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From: <fer...@us...> - 2007-10-14 20:44:09
|
Revision: 3943
http://matplotlib.svn.sourceforge.net/matplotlib/?rev=3943&view=rev
Author: fer_perez
Date: 2007-10-14 13:44:08 -0700 (Sun, 14 Oct 2007)
Log Message:
-----------
Fix typo in a formula.
Modified Paths:
--------------
trunk/py4science/workbook/trapezoid.lyx
trunk/py4science/workbook/trapezoid.tex
Modified: trunk/py4science/workbook/trapezoid.lyx
===================================================================
--- trunk/py4science/workbook/trapezoid.lyx 2007-10-14 19:11:32 UTC (rev 3942)
+++ trunk/py4science/workbook/trapezoid.lyx 2007-10-14 20:44:08 UTC (rev 3943)
@@ -120,7 +120,7 @@
) becomes
\begin_inset Formula \begin{equation}
-\int_{a}^{b}f(x)dx\approx\frac{1}{2}\sum_{i=1}^{n}\left(x_{i}-x_{i-1}\right)\left(y_{i}+y_{i+1}\right).\label{eq:trapz}\end{equation}
+\int_{a}^{b}f(x)dx\approx\frac{1}{2}\sum_{i=1}^{n}\left(x_{i}-x_{i-1}\right)\left(y_{i}+y_{i-1}\right).\label{eq:trapz}\end{equation}
\end_inset
Modified: trunk/py4science/workbook/trapezoid.tex
===================================================================
--- trunk/py4science/workbook/trapezoid.tex 2007-10-14 19:11:32 UTC (rev 3942)
+++ trunk/py4science/workbook/trapezoid.tex 2007-10-14 20:44:08 UTC (rev 3943)
@@ -23,7 +23,7 @@
\int_{a}^{b}f(x)dx\approx\frac{h}{2}\sum_{i=1}^{n}\left(f(x_{i})+f(x_{i+1})\right).\label{eq:trapzf2}\end{equation}
One frequently receives the function values already precomputed, $y_{i}=f(x_{i}),$
so equation~(\ref{eq:trapzf}) becomes \begin{equation}
-\int_{a}^{b}f(x)dx\approx\frac{1}{2}\sum_{i=1}^{n}\left(x_{i}-x_{i-1}\right)\left(y_{i}+y_{i+1}\right).\label{eq:trapz}\end{equation}
+\int_{a}^{b}f(x)dx\approx\frac{1}{2}\sum_{i=1}^{n}\left(x_{i}-x_{i-1}\right)\left(y_{i}+y_{i-1}\right).\label{eq:trapz}\end{equation}
%
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|
|
From: <jd...@us...> - 2007-10-20 21:33:00
|
Revision: 3973
http://matplotlib.svn.sourceforge.net/matplotlib/?rev=3973&view=rev
Author: jdh2358
Date: 2007-10-20 14:32:59 -0700 (Sat, 20 Oct 2007)
Log Message:
-----------
added stock records exercise
Modified Paths:
--------------
trunk/py4science/workbook/files_etc.tex
trunk/py4science/workbook/main.tex
Added Paths:
-----------
trunk/py4science/workbook/fig/stock_records.eps
trunk/py4science/workbook/fig/stock_records.png
Added: trunk/py4science/workbook/fig/stock_records.eps
===================================================================
--- trunk/py4science/workbook/fig/stock_records.eps (rev 0)
+++ trunk/py4science/workbook/fig/stock_records.eps 2007-10-20 21:32:59 UTC (rev 3973)
@@ -0,0 +1,9121 @@
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From: <jd...@us...> - 2007-10-26 02:35:25
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Revision: 4008
http://matplotlib.svn.sourceforge.net/matplotlib/?rev=4008&view=rev
Author: jdh2358
Date: 2007-10-25 19:35:23 -0700 (Thu, 25 Oct 2007)
Log Message:
-----------
added workbook
Modified Paths:
--------------
trunk/py4science/workbook/main.tex
Added Paths:
-----------
trunk/py4science/workbook/main.pdf
Added: trunk/py4science/workbook/main.pdf
===================================================================
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From: <jd...@us...> - 2007-10-26 17:43:54
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Revision: 4013
http://matplotlib.svn.sourceforge.net/matplotlib/?rev=4013&view=rev
Author: jdh2358
Date: 2007-10-26 10:43:50 -0700 (Fri, 26 Oct 2007)
Log Message:
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Added: trunk/py4science/workbook/fig/convolve_deltas.png
===================================================================
(Binary files differ)
Property changes on: trunk/py4science/workbook/fig/convolve_deltas.png
___________________________________________________________________
Name: svn:mime-type
+ application/octet-stream
Added: trunk/py4science/workbook/scripts/convolve_deltas.py
===================================================================
--- trunk/py4science/workbook/scripts/convolve_deltas.py (rev 0)
+++ trunk/py4science/workbook/scripts/convolve_deltas.py 2007-10-26 17:43:50 UTC (rev 4013)
@@ -0,0 +1,18 @@
+import numpy as npy
+from pylab import figure, show
+
+dt = 0.025
+t = npy.arange(0.0, 0.5, dt)
+Nt = len(t)
+
+
+s = npy.exp(-2*t)*npy.cos(2*3*npy.pi*t)+1.0
+fig = figure()
+ax = fig.add_subplot(111)
+ax.plot(t, s, color='blue', lw=2)
+ax.bar(t-dt/8., s, facecolor='blue', width=dt/4.)
+ax.axhline(0, color='black', lw=2)
+ax.set_xlim(xmin=0)
+fig.savefig('convolve_deltas.png', dpi=150)
+fig.savefig('convolve_deltas.eps')
+show()
Added: trunk/py4science/workbook/scripts/convolve_explain.py
===================================================================
--- trunk/py4science/workbook/scripts/convolve_explain.py (rev 0)
+++ trunk/py4science/workbook/scripts/convolve_explain.py 2007-10-26 17:43:50 UTC (rev 4013)
@@ -0,0 +1,48 @@
+import numpy as npy
+from pylab import figure, show
+
+dt = 0.01
+t = npy.arange(0.0, 10.0, dt)
+Nt = len(t)
+
+def impulse_response(t):
+ 'double exponential response function'
+ return (npy.exp(-t) - npy.exp(-5*t))
+
+i1 = npy.zeros(len(t))
+i2 = npy.zeros(len(t))
+i3 = npy.zeros(len(t))
+r = impulse_response(t)
+
+ind1, ind2, ind3 = 100, 300, 900
+i1[ind1] = 1
+i2[ind2] = 1.7
+i3[ind3] = 0.6
+
+y1 = npy.convolve(i1, r, mode='full')[:Nt]
+y2 = npy.convolve(i2, r, mode='full')[:Nt]
+y3 = npy.convolve(i3, r, mode='full')[:Nt]
+
+fig = figure()
+ax1 = ax = fig.add_subplot(311)
+ax.plot(t, r, 'k', lw=2)
+ax.set_ylabel('impulse response')
+
+ax = fig.add_subplot(312, sharex=ax1)
+ax.bar(t[ind1], i1[ind1], facecolor='blue', lw=2, edgecolor='blue', width=3*dt)
+ax.bar(t[ind2], i2[ind2], facecolor='green', lw=2, edgecolor='green', width=3*dt)
+ax.bar(t[ind3], i3[ind3], facecolor='red', lw=2, edgecolor='red', width=3*dt)
+ax.plot(t, y1, color='blue', lw=1, label='input 1')
+ax.plot(t, y2, color='green', lw=1, label='input 2')
+ax.plot(t, y3, color='red', lw=1, label='input d')
+ax.set_ylabel('3 inputs')
+
+
+ax = fig.add_subplot(313, sharex=ax1)
+ax.plot(t, y1+y2+y3, color='black', lw=2, label='sum')
+ax.set_ylabel('output')
+#ax.legend(loc='best')
+
+fig.savefig('../fig/convolve_inputs.png', dpi=150)
+fig.savefig('../fig/convolve_inputs.eps')
+show()
This was sent by the SourceForge.net collaborative development platform, the world's largest Open Source development site.
|
|
From: <jd...@us...> - 2007-10-26 17:56:08
|
Revision: 4015
http://matplotlib.svn.sourceforge.net/matplotlib/?rev=4015&view=rev
Author: jdh2358
Date: 2007-10-26 10:56:06 -0700 (Fri, 26 Oct 2007)
Log Message:
-----------
added more convolution figs
Modified Paths:
--------------
trunk/py4science/workbook/scripts/convolve_explain.py
Added Paths:
-----------
trunk/py4science/workbook/fig/convolve_explain.eps
trunk/py4science/workbook/fig/convolve_explain.png
Added: trunk/py4science/workbook/fig/convolve_explain.eps
===================================================================
--- trunk/py4science/workbook/fig/convolve_explain.eps (rev 0)
+++ trunk/py4science/workbook/fig/convolve_explain.eps 2007-10-26 17:56:06 UTC (rev 4015)
@@ -0,0 +1,6355 @@
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[truncated message content] |
|
From: <jd...@us...> - 2007-10-26 19:44:09
|
Revision: 4022
http://matplotlib.svn.sourceforge.net/matplotlib/?rev=4022&view=rev
Author: jdh2358
Date: 2007-10-26 12:44:07 -0700 (Fri, 26 Oct 2007)
Log Message:
-----------
added some more workbook files
Added Paths:
-----------
trunk/py4science/workbook/glass_dots.tex
trunk/py4science/workbook/intro_linalg.tex
trunk/py4science/workbook/intro_sigproc.tex
Added: trunk/py4science/workbook/glass_dots.tex
===================================================================
--- trunk/py4science/workbook/glass_dots.tex (rev 0)
+++ trunk/py4science/workbook/glass_dots.tex 2007-10-26 19:44:07 UTC (rev 4022)
@@ -0,0 +1,52 @@
+\section{Glass Moir\'e Patterns}
+\label{sec:glass_patterns}
+
+When a random dot pattern is scaled, rotated, and superimposed over
+the original dots, interesting visual patterns known as Glass Patterns
+emerge\footnote{L. Glass. 'Moir\'e effect from random dots' Nature 223,
+ 578580 (1969).} In this exercise, we generate random dot fields
+using numpy's uniform distribution function, and apply
+transformations to the random dot field using a scale $\mathbf{S}$
+and rotation $\mathbf{R}$ matrix $\mathbf{X_2} = \mathbf{S} \mathbf{R}
+\mathbf{X_1}$.
+
+If the scale and rotation factors are small, the transformation is
+analogous to a single step in the numerical solution of a 2D ODE, and
+the plot of both $\mathbf{X_1}$ and $\mathbf{X_2}$ will reveal the
+structure of the vecotr field flow around the fixed point (the
+invariant under the transformation); see for example the
+\textit{stable focus}, aka \textit{spiral}, in
+Figure~\ref{fig:glass_dots1}.
+
+The eigenvalues of the tranformation matrix $\mathbf{M} =
+\mathbf{S}\mathbf{R}$ determine the type of fix point:
+\textit{center}, \textit{stable focus}, \textit{saddle node},
+etc\dots. For example, if the two eigenvalues are real but differing
+in signs, the fixed point is a \textit{saddle node}. If the real
+parts of both eigenvalues are negative and the eigenvalues are
+complex, the fixed point is a \textit{stable focus}. The complex part
+of the eigenvalue determines whether there is any rotation in the
+matrix transformation, so another way to look at this is to break out
+the scaling and rotation components of the transformation
+$\textbf{M}$. If there is a rotation component, then the fixed point
+will be a \textit{center} or a \textit{focus}. If the scaling
+components are both one, the rotation will be a \textit{center}, if
+they are both less than one (contraction), it will be a \textit{stable
+ focus}. Likewise, if there is no rotation component, the fixed
+point will be a \textit{node}, and the scaling components will
+determine the type of node. If both are less than one, we have a
+\textit{stable node}, if one is greater than one and the other less
+than one, we have a \textit{saddle node}.
+
+\lstinputlisting[label=code:glass_dots1_skel,caption={IGNORED}]{skel/glass_dots1_skel.py}
+
+
+
+\begin{center}%
+\begin{figure}
+\begin{centering}\includegraphics[width=4in]{fig/glass_dots1}\par\end{centering}
+
+
+\caption{\label{fig:glass_dots1}Glass pattern showing a stable focus}
+\end{figure}
+\par\end{center}
Added: trunk/py4science/workbook/intro_linalg.tex
===================================================================
--- trunk/py4science/workbook/intro_linalg.tex (rev 0)
+++ trunk/py4science/workbook/intro_linalg.tex 2007-10-26 19:44:07 UTC (rev 4022)
@@ -0,0 +1,49 @@
+Like matlab, numpy and scipy have support for fast linear algebra
+built upon the highly optimized LAPACK, BLAS and ATLAS fortran linear
+algebra libraries. Unlike Matlab, in which everything is a matrix or
+vector, and the '*' operator always means matrix multiple, the default
+object in numpy is an \texttt{array}, and the '*' operator on arrays means
+element-wise multiplication.
+
+Instead, numpy provides a \texttt{matrix} class if you want to do
+standard matrix-matrix multiplication with the '*' operator, or the
+\texttt{dot} function if you want to do matrix multiplies with plain
+arrays. The basic linear algebra functionality is found in
+\texttt{numpy.linalg}
+
+\begin{lstlisting}
+In [1]: import numpy as npy
+In [2]: import numpy.linalg as linalg
+
+# X and Y are arrays
+In [3]: X = npy.random.rand(3,3)
+In [4]: Y = npy.random.rand(3,3)
+
+# * operator is element wise multiplication, not matrix matrix
+In [5]: print X*Y
+[[ 0.00973215 0.18086148 0.05539387]
+ [ 0.00817516 0.63354021 0.2017993 ]
+ [ 0.34287698 0.25788149 0.15508982]]
+
+# the dot function will use optimized LAPACK to do matrix-matix
+# multiply
+In [6]: print npy.dot(X, Y)
+[[ 0.10670678 0.68340331 0.39236388]
+ [ 0.27840642 1.14561885 0.62192324]
+ [ 0.48192134 1.32314856 0.51188578]]
+
+# the matrix class will create matrix objects that support matrix
+# multiplication with *
+In [7]: Xm = npy.matrix(X)
+In [8]: Ym = npy.matrix(Y)
+In [9]: print Xm*Ym
+[[ 0.10670678 0.68340331 0.39236388]
+ [ 0.27840642 1.14561885 0.62192324]
+ [ 0.48192134 1.32314856 0.51188578]]
+
+# the linalg module provides functions to compute eigenvalues,
+# determinants, etc. See help(linalg) for more info
+In [10]: print linalg.eigvals(X)
+[ 1.46131600+0.j 0.46329211+0.16501143j 0.46329211-0.16501143j]
+
+\end{lstlisting}
Added: trunk/py4science/workbook/intro_sigproc.tex
===================================================================
--- trunk/py4science/workbook/intro_sigproc.tex (rev 0)
+++ trunk/py4science/workbook/intro_sigproc.tex 2007-10-26 19:44:07 UTC (rev 4022)
@@ -0,0 +1,15 @@
+\texttt{numpy} and \texttt{scipy} provide many of the essential tools
+for digital signal processing. \texttt{scipy.signal} provides basic
+tools for digital filter design and filtering (eg Butterworth
+filters), a linear systems toolkit, standard waveforms such as square
+waves, and saw tooth functions, and some basic wavelet functionality.
+\texttt{scipy.fftpack} provides a suite of tools for Fourier domain
+analysis, including 1D, 2D, and ND discrete fourier transform and
+inverse functions, in addition to other tools such as analytic signal
+representations via the Hilbert trasformation (\texttt{numpy.fft} also
+provides basic FFT functions). \texttt{pylab} provides Matlab
+compatible functions for computing and plotting standard time series
+analyses, such as historgrams (\texttt{hist}), auto and cross
+correlations (\texttt{acorr} and \texttt{xcorr}), power spectra and
+coherence spectra (\texttt{psd}, \texttt{csd}, \texttt{cohere} and
+\texttt{specgram}.
