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import os
import sys
import types
import re
from numpy.core.numerictypes import issubclass_, issubsctype, issubdtype
from numpy.core import product, ndarray
__all__ = ['issubclass_', 'get_numpy_include', 'issubsctype',
'issubdtype', 'deprecate', 'deprecate_with_doc',
'get_numarray_include', 'get_include',
'info', 'source', 'who', 'lookfor',
'byte_bounds', 'may_share_memory', 'safe_eval']
def get_include():
"""
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()])
...
"""
import numpy
if numpy.show_config is None:
# running from numpy source directory
d = os.path.join(os.path.dirname(numpy.__file__), 'core', 'include')
else:
# using installed numpy core headers
import numpy.core as core
d = os.path.join(os.path.dirname(core.__file__), 'include')
return d
def get_numarray_include(type=None):
"""
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()])
...
"""
from numpy.numarray import get_numarray_include_dirs
include_dirs = get_numarray_include_dirs()
if type is None:
return include_dirs[0]
else:
return include_dirs + [get_include()]
if sys.version_info < (2, 4):
# Can't set __name__ in 2.3
import new
def _set_function_name(func, name):
func = new.function(func.func_code, func.func_globals,
name, func.func_defaults, func.func_closure)
return func
else:
def _set_function_name(func, name):
func.__name__ = name
return func
class _Deprecate(object):
"""
Decorator class to deprecate old functions.
Refer to `deprecate` for details.
See Also
--------
deprecate
"""
def __init__(self, old_name=None, new_name=None, message=None):
self.old_name = old_name
self.new_name = new_name
self.message = message
def __call__(self, func, *args, **kwargs):
"""
Decorator call. Refer to ``decorate``.
"""
old_name = self.old_name
new_name = self.new_name
message = self.message
import warnings
if old_name is None:
try:
old_name = func.func_name
except AttributeError:
old_name = func.__name__
if new_name is None:
depdoc = "`%s` is deprecated!" % old_name
else:
depdoc = "`%s` is deprecated, use `%s` instead!" % \
(old_name, new_name)
if message is not None:
depdoc += "\n" + message
def newfunc(*args,**kwds):
"""`arrayrange` is deprecated, use `arange` instead!"""
warnings.warn(depdoc, DeprecationWarning)
return func(*args, **kwds)
newfunc = _set_function_name(newfunc, old_name)
doc = func.__doc__
if doc is None:
doc = depdoc
else:
doc = '\n\n'.join([depdoc, doc])
newfunc.__doc__ = doc
try:
d = func.__dict__
except AttributeError:
pass
else:
newfunc.__dict__.update(d)
return newfunc
def deprecate(*args, **kwargs):
"""
Issues a DeprecationWarning, adds warning to `old_name`'s
docstring, rebinds ``old_name.__name__`` and returns the new
function object.
This function may also be used as a decorator.
Parameters
----------
func : function
The function to be deprecated.
old_name : str, optional
The name of the function to be deprecated. Default is None, in which
case the name of `func` is used.
new_name : str, optional
The new name for the function. Default is None, in which case
the deprecation message is that `old_name` is deprecated. If given,
the deprecation message is that `old_name` is deprecated and `new_name`
should be used instead.
message : str, optional
Additional explanation of the deprecation. Displayed in the docstring
after the warning.
Returns
-------
old_func : function
The deprecated function.
Examples
--------
Note that ``olduint`` returns a value after printing Deprecation Warning:
>>> 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
"""
