SF.net SVN: matplotlib: [4556] trunk/py4science/workbook

[<fer...@us...>]

Revision: 4556
          http://matplotlib.svn.sourceforge.net/matplotlib/?rev=4556&view=rev
Author:   fer_perez
Date:     2007-12-03 00:22:16 -0800 (Mon, 03 Dec 2007)

Log Message:
-----------
Reorganize a bit.  Waste 2 hours dealing with utf-8 problems with latex...

Modified Paths:
--------------
    trunk/py4science/workbook/intro_to_python.tex
    trunk/py4science/workbook/ipython_tut.tex
    trunk/py4science/workbook/main.tex
    trunk/py4science/workbook/matplotlib_tut.tex
    trunk/py4science/workbook/qsort.tex
    trunk/py4science/workbook/why_python.tex
    trunk/py4science/workbook/wrapping.tex

Added Paths:
-----------
    trunk/py4science/workbook/introduction.tex

Modified: trunk/py4science/workbook/intro_to_python.tex
===================================================================
--- trunk/py4science/workbook/intro_to_python.tex	2007-12-03 04:56:57 UTC (rev 4555)
+++ trunk/py4science/workbook/intro_to_python.tex	2007-12-03 08:22:16 UTC (rev 4556)
@@ -734,13 +734,12 @@
 
 
 \end{lyxcode}
-There are two string methods, \texttt{split} and \texttt{join}, that
-arise frequenctly in Numeric processing, specifically in the context
-of processing data files that have comma, tab, or space separated
-numbers in them. \texttt{split} takes a single string, and splits
-it on the indicated character to a sequence of strings. This is useful
-to take a single line of space or comma separated values and split
-them into individual numbers
+There are two string methods, \texttt{split} and \texttt{join}, that arise
+frequenctly in numerical processing, specifically in the context of processing
+data files that have comma, tab, or space separated numbers in
+them. \texttt{split} takes a single string, and splits it on the indicated
+character to a sequence of strings. This is useful to take a single line of
+space or comma separated values and split them into individual numbers
 
 \begin{lyxcode}
 \textcolor{blue}{\#~s~is~a~single~string~and~we~split~it~into~a~list~of~strings}

Added: trunk/py4science/workbook/introduction.tex
===================================================================
--- trunk/py4science/workbook/introduction.tex	                        (rev 0)
+++ trunk/py4science/workbook/introduction.tex	2007-12-03 08:22:16 UTC (rev 4556)
@@ -0,0 +1,41 @@
+\chapter*{Introduction}
+
+This book is currently a work in progress, and ultimately we hope it will
+evolve into an open, community-driven document developed in tandem with the
+underlying tools, by the same scientists who have written them.
+
+The book is aimed at practicing scientists, students and in general anyone who
+is looking for a modern, high-level and open platform for scientific
+computing.  The Python language is in the opinion of the authors the leading
+candidate today for this role.
+
+The book is broadly divided in two parts: the first is a general discussion of
+the Python tools used for scientific work, with an explanatory approach.  It is
+not a complete Python reference book, as there are many excellent resources for
+the base language, both in print and online.  But beyond the basic language and
+the NumPy book, it should serve as reasonably self-contained description of the
+core libraries for common numerical tasks.
+
+The second part is meant as a practical workbook, and the build system used to
+produce the document will in the future allow users to create custom versions
+with only the examples that they deem practical for any given audience.  This
+workbook approach grew out of a sequence of workshops taught by the editors at
+a number of research institutions and universities, and we've found it to be
+extremely convenient.
+
+The workbook is structured as a collection of problems, meant to be solved by
+the reader as programming exercises.  The entire book can be compiled in one of
+two forms: either with the examples in `skeleton' form, where they contain
+incomplete code meant to be filled in, or with the full solution code.  This
+should enable instructors to hand out the skeleton workbook at courses and
+workshops, with the solutions being available as well for after the teaching is
+over.
+
+We hope that the community will continue to contribute many more examples, so
+that ultimately the projects allows for the easy construction of custom
+workbooks tailored to the needs of different audiences.
+
+\begin{flushright}
+John D. Hunter and Fernando P\xE9rez, editors.
+
+\end{flushright}

