Revision: 4551
http://matplotlib.svn.sourceforge.net/matplotlib/?rev=4551&view=rev
Author: fer_perez
Date: 2007-12-02 17:50:55 -0800 (Sun, 02 Dec 2007)
Log Message:
-----------
Update reST in requirements doc
Modified Paths:
--------------
trunk/py4science/classes/0712_ncar_agenda.txt
trunk/py4science/doc/requirements.txt
trunk/py4science/workbook/main.tex
Modified: trunk/py4science/classes/0712_ncar_agenda.txt
===================================================================
--- trunk/py4science/classes/0712_ncar_agenda.txt 2007-12-03 01:39:21 UTC
(rev 4550)
+++ trunk/py4science/classes/0712_ncar_agenda.txt 2007-12-03 01:50:55 UTC
(rev 4551)
@@ -4,161 +4,149 @@
Initials indicate who presents what:
-JDH - John D. Hunter
-FP - Fernando Perez
-JW - Jeff Whitaker
+ * JDH: John D. Hunter
+ * FP: Fernando Perez
+ * JW: Jeff Whitaker
Day 1 (Friday December 7)
=========================
-830-900 Installation and configuration (optional)
-This half hour will be spent helping with installation issues, before the
-real workshop begins. If you've already set things up on your system
-(meaning you have ipython, numpy, matplotlib and scipy installed and
-running), feel free to skip this.
+830-900: Installation and configuration (optional)
+ This half hour will be spent helping with installation issues, before the
+ real workshop begins. If you've already set things up on your system
+ (meaning you have ipython, numpy, matplotlib and scipy installed and
+ running), feel free to skip this.
-900-905 Introduction
-Official start of the workshop, introduce instructors.
+900-905: Introduction
+ Official start of the workshop, introduce instructors.
-905-945 JDH: Python for scientific computing
-A high-level overview of the topic of Python in a scientific context.
+905-945 (JDH): Python for scientific computing
+ A high-level overview of the topic of Python in a scientific context.
-950-1045 FP: Workflow, guided by a simple example: trapezoid integration.
+950-1045 (FP): Workflow, guided by a simple examples.
+ This section will be used to illustrate basic workflow for students, by
+ having them 'type along' a very simple exercise, trapezoid rule integration.
+ We'll discuss the basics of numpy arrays and will solve the trapezoid
+ integration exercise together.
-This section will be used to illustrate basic workflow for students, by having
-them 'type along' a very simple exercise, trapezoid rule integration. We'll
-discuss the basics of numpy arrays and will solve the trapezoid integration
-exercise together.
+----
-Editor: (X)Emacs, Vi(m), etc.
+1045-1100: Coffee break
-ipython. Saving and reloading files, interactive use of variables, %run,
-%debug, %xmode verbose.
+----
-Getting help:
- - pydoc (-g, -p)
+1100-1145 (FP): Introductory examples.
+ We'll have two exercises, so students who finish the first one early don't
+ get bored and can do a second one:
- - The standard docs (bookmark them)
+ * FFTs: 2-d image denoising via FFT.
+ * Numerical integration and root finding.
- - ipython ?/??, help(), the tab key. numpy.*cos*? search.
+----
- - The open source process: mailing lists, wikis, svn. Python
- cookbook. Participate!
+1145-1230: Lunch Break
-Basic setup:
- - ipython
- - matplotlib (latex, etc).
- - Modules: import/reload, PYTHONPATH.
+----
-- Urllib Yahoo finance demo.
-
-ToDo: Add numerical error measure of trapezoid rule.
+1300-1400 (JDH): Basic numpy/pylab usage.
+ A linear algebra/2d data visualization demo using numpy and matplotlib will
+ then be extended as an exercise by the students. If time allows, an ODE
+ example will be presented:
-ToDo Add in workflow comparison with scipy's integration. compare timing and
- eror.
+ * Glass2 demo: linear algebra, event handling in interactive plots.
+ * Glass1 exercise: simplified version of the above as an exercise.
+ * ODEs - Lotka Volterra equations.
-ToDo: write cheat-sheet.
+1400-1500 (JW): Basemap: geographical datasets.
+ Basemap_ is a matplotlib toolkit that plots data on map projections (with
+ continental and political boundaries).
