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2
.env/.gitignore vendored
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# created by virtualenv automatically
*

87
.env/bin/activate
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# This file must be used with "source bin/activate" *from bash*
# you cannot run it directly
if [ "${BASH_SOURCE-}" = "$0" ]; then
echo "You must source this script: \$ source $0" >&2
exit 33
fi
deactivate () {
unset -f pydoc >/dev/null 2>&1 || true
# reset old environment variables
# ! [ -z ${VAR+_} ] returns true if VAR is declared at all
if ! [ -z "${_OLD_VIRTUAL_PATH:+_}" ] ; then
PATH="$_OLD_VIRTUAL_PATH"
export PATH
unset _OLD_VIRTUAL_PATH
fi
if ! [ -z "${_OLD_VIRTUAL_PYTHONHOME+_}" ] ; then
PYTHONHOME="$_OLD_VIRTUAL_PYTHONHOME"
export PYTHONHOME
unset _OLD_VIRTUAL_PYTHONHOME
fi
# The hash command must be called to get it to forget past
# commands. Without forgetting past commands the $PATH changes
# we made may not be respected
hash -r 2>/dev/null
if ! [ -z "${_OLD_VIRTUAL_PS1+_}" ] ; then
PS1="$_OLD_VIRTUAL_PS1"
export PS1
unset _OLD_VIRTUAL_PS1
fi
unset VIRTUAL_ENV
unset VIRTUAL_ENV_PROMPT
if [ ! "${1-}" = "nondestructive" ] ; then
# Self destruct!
unset -f deactivate
fi
}
# unset irrelevant variables
deactivate nondestructive
VIRTUAL_ENV='/mnt/shared/Studia/informatyka/06-WPO-23Z-projekt-python/.env'
if ([ "$OSTYPE" = "cygwin" ] || [ "$OSTYPE" = "msys" ]) && $(command -v cygpath &> /dev/null) ; then
VIRTUAL_ENV=$(cygpath -u "$VIRTUAL_ENV")
fi
export VIRTUAL_ENV
_OLD_VIRTUAL_PATH="$PATH"
PATH="$VIRTUAL_ENV/bin:$PATH"
export PATH
if [ "x" != x ] ; then
VIRTUAL_ENV_PROMPT=""
else
VIRTUAL_ENV_PROMPT=$(basename "$VIRTUAL_ENV")
fi
export VIRTUAL_ENV_PROMPT
# unset PYTHONHOME if set
if ! [ -z "${PYTHONHOME+_}" ] ; then
_OLD_VIRTUAL_PYTHONHOME="$PYTHONHOME"
unset PYTHONHOME
fi
if [ -z "${VIRTUAL_ENV_DISABLE_PROMPT-}" ] ; then
_OLD_VIRTUAL_PS1="${PS1-}"
PS1="(${VIRTUAL_ENV_PROMPT}) ${PS1-}"
export PS1
fi
# Make sure to unalias pydoc if it's already there
alias pydoc 2>/dev/null >/dev/null && unalias pydoc || true
pydoc () {
python -m pydoc "$@"
}
# The hash command must be called to get it to forget past
# commands. Without forgetting past commands the $PATH changes
# we made may not be respected
hash -r 2>/dev/null

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.env/bin/activate.csh
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# This file must be used with "source bin/activate.csh" *from csh*.
# You cannot run it directly.
# Created by Davide Di Blasi <davidedb@gmail.com>.
set newline='\
'
alias deactivate 'test $?_OLD_VIRTUAL_PATH != 0 && setenv PATH "$_OLD_VIRTUAL_PATH:q" && unset _OLD_VIRTUAL_PATH; rehash; test $?_OLD_VIRTUAL_PROMPT != 0 && set prompt="$_OLD_VIRTUAL_PROMPT:q" && unset _OLD_VIRTUAL_PROMPT; unsetenv VIRTUAL_ENV; unsetenv VIRTUAL_ENV_PROMPT; test "\!:*" != "nondestructive" && unalias deactivate && unalias pydoc'
# Unset irrelevant variables.
deactivate nondestructive
setenv VIRTUAL_ENV '/mnt/shared/Studia/informatyka/06-WPO-23Z-projekt-python/.env'
set _OLD_VIRTUAL_PATH="$PATH:q"
setenv PATH "$VIRTUAL_ENV:q/bin:$PATH:q"
if ('' != "") then
setenv VIRTUAL_ENV_PROMPT ''
else
setenv VIRTUAL_ENV_PROMPT "$VIRTUAL_ENV:t:q"
endif
if ( $?VIRTUAL_ENV_DISABLE_PROMPT ) then
if ( $VIRTUAL_ENV_DISABLE_PROMPT == "" ) then
set do_prompt = "1"
else
set do_prompt = "0"
endif
else
set do_prompt = "1"
endif
if ( $do_prompt == "1" ) then
# Could be in a non-interactive environment,
# in which case, $prompt is undefined and we wouldn't
# care about the prompt anyway.
if ( $?prompt ) then
set _OLD_VIRTUAL_PROMPT="$prompt:q"
if ( "$prompt:q" =~ *"$newline:q"* ) then
:
else
set prompt = '('"$VIRTUAL_ENV_PROMPT:q"') '"$prompt:q"
endif
endif
endif
unset env_name
unset do_prompt
alias pydoc python -m pydoc
rehash

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.env/bin/activate.fish
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# This file must be used using `source bin/activate.fish` *within a running fish ( http://fishshell.com ) session*.
# Do not run it directly.
function _bashify_path -d "Converts a fish path to something bash can recognize"
set fishy_path $argv
set bashy_path $fishy_path[1]
for path_part in $fishy_path[2..-1]
set bashy_path "$bashy_path:$path_part"
end
echo $bashy_path
end
function _fishify_path -d "Converts a bash path to something fish can recognize"
echo $argv | tr ':' '\n'
end
function deactivate -d 'Exit virtualenv mode and return to the normal environment.'
# reset old environment variables
if test -n "$_OLD_VIRTUAL_PATH"
# https://github.com/fish-shell/fish-shell/issues/436 altered PATH handling
if test (echo $FISH_VERSION | head -c 1) -lt 3
set -gx PATH (_fishify_path "$_OLD_VIRTUAL_PATH")
else
set -gx PATH $_OLD_VIRTUAL_PATH
end
set -e _OLD_VIRTUAL_PATH
end
if test -n "$_OLD_VIRTUAL_PYTHONHOME"
set -gx PYTHONHOME "$_OLD_VIRTUAL_PYTHONHOME"
set -e _OLD_VIRTUAL_PYTHONHOME
end
if test -n "$_OLD_FISH_PROMPT_OVERRIDE"
and functions -q _old_fish_prompt
# Set an empty local `$fish_function_path` to allow the removal of `fish_prompt` using `functions -e`.
set -l fish_function_path
# Erase virtualenv's `fish_prompt` and restore the original.
functions -e fish_prompt
functions -c _old_fish_prompt fish_prompt
functions -e _old_fish_prompt
set -e _OLD_FISH_PROMPT_OVERRIDE
end
set -e VIRTUAL_ENV
set -e VIRTUAL_ENV_PROMPT
if test "$argv[1]" != 'nondestructive'
# Self-destruct!
functions -e pydoc
functions -e deactivate
functions -e _bashify_path
functions -e _fishify_path
end
end
# Unset irrelevant variables.
deactivate nondestructive
set -gx VIRTUAL_ENV '/mnt/shared/Studia/informatyka/06-WPO-23Z-projekt-python/.env'
# https://github.com/fish-shell/fish-shell/issues/436 altered PATH handling
if test (echo $FISH_VERSION | head -c 1) -lt 3
set -gx _OLD_VIRTUAL_PATH (_bashify_path $PATH)
else
set -gx _OLD_VIRTUAL_PATH $PATH
end
set -gx PATH "$VIRTUAL_ENV"'/bin' $PATH
# Prompt override provided?
# If not, just use the environment name.
if test -n ''
set -gx VIRTUAL_ENV_PROMPT ''
else
set -gx VIRTUAL_ENV_PROMPT (basename "$VIRTUAL_ENV")
end
# Unset `$PYTHONHOME` if set.
if set -q PYTHONHOME
set -gx _OLD_VIRTUAL_PYTHONHOME $PYTHONHOME
set -e PYTHONHOME
end
function pydoc
python -m pydoc $argv
end
if test -z "$VIRTUAL_ENV_DISABLE_PROMPT"
# Copy the current `fish_prompt` function as `_old_fish_prompt`.
functions -c fish_prompt _old_fish_prompt
function fish_prompt
# Run the user's prompt first; it might depend on (pipe)status.
set -l prompt (_old_fish_prompt)
printf '(%s) ' $VIRTUAL_ENV_PROMPT
string join -- \n $prompt # handle multi-line prompts
end
set -gx _OLD_FISH_PROMPT_OVERRIDE "$VIRTUAL_ENV"
end

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.env/bin/activate.nu
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# virtualenv activation module
# Activate with `overlay use activate.nu`
# Deactivate with `deactivate`, as usual
#
# To customize the overlay name, you can call `overlay use activate.nu as foo`,
# but then simply `deactivate` won't work because it is just an alias to hide
# the "activate" overlay. You'd need to call `overlay hide foo` manually.
export-env {
def is-string [x] {
($x | describe) == 'string'
}
def has-env [...names] {
$names | each {|n|
$n in $env
} | all {|i| $i == true}
}
# Emulates a `test -z`, but btter as it handles e.g 'false'
def is-env-true [name: string] {
if (has-env $name) {
# Try to parse 'true', '0', '1', and fail if not convertible
let parsed = (do -i { $env | get $name | into bool })
if ($parsed | describe) == 'bool' {
$parsed
} else {
not ($env | get -i $name | is-empty)
}
} else {
false
}
}
let virtual_env = '/mnt/shared/Studia/informatyka/06-WPO-23Z-projekt-python/.env'
let bin = 'bin'
let is_windows = ($nu.os-info.family) == 'windows'
let path_name = (if (has-env 'Path') {
'Path'
} else {
'PATH'
}
)
let venv_path = ([$virtual_env $bin] | path join)
let new_path = ($env | get $path_name | prepend $venv_path)
# If there is no default prompt, then use the env name instead
let virtual_env_prompt = (if ('' | is-empty) {
($virtual_env | path basename)
} else {
''
})
let new_env = {
$path_name : $new_path
VIRTUAL_ENV : $virtual_env
VIRTUAL_ENV_PROMPT : $virtual_env_prompt
}
let new_env = (if (is-env-true 'VIRTUAL_ENV_DISABLE_PROMPT') {
$new_env
} else {
# Creating the new prompt for the session
let virtual_prefix = $'(char lparen)($virtual_env_prompt)(char rparen) '
# Back up the old prompt builder
let old_prompt_command = (if (has-env 'PROMPT_COMMAND') {
$env.PROMPT_COMMAND
} else {
''
})
let new_prompt = (if (has-env 'PROMPT_COMMAND') {
if 'closure' in ($old_prompt_command | describe) {
{|| $'($virtual_prefix)(do $old_prompt_command)' }
} else {
{|| $'($virtual_prefix)($old_prompt_command)' }
}
} else {
{|| $'($virtual_prefix)' }
})
$new_env | merge {
PROMPT_COMMAND : $new_prompt
VIRTUAL_PREFIX : $virtual_prefix
}
})
# Environment variables that will be loaded as the virtual env
load-env $new_env
}
export alias pydoc = python -m pydoc
export alias deactivate = overlay hide activate

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.env/bin/activate.ps1
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$script:THIS_PATH = $myinvocation.mycommand.path
$script:BASE_DIR = Split-Path (Resolve-Path "$THIS_PATH/..") -Parent
function global:deactivate([switch] $NonDestructive) {
if (Test-Path variable:_OLD_VIRTUAL_PATH) {
$env:PATH = $variable:_OLD_VIRTUAL_PATH
Remove-Variable "_OLD_VIRTUAL_PATH" -Scope global
}
if (Test-Path function:_old_virtual_prompt) {
$function:prompt = $function:_old_virtual_prompt
Remove-Item function:\_old_virtual_prompt
}
if ($env:VIRTUAL_ENV) {
Remove-Item env:VIRTUAL_ENV -ErrorAction SilentlyContinue
}
if ($env:VIRTUAL_ENV_PROMPT) {
Remove-Item env:VIRTUAL_ENV_PROMPT -ErrorAction SilentlyContinue
}
if (!$NonDestructive) {
# Self destruct!
Remove-Item function:deactivate
Remove-Item function:pydoc
}
}
function global:pydoc {
python -m pydoc $args
}
# unset irrelevant variables
deactivate -nondestructive
$VIRTUAL_ENV = $BASE_DIR
$env:VIRTUAL_ENV = $VIRTUAL_ENV
if ("" -ne "") {
$env:VIRTUAL_ENV_PROMPT = ""
}
else {
$env:VIRTUAL_ENV_PROMPT = $( Split-Path $env:VIRTUAL_ENV -Leaf )
}
New-Variable -Scope global -Name _OLD_VIRTUAL_PATH -Value $env:PATH
$env:PATH = "$env:VIRTUAL_ENV/bin:" + $env:PATH
if (!$env:VIRTUAL_ENV_DISABLE_PROMPT) {
function global:_old_virtual_prompt {
""
}
$function:_old_virtual_prompt = $function:prompt
function global:prompt {
# Add the custom prefix to the existing prompt
$previous_prompt_value = & $function:_old_virtual_prompt
("(" + $env:VIRTUAL_ENV_PROMPT + ") " + $previous_prompt_value)
}
}

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.env/bin/activate_this.py
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"""
Activate virtualenv for current interpreter:
Use exec(open(this_file).read(), {'__file__': this_file}).
This can be used when you must use an existing Python interpreter, not the virtualenv bin/python.
""" # noqa: D415
from __future__ import annotations
import os
import site
import sys
try:
abs_file = os.path.abspath(__file__)
except NameError as exc:
msg = "You must use exec(open(this_file).read(), {'__file__': this_file})"
raise AssertionError(msg) from exc
bin_dir = os.path.dirname(abs_file)
base = bin_dir[: -len("bin") - 1] # strip away the bin part from the __file__, plus the path separator
# prepend bin to PATH (this file is inside the bin directory)
os.environ["PATH"] = os.pathsep.join([bin_dir, *os.environ.get("PATH", "").split(os.pathsep)])
os.environ["VIRTUAL_ENV"] = base # virtual env is right above bin directory
os.environ["VIRTUAL_ENV_PROMPT"] = "" or os.path.basename(base) # noqa: SIM222
# add the virtual environments libraries to the host python import mechanism
prev_length = len(sys.path)
for lib in "../lib/python3.11/site-packages".split(os.pathsep):
path = os.path.realpath(os.path.join(bin_dir, lib))
site.addsitedir(path.decode("utf-8") if "" else path)
sys.path[:] = sys.path[prev_length:] + sys.path[0:prev_length]
sys.real_prefix = sys.prefix
sys.prefix = base

