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<table width="100%" cellspacing=0 cellpadding=2 border=0 summary="heading">
<tr bgcolor="#7799ee">
<td valign=bottom> <br>
<font color="#ffffff" face="helvetica, arial"> <br><big><big><strong><a href="matplotlib.html"><font color="#ffffff">matplotlib</font></a>.matlab</strong></big></big></font></td
><td align=right valign=bottom
><font color="#ffffff" face="helvetica, arial"><a href=".">index</a><br><a href="file:/usr/local/lib/python2.3/site-packages/matplotlib/matlab.py">/usr/local/lib/python2.3/site-packages/matplotlib/matlab.py</a></font></td></tr></table>
<p><tt>This is a matlab style functional interface the matplotlib.<br>
<br>
The following matlab compatible commands are provided<br>
<br>
Plotting commands<br>
<br>
axes - Create a new axes<br>
axis - Set or return the current axis limits<br>
bar - make a bar chart<br>
cla - clear current axes<br>
clf - clear a figure window<br>
close - close a figure window<br>
cohere - make a plot of coherence<br>
csd - make a plot of cross spectral density<br>
errorbar - make an errorbar graph<br>
figure - create or change active figure<br>
fill - make filled polygons<br>
gca - return the current axes<br>
gcf - return the current figure<br>
get - get a handle graphics property<br>
hist - make a histogram<br>
loglog - a log log plot<br>
imshow - plot image data<br>
pcolor - make a pseudocolor plot<br>
plot - make a line plot<br>
psd - make a plot of power spectral density<br>
savefig - save the current figure<br>
scatter - make a scatter plot<br>
set - set a handle graphics property<br>
semilogx - log x axis<br>
semilogy - log y axis<br>
show - show the figures<br>
specgram - a spectrogram plot<br>
stem - make a stem plot<br>
subplot - make a subplot (numrows, numcols, axesnum)<br>
text - add some text at location x,y to the current axes<br>
title - add a title to the current axes<br>
xlabel - add an xlabel to the current axes<br>
ylabel - add a ylabel to the current axes<br>
<br>
Matrix commands<br>
<br>
cumprod - the cumulative product along a dimension<br>
cumsum - the cumulative sum along a dimension<br>
detrend - remove the mean or besdt fit line from an array<br>
diag - the k-th diagonal of matrix <br>
diff - the n-th differnce of an array<br>
eig - the eigenvalues and eigen vectors of v<br>
eye - a matrix where the k-th diagonal is ones, else zero <br>
find - return the indices where a condition is nonzero <br>
fliplr - flip the rows of a matrix up/down<br>
flipud - flip the columns of a matrix left/right<br>
linspace - a linear spaced vector of N values from min to max inclusive<br>
meshgrid - repeat x and y to make regular matrices<br>
ones - an array of ones<br>
rand - an array from the uniform distribution [0,1]<br>
randn - an array from the normal distribution<br>
rot90 - rotate matrix k*90 degress counterclockwise<br>
squeeze - squeeze an array removing any dimensions of length 1<br>
tri - a triangular matrix<br>
tril - a lower triangular matrix<br>
triu - an upper triangular matrix<br>
vander - the Vandermonde matrix of vector x<br>
svd - singular value decomposition<br>
zeros - a matrix of zeros<br>
<br>
Probability<br>
<br>
levypdf - The levy probability density function from the char. func.<br>
normpdf - The Gaussian probability density function<br>
rand - random numbers from the uniform distribution<br>
randn - random numbers from the normal distribution<br>
<br>
Statistics<br>
<br>
corrcoef - correlation coefficient<br>
cov - covariance matrix<br>
max - the maximum along dimension m<br>
mean - the mean along dimension m<br>
median - the median along dimension m<br>
min - the minimum along dimension m<br>
norm - the norm of vector x<br>
prod - the product along dimension m<br>
ptp - the max-min along dimension m<br>
std - the standard deviation along dimension m<br>
sum - the sum along dimension m<br>
<br>
Time series analysis<br>
<br>
bartlett - M-point Bartlett window<br>
blackman - M-point Blackman window<br>
cohere - the coherence using average periodiogram<br>
csd - the cross spectral density using average periodiogram<br>
fft - the fast Fourier transform of vector x<br>
hamming - M-point Hamming window<br>
hanning - M-point Hanning window<br>
hist - compute the histogram of x<br>
kaiser - M length Kaiser window<br>
psd - the power spectral density using average periodiogram<br>
sinc - the sinc function of array x<br>
<br>
Other<br>
<br>
angle - the angle of a complex array<br>
load - load ASCII data into array<br>
polyfit - fit x, y to an n-th order polynomial<br>
polyval - evaluate an n-th order polynomial<br>
roots - the roots of the polynomial coefficients in p<br>
save - save an array to an ASCII file<br>
trapz - trapezoidal integration<br>
<br>
<br>
Credits: The plotting commands were provided by<br>
John D. Hunter <jdhunter@ace.bsd.uhicago.edu><br>
<br>
Most of the other commands are from the Numeric, MLab and FFT, with<br>
the exception of those in mlab.py provided by matplotlib.</tt></p>
<p>
<table width="100%" cellspacing=0 cellpadding=2 border=0 summary="section">
<tr bgcolor="#aa55cc">
<td colspan=3 valign=bottom> <br>
<font color="#fffff" face="helvetica, arial"><big><strong>Modules</strong></big></font></td></tr>
<tr><td bgcolor="#aa55cc"><tt> </tt></td><td> </td>
<td width="100%"><table width="100%" summary="list"><tr><td width="25%" valign=top><a href="numarray.linear_algebra.LinearAlgebra2.html">numarray.linear_algebra.LinearAlgebra2</a><br>
<a href="numarray.linear_algebra.mlab.html">numarray.linear_algebra.mlab</a><br>
<a href="numarray.random_array.html">numarray.random_array</a><br>
<a href="matplotlib._matlab_helpers.html">matplotlib._matlab_helpers</a><br>
<a href="numarray.arrayprint.html">numarray.arrayprint</a><br>