-1045:1100 --- Coffee break ---
+.. _Basemap:
http://matplotlib.sourceforge.net/matplotlib.toolkits.basemap.basemap.html
+----
-1100:1145 FP: Introductory examples.
+1500 End of main work for Friday
-We'll have two exercises, so students who finish the first one early don't get
-bored and can do a second one.
+----
-FFTs: 2-d image denoising via FFT.
+1500-1700: Open data access standards and protocols (optional material)
+ We realize there's a Christmas party, so we'll keep this part optional, feel
+ free to skip out as the needs for wine and cheese dictate. We'll look at the
+ Python implementation of the OpenDAP protocol and a package for easy
+ construction and manipulation of HDF5 datsets:
-Numerical integration and root finding: Find t such that
+ * (FP) - OpenDAP_ via the PyDAP_ implementation.
+ * (JDH) - PyTables_: an HDF5 library.
- \int_0^t{ f(s) ds} = u
+.. _OPenDAP: http://pydap.org
+.. _PyDAP: http://opendap.org
+.. _PyTables: http://www.pytables.org
-for a known, monotonically increasing f(s) and a fixed u.
-
-1145:12:30 --- Lunch Break ---
-
-1300:1400 JDH: Basic numpy/pylab
-
-* Glass2 demo: linear algebra, event handling in interactive plots.
-* Glass1 exercise: simplified version of the above as an exercise.
-* ODEs - Lotka Volterra equations.
-
-1400:1500 JW, Basemap: geographical datasets.
-
-1500 --- End of main work for Friday ---
-
-1500:1700: Optional material (there's a Christmas party)
-
-PyDAP/OpenDAP
-PyTables
-
-
+
Day 2 (Saturday December 8)
===========================
-900:930 FP: Traits, Mayavi2 demo. Automatic GUI generation, VTK library, the
-MayaVi visualization application. This is a demo of capabilities, not an
-exercise.
+900-930 (FP): Traits_, TVTK_ and MayaVi2_
+ Automatic GUI generation, VTK library, the MayaVi visualization application.
+ This is a demo of capabilities, not an exercise.
-930:1030 FP - Lightweight tools for low-level code reuse
+.. _Traits: http://code.enthought.com/traits
+.. _TVTK: https://svn.enthought.com/enthought/wiki/TVTK
+.. _MayaVi2: http://code.enthought.com/mayavi2
+
+9300-1030 (FP): - Lightweight tools for low-level code reuse
+ These two tools ship by default with NumPy (f2py) and SciPy (weave), and
+ allow you to easily access low-level codes or optimize numerical hotspots:
+
+ * f2py: Fortran code wrapping exercise.
+ * weave: C/C++ inlining exercise.
-f2py: Fortran code wrapping exercise.
-weave: C/C++ inlining exercise.
+----
-1030:1045 --- Coffee break ---
+1030-1045: Coffee break
-1045:1200 JDH - Other tools for C/C++ code reuse, demos/slides.
+----
-* ctypes: easy access to dynamically linked libraries.
-* pyrex: blend of python/C for automatic generation of native code.
-* SWIG: automatic wrapping of C/C++ libraries.
-* Boost.Python: automatic wrapping of C++ libraries with template support.
-* A tour of scipy's code base, which uses several of these techniques.
-
-1200:1300 --- Lunch break ---
+1045-1200 (JDH): Other tools for C/C++ code reuse
+ This will be a demo of a number of other tools that exist in Python for
+ accessing C and C++ codes, each with its own set of strengths:
+
+ * ctypes_: easy access to dynamically linked libraries.
+ * pyrex_: blend of python/C for automatic generation of native code.
+ * SWIG_: automatic wrapping of C/C++ libraries.
+ * `Boost.Python`_: automatic wrapping of C++ libraries with template support.
+ * A tour of scipy's code base, which uses several of these techniques.
-1300:1330: JDH - SVN workflow, contributing to the workbook. (optional
mailing list
- subscription)
+.. _ctypes: http://python.net/crew/theller/ctypes
+.. _pyrex: http://www.cosc.canterbury.ac.nz/greg.ewing/python/Pyrex
+.. _SWIG: http://www.swig.org
+.. _`Boost.Python`: http://www.boost.org/libs/python/doc
-1330:1400: JDH - Type along data smoothing, convolutions, scipy.filter
+----
-1400:1430: FP - Basic data fitting, scipy.optimize
+1200-1300: Lunch break
-1430:1500: FP - Sage intro/demo.