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.env/bin/f2py
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#!/mnt/shared/Studia/informatyka/06-WPO-23Z-projekt-python/.env/bin/python
# -*- coding: utf-8 -*-
import re
import sys
from numpy.f2py.f2py2e import main
if __name__ == '__main__':
sys.argv[0] = re.sub(r'(-script\.pyw|\.exe)?$', '', sys.argv[0])
sys.exit(main())

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.env/bin/pip
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#!/mnt/shared/Studia/informatyka/06-WPO-23Z-projekt-python/.env/bin/python
# -*- coding: utf-8 -*-
import re
import sys
from pip._internal.cli.main import main
if __name__ == '__main__':
sys.argv[0] = re.sub(r'(-script\.pyw|\.exe)?$', '', sys.argv[0])
sys.exit(main())

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.env/bin/pip3
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#!/mnt/shared/Studia/informatyka/06-WPO-23Z-projekt-python/.env/bin/python
# -*- coding: utf-8 -*-
import re
import sys
from pip._internal.cli.main import main
if __name__ == '__main__':
sys.argv[0] = re.sub(r'(-script\.pyw|\.exe)?$', '', sys.argv[0])
sys.exit(main())

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.env/bin/pip3.11
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#!/mnt/shared/Studia/informatyka/06-WPO-23Z-projekt-python/.env/bin/python
# -*- coding: utf-8 -*-
import re
import sys
from pip._internal.cli.main import main
if __name__ == '__main__':
sys.argv[0] = re.sub(r'(-script\.pyw|\.exe)?$', '', sys.argv[0])
sys.exit(main())

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.env/bin/python
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/usr/bin/python3.11

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.env/bin/python3
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python

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.env/bin/python3.11
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python

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.env/bin/wheel
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#!/mnt/shared/Studia/informatyka/06-WPO-23Z-projekt-python/.env/bin/python
# -*- coding: utf-8 -*-
import re
import sys
from wheel.cli import main
if __name__ == '__main__':
sys.argv[0] = re.sub(r'(-script\.pyw|\.exe)?$', '', sys.argv[0])
sys.exit(main())

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.env/bin/wheel-3.11
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#!/mnt/shared/Studia/informatyka/06-WPO-23Z-projekt-python/.env/bin/python
# -*- coding: utf-8 -*-
import re
import sys
from wheel.cli import main
if __name__ == '__main__':
sys.argv[0] = re.sub(r'(-script\.pyw|\.exe)?$', '', sys.argv[0])
sys.exit(main())

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.env/bin/wheel3
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#!/mnt/shared/Studia/informatyka/06-WPO-23Z-projekt-python/.env/bin/python
# -*- coding: utf-8 -*-
import re
import sys
from wheel.cli import main
if __name__ == '__main__':
sys.argv[0] = re.sub(r'(-script\.pyw|\.exe)?$', '', sys.argv[0])
sys.exit(main())

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.env/bin/wheel3.11
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#!/mnt/shared/Studia/informatyka/06-WPO-23Z-projekt-python/.env/bin/python
# -*- coding: utf-8 -*-
import re
import sys
from wheel.cli import main
if __name__ == '__main__':
sys.argv[0] = re.sub(r'(-script\.pyw|\.exe)?$', '', sys.argv[0])
sys.exit(main())

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.env/lib/python3.11/site-packages/_distutils_hack/__init__.py
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# don't import any costly modules
import sys
import os
is_pypy = '__pypy__' in sys.builtin_module_names
def warn_distutils_present():
if 'distutils' not in sys.modules:
return
if is_pypy and sys.version_info < (3, 7):
# PyPy for 3.6 unconditionally imports distutils, so bypass the warning
# https://foss.heptapod.net/pypy/pypy/-/blob/be829135bc0d758997b3566062999ee8b23872b4/lib-python/3/site.py#L250
return
import warnings
warnings.warn(
"Distutils was imported before Setuptools, but importing Setuptools "
"also replaces the `distutils` module in `sys.modules`. This may lead "
"to undesirable behaviors or errors. To avoid these issues, avoid "
"using distutils directly, ensure that setuptools is installed in the "
"traditional way (e.g. not an editable install), and/or make sure "
"that setuptools is always imported before distutils."
)
def clear_distutils():
if 'distutils' not in sys.modules:
return
import warnings
warnings.warn("Setuptools is replacing distutils.")
mods = [
name
for name in sys.modules
if name == "distutils" or name.startswith("distutils.")
]
for name in mods:
del sys.modules[name]
def enabled():
"""
Allow selection of distutils by environment variable.
"""
which = os.environ.get('SETUPTOOLS_USE_DISTUTILS', 'local')
return which == 'local'
def ensure_local_distutils():
import importlib
clear_distutils()
# With the DistutilsMetaFinder in place,
# perform an import to cause distutils to be
# loaded from setuptools._distutils. Ref #2906.
with shim():
importlib.import_module('distutils')
# check that submodules load as expected
core = importlib.import_module('distutils.core')
assert '_distutils' in core.__file__, core.__file__
assert 'setuptools._distutils.log' not in sys.modules
def do_override():
"""
Ensure that the local copy of distutils is preferred over stdlib.
See https://github.com/pypa/setuptools/issues/417#issuecomment-392298401
for more motivation.
"""
if enabled():
warn_distutils_present()
ensure_local_distutils()
class _TrivialRe:
def __init__(self, *patterns):
self._patterns = patterns
def match(self, string):
return all(pat in string for pat in self._patterns)
class DistutilsMetaFinder:
def find_spec(self, fullname, path, target=None):
# optimization: only consider top level modules and those
# found in the CPython test suite.
if path is not None and not fullname.startswith('test.'):
return
method_name = 'spec_for_{fullname}'.format(**locals())
method = getattr(self, method_name, lambda: None)
return method()
def spec_for_distutils(self):
if self.is_cpython():
return
import importlib
import importlib.abc
import importlib.util
try:
mod = importlib.import_module('setuptools._distutils')
except Exception:
# There are a couple of cases where setuptools._distutils
# may not be present:
# - An older Setuptools without a local distutils is
# taking precedence. Ref #2957.
# - Path manipulation during sitecustomize removes
# setuptools from the path but only after the hook
# has been loaded. Ref #2980.
# In either case, fall back to stdlib behavior.
return
class DistutilsLoader(importlib.abc.Loader):
def create_module(self, spec):
mod.__name__ = 'distutils'
return mod
def exec_module(self, module):
pass
return importlib.util.spec_from_loader(
'distutils', DistutilsLoader(), origin=mod.__file__
)
@staticmethod
def is_cpython():
"""
Suppress supplying distutils for CPython (build and tests).
Ref #2965 and #3007.
"""
return os.path.isfile('pybuilddir.txt')
def spec_for_pip(self):
"""
Ensure stdlib distutils when running under pip.
See pypa/pip#8761 for rationale.
"""
if sys.version_info >= (3, 12) or self.pip_imported_during_build():
return
clear_distutils()
self.spec_for_distutils = lambda: None
@classmethod
def pip_imported_during_build(cls):
"""
Detect if pip is being imported in a build script. Ref #2355.
"""
import traceback
return any(
cls.frame_file_is_setup(frame) for frame, line in traceback.walk_stack(None)
)
@staticmethod
def frame_file_is_setup(frame):
"""
Return True if the indicated frame suggests a setup.py file.
"""
# some frames may not have __file__ (#2940)
return frame.f_globals.get('__file__', '').endswith('setup.py')
def spec_for_sensitive_tests(self):
"""
Ensure stdlib distutils when running select tests under CPython.
python/cpython#91169
"""
clear_distutils()
self.spec_for_distutils = lambda: None
sensitive_tests = (
[
'test.test_distutils',
'test.test_peg_generator',
'test.test_importlib',
]
if sys.version_info < (3, 10)
else [
'test.test_distutils',
]
)
for name in DistutilsMetaFinder.sensitive_tests:
setattr(
DistutilsMetaFinder,
f'spec_for_{name}',
DistutilsMetaFinder.spec_for_sensitive_tests,
)
DISTUTILS_FINDER = DistutilsMetaFinder()
def add_shim():
DISTUTILS_FINDER in sys.meta_path or insert_shim()
class shim:
def __enter__(self):
insert_shim()
def __exit__(self, exc, value, tb):
_remove_shim()
def insert_shim():
sys.meta_path.insert(0, DISTUTILS_FINDER)
def _remove_shim():
try:
sys.meta_path.remove(DISTUTILS_FINDER)
except ValueError:
pass
if sys.version_info < (3, 12):
# DistutilsMetaFinder can only be disabled in Python < 3.12 (PEP 632)
remove_shim = _remove_shim

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.env/lib/python3.11/site-packages/_distutils_hack/override.py
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__import__('_distutils_hack').do_override()

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.env/lib/python3.11/site-packages/_virtualenv.pth
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import _virtualenv

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.env/lib/python3.11/site-packages/_virtualenv.py
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"""Patches that are applied at runtime to the virtual environment."""
from __future__ import annotations
import os
import sys
from contextlib import suppress
VIRTUALENV_PATCH_FILE = os.path.join(__file__)
def patch_dist(dist):
"""
Distutils allows user to configure some arguments via a configuration file:
https://docs.python.org/3/install/index.html#distutils-configuration-files.
Some of this arguments though don't make sense in context of the virtual environment files, let's fix them up.
""" # noqa: D205
# we cannot allow some install config as that would get packages installed outside of the virtual environment
old_parse_config_files = dist.Distribution.parse_config_files
def parse_config_files(self, *args, **kwargs):
result = old_parse_config_files(self, *args, **kwargs)
install = self.get_option_dict("install")
if "prefix" in install: # the prefix governs where to install the libraries
install["prefix"] = VIRTUALENV_PATCH_FILE, os.path.abspath(sys.prefix)
for base in ("purelib", "platlib", "headers", "scripts", "data"):
key = f"install_{base}"
if key in install: # do not allow global configs to hijack venv paths
install.pop(key, None)
return result
dist.Distribution.parse_config_files = parse_config_files
# Import hook that patches some modules to ignore configuration values that break package installation in case
# of virtual environments.
_DISTUTILS_PATCH = "distutils.dist", "setuptools.dist"
# https://docs.python.org/3/library/importlib.html#setting-up-an-importer
class _Finder:
"""A meta path finder that allows patching the imported distutils modules."""
fullname = None
# lock[0] is threading.Lock(), but initialized lazily to avoid importing threading very early at startup,
# because there are gevent-based applications that need to be first to import threading by themselves.
# See https://github.com/pypa/virtualenv/issues/1895 for details.
lock = [] # noqa: RUF012
def find_spec(self, fullname, path, target=None): # noqa: ARG002
if fullname in _DISTUTILS_PATCH and self.fullname is None:
# initialize lock[0] lazily
if len(self.lock) == 0:
import threading
lock = threading.Lock()
# there is possibility that two threads T1 and T2 are simultaneously running into find_spec,
# observing .lock as empty, and further going into hereby initialization. However due to the GIL,
# list.append() operation is atomic and this way only one of the threads will "win" to put the lock
# - that every thread will use - into .lock[0].
# https://docs.python.org/3/faq/library.html#what-kinds-of-global-value-mutation-are-thread-safe
self.lock.append(lock)
from functools import partial
from importlib.util import find_spec
with self.lock[0]:
self.fullname = fullname
try:
spec = find_spec(fullname, path)
if spec is not None:
# https://www.python.org/dev/peps/pep-0451/#how-loading-will-work
is_new_api = hasattr(spec.loader, "exec_module")
func_name = "exec_module" if is_new_api else "load_module"
old = getattr(spec.loader, func_name)
func = self.exec_module if is_new_api else self.load_module
if old is not func:
with suppress(AttributeError): # C-Extension loaders are r/o such as zipimporter with <3.7
setattr(spec.loader, func_name, partial(func, old))
return spec
finally:
self.fullname = None
return None
@staticmethod
def exec_module(old, module):
old(module)
if module.__name__ in _DISTUTILS_PATCH:
patch_dist(module)
@staticmethod
def load_module(old, name):
module = old(name)
if module.__name__ in _DISTUTILS_PATCH:
patch_dist(module)
return module
sys.meta_path.insert(0, _Finder())

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.env/lib/python3.11/site-packages/distutils-precedence.pth
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import os; var = 'SETUPTOOLS_USE_DISTUTILS'; enabled = os.environ.get(var, 'local') == 'local'; enabled and __import__('_distutils_hack').add_shim();

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.env/lib/python3.11/site-packages/numpy-1.26.3.dist-info/INSTALLER
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pip