<a href="copy.html">copy</a><br>
<a href="copy_reg.html">copy_reg</a><br>
</td><td width="25%" valign=top><a href="numarray.generic.html">numarray.generic</a><br>
<a href="numarray.libnumarray.html">numarray.libnumarray</a><br>
<a href="math.html">math</a><br>
<a href="numarray.memory.html">numarray.memory</a><br>
<a href="matplotlib.mlab.html">matplotlib.mlab</a><br>
<a href="numarray.numarrayall.html">numarray.numarrayall</a><br>
<a href="numarray.numarraycore.html">numarray.numarraycore</a><br>
</td><td width="25%" valign=top><a href="numarray.numerictypes.html">numarray.numerictypes</a><br>
<a href="matplotlib.numerix.html">matplotlib.numerix</a><br>
<a href="numarray.numinclude.html">numarray.numinclude</a><br>
<a href="numarray.numtest.html">numarray.numtest</a><br>
<a href="operator.html">operator</a><br>
<a href="os.html">os</a><br>
<a href="numarray.safethread.html">numarray.safethread</a><br>
</td><td width="25%" valign=top><a href="sys.html">sys</a><br>
<a href="numarray.typeconv.html">numarray.typeconv</a><br>
<a href="types.html">types</a><br>
<a href="numarray.ufunc.html">numarray.ufunc</a><br>
</td></tr></table></td></tr></table><p>
<table width="100%" cellspacing=0 cellpadding=2 border=0 summary="section">
<tr bgcolor="#eeaa77">
<td colspan=3 valign=bottom> <br>
<font color="#ffffff" face="helvetica, arial"><big><strong>Functions</strong></big></font></td></tr>
<tr><td bgcolor="#eeaa77"><tt> </tt></td><td> </td>
<td width="100%"><dl><dt><a name="-and_"><strong>and_</strong></a>(...)</dt><dd><tt><a href="#-and_">and_</a>(a, b) -- Same as a & b.</tt></dd></dl>
<dl><dt><a name="-axes"><strong>axes</strong></a>(*args, **kwargs)</dt><dd><tt>Add an axis at positon rect specified by<br>
<br>
<a href="#-axes">axes</a>() by itself creates a default full <a href="#-subplot">subplot</a>(111) window axis<br>
<br>
<a href="#-axes">axes</a>(rect, axisbg='w') where rect=[left, bottom, width, height] in<br>
normalized (0,1) units background is the background color for<br>
the axis, default white<br>
<br>
<a href="#-axes">axes</a>(h) where h is an axes instance makes h the<br>
current axis An Axes instance is returned<br>
<br>
axisbg is a color format string which sets the background color of<br>
the axes<br>
<br>
If axisbg is a length 1 string, assume it's a color format string<br>
(see plot for legal color strings). If it is a length 7 string,<br>
assume it's a hex color string, as used in html, eg, '#eeefff'.<br>
If it is a len(3) tuple, assume it's an rgb value where r,g,b in<br>
[0,1].</tt></dd></dl>
<dl><dt><a name="-axis"><strong>axis</strong></a>(*v)</dt><dd><tt><a href="#-axis">axis</a>() returns the current axis as a length a length 4 vector<br>
<br>
<a href="#-axis">axis</a>(v) where v= [xmin xmax ymin ymax] sets the min and max of the<br>
x and y axis limits<br>
<br>
<a href="#-axis">axis</a>('off') turns off the axis lines and labels</tt></dd></dl>
<dl><dt><a name="-bar"><strong>bar</strong></a>(*args, **kwargs)</dt><dd><tt>BAR(left, height)<br>
<br>
Make a bar plot with rectangles at<br>
left, left+width, 0, height<br>
left and height are Numeric arrays<br>
<br>
Return value is a list of Rectangle patch instances<br>
<br>
BAR(left, height, width, bottom,<br>
color, yerr, xerr, capsize, yoff)<br>
<br>
xerr and yerr, if not None, will be used to generate errorbars<br>
on the bar chart<br>
<br>
color specifies the color of the bar<br>
<br>
capsize determines the length in points of the error bar caps<br>
<br>
<br>
The optional arguments color, width and bottom can be either<br>
scalars or len(x) sequences<br>
<br>
This enables you to use bar as the basis for stacked bar<br>
charts, or candlestick plots</tt></dd></dl>
<dl><dt><a name="-cla"><strong>cla</strong></a>()</dt><dd><tt>Clear the current axes</tt></dd></dl>
<dl><dt><a name="-clf"><strong>clf</strong></a>()</dt><dd><tt>Clear the current figure</tt></dd></dl>
<dl><dt><a name="-close"><strong>close</strong></a>(*args)</dt><dd><tt>Close a figure window<br>
<br>
<a href="#-close">close</a>() by itself closes the current figure<br>
<br>
<a href="#-close">close</a>(num) closes figure number num<br>
<br>
<a href="#-close">close</a>(h) where h is a figure handle(instance) closes that figure<br>
<br>
<a href="#-close">close</a>('all') closes all the figure windows</tt></dd></dl>
<dl><dt><a name="-cohere"><strong>cohere</strong></a>(x, y, NFFT<font color="#909090">=256</font>, Fs<font color="#909090">=2</font>, detrend<font color="#909090">=<function detrend_none></font>, window<font color="#909090">=<function window_hanning></font>, noverlap<font color="#909090">=0</font>)</dt><dd><tt>Compute the coherence between x and y. Coherence is the<br>
normalized cross spectral density<br>
<br>
Cxy = |Pxy|^2/(Pxx*Pyy)<br>
<br>
The return value is (Cxy, f), where f are the frequencies of the<br>
coherence vector. See the docs for psd and csd for information<br>
about the function arguments NFFT, detrend, windowm noverlap, as<br>
well as the methods used to compute Pxy, Pxx and Pyy.<br>
<br>
Returns the tuple Cxy, freqs<br>
<br>
Refs:<br>
Bendat & Piersol -- Random Data: Analysis and Measurement<br>
Procedures, John Wiley & Sons (1986)</tt></dd></dl>
<dl><dt><a name="-colors"><strong>colors</strong></a>()</dt><dd><tt>This is a do nothing function to provide you with help on how<br>
matplotlib handles colors.<br>
<br>
Commands which take color arguments can use several formats to<br>
specify the colors. For the basic builtin colors, you can use a<br>
single letter<br>
<br>
b : blue<br>
g : green<br>
r : red<br>
c : cyan<br>
m : magenta<br>
y : yellow<br>
k : black <br>
w : white<br>
<br>
<br>
For a greater range of colors, you have two options. You can<br>
specify the color using an html hex string, as in<br>
<br>