This was sent by the SourceForge.net collaborative development platform, the world's largest Open Source development site.
|
|
From: <jd...@us...> - 2007-10-26 19:55:27
|
Revision: 4026
http://matplotlib.svn.sourceforge.net/matplotlib/?rev=4026&view=rev
Author: jdh2358
Date: 2007-10-26 12:55:21 -0700 (Fri, 26 Oct 2007)
Log Message:
-----------
updated workbook
Modified Paths:
--------------
trunk/py4science/workbook/convolution.tex
trunk/py4science/workbook/fft_imdenoise.tex
trunk/py4science/workbook/main.pdf
trunk/py4science/workbook/quad_newton.tex
trunk/py4science/workbook/trapezoid.tex
Modified: trunk/py4science/workbook/convolution.tex
===================================================================
--- trunk/py4science/workbook/convolution.tex 2007-10-26 19:50:21 UTC (rev 4025)
+++ trunk/py4science/workbook/convolution.tex 2007-10-26 19:55:21 UTC (rev 4026)
@@ -1,3 +1,5 @@
+\section{Convolution}
+\label{sec:convolution}
\begin{center}%
Modified: trunk/py4science/workbook/fft_imdenoise.tex
===================================================================
--- trunk/py4science/workbook/fft_imdenoise.tex 2007-10-26 19:50:21 UTC (rev 4025)
+++ trunk/py4science/workbook/fft_imdenoise.tex 2007-10-26 19:55:21 UTC (rev 4026)
@@ -1,3 +1,6 @@
+\section{FFT Image Denoising}
+\label{sec:fft_imdenoise}
+
Convolution of an input with with a linear filter in the termporal or
spatial domain is equivalent to multiplication by the fourier
transforms of the input and the filter in the spectral domain. This
Modified: trunk/py4science/workbook/main.pdf
===================================================================
--- trunk/py4science/workbook/main.pdf 2007-10-26 19:50:21 UTC (rev 4025)
+++ trunk/py4science/workbook/main.pdf 2007-10-26 19:55:21 UTC (rev 4026)
@@ -90,9 +90,21 @@
(Chapter 6. Signal processing)
endobj
65 0 obj
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+<< /S /GoTo /D (section.6.1) >>
endobj
-68 0 obj <<
+68 0 obj
+(1. Convolution)
+endobj
+69 0 obj
+<< /S /GoTo /D (section.6.2) >>
+endobj
+72 0 obj
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+endobj
+73 0 obj
+<< /S /GoTo /D [74 0 R /Fit ] >>
+endobj
+76 0 obj <<
/Length 292
/Filter /FlateDecode
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@@ -101,24 +113,24 @@
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|
From: <jd...@us...> - 2007-10-26 20:21:36
|
Revision: 4031
http://matplotlib.svn.sourceforge.net/matplotlib/?rev=4031&view=rev
Author: jdh2358
Date: 2007-10-26 13:21:23 -0700 (Fri, 26 Oct 2007)
Log Message:
-----------
added stats workbooks skel
Modified Paths:
--------------
trunk/py4science/workbook/main.pdf
trunk/py4science/workbook/main.tex
Added Paths:
-----------
trunk/py4science/workbook/intro_stats.tex
trunk/py4science/workbook/stats_descriptives.tex
trunk/py4science/workbook/stats_distributions.tex
Added: trunk/py4science/workbook/intro_stats.tex
===================================================================
--- trunk/py4science/workbook/intro_stats.tex (rev 0)
+++ trunk/py4science/workbook/intro_stats.tex 2007-10-26 20:21:23 UTC (rev 4031)
@@ -0,0 +1 @@
+TODO
\ No newline at end of file
Modified: trunk/py4science/workbook/main.pdf
===================================================================
--- trunk/py4science/workbook/main.pdf 2007-10-26 20:20:12 UTC (rev 4030)
+++ trunk/py4science/workbook/main.pdf 2007-10-26 20:21:23 UTC (rev 4031)
@@ -102,9 +102,27 @@
(2. FFT Image Denoising)
endobj
73 0 obj
-<< /S /GoTo /D [74 0 R /Fit ] >>
+<< /S /GoTo /D (chapter.7) >>
endobj
-76 0 obj <<
+76 0 obj
+(Chapter 7. Statistics)
+endobj
+77 0 obj
+<< /S /GoTo /D (section.7.1) >>
+endobj
+80 0 obj
+(1. Descriptive statistics)
+endobj
+81 0 obj
+<< /S /GoTo /D (section.7.2) >>
+endobj
+84 0 obj
+(2. Statistical distributions)
+endobj
+85 0 obj
+<< /S /GoTo /D [86 0 R /Fit ] >>
+endobj
+88 0 obj <<
/Length 292
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>>
@@ -113,24 +131,24 @@
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|
From: <jd...@us...> - 2007-10-27 04:09:16
|
Revision: 4033
http://matplotlib.svn.sourceforge.net/matplotlib/?rev=4033&view=rev
Author: jdh2358
Date: 2007-10-26 21:09:13 -0700 (Fri, 26 Oct 2007)
Log Message:
-----------
late nite pomona workbook build
Modified Paths:
--------------
trunk/py4science/workbook/convolution.tex
trunk/py4science/workbook/intro_stats.tex
trunk/py4science/workbook/main.pdf
trunk/py4science/workbook/stats_distributions.tex
Modified: trunk/py4science/workbook/convolution.tex
===================================================================
--- trunk/py4science/workbook/convolution.tex 2007-10-27 04:00:59 UTC (rev 4032)
+++ trunk/py4science/workbook/convolution.tex 2007-10-27 04:09:13 UTC (rev 4033)
@@ -4,7 +4,7 @@
The output of a linear system is given by the convolution of its
impulse response function with the input. Mathematically
\[
- y(t) = \int_0^\t x(\tau)r(t-\tau)d\tau
+ y(t) = \int_0^t x(\tau)r(t-\tau)d\tau
\]
This fundamental relationship lies at the heart of linear systems
analysis. It is used to model the dynamics of calcium buffers in
@@ -85,7 +85,7 @@
function $r$ by studying the amplitude and phase spectrum of its
transform $R$. In the example below, however, we simply use the
multiplication property to perform the same convolution in Fourier
-space to confirm the numerical result from \textt{numpy.convolve}.
+space to confirm the numerical result from \texttt{numpy.convolve}.
\lstinputlisting[label=code:convolution_demo,caption={IGNORED}]{skel/convolution_demo_skel.py}
Modified: trunk/py4science/workbook/intro_stats.tex
===================================================================
--- trunk/py4science/workbook/intro_stats.tex 2007-10-27 04:00:59 UTC (rev 4032)
+++ trunk/py4science/workbook/intro_stats.tex 2007-10-27 04:09:13 UTC (rev 4033)
@@ -1,16 +1,16 @@
-\textt{R}, a statistical package based on \textt{S}, is viewd by some
+\texttt{R}, a statistical package based on \texttt{S}, is viewd by some
as the best statistical software on the planet, and in the open source
world it is the clear choice for sophisticated statistical analysis.
-Like python, \textt{R} is an interpreted language written in C with an
+Like python, \texttt{R} is an interpreted language written in C with an
interactive shell. Unlike python, which is a general purpose
-programming language, \textt{R} is a specialized statistical language.
+programming language, \texttt{R} is a specialized statistical language.
Since python is a excellent glue language, with facilities for
providing a transparent interface to FORTRAN, C, C++ and other
languages, it should come as no surprise that you can harness
-\textt{R}'s immense statistical power from python, through the
+\texttt{R}'s immense statistical power from python, through the
\texttt{rpy} third part extension library.
-However, \textt{R} is not without its warts. As a language, it lacks
+However, \texttt{R} is not without its warts. As a language, it lacks
python's elegance and advanced programming constructs and idioms. It
is also GPL, which means you cannot distribute code based upon it
unhindered: the code you distribute must be GPL as well (python, and
@@ -19,10 +19,10 @@
application).
Fortunately, the core tools scientific libraries for python (primarily
-\textt{numpy} and \texttt{scipy.stats}) provide a wide array of
+\texttt{numpy} and \texttt{scipy.stats}) provide a wide array of
statistical tools, from basic descriptive statistics (mean, variance,
skew, kurtosis, correlation, \dots) to hypothesis testing (t-tests,
-$\Chi$-Square, analysis of variance, general linear models, \dots) to
+$\chi$-Square, analysis of variance, general linear models, \dots) to
analytical and numerical tools for working with almost every discrete
and continuous statistical distribution you can think of (normal,
gamma, poisson, weibull, lognormal, levy stable, \dots).
Modified: trunk/py4science/workbook/main.pdf
===================================================================
--- trunk/py4science/workbook/main.pdf 2007-10-27 04:00:59 UTC (rev 4032)
+++ trunk/py4science/workbook/main.pdf 2007-10-27 04:09:13 UTC (rev 4033)
@@ -171,7 +171,7 @@
/ProcSet [ /PDF ]
>> endobj
104 0 obj <<
-/Length 1521
+/Length 1520
/Filter /FlateDecode
>>
stream
@@ -181,8 +181,9 @@
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|
From: <jd...@us...> - 2007-10-27 04:32:05
|
Revision: 4035
http://matplotlib.svn.sourceforge.net/matplotlib/?rev=4035&view=rev
Author: jdh2358
Date: 2007-10-26 21:31:54 -0700 (Fri, 26 Oct 2007)
Log Message:
-----------
added andy
Modified Paths:
--------------
trunk/py4science/workbook/main.pdf
trunk/py4science/workbook/main.tex
Modified: trunk/py4science/workbook/main.pdf
===================================================================
--- trunk/py4science/workbook/main.pdf 2007-10-27 04:10:21 UTC (rev 4034)
+++ trunk/py4science/workbook/main.pdf 2007-10-27 04:31:54 UTC (rev 4035)
@@ -123,13 +123,14 @@
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88 0 obj <<
-/Length 292
+/Length 321
/Filter /FlateDecode
>>
stream
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|
From: <jd...@us...> - 2007-10-30 19:14:22
|
Revision: 4065
http://matplotlib.svn.sourceforge.net/matplotlib/?rev=4065&view=rev
Author: jdh2358
Date: 2007-10-30 12:14:17 -0700 (Tue, 30 Oct 2007)
Log Message:
-----------
added lotka volterra skeleton to workbook
Modified Paths:
--------------
trunk/py4science/workbook/main.tex
Added Paths:
-----------
trunk/py4science/workbook/fig/lotka_volterra.eps
trunk/py4science/workbook/fig/lotka_volterra.png
trunk/py4science/workbook/intro_dynsys.tex
trunk/py4science/workbook/lotka_volterra.tex
Added: trunk/py4science/workbook/fig/lotka_volterra.eps
===================================================================
--- trunk/py4science/workbook/fig/lotka_volterra.eps (rev 0)
+++ trunk/py4science/workbook/fig/lotka_volterra.eps 2007-10-30 19:14:17 UTC (rev 4065)
@@ -0,0 +1,12596 @@
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+%%Title: lotka_volterra.eps
+%%Creator: matplotlib version 0.90.1, http://matplotlib.sourceforge.net/
+%%CreationDate: Tue Oct 30 14:09:28 2007
+%%Orientation: portrait
+%%BoundingBox: 18 180 594 612
+%%EndComments
+%%BeginProlog
+/mpldict 8 dict def
+mpldict begin
+/m { moveto } bind def
+/l { lineto } bind def
+/r { rlineto } bind def
+/box {
+m
+1 index 0 r
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+neg 0 r
+closepath
+} bind def
+/clipbox {
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+clip
+newpath
+} bind def
+/ellipse {
+newpath
+matrix currentmatrix 7 1 roll
+translate
+scale
+0 0 1 5 3 roll arc
+setmatrix
+closepath
+} bind def
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[truncated message content] |
|
From: <jd...@us...> - 2007-10-30 19:18:30
|
Revision: 4066
http://matplotlib.svn.sourceforge.net/matplotlib/?rev=4066&view=rev
Author: jdh2358
Date: 2007-10-30 12:18:26 -0700 (Tue, 30 Oct 2007)
Log Message:
-----------
increased fig size
Modified Paths:
--------------
trunk/py4science/workbook/fft_imdenoise.tex
trunk/py4science/workbook/lotka_volterra.tex
trunk/py4science/workbook/stats_descriptives.tex
trunk/py4science/workbook/stats_distributions.tex
trunk/py4science/workbook/trapezoid.tex
Modified: trunk/py4science/workbook/fft_imdenoise.tex
===================================================================
--- trunk/py4science/workbook/fft_imdenoise.tex 2007-10-30 19:14:17 UTC (rev 4065)
+++ trunk/py4science/workbook/fft_imdenoise.tex 2007-10-30 19:18:26 UTC (rev 4066)
@@ -44,7 +44,7 @@
\lstinputlisting[label=code:fft_imdenoise_skel,caption={IGNORED}]{skel/fft_imdenoise_skel.py}
\begin{figure}
-\begin{centering}\includegraphics[width=3in]{fig/fft_imdenoise}\par\end{centering}
+\begin{centering}\includegraphics[width=4in]{fig/fft_imdenoise}\par\end{centering}
\caption{\label{fig:fft_imdenoise}High freqeuency noise filtering of a 2D image in the Fourier domain. The upper panels show the original image (left) and spectral power (right) and the lower panels show the same data with the high frequency power set to zero. Although the input and output images are grayscale, you can provide colormaps to \texttt{pylab.imshow} to plot them in psudo-color}
\end{figure}
Modified: trunk/py4science/workbook/lotka_volterra.tex
===================================================================
--- trunk/py4science/workbook/lotka_volterra.tex 2007-10-30 19:14:17 UTC (rev 4065)
+++ trunk/py4science/workbook/lotka_volterra.tex 2007-10-30 19:18:26 UTC (rev 4066)
@@ -4,7 +4,7 @@
\lstinputlisting[label=code:lotka_volterra_skel,caption={IGNORED}]{skel/lotka_volterra_skel.py}
\begin{figure}
-\begin{centering}\includegraphics[width=3in]{fig/lotka_volterra}\par\end{centering}
+\begin{centering}\includegraphics[width=4in]{fig/lotka_volterra}\par\end{centering}
\caption{\label{fig:lotka_volterra}Upper panel shows population
trajectories for rabbits (blue) and foxes (green) simulated using
Modified: trunk/py4science/workbook/stats_descriptives.tex
===================================================================
--- trunk/py4science/workbook/stats_descriptives.tex 2007-10-30 19:14:17 UTC (rev 4065)
+++ trunk/py4science/workbook/stats_descriptives.tex 2007-10-30 19:18:26 UTC (rev 4066)
@@ -47,7 +47,7 @@
\lstinputlisting[label=code:stats_descriptives_skel,caption={IGNORED}]{skel/stats_descriptives_skel.py}
\begin{figure}
-\begin{centering}\includegraphics[width=3in]{fig/stats_descriptives}\par\end{centering}
+\begin{centering}\includegraphics[width=4in]{fig/stats_descriptives}\par\end{centering}
\caption{\label{fig:stats_descriptives}}
\end{figure}
Modified: trunk/py4science/workbook/stats_distributions.tex
===================================================================
--- trunk/py4science/workbook/stats_distributions.tex 2007-10-30 19:14:17 UTC (rev 4065)
+++ trunk/py4science/workbook/stats_distributions.tex 2007-10-30 19:18:26 UTC (rev 4066)
@@ -81,7 +81,7 @@
\lstinputlisting[label=code:stats_distributions_skel,caption={IGNORED}]{skel/stats_distributions_skel.py}
\begin{figure}
-\begin{centering}\includegraphics[width=3in]{fig/stats_distributions}\par\end{centering}
+\begin{centering}\includegraphics[width=4in]{fig/stats_distributions}\par\end{centering}
\caption{\label{fig:stats_distributions}}
\end{figure}
Modified: trunk/py4science/workbook/trapezoid.tex
===================================================================
--- trunk/py4science/workbook/trapezoid.tex 2007-10-30 19:14:17 UTC (rev 4065)
+++ trunk/py4science/workbook/trapezoid.tex 2007-10-30 19:18:26 UTC (rev 4066)
@@ -28,7 +28,7 @@
%
\begin{figure}
-\begin{centering}\includegraphics[width=3in]{fig/Composite_trapezoidal_rule_illustration}\par\end{centering}
+\begin{centering}\includegraphics[width=4in]{fig/Composite_trapezoidal_rule_illustration}\par\end{centering}
\caption{\label{fig:trapezoid}Illustration of the composite trapezoidal rule
This was sent by the SourceForge.net collaborative development platform, the world's largest Open Source development site.
|
|
From: <jd...@us...> - 2007-10-30 20:21:42
|
Revision: 4067
http://matplotlib.svn.sourceforge.net/matplotlib/?rev=4067&view=rev
Author: jdh2358
Date: 2007-10-30 13:21:39 -0700 (Tue, 30 Oct 2007)
Log Message:
-----------
added solved vs skeletons Makefile build
Modified Paths:
--------------
trunk/py4science/workbook/convolution.tex
trunk/py4science/workbook/fft_imdenoise.tex
trunk/py4science/workbook/files_etc.tex
trunk/py4science/workbook/glass_dots.tex
trunk/py4science/workbook/lotka_volterra.tex
trunk/py4science/workbook/qsort.tex
trunk/py4science/workbook/quad_newton.tex
trunk/py4science/workbook/stats_descriptives.tex
trunk/py4science/workbook/stats_distributions.tex
trunk/py4science/workbook/trapezoid.tex
trunk/py4science/workbook/wallis_pi.tex
trunk/py4science/workbook/wordfreqs.tex
Added Paths:
-----------
trunk/py4science/workbook/Makefile
trunk/py4science/workbook/examples_skel/
trunk/py4science/workbook/examples_skel/convolution_demo.py
trunk/py4science/workbook/examples_skel/fft_imdenoise.py
trunk/py4science/workbook/examples_skel/glass_dots1.py
trunk/py4science/workbook/examples_skel/lotka_volterra.py
trunk/py4science/workbook/examples_skel/noisy_sine.py
trunk/py4science/workbook/examples_skel/qsort.py
trunk/py4science/workbook/examples_skel/quad_newton.py
trunk/py4science/workbook/examples_skel/stats_descriptives.py
trunk/py4science/workbook/examples_skel/stats_distributions.py
trunk/py4science/workbook/examples_skel/stock_records.py
trunk/py4science/workbook/examples_skel/trapezoid.py
trunk/py4science/workbook/examples_skel/wallis_pi.py
trunk/py4science/workbook/examples_skel/wordfreqs.py
trunk/py4science/workbook/examples_solved
Removed Paths:
-------------
trunk/py4science/workbook/skel
Added: trunk/py4science/workbook/Makefile
===================================================================
--- trunk/py4science/workbook/Makefile (rev 0)
+++ trunk/py4science/workbook/Makefile 2007-10-30 20:21:39 UTC (rev 4067)
@@ -0,0 +1,11 @@
+solved:
+ rm -rf examples
+ ln -s examples_solved examples
+ cp main.tex workbook_solved.tex
+ pdflatex workbook_solved
+
+skeletons:
+ rm -rf examples
+ ln -s examples_skel examples
+ cp main.tex workbook_skeletons.tex
+ pdflatex workbook_skeletons
Modified: trunk/py4science/workbook/convolution.tex
===================================================================
--- trunk/py4science/workbook/convolution.tex 2007-10-30 19:18:26 UTC (rev 4066)
+++ trunk/py4science/workbook/convolution.tex 2007-10-30 20:21:39 UTC (rev 4067)
@@ -87,7 +87,7 @@
multiplication property to perform the same convolution in Fourier
space to confirm the numerical result from \texttt{numpy.convolve}.
