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__pycache__	[ DIR ]	drwxr-xr-x
compiled	[ DIR ]	drwxr-xr-x
context	[ DIR ]	drwxr-xr-x
__init__.py	17.11 KB	-rw-r--r--
analysis.py	7.81 KB	-rw-r--r--
arguments.py	11.23 KB	-rw-r--r--
base_context.py	9.42 KB	-rw-r--r--
cache.py	2.43 KB	-rw-r--r--
docstrings.py	10.46 KB	-rw-r--r--
dynamic.py	8.43 KB	-rw-r--r--
filters.py	15.69 KB	-rw-r--r--
finder.py	11.59 KB	-rw-r--r--
flow_analysis.py	4.21 KB	-rw-r--r--
helpers.py	6.88 KB	-rw-r--r--
imports.py	21.75 KB	-rw-r--r--
jedi_typing.py	2.6 KB	-rw-r--r--
lazy_context.py	1.63 KB	-rw-r--r--
param.py	7.74 KB	-rw-r--r--
parser_cache.py	172 B	-rw-r--r--
pep0484.py	11.4 KB	-rw-r--r--
recursion.py	4.17 KB	-rw-r--r--
stdlib.py	11.01 KB	-rw-r--r--
syntax_tree.py	25.63 KB	-rw-r--r--
sys_path.py	8 KB	-rw-r--r--
usages.py	2.33 KB	-rw-r--r--
utils.py	3.48 KB	-rw-r--r--

Code Editor : docstrings.py

"""
Docstrings are another source of information for functions and classes.
:mod:`jedi.evaluate.dynamic` tries to find all executions of functions, while
the docstring parsing is much easier. There are three different types of
docstrings that |jedi| understands:

- `Sphinx <http://sphinx-doc.org/markup/desc.html#info-field-lists>`_
- `Epydoc <http://epydoc.sourceforge.net/manual-fields.html>`_
- `Numpydoc <https://github.com/numpy/numpy/blob/master/doc/HOWTO_DOCUMENT.rst.txt>`_

For example, the sphinx annotation ``:type foo: str`` clearly states that the
type of ``foo`` is ``str``.

As an addition to parameter searching, this module also provides return
annotations.
"""

import re
from textwrap import dedent

from parso import parse, ParserSyntaxError

from jedi._compatibility import u
from jedi.evaluate.utils import indent_block
from jedi.evaluate.cache import evaluator_method_cache
from jedi.evaluate.base_context import iterator_to_context_set, ContextSet, \
    NO_CONTEXTS
from jedi.evaluate.lazy_context import LazyKnownContexts

DOCSTRING_PARAM_PATTERNS = [
    r'\s*:type\s+%s:\s*([^\n]+)',  # Sphinx
    r'\s*:param\s+(\w+)\s+%s:[^\n]*',  # Sphinx param with type
    r'\s*@type\s+%s:\s*([^\n]+)',  # Epydoc
]

DOCSTRING_RETURN_PATTERNS = [
    re.compile(r'\s*:rtype:\s*([^\n]+)', re.M),  # Sphinx
    re.compile(r'\s*@rtype:\s*([^\n]+)', re.M),  # Epydoc
]

REST_ROLE_PATTERN = re.compile(r':[^`]+:`([^`]+)`')

_numpy_doc_string_cache = None

def _get_numpy_doc_string_cls():
    global _numpy_doc_string_cache
    if isinstance(_numpy_doc_string_cache, ImportError):
        raise _numpy_doc_string_cache
    try:
        from numpydoc.docscrape import NumpyDocString
        _numpy_doc_string_cache = NumpyDocString
    except ImportError as e:
        _numpy_doc_string_cache = e
        raise
    return _numpy_doc_string_cache