# Deprecate may be run as a function or as a decorator
# If run as a function, we initialise the decorator class
# and execute its __call__ method.
if args:
fn = args[0]
args = args[1:]
# backward compatibility -- can be removed
# after next release
if 'newname' in kwargs:
kwargs['new_name'] = kwargs.pop('newname')
if 'oldname' in kwargs:
kwargs['old_name'] = kwargs.pop('oldname')
return _Deprecate(*args, **kwargs)(fn)
else:
return _Deprecate(*args, **kwargs)
deprecate_with_doc = lambda msg: _Deprecate(message=msg)
get_numpy_include = deprecate(get_include, 'get_numpy_include', 'get_include')
#--------------------------------------------
# Determine if two arrays can share memory
#--------------------------------------------
def byte_bounds(a):
"""
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
"""
ai = a.__array_interface__
a_data = ai['data'][0]
astrides = ai['strides']
ashape = ai['shape']
nd_a = len(ashape)
bytes_a = int(ai['typestr'][2:])
a_low = a_high = a_data
if astrides is None: # contiguous case
a_high += product(ashape, dtype=int)*bytes_a
else:
for shape, stride in zip(ashape, astrides):
if stride < 0:
a_low += (shape-1)*stride
else:
a_high += (shape-1)*stride
a_high += bytes_a
return a_low, a_high
def may_share_memory(a, b):
"""
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
"""
a_low, a_high = byte_bounds(a)
b_low, b_high = byte_bounds(b)
if b_low >= a_high or a_low >= b_high:
return False
return True
#-----------------------------------------------------------------------------
# Function for output and information on the variables used.
#-----------------------------------------------------------------------------
def who(vardict=None):
"""
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
"""
if vardict is None:
frame = sys._getframe().f_back
vardict = frame.f_globals
sta = []
cache = {}
for name in vardict.keys():
if isinstance(vardict[name],ndarray):
var = vardict[name]
idv = id(var)
if idv in cache.keys():
namestr = name + " (%s)" % cache[idv]
original=0
else:
cache[idv] = name
namestr = name
original=1
shapestr = " x ".join(map(str, var.shape))
bytestr = str(var.nbytes)
sta.append([namestr, shapestr, bytestr, var.dtype.name,
original])
maxname = 0
maxshape = 0
maxbyte = 0
totalbytes = 0
for k in range(len(sta)):
val = sta[k]
if maxname < len(val[0]):
maxname = len(val[0])
if maxshape < len(val[1]):
maxshape = len(val[1])
if maxbyte < len(val[2]):
maxbyte = len(val[2])
if val[4]:
totalbytes += int(val[2])
if len(sta) > 0:
sp1 = max(10,maxname)
sp2 = max(10,maxshape)
sp3 = max(10,maxbyte)
prval = "Name %s Shape %s Bytes %s Type" % (sp1*' ', sp2*' ', sp3*' ')
print prval + "\n" + "="*(len(prval)+5) + "\n"
for k in range(len(sta)):
val = sta[k]
print "%s %s %s %s %s %s %s" % (val[0], ' '*(sp1-len(val[0])+4),
val[1], ' '*(sp2-len(val[1])+5),
val[2], ' '*(sp3-len(val[2])+5),
val[3])
print "\nUpper bound on total bytes = %d" % totalbytes
return
#-----------------------------------------------------------------------------
# NOTE: pydoc defines a help function which works simliarly to this
# except it uses a pager to take over the screen.
# combine name and arguments and split to multiple lines of
# width characters. End lines on a comma and begin argument list
# indented with the rest of the arguments.
def _split_line(name, arguments, width):
firstwidth = len(name)
k = firstwidth
newstr = name
sepstr = ", "
arglist = arguments.split(sepstr)
for argument in arglist:
if k == firstwidth:
addstr = ""
else:
addstr = sepstr
k = k + len(argument) + len(addstr)
if k > width:
k = firstwidth + 1 + len(argument)
newstr = newstr + ",\n" + " "*(firstwidth+2) + argument
else:
newstr = newstr + addstr + argument
return newstr
_namedict = None
_dictlist = None
# Traverse all module directories underneath globals
# to see if something is defined
def _makenamedict(module='numpy'):
module = __import__(module, globals(), locals(), [])
thedict = {module.__name__:module.__dict__}
dictlist = [module.__name__]
totraverse = [module.__dict__]
while 1:
if len(totraverse) == 0:
break
thisdict = totraverse.pop(0)
for x in thisdict.keys():
if isinstance(thisdict[x],types.ModuleType):
modname = thisdict[x].__name__
if modname not in dictlist:
moddict = thisdict[x].__dict__
dictlist.append(modname)
totraverse.append(moddict)
thedict[modname] = moddict
return thedict, dictlist
def info(object=None,maxwidth=76,output=sys.stdout,toplevel='numpy'):
"""
Get help information for a function, class, or module.
Parameters
----------
object : object or str, optional
Input object or name to get information about. If `object` is a
numpy object, its docstring is given. If it is a string, available
modules are searched for matching objects.