color = '#eeefff'<br>
<br>
or you can pass an R,G,B tuple, where each of R,G,B are in the<br>
range [0,1]. The example below creates a subplot with a dark<br>
slate gray background<br>
<br>
<a href="#-subplot">subplot</a>(111, axisbg=(0.1843, 0.3098, 0.3098))<br>
<br>
Here is an example that creates a pale turqoise title<br>
<br>
<a href="#-title">title</a>('Is this the best color?', color='#afeeee')</tt></dd></dl>
<dl><dt><a name="-csd"><strong>csd</strong></a>(x, y, NFFT<font color="#909090">=256</font>, Fs<font color="#909090">=2</font>, detrend<font color="#909090">=<function detrend_none></font>, window<font color="#909090">=<function window_hanning></font>, noverlap<font color="#909090">=0</font>)</dt><dd><tt>The cross spectral density Pxy by Welches average periodogram<br>
method. The vectors x and y are divided into NFFT length<br>
segments. Each segment is detrended by function detrend and<br>
windowed by function window. noverlap gives the length of the<br>
overlap between segments. The product of the direct FFTs of x and<br>
y are averaged over each segment to compute Pxy, with a scaling to<br>
correct for power loss due to windowing. Fs is the sampling<br>
frequency.<br>
<br>
NFFT must be a power of 2<br>
<br>
detrend and window are functions, unlike in matlab where they are<br>
vectors. For detrending you can use detrend_none, detrend_mean,<br>
detrend_linear or a custom function. For windowing, you can use<br>
window_none, window_hanning, or a custom function<br>
<br>
Returns the tuple Pxy, freqs. Pxy is the cross spectrum (complex<br>
valued), and 10*log10(|Pxy|) is plotted<br>
<br>
Refs:<br>
Bendat & Piersol -- Random Data: Analysis and Measurement<br>
Procedures, John Wiley & Sons (1986)</tt></dd></dl>
<dl><dt><a name="-cumproduct"><strong>cumproduct</strong></a> = accumulate(...)</dt><dd><tt>accumulate performs the operation along the dimension, accumulating subtotals</tt></dd></dl>
<dl><dt><a name="-errorbar"><strong>errorbar</strong></a>(x, y, yerr<font color="#909090">=None</font>, xerr<font color="#909090">=None</font>, fmt<font color="#909090">='b-'</font>, capsize<font color="#909090">=3</font>)</dt><dd><tt>Plot x versus y with error deltas in yerr and xerr.<br>
Vertical errorbars are plotted if yerr is not None<br>
Horizontal errorbars are plotted if xerr is not None<br>
<br>
xerr and yerr may be any of:<br>
a rank-0, Nx1 Numpy array - symmetric errorbars +/- value<br>
an N-element list or tuple - symmetric errorbars +/- value<br>
a rank-1, Nx2 Numpy array - asymmetric errorbars -column1/+column2<br>
<br>
fmt is the plot format symbol for y<br>
<br>
Return value is a length 2 tuple. The first element is a list of<br>
y symbol lines. The second element is a list of error bar lines.<br>
<br>
capsize is the size of the error bar caps in points</tt></dd></dl>
<dl><dt><a name="-figlegend"><strong>figlegend</strong></a>(handles, labels, loc)</dt><dd><tt>Place a legend in the figure. Labels are a sequence of<br>
strings, handles is a sequence of line or patch instances, and<br>
loc can be a string or an integer specifying the legend<br>
location<br>
<br>
USAGE: <br>
<a href="#-legend">legend</a>( (line1, line2, line3),<br>
('label1', 'label2', 'label3'),<br>
'upper right')<br>
<br>
See help(legend) for information about the location codes</tt></dd></dl>
<dl><dt><a name="-figure"><strong>figure</strong></a>(num<font color="#909090">=1</font>, figsize<font color="#909090">=[8.0, 6.0]</font>, dpi<font color="#909090">=80.0</font>, facecolor<font color="#909090">=0.75</font>, edgecolor<font color="#909090">='w'</font>)</dt><dd><tt><a href="#-figure">figure</a>(num = 1, figsize=(8, 6), dpi=80, facecolor='w', edgecolor='k')<br>
<br>
<br>
Create a new figure and return a handle to it<br>
<br>
If <a href="#-figure">figure</a>(num) already exists, make it active and return the<br>
handle to it.<br>
<br>
<a href="#-figure">figure</a>(1)<br>
<br>
figsize - width in height x inches<br>
dpi - resolution<br>
facecolor - the background color <br>
edgecolor - the border color<br>
<br>
rcParams gives the default values from the .matplotlibrc file</tt></dd></dl>
<dl><dt><a name="-fill"><strong>fill</strong></a>(*args, **kwargs)</dt><dd><tt>plot filled polygons. *args is a variable length argument,<br>
allowing for multiple x,y pairs with an optional color format<br>
string. For example, all of the following are legal, assuming a<br>
is the Axis instance:<br>
<br>
<a href="#-fill">fill</a>(x,y) # plot polygon with vertices at x,y<br>
<a href="#-fill">fill</a>(x,y, 'b' ) # plot polygon with vertices at x,y in blue<br>
<br>
An arbitrary number of x, y, color groups can be specified, as in <br>
<a href="#-fill">fill</a>(x1, y1, 'g', x2, y2, 'r') <br>
<br>
Return value is a list of patches that were added<br>
<br>
The following color strings are supported<br>
<br>
b : blue<br>
g : green<br>
r : red<br>
c : cyan<br>
m : magenta<br>
y : yellow<br>
k : black <br>
w : white<br>
<br>
The kwargs that are can be used to set line properties (any<br>
property that has a set_* method). You can use this to set edge<br>
color, face color, etc.<br>
<br>
Example code:<br>
<br>
from matplotlib.matlab import *<br>
t = arange(0.0, 1.01, 0.01)<br>
s = sin(2*2*pi*t)<br>
<br>
<a href="#-fill">fill</a>(t, s, 'r')<br>
<a href="#-grid">grid</a>(True)<br>
show()</tt></dd></dl>
<dl><dt><a name="-gca"><strong>gca</strong></a>()</dt><dd><tt>Return the current axis instance. This can be used to control<br>
axis properties either using set or the Axes methods.<br>
<br>
Example:<br>
<br>
<a href="#-plot">plot</a>(t,s)<br>
<a href="#-set">set</a>(<a href="#-gca">gca</a>(), 'xlim', [0,10]) # set the x axis limits<br>
<br>
or<br>
<br>
<a href="#-plot">plot</a>(t,s)<br>
a = <a href="#-gca">gca</a>()<br>