-\lstinputlisting[label=code:convolution_demo,caption={IGNORED}]{skel/convolution_demo_skel.py}
+\lstinputlisting[label=code:convolution_demo,caption={IGNORED}]{examples/convolution_demo.py}
Added: trunk/py4science/workbook/examples_skel/convolution_demo.py
===================================================================
--- trunk/py4science/workbook/examples_skel/convolution_demo.py (rev 0)
+++ trunk/py4science/workbook/examples_skel/convolution_demo.py 2007-10-30 20:21:39 UTC (rev 4067)
@@ -0,0 +1 @@
+link ../../examples/skel/convolution_demo_skel.py
\ No newline at end of file
Property changes on: trunk/py4science/workbook/examples_skel/convolution_demo.py
___________________________________________________________________
Name: svn:special
+ *
Added: trunk/py4science/workbook/examples_skel/fft_imdenoise.py
===================================================================
--- trunk/py4science/workbook/examples_skel/fft_imdenoise.py (rev 0)
+++ trunk/py4science/workbook/examples_skel/fft_imdenoise.py 2007-10-30 20:21:39 UTC (rev 4067)
@@ -0,0 +1 @@
+link ../../examples/skel/fft_imdenoise_skel.py
\ No newline at end of file
Property changes on: trunk/py4science/workbook/examples_skel/fft_imdenoise.py
___________________________________________________________________
Name: svn:special
+ *
Added: trunk/py4science/workbook/examples_skel/glass_dots1.py
===================================================================
--- trunk/py4science/workbook/examples_skel/glass_dots1.py (rev 0)
+++ trunk/py4science/workbook/examples_skel/glass_dots1.py 2007-10-30 20:21:39 UTC (rev 4067)
@@ -0,0 +1 @@
+link ../../examples/skel/glass_dots1_skel.py
\ No newline at end of file
Property changes on: trunk/py4science/workbook/examples_skel/glass_dots1.py
___________________________________________________________________
Name: svn:special
+ *
Added: trunk/py4science/workbook/examples_skel/lotka_volterra.py
===================================================================
--- trunk/py4science/workbook/examples_skel/lotka_volterra.py (rev 0)
+++ trunk/py4science/workbook/examples_skel/lotka_volterra.py 2007-10-30 20:21:39 UTC (rev 4067)
@@ -0,0 +1 @@
+link ../../examples/skel/lotka_volterra_skel.py
\ No newline at end of file
Property changes on: trunk/py4science/workbook/examples_skel/lotka_volterra.py
___________________________________________________________________
Name: svn:special
+ *
Added: trunk/py4science/workbook/examples_skel/noisy_sine.py
===================================================================
--- trunk/py4science/workbook/examples_skel/noisy_sine.py (rev 0)
+++ trunk/py4science/workbook/examples_skel/noisy_sine.py 2007-10-30 20:21:39 UTC (rev 4067)
@@ -0,0 +1 @@
+link ../../examples/skel/noisy_sine_skel.py
\ No newline at end of file
Property changes on: trunk/py4science/workbook/examples_skel/noisy_sine.py
___________________________________________________________________
Name: svn:special
+ *
Added: trunk/py4science/workbook/examples_skel/qsort.py
===================================================================
--- trunk/py4science/workbook/examples_skel/qsort.py (rev 0)
+++ trunk/py4science/workbook/examples_skel/qsort.py 2007-10-30 20:21:39 UTC (rev 4067)
@@ -0,0 +1 @@
+link ../../examples/skel/qsort_skel.py
\ No newline at end of file
Property changes on: trunk/py4science/workbook/examples_skel/qsort.py
___________________________________________________________________
Name: svn:special
+ *
Added: trunk/py4science/workbook/examples_skel/quad_newton.py
===================================================================
--- trunk/py4science/workbook/examples_skel/quad_newton.py (rev 0)
+++ trunk/py4science/workbook/examples_skel/quad_newton.py 2007-10-30 20:21:39 UTC (rev 4067)
@@ -0,0 +1 @@
+link ../../examples/skel/quad_newton_skel.py
\ No newline at end of file
Property changes on: trunk/py4science/workbook/examples_skel/quad_newton.py
___________________________________________________________________
Name: svn:special
+ *
Added: trunk/py4science/workbook/examples_skel/stats_descriptives.py
===================================================================
--- trunk/py4science/workbook/examples_skel/stats_descriptives.py (rev 0)
+++ trunk/py4science/workbook/examples_skel/stats_descriptives.py 2007-10-30 20:21:39 UTC (rev 4067)
@@ -0,0 +1 @@
+link ../../examples/skel/stats_descriptives_skel.py
\ No newline at end of file
Property changes on: trunk/py4science/workbook/examples_skel/stats_descriptives.py
___________________________________________________________________
Name: svn:special
+ *
Added: trunk/py4science/workbook/examples_skel/stats_distributions.py
===================================================================
--- trunk/py4science/workbook/examples_skel/stats_distributions.py (rev 0)
+++ trunk/py4science/workbook/examples_skel/stats_distributions.py 2007-10-30 20:21:39 UTC (rev 4067)
@@ -0,0 +1 @@
+link ../../examples/skel/stats_distributions_skel.py
\ No newline at end of file
Property changes on: trunk/py4science/workbook/examples_skel/stats_distributions.py
___________________________________________________________________
Name: svn:special
+ *
Added: trunk/py4science/workbook/examples_skel/stock_records.py
===================================================================
--- trunk/py4science/workbook/examples_skel/stock_records.py (rev 0)
+++ trunk/py4science/workbook/examples_skel/stock_records.py 2007-10-30 20:21:39 UTC (rev 4067)
@@ -0,0 +1 @@
+link ../../examples/skel/stock_records_skel.py
\ No newline at end of file
Property changes on: trunk/py4science/workbook/examples_skel/stock_records.py
___________________________________________________________________
Name: svn:special
+ *
Added: trunk/py4science/workbook/examples_skel/trapezoid.py
===================================================================
--- trunk/py4science/workbook/examples_skel/trapezoid.py (rev 0)
+++ trunk/py4science/workbook/examples_skel/trapezoid.py 2007-10-30 20:21:39 UTC (rev 4067)
@@ -0,0 +1 @@
+link ../../examples/skel/trapezoid_skel.py
\ No newline at end of file
Property changes on: trunk/py4science/workbook/examples_skel/trapezoid.py
___________________________________________________________________
Name: svn:special
+ *
Added: trunk/py4science/workbook/examples_skel/wallis_pi.py
===================================================================
--- trunk/py4science/workbook/examples_skel/wallis_pi.py (rev 0)
+++ trunk/py4science/workbook/examples_skel/wallis_pi.py 2007-10-30 20:21:39 UTC (rev 4067)
@@ -0,0 +1 @@
+link ../../examples/skel/wallis_pi_skel.py
\ No newline at end of file
Property changes on: trunk/py4science/workbook/examples_skel/wallis_pi.py
___________________________________________________________________
Name: svn:special
+ *
Added: trunk/py4science/workbook/examples_skel/wordfreqs.py
===================================================================
--- trunk/py4science/workbook/examples_skel/wordfreqs.py (rev 0)
+++ trunk/py4science/workbook/examples_skel/wordfreqs.py 2007-10-30 20:21:39 UTC (rev 4067)
@@ -0,0 +1 @@
+link ../../examples/skel/wordfreqs_skel.py
\ No newline at end of file
Property changes on: trunk/py4science/workbook/examples_skel/wordfreqs.py
___________________________________________________________________
Name: svn:special
+ *
Added: trunk/py4science/workbook/examples_solved
===================================================================
--- trunk/py4science/workbook/examples_solved (rev 0)
+++ trunk/py4science/workbook/examples_solved 2007-10-30 20:21:39 UTC (rev 4067)
@@ -0,0 +1 @@
+link ../examples
\ No newline at end of file
Property changes on: trunk/py4science/workbook/examples_solved
___________________________________________________________________
Name: svn:special
+ *
Modified: trunk/py4science/workbook/fft_imdenoise.tex
===================================================================
--- trunk/py4science/workbook/fft_imdenoise.tex 2007-10-30 19:18:26 UTC (rev 4066)
+++ trunk/py4science/workbook/fft_imdenoise.tex 2007-10-30 20:21:39 UTC (rev 4067)
@@ -41,7 +41,7 @@
to one. This serves to enhance contrast among the darker elements of
the image, so it is not completely dominated by the brighter segments
-\lstinputlisting[label=code:fft_imdenoise_skel,caption={IGNORED}]{skel/fft_imdenoise_skel.py}
+\lstinputlisting[label=code:fft_imdenoise,caption={IGNORED}]{examples/fft_imdenoise.py}
\begin{figure}
\begin{centering}\includegraphics[width=4in]{fig/fft_imdenoise}\par\end{centering}
Modified: trunk/py4science/workbook/files_etc.tex
===================================================================
--- trunk/py4science/workbook/files_etc.tex 2007-10-30 19:18:26 UTC (rev 4066)
+++ trunk/py4science/workbook/files_etc.tex 2007-10-30 20:21:39 UTC (rev 4067)
@@ -44,7 +44,7 @@
Here is the exercise skeleton of the script to create and plot the
data file
-\lstinputlisting[label=code:noisy_sine_skel,caption={IGNORED}]{skel/noisy_sine_skel.py}
+\lstinputlisting[label=code:noisy_sine,caption={IGNORED}]{examples/noisy_sine.py}
and the graph will look something like Figure~\ref{fig:noisy_sine}
@@ -189,7 +189,7 @@
in 2003 and held to the present) for each stock. Here is the exercise
skeleton.:
-\lstinputlisting[label=code:stock_records_skel,caption={IGNORED}]{skel/stock_records_skel.py}
+\lstinputlisting[label=code:stock_records,caption={IGNORED}]{examples/stock_records.py}
The graph will look something like Figure~\ref{fig:stock_records}.
Modified: trunk/py4science/workbook/glass_dots.tex
===================================================================
--- trunk/py4science/workbook/glass_dots.tex 2007-10-30 19:18:26 UTC (rev 4066)
+++ trunk/py4science/workbook/glass_dots.tex 2007-10-30 20:21:39 UTC (rev 4067)
@@ -38,7 +38,7 @@
\textit{stable node}, if one is greater than one and the other less
than one, we have a \textit{saddle node}.
-\lstinputlisting[label=code:glass_dots1_skel,caption={IGNORED}]{skel/glass_dots1_skel.py}
+\lstinputlisting[label=code:glass_dots1,caption={IGNORED}]{examples/glass_dots1.py}
Modified: trunk/py4science/workbook/lotka_volterra.tex
===================================================================
--- trunk/py4science/workbook/lotka_volterra.tex 2007-10-30 19:18:26 UTC (rev 4066)
+++ trunk/py4science/workbook/lotka_volterra.tex 2007-10-30 20:21:39 UTC (rev 4067)
@@ -1,7 +1,7 @@
\section{Lotka-Volterra Equations}
\label{sec:lotka_volterra}
-\lstinputlisting[label=code:lotka_volterra_skel,caption={IGNORED}]{skel/lotka_volterra_skel.py}
+\lstinputlisting[label=code:lotka_volterra,caption={IGNORED}]{examples/lotka_volterra.py}
\begin{figure}
\begin{centering}\includegraphics[width=4in]{fig/lotka_volterra}\par\end{centering}
Modified: trunk/py4science/workbook/qsort.tex
===================================================================
--- trunk/py4science/workbook/qsort.tex 2007-10-30 19:18:26 UTC (rev 4066)
+++ trunk/py4science/workbook/qsort.tex 2007-10-30 20:21:39 UTC (rev 4067)
@@ -20,11 +20,11 @@
represents the sorting operation, $+$ indicates list concatenation
and $[p]$ is the list containing the pivot as its single element.
\end{enumerate}
-The listing~\ref{code:qsort_skel} contains a skeleton with no implementation
+The listing~\ref{code:qsort} contains a skeleton with no implementation
but with tests already written (in the form of \emph{unit tests},
as described in the introduction).
-\lstinputlisting[label=code:qsort_skel,caption={IGNORED}]{skel/qsort_skel.py}
+\lstinputlisting[label=code:qsort,caption={IGNORED}]{examples/qsort.py}
\subsection*{Hints}
Modified: trunk/py4science/workbook/quad_newton.tex
===================================================================
--- trunk/py4science/workbook/quad_newton.tex 2007-10-30 19:18:26 UTC (rev 4066)
+++ trunk/py4science/workbook/quad_newton.tex 2007-10-30 20:21:39 UTC (rev 4067)
@@ -26,9 +26,9 @@
u=\frac{1}{4}.\]
-The listing~\ref{code:quad_newton_skel} contains a skeleton that
+The listing~\ref{code:quad_newton} contains a skeleton that
includes for comparison the correct numerical value.
-\lstinputlisting[label=code:quad_newton_skel,caption={IGNORED}]{skel/quad_newton_skel.py}
+\lstinputlisting[label=code:quad_newton,caption={IGNORED}]{examples/quad_newton.py}
Deleted: trunk/py4science/workbook/skel
===================================================================
--- trunk/py4science/workbook/skel 2007-10-30 19:18:26 UTC (rev 4066)
+++ trunk/py4science/workbook/skel 2007-10-30 20:21:39 UTC (rev 4067)
@@ -1 +0,0 @@
-link ../examples/skel
\ No newline at end of file
Modified: trunk/py4science/workbook/stats_descriptives.tex
===================================================================
--- trunk/py4science/workbook/stats_descriptives.tex 2007-10-30 19:18:26 UTC (rev 4066)
+++ trunk/py4science/workbook/stats_descriptives.tex 2007-10-30 20:21:39 UTC (rev 4067)
@@ -44,7 +44,7 @@
\end{lstlisting}
-\lstinputlisting[label=code:stats_descriptives_skel,caption={IGNORED}]{skel/stats_descriptives_skel.py}
+\lstinputlisting[label=code:stats_descriptives,caption={IGNORED}]{examples/stats_descriptives.py}
\begin{figure}
\begin{centering}\includegraphics[width=4in]{fig/stats_descriptives}\par\end{centering}
Modified: trunk/py4science/workbook/stats_distributions.tex
===================================================================
--- trunk/py4science/workbook/stats_distributions.tex 2007-10-30 19:18:26 UTC (rev 4066)
+++ trunk/py4science/workbook/stats_distributions.tex 2007-10-30 20:21:39 UTC (rev 4067)
@@ -78,7 +78,7 @@
should look something like those in
Figure~\ref{fig:stats_distributions}.
-\lstinputlisting[label=code:stats_distributions_skel,caption={IGNORED}]{skel/stats_distributions_skel.py}
+\lstinputlisting[label=code:stats_distributions,caption={IGNORED}]{examples/stats_distributions.py}
\begin{figure}
\begin{centering}\includegraphics[width=4in]{fig/stats_distributions}\par\end{centering}
Modified: trunk/py4science/workbook/trapezoid.tex
===================================================================
--- trunk/py4science/workbook/trapezoid.tex 2007-10-30 19:18:26 UTC (rev 4066)
+++ trunk/py4science/workbook/trapezoid.tex 2007-10-30 20:21:39 UTC (rev 4067)
@@ -36,11 +36,11 @@
\end{figure}
-Listing~\ref{code:trapezoid_skel} contains a skeleton for this problem,
+Listing~\ref{code:trapezoid} contains a skeleton for this problem,
written in the form of two incomplete functions and a set of automatic
tests (in the form of \emph{unit tests}, as described in the introduction).
-\lstinputlisting[label=code:trapezoid_skel,caption={IGNORED}]{skel/trapezoid_skel.py}
+\lstinputlisting[label=code:trapezoid,caption={IGNORED}]{examples/trapezoid.py}
In this exercise, you'll need to write two functions, \texttt{trapz}
and \texttt{trapzf}. \texttt{trapz} applies the trapezoid formula
Modified: trunk/py4science/workbook/wallis_pi.tex
===================================================================
--- trunk/py4science/workbook/wallis_pi.tex 2007-10-30 19:18:26 UTC (rev 4066)
+++ trunk/py4science/workbook/wallis_pi.tex 2007-10-30 20:21:39 UTC (rev 4067)
@@ -6,12 +6,12 @@
\pi=\prod_{i=1}^{\infty}\frac{4i^{2}}{4i^{2}-1}.\end{equation}
-The listing~\ref{code:wallis_pi_skel} contains a skeleton with no
+The listing~\ref{code:wallis_pi} contains a skeleton with no
implementation but with some plotting commands already inserted, so
that you can visualize the convergence rate of this formula as more
terms are kept.
-\lstinputlisting[label=code:wallis_pi_skel,caption={IGNORED}]{skel/wallis_pi_skel.py}
+\lstinputlisting[label=code:wallis_pi,caption={IGNORED}]{examples/wallis_pi.py}
After running the script successfully, you should obtain a plot similar
to Figure~\ref{fig:wallis_pi}.
Modified: trunk/py4science/workbook/wordfreqs.tex
===================================================================
--- trunk/py4science/workbook/wordfreqs.tex 2007-10-30 19:18:26 UTC (rev 4066)
+++ trunk/py4science/workbook/wordfreqs.tex 2007-10-30 20:21:39 UTC (rev 4067)
@@ -14,10 +14,10 @@
In this problem, you will need to count the frequencies of all the
words contained in a compressed text file supplied as input.
-The listing~\ref{code:wordfreqs_skel} contains a skeleton for this
+The listing~\ref{code:wordfreqs} contains a skeleton for this
problem, with \texttt{XXX} marking various places that are incomplete.
