the plot command to the pick event, and plot the original +time series of the data that generated the clicked on points. If more +than one point is within the tolerance of the clicked on point, you +can use multiple subplots to plot the multiple time series. + +Exercise solution:: + + """ + compute the mean and stddev of 100 data sets and plot mean vs stddev. + When you click on one of the mu, sigma points, plot the raw data from + the dataset that generated the mean and stddev + """ + import numpy as np + import matplotlib.pyplot as plt + + X = np.random.rand(100, 1000) + xs = np.mean(X, axis=1) + ys = np.std(X, axis=1) + + fig = plt.figure() + ax = fig.add_subplot(111) + ax.set_title('click on point to plot time series') + line, = ax.plot(xs, ys, 'o', picker=5) # 5 points tolerance + + + def onpick(event): + + if event.artist!=line: return True + + N = len(event.ind) + if not N: return True + + + figi = plt.figure() + for subplotnum, dataind in enumerate(event.ind): + ax = figi.add_subplot(N,1,subplotnum+1) + ax.plot(X[dataind]) + ax.text(0.05, 0.9, 'mu=%1.3f\nsigma=%1.3f'%(xs[dataind], ys[dataind]), + transform=ax.transAxes, va='top') + ax.set_ylim(-0.5, 1.5) + figi.show() + return True + + fig.canvas.mpl_connect('pick_event', onpick) + plt.show() diff --git a/examples/api/font_family_rc.py b/examples/api/font_family_rc.py index 6c096818d808..d379e3163471 100644 --- a/examples/api/font_family_rc.py +++ b/examples/api/font_family_rc.py @@ -1,31 +1,31 @@ -""" -You can explicitly set which font family is picked up for a given font -style (eg 'serif', 'sans-serif', or 'monospace'). - -In the example below, we only allow one font family (Tahoma) for the -san-serif font style. You the default family with the font.family rc -param, eg:: - - rcParams['font.family'] = 'sans-serif' - -and for the font.family you set a list of font styles to try to find -in order:: - - rcParams['font.sans-serif'] = ['Tahoma', 'Bitstream Vera Sans', 'Lucida Grande', 'Verdana'] - -""" - -# -*- noplot -*- - -from matplotlib import rcParams -rcParams['font.family'] = 'sans-serif' -rcParams['font.sans-serif'] = ['Tahoma'] -import matplotlib.pyplot as plt - -fig = plt.figure() -ax = fig.add_subplot(111) -ax.plot([1,2,3], label='test') - -ax.legend() -plt.show() - +""" +You can explicitly set which font family is picked up for a given font +style (eg 'serif', 'sans-serif', or 'monospace'). + +In the example below, we only allow one font family (Tahoma) for the +san-serif font style. You the default family with the font.family rc +param, eg:: + + rcParams['font.family'] = 'sans-serif' + +and for the font.family you set a list of font styles to try to find +in order:: + + rcParams['font.sans-serif'] = ['Tahoma', 'Bitstream Vera Sans', 'Lucida Grande', 'Verdana'] + +""" + +# -*- noplot -*- + +from matplotlib import rcParams +rcParams['font.family'] = 'sans-serif' +rcParams['font.sans-serif'] = ['Tahoma'] +import matplotlib.pyplot as plt + +fig = plt.figure() +ax = fig.add_subplot(111) +ax.plot([1,2,3], label='test') + +ax.legend() +plt.show() + diff --git a/examples/api/font_file.py b/examples/api/font_file.py index 489862f1d34e..ced762d2edbe 100644 --- a/examples/api/font_file.py +++ b/examples/api/font_file.py @@ -1,19 +1,19 @@ -# -*- noplot -*- -""" -Although it is usually not a good idea to explicitly point to a single -ttf file for a font instance, you can do so using the -font_manager.FontProperties fname argument (for a more flexible -solution, see the font_fmaily_rc.py and fonts_demo.py examples). -""" -import matplotlib.font_manager as fm - -import matplotlib.pyplot as plt - -fig = plt.figure() -ax = fig.add_subplot(111) -ax.plot([1,2,3]) - -prop = fm.FontProperties(fname='/Library/Fonts/Tahoma.ttf') -ax.set_title('this is a special font', fontproperties=prop) -plt.show() - +# -*- noplot -*- +""" +Although it is usually not a good idea to explicitly point to a single +ttf file for a font instance, you can do so using the +font_manager.FontProperties fname argument (for a more flexible +solution, see the font_fmaily_rc.py and fonts_demo.py examples). +""" +import matplotlib.font_manager as fm + +import matplotlib.pyplot as plt + +fig = plt.figure() +ax = fig.add_subplot(111) +ax.plot([1,2,3]) + +prop = fm.FontProperties(fname='/Library/Fonts/Tahoma.ttf') +ax.set_title('this is a special font', fontproperties=prop) +plt.show() + diff --git a/examples/data/demodata.csv b/examples/data/demodata.csv index d9656f9da605..c167c4c73479 100644 --- a/examples/data/demodata.csv +++ b/examples/data/demodata.csv @@ -1,11 +1,11 @@ -clientid,date,weekdays,gains,prices,up -0,2008-04-30,Wed,-0.52458192906686452,7791404.0091921333,False -1,2008-05-01,Thu,0.076191536201738269,3167180.7366340165,True -2,2008-05-02,Fri,-0.86850970062880861,9589766.9613829032,False -3,2008-05-03,Sat,-0.42701083852713395,8949415.1867596991,False -4,2008-05-04,Sun,0.2532553652693274,937163.44375252665,True -5,2008-05-05,Mon,-0.68151636911081892,949579.88022264629,False -6,2008-05-06,Tue,0.0071911579626532168,7268426.906552773,True -7,2008-05-07,Wed,0.67449747200412147,7517014.782897247,True -8,2008-05-08,Thu,-1.1841008656818983,1920959.5423492221,False -9,2008-05-09,Fri,-1.5803692595811152,8456240.6198725495,False +clientid,date,weekdays,gains,prices,up +0,2008-04-30,Wed,-0.52458192906686452,7791404.0091921333,False +1,2008-05-01,Thu,0.076191536201738269,3167180.7366340165,True +2,2008-05-02,Fri,-0.86850970062880861,9589766.9613829032,False +3,2008-05-03,Sat,-0.42701083852713395,8949415.1867596991,False +4,2008-05-04,Sun,0.2532553652693274,937163.44375252665,True +5,2008-05-05,Mon,-0.68151636911081892,949579.88022264629,False +6,2008-05-06,Tue,0.0071911579626532168,7268426.906552773,True +7,2008-05-07,Wed,0.67449747200412147,7517014.782897247,True +8,2008-05-08,Thu,-1.1841008656818983,1920959.5423492221,False +9,2008-05-09,Fri,-1.5803692595811152,8456240.6198725495,False diff --git a/lib/matplotlib/mpl-data/images/stock_close.xpm