From: <efiring@us...>  20100626 07:45:33

Revision: 8468 http://matplotlib.svn.sourceforge.net/matplotlib/?rev=8468&view=rev Author: efiring Date: 20100626 07:45:26 +0000 (Sat, 26 Jun 2010) Log Message:  docs: be consistent in referring to MATLAB. For the legal rationale, see Joe Harrington's post to scipydev: http://article.gmane.org/gmane.comp.python.scientific.user/25399/match=matlab+trademark Modified Paths:  trunk/matplotlib/doc/_templates/index.html trunk/matplotlib/doc/api/api_changes.rst trunk/matplotlib/doc/faq/usage_faq.rst trunk/matplotlib/doc/users/artists.rst trunk/matplotlib/doc/users/installing.rst trunk/matplotlib/doc/users/intro.rst trunk/matplotlib/doc/users/pyplot_tutorial.rst trunk/matplotlib/doc/users/screenshots.rst trunk/matplotlib/doc/users/whats_new.rst Modified: trunk/matplotlib/doc/_templates/index.html ===================================================================  trunk/matplotlib/doc/_templates/index.html 20100626 07:37:44 UTC (rev 8467) +++ trunk/matplotlib/doc/_templates/index.html 20100626 07:45:26 UTC (rev 8468) @@ 9,10 +9,19 @@ publication quality figures in a variety of hardcopy formats and interactive environments across platforms. matplotlib can be used in python scripts, the python and <a  href="http://ipython.scipy.org">ipython</a>; shell (ala matlab or  mathematica), web application servers, and six graphical user + href="http://ipython.scipy.org">ipython</a>; shell (ala + MATLAB<sup>®<a name="matlab" href="#ftn.matlab">*</a></sup> + or + Mathematica<sup>®<a name="mathematica" + href="#ftn.mathematica">†</a></sup>), + web application servers, and six graphical user interface toolkits.</p> + + + + + <p>matplotlib tries to make easy things easy and hard things possible. You can generate plots, histograms, power spectra, bar charts, errorcharts, scatterplots, etc, with just a few lines of code. @@ 35,7 +44,7 @@ <p>For the power user, you have full control of line styles, font properties, axes properties, etc, via an object oriented interface  or via a set of functions familiar to Matlab® users. + or via a set of functions familiar to MATLAB users. The pylab mode provides all of the <a href="api/pyplot_api.html">pyplot</a> plotting functions listed below, as well as nonplotting functions from <a href="http://scipy.org/Numpy_Example_List_With_Doc">numpy</a>; and @@ 1193,4 +1202,16 @@ </tr> </table> + +<div class="footnote"><p> +<sup><a name="ftn.matlab" href="#matlab">*</a></sup> +MATLAB is a registered trademark of The MathWorks, Inc. +</p> +<p> +<sup><a name="ftn.mathematica" href="#mathematica">†</a></sup> +Mathematica is a registered trademark of Wolfram Research, Inc. +</p> + + + {% endblock %} Modified: trunk/matplotlib/doc/api/api_changes.rst ===================================================================  trunk/matplotlib/doc/api/api_changes.rst 20100626 07:37:44 UTC (rev 8467) +++ trunk/matplotlib/doc/api/api_changes.rst 20100626 07:45:26 UTC (rev 8468) @@ 179,7 +179,7 @@ to scale onesided densities by a factor of 2. Also, optionally scale the densities by the sampling frequency, which gives true values of densities that can be integrated by the returned frequency values.  This also gives better MatLab compatibility. The corresponding + This also gives better MATLAB compatibility. The corresponding :class:`matplotlib.axes.Axes` methods and :mod:`matplotlib.pyplot` functions were updated as well. @@ 1094,7 +1094,7 @@  pylab figure now defaults to num=None, which creates a new figure with a guaranteed unique number   contour method syntax changed  now it is matlab compatible +  contour method syntax changed  now it is MATLAB compatible unchanged: contour(Z) old: contour(Z, x=Y, y=Y) @@ 1183,7 +1183,7 @@ ::  mpl_connect and mpl_disconnect in the matlab interface renamed to + mpl_connect and mpl_disconnect in the MATLAB interface renamed to connect and disconnect Did away with the text methods for angle since they were ambiguous. @@ 1272,7 +1272,7 @@ Changes for 0.54 ================ matlab interface +MATLAB interface  dpi @@ 1289,7 +1289,7 @@ pcolor and scatter ~~~~~~~~~~~~~~~~~~ There are two changes to the matlab interface API, both involving the +There are two changes to the MATLAB interface API, both involving the patch drawing commands. For efficiency, pcolor and scatter have been rewritten to use polygon collections, which are a new set of objects from matplotlib.collections designed to enable efficient handling of @@ 1323,7 +1323,7 @@ for a discussion on how to set the properties as a sequence. For scatter, the size argument is now in points^2 (the area of the symbol in points) as in matlab and is not in data coords as before. +symbol in points) as in MATLAB and is not in data coords as before. Using sizes in data coords caused several problems. So you will need to adjust your size arguments accordingly or use scatter_classic. @@ 1491,7 +1491,7 @@ * backends must implement