-\lstinputlisting[label=code:wordfreqs_skel,caption={IGNORED}]{skel/wordfreqs_skel.py}
+\lstinputlisting[label=code:wordfreqs,caption={IGNORED}]{examples/wordfreqs.py}
\subsection*{Hints}
This was sent by the SourceForge.net collaborative development platform, the world's largest Open Source development site.
|
|
From: <jd...@us...> - 2007-11-03 12:32:03
|
Revision: 4100
http://matplotlib.svn.sourceforge.net/matplotlib/?rev=4100&view=rev
Author: jdh2358
Date: 2007-11-03 05:31:59 -0700 (Sat, 03 Nov 2007)
Log Message:
-----------
added readme and exercise template
Added Paths:
-----------
trunk/py4science/workbook/README
trunk/py4science/workbook/template.tex
Added: trunk/py4science/workbook/README
===================================================================
--- trunk/py4science/workbook/README (rev 0)
+++ trunk/py4science/workbook/README 2007-11-03 12:31:59 UTC (rev 4100)
@@ -0,0 +1,22 @@
+This is the workbook for the py4science course. It is made up of a
+bunch of different units, each of which lives in a separate *.tex
+file. To make a new unit, copy template.tex to yourfile.tex and fill
+it in. We use the latex listing package for including python source
+code.
+
+For every unit, create an example in ../examples/your_example.py, a
+skeleton in ../examples/your_example_skel.py, and a symlink from
+../examples/your_example_skel.py -> examples_skel/your_example.py
+(../examples is in the top level of the py4science repository). If
+there are any figures, you should create a PNG and EPS version of each
+and add them to the fig subdirectory of the workbook directory. You
+will need to svn add your tex file, example, skeleton, skeleton
+symlink, and figures.
+
+You can build the workbook in skeleton form with
+
+ > make skeletons # create workbook_skeletons.pdf
+
+and in solved form with
+
+ > make solved # creates workbook_solved.pdf
Added: trunk/py4science/workbook/template.tex
===================================================================
--- trunk/py4science/workbook/template.tex (rev 0)
+++ trunk/py4science/workbook/template.tex 2007-11-03 12:31:59 UTC (rev 4100)
@@ -0,0 +1,37 @@
+\section{Your Example}
+\label{sec:your_example}
+
+This is your introduction. Refer to python packages like
+\texttt{numpy} and \texttt{matplotlib}, as well as functions like
+\texttt{n.arange}, with ``texttt''.
+
+Refer to figures like Figure~\ref{fig:your_figure}. Your example code
+should be included like.
+
+\lstinputlisting[label=code:your_example,caption={IGNORED}]{examples/your_example.py}
+
+and your figures should be saved in the \textff{fig} subdirectory with
+a PNG and EPS version. You should include it like
+
+\begin{figure}
+\begin{centering}\includegraphics[width=4in]{fig/your_example}\par\end{centering}
+
+\caption{\label{fig:your_example}Your figure caption here}
+\end{figure}
+
+If you want to include python code inline, including ipython sessions use the following environment. I sometimes decorate ipython sessions with comments.
+
+\begin{listing}
+# use namespaces!
+In [1]: import numpy as n
+
+In [2]: x = n.random.rand(10)
+
+In [3]: x**2
+Out[3]:
+array([ 0.08694464, 0.99225328, 0.05017794, 0.44827437, 0.25938905,
+ 0.10617795, 0.27397649, 0.33391573, 0.27975237, 0.06808894])
+
+In [4]:
+
+\end{listing}
\ No newline at end of file
This was sent by the SourceForge.net collaborative development platform, the world's largest Open Source development site.
|
|
From: <js...@us...> - 2007-11-21 20:50:17
|
Revision: 4409
http://matplotlib.svn.sourceforge.net/matplotlib/?rev=4409&view=rev
Author: jswhit
Date: 2007-11-21 12:50:10 -0800 (Wed, 21 Nov 2007)
Log Message:
-----------
add 'Plotting on Maps' chapter
Modified Paths:
--------------
trunk/py4science/workbook/main.tex
Added Paths:
-----------
trunk/py4science/workbook/basemap.tex
trunk/py4science/workbook/fig/basemap1.eps
trunk/py4science/workbook/fig/basemap1.pdf
trunk/py4science/workbook/fig/basemap1.png
trunk/py4science/workbook/fig/basemap2.eps
trunk/py4science/workbook/fig/basemap2.png
trunk/py4science/workbook/fig/basemap3.eps
trunk/py4science/workbook/fig/basemap3.pdf
trunk/py4science/workbook/fig/basemap3.png
trunk/py4science/workbook/fig/basemap4.eps
trunk/py4science/workbook/fig/basemap4.pdf
trunk/py4science/workbook/fig/basemap4.png
trunk/py4science/workbook/fig/basemap5.pdf
trunk/py4science/workbook/fig/basemap5.png
Added: trunk/py4science/workbook/basemap.tex
===================================================================
--- trunk/py4science/workbook/basemap.tex (rev 0)
+++ trunk/py4science/workbook/basemap.tex 2007-11-21 20:50:10 UTC (rev 4409)
@@ -0,0 +1,125 @@
+The matplotlib basemap toolkit is an add-on for matplotlib that provides
+the capability to draw maps of the earth in various map projections,
+and plot data on those maps. This section shows how to use basemap
+to create simple maps, draw coastlines and political boundaries, draw
+lines of constant latitude and longitude, and plot geophysical data
+on the maps.
+
+
+\section{Setting up the map.}
+
+In order to represent the curved surface of the earth in a two-dimensional
+map, a map projection is needed. Since this cannot be done without
+distortion, there are many map projections, each with it's own advantages
+and disadvantages. Basemap provides 19 different map projections.
+Some are global, some can only represent a portion of the globe. When
+a Basemap class instance is created, the desired map projection must
+be specified, along with information about the portion of the earth's
+surface that the map projection will describe. There are two basic
+ways of doing this. One is to provide the latitude and longitude values
+of each of the four corners of the rectangular map projection region.
+The other is to provide the lat/lon value of the center of the map
+projection region along with the width and height of the region in
+map projection coordinates. The first script illustrates how to use
+both of these methods to create a simple map. It also shows how to
+draw the continents and political boundaries on the map.
+
+Here is an example script that creates a map by specifying the latitudes
+and longitudes of the four corners
+
+\lstinputlisting[label=code:basemap1_skel,caption={IGNORED}]{../examples/basemap1.py}
+
+After running this script, you should see a plot that looks similar
+to Figure 1.
+
+\begin{figure}[h]
+\includegraphics[scale=0.75]{fig/basemap1}
+
+\caption{A map created by specifying the latitudes and longitudes of the four
+corners.}
+
+\end{figure}
+
+\medskip{}
+Here is an example script that creates a map by specifying the center
+of the map, plus the width and height in meters.
+
+\lstinputlisting[label=code:basemap2_skel,caption={IGNORED}]{../examples/basemap2.py}
+
+After running this script, you should see a plot that looks nearly
+identical to Figure 1.\medskip{}
+
+
+The Basemap class instance can be used to convert latitudes and longitudes
+to coordinates on the map. To do this, simply call the instance as
+if it were a function, passing it the longitude and latitudes values
+to convert. The corresponding x and y values in map projection coordinates
+will be returned. The following example script shows how to use this
+to plot the locations of two cities (New York and London). The Basemap
+method drawgreatcircle is then used to draw the great circle route
+between these cities on the map.
+
+\lstinputlisting[label=code:basemap3_skel,caption={IGNORED}]{../examples/basemap3.py}
+
+This should produce something similar to Figure 2.
+
+\begin{figure}[h]
+\includegraphics[scale=0.75]{fig/basemap3}
+
+\caption{Drawing the locations of two cities, and connecting them along a great
+circle.}
+
+\end{figure}
+
+\medskip{}
+Most maps include a graticule grid, a reference network of labelled
+latitude and longitude lines. Basemap does this with the drawparallels
+and drawmeridians instance methods. The longitude and latitude lines
+can be labelled where the intersect the map projection boundary. Following
+is an example script that draws a graticule on the map we've been
+working with.
+
+\lstinputlisting[label=code:basemap4_skel,caption={IGNORED}]{../examples/basemap4.py}
+
+Running this script should produce a plot that looks like Figure 3.
+
+\begin{figure}[h]
+\includegraphics[scale=0.75]{fig/basemap4}
+
+\caption{Drawing labelled meridians and parallels on the map (a graticule grid).}
+
+\end{figure}
+
+
+\section{Plotting geophysical data on the map.}
+
+One of the most common uses of Basemap is to visualize earth science
+data, such as output from climate models. These data often come on
+latitude/longitude grids. One common data format for storing such
+grids is NetCDF. Basemap includes a NetCDF file reader (written in
+pure python by Roberto D'Almeida). There are python packages available
+for reading just about every other scientific data format imaginable,
+including HDF, GRIB, FITS and many others. Following is an example
+of how to read sea-surface temperature data from a NetCDF file and
+plot it on a global mollweide projection.
+
+\lstinputlisting[label=code:basemap5_skel,caption={IGNORED}]{../examples/basemap5.py}
+
+The resulting plot should look like Figure 4.
+
+\pagebreak
+
+\begin{figure}[h]
+\includegraphics[scale=0.75]{fig/basemap5}
+
+\caption{Sea surface temperature on a global mollweide projection.}
+
+\end{figure}
+
+\medskip{}
+
+Basemap also is capable of reading ESRI shapefiles, a very common
+GIS format. The script fillstates.py in the examples directory of
+the basemap source distribution shows how to read and plot polygons
+in a shapefile. There are many other useful examples in that directory
+that illustrate various ways of using basemap.%
Added: trunk/py4science/workbook/fig/basemap1.eps
===================================================================
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[truncated message content] |
|
From: <fer...@us...> - 2007-12-03 00:12:27
|
Revision: 4548
http://matplotlib.svn.sourceforge.net/matplotlib/?rev=4548&view=rev
Author: fer_perez
Date: 2007-12-02 16:12:18 -0800 (Sun, 02 Dec 2007)
Log Message:
-----------
Commits to continue the merged book.
Modified Paths:
--------------
trunk/py4science/workbook/Makefile
trunk/py4science/workbook/basemap.tex
trunk/py4science/workbook/convolution.tex
trunk/py4science/workbook/fft_imdenoise.tex
trunk/py4science/workbook/files_etc.tex
trunk/py4science/workbook/glass_dots.tex
trunk/py4science/workbook/lotka_volterra.tex
trunk/py4science/workbook/main.tex
trunk/py4science/workbook/qsort.tex
trunk/py4science/workbook/quad_newton.tex
trunk/py4science/workbook/stats_descriptives.tex
trunk/py4science/workbook/stats_distributions.tex
trunk/py4science/workbook/template.tex
trunk/py4science/workbook/trapezoid.tex
trunk/py4science/workbook/wallis_pi.tex
trunk/py4science/workbook/wordfreqs.tex
Added Paths:
-----------
trunk/py4science/workbook/examples
trunk/py4science/workbook/fig/hothead.png
trunk/py4science/workbook/fig/ipscr_code.png
trunk/py4science/workbook/fig/ipscr_meth_src.png
trunk/py4science/workbook/fig/ipscr_traceback.png
trunk/py4science/workbook/fig/load_ascii.png
trunk/py4science/workbook/fig/mpl_image_hot.png
trunk/py4science/workbook/fig/mpl_image_jet.png
trunk/py4science/workbook/fig/mpl_one_two_three.png
trunk/py4science/workbook/fig/mpl_ratner.png
trunk/py4science/workbook/fig/mpl_set_get1.png
trunk/py4science/workbook/fig/mpl_set_get2.png
trunk/py4science/workbook/fig/mpl_subplot_demo.png
trunk/py4science/workbook/fig/mpl_toolbar.png
trunk/py4science/workbook/intro_to_python.tex
trunk/py4science/workbook/ipython_tut.tex
trunk/py4science/workbook/matplotlib_tut.tex
trunk/py4science/workbook/problems_solved
trunk/py4science/workbook/python.bib
trunk/py4science/workbook/python2.bib
trunk/py4science/workbook/snippets
trunk/py4science/workbook/why_python.tex
trunk/py4science/workbook/wrapping.tex
Modified: trunk/py4science/workbook/Makefile
===================================================================
--- trunk/py4science/workbook/Makefile 2007-12-02 17:27:41 UTC (rev 4547)
+++ trunk/py4science/workbook/Makefile 2007-12-03 00:12:18 UTC (rev 4548)
@@ -1,14 +1,16 @@
solved:
- rm -f examples
- ln -s examples_solved examples
+ rm -f problems
+ ln -s problems_solved problems
rm -f workbook_solved.tex
ln -s main.tex workbook_solved.tex
pdflatex workbook_solved
+ bibtex workbook_solved
+ pdflatex workbook_solved
rm -f workbook_solved.tex
skeletons:
- rm -f examples
- ln -s examples_skel examples
+ rm -f problems
+ ln -s problems_skel problems
rm -f workbook_skeletons.tex
ln -s main.tex workbook_skeletons.tex
pdflatex workbook_skeletons
Modified: trunk/py4science/workbook/basemap.tex
===================================================================
--- trunk/py4science/workbook/basemap.tex 2007-12-02 17:27:41 UTC (rev 4547)
+++ trunk/py4science/workbook/basemap.tex 2007-12-03 00:12:18 UTC (rev 4548)
@@ -27,7 +27,7 @@
Here is an example script that creates a map by specifying the latitudes
and longitudes of the four corners
-\lstinputlisting[label=code:basemap1_skel,caption={IGNORED}]{../examples/basemap1.py}
+\lstinputlisting[label=code:basemap1_skel,caption={IGNORED}]{problems/basemap1.py}
After running this script, you should see a plot that looks similar
to Figure 1.
@@ -44,7 +44,7 @@
Here is an example script that creates a map by specifying the center
of the map, plus the width and height in meters.
-\lstinputlisting[label=code:basemap2_skel,caption={IGNORED}]{../examples/basemap2.py}
+\lstinputlisting[label=code:basemap2_skel,caption={IGNORED}]{problems/basemap2.py}
After running this script, you should see a plot that looks nearly
identical to Figure 1.\medskip{}
@@ -59,7 +59,7 @@
method drawgreatcircle is then used to draw the great circle route
between these cities on the map.
-\lstinputlisting[label=code:basemap3_skel,caption={IGNORED}]{../examples/basemap3.py}
+\lstinputlisting[label=code:basemap3_skel,caption={IGNORED}]{problems/basemap3.py}
This should produce something similar to Figure 2.
@@ -79,7 +79,7 @@
is an example script that draws a graticule on the map we've been
working with.
-\lstinputlisting[label=code:basemap4_skel,caption={IGNORED}]{../examples/basemap4.py}
+\lstinputlisting[label=code:basemap4_skel,caption={IGNORED}]{problems/basemap4.py}
Running this script should produce a plot that looks like Figure 3.
@@ -109,7 +109,7 @@
of how to read sea-surface temperature data from a NetCDF file and
plot it on a global mollweide projection.
-\lstinputlisting[label=code:basemap5_skel,caption={IGNORED}]{../examples/basemap5.py}
+\lstinputlisting[label=code:basemap5_skel,caption={IGNORED}]{problems/basemap5.py}
The resulting plot should look like Figure 4.
Modified: trunk/py4science/workbook/convolution.tex
===================================================================
--- trunk/py4science/workbook/convolution.tex 2007-12-02 17:27:41 UTC (rev 4547)
+++ trunk/py4science/workbook/convolution.tex 2007-12-03 00:12:18 UTC (rev 4548)
@@ -87,7 +87,7 @@
multiplication property to perform the same convolution in Fourier
space to confirm the numerical result from \texttt{numpy.convolve}.
-\lstinputlisting[label=code:convolution_demo,caption={IGNORED}]{examples/convolution_demo.py}
+\lstinputlisting[label=code:convolution_demo,caption={IGNORED}]{problems/convolution_demo.py}
Added: trunk/py4science/workbook/examples
===================================================================
--- trunk/py4science/workbook/examples (rev 0)
+++ trunk/py4science/workbook/examples 2007-12-03 00:12:18 UTC (rev 4548)
@@ -0,0 +1 @@
+link ../book/examples
\ No newline at end of file
Property changes on: trunk/py4science/workbook/examples
___________________________________________________________________
Name: svn:special
+ *
Modified: trunk/py4science/workbook/fft_imdenoise.tex
===================================================================
--- trunk/py4science/workbook/fft_imdenoise.tex 2007-12-02 17:27:41 UTC (rev 4547)
+++ trunk/py4science/workbook/fft_imdenoise.tex 2007-12-03 00:12:18 UTC (rev 4548)
@@ -41,7 +41,7 @@
to one. This serves to enhance contrast among the darker elements of
the image, so it is not completely dominated by the brighter segments
-\lstinputlisting[label=code:fft_imdenoise,caption={IGNORED}]{examples/fft_imdenoise.py}
+\lstinputlisting[label=code:fft_imdenoise,caption={IGNORED}]{problems/fft_imdenoise.py}
\begin{figure}
\begin{centering}\includegraphics[width=4in]{fig/fft_imdenoise}\par\end{centering}
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Modified: trunk/py4science/workbook/files_etc.tex
===================================================================
--- trunk/py4science/workbook/files_etc.tex 2007-12-02 17:27:41 UTC (rev 4547)
+++ trunk/py4science/workbook/files_etc.tex 2007-12-03 00:12:18 UTC (rev 4548)
@@ -44,7 +44,7 @@
Here is the exercise skeleton of the script to create and plot the
data file
-\lstinputlisting[label=code:noisy_sine,caption={IGNORED}]{examples/noisy_sine.py}
+\lstinputlisting[label=code:noisy_sine,caption={IGNORED}]{problems/noisy_sine.py}
and the graph will look something like Figure~\ref{fig:noisy_sine}
@@ -189,7 +189,7 @@
in 2003 and held to the present) for each stock. Here is the exercise
skeleton.:
-\lstinputlisting[label=code:stock_records,caption={IGNORED}]{examples/stock_records.py}
+\lstinputlisting[label=code:stock_records,caption={IGNORED}]{problems/stock_records.py}
The graph will look something like Figure~\ref{fig:stock_records}.