b/lib/matplotlib/mpl-data/images/stock_close.xpm index 4ae0b95b02c9..005ce14dbf37 100644 --- a/lib/matplotlib/mpl-data/images/stock_close.xpm +++ b/lib/matplotlib/mpl-data/images/stock_close.xpm @@ -1,21 +1,21 @@ -/* XPM */ -static char * stock_close_xpm[] = { -"16 16 2 1", -" g None", -". g #000000", -" ", -" ", -" . . ", -" . ... ", -" .. .... ", -" .. ... ", -" ..... ", -" ... ", -" ..... ", -" ....... ", -" ... .... ", -" ... .... ", -" ... .. ", -" ", -" ", -" "}; +/* XPM */ +static char * stock_close_xpm[] = { +"16 16 2 1", +" g None", +". g #000000", +" ", +" ", +" . . ", +" . ... ", +" .. .... ", +" .. ... ", +" ..... ", +" ... ", +" ..... ", +" ....... ", +" ... .... ", +" ... .... ", +" ... .. ", +" ", +" ", +" "}; diff --git a/lib/matplotlib/mpl-data/images/stock_down.xpm b/lib/matplotlib/mpl-data/images/stock_down.xpm index a394f91e9de0..26d0c28ab927 100644 --- a/lib/matplotlib/mpl-data/images/stock_down.xpm +++ b/lib/matplotlib/mpl-data/images/stock_down.xpm @@ -1,44 +1,44 @@ -/* XPM */ -static char * stock_down_xpm[] = { -"16 16 25 1", -" c None", -". c #000000", -"+ c #B5C9DC", -"@ c #9BB6D0", -"# c #91B0CC", -"$ c #49749C", -"% c #456F96", -"& c #AFC5DA", -"* c #A0BAD3", -"= c #9EB8D1", -"- c #3F6588", -"; c #375978", -"> c #B2C7DB", -", c #9CB7D1", -"' c #9AB5CF", -") c #B6CADD", -"! c #5B88B2", -"~ c #A4BDD5", -"{ c #2A435B", -"] c #5080AD", -"^ c #97B3CE", -"/ c #080D11", -"( c #5F8BB4", -"_ c #95B2CE", -": c #4C79A3", -" ", -" ", -" ....... ", -" .+@#$%. ", -" .&*=-;. ", -" .>,'-;. ", -" .),'-;. ", -" .)@@-;. ", -" ....)',-;.... ", -" .!=~*,---{. ", -" .],,,--{. ", -" .]^*-{. ", -" /(_{. ", -" .:. ", -" . ", -" "}; +/* XPM */ +static char * stock_down_xpm[] = { +"16 16 25 1", +" c None", +". c #000000", +"+ c #B5C9DC", +"@ c #9BB6D0", +"# c #91B0CC", +"$ c #49749C", +"% c #456F96", +"& c #AFC5DA", +"* c #A0BAD3", +"= c #9EB8D1", +"- c #3F6588", +"; c #375978", +"> c #B2C7DB", +", c #9CB7D1", +"' c #9AB5CF", +") c #B6CADD", +"! c #5B88B2", +"~ c #A4BDD5", +"{ c #2A435B", +"] c #5080AD", +"^ c #97B3CE", +"/ c #080D11", +"( c #5F8BB4", +"_ c #95B2CE", +": c #4C79A3", +" ", +" ", +" ....... ", +" .+@#$%. ", +" .&*=-;. ", +" .>,'-;. ", +" .),'-;. ", +" .)@@-;. ", +" ....)',-;.... ", +" .!=~*,---{. ", +" .],,,--{. ", +" .]^*-{. ", +" /(_{. ", +" .:. ", +" . ", +" "}; diff --git a/lib/matplotlib/mpl-data/images/stock_left.xpm b/lib/matplotlib/mpl-data/images/stock_left.xpm index 1e26e19406c2..503bc36616e2 100644 --- a/lib/matplotlib/mpl-data/images/stock_left.xpm +++ b/lib/matplotlib/mpl-data/images/stock_left.xpm @@ -1,45 +1,45 @@ -/* XPM */ -static char * stock_left_xpm[] = { -"16 16 26 1", -" c None", -". c #000000", -"+ c #C4D4E3", -"@ c #A3BCD4", -"# c #A6BED5", -"$ c #AAC1D7", -"% c #ABC2D8", -"& c #AFC5DA", -"* c #AEC4D9", -"= c #6892B9", -"- c #9CB7D1", -"; c #A4BDD5", -"> c #9FB9D2", -", c #9BB6D0", -"' c #9AB5CF", -") c #49759E", -"! c #1C2D3D", -"~ c #C5D5E4", -"{ c #A0BAD3", -"] c #9EB8D1", -"^ c #4B78A2", -"/ c #2A435B", -"( c #3F6588", -"_ c #34536F", -": c #29425A", -"< c #2D4760", -" ", -" . ", -" .. ", -" .