a.set_xlim([0,10]) # does the same</tt></dd></dl>
<dl><dt><a name="-gcf"><strong>gcf</strong></a>()</dt><dd><tt>Return a handle to the current figure</tt></dd></dl>
<dl><dt><a name="-get"><strong>get</strong></a>(o, s)</dt><dd><tt>Return the value of handle property s<br>
<br>
h is an instance of a class, eg a Line2D or an Axes or Text.<br>
if s is 'somename', this function returns<br>
<br>
o.get_somename()</tt></dd></dl>
<dl><dt><a name="-get_current_fig_manager"><strong>get_current_fig_manager</strong></a>()</dt></dl>
<dl><dt><a name="-get_plot_commands"><strong>get_plot_commands</strong></a>()</dt></dl>
<dl><dt><a name="-grid"><strong>grid</strong></a>(b)</dt><dd><tt>Set the figure grid to be on or off (b is a boolean)</tt></dd></dl>
<dl><dt><a name="-hist"><strong>hist</strong></a>(x, bins<font color="#909090">=10</font>, noplot<font color="#909090">=0</font>, normed<font color="#909090">=0</font>)</dt><dd><tt>Compute the histogram of x. bins is either an integer number of<br>
bins or a sequence giving the bins. x are the data to be binned.<br>
<br>
if noplot is True, just compute the histogram and return the<br>
number of observations and the bins as an (n, bins) tuple.<br>
<br>
If noplot is False, compute the histogram and plot it, returning<br>
n, bins, patches<br>
<br>
If normed is true, the first element of the return tuple will be the<br>
counts normalized to form a probability distribtion, ie,<br>
n/(len(x)*dbin)</tt></dd></dl>
<dl><dt><a name="-hlines"><strong>hlines</strong></a>(*args, **kwargs)</dt><dd><tt>lines = <a href="#-hlines">hlines</a>(self, y, xmin, xmax, fmt='k-')<br>
<br>
plot horizontal lines at each y from xmin to xmax. xmin or<br>
xmax can be scalars or len(x) numpy arrays. If they are<br>
scalars, then the respective values are constant, else the<br>
widths of the lines are determined by xmin and xmax<br>
<br>
Returns a list of line instances that were added</tt></dd></dl>
<dl><dt><a name="-imshow"><strong>imshow</strong></a>(*args, **kwargs)</dt><dd><tt>Display the image in array X to current axes. X must be a<br>
float array<br>
<br>
Usage: <br>
<br>
IMSHOW(X)<br>
<br>
If X is MxN, assume luminance (grayscale)<br>
If X is MxNx3, assume RGB<br>
If X is MxNx4, assume RGBA<br>
<br>
IMSHOW(X, cmap)<br>
cmap is a colors.Colormap instance used to make a pseudo-color plot<br>
<br>
An Image instance is returned</tt></dd></dl>
<dl><dt><a name="-legend"><strong>legend</strong></a>(*args, **kwargs)</dt><dd><tt>Place a legend on the current axes at location loc. Labels are a<br>
sequence of strings and loc can be a string or an integer<br>
specifying the legend location<br>
<br>
USAGE: <br>
<br>
Make a legend with existing lines<br>
<a href="#-legend">legend</a>( LABELS )<br>
>>> <a href="#-legend">legend</a>( ('label1', 'label2', 'label3') ) <br>
<br>
Make a legend for Line2D instances lines1, line2, line3<br>
<a href="#-legend">legend</a>( LINES, LABELS )<br>
>>> <a href="#-legend">legend</a>( (line1, line2, line3), ('label1', 'label2', 'label3') )<br>
<br>
Make a legend at LOC<br>
<a href="#-legend">legend</a>( LABELS, LOC ) or<br>
<a href="#-legend">legend</a>( LINES, LABELS, LOC )<br>
>>> <a href="#-legend">legend</a>( ('label1', 'label2', 'label3'), loc='upper left')<br>
>>> <a href="#-legend">legend</a>( (line1, line2, line3),<br>
('label1', 'label2', 'label3'),<br>
loc=2)<br>
<br>
The LOC location codes are<br>
<br>
The LOC location codes are<br>
<br>
'best' : 0, (currently not supported, defaults to upper right)<br>
'upper right' : 1, (default)<br>
'upper left' : 2,<br>
'lower left' : 3,<br>
'lower right' : 4,<br>
'right' : 5,<br>
'center left' : 6,<br>
'center right' : 7,<br>
'lower center' : 8,<br>
'upper center' : 9,<br>
'center' : 10,<br>
<br>
If none of these are suitable, loc can be a 2-tuple giving x,y<br>
in axes coords, ie,<br>
<br>
loc = 0, 1 is left top<br>
loc = 0.5, 0.5 is center, center<br>
<br>
and so on<br>
<br>
<br>
The legend instance is returned</tt></dd></dl>
<dl><dt><a name="-load"><strong>load</strong></a>(fname)</dt><dd><tt>Load ASCII data from fname into an array and return the array.<br>
<br>
The data must be regular, same number of values in every row<br>
<br>
fname can be a filename or a file handle<br>
<br>
matfile data is not currently supported, but see<br>
Nigel Wade's matfile <a href="ftp://ion.le.ac.uk/matfile/matfile.tar.gz">ftp://ion.le.ac.uk/matfile/matfile.tar.gz</a><br>
<br>
Example usage:<br>
<br>
x,y = <a href="#-load">load</a>('test.dat') # data in two columns<br>
<br>
X = <a href="#-load">load</a>('test.dat') # a matrix of data<br>
<br>
x = <a href="#-load">load</a>('test.dat') # a single column of data</tt></dd></dl>
<dl><dt><a name="-loglog"><strong>loglog</strong></a>(*args, **kwargs)</dt><dd><tt>Make a loglog plot with log scaling on the a and y axis. The args<br>
to semilog x are the same as the args to plot. See help plot for<br>
more info</tt></dd></dl>
<dl><dt><a name="-pcolor"><strong>pcolor</strong></a>(*args, **kwargs)</dt><dd><tt>pcolor_patch(C) - make a pseudocolor plot of matrix C<br>
<br>
<a href="#-pcolor">pcolor</a>(X, Y, C) - a pseudo color plot of C on the matrices X and Y <br>
<br>
Shading:<br>
<br>
The optional keyword arg shading ('flat' or 'faceted') will<br>
determine whether the black grid is drawn around each pcolor<br>
square. Defaul 'faceteted'<br>
e.g., <br>
<a href="#-pcolor">pcolor</a>(C, shading='flat') <br>
<a href="#-pcolor">pcolor</a>(X, Y, C, shading='faceted')<br>
<br>
returns a list of patch objects.<br>
<br>
Note, the behavior of meshgrid in matlab is a bit<br>
counterintuitive for x and y arrays. For example,<br>
<br>
x = arange(7)<br>
y = arange(5)<br>
X, Y = meshgrid(x,y)<br>
<br>
Z = rand( len(x), len(y))<br>
<a href="#-pcolor">pcolor</a>(X, Y, Z)<br>
<br>
