diff --git a/evalkit_cambrian/lib/python3.10/site-packages/aiofiles-23.2.1.dist-info/INSTALLER b/evalkit_cambrian/lib/python3.10/site-packages/aiofiles-23.2.1.dist-info/INSTALLER new file mode 100644 index 0000000000000000000000000000000000000000..a1b589e38a32041e49332e5e81c2d363dc418d68 --- /dev/null +++ b/evalkit_cambrian/lib/python3.10/site-packages/aiofiles-23.2.1.dist-info/INSTALLER @@ -0,0 +1 @@ +pip diff --git a/evalkit_cambrian/lib/python3.10/site-packages/aiofiles-23.2.1.dist-info/METADATA b/evalkit_cambrian/lib/python3.10/site-packages/aiofiles-23.2.1.dist-info/METADATA new file mode 100644 index 0000000000000000000000000000000000000000..61b6e3ee620bb606cf5b99646c8fcb84ee128419 --- /dev/null +++ b/evalkit_cambrian/lib/python3.10/site-packages/aiofiles-23.2.1.dist-info/METADATA @@ -0,0 +1,291 @@ +Metadata-Version: 2.1 +Name: aiofiles +Version: 23.2.1 +Summary: File support for asyncio. +Project-URL: Changelog, https://github.com/Tinche/aiofiles#history +Project-URL: Bug Tracker, https://github.com/Tinche/aiofiles/issues +Project-URL: repository, https://github.com/Tinche/aiofiles +Author-email: Tin Tvrtkovic +License: Apache-2.0 +License-File: LICENSE +License-File: NOTICE +Classifier: Development Status :: 5 - Production/Stable +Classifier: Framework :: AsyncIO +Classifier: License :: OSI Approved :: Apache Software License +Classifier: Operating System :: OS Independent +Classifier: Programming Language :: Python :: 3.7 +Classifier: Programming Language :: Python :: 3.8 +Classifier: Programming Language :: Python :: 3.9 +Classifier: Programming Language :: Python :: 3.10 +Classifier: Programming Language :: Python :: 3.11 +Classifier: Programming Language :: Python :: 3.12 +Classifier: Programming Language :: Python :: Implementation :: CPython +Classifier: Programming Language :: Python :: Implementation :: PyPy +Requires-Python: >=3.7 +Description-Content-Type: text/markdown + +# aiofiles: file support for asyncio + +[![PyPI](https://img.shields.io/pypi/v/aiofiles.svg)](https://pypi.python.org/pypi/aiofiles) +[![Build](https://github.com/Tinche/aiofiles/workflows/CI/badge.svg)](https://github.com/Tinche/aiofiles/actions) +[![Coverage](https://img.shields.io/endpoint?url=https://gist.githubusercontent.com/Tinche/882f02e3df32136c847ba90d2688f06e/raw/covbadge.json)](https://github.com/Tinche/aiofiles/actions/workflows/main.yml) +[![Supported Python versions](https://img.shields.io/pypi/pyversions/aiofiles.svg)](https://github.com/Tinche/aiofiles) +[![Black](https://img.shields.io/badge/code%20style-black-000000.svg)](https://github.com/psf/black) + +**aiofiles** is an Apache2 licensed library, written in Python, for handling local +disk files in asyncio applications. + +Ordinary local file IO is blocking, and cannot easily and portably be made +asynchronous. This means doing file IO may interfere with asyncio applications, +which shouldn't block the executing thread. aiofiles helps with this by +introducing asynchronous versions of files that support delegating operations to +a separate thread pool. + +```python +async with aiofiles.open('filename', mode='r') as f: + contents = await f.read() +print(contents) +'My file contents' +``` + +Asynchronous iteration is also supported. + +```python +async with aiofiles.open('filename') as f: + async for line in f: + ... +``` + +Asynchronous interface to tempfile module. + +```python +async with aiofiles.tempfile.TemporaryFile('wb') as f: + await f.write(b'Hello, World!') +``` + +## Features + +- a file API very similar to Python's standard, blocking API +- support for buffered and unbuffered binary files, and buffered text files +- support for `async`/`await` ([PEP 492](https://peps.python.org/pep-0492/)) constructs +- async interface to tempfile module + +## Installation + +To install aiofiles, simply: + +```bash +$ pip install aiofiles +``` + +## Usage + +Files are opened using the `aiofiles.open()` coroutine, which in addition to +mirroring the builtin `open` accepts optional `loop` and `executor` +arguments. If `loop` is absent, the default loop will be used, as per the +set asyncio policy. If `executor` is not specified, the default event loop +executor will be used. + +In case of success, an asynchronous file object is returned with an +API identical to an ordinary file, except the following methods are coroutines +and delegate to an executor: + +- `close` +- `flush` +- `isatty` +- `read` +- `readall` +- `read1` +- `readinto` +- `readline` +- `readlines` +- `seek` +- `seekable` +- `tell` +- `truncate` +- `writable` +- `write` +- `writelines` + +In case of failure, one of the usual exceptions will be raised. + +`aiofiles.stdin`, `aiofiles.stdout`, `aiofiles.stderr`, +`aiofiles.stdin_bytes`, `aiofiles.stdout_bytes`, and +`aiofiles.stderr_bytes` provide async access to `sys.stdin`, +`sys.stdout`, `sys.stderr`, and their corresponding `.buffer` properties. + +The `aiofiles.os` module contains executor-enabled coroutine versions of +several useful `os` functions that deal with files: + +- `stat` +- `statvfs` +- `sendfile` +- `rename` +- `renames` +- `replace` +- `remove` +- `unlink` +- `mkdir` +- `makedirs` +- `rmdir` +- `removedirs` +- `link` +- `symlink` +- `readlink` +- `listdir` +- `scandir` +- `access` +- `path.exists` +- `path.isfile` +- `path.isdir` +- `path.islink` +- `path.ismount` +- `path.getsize` +- `path.getatime` +- `path.getctime` +- `path.samefile` +- `path.sameopenfile` + +### Tempfile + +**aiofiles.tempfile** implements the following interfaces: + +- TemporaryFile +- NamedTemporaryFile +- SpooledTemporaryFile +- TemporaryDirectory + +Results return wrapped with a context manager allowing use with async with and async for. + +```python +async with aiofiles.tempfile.NamedTemporaryFile('wb+') as f: + await f.write(b'Line1\n Line2') + await f.seek(0) + async for line in f: + print(line) + +async with aiofiles.tempfile.TemporaryDirectory() as d: + filename = os.path.join(d, "file.ext") +``` + +### Writing tests for aiofiles + +Real file IO can be mocked by patching `aiofiles.threadpool.sync_open` +as desired. The return type also needs to be registered with the +`aiofiles.threadpool.wrap` dispatcher: + +```python +aiofiles.threadpool.wrap.register(mock.MagicMock)( + lambda *args, **kwargs: threadpool.AsyncBufferedIOBase(*args, **kwargs)) + +async def test_stuff(): + data = 'data' + mock_file = mock.MagicMock() + + with mock.patch('aiofiles.threadpool.sync_open', return_value=mock_file) as mock_open: + async with aiofiles.open('filename', 'w') as f: + await f.write(data) + + mock_file.write.assert_called_once_with(data) +``` + +### History + +#### 23.2.1 (2023-08-09) + +- Import `os.statvfs` conditionally to fix importing on non-UNIX systems. + [#171](https://github.com/Tinche/aiofiles/issues/171) [#172](https://github.com/Tinche/aiofiles/pull/172) + +#### 23.2.0 (2023-08-09) + +- aiofiles is now tested on Python 3.12 too. + [#166](https://github.com/Tinche/aiofiles/issues/166) [#168](https://github.com/Tinche/aiofiles/pull/168) +- On Python 3.12, `aiofiles.tempfile.NamedTemporaryFile` now accepts a `delete_on_close` argument, just like the stdlib version. +- On Python 3.12, `aiofiles.tempfile.NamedTemporaryFile` no longer exposes a `delete` attribute, just like the stdlib version. +- Added `aiofiles.os.statvfs` and `aiofiles.os.path.ismount`. + [#162](https://github.com/Tinche/aiofiles/pull/162) +- Use [PDM](https://pdm.fming.dev/latest/) instead of Poetry. + [#169](https://github.com/Tinche/aiofiles/pull/169) + +#### 23.1.0 (2023-02-09) + +- Added `aiofiles.os.access`. + [#146](https://github.com/Tinche/aiofiles/pull/146) +- Removed `aiofiles.tempfile.temptypes.AsyncSpooledTemporaryFile.softspace`. + [#151](https://github.com/Tinche/aiofiles/pull/151) +- Added `aiofiles.stdin`, `aiofiles.stdin_bytes`, and other stdio streams. + [#154](https://github.com/Tinche/aiofiles/pull/154) +- Transition to `asyncio.get_running_loop` (vs `asyncio.get_event_loop`) internally. + +#### 22.1.0 (2022-09-04) + +- Added `aiofiles.os.path.islink`. + [#126](https://github.com/Tinche/aiofiles/pull/126) +- Added `aiofiles.os.readlink`. + [#125](https://github.com/Tinche/aiofiles/pull/125) +- Added `aiofiles.os.symlink`. + [#124](https://github.com/Tinche/aiofiles/pull/124) +- Added `aiofiles.os.unlink`. + [#123](https://github.com/Tinche/aiofiles/pull/123) +- Added `aiofiles.os.link`. + [#121](https://github.com/Tinche/aiofiles/pull/121) +- Added `aiofiles.os.renames`. + [#120](https://github.com/Tinche/aiofiles/pull/120) +- Added `aiofiles.os.{listdir, scandir}`. + [#143](https://github.com/Tinche/aiofiles/pull/143) +- Switched to CalVer. +- Dropped Python 3.6 support. If you require it, use version 0.8.0. +- aiofiles is now tested on Python 3.11. + +#### 0.8.0 (2021-11-27) + +- aiofiles is now tested on Python 3.10. +- Added `aiofiles.os.replace`. + [#107](https://github.com/Tinche/aiofiles/pull/107) +- Added `aiofiles.os.{makedirs, removedirs}`. +- Added `aiofiles.os.path.{exists, isfile, isdir, getsize, getatime, getctime, samefile, sameopenfile}`. + [#63](https://github.com/Tinche/aiofiles/pull/63) +- Added `suffix`, `prefix`, `dir` args to `aiofiles.tempfile.TemporaryDirectory`. + [#116](https://github.com/Tinche/aiofiles/pull/116) + +#### 0.7.0 (2021-05-17) + +- Added the `aiofiles.tempfile` module for async temporary files. + [#56](https://github.com/Tinche/aiofiles/pull/56) +- Switched to Poetry and GitHub actions. +- Dropped 3.5 support. + +#### 0.6.0 (2020-10-27) + +- `aiofiles` is now tested on ppc64le. +- Added `name` and `mode` properties to async file objects. + [#82](https://github.com/Tinche/aiofiles/pull/82) +- Fixed a DeprecationWarning internally. + [#75](https://github.com/Tinche/aiofiles/pull/75) +- Python 3.9 support and tests. + +#### 0.5.0 (2020-04-12) + +- Python 3.8 support. Code base modernization (using `async/await` instead of `asyncio.coroutine`/`yield from`). +- Added `aiofiles.os.remove`, `aiofiles.os.rename`, `aiofiles.os.mkdir`, `aiofiles.os.rmdir`. + [#62](https://github.com/Tinche/aiofiles/pull/62) + +#### 0.4.0 (2018-08-11) + +- Python 3.7 support. +- Removed Python 3.3/3.4 support. If you use these versions, stick to aiofiles 0.3.x. + +#### 0.3.2 (2017-09-23) + +- The LICENSE is now included in the sdist. + [#31](https://github.com/Tinche/aiofiles/pull/31) + +#### 0.3.1 (2017-03-10) + +- Introduced a changelog. +- `aiofiles.os.sendfile` will now work if the standard `os` module contains a `sendfile` function. + +### Contributing + +Contributions are very welcome. Tests can be run with `tox`, please ensure +the coverage at least stays the same before you submit a pull request. diff --git a/evalkit_cambrian/lib/python3.10/site-packages/aiofiles-23.2.1.dist-info/RECORD b/evalkit_cambrian/lib/python3.10/site-packages/aiofiles-23.2.1.dist-info/RECORD new file mode 100644 index 0000000000000000000000000000000000000000..1bde3a70b6e92a5b197eb483b842dec822cb0250 --- /dev/null +++ b/evalkit_cambrian/lib/python3.10/site-packages/aiofiles-23.2.1.dist-info/RECORD @@ -0,0 +1,27 @@ +aiofiles-23.2.1.dist-info/INSTALLER,sha256=zuuue4knoyJ-UwPPXg8fezS7VCrXJQrAP7zeNuwvFQg,4 +aiofiles-23.2.1.dist-info/METADATA,sha256=cot28p_PNjdl_MK--l9Qu2e6QOv9OxdHrKbjLmYf9Uw,9673 +aiofiles-23.2.1.dist-info/RECORD,, +aiofiles-23.2.1.dist-info/REQUESTED,sha256=47DEQpj8HBSa-_TImW-5JCeuQeRkm5NMpJWZG3hSuFU,0 +aiofiles-23.2.1.dist-info/WHEEL,sha256=KGYbc1zXlYddvwxnNty23BeaKzh7YuoSIvIMO4jEhvw,87 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b/evalkit_cambrian/lib/python3.10/site-packages/aiofiles-23.2.1.dist-info/WHEEL new file mode 100644 index 0000000000000000000000000000000000000000..9a7c9d3aa0dbdfb11d17aa156b7fc731dcd4a038 --- /dev/null +++ b/evalkit_cambrian/lib/python3.10/site-packages/aiofiles-23.2.1.dist-info/WHEEL @@ -0,0 +1,4 @@ +Wheel-Version: 1.0 +Generator: hatchling 1.17.1 +Root-Is-Purelib: true +Tag: py3-none-any diff --git a/evalkit_cambrian/lib/python3.10/site-packages/aiofiles-23.2.1.dist-info/licenses/NOTICE b/evalkit_cambrian/lib/python3.10/site-packages/aiofiles-23.2.1.dist-info/licenses/NOTICE new file mode 100644 index 0000000000000000000000000000000000000000..d134f281ef732a3697f32b24b42ce8c4c07f9019 --- /dev/null +++ b/evalkit_cambrian/lib/python3.10/site-packages/aiofiles-23.2.1.dist-info/licenses/NOTICE @@ -0,0 +1,2 @@ +Asyncio support for files +Copyright 2016 Tin Tvrtkovic diff --git a/evalkit_cambrian/lib/python3.10/site-packages/aiohttp/_headers.pxi b/evalkit_cambrian/lib/python3.10/site-packages/aiohttp/_headers.pxi new file mode 100644 index 0000000000000000000000000000000000000000..3744721d4786a6c79b90aa349c8d02fa66204ecc --- /dev/null +++ b/evalkit_cambrian/lib/python3.10/site-packages/aiohttp/_headers.pxi @@ -0,0 +1,83 @@ +# The file is autogenerated from aiohttp/hdrs.py +# Run ./tools/gen.py to update it after the origin changing. + +from . import hdrs +cdef tuple headers = ( + hdrs.ACCEPT, + hdrs.ACCEPT_CHARSET, + hdrs.ACCEPT_ENCODING, + hdrs.ACCEPT_LANGUAGE, + hdrs.ACCEPT_RANGES, + hdrs.ACCESS_CONTROL_ALLOW_CREDENTIALS, + hdrs.ACCESS_CONTROL_ALLOW_HEADERS, + hdrs.ACCESS_CONTROL_ALLOW_METHODS, + hdrs.ACCESS_CONTROL_ALLOW_ORIGIN, + hdrs.ACCESS_CONTROL_EXPOSE_HEADERS, + hdrs.ACCESS_CONTROL_MAX_AGE, + hdrs.ACCESS_CONTROL_REQUEST_HEADERS, + hdrs.ACCESS_CONTROL_REQUEST_METHOD, + hdrs.AGE, + hdrs.ALLOW, + hdrs.AUTHORIZATION, + hdrs.CACHE_CONTROL, + hdrs.CONNECTION, + hdrs.CONTENT_DISPOSITION, + hdrs.CONTENT_ENCODING, + hdrs.CONTENT_LANGUAGE, + hdrs.CONTENT_LENGTH, + hdrs.CONTENT_LOCATION, + hdrs.CONTENT_MD5, + hdrs.CONTENT_RANGE, + hdrs.CONTENT_TRANSFER_ENCODING, + hdrs.CONTENT_TYPE, + hdrs.COOKIE, + hdrs.DATE, + hdrs.DESTINATION, + hdrs.DIGEST, + hdrs.ETAG, + hdrs.EXPECT, + hdrs.EXPIRES, + hdrs.FORWARDED, + hdrs.FROM, + hdrs.HOST, + hdrs.IF_MATCH, + hdrs.IF_MODIFIED_SINCE, + hdrs.IF_NONE_MATCH, + hdrs.IF_RANGE, + hdrs.IF_UNMODIFIED_SINCE, + hdrs.KEEP_ALIVE, + hdrs.LAST_EVENT_ID, + hdrs.LAST_MODIFIED, + hdrs.LINK, + hdrs.LOCATION, + hdrs.MAX_FORWARDS, + hdrs.ORIGIN, + hdrs.PRAGMA, + hdrs.PROXY_AUTHENTICATE, + hdrs.PROXY_AUTHORIZATION, + hdrs.RANGE, + hdrs.REFERER, + hdrs.RETRY_AFTER, + hdrs.SEC_WEBSOCKET_ACCEPT, + hdrs.SEC_WEBSOCKET_EXTENSIONS, + hdrs.SEC_WEBSOCKET_KEY, + hdrs.SEC_WEBSOCKET_KEY1, + hdrs.SEC_WEBSOCKET_PROTOCOL, + hdrs.SEC_WEBSOCKET_VERSION, + hdrs.SERVER, + hdrs.SET_COOKIE, + hdrs.TE, + hdrs.TRAILER, + hdrs.TRANSFER_ENCODING, + hdrs.URI, + hdrs.UPGRADE, + hdrs.USER_AGENT, + hdrs.VARY, + hdrs.VIA, + hdrs.WWW_AUTHENTICATE, + hdrs.WANT_DIGEST, + hdrs.WARNING, + hdrs.X_FORWARDED_FOR, + hdrs.X_FORWARDED_HOST, + hdrs.X_FORWARDED_PROTO, +) diff --git a/evalkit_cambrian/lib/python3.10/site-packages/aiohttp/abc.py b/evalkit_cambrian/lib/python3.10/site-packages/aiohttp/abc.py new file mode 100644 index 0000000000000000000000000000000000000000..5794a9108b076a81120dc09bbcca892dd9fcf1f3 --- /dev/null +++ b/evalkit_cambrian/lib/python3.10/site-packages/aiohttp/abc.py @@ -0,0 +1,253 @@ +import asyncio +import logging +import socket +import zlib +from abc import ABC, abstractmethod +from collections.abc import Sized +from http.cookies import BaseCookie, Morsel +from typing import ( + TYPE_CHECKING, + Any, + Awaitable, + Callable, + Dict, + Generator, + Iterable, + List, + Optional, + Tuple, + TypedDict, + Union, +) + +from multidict import CIMultiDict +from yarl import URL + +from .typedefs import LooseCookies + +if TYPE_CHECKING: + from .web_app import Application + from .web_exceptions import HTTPException + from .web_request import BaseRequest, Request + from .web_response import StreamResponse +else: + BaseRequest = Request = Application = StreamResponse = None + HTTPException = None + + +class AbstractRouter(ABC): + def __init__(self) -> None: + self._frozen = False + + def post_init(self, app: Application) -> None: + """Post init stage. + + Not an abstract method for sake of backward compatibility, + but if the router wants to be aware of the application + it can override this. + """ + + @property + def frozen(self) -> bool: + return self._frozen + + def freeze(self) -> None: + """Freeze router.""" + self._frozen = True + + @abstractmethod + async def resolve(self, request: Request) -> "AbstractMatchInfo": + """Return MATCH_INFO for given request""" + + +class AbstractMatchInfo(ABC): + + __slots__ = () + + @property # pragma: no branch + @abstractmethod + def handler(self) -> Callable[[Request], Awaitable[StreamResponse]]: + """Execute matched request handler""" + + @property + @abstractmethod + def expect_handler( + self, + ) -> Callable[[Request], Awaitable[Optional[StreamResponse]]]: + """Expect handler for 100-continue processing""" + + @property # pragma: no branch + @abstractmethod + def http_exception(self) -> Optional[HTTPException]: + """HTTPException instance raised on router's resolving, or None""" + + @abstractmethod # pragma: no branch + def get_info(self) -> Dict[str, Any]: + """Return a dict with additional info useful for introspection""" + + @property # pragma: no branch + @abstractmethod + def apps(self) -> Tuple[Application, ...]: + """Stack of nested applications. + + Top level application is left-most element. + + """ + + @abstractmethod + def add_app(self, app: Application) -> None: + """Add application to the nested apps stack.""" + + @abstractmethod + def freeze(self) -> None: + """Freeze the match info. + + The method is called after route resolution. + + After the call .add_app() is forbidden. + + """ + + +class AbstractView(ABC): + """Abstract class based view.""" + + def __init__(self, request: Request) -> None: + self._request = request + + @property + def request(self) -> Request: + """Request instance.""" + return self._request + + @abstractmethod + def __await__(self) -> Generator[Any, None, StreamResponse]: + """Execute the view handler.""" + + +class ResolveResult(TypedDict): + """Resolve result. + + This is the result returned from an AbstractResolver's + resolve method. + + :param hostname: The hostname that was provided. + :param host: The IP address that was resolved. + :param port: The port that was resolved. + :param family: The address family that was resolved. + :param proto: The protocol that was resolved. + :param flags: The flags that were resolved. + """ + + hostname: str + host: str + port: int + family: int + proto: int + flags: int + + +class AbstractResolver(ABC): + """Abstract DNS resolver.""" + + @abstractmethod + async def resolve( + self, host: str, port: int = 0, family: socket.AddressFamily = socket.AF_INET + ) -> List[ResolveResult]: + """Return IP address for given hostname""" + + @abstractmethod + async def close(self) -> None: + """Release resolver""" + + +if TYPE_CHECKING: + IterableBase = Iterable[Morsel[str]] +else: + IterableBase = Iterable + + +ClearCookiePredicate = Callable[["Morsel[str]"], bool] + + +class AbstractCookieJar(Sized, IterableBase): + """Abstract Cookie Jar.""" + + def __init__(self, *, loop: Optional[asyncio.AbstractEventLoop] = None) -> None: + self._loop = loop or asyncio.get_running_loop() + + @property + @abstractmethod + def quote_cookie(self) -> bool: + """Return True if cookies should be quoted.""" + + @abstractmethod + def clear(self, predicate: Optional[ClearCookiePredicate] = None) -> None: + """Clear all cookies if no predicate is passed.""" + + @abstractmethod + def clear_domain(self, domain: str) -> None: + """Clear all cookies for domain and all subdomains.""" + + @abstractmethod + def update_cookies(self, cookies: LooseCookies, response_url: URL = URL()) -> None: + """Update cookies.""" + + @abstractmethod + def filter_cookies(self, request_url: URL) -> "BaseCookie[str]": + """Return the jar's cookies filtered by their attributes.""" + + +class AbstractStreamWriter(ABC): + """Abstract stream writer.""" + + buffer_size: int = 0 + output_size: int = 0 + length: Optional[int] = 0 + + @abstractmethod + async def write(self, chunk: Union[bytes, bytearray, memoryview]) -> None: + """Write chunk into stream.""" + + @abstractmethod + async def write_eof(self, chunk: bytes = b"") -> None: + """Write last chunk.""" + + @abstractmethod + async def drain(self) -> None: + """Flush the write buffer.""" + + @abstractmethod + def enable_compression( + self, encoding: str = "deflate", strategy: int = zlib.Z_DEFAULT_STRATEGY + ) -> None: + """Enable HTTP body compression""" + + @abstractmethod + def enable_chunking(self) -> None: + """Enable HTTP chunked mode""" + + @abstractmethod + async def write_headers( + self, status_line: str, headers: "CIMultiDict[str]" + ) -> None: + """Write HTTP headers""" + + +class AbstractAccessLogger(ABC): + """Abstract writer to access log.""" + + __slots__ = ("logger", "log_format") + + def __init__(self, logger: logging.Logger, log_format: str) -> None: + self.logger = logger + self.log_format = log_format + + @abstractmethod + def log(self, request: BaseRequest, response: StreamResponse, time: float) -> None: + """Emit log to logger.""" + + @property + def enabled(self) -> bool: + """Check if logger is enabled.""" + return True diff --git a/evalkit_cambrian/lib/python3.10/site-packages/aiohttp/base_protocol.py b/evalkit_cambrian/lib/python3.10/site-packages/aiohttp/base_protocol.py new file mode 100644 index 0000000000000000000000000000000000000000..b0a67ed6ff68ca5bc48be9ac472ee755369b2720 --- /dev/null +++ b/evalkit_cambrian/lib/python3.10/site-packages/aiohttp/base_protocol.py @@ -0,0 +1,100 @@ +import asyncio +from typing import Optional, cast + +from .client_exceptions import ClientConnectionResetError +from .helpers import set_exception +from .tcp_helpers import tcp_nodelay + + +class BaseProtocol(asyncio.Protocol): + __slots__ = ( + "_loop", + "_paused", + "_drain_waiter", + "_connection_lost", + "_reading_paused", + "transport", + ) + + def __init__(self, loop: asyncio.AbstractEventLoop) -> None: + self._loop: asyncio.AbstractEventLoop = loop + self._paused = False + self._drain_waiter: Optional[asyncio.Future[None]] = None + self._reading_paused = False + + self.transport: Optional[asyncio.Transport] = None + + @property + def connected(self) -> bool: + """Return True if the connection is open.""" + return self.transport is not None + + @property + def writing_paused(self) -> bool: + return self._paused + + def pause_writing(self) -> None: + assert not self._paused + self._paused = True + + def resume_writing(self) -> None: + assert self._paused + self._paused = False + + waiter = self._drain_waiter + if waiter is not None: + self._drain_waiter = None + if not waiter.done(): + waiter.set_result(None) + + def pause_reading(self) -> None: + if not self._reading_paused and self.transport is not None: + try: + self.transport.pause_reading() + except (AttributeError, NotImplementedError, RuntimeError): + pass + self._reading_paused = True + + def resume_reading(self) -> None: + if self._reading_paused and self.transport is not None: + try: + self.transport.resume_reading() + except (AttributeError, NotImplementedError, RuntimeError): + pass + self._reading_paused = False + + def connection_made(self, transport: asyncio.BaseTransport) -> None: + tr = cast(asyncio.Transport, transport) + tcp_nodelay(tr, True) + self.transport = tr + + def connection_lost(self, exc: Optional[BaseException]) -> None: + # Wake up the writer if currently paused. + self.transport = None + if not self._paused: + return + waiter = self._drain_waiter + if waiter is None: + return + self._drain_waiter = None + if waiter.done(): + return + if exc is None: + waiter.set_result(None) + else: + set_exception( + waiter, + ConnectionError("Connection lost"), + exc, + ) + + async def _drain_helper(self) -> None: + if self.transport is None: + raise ClientConnectionResetError("Connection lost") + if not self._paused: + return + waiter = self._drain_waiter + if waiter is None: + waiter = self._loop.create_future() + self._drain_waiter = waiter + await asyncio.shield(waiter) diff --git a/evalkit_cambrian/lib/python3.10/site-packages/aiohttp/client.py b/evalkit_cambrian/lib/python3.10/site-packages/aiohttp/client.py new file mode 100644 index 0000000000000000000000000000000000000000..3b1dc08544ff9d97e1e5e56f93b7b974977a2805 --- /dev/null +++ b/evalkit_cambrian/lib/python3.10/site-packages/aiohttp/client.py @@ -0,0 +1,1576 @@ +"""HTTP Client for asyncio.""" + +import asyncio +import base64 +import hashlib +import json +import os +import sys +import traceback +import warnings +from contextlib import suppress +from types import TracebackType +from typing import ( + TYPE_CHECKING, + Any, + Awaitable, + Callable, + Coroutine, + Final, + FrozenSet, + Generator, + Generic, + Iterable, + List, + Mapping, + Optional, + Set, + Tuple, + Type, + TypedDict, + TypeVar, + Union, +) + +import attr +from multidict import CIMultiDict, MultiDict, MultiDictProxy, istr +from yarl import URL + +from . import hdrs, http, payload +from ._websocket.reader import WebSocketDataQueue +from .abc import AbstractCookieJar +from .client_exceptions import ( + ClientConnectionError, + ClientConnectionResetError, + ClientConnectorCertificateError, + ClientConnectorDNSError, + ClientConnectorError, + ClientConnectorSSLError, + ClientError, + ClientHttpProxyError, + ClientOSError, + ClientPayloadError, + ClientProxyConnectionError, + ClientResponseError, + ClientSSLError, + ConnectionTimeoutError, + ContentTypeError, + InvalidURL, + InvalidUrlClientError, + InvalidUrlRedirectClientError, + NonHttpUrlClientError, + NonHttpUrlRedirectClientError, + RedirectClientError, + ServerConnectionError, + ServerDisconnectedError, + ServerFingerprintMismatch, + ServerTimeoutError, + SocketTimeoutError, + TooManyRedirects, + WSMessageTypeError, + WSServerHandshakeError, +) +from .client_reqrep import ( + ClientRequest as ClientRequest, + ClientResponse as ClientResponse, + Fingerprint as Fingerprint, + RequestInfo as RequestInfo, + _merge_ssl_params, +) +from .client_ws import ( + DEFAULT_WS_CLIENT_TIMEOUT, + ClientWebSocketResponse as ClientWebSocketResponse, + ClientWSTimeout as ClientWSTimeout, +) +from .connector import ( + HTTP_AND_EMPTY_SCHEMA_SET, + BaseConnector as BaseConnector, + NamedPipeConnector as NamedPipeConnector, + TCPConnector as TCPConnector, + UnixConnector as UnixConnector, +) +from .cookiejar import CookieJar +from .helpers import ( + _SENTINEL, + DEBUG, + EMPTY_BODY_METHODS, + BasicAuth, + TimeoutHandle, + get_env_proxy_for_url, + sentinel, + strip_auth_from_url, +) +from .http import WS_KEY, HttpVersion, WebSocketReader, WebSocketWriter +from .http_websocket import WSHandshakeError, ws_ext_gen, ws_ext_parse +from .tracing import Trace, TraceConfig +from .typedefs import JSONEncoder, LooseCookies, LooseHeaders, Query, StrOrURL + +__all__ = ( + # client_exceptions + "ClientConnectionError", + "ClientConnectionResetError", + "ClientConnectorCertificateError", + "ClientConnectorDNSError", + "ClientConnectorError", + "ClientConnectorSSLError", + "ClientError", + "ClientHttpProxyError", + "ClientOSError", + "ClientPayloadError", + "ClientProxyConnectionError", + "ClientResponseError", + "ClientSSLError", + "ConnectionTimeoutError", + "ContentTypeError", + "InvalidURL", + "InvalidUrlClientError", + "RedirectClientError", + "NonHttpUrlClientError", + "InvalidUrlRedirectClientError", + "NonHttpUrlRedirectClientError", + "ServerConnectionError", + "ServerDisconnectedError", + "ServerFingerprintMismatch", + "ServerTimeoutError", + "SocketTimeoutError", + "TooManyRedirects", + "WSServerHandshakeError", + # client_reqrep + "ClientRequest", + "ClientResponse", + "Fingerprint", + "RequestInfo", + # connector + "BaseConnector", + "TCPConnector", + "UnixConnector", + "NamedPipeConnector", + # client_ws + "ClientWebSocketResponse", + # client + "ClientSession", + "ClientTimeout", + "ClientWSTimeout", + "request", + "WSMessageTypeError", +) + + +if TYPE_CHECKING: + from ssl import SSLContext +else: + SSLContext = None + +if sys.version_info >= (3, 11) and TYPE_CHECKING: + from typing import Unpack + + +class _RequestOptions(TypedDict, total=False): + params: Query + data: Any + json: Any + cookies: Union[LooseCookies, None] + headers: Union[LooseHeaders, None] + skip_auto_headers: Union[Iterable[str], None] + auth: Union[BasicAuth, None] + allow_redirects: bool + max_redirects: int + compress: Union[str, bool, None] + chunked: Union[bool, None] + expect100: bool + raise_for_status: Union[None, bool, Callable[[ClientResponse], Awaitable[None]]] + read_until_eof: bool + proxy: Union[StrOrURL, None] + proxy_auth: Union[BasicAuth, None] + timeout: "Union[ClientTimeout, _SENTINEL, None]" + ssl: Union[SSLContext, bool, Fingerprint] + server_hostname: Union[str, None] + proxy_headers: Union[LooseHeaders, None] + trace_request_ctx: Union[Mapping[str, Any], None] + read_bufsize: Union[int, None] + auto_decompress: Union[bool, None] + max_line_size: Union[int, None] + max_field_size: Union[int, None] + + +@attr.s(auto_attribs=True, frozen=True, slots=True) +class ClientTimeout: + total: Optional[float] = None + connect: Optional[float] = None + sock_read: Optional[float] = None + sock_connect: Optional[float] = None + ceil_threshold: float = 5 + + # pool_queue_timeout: Optional[float] = None + # dns_resolution_timeout: Optional[float] = None + # socket_connect_timeout: Optional[float] = None + # connection_acquiring_timeout: Optional[float] = None + # new_connection_timeout: Optional[float] = None + # http_header_timeout: Optional[float] = None + # response_body_timeout: Optional[float] = None + + # to create a timeout specific for a single request, either + # - create a completely new one to overwrite the default + # - or use http://www.attrs.org/en/stable/api.html#attr.evolve + # to overwrite the defaults + + +# 5 Minute default read timeout +DEFAULT_TIMEOUT: Final[ClientTimeout] = ClientTimeout(total=5 * 60, sock_connect=30) + +# https://www.rfc-editor.org/rfc/rfc9110#section-9.2.2 +IDEMPOTENT_METHODS = frozenset({"GET", "HEAD", "OPTIONS", "TRACE", "PUT", "DELETE"}) + +_RetType = TypeVar("_RetType", ClientResponse, ClientWebSocketResponse) +_CharsetResolver = Callable[[ClientResponse, bytes], str] + + +class ClientSession: + """First-class interface for making HTTP requests.""" + + ATTRS = frozenset( + [ + "_base_url", + "_base_url_origin", + "_source_traceback", + "_connector", + "_loop", + "_cookie_jar", + "_connector_owner", + "_default_auth", + "_version", + "_json_serialize", + "_requote_redirect_url", + "_timeout", + "_raise_for_status", + "_auto_decompress", + "_trust_env", + "_default_headers", + "_skip_auto_headers", + "_request_class", + "_response_class", + "_ws_response_class", + "_trace_configs", + "_read_bufsize", + "_max_line_size", + "_max_field_size", + "_resolve_charset", + "_default_proxy", + "_default_proxy_auth", + "_retry_connection", + "requote_redirect_url", + ] + ) + + _source_traceback: Optional[traceback.StackSummary] = None + _connector: Optional[BaseConnector] = None + + def __init__( + self, + base_url: Optional[StrOrURL] = None, + *, + connector: Optional[BaseConnector] = None, + loop: Optional[asyncio.AbstractEventLoop] = None, + cookies: Optional[LooseCookies] = None, + headers: Optional[LooseHeaders] = None, + proxy: Optional[StrOrURL] = None, + proxy_auth: Optional[BasicAuth] = None, + skip_auto_headers: Optional[Iterable[str]] = None, + auth: Optional[BasicAuth] = None, + json_serialize: JSONEncoder = json.dumps, + request_class: Type[ClientRequest] = ClientRequest, + response_class: Type[ClientResponse] = ClientResponse, + ws_response_class: Type[ClientWebSocketResponse] = ClientWebSocketResponse, + version: HttpVersion = http.HttpVersion11, + cookie_jar: Optional[AbstractCookieJar] = None, + connector_owner: bool = True, + raise_for_status: Union[ + bool, Callable[[ClientResponse], Awaitable[None]] + ] = False, + read_timeout: Union[float, _SENTINEL] = sentinel, + conn_timeout: Optional[float] = None, + timeout: Union[object, ClientTimeout] = sentinel, + auto_decompress: bool = True, + trust_env: bool = False, + requote_redirect_url: bool = True, + trace_configs: Optional[List[TraceConfig]] = None, + read_bufsize: int = 2**16, + max_line_size: int = 8190, + max_field_size: int = 8190, + fallback_charset_resolver: _CharsetResolver = lambda r, b: "utf-8", + ) -> None: + # We initialise _connector to None immediately, as it's referenced in __del__() + # and could cause issues if an exception occurs during initialisation. + self._connector: Optional[BaseConnector] = None + + if loop is None: + if connector is not None: + loop = connector._loop + + loop = loop or asyncio.get_running_loop() + + if base_url is None or isinstance(base_url, URL): + self._base_url: Optional[URL] = base_url + self._base_url_origin = None if base_url is None else base_url.origin() + else: + self._base_url = URL(base_url) + self._base_url_origin = self._base_url.origin() + assert self._base_url.absolute, "Only absolute URLs are supported" + if self._base_url is not None and not self._base_url.path.endswith("/"): + raise ValueError("base_url must have a trailing '/'") + + if timeout is sentinel or timeout is None: + self._timeout = DEFAULT_TIMEOUT + if read_timeout is not sentinel: + warnings.warn( + "read_timeout is deprecated, use timeout argument instead", + DeprecationWarning, + stacklevel=2, + ) + self._timeout = attr.evolve(self._timeout, total=read_timeout) + if conn_timeout is not None: + self._timeout = attr.evolve(self._timeout, connect=conn_timeout) + warnings.warn( + "conn_timeout is deprecated, use timeout argument instead", + DeprecationWarning, + stacklevel=2, + ) + else: + if not isinstance(timeout, ClientTimeout): + raise ValueError( + f"timeout parameter cannot be of {type(timeout)} type, " + "please use 'timeout=ClientTimeout(...)'", + ) + self._timeout = timeout + if read_timeout is not sentinel: + raise ValueError( + "read_timeout and timeout parameters " + "conflict, please setup " + "timeout.read" + ) + if conn_timeout is not None: + raise ValueError( + "conn_timeout and timeout parameters " + "conflict, please setup " + "timeout.connect" + ) + + if connector is None: + connector = TCPConnector(loop=loop) + + if connector._loop is not loop: + raise RuntimeError("Session and connector has to use same event loop") + + self._loop = loop + + if loop.get_debug(): + self._source_traceback = traceback.extract_stack(sys._getframe(1)) + + if cookie_jar is None: + cookie_jar = CookieJar(loop=loop) + self._cookie_jar = cookie_jar + + if cookies: + self._cookie_jar.update_cookies(cookies) + + self._connector = connector + self._connector_owner = connector_owner + self._default_auth = auth + self._version = version + self._json_serialize = json_serialize + self._raise_for_status = raise_for_status + self._auto_decompress = auto_decompress + self._trust_env = trust_env + self._requote_redirect_url = requote_redirect_url + self._read_bufsize = read_bufsize + self._max_line_size = max_line_size + self._max_field_size = max_field_size + + # Convert to list of tuples + if headers: + real_headers: CIMultiDict[str] = CIMultiDict(headers) + else: + real_headers = CIMultiDict() + self._default_headers: CIMultiDict[str] = real_headers + if skip_auto_headers is not None: + self._skip_auto_headers = frozenset(istr(i) for i in skip_auto_headers) + else: + self._skip_auto_headers = frozenset() + + self._request_class = request_class + self._response_class = response_class + self._ws_response_class = ws_response_class + + self._trace_configs = trace_configs or [] + for trace_config in self._trace_configs: + trace_config.freeze() + + self._resolve_charset = fallback_charset_resolver + + self._default_proxy = proxy + self._default_proxy_auth = proxy_auth + self._retry_connection: bool = True + + def __init_subclass__(cls: Type["ClientSession"]) -> None: + warnings.warn( + "Inheritance class {} from ClientSession " + "is discouraged".format(cls.