Modified: trunk/py4science/workbook/glass_dots.tex
===================================================================
--- trunk/py4science/workbook/glass_dots.tex 2007-12-02 17:27:41 UTC (rev 4547)
+++ trunk/py4science/workbook/glass_dots.tex 2007-12-03 00:12:18 UTC (rev 4548)
@@ -38,7 +38,7 @@
\textit{stable node}, if one is greater than one and the other less
than one, we have a \textit{saddle node}.
-\lstinputlisting[label=code:glass_dots1,caption={IGNORED}]{examples/glass_dots1.py}
+\lstinputlisting[label=code:glass_dots1,caption={IGNORED}]{problems/glass_dots1.py}
Added: trunk/py4science/workbook/intro_to_python.tex
===================================================================
--- trunk/py4science/workbook/intro_to_python.tex (rev 0)
+++ trunk/py4science/workbook/intro_to_python.tex 2007-12-03 00:12:18 UTC (rev 4548)
@@ -0,0 +1,1474 @@
+
+\chapter[Python intro]{A whirlwind tour of python and the standard library}
+
+This is a quick-and-dirty introduction to the python language for
+the impatient scientist. There are many top notch, comprehensive introductions
+and tutorials for python. For absolute beginners, there is the \textit{Python
+Beginner's Guide}.%
+\footnote{http://www.python.org/moin/BeginnersGuide%
+} The official \textit{Python Tutorial} can be read online%
+\footnote{http://docs.python.org/tut/tut.html%
+} or downloaded%
+\footnote{http://docs.python.org/download.html%
+} in a variety of formats. There are over 100 python tutorials collected
+online.%
+\footnote{http://www.awaretek.com/tutorials.html%
+}
+
+There are also many excellent books. Targetting newbies is Mark Pilgrim's
+\textit{Dive into Python} which in available in print and for free
+online%
+\footnote{http://diveintopython.org/toc/index.html%
+}, though for absolute newbies even this may be too hard \cite{Dive}.
+For experienced programmers, David Beasley's \textit{Python Essential
+Reference} is an excellent introduction to python, but is a bit dated
+since it only covers python2.1 \cite{Beasley}. Likwise Alex Martelli's
+\textit{Python in a Nutshell} is highly regarded and a bit more current
+-- a 2nd edition is in the works\cite{Nutshell}. And \textit{The
+Python Cookbook} is an extremely useful collection of python idioms,
+tips and tricks \cite{Cookbook}.
+
+But the typical scientist I encounter wants to solve a specific problem,
+eg, to make a certain kind of graph, to numerically integrate an equation,
+or to fit some data to a parametric model, and doesn't have the time
+or interest to read several books or tutorials to get what they want.
+This guide is for them: a short overview of the language to help them
+get to what they want as quickly as possible. We get to advanced material
+pretty quickly, so it may be touch sledding if you are a python newbie.
+Take in what you can, and if you start getting dizzy, skip ahead to
+the next section; you can always come back to absorb more detail later,
+after you get your real work done.
+
+
+\section{Hello Python}
+
+Python is a dynamically typed, object oriented, interpreted language.
+Interpreted means that your program interacts with the python interpreter,
+similar to Matlab, Perl, Tcl and Java, and unlike FORTRAN, C, or C++
+which are compiled. So let's fire up the python interpreter and get
+started. I'm not going to cover installing python -- it's standard
+on most linux boxes and for windows there is a friendly GUI installer.
+To run the python interpreter, on windows, you can click \texttt{Start->All
+Programs->Python 2.4->Python (command line)} or better yet, install
+\texttt{ipython}, a python shell on steroids, and use that. On linux
+/ unix systems, you just need to type \texttt{python} or \texttt{ipython}
+at the command line. The \texttt{>\,{}>\,{}>} is the default python
+shell prompt, so don't type it in the examples below
+
+\begin{lyxcode}
+>\,{}>\,{}>~print~'hello~world'
+
+hello~world
+
+
+\end{lyxcode}
+As this example shows, \textit{hello world} in python is pretty easy
+-- one common phrase you hear in the python community is that {}``it
+fits your brain''. -- the basic idea is that coding in python feels
+natural. Compare python's version with \textit{hello world} in C++
+
+\begin{lyxcode}
+//~C++
+
+\#include~<iostream>
+
+int~main~()
+
+\{~~~
+
+~~std::cout~<\,{}<~\char`\"{}Hello~World\char`\"{}~<\,{}<~std::endl;
+
+~~return~0;
+
+\}
+\end{lyxcode}
+
+\section[Calculator]{\label{sec:into_calculator}Python is a calculator}
+
+Aside from my daughter's solar powered cash-register calculator, Python
+is the only calculator I use. From the python shell, you can type
+arbitrary arithmetic expressions.
+
+\begin{lyxcode}
+>\,{}>\,{}>~2+2
+
+4
+
+>\,{}>\,{}>~2{*}{*}10
+
+1024
+
+>\,{}>\,{}>~10/5
+
+2
+
+>\,{}>\,{}>~2+(24.3~+~.9)/.24
+
+107.0
+
+>\,{}>\,{}>~2/3
+
+0
+\end{lyxcode}
+The last line is a standard newbie gotcha -- if both the left and
+right operands are integers, python returns an integer. To do floating
+point division, make sure at least one of the numbers is a float
+
+\begin{lyxcode}
+>\,{}>\,{}>~2.0/3
+
+0.66666666666666663
+\end{lyxcode}
+The distinction between integer and floating point division is a common
+source of frustration among newbies and is slated for destruction
+in the mythical Python 3000.%
+\footnote{Python 3000 is a future python release that will clean up several
+things that Guido considers to be warts.%
+} Since default integer division will be removed in the future, you
+can invoke the time machine with the \texttt{from \_\_future\_\_}
+directives; these directives allow python programmers today to use
+features that will become standard in future releases but are not
+included by default because they would break existing code. From future
+directives should be among the first lines you type in your python
+code if you are going to use them, otherwise they may not work. The
+future division operator will assume floating point division by default,%
+\footnote{You may have noticed that 2/3 was represented as 0.66666666666666663
+and not 0.66666666666666666 as might be expected. This is because
+computers are binary calculators, and there is no exact binary representation
+of 2/3, just as there is no exact binary representation of 0.1
+
+\begin{lyxcode}
+>\,{}>\,{}>~0.1
+
+0.10000000000000001
+\end{lyxcode}
+Some languages try and hide this from you, but python is explicit.%
+}and provides another operator // to do classic integer division.
+
+\begin{lyxcode}
+>\,{}>\,{}>~from~\_\_future\_\_~import~division
+
+>\,{}>\,{}>~2/3
+
+0.66666666666666663
+
+>\,{}>\,{}>~2//3
+
+0
+\end{lyxcode}
+python has four basic numeric types: int, long, float and complex,
+but unlike C++, BASIC, FORTRAN or Java, you don't have to declare
+these types. python can infer them
+
+\begin{lyxcode}
+>\,{}>\,{}>~type(1)
+
+<type~'int'>
+
+>\,{}>\,{}>~type(1.0)
+
+<type~'float'>
+
+>\,{}>\,{}>~type(2{*}{*}200)
+
+<type~'long'>
+
+
+\end{lyxcode}
+$2^{200}$is a huge number!
+
+\begin{lyxcode}
+>\,{}>\,{}>~2{*}{*}200
+
+1606938044258990275541962092341162602522202993782792835301376L
+\end{lyxcode}
+but python will blithely compute it and much larger numbers for you
+as long as you have CPU and memory to handle them. The integer type,
+if it overflows, will automatically convert to a python \texttt{long}
+(as indicated by the appended \texttt{L} in the output above) and
+has no built-in upper bound on size, unlike C/C++ longs.
+
+Python has built in support for complex numbers. Eg, we can verify
+$i^{2}=-1$
+
+\begin{lyxcode}
+>\,{}>\,{}>~x~=~complex(0,1)
+
+>\,{}>\,{}>~x{*}x
+
+(-1+0j)
+\end{lyxcode}
+To access the real and imaginary parts of a complex number, use the
+\texttt{real} and \texttt{imag} attributes
+
+\begin{lyxcode}
+>\,{}>\,{}>~x.real
+
+0.0
+
+>\,{}>\,{}>~x.imag
+
+1.0
+\end{lyxcode}
+If you come from other languages like Matlab, the above may be new
+to you. In matlab, you might do something like this (>\,{}> is the
+standard matlab shell prompt)
+
+\begin{lyxcode}
+>\,{}>~x~=~0+j
+
+x~=
+
+~~~0.0000~+~1.0000i
+
+
+
+>\,{}>~real(x)
+
+ans~=
+
+~~~~~0
+
+
+
+>\,{}>~imag(x)
+
+ans~=
+
+~~~~~1
+
+
+
+
+\end{lyxcode}
+That is, in Matlab, you use a \textit{function} to access the real
+and imaginary parts of the data, but in python these are attributes
+of the complex object itself. This is a core feature of python and
+other object oriented languages: an object carries its data and methods
+around with it. One might say: {}``a complex number knows it's real
+and imaginary parts'' or {}``a complex number knows how to take
+its conjugate'', you don't need external functions for these operations
+
+\begin{lyxcode}
+>\,{}>\,{}>~x.conjugate
+
+<built-in~method~conjugate~of~complex~object~at~0xb6a62368>
+
+>\,{}>\,{}>~x.conjugate()
+
+-1j
+\end{lyxcode}
+On the first line, I just followed along from the example above with
+\texttt{real} and \texttt{imag} and typed \texttt{x.conjugate} and
+python printed the representation \texttt{<built-in method conjugate
+of complex object at 0xb6a62368>.} This means that \texttt{conjugate}
+is a \textit{method}, a.k.a a function, and in python we need to use
+parentheses to call a function. If the method has arguments, like
+the \texttt{x} in \texttt{sin(x)}, you place them inside the parentheses,
+and if it has no arguments, like \texttt{conjugate}, you simply provide
+the open and closing parentheses. \texttt{real}, \texttt{imag} and
+\texttt{conjugate} are attributes of the complex object, and \texttt{conjugate}
+is a \textit{callable} attribute, known as a \textit{method}.
+
+OK, now you are an object oriented programmer. There are several key
+ideas in object oriented programming, and this is one of them: an
+object carries around with it data (simple attributes) and methods
+(callable attributes) that provide additional information about the
+object and perform services. It's one stop shopping -- no need to
+go to external functions and libraries to deal with it -- the object
+knows how to deal with itself.
+
+
+\section[Standard Library]{Accessing the standard library}
+
+Arithmetic is fine, but before long you may find yourself tiring of
+it and wanting to compute logarithms and exponents, sines and cosines
+
+\begin{lyxcode}
+>\,{}>\,{}>~log(10)
+
+Traceback~(most~recent~call~last):
+
+~~File~\char`\"{}<stdin>\char`\"{},~line~1,~in~?
+
+NameError:~name~'log'~is~not~defined
+\end{lyxcode}
+These functions are not built into python, but don't despair, they
+are built into the python standard library. To access a function from
+the standard library, or an external library for that matter, you
+must import it.
+
+\begin{lyxcode}
+>\,{}>\,{}>~import~math
+
+>\,{}>\,{}>~math.log(10)
+
+2.3025850929940459
+
+>\,{}>\,{}>~math.sin(math.pi)
+
+1.2246063538223773e-16
+\end{lyxcode}
+Note that the default \texttt{log} function is a base 2 logarithm
+(use \texttt{math.log10} for base 10 logs) and that floating point
+math is inherently imprecise, since analytically$\sin(\pi)=0$.
+
+It's kind of a pain to keep typing \texttt{math.log} and \texttt{math.sin}
+and \texttt{math.p}i, and python is accomodating. There are additional
+forms of \texttt{import} that will let you save more or less typing
+depending on your desires
+
+\begin{lyxcode}
+\textcolor{blue}{\#~Appreviate~the~module~name:~m~is~an~alias}
+
+>\,{}>\,{}>~import~math~as~m
+
+>\,{}>\,{}>~m.cos(2{*}m.pi)
+
+1.0
+
+
+
+\textcolor{blue}{\#~Import~just~the~names~you~need}
+
+>\,{}>\,{}>~from~math~import~exp,~log
+
+>\,{}>\,{}>~log(exp(1))
+
+1.0
+
+
+
+\textcolor{blue}{\#~Import~everything~-~use~with~caution!}
+
+>\,{}>\,{}>~from~math~import~{*}
+
+>\,{}>\,{}>~sin(2{*}pi{*}10)
+
+-2.4492127076447545e-15
+\end{lyxcode}
+To help you learn more about what you can find in the math library,
+python has nice introspection capabilities -- introspection is a way
+of asking an object about itself. For example, to find out what is
+available in the math library, we can get a directory of everything
+available with the \texttt{dir} command%
+\footnote{In addition to the introdpection and help provided in the python interpreter,
+the official documentation of the python standard library is very
+good and up-to-date http://docs.python.org/lib/lib.html .%
+}
+
+\begin{lyxcode}
+>\,{}>\,{}>~dir(math)
+
+{[}'\_\_doc\_\_',~'\_\_file\_\_',~'\_\_name\_\_',~'acos',~'asin',~'atan',~'atan2',~'ceil',~'cos',~'cosh',~'degrees',~'e',~'exp',~'fabs',~'floor',~'fmod',~'frexp',~'hypot',~'ldexp',~'log',~'log10',~'modf',~'pi',~'pow',~'radians',~'sin',~'sinh',~'sqrt',~'tan',~'tanh']
+\end{lyxcode}
+This gives us just a listing of the names that are in the math module
+-- they are fairly self descriptive, but if you want more, you can
+call \texttt{help} on any of these functions for more information
+
+\begin{lyxcode}
+>\,{}>\,{}>~help(math.sin)~
+
+Help~on~built-in~function~sin:
+
+sin(...)
+
+sin(x)
+
+Return~the~sine~of~x~(measured~in~radians).
+\end{lyxcode}
+and for the whole math library
+
+\begin{lyxcode}
+>\,{}>\,{}>~help(math)~
+
+Help~on~module~math:
+
+~
+
+NAME
+
+~~~~math
+
+~
+
+FILE
+
+~~~~/usr/local/lib/python2.3/lib-dynload/math.so
+
+~
+
+DESCRIPTION
+
+~~~~This~module~is~always~available.~~It~provides~access~to~the
+
+~~~~mathematical~functions~defined~by~the~C~standard.
+
+~
+
+FUNCTIONS
+
+~~~~acos(...)
+
+~~~~~~~~acos(x)
+
+~~~~~~~~~
+
+~~~~~~~~Return~the~arc~cosine~(measured~in~radians)~of~x.
+
+~~~~~
+
+~~~~asin(...)
+
+~~~~~~~~asin(x)
+
+~~~~~~~~~
+
+~~~~~~~~Return~the~arc~sine~(measured~in~radians)~of~x.
+
+~~~~~
+\end{lyxcode}
+And much more which is snipped. Likewise, we can get information on
+the complex object in the same way
+
+\begin{lyxcode}
+>\,{}>\,{}>~x~=~complex(0,1)
+
+>\,{}>\,{}>~dir(x)
+
+{[}'\_\_abs\_\_',~'\_\_add\_\_',~'\_\_class\_\_',~'\_\_coerce\_\_',~'\_\_delattr\_\_',~'\_\_div\_\_',~'\_\_divmod\_\_',~'\_\_doc\_\_',~'\_\_eq\_\_',~'\_\_float\_\_',~'\_\_floordiv\_\_',~'\_\_ge\_\_',~'\_\_getattribute\_\_',~'\_\_getnewargs\_\_',~'\_\_gt\_\_',~'\_\_hash\_\_',~'\_\_init\_\_',~'\_\_int\_\_',~'\_\_le\_\_',~'\_\_long\_\_',~'\_\_lt\_\_',~'\_\_mod\_\_',~'\_\_mul\_\_',~'\_\_ne\_\_',~'\_\_neg\_\_',~'\_\_new\_\_',~'\_\_nonzero\_\_',~'\_\_pos\_\_',~'\_\_pow\_\_',~'\_\_radd\_\_',~'\_\_rdiv\_\_',~'\_\_rdivmod\_\_',~'\_\_reduce\_\_',~'\_\_reduce\_ex\_\_',~'\_\_repr\_\_',~'\_\_rfloordiv\_\_',~'\_\_rmod\_\_',~'\_\_rmul\_\_',~'\_\_rpow\_\_',~'\_\_rsub\_\_',~'\_\_rtruediv\_\_',~'\_\_setattr\_\_',~'\_\_str\_\_',~'\_\_sub\_\_',~'\_\_truediv\_\_',~'conjugate',~'imag',~'real']
+
+
+\end{lyxcode}
+Notice that called \texttt{dir} or \texttt{help} on the \texttt{math}
+\textit{module}, the \texttt{math.sin} \textit{function}, and the
+\texttt{complex} \textit{number} \texttt{x}. That's because modules,
+functions and numbers are all \textit{objects}, and we use the same
+object introspection and help capabilites on them. We can find out
+what type of object they are by calling \texttt{type} on them, which
+is another function in python's introspection arsenal
+
+\begin{lyxcode}
+>\,{}>\,{}>~type(math)
+
+<type~'module'>
+
+>\,{}>\,{}>~type(math.sin)
+
+<type~'builtin\_function\_or\_method'>
+
+>\,{}>\,{}>~type(x)
+
+<type~'complex'>
+
+
+\end{lyxcode}
+Now, you may be wondering: what were all those god-awful looking double
+underscore methods, like \texttt{\_\_abs\_\_} and \texttt{\_\_mul\_\_}
+in the \texttt{dir} listing of the complex object above? These are
+methods that define what it means to be a numeric type in python,
+and the complex object implements these methods so that complex numbers
+act like the way should, eg \texttt{\_\_mul\_\_} implements the rules
+of complex multiplication. The nice thing about this is that python
+specifies an application programming interface (API) that is the definition
+of what it means to be a number in python. And this means you can
+define your own numeric types, as long as you implement the required
+special double underscore methods for your custom type. double underscore
+methods are very important in python; although the typical newbie
+never sees them or thinks about them, they are there under the hood
+providing all the python magic, and more importantly, showing the
+way to let you make magic.