+. ", -" .+@....... ", -" .+#$%&%*@=. ", -" .+-;@>,,>'). ", -" !~>{]]->>>>^. ", -" ./((((((((_. ", -" ./((:::::<. ", -" ./(....... ", -" ./. ", -" .. ", -" . ", -" ", -" "}; +/* XPM */ +static char * stock_left_xpm[] = { +"16 16 26 1", +" c None", +". c #000000", +"+ c #C4D4E3", +"@ c #A3BCD4", +"# c #A6BED5", +"$ c #AAC1D7", +"% c #ABC2D8", +"& c #AFC5DA", +"* c #AEC4D9", +"= c #6892B9", +"- c #9CB7D1", +"; c #A4BDD5", +"> c #9FB9D2", +", c #9BB6D0", +"' c #9AB5CF", +") c #49759E", +"! c #1C2D3D", +"~ c #C5D5E4", +"{ c #A0BAD3", +"] c #9EB8D1", +"^ c #4B78A2", +"/ c #2A435B", +"( c #3F6588", +"_ c #34536F", +": c #29425A", +"< c #2D4760", +" ", +" . ", +" .. ", +" .+. ", +" .+@....... ", +" .+#$%&%*@=. ", +" .+-;@>,,>'). ", +" !~>{]]->>>>^. ", +" ./((((((((_. ", +" ./((:::::<. ", +" ./(....... ", +" ./. ", +" .. ", +" . ", +" ", +" "}; diff --git a/lib/matplotlib/mpl-data/images/stock_refresh.xpm b/lib/matplotlib/mpl-data/images/stock_refresh.xpm index d686de9f2920..1659cff3dd52 100644 --- a/lib/matplotlib/mpl-data/images/stock_refresh.xpm +++ b/lib/matplotlib/mpl-data/images/stock_refresh.xpm @@ -1,35 +1,35 @@ -/* XPM */ -static char * stock_refresh_xpm[] = { -"16 16 16 1", -" c None", -". c #000000", -"+ c #8FA8BE", -"@ c #D5DEE6", -"# c #BBCBD8", -"$ c #A6BACB", -"% c #A2B7C9", -"& c #83A0B8", -"* c #7393AE", -"= c #4F6F8A", -"- c #48667F", -"; c #92ABC0", -"> c #33485A", -", c #22303B", -"' c #7897B1", -") c #4B6A84", -" ", -" . ", -" ..+. ", -" .@#$%. ", -" .&*==-. ", -" .;>.,. ", -" .'. . . ", -" .). .. ", -" .. .@. ", -" . . .=. ", -" .@.>=. ", -" .@===>. ", -" .'=>>. ", -" .'.. ", -" . ", -" "}; +/* XPM */ +static char * stock_refresh_xpm[] = { +"16 16 16 1", +" c None", +". c #000000", +"+ c #8FA8BE", +"@ c #D5DEE6", +"# c #BBCBD8", +"$ c #A6BACB", +"% c #A2B7C9", +"& c #83A0B8", +"* c #7393AE", +"= c #4F6F8A", +"- c #48667F", +"; c #92ABC0", +"> c #33485A", +", c #22303B", +"' c #7897B1", +") c #4B6A84", +" ", +" . ", +" ..+. ", +" .@#$%. ", +" .&*==-. ", +" .;>.,. ", +" .'. . . ", +" .). .. ", +" .. .@. ", +" . . .=. ", +" .@.>=. ", +" .@===>. ", +" .'=>>. ", +" .'.. ", +" . ", +" "}; diff --git a/lib/matplotlib/mpl-data/images/stock_right.xpm b/lib/matplotlib/mpl-data/images/stock_right.xpm index 2f1607d67b3d..f63c1d34051b 100644 --- a/lib/matplotlib/mpl-data/images/stock_right.xpm +++ b/lib/matplotlib/mpl-data/images/stock_right.xpm @@ -1,44 +1,44 @@ -/* XPM */ -static char * stock_right_xpm[] = { -"16 16 25 1", -" c None", -". c #000000", -"+ c #5B88B2", -"@ c #9EB8D1", -"# c #5080AD", -"$ c #B5C9DC", -"% c #AFC5DA", -"& c #B2C7DB", -"* c #B6CADD", -"= c #A4BDD5", -"- c #9CB7D1", -"; c #080D11", -"> c #9BB6D0", -", c #A0BAD3", -"' c #9AB5CF", -") c #97B3CE", -"! c #5F8BB4", -"~ c #91B0CC", -"{ c #95B2CE", -"] c #4C79A3", -"^ c #49749C", -"/ c #3F6588", -"( c #2A435B", -"_ c #456F96", -": c #375978", -" ", -" . ", -" .. ", -" .+. ", -" .......@#. ", -" .