__name__), + DeprecationWarning, + stacklevel=2, + ) + + if DEBUG: + + def __setattr__(self, name: str, val: Any) -> None: + if name not in self.ATTRS: + warnings.warn( + "Setting custom ClientSession.{} attribute " + "is discouraged".format(name), + DeprecationWarning, + stacklevel=2, + ) + super().__setattr__(name, val) + + def __del__(self, _warnings: Any = warnings) -> None: + if not self.closed: + kwargs = {"source": self} + _warnings.warn( + f"Unclosed client session {self!r}", ResourceWarning, **kwargs + ) + context = {"client_session": self, "message": "Unclosed client session"} + if self._source_traceback is not None: + context["source_traceback"] = self._source_traceback + self._loop.call_exception_handler(context) + + if sys.version_info >= (3, 11) and TYPE_CHECKING: + + def request( + self, + method: str, + url: StrOrURL, + **kwargs: Unpack[_RequestOptions], + ) -> "_RequestContextManager": ... + + else: + + def request( + self, method: str, url: StrOrURL, **kwargs: Any + ) -> "_RequestContextManager": + """Perform HTTP request.""" + return _RequestContextManager(self._request(method, url, **kwargs)) + + def _build_url(self, str_or_url: StrOrURL) -> URL: + url = URL(str_or_url) + if self._base_url is None: + return url + else: + assert not url.absolute + return self._base_url.join(url) + + async def _request( + self, + method: str, + str_or_url: StrOrURL, + *, + params: Query = None, + data: Any = None, + json: Any = None, + cookies: Optional[LooseCookies] = None, + headers: Optional[LooseHeaders] = None, + skip_auto_headers: Optional[Iterable[str]] = None, + auth: Optional[BasicAuth] = None, + allow_redirects: bool = True, + max_redirects: int = 10, + compress: Union[str, bool, None] = None, + chunked: Optional[bool] = None, + expect100: bool = False, + raise_for_status: Union[ + None, bool, Callable[[ClientResponse], Awaitable[None]] + ] = None, + read_until_eof: bool = True, + proxy: Optional[StrOrURL] = None, + proxy_auth: Optional[BasicAuth] = None, + timeout: Union[ClientTimeout, _SENTINEL] = sentinel, + verify_ssl: Optional[bool] = None, + fingerprint: Optional[bytes] = None, + ssl_context: Optional[SSLContext] = None, + ssl: Union[SSLContext, bool, Fingerprint] = True, + server_hostname: Optional[str] = None, + proxy_headers: Optional[LooseHeaders] = None, + trace_request_ctx: Optional[Mapping[str, Any]] = None, + read_bufsize: Optional[int] = None, + auto_decompress: Optional[bool] = None, + max_line_size: Optional[int] = None, + max_field_size: Optional[int] = None, + ) -> ClientResponse: + + # NOTE: timeout clamps existing connect and read timeouts. We cannot + # set the default to None because we need to detect if the user wants + # to use the existing timeouts by setting timeout to None. + + if self.closed: + raise RuntimeError("Session is closed") + + ssl = _merge_ssl_params(ssl, verify_ssl, ssl_context, fingerprint) + + if data is not None and json is not None: + raise ValueError( + "data and json parameters can not be used at the same time" + ) + elif json is not None: + data = payload.JsonPayload(json, dumps=self._json_serialize) + + if not isinstance(chunked, bool) and chunked is not None: + warnings.warn("Chunk size is deprecated #1615", DeprecationWarning) + + redirects = 0 + history: List[ClientResponse] = [] + version = self._version + params = params or {} + + # Merge with default headers and transform to CIMultiDict + headers = self._prepare_headers(headers) + + try: + url = self._build_url(str_or_url) + except ValueError as e: + raise InvalidUrlClientError(str_or_url) from e + + assert self._connector is not None + if url.scheme not in self._connector.allowed_protocol_schema_set: + raise NonHttpUrlClientError(url) + + skip_headers: Optional[Iterable[istr]] + if skip_auto_headers is not None: + skip_headers = { + istr(i) for i in skip_auto_headers + } | self._skip_auto_headers + elif self._skip_auto_headers: + skip_headers = self._skip_auto_headers + else: + skip_headers = None + + if proxy is None: + proxy = self._default_proxy + if proxy_auth is None: + proxy_auth = self._default_proxy_auth + + if proxy is None: + proxy_headers = None + else: + proxy_headers = self._prepare_headers(proxy_headers) + try: + proxy = URL(proxy) + except ValueError as e: + raise InvalidURL(proxy) from e + + if timeout is sentinel: + real_timeout: ClientTimeout = self._timeout + else: + if not isinstance(timeout, ClientTimeout): + real_timeout = ClientTimeout(total=timeout) + else: + real_timeout = timeout + # timeout is cumulative for all request operations + # (request, redirects, responses, data consuming) + tm = TimeoutHandle( + self._loop, real_timeout.total, ceil_threshold=real_timeout.ceil_threshold + ) + handle = tm.start() + + if read_bufsize is None: + read_bufsize = self._read_bufsize + + if auto_decompress is None: + auto_decompress = self._auto_decompress + + if max_line_size is None: + max_line_size = self._max_line_size + + if max_field_size is None: + max_field_size = self._max_field_size + + traces = [ + Trace( + self, + trace_config, + trace_config.trace_config_ctx(trace_request_ctx=trace_request_ctx), + ) + for trace_config in self._trace_configs + ] + + for trace in traces: + await trace.send_request_start(method, url.update_query(params), headers) + + timer = tm.timer() + try: + with timer: + # https://www.rfc-editor.org/rfc/rfc9112.html#name-retrying-requests + retry_persistent_connection = ( + self._retry_connection and method in IDEMPOTENT_METHODS + ) + while True: + url, auth_from_url = strip_auth_from_url(url) + if not url.raw_host: + # NOTE: Bail early, otherwise, causes `InvalidURL` through + # NOTE: `self._request_class()` below. + err_exc_cls = ( + InvalidUrlRedirectClientError + if redirects + else InvalidUrlClientError + ) + raise err_exc_cls(url) + # If `auth` was passed for an already authenticated URL, + # disallow only if this is the initial URL; this is to avoid issues + # with sketchy redirects that are not the caller's responsibility + if not history and (auth and auth_from_url): + raise ValueError( + "Cannot combine AUTH argument with " + "credentials encoded in URL" + ) + + # Override the auth with the one from the URL only if we + # have no auth, or if we got an auth from a redirect URL + if auth is None or (history and auth_from_url is not None): + auth = auth_from_url + + if ( + auth is None + and self._default_auth + and ( + not self._base_url or self._base_url_origin == url.origin() + ) + ): + auth = self._default_auth + # It would be confusing if we support explicit + # Authorization header with auth argument + if ( + headers is not None + and auth is not None + and hdrs.AUTHORIZATION in headers + ): + raise ValueError( + "Cannot combine AUTHORIZATION header " + "with AUTH argument or credentials " + "encoded in URL" + ) + + all_cookies = self._cookie_jar.filter_cookies(url) + + if cookies is not None: + tmp_cookie_jar = CookieJar( + quote_cookie=self._cookie_jar.quote_cookie + ) + tmp_cookie_jar.update_cookies(cookies) + req_cookies = tmp_cookie_jar.filter_cookies(url) + if req_cookies: + all_cookies.load(req_cookies) + + if proxy is not None: + proxy = URL(proxy) + elif self._trust_env: + with suppress(LookupError): + proxy, proxy_auth = get_env_proxy_for_url(url) + + req = self._request_class( + method, + url, + params=params, + headers=headers, + skip_auto_headers=skip_headers, + data=data, + cookies=all_cookies, + auth=auth, + version=version, + compress=compress, + chunked=chunked, + expect100=expect100, + loop=self._loop, + response_class=self._response_class, + proxy=proxy, + proxy_auth=proxy_auth, + timer=timer, + session=self, + ssl=ssl if ssl is not None else True, + server_hostname=server_hostname, + proxy_headers=proxy_headers, + traces=traces, + trust_env=self.trust_env, + ) + + # connection timeout + try: + conn = await self._connector.connect( + req, traces=traces, timeout=real_timeout + ) + except asyncio.TimeoutError as exc: + raise ConnectionTimeoutError( + f"Connection timeout to host {url}" + ) from exc + + assert conn.transport is not None + + assert conn.protocol is not None + conn.protocol.set_response_params( + timer=timer, + skip_payload=method in EMPTY_BODY_METHODS, + read_until_eof=read_until_eof, + auto_decompress=auto_decompress, + read_timeout=real_timeout.sock_read, + read_bufsize=read_bufsize, + timeout_ceil_threshold=self._connector._timeout_ceil_threshold, + max_line_size=max_line_size, + max_field_size=max_field_size, + ) + + try: + try: + resp = await req.send(conn) + try: + await resp.start(conn) + except BaseException: + resp.close() + raise + except BaseException: + conn.close() + raise + except (ClientOSError, ServerDisconnectedError): + if retry_persistent_connection: + retry_persistent_connection = False + continue + raise + except ClientError: + raise + except OSError as exc: + if exc.errno is None and isinstance(exc, asyncio.TimeoutError): + raise + raise ClientOSError(*exc.args) from exc + + if cookies := resp._cookies: + self._cookie_jar.update_cookies(cookies, resp.url) + + # redirects + if resp.status in (301, 302, 303, 307, 308) and allow_redirects: + + for trace in traces: + await trace.send_request_redirect( + method, url.update_query(params), headers, resp + ) + + redirects += 1 + history.append(resp) + if max_redirects and redirects >= max_redirects: + resp.close() + raise TooManyRedirects( + history[0].request_info, tuple(history) + ) + + # For 301 and 302, mimic IE, now changed in RFC + # https://github.com/kennethreitz/requests/pull/269 + if (resp.status == 303 and resp.method != hdrs.METH_HEAD) or ( + resp.status in (301, 302) and resp.method == hdrs.METH_POST + ): + method = hdrs.METH_GET + data = None + if headers.get(hdrs.CONTENT_LENGTH): + headers.pop(hdrs.CONTENT_LENGTH) + + r_url = resp.headers.get(hdrs.LOCATION) or resp.headers.get( + hdrs.URI + ) + if r_url is None: + # see github.com/aio-libs/aiohttp/issues/2022 + break + else: + # reading from correct redirection + # response is forbidden + resp.release() + + try: + parsed_redirect_url = URL( + r_url, encoded=not self._requote_redirect_url + ) + except ValueError as e: + raise InvalidUrlRedirectClientError( + r_url, + "Server attempted redirecting to a location that does not look like a URL", + ) from e + + scheme = parsed_redirect_url.scheme + if scheme not in HTTP_AND_EMPTY_SCHEMA_SET: + resp.close() + raise NonHttpUrlRedirectClientError(r_url) + elif not scheme: + parsed_redirect_url = url.join(parsed_redirect_url) + + try: + redirect_origin = parsed_redirect_url.origin() + except ValueError as origin_val_err: + raise InvalidUrlRedirectClientError( + parsed_redirect_url, + "Invalid redirect URL origin", + ) from origin_val_err + + if url.origin() != redirect_origin: + auth = None + headers.pop(hdrs.AUTHORIZATION, None) + + url = parsed_redirect_url + params = {} + resp.release() + continue + + break + + # check response status + if raise_for_status is None: + raise_for_status = self._raise_for_status + + if raise_for_status is None: + pass + elif callable(raise_for_status): + await raise_for_status(resp) + elif raise_for_status: + resp.raise_for_status() + + # register connection + if handle is not None: + if resp.connection is not None: + resp.connection.add_callback(handle.cancel) + else: + handle.cancel() + + resp._history = tuple(history) + + for trace in traces: + await trace.send_request_end( + method, url.update_query(params), headers, resp + ) + return resp + + except BaseException as e: + # cleanup timer + tm.close() + if handle: + handle.cancel() + handle = None + + for trace in traces: + await trace.send_request_exception( + method, url.update_query(params), headers, e + ) + raise + + def ws_connect( + self, + url: StrOrURL, + *, + method: str = hdrs.METH_GET, + protocols: Iterable[str] = (), + timeout: Union[ClientWSTimeout, _SENTINEL] = sentinel, + receive_timeout: Optional[float] = None, + autoclose: bool = True, + autoping: bool = True, + heartbeat: Optional[float] = None, + auth: Optional[BasicAuth] = None, + origin: Optional[str] = None, + params: Query = None, + headers: Optional[LooseHeaders] = None, + proxy: Optional[StrOrURL] = None, + proxy_auth: Optional[BasicAuth] = None, + ssl: Union[SSLContext, bool, Fingerprint] = True, + verify_ssl: Optional[bool] = None, + fingerprint: Optional[bytes] = None, + ssl_context: Optional[SSLContext] = None, + server_hostname: Optional[str] = None, + proxy_headers: Optional[LooseHeaders] = None, + compress: int = 0, + max_msg_size: int = 4 * 1024 * 1024, + ) -> "_WSRequestContextManager": + """Initiate websocket connection.""" + return _WSRequestContextManager( + self._ws_connect( + url, + method=method, + protocols=protocols, + timeout=timeout, + receive_timeout=receive_timeout, + autoclose=autoclose, + autoping=autoping, + heartbeat=heartbeat, + auth=auth, + origin=origin, + params=params, + headers=headers, + proxy=proxy, + proxy_auth=proxy_auth, + ssl=ssl, + verify_ssl=verify_ssl, + fingerprint=fingerprint, + ssl_context=ssl_context, + server_hostname=server_hostname, + proxy_headers=proxy_headers, + compress=compress, + max_msg_size=max_msg_size, + ) + ) + + async def _ws_connect( + self, + url: StrOrURL, + *, + method: str = hdrs.METH_GET, + protocols: Iterable[str] = (), + timeout: Union[ClientWSTimeout, _SENTINEL] = sentinel, + receive_timeout: Optional[float] = None, + autoclose: bool = True, + autoping: bool = True, + heartbeat: Optional[float] = None, + auth: Optional[BasicAuth] = None, + origin: Optional[str] = None, + params: Query = None, + headers: Optional[LooseHeaders] = None, + proxy: Optional[StrOrURL] = None, + proxy_auth: Optional[BasicAuth] = None, + ssl: Union[SSLContext, bool, Fingerprint] = True, + verify_ssl: Optional[bool] = None, + fingerprint: Optional[bytes] = None, + ssl_context: Optional[SSLContext] = None, + server_hostname: Optional[str] = None, + proxy_headers: Optional[LooseHeaders] = None, + compress: int = 0, + max_msg_size: int = 4 * 1024 * 1024, + ) -> ClientWebSocketResponse: + if timeout is not sentinel: + if isinstance(timeout, ClientWSTimeout): + ws_timeout = timeout + else: + warnings.warn( + "parameter 'timeout' of type 'float' " + "is deprecated, please use " + "'timeout=ClientWSTimeout(ws_close=...)'", + DeprecationWarning, + stacklevel=2, + ) + ws_timeout = ClientWSTimeout(ws_close=timeout) + else: + ws_timeout = DEFAULT_WS_CLIENT_TIMEOUT + if receive_timeout is not None: + warnings.warn( + "float parameter 'receive_timeout' " + "is deprecated, please use parameter " + "'timeout=ClientWSTimeout(ws_receive=...)'", + DeprecationWarning, + stacklevel=2, + ) + ws_timeout = attr.evolve(ws_timeout, ws_receive=receive_timeout) + + if headers is None: + real_headers: CIMultiDict[str] = CIMultiDict() + else: + real_headers = CIMultiDict(headers) + + default_headers = { + hdrs.UPGRADE: "websocket", + hdrs.CONNECTION: "Upgrade", + hdrs.SEC_WEBSOCKET_VERSION: "13", + } + + for key, value in default_headers.items(): + real_headers.setdefault(key, value) + + sec_key = base64.b64encode(os.urandom(16)) + real_headers[hdrs.SEC_WEBSOCKET_KEY] = sec_key.decode() + + if protocols: + real_headers[hdrs.SEC_WEBSOCKET_PROTOCOL] = ",".join(protocols) + if origin is not None: + real_headers[hdrs.ORIGIN] = origin + if compress: + extstr = ws_ext_gen(compress=compress) + real_headers[hdrs.SEC_WEBSOCKET_EXTENSIONS] = extstr + + # For the sake of backward compatibility, if user passes in None, convert it to True + if ssl is None: + warnings.warn( + "ssl=None is deprecated, please use ssl=True", + DeprecationWarning, + stacklevel=2, + ) + ssl = True + ssl = _merge_ssl_params(ssl, verify_ssl, ssl_context, fingerprint) + + # send request + resp = await self.request( + method, + url, + params=params, + headers=real_headers, + read_until_eof=False, + auth=auth, + proxy=proxy, + proxy_auth=proxy_auth, + ssl=ssl, + server_hostname=server_hostname, + proxy_headers=proxy_headers, + ) + + try: + # check handshake + if resp.status != 101: + raise WSServerHandshakeError( + resp.request_info, + resp.history, + message="Invalid response status", + status=resp.status, + headers=resp.headers, + ) + + if resp.headers.get(hdrs.UPGRADE, "").lower() != "websocket": + raise WSServerHandshakeError( + resp.request_info, + resp.history, + message="Invalid upgrade header", + status=resp.status, + headers=resp.headers, + ) + + if resp.headers.get(hdrs.CONNECTION, "").lower() != "upgrade": + raise WSServerHandshakeError( + resp.request_info, + resp.history, + message="Invalid connection header", + status=resp.status, + headers=resp.headers, + ) + + # key calculation + r_key = resp.headers.get(hdrs.SEC_WEBSOCKET_ACCEPT, "") + match = base64.b64encode(hashlib.sha1(sec_key + WS_KEY).digest()).decode() + if r_key != match: + raise WSServerHandshakeError( + resp.request_info, + resp.history, + message="Invalid challenge response", + status=resp.status, + headers=resp.headers, + ) + + # websocket protocol + protocol = None + if protocols and hdrs.SEC_WEBSOCKET_PROTOCOL in resp.headers: + resp_protocols = [ + proto.strip() + for proto in resp.headers[hdrs.SEC_WEBSOCKET_PROTOCOL].split(",") + ] + + for proto in resp_protocols: + if proto in protocols: + protocol = proto + break + + # websocket compress + notakeover = False + if compress: + compress_hdrs = resp.headers.get(hdrs.SEC_WEBSOCKET_EXTENSIONS) + if compress_hdrs: + try: + compress, notakeover = ws_ext_parse(compress_hdrs) + except WSHandshakeError as exc: + raise WSServerHandshakeError( + resp.request_info, + resp.history, + message=exc.args[0], + status=resp.status, + headers=resp.headers, + ) from exc + else: + compress = 0 + notakeover = False + + conn = resp.connection + assert conn is not None + conn_proto = conn.protocol + assert conn_proto is not None + + # For WS connection the read_timeout must be either receive_timeout or greater + # None == no timeout, i.e. infinite timeout, so None is the max timeout possible + if ws_timeout.ws_receive is None: + # Reset regardless + conn_proto.read_timeout = None + elif conn_proto.read_timeout is not None: + conn_proto.read_timeout = max( + ws_timeout.ws_receive, conn_proto.read_timeout + ) + + transport = conn.transport + assert transport is not None + reader = WebSocketDataQueue(conn_proto, 2**16, loop=self._loop) + conn_proto.set_parser(WebSocketReader(reader, max_msg_size), reader) + writer = WebSocketWriter( + conn_proto, + transport, + use_mask=True, + compress=compress, + notakeover=notakeover, + ) + except BaseException: + resp.close() + raise + else: + return self._ws_response_class( + reader, + writer, + protocol, + resp, + ws_timeout, + autoclose, + autoping, + self._loop, + heartbeat=heartbeat, + compress=compress, + client_notakeover=notakeover, + ) + + def _prepare_headers(self, headers: Optional[LooseHeaders]) -> "CIMultiDict[str]": + """Add default headers and transform it to CIMultiDict""" + # Convert headers to MultiDict + result = CIMultiDict(self._default_headers) + if headers: + if not isinstance(headers, (MultiDictProxy, MultiDict)): + headers = CIMultiDict(headers) + added_names: Set[str] = set() + for key, value in headers.items(): + if key in added_names: + result.add(key, value) + else: + result[key] = value + added_names.add(key) + return result + + if sys.version_info >= (3, 11) and TYPE_CHECKING: + + def get( + self, + url: StrOrURL, + **kwargs: Unpack[_RequestOptions], + ) -> "_RequestContextManager": ... + + def options( + self, + url: StrOrURL, + **kwargs: Unpack[_RequestOptions], + ) -> "_RequestContextManager": ... + + def head( + self, + url: StrOrURL, + **kwargs: Unpack[_RequestOptions], + ) -> "_RequestContextManager": ... + + def post( + self, + url: StrOrURL, + **kwargs: Unpack[_RequestOptions], + ) -> "_RequestContextManager": ... + + def put( + self, + url: StrOrURL, + **kwargs: Unpack[_RequestOptions], + ) -> "_RequestContextManager": ... + + def patch( + self, + url: StrOrURL, + **kwargs: Unpack[_RequestOptions], + ) -> "_RequestContextManager": ... + + def delete( + self, + url: StrOrURL, + **kwargs: Unpack[_RequestOptions], + ) -> "_RequestContextManager": ... + + else: + + def get( + self, url: StrOrURL, *, allow_redirects: bool = True, **kwargs: Any + ) -> "_RequestContextManager": + """Perform HTTP GET request.""" + return _RequestContextManager( + self._request( + hdrs.METH_GET, url, allow_redirects=allow_redirects, **kwargs + ) + ) + + def options( + self, url: StrOrURL, *, allow_redirects: bool = True, **kwargs: Any + ) -> "_RequestContextManager": + """Perform HTTP OPTIONS request.""" + return _RequestContextManager( + self._request( + hdrs.METH_OPTIONS, url, allow_redirects=allow_redirects, **kwargs + ) + ) + + def head( + self, url: StrOrURL, *, allow_redirects: bool = False, **kwargs: Any + ) -> "_RequestContextManager": + """Perform HTTP HEAD request.""" + return _RequestContextManager( + self._request( + hdrs.METH_HEAD, url, allow_redirects=allow_redirects, **kwargs + ) + ) + + def post( + self, url: StrOrURL, *, data: Any = None, **kwargs: Any + ) -> "_RequestContextManager": + """Perform HTTP POST request.""" + return _RequestContextManager( + self._request(hdrs.METH_POST, url, data=data, **kwargs) + ) + + def put( + self, url: StrOrURL, *, data: Any = None, **kwargs: Any + ) -> "_RequestContextManager": + """Perform HTTP PUT request.""" + return _RequestContextManager( + self._request(hdrs.METH_PUT, url, data=data, **kwargs) + ) + + def patch( + self, url: StrOrURL, *, data: Any = None, **kwargs: Any + ) -> "_RequestContextManager": + """Perform HTTP PATCH request.""" + return _RequestContextManager( + self._request(hdrs.METH_PATCH, url, data=data, **kwargs) + ) + + def delete(self, url: StrOrURL, **kwargs: Any) -> "_RequestContextManager": + """Perform HTTP DELETE request.""" + return _RequestContextManager( + self._request(hdrs.METH_DELETE, url, **kwargs) + ) + + async def close(self) -> None: + """Close underlying connector. + + Release all acquired resources. + """ + if not self.closed: + if self._connector is not None and self._connector_owner: + await self._connector.close() + self._connector = None + + @property + def closed(self) -> bool: + """Is client session closed. + + A readonly property. + """ + return self._connector is None or self._connector.closed + + @property + def connector(self) -> Optional[BaseConnector]: + """Connector instance used for the session.""" + return self._connector + + @property + def cookie_jar(self) -> AbstractCookieJar: + """The session cookies.""" + return self._cookie_jar + + @property + def version(self) -> Tuple[int, int]: + """The session HTTP protocol version.""" + return self._version + + @property + def requote_redirect_url(self) -> bool: + """Do URL requoting on redirection handling.""" + return self._requote_redirect_url + + @requote_redirect_url.setter + def requote_redirect_url(self, val: bool) -> None: + """Do URL requoting on redirection handling.""" + warnings.warn( + "session.requote_redirect_url modification is deprecated #2778", + DeprecationWarning, + stacklevel=2, + ) + self._requote_redirect_url = val + + @property + def loop(self) -> asyncio.AbstractEventLoop: + """Session's loop.""" + warnings.warn( + "client.loop property is deprecated", DeprecationWarning, stacklevel=2 + ) + return self._loop + + @property + def timeout(self) -> ClientTimeout: + """Timeout for the session.""" + return self._timeout + + @property + def headers(self) -> "CIMultiDict[str]": + """The default headers of the client session.""" + return self._default_headers + + @property + def skip_auto_headers(self) -> FrozenSet[istr]: + """Headers for which autogeneration should be skipped""" + return self._skip_auto_headers + + @property + def auth(self) -> Optional[BasicAuth]: + """An object that represents HTTP Basic Authorization""" + return self._default_auth + + @property + def json_serialize(self) -> JSONEncoder: + """Json serializer callable""" + return self._json_serialize + + @property + def connector_owner(self) -> bool: + """Should connector be closed on session closing""" + return self._connector_owner + + @property + def raise_for_status( + self, + ) -> Union[bool, Callable[[ClientResponse], Awaitable[None]]]: + """Should `ClientResponse.raise_for_status()` be called for each response.""" + return self._raise_for_status + + @property + def auto_decompress(self) -> bool: + """Should the body response be automatically decompressed.""" + return self._auto_decompress + + @property + def trust_env(self) -> bool: + """ + Should proxies information from environment or netrc be trusted. + + Information is from HTTP_PROXY / HTTPS_PROXY environment variables + or ~/.netrc file if present. + """ + return self._trust_env + + @property + def trace_configs(self) -> List[TraceConfig]: + """A list of TraceConfig instances used for client tracing""" + return self._trace_configs + + def detach(self) -> None: + """Detach connector from session without closing the former. + + Session is switched to closed state anyway. + """ + self._connector = None + + def __enter__(self) -> None: + raise TypeError("Use async with instead") + + def __exit__( + self, + exc_type: Optional[Type[BaseException]], + exc_val: Optional[BaseException], + exc_tb: Optional[TracebackType], + ) -> None: + # __exit__ should exist in pair with __enter__ but never executed + pass # pragma: no cover + + async def __aenter__(self) -> "ClientSession": + return self + + async def __aexit__( + self, + exc_type: Optional[Type[BaseException]], + exc_val: Optional[BaseException], + exc_tb: Optional[TracebackType], + ) -> None: + await self.close() + + +class _BaseRequestContextManager(Coroutine[Any, Any, _RetType], Generic[_RetType]): + + __slots__ = ("_coro", "_resp") + + def __init__(self, coro: Coroutine["asyncio.Future[Any]", None, _RetType]) -> None: + self._coro: Coroutine["asyncio.Future[Any]", None, _RetType] = coro + + def send(self, arg: None) -> "asyncio.Future[Any]": + return self._coro.send(arg) + + def throw(self, *args: Any, **kwargs: Any) -> "asyncio.Future[Any]": + return self._coro.throw(*args, **kwargs) + + def close(self) -> None: + return self._coro.close() + + def __await__(self) -> Generator[Any, None, _RetType]: + ret = self._coro.__await__() + return ret + + def __iter__(self) -> Generator[Any, None, _RetType]: + return self.__await__() + + async def __aenter__(self) -> _RetType: + self._resp: _RetType = await self._coro + return await self._resp.__aenter__() + + async def __aexit__( + self, + exc_type: Optional[Type[BaseException]], + exc: Optional[BaseException], + tb: Optional[TracebackType], + ) -> None: + await self._resp.__aexit__(exc_type, exc, tb) + + +_RequestContextManager = _BaseRequestContextManager[ClientResponse] +_WSRequestContextManager = _BaseRequestContextManager[ClientWebSocketResponse] + + +class _SessionRequestContextManager: + + __slots__ = ("_coro", "_resp", "_session") + + def __init__( + self, + coro: Coroutine["asyncio.Future[Any]", None, ClientResponse], + session: ClientSession, + ) -> None: + self._coro = coro + self._resp: Optional[ClientResponse] = None + self._session = session + + async def __aenter__(self) -> ClientResponse: + try: + self._resp = await self._coro + except BaseException: + await self._session.close() + raise + else: + return self._resp + + async def __aexit__( + self, + exc_type: Optional[Type[BaseException]], + exc: Optional[BaseException], + tb: Optional[TracebackType], + ) -> None: + assert self._resp is not None + self._resp.close() + await self._session.close() + + +def request( + method: str, + url: StrOrURL, + *, + params: Query = None, + data: Any = None, + json: Any = None, + headers: Optional[LooseHeaders] = None, + skip_auto_headers: Optional[Iterable[str]] = None, + auth: Optional[BasicAuth] = None, + allow_redirects: bool = True, + max_redirects: int = 10, + compress: Optional[str] = None, + chunked: Optional[bool] = None, + expect100: bool = False, + raise_for_status: Optional[bool] = None, + read_until_eof: bool = True, + proxy: Optional[StrOrURL] = None, + proxy_auth: Optional[BasicAuth] = None, + timeout: Union[ClientTimeout, object] = sentinel, + cookies: Optional[LooseCookies] = None, + version: HttpVersion = http.HttpVersion11, + connector: Optional[BaseConnector] = None, + read_bufsize: Optional[int] = None, + loop: Optional[asyncio.AbstractEventLoop] = None, + max_line_size: int = 8190, + max_field_size: int = 8190, +) -> _SessionRequestContextManager: + """Constructs and sends a request. + + Returns response object. + method - HTTP method + url - request url + params - (optional) Dictionary or bytes to be sent in the query + string of the new request + data - (optional) Dictionary, bytes, or file-like object to + send in the body of the request + json - (optional) Any json compatible python object + headers - (optional) Dictionary of HTTP Headers to send with + the request + cookies - (optional) Dict object to send with the request + auth - (optional) BasicAuth named tuple represent HTTP Basic Auth + auth - aiohttp.helpers.BasicAuth + allow_redirects - (optional) If set to False, do not follow + redirects + version - Request HTTP version. + compress - Set to True if request has to be compressed + with deflate encoding. + chunked - Set to chunk size for chunked transfer encoding. + expect100 - Expect 100-continue response from server. + connector - BaseConnector sub-class instance to support + connection pooling. + read_until_eof - Read response until eof if response + does not have Content-Length header. + loop - Optional event loop. + timeout - Optional ClientTimeout settings structure, 5min + total timeout by default. + Usage:: + >>> import aiohttp + >>> resp = await aiohttp.request('GET', 'http://python.org/') + >>> resp + + >>> data = await resp.read() + """ + connector_owner = False + if connector is None: + connector_owner = True + connector = TCPConnector(loop=loop, force_close=True) + + session = ClientSession( + loop=loop, + cookies=cookies, + version=version, + timeout=timeout, + connector=connector, + connector_owner=connector_owner, + ) + + return _SessionRequestContextManager( + session._request( + method, + url, + params=params, + data=data, + json=json, + headers=headers, + skip_auto_headers=skip_auto_headers, + auth=auth, + allow_redirects=allow_redirects, + max_redirects=max_redirects, + compress=compress, + chunked=chunked, + expect100=expect100, + raise_for_status=raise_for_status, + read_until_eof=read_until_eof, + proxy=proxy, + proxy_auth=proxy_auth, + read_bufsize=read_bufsize, + max_line_size=max_line_size, + max_field_size=max_field_size, + ), + session, + ) diff --git a/evalkit_cambrian/lib/python3.10/site-packages/aiohttp/cookiejar.py b/evalkit_cambrian/lib/python3.10/site-packages/aiohttp/cookiejar.py new file mode 100644 index 0000000000000000000000000000000000000000..f6b9a9217673b8322dbafb79dcde6dcde1bfd542 --- /dev/null +++ b/evalkit_cambrian/lib/python3.10/site-packages/aiohttp/cookiejar.py @@ -0,0 +1,495 @@ +import asyncio +import calendar +import contextlib +import datetime +import heapq +import itertools +import os # noqa +import pathlib +import pickle +import re +import time +import warnings +from collections import defaultdict +from http.cookies import BaseCookie, Morsel, SimpleCookie +from typing import ( + DefaultDict, + Dict, + Iterable, + Iterator, + List, + Mapping, + Optional, + Set, + Tuple, + Union, + cast, +) + +from yarl import URL + +from .abc import AbstractCookieJar, ClearCookiePredicate +from .helpers import is_ip_address +from .typedefs import LooseCookies, PathLike, StrOrURL + +__all__ = ("CookieJar", "DummyCookieJar") + + +CookieItem = Union[str, "Morsel[str]"] + +# We cache these string methods here as their use is in performance critical code. +_FORMAT_PATH = "{}/{}".format +_FORMAT_DOMAIN_REVERSED = "{1}.{0}".format + +# The minimum number of scheduled cookie expirations before we start cleaning up +# the expiration heap. This is a performance optimization to avoid cleaning up the +# heap too often when there are only a few scheduled expirations. +_MIN_SCHEDULED_COOKIE_EXPIRATION = 100 + + +class CookieJar(AbstractCookieJar): + """Implements cookie storage adhering to RFC 6265.""" + + DATE_TOKENS_RE = re.compile( + r"[\x09\x20-\x2F\x3B-\x40\x5B-\x60\x7B-\x7E]*" + r"(?P[\x00-\x08\x0A-\x1F\d:a-zA-Z\x7F-\xFF]+)" + ) + + DATE_HMS_TIME_RE = re.compile(r"(\d{1,2}):(\d{1,2}):(\d{1,2})") + + DATE_DAY_OF_MONTH_RE = re.compile(r"(\d{1,2})") + + DATE_MONTH_RE = re.compile( + "(jan)|(feb)|(mar)|(apr)|(may)|(jun)|(jul)|(aug)|(sep)|(oct)|(nov)|(dec)", + re.I, + ) + + DATE_YEAR_RE = re.compile(r"(\d{2,4})") + + # calendar.timegm() fails for timestamps after datetime.datetime.max + # Minus one as a loss of precision occurs when timestamp() is called. + MAX_TIME = ( + int(datetime.datetime.max.replace(tzinfo=datetime.timezone.utc).timestamp()) - 1 + ) + try: + calendar.timegm(time.gmtime(MAX_TIME)) + except (OSError, ValueError): + # Hit the maximum representable time on Windows + # https://learn.microsoft.com/en-us/cpp/c-runtime-library/reference/localtime-localtime32-localtime64 + # Throws ValueError on PyPy 3.9, OSError elsewhere + MAX_TIME = calendar.timegm((3000, 12, 31, 23, 59, 59, -1, -1, -1)) + except OverflowError: + # #4515: datetime.max may not be representable on 32-bit platforms + MAX_TIME = 2**31 - 1 + # Avoid minuses in the future, 3x faster + SUB_MAX_TIME = MAX_TIME - 1 + + def __init__( + self, + *, + unsafe: bool = False, + quote_cookie: bool = True, + treat_as_secure_origin: Union[StrOrURL, List[StrOrURL], None] = None, + loop: Optional[asyncio.AbstractEventLoop] = None, + ) -> None: + super().