+
+
+\section{\label{sec:intro_string}Strings}
+
+We've encountered a number of types of objects above: int, float,
+long, complex, method/function and module. We'll continue our tour
+with an introduction to strings, which are critical components of
+almost every program. You can create strings in a number of different
+ways, with single quotes, double quotes, or triple quotes -- this
+diversity of methods makes it easy if you need to embed string characters
+in the string itself
+
+\begin{lyxcode}
+\textcolor{blue}{\#~single,~double~and~triple~quoted~strings}
+
+>\,{}>\,{}>~s~=~'Hi~Mom!'
+
+>\,{}>\,{}>~s~=~\char`\"{}Hi~Mom!\char`\"{}
+
+>\,{}>\,{}>~s~=~\char`\"{}\char`\"{}\char`\"{}Porky~said,~\char`\"{}That's~all~folks!\char`\"{}~\char`\"{}\char`\"{}\char`\"{}
+\end{lyxcode}
+You can add strings together to concatenate them
+
+\begin{lyxcode}
+\textcolor{blue}{\#~concatenating~strings}
+
+>\,{}>\,{}>~first~=~'John'
+
+>\,{}>\,{}>~last~=~'Hunter'
+
+>\,{}>\,{}>~first+last
+
+'JohnHunter'
+\end{lyxcode}
+or call string methods to process them: upcase them or downcase them,
+or replace one character with another
+
+\begin{lyxcode}
+\textcolor{blue}{\#~string~methods}
+
+>\,{}>\,{}>~last.lower()
+
+'hunter'
+
+>\,{}>\,{}>~last.upper()
+
+'HUNTER'
+
+>\,{}>\,{}>~last.replace('h',~'p')
+
+'Hunter'
+
+>\,{}>\,{}>~last.replace('H',~'P')
+
+'Punter'~
+\end{lyxcode}
+Note that in all of these examples, the string \texttt{last} is unchanged.
+All of these methods operate on the string and return a new string,
+leaving the original unchanged. In fact, python strings cannot be
+changed by any python code at all: they are \textit{immutable} (unchangeable).
+The concept of mutable and immutable objects in python is an important
+one, and it will come up again, because only immutable objects can
+be used as keys in python dictionaries and elements of python sets.
+
+You can access individual characters, or slices of the string (substrings),
+using indexing. A string in sequence of characters, and strings implement
+the sequence protocol in python -- we'll see more examples of python
+sequences later -- and all sequences have the same syntax for accessing
+their elements. Python uses 0 based indexing which means the first
+element is at index 0; you can use negative indices to access the
+last elements in the sequence
+
+\begin{lyxcode}
+\textcolor{blue}{\#~string~indexing}
+
+>\,{}>\,{}>~last~=~'Hunter'
+
+>\,{}>\,{}>~last{[}0]
+
+'H'
+
+>\,{}>\,{}>~last{[}1]
+
+'u'
+
+>\,{}>\,{}>~last{[}-1]~
+
+'r'~
+\end{lyxcode}
+To access substrings, or generically in terms of the sequence protocol,
+slices, you use a colon to indicate a range
+
+\begin{lyxcode}
+\textcolor{blue}{\#~string~slicing}
+
+>\,{}>\,{}>~last{[}0:2]
+
+'Hu'
+
+>\,{}>\,{}>~last{[}2:4]
+
+'nt'
+\end{lyxcode}
+As this example shows, python uses {}``one-past-the-end'' indexing
+when defining a range; eg, in the range \texttt{indmin:indmax}, the
+element of \texttt{imax} is not included. You can use negative indices
+when slicing too; eg, to get everything before the last character
+
+\begin{lyxcode}
+>\,{}>\,{}>~last{[}0:-1]
+
+'Hunte'
+\end{lyxcode}
+You can also leave out either the min or max indicator; if they are
+left out, 0 is assumed to be the \texttt{indmin} and one past the
+end of the sequence is assumed to be \texttt{indmax}
+
+\begin{lyxcode}
+>\,{}>\,{}>~last{[}:3]
+
+'Hun'
+
+>\,{}>\,{}>~last{[}3:]
+
+'ter'
+\end{lyxcode}
+There is a third number that can be placed in a slice, a step, with
+syntax indmin:indmax:step; eg, a step of 2 will skip every second
+letter
+
+\begin{lyxcode}
+>\,{}>\,{}>~last{[}1:6:2]
+
+'utr'
+\end{lyxcode}
+Although this may be more that you want to know about slicing strings,
+the time spent here is worthwhile. As mentioned above, all python
+sequences obey these rules. In addition to strings, lists and tuples,
+which are built-in python sequence data types and are discussed in
+the next section, the numeric arrays widely used in scientific computing
+also implement the sequence protocol, and thus have the same slicing
+rules.
+
+\begin{xca}
+What would you expect last{[}:] to return?
+\end{xca}
+One thing that comes up all the time is the need to create strings
+out of other strings and numbers, eg to create filenames from a combination
+of a base directory, some base filename, and some numbers. Scientists
+like to create lots of data files like and then write code to loop
+over these files and analyze them. We're going to show how to do that,
+starting with the newbie way and progressively building up to the
+way of python zen master. All of the methods below \textit{work},
+but the zen master way will more efficient, more scalable (eg to larger
+numbers of files) and cross-platform.%
+\footnote{{}``But it works'' is a common defense of bad code; my rejoinder
+to this is {}``A computer scientist is someone who fixes things that
+aren't broken''. %
+} Here's the newbie way: we also introduce the for-loop here in the
+spirit of diving into python -- note that python uses whitespace indentation
+to delimit the for-loop code block
+
+\begin{lyxcode}
+\textcolor{blue}{\#~The~newbie~way}
+
+for~i~in~(1,2,3,4):
+
+~~~~fname~=~'data/myexp0'~+~str(i)~+~'.dat'
+
+~~~~print~fname
+\end{lyxcode}
+Now as promised, this will print out the 4 file names above, but it
+has three flaws: it doesn't scale to 10 or more files, it is inefficient,
+and it is not cross platform. It doesn't scale because it hard-codes
+the '\texttt{0}' after \texttt{myexp}, it is inefficient because to
+add several strings requires the creation of temporary strings, and
+it is not cross-platform because it hard-codes the directory separator
+'/'.
+
+\begin{lyxcode}
+\textcolor{blue}{\#~On~the~path~to~elightenment}
+
+for~i~in~(1,2,3,4):
+
+~~~~fname~=~'data/myexp\%02d.dat'\%i
+
+~~~~print~fname
+\end{lyxcode}
+This example uses string interpolation, the funny \% thing. If you
+are familiar with C programming, this will be no surprise to you (on
+linux/unix systems do \texttt{man sprintf} at the unix shell). The
+percent character is a string formatting character: \texttt{\%02d}
+means to take an integer (the \texttt{d} part) and print it with two
+digits, padding zero on the left (the \texttt{\%02} part). There is
+more to be said about string interpolation, but let's finish the job
+at hand. This example is better than the newbie way because is scales
+up to files numbered 0-99, and it is more efficient because it avoids
+the creation of temporary strings. For the platform independent part,
+we go to the python standard library \texttt{os.path}, which provides
+a host of functions for platform-independent manipulations of filenames,
+extensions and paths. Here we use \texttt{os.path.join} to combine
+the directory with the filename in a platform independent way. On
+windows, it will use the windows path separator '\textbackslash{}'
+and on unix it will use '/'.
+
+\begin{lyxcode}
+\textcolor{blue}{\#~the~zen~master~approach}
+
+import~os
+
+for~i~in~(1,2,3,4):
+
+~~~~fname~=~os.path.join('data',~'myexp\%02d.dat'\%i)
+
+~~~~print~fname
+\end{lyxcode}
+\begin{xca}
+Suppose you have data files named like
+\end{xca}
+\begin{lyxcode}
+data/2005/exp0100.dat
+
+data/2005/exp0101.dat
+
+data/2005/exp0102.dat
+
+...
+
+data/2005/exp1000.dat
+\end{lyxcode}
+Write the python code that iterates over these files, constructing
+the filenames as strings in using \texttt{os.path.join} to construct
+the paths in a platform-independent way. \textit{Hint}: read the help
+for \texttt{os.path.join}!
+
+OK, I promised to torture you a bit more with string interpolation
+-- don't worry, I remembered. The ability to properly format your
+data when printing it is crucial in scientific endeavors: how many
+signficant digits do you want, do you want to use integer, floating
+point representation or exponential notation? These three choices
+are provided with \texttt{\%d}, \texttt{\%f} and \texttt{\%e}, with
+lots of variations on the theme to indicate precision and more
+
+\begin{lyxcode}
+>\,{}>\,{}>~'warm~for~\%d~minutes~at~\%1.1f~C'~\%~(30,~37.5)
+
+'warm~for~30~minutes~at~37.5~C'
+
+
+
+>\,{}>\,{}>~'The~mass~of~the~sun~is~\%1.4e~kg'\%~(1.98892{*}10{*}{*}30)
+
+'The~mass~of~the~sun~is~1.9889e+30~kg'
+
+
+\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
+
+\begin{lyxcode}
+\textcolor{blue}{\#~s~is~a~single~string~and~we~split~it~into~a~list~of~strings}
+
+\textcolor{blue}{\#~for~further~processing}
+
+>\,{}>\,{}>~s~=~'1.0~2.0~3.0~4.0~5.0'
+
+>\,{}>\,{}>~s.split('~')
+
+{[}'1.0',~'2.0',~'3.0',~'4.0',~'5.0']
+\end{lyxcode}
+The return value, with square brackets, indicates that python has
+returned a list of strings. These individual strings need further
+processing to convert them into actual floats, but that is the first
+step. The conversion to floats will be discussed in the next session,
+when we learn about list comprehensions. The converse method is join,
+which is often used to create string output to an ASCII file from
+a list of numbers. In this case you want to join a list of numbers
+into a single line for printing to a file. The example below will
+be clearer after the next section, in which lists are discussed
+
+\begin{lyxcode}
+\textcolor{blue}{\#~vals~is~a~list~of~floats~and~we~convert~it~to~a~single}
+
+\textcolor{blue}{\#~space~separated~string}
+
+>\,{}>\,{}>~vals~=~{[}1.0,~2.0,~3.0,~4.0,~5.0]
+
+>\,{}>\,{}>~'~'.join({[}str(val)~for~val~in~vals])
+
+'1.0~2.0~3.0~4.0~5.0'
+\end{lyxcode}
+There are two new things in the example above. One, we called the
+join method directly on a string itself, and not on a variable name.
+Eg, in the previous examples, we always used the name of the object
+when accessing attributes, eg \texttt{x.real} or \texttt{s.upper()}.
+In this example, we call the \texttt{join} method on the string which
+is a single space. The second new feature is that we use a list comprehension
+\texttt{{[}str(val) for val in vals]} as the argument to \texttt{join}.
+\texttt{join} requires a sequence of strings, and the list comprehension
+converts a list of floats to a strings. This can be confusing at first,
+so don't dispair if it is. But it is worth bringing up early because
+list comprehensions are a very useful feature of python. To help elucidate,
+compare \texttt{vals}, which is a list of floats, with the conversion
+of \texttt{vals} to a list of strings using list comprehensions in
+the next line
+
+\begin{lyxcode}
+\textcolor{blue}{\#~converting~a~list~of~floats~to~a~list~of~strings}
+
+>\,{}>\,{}>~vals
+
+{[}1.0,~2.0,~3.0,~4.0,~5.0]
+
+>\,{}>\,{}>~{[}str(val)~for~val~in~vals]~
+
+{[}'1.0',~'2.0',~'3.0',~'4.0',~'5.0']
+\end{lyxcode}
+
+\section[Data Structures]{The basic python data structures}
+
+Strings, covered in the last section, are sequences of characters.
+python has two additional built-in sequence types which can hold arbitrary
+elements: tuples and lists. tuples are created using parentheses,
+and lists are created using square brackets
+
+\begin{lyxcode}
+\textcolor{blue}{\#~a~tuple~and~a~list~of~elements~of~the~same~type}
+
+\textcolor{blue}{\#~(homogeneous)}
+
+>\,{}>\,{}>~t~=~(1,2,3,4)~~\#~tuple
+
+>\,{}>\,{}>~l~=~{[}1,2,3,4]~~\#~list
+\end{lyxcode}
+Both tuples and lists can also be used to hold elements of different
+types
+
+\begin{lyxcode}
+\textcolor{blue}{\#~a~tuple~and~list~of~int,~string,~float}
+
+>\,{}>\,{}>~t~=~(1,'john',~3.0)
+
+>\,{}>\,{}>~l~=~{[}1,'john',~3.0]
+\end{lyxcode}
+Tuples and lists have the same indexing and slicing rules as each
+other, and as string discussed above, because both implement the python
+sequence protocol, with the only difference being that tuple slices
+return tuples (indicated by the parentheses below) and list slices
+return lists (indicated by the square brackets)
+
+\begin{lyxcode}
+\#~indexing~and~slicing~tuples~and~lists
+
+>\,{}>\,{}>~t{[}0]
+
+1
+
+>\,{}>\,{}>~l{[}0]
+
+1
+
+>\,{}>\,{}>~t{[}:-1]
+
+(1,~'john')
+
+>\,{}>\,{}>~l{[}:-1]
+
+{[}1,~'john']
+\end{lyxcode}
+So why the difference between tuples and lists? A number of explanations
+have been offered on the mailing lists, but the only one that makes
+a difference to me is that tuples are immutable, like strings, and
+hence can be used as keys to python dictionaries and included as elements
+of sets, and lists are mutable, and cannot. So a tuple, once created,
+can never be changed, but a list can. For example, if we try to reassign
+the first element of the tuple above, we get an error
+
+\begin{lyxcode}
+>\,{}>\,{}>~t{[}0]~=~'why~not?'
+
+Traceback~(most~recent~call~last):
+
+~File~\char`\"{}<stdin>\char`\"{},~line~1,~in~?
+
+TypeError:~object~doesn't~support~item~assignment
+\end{lyxcode}
+But the same operation is perfectly accetable for lists
+
+\begin{lyxcode}
+>\,{}>\,{}>~l{[}0]~=~'why~not?'
+
+>\,{}>\,{}>~l
+
+{[}'why~not?',~'john',~3.0]
+\end{lyxcode}
+lists also have a lot of methods, tuples have none, save the special
+double underscore methods that are required for python objects and
+sequences
+
+\begin{lyxcode}
+\textcolor{blue}{\#~tuples~contain~only~{}``hidden''~double~underscore~methods}
+
+>\,{}>\,{}>~dir(t)
+
+{[}'\_\_add\_\_',~'\_\_class\_\_',~'\_\_contains\_\_',~'\_\_delattr\_\_',~'\_\_doc\_\_',~'\_\_eq\_\_',~'\_\_ge\_\_',~'\_\_getattribute\_\_',~'\_\_getitem\_\_',~'\_\_getnewargs\_\_',~'\_\_getslice\_\_',~'\_\_gt\_\_',~'\_\_hash\_\_',~'\_\_init\_\_',~'\_\_iter\_\_',~'\_\_le\_\_',~'\_\_len\_\_',~'\_\_lt\_\_',~'\_\_mul\_\_',~'\_\_ne\_\_',~'\_\_new\_\_',~'\_\_reduce\_\_',~'\_\_reduce\_ex\_\_',~'\_\_repr\_\_',~'\_\_rmul\_\_',~'\_\_setattr\_\_',~'\_\_str\_\_']
+
+
+
+\textcolor{blue}{\#~but~lists~contain~other~methods,~eg~append,~extend~and}
+
+\textcolor{blue}{\#~reverse}
+
+>\,{}>\,{}>~dir(l)
+
+{[}'\_\_add\_\_',~'\_\_class\_\_',~'\_\_contains\_\_',~'\_\_delattr\_\_',~'\_\_delitem\_\_',~'\_\_delslice\_\_',~'\_\_doc\_\_',~'\_\_eq\_\_',~'\_\_ge\_\_',~'\_\_getattribute\_\_',~'\_\_getitem\_\_',~'\_\_getslice\_\_',~'\_\_gt\_\_',~'\_\_hash\_\_',~'\_\_iadd\_\_',~'\_\_imul\_\_',~'\_\_init\_\_',~'\_\_iter\_\_',~'\_\_le\_\_',~'\_\_len\_\_',~'\_\_lt\_\_',~'\_\_mul\_\_',~'\_\_ne\_\_',~'\_\_new\_\_',~'\_\_reduce\_\_',~'\_\_reduce\_ex\_\_',~'\_\_repr\_\_',~'\_\_rmul\_\_',~'\_\_setattr\_\_',~'\_\_setitem\_\_',~'\_\_setslice\_\_',~'\_\_str\_\_',~'append',~'count',~'extend',~'index',~'insert',~'pop',~'remove',~'reverse',~'sort']
+\end{lyxcode}
+Many of these list methods change, or mutate, the list, eg append
+adds an element to the list\texttt{: extend} extends the list with
+a sequence of elements, \texttt{sort} sorts the list in place, \texttt{reverse}
+reverses it in place, \texttt{pop} takes an element off the list and
+returns it.