+----
-1500:1515 - Wrapup.
+1300-1330 (JDH): Participating in the open source process
+ We'll discuss the SVN workflow, contributing to the workbook and the projects
+ used in this course, etc.
+1330-1400 (JDH): Data smoothing
+ Type along data smoothing, convolutions, scipy.filter
+1400-1430 (FP): Fitting
+ Basic data fitting, scipy.optimize exercise.
-Unused examples and exercises, extra ideas
-==========================================
+1430-1500 (FP): SAGE
+ An overview and brief demo of the Sage_ project, an ambitious and rapidly
+ growing Python project to offer free mathematical software (as well as
+ integration with commercial systems).
-* Visual (VPython): Show some examples, explain. Target shooting exercise.
+.. _Sage: http://sagemath.org
-* One-dimensional FFT - Bode plot.
-
-* Spectral interpolation.
-
-* Steinman interpolation.
-
-* Extended precision root finding: manually implement newton's method using
- clnum or mpfr.
-
-* Bessel functions: special functions library, array manipulations to check
-recursion relation (30 min).
-
-* Descriptive statistics, statistical distributions (1 hr).
-
-* SVD/eigenfaces (1 hr).
-
-* Logistic map (1 hr).
-
-* Beautiful soup: screen-scraping HTML for data extraction (30 min).
-
-* Word frequencies: use of dictionaries and text processing (20 min).
-
-* Prime numbers: the Sieve of Erathostenes. Illustrates lists and sets (30
- min).
-
-* Wallis' pi: arbitrary precision integers (30 min).
+1500-1515: Wrapup
+ We'll have a bit of time for discussion, feedback and any questions that may
+ have been left.
+
\ No newline at end of file
Modified: trunk/py4science/doc/requirements.txt
===================================================================
--- trunk/py4science/doc/requirements.txt 2007-12-03 01:39:21 UTC (rev
4550)
+++ trunk/py4science/doc/requirements.txt 2007-12-03 01:50:55 UTC (rev
4551)
@@ -1,3 +1,17 @@
+======================================
+ Requirements for the Python workshop
+======================================
+
+.. contents::
+..
+ 1 Core requirements
+ 2 Basic configuration
+ 3 Testing
+ 4 Checking your versions
+ 5 Platform specific instructions
+ 6 Optional packages
+
+
Core requirements
=================
@@ -42,7 +56,7 @@
with the rest of your matplotlib install. Create a directory in your
home directory called .matplotlib and copy this file into it (or
simply edit in place in mpl-data) and change the line that starts with
-'backend' to
+'backend' to::
backend : WXAgg
@@ -53,7 +67,7 @@
If you can execute the following commands w/o error, and have a plot
window pop up, you have an installation that will work for 90% of the
-exercises in the workshop
+exercises in the workshop::
> ipython -pylab
Python 2.5 (r25:51918, Sep 19 2006, 08:49:13)
@@ -82,7 +96,7 @@
- scipy >= 0.5.2
-The example code below shows you how to check your versions:
+The example code below shows you how to check your versions::
In [4]: import numpy
@@ -113,6 +127,7 @@
win32
+
Optional packages
=================
@@ -157,4 +172,3 @@
with documentation at
http://matplotlib.sourceforge.net/matplotlib.toolkits.basemap.basemap.html
-
Modified: trunk/py4science/workbook/main.tex
===================================================================
--- trunk/py4science/workbook/main.tex 2007-12-03 01:39:21 UTC (rev 4550)
+++ trunk/py4science/workbook/main.tex 2007-12-03 01:50:55 UTC (rev 4551)
@@ -22,7 +22,7 @@
\providecommand{\tabularnewline}{\\}
%%%%%%%%%%%%%%%%%%%%%%%%%%%%%% Textclass specific LaTeX commands.
- \theoremstyle{plain}
+\theoremstyle{plain}
\newtheorem{thm}{Theorem}[section]
\newenvironment{lyxcode}
{\begin{list}{}{
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