Modified: trunk/py4science/workbook/ipython_tut.tex
===================================================================
--- trunk/py4science/workbook/ipython_tut.tex	2007-12-03 04:56:57 UTC (rev 4555)
+++ trunk/py4science/workbook/ipython_tut.tex	2007-12-03 08:22:16 UTC (rev 4556)
@@ -1020,8 +1020,6 @@
 \item \texttt{\$HOME/.ipython/ipythonrc}: load basic things you always want. 
 \item \texttt{\$HOME/.ipython/ipythonrc-math}: load (1) and basic math-related
 modules. 
-\item \texttt{\$HOME/.ipython/ipythonrc-numeric}: load (1) and Numeric and
-plotting modules.
 \end{enumerate}
 Since it is possible to create an endless loop by having circular
 file inclusions, IPython will stop if it reaches 15 recursive inclusions.

Modified: trunk/py4science/workbook/main.tex
===================================================================
--- trunk/py4science/workbook/main.tex	2007-12-03 04:56:57 UTC (rev 4555)
+++ trunk/py4science/workbook/main.tex	2007-12-03 08:22:16 UTC (rev 4556)
@@ -45,6 +45,9 @@
 
 \usepackage{babel}
 \makeatother
+
+%%%%%%%%%%%%%%%%%%%%%%%%%%%%%% Begin main document - front matter
+
 \begin{document}
 
 \title{ \vspace{3cm} 
@@ -67,10 +70,15 @@
 \tableofcontents{}
 
 
-\part{Discussion}
+%%%%%%%%%%%%%%%%%%%%%%%%%%%%%% Main document - body
 
+\input{introduction.tex}
+
+\part{General Discussion}
+
 % In this part, each tex file is a chapter by itself, since it is more or less
 % meant to be used in whole.
+
 \input{why_python.tex}
 
 \input{intro_to_python.tex}
@@ -83,7 +91,8 @@
 
 
 
-\part{Workbook}
+\part{Workbook\\
+A Problem Collection}
 
 % This part specifies the chapter declarations in the main file, while the
 % chapters are made of individual TeX files which themselves should be written
@@ -91,26 +100,19 @@
 % few closely related topics, and to allow users to build them with as many or
 % as few actual sections as desired for a given audience.
 
-\chapter{Introduction}
-
+\chapter{Introduction to the workbook}
 \input{intro.tex}
 
-\chapter{Simple non-numerical problems}
-
+\chapter{Simple non-numerical Problems}
 \input{qsort.tex}
-
 \input{wordfreqs.tex}
 
 \chapter{Working with files, the internet, and numpy arrays}
-
 \input{files_etc.tex}
 
-\chapter{Elementary Numerics}
-
+\chapter{Elementary numerics}
 \input{wallis_pi.tex}
-
 \input{trapezoid.tex}
-
 \input{quad_newton.tex}
 
 \chapter{Linear algebra}
@@ -122,7 +124,7 @@
 \input{convolution.tex}
 \input{fft_imdenoise.tex}
 
-\chapter{Dynamical Systems}
+\chapter{Dynamical systems}
 \input{intro_dynsys.tex}
 \input{lotka_volterra.tex}
 
@@ -131,9 +133,11 @@
 \input{stats_descriptives.tex}
 \input{stats_distributions.tex}
 
-\chapter{Plotting on Maps}
+\chapter{Plotting on maps}
 \input{basemap.tex}
 
+%%% Bibliography section
+
 \bibliographystyle{plain}
 \bibliography{python,python2}
 

