438 lines
12 KiB
Python
438 lines
12 KiB
Python
from __future__ import annotations
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import bz2
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from functools import wraps
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import gzip
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import io
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import socket
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import tarfile
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from typing import (
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TYPE_CHECKING,
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Any,
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Callable,
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)
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import zipfile
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from pandas._typing import (
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FilePath,
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ReadPickleBuffer,
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)
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from pandas.compat import get_lzma_file
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from pandas.compat._optional import import_optional_dependency
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import pandas as pd
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from pandas._testing._random import rands
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from pandas._testing.contexts import ensure_clean
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from pandas.io.common import urlopen
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if TYPE_CHECKING:
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from pandas import (
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DataFrame,
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Series,
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)
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# skip tests on exceptions with these messages
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_network_error_messages = (
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# 'urlopen error timed out',
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# 'timeout: timed out',
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# 'socket.timeout: timed out',
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"timed out",
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"Server Hangup",
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"HTTP Error 503: Service Unavailable",
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"502: Proxy Error",
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"HTTP Error 502: internal error",
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"HTTP Error 502",
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"HTTP Error 503",
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"HTTP Error 403",
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"HTTP Error 400",
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"Temporary failure in name resolution",
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"Name or service not known",
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"Connection refused",
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"certificate verify",
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)
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# or this e.errno/e.reason.errno
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_network_errno_vals = (
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101, # Network is unreachable
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111, # Connection refused
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110, # Connection timed out
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104, # Connection reset Error
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54, # Connection reset by peer
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60, # urllib.error.URLError: [Errno 60] Connection timed out
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)
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# Both of the above shouldn't mask real issues such as 404's
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# or refused connections (changed DNS).
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# But some tests (test_data yahoo) contact incredibly flakey
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# servers.
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# and conditionally raise on exception types in _get_default_network_errors
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def _get_default_network_errors():
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# Lazy import for http.client & urllib.error
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# because it imports many things from the stdlib
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import http.client
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import urllib.error
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return (
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OSError,
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http.client.HTTPException,
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TimeoutError,
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urllib.error.URLError,
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socket.timeout,
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)
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def optional_args(decorator):
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"""
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allows a decorator to take optional positional and keyword arguments.
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Assumes that taking a single, callable, positional argument means that
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it is decorating a function, i.e. something like this::
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@my_decorator
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def function(): pass
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Calls decorator with decorator(f, *args, **kwargs)
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"""
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@wraps(decorator)
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def wrapper(*args, **kwargs):
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def dec(f):
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return decorator(f, *args, **kwargs)
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is_decorating = not kwargs and len(args) == 1 and callable(args[0])
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if is_decorating:
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f = args[0]
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args = ()
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return dec(f)
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else:
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return dec
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return wrapper
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@optional_args
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def network(
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t,
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url="https://www.google.com",
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raise_on_error=False,
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check_before_test=False,
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error_classes=None,
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skip_errnos=_network_errno_vals,
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_skip_on_messages=_network_error_messages,
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):
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"""
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Label a test as requiring network connection and, if an error is
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encountered, only raise if it does not find a network connection.
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In comparison to ``network``, this assumes an added contract to your test:
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you must assert that, under normal conditions, your test will ONLY fail if
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it does not have network connectivity.
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You can call this in 3 ways: as a standard decorator, with keyword
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arguments, or with a positional argument that is the url to check.
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Parameters
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----------
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t : callable
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The test requiring network connectivity.
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url : path
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The url to test via ``pandas.io.common.urlopen`` to check
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for connectivity. Defaults to 'https://www.google.com'.
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raise_on_error : bool
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If True, never catches errors.
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check_before_test : bool
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If True, checks connectivity before running the test case.
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error_classes : tuple or Exception
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error classes to ignore. If not in ``error_classes``, raises the error.
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defaults to OSError. Be careful about changing the error classes here.
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skip_errnos : iterable of int
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Any exception that has .errno or .reason.erno set to one
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of these values will be skipped with an appropriate
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message.
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_skip_on_messages: iterable of string
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any exception e for which one of the strings is
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a substring of str(e) will be skipped with an appropriate
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message. Intended to suppress errors where an errno isn't available.
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Notes
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-----
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* ``raise_on_error`` supersedes ``check_before_test``
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Returns
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-------
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t : callable
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The decorated test ``t``, with checks for connectivity errors.
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Example
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-------
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Tests decorated with @network will fail if it's possible to make a network
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connection to another URL (defaults to google.com)::
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>>> from pandas import _testing as tm
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>>> @tm.network
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... def test_network():
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... with pd.io.common.urlopen("rabbit://bonanza.com"):
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... pass
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>>> test_network() # doctest: +SKIP
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Traceback
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...
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URLError: <urlopen error unknown url type: rabbit>
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You can specify alternative URLs::
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>>> @tm.network("https://www.yahoo.com")
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... def test_something_with_yahoo():
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... raise OSError("Failure Message")
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>>> test_something_with_yahoo() # doctest: +SKIP
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Traceback (most recent call last):
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...
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OSError: Failure Message
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If you set check_before_test, it will check the url first and not run the
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test on failure::
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>>> @tm.network("failing://url.blaher", check_before_test=True)
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... def test_something():
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... print("I ran!")
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... raise ValueError("Failure")
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>>> test_something() # doctest: +SKIP
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Traceback (most recent call last):
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...
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Errors not related to networking will always be raised.