will fail in matlab and matplotlib. You will probably be<br>
happy with<br>
<br>
<a href="#-pcolor">pcolor</a>(X, Y, transpose(Z))<br>
<br>
Likewise, for nonsquare Z,<br>
<br>
<a href="#-pcolor">pcolor</a>(transpose(Z))<br>
<br>
will make the x and y axes in the plot agree with the numrows<br>
and numcols of Z</tt></dd></dl>
<dl><dt><a name="-plot"><strong>plot</strong></a>(*args, **kwargs)</dt><dd><tt>plot lines. *args is a variable length argument, allowing for<br>
multiple x, y pairs with an optional format string. For<br>
example, all of the following are legal<br>
<br>
<a href="#-plot">plot</a>(x,y) # plot Numeric arrays y vs x<br>
<a href="#-plot">plot</a>(x,y, 'bo') # plot Numeric arrays y vs x with blue circles<br>
<a href="#-plot">plot</a>(y) # plot y using x = arange(len(y))<br>
<a href="#-plot">plot</a>(y, 'r+') # ditto with red plusses<br>
<br>
An arbitrary number of x, y, fmt groups can be specified, as in <br>
<br>
<a href="#-plot">plot</a>(x1, y1, 'g^', x2, y2, 'l-') <br>
<br>
Return value is a list of lines that were added<br>
<br>
The following line styles are supported:<br>
<br>
- : solid line<br>
-- : dashed line<br>
-. : dash-dot line<br>
: : dotted line<br>
. : points<br>
, : pixels<br>
o : circle symbols<br>
^ : triangle up symbols<br>
v : triangle down symbols<br>
< : triangle left symbols<br>
> : triangle right symbols<br>
s : square symbols<br>
+ : plus symbols<br>
x : cross symbols<br>
d : diamond symbols<br>
<br>
The following color strings are supported<br>
<br>
b : blue<br>
g : green<br>
r : red<br>
c : cyan<br>
m : magenta<br>
y : yellow<br>
k : black <br>
w : white<br>
<br>
Line styles and colors are combined in a single format string<br>
<br>
The kwargs that are can be used to set line properties (any<br>
property that has a set_* method). You can use this to set a line<br>
label (for auto legends), linewidth, anitialising, marker face<br>
color, etc. Here is an example:<br>
<br>
<a href="#-plot">plot</a>([1,2,3], [1,2,3], 'go-', label='line 1', linewidth=2)<br>
<a href="#-plot">plot</a>([1,2,3], [1,4,9], 'rs', label='line 2')<br>
<a href="#-axis">axis</a>([0, 4, 0, 10])<br>
<a href="#-legend">legend</a>()<br>
<br>
If you make multiple lines with one plot command, the kwargs apply<br>
to all those lines, eg<br>
<br>
<a href="#-plot">plot</a>(x1, y1, x2, y2, antialising=False)<br>
<br>
Neither line will be antialiased.</tt></dd></dl>
<dl><dt><a name="-plot_date"><strong>plot_date</strong></a>(*args, **kwargs)</dt><dd><tt>PLOT_DATE(d, y, converter, fmt='bo', **kwargs)<br>
<br>
d is a sequence of dates; converter is a dates.DateConverter<br>
instance that converts your dates to seconds since the epoch for<br>
plotting. y are the y values at those dates. fmt is a plot<br>
format string. kwargs are passed on to plot. See plot for more<br>
information.</tt></dd></dl>
<dl><dt><a name="-plotting"><strong>plotting</strong></a>()</dt><dd><tt>Plotting commands<br>
axes - Create a new axes<br>
axis - Set or return the current axis limits<br>
bar - make a bar chart<br>
cla - clear current axes<br>
clf - clear a figure window<br>
close - close a figure window<br>
cohere - make a plot of coherence<br>
csd - make a plot of cross spectral density<br>
errorbar - make an errorbar graph<br>
figure - create or change active figure<br>
fill - make filled polygons<br>
gca - return the current axes<br>
gcf - return the current figure<br>
get - get a handle graphics property<br>
hist - make a histogram<br>
loglog - a log log plot<br>
imshow - plot image data<br>
pcolor - make a pseudocolor plot<br>
plot - make a line plot<br>
psd - make a plot of power spectral density<br>
savefig - save the current figure<br>
scatter - make a scatter plot<br>
set - set a handle graphics property<br>
semilogx - log x axis<br>
semilogy - log y axis<br>
show - show the figures<br>
specgram - a spectrogram plot<br>
stem - make a stem plot<br>
subplot - make a subplot (numrows, numcols, axesnum)<br>
text - add some text at location x,y to the current axes<br>
title - add a title to the current axes<br>
xlabel - add an xlabel to the current axes<br>
ylabel - add a ylabel to the current axes</tt></dd></dl>
<dl><dt><a name="-product"><strong>product</strong></a> = reduce(...)</dt><dd><tt>reduce performs the operation along the specified dimension, eliminating it. Returns scalars rather than rank-0 numarray.</tt></dd></dl>
<dl><dt><a name="-psd"><strong>psd</strong></a>(x, NFFT<font color="#909090">=256</font>, Fs<font color="#909090">=2</font>, detrend<font color="#909090">=<function detrend_none></font>, window<font color="#909090">=<function window_hanning></font>, noverlap<font color="#909090">=0</font>)</dt><dd><tt>The power spectral density by Welches average periodogram method.<br>
The vector x is divided into NFFT length segments. Each segment<br>
is detrended by function detrend and windowed by function window.<br>
noperlap gives the length of the overlap between segments. The<br>
absolute(fft(segment))**2 of each segment are averaged to compute Pxx,<br>
with a scaling to correct for power loss due to windowing. Fs is<br>
the sampling frequency.<br>
<br>
-- NFFT must be a power of 2<br>
<br>
-- detrend and window are functions, unlike in matlab where they<br>
are vectors. For detrending you can use detrend_none,<br>
detrend_mean, detrend_linear or a custom function. For<br>
windowing, you can use window_none, window_hanning, or a custom<br>
function<br>
<br>
-- if length x < NFFT, it will be zero padded to NFFT<br>
<br>
<br>
Returns the tuple Pxx, freqs<br>
<br>
For plotting, the power is plotted as 10*log10(pxx)) for decibels,<br>
though pxx itself is returned<br>
<br>
Refs:<br>
Bendat & Piersol -- Random Data: Analysis and Measurement<br>
Procedures, John Wiley & Sons (1986)</tt></dd></dl>
