def test_analyze_explicit_script(linter: PyLinter) -> None:
linter.set_reporter(testutils.GenericTestReporter())
linter.check([os.path.join(DATA_DIR, "ascript")])
assert len(linter.reporter.messages) == 1
assert linter.reporter.messages[0] == Message(
msg_id="C0301",
symbol="line-too-long",
msg="Line too long (175/100)",
confidence=interfaces.Confidence(
name="UNDEFINED",
description="Warning without any associated confidence level.",
),
location=MessageLocationTuple(
abspath=os.path.join(abspath(dirname(__file__)),
"ascript").replace(
f"lint{os.path.sep}ascript",
f"data{os.path.sep}ascript"),
path=f"tests{os.path.sep}data{os.path.sep}ascript",
module="data.ascript",
obj="",
line=2,
column=0,
end_line=None,
end_column=None,
),
)
def test_new_message(
message_definitions: ValuesView[MessageDefinition]) -> None:
def build_message(message_definition_: MessageDefinition,
location_value: MessageLocationTuple) -> Message:
return Message(
symbol=message_definition_.symbol,
msg_id=message_definition_.msgid,
location=location_value,
msg=message_definition_.msg,
confidence=HIGH,
)
template = "{path}:{line}:{column}: {msg_id}: {msg} ({symbol})"
for message_definition in message_definitions:
if message_definition.msgid == "E1234":
e1234_message_definition = message_definition
if message_definition.msgid == "W1234":
w1234_message_definition = message_definition
e1234_location_values = MessageLocationTuple(
abspath="1",
path="2",
module="3",
obj="4",
line=5,
column=6,
end_line=5,
end_column=9,
)
w1234_location_values = MessageLocationTuple(
abspath="7",
path="8",
module="9",
obj="10",
line=11,
column=12,
end_line=11,
end_column=14,
)
expected = (
"2:5:6: E1234: Duplicate keyword argument %r in %s call (duplicate-keyword-arg)"
)
e1234 = build_message(e1234_message_definition, e1234_location_values)
w1234 = build_message(w1234_message_definition, w1234_location_values)
assert e1234.format(template) == expected
assert w1234.format(template) == "8:11:12: W1234: message (msg-symbol)"
def test_addmessage(linter: PyLinter) -> None:
linter.set_reporter(testutils.GenericTestReporter())
linter.open()
linter.set_current_module("0123")
linter.add_message("C0301", line=1, args=(1, 2))
linter.add_message("line-too-long", line=2, args=(3, 4))
assert len(linter.reporter.messages) == 2
assert linter.reporter.messages[0] == Message(
msg_id="C0301",
symbol="line-too-long",
msg="Line too long (1/2)",
confidence=interfaces.Confidence(
name="UNDEFINED",
description="Warning without any associated confidence level.",
),
location=MessageLocationTuple(
abspath="0123",
path="0123",
module="0123",
obj="",
line=1,
column=0,
end_line=None,
end_column=None,
),
)
assert linter.reporter.messages[1] == Message(
msg_id="C0301",
symbol="line-too-long",
msg="Line too long (3/4)",
confidence=interfaces.Confidence(
name="UNDEFINED",
description="Warning without any associated confidence level.",
),
location=MessageLocationTuple(
abspath="0123",
path="0123",
module="0123",
obj="",
line=2,
column=0,
end_line=None,
end_column=None,
),
)
def inner(confidence: Confidence = HIGH) -> Message:
return Message(
symbol="missing-docstring",
msg_id="C0123",
location=MessageLocationTuple("abspath", "path", "module", "obj",
1, 2, 1, 3),
msg="msg",
confidence=confidence,
)
def check_parallel(
linter: "PyLinter",
jobs: int,
files: Iterable[FileItem],
arguments: Union[None, str, Sequence[str]] = None,
) -> None:
"""Use the given linter to lint the files with given amount of workers (jobs).
This splits the work filestream-by-filestream. If you need to do work across
multiple files, as in the similarity-checker, then inherit from MapReduceMixin and
implement the map/reduce mixin functionality.