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"""
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import pytest
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if error_classes is None:
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error_classes = _get_default_network_errors()
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t.network = True
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@wraps(t)
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def wrapper(*args, **kwargs):
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if (
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check_before_test
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and not raise_on_error
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and not can_connect(url, error_classes)
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):
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pytest.skip(
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f"May not have network connectivity because cannot connect to {url}"
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)
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try:
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return t(*args, **kwargs)
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except Exception as err:
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errno = getattr(err, "errno", None)
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if not errno and hasattr(errno, "reason"):
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# error: "Exception" has no attribute "reason"
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errno = getattr(err.reason, "errno", None) # type: ignore[attr-defined]
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if errno in skip_errnos:
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pytest.skip(f"Skipping test due to known errno and error {err}")
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e_str = str(err)
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if any(m.lower() in e_str.lower() for m in _skip_on_messages):
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pytest.skip(
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f"Skipping test because exception message is known and error {err}"
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)
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if not isinstance(err, error_classes) or raise_on_error:
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raise
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else:
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pytest.skip(
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f"Skipping test due to lack of connectivity and error {err}"
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)
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return wrapper
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def can_connect(url, error_classes=None) -> bool:
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"""
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Try to connect to the given url. True if succeeds, False if OSError
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raised
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Parameters
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----------
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url : basestring
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The URL to try to connect to
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Returns
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-------
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connectable : bool
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Return True if no OSError (unable to connect) or URLError (bad url) was
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raised
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"""
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if error_classes is None:
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error_classes = _get_default_network_errors()
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try:
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with urlopen(url, timeout=20) as response:
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# Timeout just in case rate-limiting is applied
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if response.status != 200:
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return False
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except error_classes:
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return False
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else:
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return True
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# ------------------------------------------------------------------
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# File-IO
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def round_trip_pickle(
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obj: Any, path: FilePath | ReadPickleBuffer | None = None
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) -> DataFrame | Series:
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"""
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Pickle an object and then read it again.
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Parameters
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----------
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obj : any object
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The object to pickle and then re-read.
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path : str, path object or file-like object, default None
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The path where the pickled object is written and then read.
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Returns
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-------
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pandas object
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The original object that was pickled and then re-read.
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"""
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_path = path
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if _path is None:
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_path = f"__{rands(10)}__.pickle"
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with ensure_clean(_path) as temp_path:
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pd.to_pickle(obj, temp_path)
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return pd.read_pickle(temp_path)
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def round_trip_pathlib(writer, reader, path: str | None = None):
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"""
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Write an object to file specified by a pathlib.Path and read it back
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Parameters
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----------
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writer : callable bound to pandas object
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IO writing function (e.g. DataFrame.to_csv )
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reader : callable
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IO reading function (e.g. pd.read_csv )
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path : str, default None
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The path where the object is written and then read.
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Returns
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-------
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pandas object
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The original object that was serialized and then re-read.
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"""
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import pytest
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Path = pytest.importorskip("pathlib").Path
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if path is None:
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path = "___pathlib___"
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with ensure_clean(path) as path:
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writer(Path(path))
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obj = reader(Path(path))
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return obj
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def round_trip_localpath(writer, reader, path: str | None = None):
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"""
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Write an object to file specified by a py.path LocalPath and read it back.
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Parameters
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----------
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writer : callable bound to pandas object
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IO writing function (e.g. DataFrame.to_csv )
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reader : callable
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IO reading function (e.g. pd.read_csv )
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path : str, default None
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The path where the object is written and then read.
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Returns
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-------
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pandas object
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The original object that was serialized and then re-read.
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"""
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import pytest
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LocalPath = pytest.importorskip("py.path").local
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if path is None:
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path = "___localpath___"
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with ensure_clean(path) as path:
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writer(LocalPath(path))
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obj = reader(LocalPath(path))
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return obj
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def write_to_compressed(compression, path, data, dest="test"):
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"""
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Write data to a compressed file.
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Parameters
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----------
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compression : {'gzip', 'bz2', 'zip', 'xz', 'zstd'}
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The compression type to use.
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path : str
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The file path to write the data.
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data : str
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The data to write.
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dest : str, default "test"
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The destination file (for ZIP only)
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Raises
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------
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ValueError : An invalid compression value was passed in.
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"""
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args: tuple[Any, ...] = (data,)
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mode = "wb"
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method = "write"
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compress_method: Callable
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if compression == "zip":
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compress_method = zipfile.ZipFile
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mode = "w"
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args = (dest, data)
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method = "writestr"
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elif compression == "tar":
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compress_method = tarfile.TarFile
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mode = "w"
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file = tarfile.TarInfo(name=dest)
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bytes = io.BytesIO(data)
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file.size = len(data)
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args = (file, bytes)
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method = "addfile"
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elif compression == "gzip":
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compress_method = gzip.GzipFile
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elif compression == "bz2":
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compress_method = bz2.BZ2File
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elif compression == "zstd":
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compress_method = import_optional_dependency("zstandard").open
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elif compression == "xz":
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compress_method = get_lzma_file()
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else:
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raise ValueError(f"Unrecognized compression type: {compression}")
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with compress_method(path, mode=mode) as f:
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getattr(f, method)(*args)
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# ------------------------------------------------------------------
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# Plotting
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def close(fignum=None) -> None:
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from matplotlib.pyplot import (
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close as _close,
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get_fignums,
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)
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if fignum is None:
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for fignum in get_fignums():
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_close(fignum)
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else:
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_close(fignum)
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