Inzynierka_Gwiazdy/machine_learning/Lib/site-packages/pandas/_testing/contexts.py
2023-09-20 19:46:58 +02:00

220 lines
5.2 KiB
Python

from __future__ import annotations
from contextlib import contextmanager
import os
from pathlib import Path
import tempfile
from typing import (
IO,
Any,
Generator,
)
import uuid
from pandas._typing import (
BaseBuffer,
CompressionOptions,
FilePath,
)
from pandas.compat import PYPY
from pandas.errors import ChainedAssignmentError
from pandas import set_option
from pandas.io.common import get_handle
@contextmanager
def decompress_file(
path: FilePath | BaseBuffer, compression: CompressionOptions
) -> Generator[IO[bytes], None, None]:
"""
Open a compressed file and return a file object.
Parameters
----------
path : str
The path where the file is read from.
compression : {'gzip', 'bz2', 'zip', 'xz', 'zstd', None}
Name of the decompression to use
Returns
-------
file object
"""
with get_handle(path, "rb", compression=compression, is_text=False) as handle:
yield handle.handle
@contextmanager
def set_timezone(tz: str) -> Generator[None, None, None]:
"""
Context manager for temporarily setting a timezone.
Parameters
----------
tz : str
A string representing a valid timezone.
Examples
--------
>>> from datetime import datetime
>>> from dateutil.tz import tzlocal
>>> tzlocal().tzname(datetime(2021, 1, 1)) # doctest: +SKIP
'IST'
>>> with set_timezone('US/Eastern'):
... tzlocal().tzname(datetime(2021, 1, 1))
...
'EST'
"""
import time
def setTZ(tz) -> None:
if tz is None:
try:
del os.environ["TZ"]
except KeyError:
pass
else:
os.environ["TZ"] = tz
time.tzset()
orig_tz = os.environ.get("TZ")
setTZ(tz)
try:
yield
finally:
setTZ(orig_tz)
@contextmanager
def ensure_clean(
filename=None, return_filelike: bool = False, **kwargs: Any
) -> Generator[Any, None, None]:
"""
Gets a temporary path and agrees to remove on close.
This implementation does not use tempfile.mkstemp to avoid having a file handle.
If the code using the returned path wants to delete the file itself, windows
requires that no program has a file handle to it.
Parameters
----------
filename : str (optional)
suffix of the created file.
return_filelike : bool (default False)
if True, returns a file-like which is *always* cleaned. Necessary for
savefig and other functions which want to append extensions.
**kwargs
Additional keywords are passed to open().
"""
folder = Path(tempfile.gettempdir())
if filename is None:
filename = ""
filename = str(uuid.uuid4()) + filename
path = folder / filename
path.touch()
handle_or_str: str | IO = str(path)
if return_filelike:
kwargs.setdefault("mode", "w+b")
handle_or_str = open(path, **kwargs)
try:
yield handle_or_str
finally:
if not isinstance(handle_or_str, str):
handle_or_str.close()
if path.is_file():
path.unlink()
@contextmanager
def ensure_safe_environment_variables() -> Generator[None, None, None]:
"""
Get a context manager to safely set environment variables
All changes will be undone on close, hence environment variables set
within this contextmanager will neither persist nor change global state.
"""
saved_environ = dict(os.environ)
try:
yield
finally:
os.environ.clear()
os.environ.update(saved_environ)
@contextmanager
def with_csv_dialect(name, **kwargs) -> Generator[None, None, None]:
"""
Context manager to temporarily register a CSV dialect for parsing CSV.
Parameters
----------
name : str
The name of the dialect.
kwargs : mapping
The parameters for the dialect.
Raises
------
ValueError : the name of the dialect conflicts with a builtin one.
See Also
--------
csv : Python's CSV library.
"""
import csv
_BUILTIN_DIALECTS = {"excel", "excel-tab", "unix"}
if name in _BUILTIN_DIALECTS:
raise ValueError("Cannot override builtin dialect.")
csv.register_dialect(name, **kwargs)
try:
yield
finally:
csv.unregister_dialect(name)
@contextmanager
def use_numexpr(use, min_elements=None) -> Generator[None, None, None]:
from pandas.core.computation import expressions as expr
if min_elements is None:
min_elements = expr._MIN_ELEMENTS
olduse = expr.USE_NUMEXPR
oldmin = expr._MIN_ELEMENTS
set_option("compute.use_numexpr", use)
expr._MIN_ELEMENTS = min_elements
try:
yield
finally:
expr._MIN_ELEMENTS = oldmin
set_option("compute.use_numexpr", olduse)
def raises_chained_assignment_error():
if PYPY:
from contextlib import nullcontext
return nullcontext()
else:
from pandas._testing import assert_produces_warning
return assert_produces_warning(
ChainedAssignmentError,
match=(
"A value is trying to be set on a copy of a DataFrame or Series "
"through chained assignment"
),
)