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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