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.env/lib/python3.11/site-packages/numpy-1.26.3.dist-info/LICENSE.txt
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Copyright (c) 2005-2023, NumPy Developers.
All rights reserved.
Redistribution and use in source and binary forms, with or without
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met:
* Redistributions of source code must retain the above copyright
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* Redistributions in binary form must reproduce the above
copyright notice, this list of conditions and the following
disclaimer in the documentation and/or other materials provided
with the distribution.
* Neither the name of the NumPy Developers nor the names of any
contributors may be used to endorse or promote products derived
from this software without specific prior written permission.
THIS SOFTWARE IS PROVIDED BY THE COPYRIGHT HOLDERS AND CONTRIBUTORS
"AS IS" AND ANY EXPRESS OR IMPLIED WARRANTIES, INCLUDING, BUT NOT
LIMITED TO, THE IMPLIED WARRANTIES OF MERCHANTABILITY AND FITNESS FOR
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LIMITED TO, PROCUREMENT OF SUBSTITUTE GOODS OR SERVICES; LOSS OF USE,
DATA, OR PROFITS; OR BUSINESS INTERRUPTION) HOWEVER CAUSED AND ON ANY
THEORY OF LIABILITY, WHETHER IN CONTRACT, STRICT LIABILITY, OR TORT
(INCLUDING NEGLIGENCE OR OTHERWISE) ARISING IN ANY WAY OUT OF THE USE
OF THIS SOFTWARE, EVEN IF ADVISED OF THE POSSIBILITY OF SUCH DAMAGE.
----
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Files: numpy/linalg/lapack_lite/*
License: BSD-3-Clause
For details, see numpy/linalg/lapack_lite/LICENSE.txt
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Files: tools/npy_tempita/*
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For details, see tools/npy_tempita/license.txt
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Files: numpy/core/src/multiarray/dragon4.c
License: MIT
For license text, see numpy/core/src/multiarray/dragon4.c
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Files: numpy/core/include/numpy/libdivide/*
License: Zlib
For license text, see numpy/core/include/numpy/libdivide/LICENSE.txt
Note that the following files are vendored in the repository and sdist but not
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Name: Meson
Files: vendored-meson/meson/*
License: Apache 2.0
For license text, see vendored-meson/meson/COPYING
Name: meson-python
Files: vendored-meson/meson-python/*
License: MIT
For license text, see vendored-meson/meson-python/LICENSE
Name: spin
Files: .spin/cmds.py
License: BSD-3
For license text, see .spin/LICENSE
----
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Files: numpy.libs/libopenblas*.so
Description: bundled as a dynamically linked library
Availability: https://github.com/OpenMathLib/OpenBLAS/
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AND ANY EXPRESS OR IMPLIED WARRANTIES, INCLUDING, BUT NOT LIMITED TO, THE
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Name: LAPACK
Files: numpy.libs/libopenblas*.so
Description: bundled in OpenBLAS
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Name: GCC runtime library
Files: numpy.libs/libgfortran*.so
Description: dynamically linked to files compiled with gcc
Availability: https://gcc.gnu.org/git/?p=gcc.git;a=tree;f=libgfortran
License: GPL-3.0-with-GCC-exception
Copyright (C) 2002-2017 Free Software Foundation, Inc.
Libgfortran is free software; you can redistribute it and/or modify
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Libgfortran is distributed in the hope that it will be useful,
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----
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listed below does not necessarily imply the conditions apply to the
present binary release):
----
GCC RUNTIME LIBRARY EXCEPTION
Version 3.1, 31 March 2009
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----
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Name: libquadmath
Files: numpy.libs/libquadmath*.so
Description: dynamically linked to files compiled with gcc
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License: LGPL-2.1-or-later
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This file is part of the libquadmath library.
Libquadmath is free software; you can redistribute it and/or
modify it under the terms of the GNU Library General Public
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https://www.gnu.org/licenses/old-licenses/lgpl-2.1.html

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Wheel-Version: 1.0
Generator: meson
Root-Is-Purelib: false
Tag: cp311-cp311-manylinux_2_17_x86_64
Tag: cp311-cp311-manylinux2014_x86_64

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[array_api]
numpy = numpy.array_api
[pyinstaller40]
hook-dirs = numpy:_pyinstaller_hooks_dir
[console_scripts]
f2py = numpy.f2py.f2py2e:main

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# This file is generated by numpy's build process
# It contains system_info results at the time of building this package.
from enum import Enum
from numpy.core._multiarray_umath import (
__cpu_features__,
__cpu_baseline__,
__cpu_dispatch__,
)
__all__ = ["show"]
_built_with_meson = True
class DisplayModes(Enum):
stdout = "stdout"
dicts = "dicts"
def _cleanup(d):
"""
Removes empty values in a `dict` recursively
This ensures we remove values that Meson could not provide to CONFIG
"""
if isinstance(d, dict):
return {k: _cleanup(v) for k, v in d.items() if v and _cleanup(v)}
else:
return d
CONFIG = _cleanup(
{
"Compilers": {
"c": {
"name": "gcc",
"linker": "ld.bfd",
"version": "10.2.1",
"commands": "cc",
},
"cython": {
"name": "cython",
"linker": "cython",
"version": "3.0.7",
"commands": "cython",
},
"c++": {
"name": "gcc",
"linker": "ld.bfd",
"version": "10.2.1",
"commands": "c++",
},
},
"Machine Information": {
"host": {
"cpu": "x86_64",
"family": "x86_64",
"endian": "little",
"system": "linux",
},
"build": {
"cpu": "x86_64",
"family": "x86_64",
"endian": "little",
"system": "linux",
},
"cross-compiled": bool("False".lower().replace("false", "")),
},
"Build Dependencies": {
"blas": {
"name": "openblas64",
"found": bool("True".lower().replace("false", "")),
"version": "0.3.23.dev",
"detection method": "pkgconfig",
"include directory": r"/usr/local/include",
"lib directory": r"/usr/local/lib",
"openblas configuration": "USE_64BITINT=1 DYNAMIC_ARCH=1 DYNAMIC_OLDER= NO_CBLAS= NO_LAPACK= NO_LAPACKE= NO_AFFINITY=1 USE_OPENMP= HASWELL MAX_THREADS=2",
"pc file directory": r"/usr/local/lib/pkgconfig",
},
"lapack": {
"name": "dep139639528180752",
"found": bool("True".lower().replace("false", "")),
"version": "1.26.3",
"detection method": "internal",
"include directory": r"unknown",
"lib directory": r"unknown",
"openblas configuration": "unknown",
"pc file directory": r"unknown",
},
},
"Python Information": {
"path": r"/opt/python/cp311-cp311/bin/python",
"version": "3.11",
},
"SIMD Extensions": {
"baseline": __cpu_baseline__,
"found": [
feature for feature in __cpu_dispatch__ if __cpu_features__[feature]
],
"not found": [
feature for feature in __cpu_dispatch__ if not __cpu_features__[feature]
],
},
}
)
def _check_pyyaml():
import yaml
return yaml
def show(mode=DisplayModes.stdout.value):
"""
Show libraries and system information on which NumPy was built
and is being used
Parameters
----------
mode : {`'stdout'`, `'dicts'`}, optional.
Indicates how to display the config information.
`'stdout'` prints to console, `'dicts'` returns a dictionary
of the configuration.
Returns
-------
out : {`dict`, `None`}
If mode is `'dicts'`, a dict is returned, else None
See Also
--------
get_include : Returns the directory containing NumPy C
header files.
Notes
-----
1. The `'stdout'` mode will give more readable
output if ``pyyaml`` is installed
"""
if mode == DisplayModes.stdout.value:
try: # Non-standard library, check import
yaml = _check_pyyaml()
print(yaml.dump(CONFIG))
except ModuleNotFoundError:
import warnings
import json
warnings.warn("Install `pyyaml` for better output", stacklevel=1)
print(json.dumps(CONFIG, indent=2))
elif mode == DisplayModes.dicts.value:
return CONFIG
else:
raise AttributeError(
f"Invalid `mode`, use one of: {', '.join([e.value for e in DisplayModes])}"
)

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"""
NumPy
=====
Provides
1. An array object of arbitrary homogeneous items
2. Fast mathematical operations over arrays
3. Linear Algebra, Fourier Transforms, Random Number Generation
How to use the documentation
----------------------------
Documentation is available in two forms: docstrings provided
with the code, and a loose standing reference guide, available from
`the NumPy homepage <https://numpy.org>`_.
We recommend exploring the docstrings using
`IPython <https://ipython.org>`_, an advanced Python shell with
TAB-completion and introspection capabilities. See below for further
instructions.
The docstring examples assume that `numpy` has been imported as ``np``::
>>> import numpy as np
Code snippets are indicated by three greater-than signs::
>>> x = 42
>>> x = x + 1
Use the built-in ``help`` function to view a function's docstring::
>>> help(np.sort)
... # doctest: +SKIP
For some objects, ``np.info(obj)`` may provide additional help. This is
particularly true if you see the line "Help on ufunc object:" at the top
of the help() page. Ufuncs are implemented in C, not Python, for speed.
The native Python help() does not know how to view their help, but our
np.info() function does.
To search for documents containing a keyword, do::
>>> np.lookfor('keyword')
... # doctest: +SKIP
General-purpose documents like a glossary and help on the basic concepts
of numpy are available under the ``doc`` sub-module::
>>> from numpy import doc
>>> help(doc)
... # doctest: +SKIP
Available subpackages
---------------------
lib
Basic functions used by several sub-packages.
random
Core Random Tools
linalg
Core Linear Algebra Tools
fft
Core FFT routines
polynomial
Polynomial tools
testing
NumPy testing tools
distutils
Enhancements to distutils with support for
Fortran compilers support and more (for Python <= 3.11).
Utilities
---------
test
Run numpy unittests
show_config
Show numpy build configuration
matlib
Make everything matrices.
__version__
NumPy version string
Viewing documentation using IPython
-----------------------------------
Start IPython and import `numpy` usually under the alias ``np``: `import
numpy as np`. Then, directly past or use the ``%cpaste`` magic to paste
examples into the shell. To see which functions are available in `numpy`,
type ``np.<TAB>`` (where ``<TAB>`` refers to the TAB key), or use
``np.*cos*?<ENTER>`` (where ``<ENTER>`` refers to the ENTER key) to narrow
down the list. To view the docstring for a function, use
``np.cos?<ENTER>`` (to view the docstring) and ``np.cos??<ENTER>`` (to view
the source code).
Copies vs. in-place operation
-----------------------------
Most of the functions in `numpy` return a copy of the array argument
(e.g., `np.sort`). In-place versions of these functions are often
available as array methods, i.e. ``x = np.array([1,2,3]); x.sort()``.
Exceptions to this rule are documented.
"""
import sys
import warnings
from ._globals import _NoValue, _CopyMode
# These exceptions were moved in 1.25 and are hidden from __dir__()
from .exceptions import (
ComplexWarning, ModuleDeprecationWarning, VisibleDeprecationWarning,
TooHardError, AxisError)
# If a version with git hash was stored, use that instead
from . import version
from .version import __version__
# We first need to detect if we're being called as part of the numpy setup
# procedure itself in a reliable manner.
try:
__NUMPY_SETUP__
except NameError:
__NUMPY_SETUP__ = False
if __NUMPY_SETUP__:
sys.stderr.write('Running from numpy source directory.\n')
else:
# Allow distributors to run custom init code before importing numpy.core
from . import _distributor_init
try:
from numpy.__config__ import show as show_config
except ImportError as e:
msg = """Error importing numpy: you should not try to import numpy from
its source directory; please exit the numpy source tree, and relaunch
your python interpreter from there."""
raise ImportError(msg) from e
__all__ = [
'exceptions', 'ModuleDeprecationWarning', 'VisibleDeprecationWarning',
'ComplexWarning', 'TooHardError', 'AxisError']
# mapping of {name: (value, deprecation_msg)}
__deprecated_attrs__ = {}
from . import core
from .core import *
from . import compat
from . import exceptions
from . import dtypes
from . import lib
# NOTE: to be revisited following future namespace cleanup.
# See gh-14454 and gh-15672 for discussion.
from .lib import *
from . import linalg
from . import fft
from . import polynomial
from . import random
from . import ctypeslib
from . import ma
from . import matrixlib as _mat
from .matrixlib import *
# Deprecations introduced in NumPy 1.20.0, 2020-06-06
import builtins as _builtins
_msg = (
"module 'numpy' has no attribute '{n}'.\n"
"`np.{n}` was a deprecated alias for the builtin `{n}`. "
"To avoid this error in existing code, use `{n}` by itself. "
"Doing this will not modify any behavior and is safe. {extended_msg}\n"
"The aliases was originally deprecated in NumPy 1.20; for more "
"details and guidance see the original release note at:\n"
" https://numpy.org/devdocs/release/1.20.0-notes.html#deprecations")
_specific_msg = (
"If you specifically wanted the numpy scalar type, use `np.{}` here.")
_int_extended_msg = (
"When replacing `np.{}`, you may wish to use e.g. `np.int64` "
"or `np.int32` to specify the precision. If you wish to review "
"your current use, check the release note link for "
"additional information.")
_type_info = [
("object", ""), # The NumPy scalar only exists by name.
("bool", _specific_msg.format("bool_")),
("float", _specific_msg.format("float64")),
("complex", _specific_msg.format("complex128")),
("str", _specific_msg.format("str_")),
("int", _int_extended_msg.format("int"))]
__former_attrs__ = {
n: _msg.format(n=n, extended_msg=extended_msg)
for n, extended_msg in _type_info
}
# Future warning introduced in NumPy 1.24.0, 2022-11-17
_msg = (
"`np.{n}` is a deprecated alias for `{an}`. (Deprecated NumPy 1.24)")
# Some of these are awkward (since `np.str` may be preferable in the long
# term), but overall the names ending in 0 seem undesirable
_type_info = [
("bool8", bool_, "np.bool_"),
("int0", intp, "np.intp"),
("uint0", uintp, "np.uintp"),
("str0", str_, "np.str_"),
("bytes0", bytes_, "np.bytes_"),
("void0", void, "np.void"),
("object0", object_,
"`np.object0` is a deprecated alias for `np.object_`. "
"`object` can be used instead. (Deprecated NumPy 1.24)")]
# Some of these could be defined right away, but most were aliases to
# the Python objects and only removed in NumPy 1.24. Defining them should
# probably wait for NumPy 1.26 or 2.0.
# When defined, these should possibly not be added to `__all__` to avoid
# import with `from numpy import *`.
__future_scalars__ = {"bool", "long", "ulong", "str", "bytes", "object"}
__deprecated_attrs__.update({
n: (alias, _msg.format(n=n, an=an)) for n, alias, an in _type_info})
import math
__deprecated_attrs__['math'] = (math,
"`np.math` is a deprecated alias for the standard library `math` "
"module (Deprecated Numpy 1.25). Replace usages of `np.math` with "
"`math`")
del math, _msg, _type_info
from .core import abs
# now that numpy modules are imported, can initialize limits
core.getlimits._register_known_types()
__all__.extend(['__version__', 'show_config'])
__all__.extend(core.__all__)
__all__.extend(_mat.__all__)
__all__.extend(lib.__all__)
__all__.extend(['linalg', 'fft', 'random', 'ctypeslib', 'ma'])
# Remove min and max from __all__ to avoid `from numpy import *` override
# the builtins min/max. Temporary fix for 1.25.x/1.26.x, see gh-24229.
__all__.remove('min')
__all__.remove('max')
__all__.remove('round')
# Remove one of the two occurrences of `issubdtype`, which is exposed as
# both `numpy.core.issubdtype` and `numpy.lib.issubdtype`.
__all__.remove('issubdtype')
# These are exported by np.core, but are replaced by the builtins below
# remove them to ensure that we don't end up with `np.long == np.int_`,
# which would be a breaking change.
del long, unicode
__all__.remove('long')
__all__.remove('unicode')
# Remove things that are in the numpy.lib but not in the numpy namespace
# Note that there is a test (numpy/tests/test_public_api.py:test_numpy_namespace)
# that prevents adding more things to the main namespace by accident.
# The list below will grow until the `from .lib import *` fixme above is
# taken care of
__all__.remove('Arrayterator')
del Arrayterator
# These names were removed in NumPy 1.20. For at least one release,
# attempts to access these names in the numpy namespace will trigger
# a warning, and calling the function will raise an exception.
_financial_names = ['fv', 'ipmt', 'irr', 'mirr', 'nper', 'npv', 'pmt',
'ppmt', 'pv', 'rate']
__expired_functions__ = {
name: (f'In accordance with NEP 32, the function {name} was removed '
'from NumPy version 1.20. A replacement for this function '
'is available in the numpy_financial library: '
'https://pypi.org/project/numpy-financial')
for name in _financial_names}
# Filter out Cython harmless warnings
warnings.filterwarnings("ignore", message="numpy.dtype size changed")
warnings.filterwarnings("ignore", message="numpy.ufunc size changed")
warnings.filterwarnings("ignore", message="numpy.ndarray size changed")
# oldnumeric and numarray were removed in 1.9. In case some packages import
# but do not use them, we define them here for backward compatibility.
oldnumeric = 'removed'
numarray = 'removed'
def __getattr__(attr):
# Warn for expired attributes, and return a dummy function
# that always raises an exception.
import warnings
import math
try:
msg = __expired_functions__[attr]
except KeyError:
pass
else:
warnings.warn(msg, DeprecationWarning, stacklevel=2)
def _expired(*args, **kwds):
raise RuntimeError(msg)
return _expired
# Emit warnings for deprecated attributes
try:
val, msg = __deprecated_attrs__[attr]
except KeyError:
pass
else:
warnings.warn(msg, DeprecationWarning, stacklevel=2)
return val
if attr in __future_scalars__:
# And future warnings for those that will change, but also give
# the AttributeError
warnings.warn(
f"In the future `np.{attr}` will be defined as the "
"corresponding NumPy scalar.", FutureWarning, stacklevel=2)
if attr in __former_attrs__:
raise AttributeError(__former_attrs__[attr])
if attr == 'testing':
import numpy.testing as testing
return testing
elif attr == 'Tester':
"Removed in NumPy 1.25.0"
raise RuntimeError("Tester was removed in NumPy 1.25.")
raise AttributeError("module {!r} has no attribute "
"{!r}".format(__name__, attr))
def __dir__():
public_symbols = globals().keys() | {'testing'}
public_symbols -= {
"core", "matrixlib",
# These were moved in 1.25 and may be deprecated eventually:
"ModuleDeprecationWarning", "VisibleDeprecationWarning",
"ComplexWarning", "TooHardError", "AxisError"
}
return list(public_symbols)
# Pytest testing
from numpy._pytesttester import PytestTester
test = PytestTester(__name__)
del PytestTester
def _sanity_check():
"""
Quick sanity checks for common bugs caused by environment.
There are some cases e.g. with wrong BLAS ABI that cause wrong
results under specific runtime conditions that are not necessarily
achieved during test suite runs, and it is useful to catch those early.
See https://github.com/numpy/numpy/issues/8577 and other
similar bug reports.
"""
try:
x = ones(2, dtype=float32)
if not abs(x.dot(x) - float32(2.0)) < 1e-5:
raise AssertionError()
except AssertionError:
msg = ("The current Numpy installation ({!r}) fails to "
"pass simple sanity checks. This can be caused for example "
"by incorrect BLAS library being linked in, or by mixing "
"package managers (pip, conda, apt, ...). Search closed "
"numpy issues for similar problems.")
raise RuntimeError(msg.format(__file__)) from None
_sanity_check()
del _sanity_check
def _mac_os_check():
"""
Quick Sanity check for Mac OS look for accelerate build bugs.
Testing numpy polyfit calls init_dgelsd(LAPACK)
"""
try:
c = array([3., 2., 1.])
x = linspace(0, 2, 5)
y = polyval(c, x)
_ = polyfit(x, y, 2, cov=True)
except ValueError:
pass
if sys.platform == "darwin":
from . import exceptions
with warnings.catch_warnings(record=True) as w:
_mac_os_check()
# Throw runtime error, if the test failed Check for warning and error_message
if len(w) > 0:
for _wn in w:
if _wn.category is exceptions.RankWarning:
# Ignore other warnings, they may not be relevant (see gh-25433).
error_message = f"{_wn.category.__name__}: {str(_wn.message)}"
msg = (
"Polyfit sanity test emitted a warning, most likely due "
"to using a buggy Accelerate backend."
"\nIf you compiled yourself, more information is available at:"
"\nhttps://numpy.org/devdocs/building/index.html"
"\nOtherwise report this to the vendor "
"that provided NumPy.\n\n{}\n".format(error_message))
raise RuntimeError(msg)
del _wn
del w
del _mac_os_check
# We usually use madvise hugepages support, but on some old kernels it
# is slow and thus better avoided.
# Specifically kernel version 4.6 had a bug fix which probably fixed this:
# https://github.com/torvalds/linux/commit/7cf91a98e607c2f935dbcc177d70011e95b8faff
import os
use_hugepage = os.environ.get("NUMPY_MADVISE_HUGEPAGE", None)
if sys.platform == "linux" and use_hugepage is None:
# If there is an issue with parsing the kernel version,
# set use_hugepages to 0. Usage of LooseVersion will handle
# the kernel version parsing better, but avoided since it
# will increase the import time. See: #16679 for related discussion.
try:
use_hugepage = 1
kernel_version = os.uname().release.split(".")[:2]
kernel_version = tuple(int(v) for v in kernel_version)
if kernel_version < (4, 6):
use_hugepage = 0
except ValueError:
use_hugepages = 0
elif use_hugepage is None:
# This is not Linux, so it should not matter, just enable anyway
use_hugepage = 1
else:
use_hugepage = int(use_hugepage)
# Note that this will currently only make a difference on Linux
core.multiarray._set_madvise_hugepage(use_hugepage)
del use_hugepage
# Give a warning if NumPy is reloaded or imported on a sub-interpreter
# We do this from python, since the C-module may not be reloaded and
# it is tidier organized.
core.multiarray._multiarray_umath._reload_guard()
# default to "weak" promotion for "NumPy 2".
core._set_promotion_state(
os.environ.get("NPY_PROMOTION_STATE",
"weak" if _using_numpy2_behavior() else "legacy"))
# Tell PyInstaller where to find hook-numpy.py
def _pyinstaller_hooks_dir():
from pathlib import Path
return [str(Path(__file__).with_name("_pyinstaller").resolve())]
# Remove symbols imported for internal use
del os
# Remove symbols imported for internal use
del sys, warnings

