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\Kc @ s d d k Z d d k Z d d k Z d d k Z d d k l Z l Z l Z d d k l Z l
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issubdtype( t productt ndarrayR t get_numpy_includeR R t deprecatet deprecate_with_doct get_numarray_includet get_includet infot sourcet whot lookfort byte_boundst may_share_memoryt safe_evalc C s~ d d k } | i d j o+ t i i t i i | i d d } n4 d d k i } t i i t i i | i d } | S( s
Return the directory that contains the NumPy \*.h header files.
Extension modules that need to compile against NumPy should use this
function to locate the appropriate include directory.
Notes
-----
When using ``distutils``, for example in ``setup.py``.
::
import numpy as np
...
Extension('extension_name', ...
include_dirs=[np.get_include()])
...
iNt coret include(
t numpyt show_configt Nonet ost patht joint dirnamet __file__t
numpy.coreR ( R t dR ( ( s5 /usr/lib64/python2.6/site-packages/numpy/lib/utils.pyR s +$c C sA d d k l } | } | d j o | d S| t g Sd S( s
Return the directory that contains the numarray \*.h header files.
Extension modules that need to compile against numarray should use this
function to locate the appropriate include directory.
Parameters
----------
type : any, optional
If `type` is not None, the location of the NumPy headers is returned
as well.
Returns
-------
dirs : str or list of str
If `type` is None, `dirs` is a string containing the path to the
numarray headers.
If `type` is not None, `dirs` is a list of strings with first the
path(s) to the numarray headers, followed by the path to the NumPy
headers.
Notes
-----
Useful when using ``distutils``, for example in ``setup.py``.
::
import numpy as np
...
Extension('extension_name', ...
include_dirs=[np.get_numarray_include()])
...
i( t get_numarray_include_dirsi N( t numpy.numarrayR R R ( t typeR t include_dirs( ( s5 /usr/lib64/python2.6/site-packages/numpy/lib/utils.pyR , s
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i i c C s+ t i | i | i | | i | i } | S( N( t newt functiont func_codet func_globalst
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Decorator class to deprecate old functions.
Refer to `deprecate` for details.
See Also
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deprecate
c C s | | _ | | _ | | _ d S( N( t old_namet new_namet message( t selfR, R- R. ( ( s5 /usr/lib64/python2.6/site-packages/numpy/lib/utils.pyt __init__m s c
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Decorator call. Refer to ``decorate``.
iNs `%s` is deprecated!s% `%s` is deprecated, use `%s` instead!s
c s i t | | S( s1 `arrayrange` is deprecated, use `arange` instead!( t warnt DeprecationWarning( t argst kwds( t depdoct warningsR' ( s5 /usr/lib64/python2.6/site-packages/numpy/lib/utils.pyt newfunc s s
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c O s | op | d } | d } d | j o | i d | d >> olduint = np.deprecate(np.uint)
>>> olduint(6)
/usr/lib/python2.5/site-packages/numpy/lib/utils.py:114:
DeprecationWarning: uint32 is deprecated
warnings.warn(str1, DeprecationWarning)
6
i i t newnameR- t oldnameR, N( t popR+ ( R3 R= t fn( ( s5 /usr/lib64/python2.6/site-packages/numpy/lib/utils.pyR s -
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Returns pointers to the end-points of an array.
Parameters
----------
a : ndarray
Input array. It must conform to the Python-side of the array interface.
Returns
-------
(low, high) : tuple of 2 integers
The first integer is the first byte of the array, the second integer is
just past the last byte of the array. If `a` is not contiguous it
will not use every byte between the (`low`, `high`) values.
Examples
--------
>>> I = np.eye(2, dtype='f'); I.dtype
dtype('float32')
>>> low, high = np.byte_bounds(I)
>>> high - low == I.size*I.itemsize
True
>>> I = np.eye(2, dtype='G'); I.dtype
dtype('complex192')
>>> low, high = np.byte_bounds(I)
>>> high - low == I.size*I.itemsize
True
t datai t stridest shapet typestri t dtypei N( t __array_interface__t lent intR R t zip( t at ait a_datat astridest ashapet nd_at bytes_at a_lowt a_highRI t stride( ( s5 /usr/lib64/python2.6/site-packages/numpy/lib/utils.pyR s
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Determine if two arrays can share memory
The memory-bounds of a and b are computed. If they overlap then
this function returns True. Otherwise, it returns False.
A return of True does not necessarily mean that the two arrays
share any element. It just means that they *might*.
Parameters
----------
a, b : ndarray
Returns
-------
out : bool
Examples
--------
>>> np.may_share_memory(np.array([1,2]), np.array([5,8,9]))
False
( R t Falset True( RP t bRW RX t b_lowt b_high( ( s5 /usr/lib64/python2.6/site-packages/numpy/lib/utils.pyR s
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Print the Numpy arrays in the given dictionary.
If there is no dictionary passed in or `vardict` is None then returns
Numpy arrays in the globals() dictionary (all Numpy arrays in the
namespace).
Parameters
----------
vardict : dict, optional
A dictionary possibly containing ndarrays. Default is globals().
Returns
-------
out : None
Returns 'None'.
Notes
-----
Prints out the name, shape, bytes and type of all of the ndarrays present
in `vardict`.
Examples
--------
>>> a = np.arange(10)
>>> b = np.ones(20)
>>> np.who()
Name Shape Bytes Type
===========================================================
a 10 40 int32
b 20 160 float64
Upper bound on total bytes = 200
>>> d = {'x': np.arange(2.0), 'y': np.arange(3.0), 'txt': 'Some str',
... 'idx':5}
>>> np.whos(d)
Name Shape Bytes Type
===========================================================
y 3 24 float64
x 2 16 float64
Upper bound on total bytes = 40
s (%s)i i s x i i i
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R_ i ( RM t split(
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oS | | i } | | j o5 | | i } | i | | i | | | | >> np.info(np.polyval) # doctest: +SKIP
polyval(p, x)
Evaluate the polynomial p at x.
