if length.value > len(inferred_sequence.elts):
# In this case, this will raise a ValueError
raise UseInferenceDefault
try:
elts = random.sample(inferred_sequence.elts, length.value)
except ValueError as exc:
raise UseInferenceDefault from exc
new_node = List(lineno=node.lineno, col_offset=node.col_offset, parent=node.scope())
new_elts = [
_clone_node_with_lineno(elt, parent=new_node, lineno=new_node.lineno)
for elt in elts
]
new_node.postinit(new_elts)
return iter((new_node,))
def _looks_like_random_sample(node):
func = node.func
if isinstance(func, Attribute):
return func.attrname == "sample"
if isinstance(func, Name):
return func.name == "sample"
return False
AstroidManager().register_transform(
Call, inference_tip(infer_random_sample), _looks_like_random_sample
)
return False
for decorator in node.decorators.nodes:
if not isinstance(decorator, Call):
continue
if _looks_like_functools_member(decorator, "lru_cache"):
return True
return False
def _looks_like_functools_member(node, member) -> bool:
"""Check if the given Call node is a functools.partial call"""
if isinstance(node.func, Name):
return node.func.name == member
if isinstance(node.func, Attribute):
return (node.func.attrname == member
and isinstance(node.func.expr, Name)
and node.func.expr.name == "functools")
return False
_looks_like_partial = partial(_looks_like_functools_member, member="partial")
AstroidManager().register_transform(FunctionDef, _transform_lru_cache,
_looks_like_lru_cache)
AstroidManager().register_transform(
Call,
inference_tip(_functools_partial_inference),
_looks_like_partial,
)
# Licensed under the LGPL: https://www.gnu.org/licenses/old-licenses/lgpl-2.1.en.html
# For details: https://github.com/PyCQA/astroid/blob/main/LICENSE
"""Astroid hooks for numpy.core.function_base module."""
import functools
from astroid.brain.brain_numpy_utils import infer_numpy_member, looks_like_numpy_member
from astroid.inference_tip import inference_tip
from astroid.manager import AstroidManager
from astroid.nodes.node_classes import Attribute
METHODS_TO_BE_INFERRED = {
"linspace":
"""def linspace(start, stop, num=50, endpoint=True, retstep=False, dtype=None, axis=0):
return numpy.ndarray([0, 0])""",
"logspace":
"""def logspace(start, stop, num=50, endpoint=True, base=10.0, dtype=None, axis=0):
return numpy.ndarray([0, 0])""",
"geomspace":
"""def geomspace(start, stop, num=50, endpoint=True, dtype=None, axis=0):
return numpy.ndarray([0, 0])""",
}
for func_name, func_src in METHODS_TO_BE_INFERRED.items():
inference_function = functools.partial(infer_numpy_member, func_src)
AstroidManager().register_transform(
Attribute,
inference_tip(inference_function),
functools.partial(looks_like_numpy_member, func_name),
)
def swapaxes(self, axis1, axis2): return np.ndarray([0, 0])
def take(self, indices, axis=None, out=None, mode='raise'): return np.ndarray([0, 0])
def tobytes(self, order='C'): return b''
def tofile(self, fid, sep="", format="%s"): return None
def tolist(self, ): return []
def tostring(self, order='C'): return b''
def trace(self, offset=0, axis1=0, axis2=1, dtype=None, out=None): return np.ndarray([0, 0])
def transpose(self, *axes): return np.ndarray([0, 0])
def var(self, axis=None, dtype=None, out=None, ddof=0, keepdims=False): return np.ndarray([0, 0])
def view(self, dtype=None, type=None): return np.ndarray([0, 0])
"""
if numpy_supports_type_hints():
ndarray += """
@classmethod
def __class_getitem__(cls, value):
return cls
"""
node = extract_node(ndarray)
return node.infer(context=context)
def _looks_like_numpy_ndarray(node):
return isinstance(node, Attribute) and node.attrname == "ndarray"
AstroidManager().register_transform(
Attribute,
inference_tip(infer_numpy_ndarray),
_looks_like_numpy_ndarray,
)
# In this case, this will raise a ValueError
raise UseInferenceDefault
try:
elts = random.sample(inferred_sequence.elts, length.value)
except ValueError as exc:
raise UseInferenceDefault from exc
new_node = List(lineno=node.lineno,
col_offset=node.col_offset,
parent=node.scope())
new_elts = [
_clone_node_with_lineno(elt, parent=new_node, lineno=new_node.lineno)
for elt in elts
]
new_node.postinit(new_elts)
return iter((new_node, ))
def _looks_like_random_sample(node):
func = node.func
if isinstance(func, Attribute):
return func.attrname == "sample"
if isinstance(func, Name):
return func.name == "sample"
return False
AstroidManager().register_transform(Call, inference_tip(infer_random_sample),
_looks_like_random_sample)