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"""
This private module only contains stubs for interoperability with
NumPy 2.0 pickled arrays. It may not be used by the end user.
"""

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from numpy.core import _dtype
_globals = globals()
for item in _dtype.__dir__():
_globals[item] = getattr(_dtype, item)

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from numpy.core import _dtype_ctypes
_globals = globals()
for item in _dtype_ctypes.__dir__():
_globals[item] = getattr(_dtype_ctypes, item)

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from numpy.core import _internal
_globals = globals()
for item in _internal.__dir__():
_globals[item] = getattr(_internal, item)

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from numpy.core import _multiarray_umath
_globals = globals()
for item in _multiarray_umath.__dir__():
_globals[item] = getattr(_multiarray_umath, item)

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from numpy.core import multiarray
_globals = globals()
for item in multiarray.__dir__():
_globals[item] = getattr(multiarray, item)

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from numpy.core import umath
_globals = globals()
for item in umath.__dir__():
_globals[item] = getattr(umath, item)

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""" Distributor init file
Distributors: you can add custom code here to support particular distributions
of numpy.
For example, this is a good place to put any BLAS/LAPACK initialization code.
The numpy standard source distribution will not put code in this file, so you
can safely replace this file with your own version.
"""
try:
from . import _distributor_init_local
except ImportError:
pass

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"""
Module defining global singleton classes.
This module raises a RuntimeError if an attempt to reload it is made. In that
way the identities of the classes defined here are fixed and will remain so
even if numpy itself is reloaded. In particular, a function like the following
will still work correctly after numpy is reloaded::
def foo(arg=np._NoValue):
if arg is np._NoValue:
...
That was not the case when the singleton classes were defined in the numpy
``__init__.py`` file. See gh-7844 for a discussion of the reload problem that
motivated this module.
"""
import enum
from ._utils import set_module as _set_module
__all__ = ['_NoValue', '_CopyMode']
# Disallow reloading this module so as to preserve the identities of the
# classes defined here.
if '_is_loaded' in globals():
raise RuntimeError('Reloading numpy._globals is not allowed')
_is_loaded = True
class _NoValueType:
"""Special keyword value.
The instance of this class may be used as the default value assigned to a
keyword if no other obvious default (e.g., `None`) is suitable,
Common reasons for using this keyword are:
- A new keyword is added to a function, and that function forwards its
inputs to another function or method which can be defined outside of
NumPy. For example, ``np.std(x)`` calls ``x.std``, so when a ``keepdims``
keyword was added that could only be forwarded if the user explicitly
specified ``keepdims``; downstream array libraries may not have added
the same keyword, so adding ``x.std(..., keepdims=keepdims)``
unconditionally could have broken previously working code.
- A keyword is being deprecated, and a deprecation warning must only be
emitted when the keyword is used.
"""
__instance = None
def __new__(cls):
# ensure that only one instance exists
if not cls.__instance:
cls.__instance = super().__new__(cls)
return cls.__instance
def __repr__(self):
return "<no value>"
_NoValue = _NoValueType()
@_set_module("numpy")
class _CopyMode(enum.Enum):
"""
An enumeration for the copy modes supported
by numpy.copy() and numpy.array(). The following three modes are supported,
- ALWAYS: This means that a deep copy of the input
array will always be taken.
- IF_NEEDED: This means that a deep copy of the input
array will be taken only if necessary.
- NEVER: This means that the deep copy will never be taken.
If a copy cannot be avoided then a `ValueError` will be
raised.
Note that the buffer-protocol could in theory do copies. NumPy currently
assumes an object exporting the buffer protocol will never do this.
"""
ALWAYS = True
IF_NEEDED = False
NEVER = 2
def __bool__(self):
# For backwards compatibility
if self == _CopyMode.ALWAYS:
return True
if self == _CopyMode.IF_NEEDED:
return False
raise ValueError(f"{self} is neither True nor False.")

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"""This hook should collect all binary files and any hidden modules that numpy
needs.
Our (some-what inadequate) docs for writing PyInstaller hooks are kept here:
https://pyinstaller.readthedocs.io/en/stable/hooks.html
"""
from PyInstaller.compat import is_conda, is_pure_conda
from PyInstaller.utils.hooks import collect_dynamic_libs, is_module_satisfies
# Collect all DLLs inside numpy's installation folder, dump them into built
# app's root.
binaries = collect_dynamic_libs("numpy", ".")
# If using Conda without any non-conda virtual environment manager:
if is_pure_conda:
# Assume running the NumPy from Conda-forge and collect it's DLLs from the
# communal Conda bin directory. DLLs from NumPy's dependencies must also be
# collected to capture MKL, OpenBlas, OpenMP, etc.
from PyInstaller.utils.hooks import conda_support
datas = conda_support.collect_dynamic_libs("numpy", dependencies=True)
# Submodules PyInstaller cannot detect. `_dtype_ctypes` is only imported
# from C and `_multiarray_tests` is used in tests (which are not packed).
hiddenimports = ['numpy.core._dtype_ctypes', 'numpy.core._multiarray_tests']
# Remove testing and building code and packages that are referenced throughout
# NumPy but are not really dependencies.
excludedimports = [
"scipy",
"pytest",
"f2py",
"setuptools",
"numpy.f2py",
"distutils",
"numpy.distutils",
]

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"""A crude *bit of everything* smoke test to verify PyInstaller compatibility.
PyInstaller typically goes wrong by forgetting to package modules, extension
modules or shared libraries. This script should aim to touch as many of those
as possible in an attempt to trip a ModuleNotFoundError or a DLL load failure
due to an uncollected resource. Missing resources are unlikely to lead to
arithmetic errors so there's generally no need to verify any calculation's
output - merely that it made it to the end OK. This script should not
explicitly import any of numpy's submodules as that gives PyInstaller undue
hints that those submodules exist and should be collected (accessing implicitly
loaded submodules is OK).
"""
import numpy as np
a = np.arange(1., 10.).reshape((3, 3)) % 5
np.linalg.det(a)
a @ a
a @ a.T
np.linalg.inv(a)
np.sin(np.exp(a))
np.linalg.svd(a)
np.linalg.eigh(a)
np.unique(np.random.randint(0, 10, 100))
np.sort(np.random.uniform(0, 10, 100))
np.fft.fft(np.exp(2j * np.pi * np.arange(8) / 8))
np.ma.masked_array(np.arange(10), np.random.rand(10) < .5).sum()
np.polynomial.Legendre([7, 8, 9]).roots()
print("I made it!")

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import subprocess
from pathlib import Path
import pytest
# PyInstaller has been very unproactive about replacing 'imp' with 'importlib'.
@pytest.mark.filterwarnings('ignore::DeprecationWarning')
# It also leaks io.BytesIO()s.
@pytest.mark.filterwarnings('ignore::ResourceWarning')
@pytest.mark.parametrize("mode", ["--onedir", "--onefile"])
@pytest.mark.slow
def test_pyinstaller(mode, tmp_path):
"""Compile and run pyinstaller-smoke.py using PyInstaller."""
pyinstaller_cli = pytest.importorskip("PyInstaller.__main__").run
source = Path(__file__).with_name("pyinstaller-smoke.py").resolve()
args = [
# Place all generated files in ``tmp_path``.
'--workpath', str(tmp_path / "build"),
'--distpath', str(tmp_path / "dist"),
'--specpath', str(tmp_path),
mode,
str(source),
]
pyinstaller_cli(args)
if mode == "--onefile":
exe = tmp_path / "dist" / source.stem
else:
exe = tmp_path / "dist" / source.stem / source.stem
p = subprocess.run([str(exe)], check=True, stdout=subprocess.PIPE)
assert p.stdout.strip() == b"I made it!"