If None, information about `info` itself is returned.
maxwidth : int, optional
Printing width.
output : file like object, optional
File like object that the output is written to, default is ``stdout``.
The object has to be opened in 'w' or 'a' mode.
toplevel : str, optional
Start search at this level.
See Also
--------
source, lookfor
Notes
-----
When used interactively with an object, ``np.info(obj)`` is equivalent to
``help(obj)`` on the Python prompt or ``obj?`` on the IPython prompt.
Examples
--------
>>> 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. ***
"""
global _namedict, _dictlist
# Local import to speed up numpy's import time.
import pydoc, inspect
if hasattr(object,'_ppimport_importer') or \
hasattr(object, '_ppimport_module'):
object = object._ppimport_module
elif hasattr(object, '_ppimport_attr'):
object = object._ppimport_attr
if object is None:
info(info)
elif isinstance(object, ndarray):
import numpy.numarray as nn
nn.info(object, output=output, numpy=1)
elif isinstance(object, str):
if _namedict is None:
_namedict, _dictlist = _makenamedict(toplevel)
numfound = 0
objlist = []
for namestr in _dictlist:
try:
obj = _namedict[namestr][object]
if id(obj) in objlist:
print >> output, "\n *** Repeat reference found in %s *** " % namestr
else:
objlist.append(id(obj))
print >> output, " *** Found in %s ***" % namestr
info(obj)
print >> output, "-"*maxwidth
numfound += 1
except KeyError:
pass
if numfound == 0:
print >> output, "Help for %s not found." % object
else:
print >> output, "\n *** Total of %d references found. ***" % numfound
elif inspect.isfunction(object):
name = object.func_name
arguments = inspect.formatargspec(*inspect.getargspec(object))
if len(name+arguments) > maxwidth:
argstr = _split_line(name, arguments, maxwidth)
else:
argstr = name + arguments
print >> output, " " + argstr + "\n"
print >> output, inspect.getdoc(object)
elif inspect.isclass(object):
name = object.__name__
arguments = "()"
try:
if hasattr(object, '__init__'):
arguments = inspect.formatargspec(*inspect.getargspec(object.__init__.im_func))
arglist = arguments.split(', ')
if len(arglist) > 1:
arglist[1] = "("+arglist[1]
arguments = ", ".join(arglist[1:])
except:
pass
if len(name+arguments) > maxwidth:
argstr = _split_line(name, arguments, maxwidth)
else:
argstr = name + arguments
print >> output, " " + argstr + "\n"
doc1 = inspect.getdoc(object)
if doc1 is None:
if hasattr(object,'__init__'):
print >> output, inspect.getdoc(object.__init__)
else:
print >> output, inspect.getdoc(object)
methods = pydoc.allmethods(object)
if methods != []:
print >> output, "\n\nMethods:\n"
for meth in methods:
if meth[0] == '_':
continue
thisobj = getattr(object, meth, None)
if thisobj is not None:
methstr, other = pydoc.splitdoc(inspect.getdoc(thisobj) or "None")
print >> output, " %s -- %s" % (meth, methstr)
elif type(object) is types.InstanceType: ## check for __call__ method
print >> output, "Instance of class: ", object.__class__.__name__
print >> output
if hasattr(object, '__call__'):
arguments = inspect.formatargspec(*inspect.getargspec(object.__call__.im_func))
arglist = arguments.split(', ')
if len(arglist) > 1:
arglist[1] = "("+arglist[1]
arguments = ", ".join(arglist[1:])
else:
arguments = "()"
if hasattr(object,'name'):
name = "%s" % object.name
else:
name = "<name>"
if len(name+arguments) > maxwidth:
argstr = _split_line(name, arguments, maxwidth)
else:
argstr = name + arguments
print >> output, " " + argstr + "\n"
doc = inspect.getdoc(object.__call__)
if doc is not None:
print >> output, inspect.getdoc(object.__call__)
print >> output, inspect.getdoc(object)
else:
print >> output, inspect.getdoc(object)
elif inspect.ismethod(object):
name = object.__name__
arguments = inspect.formatargspec(*inspect.getargspec(object.im_func))
arglist = arguments.split(', ')
if len(arglist) > 1:
arglist[1] = "("+arglist[1]
arguments = ", ".join(arglist[1:])
else:
arguments = "()"
if len(name+arguments) > maxwidth:
argstr = _split_line(name, arguments, maxwidth)
else:
argstr = name + arguments
print >> output, " " + argstr + "\n"
print >> output, inspect.getdoc(object)
elif hasattr(object, '__doc__'):
print >> output, inspect.getdoc(object)
def source(object, output=sys.stdout):
"""
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.