"""
# The linter is inherited by all the pool's workers, i.e. the linter
# is identical to the linter object here. This is required so that
# a custom PyLinter object can be used.
initializer = functools.partial(_worker_initialize, arguments=arguments)
with multiprocessing.Pool(jobs,
initializer=initializer,
initargs=[dill.dumps(linter)]) as pool:
linter.open()
all_stats = []
all_mapreduce_data = collections.defaultdict(list)
# Maps each file to be worked on by a single _worker_check_single_file() call,
# collecting any map/reduce data by checker module so that we can 'reduce' it
# later.
for (
worker_idx, # used to merge map/reduce data across workers
module,
file_path,
base_name,
messages,
stats,
msg_status,
mapreduce_data,
) in pool.imap_unordered(_worker_check_single_file, files):
linter.file_state.base_name = base_name
linter.set_current_module(module, file_path)
for msg in messages:
msg = Message(msg[0], msg[1], MessageLocationTuple(*msg[2]),
msg[3], msg[4])
linter.reporter.handle_message(msg)
all_stats.append(stats)
all_mapreduce_data[worker_idx].append(mapreduce_data)
linter.msg_status |= msg_status
pool.close()
pool.join()
_merge_mapreduce_data(linter, all_mapreduce_data)
linter.stats = merge_stats([linter.stats] + all_stats)
# Insert stats data to local checkers.
for checker in linter.get_checkers():
if checker is not linter:
checker.stats = linter.stats
def __init__(
self,
msg_id: str,
symbol: str,
location: tuple[str, str, str, str, int, int] | MessageLocationTuple,
msg: str,
confidence: Confidence | None,
) -> None:
if not isinstance(location, MessageLocationTuple):
warn(
"In pylint 3.0, Messages will only accept a MessageLocationTuple as location parameter",
DeprecationWarning,
)
location = MessageLocationTuple(
location[0],
location[1],
location[2],
location[3],
location[4],
location[5],
None,
None,
)
self.msg_id = msg_id
self.symbol = symbol
self.msg = msg
self.C = msg_id[0]
self.category = MSG_TYPES[msg_id[0]]
self.confidence = confidence or UNDEFINED
self.abspath = location.abspath
self.path = location.path
self.module = location.module
self.obj = location.obj
self.line = location.line
self.column = location.column
self.end_line = location.end_line
self.end_column = location.end_column
def check_parallel(linter, jobs, files: Iterable[FileItem], arguments=None):
"""Use the given linter to lint the files with given amount of workers (jobs)
This splits the work filestream-by-filestream. If you need to do work across
multiple files, as in the similarity-checker, then inherit from MapReduceMixin and
implement the map/reduce mixin functionality"""
# The reporter does not need to be passed to worker processes, i.e. the reporter does
original_reporter = linter.reporter
linter.reporter = None
# The linter is inherited by all the pool's workers, i.e. the linter
# is identical to the linter object here. This is required so that
# a custom PyLinter object can be used.
initializer = functools.partial(_worker_initialize, arguments=arguments)
pool = multiprocessing.Pool( # pylint: disable=consider-using-with
jobs,
initializer=initializer,
initargs=[linter])
# ..and now when the workers have inherited the linter, the actual reporter
# can be set back here on the parent process so that results get stored into
# correct reporter
linter.set_reporter(original_reporter)
linter.open()
try:
all_stats = []
all_mapreduce_data = collections.defaultdict(list)
# Maps each file to be worked on by a single _worker_check_single_file() call,
# collecting any map/reduce data by checker module so that we can 'reduce' it
# later.
for (
worker_idx, # used to merge map/reduce data across workers
module,
file_path,
base_name,
messages,
stats,
msg_status,
mapreduce_data,
) in pool.imap_unordered(_worker_check_single_file, files):
linter.file_state.base_name = base_name
linter.set_current_module(module, file_path)
for msg in messages:
msg = Message(msg[0], msg[1], MessageLocationTuple(*msg[2]),
msg[3], msg[4])
linter.reporter.handle_message(
msg
) # type: ignore[attr-defined] # linter.set_reporter() call above makes linter have a reporter attr
all_stats.append(stats)
all_mapreduce_data[worker_idx].append(mapreduce_data)
linter.msg_status |= msg_status
finally:
pool.close()
pool.join()
_merge_mapreduce_data(linter, all_mapreduce_data)
linter.stats = merge_stats([linter.stats] + all_stats)
# Insert stats data to local checkers.
for checker in linter.get_checkers():
if checker is not linter:
checker.stats = linter.stats
