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<font color="#ffffff" face="helvetica, arial"> <br><big><big><strong><a href="matplotlib.html"><font color="#ffffff">matplotlib</font></a>.numerix</strong></big></big> (version 1.2.0.dev5564)</font></td
><td align=right valign=bottom
><font color="#ffffff" face="helvetica, arial"><a href=".">index</a><br><a href="file:/home/jdhunter/dev/lib64/python2.5/site-packages/matplotlib/numerix/__init__.py">/home/jdhunter/dev/lib64/python2.5/site-packages/matplotlib/numerix/__init__.py</a></font></td></tr></table>
<p><tt>numerix imports either Numeric or numarray based on various selectors.<br>
<br>
0. If the value "--numpy","--numarray" or "--Numeric" is specified on the<br>
command line, then numerix imports the specified<br>
array package.<br>
<br>
1. The value of numerix in matplotlibrc: either Numeric or numarray<br>
<br>
2. If none of the above is done, the default array package is Numeric.<br>
Because the matplotlibrc always provides *some* value for numerix<br>
(it has it's own system of default values), this default is most<br>
likely never used.<br>
<br>
To summarize: the commandline is examined first, the rc file second,<br>
and the default array package is Numeric.</tt></p>
<p>
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<font color="#ffffff" face="helvetica, arial"><big><strong>Package Contents</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="matplotlib.numerix._na_imports.html">_na_imports</a><br>
<a href="matplotlib.numerix._nc_imports.html">_nc_imports</a><br>
<a href="matplotlib.numerix._sp_imports.html">_sp_imports</a><br>
</td><td width="25%" valign=top><a href="matplotlib.numerix.fft.html"><strong>fft</strong> (package)</a><br>
<a href="matplotlib.numerix.linear_algebra.html"><strong>linear_algebra</strong> (package)</a><br>
<a href="matplotlib.numerix.ma.html"><strong>ma</strong> (package)</a><br>
</td><td width="25%" valign=top><a href="matplotlib.numerix.mlab.html"><strong>mlab</strong> (package)</a><br>
<a href="matplotlib.numerix.npyma.html"><strong>npyma</strong> (package)</a><br>
<a href="matplotlib.numerix.random_array.html"><strong>random_array</strong> (package)</a><br>
</td><td width="25%" valign=top></td></tr></table></td></tr></table><p>
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<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="-byteswapped"><strong>byteswapped</strong></a>(a)</dt></dl>
<dl><dt><a name="-concatenate"><strong>concatenate</strong></a>(...)</dt><dd><tt><a href="#-concatenate">concatenate</a>((a1, a2, ...), axis=0)<br>
<br>
Join arrays together.<br>
<br>
The tuple of sequences (a1, a2, ...) are joined along the given axis<br>
(default is the first one) into a single numpy array.<br>
<br>
Examples<br>
--------<br>
>>> np.<a href="#-concatenate">concatenate</a>( ([0,1,2], [5,6,7]) )<br>
array([0, 1, 2, 5, 6, 7])</tt></dd></dl>
<dl><dt><a name="-dot"><strong>dot</strong></a>(...)</dt></dl>
<dl><dt><a name="-dump"><strong>dump</strong></a>(...)</dt><dd><tt><a href="#-dump">dump</a>(obj, file, protocol=0) -- Write an object in pickle format to the given file.<br>
<br>
See the Pickler docstring for the meaning of optional argument proto.</tt></dd></dl>
<dl><dt><a name="-dumps"><strong>dumps</strong></a>(...)</dt><dd><tt><a href="#-dumps">dumps</a>(obj, protocol=0) -- Return a string containing an object in pickle format.<br>
<br>
See the Pickler docstring for the meaning of optional argument proto.</tt></dd></dl>
<dl><dt><a name="-innerproduct"><strong>innerproduct</strong></a> = inner(...)</dt><dd><tt>inner(a,b)<br>
<br>
Returns the dot product of two arrays, which has shape a.shape[:-1] +<br>
b.shape[:-1] with elements computed by the product of the elements<br>
from the last dimensions of a and b.</tt></dd></dl>
<dl><dt><a name="-iscontiguous"><strong>iscontiguous</strong></a>(a)</dt></dl>
<dl><dt><a name="-itemsize"><strong>itemsize</strong></a>(a)</dt></dl>
<dl><dt><a name="-matrixmultiply"><strong>matrixmultiply</strong></a> = dot(...)</dt></dl>
<dl><dt><a name="-putmask"><strong>putmask</strong></a>(...)</dt><dd><tt><a href="#-putmask">putmask</a>(a, mask, values)<br>
<br>
Sets a.flat[n] = values[n] for each n where mask.flat[n] is true.<br>
<br>
If values is not the same size as `a` and `mask` then it will repeat.<br>
This gives behavior different from a[mask] = values.<br>
<br>
Parameters<br>
----------<br>
a : {array_like}<br>
Array to put data into<br>
mask : {array_like}<br>
Boolean mask array<br>
values : {array_like}<br>
Values to put</tt></dd></dl>
<dl><dt><a name="-rand"><strong>rand</strong></a>(...)</dt><dd><tt>Return an array of the given dimensions which is initialized to<br>
random numbers from a uniform distribution in the range [0,1).<br>
<br>
<a href="#-rand">rand</a>(d0, d1, ..., dn) -> random values<br>
<br>
