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added joshuas movie demo
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‎doc/faq/howto_faq.rst‎

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@@ -299,7 +299,7 @@ The :mod:`matplotlib.nxutils` provides two high performance methods:
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for a single point use :func:`~matplotlib.nxutils.pnpoly` and for an
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array of points use :func:`~matplotlib.nxutils.points_inside_poly`.
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For a discussion of the implementation see `pnpoly
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<http://www.ecse.rpi.edu/Homepages/wrf/Research/Short_Notes/pnpoly.html>`_.
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<http://www.ecse.rpi.edu/Homepages/wrf/Research/Short_Notes/pnpoly.html>`_.
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.. sourcecode:: ipython
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@@ -334,5 +334,140 @@ For a discussion of the implementation see `pnpoly
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Out[32]: array([False, False, False, False, False, False, False, True, False, True], dtype=bool)
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.. htmlonly::
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For a complete example, see :ref:`event_handling-lasso_demo`.
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.. _how-to-submit-patch:
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How do I submit a patch?
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========================
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First obtain a copy of matplotlib svn (see :ref:`install-svn`) and
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make your changes to the matplotlib source code or documentation and
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apply a `svn diff`. If it is feasible, do your diff from the top
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level directory, the one that contains :file:`setup.py`. Eg,::
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> cd /path/to/matplotlib/source
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> svn diff > mypatch.diff
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and then post your patch to the `matplotlib-devel
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<http://sourceforge.net/mail/?group_id=80706>`_ mailing list. If you
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do not get a response within 24 hours, post your patch to the
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sourceforge patch `tracker
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<http://sourceforge.net/tracker2/?atid=560722&group_id=80706&func=browse>`_,
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and follow up on the mailing list with a link to the sourceforge patch
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submissions. If you still do not hear anything within a week (this
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shouldn't happen!), send us a kind and gentle reminder on the mailing
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list.
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If you have made lots of local changes and do not want to a diff
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against the entire tree, but rather against a single directory or
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file, that is fine, but we do prefer svn diffs against HEAD.
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You should check out the guide to developing matplotlib to make sure
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your patch abides by our coding conventions
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:ref:`developers-guide-index`.
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.. _howto-click-maps:
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Clickable images for HTML
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=========================
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Andrew Dalke of `Dalke Scientific <http://www.dalkescientific.com>`_
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has written a nice `article
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<http://www.dalkescientific.com/writings/diary/archive/2005/04/24/interactive_html.html>`_
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on how to make html click maps with matplotlib agg PNGs. We would
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also like to add this functionality to SVG and add a SWF backend to
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support these kind of images. If you are interested in contributing
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to these efforts that would be great.
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.. _howto-set-zorder:
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How do I control the depth of plot elements?
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=============================================
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Within an axes, the order that the various lines, markers, text,
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collections, etc appear is determined by the
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:meth:`matplotlib.artist.Artist.set_zorder` property. The default
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order is patches, lines, text, with collections of lines and
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collections of patches appearing at the same level as regular lines
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and patches, respectively::
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line, = ax.plot(x, y, zorder=10)
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.. htmlonly::
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See :ref:`pylab_examples-zorder_demo` for a complete example.
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You can also use the Axes property
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:meth:`matplotlib.axes.Axes.set_axisbelow` to control whether the grid
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lines are placed above or below your other plot elements.
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.. _howto-axis-equal:
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How to I make the aspect ratio for plots equal?
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===============================================
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The Axes property :meth:`matplotlib.axes.Axes.set_aspect` controls the
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aspect ratio of the axes. You can set it to be 'auto', 'equal', or
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some ratio which controls the ratio::
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ax = fig.add_subplot(111, aspect='equal')
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.. htmlonly::
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See :ref:`pylab_examples-equal_aspect_ratio` for a complete example.
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.. _howto-movie:
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How do I make a movie?
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======================
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If you want to take an animated plot and turn it into a movie, the
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best approach is to save a series of image files (eg PNG) and use an
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external tool to convert them to a movie. You can use ` mencoder
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<http://www.mplayerhq.hu/DOCS/HTML/en/mencoder.html>`_,
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which is part of the `mplayer <http://www.mplayerhq.hu>`_ suite
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for this::
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#fps (frames per second) controls the play speed
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mencoder 'mf://*.png' -mf type=png:fps=10 -ovc \\
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lavc -lavcopts vcodec=wmv2 -oac copy -o animation.avi
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The swiss army knife of image tools, ImageMagick's `convert
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<http://www.imagemagick.org/script/convert.php>`_ works for this as
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well.<p>
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Here is a simple example script that saves some PNGs, makes them into
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a movie, and then cleans up::
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import os, sys
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import matplotlib.pyplot as plt
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files = []
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fig = plt.figure(figsize=(5,5))
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ax = fig.add_subplot(111)
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for i in range(50): # 50 frames
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ax.cla()
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ax.imshow(rand(5,5), interpolation='nearest')
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fname = '_tmp%03d.png'%i
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print 'Saving frame', fname
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fig.savefig(fname)
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files.append(fname)
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print 'Making movie animation.mpg - this make take a while'
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os.system("mencoder 'mf://_tmp*.png' -mf type=png:fps=10 \\
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-ovc lavc -lavcopts vcodec=wmv2 -oac copy -o animation.mpg")
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.. htmlonly::
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See :ref:`animation-movie_demo` for a complete example.

