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SF.net SVN: matplotlib:[6241] trunk/matplotlib
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From: <jd...@us...> - 2008-10-17 15:35:15
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Revision: 6241
http://matplotlib.svn.sourceforge.net/matplotlib/?rev=6241&view=rev
Author: jdh2358
Date: 2008-10-17 15:35:05 +0000 (Fri, 17 Oct 2008)
Log Message:
-----------
added joshuas movie demo
Modified Paths:
--------------
trunk/matplotlib/doc/faq/howto_faq.rst
trunk/matplotlib/doc/faq/installing_faq.rst
Added Paths:
-----------
trunk/matplotlib/examples/animation/animation_blit_gtk.py
trunk/matplotlib/examples/animation/movie_demo.py
Removed Paths:
-------------
trunk/matplotlib/examples/animation/animation_blit.py
Modified: trunk/matplotlib/doc/faq/howto_faq.rst
===================================================================
--- trunk/matplotlib/doc/faq/howto_faq.rst 2008-10-17 14:31:38 UTC (rev 6240)
+++ trunk/matplotlib/doc/faq/howto_faq.rst 2008-10-17 15:35:05 UTC (rev 6241)
@@ -299,7 +299,7 @@
for a single point use :func:`~matplotlib.nxutils.pnpoly` and for an
array of points use :func:`~matplotlib.nxutils.points_inside_poly`.
For a discussion of the implementation see `pnpoly
-<http://www.ecse.rpi.edu/Homepages/wrf/Research/Short_Notes/pnpoly.html>`_.
+<http://www.ecse.rpi.edu/Homepages/wrf/Research/Short_Notes/pnpoly.html>`_.
.. sourcecode:: ipython
@@ -334,5 +334,140 @@
Out[32]: array([False, False, False, False, False, False, False, True, False, True], dtype=bool)
.. htmlonly::
-
+
For a complete example, see :ref:`event_handling-lasso_demo`.
+
+
+.. _how-to-submit-patch:
+
+How do I submit a patch?
+========================
+
+First obtain a copy of matplotlib svn (see :ref:`install-svn`) and
+make your changes to the matplotlib source code or documentation and
+apply a `svn diff`. If it is feasible, do your diff from the top
+level directory, the one that contains :file:`setup.py`. Eg,::
+
+ > cd /path/to/matplotlib/source
+ > svn diff > mypatch.diff
+
+and then post your patch to the `matplotlib-devel
+<http://sourceforge.net/mail/?group_id=80706>`_ mailing list. If you
+do not get a response within 24 hours, post your patch to the
+sourceforge patch `tracker
+<http://sourceforge.net/tracker2/?atid=560722&group_id=80706&func=browse>`_,
+and follow up on the mailing list with a link to the sourceforge patch
+submissions. If you still do not hear anything within a week (this
+shouldn't happen!), send us a kind and gentle reminder on the mailing
+list.
+
+If you have made lots of local changes and do not want to a diff
+against the entire tree, but rather against a single directory or
+file, that is fine, but we do prefer svn diffs against HEAD.
+
+You should check out the guide to developing matplotlib to make sure
+your patch abides by our coding conventions
+:ref:`developers-guide-index`.
+
+
+.. _howto-click-maps:
+
+Clickable images for HTML
+=========================
+
+Andrew Dalke of `Dalke Scientific <http://www.dalkescientific.com>`_
+has written a nice `article
+<http://www.dalkescientific.com/writings/diary/archive/2005/04/24/interactive_html.html>`_
+on how to make html click maps with matplotlib agg PNGs. We would
+also like to add this functionality to SVG and add a SWF backend to
+support these kind of images. If you are interested in contributing
+to these efforts that would be great.
+
+.. _howto-set-zorder:
+
+How do I control the depth of plot elements?
+=============================================
+
+Within an axes, the order that the various lines, markers, text,
+collections, etc appear is determined by the
+:meth:`matplotlib.artist.Artist.set_zorder` property. The default
+order is patches, lines, text, with collections of lines and
+collections of patches appearing at the same level as regular lines
+and patches, respectively::
+
+ line, = ax.plot(x, y, zorder=10)
+
+
+
+.. htmlonly::
+
+ See :ref:`pylab_examples-zorder_demo` for a complete example.