__init__(loop=loop) + self._cookies: DefaultDict[Tuple[str, str], SimpleCookie] = defaultdict( + SimpleCookie + ) + self._morsel_cache: DefaultDict[Tuple[str, str], Dict[str, Morsel[str]]] = ( + defaultdict(dict) + ) + self._host_only_cookies: Set[Tuple[str, str]] = set() + self._unsafe = unsafe + self._quote_cookie = quote_cookie + if treat_as_secure_origin is None: + treat_as_secure_origin = [] + elif isinstance(treat_as_secure_origin, URL): + treat_as_secure_origin = [treat_as_secure_origin.origin()] + elif isinstance(treat_as_secure_origin, str): + treat_as_secure_origin = [URL(treat_as_secure_origin).origin()] + else: + treat_as_secure_origin = [ + URL(url).origin() if isinstance(url, str) else url.origin() + for url in treat_as_secure_origin + ] + self._treat_as_secure_origin = treat_as_secure_origin + self._expire_heap: List[Tuple[float, Tuple[str, str, str]]] = [] + self._expirations: Dict[Tuple[str, str, str], float] = {} + + @property + def quote_cookie(self) -> bool: + return self._quote_cookie + + def save(self, file_path: PathLike) -> None: + file_path = pathlib.Path(file_path) + with file_path.open(mode="wb") as f: + pickle.dump(self._cookies, f, pickle.HIGHEST_PROTOCOL) + + def load(self, file_path: PathLike) -> None: + file_path = pathlib.Path(file_path) + with file_path.open(mode="rb") as f: + self._cookies = pickle.load(f) + + def clear(self, predicate: Optional[ClearCookiePredicate] = None) -> None: + if predicate is None: + self._expire_heap.clear() + self._cookies.clear() + self._morsel_cache.clear() + self._host_only_cookies.clear() + self._expirations.clear() + return + + now = time.time() + to_del = [ + key + for (domain, path), cookie in self._cookies.items() + for name, morsel in cookie.items() + if ( + (key := (domain, path, name)) in self._expirations + and self._expirations[key] <= now + ) + or predicate(morsel) + ] + if to_del: + self._delete_cookies(to_del) + + def clear_domain(self, domain: str) -> None: + self.clear(lambda x: self._is_domain_match(domain, x["domain"])) + + def __iter__(self) -> "Iterator[Morsel[str]]": + self._do_expiration() + for val in self._cookies.values(): + yield from val.values() + + def __len__(self) -> int: + """Return number of cookies. + + This function does not iterate self to avoid unnecessary expiration + checks. + """ + return sum(len(cookie.values()) for cookie in self._cookies.values()) + + def _do_expiration(self) -> None: + """Remove expired cookies.""" + if not (expire_heap_len := len(self._expire_heap)): + return + + # If the expiration heap grows larger than the number expirations + # times two, we clean it up to avoid keeping expired entries in + # the heap and consuming memory. We guard this with a minimum + # threshold to avoid cleaning up the heap too often when there are + # only a few scheduled expirations. + if ( + expire_heap_len > _MIN_SCHEDULED_COOKIE_EXPIRATION + and expire_heap_len > len(self._expirations) * 2 + ): + # Remove any expired entries from the expiration heap + # that do not match the expiration time in the expirations + # as it means the cookie has been re-added to the heap + # with a different expiration time. + self._expire_heap = [ + entry + for entry in self._expire_heap + if self._expirations.get(entry[1]) == entry[0] + ] + heapq.heapify(self._expire_heap) + + now = time.time() + to_del: List[Tuple[str, str, str]] = [] + # Find any expired cookies and add them to the to-delete list + while self._expire_heap: + when, cookie_key = self._expire_heap[0] + if when > now: + break + heapq.heappop(self._expire_heap) + # Check if the cookie hasn't been re-added to the heap + # with a different expiration time as it will be removed + # later when it reaches the top of the heap and its + # expiration time is met. + if self._expirations.get(cookie_key) == when: + to_del.append(cookie_key) + + if to_del: + self._delete_cookies(to_del) + + def _delete_cookies(self, to_del: List[Tuple[str, str, str]]) -> None: + for domain, path, name in to_del: + self._host_only_cookies.discard((domain, name)) + self._cookies[(domain, path)].pop(name, None) + self._morsel_cache[(domain, path)].pop(name, None) + self._expirations.pop((domain, path, name), None) + + def _expire_cookie(self, when: float, domain: str, path: str, name: str) -> None: + cookie_key = (domain, path, name) + if self._expirations.get(cookie_key) == when: + # Avoid adding duplicates to the heap + return + heapq.heappush(self._expire_heap, (when, cookie_key)) + self._expirations[cookie_key] = when + + def update_cookies(self, cookies: LooseCookies, response_url: URL = URL()) -> None: + """Update cookies.""" + hostname = response_url.raw_host + + if not self._unsafe and is_ip_address(hostname): + # Don't accept cookies from IPs + return + + if isinstance(cookies, Mapping): + cookies = cookies.items() + + for name, cookie in cookies: + if not isinstance(cookie, Morsel): + tmp = SimpleCookie() + tmp[name] = cookie # type: ignore[assignment] + cookie = tmp[name] + + domain = cookie["domain"] + + # ignore domains with trailing dots + if domain and domain[-1] == ".": + domain = "" + del cookie["domain"] + + if not domain and hostname is not None: + # Set the cookie's domain to the response hostname + # and set its host-only-flag + self._host_only_cookies.add((hostname, name)) + domain = cookie["domain"] = hostname + + if domain and domain[0] == ".": + # Remove leading dot + domain = domain[1:] + cookie["domain"] = domain + + if hostname and not self._is_domain_match(domain, hostname): + # Setting cookies for different domains is not allowed + continue + + path = cookie["path"] + if not path or path[0] != "/": + # Set the cookie's path to the response path + path = response_url.path + if not path.startswith("/"): + path = "/" + else: + # Cut everything from the last slash to the end + path = "/" + path[1 : path.rfind("/")] + cookie["path"] = path + path = path.rstrip("/") + + if max_age := cookie["max-age"]: + try: + delta_seconds = int(max_age) + max_age_expiration = min(time.time() + delta_seconds, self.MAX_TIME) + self._expire_cookie(max_age_expiration, domain, path, name) + except ValueError: + cookie["max-age"] = "" + + elif expires := cookie["expires"]: + if expire_time := self._parse_date(expires): + self._expire_cookie(expire_time, domain, path, name) + else: + cookie["expires"] = "" + + key = (domain, path) + if self._cookies[key].get(name) != cookie: + # Don't blow away the cache if the same + # cookie gets set again + self._cookies[key][name] = cookie + self._morsel_cache[key].pop(name, None) + + self._do_expiration() + + def filter_cookies(self, request_url: URL = URL()) -> "BaseCookie[str]": + """Returns this jar's cookies filtered by their attributes.""" + filtered: Union[SimpleCookie, "BaseCookie[str]"] = ( + SimpleCookie() if self._quote_cookie else BaseCookie() + ) + if not self._cookies: + # Skip do_expiration() if there are no cookies. + return filtered + self._do_expiration() + if not self._cookies: + # Skip rest of function if no non-expired cookies. + return filtered + if type(request_url) is not URL: + warnings.warn( + "filter_cookies expects yarl.URL instances only," + f"and will stop working in 4.x, got {type(request_url)}", + DeprecationWarning, + stacklevel=2, + ) + request_url = URL(request_url) + hostname = request_url.raw_host or "" + + is_not_secure = request_url.scheme not in ("https", "wss") + if is_not_secure and self._treat_as_secure_origin: + request_origin = URL() + with contextlib.suppress(ValueError): + request_origin = request_url.origin() + is_not_secure = request_origin not in self._treat_as_secure_origin + + # Send shared cookie + for c in self._cookies[("", "")].values(): + filtered[c.key] = c.value + + if is_ip_address(hostname): + if not self._unsafe: + return filtered + domains: Iterable[str] = (hostname,) + else: + # Get all the subdomains that might match a cookie (e.g. "foo.bar.com", "bar.com", "com") + domains = itertools.accumulate( + reversed(hostname.split(".")), _FORMAT_DOMAIN_REVERSED + ) + + # Get all the path prefixes that might match a cookie (e.g. "", "/foo", "/foo/bar") + paths = itertools.accumulate(request_url.path.split("/"), _FORMAT_PATH) + # Create every combination of (domain, path) pairs. + pairs = itertools.product(domains, paths) + + path_len = len(request_url.path) + # Point 2: https://www.rfc-editor.org/rfc/rfc6265.html#section-5.4 + for p in pairs: + for name, cookie in self._cookies[p].items(): + domain = cookie["domain"] + + if (domain, name) in self._host_only_cookies and domain != hostname: + continue + + # Skip edge case when the cookie has a trailing slash but request doesn't. + if len(cookie["path"]) > path_len: + continue + + if is_not_secure and cookie["secure"]: + continue + + # We already built the Morsel so reuse it here + if name in self._morsel_cache[p]: + filtered[name] = self._morsel_cache[p][name] + continue + + # It's critical we use the Morsel so the coded_value + # (based on cookie version) is preserved + mrsl_val = cast("Morsel[str]", cookie.get(cookie.key, Morsel())) + mrsl_val.set(cookie.key, cookie.value, cookie.coded_value) + self._morsel_cache[p][name] = mrsl_val + filtered[name] = mrsl_val + + return filtered + + @staticmethod + def _is_domain_match(domain: str, hostname: str) -> bool: + """Implements domain matching adhering to RFC 6265.""" + if hostname == domain: + return True + + if not hostname.endswith(domain): + return False + + non_matching = hostname[: -len(domain)] + + if not non_matching.endswith("."): + return False + + return not is_ip_address(hostname) + + @classmethod + def _parse_date(cls, date_str: str) -> Optional[int]: + """Implements date string parsing adhering to RFC 6265.""" + if not date_str: + return None + + found_time = False + found_day = False + found_month = False + found_year = False + + hour = minute = second = 0 + day = 0 + month = 0 + year = 0 + + for token_match in cls.DATE_TOKENS_RE.finditer(date_str): + + token = token_match.group("token") + + if not found_time: + time_match = cls.DATE_HMS_TIME_RE.match(token) + if time_match: + found_time = True + hour, minute, second = (int(s) for s in time_match.groups()) + continue + + if not found_day: + day_match = cls.DATE_DAY_OF_MONTH_RE.match(token) + if day_match: + found_day = True + day = int(day_match.group()) + continue + + if not found_month: + month_match = cls.DATE_MONTH_RE.match(token) + if month_match: + found_month = True + assert month_match.lastindex is not None + month = month_match.lastindex + continue + + if not found_year: + year_match = cls.DATE_YEAR_RE.match(token) + if year_match: + found_year = True + year = int(year_match.group()) + + if 70 <= year <= 99: + year += 1900 + elif 0 <= year <= 69: + year += 2000 + + if False in (found_day, found_month, found_year, found_time): + return None + + if not 1 <= day <= 31: + return None + + if year < 1601 or hour > 23 or minute > 59 or second > 59: + return None + + return calendar.timegm((year, month, day, hour, minute, second, -1, -1, -1)) + + +class DummyCookieJar(AbstractCookieJar): + """Implements a dummy cookie storage. + + It can be used with the ClientSession when no cookie processing is needed. + + """ + + def __init__(self, *, loop: Optional[asyncio.AbstractEventLoop] = None) -> None: + super().__init__(loop=loop) + + def __iter__(self) -> "Iterator[Morsel[str]]": + while False: + yield None + + def __len__(self) -> int: + return 0 + + @property + def quote_cookie(self) -> bool: + return True + + def clear(self, predicate: Optional[ClearCookiePredicate] = None) -> None: + pass + + def clear_domain(self, domain: str) -> None: + pass + + def update_cookies(self, cookies: LooseCookies, response_url: URL = URL()) -> None: + pass + + def filter_cookies(self, request_url: URL) -> "BaseCookie[str]": + return SimpleCookie() diff --git a/evalkit_cambrian/lib/python3.10/site-packages/aiohttp/formdata.py b/evalkit_cambrian/lib/python3.10/site-packages/aiohttp/formdata.py new file mode 100644 index 0000000000000000000000000000000000000000..73056f4bc45f2ec140c00e7a3983e7e0e21c4343 --- /dev/null +++ b/evalkit_cambrian/lib/python3.10/site-packages/aiohttp/formdata.py @@ -0,0 +1,182 @@ +import io +import warnings +from typing import Any, Iterable, List, Optional +from urllib.parse import urlencode + +from multidict import MultiDict, MultiDictProxy + +from . import hdrs, multipart, payload +from .helpers import guess_filename +from .payload import Payload + +__all__ = ("FormData",) + + +class FormData: + """Helper class for form body generation. + + Supports multipart/form-data and application/x-www-form-urlencoded. + """ + + def __init__( + self, + fields: Iterable[Any] = (), + quote_fields: bool = True, + charset: Optional[str] = None, + *, + default_to_multipart: bool = False, + ) -> None: + self._writer = multipart.MultipartWriter("form-data") + self._fields: List[Any] = [] + self._is_multipart = default_to_multipart + self._is_processed = False + self._quote_fields = quote_fields + self._charset = charset + + if isinstance(fields, dict): + fields = list(fields.items()) + elif not isinstance(fields, (list, tuple)): + fields = (fields,) + self.add_fields(*fields) + + @property + def is_multipart(self) -> bool: + return self._is_multipart + + def add_field( + self, + name: str, + value: Any, + *, + content_type: Optional[str] = None, + filename: Optional[str] = None, + content_transfer_encoding: Optional[str] = None, + ) -> None: + + if isinstance(value, io.IOBase): + self._is_multipart = True + elif isinstance(value, (bytes, bytearray, memoryview)): + msg = ( + "In v4, passing bytes will no longer create a file field. " + "Please explicitly use the filename parameter or pass a BytesIO object." + ) + if filename is None and content_transfer_encoding is None: + warnings.warn(msg, DeprecationWarning) + filename = name + + type_options: MultiDict[str] = MultiDict({"name": name}) + if filename is not None and not isinstance(filename, str): + raise TypeError("filename must be an instance of str. Got: %s" % filename) + if filename is None and isinstance(value, io.IOBase): + filename = guess_filename(value, name) + if filename is not None: + type_options["filename"] = filename + self._is_multipart = True + + headers = {} + if content_type is not None: + if not isinstance(content_type, str): + raise TypeError( + "content_type must be an instance of str. Got: %s" % content_type + ) + headers[hdrs.CONTENT_TYPE] = content_type + self._is_multipart = True + if content_transfer_encoding is not None: + if not isinstance(content_transfer_encoding, str): + raise TypeError( + "content_transfer_encoding must be an instance" + " of str. Got: %s" % content_transfer_encoding + ) + msg = ( + "content_transfer_encoding is deprecated. " + "To maintain compatibility with v4 please pass a BytesPayload." + ) + warnings.warn(msg, DeprecationWarning) + self._is_multipart = True + + self._fields.append((type_options, headers, value)) + + def add_fields(self, *fields: Any) -> None: + to_add = list(fields) + + while to_add: + rec = to_add.pop(0) + + if isinstance(rec, io.IOBase): + k = guess_filename(rec, "unknown") + self.add_field(k, rec) # type: ignore[arg-type] + + elif isinstance(rec, (MultiDictProxy, MultiDict)): + to_add.extend(rec.items()) + + elif isinstance(rec, (list, tuple)) and len(rec) == 2: + k, fp = rec + self.add_field(k, fp) # type: ignore[arg-type] + + else: + raise TypeError( + "Only io.IOBase, multidict and (name, file) " + "pairs allowed, use .add_field() for passing " + "more complex parameters, got {!r}".format(rec) + ) + + def _gen_form_urlencoded(self) -> payload.BytesPayload: + # form data (x-www-form-urlencoded) + data = [] + for type_options, _, value in self._fields: + data.append((type_options["name"], value)) + + charset = self._charset if self._charset is not None else "utf-8" + + if charset == "utf-8": + content_type = "application/x-www-form-urlencoded" + else: + content_type = "application/x-www-form-urlencoded; charset=%s" % charset + + return payload.BytesPayload( + urlencode(data, doseq=True, encoding=charset).encode(), + content_type=content_type, + ) + + def _gen_form_data(self) -> multipart.MultipartWriter: + """Encode a list of fields using the multipart/form-data MIME format""" + if self._is_processed: + raise RuntimeError("Form data has been processed already") + for dispparams, headers, value in self._fields: + try: + if hdrs.CONTENT_TYPE in headers: + part = payload.get_payload( + value, + content_type=headers[hdrs.CONTENT_TYPE], + headers=headers, + encoding=self._charset, + ) + else: + part = payload.get_payload( + value, headers=headers, encoding=self._charset + ) + except Exception as exc: + raise TypeError( + "Can not serialize value type: %r\n " + "headers: %r\n value: %r" % (type(value), headers, value) + ) from exc + + if dispparams: + part.set_content_disposition( + "form-data", quote_fields=self._quote_fields, **dispparams + ) + # FIXME cgi.FieldStorage doesn't likes body parts with + # Content-Length which were sent via chunked transfer encoding + assert part.headers is not None + part.headers.popall(hdrs.CONTENT_LENGTH, None) + + self._writer.append_payload(part) + + self._is_processed = True + return self._writer + + def __call__(self) -> Payload: + if self._is_multipart: + return self._gen_form_data() + else: + return self._gen_form_urlencoded() diff --git a/evalkit_cambrian/lib/python3.10/site-packages/aiohttp/helpers.py b/evalkit_cambrian/lib/python3.10/site-packages/aiohttp/helpers.py new file mode 100644 index 0000000000000000000000000000000000000000..8038931ebec525c614ffd3539f38fd4e8214c2fb --- /dev/null +++ b/evalkit_cambrian/lib/python3.10/site-packages/aiohttp/helpers.py @@ -0,0 +1,944 @@ +"""Various helper functions""" + +import asyncio +import base64 +import binascii +import contextlib +import datetime +import enum +import functools +import inspect +import netrc +import os +import platform +import re +import sys +import time +import weakref +from collections import namedtuple +from contextlib import suppress +from email.parser import HeaderParser +from email.utils import parsedate +from math import ceil +from pathlib import Path +from types import TracebackType +from typing import ( + Any, + Callable, + ContextManager, + Dict, + Generator, + Generic, + Iterable, + Iterator, + List, + Mapping, + Optional, + Protocol, + Tuple, + Type, + TypeVar, + Union, + get_args, + overload, +) +from urllib.parse import quote +from urllib.request import getproxies, proxy_bypass + +import attr +from multidict import MultiDict, MultiDictProxy, MultiMapping +from propcache.api import under_cached_property as reify +from yarl import URL + +from . import hdrs +from .log import client_logger + +if sys.version_info >= (3, 11): + import asyncio as async_timeout +else: + import async_timeout + +__all__ = ("BasicAuth", "ChainMapProxy", "ETag", "reify") + +IS_MACOS = platform.system() == "Darwin" +IS_WINDOWS = platform.system() == "Windows" + +PY_310 = sys.version_info >= (3, 10) +PY_311 = sys.version_info >= (3, 11) + + +_T = TypeVar("_T") +_S = TypeVar("_S") + +_SENTINEL = enum.Enum("_SENTINEL", "sentinel") +sentinel = _SENTINEL.sentinel + +NO_EXTENSIONS = bool(os.environ.get("AIOHTTP_NO_EXTENSIONS")) + +# https://datatracker.ietf.org/doc/html/rfc9112#section-6.3-2.1 +EMPTY_BODY_STATUS_CODES = frozenset((204, 304, *range(100, 200))) +# https://datatracker.ietf.org/doc/html/rfc9112#section-6.3-2.1 +# https://datatracker.ietf.org/doc/html/rfc9112#section-6.3-2.2 +EMPTY_BODY_METHODS = hdrs.METH_HEAD_ALL + +DEBUG = sys.flags.dev_mode or ( + not sys.flags.ignore_environment and bool(os.environ.get("PYTHONASYNCIODEBUG")) +) + + +CHAR = {chr(i) for i in range(0, 128)} +CTL = {chr(i) for i in range(0, 32)} | { + chr(127), +} +SEPARATORS = { + "(", + ")", + "<", + ">", + "@", + ",", + ";", + ":", + "\\", + '"', + "/", + "[", + "]", + "?", + "=", + "{", + "}", + " ", + chr(9), +} +TOKEN = CHAR ^ CTL ^ SEPARATORS + + +class noop: + def __await__(self) -> Generator[None, None, None]: + yield + + +class BasicAuth(namedtuple("BasicAuth", ["login", "password", "encoding"])): + """Http basic authentication helper.""" + + def __new__( + cls, login: str, password: str = "", encoding: str = "latin1" + ) -> "BasicAuth": + if login is None: + raise ValueError("None is not allowed as login value") + + if password is None: + raise ValueError("None is not allowed as password value") + + if ":" in login: + raise ValueError('A ":" is not allowed in login (RFC 1945#section-11.1)') + + return super().__new__(cls, login, password, encoding) + + @classmethod + def decode(cls, auth_header: str, encoding: str = "latin1") -> "BasicAuth": + """Create a BasicAuth object from an Authorization HTTP header.""" + try: + auth_type, encoded_credentials = auth_header.split(" ", 1) + except ValueError: + raise ValueError("Could not parse authorization header.") + + if auth_type.lower() != "basic": + raise ValueError("Unknown authorization method %s" % auth_type) + + try: + decoded = base64.b64decode( + encoded_credentials.encode("ascii"), validate=True + ).decode(encoding) + except binascii.Error: + raise ValueError("Invalid base64 encoding.") + + try: + # RFC 2617 HTTP Authentication + # https://www.ietf.org/rfc/rfc2617.txt + # the colon must be present, but the username and password may be + # otherwise blank. + username, password = decoded.split(":", 1) + except ValueError: + raise ValueError("Invalid credentials.") + + return cls(username, password, encoding=encoding) + + @classmethod + def from_url(cls, url: URL, *, encoding: str = "latin1") -> Optional["BasicAuth"]: + """Create BasicAuth from url.""" + if not isinstance(url, URL): + raise TypeError("url should be yarl.URL instance") + # Check raw_user and raw_password first as yarl is likely + # to already have these values parsed from the netloc in the cache. + if url.raw_user is None and url.raw_password is None: + return None + return cls(url.user or "", url.password or "", encoding=encoding) + + def encode(self) -> str: + """Encode credentials.""" + creds = (f"{self.login}:{self.password}").encode(self.encoding) + return "Basic %s" % base64.b64encode(creds).decode(self.encoding) + + +def strip_auth_from_url(url: URL) -> Tuple[URL, Optional[BasicAuth]]: + """Remove user and password from URL if present and return BasicAuth object.""" + # Check raw_user and raw_password first as yarl is likely + # to already have these values parsed from the netloc in the cache. + if url.raw_user is None and url.raw_password is None: + return url, None + return url.with_user(None), BasicAuth(url.user or "", url.password or "") + + +def netrc_from_env() -> Optional[netrc.netrc]: + """Load netrc from file. + + Attempt to load it from the path specified by the env-var + NETRC or in the default location in the user's home directory. + + Returns None if it couldn't be found or fails to parse. + """ + netrc_env = os.environ.get("NETRC") + + if netrc_env is not None: + netrc_path = Path(netrc_env) + else: + try: + home_dir = Path.home() + except RuntimeError as e: # pragma: no cover + # if pathlib can't resolve home, it may raise a RuntimeError + client_logger.debug( + "Could not resolve home directory when " + "trying to look for .netrc file: %s", + e, + ) + return None + + netrc_path = home_dir / ("_netrc" if IS_WINDOWS else ".netrc") + + try: + return netrc.netrc(str(netrc_path)) + except netrc.NetrcParseError as e: + client_logger.warning("Could not parse .netrc file: %s", e) + except OSError as e: + netrc_exists = False + with contextlib.suppress(OSError): + netrc_exists = netrc_path.is_file() + # we couldn't read the file (doesn't exist, permissions, etc.) + if netrc_env or netrc_exists: + # only warn if the environment wanted us to load it, + # or it appears like the default file does actually exist + client_logger.warning("Could not read .netrc file: %s", e) + + return None + + +@attr.s(auto_attribs=True, frozen=True, slots=True) +class ProxyInfo: + proxy: URL + proxy_auth: Optional[BasicAuth] + + +def basicauth_from_netrc(netrc_obj: Optional[netrc.netrc], host: str) -> BasicAuth: + """ + Return :py:class:`~aiohttp.BasicAuth` credentials for ``host`` from ``netrc_obj``. + + :raises LookupError: if ``netrc_obj`` is :py:data:`None` or if no + entry is found for the ``host``. + """ + if netrc_obj is None: + raise LookupError("No .netrc file found") + auth_from_netrc = netrc_obj.authenticators(host) + + if auth_from_netrc is None: + raise LookupError(f"No entry for {host!s} found in the `.netrc` file.") + login, account, password = auth_from_netrc + + # TODO(PY311): username = login or account + # Up to python 3.10, account could be None if not specified, + # and login will be empty string if not specified. From 3.11, + # login and account will be empty string if not specified. + username = login if (login or account is None) else account + + # TODO(PY311): Remove this, as password will be empty string + # if not specified + if password is None: + password = "" + + return BasicAuth(username, password) + + +def proxies_from_env() -> Dict[str, ProxyInfo]: + proxy_urls = { + k: URL(v) + for k, v in getproxies().items() + if k in ("http", "https", "ws", "wss") + } + netrc_obj = netrc_from_env() + stripped = {k: strip_auth_from_url(v) for k, v in proxy_urls.items()} + ret = {} + for proto, val in stripped.items(): + proxy, auth = val + if proxy.scheme in ("https", "wss"): + client_logger.warning( + "%s proxies %s are not supported, ignoring", proxy.scheme.upper(), proxy + ) + continue + if netrc_obj and auth is None: + if proxy.host is not None: + try: + auth = basicauth_from_netrc(netrc_obj, proxy.host) + except LookupError: + auth = None + ret[proto] = ProxyInfo(proxy, auth) + return ret + + +def get_env_proxy_for_url(url: URL) -> Tuple[URL, Optional[BasicAuth]]: + """Get a permitted proxy for the given URL from the env.""" + if url.host is not None and proxy_bypass(url.host): + raise LookupError(f"Proxying is disallowed for `{url.host!r}`") + + proxies_in_env = proxies_from_env() + try: + proxy_info = proxies_in_env[url.scheme] + except KeyError: + raise LookupError(f"No proxies found for `{url!s}` in the env") + else: + return proxy_info.proxy, proxy_info.proxy_auth + + +@attr.s(auto_attribs=True, frozen=True, slots=True) +class MimeType: + type: str + subtype: str + suffix: str + parameters: "MultiDictProxy[str]" + + +@functools.lru_cache(maxsize=56) +def parse_mimetype(mimetype: str) -> MimeType: + """Parses a MIME type into its components. + + mimetype is a MIME type string. + + Returns a MimeType object. + + Example: + + >>> parse_mimetype('text/html; charset=utf-8') + MimeType(type='text', subtype='html', suffix='', + parameters={'charset': 'utf-8'}) + + """ + if not mimetype: + return MimeType( + type="", subtype="", suffix="", parameters=MultiDictProxy(MultiDict()) + ) + + parts = mimetype.split(";") + params: MultiDict[str] = MultiDict() + for item in parts[1:]: + if not item: + continue + key, _, value = item.partition("=") + params.add(key.lower().strip(), value.strip(' "')) + + fulltype = parts[0].strip().lower() + if fulltype == "*": + fulltype = "*/*" + + mtype, _, stype = fulltype.partition("/") + stype, _, suffix = stype.partition("+") + + return MimeType( + type=mtype, subtype=stype, suffix=suffix, parameters=MultiDictProxy(params) + ) + + +def guess_filename(obj: Any, default: Optional[str] = None) -> Optional[str]: + name = getattr(obj, "name", None) + if name and isinstance(name, str) and name[0] != "<" and name[-1] != ">": + return Path(name).name + return default + + +not_qtext_re = re.compile(r"[^\041\043-\133\135-\176]") +QCONTENT = {chr(i) for i in range(0x20, 0x7F)} | {"\t"} + + +def quoted_string(content: str) -> str: + """Return 7-bit content as quoted-string. + + Format content into a quoted-string as defined in RFC5322 for + Internet Message Format. Notice that this is not the 8-bit HTTP + format, but the 7-bit email format. Content must be in usascii or + a ValueError is raised. + """ + if not (QCONTENT > set(content)): + raise ValueError(f"bad content for quoted-string {content!r}") + return not_qtext_re.sub(lambda x: "\\" + x.group(0), content) + + +def content_disposition_header( + disptype: str, quote_fields: bool = True, _charset: str = "utf-8", **params: str +) -> str: + """Sets ``Content-Disposition`` header for MIME. + + This is the MIME payload Content-Disposition header from RFC 2183 + and RFC 7579 section 4.2, not the HTTP Content-Disposition from + RFC 6266. + + disptype is a disposition type: inline, attachment, form-data. + Should be valid extension token (see RFC 2183) + + quote_fields performs value quoting to 7-bit MIME headers + according to RFC 7578. Set to quote_fields to False if recipient + can take 8-bit file names and field values. + + _charset specifies the charset to use when quote_fields is True. + + params is a dict with disposition params. + """ + if not disptype or not (TOKEN > set(disptype)): + raise ValueError(f"bad content disposition type {disptype!r}") + + value = disptype + if params: + lparams = [] + for key, val in params.items(): + if not key or not (TOKEN > set(key)): + raise ValueError(f"bad content disposition parameter {key!r}={val!r}") + if quote_fields: + if key.lower() == "filename": + qval = quote(val, "", encoding=_charset) + lparams.append((key, '"%s"' % qval)) + else: + try: + qval = quoted_string(val) + except ValueError: + qval = "".join( + (_charset, "''", quote(val, "", encoding=_charset)) + ) + lparams.append((key + "*", qval)) + else: + lparams.append((key, '"%s"' % qval)) + else: + qval = val.replace("\\", "\\\\").replace('"', '\\"') + lparams.append((key, '"%s"' % qval)) + sparams = "; ".join("=".join(pair) for pair in lparams) + value = "; ".join((value, sparams)) + return value + + +def is_ip_address(host: Optional[str]) -> bool: + """Check if host looks like an IP Address. + + This check is only meant as a heuristic to ensure that + a host is not a domain name. + """ + if not host: + return False + # For a host to be an ipv4 address, it must be all numeric. + # The host must contain a colon to be an IPv6 address. + return ":" in host or host.replace(".", "").isdigit() + + +_cached_current_datetime: Optional[int] = None +_cached_formatted_datetime = "" + + +def rfc822_formatted_time() -> str: + global _cached_current_datetime + global _cached_formatted_datetime + + now = int(time.time()) + if now != _cached_current_datetime: + # Weekday and month names for HTTP date/time formatting; + # always English! + # Tuples are constants stored in codeobject! + _weekdayname = ("Mon", "Tue", "Wed", "Thu", "Fri", "Sat", "Sun") + _monthname = ( + "", # Dummy so we can use 1-based month numbers + "Jan", + "Feb", + "Mar", + "Apr", + "May", + "Jun", + "Jul", + "Aug", + "Sep", + "Oct", + "Nov", + "Dec", + ) + + year, month, day, hh, mm, ss, wd, *tail = time.gmtime(now) + _cached_formatted_datetime = "%s, %02d %3s %4d %02d:%02d:%02d GMT" % ( + _weekdayname[wd], + day, + _monthname[month], + year, + hh, + mm, + ss, + ) + _cached_current_datetime = now + return _cached_formatted_datetime + + +def _weakref_handle(info: "Tuple[weakref.ref[object], str]") -> None: + ref, name = info + ob = ref() + if ob is not None: + with suppress(Exception): + getattr(ob, name)() + + +def weakref_handle( + ob: object, + name: str, + timeout: float, + loop: asyncio.AbstractEventLoop, + timeout_ceil_threshold: float = 5, +) -> Optional[asyncio.TimerHandle]: + if timeout is not None and timeout > 0: + when = loop.time() + timeout + if timeout >= timeout_ceil_threshold: + when = ceil(when) + + return loop.call_at(when, _weakref_handle, (weakref.ref(ob), name)) + return None + + +def call_later( + cb: Callable[[], Any], + timeout: float, + loop: asyncio.AbstractEventLoop, + timeout_ceil_threshold: float = 5, +) -> Optional[asyncio.TimerHandle]: + if timeout is None or timeout <= 0: + return None + now = loop.time() + when = calculate_timeout_when(now, timeout, timeout_ceil_threshold) + return loop.call_at(when, cb) + + +def calculate_timeout_when( + loop_time: float, + timeout: float, + timeout_ceiling_threshold: float, +) -> float: + """Calculate when to execute a timeout.""" + when = loop_time + timeout + if timeout > timeout_ceiling_threshold: + return ceil(when) + return when + + +class TimeoutHandle: + """Timeout handle""" + + __slots__ = ("_timeout", "_loop", "_ceil_threshold", "_callbacks") + + def __init__( + self, + loop: asyncio.AbstractEventLoop, + timeout: Optional[float], + ceil_threshold: float = 5, + ) -> None: + self._timeout = timeout + self._loop = loop + self._ceil_threshold = ceil_threshold + self._callbacks: List[ + Tuple[Callable[..., None], Tuple[Any, ...], Dict[str, Any]] + ] = [] + + def register( + self, callback: Callable[..., None], *args: Any, **kwargs: Any + ) -> None: + self._callbacks.append((callback, args, kwargs)) + + def close(self) -> None: + self._callbacks.clear() + + def start(self) -> Optional[asyncio.TimerHandle]: + timeout = self._timeout + if timeout is not None and timeout > 0: + when = self._loop.time() + timeout + if timeout >= self._ceil_threshold: + when = ceil(when) + return self._loop.call_at(when, self.