$%&***=-#; ", -" .>,-->',-)!. ", -" .~@''>---,{]. ", -" .^////////(. ", -" ._::::://(. ", -" ......./(. ", -" .(. ", -" .. ", -" . ", -" ", -" "}; +/* XPM */ +static char * stock_right_xpm[] = { +"16 16 25 1", +" c None", +". c #000000", +"+ c #5B88B2", +"@ c #9EB8D1", +"# c #5080AD", +"$ c #B5C9DC", +"% c #AFC5DA", +"& c #B2C7DB", +"* c #B6CADD", +"= c #A4BDD5", +"- c #9CB7D1", +"; c #080D11", +"> c #9BB6D0", +", c #A0BAD3", +"' c #9AB5CF", +") c #97B3CE", +"! c #5F8BB4", +"~ c #91B0CC", +"{ c #95B2CE", +"] c #4C79A3", +"^ c #49749C", +"/ c #3F6588", +"( c #2A435B", +"_ c #456F96", +": c #375978", +" ", +" . ", +" .. ", +" .+. ", +" .......@#. ", +" .$%&***=-#; ", +" .>,-->',-)!. ", +" .~@''>---,{]. ", +" .^////////(. ", +" ._::::://(. ", +" ......./(. ", +" .(. ", +" .. ", +" . ", +" ", +" "}; diff --git a/lib/matplotlib/mpl-data/images/stock_save_as.xpm b/lib/matplotlib/mpl-data/images/stock_save_as.xpm index 63f65f797f63..0981c7c57c61 100644 --- a/lib/matplotlib/mpl-data/images/stock_save_as.xpm +++ b/lib/matplotlib/mpl-data/images/stock_save_as.xpm @@ -1,130 +1,130 @@ -/* XPM */ -static char * stock_save_as_xpm[] = { -"16 16 111 2", -" c None", -". c #000000", -"+ c #F7F8FA", -"@ c #CBDDEB", -"# c #C88A80", -"$ c #D18F84", -"% c #CF8D82", -"& c #A49626", -"* c #634A1E", -"= c #A8BBCC", -"- c #BFD5E8", -"; c #DBE7F1", -"> c #8DA9BE", -", c #B7877E", -"' c #C77568", -") c #C77467", -"! c #C57366", -"~ c #FCEB3D", -"{ c #F7B544", -"] c #61522E", -"^ c #72899A", -"/ c #54697C", -"( c #CFE0ED", -"_ c #D7D7D7", -": c #FEFEFE", -"< c #FCFCFC", -"[ c #F9DF39", -"} c #F7B545", -"| c #6C5F34", -"1 c #B4B4B4", -"2 c #84A0B5", -"3 c #4F6475", -"4 c #D6D6D6", -"5 c #F8D837", -"6 c #EFB44D", -"7 c #584D2B", -"8 c #8F8F8F", -"9 c #F1F1F1", -"0 c #819AAE", -"a c #496072", -"b c #FDFDFD", -"c c #F6D236", -"d c #EDA43E", -"e c #584E2B", -"f c #AAAAAA", -"g c #D3D3D3", -"h c #485F71", -"i c #D5D5D5", -"j c #D7AE74", -"k c #61562F", -"l c #737373", -"m c #C5C5C5", -"n c #B0B0B0", -"o c #7F98AC", -"p c #EDEDED", -"q c #4F4115", -"r c #8D8D8D", -"s c #EBEBEB", -"t c #ECECEC", -"u c #ACBDCB", -"v c #6F767D", -"w c #9AA3AC", -"x c #BFCBD6", -"y c #BDC9D4", -"z c #A1B6C4", -"A c #8BA7BC", -"B c #809CB0", -"C c #6C8394", -"D c #7D97AB", -"E c #7D97AC", -"F c #A4ACB8", -"G c #B9B9B9", -"H c #C7C7C7", -"I c #E1E1E1", -"J c #D4D4D4", -"K c #9C9D9D", -"L c #2F4656", -"M c #80868C", -"N c #183042", -"O c #33495A", -"P c #132D3C", -"Q c #586D80", -"R c #97A5B0", -"S c #86A4B9", -"T c #CDCDCD", -"U c #2E4353", -"V c #5A7082", -"W c #BFBFBF", -"X c #112835", -"Y c #9DA9B0", -"Z c #6B7882", -"` c #829DB1", -" . c #CBCBCB", -".. c #E5E5E5", -"+. c #213648", -"@. c #5F7989", -"#. c #C2C2C2", -"$. c #B2B2B2", -"%. c #112C3A", -"&. c #9FA9B0", -"*. c #59636D", -"=. c #A1A1A1", -"-. c #C0C0C0", -";. c #909090", -">. c #868686", -",. c #6E6E6E", -"'. c #7A7A7A", -"). c #2D3949", -"!. c #3E4F5C", -"~. c #80878F", -"{. c #1A3140", -" . . . . . . . . . . . . . . ", -". + @ # $ $ $ $ % . & * . = - . ", -". ; > , ' ) ) ! . ~ { ] . ^ / . ", -". ( > _ : : < . [ } | . 