Note: This is a convenience function. If you want an<br>
interface that takes a tuple as the first argument<br>
use numpy.random.random_sample(shape_tuple).</tt></dd></dl>
<dl><dt><a name="-randn"><strong>randn</strong></a>(...)</dt><dd><tt>Returns zero-mean, unit-variance Gaussian random numbers in an<br>
array of shape (d0, d1, ..., dn).<br>
<br>
<a href="#-randn">randn</a>(d0, d1, ..., dn) -> random values<br>
<br>
Note: This is a convenience function. If you want an<br>
interface that takes a tuple as the first argument<br>
use numpy.random.standard_normal(shape_tuple).</tt></dd></dl>
<dl><dt><a name="-typecode"><strong>typecode</strong></a>(a)</dt></dl>
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<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>Character</strong> = 'c'<br>
<strong>Complex</strong> = 'D'<br>
<strong>Complex0</strong> = 'F'<br>
<strong>Complex16</strong> = 'F'<br>
<strong>Complex32</strong> = 'F'<br>
<strong>Complex64</strong> = 'D'<br>
<strong>Complex8</strong> = 'F'<br>
<strong>Float</strong> = 'd'<br>
<strong>Float0</strong> = 'f'<br>
<strong>Float16</strong> = 'f'<br>
<strong>Float32</strong> = 'f'<br>
<strong>Float64</strong> = 'd'<br>
<strong>Float8</strong> = 'f'<br>
<strong>Infinity</strong> = inf<br>
<strong>Int</strong> = 'l'<br>
<strong>Int0</strong> = 'b'<br>
<strong>Int16</strong> = 'h'<br>
<strong>Int32</strong> = 'i'<br>
<strong>Int64</strong> = 'l'<br>
<strong>Int8</strong> = 'b'<br>
<strong>LittleEndian</strong> = True<br>
<strong>NewAxis</strong> = None<br>
<strong>PyObject</strong> = 'O'<br>
<strong>UInt</strong> = 'u'<br>
<strong>UInt16</strong> = 'H'<br>
<strong>UInt32</strong> = 'I'<br>
<strong>UInt64</strong> = 'L'<br>
<strong>UInt8</strong> = 'B'<br>
<strong>UnsignedInt</strong> = 'u'<br>
<strong>UnsignedInt16</strong> = 'H'<br>
<strong>UnsignedInt32</strong> = 'I'<br>
<strong>UnsignedInt64</strong> = 'L'<br>
<strong>UnsignedInt8</strong> = 'B'<br>
<strong>UnsignedInteger</strong> = 'u'<br>
<strong>__version__</strong> = '1.2.0.dev5564'<br>
<strong>absolute</strong> = <ufunc 'absolute'><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_or</strong> = <ufunc 'bitwise_or'><br>
<strong>bitwise_xor</strong> = <ufunc 'bitwise_xor'><br>
<strong>ceil</strong> = <ufunc 'ceil'><br>
<strong>conjugate</strong> = <ufunc 'conjugate'><br>
<strong>cos</strong> = <ufunc 'cos'><br>
<strong>cosh</strong> = <ufunc 'cosh'><br>
<strong>divide</strong> = <ufunc 'divide'><br>
<strong>divide_safe</strong> = <ufunc 'divide'><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>floor_divide</strong> = <ufunc 'floor_divide'><br>
<strong>fmod</strong> = <ufunc 'fmod'><br>
<strong>g</strong> = {'ArrayType': <type 'numpy.ndarray'>, 'Character': 'c', 'Complex': 'D', 'Complex0': 'F', 'Complex16': 'F', 'Complex32': 'F', 'Complex64': 'D', 'Complex8': 'F', 'DumpArray': <function DumpArray at 0x261c140>, 'Float': 'd', ...}<br>
<strong>greater</strong> = <ufunc 'greater'><br>
<strong>greater_equal</strong> = <ufunc 'greater_equal'><br>
<strong>hypot</strong> = <ufunc 'hypot'><br>
<strong>infinity</strong> = inf<br>
<strong>invert</strong> = <ufunc 'invert'><br>
<strong>isnan</strong> = <ufunc 'isnan'><br>
<strong>l</strong> = {'ArrayType': <type 'numpy.ndarray'>, 'Character': 'c', 'Complex': 'D', 'Complex0': 'F', 'Complex16': 'F', 'Complex32': 'F', 'Complex64': 'D', 'Complex8': 'F', 'DumpArray': <function DumpArray at 0x261c140>, 'Float': 'd', ...}<br>
<strong>left_shift</strong> = <ufunc 'left_shift'><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>maximum</strong> = <ufunc 'maximum'><br>
<strong>minimum</strong> = <ufunc 'minimum'><br>
<strong>multiply</strong> = <ufunc 'multiply'><br>
<strong>nan</strong> = nan<br>
<strong>negative</strong> = <ufunc 'negative'><br>
<strong>newaxis</strong> = None<br>
<strong>not_equal</strong> = <ufunc 'not_equal'><br>
<strong>nx</strong> = <matplotlib.numerix._sp_imports._TypeNamespace instance at 0x2630248><br>
<strong>pi</strong> = 3.1415926535897931<br>
<strong>power</strong> = <ufunc 'power'><br>
<strong>rcParams</strong> = {'figure.subplot.right': 0.90000000000000002, 'm...persize': 'letter', 'svg.embed_char_paths': True}<br>
<strong>remainder</strong> = <ufunc 'remainder'><br>
<strong>right_shift</strong> = <ufunc 'right_shift'><br>
<strong>sign</strong> = <ufunc 'sign'><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>true_divide</strong> = <ufunc 'true_divide'><br>
<strong>typecodes</strong> = {'Character': 'c', 'Complex': 'FD', 'Float': 'fd', 'Integer': 'bhil', 'UnsignedInteger': 'BHIL'}<br>
<strong>use_maskedarray</strong> = False<br>
<strong>verbose</strong> = <matplotlib.Verbose instance at 0x25ee200><br>
<strong>version</strong> = 'numpy 1.2.0.dev5564'<br>
<strong>which</strong> = ('numpy', 'rc')</td></tr></table>
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