+
+You can also use the Axes property
+:meth:`matplotlib.axes.Axes.set_axisbelow` to control whether the grid
+lines are placed above or below your other plot elements.
+
+.. _howto-axis-equal:
+
+How to I make the aspect ratio for plots equal?
+===============================================
+
+The Axes property :meth:`matplotlib.axes.Axes.set_aspect` controls the
+aspect ratio of the axes. You can set it to be 'auto', 'equal', or
+some ratio which controls the ratio::
+
+ ax = fig.add_subplot(111, aspect='equal')
+
+
+
+.. htmlonly::
+
+ See :ref:`pylab_examples-equal_aspect_ratio` for a complete example.
+
+
+.. _howto-movie:
+
+How do I make a movie?
+======================
+
+
+If you want to take an animated plot and turn it into a movie, the
+best approach is to save a series of image files (eg PNG) and use an
+external tool to convert them to a movie. You can use ` mencoder
+<http://www.mplayerhq.hu/DOCS/HTML/en/mencoder.html>`_,
+which is part of the `mplayer <http://www.mplayerhq.hu>`_ suite
+for this::
+
+
+ #fps (frames per second) controls the play speed
+ mencoder 'mf://*.png' -mf type=png:fps=10 -ovc \\
+ lavc -lavcopts vcodec=wmv2 -oac copy -o animation.avi
+
+The swiss army knife of image tools, ImageMagick's `convert
+<http://www.imagemagick.org/script/convert.php>`_ works for this as
+well.<p>
+
+Here is a simple example script that saves some PNGs, makes them into
+a movie, and then cleans up::
+
+ import os, sys
+ import matplotlib.pyplot as plt
+
+ files = []
+ fig = plt.figure(figsize=(5,5))
+ ax = fig.add_subplot(111)
+ for i in range(50): # 50 frames
+ ax.cla()
+ ax.imshow(rand(5,5), interpolation='nearest')
+ fname = '_tmp%03d.png'%i
+ print 'Saving frame', fname
+ fig.savefig(fname)
+ files.append(fname)
+
+ print 'Making movie animation.mpg - this make take a while'
+ os.system("mencoder 'mf://_tmp*.png' -mf type=png:fps=10 \\
+ -ovc lavc -lavcopts vcodec=wmv2 -oac copy -o animation.mpg")
+
+.. htmlonly::
+
+ See :ref:`animation-movie_demo` for a complete example.
Modified: trunk/matplotlib/doc/faq/installing_faq.rst
===================================================================
--- trunk/matplotlib/doc/faq/installing_faq.rst 2008-10-17 14:31:38 UTC (rev 6240)
+++ trunk/matplotlib/doc/faq/installing_faq.rst 2008-10-17 15:35:05 UTC (rev 6241)
@@ -105,6 +105,8 @@
> cd matplotlib
> python setup.py install
+
+
Backends
========
Deleted: trunk/matplotlib/examples/animation/animation_blit.py
===================================================================
--- trunk/matplotlib/examples/animation/animation_blit.py 2008-10-17 14:31:38 UTC (rev 6240)
+++ trunk/matplotlib/examples/animation/animation_blit.py 2008-10-17 15:35:05 UTC (rev 6241)
@@ -1,57 +0,0 @@
-#!/usr/bin/env python
-
-# For detailed comments on animation and the techniques used here, see
-# the wiki entry
-# http://www.scipy.org/wikis/topical_software/MatplotlibAnimation
-import sys
-import time
-
-import gtk, gobject
-
-import matplotlib
-matplotlib.use('GTKAgg')
-import numpy as npy
-import pylab as p
-
-
-ax = p.subplot(111)
-canvas = ax.figure.canvas
-
-p.subplots_adjust(left=0.3, bottom=0.3) # check for flipy bugs
-p.grid() # to ensure proper background restore
-
-# create the initial line
-x = npy.arange(0,2*npy.pi,0.01)
-line, = p.plot(x, npy.sin(x), animated=True, lw=2)
-
-# for profiling
-tstart = time.time()
-
-def update_line(*args):
- if update_line.background is None:
- update_line.background = canvas.copy_from_bbox(ax.bbox)
-
- # restore the clean slate background
- canvas.restore_region(update_line.background)
- # update the data
