AIM-PIbd-32-Kurbanova-A-A/aimenv/Lib/site-packages/numpy/tests/test_public_api.py
2024-10-02 22:15:59 +04:00

683 lines
22 KiB
Python

import sys
import sysconfig
import subprocess
import pkgutil
import types
import importlib
import inspect
import warnings
import numpy as np
import numpy
from numpy.testing import IS_WASM
import pytest
try:
import ctypes
except ImportError:
ctypes = None
def check_dir(module, module_name=None):
"""Returns a mapping of all objects with the wrong __module__ attribute."""
if module_name is None:
module_name = module.__name__
results = {}
for name in dir(module):
if name == "core":
continue
item = getattr(module, name)
if (hasattr(item, '__module__') and hasattr(item, '__name__')
and item.__module__ != module_name):
results[name] = item.__module__ + '.' + item.__name__
return results
def test_numpy_namespace():
# We override dir to not show these members
allowlist = {
'recarray': 'numpy.rec.recarray',
'show_config': 'numpy.__config__.show',
}
bad_results = check_dir(np)
# pytest gives better error messages with the builtin assert than with
# assert_equal
assert bad_results == allowlist
@pytest.mark.skipif(IS_WASM, reason="can't start subprocess")
@pytest.mark.parametrize('name', ['testing'])
def test_import_lazy_import(name):
"""Make sure we can actually use the modules we lazy load.
While not exported as part of the public API, it was accessible. With the
use of __getattr__ and __dir__, this isn't always true It can happen that
an infinite recursion may happen.
This is the only way I found that would force the failure to appear on the
badly implemented code.
We also test for the presence of the lazily imported modules in dir
"""
exe = (sys.executable, '-c', "import numpy; numpy." + name)
result = subprocess.check_output(exe)
assert not result
# Make sure they are still in the __dir__
assert name in dir(np)
def test_dir_testing():
"""Assert that output of dir has only one "testing/tester"
attribute without duplicate"""
assert len(dir(np)) == len(set(dir(np)))
def test_numpy_linalg():
bad_results = check_dir(np.linalg)
assert bad_results == {}
def test_numpy_fft():
bad_results = check_dir(np.fft)
assert bad_results == {}
@pytest.mark.skipif(ctypes is None,
reason="ctypes not available in this python")
def test_NPY_NO_EXPORT():
cdll = ctypes.CDLL(np._core._multiarray_tests.__file__)
# Make sure an arbitrary NPY_NO_EXPORT function is actually hidden
f = getattr(cdll, 'test_not_exported', None)
assert f is None, ("'test_not_exported' is mistakenly exported, "
"NPY_NO_EXPORT does not work")
# Historically NumPy has not used leading underscores for private submodules
# much. This has resulted in lots of things that look like public modules
# (i.e. things that can be imported as `import numpy.somesubmodule.somefile`),
# but were never intended to be public. The PUBLIC_MODULES list contains
# modules that are either public because they were meant to be, or because they
# contain public functions/objects that aren't present in any other namespace
# for whatever reason and therefore should be treated as public.
#
# The PRIVATE_BUT_PRESENT_MODULES list contains modules that look public (lack
# of underscores) but should not be used. For many of those modules the
# current status is fine. For others it may make sense to work on making them
# private, to clean up our public API and avoid confusion.
PUBLIC_MODULES = ['numpy.' + s for s in [
"ctypeslib",
"dtypes",
"exceptions",
"f2py",
"fft",
"lib",
"lib.array_utils",
"lib.format",
"lib.introspect",
"lib.mixins",
"lib.npyio",
"lib.recfunctions", # note: still needs cleaning, was forgotten for 2.0
"lib.scimath",
"lib.stride_tricks",
"linalg",
"ma",
"ma.extras",
"ma.mrecords",
"polynomial",
"polynomial.chebyshev",
"polynomial.hermite",
"polynomial.hermite_e",
"polynomial.laguerre",