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"""
Pytest test running.
This module implements the ``test()`` function for NumPy modules. The usual
boiler plate for doing that is to put the following in the module
``__init__.py`` file::
from numpy._pytesttester import PytestTester
test = PytestTester(__name__)
del PytestTester
Warnings filtering and other runtime settings should be dealt with in the
``pytest.ini`` file in the numpy repo root. The behavior of the test depends on
whether or not that file is found as follows:
* ``pytest.ini`` is present (develop mode)
All warnings except those explicitly filtered out are raised as error.
* ``pytest.ini`` is absent (release mode)
DeprecationWarnings and PendingDeprecationWarnings are ignored, other
warnings are passed through.
In practice, tests run from the numpy repo are run in develop mode. That
includes the standard ``python runtests.py`` invocation.
This module is imported by every numpy subpackage, so lies at the top level to
simplify circular import issues. For the same reason, it contains no numpy
imports at module scope, instead importing numpy within function calls.
"""
import sys
import os
__all__ = ['PytestTester']
def _show_numpy_info():
import numpy as np
print("NumPy version %s" % np.__version__)
relaxed_strides = np.ones((10, 1), order="C").flags.f_contiguous
print("NumPy relaxed strides checking option:", relaxed_strides)
info = np.lib.utils._opt_info()
print("NumPy CPU features: ", (info if info else 'nothing enabled'))
class PytestTester:
"""
Pytest test runner.
A test function is typically added to a package's __init__.py like so::
from numpy._pytesttester import PytestTester
test = PytestTester(__name__).test
del PytestTester
Calling this test function finds and runs all tests associated with the
module and all its sub-modules.
Attributes
----------
module_name : str
Full path to the package to test.
Parameters
----------
module_name : module name
The name of the module to test.
Notes
-----
Unlike the previous ``nose``-based implementation, this class is not
publicly exposed as it performs some ``numpy``-specific warning
suppression.
"""
def __init__(self, module_name):
self.module_name = module_name
def __call__(self, label='fast', verbose=1, extra_argv=None,
doctests=False, coverage=False, durations=-1, tests=None):
"""
Run tests for module using pytest.
Parameters
----------
label : {'fast', 'full'}, optional
Identifies the tests to run. When set to 'fast', tests decorated
with `pytest.mark.slow` are skipped, when 'full', the slow marker
is ignored.
verbose : int, optional
Verbosity value for test outputs, in the range 1-3. Default is 1.
extra_argv : list, optional
List with any extra arguments to pass to pytests.
doctests : bool, optional
.. note:: Not supported
coverage : bool, optional
If True, report coverage of NumPy code. Default is False.
Requires installation of (pip) pytest-cov.
durations : int, optional
If < 0, do nothing, If 0, report time of all tests, if > 0,
report the time of the slowest `timer` tests. Default is -1.
tests : test or list of tests
Tests to be executed with pytest '--pyargs'
Returns
-------
result : bool
Return True on success, false otherwise.
Notes
-----
Each NumPy module exposes `test` in its namespace to run all tests for
it. For example, to run all tests for numpy.lib:
>>> np.lib.test() #doctest: +SKIP
Examples
--------
>>> result = np.lib.test() #doctest: +SKIP
...
1023 passed, 2 skipped, 6 deselected, 1 xfailed in 10.39 seconds
>>> result
True
"""
import pytest
import warnings
module = sys.modules[self.module_name]
module_path = os.path.abspath(module.__path__[0])
# setup the pytest arguments
pytest_args = ["-l"]
# offset verbosity. The "-q" cancels a "-v".
pytest_args += ["-q"]
if sys.version_info < (3, 12):
with warnings.catch_warnings():
warnings.simplefilter("always")
# Filter out distutils cpu warnings (could be localized to
# distutils tests). ASV has problems with top level import,
# so fetch module for suppression here.
from numpy.distutils import cpuinfo
with warnings.catch_warnings(record=True):
# Ignore the warning from importing the array_api submodule. This
# warning is done on import, so it would break pytest collection,
# but importing it early here prevents the warning from being
# issued when it imported again.
import numpy.array_api
# Filter out annoying import messages. Want these in both develop and
# release mode.
pytest_args += [
"-W ignore:Not importing directory",
"-W ignore:numpy.dtype size changed",
"-W ignore:numpy.ufunc size changed",
"-W ignore::UserWarning:cpuinfo",
]
# When testing matrices, ignore their PendingDeprecationWarnings
pytest_args += [
"-W ignore:the matrix subclass is not",
"-W ignore:Importing from numpy.matlib is",
]
if doctests:
pytest_args += ["--doctest-modules"]
if extra_argv:
pytest_args += list(extra_argv)
if verbose > 1:
pytest_args += ["-" + "v"*(verbose - 1)]
if coverage:
pytest_args += ["--cov=" + module_path]
if label == "fast":
# not importing at the top level to avoid circular import of module
from numpy.testing import IS_PYPY
if IS_PYPY:
pytest_args += ["-m", "not slow and not slow_pypy"]
else:
pytest_args += ["-m", "not slow"]
elif label != "full":
pytest_args += ["-m", label]
if durations >= 0:
pytest_args += ["--durations=%s" % durations]
if tests is None:
tests = [self.module_name]
pytest_args += ["--pyargs"] + list(tests)
# run tests.
_show_numpy_info()
try:
code = pytest.main(pytest_args)
except SystemExit as exc:
code = exc.code
return code == 0

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from collections.abc import Iterable
from typing import Literal as L
__all__: list[str]
class PytestTester:
module_name: str
def __init__(self, module_name: str) -> None: ...
def __call__(
self,
label: L["fast", "full"] = ...,
verbose: int = ...,
extra_argv: None | Iterable[str] = ...,
doctests: L[False] = ...,
coverage: bool = ...,
durations: int = ...,
tests: None | Iterable[str] = ...,
) -> bool: ...

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"""Private counterpart of ``numpy.typing``."""
from __future__ import annotations
from .. import ufunc
from .._utils import set_module
from typing import TYPE_CHECKING, final
@final # Disallow the creation of arbitrary `NBitBase` subclasses
@set_module("numpy.typing")
class NBitBase:
"""
A type representing `numpy.number` precision during static type checking.
Used exclusively for the purpose static type checking, `NBitBase`
represents the base of a hierarchical set of subclasses.
Each subsequent subclass is herein used for representing a lower level
of precision, *e.g.* ``64Bit > 32Bit > 16Bit``.
.. versionadded:: 1.20
Examples
--------
Below is a typical usage example: `NBitBase` is herein used for annotating
a function that takes a float and integer of arbitrary precision
as arguments and returns a new float of whichever precision is largest
(*e.g.* ``np.float16 + np.int64 -> np.float64``).
.. code-block:: python
>>> from __future__ import annotations
>>> from typing import TypeVar, TYPE_CHECKING
>>> import numpy as np
>>> import numpy.typing as npt
>>> T1 = TypeVar("T1", bound=npt.NBitBase)
>>> T2 = TypeVar("T2", bound=npt.NBitBase)
>>> def add(a: np.floating[T1], b: np.integer[T2]) -> np.floating[T1 | T2]:
... return a + b
>>> a = np.float16()
>>> b = np.int64()
>>> out = add(a, b)
>>> if TYPE_CHECKING:
... reveal_locals()
... # note: Revealed local types are:
... # note: a: numpy.floating[numpy.typing._16Bit*]
... # note: b: numpy.signedinteger[numpy.typing._64Bit*]
... # note: out: numpy.floating[numpy.typing._64Bit*]
"""
def __init_subclass__(cls) -> None:
allowed_names = {
"NBitBase", "_256Bit", "_128Bit", "_96Bit", "_80Bit",
"_64Bit", "_32Bit", "_16Bit", "_8Bit",
}
if cls.__name__ not in allowed_names:
raise TypeError('cannot inherit from final class "NBitBase"')
super().__init_subclass__()
# Silence errors about subclassing a `@final`-decorated class
class _256Bit(NBitBase): # type: ignore[misc]
pass
class _128Bit(_256Bit): # type: ignore[misc]
pass
class _96Bit(_128Bit): # type: ignore[misc]
pass
class _80Bit(_96Bit): # type: ignore[misc]
pass
class _64Bit(_80Bit): # type: ignore[misc]
pass
class _32Bit(_64Bit): # type: ignore[misc]
pass
class _16Bit(_32Bit): # type: ignore[misc]
pass
class _8Bit(_16Bit): # type: ignore[misc]
pass
from ._nested_sequence import (
_NestedSequence as _NestedSequence,
)
from ._nbit import (
_NBitByte as _NBitByte,
_NBitShort as _NBitShort,
_NBitIntC as _NBitIntC,
_NBitIntP as _NBitIntP,
_NBitInt as _NBitInt,
_NBitLongLong as _NBitLongLong,
_NBitHalf as _NBitHalf,
_NBitSingle as _NBitSingle,
_NBitDouble as _NBitDouble,
_NBitLongDouble as _NBitLongDouble,
)
from ._char_codes import (
_BoolCodes as _BoolCodes,
_UInt8Codes as _UInt8Codes,
_UInt16Codes as _UInt16Codes,
_UInt32Codes as _UInt32Codes,
_UInt64Codes as _UInt64Codes,
_Int8Codes as _Int8Codes,
_Int16Codes as _Int16Codes,
_Int32Codes as _Int32Codes,
_Int64Codes as _Int64Codes,
_Float16Codes as _Float16Codes,
_Float32Codes as _Float32Codes,
_Float64Codes as _Float64Codes,
_Complex64Codes as _Complex64Codes,
_Complex128Codes as _Complex128Codes,
_ByteCodes as _ByteCodes,
_ShortCodes as _ShortCodes,
_IntCCodes as _IntCCodes,
_IntPCodes as _IntPCodes,
_IntCodes as _IntCodes,
_LongLongCodes as _LongLongCodes,
_UByteCodes as _UByteCodes,
_UShortCodes as _UShortCodes,
_UIntCCodes as _UIntCCodes,
_UIntPCodes as _UIntPCodes,
_UIntCodes as _UIntCodes,
_ULongLongCodes as _ULongLongCodes,
_HalfCodes as _HalfCodes,
_SingleCodes as _SingleCodes,
_DoubleCodes as _DoubleCodes,
_LongDoubleCodes as _LongDoubleCodes,
_CSingleCodes as _CSingleCodes,
_CDoubleCodes as _CDoubleCodes,
_CLongDoubleCodes as _CLongDoubleCodes,
_DT64Codes as _DT64Codes,
_TD64Codes as _TD64Codes,
_StrCodes as _StrCodes,
_BytesCodes as _BytesCodes,
_VoidCodes as _VoidCodes,
_ObjectCodes as _ObjectCodes,
)
from ._scalars import (
_CharLike_co as _CharLike_co,
_BoolLike_co as _BoolLike_co,
_UIntLike_co as _UIntLike_co,
_IntLike_co as _IntLike_co,
_FloatLike_co as _FloatLike_co,
_ComplexLike_co as _ComplexLike_co,
_TD64Like_co as _TD64Like_co,
_NumberLike_co as _NumberLike_co,
_ScalarLike_co as _ScalarLike_co,
_VoidLike_co as _VoidLike_co,
)
from ._shape import (
_Shape as _Shape,
_ShapeLike as _ShapeLike,
)
from ._dtype_like import (
DTypeLike as DTypeLike,
_DTypeLike as _DTypeLike,
_SupportsDType as _SupportsDType,
_VoidDTypeLike as _VoidDTypeLike,
_DTypeLikeBool as _DTypeLikeBool,
_DTypeLikeUInt as _DTypeLikeUInt,
_DTypeLikeInt as _DTypeLikeInt,
_DTypeLikeFloat as _DTypeLikeFloat,
_DTypeLikeComplex as _DTypeLikeComplex,
_DTypeLikeTD64 as _DTypeLikeTD64,
_DTypeLikeDT64 as _DTypeLikeDT64,
_DTypeLikeObject as _DTypeLikeObject,
_DTypeLikeVoid as _DTypeLikeVoid,
_DTypeLikeStr as _DTypeLikeStr,
_DTypeLikeBytes as _DTypeLikeBytes,
_DTypeLikeComplex_co as _DTypeLikeComplex_co,
)
from ._array_like import (
NDArray as NDArray,
ArrayLike as ArrayLike,
_ArrayLike as _ArrayLike,
_FiniteNestedSequence as _FiniteNestedSequence,
_SupportsArray as _SupportsArray,
_SupportsArrayFunc as _SupportsArrayFunc,
_ArrayLikeInt as _ArrayLikeInt,
_ArrayLikeBool_co as _ArrayLikeBool_co,
_ArrayLikeUInt_co as _ArrayLikeUInt_co,
_ArrayLikeInt_co as _ArrayLikeInt_co,
_ArrayLikeFloat_co as _ArrayLikeFloat_co,
_ArrayLikeComplex_co as _ArrayLikeComplex_co,
_ArrayLikeNumber_co as _ArrayLikeNumber_co,
_ArrayLikeTD64_co as _ArrayLikeTD64_co,
_ArrayLikeDT64_co as _ArrayLikeDT64_co,
_ArrayLikeObject_co as _ArrayLikeObject_co,
_ArrayLikeVoid_co as _ArrayLikeVoid_co,
_ArrayLikeStr_co as _ArrayLikeStr_co,
_ArrayLikeBytes_co as _ArrayLikeBytes_co,
_ArrayLikeUnknown as _ArrayLikeUnknown,
_UnknownType as _UnknownType,
)
if TYPE_CHECKING:
from ._ufunc import (
_UFunc_Nin1_Nout1 as _UFunc_Nin1_Nout1,
_UFunc_Nin2_Nout1 as _UFunc_Nin2_Nout1,
_UFunc_Nin1_Nout2 as _UFunc_Nin1_Nout2,
_UFunc_Nin2_Nout2 as _UFunc_Nin2_Nout2,
_GUFunc_Nin2_Nout1 as _GUFunc_Nin2_Nout1,
)
else:
# Declare the (type-check-only) ufunc subclasses as ufunc aliases during
# runtime; this helps autocompletion tools such as Jedi (numpy/numpy#19834)
_UFunc_Nin1_Nout1 = ufunc
_UFunc_Nin2_Nout1 = ufunc
_UFunc_Nin1_Nout2 = ufunc
_UFunc_Nin2_Nout2 = ufunc
_GUFunc_Nin2_Nout1 = ufunc