__call__) + else: + return None + + def timer(self) -> "BaseTimerContext": + if self._timeout is not None and self._timeout > 0: + timer = TimerContext(self._loop) + self.register(timer.timeout) + return timer + else: + return TimerNoop() + + def __call__(self) -> None: + for cb, args, kwargs in self._callbacks: + with suppress(Exception): + cb(*args, **kwargs) + + self._callbacks.clear() + + +class BaseTimerContext(ContextManager["BaseTimerContext"]): + + __slots__ = () + + def assert_timeout(self) -> None: + """Raise TimeoutError if timeout has been exceeded.""" + + +class TimerNoop(BaseTimerContext): + + __slots__ = () + + def __enter__(self) -> BaseTimerContext: + return self + + def __exit__( + self, + exc_type: Optional[Type[BaseException]], + exc_val: Optional[BaseException], + exc_tb: Optional[TracebackType], + ) -> None: + return + + +class TimerContext(BaseTimerContext): + """Low resolution timeout context manager""" + + __slots__ = ("_loop", "_tasks", "_cancelled", "_cancelling") + + def __init__(self, loop: asyncio.AbstractEventLoop) -> None: + self._loop = loop + self._tasks: List[asyncio.Task[Any]] = [] + self._cancelled = False + self._cancelling = 0 + + def assert_timeout(self) -> None: + """Raise TimeoutError if timer has already been cancelled.""" + if self._cancelled: + raise asyncio.TimeoutError from None + + def __enter__(self) -> BaseTimerContext: + task = asyncio.current_task(loop=self._loop) + if task is None: + raise RuntimeError("Timeout context manager should be used inside a task") + + if sys.version_info >= (3, 11): + # Remember if the task was already cancelling + # so when we __exit__ we can decide if we should + # raise asyncio.TimeoutError or let the cancellation propagate + self._cancelling = task.cancelling() + + if self._cancelled: + raise asyncio.TimeoutError from None + + self._tasks.append(task) + return self + + def __exit__( + self, + exc_type: Optional[Type[BaseException]], + exc_val: Optional[BaseException], + exc_tb: Optional[TracebackType], + ) -> Optional[bool]: + enter_task: Optional[asyncio.Task[Any]] = None + if self._tasks: + enter_task = self._tasks.pop() + + if exc_type is asyncio.CancelledError and self._cancelled: + assert enter_task is not None + # The timeout was hit, and the task was cancelled + # so we need to uncancel the last task that entered the context manager + # since the cancellation should not leak out of the context manager + if sys.version_info >= (3, 11): + # If the task was already cancelling don't raise + # asyncio.TimeoutError and instead return None + # to allow the cancellation to propagate + if enter_task.uncancel() > self._cancelling: + return None + raise asyncio.TimeoutError from exc_val + return None + + def timeout(self) -> None: + if not self._cancelled: + for task in set(self._tasks): + task.cancel() + + self._cancelled = True + + +def ceil_timeout( + delay: Optional[float], ceil_threshold: float = 5 +) -> async_timeout.Timeout: + if delay is None or delay <= 0: + return async_timeout.timeout(None) + + loop = asyncio.get_running_loop() + now = loop.time() + when = now + delay + if delay > ceil_threshold: + when = ceil(when) + return async_timeout.timeout_at(when) + + +class HeadersMixin: + """Mixin for handling headers.""" + + ATTRS = frozenset(["_content_type", "_content_dict", "_stored_content_type"]) + + _headers: MultiMapping[str] + _content_type: Optional[str] = None + _content_dict: Optional[Dict[str, str]] = None + _stored_content_type: Union[str, None, _SENTINEL] = sentinel + + def _parse_content_type(self, raw: Optional[str]) -> None: + self._stored_content_type = raw + if raw is None: + # default value according to RFC 2616 + self._content_type = "application/octet-stream" + self._content_dict = {} + else: + msg = HeaderParser().parsestr("Content-Type: " + raw) + self._content_type = msg.get_content_type() + params = msg.get_params(()) + self._content_dict = dict(params[1:]) # First element is content type again + + @property + def content_type(self) -> str: + """The value of content part for Content-Type HTTP header.""" + raw = self._headers.get(hdrs.CONTENT_TYPE) + if self._stored_content_type != raw: + self._parse_content_type(raw) + assert self._content_type is not None + return self._content_type + + @property + def charset(self) -> Optional[str]: + """The value of charset part for Content-Type HTTP header.""" + raw = self._headers.get(hdrs.CONTENT_TYPE) + if self._stored_content_type != raw: + self._parse_content_type(raw) + assert self._content_dict is not None + return self._content_dict.get("charset") + + @property + def content_length(self) -> Optional[int]: + """The value of Content-Length HTTP header.""" + content_length = self._headers.get(hdrs.CONTENT_LENGTH) + return None if content_length is None else int(content_length) + + +def set_result(fut: "asyncio.Future[_T]", result: _T) -> None: + if not fut.done(): + fut.set_result(result) + + +_EXC_SENTINEL = BaseException() + + +class ErrorableProtocol(Protocol): + def set_exception( + self, + exc: BaseException, + exc_cause: BaseException = ..., + ) -> None: ... # pragma: no cover + + +def set_exception( + fut: "asyncio.Future[_T] | ErrorableProtocol", + exc: BaseException, + exc_cause: BaseException = _EXC_SENTINEL, +) -> None: + """Set future exception. + + If the future is marked as complete, this function is a no-op. + + :param exc_cause: An exception that is a direct cause of ``exc``. + Only set if provided. + """ + if asyncio.isfuture(fut) and fut.done(): + return + + exc_is_sentinel = exc_cause is _EXC_SENTINEL + exc_causes_itself = exc is exc_cause + if not exc_is_sentinel and not exc_causes_itself: + exc.__cause__ = exc_cause + + fut.set_exception(exc) + + +@functools.total_ordering +class AppKey(Generic[_T]): + """Keys for static typing support in Application.""" + + __slots__ = ("_name", "_t", "__orig_class__") + + # This may be set by Python when instantiating with a generic type. We need to + # support this, in order to support types that are not concrete classes, + # like Iterable, which can't be passed as the second parameter to __init__. + __orig_class__: Type[object] + + def __init__(self, name: str, t: Optional[Type[_T]] = None): + # Prefix with module name to help deduplicate key names. + frame = inspect.currentframe() + while frame: + if frame.f_code.co_name == "": + module: str = frame.f_globals["__name__"] + break + frame = frame.f_back + + self._name = module + "." + name + self._t = t + + def __lt__(self, other: object) -> bool: + if isinstance(other, AppKey): + return self._name < other._name + return True # Order AppKey above other types. + + def __repr__(self) -> str: + t = self._t + if t is None: + with suppress(AttributeError): + # Set to type arg. + t = get_args(self.__orig_class__)[0] + + if t is None: + t_repr = "<>" + elif isinstance(t, type): + if t.__module__ == "builtins": + t_repr = t.__qualname__ + else: + t_repr = f"{t.__module__}.{t.__qualname__}" + else: + t_repr = repr(t) + return f"" + + +class ChainMapProxy(Mapping[Union[str, AppKey[Any]], Any]): + __slots__ = ("_maps",) + + def __init__(self, maps: Iterable[Mapping[Union[str, AppKey[Any]], Any]]) -> None: + self._maps = tuple(maps) + + def __init_subclass__(cls) -> None: + raise TypeError( + "Inheritance class {} from ChainMapProxy " + "is forbidden".format(cls.__name__) + ) + + @overload # type: ignore[override] + def __getitem__(self, key: AppKey[_T]) -> _T: ... + + @overload + def __getitem__(self, key: str) -> Any: ... + + def __getitem__(self, key: Union[str, AppKey[_T]]) -> Any: + for mapping in self._maps: + try: + return mapping[key] + except KeyError: + pass + raise KeyError(key) + + @overload # type: ignore[override] + def get(self, key: AppKey[_T], default: _S) -> Union[_T, _S]: ... + + @overload + def get(self, key: AppKey[_T], default: None = ...) -> Optional[_T]: ... + + @overload + def get(self, key: str, default: Any = ...) -> Any: ... + + def get(self, key: Union[str, AppKey[_T]], default: Any = None) -> Any: + try: + return self[key] + except KeyError: + return default + + def __len__(self) -> int: + # reuses stored hash values if possible + return len(set().union(*self._maps)) + + def __iter__(self) -> Iterator[Union[str, AppKey[Any]]]: + d: Dict[Union[str, AppKey[Any]], Any] = {} + for mapping in reversed(self._maps): + # reuses stored hash values if possible + d.update(mapping) + return iter(d) + + def __contains__(self, key: object) -> bool: + return any(key in m for m in self._maps) + + def __bool__(self) -> bool: + return any(self._maps) + + def __repr__(self) -> str: + content = ", ".join(map(repr, self._maps)) + return f"ChainMapProxy({content})" + + +# https://tools.ietf.org/html/rfc7232#section-2.3 +_ETAGC = r"[!\x23-\x7E\x80-\xff]+" +_ETAGC_RE = re.compile(_ETAGC) +_QUOTED_ETAG = rf'(W/)?"({_ETAGC})"' +QUOTED_ETAG_RE = re.compile(_QUOTED_ETAG) +LIST_QUOTED_ETAG_RE = re.compile(rf"({_QUOTED_ETAG})(?:\s*,\s*|$)|(.)") + +ETAG_ANY = "*" + + +@attr.s(auto_attribs=True, frozen=True, slots=True) +class ETag: + value: str + is_weak: bool = False + + +def validate_etag_value(value: str) -> None: + if value != ETAG_ANY and not _ETAGC_RE.fullmatch(value): + raise ValueError( + f"Value {value!r} is not a valid etag. Maybe it contains '\"'?" + ) + + +def parse_http_date(date_str: Optional[str]) -> Optional[datetime.datetime]: + """Process a date string, return a datetime object""" + if date_str is not None: + timetuple = parsedate(date_str) + if timetuple is not None: + with suppress(ValueError): + return datetime.datetime(*timetuple[:6], tzinfo=datetime.timezone.utc) + return None + + +@functools.lru_cache +def must_be_empty_body(method: str, code: int) -> bool: + """Check if a request must return an empty body.""" + return ( + code in EMPTY_BODY_STATUS_CODES + or method in EMPTY_BODY_METHODS + or (200 <= code < 300 and method in hdrs.METH_CONNECT_ALL) + ) + + +def should_remove_content_length(method: str, code: int) -> bool: + """Check if a Content-Length header should be removed. + + This should always be a subset of must_be_empty_body + """ + # https://www.rfc-editor.org/rfc/rfc9110.html#section-8.6-8 + # https://www.rfc-editor.org/rfc/rfc9110.html#section-15.4.5-4 + return code in EMPTY_BODY_STATUS_CODES or ( + 200 <= code < 300 and method in hdrs.METH_CONNECT_ALL + ) diff --git a/evalkit_cambrian/lib/python3.10/site-packages/aiohttp/http.py b/evalkit_cambrian/lib/python3.10/site-packages/aiohttp/http.py new file mode 100644 index 0000000000000000000000000000000000000000..a1feae2d9b8fe631d539a15dbf8e5ea2914d70d5 --- /dev/null +++ b/evalkit_cambrian/lib/python3.10/site-packages/aiohttp/http.py @@ -0,0 +1,72 @@ +import sys +from http import HTTPStatus +from typing import Mapping, Tuple + +from . import __version__ +from .http_exceptions import HttpProcessingError as HttpProcessingError +from .http_parser import ( + HeadersParser as HeadersParser, + HttpParser as HttpParser, + HttpRequestParser as HttpRequestParser, + HttpResponseParser as HttpResponseParser, + RawRequestMessage as RawRequestMessage, + RawResponseMessage as RawResponseMessage, +) +from .http_websocket import ( + WS_CLOSED_MESSAGE as WS_CLOSED_MESSAGE, + WS_CLOSING_MESSAGE as WS_CLOSING_MESSAGE, + WS_KEY as WS_KEY, + WebSocketError as WebSocketError, + WebSocketReader as WebSocketReader, + WebSocketWriter as WebSocketWriter, + WSCloseCode as WSCloseCode, + WSMessage as WSMessage, + WSMsgType as WSMsgType, + ws_ext_gen as ws_ext_gen, + ws_ext_parse as ws_ext_parse, +) +from .http_writer import ( + HttpVersion as HttpVersion, + HttpVersion10 as HttpVersion10, + HttpVersion11 as HttpVersion11, + StreamWriter as StreamWriter, +) + +__all__ = ( + "HttpProcessingError", + "RESPONSES", + "SERVER_SOFTWARE", + # .http_writer + "StreamWriter", + "HttpVersion", + "HttpVersion10", + "HttpVersion11", + # .http_parser + "HeadersParser", + "HttpParser", + "HttpRequestParser", + "HttpResponseParser", + "RawRequestMessage", + "RawResponseMessage", + # .http_websocket + "WS_CLOSED_MESSAGE", + "WS_CLOSING_MESSAGE", + "WS_KEY", + "WebSocketReader", + "WebSocketWriter", + "ws_ext_gen", + "ws_ext_parse", + "WSMessage", + "WebSocketError", + "WSMsgType", + "WSCloseCode", +) + + +SERVER_SOFTWARE: str = "Python/{0[0]}.{0[1]} aiohttp/{1}".format( + sys.version_info, __version__ +) + +RESPONSES: Mapping[int, Tuple[str, str]] = { + v: (v.phrase, v.description) for v in HTTPStatus.__members__.values() +} diff --git a/evalkit_cambrian/lib/python3.10/site-packages/aiohttp/http_parser.py b/evalkit_cambrian/lib/python3.10/site-packages/aiohttp/http_parser.py new file mode 100644 index 0000000000000000000000000000000000000000..1b8b5b4d49e1cc14a9d9659da7f5533d77a44cc7 --- /dev/null +++ b/evalkit_cambrian/lib/python3.10/site-packages/aiohttp/http_parser.py @@ -0,0 +1,1046 @@ +import abc +import asyncio +import re +import string +from contextlib import suppress +from enum import IntEnum +from typing import ( + Any, + ClassVar, + Final, + Generic, + List, + Literal, + NamedTuple, + Optional, + Pattern, + Set, + Tuple, + Type, + TypeVar, + Union, +) + +from multidict import CIMultiDict, CIMultiDictProxy, istr +from yarl import URL + +from . import hdrs +from .base_protocol import BaseProtocol +from .compression_utils import HAS_BROTLI, BrotliDecompressor, ZLibDecompressor +from .helpers import ( + _EXC_SENTINEL, + DEBUG, + EMPTY_BODY_METHODS, + EMPTY_BODY_STATUS_CODES, + NO_EXTENSIONS, + BaseTimerContext, + set_exception, +) +from .http_exceptions import ( + BadHttpMessage, + BadHttpMethod, + BadStatusLine, + ContentEncodingError, + ContentLengthError, + InvalidHeader, + InvalidURLError, + LineTooLong, + TransferEncodingError, +) +from .http_writer import HttpVersion, HttpVersion10 +from .streams import EMPTY_PAYLOAD, StreamReader +from .typedefs import RawHeaders + +__all__ = ( + "HeadersParser", + "HttpParser", + "HttpRequestParser", + "HttpResponseParser", + "RawRequestMessage", + "RawResponseMessage", +) + +_SEP = Literal[b"\r\n", b"\n"] + +ASCIISET: Final[Set[str]] = set(string.printable) + +# See https://www.rfc-editor.org/rfc/rfc9110.html#name-overview +# and https://www.rfc-editor.org/rfc/rfc9110.html#name-tokens +# +# method = token +# tchar = "!" / "#" / "$" / "%" / "&" / "'" / "*" / "+" / "-" / "." / +# "^" / "_" / "`" / "|" / "~" / DIGIT / ALPHA +# token = 1*tchar +_TCHAR_SPECIALS: Final[str] = re.escape("!#$%&'*+-.^_`|~") +TOKENRE: Final[Pattern[str]] = re.compile(f"[0-9A-Za-z{_TCHAR_SPECIALS}]+") +VERSRE: Final[Pattern[str]] = re.compile(r"HTTP/(\d)\.(\d)", re.ASCII) +DIGITS: Final[Pattern[str]] = re.compile(r"\d+", re.ASCII) +HEXDIGITS: Final[Pattern[bytes]] = re.compile(rb"[0-9a-fA-F]+") + + +class RawRequestMessage(NamedTuple): + method: str + path: str + version: HttpVersion + headers: "CIMultiDictProxy[str]" + raw_headers: RawHeaders + should_close: bool + compression: Optional[str] + upgrade: bool + chunked: bool + url: URL + + +class RawResponseMessage(NamedTuple): + version: HttpVersion + code: int + reason: str + headers: CIMultiDictProxy[str] + raw_headers: RawHeaders + should_close: bool + compression: Optional[str] + upgrade: bool + chunked: bool + + +_MsgT = TypeVar("_MsgT", RawRequestMessage, RawResponseMessage) + + +class ParseState(IntEnum): + + PARSE_NONE = 0 + PARSE_LENGTH = 1 + PARSE_CHUNKED = 2 + PARSE_UNTIL_EOF = 3 + + +class ChunkState(IntEnum): + PARSE_CHUNKED_SIZE = 0 + PARSE_CHUNKED_CHUNK = 1 + PARSE_CHUNKED_CHUNK_EOF = 2 + PARSE_MAYBE_TRAILERS = 3 + PARSE_TRAILERS = 4 + + +class HeadersParser: + def __init__( + self, + max_line_size: int = 8190, + max_headers: int = 32768, + max_field_size: int = 8190, + lax: bool = False, + ) -> None: + self.max_line_size = max_line_size + self.max_headers = max_headers + self.max_field_size = max_field_size + self._lax = lax + + def parse_headers( + self, lines: List[bytes] + ) -> Tuple["CIMultiDictProxy[str]", RawHeaders]: + headers: CIMultiDict[str] = CIMultiDict() + # note: "raw" does not mean inclusion of OWS before/after the field value + raw_headers = [] + + lines_idx = 1 + line = lines[1] + line_count = len(lines) + + while line: + # Parse initial header name : value pair. + try: + bname, bvalue = line.split(b":", 1) + except ValueError: + raise InvalidHeader(line) from None + + if len(bname) == 0: + raise InvalidHeader(bname) + + # https://www.rfc-editor.org/rfc/rfc9112.html#section-5.1-2 + if {bname[0], bname[-1]} & {32, 9}: # {" ", "\t"} + raise InvalidHeader(line) + + bvalue = bvalue.lstrip(b" \t") + if len(bname) > self.max_field_size: + raise LineTooLong( + "request header name {}".format( + bname.decode("utf8", "backslashreplace") + ), + str(self.max_field_size), + str(len(bname)), + ) + name = bname.decode("utf-8", "surrogateescape") + if not TOKENRE.fullmatch(name): + raise InvalidHeader(bname) + + header_length = len(bvalue) + + # next line + lines_idx += 1 + line = lines[lines_idx] + + # consume continuation lines + continuation = self._lax and line and line[0] in (32, 9) # (' ', '\t') + + # Deprecated: https://www.rfc-editor.org/rfc/rfc9112.html#name-obsolete-line-folding + if continuation: + bvalue_lst = [bvalue] + while continuation: + header_length += len(line) + if header_length > self.max_field_size: + raise LineTooLong( + "request header field {}".format( + bname.decode("utf8", "backslashreplace") + ), + str(self.max_field_size), + str(header_length), + ) + bvalue_lst.append(line) + + # next line + lines_idx += 1 + if lines_idx < line_count: + line = lines[lines_idx] + if line: + continuation = line[0] in (32, 9) # (' ', '\t') + else: + line = b"" + break + bvalue = b"".join(bvalue_lst) + else: + if header_length > self.max_field_size: + raise LineTooLong( + "request header field {}".format( + bname.decode("utf8", "backslashreplace") + ), + str(self.max_field_size), + str(header_length), + ) + + bvalue = bvalue.strip(b" \t") + value = bvalue.decode("utf-8", "surrogateescape") + + # https://www.rfc-editor.org/rfc/rfc9110.html#section-5.5-5 + if "\n" in value or "\r" in value or "\x00" in value: + raise InvalidHeader(bvalue) + + headers.add(name, value) + raw_headers.append((bname, bvalue)) + + return (CIMultiDictProxy(headers), tuple(raw_headers)) + + +def _is_supported_upgrade(headers: CIMultiDictProxy[str]) -> bool: + """Check if the upgrade header is supported.""" + return headers.get(hdrs.UPGRADE, "").lower() in {"tcp", "websocket"} + + +class HttpParser(abc.ABC, Generic[_MsgT]): + lax: ClassVar[bool] = False + + def __init__( + self, + protocol: Optional[BaseProtocol] = None, + loop: Optional[asyncio.AbstractEventLoop] = None, + limit: int = 2**16, + max_line_size: int = 8190, + max_headers: int = 32768, + max_field_size: int = 8190, + timer: Optional[BaseTimerContext] = None, + code: Optional[int] = None, + method: Optional[str] = None, + payload_exception: Optional[Type[BaseException]] = None, + response_with_body: bool = True, + read_until_eof: bool = False, + auto_decompress: bool = True, + ) -> None: + self.protocol = protocol + self.loop = loop + self.max_line_size = max_line_size + self.max_headers = max_headers + self.max_field_size = max_field_size + self.timer = timer + self.code = code + self.method = method + self.payload_exception = payload_exception + self.response_with_body = response_with_body + self.read_until_eof = read_until_eof + + self._lines: List[bytes] = [] + self._tail = b"" + self._upgraded = False + self._payload = None + self._payload_parser: Optional[HttpPayloadParser] = None + self._auto_decompress = auto_decompress + self._limit = limit + self._headers_parser = HeadersParser( + max_line_size, max_headers, max_field_size, self.lax + ) + + @abc.abstractmethod + def parse_message(self, lines: List[bytes]) -> _MsgT: ... + + @abc.abstractmethod + def _is_chunked_te(self, te: str) -> bool: ... + + def feed_eof(self) -> Optional[_MsgT]: + if self._payload_parser is not None: + self._payload_parser.feed_eof() + self._payload_parser = None + else: + # try to extract partial message + if self._tail: + self._lines.append(self._tail) + + if self._lines: + if self._lines[-1] != "\r\n": + self._lines.append(b"") + with suppress(Exception): + return self.parse_message(self._lines) + return None + + def feed_data( + self, + data: bytes, + SEP: _SEP = b"\r\n", + EMPTY: bytes = b"", + CONTENT_LENGTH: istr = hdrs.CONTENT_LENGTH, + METH_CONNECT: str = hdrs.METH_CONNECT, + SEC_WEBSOCKET_KEY1: istr = hdrs.SEC_WEBSOCKET_KEY1, + ) -> Tuple[List[Tuple[_MsgT, StreamReader]], bool, bytes]: + + messages = [] + + if self._tail: + data, self._tail = self._tail + data, b"" + + data_len = len(data) + start_pos = 0 + loop = self.loop + + should_close = False + while start_pos < data_len: + + # read HTTP message (request/response line + headers), \r\n\r\n + # and split by lines + if self._payload_parser is None and not self._upgraded: + pos = data.find(SEP, start_pos) + # consume \r\n + if pos == start_pos and not self._lines: + start_pos = pos + len(SEP) + continue + + if pos >= start_pos: + if should_close: + raise BadHttpMessage("Data after `Connection: close`") + + # line found + line = data[start_pos:pos] + if SEP == b"\n": # For lax response parsing + line = line.rstrip(b"\r") + self._lines.append(line) + start_pos = pos + len(SEP) + + # \r\n\r\n found + if self._lines[-1] == EMPTY: + try: + msg: _MsgT = self.parse_message(self._lines) + finally: + self._lines.clear() + + def get_content_length() -> Optional[int]: + # payload length + length_hdr = msg.headers.get(CONTENT_LENGTH) + if length_hdr is None: + return None + + # Shouldn't allow +/- or other number formats. + # https://www.rfc-editor.org/rfc/rfc9110#section-8.6-2 + # msg.headers is already stripped of leading/trailing wsp + if not DIGITS.fullmatch(length_hdr): + raise InvalidHeader(CONTENT_LENGTH) + + return int(length_hdr) + + length = get_content_length() + # do not support old websocket spec + if SEC_WEBSOCKET_KEY1 in msg.headers: + raise InvalidHeader(SEC_WEBSOCKET_KEY1) + + self._upgraded = msg.upgrade and _is_supported_upgrade( + msg.headers + ) + + method = getattr(msg, "method", self.method) + # code is only present on responses + code = getattr(msg, "code", 0) + + assert self.protocol is not None + # calculate payload + empty_body = code in EMPTY_BODY_STATUS_CODES or bool( + method and method in EMPTY_BODY_METHODS + ) + if not empty_body and ( + ((length is not None and length > 0) or msg.chunked) + and not self._upgraded + ): + payload = StreamReader( + self.protocol, + timer=self.timer, + loop=loop, + limit=self._limit, + ) + payload_parser = HttpPayloadParser( + payload, + length=length, + chunked=msg.chunked, + method=method, + compression=msg.compression, + code=self.code, + response_with_body=self.response_with_body, + auto_decompress=self._auto_decompress, + lax=self.lax, + ) + if not payload_parser.done: + self._payload_parser = payload_parser + elif method == METH_CONNECT: + assert isinstance(msg, RawRequestMessage) + payload = StreamReader( + self.protocol, + timer=self.timer, + loop=loop, + limit=self._limit, + ) + self._upgraded = True + self._payload_parser = HttpPayloadParser( + payload, + method=msg.method, + compression=msg.compression, + auto_decompress=self._auto_decompress, + lax=self.lax, + ) + elif not empty_body and length is None and self.read_until_eof: + payload = StreamReader( + self.protocol, + timer=self.timer, + loop=loop, + limit=self._limit, + ) + payload_parser = HttpPayloadParser( + payload, + length=length, + chunked=msg.chunked, + method=method, + compression=msg.compression, + code=self.code, + response_with_body=self.response_with_body, + auto_decompress=self._auto_decompress, + lax=self.lax, + ) + if not payload_parser.done: + self._payload_parser = payload_parser + else: + payload = EMPTY_PAYLOAD + + messages.append((msg, payload)) + should_close = msg.should_close + else: + self._tail = data[start_pos:] + data = EMPTY + break + + # no parser, just store + elif self._payload_parser is None and self._upgraded: + assert not self._lines + break + + # feed payload + elif data and start_pos < data_len: + assert not self._lines + assert self._payload_parser is not None + try: + eof, data = self._payload_parser.feed_data(data[start_pos:], SEP) + except BaseException as underlying_exc: + reraised_exc = underlying_exc + if self.payload_exception is not None: + reraised_exc = self.payload_exception(str(underlying_exc)) + + set_exception( + self._payload_parser.payload, + reraised_exc, + underlying_exc, + ) + + eof = True + data = b"" + + if eof: + start_pos = 0 + data_len = len(data) + self._payload_parser = None + continue + else: + break + + if data and start_pos < data_len: + data = data[start_pos:] + else: + data = EMPTY + + return messages, self._upgraded, data + + def parse_headers( + self, lines: List[bytes] + ) -> Tuple[ + "CIMultiDictProxy[str]", RawHeaders, Optional[bool], Optional[str], bool, bool + ]: + """Parses RFC 5322 headers from a stream. + + Line continuations are supported. Returns list of header name + and value pairs. Header name is in upper case. + """ + headers, raw_headers = self._headers_parser.parse_headers(lines) + close_conn = None + encoding = None + upgrade = False + chunked = False + + # https://www.rfc-editor.org/rfc/rfc9110.html#section-5.5-6 + # https://www.rfc-editor.org/rfc/rfc9110.html#name-collected-abnf + singletons = ( + hdrs.CONTENT_LENGTH, + hdrs.CONTENT_LOCATION, + hdrs.CONTENT_RANGE, + hdrs.CONTENT_TYPE, + hdrs.ETAG, + hdrs.HOST, + hdrs.MAX_FORWARDS, + hdrs.SERVER, + hdrs.TRANSFER_ENCODING, + hdrs.USER_AGENT, + ) + bad_hdr = next((h for h in singletons if len(headers.getall(h, ())) > 1), None) + if bad_hdr is not None: + raise BadHttpMessage(f"Duplicate '{bad_hdr}' header found.") + + # keep-alive + conn = headers.get(hdrs.CONNECTION) + if conn: + v = conn.lower() + if v == "close": + close_conn = True + elif v == "keep-alive": + close_conn = False + # https://www.rfc-editor.org/rfc/rfc9110.html#name-101-switching-protocols + elif v == "upgrade" and headers.get(hdrs.UPGRADE): + upgrade = True + + # encoding + enc = headers.get(hdrs.CONTENT_ENCODING) + if enc: + enc = enc.lower() + if enc in ("gzip", "deflate", "br"): + encoding = enc + + # chunking + te = headers.get(hdrs.TRANSFER_ENCODING) + if te is not None: + if self._is_chunked_te(te): + chunked = True + + if hdrs.CONTENT_LENGTH in headers: + raise BadHttpMessage( + "Transfer-Encoding can't be present with Content-Length", + ) + + return (headers, raw_headers, close_conn, encoding, upgrade, chunked) + + def set_upgraded(self, val: bool) -> None: + """Set connection upgraded (to websocket) mode. + + :param bool val: new state. + """ + self._upgraded = val + + +class HttpRequestParser(HttpParser[RawRequestMessage]): + """Read request status line. + + Exception .http_exceptions.BadStatusLine + could be raised in case of any errors in status line. + Returns RawRequestMessage. + """ + + def parse_message(self, lines: List[bytes]) -> RawRequestMessage: + # request line + line = lines[0].decode("utf-8", "surrogateescape") + try: + method, path, version = line.split(" ", maxsplit=2) + except ValueError: + raise BadHttpMethod(line) from None + + if len(path) > self.max_line_size: + raise LineTooLong( + "Status line is too long", str(self.max_line_size), str(len(path)) + ) + + # method + if not TOKENRE.fullmatch(method): + raise BadHttpMethod(method) + + # version + match = VERSRE.fullmatch(version) + if match is None: + raise BadStatusLine(line) + version_o = HttpVersion(int(match.group(1)), int(match.group(2))) + + if method == "CONNECT": + # authority-form, + # https://datatracker.ietf.org/doc/html/rfc7230#section-5.3.3 + url = URL.build(authority=path, encoded=True) + elif path.startswith("/"): + # origin-form, + # https://datatracker.ietf.org/doc/html/rfc7230#section-5.3.1 + path_part, _hash_separator, url_fragment = path.partition("#") + path_part, _question_mark_separator, qs_part = path_part.partition("?") + + # NOTE: `yarl.URL.build()` is used to mimic what the Cython-based + # NOTE: parser does, otherwise it results into the same + # NOTE: HTTP Request-Line input producing different + # NOTE: `yarl.URL()` objects + url = URL.build( + path=path_part, + query_string=qs_part, + fragment=url_fragment, + encoded=True, + ) + elif path == "*" and method == "OPTIONS": + # asterisk-form, + url = URL(path, encoded=True) + else: + # absolute-form for proxy maybe, + # https://datatracker.ietf.org/doc/html/rfc7230#section-5.3.2 + url = URL(path, encoded=True) + if url.scheme == "": + # not absolute-form + raise InvalidURLError( + path.encode(errors="surrogateescape").decode("latin1") + ) + + # read headers + ( + headers, + raw_headers, + close, + compression, + upgrade, + chunked, + ) = self.parse_headers(lines) + + if close is None: # then the headers weren't set in the request + if version_o <= HttpVersion10: # HTTP 1.0 must asks to not close + close = True + else: # HTTP 1.1 must ask to close. + close = False + + return RawRequestMessage( + method, + path, + version_o, + headers, + raw_headers, + close, + compression, + upgrade, + chunked, + url, + ) + + def _is_chunked_te(self, te: str) -> bool: + if te.rsplit(",", maxsplit=1)[-1].strip(" \t").lower() == "chunked": + return True + # https://www.rfc-editor.org/rfc/rfc9112#section-6.3-2.4.3 + raise BadHttpMessage("Request has invalid `Transfer-Encoding`") + + +class HttpResponseParser(HttpParser[RawResponseMessage]): + """Read response status line and headers. + + BadStatusLine could be raised in case of any errors in status line. + Returns RawResponseMessage. + """ + + # Lax mode should only be enabled on response parser. + lax = not DEBUG + + def feed_data( + self, + data: bytes, + SEP: Optional[_SEP] = None, + *args: Any, + **kwargs: Any, + ) -> Tuple[List[Tuple[RawResponseMessage, StreamReader]], bool, bytes]: + if SEP is None: + SEP = b"\r\n" if DEBUG else b"\n" + return super().feed_data(data, SEP, *args, **kwargs) + + def parse_message(self, lines: List[bytes]) -> RawResponseMessage: + line = lines[0].decode("utf-8", "surrogateescape") + try: + version, status = line.split(maxsplit=1) + except ValueError: + raise BadStatusLine(line) from None + + try: + status, reason = status.split(maxsplit=1) + except ValueError: + status = status.strip() + reason = "" + + if len(reason) > self.max_line_size: + raise LineTooLong( + "Status line is too long", str(self.max_line_size), str(len(reason)) + ) + + # version + match = VERSRE.fullmatch(version) + if match is None: + raise BadStatusLine(line) + version_o = HttpVersion(int(match.group(1)), int(match.group(2))) + + # The status code is a three-digit ASCII number, no padding + if len(status) != 3 or not DIGITS.fullmatch(status): + raise BadStatusLine(line) + status_i = int(status) + + # read headers + ( + headers, + raw_headers, + close, + compression, + upgrade, + chunked, + ) = self.parse_headers(lines) + + if close is None: + if version_o <= HttpVersion10: + close = True + # https://www.rfc-editor.org/rfc/rfc9112.html#name-message-body-length + elif 100 <= status_i < 200 or status_i in {204, 304}: + close = False + elif hdrs.CONTENT_LENGTH in headers or hdrs.TRANSFER_ENCODING in headers: + close = False + else: + # https://www.rfc-editor.org/rfc/rfc9112.html#section-6.3-2.8 + close = True + + return RawResponseMessage( + version_o, + status_i, + reason.strip(), + headers, + raw_headers, + close, + compression, + upgrade, + chunked, + ) + + def _is_chunked_te(self, te: str) -> bool: + # https://www.rfc-editor.org/rfc/rfc9112#section-6.3-2.4.2 + return te.rsplit(",", maxsplit=1)[-1].strip(" \t").lower() == "chunked" + + +class HttpPayloadParser: + def __init__( + self, + payload: StreamReader, + length: Optional[int] = None, + chunked: bool = False, + compression: Optional[str] = None, + code: Optional[int] = None, + method: Optional[str] = None, + response_with_body: bool = True, + auto_decompress: bool = True, + lax: bool = False, + ) -> None: + self._length = 0 + self._type = ParseState.PARSE_UNTIL_EOF + self._chunk = ChunkState.PARSE_CHUNKED_SIZE + self._chunk_size = 0 + self._chunk_tail = b"" + self._auto_decompress = auto_decompress + self._lax = lax + self.done = False + + # payload decompression wrapper + if response_with_body and compression and self._auto_decompress: + real_payload: Union[StreamReader, DeflateBuffer] = DeflateBuffer( + payload, compression + ) + else: + real_payload = payload + + # payload parser + if not response_with_body: + # don't parse payload if it's not expected to be received + self._type = ParseState.PARSE_NONE + real_payload.feed_eof() + self.done = True + elif chunked: + self._type = ParseState.PARSE_CHUNKED + elif length is not None: + self._type = ParseState.PARSE_LENGTH + self._length = length + if self._length == 0: + real_payload.feed_eof() + self.done = True + + self.payload = real_payload + + def feed_eof(self) -> None: + if self._type == ParseState.PARSE_UNTIL_EOF: + self.payload.feed_eof() + elif self._type == ParseState.PARSE_LENGTH: + raise ContentLengthError( + "Not enough data for satisfy content length header." + ) + elif self._type == ParseState.PARSE_CHUNKED: + raise TransferEncodingError( + "Not enough data for satisfy transfer length header." + ) + + def feed_data( + self, chunk: bytes, SEP: _SEP = b"\r\n", CHUNK_EXT: bytes = b";" + ) -> Tuple[bool, bytes]: + # Read specified amount of bytes + if self._type == ParseState.PARSE_LENGTH: + required = self._length + chunk_len = len(chunk) + + if required >= chunk_len: + self._length = required - chunk_len + self.payload.feed_data(chunk, chunk_len) + if self._length == 0: + self.payload.feed_eof() + return True, b"" + else: + self._length = 0 + self.payload.feed_data(chunk[:required], required) + self.payload.feed_eof() + return True, chunk[required:] + + # Chunked transfer encoding parser + elif self._type == ParseState.PARSE_CHUNKED: + if self._chunk_tail: + chunk = self._chunk_tail + chunk + self._chunk_tail = b"" + + while chunk: + + # read next chunk size + if self._chunk == ChunkState.PARSE_CHUNKED_SIZE: + pos = chunk.find(SEP) + if pos >= 0: + i = chunk.find(CHUNK_EXT, 0, pos) + if i >= 0: + size_b = chunk[:i] # strip chunk-extensions + # Verify no LF in the chunk-extension + if b"\n" in (ext := chunk[i:pos]): + exc = BadHttpMessage( + f"Unexpected LF in chunk-extension: {ext!r}" + ) + set_exception(self.payload, exc) + raise exc + else: + size_b = chunk[:pos] + + if self._lax: # Allow whitespace in lax mode. + size_b = size_b.strip() + + if not re.fullmatch(HEXDIGITS, size_b): + exc = TransferEncodingError( + chunk[:pos].decode("ascii", "surrogateescape") + ) + set_exception(self.payload, exc) + raise exc + size = int(bytes(size_b), 16) + + chunk = chunk[pos + len(SEP) :] + if size == 0: # eof marker + self._chunk = ChunkState.PARSE_MAYBE_TRAILERS + if self._lax and chunk.startswith(b"\r"): + chunk = chunk[1:] + else: + self._chunk = ChunkState.PARSE_CHUNKED_CHUNK + self._chunk_size = size + self.payload.begin_http_chunk_receiving() + else: + self._chunk_tail = chunk + return False, b"" + + # read chunk and feed buffer + if self._chunk == ChunkState.PARSE_CHUNKED_CHUNK: + required = self._chunk_size + chunk_len = len(chunk) + + if required > chunk_len: + self._chunk_size = required - chunk_len + self.payload.feed_data(chunk, chunk_len) + return False, b"" + else: + self._chunk_size = 0 + self.payload.feed_data(chunk[:required], required) + chunk = chunk[required:] + self._chunk = ChunkState.PARSE_CHUNKED_CHUNK_EOF + self.payload.end_http_chunk_receiving() + + # toss the CRLF at the end of the chunk + if self._chunk == ChunkState.PARSE_CHUNKED_CHUNK_EOF: + if self._lax and chunk.startswith(b"\r"): + chunk = chunk[1:] + if chunk[: len(SEP)] == SEP: + chunk = chunk[len(SEP) :] + self._chunk = ChunkState.PARSE_CHUNKED_SIZE + else: + self._chunk_tail = chunk + return False, b"" + + # if stream does not contain trailer, after 0\r\n + # we should get another \r\n otherwise + # trailers needs to be skipped until \r\n\r\n + if self._chunk == ChunkState.PARSE_MAYBE_TRAILERS: + head = chunk[: len(SEP)] + if head == SEP: + # end of stream + self.payload.feed_eof() + return True, chunk[len(SEP) :] + # Both CR and LF, or only LF may not be received yet. It is + # expected that CRLF or LF will be shown at the very first + # byte next time, otherwise trailers should come. The last + # CRLF which marks the end of response might not be + # contained in the same TCP segment which delivered the + # size indicator. + if not head: + return False, b"" + if head == SEP[:1]: + self._chunk_tail = head + return False, b"" + self._chunk = ChunkState.PARSE_TRAILERS + + # read and discard trailer up to the CRLF terminator + if self._chunk == ChunkState.PARSE_TRAILERS: + pos = chunk.find(SEP) + if pos >= 0: + chunk = chunk[pos + len(SEP) :] + self._chunk = ChunkState.PARSE_MAYBE_TRAILERS + else: + self._chunk_tail = chunk + return False, b"" + + # Read all bytes until eof + elif self._type == ParseState.PARSE_UNTIL_EOF: + self.payload.feed_data(chunk, len(chunk)) + + return False, b"" + + +class DeflateBuffer: + """DeflateStream decompress stream and feed data into specified stream.""" + + decompressor: Any + + def __init__(self, out: StreamReader, encoding: Optional[str]) -> None: + self.out = out + self.size = 0 + self.encoding = encoding + self._started_decoding = False + + self.decompressor: Union[BrotliDecompressor, ZLibDecompressor] + if encoding == "br": + if not HAS_BROTLI: # pragma: no cover + raise ContentEncodingError( + "Can not decode content-encoding: brotli (br). " + "Please install `Brotli`" + ) + self.decompressor = BrotliDecompressor() + else: + self.decompressor = ZLibDecompressor(encoding=encoding) + + def set_exception( + self, + exc: BaseException, + exc_cause: BaseException = _EXC_SENTINEL, + ) -> None: + set_exception(self.out, exc, exc_cause) + + def feed_data(self, chunk: bytes, size: int) -> None: + if not size: + return + + self.size += size + + # RFC1950 + # bits 0..3 = CM = 0b1000 = 8 = "deflate" + # bits 4..7 = CINFO = 1..7 = windows size. + if ( + not self._started_decoding + and self.encoding == "deflate" + and chunk[0] & 0xF != 8 + ): + # Change the decoder to decompress incorrectly compressed data + # Actually we should issue a warning about non-RFC-compliant data. + self.decompressor = ZLibDecompressor( + encoding=self.encoding, suppress_deflate_header=True + ) + + try: + chunk = self.decompressor.decompress_sync(chunk) + except Exception: + raise ContentEncodingError( + "Can not decode content-encoding: %s" % self.encoding + ) + + self._started_decoding = True + + if chunk: + self.out.feed_data(chunk, len(chunk)) + + def feed_eof(self) -> None: + chunk = self.decompressor.flush() + + if chunk or self.size > 0: + self.out.feed_data(chunk, len(chunk)) + if self.encoding == "deflate" and not self.decompressor.eof: + raise ContentEncodingError("deflate") + + self.out.feed_eof() + + def begin_http_chunk_receiving(self) -> None: + self.out.begin_http_chunk_receiving() + + def end_http_chunk_receiving(self) -> None: + self.out.end_http_chunk_receiving() + + +HttpRequestParserPy = HttpRequestParser +HttpResponseParserPy = HttpResponseParser +RawRequestMessagePy = RawRequestMessage +RawResponseMessagePy = RawResponseMessage + +try: + if not NO_EXTENSIONS: + from ._http_parser import ( # type: ignore[import-not-found,no-redef] + HttpRequestParser, + HttpResponseParser, + RawRequestMessage, + RawResponseMessage, + ) + + HttpRequestParserC = HttpRequestParser + HttpResponseParserC = HttpResponseParser + RawRequestMessageC = RawRequestMessage + RawResponseMessageC = RawResponseMessage +except ImportError: # pragma: no cover + pass diff --git a/evalkit_cambrian/lib/python3.10/site-packages/aiohttp/payload.py b/evalkit_cambrian/lib/python3.10/site-packages/aiohttp/payload.py new file mode 100644 index 0000000000000000000000000000000000000000..3f6d3672db29a7f4f580c8a7a5e1fb1ac29ebf2d --- /dev/null +++ b/evalkit_cambrian/lib/python3.10/site-packages/aiohttp/payload.py @@ -0,0 +1,519 @@ +import asyncio +import enum +import io +import json +import mimetypes +import os +import sys +import warnings +from abc import ABC, abstractmethod +from itertools import chain +from typing import ( + IO, + TYPE_CHECKING, + Any, + Dict, + Final, + Iterable, + Optional, + TextIO, + Tuple, + Type, + Union, +) + +from multidict import CIMultiDict + +from . import hdrs +from .abc import AbstractStreamWriter +from .helpers import ( + _SENTINEL, + content_disposition_header, + guess_filename, + parse_mimetype, + sentinel, +) +from .streams import StreamReader +from .typedefs import JSONEncoder, _CIMultiDict + +__all__ = ( + "PAYLOAD_REGISTRY", + "get_payload", + "payload_type", + "Payload", + "BytesPayload", + "StringPayload", + "IOBasePayload", + "BytesIOPayload", + "BufferedReaderPayload", + "TextIOPayload", + "StringIOPayload", + "JsonPayload", + "AsyncIterablePayload", +) + +TOO_LARGE_BYTES_BODY: Final[int] = 2**20 # 1 MB + +if TYPE_CHECKING: + from typing import List + + +class LookupError(Exception): + pass + + +class Order(str, enum.Enum): + normal = "normal" + try_first = "try_first" + try_last = "try_last" + + +def get_payload(data: Any, *args: Any, **kwargs: Any) -> "Payload": + return PAYLOAD_REGISTRY.get(data, *args, **kwargs) + + +def register_payload( + factory: Type["Payload"], type: Any, *, order: Order = Order.normal +) -> None: + PAYLOAD_REGISTRY.register(factory, type, order=order) + + +class payload_type: + def __init__(self, type: Any, *, order: Order = Order.normal) -> None: + self.type = type + self.order = order + + def __call__(self, factory: Type["Payload"]) -> Type["Payload"]: + register_payload(factory, self.type, order=self.order) + return factory + + +PayloadType = Type["Payload"] +_PayloadRegistryItem = Tuple[PayloadType, Any] + + +class PayloadRegistry: + """Payload registry. + + note: we need zope.interface for more efficient adapter search + """ + + __slots__ = ("_first", "_normal", "_last", "_normal_lookup") + + def __init__(self) -> None: + self._first: List[_PayloadRegistryItem] = [] + self._normal: List[_PayloadRegistryItem] = [] + self._last: List[_PayloadRegistryItem] = [] + self._normal_lookup: Dict[Any, PayloadType] = {} + + def get( + self, + data: Any, + *args: Any, + _CHAIN: "Type[chain[_PayloadRegistryItem]]" = chain, + **kwargs: Any, + ) -> "Payload": + if self._first: + for factory, type_ in self._first: + if isinstance(data, type_): + return factory(data, *args, **kwargs) + # Try the fast lookup first + if lookup_factory := self._normal_lookup.get(type(data)): + return lookup_factory(data, *args, **kwargs) + # Bail early if its already a Payload + if isinstance(data, Payload): + return data + # Fallback to the slower linear search + for factory, type_ in _CHAIN(self._normal, self._last): + if isinstance(data, type_): + return factory(data, *args, **kwargs) + raise LookupError() + + def register( + self, factory: PayloadType, type: Any, *, order: Order = Order.normal + ) -> None: + if order is Order.try_first: + self._first.append((factory, type)) + elif order is Order.normal: + self._normal.append((factory, type)) + if isinstance(type, Iterable): + for t in type: + self._normal_lookup[t] = factory + else: + self._normal_lookup[type] = factory + elif order is Order.try_last: + self._last.append((factory, type)) + else: + raise ValueError(f"Unsupported order {order!r}") + + +class Payload(ABC): + + _default_content_type: str = "application/octet-stream" + _size: Optional[int] = None + + def __init__( + self, + value: Any, + headers: Optional[ + Union[_CIMultiDict, Dict[str, str], Iterable[Tuple[str, str]]] + ] = None, + content_type: Union[str, None, _SENTINEL] = sentinel, + filename: Optional[str] = None, + encoding: Optional[str] = None, + **kwargs: Any, + ) -> None: + self._encoding = encoding + self._filename = filename + self._headers: _CIMultiDict = CIMultiDict() + self._value = value + if content_type is not sentinel and content_type is not None: + self._headers[hdrs.CONTENT_TYPE] = content_type + elif self._filename is not None: + if sys.version_info >= (3, 13): + guesser = mimetypes.guess_file_type + else: + guesser = mimetypes.guess_type + content_type = guesser(self._filename)[0] + if content_type is None: + content_type = self._default_content_type + self._headers[hdrs.CONTENT_TYPE] = content_type + else: + self._headers[hdrs.CONTENT_TYPE] = self._default_content_type + if headers: + self._headers.update(headers) + + @property + def size(self) -> Optional[int]: + """Size of the payload.""" + return self._size + + @property + def filename(self) -> Optional[str]: + """Filename of the payload.""" + return self._filename + + @property + def headers(self) -> _CIMultiDict: + """Custom item headers""" + return self._headers + + @property + def _binary_headers(self) -> bytes: + return ( + "".join([k + ": " + v + "\r\n" for k, v in self.headers.items()]).encode( + "utf-8" + ) + + b"\r\n" + ) + + @property + def encoding(self) -> Optional[str]: + """Payload encoding""" + return self._encoding + + @property + def content_type(self) -> str: + """Content type""" + return self._headers[hdrs.CONTENT_TYPE] + + def set_content_disposition( + self, + disptype: str, + quote_fields: bool = True, + _charset: str = "utf-8", + **params: Any, + ) -> None: + """Sets ``Content-Disposition`` header.""" + self._headers[hdrs.CONTENT_DISPOSITION] = content_disposition_header( + disptype, quote_fields=quote_fields, _charset=_charset, **params + ) + + @abstractmethod + def decode(self, encoding: str = "utf-8", errors: str = "strict") -> str: + """Return string representation of the value. + + This is named decode() to allow compatibility with bytes objects. + """ + + @abstractmethod + async def write(self, writer: AbstractStreamWriter) -> None: + """Write payload. + + writer is an AbstractStreamWriter instance: + """ + + +class BytesPayload(Payload): + _value: bytes + + def __init__( + self, value: Union[bytes, bytearray, memoryview], *args: Any, **kwargs: Any + ) -> None: + if "content_type" not in kwargs: + kwargs["content_type"] = "application/octet-stream" + + super().__init__(value, *args, **kwargs) + + if isinstance(value, memoryview): + self._size = value.nbytes + elif isinstance(value, (bytes, bytearray)): + self._size = len(value) + else: + raise TypeError(f"value argument must be byte-ish, not {type(value)!r}") + + if self._size > TOO_LARGE_BYTES_BODY: + kwargs = {"source": self} + warnings.warn( + "Sending a large body directly with raw bytes might" + " lock the event loop. You should probably pass an " + "io.BytesIO object instead", + ResourceWarning, + **kwargs, + ) + + def decode(self, encoding: str = "utf-8", errors: str = "strict") -> str: + return self._value.decode(encoding, errors) + + async def write(self, writer: AbstractStreamWriter) -> None: + await writer.write(self._value) + + +class StringPayload(BytesPayload): + def __init__( + self, + value: str, + *args: Any, + encoding: Optional[str] = None, + content_type: Optional[str] = None, + **kwargs: Any, + ) -> None: + + if encoding is None: + if content_type is None: + real_encoding = "utf-8" + content_type = "text/plain; charset=utf-8" + else: + mimetype = parse_mimetype(content_type) + real_encoding = mimetype.parameters.get("charset", "utf-8") + else: + if content_type is None: + content_type = "text/plain; charset=%s" % encoding + real_encoding = encoding + + super().__init__( + value.encode(real_encoding), + encoding=real_encoding, + content_type=content_type, + *args, + **kwargs, + ) + + +class StringIOPayload(StringPayload): + def __init__(self, value: IO[str], *args: Any, **kwargs: Any) -> None: + super().__init__(value.read(), *args, **kwargs) + + +class IOBasePayload(Payload): + _value: io.IOBase + + def __init__( + self, value: IO[Any], disposition: str = "attachment", *args: Any, **kwargs: Any + ) -> None: + if "filename" not in kwargs: + kwargs["filename"] = guess_filename(value) + + super().__init__(value, *args, **kwargs) + + if self._filename is not None and disposition is not None: + if hdrs.CONTENT_DISPOSITION not in self.headers: + self.set_content_disposition(disposition, filename=self._filename) + + async def write(self, writer: AbstractStreamWriter) -> None: + loop = asyncio.get_event_loop() + try: + chunk = await loop.run_in_executor(None, self._value.read, 2**16) + while chunk: + await writer.write(chunk) + chunk = await loop.run_in_executor(None, self._value.read, 2**16) + finally: + await loop.run_in_executor(None, self._value.close) + + def decode(self, encoding: str = "utf-8", errors: str = "strict") -> str: + return "".join(r.decode(encoding, errors) for r in self._value.readlines()) + + +class TextIOPayload(IOBasePayload): + _value: io.TextIOBase + + def __init__( + self, + value: TextIO, + *args: Any, + encoding: Optional[str] = None, + content_type: Optional[str] = None, + **kwargs: Any, + ) -> None: + + if encoding is None: + if content_type is None: + encoding = "utf-8" + content_type = "text/plain; charset=utf-8" + else: + mimetype = parse_mimetype(content_type) + encoding = mimetype.parameters.get("charset", "utf-8") + else: + if content_type is None: + content_type = "text/plain; charset=%s" % encoding + + super().__init__( + value, + content_type=content_type, + encoding=encoding, + *args, + **kwargs, + ) + + @property + def size(self) -> Optional[int]: + try: + return os.fstat(self._value.fileno()).st_size - self._value.tell() + except OSError: + return None + + def decode(self, encoding: str = "utf-8", errors: str = "strict") -> str: + return self._value.read() + + async def write(self, writer: AbstractStreamWriter) -> None: + loop = asyncio.get_event_loop() + try: + chunk = await loop.run_in_executor(None, self._value.read, 2**16) + while chunk: + data = ( + chunk.encode(encoding=self._encoding) + if self._encoding + else chunk.encode() + ) + await writer.write(data) + chunk = await loop.run_in_executor(None, self._value.read, 2**16) + finally: + await loop.run_in_executor(None, self._value.close) + + +class BytesIOPayload(IOBasePayload): + _value: io.BytesIO + + @property + def size(self) -> int: + position = self._value.tell() + end = self._value.seek(0, os.SEEK_END) + self._value.seek(position) + return end - position + + def decode(self, encoding: str = "utf-8", errors: str = "strict") -> str: + return self._value.read().decode(encoding, errors) + + +class BufferedReaderPayload(IOBasePayload): + _value: io.BufferedIOBase + + @property + def size(self) -> Optional[int]: + try: + return os.fstat(self._value.fileno()).st_size - self._value.tell() + except (OSError, AttributeError): + # data.fileno() is not supported, e.g. + # io.BufferedReader(io.BytesIO(b'data')) + # For some file-like objects (e.g. tarfile), the fileno() attribute may + # not exist at all, and will instead raise an AttributeError. + return None + + def decode(self, encoding: str = "utf-8", errors: str = "strict") -> str: + return self._value.read().decode(encoding, errors) + + +class JsonPayload(BytesPayload): + def __init__( + self, + value: Any, + encoding: str = "utf-8", + content_type: str = "application/json", + dumps: JSONEncoder = json.dumps, + *args: Any, + **kwargs: Any, + ) -> None: + + super().__init__( + dumps(value).encode(encoding), + content_type=content_type, + encoding=encoding, + *args, + **kwargs, + ) + + +if TYPE_CHECKING: + from typing import AsyncIterable, AsyncIterator + + _AsyncIterator = AsyncIterator[bytes] + _AsyncIterable = AsyncIterable[bytes] +else: + from collections.abc import AsyncIterable, AsyncIterator + + _AsyncIterator = AsyncIterator + _AsyncIterable = AsyncIterable + + +class AsyncIterablePayload(Payload): + + _iter: Optional[_AsyncIterator] = None + _value: _AsyncIterable + + def __init__(self, value: _AsyncIterable, *args: Any, **kwargs: Any) -> None: + if not isinstance(value, AsyncIterable): + raise TypeError( + "value argument must support " + "collections.abc.AsyncIterable interface, " + "got {!r}".format(type(value)) + ) + + if "content_type" not in kwargs: + kwargs["content_type"] = "application/octet-stream" + + super().__init__(value, *args, **kwargs) + + self._iter = value.__aiter__() + + async def write(self, writer: AbstractStreamWriter) -> None: + if self._iter: + try: + # iter is not None check prevents rare cases + # when the case iterable is used twice + while True: + chunk = await self._iter.__anext__() + await writer.write(chunk) + except StopAsyncIteration: + self._iter = None + + def decode(self, encoding: str = "utf-8", errors: str = "strict") -> str: + raise TypeError("Unable to decode.") + + +class StreamReaderPayload(AsyncIterablePayload): + def __init__(self, value: StreamReader, *args: Any, **kwargs: Any) -> None: + super().__init__(value.iter_any(), *args, **kwargs) + + +PAYLOAD_REGISTRY = PayloadRegistry() +PAYLOAD_REGISTRY.register(BytesPayload, (bytes, bytearray, memoryview)) +PAYLOAD_REGISTRY.register(StringPayload, str) +PAYLOAD_REGISTRY.register(StringIOPayload, io.StringIO) +PAYLOAD_REGISTRY.register(TextIOPayload, io.TextIOBase) +PAYLOAD_REGISTRY.register(BytesIOPayload, io.BytesIO) +PAYLOAD_REGISTRY.register(BufferedReaderPayload, (io.BufferedReader, io.BufferedRandom)) +PAYLOAD_REGISTRY.register(IOBasePayload, io.IOBase) +PAYLOAD_REGISTRY.register(StreamReaderPayload, StreamReader) +# try_last for giving a chance to more specialized async interables like +# multidict.BodyPartReaderPayload override the default +PAYLOAD_REGISTRY.register(AsyncIterablePayload, AsyncIterable, order=Order.try_last) diff --git a/evalkit_cambrian/lib/python3.10/site-packages/aiohttp/web_middlewares.py b/evalkit_cambrian/lib/python3.10/site-packages/aiohttp/web_middlewares.py new file mode 100644 index 0000000000000000000000000000000000000000..2f1f5f58e6e38845d4d2d4ffdd2748fc519fa5bf --- /dev/null +++ b/evalkit_cambrian/lib/python3.10/site-packages/aiohttp/web_middlewares.py @@ -0,0 +1,121 @@ +import re +from typing import TYPE_CHECKING, Tuple, Type, TypeVar + +from .typedefs import Handler, Middleware +from .web_exceptions import HTTPMove, HTTPPermanentRedirect +from .web_request import Request +from .web_response import StreamResponse +from .web_urldispatcher import SystemRoute + +__all__ = ( + "middleware", + "normalize_path_middleware", +) + +if TYPE_CHECKING: + from .web_app import Application + +_Func = TypeVar("_Func") + + +async def _check_request_resolves(request: Request, path: str) -> Tuple[bool, Request]: + alt_request = request.clone(rel_url=path) + + match_info = await request.app.router.resolve(alt_request) + alt_request._match_info = match_info + + if match_info.http_exception is None: + return True, alt_request + + return False, request + + +def middleware(f: _Func) -> _Func: + f.__middleware_version__ = 1 # type: ignore[attr-defined] + return f + + +def normalize_path_middleware( + *, + append_slash: bool = True, + remove_slash: bool = False, + merge_slashes: bool = True, + redirect_class: Type[HTTPMove] = HTTPPermanentRedirect, +) -> Middleware: + """Factory for producing a middleware that normalizes the path of a request. + + Normalizing means: + - Add or remove a trailing slash to the path. + - Double slashes are replaced by one. + + The middleware returns as soon as it finds a path that resolves + correctly. The order if both merge and append/remove are enabled is + 1) merge slashes + 2) append/remove slash + 3) both merge slashes and append/remove slash. + If the path resolves with at least one of those conditions, it will + redirect to the new path. + + Only one of `append_slash` and `remove_slash` can be enabled. If both + are `True` the factory will raise an assertion error + + If `append_slash` is `True` the middleware will append a slash when + needed. If a resource is defined with trailing slash and the request + comes without it, it will append it automatically. + + If `remove_slash` is `True`, `append_slash` must be `False`. When enabled + the middleware will remove trailing slashes and redirect if the resource + is defined + + If merge_slashes is True, merge multiple consecutive slashes in the + path into one. + """ + correct_configuration = not (append_slash and remove_slash) + assert correct_configuration, "Cannot both remove and append slash" + + @middleware + async def impl(request: Request, handler: Handler) -> StreamResponse: + if isinstance(request.match_info.route, SystemRoute): + paths_to_check = [] + if "?" in request.raw_path: + path, query = request.raw_path.split("?", 1) + query = "?" + query + else: + query = "" + path = request.raw_path + + if merge_slashes: + paths_to_check.append(re.sub("//+", "/", path)) + if append_slash and not request.path.endswith("/"): + paths_to_check.append(path + "/") + if remove_slash and request.path.endswith("/"): + paths_to_check.append(path[:-1]) + if merge_slashes and append_slash: + paths_to_check.append(re.sub("//+", "/", path + "/")) + if merge_slashes and remove_slash: + merged_slashes = re.sub("//+", "/", path) + paths_to_check.append(merged_slashes[:-1]) + + for path in paths_to_check: + path = re.sub("^//+", "/", path) # SECURITY: GHSA-v6wp-4m6f-gcjg + resolves, request = await _check_request_resolves(request, path) + if resolves: + raise redirect_class(request.raw_path + query) + + return await handler(request) + + return impl + + +def _fix_request_current_app(app: "Application") -> Middleware: + @middleware + async def impl(request: Request, handler: Handler) -> StreamResponse: + match_info = request.match_info + prev = match_info.current_app + match_info.current_app = app + try: + return await handler(request) + finally: + match_info.current_app = prev + + return impl diff --git a/evalkit_cambrian/lib/python3.10/site-packages/nvidia_cuda_nvrtc_cu12-12.1.105.dist-info/INSTALLER b/evalkit_cambrian/lib/python3.10/site-packages/nvidia_cuda_nvrtc_cu12-12.1.105.dist-info/INSTALLER new file mode 100644 index 0000000000000000000000000000000000000000..a1b589e38a32041e49332e5e81c2d363dc418d68 --- /dev/null +++ b/evalkit_cambrian/lib/python3.10/site-packages/nvidia_cuda_nvrtc_cu12-12.1.105.dist-info/INSTALLER @@ -0,0 +1 @@ +pip diff --git a/evalkit_cambrian/lib/python3.10/site-packages/nvidia_cuda_nvrtc_cu12-12.1.105.dist-info/License.txt b/evalkit_cambrian/lib/python3.10/site-packages/nvidia_cuda_nvrtc_cu12-12.1.105.dist-info/License.txt new file mode 100644 index 0000000000000000000000000000000000000000..b491c70e0aef319022ded661e111ddbd45b8a17f --- /dev/null +++ b/evalkit_cambrian/lib/python3.10/site-packages/nvidia_cuda_nvrtc_cu12-12.1.105.dist-info/License.txt @@ -0,0 +1,1568 @@ +End User License Agreement +-------------------------- + + +Preface +------- + +The Software License Agreement in Chapter 1 and the Supplement +in Chapter 2 contain license terms and conditions that govern +the use of NVIDIA software. By accepting this agreement, you +agree to comply with all the terms and conditions applicable +to the product(s) included herein. + + +NVIDIA Driver + + +Description + +This package contains the operating system driver and +fundamental system software components for NVIDIA GPUs. + + +NVIDIA CUDA Toolkit + + +Description + +The NVIDIA CUDA Toolkit provides command-line and graphical +tools for building, debugging and optimizing the performance +of applications accelerated by NVIDIA GPUs, runtime and math +libraries, and documentation including programming guides, +user manuals, and API references. + + +Default Install Location of CUDA Toolkit + +Windows platform: + +%ProgramFiles%\NVIDIA GPU Computing Toolkit\CUDA\v#.# + +Linux platform: + +/usr/local/cuda-#.# + +Mac platform: + +/Developer/NVIDIA/CUDA-#.# + + +NVIDIA CUDA Samples + + +Description + +This package includes over 100+ CUDA examples that demonstrate +various CUDA programming principles, and efficient CUDA +implementation of algorithms in specific application domains. + + +Default Install Location of CUDA Samples + +Windows platform: + +%ProgramData%\NVIDIA Corporation\CUDA Samples\v#.# + +Linux platform: + +/usr/local/cuda-#.#/samples + +and + +$HOME/NVIDIA_CUDA-#.#_Samples + +Mac platform: + +/Developer/NVIDIA/CUDA-#.#/samples + + +NVIDIA Nsight Visual Studio Edition (Windows only) + + +Description + +NVIDIA Nsight Development Platform, Visual Studio Edition is a +development environment integrated into Microsoft Visual +Studio that provides tools for debugging, profiling, analyzing +and optimizing your GPU computing and graphics applications. + + +Default Install Location of Nsight Visual Studio Edition + +Windows platform: + +%ProgramFiles(x86)%\NVIDIA Corporation\Nsight Visual Studio Edition #.# + + +1. 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CUDA Toolkit Supplement to Software License Agreement for +NVIDIA Software Development Kits +------------------------------------------------------------ + + +Release date: August 16, 2018 +----------------------------- + +The terms in this supplement govern your use of the NVIDIA +CUDA Toolkit SDK under the terms of your license agreement +(“Agreement”) as modified by this supplement. Capitalized +terms used but not defined below have the meaning assigned to +them in the Agreement. + +This supplement is an exhibit to the Agreement and is +incorporated as an integral part of the Agreement. In the +event of conflict between the terms in this supplement and the +terms in the Agreement, the terms in this supplement govern. + + +2.1. License Scope + +The SDK is licensed for you to develop applications only for +use in systems with NVIDIA GPUs. + + +2.2. Distribution + +The portions of the SDK that are distributable under the +Agreement are listed in Attachment A. + + +2.3. 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NVIDIA does not grant to you +under this Agreement any necessary patent or other rights with +respect to any audio and/or video encoders and decoders. + + +2.5. Licensing + +If the distribution terms in this Agreement are not suitable +for your organization, or for any questions regarding this +Agreement, please contact NVIDIA at +nvidia-compute-license-questions@nvidia.com. + + +2.6. Attachment A + +The following portions of the SDK are distributable under the +Agreement: + +Component + +CUDA Runtime + +Windows + +cudart.dll, cudart_static.lib, cudadevrt.lib + +Mac OSX + +libcudart.dylib, libcudart_static.a, libcudadevrt.a + +Linux + +libcudart.so, libcudart_static.a, libcudadevrt.a + +Android + +libcudart.so, libcudart_static.a, libcudadevrt.a + +Component + +CUDA FFT Library + +Windows + +cufft.dll, cufftw.dll, cufft.lib, cufftw.lib + +Mac OSX + +libcufft.dylib, libcufft_static.a, libcufftw.dylib, +libcufftw_static.a + +Linux + +libcufft.so, libcufft_static.a, libcufftw.so, +libcufftw_static.a + +Android + +libcufft.so, libcufft_static.a, libcufftw.so, +libcufftw_static.a + +Component + +CUDA BLAS Library + +Windows + +cublas.dll, cublasLt.dll + +Mac OSX + +libcublas.dylib, libcublasLt.dylib, libcublas_static.a, +libcublasLt_static.a + +Linux + +libcublas.so, libcublasLt.so, libcublas_static.a, +libcublasLt_static.a + +Android + +libcublas.so, libcublasLt.so, libcublas_static.a, +libcublasLt_static.a + +Component + +NVIDIA "Drop-in" BLAS Library + +Windows + +nvblas.dll + +Mac OSX + +libnvblas.dylib + +Linux + +libnvblas.so + +Component + +CUDA Sparse Matrix Library + +Windows + +cusparse.dll, cusparse.lib + +Mac OSX + +libcusparse.dylib, libcusparse_static.a + +Linux + +libcusparse.so, libcusparse_static.a + +Android + +libcusparse.so, libcusparse_static.a + +Component + +CUDA Linear Solver Library + +Windows + +cusolver.dll, cusolver.lib + +Mac OSX + +libcusolver.dylib, libcusolver_static.a + +Linux + +libcusolver.so, libcusolver_static.a + +Android + +libcusolver.so, libcusolver_static.a + +Component + +CUDA Random Number Generation Library + +Windows + +curand.dll, curand.lib + +Mac OSX + +libcurand.dylib, libcurand_static.a + +Linux + +libcurand.so, libcurand_static.a + +Android + +libcurand.so, libcurand_static.a + +Component + +CUDA Accelerated Graph Library + +Component + +NVIDIA Performance Primitives Library + +Windows + +nppc.dll, nppc.lib, nppial.dll, nppial.lib, nppicc.dll, +nppicc.lib, nppicom.dll, nppicom.lib, nppidei.dll, +nppidei.lib, nppif.dll, nppif.lib, nppig.dll, nppig.lib, +nppim.dll, nppim.lib, nppist.dll, nppist.lib, nppisu.dll, +nppisu.lib, nppitc.dll, nppitc.lib, npps.dll, npps.lib + +Mac OSX + +libnppc.dylib, libnppc_static.a, libnppial.dylib, +libnppial_static.a, libnppicc.dylib, libnppicc_static.a, +libnppicom.dylib, libnppicom_static.a, libnppidei.dylib, +libnppidei_static.a, libnppif.dylib, libnppif_static.a, +libnppig.dylib, libnppig_static.a, libnppim.dylib, +libnppisu_static.a, libnppitc.dylib, libnppitc_static.a, +libnpps.dylib, libnpps_static.a + +Linux + +libnppc.so, libnppc_static.a, libnppial.so, +libnppial_static.a, libnppicc.so, libnppicc_static.a, +libnppicom.so, libnppicom_static.a, libnppidei.so, +libnppidei_static.a, libnppif.so, libnppif_static.a +libnppig.so, libnppig_static.a, libnppim.so, +libnppim_static.a, libnppist.so, libnppist_static.a, +libnppisu.so, libnppisu_static.a, libnppitc.so +libnppitc_static.a, libnpps.so, libnpps_static.a + +Android + +libnppc.so, libnppc_static.a, libnppial.so, +libnppial_static.a, libnppicc.so, libnppicc_static.a, +libnppicom.so, libnppicom_static.a, libnppidei.so, +libnppidei_static.a, libnppif.so, libnppif_static.a +libnppig.so, libnppig_static.a, libnppim.so, +libnppim_static.a, libnppist.so, libnppist_static.a, +libnppisu.so, libnppisu_static.a, libnppitc.so +libnppitc_static.a, libnpps.so, libnpps_static.a + +Component + +NVIDIA JPEG Library + +Linux + +libnvjpeg.so, libnvjpeg_static.a + +Component + +Internal common library required for statically linking to +cuBLAS, cuSPARSE, cuFFT, cuRAND, nvJPEG and NPP + +Mac OSX + +libculibos.a + +Linux + +libculibos.a + +Component + +NVIDIA Runtime Compilation Library and Header + +All + +nvrtc.h + +Windows + +nvrtc.dll, nvrtc-builtins.dll + +Mac OSX + +libnvrtc.dylib, libnvrtc-builtins.dylib + +Linux + +libnvrtc.so, libnvrtc-builtins.so + +Component + +NVIDIA Optimizing Compiler Library + +Windows + +nvvm.dll + +Mac OSX + +libnvvm.dylib + +Linux + +libnvvm.so + +Component + +NVIDIA Common Device Math Functions Library + +Windows + +libdevice.10.bc + +Mac OSX + +libdevice.10.bc + +Linux + +libdevice.10.bc + +Component + +CUDA Occupancy Calculation Header Library + +All + +cuda_occupancy.h + +Component + +CUDA Half Precision Headers + +All + +cuda_fp16.h, cuda_fp16.hpp + +Component + +CUDA Profiling Tools Interface (CUPTI) Library + +Windows + +cupti.dll + +Mac OSX + +libcupti.dylib + +Linux + +libcupti.so + +Component + +NVIDIA Tools Extension Library + +Windows + +nvToolsExt.dll, nvToolsExt.lib + +Mac OSX + +libnvToolsExt.dylib + +Linux + +libnvToolsExt.so + +Component + +NVIDIA CUDA Driver Libraries + +Linux + +libcuda.so, libnvidia-fatbinaryloader.so, +libnvidia-ptxjitcompiler.so + +The NVIDIA CUDA Driver Libraries are only distributable in +applications that meet this criteria: + + 1. The application was developed starting from a NVIDIA CUDA + container obtained from Docker Hub or the NVIDIA GPU + Cloud, and + + 2. The resulting application is packaged as a Docker + container and distributed to users on Docker Hub or the + NVIDIA GPU Cloud only. + + +2.7. Attachment B + + +Additional Licensing Obligations + +The following third party components included in the SOFTWARE +are licensed to Licensee pursuant to the following terms and +conditions: + + 1. Licensee's use of the GDB third party component is + subject to the terms and conditions of GNU GPL v3: + + This product includes copyrighted third-party software licensed + under the terms of the GNU General Public License v3 ("GPL v3"). + All third-party software packages are copyright by their respective + authors. GPL v3 terms and conditions are hereby incorporated into + the Agreement by this reference: http://www.gnu.org/licenses/gpl.txt + + Consistent with these licensing requirements, the software + listed below is provided under the terms of the specified + open source software licenses. To obtain source code for + software provided under licenses that require + redistribution of source code, including the GNU General + Public License (GPL) and GNU Lesser General Public License + (LGPL), contact oss-requests@nvidia.com. This offer is + valid for a period of three (3) years from the date of the + distribution of this product by NVIDIA CORPORATION. + + Component License + CUDA-GDB GPL v3 + + 2. Licensee represents and warrants that any and all third + party licensing and/or royalty payment obligations in + connection with Licensee's use of the H.264 video codecs + are solely the responsibility of Licensee. + + 3. Licensee's use of the Thrust library is subject to the + terms and conditions of the Apache License Version 2.0. + All third-party software packages are copyright by their + respective authors. Apache License Version 2.0 terms and + conditions are hereby incorporated into the Agreement by + this reference. + http://www.apache.org/licenses/LICENSE-2.0.html + + In addition, Licensee acknowledges the following notice: + Thrust includes source code from the Boost Iterator, + Tuple, System, and Random Number libraries. + + Boost Software License - Version 1.0 - August 17th, 2003 + . . . . + + Permission is hereby granted, free of charge, to any person or + organization obtaining a copy of the software and accompanying + documentation covered by this license (the "Software") to use, + reproduce, display, distribute, execute, and transmit the Software, + and to prepare derivative works of the Software, and to permit + third-parties to whom the Software is furnished to do so, all + subject to the following: + + The copyright notices in the Software and this entire statement, + including the above license grant, this restriction and the following + disclaimer, must be included in all copies of the Software, in whole + or in part, and all derivative works of the Software, unless such + copies or derivative works are solely in the form of machine-executable + object code generated by a source language processor. + + THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, + EXPRESS OR IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF + MERCHANTABILITY, FITNESS FOR A PARTICULAR PURPOSE, TITLE AND + NON-INFRINGEMENT. 