FigureCanvasBackend (the thing that controls the figure and handles the events if any) and  FigureManagerBackend (wraps the canvas and the window for matlab + FigureManagerBackend (wraps the canvas and the window for MATLAB interface). FigureCanvasBase implements a backend switching mechanism @@ 1514,9 +1514,9 @@ Migrating code:  Matlab interface: + MATLAB interface:  The only API change for those using the matlab interface is in how + The only API change for those using the MATLAB interface is in how you call figure redraws for dynamically updating figures. In the old API, you did @@ 1653,7 +1653,7 @@  new module transforms supplies Bound1D, Bound2D and Transform instances and more   Changes to the matlab helpers API +  Changes to the MATLAB helpers API * _matlab_helpers.GcfBase is renamed by Gcf. Backends no longer need to derive from this class. Instead, they provide a factory Modified: trunk/matplotlib/doc/faq/usage_faq.rst ===================================================================  trunk/matplotlib/doc/faq/usage_faq.rst 20100626 07:37:44 UTC (rev 8467) +++ trunk/matplotlib/doc/faq/usage_faq.rst 20100626 07:45:26 UTC (rev 8468) @@ 17,19 +17,19 @@ installed alongside :mod:`matplotlib`; and :mod:`matplotlib.pyplot` is a module in matplotlib. Pyplot provides a Matlabstyle statemachine interface to +Pyplot provides a MATLABstyle statemachine interface to the underlying objectoriented plotting library in matplotlib. Pylab combines the pyplot functionality (for plotting) with the numpy functionality (for mathematics and for working with arrays) in a single namespace, making that namespace (or environment) even more Matlablike. This is what you get if +(or environment) even more MATLABlike. This is what you get if you use the *ipython* shell with the *pylab* option, which imports everything from pylab and makes plotting fully interactive. We have been gradually converting the matplotlib examples from pure Matlabstyle, using "from pylab import \*", to a preferred +from pure MATLABstyle, using "from pylab import \*", to a preferred style in which pyplot is used for some convenience functions, either pyplot or the objectoriented style is used for the remainder of the plotting code, and numpy is used explicitly for numeric array operations. @@ 42,7 +42,7 @@ Then one calls, for example, np.arange, np.zeros, np.pi, plt.figure, plt.plot, plt.show, etc. Example, pure Matlabstyle:: +Example, pure MATLABstyle:: from pylab import * x = arange(0, 10, 0.2) @@ 71,7 +71,7 @@ plt.show() So, why do all the extra typing required as one moves away from the pure matlabstyle? For very simple things like this example, the only +MATLABstyle? For very simple things like this example, the only advantage is educational: the wordier styles are more explicit, more clear as to where things come from and what is going on. For more complicated applications, the explicitness and clarity become Modified: trunk/matplotlib/doc/users/artists.rst ===================================================================  trunk/matplotlib/doc/users/artists.rst 20100626 07:37:44 UTC (rev 8467) +++ trunk/matplotlib/doc/users/artists.rst 20100626 07:45:26 UTC (rev 8468) @@ 1,610 +1,611 @@ .. _artisttutorial:  *************** 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 <http://www.wxpython.org>`_ 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  you can work directly with PostScript, PDF Gtk+, or wxPython ``FigureCanvas`` instances, 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) and 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 01 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 <matplotlib.axes.Axes.lines>` list. In the interactive `ipython <http://ipython.scipy.org/>`_ 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...`` call:  .. sourcecode:: ipython   In [101]: ax.lines[0]  Out[101]: <matplotlib.lines.Line2D instance at 0x19a95710>   In [102]: line  Out[102]: <matplotlib.lines.Line2D instance at 0x19a95710>  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 xaxis and yaxis 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 <matplotlib.axes.Axes.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 crossreferences .. 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  .. _customizingartists:  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 <matplotlib.figure.Figure.patch>` and :attr:`Axes.patch <matplotlib.axes.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 01 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 autolabeling) 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 oldfashioned 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 = <Affine object at 0x134cca84>  verts = ((0, 0), (0, 1), (1, 1), (1, 0))  visible = True  width = 1  window_extent = <Bbox object at 0x134acbcc>  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:`artistapi` for a listing of properties for a given object.  .. _objectcontainers:  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.  .. _figurecontainer:  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 <matplotlib.figure.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 <matplotlib.figure.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]: <matplotlib.axes.Subplot instance at 0xd54b26c>   In [160]: print fig.axes  [<matplotlib.axes.Subplot instance at 0xd54b26c>, <matplotlib.axes.Axes instance at 0xd3f0b2c>]  Because the figure maintains the concept of the "current axes" (see :meth:`Figure.gca <matplotlib.figure.Figure.gca>` and :meth:`Figure.sca <matplotlib.figure.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 bottomleft of the figure and (1, 1) is the topright of the figure which you can obtain by setting the ``Artist`` transform to :attr:`fig.transFigure <matplotlib.figure.Figure.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 ================ ===============================================================  .. _axescontainer:  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 <matplotlib.axes.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  [<matplotlib.lines.Line2D instance at 0xd378b0c>]  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 <matplotlib.axes.Axes.patches>` list:  .. sourcecode:: ipython   In [233]: n, bins, rectangles = ax.hist(np.random.randn(1000), 50, facecolor='yellow')   In [234]: rectangles  Out[234]: <a list of 50 Patch objects>   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 autoscaling, 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()  <Affine object at 0x13695544>   # 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()  Axes(0.125,0.1;0.775x0.8)   # and the transformation has been set too  In [268]: print rect.get_transform()  <Affine object at 0x15009ca4>   # the default axes transformation is ax.transData  In [269]: print ax.transData  <Affine object at 0x15009ca4>   # 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.bounds  (1.0, 1.0, 5.0, 12.0)   # we can manually invoke the autoscaling 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 ============== ======================================  .. _axiscontainer:  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 yaxis, and the upper and lower ticks separately for the xaxis. The ``Axis`` also stores the data and view intervals used in autoscaling, 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 :mod:`~matplotlib.pylab` modifies in calls to :func:`~matplotlib.pylab.xlabel` and :func:`~matplotlib.pylab.ylabel`) 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]: <a list of 10 Text major ticklabel objects>   # 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]: <a list of 20 Line2D ticklines objects>   # by default you get the major ticks back  In [291]: axis.get_ticklines()  Out[291]: <a list of 20 Line2D ticklines objects>   # but you can also ask for the minor ticks  In [292]: axis.get_ticklines(minor=True)  Out[292]: <a list of 0 Line2D ticklines objects>  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=TrueFalse get_ticklines A list of Line2D instances  keyword minor=TrueFalse get_ticklocs A list of Tick locations  keyword minor=TrueFalse 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  :includesource:   .. _tickcontainer:  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 xaxis and whether the right labels and ticks are on for the yaxis.  ============== ========================================================== 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 right side ticks with dollar signs and colors them green on the right side of the yaxis  .. plot:: pyplots/dollar_ticks.py  :includesource: +.. _artisttutorial: + +*************** +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 +<http://www.wxpython.org>`_ 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  you +can work directly with PostScript, PDF Gtk+, or wxPython ``FigureCanvas`` instances, 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) and 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 01 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 +<matplotlib.axes.Axes.lines>` list. In the interactive `ipython +<http://ipython.scipy.org/>`_ 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...`` call: + +.. sourcecode:: ipython + + In [101]: ax.lines[0] + Out[101]: <matplotlib.lines.Line2D instance at 0x19a95710> + + In [102]: line + Out[102]: <matplotlib.lines.Line2D instance at 0x19a95710> + +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 xaxis +and yaxis 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 <matplotlib.axes.Axes.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 crossreferences +.. 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 + +.. _customizingartists: + +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 +<matplotlib.figure.Figure.patch>` and :attr:`Axes.patch +<matplotlib.axes.