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"""A module for creating docstrings for sphinx ``data`` domains."""
import re
import textwrap
from ._array_like import NDArray
_docstrings_list = []
def add_newdoc(name: str, value: str, doc: str) -> None:
"""Append ``_docstrings_list`` with a docstring for `name`.
Parameters
----------
name : str
The name of the object.
value : str
A string-representation of the object.
doc : str
The docstring of the object.
"""
_docstrings_list.append((name, value, doc))
def _parse_docstrings() -> str:
"""Convert all docstrings in ``_docstrings_list`` into a single
sphinx-legible text block.
"""
type_list_ret = []
for name, value, doc in _docstrings_list:
s = textwrap.dedent(doc).replace("\n", "\n ")
# Replace sections by rubrics
lines = s.split("\n")
new_lines = []
indent = ""
for line in lines:
m = re.match(r'^(\s+)[-=]+\s*$', line)
if m and new_lines:
prev = textwrap.dedent(new_lines.pop())
if prev == "Examples":
indent = ""
new_lines.append(f'{m.group(1)}.. rubric:: {prev}')
else:
indent = 4 * " "
new_lines.append(f'{m.group(1)}.. admonition:: {prev}')
new_lines.append("")
else:
new_lines.append(f"{indent}{line}")
s = "\n".join(new_lines)
s_block = f""".. data:: {name}\n :value: {value}\n {s}"""
type_list_ret.append(s_block)
return "\n".join(type_list_ret)
add_newdoc('ArrayLike', 'typing.Union[...]',
"""
A `~typing.Union` representing objects that can be coerced
into an `~numpy.ndarray`.
Among others this includes the likes of:
* Scalars.
* (Nested) sequences.
* Objects implementing the `~class.__array__` protocol.
.. versionadded:: 1.20
See Also
--------
:term:`array_like`:
Any scalar or sequence that can be interpreted as an ndarray.
Examples
--------
.. code-block:: python
>>> import numpy as np
>>> import numpy.typing as npt
>>> def as_array(a: npt.ArrayLike) -> np.ndarray:
... return np.array(a)
""")
add_newdoc('DTypeLike', 'typing.Union[...]',
"""
A `~typing.Union` representing objects that can be coerced
into a `~numpy.dtype`.
Among others this includes the likes of:
* :class:`type` objects.
* Character codes or the names of :class:`type` objects.
* Objects with the ``.dtype`` attribute.
.. versionadded:: 1.20
See Also
--------
:ref:`Specifying and constructing data types <arrays.dtypes.constructing>`
A comprehensive overview of all objects that can be coerced
into data types.
Examples
--------
.. code-block:: python
>>> import numpy as np
>>> import numpy.typing as npt
>>> def as_dtype(d: npt.DTypeLike) -> np.dtype:
... return np.dtype(d)
""")
add_newdoc('NDArray', repr(NDArray),
"""
A :term:`generic <generic type>` version of
`np.ndarray[Any, np.dtype[+ScalarType]] <numpy.ndarray>`.
Can be used during runtime for typing arrays with a given dtype
and unspecified shape.
.. versionadded:: 1.21
Examples
--------
.. code-block:: python
>>> import numpy as np
>>> import numpy.typing as npt
>>> print(npt.NDArray)
numpy.ndarray[typing.Any, numpy.dtype[+ScalarType]]
>>> print(npt.NDArray[np.float64])
numpy.ndarray[typing.Any, numpy.dtype[numpy.float64]]
>>> NDArrayInt = npt.NDArray[np.int_]
>>> a: NDArrayInt = np.arange(10)
>>> def func(a: npt.ArrayLike) -> npt.NDArray[Any]:
... return np.array(a)
""")
_docstrings = _parse_docstrings()

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from __future__ import annotations
import sys
from collections.abc import Collection, Callable, Sequence
from typing import Any, Protocol, Union, TypeVar, runtime_checkable
from numpy import (
ndarray,
dtype,
generic,
bool_,
unsignedinteger,
integer,
floating,
complexfloating,
number,
timedelta64,
datetime64,
object_,
void,
str_,
bytes_,
)
from ._nested_sequence import _NestedSequence
_T = TypeVar("_T")
_ScalarType = TypeVar("_ScalarType", bound=generic)
_ScalarType_co = TypeVar("_ScalarType_co", bound=generic, covariant=True)
_DType = TypeVar("_DType", bound=dtype[Any])
_DType_co = TypeVar("_DType_co", covariant=True, bound=dtype[Any])
NDArray = ndarray[Any, dtype[_ScalarType_co]]
# The `_SupportsArray` protocol only cares about the default dtype
# (i.e. `dtype=None` or no `dtype` parameter at all) of the to-be returned
# array.
# Concrete implementations of the protocol are responsible for adding
# any and all remaining overloads
@runtime_checkable
class _SupportsArray(Protocol[_DType_co]):
def __array__(self) -> ndarray[Any, _DType_co]: ...
@runtime_checkable
class _SupportsArrayFunc(Protocol):
"""A protocol class representing `~class.__array_function__`."""
def __array_function__(
self,
func: Callable[..., Any],
types: Collection[type[Any]],
args: tuple[Any, ...],
kwargs: dict[str, Any],
) -> object: ...
# TODO: Wait until mypy supports recursive objects in combination with typevars
_FiniteNestedSequence = Union[
_T,
Sequence[_T],
Sequence[Sequence[_T]],
Sequence[Sequence[Sequence[_T]]],
Sequence[Sequence[Sequence[Sequence[_T]]]],
]
# A subset of `npt.ArrayLike` that can be parametrized w.r.t. `np.generic`
_ArrayLike = Union[
_SupportsArray[dtype[_ScalarType]],
_NestedSequence[_SupportsArray[dtype[_ScalarType]]],
]
# A union representing array-like objects; consists of two typevars:
# One representing types that can be parametrized w.r.t. `np.dtype`
# and another one for the rest
_DualArrayLike = Union[
_SupportsArray[_DType],
_NestedSequence[_SupportsArray[_DType]],
_T,
_NestedSequence[_T],
]
if sys.version_info >= (3, 12):
from collections.abc import Buffer
ArrayLike = Buffer | _DualArrayLike[
dtype[Any],
Union[bool, int, float, complex, str, bytes],
]
else:
ArrayLike = _DualArrayLike[
dtype[Any],
Union[bool, int, float, complex, str, bytes],
]
# `ArrayLike<X>_co`: array-like objects that can be coerced into `X`
# given the casting rules `same_kind`
_ArrayLikeBool_co = _DualArrayLike[
dtype[bool_],
bool,
]
_ArrayLikeUInt_co = _DualArrayLike[
dtype[Union[bool_, unsignedinteger[Any]]],
bool,
]
_ArrayLikeInt_co = _DualArrayLike[
dtype[Union[bool_, integer[Any]]],
Union[bool, int],
]
_ArrayLikeFloat_co = _DualArrayLike[
dtype[Union[bool_, integer[Any], floating[Any]]],
Union[bool, int, float],
]
_ArrayLikeComplex_co = _DualArrayLike[
dtype[Union[
bool_,
integer[Any],
floating[Any],
complexfloating[Any, Any],
]],
Union[bool, int, float, complex],
]
_ArrayLikeNumber_co = _DualArrayLike[
dtype[Union[bool_, number[Any]]],
Union[bool, int, float, complex],
]
_ArrayLikeTD64_co = _DualArrayLike[
dtype[Union[bool_, integer[Any], timedelta64]],
Union[bool, int],
]
_ArrayLikeDT64_co = Union[
_SupportsArray[dtype[datetime64]],
_NestedSequence[_SupportsArray[dtype[datetime64]]],
]
_ArrayLikeObject_co = Union[
_SupportsArray[dtype[object_]],
_NestedSequence[_SupportsArray[dtype[object_]]],
]
_ArrayLikeVoid_co = Union[
_SupportsArray[dtype[void]],
_NestedSequence[_SupportsArray[dtype[void]]],
]
_ArrayLikeStr_co = _DualArrayLike[
dtype[str_],
str,
]
_ArrayLikeBytes_co = _DualArrayLike[
dtype[bytes_],
bytes,
]
_ArrayLikeInt = _DualArrayLike[
dtype[integer[Any]],
int,
]
# Extra ArrayLike type so that pyright can deal with NDArray[Any]
# Used as the first overload, should only match NDArray[Any],
# not any actual types.
# https://github.com/numpy/numpy/pull/22193
class _UnknownType:
...
_ArrayLikeUnknown = _DualArrayLike[
dtype[_UnknownType],
_UnknownType,
]