+
+We've seen a couple of examples of creating a list above -- let's
+look at some more using list methods
+
+\begin{lyxcode}
+>\,{}>\,{}>~x~=~{[}]~~~~~~~~~~~~~~~~~~~\textcolor{blue}{\#~create~the~empty~list}
+
+>\,{}>\,{}>~x.append(1)~~~~~~~~~~~~~~\textcolor{blue}{\#~add~the~integer~one~to~it}
+
+>\,{}>\,{}>~x.extend({[}'hi',~'mom'])~~\textcolor{blue}{\#~append~two~strings~to~it}
+
+>\,{}>\,{}>~x
+
+{[}1,~'hi',~'mom']
+
+>\,{}>\,{}>~x.reverse()~~~~~~~~~~~~~~\textcolor{blue}{\#~reverse~the~list,~in~place}
+
+>\,{}>\,{}>~x
+
+{[}'mom',~'hi',~1]
+
+>\,{}>\,{}>~len(x)
+
+3
+\end{lyxcode}
+We mentioned list comprehensions in the last section when discussing
+string methods. List comprehensions are a way of creating a list
+using a for loop in a single line of python. Let's create a list of
+the perfect cubes from 1 to 10, first with a for loop and then with
+a list comprehension. The list comprehension code will not only be
+shorter and more elegant, it can be much faster (the dots are the
+indentation block indicator from the python shell and should not be
+typed)
+
+\begin{lyxcode}
+\textcolor{blue}{\#~a~list~of~perfect~cubes~using~a~for-loop}
+
+>\,{}>\,{}>~cubes~=~{[}]
+
+>\,{}>\,{}>~for~i~in~range(1,10):
+
+...~~~~~cubes.append(i{*}{*}3)
+
+...~
+
+>\,{}>\,{}>~cubes
+
+{[}1,~8,~27,~64,~125,~216,~343,~512,~729]
+
+
+
+\textcolor{blue}{\#~functionally~equivalent~code~using~list~comprehensions}
+
+>\,{}>\,{}>~cubes~=~{[}i{*}{*}3~for~i~in~range(1,10)]
+
+>\,{}>\,{}>~cubes
+
+{[}1,~8,~27,~64,~125,~216,~343,~512,~729]
+\end{lyxcode}
+The list comprehension code is faster because it all happens at the
+C level. In the simple for-loop version, the python expression which
+appends the cube of \texttt{i} has to be evaluated by the python interpreter
+for each element of the loop. In the list comprehension example, the
+single line is parsed once and executed at the C level. The difference
+in speed can be considerable, and the list comprehension example is
+shorter and more elegant to boot.
+
+The remaining essential built-in data strucuture in python is the
+dictionary, which is an associative array that maps arbitrary immutable
+objects to arbitrary objects. int, long, float, string and tuple are
+all immutable and can be used as keys; to a dictionary list and dict
+are mutable and cannot. A dictionary takes one kind of object as the
+key, and this key points to another object which is the value. In
+a contrived but easy to comprehent examples, one might map names to
+ages
+
+\begin{lyxcode}
+>\,{}>\,{}>~ages~=~\{\}~~~~~~~~~~~~\textcolor{blue}{\#~create~an~empty~dict}
+
+>\,{}>\,{}>~ages{[}'john']~=~36
+
+>\,{}>\,{}>~ages{[}'fernando']~=~33
+
+>\,{}>\,{}>~ages~~~~~~~~~~~~~~~~~\textcolor{blue}{\#~view~the~whole~dict}
+
+\{'john':~36,~'fernando':~33\}
+
+>\,{}>\,{}>~ages{[}'john']
+
+36
+
+>\,{}>\,{}>~ages{[}'john']~=~37~~~~\textcolor{blue}{\#~reassign~john's~age}
+
+>\,{}>\,{}>~ages{[}'john']
+
+37
+\end{lyxcode}
+Dictionary lookup is very fast; Tim Peter's once joked that any python
+program which uses a dictionary is automatically 10 times faster than
+any C program, which is of course false, but makes two worthy points
+in jest: dictionary lookup is fast, and dictionaries can be used for
+important optimizations, eg, creating a cache of frequently used values.
+As a simple eaxample, suppose you needed to compute the product of
+two numbers between 1 and 100 in an inner loop -- you could use a
+dictionary to cache the cube of all odd of numbers < 100; if you were
+inteterested in all numbers, you might simply use a list to store
+the cached cubes -- I am cacheing only the odd numbers to show you
+how a dictionary can be used to represent a sparse data structure
+
+\begin{lyxcode}
+
+
+>\,{}>\,{}>~cubes~=~dict({[}~(~i,~i{*}{*}3~)~for~i~in~range(1,100,2)])
+
+>\,{}>\,{}>~cubes{[}5]
+
+125
+\end{lyxcode}
+The last example is syntactically a bit challenging, but bears careful
+study. We are initializing a dictionary with a list comprehension.
+ The list comprehension is made up of length 2 tuples \texttt{( i,
+i{*}{*}3} ). When a dictionary is initialized with a sequence of
+length 2 tuples, it assumes the first element of the tuple \texttt{i}
+is the \textit{key} and the second element i{*}{*}3is the \textit{value}.
+ Thus we have a lookup table from odd integers to to cube. Creating
+dictionaries from list comprehensions as in this example is something
+that hard-core python programmers do almost every day, and you should
+too.
+
+\begin{xca}
+Create a lookup table of the product of all pairs of numbers less
+than 100. The key will be a tuple of the two numbers \texttt{(i,j)}
+and the value will be the product. Hint: you can loop over multiple
+ranges in a list comprehension, eg \texttt{{[} something for i in
+range(Ni) for j in range(Nj)]}
+\end{xca}
+
+\section[Zen]{The Zen of Python}
+
+\begin{xca}
+\texttt{>\,{}>\,{}> import this}
+\end{xca}
+
+\section{Functions and classes}
+
+You can define functions just about anywhere in python code. The typical
+function definition takes zero or more arguments, zero or more keyword
+arguments, and is followed by a documentation string and the function
+definition, optionally returing a value. Here is a function to compute
+the hypoteneuse of a right triange
+
+\begin{lyxcode}
+def~hypot(base,~height):
+
+~~~'compute~the~hypoteneuse~of~a~right~triangle'
+
+~~~import~math
+
+~~~return~math.sqrt(base{*}{*}2~+~height{*}{*}2)
+\end{lyxcode}
+As in the case of the for-loop, leading white space is significant
+and is used to delimt the start and end of the function. In the example
+below, x = 1 is not in the function, because it is not indented
+
+\begin{lyxcode}
+def~growone(l):
+
+~~~'append~1~to~a~list~l'
+
+~~~l.append(1)
+
+x~=~1
+\end{lyxcode}
+Note that this function does not return anything, because the append
+method modifies the list that was passed in. You should be careful
+when designing functions that have side effects such as modifying
+the structures that are passed in; they should be named and documented
+in such a way that these side effects are clear.
+
+Python is pretty flexible with functions: you can define functions
+within function definitions (just be mindful of your indentation),
+you can attach attributes to functions (like other objects), you can
+pass functions as arguments to other functions. A function keyword
+argument defines a default value for a function that can be overridden.
+Below is an example which provides a normalize keyword argument. The
+default argument is \texttt{normalize=None}; the value None is a standard
+python idiom which usually means either do the default thing or do
+nothing. If \texttt{normalize} is not \texttt{None}, we assume it
+is a function that can be called to normalize our data
+
+\begin{lyxcode}
+def~psd(x,~normalize=None):
+
+~~~~'compute~the~power~spectral~density~of~x'
+
+~~~~if~normalize~is~not~None:~x~=~normalize(x)
+
+~~~\textcolor{blue}{~\#~compute~the~power~spectra~of~x~and~return~it}
+\end{lyxcode}
+This function could be called with or without a \texttt{normalize}
+keyword argument, since if the argument is not passed, the default
+of \texttt{None} is used and no normalization is done.
+
+\begin{lyxcode}
+
+
+\textcolor{blue}{\#~no~normalize~argument;~do~the~default~thing}
+
+>\,{}>\,{}>~psd(x)~~~
+
+
+
+\textcolor{blue}{\#~define~a~custom~normalize~function~unitstd~as~pass~it}
+
+\textcolor{blue}{\#~to~psd}
+
+>\,{}>\,{}>~def~unitstd(x):~return~x/std(x)
+
+>\,{}>\,{}>~psd(x,~normalize=unitstd)
+
+
+\end{lyxcode}
+In Section\ref{sec:into_calculator} we noticed that complex objects
+have the real and imag data attributes, and the conjugate method.
+An object is an instance of a class that defines it, and in python
+you can easily define your own classes. In that section, we emphasized
+that one of the important features of a classes/objects is that they
+carry around their data and methods in a single bundle. Let's look
+at the mechnics of defining classes, and creating instances (a.k.a.
+objects) of these classes. Classes have a special double underscore
+method \_\_init\_\_ that is used as the function to initialize the
+class. For this example, we'll continue with the normalize theme above,
+but in this case the normalization requires some data parameters.
+This example arises when you want to normalize an image which may
+range over 0-255 (8 bit image) or from 0-65535 (16 bit image) to the
+0-1 interval. For 16 bit images, you would normally divide everything
+by 65525, but you might want to configure this to a smaller number
+if your data doesn't use the whole intensity range to enhance contrast.
+For simplicitly, let's suppose our normalize class is only interested
+in the pixel maximum, and will divide all the data by that value.
+
+\begin{lyxcode}
+from~\_\_future\_\_~import~division~~\textcolor{blue}{\#~make~sure~we~do~float~division}
+
+class~Normalize:
+
+~~~~\char`\"{}\char`\"{}\char`\"{}
+
+~~~~A~class~to~normalize~data~by~dividing~it~by~a~maximum~value
+
+~~~~\char`\"{}\char`\"{}\char`\"{}
+
+~~~~def~\_\_init\_\_(self,~maxval):
+
+~~~~~~~~'maxval~will~be~mapped~to~1'
+
+~~~~~~~~self.maxval~=~maxval
+
+~~~~def~\_\_call\_\_(self,~data):
+
+~~~~~~~~'do~the~normalization'
+
+~~~~~~~~\textcolor{blue}{\#~in~real~life~you~would~also~want~to~clip~all~values~of}
+
+~\textcolor{blue}{~~~~~~~\#~data>maxval~so~that~the~returned~value~will~be~in~the~unit}
+
+~\textcolor{blue}{~~~~~~~\#~interval}
+
+~~~~~~~~return~data/self.maxval
+\end{lyxcode}
+The triple quoted string following the definition of class Normalize
+is the class documentation stringd, and it will bre shown to the user
+when they do \texttt{help(Normalize)}. A commonly used convention
+is to name classes with \textit{UpperCase}, but this is not required.
+self is a special variable that a class can use to refer to its own
+data and methods, and must be the first argument to all the class
+methods. The \texttt{\_\_init\_\_} method stores the normalization
+value maxval as a class attribute in \texttt{self.maxval}, and this
+value can later be reused by other class methods (as it is in \texttt{\_\_call\_\_})
+and it can be altered by the user of the class, as will illustrate
+below. The \texttt{\_\_call\_\_} method is another piece of python
+double underscore magic, it allows class instances to be used as \textit{functions},
+eg you can call them just like you can call any function. OK, now
+let's see how you could use this.
+
+The first line use used to create an \textit{instance} of the \textit{class}
+\texttt{Normalize}, and the special method \texttt{\_\_init\_\_} is
+implicitly called. The second line implicitly calls the special \texttt{\_\_call\_\_}method
+
+\begin{lyxcode}
+>\,{}>\,{}>~norm~=~Normalize(65356)~\textcolor{blue}{\#~good~for~16~bit~images}
+
+>\,{}>\,{}>~norm(255)~~~~~~~~~~~~~~~\textcolor{blue}{\#~call~this~function}
+
+0.0039017075708427688
+
+
+
+\textcolor{blue}{\#~We~can~reset~the~maxval~attribute,~and~the~call~method~}
+
+\textcolor{blue}{\#~is~automagically~updated}
+
+>\,{}>\,{}>~norm.maxval~=~255~~~~~~~\textcolor{blue}{\#~reset~the~maxval}
+
+>\,{}>\,{}>~norm(255)~~~~~~~~~~~~~~~\textcolor{blue}{\#~and~call~it~again}
+
+1.0
+
+
+
+\textcolor{blue}{\#~We~can~pass~the~norm~instance~to~the~psd~function~we~defined~above,~which~}
+
+\textcolor{blue}{\#~is~expecting~a~function}
+
+>\,{}>\,{}>~pdf(X,~normalize=norm)~~~~~~~~~~~~
+\end{lyxcode}
+\begin{xca}
+Pretend that \texttt{complex} were not built-in to the python core,
+and write your own complex class \texttt{MyComplex}. Provide \texttt{real}
+and \texttt{imag} attributes and the \texttt{conjugate} method. Define
+\texttt{\_\_abs\_\_}, \texttt{\_\_mul\_\_} and \texttt{\_\_add\_\_}
+to implement the absolute value of complex numbers, multiplication
+of complex numbers and addition of complex numbers. See the API definition
+of the python number protocol; although this is written for C programmers,
+it contains information about the required function call signatures
+for each of the double underscore methods that define the number protocol
+in python; where they use \texttt{o1} on that page, you would use
+\texttt{self} in python, and where they use \texttt{o2} you might
+use \texttt{other} in python.%
+\footnote{http://www.python.org/doc/current/api/number.html%
+} To get you started, I'll show you what the \texttt{\_\_add\_\_} method
+should look like
+\end{xca}
+\begin{lyxcode}
+\textcolor{blue}{\#~An~example~double~underscore~method~required~in~your~MyComplex}
+
+\textcolor{blue}{\#~implementation}
+
+def~\_\_add\_\_(self,~other):
+
+~~~~'add~self~to~other~and~return~a~new~MyComplex~instance'
+
+~~~~r~=~self.real~+~other.real
+
+~~~~i~=~self.imag~+~other.imag
+
+~~~~return~MyComplex(r,i)
+
+
+
+\textcolor{blue}{\#~When~you~are~finished,~test~your~implementation~with~}
+
+>\,{}>\,{}>~x~=~MyComplex(2,3)
+
+>\,{}>\,{}>~y~=~MyComplex(0,1)
+
+>\,{}>\,{}>~x.real
+
+2.0
+
+>\,{}>\,{}>~y.imag
+
+1.0
+
+>\,{}>\,{}>~x.conjugate()
+
+(2-3j)
+
+>\,{}>\,{}>~x+y
+
+(2+4j)
+
+>\,{}>\,{}>~x{*}y
+
+(-3+2j)
+
+>\,{}>\,{}>~abs(x{*}y)
+
+3.6055512754639891
+
+
+\end{lyxcode}
+
+\section[Files]{Files and file like objects}
+
+Working with files is one of the most common and important things
+we do in scientific computing because that is usually where the data
+lives. In Section\ref{sec:intro_string}, we went through the mechanics
+of automatically building file names like
+
+\begin{lyxcode}
+data/myexp01.dat
+
+data/myexp02.dat
+
+data/myexp03.dat
+
+data/myexp04.dat
+\end{lyxcode}
+but we didn't actually do anything with these files. Here we'll show
+how to read in the data and do something with it. Python makes working
+with files easy and dare I say fun. The test data set lives in \texttt{data/family.csv}
+and is a standard comma separated value file that contains information
+about my family: first name, last name, age, height in cm, weight
+in kg and birthdate. We'll open this file and parse it -- note that
+python has a standard module for parsing CSV files that is much more
+sophisticated than what I am doing here. Nevertheless, it serves as
+an easy to understand example that is close enough to real life that
+it is worth doing. Here is what the data file looks like
+
+\begin{lyxcode}
+First,Last,Age,Weight,Height,Birthday
+
+John,Hunter,36,175,180,1968-03-05
+
+Miriam,Sierig,33,135,177,1971-05-04
+
+Rahel,Hunter,7,55,134,1998-02-25
+
+Ava,Hunter,3,45,121,2001-04-26
+
+Clara,Hunter,0,15,55,2004-10-02
+\end{lyxcode}
+Here is the code to parse that file
+
+\begin{lyxcode}
+\textcolor{blue}{\#~open~the~file~for~reading}
+
+fh~=~file('../data/family.csv',~'r')
+
+\textcolor{blue}{\#~slurp~the~header,~splitting~on~the~comma}
+
+headers~=~fh.readline().split(',')
+
+\textcolor{blue}{\#~now~loop~over~the~remaining~lines~in~the~file~and~parse~them}
+
+for~line~in~fh:
+
+~~~~\textcolor{blue}{\#~remove~any~leading~or~trailing~white~space}
+
+~~~~line~=~line.strip()
+
+~~~~\textcolor{blue}{\#~split~the~line~on~the~comma~into~separate~variables}
+
+~~~~first,~last,~age,~weight,~height,~dob~=~line.split(',')
+
+~~~~\textcolor{blue}{\#~convert~some~of~these~strings~to~floats}
+
+~~~~age,~weight,~height~=~{[}float(val)~for~val~in~(age,~weight,~height)]
+
+~~~~print~first,~last,~age,~weight,~height,~dob
+\end{lyxcode}
+This example illustrates several interesting things. The syntax for
+opening a file is \texttt{file(filename, mode)} and the \texttt{mode}
+is a string like \texttt{'r'} or \texttt{'w'} that determines whether
+you are opening in read or write mode. You can also read and write
+binary files with \texttt{'rb'} and \texttt{'wb'}. There are more
+options and you should do \texttt{help(file)} to learn about them.