Modified: trunk/py4science/workbook/matplotlib_tut.tex
===================================================================
--- trunk/py4science/workbook/matplotlib_tut.tex	2007-12-03 04:56:57 UTC (rev 4555)
+++ trunk/py4science/workbook/matplotlib_tut.tex	2007-12-03 08:22:16 UTC (rev 4556)
@@ -6,17 +6,16 @@
 
 matplotlib is a library for making 2D plots of arrays in python.%
 \footnote{This short guide is not meant as a complete guide or tutorial. There
-is a more comprehensive user's guide and tutorial on the matplotlib
-web-site at http://matplotlib.sf.net.%
-} Although it has its origins in emulating the Matlab graphics commands,
-it does not require matlab, and has a pure, object oriented API. Although
-matplotlib is written primarily in python, it makes heavy use of Numeric/numarray
-and other extension code to provide good performance even for large
-arrays. matplotlib is designed with the philosophy that you should
-be able to create simple plots with just a few commands, or just one!
-If you want to see a histogram of your data, you shouldn't need to
-instantiate objects, call methods, set properties, and so on; it should
-just work. 
+  is a more comprehensive user's guide and tutorial on the matplotlib web-site
+  at http://matplotlib.sf.net.%
+} Although it has its origins in emulating the Matlab graphics commands, it
+does not require matlab, and has a pure, object oriented API. Although
+matplotlib is written primarily in python, it makes heavy use of NumPy and
+other extension code to provide good performance even for large
+arrays. matplotlib is designed with the philosophy that you should be able to
+create simple plots with just a few commands, or just one!  If you want to see
+a histogram of your data, you shouldn't need to instantiate objects, call
+methods, set properties, and so on; it should just work.
 
 The matplotlib code is divided into three parts: the \textit{pylab
 interface} is the set of functions provided by the \texttt{pylab}
@@ -355,68 +354,7 @@
 In~{[}25]:~set(t,~fontsize=20,~color='darkslategray')~
 \end{lyxcode}
 
-\section[numerix]{A common interface to Numeric and numarray}
 
-Currently the python computing community is in a state of having too
-many array pacakges, none of which satisfy everyone's needs. Although
-Numeric and numarray both provide the same set of core functions,
-they are organized differently, and matplotlib provides a compatibility
-later so you can use either one in your matplotlib scripts without
-having to change your code.
-
-Several numarray/Numeric developers are codevelopers of matplotlib,
-giving matplotlib full Numeric and numarray compatibility, thanks
-in large part to Todd Miller's \texttt{matplotlib.numerix} module
-and the numarray compatibility layer for extension code. This allows
-you to choose between Numeric or numarray at the prompt or in a config
-file. Thus when you do
-
-\begin{lyxcode}
-\textcolor{blue}{\#~import~matplotlib~and~all~the~numerix~functions}
-
-from~pylab~import~{*}
-\end{lyxcode}
-you'll not only get all the matplotlib pylab interface commands, but
-most of the Numeric or numarray package as well (depending on your
-\texttt{numerix} setting). All of the array creation and manipulation
-functions are imported, such as \texttt{array}, \texttt{arange}, \texttt{take},
-\texttt{where}, etc, as are the external module functions which reside
-in \texttt{mlab, fft} and \texttt{linear\_algebra.} 
-
-Even if you don't want to import all of the numerix symbols from the
-pytlab interface, to make your matplotlib scripts as portable as possible
-with respect to your choice of array packages, it is advised not to
-explicitly import Numeric or numarray. Rather, you should use \texttt{matplotlib.numerix}
-where possible, either by using the functions imported by \texttt{pylab},
-or by explicitly importing the \texttt{numerix} module, as in 
-
-\begin{lyxcode}
-\textcolor{blue}{\#~create~a~numerix~namespace}
-
-import~matplotlib.numerix~as~n
-
-from~matplotlib.numerix.mlab~import~mean
-
-x~=~n.arange(100)
-
-y~=~n.take(x,~range(10,20))
-
-print~mean(y)
-\end{lyxcode}
-For the remainder of this manual, the term \texttt{numerix} is used
-to mean either the Numeric or numarray package. To select numarray
-or Numeric from the prompt, run your matplotlib script with
-
-\begin{lyxcode}
-~~>~python~myscript.py~-{}-numarray~~\textcolor{blue}{\#~use~numarray}
-
-~~>~python~myscript.py~-{}-Numeric~~~\textcolor{blue}{\#~use~Numeric}
-\end{lyxcode}
-Typically, however, users will choose one or the other and make this
-setting in their rc file using either \texttt{numerix : Numeric} or
-\texttt{numerix : numarray}. 
-
-
 \section[matplotlibrc]{Customizing the default behavior with the rc file}
 