1 2 3 . ", -". ( > _ _ 4 . 5 6 7 . 8 9 0 a . ", -". ( > _ b . c d e . f g 9 0 h . ", -". ( > _ i . j k . l m n 9 o a . ", -". ( > p . q . . r g s s t 0 a . ", -". ( > u . . v w x x x y z 0 a . ", -". ( > A B C 0 0 0 0 D E 0 0 a . ", -". ( > A F G G H I J K L M 0 a . ", -". ( > 2 m m N O i m G P Q R a . ", -". ( S 0 m T U V m m W X V Y a . ", -". Z ` o ...+.@.m #.$.%.V &.a . ", -". . *.3 =.-.;.;.>.,.'.).!.~.{.. ", -" . . . . . . . . . . . . . . "}; +/* XPM */ +static char * stock_save_as_xpm[] = { +"16 16 111 2", +" c None", +". c #000000", +"+ c #F7F8FA", +"@ c #CBDDEB", +"# c #C88A80", +"$ c #D18F84", +"% c #CF8D82", +"& c #A49626", +"* c #634A1E", +"= c #A8BBCC", +"- c #BFD5E8", +"; c #DBE7F1", +"> c #8DA9BE", +", c #B7877E", +"' c #C77568", +") c #C77467", +"! c #C57366", +"~ c #FCEB3D", +"{ c #F7B544", +"] c #61522E", +"^ c #72899A", +"/ c #54697C", +"( c #CFE0ED", +"_ c #D7D7D7", +": c #FEFEFE", +"< c #FCFCFC", +"[ c #F9DF39", +"} c #F7B545", +"| c #6C5F34", +"1 c #B4B4B4", +"2 c #84A0B5", +"3 c #4F6475", +"4 c #D6D6D6", +"5 c #F8D837", +"6 c #EFB44D", +"7 c #584D2B", +"8 c #8F8F8F", +"9 c #F1F1F1", +"0 c #819AAE", +"a c #496072", +"b c #FDFDFD", +"c c #F6D236", +"d c #EDA43E", +"e c #584E2B", +"f c #AAAAAA", +"g c #D3D3D3", +"h c #485F71", +"i c #D5D5D5", +"j c #D7AE74", +"k c #61562F", +"l c #737373", +"m c #C5C5C5", +"n c #B0B0B0", +"o c #7F98AC", +"p c #EDEDED", +"q c #4F4115", +"r c #8D8D8D", +"s c #EBEBEB", +"t c #ECECEC", +"u c #ACBDCB", +"v c #6F767D", +"w c #9AA3AC", +"x c #BFCBD6", +"y c #BDC9D4", +"z c #A1B6C4", +"A c #8BA7BC", +"B c #809CB0", +"C c #6C8394", +"D c #7D97AB", +"E c #7D97AC", +"F c #A4ACB8", +"G c #B9B9B9", +"H c #C7C7C7", +"I c #E1E1E1", +"J c #D4D4D4", +"K c #9C9D9D", +"L c #2F4656", +"M c #80868C", +"N c #183042", +"O c #33495A", +"P c #132D3C", +"Q c #586D80", +"R c #97A5B0", +"S c #86A4B9", +"T c #CDCDCD", +"U c #2E4353", +"V c #5A7082", +"W c #BFBFBF", +"X c #112835", +"Y c #9DA9B0", +"Z c #6B7882", +"` c #829DB1", +" . c #CBCBCB", +".. c #E5E5E5", +"+. c #213648", +"@. c #5F7989", +"#. c #C2C2C2", +"$. c #B2B2B2", +"%. c #112C3A", +"&. c #9FA9B0", +"*. c #59636D", +"=. c #A1A1A1", +"-. c #C0C0C0", +";. c #909090", +">. c #868686", +",. c #6E6E6E", +"'. c #7A7A7A", +"). c #2D3949", +"!. c #3E4F5C", +"~. c #80878F", +"{. c #1A3140", +" . . . . . . . . . . . . . . ", +". + @ # $ $ $ $ % . & * . = - . ", +". ; > , ' ) ) ! . ~ { ] . ^ / . ", +". ( > _ : : < . [ } | . 1 2 3 . ", +". ( > _ _ 4 . 5 6 7 . 8 9 0 a . ", +". ( > _ b . c d e . f g 9 0 h . ", +". ( > _ i . j k . l m n 9 o a . ", +". ( > p . q . . r g s s t 0 a . ", +". ( > u . . v w x x x y z 0 a . ", +". ( > A B C 0 0 0 0 D E 0 0 a . ", +". ( > A F G G H I J K L M 0 a . ", +". ( > 2 m m N O i m G P Q R a . ", +". ( S 0 m T U V m m W X V Y a . ", +". Z ` o ...+.@.m #.$.%.V &.a . ", +". . *.3 =.-.;.;.>.,.'.).!.~.