Revision: 6252
http://matplotlib.svn.sourceforge.net/matplotlib/?rev=6252&view=rev
Author: jdh2358
Date: 2008-10-17 20:24:06 +0000 (Fri, 17 Oct 2008)
Log Message:
-----------
added mlab api docs
Modified Paths:
--------------
trunk/matplotlib/doc/api/index.rst
trunk/matplotlib/doc/users/index.rst
trunk/matplotlib/lib/matplotlib/axes.py
trunk/matplotlib/lib/matplotlib/mlab.py
Added Paths:
-----------
trunk/matplotlib/doc/api/mlab_api.rst
Modified: trunk/matplotlib/doc/api/index.rst
===================================================================
--- trunk/matplotlib/doc/api/index.rst 2008-10-17 19:32:16 UTC (rev 6251)
+++ trunk/matplotlib/doc/api/index.rst 2008-10-17 20:24:06 UTC (rev 6252)
@@ -23,6 +23,7 @@
colorbar_api.rst
colors_api.rst
nxutils_api.rst
+ mlab_api.rst
path_api.rst
pyplot_api.rst
index_backend_api.rst
Added: trunk/matplotlib/doc/api/mlab_api.rst
===================================================================
--- trunk/matplotlib/doc/api/mlab_api.rst (rev 0)
+++ trunk/matplotlib/doc/api/mlab_api.rst 2008-10-17 20:24:06 UTC (rev
6252)
@@ -0,0 +1,12 @@
+****************
+matplotlib mlab
+****************
+
+
+:mod:`matplotlib.mlab`
+=======================
+
+.. automodule:: matplotlib.mlab
+ :members:
+ :undoc-members:
+ :show-inheritance:
Modified: trunk/matplotlib/doc/users/index.rst
===================================================================
--- trunk/matplotlib/doc/users/index.rst 2008-10-17 19:32:16 UTC (rev
6251)
+++ trunk/matplotlib/doc/users/index.rst 2008-10-17 20:24:06 UTC (rev
6252)
@@ -21,6 +21,7 @@
index_text.rst
artists.rst
event_handling.rst
+ plotting.rst
toolkits.rst
screenshots.rst
license.rst
Modified: trunk/matplotlib/lib/matplotlib/axes.py
===================================================================
--- trunk/matplotlib/lib/matplotlib/axes.py 2008-10-17 19:32:16 UTC (rev
6251)
+++ trunk/matplotlib/lib/matplotlib/axes.py 2008-10-17 20:24:06 UTC (rev
6252)
@@ -3387,7 +3387,7 @@
maxlags=None, **kwargs)
Plot the autocorrelation of *x*. If *normed* = *True*,
- normalize the data but the autocorrelation at 0-th lag. *x* is
+ normalize the data by the autocorrelation at 0-th lag. *x* is
detrended by the *detrend* callable (default no normalization).
Data are plotted as ``plot(lags, c, **kwargs)``
@@ -4370,7 +4370,7 @@
**Example:**
- .. plot:: mpl_examples/pyplot_examples/errorbar_demo.py
+ .. plot:: mpl_examples/pylab_examples/errorbar_demo.py
"""
Modified: trunk/matplotlib/lib/matplotlib/mlab.py
===================================================================
--- trunk/matplotlib/lib/matplotlib/mlab.py 2008-10-17 19:32:16 UTC (rev
6251)
+++ trunk/matplotlib/lib/matplotlib/mlab.py 2008-10-17 20:24:06 UTC (rev
6252)
@@ -3,66 +3,94 @@
Numerical python functions written for compatability with matlab(TM)
commands with the same names.
- Matlab(TM) compatible functions:
+Matlab(TM) compatible functions
+-------------------------------
- * cohere - Coherence (normalized cross spectral density)
+:func:`cohere`
+ Coherence (normalized cross spectral density)
- * csd - Cross spectral density uing Welch's average periodogram
+:func:`csd`
+ Cross spectral density uing Welch's average periodogram
- * detrend -- Remove the mean or best fit line from an array
+:func:`detrend`
+ Remove the mean or best fit line from an array
- * find - Return the indices where some condition is true;
- numpy.nonzero is similar but more general.
+:func:`find`
+ Return the indices where some condition is true;
+ numpy.nonzero is similar but more general.
- * griddata - interpolate irregularly distributed data to a
- regular grid.
+:func:`griddata`
+ interpolate irregularly distributed data to a
+ regular grid.