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Licensee's use of the LLVM third party component is + subject to the following terms and conditions: + + ====================================================== + LLVM Release License + ====================================================== + University of Illinois/NCSA + Open Source License + + Copyright (c) 2003-2010 University of Illinois at Urbana-Champaign. + All rights reserved. + + Developed by: + + LLVM Team + + University of Illinois at Urbana-Champaign + + http://llvm.org + + Permission is hereby granted, free of charge, to any person obtaining a copy + of this software and associated documentation files (the "Software"), to + deal with the Software without restriction, including without limitation the + rights to use, copy, modify, merge, publish, distribute, sublicense, and/or + sell copies of the Software, and to permit persons to whom the Software is + furnished to do so, subject to the following conditions: + + * Redistributions of source code must retain the above copyright notice, + this list of conditions and the following disclaimers. + + * Redistributions in binary form must reproduce the above copyright + notice, this list of conditions and the following disclaimers in the + documentation and/or other materials provided with the distribution. + + * Neither the names of the LLVM Team, University of Illinois at Urbana- + Champaign, nor the names of its contributors may be used to endorse or + promote products derived from this Software without specific prior + written permission. + + THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR + IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY, + FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL + THE CONTRIBUTORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR + OTHER LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, + ARISING FROM, OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER + DEALINGS WITH THE SOFTWARE. + + 5. Licensee's use (e.g. nvprof) of the PCRE third party + component is subject to the following terms and + conditions: + + ------------ + PCRE LICENCE + ------------ + PCRE is a library of functions to support regular expressions whose syntax + and semantics are as close as possible to those of the Perl 5 language. + Release 8 of PCRE is distributed under the terms of the "BSD" licence, as + specified below. The documentation for PCRE, supplied in the "doc" + directory, is distributed under the same terms as the software itself. The + basic library functions are written in C and are freestanding. Also + included in the distribution is a set of C++ wrapper functions, and a just- + in-time compiler that can be used to optimize pattern matching. These are + both optional features that can be omitted when the library is built. + + THE BASIC LIBRARY FUNCTIONS + --------------------------- + Written by: Philip Hazel + Email local part: ph10 + Email domain: cam.ac.uk + University of Cambridge Computing Service, + Cambridge, England. + Copyright (c) 1997-2012 University of Cambridge + All rights reserved. + + PCRE JUST-IN-TIME COMPILATION SUPPORT + ------------------------------------- + Written by: Zoltan Herczeg + Email local part: hzmester + Emain domain: freemail.hu + Copyright(c) 2010-2012 Zoltan Herczeg + All rights reserved. + + STACK-LESS JUST-IN-TIME COMPILER + -------------------------------- + Written by: Zoltan Herczeg + Email local part: hzmester + Emain domain: freemail.hu + Copyright(c) 2009-2012 Zoltan Herczeg + All rights reserved. + + THE C++ WRAPPER FUNCTIONS + ------------------------- + Contributed by: Google Inc. + Copyright (c) 2007-2012, Google Inc. + All rights reserved. + + THE "BSD" LICENCE + ----------------- + Redistribution and use in source and binary forms, with or without + modification, are permitted provided that the following conditions are met: + + * Redistributions of source code must retain the above copyright notice, + this list of conditions and the following disclaimer. + + * 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 University of Cambridge nor the name of Google + Inc. nor the names of their 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 A PARTICULAR PURPOSE + ARE DISCLAIMED. IN NO EVENT SHALL THE COPYRIGHT OWNER OR CONTRIBUTORS BE + LIABLE FOR ANY DIRECT, INDIRECT, INCIDENTAL, SPECIAL, EXEMPLARY, OR + CONSEQUENTIAL DAMAGES (INCLUDING, BUT NOT 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. + + 6. Some of the cuBLAS library routines were written by or + derived from code written by Vasily Volkov and are subject + to the Modified Berkeley Software Distribution License as + follows: + + Copyright (c) 2007-2009, Regents of the University of California + + All rights reserved. + + Redistribution and use in source and binary forms, with or without + modification, are permitted provided that the following conditions are + met: + * Redistributions of source code must retain the above copyright + notice, this list of conditions and the following disclaimer. + * 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 University of California, Berkeley nor + the names of its contributors may be used to endorse or promote + products derived from this software without specific prior + written permission. + + THIS SOFTWARE IS PROVIDED BY THE AUTHOR "AS IS" AND ANY EXPRESS OR + IMPLIED WARRANTIES, INCLUDING, BUT NOT LIMITED TO, THE IMPLIED + WARRANTIES OF MERCHANTABILITY AND FITNESS FOR A PARTICULAR PURPOSE ARE + DISCLAIMED. IN NO EVENT SHALL THE AUTHOR BE LIABLE FOR ANY DIRECT, + INDIRECT, INCIDENTAL, SPECIAL, EXEMPLARY, OR CONSEQUENTIAL DAMAGES + (INCLUDING, BUT NOT 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. + + 7. Some of the cuBLAS library routines were written by or + derived from code written by Davide Barbieri and are + subject to the Modified Berkeley Software Distribution + License as follows: + + Copyright (c) 2008-2009 Davide Barbieri @ University of Rome Tor Vergata. + + All rights reserved. + + Redistribution and use in source and binary forms, with or without + modification, are permitted provided that the following conditions are + met: + * Redistributions of source code must retain the above copyright + notice, this list of conditions and the following disclaimer. + * 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. + * The name of the author may not be used to endorse or promote + products derived from this software without specific prior + written permission. + + THIS SOFTWARE IS PROVIDED BY THE AUTHOR "AS IS" AND ANY EXPRESS OR + IMPLIED WARRANTIES, INCLUDING, BUT NOT LIMITED TO, THE IMPLIED + WARRANTIES OF MERCHANTABILITY AND FITNESS FOR A PARTICULAR PURPOSE ARE + DISCLAIMED. IN NO EVENT SHALL THE AUTHOR BE LIABLE FOR ANY DIRECT, + INDIRECT, INCIDENTAL, SPECIAL, EXEMPLARY, OR CONSEQUENTIAL DAMAGES + (INCLUDING, BUT NOT 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. + + 8. Some of the cuBLAS library routines were derived from + code developed by the University of Tennessee and are + subject to the Modified Berkeley Software Distribution + License as follows: + + Copyright (c) 2010 The University of Tennessee. + + All rights reserved. + + Redistribution and use in source and binary forms, with or without + modification, are permitted provided that the following conditions are + met: + * Redistributions of source code must retain the above copyright + notice, this list of conditions and the following disclaimer. + * Redistributions in binary form must reproduce the above + copyright notice, this list of conditions and the following + disclaimer listed in this license in the documentation and/or + other materials provided with the distribution. + * Neither the name of the copyright holders nor the names of its + 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 + A PARTICULAR PURPOSE ARE DISCLAIMED. IN NO EVENT SHALL THE COPYRIGHT + OWNER OR CONTRIBUTORS BE LIABLE FOR ANY DIRECT, INDIRECT, INCIDENTAL, + SPECIAL, EXEMPLARY, OR CONSEQUENTIAL DAMAGES (INCLUDING, BUT NOT + 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. + + 9. Some of the cuBLAS library routines were written by or + derived from code written by Jonathan Hogg and are subject + to the Modified Berkeley Software Distribution License as + follows: + + Copyright (c) 2012, The Science and Technology Facilities Council (STFC). + + All rights reserved. + + Redistribution and use in source and binary forms, with or without + modification, are permitted provided that the following conditions are + met: + * Redistributions of source code must retain the above copyright + notice, this list of conditions and the following disclaimer. + * 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 STFC nor the names of its 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 + A PARTICULAR PURPOSE ARE DISCLAIMED. IN NO EVENT SHALL THE STFC BE + LIABLE FOR ANY DIRECT, INDIRECT, INCIDENTAL, SPECIAL, EXEMPLARY, OR + CONSEQUENTIAL DAMAGES (INCLUDING, BUT NOT 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. + + 10. Some of the cuBLAS library routines were written by or + derived from code written by Ahmad M. Abdelfattah, David + Keyes, and Hatem Ltaief, and are subject to the Apache + License, Version 2.0, as follows: + + -- (C) Copyright 2013 King Abdullah University of Science and Technology + Authors: + Ahmad Abdelfattah (ahmad.ahmad@kaust.edu.sa) + David Keyes (david.keyes@kaust.edu.sa) + Hatem Ltaief (hatem.ltaief@kaust.edu.sa) + + Redistribution and use in source and binary forms, with or without + modification, are permitted provided that the following conditions + are met: + + * Redistributions of source code must retain the above copyright + notice, this list of conditions and the following disclaimer. + * 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 King Abdullah University of Science and + Technology nor the names of its 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 + A PARTICULAR PURPOSE ARE DISCLAIMED. IN NO EVENT SHALL THE COPYRIGHT + HOLDERS OR CONTRIBUTORS BE LIABLE FOR ANY DIRECT, INDIRECT, INCIDENTAL, + SPECIAL, EXEMPLARY, OR CONSEQUENTIAL DAMAGES (INCLUDING, BUT NOT + 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 + + 11. Some of the cuSPARSE library routines were written by or + derived from code written by Li-Wen Chang and are subject + to the NCSA Open Source License as follows: + + Copyright (c) 2012, University of Illinois. + + All rights reserved. + + Developed by: IMPACT Group, University of Illinois, http://impact.crhc.illinois.edu + + Permission is hereby granted, free of charge, to any person obtaining + a copy of this software and associated documentation files (the + "Software"), to deal with the Software without restriction, including + without limitation the rights to use, copy, modify, merge, publish, + distribute, sublicense, and/or sell copies of the Software, and to + permit persons to whom the Software is furnished to do so, subject to + the following conditions: + * Redistributions of source code must retain the above copyright + notice, this list of conditions and the following disclaimer. + * Redistributions in binary form must reproduce the above + copyright notice, this list of conditions and the following + disclaimers in the documentation and/or other materials provided + with the distribution. + * Neither the names of IMPACT Group, University of Illinois, nor + the names of its contributors may be used to endorse or promote + products derived from this Software without specific prior + written permission. + + THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, + EXPRESS OR IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF + MERCHANTABILITY, FITNESS FOR A PARTICULAR PURPOSE AND + NONINFRINGEMENT. IN NO EVENT SHALL THE CONTRIBUTORS OR COPYRIGHT + HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER LIABILITY, WHETHER + IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM, OUT OF OR + IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS WITH THE + SOFTWARE. + + 12. Some of the cuRAND library routines were written by or + derived from code written by Mutsuo Saito and Makoto + Matsumoto and are subject to the following license: + + Copyright (c) 2009, 2010 Mutsuo Saito, Makoto Matsumoto and Hiroshima + University. All rights reserved. + + Copyright (c) 2011 Mutsuo Saito, Makoto Matsumoto, Hiroshima + University and University of Tokyo. All rights reserved. + + Redistribution and use in source and binary forms, with or without + modification, are permitted provided that the following conditions are + met: + * Redistributions of source code must retain the above copyright + notice, this list of conditions and the following disclaimer. + * 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 Hiroshima University nor the names of + its 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 + A PARTICULAR PURPOSE ARE DISCLAIMED. IN NO EVENT SHALL THE COPYRIGHT + OWNER OR CONTRIBUTORS BE LIABLE FOR ANY DIRECT, INDIRECT, INCIDENTAL, + SPECIAL, EXEMPLARY, OR CONSEQUENTIAL DAMAGES (INCLUDING, BUT NOT + 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. + + 13. Some of the cuRAND library routines were derived from + code developed by D. E. Shaw Research and are subject to + the following license: + + Copyright 2010-2011, D. E. Shaw Research. + + All rights reserved. + + Redistribution and use in source and binary forms, with or without + modification, are permitted provided that the following conditions are + met: + * Redistributions of source code must retain the above copyright + notice, this list of conditions, and the following disclaimer. + * 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 D. E. Shaw Research nor the names of its + 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 + A PARTICULAR PURPOSE ARE DISCLAIMED. IN NO EVENT SHALL THE COPYRIGHT + OWNER OR CONTRIBUTORS BE LIABLE FOR ANY DIRECT, INDIRECT, INCIDENTAL, + SPECIAL, EXEMPLARY, OR CONSEQUENTIAL DAMAGES (INCLUDING, BUT NOT + 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. + + 14. Some of the Math library routines were written by or + derived from code developed by Norbert Juffa and are + subject to the following license: + + Copyright (c) 2015-2017, Norbert Juffa + All rights reserved. + + Redistribution and use in source and binary forms, with or without + modification, are permitted provided that the following conditions + are met: + + 1. 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Licensee's use of the lz4 third party component is + subject to the following terms and conditions: + + Copyright (C) 2011-2013, Yann Collet. + BSD 2-Clause License (http://www.opensource.org/licenses/bsd-license.php) + + Redistribution and use in source and binary forms, with or without + modification, are permitted provided that the following conditions are + met: + + * Redistributions of source code must retain the above copyright + notice, this list of conditions and the following disclaimer. + * 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. + + 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 + A PARTICULAR PURPOSE ARE DISCLAIMED. IN NO EVENT SHALL THE COPYRIGHT + OWNER OR CONTRIBUTORS BE LIABLE FOR ANY DIRECT, INDIRECT, INCIDENTAL, + SPECIAL, EXEMPLARY, OR CONSEQUENTIAL DAMAGES (INCLUDING, BUT NOT + 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. + + 16. The NPP library uses code from the Boost Math Toolkit, + and is subject to the following license: + + Boost Software License - Version 1.0 - August 17th, 2003 + . . . . + + Permission is hereby granted, free of charge, to any person or + organization obtaining a copy of the software and accompanying + documentation covered by this license (the "Software") to use, + reproduce, display, distribute, execute, and transmit the Software, + and to prepare derivative works of the Software, and to permit + third-parties to whom the Software is furnished to do so, all + subject to the following: + + The copyright notices in the Software and this entire statement, + including the above license grant, this restriction and the following + disclaimer, must be included in all copies of the Software, in whole + or in part, and all derivative works of the Software, unless such + copies or derivative works are solely in the form of machine-executable + object code generated by a source language processor. + + THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, + EXPRESS OR IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF + MERCHANTABILITY, FITNESS FOR A PARTICULAR PURPOSE, TITLE AND + NON-INFRINGEMENT. IN NO EVENT SHALL THE COPYRIGHT HOLDERS OR + ANYONE DISTRIBUTING THE SOFTWARE BE LIABLE FOR ANY DAMAGES OR + OTHER LIABILITY, WHETHER IN CONTRACT, TORT OR OTHERWISE, ARISING + FROM, OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR + OTHER DEALINGS IN THE SOFTWARE. + + 17. Portions of the Nsight Eclipse Edition is subject to the + following license: + + The Eclipse Foundation makes available all content in this plug-in + ("Content"). Unless otherwise indicated below, the Content is provided + to you under the terms and conditions of the Eclipse Public License + Version 1.0 ("EPL"). A copy of the EPL is available at http:// + www.eclipse.org/legal/epl-v10.html. For purposes of the EPL, "Program" + will mean the Content. + + If you did not receive this Content directly from the Eclipse + Foundation, the Content is being redistributed by another party + ("Redistributor") and different terms and conditions may apply to your + use of any object code in the Content. 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Some of the cuBLAS library routines uses code from + OpenAI, which is subject to the following license: + + License URL + https://github.com/openai/openai-gemm/blob/master/LICENSE + + License Text + The MIT License + + Copyright (c) 2016 OpenAI (http://openai.com), 2016 Google Inc. + + Permission is hereby granted, free of charge, to any person obtaining a copy + of this software and associated documentation files (the "Software"), to deal + in the Software without restriction, including without limitation the rights + to use, copy, modify, merge, publish, distribute, sublicense, and/or sell + copies of the Software, and to permit persons to whom the Software is + furnished to do so, subject to the following conditions: + + The above copyright notice and this permission notice shall be included in + all copies or substantial portions of the Software. + + THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR + IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY, + FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. 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All rights reserved. + + Permission is hereby granted, free of charge, to any person + obtaining a copy of this software and associated documentation + files (the "Software"), to deal in the Software without restriction, + including without limitation the rights to use, copy, modify, merge, + publish, distribute, sublicense, and/or sell copies of the Software, + and to permit persons to whom the Software is furnished to do so, + subject to the following conditions: + + The above copyright notice and this permission notice shall be included + in all copies or substantial portions of the Software. + + THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS + OR IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY, + FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE + AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER + LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM, + OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE SOFTWARE. + + 20. Licensee's use of linmath.h header for CPU functions for + GL vector/matrix operations from lunarG is subject to the + Apache License Version 2.0. + + 21. The DX12-CUDA sample uses the d3dx12.h header, which is + subject to the MIT license . + +----------------- diff --git a/evalkit_cambrian/lib/python3.10/site-packages/nvidia_cuda_nvrtc_cu12-12.1.105.dist-info/METADATA b/evalkit_cambrian/lib/python3.10/site-packages/nvidia_cuda_nvrtc_cu12-12.1.105.dist-info/METADATA new file mode 100644 index 0000000000000000000000000000000000000000..468bc77c74622511aeaf7188c5f42b82819ce831 --- /dev/null +++ b/evalkit_cambrian/lib/python3.10/site-packages/nvidia_cuda_nvrtc_cu12-12.1.105.dist-info/METADATA @@ -0,0 +1,35 @@ +Metadata-Version: 2.1 +Name: nvidia-cuda-nvrtc-cu12 +Version: 12.1.105 +Summary: NVRTC native runtime libraries +Home-page: https://developer.nvidia.com/cuda-zone +Author: Nvidia CUDA Installer Team +Author-email: cuda_installer@nvidia.com +License: NVIDIA Proprietary Software +Keywords: cuda,nvidia,runtime,machine learning,deep learning +Classifier: Development Status :: 4 - Beta +Classifier: Intended Audience :: Developers +Classifier: Intended Audience :: Education +Classifier: Intended Audience :: Science/Research +Classifier: License :: Other/Proprietary License +Classifier: Natural Language :: English +Classifier: Programming Language :: Python :: 3 +Classifier: Programming Language :: Python :: 3.5 +Classifier: Programming Language :: Python :: 3.6 +Classifier: Programming Language :: Python :: 3.7 +Classifier: Programming Language :: Python :: 3.8 +Classifier: Programming Language :: Python :: 3.9 +Classifier: Programming Language :: Python :: 3.10 +Classifier: Programming Language :: Python :: 3.11 +Classifier: Programming Language :: Python :: 3 :: Only +Classifier: Topic :: Scientific/Engineering +Classifier: Topic :: Scientific/Engineering :: Mathematics +Classifier: Topic :: Scientific/Engineering :: Artificial Intelligence +Classifier: Topic :: Software Development +Classifier: Topic :: Software Development :: Libraries +Classifier: Operating System :: Microsoft :: Windows +Classifier: Operating System :: POSIX :: Linux +Requires-Python: >=3 +License-File: License.txt + +NVRTC native runtime libraries diff --git a/evalkit_cambrian/lib/python3.10/site-packages/nvidia_cuda_nvrtc_cu12-12.1.105.dist-info/REQUESTED b/evalkit_cambrian/lib/python3.10/site-packages/nvidia_cuda_nvrtc_cu12-12.1.105.dist-info/REQUESTED new file mode 100644 index 0000000000000000000000000000000000000000..e69de29bb2d1d6434b8b29ae775ad8c2e48c5391 diff --git a/evalkit_cambrian/lib/python3.10/site-packages/portalocker/__about__.py b/evalkit_cambrian/lib/python3.10/site-packages/portalocker/__about__.py new file mode 100644 index 0000000000000000000000000000000000000000..e734840017fe9d9eb07b78df901edb47ca5772f2 --- /dev/null +++ b/evalkit_cambrian/lib/python3.10/site-packages/portalocker/__about__.py @@ -0,0 +1,6 @@ +__package_name__ = 'portalocker' +__author__ = 'Rick van Hattem' +__email__ = 'wolph@wol.ph' +__version__ = '3.1.1' +__description__ = """Wraps the portalocker recipe for easy usage""" +__url__ = 'https://github.com/WoLpH/portalocker' diff --git a/evalkit_cambrian/lib/python3.10/site-packages/portalocker/__init__.py b/evalkit_cambrian/lib/python3.10/site-packages/portalocker/__init__.py new file mode 100644 index 0000000000000000000000000000000000000000..b461e9ce7341a8e52bd2a7d309c2658192cf281a --- /dev/null +++ b/evalkit_cambrian/lib/python3.10/site-packages/portalocker/__init__.py @@ -0,0 +1,79 @@ +from . import __about__, constants, exceptions, portalocker +from .utils import ( + BoundedSemaphore, + Lock, + RLock, + TemporaryFileLock, + open_atomic, +) + +try: # pragma: no cover + from .redis import RedisLock +except ImportError: # pragma: no cover + RedisLock = None # type: ignore[assignment,misc] + + +#: The package name on Pypi +__package_name__ = __about__.__package_name__ +#: Current author and maintainer, view the git history for the previous ones +__author__ = __about__.__author__ +#: Current author's email address +__email__ = __about__.__email__ +#: Version number +__version__ = '3.1.1' +#: Package description for Pypi +__description__ = __about__.__description__ +#: Package homepage +__url__ = __about__.__url__ + + +#: Exception thrown when the file is already locked by someone else +AlreadyLocked = exceptions.AlreadyLocked +#: Exception thrown if an error occurred during locking +LockException = exceptions.LockException + + +#: Lock a file. Note that this is an advisory lock on Linux/Unix systems +lock = portalocker.lock +#: Unlock a file +unlock = portalocker.unlock + +#: Place an exclusive lock. +#: Only one process may hold an exclusive lock for a given file at a given +#: time. +LOCK_EX: constants.LockFlags = constants.LockFlags.EXCLUSIVE + +#: Place a shared lock. +#: More than one process may hold a shared lock for a given file at a given +#: time. +LOCK_SH: constants.LockFlags = constants.LockFlags.SHARED + +#: Acquire the lock in a non-blocking fashion. +LOCK_NB: constants.LockFlags = constants.LockFlags.NON_BLOCKING + +#: Remove an existing lock held by this process. +LOCK_UN: constants.LockFlags = constants.LockFlags.UNBLOCK + +#: Locking flags enum +LockFlags = constants.LockFlags + +#: Locking utility class to automatically handle opening with timeouts and +#: context wrappers + +__all__ = [ + 'LOCK_EX', + 'LOCK_NB', + 'LOCK_SH', + 'LOCK_UN', + 'AlreadyLocked', + 'BoundedSemaphore', + 'Lock', + 'LockException', + 'LockFlags', + 'RLock', + 'RedisLock', + 'TemporaryFileLock', + 'lock', + 'open_atomic', + 'unlock', +] diff --git a/evalkit_cambrian/lib/python3.10/site-packages/portalocker/__main__.py b/evalkit_cambrian/lib/python3.10/site-packages/portalocker/__main__.py new file mode 100644 index 0000000000000000000000000000000000000000..f31930a1d2147fc580df33a75733eb43cf7f0e3f --- /dev/null +++ b/evalkit_cambrian/lib/python3.10/site-packages/portalocker/__main__.py @@ -0,0 +1,134 @@ +from __future__ import annotations + +import argparse +import logging +import os +import pathlib +import re +import typing + +base_path = pathlib.Path(__file__).parent.parent +src_path = base_path / 'portalocker' +dist_path = base_path / 'dist' +_default_output_path = base_path / 'dist' / 'portalocker.py' + +_NAMES_RE = re.compile(r'(?P[^()]+)$') +_RELATIVE_IMPORT_RE = re.compile( + r'^from \.(?P.*?) import (?P\(?)(?P[^()]+)$', +) +_USELESS_ASSIGNMENT_RE = re.compile(r'^(?P\w+) = \1\n$') + +_TEXT_TEMPLATE = """''' +{} +''' + +""" + +logger = logging.getLogger(__name__) + + +def main(argv: typing.Sequence[str] | None = None) -> None: + parser = argparse.ArgumentParser() + + subparsers = parser.add_subparsers(required=True) + combine_parser = subparsers.add_parser( + 'combine', + help='Combine all Python files into a single unified `portalocker.py` ' + 'file for easy distribution', + ) + combine_parser.add_argument( + '--output-file', + '-o', + type=argparse.FileType('w'), + default=str(_default_output_path), + ) + + combine_parser.set_defaults(func=combine) + args = parser.parse_args(argv) + args.func(args) + + +def _read_file( + path: pathlib.Path, + seen_files: set[pathlib.Path], +) -> typing.Iterator[str]: + if path in seen_files: + return + + names: set[str] = set() + seen_files.add(path) + paren = False + from_ = None + for line in path.open(): + if '__future__' in line: + continue + + if paren: + if ')' in line: + line = line.split(')', 1)[1] + paren = False + continue + + match = _NAMES_RE.match(line) + else: + match = _RELATIVE_IMPORT_RE.match(line) + + if match: + if not paren: + paren = bool(match.group('paren')) + from_ = match.group('from') + + if from_: + names.add(from_) + yield from _read_file(src_path / f'{from_}.py', seen_files) + else: + for name in match.group('names').split(','): + name = name.strip() + names.add(name) + yield from _read_file(src_path / f'{name}.py', seen_files) + else: + yield _clean_line(line, names) + + +def _clean_line(line: str, names: set[str]) -> str: + # Replace `some_import.spam` with `spam` + if names: + joined_names = '|'.join(names) + line = re.sub(rf'\b({joined_names})\.', '', line) + + # Replace useless assignments (e.g. `spam = spam`) + return _USELESS_ASSIGNMENT_RE.sub('', line) + + +def combine(args: argparse.Namespace) -> None: + output_file = args.output_file + pathlib.Path(output_file.name).parent.mkdir(parents=True, exist_ok=True) + + # We're handling this separately because it has to be the first import. + output_file.write('from __future__ import annotations\n') + + output_file.write( + _TEXT_TEMPLATE.format((base_path / 'README.rst').read_text()), + ) + output_file.write( + _TEXT_TEMPLATE.format((base_path / 'LICENSE').read_text()), + ) + + seen_files: set[pathlib.Path] = set() + for line in _read_file(src_path / '__init__.py', seen_files): + output_file.write(line) + + output_file.flush() + output_file.close() + + logger.info(f'Wrote combined file to {output_file.name}') + # Run black and ruff if available. 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`UNBLOCK` unlock +""" + +import enum +import os + +# The actual tests will execute the code anyhow so the following code can +# safely be ignored from the coverage tests +if os.name == 'nt': # pragma: no cover + import msvcrt + + #: exclusive lock + LOCK_EX = 0x1 + #: shared lock + LOCK_SH = 0x2 + #: non-blocking + LOCK_NB = 0x4 + #: unlock + LOCK_UN = msvcrt.LK_UNLCK # type: ignore[attr-defined] + +elif os.name == 'posix': # pragma: no cover + import fcntl + + #: exclusive lock + LOCK_EX = fcntl.LOCK_EX + #: shared lock + LOCK_SH = fcntl.LOCK_SH + #: non-blocking + LOCK_NB = fcntl.LOCK_NB + #: unlock + LOCK_UN = fcntl.LOCK_UN + +else: # pragma: no cover + raise RuntimeError('PortaLocker only defined for nt and posix platforms') + + +class LockFlags(enum.IntFlag): + #: exclusive lock + EXCLUSIVE = LOCK_EX + #: shared lock + SHARED = LOCK_SH + #: non-blocking + NON_BLOCKING = LOCK_NB + #: unlock + UNBLOCK = LOCK_UN diff --git a/evalkit_cambrian/lib/python3.10/site-packages/portalocker/exceptions.py b/evalkit_cambrian/lib/python3.10/site-packages/portalocker/exceptions.py new file mode 100644 index 0000000000000000000000000000000000000000..54d1bfa26dd18e84c7534c00573f330b004036de --- /dev/null +++ b/evalkit_cambrian/lib/python3.10/site-packages/portalocker/exceptions.py @@ -0,0 +1,29 @@ +import typing + +from portalocker import types + + +class BaseLockException(Exception): # noqa: N818 + # Error codes: + LOCK_FAILED = 1 + + def __init__( + self, + *args: typing.Any, + fh: typing.Union[types.IO, None, int] = None, + **kwargs: typing.Any, + ) -> None: + self.fh = fh + Exception.