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"""
A module with various ``typing.Protocol`` subclasses that implement
the ``__call__`` magic method.
See the `Mypy documentation`_ on protocols for more details.
.. _`Mypy documentation`: https://mypy.readthedocs.io/en/stable/protocols.html#callback-protocols
"""
from __future__ import annotations
from typing import (
TypeVar,
overload,
Any,
NoReturn,
Protocol,
)
from numpy import (
ndarray,
dtype,
generic,
bool_,
timedelta64,
number,
integer,
unsignedinteger,
signedinteger,
int8,
int_,
floating,
float64,
complexfloating,
complex128,
)
from ._nbit import _NBitInt, _NBitDouble
from ._scalars import (
_BoolLike_co,
_IntLike_co,
_FloatLike_co,
_NumberLike_co,
)
from . import NBitBase
from ._array_like import NDArray
from ._nested_sequence import _NestedSequence
_T1 = TypeVar("_T1")
_T2 = TypeVar("_T2")
_T1_contra = TypeVar("_T1_contra", contravariant=True)
_T2_contra = TypeVar("_T2_contra", contravariant=True)
_2Tuple = tuple[_T1, _T1]
_NBit1 = TypeVar("_NBit1", bound=NBitBase)
_NBit2 = TypeVar("_NBit2", bound=NBitBase)
_IntType = TypeVar("_IntType", bound=integer)
_FloatType = TypeVar("_FloatType", bound=floating)
_NumberType = TypeVar("_NumberType", bound=number)
_NumberType_co = TypeVar("_NumberType_co", covariant=True, bound=number)
_GenericType_co = TypeVar("_GenericType_co", covariant=True, bound=generic)
class _BoolOp(Protocol[_GenericType_co]):
@overload
def __call__(self, other: _BoolLike_co, /) -> _GenericType_co: ...
@overload # platform dependent
def __call__(self, other: int, /) -> int_: ...
@overload
def __call__(self, other: float, /) -> float64: ...
@overload
def __call__(self, other: complex, /) -> complex128: ...
@overload
def __call__(self, other: _NumberType, /) -> _NumberType: ...
class _BoolBitOp(Protocol[_GenericType_co]):
@overload
def __call__(self, other: _BoolLike_co, /) -> _GenericType_co: ...
@overload # platform dependent
def __call__(self, other: int, /) -> int_: ...
@overload
def __call__(self, other: _IntType, /) -> _IntType: ...
class _BoolSub(Protocol):
# Note that `other: bool_` is absent here
@overload
def __call__(self, other: bool, /) -> NoReturn: ...
@overload # platform dependent
def __call__(self, other: int, /) -> int_: ...
@overload
def __call__(self, other: float, /) -> float64: ...
@overload
def __call__(self, other: complex, /) -> complex128: ...
@overload
def __call__(self, other: _NumberType, /) -> _NumberType: ...
class _BoolTrueDiv(Protocol):
@overload
def __call__(self, other: float | _IntLike_co, /) -> float64: ...
@overload
def __call__(self, other: complex, /) -> complex128: ...
@overload
def __call__(self, other: _NumberType, /) -> _NumberType: ...
class _BoolMod(Protocol):
@overload
def __call__(self, other: _BoolLike_co, /) -> int8: ...
@overload # platform dependent
def __call__(self, other: int, /) -> int_: ...
@overload
def __call__(self, other: float, /) -> float64: ...
@overload
def __call__(self, other: _IntType, /) -> _IntType: ...
@overload
def __call__(self, other: _FloatType, /) -> _FloatType: ...
class _BoolDivMod(Protocol):
@overload
def __call__(self, other: _BoolLike_co, /) -> _2Tuple[int8]: ...
@overload # platform dependent
def __call__(self, other: int, /) -> _2Tuple[int_]: ...
@overload
def __call__(self, other: float, /) -> _2Tuple[floating[_NBit1 | _NBitDouble]]: ...
@overload
def __call__(self, other: _IntType, /) -> _2Tuple[_IntType]: ...
@overload
def __call__(self, other: _FloatType, /) -> _2Tuple[_FloatType]: ...
class _TD64Div(Protocol[_NumberType_co]):
@overload
def __call__(self, other: timedelta64, /) -> _NumberType_co: ...
@overload
def __call__(self, other: _BoolLike_co, /) -> NoReturn: ...
@overload
def __call__(self, other: _FloatLike_co, /) -> timedelta64: ...
class _IntTrueDiv(Protocol[_NBit1]):
@overload
def __call__(self, other: bool, /) -> floating[_NBit1]: ...
@overload
def __call__(self, other: int, /) -> floating[_NBit1 | _NBitInt]: ...
@overload
def __call__(self, other: float, /) -> floating[_NBit1 | _NBitDouble]: ...
@overload
def __call__(
self, other: complex, /,
) -> complexfloating[_NBit1 | _NBitDouble, _NBit1 | _NBitDouble]: ...
@overload
def __call__(self, other: integer[_NBit2], /) -> floating[_NBit1 | _NBit2]: ...
class _UnsignedIntOp(Protocol[_NBit1]):
# NOTE: `uint64 + signedinteger -> float64`
@overload
def __call__(self, other: bool, /) -> unsignedinteger[_NBit1]: ...
@overload
def __call__(
self, other: int | signedinteger[Any], /
) -> Any: ...
@overload
def __call__(self, other: float, /) -> floating[_NBit1 | _NBitDouble]: ...
@overload
def __call__(
self, other: complex, /,
) -> complexfloating[_NBit1 | _NBitDouble, _NBit1 | _NBitDouble]: ...
@overload
def __call__(
self, other: unsignedinteger[_NBit2], /
) -> unsignedinteger[_NBit1 | _NBit2]: ...
class _UnsignedIntBitOp(Protocol[_NBit1]):
@overload
def __call__(self, other: bool, /) -> unsignedinteger[_NBit1]: ...
@overload
def __call__(self, other: int, /) -> signedinteger[Any]: ...
@overload
def __call__(self, other: signedinteger[Any], /) -> signedinteger[Any]: ...
@overload
def __call__(
self, other: unsignedinteger[_NBit2], /
) -> unsignedinteger[_NBit1 | _NBit2]: ...
class _UnsignedIntMod(Protocol[_NBit1]):
@overload
def __call__(self, other: bool, /) -> unsignedinteger[_NBit1]: ...
@overload
def __call__(
self, other: int | signedinteger[Any], /
) -> Any: ...
@overload
def __call__(self, other: float, /) -> floating[_NBit1 | _NBitDouble]: ...
@overload
def __call__(
self, other: unsignedinteger[_NBit2], /
) -> unsignedinteger[_NBit1 | _NBit2]: ...
class _UnsignedIntDivMod(Protocol[_NBit1]):
@overload
def __call__(self, other: bool, /) -> _2Tuple[signedinteger[_NBit1]]: ...
@overload
def __call__(
self, other: int | signedinteger[Any], /
) -> _2Tuple[Any]: ...
@overload
def __call__(self, other: float, /) -> _2Tuple[floating[_NBit1 | _NBitDouble]]: ...
@overload
def __call__(
self, other: unsignedinteger[_NBit2], /
) -> _2Tuple[unsignedinteger[_NBit1 | _NBit2]]: ...
class _SignedIntOp(Protocol[_NBit1]):
@overload
def __call__(self, other: bool, /) -> signedinteger[_NBit1]: ...
@overload
def __call__(self, other: int, /) -> signedinteger[_NBit1 | _NBitInt]: ...
@overload
def __call__(self, other: float, /) -> floating[_NBit1 | _NBitDouble]: ...
@overload
def __call__(
self, other: complex, /,
) -> complexfloating[_NBit1 | _NBitDouble, _NBit1 | _NBitDouble]: ...
@overload
def __call__(
self, other: signedinteger[_NBit2], /,
) -> signedinteger[_NBit1 | _NBit2]: ...
class _SignedIntBitOp(Protocol[_NBit1]):
@overload
def __call__(self, other: bool, /) -> signedinteger[_NBit1]: ...
@overload
def __call__(self, other: int, /) -> signedinteger[_NBit1 | _NBitInt]: ...
@overload
def __call__(
self, other: signedinteger[_NBit2], /,
) -> signedinteger[_NBit1 | _NBit2]: ...
class _SignedIntMod(Protocol[_NBit1]):
@overload
def __call__(self, other: bool, /) -> signedinteger[_NBit1]: ...
@overload
def __call__(self, other: int, /) -> signedinteger[_NBit1 | _NBitInt]: ...
@overload
def __call__(self, other: float, /) -> floating[_NBit1 | _NBitDouble]: ...
@overload
def __call__(
self, other: signedinteger[_NBit2], /,
) -> signedinteger[_NBit1 | _NBit2]: ...
class _SignedIntDivMod(Protocol[_NBit1]):
@overload
def __call__(self, other: bool, /) -> _2Tuple[signedinteger[_NBit1]]: ...
@overload
def __call__(self, other: int, /) -> _2Tuple[signedinteger[_NBit1 | _NBitInt]]: ...
@overload
def __call__(self, other: float, /) -> _2Tuple[floating[_NBit1 | _NBitDouble]]: ...
@overload
def __call__(
self, other: signedinteger[_NBit2], /,
) -> _2Tuple[signedinteger[_NBit1 | _NBit2]]: ...
class _FloatOp(Protocol[_NBit1]):
@overload
def __call__(self, other: bool, /) -> floating[_NBit1]: ...
@overload
def __call__(self, other: int, /) -> floating[_NBit1 | _NBitInt]: ...
@overload
def __call__(self, other: float, /) -> floating[_NBit1 | _NBitDouble]: ...
@overload
def __call__(
self, other: complex, /,
) -> complexfloating[_NBit1 | _NBitDouble, _NBit1 | _NBitDouble]: ...
@overload
def __call__(
self, other: integer[_NBit2] | floating[_NBit2], /
) -> floating[_NBit1 | _NBit2]: ...
class _FloatMod(Protocol[_NBit1]):
@overload
def __call__(self, other: bool, /) -> floating[_NBit1]: ...
@overload
def __call__(self, other: int, /) -> floating[_NBit1 | _NBitInt]: ...
@overload
def __call__(self, other: float, /) -> floating[_NBit1 | _NBitDouble]: ...
@overload
def __call__(
self, other: integer[_NBit2] | floating[_NBit2], /
) -> floating[_NBit1 | _NBit2]: ...
class _FloatDivMod(Protocol[_NBit1]):
@overload
def __call__(self, other: bool, /) -> _2Tuple[floating[_NBit1]]: ...
@overload
def __call__(self, other: int, /) -> _2Tuple[floating[_NBit1 | _NBitInt]]: ...
@overload
def __call__(self, other: float, /) -> _2Tuple[floating[_NBit1 | _NBitDouble]]: ...
@overload
def __call__(
self, other: integer[_NBit2] | floating[_NBit2], /
) -> _2Tuple[floating[_NBit1 | _NBit2]]: ...
class _ComplexOp(Protocol[_NBit1]):
@overload
def __call__(self, other: bool, /) -> complexfloating[_NBit1, _NBit1]: ...
@overload
def __call__(self, other: int, /) -> complexfloating[_NBit1 | _NBitInt, _NBit1 | _NBitInt]: ...
@overload
def __call__(
self, other: complex, /,
) -> complexfloating[_NBit1 | _NBitDouble, _NBit1 | _NBitDouble]: ...
@overload
def __call__(
self,
other: (
integer[_NBit2]
| floating[_NBit2]
| complexfloating[_NBit2, _NBit2]
), /,
) -> complexfloating[_NBit1 | _NBit2, _NBit1 | _NBit2]: ...
class _NumberOp(Protocol):
def __call__(self, other: _NumberLike_co, /) -> Any: ...
class _SupportsLT(Protocol):
def __lt__(self, other: Any, /) -> object: ...
class _SupportsGT(Protocol):
def __gt__(self, other: Any, /) -> object: ...
class _ComparisonOp(Protocol[_T1_contra, _T2_contra]):
@overload
def __call__(self, other: _T1_contra, /) -> bool_: ...
@overload
def __call__(self, other: _T2_contra, /) -> NDArray[bool_]: ...
@overload
def __call__(
self,
other: _SupportsLT | _SupportsGT | _NestedSequence[_SupportsLT | _SupportsGT],
/,
) -> Any: ...

111
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@ -1,111 +0,0 @@
from typing import Literal
_BoolCodes = Literal["?", "=?", "<?", ">?", "bool", "bool_", "bool8"]
_UInt8Codes = Literal["uint8", "u1", "=u1", "<u1", ">u1"]
_UInt16Codes = Literal["uint16", "u2", "=u2", "<u2", ">u2"]
_UInt32Codes = Literal["uint32", "u4", "=u4", "<u4", ">u4"]
_UInt64Codes = Literal["uint64", "u8", "=u8", "<u8", ">u8"]
_Int8Codes = Literal["int8", "i1", "=i1", "<i1", ">i1"]
_Int16Codes = Literal["int16", "i2", "=i2", "<i2", ">i2"]
_Int32Codes = Literal["int32", "i4", "=i4", "<i4", ">i4"]
_Int64Codes = Literal["int64", "i8", "=i8", "<i8", ">i8"]
_Float16Codes = Literal["float16", "f2", "=f2", "<f2", ">f2"]
_Float32Codes = Literal["float32", "f4", "=f4", "<f4", ">f4"]
_Float64Codes = Literal["float64", "f8", "=f8", "<f8", ">f8"]
_Complex64Codes = Literal["complex64", "c8", "=c8", "<c8", ">c8"]
_Complex128Codes = Literal["complex128", "c16", "=c16", "<c16", ">c16"]
_ByteCodes = Literal["byte", "b", "=b", "<b", ">b"]
_ShortCodes = Literal["short", "h", "=h", "<h", ">h"]
_IntCCodes = Literal["intc", "i", "=i", "<i", ">i"]
_IntPCodes = Literal["intp", "int0", "p", "=p", "<p", ">p"]
_IntCodes = Literal["long", "int", "int_", "l", "=l", "<l", ">l"]
_LongLongCodes = Literal["longlong", "q", "=q", "<q", ">q"]
_UByteCodes = Literal["ubyte", "B", "=B", "<B", ">B"]
_UShortCodes = Literal["ushort", "H", "=H", "<H", ">H"]
_UIntCCodes = Literal["uintc", "I", "=I", "<I", ">I"]
_UIntPCodes = Literal["uintp", "uint0", "P", "=P", "<P", ">P"]
_UIntCodes = Literal["ulong", "uint", "L", "=L", "<L", ">L"]
_ULongLongCodes = Literal["ulonglong", "Q", "=Q", "<Q", ">Q"]
_HalfCodes = Literal["half", "e", "=e", "<e", ">e"]
_SingleCodes = Literal["single", "f", "=f", "<f", ">f"]
_DoubleCodes = Literal["double", "float", "float_", "d", "=d", "<d", ">d"]
_LongDoubleCodes = Literal["longdouble", "longfloat", "g", "=g", "<g", ">g"]
_CSingleCodes = Literal["csingle", "singlecomplex", "F", "=F", "<F", ">F"]
_CDoubleCodes = Literal["cdouble", "complex", "complex_", "cfloat", "D", "=D", "<D", ">D"]
_CLongDoubleCodes = Literal["clongdouble", "clongfloat", "longcomplex", "G", "=G", "<G", ">G"]
_StrCodes = Literal["str", "str_", "str0", "unicode", "unicode_", "U", "=U", "<U", ">U"]
_BytesCodes = Literal["bytes", "bytes_", "bytes0", "S", "=S", "<S", ">S"]
_VoidCodes = Literal["void", "void0", "V", "=V", "<V", ">V"]
_ObjectCodes = Literal["object", "object_", "O", "=O", "<O", ">O"]
_DT64Codes = Literal[
"datetime64", "=datetime64", "<datetime64", ">datetime64",
"datetime64[Y]", "=datetime64[Y]", "<datetime64[Y]", ">datetime64[Y]",
"datetime64[M]", "=datetime64[M]", "<datetime64[M]", ">datetime64[M]",
"datetime64[W]", "=datetime64[W]", "<datetime64[W]", ">datetime64[W]",
"datetime64[D]", "=datetime64[D]", "<datetime64[D]", ">datetime64[D]",
"datetime64[h]", "=datetime64[h]", "<datetime64[h]", ">datetime64[h]",
"datetime64[m]", "=datetime64[m]", "<datetime64[m]", ">datetime64[m]",
"datetime64[s]", "=datetime64[s]", "<datetime64[s]", ">datetime64[s]",
"datetime64[ms]", "=datetime64[ms]", "<datetime64[ms]", ">datetime64[ms]",
"datetime64[us]", "=datetime64[us]", "<datetime64[us]", ">datetime64[us]",
"datetime64[ns]", "=datetime64[ns]", "<datetime64[ns]", ">datetime64[ns]",
"datetime64[ps]", "=datetime64[ps]", "<datetime64[ps]", ">datetime64[ps]",
"datetime64[fs]", "=datetime64[fs]", "<datetime64[fs]", ">datetime64[fs]",
"datetime64[as]", "=datetime64[as]", "<datetime64[as]", ">datetime64[as]",
"M", "=M", "<M", ">M",
"M8", "=M8", "<M8", ">M8",
"M8[Y]", "=M8[Y]", "<M8[Y]", ">M8[Y]",
"M8[M]", "=M8[M]", "<M8[M]", ">M8[M]",
"M8[W]", "=M8[W]", "<M8[W]", ">M8[W]",
"M8[D]", "=M8[D]", "<M8[D]", ">M8[D]",
"M8[h]", "=M8[h]", "<M8[h]", ">M8[h]",
"M8[m]", "=M8[m]", "<M8[m]", ">M8[m]",
"M8[s]", "=M8[s]", "<M8[s]", ">M8[s]",
"M8[ms]", "=M8[ms]", "<M8[ms]", ">M8[ms]",
"M8[us]", "=M8[us]", "<M8[us]", ">M8[us]",
"M8[ns]", "=M8[ns]", "<M8[ns]", ">M8[ns]",
"M8[ps]", "=M8[ps]", "<M8[ps]", ">M8[ps]",
"M8[fs]", "=M8[fs]", "<M8[fs]", ">M8[fs]",
"M8[as]", "=M8[as]", "<M8[as]", ">M8[as]",
]
_TD64Codes = Literal[
"timedelta64", "=timedelta64", "<timedelta64", ">timedelta64",
"timedelta64[Y]", "=timedelta64[Y]", "<timedelta64[Y]", ">timedelta64[Y]",
"timedelta64[M]", "=timedelta64[M]", "<timedelta64[M]", ">timedelta64[M]",
"timedelta64[W]", "=timedelta64[W]", "<timedelta64[W]", ">timedelta64[W]",
"timedelta64[D]", "=timedelta64[D]", "<timedelta64[D]", ">timedelta64[D]",
"timedelta64[h]", "=timedelta64[h]", "<timedelta64[h]", ">timedelta64[h]",
"timedelta64[m]", "=timedelta64[m]", "<timedelta64[m]", ">timedelta64[m]",
"timedelta64[s]", "=timedelta64[s]", "<timedelta64[s]", ">timedelta64[s]",
"timedelta64[ms]", "=timedelta64[ms]", "<timedelta64[ms]", ">timedelta64[ms]",
"timedelta64[us]", "=timedelta64[us]", "<timedelta64[us]", ">timedelta64[us]",
"timedelta64[ns]", "=timedelta64[ns]", "<timedelta64[ns]", ">timedelta64[ns]",
"timedelta64[ps]", "=timedelta64[ps]", "<timedelta64[ps]", ">timedelta64[ps]",
"timedelta64[fs]", "=timedelta64[fs]", "<timedelta64[fs]", ">timedelta64[fs]",
"timedelta64[as]", "=timedelta64[as]", "<timedelta64[as]", ">timedelta64[as]",
"m", "=m", "<m", ">m",
"m8", "=m8", "<m8", ">m8",
"m8[Y]", "=m8[Y]", "<m8[Y]", ">m8[Y]",
"m8[M]", "=m8[M]", "<m8[M]", ">m8[M]",
"m8[W]", "=m8[W]", "<m8[W]", ">m8[W]",
"m8[D]", "=m8[D]", "<m8[D]", ">m8[D]",
"m8[h]", "=m8[h]", "<m8[h]", ">m8[h]",
"m8[m]", "=m8[m]", "<m8[m]", ">m8[m]",
"m8[s]", "=m8[s]", "<m8[s]", ">m8[s]",
"m8[ms]", "=m8[ms]", "<m8[ms]", ">m8[ms]",
"m8[us]", "=m8[us]", "<m8[us]", ">m8[us]",
"m8[ns]", "=m8[ns]", "<m8[ns]", ">m8[ns]",
"m8[ps]", "=m8[ps]", "<m8[ps]", ">m8[ps]",
"m8[fs]", "=m8[fs]", "<m8[fs]", ">m8[fs]",
"m8[as]", "=m8[as]", "<m8[as]", ">m8[as]",
]