- line.set_ydata(npy.sin(x+update_line.cnt/10.0))
- # just draw the animated artist
- try:
- ax.draw_artist(line)
- except AssertionError:
- return
- # just redraw the axes rectangle
- canvas.blit(ax.bbox)
-
- if update_line.cnt==1000:
- # print the timing info and quit
- print 'FPS:' , 1000/(time.time()-tstart)
- sys.exit()
-
- update_line.cnt += 1
- return True
-
-update_line.cnt = 0
-update_line.background = None
-gobject.idle_add(update_line)
-p.show()
Copied: trunk/matplotlib/examples/animation/animation_blit_gtk.py (from rev 6240, trunk/matplotlib/examples/animation/animation_blit.py)
===================================================================
--- trunk/matplotlib/examples/animation/animation_blit_gtk.py (rev 0)
+++ trunk/matplotlib/examples/animation/animation_blit_gtk.py 2008-10-17 15:35:05 UTC (rev 6241)
@@ -0,0 +1,68 @@
+#!/usr/bin/env python
+
+# For detailed comments on animation and the techniques used here, see
+# the wiki entry
+# http://www.scipy.org/wikis/topical_software/MatplotlibAnimation
+import time
+
+import gtk, gobject
+
+import matplotlib
+matplotlib.use('GTKAgg')
+
+import numpy as np
+import matplotlib.pyplot as plt
+
+
+fig = plt.figure()
+ax = fig.add_subplot(111)
+canvas = fig.canvas
+
+fig.subplots_adjust(left=0.3, bottom=0.3) # check for flipy bugs
+ax.grid() # to ensure proper background restore
+
+# create the initial line
+x = np.arange(0,2*np.pi,0.01)
+line, = ax.plot(x, np.sin(x), animated=True, lw=2)
+canvas.draw()
+
+# for profiling
+tstart = time.time()
+
+def update_line(*args):
+ print 'you are here', update_line.cnt
+ if update_line.background is None:
+ update_line.background = canvas.copy_from_bbox(ax.bbox)
+
+ # restore the clean slate background
+ canvas.restore_region(update_line.background)
+ # update the data
+ line.set_ydata(np.sin(x+update_line.cnt/10.0))
+ # just draw the animated artist
+ ax.draw_artist(line)
+
+ # just redraw the axes rectangle
+ canvas.blit(ax.bbox)
+
+ if update_line.cnt==1000:
+ # print the timing info and quit
+ print 'FPS:' , 1000/(time.time()-tstart)
+ gtk.mainquit()
+ raise SystemExit
+
+ update_line.cnt += 1
+ return True
+
+update_line.cnt = 0
+update_line.background = None
+
+
+def start_anim(event):
+ gobject.idle_add(update_line)
+ canvas.mpl_disconnect(start_anim.cid)
+
+start_anim.cid = canvas.mpl_connect('draw_event', start_anim)
+
+
+
+plt.show()
Added: trunk/matplotlib/examples/animation/movie_demo.py
===================================================================
--- trunk/matplotlib/examples/animation/movie_demo.py (rev 0)
+++ trunk/matplotlib/examples/animation/movie_demo.py 2008-10-17 15:35:05 UTC (rev 6241)
@@ -0,0 +1,140 @@
+#!/usr/bin/python
+#
+# Josh Lifton 2004
+#
+# Permission is hereby granted to use and abuse this document
+# so long as proper attribution is given.
+#
+# This Python script demonstrates how to use the numarray package
+# to generate and handle large arrays of data and how to use the
+# matplotlib package to generate plots from the data and then save
+# those plots as images. These images are then stitched together
+# by Mencoder to create a movie of the plotted data. This script
+# is for demonstration purposes only and is not intended to be
+# for general use. In particular, you will likely need to modify
+# the script to suit your own needs.
+#
+
+
+from matplotlib.matlab import * # For plotting graphs.
+import os # For issuing commands to the OS.
+import sys # For determining the Python version.
+
+#
+# Print the version information for the machine, OS,
+# Python interpreter, and matplotlib. The version of
+# Mencoder is printed when it is called.