 matplotlib is designed to work in a variety of settings: some people
@@ -436,13 +374,13 @@
 \begin{lyxcode}
 C:\textbackslash{}Python23\textbackslash{}share\textbackslash{}matplotlib\textbackslash{}.matplotlibrc~\textcolor{blue}{\#~windows}~/usr/share/matplotlib/.matplotlibrc~~\textcolor{blue}{\#~linux}
 \end{lyxcode}
-By default, the installer will overwrite the existing file in the
-install path, so if you want to preserve your's, please move it to
-your \texttt{HOME} dir and set the environment variable if necessary.
-In the rc file, you can set your backend , your numerix setting ,
-whether you'll be working interactively and default values for most
-of the figure properties. 
 
+By default, the installer will overwrite the existing file in the install path,
+so if you want to preserve yours, please move it to your \texttt{HOME} dir and
+set the environment variable if necessary.  In the rc file, you can set your
+backend, whether you'll be working interactively and default values for most of
+the figure properties.
+
 In the RC file, blank lines, or lines starting with a comment symbol,
 are ignored, as are trailing comments. Other lines must have the format
 

Modified: trunk/py4science/workbook/qsort.tex
===================================================================
--- trunk/py4science/workbook/qsort.tex	2007-12-03 04:56:57 UTC (rev 4555)
+++ trunk/py4science/workbook/qsort.tex	2007-12-03 08:22:16 UTC (rev 4556)
@@ -30,12 +30,16 @@
 \subsection*{Hints}
 
 \begin{itemize}
-\item Python has no particular syntactic requirements for implementing recursion,
-but it does have a maximum recursion depth. This value can be queried
-via the function \texttt{sys.getrecursionlimit()}, and it can be changed
-with \texttt{sys.setrecursionlimit(new\_value)}. 
+
+\item Python has no particular syntactic requirements for implementing
+  recursion, but it does have a maximum recursion depth. This value can be
+  queried via the function \texttt{sys.getrecursionlimit()}, and it can be
+  changed with \texttt{sys.setrecursionlimit(new\_value)}.
+
 \item Like in all recursive problems, don't forget to implement an exit
-condition!
+  condition!
+
 \item If \texttt{L} is a list, the call \texttt{len(L)} provides its length.
+
 \end{itemize}
 