"""
# Local import to speed up numpy's import time.
import inspect
try:
print >> output, "In file: %s\n" % inspect.getsourcefile(object)
print >> output, inspect.getsource(object)
except:
print >> output, "Not available for this object."
# Cache for lookfor: {id(module): {name: (docstring, kind, index), ...}...}
# where kind: "func", "class", "module", "object"
# and index: index in breadth-first namespace traversal
_lookfor_caches = {}
# regexp whose match indicates that the string may contain a function signature
_function_signature_re = re.compile(r"[a-z_]+\(.*[,=].*\)", re.I)
def lookfor(what, module=None, import_modules=True, regenerate=False,
output=None):
"""
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.
...
"""
import pydoc
# Cache
cache = _lookfor_generate_cache(module, import_modules, regenerate)
# Search
# XXX: maybe using a real stemming search engine would be better?
found = []
whats = str(what).lower().split()
if not whats: return
for name, (docstring, kind, index) in cache.iteritems():
if kind in ('module', 'object'):
# don't show modules or objects
continue
ok = True
doc = docstring.lower()
for w in whats:
if w not in doc:
ok = False
break
if ok:
found.append(name)
# Relevance sort
# XXX: this is full Harrison-Stetson heuristics now,
# XXX: it probably could be improved
kind_relevance = {'func': 1000, 'class': 1000,
'module': -1000, 'object': -1000}
def relevance(name, docstr, kind, index):
r = 0
# do the keywords occur within the start of the docstring?
first_doc = "\n".join(docstr.lower().strip().split("\n")[:3])
r += sum([200 for w in whats if w in first_doc])
# do the keywords occur in the function name?
r += sum([30 for w in whats if w in name])
# is the full name long?
r += -len(name) * 5
# is the object of bad type?
r += kind_relevance.get(kind, -1000)
# is the object deep in namespace hierarchy?
r += -name.count('.') * 10
r += max(-index / 100, -100)
return r
def relevance_sort(a, b):
dr = relevance(b, *cache[b]) - relevance(a, *cache[a])
if dr != 0: return dr
else: return cmp(a, b)
found.sort(relevance_sort)
# Pretty-print
s = "Search results for '%s'" % (' '.join(whats))
help_text = [s, "-"*len(s)]
for name in found:
doc, kind, ix = cache[name]
doclines = [line.strip() for line in doc.strip().split("\n")
if line.strip()]
# find a suitable short description
try:
first_doc = doclines[0].strip()
if _function_signature_re.search(first_doc):
first_doc = doclines[1].strip()
except IndexError:
first_doc = ""
help_text.append("%s\n %s" % (name, first_doc))
if not found:
help_text.append("Nothing found.")
# Output
if output is not None:
output.write("\n".join(help_text))
elif len(help_text) > 10:
pager = pydoc.getpager()
pager("\n".join(help_text))
else:
print "\n".join(help_text)
def _lookfor_generate_cache(module, import_modules, regenerate):
"""
Generate docstring cache for given module.
Parameters
----------
module : str, None, module
Module for which to generate docstring cache
import_modules : bool
Whether to import sub-modules in packages.
regenerate: bool
Re-generate the docstring cache
Returns
-------
cache : dict {obj_full_name: (docstring, kind, index), ...}
Docstring cache for the module, either cached one (regenerate=False)
or newly generated.