def _search_param_in_numpydocstr(docstr, param_str):
    """Search `docstr` (in numpydoc format) for type(-s) of `param_str`."""
    try:
        # This is a non-public API. If it ever changes we should be
        # prepared and return gracefully.
        params = _get_numpy_doc_string_cls()(docstr)._parsed_data['Parameters']
    except (KeyError, AttributeError, ImportError):
        return []
    for p_name, p_type, p_descr in params:
        if p_name == param_str:
            m = re.match(r'([^,]+(,[^,]+)*?)(,[ ]*optional)?$', p_type)
            if m:
                p_type = m.group(1)
            return list(_expand_typestr(p_type))
    return []

def _search_return_in_numpydocstr(docstr):
    """
    Search `docstr` (in numpydoc format) for type(-s) of function returns.
    """
    try:
        doc = _get_numpy_doc_string_cls()(docstr)
    except ImportError:
        return
    try:
        # This is a non-public API. If it ever changes we should be
        # prepared and return gracefully.
        returns = doc._parsed_data['Returns']
        returns += doc._parsed_data['Yields']
    except (KeyError, AttributeError):
        return
    for r_name, r_type, r_descr in returns:
        # Return names are optional and if so the type is in the name
        if not r_type:
            r_type = r_name
        for type_ in _expand_typestr(r_type):
            yield type_

def _expand_typestr(type_str):
    """
    Attempts to interpret the possible types in `type_str`
    """
    # Check if alternative types are specified with 'or'
    if re.search(r'\bor\b', type_str):
        for t in type_str.split('or'):
            yield t.split('of')[0].strip()
    # Check if like "list of `type`" and set type to list
    elif re.search(r'\bof\b', type_str):
        yield type_str.split('of')[0]
    # Check if type has is a set of valid literal values eg: {'C', 'F', 'A'}
    elif type_str.startswith('{'):
        node = parse(type_str, version='3.6').children[0]
        if node.type == 'atom':
            for leaf in node.children[1].children:
                if leaf.type == 'number':
                    if '.' in leaf.value:
                        yield 'float'
                    else:
                        yield 'int'
                elif leaf.type == 'string':
                    if 'b' in leaf.string_prefix.lower():
                        yield 'bytes'
                    else:
                        yield 'str'
                # Ignore everything else.

# Otherwise just work with what we have.
    else:
        yield type_str

def _search_param_in_docstr(docstr, param_str):
    """
    Search `docstr` for type(-s) of `param_str`.

>>> _search_param_in_docstr(':type param: int', 'param')
    ['int']
    >>> _search_param_in_docstr('@type param: int', 'param')
    ['int']
    >>> _search_param_in_docstr(
    ...   ':type param: :class:`threading.Thread`', 'param')
    ['threading.Thread']
    >>> bool(_search_param_in_docstr('no document', 'param'))
    False
    >>> _search_param_in_docstr(':param int param: some description', 'param')
    ['int']

"""
    # look at #40 to see definitions of those params
    patterns = [re.compile(p % re.escape(param_str))
                for p in DOCSTRING_PARAM_PATTERNS]
    for pattern in patterns:
        match = pattern.search(docstr)
        if match:
            return [_strip_rst_role(match.group(1))]

return _search_param_in_numpydocstr(docstr, param_str)

def _strip_rst_role(type_str):
    """
    Strip off the part looks like a ReST role in `type_str`.

>>> _strip_rst_role(':class:`ClassName`')  # strip off :class:
    'ClassName'
    >>> _strip_rst_role(':py:obj:`module.Object`')  # works with domain
    'module.Object'
    >>> _strip_rst_role('ClassName')  # do nothing when not ReST role
    'ClassName'

See also:
    http://sphinx-doc.org/domains.html#cross-referencing-python-objects

"""
    match = REST_ROLE_PATTERN.match(type_str)
    if match:
        return match.group(1)
    else:
        return type_str

def _evaluate_for_statement_string(module_context, string):
    code = dedent(u("""
    def pseudo_docstring_stuff():
        '''
        Create a pseudo function for docstring statements.
        Need this docstring so that if the below part is not valid Python this
        is still a function.
        '''
    {}
    """))
    if string is None:
        return []

for element in re.findall(r'((?:\w+\.)*\w+)\.', string):
        # Try to import module part in dotted name.
        # (e.g., 'threading' in 'threading.Thread').
        string = 'import %s\n' % element + string