‎doc/faq/installing_faq.rst‎

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@@ -105,6 +105,8 @@ and build and install as usual with::
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> cd matplotlib
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> python setup.py install
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Backends
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========
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examples/animation/animation_blit.py renamed to examples/animation/animation_blit_gtk.py

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# For detailed comments on animation and the techniques used here, see
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# the wiki entry
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# http://www.scipy.org/wikis/topical_software/MatplotlibAnimation
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import sys
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import time
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import gtk, gobject
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import matplotlib
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matplotlib.use('GTKAgg')
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import numpy as npy
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import pylab as p
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import numpy as np
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import matplotlib.pyplot as plt
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ax = p.subplot(111)
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canvas = ax.figure.canvas
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p.subplots_adjust(left=0.3, bottom=0.3) # check for flipy bugs
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p.grid() # to ensure proper background restore
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fig = plt.figure()
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ax = fig.add_subplot(111)
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canvas = fig.canvas
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fig.subplots_adjust(left=0.3, bottom=0.3) # check for flipy bugs
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ax.grid() # to ensure proper background restore
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# create the initial line
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x = npy.arange(0,2*npy.pi,0.01)
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line, = p.plot(x, npy.sin(x), animated=True, lw=2)
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x = np.arange(0,2*np.pi,0.01)
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line, = ax.plot(x, np.sin(x), animated=True, lw=2)
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canvas.draw()
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# for profiling
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tstart = time.time()
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def update_line(*args):
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print 'you are here', update_line.cnt
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if update_line.background is None:
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update_line.background = canvas.copy_from_bbox(ax.bbox)
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# restore the clean slate background
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canvas.restore_region(update_line.background)
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# update the data
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line.set_ydata(npy.sin(x+update_line.cnt/10.0))
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line.set_ydata(np.sin(x+update_line.cnt/10.0))
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# just draw the animated artist
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try:
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ax.draw_artist(line)
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except AssertionError:
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return
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ax.draw_artist(line)
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# just redraw the axes rectangle
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canvas.blit(ax.bbox)
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if update_line.cnt==1000:
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# print the timing info and quit
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print 'FPS:' , 1000/(time.time()-tstart)
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sys.exit()
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gtk.mainquit()
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raise SystemExit
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update_line.cnt += 1
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return True
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update_line.cnt = 0
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update_line.background = None
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gobject.idle_add(update_line)
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p.show()
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def start_anim(event):
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gobject.idle_add(update_line)
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canvas.mpl_disconnect(start_anim.cid)
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start_anim.cid = canvas.mpl_connect('draw_event', start_anim)
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plt.show()