"""
global _lookfor_caches
# Local import to speed up numpy's import time.
import inspect
from StringIO import StringIO
if module is None:
module = "numpy"
if isinstance(module, str):
try:
__import__(module)
except ImportError:
return {}
module = sys.modules[module]
elif isinstance(module, list) or isinstance(module, tuple):
cache = {}
for mod in module:
cache.update(_lookfor_generate_cache(mod, import_modules,
regenerate))
return cache
if id(module) in _lookfor_caches and not regenerate:
return _lookfor_caches[id(module)]
# walk items and collect docstrings
cache = {}
_lookfor_caches[id(module)] = cache
seen = {}
index = 0
stack = [(module.__name__, module)]
while stack:
name, item = stack.pop(0)
if id(item) in seen: continue
seen[id(item)] = True
index += 1
kind = "object"
if inspect.ismodule(item):
kind = "module"
try:
_all = item.__all__
except AttributeError:
_all = None
# import sub-packages
if import_modules and hasattr(item, '__path__'):
for pth in item.__path__:
for mod_path in os.listdir(pth):
this_py = os.path.join(pth, mod_path)
init_py = os.path.join(pth, mod_path, '__init__.py')
if os.path.isfile(this_py) and mod_path.endswith('.py'):
to_import = mod_path[:-3]
elif os.path.isfile(init_py):
to_import = mod_path
else:
continue
if to_import == '__init__':
continue
try:
# Catch SystemExit, too
base_exc = BaseException
except NameError:
# Python 2.4 doesn't have BaseException
base_exc = Exception
try:
old_stdout = sys.stdout
old_stderr = sys.stderr
try:
sys.stdout = StringIO()
sys.stderr = StringIO()
__import__("%s.%s" % (name, to_import))
finally:
sys.stdout = old_stdout
sys.stderr = old_stderr
except base_exc:
continue
for n, v in _getmembers(item):
item_name = getattr(v, '__name__', "%s.%s" % (name, n))
mod_name = getattr(v, '__module__', None)
if '.' not in item_name and mod_name:
item_name = "%s.%s" % (mod_name, item_name)
if not item_name.startswith(name + '.'):
# don't crawl foreign objects
continue
elif not (inspect.ismodule(v) or _all is None or n in _all):
continue
stack.append(("%s.%s" % (name, n), v))
elif inspect.isclass(item):
kind = "class"
for n, v in _getmembers(item):
stack.append(("%s.%s" % (name, n), v))
elif callable(item):
kind = "func"
doc = inspect.getdoc(item)
if doc is not None:
cache[name] = (doc, kind, index)
return cache
def _getmembers(item):
import inspect
try:
members = inspect.getmembers(item)
except AttributeError:
members = [(x, getattr(item, x)) for x in dir(item)
if hasattr(item, x)]
return members
#-----------------------------------------------------------------------------
# The following SafeEval class and company are adapted from Michael Spencer's
# ASPN Python Cookbook recipe:
# http://aspn.activestate.com/ASPN/Cookbook/Python/Recipe/364469
# Accordingly it is mostly Copyright 2006 by Michael Spencer.
# The recipe, like most of the other ASPN Python Cookbook recipes was made
# available under the Python license.
# http://www.python.org/license
# It has been modified to:
# * handle unary -/+
# * support True/False/None
# * raise SyntaxError instead of a custom exception.
class SafeEval(object):
"""
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
"""
def visit(self, node, **kw):
cls = node.__class__
meth = getattr(self,'visit'+cls.__name__,self.default)
return meth(node, **kw)
def default(self, node, **kw):
raise SyntaxError("Unsupported source construct: %s" % node.__class__)
def visitExpression(self, node, **kw):
for child in node.getChildNodes():
return self.visit(child, **kw)
def visitConst(self, node, **kw):
return node.value
def visitDict(self, node,**kw):
return dict([(self.visit(k),self.visit(v)) for k,v in node.items])
def visitTuple(self, node, **kw):
return tuple([self.visit(i) for i in node.nodes])
def visitList(self, node, **kw):
return [self.visit(i) for i in node.nodes]
def visitUnaryAdd(self, node, **kw):
return +self.visit(node.getChildNodes()[0])
def visitUnarySub(self, node, **kw):
return -self.visit(node.getChildNodes()[0])
def visitName(self, node, **kw):
if node.name == 'False':
return False
elif node.name == 'True':
return True
elif node.name == 'None':
return None
else:
raise SyntaxError("Unknown name: %s" % node.name)
def safe_eval(source):
"""
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
"""
# Local import to speed up numpy's import time.
import compiler
walker = SafeEval()
try:
ast = compiler.parse(source, "eval")
except SyntaxError, err:
raise
try:
return walker.visit(ast)
except SyntaxError, err:
raise
#-----------------------------------------------------------------------------
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