"polynomial.legendre",
"polynomial.polynomial",
"random",
"strings",
"testing",
"testing.overrides",
"typing",
"typing.mypy_plugin",
"version",
]]
if sys.version_info < (3, 12):
PUBLIC_MODULES += [
'numpy.' + s for s in [
"distutils",
"distutils.cpuinfo",
"distutils.exec_command",
"distutils.misc_util",
"distutils.log",
"distutils.system_info",
]
]
PUBLIC_ALIASED_MODULES = [
"numpy.char",
"numpy.emath",
"numpy.rec",
]
PRIVATE_BUT_PRESENT_MODULES = ['numpy.' + s for s in [
"compat",
"compat.py3k",
"conftest",
"core",
"core.multiarray",
"core.numeric",
"core.umath",
"core.arrayprint",
"core.defchararray",
"core.einsumfunc",
"core.fromnumeric",
"core.function_base",
"core.getlimits",
"core.numerictypes",
"core.overrides",
"core.records",
"core.shape_base",
"f2py.auxfuncs",
"f2py.capi_maps",
"f2py.cb_rules",
"f2py.cfuncs",
"f2py.common_rules",
"f2py.crackfortran",
"f2py.diagnose",
"f2py.f2py2e",
"f2py.f90mod_rules",
"f2py.func2subr",
"f2py.rules",
"f2py.symbolic",
"f2py.use_rules",
"fft.helper",
"lib.user_array", # note: not in np.lib, but probably should just be deleted
"linalg.lapack_lite",
"linalg.linalg",
"ma.core",
"ma.testutils",
"ma.timer_comparison",
"matlib",
"matrixlib",
"matrixlib.defmatrix",
"polynomial.polyutils",
"random.mtrand",
"random.bit_generator",
"testing.print_coercion_tables",
]]
if sys.version_info < (3, 12):
PRIVATE_BUT_PRESENT_MODULES += [
'numpy.' + s for s in [
"distutils.armccompiler",
"distutils.fujitsuccompiler",
"distutils.ccompiler",
'distutils.ccompiler_opt',
"distutils.command",
"distutils.command.autodist",
"distutils.command.bdist_rpm",
"distutils.command.build",
"distutils.command.build_clib",
"distutils.command.build_ext",
"distutils.command.build_py",
"distutils.command.build_scripts",
"distutils.command.build_src",
"distutils.command.config",
"distutils.command.config_compiler",
"distutils.command.develop",
"distutils.command.egg_info",
"distutils.command.install",
"distutils.command.install_clib",
"distutils.command.install_data",
"distutils.command.install_headers",
"distutils.command.sdist",
"distutils.conv_template",
"distutils.core",
"distutils.extension",
"distutils.fcompiler",
"distutils.fcompiler.absoft",
"distutils.fcompiler.arm",
"distutils.fcompiler.compaq",
"distutils.fcompiler.environment",
"distutils.fcompiler.g95",
"distutils.fcompiler.gnu",
"distutils.fcompiler.hpux",
"distutils.fcompiler.ibm",
"distutils.fcompiler.intel",
"distutils.fcompiler.lahey",
"distutils.fcompiler.mips",
"distutils.fcompiler.nag",
"distutils.fcompiler.none",
"distutils.fcompiler.pathf95",
"distutils.fcompiler.pg",
"distutils.fcompiler.nv",
"distutils.fcompiler.sun",
"distutils.fcompiler.vast",
"distutils.fcompiler.fujitsu",
"distutils.from_template",
"distutils.intelccompiler",
"distutils.lib2def",
"distutils.line_endings",
"distutils.mingw32ccompiler",
"distutils.msvccompiler",
"distutils.npy_pkg_config",
"distutils.numpy_distribution",
"distutils.pathccompiler",
"distutils.unixccompiler",
]
]
def is_unexpected(name):
"""Check if this needs to be considered."""
if '._' in name or '.tests' in name or '.setup' in name:
return False
if name in PUBLIC_MODULES:
return False
if name in PUBLIC_ALIASED_MODULES:
return False
if name in PRIVATE_BUT_PRESENT_MODULES:
return False
return True
if sys.version_info < (3, 12):
SKIP_LIST = ["numpy.distutils.msvc9compiler"]
else:
SKIP_LIST = []
# suppressing warnings from deprecated modules
@pytest.mark.filterwarnings("ignore:.*np.compat.*:DeprecationWarning")
def test_all_modules_are_expected():
"""
Test that we don't add anything that looks like a new public module by
accident. Check is based on filenames.
"""
modnames = []
for _, modname, ispkg in pkgutil.walk_packages(path=np.__path__,
prefix=np.__name__ + '.',
onerror=None):
if is_unexpected(modname) and modname not in SKIP_LIST:
