198 lines
8.1 KiB
Python
198 lines
8.1 KiB
Python
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"""
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Type inference of Python code in |jedi| is based on three assumptions:
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* The code uses as least side effects as possible. Jedi understands certain
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list/tuple/set modifications, but there's no guarantee that Jedi detects
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everything (list.append in different modules for example).
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* No magic is being used:
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- metaclasses
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- ``setattr()`` / ``__import__()``
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- writing to ``globals()``, ``locals()``, ``object.__dict__``
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* The programmer is not a total dick, e.g. like `this
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<https://github.com/davidhalter/jedi/issues/24>`_ :-)
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The actual algorithm is based on a principle I call lazy type inference. That
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said, the typical entry point for static analysis is calling
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``infer_expr_stmt``. There's separate logic for autocompletion in the API, the
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inference_state is all about inferring an expression.
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TODO this paragraph is not what jedi does anymore, it's similar, but not the
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same.
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Now you need to understand what follows after ``infer_expr_stmt``. Let's
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make an example::
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import datetime
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datetime.date.toda# <-- cursor here
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First of all, this module doesn't care about completion. It really just cares
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about ``datetime.date``. At the end of the procedure ``infer_expr_stmt`` will
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return the ``date`` class.
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To *visualize* this (simplified):
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- ``InferenceState.infer_expr_stmt`` doesn't do much, because there's no assignment.
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- ``Context.infer_node`` cares for resolving the dotted path
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- ``InferenceState.find_types`` searches for global definitions of datetime, which
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it finds in the definition of an import, by scanning the syntax tree.
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- Using the import logic, the datetime module is found.
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- Now ``find_types`` is called again by ``infer_node`` to find ``date``
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inside the datetime module.
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Now what would happen if we wanted ``datetime.date.foo.bar``? Two more
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calls to ``find_types``. However the second call would be ignored, because the
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first one would return nothing (there's no foo attribute in ``date``).
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What if the import would contain another ``ExprStmt`` like this::
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from foo import bar
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Date = bar.baz
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Well... You get it. Just another ``infer_expr_stmt`` recursion. It's really
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easy. Python can obviously get way more complicated then this. To understand
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tuple assignments, list comprehensions and everything else, a lot more code had
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to be written.
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Jedi has been tested very well, so you can just start modifying code. It's best
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to write your own test first for your "new" feature. Don't be scared of
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breaking stuff. As long as the tests pass, you're most likely to be fine.
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I need to mention now that lazy type inference is really good because it
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only *inferes* what needs to be *inferred*. All the statements and modules
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that are not used are just being ignored.
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"""
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import parso
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from jedi.file_io import FileIO
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from jedi import debug
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from jedi import settings
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from jedi.inference import imports
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from jedi.inference import recursion
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from jedi.inference.cache import inference_state_function_cache
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from jedi.inference import helpers
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from jedi.inference.names import TreeNameDefinition
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from jedi.inference.base_value import ContextualizedNode, \
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ValueSet, iterate_values
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from jedi.inference.value import ClassValue, FunctionValue
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from jedi.inference.syntax_tree import infer_expr_stmt, \
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check_tuple_assignments, tree_name_to_values
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from jedi.inference.imports import follow_error_node_imports_if_possible
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from jedi.plugins import plugin_manager
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class InferenceState(object):
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def __init__(self, project, environment=None, script_path=None):
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if environment is None:
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environment = project.get_environment()
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self.environment = environment
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self.script_path = script_path
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self.compiled_subprocess = environment.get_inference_state_subprocess(self)
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self.grammar = environment.get_grammar()
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self.latest_grammar = parso.load_grammar(version='3.7')
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self.memoize_cache = {} # for memoize decorators
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self.module_cache = imports.ModuleCache() # does the job of `sys.modules`.
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self.stub_module_cache = {} # Dict[Tuple[str, ...], Optional[ModuleValue]]
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self.compiled_cache = {} # see `inference.compiled.create()`
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self.inferred_element_counts = {}
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self.mixed_cache = {} # see `inference.compiled.mixed._create()`
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self.analysis = []
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self.dynamic_params_depth = 0
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self.is_analysis = False
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self.project = project
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self.access_cache = {}
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self.allow_descriptor_getattr = False
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self.flow_analysis_enabled = True
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self.reset_recursion_limitations()
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def import_module(self, import_names, sys_path=None, prefer_stubs=True):
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return imports.import_module_by_names(
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self, import_names, sys_path, prefer_stubs=prefer_stubs)
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@staticmethod
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@plugin_manager.decorate()
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def execute(value, arguments):
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debug.dbg('execute: %s %s', value, arguments)
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with debug.increase_indent_cm():
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value_set = value.py__call__(arguments=arguments)
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debug.dbg('execute result: %s in %s', value_set, value)
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return value_set
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@property
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@inference_state_function_cache()
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def builtins_module(self):
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module_name = u'builtins'
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if self.environment.version_info.major == 2:
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module_name = u'__builtin__'
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builtins_module, = self.import_module((module_name,), sys_path=())
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return builtins_module
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@property
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@inference_state_function_cache()
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def typing_module(self):
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typing_module, = self.import_module((u'typing',))
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return typing_module
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def reset_recursion_limitations(self):
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self.recursion_detector = recursion.RecursionDetector()
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self.execution_recursion_detector = recursion.ExecutionRecursionDetector(self)
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def get_sys_path(self, **kwargs):
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"""Convenience function"""
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return self.project._get_sys_path(self, **kwargs)
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def infer(self, context, name):
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def_ = name.get_definition(import_name_always=True)
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if def_ is not None:
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type_ = def_.type
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is_classdef = type_ == 'classdef'
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if is_classdef or type_ == 'funcdef':
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if is_classdef:
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c = ClassValue(self, context, name.parent)
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else:
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c = FunctionValue.from_context(context, name.parent)
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return ValueSet([c])
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if type_ == 'expr_stmt':
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is_simple_name = name.parent.type not in ('power', 'trailer')
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if is_simple_name:
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return infer_expr_stmt(context, def_, name)
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if type_ == 'for_stmt':
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container_types = context.infer_node(def_.children[3])
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cn = ContextualizedNode(context, def_.children[3])
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for_types = iterate_values(container_types, cn)
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n = TreeNameDefinition(context, name)
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return check_tuple_assignments(n, for_types)
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if type_ in ('import_from', 'import_name'):
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return imports.infer_import(context, name)
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if type_ == 'with_stmt':
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return tree_name_to_values(self, context, name)
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elif type_ == 'param':
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return context.py__getattribute__(name.value, position=name.end_pos)
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else:
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result = follow_error_node_imports_if_possible(context, name)
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if result is not None:
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return result
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return helpers.infer_call_of_leaf(context, name)
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def parse_and_get_code(self, code=None, path=None, encoding='utf-8',
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use_latest_grammar=False, file_io=None, **kwargs):
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if code is None:
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if file_io is None:
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file_io = FileIO(path)
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code = file_io.read()
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# We cannot just use parso, because it doesn't use errors='replace'.
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code = parso.python_bytes_to_unicode(code, encoding=encoding, errors='replace')
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if len(code) > settings._cropped_file_size:
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code = code[:settings._cropped_file_size]
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grammar = self.latest_grammar if use_latest_grammar else self.grammar
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return grammar.parse(code=code, path=path, file_io=file_io, **kwargs), code
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def parse(self, *args, **kwargs):
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return self.parse_and_get_code(*args, **kwargs)[0]
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