Revision: 7569
http://matplotlib.svn.sourceforge.net/matplotlib/?rev=7569&view=rev
Author: mdboom
Date: 2009-08-25 20:04:34 +0000 (Tue, 25 Aug 2009)
Log Message:
-----------
Fix cohere_pairs docstring so it builds with new versions of Sphinx
Modified Paths:
--------------
branches/v0_99_maint/lib/matplotlib/mlab.py
Modified: branches/v0_99_maint/lib/matplotlib/mlab.py
===================================================================
--- branches/v0_99_maint/lib/matplotlib/mlab.py 2009-08-25 15:32:13 UTC (rev
7568)
+++ branches/v0_99_maint/lib/matplotlib/mlab.py 2009-08-25 20:04:34 UTC (rev
7569)
@@ -501,77 +501,80 @@
returnPxx=False):
u"""
- Cxy, Phase, freqs = cohere_pairs( X, ij, ...)
+ Call signature::
- Compute the coherence and phase for all pairs ij, in X.
+ Cxy, Phase, freqs = cohere_pairs( X, ij, ...)
- Parameters
- ----------
- X: array
- a numSamples*numCols array
+ Compute the coherence and phase for all pairs *ij*, in *X*.
- ij: list of tuples
- Each tuple is a pair of indexes into the columns of X for which you want to
- compute coherence. For example, if X has 64 columns, and you want to
- compute all nonredundant pairs, define ij as
+ *X* is a *numSamples* * *numCols* array
+ *ij* is a list of tuples. Each tuple is a pair of indexes into
+ the columns of X for which you want to compute coherence. For
+ example, if *X* has 64 columns, and you want to compute all
+ nonredundant pairs, define *ij* as::
+
ij = []
for i in range(64):
for j in range(i+1,64):
ij.append( (i,j) )
- preferSpeedOverMemory: optional, bool
+ *preferSpeedOverMemory* is an optional bool. Defaults to true. If
+ False, limits the caching by only making one, rather than two,
+ complex cache arrays. This is useful if memory becomes critical.
+ Even when *preferSpeedOverMemory* is False, :func:`cohere_pairs`
+ will still give significant performace gains over calling
+ :func:`cohere` for each pair, and will use subtantially less
+ memory than if *preferSpeedOverMemory* is True. In my tests with
+ a 43000,64 array over all nonredundant pairs,
+ *preferSpeedOverMemory* = True delivered a 33% performance boost
+ on a 1.7GHZ Athlon with 512MB RAM compared with
+ *preferSpeedOverMemory* = False. But both solutions were more
+ than 10x faster than naively crunching all possible pairs through
+ :func:`cohere`.
- Defaults to true. If false, limits the caching by only making one, rather
- than two, complex cache arrays. This is useful if memory becomes critical.
- Even when preferSpeedOverMemory is false, cohere_pairs will still give
- significant performace gains over calling cohere for each pair, and will
- use subtantially less memory than if preferSpeedOverMemory is true. In my
- tests with a 43000,64 array over all nonredundant pairs,
- preferSpeedOverMemory=1 delivered a 33% performace boost on a 1.7GHZ Athlon
- with 512MB RAM compared with preferSpeedOverMemory=0. But both solutions
- were more than 10x faster than naievly crunching all possible pairs through
- cohere.
-
- Returns
- -------
+ Returns::
- (Cxy, Phase, freqs), where:
-
- Cxy: dictionary of (i,j) tuples -> coherence vector for that
- pair. Ie, Cxy[(i,j) = cohere(X[:,i], X[:,j]). Number of
- dictionary keys is len(ij)
+ (Cxy, Phase, freqs)
- Phase: dictionary of phases of the cross spectral density at
- each frequency for each pair. keys are (i,j).
+ where:
- freqs: vector of frequencies, equal in length to either the
- coherence or phase vectors for any i,j key.
+ - *Cxy*: dictionary of (*i*, *j*) tuples -> coherence vector for
+ that pair. I.e., ``Cxy[(i,j) = cohere(X[:,i], X[:,j])``.
+ Number of dictionary keys is ``len(ij)``.
- Eg, to make a coherence Bode plot:
+ - *Phase*: dictionary of phases of the cross spectral density at
+ each frequency for each pair. Keys are (*i*, *j*).
+ - *freqs*: vector of frequencies, equal in length to either the
+ coherence or phase vectors for any (*i*, *j*) key.
+
+ Eg., to make a coherence Bode plot::
+
subplot(211)
plot( freqs, Cxy[(12,19)])
subplot(212)
plot( freqs, Phase[(12,19)])
- For a large number of pairs, cohere_pairs can be much more
- efficient than just calling cohere for each pair, because it
- caches most of the intensive computations. If N is the number of
- pairs, this function is O(N) for most of the heavy lifting,
- whereas calling cohere for each pair is O(N^2). However, because
- of the caching, it is also more memory intensive, making 2
- additional complex arrays with approximately the same number of
- elements as X.
+ For a large number of pairs, :func:`cohere_pairs` can be much more
+ efficient than just calling :func:`cohere` for each pair, because
+ it caches most of the intensive computations. If :math:`N` is the
+ number of pairs, this function is :math:`O(N)` for most of the
+ heavy lifting, whereas calling cohere for each pair is
+ :math:`O(N^2)`. However, because of the caching, it is also more
+ memory intensive, making 2 additional complex arrays with
+ approximately the same number of elements as *X*.
- See test/cohere_pairs_test.py in the src tree for an example
- script that shows that this cohere_pairs and cohere give the same
- results for a given pair.
+ See :file:`test/cohere_pairs_test.py` in the src tree for an
+ example script that shows that this :func:`cohere_pairs` and
+ :func:`cohere` give the same results for a given pair.
- See also
- --------
- :func: psd
- """
+ .. sealso::
+
+ :func:`psd`
+ For information about the methods used to compute
+ :math:`P_{xy}`, :math:`P_{xx}` and :math:`P_{yy}`.
+ """
numRows, numCols = X.shape
# zero pad if X is too short
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