# We have a name that is new. If that's on purpose, add it to
# PUBLIC_MODULES. We don't expect to have to add anything to
# PRIVATE_BUT_PRESENT_MODULES. Use an underscore in the name!
modnames.append(modname)
if modnames:
raise AssertionError(f'Found unexpected modules: {modnames}')
# Stuff that clearly shouldn't be in the API and is detected by the next test
# below
SKIP_LIST_2 = [
'numpy.lib.math',
'numpy.matlib.char',
'numpy.matlib.rec',
'numpy.matlib.emath',
'numpy.matlib.exceptions',
'numpy.matlib.math',
'numpy.matlib.linalg',
'numpy.matlib.fft',
'numpy.matlib.random',
'numpy.matlib.ctypeslib',
'numpy.matlib.ma',
]
if sys.version_info < (3, 12):
SKIP_LIST_2 += [
'numpy.distutils.log.sys',
'numpy.distutils.log.logging',
'numpy.distutils.log.warnings',
]
def test_all_modules_are_expected_2():
"""
Method checking all objects. The pkgutil-based method in
`test_all_modules_are_expected` does not catch imports into a namespace,
only filenames. So this test is more thorough, and checks this like:
import .lib.scimath as emath
To check if something in a module is (effectively) public, one can check if
there's anything in that namespace that's a public function/object but is
not exposed in a higher-level namespace. For example for a `numpy.lib`
submodule::
mod = np.lib.mixins
for obj in mod.__all__:
if obj in np.__all__:
continue
elif obj in np.lib.__all__:
continue
else:
print(obj)
"""
def find_unexpected_members(mod_name):
members = []
module = importlib.import_module(mod_name)
if hasattr(module, '__all__'):
objnames = module.__all__
else:
objnames = dir(module)
for objname in objnames:
if not objname.startswith('_'):
fullobjname = mod_name + '.' + objname
if isinstance(getattr(module, objname), types.ModuleType):
if is_unexpected(fullobjname):
if fullobjname not in SKIP_LIST_2:
members.append(fullobjname)
return members
unexpected_members = find_unexpected_members("numpy")
for modname in PUBLIC_MODULES:
unexpected_members.extend(find_unexpected_members(modname))
if unexpected_members:
raise AssertionError("Found unexpected object(s) that look like "
"modules: {}".format(unexpected_members))
def test_api_importable():
"""
Check that all submodules listed higher up in this file can be imported
Note that if a PRIVATE_BUT_PRESENT_MODULES entry goes missing, it may
simply need to be removed from the list (deprecation may or may not be
needed - apply common sense).
"""
def check_importable(module_name):
try:
importlib.import_module(module_name)
except (ImportError, AttributeError):
return False
return True
module_names = []
for module_name in PUBLIC_MODULES:
if not check_importable(module_name):
module_names.append(module_name)
if module_names:
raise AssertionError("Modules in the public API that cannot be "
"imported: {}".format(module_names))
for module_name in PUBLIC_ALIASED_MODULES:
try:
eval(module_name)
except AttributeError:
module_names.append(module_name)
if module_names:
raise AssertionError("Modules in the public API that were not "
"found: {}".format(module_names))
with warnings.catch_warnings(record=True) as w:
warnings.filterwarnings('always', category=DeprecationWarning)
warnings.filterwarnings('always', category=ImportWarning)
for module_name in PRIVATE_BUT_PRESENT_MODULES:
if not check_importable(module_name):
module_names.append(module_name)
if module_names:
raise AssertionError("Modules that are not really public but looked "
"public and can not be imported: "
"{}".format(module_names))
@pytest.mark.xfail(
sysconfig.get_config_var("Py_DEBUG") not in (None, 0, "0"),
reason=(
"NumPy possibly built with `USE_DEBUG=True ./tools/travis-test.sh`, "
"which does not expose the `array_api` entry point. "
"See https://github.com/numpy/numpy/pull/19800"
),
)
def test_array_api_entry_point():
"""
Entry point for Array API implementation can be found with importlib and
returns the main numpy namespace.
"""
# For a development install that did not go through meson-python,
# the entrypoint will not have been installed. So ensure this test fails
# only if numpy is inside site-packages.
numpy_in_sitepackages = sysconfig.get_path('platlib') in np.__file__
eps = importlib.metadata.entry_points()
try:
xp_eps = eps.select(group="array_api")
except AttributeError:
# The select interface for entry_points was introduced in py3.10,
# deprecating its dict interface. We fallback to dict keys for finding
# Array API entry points so that running this test in <=3.9 will
# still work - see https://github.com/numpy/numpy/pull/19800.
xp_eps = eps.get("array_api", [])
if len(xp_eps) == 0:
if numpy_in_sitepackages:
msg = "No entry points for 'array_api' found"
raise AssertionError(msg) from None
return
try:
ep = next(ep for ep in xp_eps if ep.name == "numpy")
except StopIteration:
if numpy_in_sitepackages:
msg = "'numpy' not in array_api entry points"
raise AssertionError(msg) from None
return
if ep.value == 'numpy.array_api':
# Looks like the entrypoint for the current numpy build isn't
# installed, but an older numpy is also installed and hence the
# entrypoint is pointing to the old (no longer existing) location.
# This isn't a problem except for when running tests with `spin` or an
# in-place build.
return
xp = ep.load()
msg = (
f"numpy entry point value '{ep.value}' "
"does not point to our Array API implementation"
)
assert xp is numpy, msg
def test_main_namespace_all_dir_coherence():
"""
Checks if `dir(np)` and `np.__all__` are consistent and return
the same content, excluding exceptions and private members.