+#
+# This script is known to have worked for:
+#
+# OS version: ('Linux', 'flux-capacitor', '2.4.26', '#1 SMP Sa Apr 17 19:33:42 CEST 2004', 'i686')
+# Python version: 2.3.4 (#2, May 29 2004, 03:31:27) [GCC 3.3.3 (Debian 20040417)]
+# matplotlib version: 0.61.0
+# MEncoder version:
+# MEncoder 1.0pre4-3.3.3 (C) 2000-2004 MPlayer Team
+# CPU: Intel Celeron 2/Pentium III Coppermine,Geyserville 996.1 MHz (Family: 6, Stepping: 10)
+# Detected cache-line size is 32 bytes
+# CPUflags: Type: 6 MMX: 1 MMX2: 1 3DNow: 0 3DNow2: 0 SSE: 1 SSE2: 0
+# Compiled for x86 CPU with extensions: MMX MMX2 SSE
+#
+print 'Executing on', os.uname()
+print 'Python version', sys.version
+print 'matplotlib version', matplotlib.__version__
+
+
+
+#
+# First, let's create some data to work with. In this example
+# we'll use a normalized Gaussian waveform whose mean and
+# standard deviation both increase linearly with time. Such a
+# waveform can be thought of as a propagating system that loses
+# coherence over time, as might happen to the probability
+# distribution of a clock subjected to independent, identically
+# distributed Gaussian noise at each time step.
+#
+
+print 'Initializing data set...' # Let the user know what's happening.
+
+# Initialize variables needed to create and store the example data set.
+numberOfTimeSteps = 100 # Number of frames we want in the movie.
+x = arange(-10,10,0.01) # Values to be plotted on the x-axis.
+mean = -6 # Initial mean of the Gaussian.
+stddev = 0.2 # Initial standard deviation.
+meaninc = 0.1 # Mean increment.
+stddevinc = 0.1 # Standard deviation increment.
+
+# Create an array of zeros and fill it with the example data.
+y = zeros((numberOfTimeSteps,len(x)), Float64)
+for i in range(numberOfTimeSteps) :
+ y[i] = (1/sqrt(2*pi*stddev))*exp(-((x-mean)**2)/(2*stddev))
+ mean = mean + meaninc
+ stddev = stddev + stddevinc
+
+print 'Done.' # Let the user know what's happening.
+
+#
+# Now that we have an example data set (x,y) to work with, we can
+# start graphing it and saving the images.
+#
+
+for i in range(len(y)) :
+ #
+ # The next four lines are just like Matlab.
+ #
+ plot(x,y[i],'b.')
+ axis((x[0],x[-1],-0.25,1))
+ xlabel('time (ms)')
+ ylabel('probability density function')
+
+ #
+ # Notice the use of LaTeX-like markup.
+ #
+ title(r'$\cal{N}(\mu, \sigma^2)$', fontsize=20)
+
+ #
+ # The file name indicates how the image will be saved and the
+ # order it will appear in the movie. If you actually wanted each
+ # graph to be displayed on the screen, you would include commands
+ # such as show() and draw() here. See the matplotlib
+ # documentation for details. In this case, we are saving the
+ # images directly to a file without displaying them.
+ #
+ filename = str('%03d' % i) + '.png'
+ savefig(filename, dpi=100)
+
+ #
+ # Let the user know what's happening.
+ #
+ print 'Wrote file', filename
+
+ #
+ # Clear the figure to make way for the next image.
+ #
+ clf()
+
+#
+# Now that we have graphed images of the dataset, we will stitch them
+# together using Mencoder to create a movie. Each image will become
+# a single frame in the movie.
+#
+# We want to use Python to make what would normally be a command line
+# call to Mencoder. Specifically, the command line call we want to
+# emulate is (without the initial '#'):
+# mencoder mf://*.png -mf type=png:w=800:h=600:fps=25 -ovc lavc -lavcopts vcodec=mpeg4 -oac copy -o output.avi
+# See the MPlayer and Mencoder documentation for details.
+#
+
+command = ('mencoder',
+ 'mf://*.png',
+ '-mf',
+ 'type=png:w=800:h=600:fps=25',
+ '-ovc',
+ 'lavc',
+ '-lavcopts',
+ 'vcodec=mpeg4',
+ '-oac',
+ 'copy',
+ '-o',
+ 'output.avi')
+
+os.spawnvp(os.P_WAIT, 'mencoder', command)
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