<dl><dt><a name="-raise_msg_to_str"><strong>raise_msg_to_str</strong></a>(msg)</dt><dd><tt>msg is a return arg from a raise. Join with new lines</tt></dd></dl>
<dl><dt><a name="-save"><strong>save</strong></a>(fname, X, fmt<font color="#909090">='%1.4f'</font>)</dt><dd><tt>Save the data in X to file fname using fmt string to convert the<br>
data to strings<br>
<br>
fname can be a filename or a file handle <br>
<br>
Example usage:<br>
<br>
<a href="#-save">save</a>('test.out', X) # X is an array<br>
<a href="#-save">save</a>('test1.out', (x,y,z)) # x,y,z equal sized 1D arrays<br>
<a href="#-save">save</a>('test2.out', x) # x is 1D<br>
<a href="#-save">save</a>('test3.out', x, fmt='%1.4e') # use exponential notation</tt></dd></dl>
<dl><dt><a name="-savefig"><strong>savefig</strong></a>(*args, **kwargs)</dt><dd><tt>def <a href="#-savefig">savefig</a>(fname, dpi=150, facecolor='w', edgecolor='w',<br>
orientation='portrait'):<br>
Save the current figure to filename fname. dpi is the resolution<br>
in dots per inch.<br>
<br>
Output file types currently supported are jpeg and png and will be<br>
deduced by the extension to fname<br>
<br>
facecolor and edgecolor are the colors os the figure rectangle<br>
<br>
orientation is either 'landscape' or 'portrait' - not supported on<br>
all backends; currently only on postscript output.</tt></dd></dl>
<dl><dt><a name="-scatter"><strong>scatter</strong></a>(*args, **kwargs)</dt><dd><tt><a href="#-scatter">scatter</a>(self, x, y, s=None, c='b'):<br>
<br>
Make a scatter plot of x versus y. s is a size (in data<br>
coords) and can be either a scalar or an array of the same<br>
length as x or y. c is a color and can be a single color<br>
format string or an length(x) array of intensities which will<br>
be mapped by the colormap jet. <br>
<br>
If size is None a default size will be used</tt></dd></dl>
<dl><dt><a name="-semilogx"><strong>semilogx</strong></a>(*args, **kwargs)</dt><dd><tt>Make a semilog plot with log scaling on the x axis. The args to<br>
semilog x are the same as the args to plot. See help plot for<br>
more info</tt></dd></dl>
<dl><dt><a name="-semilogy"><strong>semilogy</strong></a>(*args, **kwargs)</dt><dd><tt>Make a semilog plot with log scaling on the y axis. The args to<br>
semilog x are the same as the args to plot. See help plot for<br>
more info</tt></dd></dl>
<dl><dt><a name="-set"><strong>set</strong></a>(h, *args, **kwargs)</dt><dd><tt>Set handle h property in string s to value val<br>
<br>
h can be a handle or vector of handles.<br>
<br>
h is an instance (or vector of instances) of a class, eg a Line2D<br>
or an Axes or Text.<br>
<br>
args is a list of string, value pairs. if the string<br>
is 'somename', set function calls<br>
<br>
o.set_somename(value)<br>
<br>
for every instance in h.</tt></dd></dl>
<dl><dt><a name="-specgram"><strong>specgram</strong></a>(x, NFFT<font color="#909090">=256</font>, Fs<font color="#909090">=2</font>, detrend<font color="#909090">=<function detrend_none></font>, window<font color="#909090">=<function window_hanning></font>, noverlap<font color="#909090">=128</font>, cmap<font color="#909090">=<matplotlib.colors.ColormapJet instance></font>)</dt><dd><tt>Compute a spectrogram of data in x. Data are split into NFFT<br>
length segements and the PSD of each section is computed. The<br>
windowing function window is applied to each segment, and the<br>
amount of overlap of each segment is specified with noverlap<br>
<br>
cmap is a a matplotlib.colors.ColorMap<br>
<br>
See help(psd) for information on the other arguments<br>
<br>
return value is Pxx, freqs, bins, im<br>
<br>
bins are the time points the spectrogram is calculated over<br>
freqs is an array of frequencies<br>
Pxx is a len(times) x len(freqs) array of power<br>
im is a matplotlib image</tt></dd></dl>
<dl><dt><a name="-stem"><strong>stem</strong></a>(*args, **kwargs)</dt><dd><tt><a href="#-stem">stem</a>(x, y, linefmt='b-', markerfmt='bo', basefmt='r-')<br>
<br>
A stem plot plots vertical lines (using linefmt) at each x<br>
location from the baseline to y, and places a marker there using<br>
markerfmt. A horizontal line at 0 is is plotted using basefmt<br>
<br>
return value is markerline, stemlines, baseline<br>
<br>
See<br>
<a href="http://www.mathworks.com/access/helpdesk/help/techdoc/ref/stem.html">http://www.mathworks.com/access/helpdesk/help/techdoc/ref/stem.html</a><br>
for details and examples/stem_plot.py for a demo.</tt></dd></dl>
<dl><dt><a name="-subplot"><strong>subplot</strong></a>(*args, **kwargs)</dt><dd><tt>Create a subplot command, creating axes with<br>
<br>
<a href="#-subplot">subplot</a>(numRows, numCols, plotNum)<br>
<br>
where plotNum=1 is the first plot number and increasing plotNums<br>
fill rows first. max(plotNum)==numRows*numCols<br>
<br>
You can leave out the commas if numRows<=numCols<=plotNum<10, as<br>
in<br>
<br>
<a href="#-subplot">subplot</a>(211) # 2 rows, 1 column, first (upper) plot<br>
<br>
<a href="#-subplot">subplot</a>(111) is the default axis<br>
<br>
The background color of the subplot can be specified via keyword<br>
argument 'axisbg', which takes a color string or gdk.Color as value, as in<br>
<br>
<a href="#-subplot">subplot</a>(211, axisbg='y')</tt></dd></dl>
<dl><dt><a name="-sum"><strong>sum</strong></a> = reduce(...)</dt><dd><tt>reduce performs the operation along the specified dimension, eliminating it. Returns scalars rather than rank-0 numarray.</tt></dd></dl>
<dl><dt><a name="-table"><strong>table</strong></a>(*args, **kwargs)</dt><dd><tt><a href="#-table">table</a>(cellText=None, cellColours=None,<br>
cellLoc='right', colWidths=None,<br>
rowLabels=None, rowColours=None, rowLoc='left',<br>
colLabels=None, colColours=None, colLoc='center',<br>