- * prctile - find the percentiles of a sequence
+:func:`prctile`
+ find the percentiles of a sequence
- * prepca - Principal Component Analysis
+:func:`prepca`
+ Principal Component Analysis
- * psd - Power spectral density uing Welch's average periodogram
+:func:`psd`
+ Power spectral density uing Welch's average periodogram
- * rk4 - A 4th order runge kutta integrator for 1D or ND systems
+:func:`rk4`
+ A 4th order runge kutta integrator for 1D or ND systems
- The following are deprecated; please import directly from numpy
- (with care--function signatures may differ):
+Miscellaneous functions
+-------------------------
- * conv - convolution (numpy.convolve)
- * corrcoef - The matrix of correlation coefficients
- * hist -- Histogram (numpy.histogram)
- * linspace -- Linear spaced array from min to max
- * meshgrid
- * polyfit - least squares best polynomial fit of x to y
- * polyval - evaluate a vector for a vector of polynomial coeffs
- * trapz - trapeziodal integration (trapz(x,y) -> numpy.trapz(y,x))
- * vander - the Vandermonde matrix
+Functions that don't exist in matlab(TM), but are useful anyway:
- Functions that don't exist in matlab(TM), but are useful anyway:
+:meth:`cohere_pairs`
+ Coherence over all pairs. This is not a matlab function, but we
+ compute coherence a lot in my lab, and we compute it for a lot of
+ pairs. This function is optimized to do this efficiently by
+ caching the direct FFTs.
- * cohere_pairs - Coherence over all pairs. This is not a matlab
- function, but we compute coherence a lot in my lab, and we
- compute it for a lot of pairs. This function is optimized to do
- this efficiently by caching the direct FFTs.
+:meth:`rk4`
+ A 4th order Runge-Kutta ODE integrator in case you ever find
+ yourself stranded without scipy (and the far superior
+ scipy.integrate tools)
-= record array helper functions =
- * rec2txt : pretty print a record array
- * rec2csv : store record array in CSV file
- * csv2rec : import record array from CSV file with type inspection
- * rec_append_fields: adds field(s)/array(s) to record array
- * rec_drop_fields : drop fields from record array
- * rec_join : join two record arrays on sequence of fields
- * rec_groupby : summarize data by groups (similar to SQL GROUP BY)
- * rec_summarize : helper code to filter rec array fields into new fields
+record array helper functions
+-------------------------------
+A collection of helper methods for numpyrecord arrays
+
+.. _htmlonly::
+
+ See :ref:`misc-examples-index`
+
+:meth:`rec2txt`
+ pretty print a record array
+
+:meth:`rec2csv`
+ store record array in CSV file
+
+:meth:`csv2rec`
+ import record array from CSV file with type inspection
+
+:meth:`rec_append_fields`
+ adds field(s)/array(s) to record array
+
+:meth:`rec_drop_fields`
+ drop fields from record array
+
+:meth:`rec_join`
+ join two record arrays on sequence of fields
+
+:meth:`rec_groupby`
+ summarize data by groups (similar to SQL GROUP BY)
+
+:meth:`rec_summarize`
+ helper code to filter rec array fields into new fields
+
For the rec viewer functions(e rec2csv), there are a bunch of Format
objects you can pass into the functions that will do things like color
negative values red, set percent formatting and scaling, etc.
+Example usage::
-Example usage:
-
r = csv2rec('somefile.csv', checkrows=0)
formatd = dict(
@@ -82,6 +110,40 @@
win.show_all()
gtk.main()
+
+Deprecated functions
+---------------------
+
+The following are deprecated; please import directly from numpy (with
+care--function signatures may differ):
+
+:meth:`conv`
+ convolution (numpy.convolve)
+
+:meth:`corrcoef`
+ The matrix of correlation coefficients
+
+:meth:`hist`
+ Histogram (numpy.histogram)
+
+:meth:`linspace`
+ Linear spaced array from min to max
+
+:meth:`meshgrid`
+ Make a 2D grid from 2 1 arrays (numpy.meshgrid)
+
+:meth:`polyfit`
+ least squares best polynomial fit of x to y (numpy.polyfit)
+
+:meth:`polyval`
+ evaluate a vector for a vector of polynomial coeffs (numpy.polyval)
+
+:meth:`trapz`
+ trapeziodal integration (trapz(x,y) -> numpy.trapz(y,x))
+
+:meth:`vander`
+ the Vandermonde matrix (numpy.vander)
+
"""
from __future__ import division
@@ -185,20 +247,23 @@
to calculate the Fourier frequencies, freqs, in cycles per time
unit.
- -- NFFT must be even; a power 2 is most efficient.
- -- detrend is a functions, unlike in matlab where it is a vector.
- -- window can be a function or a vector of length NFFT. To create window
- vectors see numpy.blackman, numpy.hamming, numpy.bartlett,
- scipy.signal, scipy.signal.get_window etc.