246
.env/lib/python3.11/site-packages/numpy/_typing/_dtype_like.py
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@ -1,246 +0,0 @@
from collections.abc import Sequence
from typing import (
Any,
Sequence,
Union,
TypeVar,
Protocol,
TypedDict,
runtime_checkable,
)
import numpy as np
from ._shape import _ShapeLike
from ._char_codes import (
_BoolCodes,
_UInt8Codes,
_UInt16Codes,
_UInt32Codes,
_UInt64Codes,
_Int8Codes,
_Int16Codes,
_Int32Codes,
_Int64Codes,
_Float16Codes,
_Float32Codes,
_Float64Codes,
_Complex64Codes,
_Complex128Codes,
_ByteCodes,
_ShortCodes,
_IntCCodes,
_IntPCodes,
_IntCodes,
_LongLongCodes,
_UByteCodes,
_UShortCodes,
_UIntCCodes,
_UIntPCodes,
_UIntCodes,
_ULongLongCodes,
_HalfCodes,
_SingleCodes,
_DoubleCodes,
_LongDoubleCodes,
_CSingleCodes,
_CDoubleCodes,
_CLongDoubleCodes,
_DT64Codes,
_TD64Codes,
_StrCodes,
_BytesCodes,
_VoidCodes,
_ObjectCodes,
)
_SCT = TypeVar("_SCT", bound=np.generic)
_DType_co = TypeVar("_DType_co", covariant=True, bound=np.dtype[Any])
_DTypeLikeNested = Any # TODO: wait for support for recursive types
# Mandatory keys
class _DTypeDictBase(TypedDict):
names: Sequence[str]
formats: Sequence[_DTypeLikeNested]
# Mandatory + optional keys
class _DTypeDict(_DTypeDictBase, total=False):
# Only `str` elements are usable as indexing aliases,
# but `titles` can in principle accept any object
offsets: Sequence[int]
titles: Sequence[Any]
itemsize: int
aligned: bool
# A protocol for anything with the dtype attribute
@runtime_checkable
class _SupportsDType(Protocol[_DType_co]):
@property
def dtype(self) -> _DType_co: ...
# A subset of `npt.DTypeLike` that can be parametrized w.r.t. `np.generic`
_DTypeLike = Union[
np.dtype[_SCT],
type[_SCT],
_SupportsDType[np.dtype[_SCT]],
]
# Would create a dtype[np.void]
_VoidDTypeLike = Union[
# (flexible_dtype, itemsize)
tuple[_DTypeLikeNested, int],
# (fixed_dtype, shape)
tuple[_DTypeLikeNested, _ShapeLike],
# [(field_name, field_dtype, field_shape), ...]
#
# The type here is quite broad because NumPy accepts quite a wide
# range of inputs inside the list; see the tests for some
# examples.
list[Any],
# {'names': ..., 'formats': ..., 'offsets': ..., 'titles': ...,
# 'itemsize': ...}
_DTypeDict,
# (base_dtype, new_dtype)
tuple[_DTypeLikeNested, _DTypeLikeNested],
]
# Anything that can be coerced into numpy.dtype.
# Reference: https://docs.scipy.org/doc/numpy/reference/arrays.dtypes.html
DTypeLike = Union[
np.dtype[Any],
# default data type (float64)
None,
# array-scalar types and generic types
type[Any], # NOTE: We're stuck with `type[Any]` due to object dtypes
# anything with a dtype attribute
_SupportsDType[np.dtype[Any]],
# character codes, type strings or comma-separated fields, e.g., 'float64'
str,
_VoidDTypeLike,
]
# NOTE: while it is possible to provide the dtype as a dict of
# dtype-like objects (e.g. `{'field1': ..., 'field2': ..., ...}`),
# this syntax is officially discourged and
# therefore not included in the Union defining `DTypeLike`.
#
# See https://github.com/numpy/numpy/issues/16891 for more details.
# Aliases for commonly used dtype-like objects.
# Note that the precision of `np.number` subclasses is ignored herein.
_DTypeLikeBool = Union[
type[bool],
type[np.bool_],
np.dtype[np.bool_],
_SupportsDType[np.dtype[np.bool_]],
_BoolCodes,
]
_DTypeLikeUInt = Union[
type[np.unsignedinteger],
np.dtype[np.unsignedinteger],
_SupportsDType[np.dtype[np.unsignedinteger]],
_UInt8Codes,
_UInt16Codes,
_UInt32Codes,
_UInt64Codes,
_UByteCodes,
_UShortCodes,
_UIntCCodes,
_UIntPCodes,
_UIntCodes,
_ULongLongCodes,
]
_DTypeLikeInt = Union[
type[int],
type[np.signedinteger],
np.dtype[np.signedinteger],
_SupportsDType[np.dtype[np.signedinteger]],
_Int8Codes,
_Int16Codes,
_Int32Codes,
_Int64Codes,
_ByteCodes,
_ShortCodes,
_IntCCodes,
_IntPCodes,
_IntCodes,
_LongLongCodes,
]
_DTypeLikeFloat = Union[
type[float],
type[np.floating],
np.dtype[np.floating],
_SupportsDType[np.dtype[np.floating]],
_Float16Codes,
_Float32Codes,
_Float64Codes,
_HalfCodes,
_SingleCodes,
_DoubleCodes,
_LongDoubleCodes,
]
_DTypeLikeComplex = Union[
type[complex],
type[np.complexfloating],
np.dtype[np.complexfloating],
_SupportsDType[np.dtype[np.complexfloating]],
_Complex64Codes,
_Complex128Codes,
_CSingleCodes,
_CDoubleCodes,
_CLongDoubleCodes,
]
_DTypeLikeDT64 = Union[
type[np.timedelta64],
np.dtype[np.timedelta64],
_SupportsDType[np.dtype[np.timedelta64]],
_TD64Codes,
]
_DTypeLikeTD64 = Union[
type[np.datetime64],
np.dtype[np.datetime64],
_SupportsDType[np.dtype[np.datetime64]],
_DT64Codes,
]
_DTypeLikeStr = Union[
type[str],
type[np.str_],
np.dtype[np.str_],
_SupportsDType[np.dtype[np.str_]],
_StrCodes,
]
_DTypeLikeBytes = Union[
type[bytes],
type[np.bytes_],
np.dtype[np.bytes_],
_SupportsDType[np.dtype[np.bytes_]],
_BytesCodes,
]
_DTypeLikeVoid = Union[
type[np.void],
np.dtype[np.void],
_SupportsDType[np.dtype[np.void]],
_VoidCodes,
_VoidDTypeLike,
]
_DTypeLikeObject = Union[
type,
np.dtype[np.object_],
_SupportsDType[np.dtype[np.object_]],
_ObjectCodes,
]
_DTypeLikeComplex_co = Union[
_DTypeLikeBool,
_DTypeLikeUInt,
_DTypeLikeInt,
_DTypeLikeFloat,
_DTypeLikeComplex,
]

27
.env/lib/python3.11/site-packages/numpy/_typing/_extended_precision.py
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@ -1,27 +0,0 @@
"""A module with platform-specific extended precision
`numpy.number` subclasses.
The subclasses are defined here (instead of ``__init__.pyi``) such
that they can be imported conditionally via the numpy's mypy plugin.
"""
import numpy as np
from . import (
_80Bit,
_96Bit,
_128Bit,
_256Bit,
)
uint128 = np.unsignedinteger[_128Bit]
uint256 = np.unsignedinteger[_256Bit]
int128 = np.signedinteger[_128Bit]
int256 = np.signedinteger[_256Bit]
float80 = np.floating[_80Bit]
float96 = np.floating[_96Bit]
float128 = np.floating[_128Bit]
float256 = np.floating[_256Bit]
complex160 = np.complexfloating[_80Bit, _80Bit]
complex192 = np.complexfloating[_96Bit, _96Bit]
complex256 = np.complexfloating[_128Bit, _128Bit]
complex512 = np.complexfloating[_256Bit, _256Bit]

16
.env/lib/python3.11/site-packages/numpy/_typing/_nbit.py
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@ -1,16 +0,0 @@
"""A module with the precisions of platform-specific `~numpy.number`s."""
from typing import Any
# To-be replaced with a `npt.NBitBase` subclass by numpy's mypy plugin
_NBitByte = Any
_NBitShort = Any
_NBitIntC = Any
_NBitIntP = Any
_NBitInt = Any
_NBitLongLong = Any
_NBitHalf = Any
_NBitSingle = Any
_NBitDouble = Any
_NBitLongDouble = Any

86
.env/lib/python3.11/site-packages/numpy/_typing/_nested_sequence.py
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@ -1,86 +0,0 @@
"""A module containing the `_NestedSequence` protocol."""
from __future__ import annotations
from collections.abc import Iterator
from typing import (
Any,
TypeVar,
Protocol,
runtime_checkable,
)
__all__ = ["_NestedSequence"]
_T_co = TypeVar("_T_co", covariant=True)
@runtime_checkable
class _NestedSequence(Protocol[_T_co]):
"""A protocol for representing nested sequences.
Warning
-------
`_NestedSequence` currently does not work in combination with typevars,
*e.g.* ``def func(a: _NestedSequnce[T]) -> T: ...``.
See Also
--------
collections.abc.Sequence
ABCs for read-only and mutable :term:`sequences`.
Examples
--------
.. code-block:: python
>>> from __future__ import annotations
>>> from typing import TYPE_CHECKING
>>> import numpy as np
>>> from numpy._typing import _NestedSequence
>>> def get_dtype(seq: _NestedSequence[float]) -> np.dtype[np.float64]:
... return np.asarray(seq).dtype
>>> a = get_dtype([1.0])
>>> b = get_dtype([[1.0]])
>>> c = get_dtype([[[1.0]]])
>>> d = get_dtype([[[[1.0]]]])
>>> if TYPE_CHECKING:
... reveal_locals()
... # note: Revealed local types are:
... # note: a: numpy.dtype[numpy.floating[numpy._typing._64Bit]]
... # note: b: numpy.dtype[numpy.floating[numpy._typing._64Bit]]
... # note: c: numpy.dtype[numpy.floating[numpy._typing._64Bit]]
... # note: d: numpy.dtype[numpy.floating[numpy._typing._64Bit]]
"""
def __len__(self, /) -> int:
"""Implement ``len(self)``."""
raise NotImplementedError
def __getitem__(self, index: int, /) -> _T_co | _NestedSequence[_T_co]:
"""Implement ``self[x]``."""
raise NotImplementedError
def __contains__(self, x: object, /) -> bool:
"""Implement ``x in self``."""
raise NotImplementedError
def __iter__(self, /) -> Iterator[_T_co | _NestedSequence[_T_co]]:
"""Implement ``iter(self)``."""
raise NotImplementedError
def __reversed__(self, /) -> Iterator[_T_co | _NestedSequence[_T_co]]:
"""Implement ``reversed(self)``."""
raise NotImplementedError
def count(self, value: Any, /) -> int:
"""Return the number of occurrences of `value`."""
raise NotImplementedError
def index(self, value: Any, /) -> int:
"""Return the first index of `value`."""
raise NotImplementedError

30
.env/lib/python3.11/site-packages/numpy/_typing/_scalars.py
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@ -1,30 +0,0 @@
from typing import Union, Any
import numpy as np
# NOTE: `_StrLike_co` and `_BytesLike_co` are pointless, as `np.str_` and
# `np.bytes_` are already subclasses of their builtin counterpart
_CharLike_co = Union[str, bytes]
# The 6 `<X>Like_co` type-aliases below represent all scalars that can be
# coerced into `<X>` (with the casting rule `same_kind`)
_BoolLike_co = Union[bool, np.bool_]
_UIntLike_co = Union[_BoolLike_co, np.unsignedinteger[Any]]
_IntLike_co = Union[_BoolLike_co, int, np.integer[Any]]
_FloatLike_co = Union[_IntLike_co, float, np.floating[Any]]
_ComplexLike_co = Union[_FloatLike_co, complex, np.complexfloating[Any, Any]]
_TD64Like_co = Union[_IntLike_co, np.timedelta64]
_NumberLike_co = Union[int, float, complex, np.number[Any], np.bool_]
_ScalarLike_co = Union[
int,
float,
complex,
str,
bytes,
np.generic,
]
# `_VoidLike_co` is technically not a scalar, but it's close enough
_VoidLike_co = Union[tuple[Any, ...], np.void]

7
.env/lib/python3.11/site-packages/numpy/_typing/_shape.py
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@ -1,7 +0,0 @@
from collections.abc import Sequence
from typing import Union, SupportsIndex
_Shape = tuple[int, ...]
# Anything that can be coerced to a shape tuple
_ShapeLike = Union[SupportsIndex, Sequence[SupportsIndex]]

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