{.. ", +" . . . . . . . . . . . . . . "}; diff --git a/lib/matplotlib/mpl-data/images/stock_up.xpm b/lib/matplotlib/mpl-data/images/stock_up.xpm index 359aed160ad6..994623624c88 100644 --- a/lib/matplotlib/mpl-data/images/stock_up.xpm +++ b/lib/matplotlib/mpl-data/images/stock_up.xpm @@ -1,45 +1,45 @@ -/* XPM */ -static char * stock_up_xpm[] = { -"16 16 26 1", -" c None", -". c #1C2D3D", -"+ c #000000", -"@ c #C5D5E4", -"# c #C4D4E3", -"$ c #9FB9D2", -"% c #2A435B", -"& c #9CB7D1", -"* c #A0BAD3", -"= c #3F6588", -"- c #A6BED5", -"; c #A4BDD5", -"> c #9EB8D1", -", c #A3BCD4", -"' c #AAC1D7", -") c #ABC2D8", -"! c #29425A", -"~ c #AFC5DA", -"{ c #9BB6D0", -"] c #AEC4D9", -"^ c #9AB5CF", -"/ c #6892B9", -"( c #49759E", -"_ c #4B78A2", -": c #34536F", -"< c #2D4760", -" ", -" . ", -" +@+ ", -" +#$%+ ", -" +#&*=%+ ", -" +#-;>==%+ ", -" +#,',>===%+ ", -" ++++)$&=!++++ ", -" +~{$=!+ ", -" +){$=!+ ", -" +]$$=!+ ", -" +,^$=!+ ", -" +/(_:<+ ", -" +++++++ ", -" ", -" "}; +/* XPM */ +static char * stock_up_xpm[] = { +"16 16 26 1", +" c None", +". c #1C2D3D", +"+ c #000000", +"@ c #C5D5E4", +"# c #C4D4E3", +"$ c #9FB9D2", +"% c #2A435B", +"& c #9CB7D1", +"* c #A0BAD3", +"= c #3F6588", +"- c #A6BED5", +"; c #A4BDD5", +"> c #9EB8D1", +", c #A3BCD4", +"' c #AAC1D7", +") c #ABC2D8", +"! c #29425A", +"~ c #AFC5DA", +"{ c #9BB6D0", +"] c #AEC4D9", +"^ c #9AB5CF", +"/ c #6892B9", +"( c #49759E", +"_ c #4B78A2", +": c #34536F", +"< c #2D4760", +" ", +" . ", +" +@+ ", +" +#$%+ ", +" +#&*=%+ ", +" +#-;>==%+ ", +" +#,',>===%+ ", +" ++++)$&=!++++ ", +" +~{$=!+ ", +" +){$=!+ ", +" +]$$=!+ ", +" +,^$=!+ ", +" +/(_:<+ ", +" +++++++ ", +" ", +" "}; diff --git a/lib/matplotlib/mpl-data/images/stock_zoom-in.xpm b/lib/matplotlib/mpl-data/images/stock_zoom-in.xpm index 33b16b674a9b..349e87e73200 100644 --- a/lib/matplotlib/mpl-data/images/stock_zoom-in.xpm +++ b/lib/matplotlib/mpl-data/images/stock_zoom-in.xpm @@ -1,61 +1,61 @@ -/* XPM */ -static char * stock_zoom_in_xpm[] = { -"16 16 42 1", -" c None", -". c #000000", -"+ c #262626", -"@ c #C5C5C5", -"# c #EEEEEE", -"$ c #EDEDED", -"% c #ABABAB", -"& c #464646", -"* c #878787", -"= c #F1F1F1", -"- c #FEFEFE", -"; c #FDFDFD", -"> c #FCFCFC", -", c #EAEAEA", -"' c #707070", -") c #252525", -"! c #282828", -"~ c #FBFBFB", -"{ c #E8E8E8", -"] c #B0B0B0", -"^ c #FFFFFF", -"/ c #050505", -"( c #040404", -"_ c #FAFAFA", -": c #A4A4A4", -"< c #090909", -"[ c #242424", -"} c #E5E5E5", -"| c #E4E4E4", -"1 c #F9F9F9", -"2 c #BABABA", -"3 c #E7E7E7", -"4 c #858585", -"5 c #E3E3E3", -"6 c #6D6D6D", -"7 c #A1A1A1", -"8 c #202020", -"9 c #686868", -"0 c #343434", -"a c #797979", -"b c #3A3A3A", -"c c #1F1F1F", -" .... ", -" .+@#$%&. ", -" .*=--;>,'. ", -" &=--)!;~{& ", -".]--^/(;>_:. ", -".#-//<(([_}. ", -".$;[(../[_|. ", -".%>;;((~_12. ", -" &,~><)_13& ", -" .4{___156. ", -" .&:}|7&.... ", -" .... 