Modified: trunk/py4science/workbook/why_python.tex
===================================================================
--- trunk/py4science/workbook/why_python.tex	2007-12-03 04:56:57 UTC (rev 4555)
+++ trunk/py4science/workbook/why_python.tex	2007-12-03 08:22:16 UTC (rev 4556)
@@ -43,7 +43,7 @@
 group at the Scripps Research Institute uses Python extensively to
 build a suite of applications for molecular visualization and exploration
 of drug/molecule interactions using virtual reality and 3D printing
-technology\cite{Sanner2005a,Sanner2005b}. Engineers at Google use
+technology \cite{Sanner2005a,Sanner2005b}. Engineers at Google use
 Python in automation, control and tuning of their computational grid,
 and use \texttt{SWIG} generated Python of their in-house C++ libraries
 in virtually all facets of their work \cite{Beazley1998,Stein2005}.
@@ -116,7 +116,7 @@
 Complementing these built-in features, Python is also readily extensible,
 giving it a wealth of libraries for scientific computing that have
 been in development for many years \cite{Dubois1996b,Dubois1996c}.
-\texttt{Numeric Python} supports large array manipulations, math,
+\texttt{NumPy} supports large array manipulations, math,
 optimized linear algebra, efficient Fourier transforms and random
 numbers. \texttt{scipy} is a collection of Python wrappers of high
 performance FORTRAN code (eg LAPACK, ODEPACK) for numerical analysis
@@ -239,8 +239,8 @@
 256x256 pixels, and each pixel is a 2 byte integer. We read this into
 a string using python's \texttt{file} function -- the 'rb' flag says
 to open the file in \texttt{read/binary} mode. We can then use the
-numerix \texttt{fromstring} method to convert this to an array, passing
-the type of the data (\texttt{Int16}) as an argument. We reshape the
+numpy \texttt{fromstring} method to convert this to an array, passing
+the type of the data (\texttt{int16}) as an argument. We reshape the
 array by changing the array shape attribute to 256 by 256, and pass
 this off to the matplotlib pylab command \texttt{imshow} for plotting.
 matplotlib has a number of colormaps, and the default one is jet;
@@ -277,7 +277,7 @@
 \end{lyxcode}
 creates a 20x30 array of zeros (default integer type; details on how
 to specify other types will follow). Note that the dimensions ({}``shape''
-in numarray parlance) are specified by giving the dimensions as a
+in numpy parlance) are specified by giving the dimensions as a
 comma-separated list within parentheses. The parentheses aren't necessary
 for a single dimension. As an aside, the parentheses used this way
 are being used to specify a Python tuple; more will be said about
@@ -333,36 +333,35 @@
 
 \subsection{Array numeric types}
 
-numarray supports all standard numeric types. The default integer
+numpy supports all standard numeric types. The default integer
 matches what Python uses for integers, usually 32 bit integers or
-what numarray calls \texttt{Int32}. The same is true for floats, i.e.,
-generally 64-bit doubles called \texttt{Float64} in numarray. The
-default complex type is \texttt{Complex64}. Many of the functions
+what numpy calls \texttt{int32}. The same is true for floats, i.e.,
+generally 64-bit doubles called \texttt{float64} in numpy. The
+default complex type is \texttt{complex64}. Many of the functions
 accept a type argument. For example
 
 \begin{lyxcode}
->\,{}>\,{}>~zeros(3,~Int8)~\#~Signed~byte
+>\,{}>\,{}>~zeros(3,~int8)~\#~Signed~byte
 
->\,{}>\,{}>~zeros(3,~type=UInt8)~\#~Unsigned~byte
+>\,{}>\,{}>~zeros(3,~dtype=uint8)~\#~Unsigned~byte
 
->\,{}>\,{}>~array({[}2,3],~type=Float32)
+>\,{}>\,{}>~array({[}2,3],~dtype=float32)
 
->\,{}>\,{}>~arange(4,~type=Complex64)
+>\,{}>\,{}>~arange(4,~dtype=complex64)
 \end{lyxcode}
-The possible types are \texttt{Int8, UInt8, Int16, UInt16, Int32,
-UInt32, Int64, UInt64, Float32, Float64, Complex32, Complex64.} To
-find out the type of an array use the .type() method. E.g.,
+The possible types are \texttt{int8, uint8, int16, uint16, int32,
+uint32, int64, uint64, float32, float64, complex32, complex64.} To
+find out the type of an array use the .dtype() method. E.g.,
 
 \begin{lyxcode}
->\,{}>\,{}>~arr.type()
-
-Float32
+>\,{}>\,{}>~arr.dtype()
+dtype('float32')
 \end{lyxcode}
 To convert an array to a different type use the \texttt{astype()}
 method, e.g,
 
 \begin{lyxcode}
->\,{}>\,{}>~a~=~arr.astype(Float64)
+>\,{}>\,{}>~a~=~arr.astype(float64)
 \end{lyxcode}
 
 \subsection{Printing arrays}
@@ -537,7 +536,7 @@
 All of the indexing tools available for 1-D arrays apply to \emph{n}-dimensional
 arrays as well (though combining index arrays with slices is not currently
 permitted). To understand all the indexing options in their full detail,
-read sections 4.6, 4.7 and 6 of the numarray manual.
+read sections 4.6, 4.7 and 6 of the numpy manual.
 