__init__(self, *args) + + +class LockException(BaseLockException): + pass + + +class AlreadyLocked(LockException): + pass + + +class FileToLarge(LockException): + pass diff --git a/evalkit_cambrian/lib/python3.10/site-packages/portalocker/portalocker.py b/evalkit_cambrian/lib/python3.10/site-packages/portalocker/portalocker.py new file mode 100644 index 0000000000000000000000000000000000000000..dcd7bca284b9d0743b1f61ce81583a8c6acce64a --- /dev/null +++ b/evalkit_cambrian/lib/python3.10/site-packages/portalocker/portalocker.py @@ -0,0 +1,154 @@ +from __future__ import annotations + +import os +import typing + +from . import constants, exceptions, types + +# Alias for readability. Due to import recursion issues we cannot do: +# from .constants import LockFlags +LockFlags = constants.LockFlags + + +class HasFileno(typing.Protocol): + def fileno(self) -> int: ... + + +LOCKER: typing.Callable[[int | HasFileno, int], typing.Any] | None = None + +if os.name == 'nt': # pragma: no cover + import msvcrt + + import pywintypes + import win32con + import win32file + import winerror + + __overlapped = pywintypes.OVERLAPPED() + + def lock(file_: types.IO | int, flags: LockFlags) -> None: + # Windows locking does not support locking through `fh.fileno()` so + # we cast it to make mypy and pyright happy + file_ = typing.cast(types.IO, file_) + + mode = 0 + if flags & LockFlags.NON_BLOCKING: + mode |= win32con.LOCKFILE_FAIL_IMMEDIATELY + + if flags & LockFlags.EXCLUSIVE: + mode |= win32con.LOCKFILE_EXCLUSIVE_LOCK + + # Save the old position so we can go back to that position but + # still lock from the beginning of the file + savepos = file_.tell() + if savepos: + file_.seek(0) + + os_fh = msvcrt.get_osfhandle(file_.fileno()) # type: ignore[attr-defined] + try: + win32file.LockFileEx(os_fh, mode, 0, -0x10000, __overlapped) + except pywintypes.error as exc_value: + # error: (33, 'LockFileEx', 'The process cannot access the file + # because another process has locked a portion of the file.') + if exc_value.winerror == winerror.ERROR_LOCK_VIOLATION: + raise exceptions.AlreadyLocked( + exceptions.LockException.LOCK_FAILED, + exc_value.strerror, + fh=file_, + ) from exc_value + else: + # Q: Are there exceptions/codes we should be dealing with + # here? + raise + finally: + if savepos: + file_.seek(savepos) + + def unlock(file_: types.IO) -> None: + try: + savepos = file_.tell() + if savepos: + file_.seek(0) + + os_fh = msvcrt.get_osfhandle(file_.fileno()) # type: ignore[attr-defined] + try: + win32file.UnlockFileEx( + os_fh, + 0, + -0x10000, + __overlapped, + ) + except pywintypes.error as exc: + if exc.winerror != winerror.ERROR_NOT_LOCKED: + # Q: Are there exceptions/codes we should be + # dealing with here? + raise + finally: + if savepos: + file_.seek(savepos) + except OSError as exc: + raise exceptions.LockException( + exceptions.LockException.LOCK_FAILED, + exc.strerror, + fh=file_, + ) from exc + +elif os.name == 'posix': # pragma: no cover + import errno + import fcntl + + # The locking implementation. + # Expected values are either fcntl.flock() or fcntl.lockf(), + # but any callable that matches the syntax will be accepted. + LOCKER = fcntl.flock # pyright: ignore[reportConstantRedefinition] + + def lock(file: int | types.IO, flags: LockFlags) -> None: # type: ignore[misc] + assert LOCKER is not None, 'We need a locking function in `LOCKER` ' + # Locking with NON_BLOCKING without EXCLUSIVE or SHARED enabled + # results in an error + if (flags & LockFlags.NON_BLOCKING) and not flags & ( + LockFlags.SHARED | LockFlags.EXCLUSIVE + ): + raise RuntimeError( + 'When locking in non-blocking mode the SHARED ' + 'or EXCLUSIVE flag must be specified as well', + ) + + try: + LOCKER(file, flags) + except OSError as exc_value: + # Python can use one of several different exception classes to + # represent timeout (most likely is BlockingIOError and IOError), + # but these errors may also represent other failures. On some + # systems, `IOError is OSError` which means checking for either + # IOError or OSError can mask other errors. + # The safest check is to catch OSError (from which the others + # inherit) and check the errno (which should be EACCESS or EAGAIN + # according to the spec). + if exc_value.errno in (errno.EACCES, errno.EAGAIN): + # A timeout exception, wrap this so the outer code knows to try + # again (if it wants to). + raise exceptions.AlreadyLocked( + exc_value, + fh=file, + ) from exc_value + else: + # Something else went wrong; don't wrap this so we stop + # immediately. + raise exceptions.LockException( + exc_value, + fh=file, + ) from exc_value + except EOFError as exc_value: + # On NFS filesystems, flock can raise an EOFError + raise exceptions.LockException( + exc_value, + fh=file, + ) from exc_value + + def unlock(file: types.IO) -> None: # type: ignore[misc] + assert LOCKER is not None, 'We need a locking function in `LOCKER` ' + LOCKER(file.fileno(), LockFlags.UNBLOCK) + +else: # pragma: no cover + raise RuntimeError('PortaLocker only defined for nt and posix platforms') diff --git a/evalkit_cambrian/lib/python3.10/site-packages/portalocker/py.typed b/evalkit_cambrian/lib/python3.10/site-packages/portalocker/py.typed new file mode 100644 index 0000000000000000000000000000000000000000..e69de29bb2d1d6434b8b29ae775ad8c2e48c5391 diff --git a/evalkit_cambrian/lib/python3.10/site-packages/portalocker/types.py b/evalkit_cambrian/lib/python3.10/site-packages/portalocker/types.py new file mode 100644 index 0000000000000000000000000000000000000000..c0ee5cca5c1f9cffedcdef4cd8cb8bb7567f4f88 --- /dev/null +++ b/evalkit_cambrian/lib/python3.10/site-packages/portalocker/types.py @@ -0,0 +1,62 @@ +from __future__ import annotations + +import pathlib +import typing +from typing import Union + +# fmt: off +Mode = typing.Literal[ + # Text modes + # Read text + 'r', 'rt', 'tr', + # Write text + 'w', 'wt', 'tw', + # Append text + 'a', 'at', 'ta', + # Exclusive creation text + 'x', 'xt', 'tx', + # Read and write text + 'r+', '+r', 'rt+', 'r+t', '+rt', 'tr+', 't+r', '+tr', + # Write and read text + 'w+', '+w', 'wt+', 'w+t', '+wt', 'tw+', 't+w', '+tw', + # Append and read text + 'a+', '+a', 'at+', 'a+t', '+at', 'ta+', 't+a', '+ta', + # Exclusive creation and read text + 'x+', '+x', 'xt+', 'x+t', '+xt', 'tx+', 't+x', '+tx', + # Universal newline support + 'U', 'rU', 'Ur', 'rtU', 'rUt', 'Urt', 'trU', 'tUr', 'Utr', + + # Binary modes + # Read binary + 'rb', 'br', + # Write binary + 'wb', 'bw', + # Append binary + 'ab', 'ba', + # Exclusive creation binary + 'xb', 'bx', + # Read and write binary + 'rb+', 'r+b', '+rb', 'br+', 'b+r', '+br', + # Write and read binary + 'wb+', 'w+b', '+wb', 'bw+', 'b+w', '+bw', + # Append and read binary + 'ab+', 'a+b', '+ab', 'ba+', 'b+a', '+ba', + # Exclusive creation and read binary + 'xb+', 'x+b', '+xb', 'bx+', 'b+x', '+bx', + # Universal newline support in binary mode + 'rbU', 'rUb', 'Urb', 'brU', 'bUr', 'Ubr', +] +Filename = Union[str, pathlib.Path] +IO: typing.TypeAlias = Union[ # type: ignore[name-defined] + typing.IO[str], + typing.IO[bytes], +] + + +class FileOpenKwargs(typing.TypedDict): + buffering: int | None + encoding: str | None + errors: str | None + newline: str | None + closefd: bool | None + opener: typing.Callable[[str, int], int] | None diff --git a/evalkit_cambrian/lib/python3.10/site-packages/portalocker/utils.py b/evalkit_cambrian/lib/python3.10/site-packages/portalocker/utils.py new file mode 100644 index 0000000000000000000000000000000000000000..e2d6b11399ecbdc8f8f2d30c9a43d2828f1caf3a --- /dev/null +++ b/evalkit_cambrian/lib/python3.10/site-packages/portalocker/utils.py @@ -0,0 +1,587 @@ +from __future__ import annotations + +import abc +import atexit +import contextlib +import logging +import os +import pathlib +import random +import tempfile +import time +import typing +import warnings + +from . import constants, exceptions, portalocker, types +from .types import Filename, Mode + +logger = logging.getLogger(__name__) + +DEFAULT_TIMEOUT = 5 +DEFAULT_CHECK_INTERVAL = 0.25 +DEFAULT_FAIL_WHEN_LOCKED = False +LOCK_METHOD = constants.LockFlags.EXCLUSIVE | constants.LockFlags.NON_BLOCKING + +__all__ = [ + 'Lock', + 'open_atomic', +] + + +def coalesce(*args: typing.Any, test_value: typing.Any = None) -> typing.Any: + """Simple coalescing function that returns the first value that is not + equal to the `test_value`. Or `None` if no value is valid. Usually this + means that the last given value is the default value. + + Note that the `test_value` is compared using an identity check + (i.e. `value is not test_value`) so changing the `test_value` won't work + for all values. + + >>> coalesce(None, 1) + 1 + >>> coalesce() + + >>> coalesce(0, False, True) + 0 + >>> coalesce(0, False, True, test_value=0) + False + + # This won't work because of the `is not test_value` type testing: + >>> coalesce([], dict(spam='eggs'), test_value=[]) + [] + """ + return next((arg for arg in args if arg is not test_value), None) + + +@contextlib.contextmanager +def open_atomic( + filename: Filename, + binary: bool = True, +) -> typing.Iterator[types.IO]: + """Open a file for atomic writing. Instead of locking this method allows + you to write the entire file and move it to the actual location. Note that + this makes the assumption that a rename is atomic on your platform which + is generally the case but not a guarantee. + + http://docs.python.org/library/os.html#os.rename + + >>> filename = 'test_file.txt' + >>> if os.path.exists(filename): + ... os.remove(filename) + + >>> with open_atomic(filename) as fh: + ... written = fh.write(b'test') + >>> assert os.path.exists(filename) + >>> os.remove(filename) + + >>> import pathlib + >>> path_filename = pathlib.Path('test_file.txt') + + >>> with open_atomic(path_filename) as fh: + ... written = fh.write(b'test') + >>> assert path_filename.exists() + >>> path_filename.unlink() + """ + # `pathlib.Path` cast in case `path` is a `str` + path: pathlib.Path + if isinstance(filename, pathlib.Path): + path = filename + else: + path = pathlib.Path(filename) + + assert not path.exists(), f'{path!r} exists' + + # Create the parent directory if it doesn't exist + path.parent.mkdir(parents=True, exist_ok=True) + + with tempfile.NamedTemporaryFile( + mode=(binary and 'wb') or 'w', + dir=str(path.parent), + delete=False, + ) as temp_fh: + yield temp_fh + temp_fh.flush() + os.fsync(temp_fh.fileno()) + + try: + os.rename(temp_fh.name, path) + finally: + with contextlib.suppress(Exception): + os.remove(temp_fh.name) + + +class LockBase(abc.ABC): # pragma: no cover + #: timeout when trying to acquire a lock + timeout: float + #: check interval while waiting for `timeout` + check_interval: float + #: skip the timeout and immediately fail if the initial lock fails + fail_when_locked: bool + + def __init__( + self, + timeout: float | None = None, + check_interval: float | None = None, + fail_when_locked: bool | None = None, + ) -> None: + self.timeout = coalesce(timeout, DEFAULT_TIMEOUT) + self.check_interval = coalesce(check_interval, DEFAULT_CHECK_INTERVAL) + self.fail_when_locked = coalesce( + fail_when_locked, + DEFAULT_FAIL_WHEN_LOCKED, + ) + + @abc.abstractmethod + def acquire( + self, + timeout: float | None = None, + check_interval: float | None = None, + fail_when_locked: bool | None = None, + ) -> typing.IO[typing.AnyStr]: ... + + def _timeout_generator( + self, + timeout: float | None, + check_interval: float | None, + ) -> typing.Iterator[int]: + f_timeout = coalesce(timeout, self.timeout, 0.0) + f_check_interval = coalesce(check_interval, self.check_interval, 0.0) + + yield 0 + i = 0 + + start_time = time.perf_counter() + while start_time + f_timeout > time.perf_counter(): + i += 1 + yield i + + # Take low lock checks into account to stay within the interval + since_start_time = time.perf_counter() - start_time + time.sleep(max(0.001, (i * f_check_interval) - since_start_time)) + + @abc.abstractmethod + def release(self) -> None: ... + + def __enter__(self) -> typing.IO[typing.AnyStr]: + return self.acquire() + + def __exit__( + self, + exc_type: type[BaseException] | None, + exc_value: BaseException | None, + traceback: typing.Any, # Should be typing.TracebackType + ) -> bool | None: + self.release() + return None + + def __delete__(self, instance: LockBase) -> None: + instance.release() + + +class Lock(LockBase): + """Lock manager with built-in timeout + + Args: + filename: filename + mode: the open mode, 'a' or 'ab' should be used for writing. When mode + contains `w` the file will be truncated to 0 bytes. + timeout: timeout when trying to acquire a lock + check_interval: check interval while waiting + fail_when_locked: after the initial lock failed, return an error + or lock the file. This does not wait for the timeout. + **file_open_kwargs: The kwargs for the `open(...)` call + + fail_when_locked is useful when multiple threads/processes can race + when creating a file. If set to true than the system will wait till + the lock was acquired and then return an AlreadyLocked exception. + + Note that the file is opened first and locked later. So using 'w' as + mode will result in truncate _BEFORE_ the lock is checked. + """ + + fh: types.IO | None + filename: str + mode: str + truncate: bool + timeout: float + check_interval: float + fail_when_locked: bool + flags: constants.LockFlags + file_open_kwargs: dict[str, typing.Any] + + def __init__( + self, + filename: Filename, + mode: Mode = 'a', + timeout: float | None = None, + check_interval: float = DEFAULT_CHECK_INTERVAL, + fail_when_locked: bool = DEFAULT_FAIL_WHEN_LOCKED, + flags: constants.LockFlags = LOCK_METHOD, + **file_open_kwargs: typing.Any, + ) -> None: + if 'w' in mode: + truncate = True + mode = typing.cast(Mode, mode.replace('w', 'a')) + else: + truncate = False + + if timeout is None: + timeout = DEFAULT_TIMEOUT + elif not (flags & constants.LockFlags.NON_BLOCKING): + warnings.warn( + 'timeout has no effect in blocking mode', + stacklevel=1, + ) + + self.fh = None + self.filename = str(filename) + self.mode = mode + self.truncate = truncate + self.flags = flags + self.file_open_kwargs = file_open_kwargs + super().__init__(timeout, check_interval, fail_when_locked) + + def acquire( + self, + timeout: float | None = None, + check_interval: float | None = None, + fail_when_locked: bool | None = None, + ) -> typing.IO[typing.AnyStr]: + """Acquire the locked filehandle""" + + fail_when_locked = coalesce(fail_when_locked, self.fail_when_locked) + + if ( + not (self.flags & constants.LockFlags.NON_BLOCKING) + and timeout is not None + ): + warnings.warn( + 'timeout has no effect in blocking mode', + stacklevel=1, + ) + + # If we already have a filehandle, return it + fh = self.fh + if fh: + # Due to type invariance we need to cast the type + return typing.cast(typing.IO[typing.AnyStr], fh) + + # Get a new filehandler + fh = self._get_fh() + + def try_close() -> None: # pragma: no cover + # Silently try to close the handle if possible, ignore all issues + if fh is not None: + with contextlib.suppress(Exception): + fh.close() + + exception = None + # Try till the timeout has passed + for _ in self._timeout_generator(timeout, check_interval): + exception = None + try: + # Try to lock + fh = self._get_lock(fh) + break + except exceptions.LockException as exc: + # Python will automatically remove the variable from memory + # unless you save it in a different location + exception = exc + + # We already tried to the get the lock + # If fail_when_locked is True, stop trying + if fail_when_locked: + try_close() + raise exceptions.AlreadyLocked(exception) from exc + except Exception as exc: + # Something went wrong with the locking mechanism. + # Wrap in a LockException and re-raise: + try_close() + raise exceptions.LockException(exc) from exc + + # Wait a bit + + if exception: + try_close() + # We got a timeout... reraising + raise exception + + # Prepare the filehandle (truncate if needed) + fh = self._prepare_fh(fh) + + self.fh = fh + return typing.cast(typing.IO[typing.AnyStr], fh) + + def __enter__(self) -> typing.IO[typing.AnyStr]: + return self.acquire() + + def release(self) -> None: + """Releases the currently locked file handle""" + if self.fh: + portalocker.unlock(self.fh) + self.fh.close() + self.fh = None + + def _get_fh(self) -> types.IO: + """Get a new filehandle""" + return typing.cast( + types.IO, + open( # noqa: SIM115 + self.filename, + self.mode, + **self.file_open_kwargs, + ), + ) + + def _get_lock(self, fh: types.IO) -> types.IO: + """ + Try to lock the given filehandle + + returns LockException if it fails""" + portalocker.lock(fh, self.flags) + return fh + + def _prepare_fh(self, fh: types.IO) -> types.IO: + """ + Prepare the filehandle for usage + + If truncate is a number, the file will be truncated to that amount of + bytes + """ + if self.truncate: + fh.seek(0) + fh.truncate(0) + + return fh + + +class RLock(Lock): + """ + A reentrant lock, functions in a similar way to threading.RLock in that it + can be acquired multiple times. When the corresponding number of release() + calls are made the lock will finally release the underlying file lock. + """ + + def __init__( + self, + filename: Filename, + mode: Mode = 'a', + timeout: float = DEFAULT_TIMEOUT, + check_interval: float = DEFAULT_CHECK_INTERVAL, + fail_when_locked: bool = False, + flags: constants.LockFlags = LOCK_METHOD, + ) -> None: + super().__init__( + filename, + mode, + timeout, + check_interval, + fail_when_locked, + flags, + ) + self._acquire_count = 0 + + def acquire( + self, + timeout: float | None = None, + check_interval: float | None = None, + fail_when_locked: bool | None = None, + ) -> typing.IO[typing.AnyStr]: + fh: typing.IO[typing.AnyStr] + if self._acquire_count >= 1: + fh = typing.cast(typing.IO[typing.AnyStr], self.fh) + else: + fh = super().acquire(timeout, check_interval, fail_when_locked) + self._acquire_count += 1 + assert fh is not None + return fh + + def release(self) -> None: + if self._acquire_count == 0: + raise exceptions.LockException( + 'Cannot release more times than acquired', + ) + + if self._acquire_count == 1: + super().release() + self._acquire_count -= 1 + + +class TemporaryFileLock(Lock): + def __init__( + self, + filename: str = '.lock', + timeout: float = DEFAULT_TIMEOUT, + check_interval: float = DEFAULT_CHECK_INTERVAL, + fail_when_locked: bool = True, + flags: constants.LockFlags = LOCK_METHOD, + ) -> None: + Lock.__init__( + self, + filename=filename, + mode='w', + timeout=timeout, + check_interval=check_interval, + fail_when_locked=fail_when_locked, + flags=flags, + ) + atexit.register(self.release) + + def release(self) -> None: + Lock.release(self) + if os.path.isfile(self.filename): # pragma: no branch + os.unlink(self.filename) + + +class BoundedSemaphore(LockBase): + """ + Bounded semaphore to prevent too many parallel processes from running + + This method is deprecated because multiple processes that are completely + unrelated could end up using the same semaphore. To prevent this, + use `NamedBoundedSemaphore` instead. The + `NamedBoundedSemaphore` is a drop-in replacement for this class. + + >>> semaphore = BoundedSemaphore(2, directory='') + >>> str(semaphore.get_filenames()[0]) + 'bounded_semaphore.00.lock' + >>> str(sorted(semaphore.get_random_filenames())[1]) + 'bounded_semaphore.01.lock' + """ + + lock: Lock | None + + def __init__( + self, + maximum: int, + name: str = 'bounded_semaphore', + filename_pattern: str = '{name}.{number:02d}.lock', + directory: str = tempfile.gettempdir(), + timeout: float | None = DEFAULT_TIMEOUT, + check_interval: float | None = DEFAULT_CHECK_INTERVAL, + fail_when_locked: bool | None = True, + ) -> None: + self.maximum = maximum + self.name = name + self.filename_pattern = filename_pattern + self.directory = directory + self.lock: Lock | None = None + super().__init__( + timeout=timeout, + check_interval=check_interval, + fail_when_locked=fail_when_locked, + ) + + if not name or name == 'bounded_semaphore': + warnings.warn( + '`BoundedSemaphore` without an explicit `name` ' + 'argument is deprecated, use NamedBoundedSemaphore', + DeprecationWarning, + stacklevel=1, + ) + + def get_filenames(self) -> typing.Sequence[pathlib.Path]: + return [self.get_filename(n) for n in range(self.maximum)] + + def get_random_filenames(self) -> typing.Sequence[pathlib.Path]: + filenames = list(self.get_filenames()) + random.shuffle(filenames) + return filenames + + def get_filename(self, number: int) -> pathlib.Path: + return pathlib.Path(self.directory) / self.filename_pattern.format( + name=self.name, + number=number, + ) + + def acquire( # type: ignore[override] + self, + timeout: float | None = None, + check_interval: float | None = None, + fail_when_locked: bool | None = None, + ) -> Lock | None: + assert not self.lock, 'Already locked' + + filenames = self.get_filenames() + + for n in self._timeout_generator(timeout, check_interval): # pragma: + logger.debug('trying lock (attempt %d) %r', n, filenames) + # no branch + if self.try_lock(filenames): # pragma: no branch + return self.lock # pragma: no cover + + if fail_when_locked := coalesce( + fail_when_locked, + self.fail_when_locked, + ): + raise exceptions.AlreadyLocked() + + return None + + def try_lock(self, filenames: typing.Sequence[Filename]) -> bool: + filename: Filename + for filename in filenames: + logger.debug('trying lock for %r', filename) + self.lock = Lock(filename, fail_when_locked=True) + try: + self.lock.acquire() + except exceptions.AlreadyLocked: + self.lock = None + else: + logger.debug('locked %r', filename) + return True + + return False + + def release(self) -> None: # pragma: no cover + if self.lock is not None: + self.lock.release() + self.lock = None + + +class NamedBoundedSemaphore(BoundedSemaphore): + """ + Bounded semaphore to prevent too many parallel processes from running + + It's also possible to specify a timeout when acquiring the lock to wait + for a resource to become available. This is very similar to + `threading.BoundedSemaphore` but works across multiple processes and across + multiple operating systems. + + Because this works across multiple processes it's important to give the + semaphore a name. This name is used to create the lock files. If you + don't specify a name, a random name will be generated. This means that + you can't use the same semaphore in multiple processes unless you pass the + semaphore object to the other processes. + + >>> semaphore = NamedBoundedSemaphore(2, name='test') + >>> str(semaphore.get_filenames()[0]) + '...test.00.lock' + + >>> semaphore = NamedBoundedSemaphore(2) + >>> 'bounded_semaphore' in str(semaphore.get_filenames()[0]) + True + + """ + + def __init__( + self, + maximum: int, + name: str | None = None, + filename_pattern: str = '{name}.{number:02d}.lock', + directory: str = tempfile.gettempdir(), + timeout: float | None = DEFAULT_TIMEOUT, + check_interval: float | None = DEFAULT_CHECK_INTERVAL, + fail_when_locked: bool | None = True, + ) -> None: + if name is None: + name = f'bounded_semaphore.{random.randint(0, 1000000):d}' + super().__init__( + maximum, + name, + filename_pattern, + directory, + timeout, + check_interval, + fail_when_locked, + ) diff --git a/evalkit_cambrian/lib/python3.10/site-packages/prompt_toolkit-3.0.48.dist-info/AUTHORS.rst b/evalkit_cambrian/lib/python3.10/site-packages/prompt_toolkit-3.0.48.dist-info/AUTHORS.rst new file mode 100644 index 0000000000000000000000000000000000000000..f7c8f60f4006dee49df0321c3786f9e413fa2cce --- /dev/null +++ b/evalkit_cambrian/lib/python3.10/site-packages/prompt_toolkit-3.0.48.dist-info/AUTHORS.rst @@ -0,0 +1,11 @@ +Authors +======= + +Creator +------- +Jonathan Slenders + +Contributors +------------ + +- Amjith Ramanujam diff --git a/evalkit_cambrian/lib/python3.10/site-packages/prompt_toolkit-3.0.48.dist-info/INSTALLER b/evalkit_cambrian/lib/python3.10/site-packages/prompt_toolkit-3.0.48.dist-info/INSTALLER new file mode 100644 index 0000000000000000000000000000000000000000..a1b589e38a32041e49332e5e81c2d363dc418d68 --- /dev/null +++ b/evalkit_cambrian/lib/python3.10/site-packages/prompt_toolkit-3.0.48.dist-info/INSTALLER @@ -0,0 +1 @@ +pip diff --git 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file mode 100644 index 0000000000000000000000000000000000000000..5d683570a4594106530f7cc58866d97d45e0b396 --- /dev/null +++ b/evalkit_cambrian/lib/python3.10/site-packages/regex-2024.11.6.dist-info/METADATA @@ -0,0 +1,1060 @@ +Metadata-Version: 2.1 +Name: regex +Version: 2024.11.6 +Summary: Alternative regular expression module, to replace re. +Home-page: https://github.com/mrabarnett/mrab-regex +Author: Matthew Barnett +Author-email: regex@mrabarnett.plus.com +License: Apache Software License +Classifier: Development Status :: 5 - Production/Stable +Classifier: Intended Audience :: Developers +Classifier: License :: OSI Approved :: Apache Software License +Classifier: Operating System :: OS Independent +Classifier: Programming Language :: Python :: 3.8 +Classifier: Programming Language :: Python :: 3.9 +Classifier: Programming Language :: Python :: 3.10 +Classifier: Programming Language :: Python :: 3.11 +Classifier: Programming Language :: Python :: 3.12 +Classifier: Programming Language :: Python :: 3.13 +Classifier: Topic :: Scientific/Engineering :: Information Analysis +Classifier: Topic :: Software Development :: Libraries :: Python Modules +Classifier: Topic :: Text Processing +Classifier: Topic :: Text Processing :: General +Requires-Python: >=3.8 +Description-Content-Type: text/x-rst +License-File: LICENSE.txt + +Introduction +------------ + +This regex implementation is backwards-compatible with the standard 're' module, but offers additional functionality. + +Python 2 +-------- + +Python 2 is no longer supported. The last release that supported Python 2 was 2021.11.10. + +PyPy +---- + +This module is targeted at CPython. It expects that all codepoints are the same width, so it won't behave properly with PyPy outside U+0000..U+007F because PyPy stores strings as UTF-8. + +Multithreading +-------------- + +The regex module releases the GIL during matching on instances of the built-in (immutable) string classes, enabling other Python threads to run concurrently. It is also possible to force the regex module to release the GIL during matching by calling the matching methods with the keyword argument ``concurrent=True``. The behaviour is undefined if the string changes during matching, so use it *only* when it is guaranteed that that won't happen. + +Unicode +------- + +This module supports Unicode 16.0.0. Full Unicode case-folding is supported. + +Flags +----- + +There are 2 kinds of flag: scoped and global. Scoped flags can apply to only part of a pattern and can be turned on or off; global flags apply to the entire pattern and can only be turned on. + +The scoped flags are: ``ASCII (?a)``, ``FULLCASE (?f)``, ``IGNORECASE (?i)``, ``LOCALE (?L)``, ``MULTILINE (?m)``, ``DOTALL (?s)``, ``UNICODE (?u)``, ``VERBOSE (?x)``, ``WORD (?w)``. + +The global flags are: ``BESTMATCH (?b)``, ``ENHANCEMATCH (?e)``, ``POSIX (?p)``, ``REVERSE (?r)``, ``VERSION0 (?V0)``, ``VERSION1 (?V1)``. + +If neither the ``ASCII``, ``LOCALE`` nor ``UNICODE`` flag is specified, it will default to ``UNICODE`` if the regex pattern is a Unicode string and ``ASCII`` if it's a bytestring. + +The ``ENHANCEMATCH`` flag makes fuzzy matching attempt to improve the fit of the next match that it finds. + +The ``BESTMATCH`` flag makes fuzzy matching search for the best match instead of the next match. + +Old vs new behaviour +-------------------- + +In order to be compatible with the re module, this module has 2 behaviours: + +* **Version 0** behaviour (old behaviour, compatible with the re module): + + Please note that the re module's behaviour may change over time, and I'll endeavour to match that behaviour in version 0. + + * Indicated by the ``VERSION0`` flag. + + * Zero-width matches are not handled correctly in the re module before Python 3.7. The behaviour in those earlier versions is: + + * ``.split`` won't split a string at a zero-width match. + + * ``.sub`` will advance by one character after a zero-width match. + + * Inline flags apply to the entire pattern, and they can't be turned off. + + * Only simple sets are supported. + + * Case-insensitive matches in Unicode use simple case-folding by default. + +* **Version 1** behaviour (new behaviour, possibly different from the re module): + + * Indicated by the ``VERSION1`` flag. + + * Zero-width matches are handled correctly. + + * Inline flags apply to the end of the group or pattern, and they can be turned off. + + * Nested sets and set operations are supported. + + * Case-insensitive matches in Unicode use full case-folding by default. + +If no version is specified, the regex module will default to ``regex.DEFAULT_VERSION``. + +Case-insensitive matches in Unicode +----------------------------------- + +The regex module supports both simple and full case-folding for case-insensitive matches in Unicode. Use of full case-folding can be turned on using the ``FULLCASE`` flag. Please note that this flag affects how the ``IGNORECASE`` flag works; the ``FULLCASE`` flag itself does not turn on case-insensitive matching. + +Version 0 behaviour: the flag is off by default. + +Version 1 behaviour: the flag is on by default. + +Nested sets and set operations +------------------------------ + +It's not possible to support both simple sets, as used in the re module, and nested sets at the same time because of a difference in the meaning of an unescaped ``"["`` in a set. + +For example, the pattern ``[[a-z]--[aeiou]]`` is treated in the version 0 behaviour (simple sets, compatible with the re module) as: + +* Set containing "[" and the letters "a" to "z" + +* Literal "--" + +* Set containing letters "a", "e", "i", "o", "u" + +* Literal "]" + +but in the version 1 behaviour (nested sets, enhanced behaviour) as: + +* Set which is: + + * Set containing the letters "a" to "z" + +* but excluding: + + * Set containing the letters "a", "e", "i", "o", "u" + +Version 0 behaviour: only simple sets are supported. + +Version 1 behaviour: nested sets and set operations are supported. + +Notes on named groups +--------------------- + +All groups have a group number, starting from 1. + +Groups with the same group name will have the same group number, and groups with a different group name will have a different group number. + +The same name can be used by more than one group, with later captures 'overwriting' earlier captures. All the captures of the group will be available from the ``captures`` method of the match object. + +Group numbers will be reused across different branches of a branch reset, eg. ``(?|(first)|(second))`` has only group 1. If groups have different group names then they will, of course, have different group numbers, eg. ``(?|(?Pfirst)|(?Psecond))`` has group 1 ("foo") and group 2 ("bar"). + +In the regex ``(\s+)(?|(?P[A-Z]+)|(\w+) (?P[0-9]+)`` there are 2 groups: + +* ``(\s+)`` is group 1. + +* ``(?P[A-Z]+)`` is group 2, also called "foo". + +* ``(\w+)`` is group 2 because of the branch reset. + +* ``(?P[0-9]+)`` is group 2 because it's called "foo". + +If you want to prevent ``(\w+)`` from being group 2, you need to name it (different name, different group number). + +Additional features +------------------- + +The issue numbers relate to the Python bug tracker, except where listed otherwise. + +Added ``\p{Horiz_Space}`` and ``\p{Vert_Space}`` (`GitHub issue 477 `_) +^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ + +``\p{Horiz_Space}`` or ``\p{H}`` matches horizontal whitespace and ``\p{Vert_Space}`` or ``\p{V}`` matches vertical whitespace. + +Added support for lookaround in conditional pattern (`Hg issue 163 `_) +^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ + +The test of a conditional pattern can be a lookaround. + +.. sourcecode:: python + + >>> regex.match(r'(?(?=\d)\d+|\w+)', '123abc') + + >>> regex.match(r'(?(?=\d)\d+|\w+)', 'abc123') + + +This is not quite the same as putting a lookaround in the first branch of a pair of alternatives. + +.. sourcecode:: python + + >>> print(regex.match(r'(?:(?=\d)\d+\b|\w+)', '123abc')) + + >>> print(regex.match(r'(?(?=\d)\d+\b|\w+)', '123abc')) + None + +In the first example, the lookaround matched, but the remainder of the first branch failed to match, and so the second branch was attempted, whereas in the second example, the lookaround matched, and the first branch failed to match, but the second branch was **not** attempted. + +Added POSIX matching (leftmost longest) (`Hg issue 150 `_) +^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ + +The POSIX standard for regex is to return the leftmost longest match. This can be turned on using the ``POSIX`` flag. + +.. sourcecode:: python + + >>> # Normal matching. + >>> regex.search(r'Mr|Mrs', 'Mrs') + + >>> regex.search(r'one(self)?(selfsufficient)?', 'oneselfsufficient') + + >>> # POSIX matching. + >>> regex.search(r'(?p)Mr|Mrs', 'Mrs') + + >>> regex.search(r'(?p)one(self)?(selfsufficient)?', 'oneselfsufficient') + + +Note that it will take longer to find matches because when it finds a match at a certain position, it won't return that immediately, but will keep looking to see if there's another longer match there. + +Added ``(?(DEFINE)...)`` (`Hg issue 152 `_) +^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ + +If there's no group called "DEFINE", then ... will be ignored except that any groups defined within it can be called and that the normal rules for numbering groups still apply. + +.. sourcecode:: python + + >>> regex.search(r'(?(DEFINE)(?P\d+)(?P\w+))(?&quant) (?&item)', '5 elephants') + + +Added ``(*PRUNE)``, ``(*SKIP)`` and ``(*FAIL)`` (`Hg issue 153 `_) +^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ + +``(*PRUNE)`` discards the backtracking info up to that point. When used in an atomic group or a lookaround, it won't affect the enclosing pattern. + +``(*SKIP)`` is similar to ``(*PRUNE)``, except that it also sets where in the text the next attempt to match will start. When used in an atomic group or a lookaround, it won't affect the enclosing pattern. + +``(*FAIL)`` causes immediate backtracking. ``(*F)`` is a permitted abbreviation. + +Added ``\K`` (`Hg issue 151 `_) +^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ + +Keeps the part of the entire match after the position where ``\K`` occurred; the part before it is discarded. + +It does not affect what groups return. + +.. sourcecode:: python + + >>> m = regex.search(r'(\w\w\K\w\w\w)', 'abcdef') + >>> m[0] + 'cde' + >>> m[1] + 'abcde' + >>> + >>> m = regex.search(r'(?r)(\w\w\K\w\w\w)', 'abcdef') + >>> m[0] + 'bc' + >>> m[1] + 'bcdef' + +Added capture subscripting for ``expandf`` and ``subf``/``subfn`` (`Hg issue 133 `_) +^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ + +You can use subscripting to get the captures of a repeated group. + +.. sourcecode:: python + + >>> m = regex.match(r"(\w)+", "abc") + >>> m.expandf("{1}") + 'c' + >>> m.expandf("{1[0]} {1[1]} {1[2]}") + 'a b c' + >>> m.expandf("{1[-1]} {1[-2]} {1[-3]}") + 'c b a' + >>> + >>> m = regex.match(r"(?P\w)+", "abc") + >>> m.expandf("{letter}") + 'c' + >>> m.expandf("{letter[0]} {letter[1]} {letter[2]}") + 'a b c' + >>> m.expandf("{letter[-1]} {letter[-2]} {letter[-3]}") + 'c b a' + +Added support for referring to a group by number using ``(?P=...)`` +^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ + +This is in addition to the existing ``\g<...>``. + +Fixed the handling of locale-sensitive regexes +^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ + +The ``LOCALE`` flag is intended for legacy code and has limited support. You're still recommended to use Unicode instead. + +Added partial matches (`Hg issue 102 `_) +^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ + +A partial match is one that matches up to the end of string, but that string has been truncated and you want to know whether a complete match could be possible if the string had not been truncated. + +Partial matches are supported by ``match``, ``search``, ``fullmatch`` and ``finditer`` with the ``partial`` keyword argument. + +Match objects have a ``partial`` attribute, which is ``True`` if it's a partial match. + +For example, if you wanted a user to enter a 4-digit number and check it character by character as it was being entered: + +.. sourcecode:: python + + >>> pattern = regex.compile(r'\d{4}') + + >>> # Initially, nothing has been entered: + >>> print(pattern.fullmatch('', partial=True)) + + + >>> # An empty string is OK, but it's only a partial match. + >>> # The user enters a letter: + >>> print(pattern.fullmatch('a', partial=True)) + None + >>> # It'll never match. + + >>> # The user deletes that and enters a digit: + >>> print(pattern.fullmatch('1', partial=True)) + + >>> # It matches this far, but it's only a partial match. + + >>> # The user enters 2 more digits: + >>> print(pattern.fullmatch('123', partial=True)) + + >>> # It matches this far, but it's only a partial match. + + >>> # The user enters another digit: + >>> print(pattern.fullmatch('1234', partial=True)) + + >>> # It's a complete match. + + >>> # If the user enters another digit: + >>> print(pattern.fullmatch('12345', partial=True)) + None + >>> # It's no longer a match. + + >>> # This is a partial match: + >>> pattern.match('123', partial=True).partial + True + + >>> # This is a complete match: + >>> pattern.match('1233', partial=True).partial + False + +``*`` operator not working correctly with sub() (`Hg issue 106 `_) +^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ + +Sometimes it's not clear how zero-width matches should be handled. For example, should ``.*`` match 0 characters directly after matching >0 characters? + +.. sourcecode:: python + + >>> regex.sub('.*', 'x', 'test') + 'xx' + >>> regex.sub('.