88.. ", -" .90.. ", -" .ab..", -" .9c.", -" .. "}; +/* XPM */ +static char * stock_zoom_in_xpm[] = { +"16 16 42 1", +" c None", +". c #000000", +"+ c #262626", +"@ c #C5C5C5", +"# c #EEEEEE", +"$ c #EDEDED", +"% c #ABABAB", +"& c #464646", +"* c #878787", +"= c #F1F1F1", +"- c #FEFEFE", +"; c #FDFDFD", +"> c #FCFCFC", +", c #EAEAEA", +"' c #707070", +") c #252525", +"! c #282828", +"~ c #FBFBFB", +"{ c #E8E8E8", +"] c #B0B0B0", +"^ c #FFFFFF", +"/ c #050505", +"( c #040404", +"_ c #FAFAFA", +": c #A4A4A4", +"< c #090909", +"[ c #242424", +"} c #E5E5E5", +"| c #E4E4E4", +"1 c #F9F9F9", +"2 c #BABABA", +"3 c #E7E7E7", +"4 c #858585", +"5 c #E3E3E3", +"6 c #6D6D6D", +"7 c #A1A1A1", +"8 c #202020", +"9 c #686868", +"0 c #343434", +"a c #797979", +"b c #3A3A3A", +"c c #1F1F1F", +" .... ", +" .+@#$%&. ", +" .*=--;>,'. ", +" &=--)!;~{& ", +".]--^/(;>_:. ", +".#-//<(([_}. ", +".$;[(../[_|. ", +".%>;;((~_12. ", +" &,~><)_13& ", +" .4{___156. ", +" .&:}|7&.... ", +" .... 88.. ", +" .90.. ", +" .ab..", +" .9c.", +" .. "}; diff --git a/lib/matplotlib/mpl-data/images/stock_zoom-out.xpm b/lib/matplotlib/mpl-data/images/stock_zoom-out.xpm index 044355850404..a9d479124a67 100644 --- a/lib/matplotlib/mpl-data/images/stock_zoom-out.xpm +++ b/lib/matplotlib/mpl-data/images/stock_zoom-out.xpm @@ -1,59 +1,59 @@ -/* XPM */ -static char * stock_zoom_out_xpm[] = { -"16 16 40 1", -" c None", -". c #000000", -"+ c #262626", -"@ c #C5C5C5", -"# c #EEEEEE", -"$ c #EDEDED", -"% c #ABABAB", -"& c #464646", -"* c #878787", -"= c #F1F1F1", -"- c #FEFEFE", -"; c #FDFDFD", -"> c #FCFCFC", -", c #EAEAEA", -"' c #707070", -") c #FBFBFB", -"! c #E8E8E8", -"~ c #B0B0B0", -"{ c #FFFFFF", -"] c #FAFAFA", -"^ c #A4A4A4", -"/ c #050505", -"( c #090909", -"_ c #040404", -": c #242424", -"< c #E5E5E5", -"[ c #E4E4E4", -"} c #F9F9F9", -"| c #BABABA", -"1 c #E7E7E7", -"2 c #858585", -"3 c #E3E3E3", -"4 c #6D6D6D", -"5 c #A1A1A1", -"6 c #202020", -"7 c #686868", -"8 c #343434", -"9 c #797979", -"0 c #3A3A3A", -"a c #1F1F1F", -" .... ", -" .+@#$%&. ", -" .*=--;>,'. ", -" &=----;)!& ", -".~--{--;>]^. ", -".#-//(__:]<. ", -".$;:_../:][. ", -".%>;;;>)]}|. ", -" &,)>))]}1& ", -" .2!]]]}34. ", -" .&^<[5&.... ", -" .... 66.. ", -" .78.. ", -" .90..", -" .7a.", -" .. "}; +/* XPM */ +static char * stock_zoom_out_xpm[] = { +"16 16 40 1", +" c None", +". c #000000", +"+ c #262626", +"@ c #C5C5C5", +"# c #EEEEEE", +"$ c #EDEDED", +"% c #ABABAB", +"& c #464646", +"* c #878787", +"= c #F1F1F1", +"- c #FEFEFE", +"; c #FDFDFD", +"> c #FCFCFC", +", c #EAEAEA", +"' c #707070", +") c #FBFBFB", +"! c #E8E8E8", +"~ c #B0B0B0", +"{ c #FFFFFF", +"] c #FAFAFA", +"^ c #A4A4A4", +"/ c #050505", +"( c #090909", +"_ c #040404", +": c #242424", +"< c #E5E5E5", +"[ c #E4E4E4", +"} c #F9F9F9", +"| c #BABABA", +"1 c #E7E7E7", +"2 c #858585", +"3 c #E3E3E3", +"4 c #6D6D6D", +"5 c #A1A1A1", +"6 c #202020", +"7 c #686868", +"8 c #343434", +"9 c #797979", +"0 c #3A3A3A", +"a c #1F1F1F", +" .... ", +" .+@#$%&. ", +" .*=--;>,'. ", +" &=----;)!& ", +".~--{--;>]^. ", +".#-//(__:]<. ", +".$;:_../:][. ", +".%>;;;>)]}|. ", +" &,)>))]}1& ", +" .2!]]]}34. ", +" .&^<[5&.... ", +" .... 66.. ", +" .78.. ", +" .90..", +" .7a.", +" .. "};