‎examples/animation/movie_demo.py‎

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#!/usr/bin/python
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#
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# Josh Lifton 2004
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#
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# Permission is hereby granted to use and abuse this document
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# so long as proper attribution is given.
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#
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# This Python script demonstrates how to use the numarray package
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# to generate and handle large arrays of data and how to use the
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# matplotlib package to generate plots from the data and then save
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# those plots as images. These images are then stitched together
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# by Mencoder to create a movie of the plotted data. This script
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# is for demonstration purposes only and is not intended to be
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# for general use. In particular, you will likely need to modify
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# the script to suit your own needs.
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#
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from matplotlib.matlab import * # For plotting graphs.
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import os # For issuing commands to the OS.
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import sys # For determining the Python version.
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#
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# Print the version information for the machine, OS,
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# Python interpreter, and matplotlib. The version of
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# Mencoder is printed when it is called.
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#
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# This script is known to have worked for:
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#
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# OS version: ('Linux', 'flux-capacitor', '2.4.26', '#1 SMP Sa Apr 17 19:33:42 CEST 2004', 'i686')
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# Python version: 2.3.4 (#2, May 29 2004, 03:31:27) [GCC 3.3.3 (Debian 20040417)]
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# matplotlib version: 0.61.0
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# MEncoder version:
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# MEncoder 1.0pre4-3.3.3 (C) 2000-2004 MPlayer Team
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# CPU: Intel Celeron 2/Pentium III Coppermine,Geyserville 996.1 MHz (Family: 6, Stepping: 10)
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# Detected cache-line size is 32 bytes
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# CPUflags: Type: 6 MMX: 1 MMX2: 1 3DNow: 0 3DNow2: 0 SSE: 1 SSE2: 0
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# Compiled for x86 CPU with extensions: MMX MMX2 SSE
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#
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print 'Executing on', os.uname()
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print 'Python version', sys.version
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print 'matplotlib version', matplotlib.__version__
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#
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# First, let's create some data to work with. In this example
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# we'll use a normalized Gaussian waveform whose mean and
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# standard deviation both increase linearly with time. Such a
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# waveform can be thought of as a propagating system that loses
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# coherence over time, as might happen to the probability
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# distribution of a clock subjected to independent, identically
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# distributed Gaussian noise at each time step.
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#
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print 'Initializing data set...' # Let the user know what's happening.
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# Initialize variables needed to create and store the example data set.
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numberOfTimeSteps = 100 # Number of frames we want in the movie.
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x = arange(-10,10,0.01) # Values to be plotted on the x-axis.
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mean = -6 # Initial mean of the Gaussian.
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stddev = 0.2 # Initial standard deviation.
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meaninc = 0.1 # Mean increment.
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stddevinc = 0.1 # Standard deviation increment.
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# Create an array of zeros and fill it with the example data.
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y = zeros((numberOfTimeSteps,len(x)), Float64)
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for i in range(numberOfTimeSteps) :
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y[i] = (1/sqrt(2*pi*stddev))*exp(-((x-mean)**2)/(2*stddev))
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mean = mean + meaninc
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stddev = stddev + stddevinc
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print 'Done.' # Let the user know what's happening.
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#
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# Now that we have an example data set (x,y) to work with, we can
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# start graphing it and saving the images.
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#
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for i in range(len(y)) :
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#
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# The next four lines are just like Matlab.
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#
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plot(x,y[i],'b.')
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axis((x[0],x[-1],-0.25,1))
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xlabel('time (ms)')
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ylabel('probability density function')
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#
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# Notice the use of LaTeX-like markup.
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#
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title(r'$\cal{N}(\mu, \sigma^2)$', fontsize=20)
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#
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# The file name indicates how the image will be saved and the
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# order it will appear in the movie. If you actually wanted each
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# graph to be displayed on the screen, you would include commands
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# such as show() and draw() here. See the matplotlib
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# documentation for details. In this case, we are saving the
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# images directly to a file without displaying them.
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#
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filename = str('%03d' % i) + '.png'
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savefig(filename, dpi=100)
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#
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# Let the user know what's happening.
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#
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print 'Wrote file', filename
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#
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# Clear the figure to make way for the next image.
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#
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clf()
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#
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# Now that we have graphed images of the dataset, we will stitch them
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# together using Mencoder to create a movie. Each image will become
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# a single frame in the movie.
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#
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# We want to use Python to make what would normally be a command line
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# call to Mencoder. Specifically, the command line call we want to
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# emulate is (without the initial '#'):
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# mencoder mf://*.png -mf type=png:w=800:h=600:fps=25 -ovc lavc -lavcopts vcodec=mpeg4 -oac copy -o output.avi
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# See the MPlayer and Mencoder documentation for details.
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#
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command = ('mencoder',
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'mf://*.png',
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'-mf',
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'type=png:w=800:h=600:fps=25',
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'-ovc',
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'lavc',
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'-lavcopts',
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'vcodec=mpeg4',
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'-oac',
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'copy',
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'-o',
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'output.avi')
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os.spawnvp(os.P_WAIT, 'mencoder', command)

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