- -- if length x < NFFT, it will be zero padded to NFFT
+ *NFFT*
+ The length of the FFT window. Must be even; a power 2 is most
efficient.
+ *detrend*
+ is a function, unlike in matlab where it is a vector.
- Returns the tuple Pxx, freqs
+ *window*
+ can be a function or a vector of length NFFT. To create window
+ vectors see numpy.blackman, numpy.hamming, numpy.bartlett,
+ scipy.signal, scipy.signal.get_window etc.
- Refs:
- Bendat & Piersol -- Random Data: Analysis and Measurement
- Procedures, John Wiley & Sons (1986)
+ If length x < NFFT, it will be zero padded to NFFT
+ Returns the tuple (*Pxx*, *freqs*)
+
+ Refs: Bendat & Piersol -- Random Data: Analysis and Measurement
Procedures, John Wiley & Sons (1986)
+
"""
# I think we could remove this condition without hurting anything.
if NFFT % 2:
@@ -409,7 +474,7 @@
window=window_hanning, noverlap=0):
"""
The coherence between x and y. Coherence is the normalized
- cross spectral density
+ cross spectral density:
.. math::
@@ -470,7 +535,7 @@
Method: if X is a the Vandermonde Matrix computed from x (see
- http://mathworld.wolfram.com/VandermondeMatrix.html), then the
+ `vandermonds <http://mathworld.wolfram.com/VandermondeMatrix.html>`_),
then the
polynomial least squares solution is given by the 'p' in
X*p = y
@@ -487,7 +552,7 @@
numpy.linalg.lstsq.
For more info, see
- http://mathworld.wolfram.com/LeastSquaresFittingPolynomial.html,
+ `least squares fitting
<http://mathworld.wolfram.com/LeastSquaresFittingPolynomial.html>`_,
but note that the k's and n's in the superscripts and subscripts
on that page. The linear algebra is correct, however.
@@ -898,14 +963,21 @@
def rk4(derivs, y0, t):
"""
- Integrate 1D or ND system of ODEs from initial state y0 at sample
- times t. derivs returns the derivative of the system and has the
- signature
+ Integrate 1D or ND system of ODEs using 4-th order Runge-Kutta. This is a
toy implementation which may be useful if you find yourself stranded on a
system w/o scipy. Otherwise use ``scipy.integrate``
- dy = derivs(yi, ti)
+ *y0*
+ initial state vector
+
+ *t*
+ sample times
- Example 1 :
+ *derivs*
+ returns the derivative of the system and has the
+ signature ``dy = derivs(yi, ti)``
+
+ Example 1 ::
+
## 2D system
def derivs6(x,t):
@@ -917,7 +989,7 @@
y0 = (1,2)
yout = rk4(derivs6, y0, t)
- Example 2:
+ Example 2::
## 1D system
alpha = 2
@@ -963,7 +1035,7 @@
"""
Bivariate gaussan distribution for equal shape X, Y
- http://mathworld.wolfram.com/BivariateNormalDistribution.html
+ See `bivariate normal
<http://mathworld.wolfram.com/BivariateNormalDistribution.html>`_ at mathworld.
"""
Xmu = X-mux
Ymu = Y-muy
@@ -1074,13 +1146,16 @@
*x* is a very long trajectory from a map, and *fprime* returns the
derivative of *x*.
- Returns :math:`\lambda = \frac{1}{n}\sum \ln|f^'(x_i)|`
+ Returns :
+ .. math::
+ \lambda = \frac{1}{n}\sum \ln|f^'(x_i)|
+
.. seealso::
Sec 10.5 Strogatz (1994) "Nonlinear Dynamics and Chaos".
`Wikipedia article on Lyapunov Exponent
- http://en.wikipedia.org/wiki/Lyapunov_exponent`_.
+ <http://en.wikipedia.org/wiki/Lyapunov_exponent>`_.
.. note::
What the function here calculates may not be what you really want;
@@ -1510,14 +1585,15 @@
return np.diag(diag)
def identity(n, rank=2, dtype='l', typecode=None):
- """identity(n,r) returns the identity matrix of shape (n,n,...,n) (rank r).
+ """Returns the identity matrix of shape (n,n,...,n) (rank r).
For ranks higher than 2, this object is simply a multi-index Kronecker
- delta:
- / 1 if i0=i1=...=iR,
- id[i0,i1,...,iR] = -|
- \ 0 otherwise.
+ delta::
+ / 1 if i0=i1=...=iR,
+ id[i0,i1,...,iR] = -|
+ \ 0 otherwise.
+
Optionally a dtype (or typecode) may be given (it defaults to 'l').
Since rank defaults to 2, this function behaves in the default case (when
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