loc='bottom', bbox=None):<br>
<br>
Add a table to the current axes. Returns a table instance. For<br>
finer grained control over tables, use the Table class and add it<br>
to the axes with add_table.<br>
<br>
Thanks to John Gill for providing the class and table.</tt></dd></dl>
<dl><dt><a name="-text"><strong>text</strong></a>(x, y, label, fontdict<font color="#909090">=None</font>, **kwargs)</dt><dd><tt>Add text to axis at location x,y<br>
<br>
fontdict is a dictionary to override the default text properties.<br>
If fontdict is None, the default is<br>
<br>
'fontsize' : 'x-small',<br>
'verticalalignment' : 'bottom',<br>
'horizontalalignment' : 'left'<br>
<br>
**kwargs can in turn be used to override the fontdict, as in<br>
<br>
a.<a href="#-text">text</a>(x,y,label, fontsize='medium')<br>
<br>
This command supplies no override dict, and so will have<br>
'verticalalignment'='bottom' and 'horizontalalignment'='left' but<br>
the keyword arg 'fontsize' will create a fontsize of medium or 12<br>
<br>
The purpose these options is to make it easy for you to create a<br>
default font theme for your plots by creating a single dictionary,<br>
and then being able to selective change individual attributes for<br>
the varous text creation commands, as in<br>
<br>
fonts = {<br>
'color' : 'k',<br>
'fontname' : 'Courier',<br>
'fontweight' : 'bold'<br>
}<br>
<br>
<a href="#-title">title</a>('My title', fonts, fontsize='medium')<br>
<a href="#-xlabel">xlabel</a>('My xlabel', fonts, fontsize='small')<br>
<a href="#-ylabel">ylabel</a>('My ylabel', fonts, fontsize='small')<br>
<a href="#-text">text</a>(12, 20, 'some text', fonts, fontsize='x-small')<br>
<br>
The Text defaults are<br>
<br>
'color' : 'k',<br>
'fontname' : 'Sans',<br>
'fontsize' : 'small',<br>
'fontweight' : 'bold',<br>
'fontangle' : 'normal',<br>
'horizontalalignment' : 'left'<br>
'rotation' : 'horizontal',<br>
'verticalalignment' : 'bottom',<br>
'transx' : <a href="#-gca">gca</a>().xaxis.transData,<br>
'transy' : <a href="#-gca">gca</a>().yaxis.transData, <br>
<br>
transx and transy specify that text is in data coords,<br>
alternatively, you can specify text in axis coords (0,0 lower<br>
left and 1,1 upper right). The example below places text in<br>
the center of the axes<br>
<br>
ax = <a href="#-subplot">subplot</a>(111)<br>
<a href="#-text">text</a>(0.5, 0.5,'matplotlib', <br>
horizontalalignment='center',<br>
verticalalignment='center',<br>
transx = ax.xaxis.transAxis,<br>
transy = ax.yaxis.transAxis,<br>
)</tt></dd></dl>
<dl><dt><a name="-title"><strong>title</strong></a>(s, *args, **kwargs)</dt><dd><tt>Set the title of the current axis to s<br>
<br>
Default font override is:<br>
override = {<br>
'fontsize' : 'medium',<br>
'verticalalignment' : 'bottom',<br>
'horizontalalignment' : 'center'<br>
}<br>
<br>
See the text docstring for information of how override and the<br>
optional args work</tt></dd></dl>
<dl><dt><a name="-vlines"><strong>vlines</strong></a>(*args, **kwargs)</dt><dd><tt>lines = <a href="#-vlines">vlines</a>(x, ymin, ymax, color='k'):<br>
<br>
Plot vertical lines at each x from ymin to ymax. ymin or ymax<br>
can be scalars or len(x) numpy arrays. If they are scalars,<br>
then the respective values are constant, else the heights of<br>
the lines are determined by ymin and ymax<br>
<br>
Returns a list of lines that were added</tt></dd></dl>
<dl><dt><a name="-xlabel"><strong>xlabel</strong></a>(s, *args, **kwargs)</dt><dd><tt>Set the x axis label of the current axis to s<br>
<br>
Default override is<br>
<br>
override = {<br>
'fontsize' : 'small',<br>
'verticalalignment' : 'top',<br>
'horizontalalignment' : 'center'<br>
}<br>
<br>
See the text docstring for information of how override and<br>
the optional args work</tt></dd></dl>
<dl><dt><a name="-ylabel"><strong>ylabel</strong></a>(s, *args, **kwargs)</dt><dd><tt>Set the y axis label of the current axis to s<br>
<br>
Defaults override is<br>
<br>
override = {<br>
'fontsize' : 'small',<br>
'verticalalignment' : 'center',<br>
'horizontalalignment' : 'right',<br>
'rotation'='vertical' : }<br>
<br>
See the text docstring for information of how override and the<br>
optional args work</tt></dd></dl>
</td></tr></table><p>
<table width="100%" cellspacing=0 cellpadding=2 border=0 summary="section">
<tr bgcolor="#55aa55">
<td colspan=3 valign=bottom> <br>
<font color="#ffffff" face="helvetica, arial"><big><strong>Data</strong></big></font></td></tr>
<tr><td bgcolor="#55aa55"><tt> </tt></td><td> </td>
<td width="100%"><strong>Any</strong> = Any<br>
<strong>Bool</strong> = Bool<br>
<strong>Byte</strong> = Int8<br>
<strong>CLIP</strong> = 0<br>
<strong>Complex</strong> = Complex64<br>
<strong>Complex32</strong> = Complex32<br>
<strong>Complex64</strong> = Complex64<br>
<strong>Error</strong> = <numarray.ufunc.NumError instance><br>
<strong>False</strong> = False<br>
<strong>Float</strong> = Float64<br>
<strong>Float32</strong> = Float32<br>
<strong>Float64</strong> = Float64<br>
<strong>Int</strong> = Int32<br>
<strong>Int16</strong> = Int16<br>
<strong>Int32</strong> = Int32<br>
<strong>Int64</strong> = Int64<br>
<strong>Int8</strong> = Int8<br>
<strong>Long</strong> = Int32<br>
<strong>MAX_ALIGN</strong> = 8<br>
<strong>MAX_INT_SIZE</strong> = 8<br>
<strong>MAX_LINE_WIDTH</strong> = None<br>
<strong>MaybeLong</strong> = Int32<br>
<strong>NewAxis</strong> = None<br>
<strong>Object</strong> = Object<br>
<strong>PRECISION</strong> = None<br>
<strong>Py2NumType</strong> = {<type 'float'>: Float64, <type 'int'>: Int32, <type 'long'>: Int32, <type 'bool'>: Bool, <type 'complex'>: Complex64}<br>
<strong>PyINT_TYPES</strong> = {<type 'int'>: 1, <type 'long'>: 1, <type 'bool'>: 1}<br>
<strong>PyLevel2Type</strong> = {-1: <type 'bool'>, 0: <type 'int'>, 1: <type 'long'>, 2: <type 'float'>, 3: <type 'complex'>}<br>