# Take the default grammar here, if we load the Python 2.7 grammar here, it
    # will be impossible to use `...` (Ellipsis) as a token. Docstring types
    # don't need to conform with the current grammar.
    grammar = module_context.evaluator.latest_grammar
    try:
        module = grammar.parse(code.format(indent_block(string)), error_recovery=False)
    except ParserSyntaxError:
        return []
    try:
        funcdef = next(module.iter_funcdefs())
        # First pick suite, then simple_stmt and then the node,
        # which is also not the last item, because there's a newline.
        stmt = funcdef.children[-1].children[-1].children[-2]
    except (AttributeError, IndexError):
        return []

if stmt.type not in ('name', 'atom', 'atom_expr'):
        return []

from jedi.evaluate.context import FunctionContext
    function_context = FunctionContext(
        module_context.evaluator,
        module_context,
        funcdef
    )
    func_execution_context = function_context.get_function_execution()
    # Use the module of the param.
    # TODO this module is not the module of the param in case of a function
    # call. In that case it's the module of the function call.
    # stuffed with content from a function call.
    return list(_execute_types_in_stmt(func_execution_context, stmt))

def _execute_types_in_stmt(module_context, stmt):
    """
    Executing all types or general elements that we find in a statement. This
    doesn't include tuple, list and dict literals, because the stuff they
    contain is executed. (Used as type information).
    """
    definitions = module_context.eval_node(stmt)
    return ContextSet.from_sets(
        _execute_array_values(module_context.evaluator, d)
        for d in definitions
    )

def _execute_array_values(evaluator, array):
    """
    Tuples indicate that there's not just one return value, but the listed
    ones.  `(str, int)` means that it returns a tuple with both types.
    """
    from jedi.evaluate.context.iterable import SequenceLiteralContext, FakeSequence
    if isinstance(array, SequenceLiteralContext):
        values = []
        for lazy_context in array.py__iter__():
            objects = ContextSet.from_sets(
                _execute_array_values(evaluator, typ)
                for typ in lazy_context.infer()
            )
            values.append(LazyKnownContexts(objects))
        return {FakeSequence(evaluator, array.array_type, values)}
    else:
        return array.execute_evaluated()

@evaluator_method_cache()
def infer_param(execution_context, param):
    from jedi.evaluate.context.instance import InstanceArguments
    from jedi.evaluate.context import FunctionExecutionContext

def eval_docstring(docstring):
        return ContextSet.from_iterable(
            p
            for param_str in _search_param_in_docstr(docstring, param.name.value)
            for p in _evaluate_for_statement_string(module_context, param_str)
        )
    module_context = execution_context.get_root_context()
    func = param.get_parent_function()
    if func.type == 'lambdef':
        return NO_CONTEXTS

types = eval_docstring(execution_context.py__doc__())
    if isinstance(execution_context, FunctionExecutionContext) \
            and isinstance(execution_context.var_args, InstanceArguments) \
            and execution_context.function_context.py__name__() == '__init__':
        class_context = execution_context.var_args.instance.class_context
        types |= eval_docstring(class_context.py__doc__())

return types

@evaluator_method_cache()
@iterator_to_context_set
def infer_return_types(function_context):
    def search_return_in_docstr(code):
        for p in DOCSTRING_RETURN_PATTERNS:
            match = p.search(code)
            if match:
                yield _strip_rst_role(match.group(1))
        # Check for numpy style return hint
        for type_ in _search_return_in_numpydocstr(code):
            yield type_

for type_str in search_return_in_docstr(function_context.py__doc__()):
        for type_eval in _evaluate_for_statement_string(function_context.get_root_context(), type_str):
            yield type_eval

${this.title}

Code Editor : docstrings.py