SF.net SVN: matplotlib: [4040] trunk/py4science

[<jd...@us...>]

Revision: 4040
          http://matplotlib.svn.sourceforge.net/matplotlib/?rev=4040&view=rev
Author:   jdh2358
Date:     2007-10-28 09:35:18 -0700 (Sun, 28 Oct 2007)

Log Message:
-----------
fixed some bugs in convolve

Modified Paths:
--------------
    trunk/py4science/examples/skel/convolution_demo_skel.py
    trunk/py4science/workbook/convolution.tex
    trunk/py4science/workbook/intro_sigproc.tex

Modified: trunk/py4science/examples/skel/convolution_demo_skel.py
===================================================================
--- trunk/py4science/examples/skel/convolution_demo_skel.py	2007-10-28 15:18:02 UTC (rev 4039)
+++ trunk/py4science/examples/skel/convolution_demo_skel.py	2007-10-28 16:35:18 UTC (rev 4040)
@@ -40,7 +40,7 @@
 # evaluate the impulse response function, and numerically convolve it
 # with the input x
 r = XXX # evaluate the impulse function
-y = XXX # convultion of x with r
+y = XXX # convolution of x with r
 y = XXX # extract just the length Nt part
 
 # compute y by applying F^-1[F(x) * F(r)].  The fft assumes the signal

Modified: trunk/py4science/workbook/convolution.tex
===================================================================
--- trunk/py4science/workbook/convolution.tex	2007-10-28 15:18:02 UTC (rev 4039)
+++ trunk/py4science/workbook/convolution.tex	2007-10-28 16:35:18 UTC (rev 4040)
@@ -3,9 +3,9 @@
 
 The output of a linear system is given by the convolution of its
 impulse response function with the input.  Mathematically
-\[
+\begin{equation}
   y(t) = \int_0^t x(\tau)r(t-\tau)d\tau
-\]
+\end{equation}
 This fundamental relationship lies at the heart of linear systems
 analysis.  It is used to model the dynamics of calcium buffers in
 neuronal synapses, where incoming action potentials are represented as

Modified: trunk/py4science/workbook/intro_sigproc.tex
===================================================================
--- trunk/py4science/workbook/intro_sigproc.tex	2007-10-28 15:18:02 UTC (rev 4039)
+++ trunk/py4science/workbook/intro_sigproc.tex	2007-10-28 16:35:18 UTC (rev 4040)
@@ -12,4 +12,4 @@
 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}.  
+\texttt{specgram}).  


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