"""
def _remove_private_members(member_set):
return {m for m in member_set if not m.startswith('_')}
def _remove_exceptions(member_set):
return member_set.difference({
"bool" # included only in __dir__
})
all_members = _remove_private_members(np.__all__)
all_members = _remove_exceptions(all_members)
dir_members = _remove_private_members(np.__dir__())
dir_members = _remove_exceptions(dir_members)
assert all_members == dir_members, (
"Members that break symmetry: "
f"{all_members.symmetric_difference(dir_members)}"
)
@pytest.mark.filterwarnings(
r"ignore:numpy.core(\.\w+)? is deprecated:DeprecationWarning"
)
def test_core_shims_coherence():
"""
Check that all "semi-public" members of `numpy._core` are also accessible
from `numpy.core` shims.
"""
import numpy.core as core
for member_name in dir(np._core):
# Skip private and test members. Also if a module is aliased,
# no need to add it to np.core
if (
member_name.startswith("_")
or member_name in ["tests", "strings"]
or f"numpy.{member_name}" in PUBLIC_ALIASED_MODULES
):
continue
member = getattr(np._core, member_name)
# np.core is a shim and all submodules of np.core are shims
# but we should be able to import everything in those shims
# that are available in the "real" modules in np._core
if inspect.ismodule(member):
submodule = member
submodule_name = member_name
for submodule_member_name in dir(submodule):
# ignore dunder names
if submodule_member_name.startswith("__"):
continue
submodule_member = getattr(submodule, submodule_member_name)
core_submodule = __import__(
f"numpy.core.{submodule_name}",
fromlist=[submodule_member_name]
)
assert submodule_member is getattr(
core_submodule, submodule_member_name
)
else:
assert member is getattr(core, member_name)
def test_functions_single_location():
"""
Check that each public function is available from one location only.
Test performs BFS search traversing NumPy's public API. It flags
any function-like object that is accessible from more that one place.
"""
from typing import Any, Callable, Dict, List, Set, Tuple
from numpy._core._multiarray_umath import (
_ArrayFunctionDispatcher as dispatched_function
)
visited_modules: Set[types.ModuleType] = {np}
visited_functions: Set[Callable[..., Any]] = set()
# Functions often have `__name__` overridden, therefore we need
# to keep track of locations where functions have been found.
functions_original_paths: Dict[Callable[..., Any], str] = dict()
# Here we aggregate functions with more than one location.
# It must be empty for the test to pass.
duplicated_functions: List[Tuple] = []
modules_queue = [np]
while len(modules_queue) > 0:
module = modules_queue.pop()
for member_name in dir(module):
member = getattr(module, member_name)
# first check if we got a module
if (
inspect.ismodule(member) and # it's a module
"numpy" in member.__name__ and # inside NumPy
not member_name.startswith("_") and # not private
"numpy._core" not in member.__name__ and # outside _core
# not a legacy or testing module
member_name not in ["f2py", "ma", "testing", "tests"] and
member not in visited_modules # not visited yet
):
modules_queue.append(member)
visited_modules.add(member)
# else check if we got a function-like object
elif (
inspect.isfunction(member) or
isinstance(member, (dispatched_function, np.ufunc))
):
if member in visited_functions:
# skip main namespace functions with aliases
if (
member.__name__ in [
"absolute", # np.abs
"arccos", # np.acos
"arccosh", # np.acosh
"arcsin", # np.asin
"arcsinh", # np.asinh
"arctan", # np.atan
"arctan2", # np.atan2
"arctanh", # np.atanh
"left_shift", # np.bitwise_left_shift
"right_shift", # np.bitwise_right_shift
"conjugate", # np.conj
"invert", # np.bitwise_not & np.bitwise_invert
"remainder", # np.mod
"divide", # np.true_divide
"concatenate", # np.concat
"power", # np.pow
"transpose", # np.permute_dims
] and
module.__name__ == "numpy"
):
continue
# skip trimcoef from numpy.polynomial as it is
# duplicated by design.
if (
member.__name__ == "trimcoef" and
module.__name__.startswith("numpy.polynomial")
):
continue
# skip ufuncs that are exported in np.strings as well
if member.__name__ in (
"add",
"equal",
"not_equal",
"greater",
"greater_equal",
"less",
"less_equal",
) and module.__name__ == "numpy.strings":
continue
# numpy.char reexports all numpy.strings functions for
# backwards-compatibility
if module.__name__ == "numpy.char":
continue
# function is present in more than one location!
duplicated_functions.append(
(member.__name__,
module.__name__,
functions_original_paths[member])
)
else:
visited_functions.add(member)
functions_original_paths[member] = module.__name__
del visited_functions, visited_modules, functions_original_paths
assert len(duplicated_functions) == 0, duplicated_functions