<strong>PyNUMERIC_TYPES</strong> = {<type 'float'>: 2, <type 'int'>: 0, <type 'long'>: 1, <type 'bool'>: -1, <type 'complex'>: 3}<br>
<strong>PyREAL_TYPES</strong> = {<type 'float'>: 1, <type 'int'>: 1, <type 'long'>: 1, <type 'bool'>: 1}<br>
<strong>RAISE</strong> = 2<br>
<strong>SUPPRESS_SMALL</strong> = None<br>
<strong>Short</strong> = Int16<br>
<strong>True</strong> = True<br>
<strong>UInt16</strong> = UInt16<br>
<strong>UInt32</strong> = UInt32<br>
<strong>UInt64</strong> = UInt64<br>
<strong>UInt8</strong> = UInt8<br>
<strong>WRAP</strong> = 1<br>
<strong>a</strong> = 'matplotlib.transforms'<br>
<strong>abs</strong> = <UFunc: 'abs'><br>
<strong>absolute</strong> = <UFunc: 'abs'><br>
<strong>add</strong> = <UFunc: 'add'><br>
<strong>arccos</strong> = <UFunc: 'arccos'><br>
<strong>arccosh</strong> = <UFunc: 'arccosh'><br>
<strong>arcsin</strong> = <UFunc: 'arcsin'><br>
<strong>arcsinh</strong> = <UFunc: 'arcsinh'><br>
<strong>arctan</strong> = <UFunc: 'arctan'><br>
<strong>arctan2</strong> = <UFunc: 'arctan2'><br>
<strong>arctanh</strong> = <UFunc: 'arctanh'><br>
<strong>bitwise_and</strong> = <UFunc: 'bitwise_and'><br>
<strong>bitwise_not</strong> = <UFunc: 'bitwise_not'><br>
<strong>bitwise_or</strong> = <UFunc: 'bitwise_or'><br>
<strong>bitwise_xor</strong> = <UFunc: 'bitwise_xor'><br>
<strong>ceil</strong> = <UFunc: 'ceil'><br>
<strong>cos</strong> = <UFunc: 'cos'><br>
<strong>cosh</strong> = <UFunc: 'cosh'><br>
<strong>divide</strong> = <UFunc: 'divide'><br>
<strong>division</strong> = _Feature((2, 2, 0, 'alpha', 2), (3, 0, 0, 'alpha', 0), 8192)<br>
<strong>e</strong> = 2.7182818284590451<br>
<strong>equal</strong> = <UFunc: 'equal'><br>
<strong>exp</strong> = <UFunc: 'exp'><br>
<strong>fabs</strong> = <UFunc: 'fabs'><br>
<strong>floor</strong> = <UFunc: 'floor'><br>
<strong>fmod</strong> = <UFunc: 'remainder'><br>
<strong>genericCoercions</strong> = {('Bool', 'Bool'): 'Bool', ('Bool', 'Complex32'): 'Complex32', ('Bool', 'Complex64'): 'Complex64', ('Bool', 'Float32'): 'Float32', ('Bool', 'Float64'): 'Float64', ('Bool', 'Int16'): 'Int16', ('Bool', 'Int32'): 'Int32', ('Bool', 'Int64'): 'Int64', ('Bool', 'Int8'): 'Int8', ('Bool', 'Object'): 'Object', ...}<br>
<strong>genericPromotionExclusions</strong> = {'Bool': (), 'Complex32': (), 'Complex64': (), 'Float32': (), 'Float64': (11,), 'Int16': (), 'Int32': (), 'Int64': (9,), 'Int8': (), 'UInt16': (), ...}<br>
<strong>genericTypeRank</strong> = ['Bool', 'Int8', 'UInt8', 'Int16', 'UInt16', 'Int32', 'UInt32', 'Int64', 'UInt64', 'Float32', 'Float64', 'Complex32', 'Complex64', 'Object']<br>
<strong>greater</strong> = <UFunc: 'greater'><br>
<strong>greater_equal</strong> = <UFunc: 'greater_equal'><br>
<strong>hypot</strong> = <UFunc: 'hypot'><br>
<strong>ieeemask</strong> = <UFunc: 'ieeemask'><br>
<strong>isBigEndian</strong> = False<br>
<strong>less</strong> = <UFunc: 'less'><br>
<strong>less_equal</strong> = <UFunc: 'less_equal'><br>
<strong>log</strong> = <UFunc: 'log'><br>
<strong>log10</strong> = <UFunc: 'log10'><br>
<strong>logical_and</strong> = <UFunc: 'logical_and'><br>
<strong>logical_not</strong> = <UFunc: 'logical_not'><br>
<strong>logical_or</strong> = <UFunc: 'logical_or'><br>
<strong>logical_xor</strong> = <UFunc: 'logical_xor'><br>
<strong>lshift</strong> = <UFunc: 'lshift'><br>
<strong>maximum</strong> = <UFunc: 'maximum'><br>
<strong>minimum</strong> = <UFunc: 'minimum'><br>
<strong>minus</strong> = <UFunc: 'minus'><br>
<strong>multiply</strong> = <UFunc: 'multiply'><br>
<strong>negative</strong> = <UFunc: 'minus'><br>
<strong>not_equal</strong> = <UFunc: 'not_equal'><br>
<strong>numarray_nonzero</strong> = <numarray.ufunc._NonzeroUFunc instance><br>
<strong>pi</strong> = 3.1415926535897931<br>
<strong>power</strong> = <UFunc: 'power'><br>
<strong>pythonTypeMap</strong> = {<type 'float'>: ('Float64', 'float'), <type 'int'>: ('Int32', 'int'), <type 'long'>: ('Int32', 'int'), <type 'complex'>: ('Complex64', 'complex')}<br>
<strong>pythonTypeRank</strong> = [<type 'int'>, <type 'long'>, <type 'float'>, <type 'complex'>]<br>
<strong>rcParams</strong> = {'axes.edgecolor': 'k', 'axes.facecolor': 'w', 'axes.grid': False, 'axes.labelcolor': 'k', 'axes.labelsize': 12.0, 'axes.linewidth': 0.5, 'axes.titlesize': 14.0, 'backend': 'GTKAgg', 'datapath': '/usr/local/share/matplotlib', 'figure.dpi': 80.0, ...}<br>
<strong>readme</strong> = '<font color="#c040c0">\n</font>MLab2.py, release 1<font color="#c040c0">\n\n</font>Created on February 2003 b...<font color="#c040c0">\n</font>Look at: http://pdilib.sf.net for new releases.<font color="#c040c0">\n</font>'<br>
<strong>remainder</strong> = <UFunc: 'remainder'><br>
<strong>rshift</strong> = <UFunc: 'rshift'><br>
<strong>scalarTypeMap</strong> = {<type 'float'>: 'Float64', <type 'int'>: 'Int32', <type 'long'>: 'Int32', <type 'complex'>: 'Complex64'}<br>
<strong>sin</strong> = <UFunc: 'sin'><br>
<strong>sinh</strong> = <UFunc: 'sinh'><br>
<strong>sqrt</strong> = <UFunc: 'sqrt'><br>
<strong>subtract</strong> = <UFunc: 'subtract'><br>
<strong>tan</strong> = <UFunc: 'tan'><br>
<strong>tanh</strong> = <UFunc: 'tanh'><br>
<strong>typeDict</strong> = {'1': Int8, 'Any': Any, 'Bool': Bool, 'Byte': Int8, 'Complex': Complex64, 'Complex32': Complex32, 'Complex64': Complex64, 'D': Complex64, 'F': Complex32, 'Float': Float64, ...}<br>
<strong>typecode</strong> = {Bool: '1', Int8: '1', UInt8: 'b', Int16: 's', UInt16: 'w', Int32: 'l', Int64: 'N', Float32: 'f', Float64: 'd', Complex32: 'F', ...}<br>
<strong>typecodes</strong> = {'Character': 'c', 'Complex': 'FD', 'Float': 'fd', 'Integer': '1silN', 'UnsignedInteger': 'bwu'}<br>
<strong>which</strong> = ('numarray', 'rc')</td></tr></table>
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