...
When using a string for `object` it is possible to get multiple results.
>>> np.info('fft') # doctest: +SKIP
*** Found in numpy ***
Core FFT routines
...
*** Found in numpy.fft ***
fft(a, n=None, axis=-1)
...
*** Repeat reference found in numpy.fft.fftpack ***
*** Total of 3 references found. ***
iNt _ppimport_importert _ppimport_modulet _ppimport_attrt outputR i i s+
*** Repeat reference found in %s *** s *** Found in %s ***t -s Help for %s not found.s+
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Methods:
t _R s %s -- %ss Instance of class: R? R( s %ss R: () t pydoct inspectt hasattrR R R R
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s 2
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c C sR d d k } y. | d | i | IJ| | i | IJWn | d IJn Xd S( s
Print or write to a file the source code for a Numpy object.
The source code is only returned for objects written in Python. Many
functions and classes are defined in C and will therefore not return
useful information.
Parameters
----------
object : numpy object
Input object. This can be any object (function, class, module, ...).
output : file object, optional
If `output` not supplied then source code is printed to screen
(sys.stdout). File object must be created with either write 'w' or
append 'a' modes.
See Also
--------
lookfor, info
Examples
--------
>>> np.source(np.interp)
In file: /usr/lib/python2.6/dist-packages/numpy/lib/function_base.py
def interp(x, xp, fp, left=None, right=None):
""".... (full docstring printed)"""
if isinstance(x, (float, int, number)):
return compiled_interp([x], xp, fp, left, right).item()
else:
return compiled_interp(x, xp, fp, left, right)
The source code is only returned for objects written in Python.
>>> np.source(np.array)
Not available for this object.
iNs In file: %s
s Not available for this object.( R t
getsourcefilet getsource( R R R ( ( s5 /usr/lib64/python2.6/site-packages/numpy/lib/utils.pyR s ' s [a-z_]+\(.*[,=].*\)c s d d k } t | | | g } t | i i p d Sx i D]| \ } \ } } }
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Do a keyword search on docstrings.
A list of of objects that matched the search is displayed,
sorted by relevance. All given keywords need to be found in the
docstring for it to be returned as a result, but the order does
not matter.
Parameters
----------
what : str
String containing words to look for.
module : str or list, optional
Name of module(s) whose docstrings to go through.
import_modules : bool, optional
Whether to import sub-modules in packages. Default is True.
regenerate : bool, optional
Whether to re-generate the docstring cache. Default is False.
output : file-like, optional
File-like object to write the output to. If omitted, use a pager.
See Also
--------
source, info
Notes
-----
Relevance is determined only roughly, by checking if the keywords occur
in the function name, at the start of a docstring, etc.
Examples
--------
>>> np.lookfor('binary representation')
Search results for 'binary representation'
------------------------------------------
numpy.binary_repr
Return the binary representation of the input number as a string.
numpy.base_repr
Return a string representation of a number in the given base system.
...
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Object to evaluate constant string expressions.
This includes strings with lists, dicts and tuples using the abstract
syntax tree created by ``compiler.parse``.
For an example of usage, see `safe_eval`.
See Also
--------
safe_eval
c K s2 | i } t | d | i | i } | | | S( Nt visit( R R R* t default( R/ t nodet kwt clsR ( ( s5 /usr/lib64/python2.6/site-packages/numpy/lib/utils.pyR s c K s t d | i d S( Ns Unsupported source construct: %s( t SyntaxErrorR ( R/ R R ( ( s5 /usr/lib64/python2.6/site-packages/numpy/lib/utils.pyR s c K s( x! | i D] } | i | | SWd S( N( t
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visitConst s c K sF t g } | i D]+ \ } } | | i | | i | f q ~ S( N( t dictt itemsR ( R/ R R R R| R ( ( s5 /usr/lib64/python2.6/site-packages/numpy/lib/utils.pyt visitDict s c K s1 t g } | i D] } | | i | q ~ S( N( R t nodesR ( R/ R R R t i( ( s5 /usr/lib64/python2.6/site-packages/numpy/lib/utils.pyt
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Protected string evaluation.
Evaluate a string containing a Python literal expression without
allowing the execution of arbitrary non-literal code.
Parameters
----------
source : str
The string to evaluate.
Returns
-------
obj : object
The result of evaluating `source`.
Raises
------
SyntaxError
If the code has invalid Python syntax, or if it contains non-literal
code.
Examples
--------
>>> np.safe_eval('1')
1
>>> np.safe_eval('[1, 2, 3]')
[1, 2, 3]
>>> np.safe_eval('{"foo": ("bar", 10.0)}')
{'foo': ('bar', 10.0)}
>>> np.safe_eval('import os')
Traceback (most recent call last):
...
SyntaxError: invalid syntax
>>> safe_eval('open("/home/user/.ssh/id_dsa").read()')
Traceback (most recent call last):
...
SyntaxError: Unsupported source construct: compiler.ast.CallFunc
iNt eval( t compilerR t parseR R ( R R- t walkert astt err( ( s5 /usr/lib64/python2.6/site-packages/numpy/lib/utils.pyR s , ( i i (, R Ra R t ret numpy.core.numerictypesR R R R R R R R R R t version_infoR! R) R R+ R R R R R R R R R R R R
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