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 01 +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 autolabeling) +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 oldfashioned 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 = <Affine object at 0x134cca84> + verts = ((0, 0), (0, 1), (1, 1), (1, 0)) + visible = True + width = 1 + window_extent = <Bbox object at 0x134acbcc> + 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:`artistapi` for a listing of properties for a given object. + +.. _objectcontainers: + +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. + +.. _figurecontainer: + +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 <matplotlib.figure.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 +<matplotlib.figure.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]: <matplotlib.axes.Subplot instance at 0xd54b26c> + + In [160]: print fig.axes + [<matplotlib.axes.Subplot instance at 0xd54b26c>, <matplotlib.axes.Axes instance at 0xd3f0b2c>] + +Because the figure maintains the concept of the "current axes" (see +:meth:`Figure.gca <matplotlib.figure.Figure.gca>` and +:meth:`Figure.sca <matplotlib.figure.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 bottomleft of +the figure and (1, 1) is the topright of the figure which you can +obtain by setting the ``Artist`` transform to :attr:`fig.transFigure +<matplotlib.figure.Figure.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 +================ =============================================================== + +.. _axescontainer: + +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 +<matplotlib.axes.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 + [<matplotlib.lines.Line2D instance at 0xd378b0c>] + +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 +<matplotlib.axes.Axes.patches>` list: + +.. sourcecode:: ipython + + In [233]: n, bins, rectangles = ax.hist(np.random.randn(1000), 50, facecolor='yellow') + + In [234]: rectangles + Out[234]: <a list of 50 Patch objects> + + 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 autoscaling, 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() + <Affine object at 0x13695544> + + # 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() + Axes(0.125,0.1;0.775x0.8) + + # and the transformation has been set too + In [268]: print rect.get_transform() + <Affine object at 0x15009ca4> + + # the default axes transformation is ax.transData + In [269]: print ax.transData + <Affine object at 0x15009ca4> + + # 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.bounds + (1.0, 1.0, 5.0, 12.0) + + # we can manually invoke the autoscaling 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 +============== ====================================== + +.. _axiscontainer: + +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 yaxis, and +the upper and lower ticks separately for the xaxis. The ``Axis`` +also stores the data and view intervals used in autoscaling, 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 :mod:`~matplotlib.pylab` modifies in calls to :func:`~matplotlib.pylab.xlabel` and :func:`~matplotlib.pylab.ylabel`) 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]: <a list of 10 Text major ticklabel objects> + + # 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]: <a list of 20 Line2D ticklines objects> + + # by default you get the major ticks back + In [291]: axis.get_ticklines() + Out[291]: <a list of 20 Line2D ticklines objects> + + # but you can also ask for the minor ticks + In [292]: axis.get_ticklines(minor=True) + Out[292]: <a list of 0 Line2D ticklines objects> + +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=TrueFalse +get_ticklines A list of Line2D instances  keyword minor=TrueFalse +get_ticklocs A list of Tick locations  keyword minor=TrueFalse +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 + :includesource: + + +.. _tickcontainer: + +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 xaxis and whether +the right labels and ticks are on for the yaxis. + +============== ========================================================== +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 right side ticks with +dollar signs and colors them green on the right side of the yaxis + +.. plot:: pyplots/dollar_ticks.py + :includesource: Modified: trunk/matplotlib/doc/users/installing.rst ===================================================================  trunk/matplotlib/doc/users/installing.rst 20100626 07:37:44 UTC (rev 8467) +++ trunk/matplotlib/doc/users/installing.rst 20100626 07:45:26 UTC (rev 8468) @@ 64,7 +64,7 @@ Once you have ipython, numpy and matplotlib installed, in ipython's "pylab" mode you have a matlablike environment that automatically +"pylab" mode you have a MATLABlike environment that automatically handles most of the configuration details for you, so you can get up and running quickly:: Modified: trunk/matplotlib/doc/users/intro.rst ===================================================================  trunk/matplotlib/doc/users/intro.rst 20100626 07:37:44 UTC (rev 8467) +++ trunk/matplotlib/doc/users/intro.rst 20100626 07:45:26 UTC (rev 8468) @@ 3,7 +3,7 @@ matplotlib is a library for making 2D plots of arrays in `Python <http://www.python.org>`_. Although it has its origins in emulating the `MATLAB™ <http://www.mathworks.com>`_ graphics commands, it is +the MATLAB graphics commands, it is independent of MATLAB, and can be used in a Pythonic, object oriented way. Although matplotlib is written primarily in pure Python, it makes heavy use of `NumPy <http://www.numpy.org>`_ and other extension Modified: trunk/matplotlib/doc/users/pyplot_tutorial.rst ===================================================================  trunk/matplotlib/doc/users/pyplot_tutorial.rst 20100626 07:37:44 UTC (rev 8467) +++ trunk/matplotlib/doc/users/pyplot_tutorial.rst 20100626 07:45:26 UTC (rev 8468) @@ 5,7 +5,7 @@ *************** :mod:`matplotlib.pyplot` is a collection of command style functions that make matplotlib work like `Matlab™ <http://www.mathworks.com>`_. +that make matplotlib work like MATLAB. Each ``pyplot`` function makes some change to a figure: eg, create a figure, create a plotting area in a figure, plot some lines in a plotting area, decorate the plot @@ 33,7 +33,7 @@ For every x, y pair of arguments, there is an optional third argument which is the format string that indicates the color and line type of the plot. The letters and symbols of the format string are from Matlab, and you concatenate a color string with a line style string. +MATLAB, and you concatenate a color string with a line style string. The default format string is 'b', which is a solid blue line. For example, to plot the above with red circles, you would issue @@ 79,15 +79,15 @@ line.set_antialiased(False) # turn off antialising * Use the :func:`~matplotlib.pyplot.setp` command. The example below  uses a Matlabstyle command to set multiple properties + uses a MATLABstyle command to set multiple properties on a list of lines. ``setp`` works transparently with a list of objects or a single object. You can either use python keyword arguments or  Matlabstyle string/value pairs:: + MATLABstyle string/value pairs:: lines = plt.plot(x1, y1, x2, y2) # use keyword args plt.setp(lines, color='r', linewidth=2.0)  # or Matlab style string value pairs + # or MATLAB style string value pairs plt.setp(lines, 'color', 'r', 'linewidth', 2.0) @@ 150,7 +150,7 @@ ====================================== Matlab, and :mod:`~matplotlib.pyplot`, have the concept of the current +MATLAB, and :mod:`~matplotlib.pyplot`, have the concept of the current figure and the current axes. All plotting commands apply to the current axes. The function :func:`~matplotlib.pyplot.gca` returns the current axes (a :class:`matplotlib.axes.Axes` instance), and Modified: trunk/matplotlib/doc/users/screenshots.rst ===================================================================  trunk/matplotlib/doc/users/screenshots.rst 20100626 07:37:44 UTC (rev 8467) +++ trunk/matplotlib/doc/users/screenshots.rst 20100626 07:45:26 UTC (rev 8468) @@ 97,7 +97,7 @@ ========== The :func:`~matplotlib.pyplot.pie` command uses a matlab(TM) compatible syntax to produce pie charts. Optional +uses a MATLAB compatible syntax to produce pie charts. Optional features include autolabeling the percentage of area, exploding one or more wedges out from the center of the pie, and a shadow effect. Take a close look at the attached code that produced this figure; nine @@ 230,7 +230,7 @@ ======= The :func:`~matplotlib.pyplot.legend` command automatically generates figure legends, with Matlab compatible legend placement +generates figure legends, with MATLAB compatible legend placement commands. Thanks to Charles Twardy for input on the legend command Modified: trunk/matplotlib/doc/users/whats_new.rst ===================================================================  trunk/matplotlib/doc/users/whats_new.rst 20100626 07:37:44 UTC (rev 8467) +++ trunk/matplotlib/doc/users/whats_new.rst 20100626 07:45:26 UTC (rev 8468) @@ 130,7 +130,7 @@ Ryan May did a lot of work to rationalize the amplitude scaling of :func:`~matplotlib.pyplot.psd` and friends. See :ref:`pylab_examplespsd_demo2`. and :ref:`pylab_examplespsd_demo3`. The changes should increase `MATLAB™ <http://www.mathworks.com>`_ +The changes should increase MATLAB compatabililty and increase scaling options. .. _fillbetween: @@ 167,7 +167,7 @@ Updated spectral methods (psd, csd, etc.) to scale onesided densities by a factor of 2 and, optionally, scale all densities by  the sampling frequency. This gives better MatLab + the sampling frequency. This gives better MATLAB compatibility. RM Fixed alignment of ticks in colorbars. MGD This was sent by the SourceForge.net collaborative development platform, the world's largest Open Source development site. 