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SF.net SVN: matplotlib: [3971] trunk/py4science
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From: <jd...@us...> - 2007-10-20 17:57:39
|
Revision: 3971
http://matplotlib.svn.sourceforge.net/matplotlib/?rev=3971&view=rev
Author: jdh2358
Date: 2007-10-20 10:57:36 -0700 (Sat, 20 Oct 2007)
Log Message:
-----------
added files workbook
Added Paths:
-----------
trunk/py4science/examples/skel/noisy_sine_skel.py
trunk/py4science/workbook/fig/noisy_sine.eps
trunk/py4science/workbook/fig/noisy_sine.png
trunk/py4science/workbook/files_etc.tex
Added: trunk/py4science/examples/skel/noisy_sine_skel.py
===================================================================
--- trunk/py4science/examples/skel/noisy_sine_skel.py (rev 0)
+++ trunk/py4science/examples/skel/noisy_sine_skel.py 2007-10-20 17:57:36 UTC (rev 3971)
@@ -0,0 +1,22 @@
+from scipy import arange, sin, pi, randn, zeros
+import pylab as p
+
+a = 2 # 2 volt amplitude
+f = 10 # 10 Hz frequency
+sigma = 0.5 # 0.5 volt standard deviation noise
+
+# create the t and v arrays; see the scipy commands arange, sin, and randn
+t = XXX # an evenly sampled time array
+v = XXX # a noisy sine wave
+
+# create a 2D array X and put t in the 1st column and v in the 2nd;
+# see the numpy command zeros
+X = XXX
+
+# save the output file as ASCII; see the pylab command save
+XXX
+
+# plot the arrays t vs v and label the x-axis, y-axis and title save
+# the output figure as noisy_sine.png. See the pylab commands plot,
+# xlabel, ylabel, grid, show
+XXX
Added: trunk/py4science/workbook/fig/noisy_sine.eps
===================================================================
--- trunk/py4science/workbook/fig/noisy_sine.eps (rev 0)
+++ trunk/py4science/workbook/fig/noisy_sine.eps 2007-10-20 17:57:36 UTC (rev 3971)
@@ -0,0 +1,954 @@
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+\section{Working with files, the web, arrays, etc\dots}
+\label{sec:working_with_files}
+
+This section is a general overview to show how easy it is to load
+and manipulate data on the file system and over the web using python's
+built in data structures and numpy arrays. The goal is to exercise
+basic programming skills like building filename or web addresses to
+automate certain tasks like loading a series of data files or
+downloading a bunch of related files off the web, as well as to
+illustrate basic numpy and pylab skills.
+
+\subsection{Loading and saving ASCII data}
+\label{sec:ascii_data}
+
+The simplest file format is a plain text ASCII file of numbers.
+Although there are many better formats out there for saveing and
+loading data, this format is extremely common because it has the
+advantages of being human readable, and thus will survive the test of
+time as the \textit{en vogue} programming languages, analysis
+applications and data formats come and go, it is easy to parse, and it
+is supported by almost all languages and applications.
+
+In this exercise we will create a data set of two arrays, the first
+one regularly sampled time \textit{t} from 0..2 seconds with 20~ms
+time step , and the second one an array \texttt{v} of sinusoidal
+voltages corrupted by some noise. Let's assume the sine wave has
+amplitude 2~V, frequency 10~Hz, and zero mean Gaussian distrubuted
+white noise with standard deviation 0.5~V. Your task is to write two
+scripts.
+
+The first script should create the vectors \texttt{t} and \texttt{v},
+plot the time series of \texttt{t} versus \texttt{v}, save them in a
+two dimensional numpy array \texttt{X}, and then dump the array
+\texttt{X} to a plain text ASCII file called
+\texttt{'noisy_sine.dat'}. The file will look like (not identical
+because of the noise)
+
+\begin{verbatim}
+0.000000000000000000e+00 1.550947826934816025e-02
+2.000000000000000042e-02 2.493944587057004725e+00
+4.000000000000000083e-02 9.497694074551737975e-01
+5.999999999999999778e-02 -9.185779287524413750e-01
+8.000000000000000167e-02 -2.811127590689064704e+00
+... and so on
+\end{verbatim}
+
+Here is the exercise skeleton of the script to create and plot the
+data file
+
+\lstinputlisting[label=code:noisy_sine_skel,caption={IGNORED}]{skel/noise_sine_skel.py}
+
+and the graph will look something like Figure~\ref{fig:noisy_sine}
+
+\begin{center}%
+\begin{figure}
+\begin{centering}\includegraphics[width=4in]{fig/noise_sine}\par\end{centering}
+
+
+\caption{\label{fig:noisy_sine}A 10~Hz sine wave corrupted by noise}
+\end{figure}
+\par\end{center}
+
+The second part of this exercise is to write a script which loads data
+from the data file into an array \texttt{X}, extracts the columns into
+arrays \texttt{t} and \texttt{v}, and computes the RMS
+(root-mean-square) intensity of the signal using the \textt{load}
+command.
+
+
+\subsection{Loading and saving binary data}
+\label{sec:binary_data}
+
+ASCII is bloated and slow for working with large arrays, and so binary
+data should be used if performance is a consideration. To save the
+array \texttt{X} in binary form, use the numpy \texttt{tostring} method
+
+\begin{lstlisting}
+# open the file for writing binary and write the binary string
+file('../data/binary_data.dat', 'wb').write(X.tostring())
+\end{lstlisting}
+
+\noindent This data can later be loaded into a numpy array using
+\texttt{fromstring}. This method takes two arguments, a string and a
+data type (note that numarray users can use \texttt{fromfile} which is
+more efficient for importing data directly from a file).
+
+\lstinputlisting{code/load_binary_data.py}
+
+\noindent Note that although Numpy and numarray use different
+typecode arguments (Numeric uses strings whereas numarray uses type
+objects), the matplotlib.numpy compatibility layer provides symbols
+which will work with either \rc{numpy} rc setting.
+
+\subsection{Processing several data files}
+\label{sec:multiple_files}
+
+Since python is a programming language \textit{par excellence}, it is
+easy to process data in batch. When I started the gradual transition
+from a full time \matlab\ user to a full time python user, I began
+processing my data in python and saving the results to data files for
+plotting in \matlab. When that became too cumbersome, I decided to
+write matplotlib so I could have all the functionality I needed in one
+environment. Here is a brief example showing how to iterate over
+several data files, named \fname{basename001.dat, basename002.dat,
+ basename003.dat, ... basename100.dat} and plot all of the traces to
+the same axes. I'll assume for this example that each file is a 1D
+ASCII array, which I can load with the \texttt{load} command.
+
+\begin{lstlisting}
+hold(True) # set the hold state to be on
+for i in range(1,101): #start at 1, end at 100
+ fname = 'basename%03d.dat'%i # %03d pads the integers with zeros
+ x = load(fname)
+ plot(x)
+\end{lstlisting}
+
+
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