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 = "" 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 #-----------------------------------------------------------------------------