+We then use the file \texttt{readline} method to read in the first
+line of the file. This returns a string (the line of text) and we
+call the string method \texttt{split(',')} to split that string wherever
+it sees a comma, and this returns a list of strings which are the
+headers
+
+\begin{lyxcode}
+>\,{}>\,{}>~headers
+
+{[}'First',~'Last',~'Age',~'Weight',~'Height',~'Birthday\textbackslash{}n']
+\end{lyxcode}
+The new line character \texttt{'\textbackslash{}n'} at the end of
+\texttt{'Birthday\textbackslash{}n'} indicates we forgot to strip
+the string of whitespace. To fix that, we should have done
+
+\begin{lyxcode}
+>\,{}>\,{}>~headers~=~fh.readline().strip().split(',')
+
+>\,{}>\,{}>~headers
+
+{[}'First',~'Last',~'Age',~'Weight',~'Height',~'Birthday']~
+\end{lyxcode}
+Notice how this works like a pipeline: \texttt{fh.readline} returns
+a line of text as a string; we call the string method \texttt{strip}
+which returns a string with all white space (spaces, tabs, newlines)
+removed from the left and right; we then call the \texttt{split} method
+on this stripped string to split it into a list of strings.
+
+Next we start to loop over the file -- this is a nice feature of python
+file handles, you can iterate over them as a sequence. We've learned
+our lesson about trailing newlines, so we first strip the line with
+\texttt{line = line.strip()}. The rest is string processing, splitting
+the line on a comma as we did for the head...
[truncated message content] |
|
From: <fer...@us...> - 2007-12-03 08:22:18
|
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.
This was sent by the SourceForge.net collaborative development platform, the world's largest Open Source development site.
|
|
From: <fer...@us...> - 2007-12-04 06:24:10
|
Revision: 4573
http://matplotlib.svn.sourceforge.net/matplotlib/?rev=4573&view=rev
Author: fer_perez
Date: 2007-12-03 22:24:07 -0800 (Mon, 03 Dec 2007)
Log Message:
-----------
Get skeleton to build correctly, rewrite makefile to do as few latex runs as possible
Modified Paths:
--------------
trunk/py4science/workbook/Makefile
Added Paths:
-----------
trunk/py4science/workbook/problems_skel/basemap1.py
trunk/py4science/workbook/problems_skel/basemap2.py
trunk/py4science/workbook/problems_skel/basemap3.py
trunk/py4science/workbook/problems_skel/basemap4.py
trunk/py4science/workbook/problems_skel/basemap5.py
Modified: trunk/py4science/workbook/Makefile
===================================================================
--- trunk/py4science/workbook/Makefile 2007-12-04 03:18:39 UTC (rev 4572)
+++ trunk/py4science/workbook/Makefile 2007-12-04 06:24:07 UTC (rev 4573)
@@ -1,20 +1,55 @@
-solved:
+# Makefile for the py4science book.
+
+# Dependencies
+
+# Warning: list the .aux *before* the bbl. It's probably possible to get the
+# rules right, but I'm not sure how.
+SKEL_SRC = workbook_skeletons.aux workbook_skeletons.bbl \
+workbook_skeletons.tex main.tex
+
+SOL_SRC = $(subst _skeletons,_solved,$(SKEL_SRC))
+
+# programs
+PDFTEX = pdflatex -file-line-error
+FASTPDFTEX = pdflatex -file-line-error -draftmode -halt-on-error
+
+# convenience shorthand for targets
+sol: workbook_solved.pdf
+skel: workbook_skeletons.pdf
+all: sol skel
+
+# actual targets
+workbook_solved.pdf: $(SOL_SRC)
rm -f problems
- ln -s problems_solved problems
- rm -f workbook_solved.tex
- ln -s main.tex workbook_solved.tex
- pdflatex workbook_solved
- bibtex workbook_solved
- pdflatex workbook_solved
- rm -f workbook_solved.tex
+ ln -sf problems_solved problems
+ $(PDFTEX) workbook_solved
+ $(PDFTEX) workbook_solved
-skeletons:
+workbook_skeletons.pdf: $(SKEL_SRC)
rm -f problems
- ln -s problems_skel problems
- rm -f workbook_skeletons.tex
- ln -s main.tex workbook_skeletons.tex
- pdflatex workbook_skeletons
- rm -f workbook_skeletons.tex
+ ln -sf problems_skel problems
+ $(PDFTEX) workbook_skeletons
+workbook_skeletons.tex:
+ ln -sf main.tex workbook_skeletons.tex
+
+workbook_solved.tex:
+ ln -sf main.tex workbook_solved.tex
+
clean:
- rm -f *~ *.aux *.log *.toc *.out
\ No newline at end of file
+ rm -f *~ *.aux *.log *.toc *.out *.bbl *.blg
+
+distclean: clean
+ rm -f workbook_solved.pdf workbook_skeletons.pdf
+
+# Basic rules
+empty :=
+%.bbl : %.aux
+ bibtex $(subst .aux,$(empty),$<)
+ $(FASTPDFTEX) $(subst .aux,$(empty),$<)
+
+%.aux : %.tex
+ $(FASTPDFTEX) $<
+
+# Phony targets
+.PHONY: all clean
Added: trunk/py4science/workbook/problems_skel/basemap1.py
===================================================================
--- trunk/py4science/workbook/problems_skel/basemap1.py (rev 0)
+++ trunk/py4science/workbook/problems_skel/basemap1.py 2007-12-04 06:24:07 UTC (rev 4573)
@@ -0,0 +1 @@
+link ../../examples/skel/basemap1_skel.py
\ No newline at end of file
Property changes on: trunk/py4science/workbook/problems_skel/basemap1.py
___________________________________________________________________
Name: svn:special
+ *
Added: trunk/py4science/workbook/problems_skel/basemap2.py
===================================================================
--- trunk/py4science/workbook/problems_skel/basemap2.py (rev 0)
+++ trunk/py4science/workbook/problems_skel/basemap2.py 2007-12-04 06:24:07 UTC (rev 4573)
@@ -0,0 +1 @@
+link ../../examples/skel/basemap2_skel.py
\ No newline at end of file
Property changes on: trunk/py4science/workbook/problems_skel/basemap2.py
___________________________________________________________________
Name: svn:special
+ *
Added: trunk/py4science/workbook/problems_skel/basemap3.py
===================================================================
--- trunk/py4science/workbook/problems_skel/basemap3.py (rev 0)
+++ trunk/py4science/workbook/problems_skel/basemap3.py 2007-12-04 06:24:07 UTC (rev 4573)
@@ -0,0 +1 @@
+link ../../examples/skel/basemap3_skel.py
\ No newline at end of file
Property changes on: trunk/py4science/workbook/problems_skel/basemap3.py
___________________________________________________________________
Name: svn:special
+ *
Added: trunk/py4science/workbook/problems_skel/basemap4.py
===================================================================
--- trunk/py4science/workbook/problems_skel/basemap4.py (rev 0)
+++ trunk/py4science/workbook/problems_skel/basemap4.py 2007-12-04 06:24:07 UTC (rev 4573)
@@ -0,0 +1 @@
+link ../../examples/skel/basemap4_skel.py
\ No newline at end of file
Property changes on: trunk/py4science/workbook/problems_skel/basemap4.py
___________________________________________________________________
Name: svn:special
+ *
Added: trunk/py4science/workbook/problems_skel/basemap5.py
===================================================================
--- trunk/py4science/workbook/problems_skel/basemap5.py (rev 0)
+++ trunk/py4science/workbook/problems_skel/basemap5.py 2007-12-04 06:24:07 UTC (rev 4573)
@@ -0,0 +1 @@
+link ../../examples/skel/basemap5_skel.py
\ No newline at end of file
Property changes on: trunk/py4science/workbook/problems_skel/basemap5.py
___________________________________________________________________
Name: svn:special
+ *
This was sent by the SourceForge.net collaborative development platform, the world's largest Open Source development site.
|
|
From: <jd...@us...> - 2007-12-07 04:48:32
|
Revision: 4663
http://matplotlib.svn.sourceforge.net/matplotlib/?rev=4663&view=rev
Author: jdh2358
Date: 2007-12-06 20:48:30 -0800 (Thu, 06 Dec 2007)
Log Message:
-----------
updated pyrex workbook
Modified Paths:
--------------
trunk/py4science/workbook/main.tex
Added Paths:
-----------
trunk/py4science/workbook/intro_pyrex.tex
trunk/py4science/workbook/pyrex_ringbuf.tex
Added: trunk/py4science/workbook/intro_pyrex.tex
===================================================================
--- trunk/py4science/workbook/intro_pyrex.tex (rev 0)
+++ trunk/py4science/workbook/intro_pyrex.tex 2007-12-07 04:48:30 UTC (rev 4663)
@@ -0,0 +1,167 @@
+\texttt{pyrex} is a pure python packages that utilizes a custom
+language which is a hybid of C and python to write code that looks
+like python, but is converted by \texttt{pyrex} into python C
+extension code. It can be used to write custom C extension modules in
+a python like module to remove performance bottlenecks in code, as
+well as to wrap and existing C API with a python binding. \textt{pyrex}
+generates C code, so you can use it to automatically generate C
+extensions that you can ship with your code and users can build your
+code without \texttt{pyrex} installed.
+
+\section{Writing C extensions \texttt{pyrex}}
+
+The canonical \texttt{pyrex} example generates a list of \texttt{N}
+prime numbers, and illustrates the hybrid nature of \texttt{pyrex}
+syntax
+
+\begin{lstlisting}
+
+# name this file with the pyx extension for pyrex, rather than the py
+# extension for python, eg primes.pyx
+def primes(int kmax):
+ # pyrex uses cdef to declare a c type
+ cdef int n, k, i
+ cdef int p[1000]
+
+ # you can use normal python too, eg a python list
+ result = []
+ if kmax > 1000:
+ kmax = 1000
+ k = 0
+ n = 2
+ while k < kmax:
+ i = 0
+ while i < k and n % p[i] <> 0:
+ i = i + 1
+ if i == k:
+ p[k] = n
+ k = k + 1
+ result.append(n)
+ n = n + 1
+ return result
+
+\end{lstlisting}
+
+To build our python extension, we will use the \texttt{pyrex.distutils}
+extensions. Here is a typical setup.py
+
+\begin{lstlisting}
+from distutils.core import setup
+
+# we use the Pyrex distutils Extension class rather than the standard
+# python one
+#from distutils.extension import Extension
+
+from Pyrex.Distutils.extension import Extension
+from Pyrex.Distutils import build_ext
+
+setup(
+ name = 'Demos',
+ ext_modules=[
+ Extension("primes", ["primes.pyx"]),
+ ],
+ cmdclass = {'build_ext': build_ext}
+)
+
+\end{lstlisting}
+
+and we can build it in place using
+
+\begin{lstlisting}
+python setup.py build_ext --inplace
+\end{lstlisting}
+
+This creates a primes.c module which is the generated C code that we
+can ship with our python code to users who may not have \texttt{pyrex}
+installed, and a primes.so file which is the python shared library
+extension. We can now fire up ipython, import primes, and call our
+function with C performance. Here is an example shell session in
+which we build and test our new extension code
+
+\begin{lstlisting}
+# our single pyx file from above
+pyrex_demos> ls primes*
+primes.pyx
+
+# build the module in place
+pyrex_demos> python setup.py build_ext --inplace
+running build_ext
+pyrexc primes.pyx --> primes.c
+building 'primes' extension
+creating build
+creating build/temp.macosx-10.3-fat-2.5
+gcc -arch ppc -arch i386 -isysroot /Developer/SDKs/MacOSX10.4u.sdk -fno-strict-aliasing -Wno-long-double -no-cpp-precomp -mno-fused-madd -fno-common -dynamic -DNDEBUG -g -O3 -I/Library/Frameworks/Python.framework/Versions/2.5/include/python2.5 -c primes.c -o build/temp.macosx-10.3-fat-2.5/primes.o
+gcc -arch i386 -arch ppc -isysroot /Developer/SDKs/MacOSX10.4u.sdk -g -bundle -undefined dynamic_lookup build/temp.macosx-10.3-fat-2.5/primes.o -o primes.so
+
+# now we have the original pyx and also the autogenerated C file and
+# the extension module
+pyrex_demos> ls primes*
+primes.cprimes.pyxprimes.so
+
+# let's test drive this in ipython
+pyrex_demos> ipython
+IPython 0.8.3.svn.r2876 -- An enhanced Interactive Python.
+
+In [1]: import primes
+
+In [2]: dir(primes)
+Out[2]: ['__builtins__', '__doc__', '__file__', '__name__', 'primes']
+
+In [3]: print primes.primes(20)
+[2, 3, 5, 7, 11, 13, 17, 19, 23, 29, 31, 37, 41, 43, 47, 53, 59, 61, 67, 71]
+
+
+\end{lstlisting}
+
+
+\section{Working with \texttt{numpy} arrays}
+
+\textt{numpy} arrays are the core of high performance computing in
+python, and on of the most common data formats for passing large data
+sets around between pyhton code and other wrappers. There are many
+things that arrays do very well and are practically as fast as a
+native C or Fortran implementations, eg convolutions and FFTs. But
+there are somethings that can be painfully slow in python when working
+with arrays, for example iterative algorithms over an array of values.
+For these cases, it is nice to be able to quickly generate some python
+extension code for working with \texttt{numpy} array data.
+
+\texttt{numpy} provides a file which exposes its C API for use in
+\texttt{pyrex} extension code, you can find it, and another file which
+\texttt{numpy} uses to expose the requisite bits of the Python C API
+which it needs, in the \texttt{numpy} source code directory
+\texttt{numpy/doc/pyrex}. These files are \texttt{c_numpy.pxd} and
+\texttt{c_python.pxd}. In addition, \texttt{numpy} provides and
+example file \texttt{numpyx.pyx}that shows you how to build a pyx
+extension file for multi-dimensional array sof different data types
+(eg int, float, python object). Here we will be a little less
+ambitious for starters, and write a simple toy function that sums a 1D
+array of floats.
+
+\begin{lstlisting}
+
+# import the numpy c API (you need to have c_python.pxd and
+# c_numpy.pxd from the numpy source directory in your build directory
+cimport c_numpy
+
+# since this is pyrex, we can import normal python modules too
+import numpy
+
+# numpy must be initialized -- don't forget to do this when writing
+# numpy extension code. It's a common gotcha
+c_numpy.import_array()
+
+def sum_elements(c_numpy.ndarray arr):
+ cdef int i
+ cdef double x, val
+
+ x = 0.
+ val = 0.
+ for i from 0<=i<arr.dimensions[0]:
+ val = (<double*>(arr.data + i*arr.strides[0]))[0]
+ x = x + val
+
+ return x
+
+\end{lstlisting}
+
Modified: trunk/py4science/workbook/main.tex
===================================================================
--- trunk/py4science/workbook/main.tex 2007-12-07 04:43:36 UTC (rev 4662)
+++ trunk/py4science/workbook/main.tex 2007-12-07 04:48:30 UTC (rev 4663)
@@ -50,12 +50,12 @@
\begin{document}
-\title{ \vspace{3cm}
+\title{ \vspace{3cm}
Practical Scientific Computing\\
in Python}
-\author{ \vspace{1cm}
+\author{ \vspace{1cm}
Editors:\\
John D. Hunter\\
Fernando P\xE9rez
@@ -136,6 +136,11 @@
\chapter{Plotting on maps}
\input{basemap.tex}
+\chapter{Performance python: interfacing with other languages}
+\input{intro_pyrex.tex}
+\input{pyrex_ringbuf.tex}
+
+
%%% Bibliography section
\bibliographystyle{plain}
Added: trunk/py4science/workbook/pyrex_ringbuf.tex
===================================================================
--- trunk/py4science/workbook/pyrex_ringbuf.tex (rev 0)
+++ trunk/py4science/workbook/pyrex_ringbuf.tex 2007-12-07 04:48:30 UTC (rev 4663)
@@ -0,0 +1,43 @@
+This exercise introduces \texttt{pyrex} to wrap a C library for
+trailing statistics.
+
+Computation of trailing windowed statistics is common in many
+quantitative data driven disciplines, particularly where there is
+noisy data. Common uses of windowed statistics are the trailing
+moving average, standard deviation, minumum and maximum. Two common
+use cases which pose computational challenges for python: real time
+updating of trailing statistics as live data comes in, and posthoc
+computation of trailing statistics over a large data array. In the
+second case, for some statistics we can use convolution and related
+techniques for efficient computation, eg of the trailing 30 sample
+average
+
+\begin{lstlisting}
+ numpy.convolve(x, numpy.ones(30), mode=valid')[:len(x)]
+\end{lstlisting}
+
+but for other statistics like the trailing 30 day maximum at each
+point, efficient routines like convolution are of no help.
+
+This exercise introduces \texttt{pyrex} to efficiently solve the problem of
+trailing statistics over arrays as well as for a live, incoming data
+stream. A pure C library, \texttt{ringbuf}, defines a circular C
+buffer and attached methods for efficiently computing trailing
+averages, and \texttt{pyrex} is used to provide a pythonic API on top of this
+extension code. The rigid segregation between the C library and the
+python wrappers insures that the C code can be used in other projects,
+be it a matlab (TM) extension or some other C library. The goal of
+the exercise is to compute the trailing statistics \textit{mean},
+\textit{median}, \textit{stddev}, \textit{min} and \textit{max} using
+three approaches:
+
+\begin{itemize}
+ \item with brute force using \texttt{numpy} arrays, slices and methods
+
+ \item with python bindings to the \texttt{ringbuf} code
+ \texttt{ringbuf.Ringbuf}.
+
+ \item using a \texttt{pyrex} extension to the
+ \texttt{ringbuf.runstats} code
+
+\end{itemize}
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