 
 \subsection{Compatibility of dimensions}
@@ -711,7 +710,7 @@
 \subsection{Array functions}
 
 There are many array utility functions. The following lists the more
-useful ones with a one line description. See the numarray manual for
+useful ones with a one line description. See the numpy manual for
 details on how they are used. Arguments shown with argument=value
 indicate what the default value is if called without a value for that
 argument.
@@ -858,9 +857,7 @@
 \item [{\texttt{\emph{a}}\texttt{.repeat}\textmd{(a,repeats,axis=0):}}]~
 \item [{\texttt{\emph{a}}\texttt{.resize}\textmd{(shape):}}]~
 \item [{\texttt{\emph{a}}\texttt{.size}\textmd{():}}] same as nelements
-\item [{\texttt{\emph{a}}\texttt{.type}\textmd{():}}] returns type of array
-\item [{\texttt{\emph{a}}\texttt{.typecode}\textmd{():}}] returns corresponding
-typecode character used by Numeric 
+\item [{\texttt{\emph{a}}\texttt{.dtype}\textmd{():}}] returns type of array
 \item [{\texttt{\emph{a}}\texttt{.tofile}\textmd{(}\textmd{\emph{file}}\textmd{):}}] write
 binary data to file
 \item [{\texttt{\emph{a}}\texttt{.tolist}\textmd{():}}] convert data to

Modified: trunk/py4science/workbook/wrapping.tex
===================================================================
--- trunk/py4science/workbook/wrapping.tex	2007-12-03 04:56:57 UTC (rev 4555)
+++ trunk/py4science/workbook/wrapping.tex	2007-12-03 08:22:16 UTC (rev 4556)
@@ -125,8 +125,8 @@
 causes f2py to keep cached copies of the scratch areas, so no unnecessary
 mallocs should be triggered.
 
-Since f2py relies on Numeric arrays, all dimensions can be determined
-from the arrays themselves and it is not necessary to pass them explicitly.
+Since f2py relies on NumPy arrays, all dimensions can be determined from the
+arrays themselves and it is not necessary to pass them explicitly.
 
 With all this, the resulting f2py-generated docstring becomes: 
 
@@ -232,7 +232,7 @@
 \subsection{Passing offset arrays to Fortran routines}
 
 It is possible to pass offset arrays (like pointers to the middle
-of other arrays) by using Numeric's slice notation.
+of other arrays) by using NumPy's slice notation.
 
 The print\_dvec function below simply prints its argument as \char`\"{}print{*},'x',x\char`\"{}.
 We show some examples of how it behaves with both 1 and 2-d arrays: 
@@ -339,7 +339,7 @@
 
 \subsection{On matrix ordering and in-memory copies}
 
-Numeric (which f2py relies on) is C-based, and therefore its arrays
+NumPy (which f2py relies on) is C-based, and therefore its arrays
 are stored in row-major order. Fortran stores its arrays in column-major
 order. This means that copying issues must be dealt with. Below we
 reproduce some comments from Pearu on this topic given in the f2py
@@ -348,7 +348,7 @@
 \begin{quote}
 To avoid copying, you should create array that has internally Fortran
 data ordering. This is achived, for example, by reading/creating your
-data in Fortran ordering to Numeric array and then doing Numeric.transpose
+data in Fortran ordering to NumPy array and then doing numpy.transpose
 on that. Every f2py generated extension module provides also function 
 
 has\_column\_major\_storage
@@ -360,7 +360,7 @@
 
 Also note that copying done by f2py generated interface is carried
 out in C on the raw data and therefore it is extremely fast compared
-to if you would make a copy in Python, even when using Numeric. Tests
+to if you would make a copy in Python, even when using NumPy. Tests
 with say 1000x1000 matrices show that there is no noticable performance
 hit when copying is carried out, in fact, sometimes making a copy
 may speed up things a bit -- I was quite surprised about that myself.


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