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"""
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IPython extension: %lookfor command for searching docstrings
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"""
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# Pauli Virtanen <pav@iki.fi>, 2008.
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import re, inspect, pkgutil, pydoc
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#------------------------------------------------------------------------------
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# Lookfor functionality
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#------------------------------------------------------------------------------
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# Cache for lookfor: {id(module): {name: (docstring, kind, index), ...}...}
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# where kind: "func", "class", "module", "object"
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# and index: index in breadth-first namespace traversal
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_lookfor_caches = {}
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# regexp whose match indicates that the string may contain a function signature
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_function_signature_re = re.compile(r"[a-z_]+\(.*[,=].*\)", re.I)
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def lookfor(what, modules=None, import_modules=True, regenerate=False):
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"""
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Search for objects whose documentation contains all given words.
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Shows a summary of matching objects, sorted roughly by relevance.
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Parameters
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----------
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what : str
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String containing words to look for.
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module : str, module
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Module whose docstrings to go through.
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import_modules : bool
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Whether to import sub-modules in packages.
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Will import only modules in __all__
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regenerate: bool
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Re-generate the docstring cache
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"""
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# Cache
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cache = {}
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for module in modules:
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try:
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c = _lookfor_generate_cache(module, import_modules, regenerate)
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cache.update(c)
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except ImportError:
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pass
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# Search
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# XXX: maybe using a real stemming search engine would be better?
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found = []
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whats = str(what).lower().split()
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if not whats: return
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for name, (docstring, kind, index) in cache.iteritems():
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if kind in ('module', 'object'):
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# don't show modules or objects
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continue
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ok = True
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doc = docstring.lower()
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for w in whats:
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if w not in doc:
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ok = False
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break
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if ok:
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found.append(name)
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# Relevance sort
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# XXX: this is full Harrison-Stetson heuristics now,
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# XXX: it probably could be improved
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kind_relevance = {'func': 1000, 'class': 1000,
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'module': -1000, 'object': -1000}
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def relevance(name, docstr, kind, index):
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r = 0
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# do the keywords occur within the start of the docstring?
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first_doc = "\n".join(docstr.lower().strip().split("\n")[:3])
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r += sum([200 for w in whats if w in first_doc])
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# do the keywords occur in the function name?
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r += sum([30 for w in whats if w in name])
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# is the full name long?
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r += -len(name) * 5
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# is the object of bad type?
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r += kind_relevance.get(kind, -1000)
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# is the object deep in namespace hierarchy?
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r += -name.count('.') * 10
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r += max(-index / 100, -100)
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return r
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def relevance_sort(a, b):
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dr = relevance(b, *cache[b]) - relevance(a, *cache[a])
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if dr != 0: return dr
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else: return cmp(a, b)
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found.sort(relevance_sort)
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# Pretty-print
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s = "Search results for '%s'" % (' '.join(whats))
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help_text = [s, "-"*len(s)]
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for name in found:
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doc, kind, ix = cache[name]
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doclines = [line.strip() for line in doc.strip().split("\n")
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if line.strip()]
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# find a suitable short description
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try:
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first_doc = doclines[0].strip()
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if _function_signature_re.search(first_doc):
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first_doc = doclines[1].strip()
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except IndexError:
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first_doc = ""
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help_text.append("%s\n %s" % (name, first_doc))
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# Output
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if len(help_text) > 10:
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pager = pydoc.getpager()
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pager("\n".join(help_text))
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else:
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print "\n".join(help_text)
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def _lookfor_generate_cache(module, import_modules, regenerate):
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"""
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Generate docstring cache for given module.
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Parameters
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----------
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module : str, None, module
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Module for which to generate docstring cache
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import_modules : bool
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Whether to import sub-modules in packages.
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Will import only modules in __all__
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regenerate: bool
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Re-generate the docstring cache
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Returns
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-------
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cache : dict {obj_full_name: (docstring, kind, index), ...}
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Docstring cache for the module, either cached one (regenerate=False)
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or newly generated.
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"""
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global _lookfor_caches
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if module is None:
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module = "numpy"
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if isinstance(module, str):
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module = __import__(module)
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if id(module) in _lookfor_caches and not regenerate:
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return _lookfor_caches[id(module)]
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# walk items and collect docstrings
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cache = {}
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_lookfor_caches[id(module)] = cache
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seen = {}
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index = 0
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stack = [(module.__name__, module)]
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while stack:
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name, item = stack.pop(0)
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if id(item) in seen: continue
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seen[id(item)] = True
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index += 1
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kind = "object"
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if inspect.ismodule(item):
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kind = "module"
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try:
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_all = item.__all__
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except AttributeError:
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_all = None
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# import sub-packages
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if import_modules and hasattr(item, '__path__'):
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for m in pkgutil.iter_modules(item.__path__):
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if _all is not None and m[1] not in _all:
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continue
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try:
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__import__("%s.%s" % (name, m[1]))
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except ImportError:
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continue
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for n, v in inspect.getmembers(item):
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if _all is not None and n not in _all:
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continue
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stack.append(("%s.%s" % (name, n), v))
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elif inspect.isclass(item):
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kind = "class"
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for n, v in inspect.getmembers(item):
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stack.append(("%s.%s" % (name, n), v))
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elif callable(item):
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kind = "func"
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doc = inspect.getdoc(item)
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if doc is not None:
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cache[name] = (doc, kind, index)
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return cache
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#------------------------------------------------------------------------------
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# IPython connectivity
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#------------------------------------------------------------------------------
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from IPython.core import ipapi
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ip = ipapi.get()
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_lookfor_modules = ['numpy', 'scipy']
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def lookfor_f(self, arg=''):
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r"""
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Search for objects whose documentation contains all given words.
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Shows a summary of matching objects, sorted roughly by relevance.
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Usage
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-----
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%lookfor +numpy some words
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Search module 'numpy'
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%lookfor_modules numpy scipy
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Set default modules whose docstrings to search
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"""
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lookfor(arg, modules=_lookfor_modules)
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def lookfor_modules_f(self, arg=''):
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global _lookfor_modules
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if not arg:
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print "Modules included in %lookfor search:", _lookfor_modules
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else:
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_lookfor_modules = arg.split()
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ip.expose_magic('lookfor', lookfor_f)
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ip.expose_magic('lookfor_modules', lookfor_modules_f)
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