Revision: 7414
http://matplotlib.svn.sourceforge.net/matplotlib/?rev=7414&view=rev
Author: jdh2358
Date: 2009-08-07 10:15:04 +0000 (Fri, 07 Aug 2009)
Log Message:
-----------
some doc fixes
Modified Paths:
--------------
branches/v0_99_maint/doc/users/annotations.rst
branches/v0_99_maint/doc/users/artists.rst
branches/v0_99_maint/doc/users/event_handling.rst
branches/v0_99_maint/doc/users/pyplot_tutorial.rst
Modified: branches/v0_99_maint/doc/users/annotations.rst
===================================================================
--- branches/v0_99_maint/doc/users/annotations.rst 2009-08-07 00:42:20 UTC
(rev 7413)
+++ branches/v0_99_maint/doc/users/annotations.rst 2009-08-07 10:15:04 UTC
(rev 7414)
@@ -78,6 +78,4 @@
.. plot:: pyplots/annotation_polar.py
:include-source:
-See the `annotations demo
-<http://matplotlib.sf.net/examples/pylab_examples/annotation_demo.py>`_ for
more
-examples.
+For more on all the wild and wonderful things you can do with annotations,
including fancy arrows, see :ref:`plotting-guide-annotation` and
:ref:`pylab_examples-annotation_demo`.
Modified: branches/v0_99_maint/doc/users/artists.rst
===================================================================
--- branches/v0_99_maint/doc/users/artists.rst 2009-08-07 00:42:20 UTC (rev
7413)
+++ branches/v0_99_maint/doc/users/artists.rst 2009-08-07 10:15:04 UTC (rev
7414)
@@ -19,27 +19,7 @@
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::
+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()
@@ -85,7 +65,7 @@
<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(x, y, 'o')`` call:
+returned by the ``line, = ax.plot...`` call:
.. sourcecode:: ipython
@@ -536,20 +516,7 @@
: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.:
+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
@@ -636,7 +603,7 @@
label2On boolean which determines whether to draw tick label
============== ==========================================================
-Here is an example which sets the formatter for the upper ticks with
+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
Modified: branches/v0_99_maint/doc/users/event_handling.rst
===================================================================
--- branches/v0_99_maint/doc/users/event_handling.rst 2009-08-07 00:42:20 UTC
(rev 7413)
+++ branches/v0_99_maint/doc/users/event_handling.rst 2009-08-07 10:15:04 UTC
(rev 7414)
@@ -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
-<http://www.scipy.org/Cookbook/Matplotlib/Animations>`_ 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 6 user interface toolkits (wxpython, tkinter,
+qt, gtk, fltk abd macosx) 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, any character, '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
+<http://www.scipy.org/Cookbook/Matplotlib/Animations>`_ 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()
Modified: branches/v0_99_maint/doc/users/pyplot_tutorial.rst
===================================================================
--- branches/v0_99_maint/doc/users/pyplot_tutorial.rst 2009-08-07 00:42:20 UTC
(rev 7413)
+++ branches/v0_99_maint/doc/users/pyplot_tutorial.rst 2009-08-07 10:15:04 UTC
(rev 7414)
@@ -74,7 +74,7 @@
one line so it is a list of length 1. I use tuple unpacking in the
``line, = plot(x, y, 'o')`` to get the first element of the list::
- line, = plt.plot(x, y, 'o')
+ line, = plt.plot(x, y, '-')
line.set_antialiased(False) # turn off antialising
* Use the :func:`~matplotlib.pyplot.setp` command. The example below
@@ -156,7 +156,7 @@
:func:`~matplotlib.pyplot.gcf` returns the current figure
(:class:`matplotlib.figure.Figure` instance). Normally, you don't have
to worry about this, because it is all taken care of behind the
-scenes. Below is an script to create two subplots.
+scenes. Below is a script to create two subplots.
.. plot:: pyplots/pyplot_two_subplots.py
:include-source:
@@ -165,18 +165,16 @@
``figure(1)`` will be created by default, just as a ``subplot(111)``
will be created by default if you don't manually specify an axes. The
:func:`~matplotlib.pyplot.subplot` command specifies ``numrows,
-numcols, fignum`` where ``fignum`` ranges from 1 to
-``numrows*numcols``. The commas in the ``subplot command are optional
+ numcols, fignum`` where ``fignum`` ranges from 1 to
+``numrows*numcols``. The commas in the ``subplot`` command are optional
if ``numrows*numcols<10``. So ``subplot(211)`` is identical to
``subplot(2,1,1)``. You can create an arbitrary number of subplots
and axes. If you want to place an axes manually, ie, not on a
rectangular grid, use the :func:`~matplotlib.pyplot.axes` command,
which allows you to specify the location as ``axes([left, bottom,
width, height])`` where all values are in fractional (0 to 1)
-coordinates. See `axes_demo.py
-<http://matplotlib.sf.net/examples/axes_demo.py>`_ for an example of
-placing axes manually and `line_styles.py
-<http://matplotlib.sf.net/examples/line_styles.py>`_ for an example
+coordinates. See :ref:`pylab_examples-axes_demo` for an example of
+placing axes manually and :ref:`pylab_examples-line_styles` for an example
with lots-o-subplots.
@@ -269,6 +267,6 @@
In this basic example, both the ``xy`` (arrow tip) and ``xytext``
locations (text location) are in data coordinates. There are a
variety of other coordinate systems one can choose -- see
-:ref:`annotations-tutorial` for details. More examples can be found
-in the `annotations demo
-<http://matplotlib.sf.net/examples/pylab_examples/annotation_demo.html>`_
+:ref:`annotations-tutorial` and :ref:`plotting-guide-annotation`
+for details. More examples can be found
+in :ref:`pylab_examples-annotation_demo`.
This was sent by the SourceForge.net collaborative development platform, the
world's largest Open Source development site.
------------------------------------------------------------------------------
Let Crystal Reports handle the reporting - Free Crystal Reports 2008 30-Day
trial. Simplify your report design, integration and deployment - and focus on
what you do best, core application coding. Discover what's new with
Crystal Reports now. http://p.sf.net/sfu/bobj-july
_______________________________________________
Matplotlib-checkins mailing list
[email protected]
https://lists.sourceforge.net/lists/listinfo/matplotlib-checkins