*?', '|', 'test') + '|||||||||' + +Added ``capturesdict`` (`Hg issue 86 `_) +^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ + +``capturesdict`` is a combination of ``groupdict`` and ``captures``: + +``groupdict`` returns a dict of the named groups and the last capture of those groups. + +``captures`` returns a list of all the captures of a group + +``capturesdict`` returns a dict of the named groups and lists of all the captures of those groups. + +.. sourcecode:: python + + >>> m = regex.match(r"(?:(?P\w+) (?P\d+)\n)+", "one 1\ntwo 2\nthree 3\n") + >>> m.groupdict() + {'word': 'three', 'digits': '3'} + >>> m.captures("word") + ['one', 'two', 'three'] + >>> m.captures("digits") + ['1', '2', '3'] + >>> m.capturesdict() + {'word': ['one', 'two', 'three'], 'digits': ['1', '2', '3']} + +Added ``allcaptures`` and ``allspans`` (`Git issue 474 `_) +^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ + +``allcaptures`` returns a list of all the captures of all the groups. + +``allspans`` returns a list of all the spans of the all captures of all the groups. + +.. sourcecode:: python + + >>> m = regex.match(r"(?:(?P\w+) (?P\d+)\n)+", "one 1\ntwo 2\nthree 3\n") + >>> m.allcaptures() + (['one 1\ntwo 2\nthree 3\n'], ['one', 'two', 'three'], ['1', '2', '3']) + >>> m.allspans() + ([(0, 20)], [(0, 3), (6, 9), (12, 17)], [(4, 5), (10, 11), (18, 19)]) + +Allow duplicate names of groups (`Hg issue 87 `_) +^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ + +Group names can be duplicated. + +.. sourcecode:: python + + >>> # With optional groups: + >>> + >>> # Both groups capture, the second capture 'overwriting' the first. + >>> m = regex.match(r"(?P\w+)? or (?P\w+)?", "first or second") + >>> m.group("item") + 'second' + >>> m.captures("item") + ['first', 'second'] + >>> # Only the second group captures. + >>> m = regex.match(r"(?P\w+)? or (?P\w+)?", " or second") + >>> m.group("item") + 'second' + >>> m.captures("item") + ['second'] + >>> # Only the first group captures. + >>> m = regex.match(r"(?P\w+)? or (?P\w+)?", "first or ") + >>> m.group("item") + 'first' + >>> m.captures("item") + ['first'] + >>> + >>> # With mandatory groups: + >>> + >>> # Both groups capture, the second capture 'overwriting' the first. + >>> m = regex.match(r"(?P\w*) or (?P\w*)?", "first or second") + >>> m.group("item") + 'second' + >>> m.captures("item") + ['first', 'second'] + >>> # Again, both groups capture, the second capture 'overwriting' the first. + >>> m = regex.match(r"(?P\w*) or (?P\w*)", " or second") + >>> m.group("item") + 'second' + >>> m.captures("item") + ['', 'second'] + >>> # And yet again, both groups capture, the second capture 'overwriting' the first. + >>> m = regex.match(r"(?P\w*) or (?P\w*)", "first or ") + >>> m.group("item") + '' + >>> m.captures("item") + ['first', ''] + +Added ``fullmatch`` (`issue #16203 `_) +^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ + +``fullmatch`` behaves like ``match``, except that it must match all of the string. + +.. sourcecode:: python + + >>> print(regex.fullmatch(r"abc", "abc").span()) + (0, 3) + >>> print(regex.fullmatch(r"abc", "abcx")) + None + >>> print(regex.fullmatch(r"abc", "abcx", endpos=3).span()) + (0, 3) + >>> print(regex.fullmatch(r"abc", "xabcy", pos=1, endpos=4).span()) + (1, 4) + >>> + >>> regex.match(r"a.*?", "abcd").group(0) + 'a' + >>> regex.fullmatch(r"a.*?", "abcd").group(0) + 'abcd' + +Added ``subf`` and ``subfn`` +^^^^^^^^^^^^^^^^^^^^^^^^^^^^ + +``subf`` and ``subfn`` are alternatives to ``sub`` and ``subn`` respectively. When passed a replacement string, they treat it as a format string. + +.. sourcecode:: python + + >>> regex.subf(r"(\w+) (\w+)", "{0} => {2} {1}", "foo bar") + 'foo bar => bar foo' + >>> regex.subf(r"(?P\w+) (?P\w+)", "{word2} {word1}", "foo bar") + 'bar foo' + +Added ``expandf`` to match object +^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ + +``expandf`` is an alternative to ``expand``. When passed a replacement string, it treats it as a format string. + +.. sourcecode:: python + + >>> m = regex.match(r"(\w+) (\w+)", "foo bar") + >>> m.expandf("{0} => {2} {1}") + 'foo bar => bar foo' + >>> + >>> m = regex.match(r"(?P\w+) (?P\w+)", "foo bar") + >>> m.expandf("{word2} {word1}") + 'bar foo' + +Detach searched string +^^^^^^^^^^^^^^^^^^^^^^ + +A match object contains a reference to the string that was searched, via its ``string`` attribute. The ``detach_string`` method will 'detach' that string, making it available for garbage collection, which might save valuable memory if that string is very large. + +.. sourcecode:: python + + >>> m = regex.search(r"\w+", "Hello world") + >>> print(m.group()) + Hello + >>> print(m.string) + Hello world + >>> m.detach_string() + >>> print(m.group()) + Hello + >>> print(m.string) + None + +Recursive patterns (`Hg issue 27 `_) +^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ + +Recursive and repeated patterns are supported. + +``(?R)`` or ``(?0)`` tries to match the entire regex recursively. ``(?1)``, ``(?2)``, etc, try to match the relevant group. + +``(?&name)`` tries to match the named group. + +.. sourcecode:: python + + >>> regex.match(r"(Tarzan|Jane) loves (?1)", "Tarzan loves Jane").groups() + ('Tarzan',) + >>> regex.match(r"(Tarzan|Jane) loves (?1)", "Jane loves Tarzan").groups() + ('Jane',) + + >>> m = regex.search(r"(\w)(?:(?R)|(\w?))\1", "kayak") + >>> m.group(0, 1, 2) + ('kayak', 'k', None) + +The first two examples show how the subpattern within the group is reused, but is _not_ itself a group. In other words, ``"(Tarzan|Jane) loves (?1)"`` is equivalent to ``"(Tarzan|Jane) loves (?:Tarzan|Jane)"``. + +It's possible to backtrack into a recursed or repeated group. + +You can't call a group if there is more than one group with that group name or group number (``"ambiguous group reference"``). + +The alternative forms ``(?P>name)`` and ``(?P&name)`` are also supported. + +Full Unicode case-folding is supported +^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ + +In version 1 behaviour, the regex module uses full case-folding when performing case-insensitive matches in Unicode. + +.. sourcecode:: python + + >>> regex.match(r"(?iV1)strasse", "stra\N{LATIN SMALL LETTER SHARP S}e").span() + (0, 6) + >>> regex.match(r"(?iV1)stra\N{LATIN SMALL LETTER SHARP S}e", "STRASSE").span() + (0, 7) + +In version 0 behaviour, it uses simple case-folding for backward compatibility with the re module. + +Approximate "fuzzy" matching (`Hg issue 12 `_, `Hg issue 41 `_, `Hg issue 109 `_) +^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ + +Regex usually attempts an exact match, but sometimes an approximate, or "fuzzy", match is needed, for those cases where the text being searched may contain errors in the form of inserted, deleted or substituted characters. + +A fuzzy regex specifies which types of errors are permitted, and, optionally, either the minimum and maximum or only the maximum permitted number of each type. (You cannot specify only a minimum.) + +The 3 types of error are: + +* Insertion, indicated by "i" + +* Deletion, indicated by "d" + +* Substitution, indicated by "s" + +In addition, "e" indicates any type of error. + +The fuzziness of a regex item is specified between "{" and "}" after the item. + +Examples: + +* ``foo`` match "foo" exactly + +* ``(?:foo){i}`` match "foo", permitting insertions + +* ``(?:foo){d}`` match "foo", permitting deletions + +* ``(?:foo){s}`` match "foo", permitting substitutions + +* ``(?:foo){i,s}`` match "foo", permitting insertions and substitutions + +* ``(?:foo){e}`` match "foo", permitting errors + +If a certain type of error is specified, then any type not specified will **not** be permitted. + +In the following examples I'll omit the item and write only the fuzziness: + +* ``{d<=3}`` permit at most 3 deletions, but no other types + +* ``{i<=1,s<=2}`` permit at most 1 insertion and at most 2 substitutions, but no deletions + +* ``{1<=e<=3}`` permit at least 1 and at most 3 errors + +* ``{i<=2,d<=2,e<=3}`` permit at most 2 insertions, at most 2 deletions, at most 3 errors in total, but no substitutions + +It's also possible to state the costs of each type of error and the maximum permitted total cost. + +Examples: + +* ``{2i+2d+1s<=4}`` each insertion costs 2, each deletion costs 2, each substitution costs 1, the total cost must not exceed 4 + +* ``{i<=1,d<=1,s<=1,2i+2d+1s<=4}`` at most 1 insertion, at most 1 deletion, at most 1 substitution; each insertion costs 2, each deletion costs 2, each substitution costs 1, the total cost must not exceed 4 + +You can also use "<" instead of "<=" if you want an exclusive minimum or maximum. + +You can add a test to perform on a character that's substituted or inserted. + +Examples: + +* ``{s<=2:[a-z]}`` at most 2 substitutions, which must be in the character set ``[a-z]``. + +* ``{s<=2,i<=3:\d}`` at most 2 substitutions, at most 3 insertions, which must be digits. + +By default, fuzzy matching searches for the first match that meets the given constraints. The ``ENHANCEMATCH`` flag will cause it to attempt to improve the fit (i.e. reduce the number of errors) of the match that it has found. + +The ``BESTMATCH`` flag will make it search for the best match instead. + +Further examples to note: + +* ``regex.search("(dog){e}", "cat and dog")[1]`` returns ``"cat"`` because that matches ``"dog"`` with 3 errors (an unlimited number of errors is permitted). + +* ``regex.search("(dog){e<=1}", "cat and dog")[1]`` returns ``" dog"`` (with a leading space) because that matches ``"dog"`` with 1 error, which is within the limit. + +* ``regex.search("(?e)(dog){e<=1}", "cat and dog")[1]`` returns ``"dog"`` (without a leading space) because the fuzzy search matches ``" dog"`` with 1 error, which is within the limit, and the ``(?e)`` then it attempts a better fit. + +In the first two examples there are perfect matches later in the string, but in neither case is it the first possible match. + +The match object has an attribute ``fuzzy_counts`` which gives the total number of substitutions, insertions and deletions. + +.. sourcecode:: python + + >>> # A 'raw' fuzzy match: + >>> regex.fullmatch(r"(?:cats|cat){e<=1}", "cat").fuzzy_counts + (0, 0, 1) + >>> # 0 substitutions, 0 insertions, 1 deletion. + + >>> # A better match might be possible if the ENHANCEMATCH flag used: + >>> regex.fullmatch(r"(?e)(?:cats|cat){e<=1}", "cat").fuzzy_counts + (0, 0, 0) + >>> # 0 substitutions, 0 insertions, 0 deletions. + +The match object also has an attribute ``fuzzy_changes`` which gives a tuple of the positions of the substitutions, insertions and deletions. + +.. sourcecode:: python + + >>> m = regex.search('(fuu){i<=2,d<=2,e<=5}', 'anaconda foo bar') + >>> m + + >>> m.fuzzy_changes + ([], [7, 8], [10, 11]) + +What this means is that if the matched part of the string had been: + +.. sourcecode:: python + + 'anacondfuuoo bar' + +it would've been an exact match. + +However, there were insertions at positions 7 and 8: + +.. sourcecode:: python + + 'anaconda fuuoo bar' + ^^ + +and deletions at positions 10 and 11: + +.. sourcecode:: python + + 'anaconda f~~oo bar' + ^^ + +So the actual string was: + +.. sourcecode:: python + + 'anaconda foo bar' + +Named lists ``\L`` (`Hg issue 11 `_) +^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ + +There are occasions where you may want to include a list (actually, a set) of options in a regex. + +One way is to build the pattern like this: + +.. sourcecode:: python + + >>> p = regex.compile(r"first|second|third|fourth|fifth") + +but if the list is large, parsing the resulting regex can take considerable time, and care must also be taken that the strings are properly escaped and properly ordered, for example, "cats" before "cat". + +The new alternative is to use a named list: + +.. sourcecode:: python + + >>> option_set = ["first", "second", "third", "fourth", "fifth"] + >>> p = regex.compile(r"\L", options=option_set) + +The order of the items is irrelevant, they are treated as a set. The named lists are available as the ``.named_lists`` attribute of the pattern object : + +.. sourcecode:: python + + >>> print(p.named_lists) + {'options': frozenset({'third', 'first', 'fifth', 'fourth', 'second'})} + +If there are any unused keyword arguments, ``ValueError`` will be raised unless you tell it otherwise: + +.. sourcecode:: python + + >>> option_set = ["first", "second", "third", "fourth", "fifth"] + >>> p = regex.compile(r"\L", options=option_set, other_options=[]) + Traceback (most recent call last): + File "", line 1, in + File "C:\Python310\lib\site-packages\regex\regex.py", line 353, in compile + return _compile(pattern, flags, ignore_unused, kwargs, cache_pattern) + File "C:\Python310\lib\site-packages\regex\regex.py", line 500, in _compile + complain_unused_args() + File "C:\Python310\lib\site-packages\regex\regex.py", line 483, in complain_unused_args + raise ValueError('unused keyword argument {!a}'.format(any_one)) + ValueError: unused keyword argument 'other_options' + >>> p = regex.compile(r"\L", options=option_set, other_options=[], ignore_unused=True) + >>> p = regex.compile(r"\L", options=option_set, other_options=[], ignore_unused=False) + Traceback (most recent call last): + File "", line 1, in + File "C:\Python310\lib\site-packages\regex\regex.py", line 353, in compile + return _compile(pattern, flags, ignore_unused, kwargs, cache_pattern) + File "C:\Python310\lib\site-packages\regex\regex.py", line 500, in _compile + complain_unused_args() + File "C:\Python310\lib\site-packages\regex\regex.py", line 483, in complain_unused_args + raise ValueError('unused keyword argument {!a}'.format(any_one)) + ValueError: unused keyword argument 'other_options' + >>> + +Start and end of word +^^^^^^^^^^^^^^^^^^^^^ + +``\m`` matches at the start of a word. + +``\M`` matches at the end of a word. + +Compare with ``\b``, which matches at the start or end of a word. + +Unicode line separators +^^^^^^^^^^^^^^^^^^^^^^^ + +Normally the only line separator is ``\n`` (``\x0A``), but if the ``WORD`` flag is turned on then the line separators are ``\x0D\x0A``, ``\x0A``, ``\x0B``, ``\x0C`` and ``\x0D``, plus ``\x85``, ``\u2028`` and ``\u2029`` when working with Unicode. + +This affects the regex dot ``"."``, which, with the ``DOTALL`` flag turned off, matches any character except a line separator. It also affects the line anchors ``^`` and ``$`` (in multiline mode). + +Set operators +^^^^^^^^^^^^^ + +**Version 1 behaviour only** + +Set operators have been added, and a set ``[...]`` can include nested sets. + +The operators, in order of increasing precedence, are: + +* ``||`` for union ("x||y" means "x or y") + +* ``~~`` (double tilde) for symmetric difference ("x~~y" means "x or y, but not both") + +* ``&&`` for intersection ("x&&y" means "x and y") + +* ``--`` (double dash) for difference ("x--y" means "x but not y") + +Implicit union, ie, simple juxtaposition like in ``[ab]``, has the highest precedence. Thus, ``[ab&&cd]`` is the same as ``[[a||b]&&[c||d]]``. + +Examples: + +* ``[ab]`` # Set containing 'a' and 'b' + +* ``[a-z]`` # Set containing 'a' .. 'z' + +* ``[[a-z]--[qw]]`` # Set containing 'a' .. 'z', but not 'q' or 'w' + +* ``[a-z--qw]`` # Same as above + +* ``[\p{L}--QW]`` # Set containing all letters except 'Q' and 'W' + +* ``[\p{N}--[0-9]]`` # Set containing all numbers except '0' .. '9' + +* ``[\p{ASCII}&&\p{Letter}]`` # Set containing all characters which are ASCII and letter + +regex.escape (`issue #2650 `_) +^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ + +regex.escape has an additional keyword parameter ``special_only``. When True, only 'special' regex characters, such as '?', are escaped. + +.. sourcecode:: python + + >>> regex.escape("foo!?", special_only=False) + 'foo\\!\\?' + >>> regex.escape("foo!?", special_only=True) + 'foo!\\?' + +regex.escape (`Hg issue 249 `_) +^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ + +regex.escape has an additional keyword parameter ``literal_spaces``. When True, spaces are not escaped. + +.. sourcecode:: python + + >>> regex.escape("foo bar!?", literal_spaces=False) + 'foo\\ bar!\\?' + >>> regex.escape("foo bar!?", literal_spaces=True) + 'foo bar!\\?' + +Repeated captures (`issue #7132 `_) +^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ + +A match object has additional methods which return information on all the successful matches of a repeated group. These methods are: + +* ``matchobject.captures([group1, ...])`` + + * Returns a list of the strings matched in a group or groups. Compare with ``matchobject.group([group1, ...])``. + +* ``matchobject.starts([group])`` + + * Returns a list of the start positions. Compare with ``matchobject.start([group])``. + +* ``matchobject.ends([group])`` + + * Returns a list of the end positions. Compare with ``matchobject.end([group])``. + +* ``matchobject.spans([group])`` + + * Returns a list of the spans. Compare with ``matchobject.span([group])``. + +.. sourcecode:: python + + >>> m = regex.search(r"(\w{3})+", "123456789") + >>> m.group(1) + '789' + >>> m.captures(1) + ['123', '456', '789'] + >>> m.start(1) + 6 + >>> m.starts(1) + [0, 3, 6] + >>> m.end(1) + 9 + >>> m.ends(1) + [3, 6, 9] + >>> m.span(1) + (6, 9) + >>> m.spans(1) + [(0, 3), (3, 6), (6, 9)] + +Atomic grouping ``(?>...)`` (`issue #433030 `_) +^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ + +If the following pattern subsequently fails, then the subpattern as a whole will fail. + +Possessive quantifiers +^^^^^^^^^^^^^^^^^^^^^^ + +``(?:...)?+`` ; ``(?:...)*+`` ; ``(?:...)++`` ; ``(?:...){min,max}+`` + +The subpattern is matched up to 'max' times. If the following pattern subsequently fails, then all the repeated subpatterns will fail as a whole. For example, ``(?:...)++`` is equivalent to ``(?>(?:...)+)``. + +Scoped flags (`issue #433028 `_) +^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ + +``(?flags-flags:...)`` + +The flags will apply only to the subpattern. Flags can be turned on or off. + +Definition of 'word' character (`issue #1693050 `_) +^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ + +The definition of a 'word' character has been expanded for Unicode. It conforms to the Unicode specification at ``http://www.unicode.org/reports/tr29/``. + +Variable-length lookbehind +^^^^^^^^^^^^^^^^^^^^^^^^^^ + +A lookbehind can match a variable-length string. + +Flags argument for regex.split, regex.sub and regex.subn (`issue #3482 `_) +^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ + +``regex.split``, ``regex.sub`` and ``regex.subn`` support a 'flags' argument. + +Pos and endpos arguments for regex.sub and regex.subn +^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ + +``regex.sub`` and ``regex.subn`` support 'pos' and 'endpos' arguments. + +'Overlapped' argument for regex.findall and regex.finditer +^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ + +``regex.findall`` and ``regex.finditer`` support an 'overlapped' flag which permits overlapped matches. + +Splititer +^^^^^^^^^ + +``regex.splititer`` has been added. It's a generator equivalent of ``regex.split``. + +Subscripting match objects for groups +^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ + +A match object accepts access to the groups via subscripting and slicing: + +.. sourcecode:: python + + >>> m = regex.search(r"(?P.*?)(?P\d+)(?P.*)", "pqr123stu") + >>> print(m["before"]) + pqr + >>> print(len(m)) + 4 + >>> print(m[:]) + ('pqr123stu', 'pqr', '123', 'stu') + +Named groups +^^^^^^^^^^^^ + +Groups can be named with ``(?...)`` as well as the existing ``(?P...)``. + +Group references +^^^^^^^^^^^^^^^^ + +Groups can be referenced within a pattern with ``\g``. This also allows there to be more than 99 groups. + +Named characters ``\N{name}`` +^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ + +Named characters are supported. Note that only those known by Python's Unicode database will be recognised. + +Unicode codepoint properties, including scripts and blocks +^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ + +``\p{property=value}``; ``\P{property=value}``; ``\p{value}`` ; ``\P{value}`` + +Many Unicode properties are supported, including blocks and scripts. ``\p{property=value}`` or ``\p{property:value}`` matches a character whose property ``property`` has value ``value``. The inverse of ``\p{property=value}`` is ``\P{property=value}`` or ``\p{^property=value}``. + +If the short form ``\p{value}`` is used, the properties are checked in the order: ``General_Category``, ``Script``, ``Block``, binary property: + +* ``Latin``, the 'Latin' script (``Script=Latin``). + +* ``BasicLatin``, the 'BasicLatin' block (``Block=BasicLatin``). + +* ``Alphabetic``, the 'Alphabetic' binary property (``Alphabetic=Yes``). + +A short form starting with ``Is`` indicates a script or binary property: + +* ``IsLatin``, the 'Latin' script (``Script=Latin``). + +* ``IsAlphabetic``, the 'Alphabetic' binary property (``Alphabetic=Yes``). + +A short form starting with ``In`` indicates a block property: + +* ``InBasicLatin``, the 'BasicLatin' block (``Block=BasicLatin``). + +POSIX character classes +^^^^^^^^^^^^^^^^^^^^^^^ + +``[[:alpha:]]``; ``[[:^alpha:]]`` + +POSIX character classes are supported. These are normally treated as an alternative form of ``\p{...}``. + +The exceptions are ``alnum``, ``digit``, ``punct`` and ``xdigit``, whose definitions are different from those of Unicode. + +``[[:alnum:]]`` is equivalent to ``\p{posix_alnum}``. + +``[[:digit:]]`` is equivalent to ``\p{posix_digit}``. + +``[[:punct:]]`` is equivalent to ``\p{posix_punct}``. + +``[[:xdigit:]]`` is equivalent to ``\p{posix_xdigit}``. + +Search anchor ``\G`` +^^^^^^^^^^^^^^^^^^^^ + +A search anchor has been added. It matches at the position where each search started/continued and can be used for contiguous matches or in negative variable-length lookbehinds to limit how far back the lookbehind goes: + +.. sourcecode:: python + + >>> regex.findall(r"\w{2}", "abcd ef") + ['ab', 'cd', 'ef'] + >>> regex.findall(r"\G\w{2}", "abcd ef") + ['ab', 'cd'] + +* The search starts at position 0 and matches 'ab'. + +* The search continues at position 2 and matches 'cd'. + +* The search continues at position 4 and fails to match any letters. + +* The anchor stops the search start position from being advanced, so there are no more results. + +Reverse searching +^^^^^^^^^^^^^^^^^ + +Searches can also work backwards: + +.. sourcecode:: python + + >>> regex.findall(r".", "abc") + ['a', 'b', 'c'] + >>> regex.findall(r"(?r).", "abc") + ['c', 'b', 'a'] + +Note that the result of a reverse search is not necessarily the reverse of a forward search: + +.. sourcecode:: python + + >>> regex.findall(r"..", "abcde") + ['ab', 'cd'] + >>> regex.findall(r"(?r)..", "abcde") + ['de', 'bc'] + +Matching a single grapheme ``\X`` +^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ + +The grapheme matcher is supported. It conforms to the Unicode specification at ``http://www.unicode.org/reports/tr29/``. + +Branch reset ``(?|...|...)`` +^^^^^^^^^^^^^^^^^^^^^^^^^^^^ + +Group numbers will be reused across the alternatives, but groups with different names will have different group numbers. + +.. sourcecode:: python + + >>> regex.match(r"(?|(first)|(second))", "first").groups() + ('first',) + >>> regex.match(r"(?|(first)|(second))", "second").groups() + ('second',) + +Note that there is only one group. + +Default Unicode word boundary +^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ + +The ``WORD`` flag changes the definition of a 'word boundary' to that of a default Unicode word boundary. This applies to ``\b`` and ``\B``. + +Timeout +^^^^^^^ + +The matching methods and functions support timeouts. The timeout (in seconds) applies to the entire operation: + +.. sourcecode:: python + + >>> from time import sleep + >>> + >>> def fast_replace(m): + ... return 'X' + ... + >>> def slow_replace(m): + ... sleep(0.5) + ... return 'X' + ... + >>> regex.sub(r'[a-z]', fast_replace, 'abcde', timeout=2) + 'XXXXX' + >>> regex.sub(r'[a-z]', slow_replace, 'abcde', timeout=2) + Traceback (most recent call last): + File "", line 1, in + File "C:\Python310\lib\site-packages\regex\regex.py", line 278, in sub + return pat.sub(repl, string, count, pos, endpos, concurrent, timeout) + TimeoutError: regex timed out diff --git a/evalkit_cambrian/lib/python3.10/site-packages/stack_data/__init__.py b/evalkit_cambrian/lib/python3.10/site-packages/stack_data/__init__.py new file mode 100644 index 0000000000000000000000000000000000000000..828121483c01cfb4a67782f74359c2606f9dba89 --- /dev/null +++ b/evalkit_cambrian/lib/python3.10/site-packages/stack_data/__init__.py @@ -0,0 +1,10 @@ +from .core import Source, FrameInfo, markers_from_ranges, Options, LINE_GAP, Line, Variable, RangeInLine, \ + RepeatedFrames, MarkerInLine, style_with_executing_node, BlankLineRange, BlankLines +from .formatting import Formatter +from .serializing import Serializer + +try: + from .version import __version__ +except ImportError: + # version.py is auto-generated with the git tag when building + __version__ = "???" diff --git a/evalkit_cambrian/lib/python3.10/site-packages/stack_data/__pycache__/__init__.cpython-310.pyc b/evalkit_cambrian/lib/python3.10/site-packages/stack_data/__pycache__/__init__.cpython-310.pyc new file mode 100644 index 0000000000000000000000000000000000000000..c774c7fd996d81c9aa34d77be51fb6931798327c Binary files /dev/null and b/evalkit_cambrian/lib/python3.10/site-packages/stack_data/__pycache__/__init__.cpython-310.pyc differ diff --git a/evalkit_cambrian/lib/python3.10/site-packages/stack_data/__pycache__/core.cpython-310.pyc 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100644 index 0000000000000000000000000000000000000000..71290667c812b144ffd85b21e4e195cc36324d3b Binary files /dev/null and b/evalkit_cambrian/lib/python3.10/site-packages/stack_data/__pycache__/serializing.cpython-310.pyc differ diff --git a/evalkit_cambrian/lib/python3.10/site-packages/stack_data/formatting.py b/evalkit_cambrian/lib/python3.10/site-packages/stack_data/formatting.py new file mode 100644 index 0000000000000000000000000000000000000000..e4c6f07fc8f1a002260e10c3ec63706a9694db92 --- /dev/null +++ b/evalkit_cambrian/lib/python3.10/site-packages/stack_data/formatting.py @@ -0,0 +1,234 @@ +import inspect +import sys +import traceback +from types import FrameType, TracebackType +from typing import Union, Iterable + +from stack_data import (style_with_executing_node, Options, Line, FrameInfo, LINE_GAP, + Variable, RepeatedFrames, BlankLineRange, BlankLines) +from stack_data.utils import assert_ + + +class Formatter: + def __init__( + self, *, + options=None, + pygmented=False, + show_executing_node=True, + pygments_formatter_cls=None, + pygments_formatter_kwargs=None, + pygments_style="monokai", + executing_node_modifier="bg:#005080", + executing_node_underline="^", + current_line_indicator="-->", + line_gap_string="(...)", + line_number_gap_string=":", + line_number_format_string="{:4} | ", + show_variables=False, + use_code_qualname=True, + show_linenos=True, + strip_leading_indent=True, + html=False, + chain=True, + collapse_repeated_frames=True + ): + if options is None: + options = Options() + + if pygmented and not options.pygments_formatter: + if show_executing_node: + pygments_style = style_with_executing_node( + pygments_style, executing_node_modifier + ) + + if pygments_formatter_cls is None: + from pygments.formatters.terminal256 import Terminal256Formatter \ + as pygments_formatter_cls + + options.pygments_formatter = pygments_formatter_cls( + style=pygments_style, + **pygments_formatter_kwargs or {}, + ) + + self.pygmented = pygmented + self.show_executing_node = show_executing_node + assert_( + len(executing_node_underline) == 1, + ValueError("executing_node_underline must be a single character"), + ) + self.executing_node_underline = executing_node_underline + self.current_line_indicator = current_line_indicator or "" + self.line_gap_string = line_gap_string + self.line_number_gap_string = line_number_gap_string + self.line_number_format_string = line_number_format_string + self.show_variables = show_variables + self.show_linenos = show_linenos + self.use_code_qualname = use_code_qualname + self.strip_leading_indent = strip_leading_indent + self.html = html + self.chain = chain + self.options = options + self.collapse_repeated_frames = collapse_repeated_frames + if not self.show_linenos and self.options.blank_lines == BlankLines.SINGLE: + raise ValueError( + "BlankLines.SINGLE option can only be used when show_linenos=True" + ) + + def set_hook(self): + def excepthook(_etype, evalue, _tb): + self.print_exception(evalue) + + sys.excepthook = excepthook + + def print_exception(self, e=None, *, file=None): + self.print_lines(self.format_exception(e), file=file) + + def print_stack(self, frame_or_tb=None, *, file=None): + if frame_or_tb is None: + frame_or_tb = inspect.currentframe().f_back + + self.print_lines(self.format_stack(frame_or_tb), file=file) + + def print_lines(self, lines, *, file=None): + if file is None: + file = sys.stderr + for line in lines: + print(line, file=file, end="") + + def format_exception(self, e=None) -> Iterable[str]: + if e is None: + e = sys.exc_info()[1] + + if self.chain: + if e.__cause__ is not None: + yield from self.format_exception(e.__cause__) + yield traceback._cause_message + elif (e.__context__ is not None + and not e.__suppress_context__): + yield from self.format_exception(e.__context__) + yield traceback._context_message + + yield 'Traceback (most recent call last):\n' + yield from self.format_stack(e.__traceback__) + yield from traceback.format_exception_only(type(e), e) + + def format_stack(self, frame_or_tb=None) -> Iterable[str]: + if frame_or_tb is None: + frame_or_tb = inspect.currentframe().f_back + + yield from self.format_stack_data( + FrameInfo.stack_data( + frame_or_tb, + self.options, + collapse_repeated_frames=self.collapse_repeated_frames, + ) + ) + + def format_stack_data( + self, stack: Iterable[Union[FrameInfo, RepeatedFrames]] + ) -> Iterable[str]: + for item in stack: + if isinstance(item, FrameInfo): + yield from self.format_frame(item) + else: + yield self.format_repeated_frames(item) + + def format_repeated_frames(self, repeated_frames: RepeatedFrames) -> str: + return ' [... skipping similar frames: {}]\n'.format( + repeated_frames.description + ) + + def format_frame(self, frame: Union[FrameInfo, FrameType, TracebackType]) -> Iterable[str]: + if not isinstance(frame, FrameInfo): + frame = FrameInfo(frame, self.options) + + yield self.format_frame_header(frame) + + for line in frame.lines: + if isinstance(line, Line): + yield self.format_line(line) + elif isinstance(line, BlankLineRange): + yield self.format_blank_lines_linenumbers(line) + else: + assert_(line is LINE_GAP) + yield self.line_gap_string + "\n" + + if self.show_variables: + try: + yield from self.format_variables(frame) + except Exception: + pass + + def format_frame_header(self, frame_info: FrameInfo) -> str: + return ' File "{frame_info.filename}", line {frame_info.lineno}, in {name}\n'.format( + frame_info=frame_info, + name=( + frame_info.executing.code_qualname() + if self.use_code_qualname else + frame_info.code.co_name + ), + ) + + def format_line(self, line: Line) -> str: + result = "" + if self.current_line_indicator: + if line.is_current: + result = self.current_line_indicator + else: + result = " " * len(self.current_line_indicator) + result += " " + else: + result = " " + + if self.show_linenos: + result += self.line_number_format_string.format(line.lineno) + + prefix = result + + result += line.render( + pygmented=self.pygmented, + escape_html=self.html, + strip_leading_indent=self.strip_leading_indent, + ) + "\n" + + if self.show_executing_node and not self.pygmented: + for line_range in line.executing_node_ranges: + start = line_range.start - line.leading_indent + end = line_range.end - line.leading_indent + # if end <= start, we have an empty line inside a highlighted + # block of code. In this case, we need to avoid inserting + # an extra blank line with no markers present. + if end > start: + result += ( + " " * (start + len(prefix)) + + self.executing_node_underline * (end - start) + + "\n" + ) + return result + + + def format_blank_lines_linenumbers(self, blank_line): + if self.current_line_indicator: + result = " " * len(self.current_line_indicator) + " " + else: + result = " " + if blank_line.begin_lineno == blank_line.end_lineno: + return result + self.line_number_format_string.format(blank_line.begin_lineno) + "\n" + return result + " {}\n".format(self.line_number_gap_string) + + + def format_variables(self, frame_info: FrameInfo) -> Iterable[str]: + for var in sorted(frame_info.variables, key=lambda v: v.name): + try: + yield self.format_variable(var) + "\n" + except Exception: + pass + + def format_variable(self, var: Variable) -> str: + return "{} = {}".format( + var.name, + self.format_variable_value(var.value), + ) + + def format_variable_value(self, value) -> str: + return repr(value) diff --git a/evalkit_cambrian/lib/python3.10/site-packages/stack_data/py.typed b/evalkit_cambrian/lib/python3.10/site-packages/stack_data/py.typed new file mode 100644 index 0000000000000000000000000000000000000000..4402a14d48faf6be4c7f82990277198e07df1c8f --- /dev/null +++ b/evalkit_cambrian/lib/python3.10/site-packages/stack_data/py.typed @@ -0,0 +1 @@ +# Marker file for PEP 561. The ``stack_data`` package uses inline types. diff --git a/evalkit_cambrian/lib/python3.10/site-packages/stack_data/serializing.py b/evalkit_cambrian/lib/python3.10/site-packages/stack_data/serializing.py new file mode 100644 index 0000000000000000000000000000000000000000..0d813f69c4f49168c921c8405819c85fe963aebe --- /dev/null +++ b/evalkit_cambrian/lib/python3.10/site-packages/stack_data/serializing.py @@ -0,0 +1,201 @@ +import inspect +import logging +import sys +import traceback +from collections import Counter +from html import escape as escape_html +from types import FrameType, TracebackType +from typing import Union, Iterable, List + +from stack_data import ( + style_with_executing_node, + Options, + Line, + FrameInfo, + Variable, + RepeatedFrames, +) +from stack_data.utils import some_str + +log = logging.getLogger(__name__) + + +class Serializer: + def __init__( + self, + *, + options=None, + pygmented=False, + show_executing_node=True, + pygments_formatter_cls=None, + pygments_formatter_kwargs=None, + pygments_style="monokai", + executing_node_modifier="bg:#005080", + use_code_qualname=True, + strip_leading_indent=True, + html=False, + chain=True, + collapse_repeated_frames=True, + show_variables=False, + ): + if options is None: + options = Options() + + if pygmented and not options.pygments_formatter: + if show_executing_node: + pygments_style = style_with_executing_node( + pygments_style, executing_node_modifier + ) + + if pygments_formatter_cls is None: + if html: + from pygments.formatters.html import ( + HtmlFormatter as pygments_formatter_cls, + ) + else: + from pygments.formatters.terminal256 import ( + Terminal256Formatter as pygments_formatter_cls, + ) + + options.pygments_formatter = pygments_formatter_cls( + style=pygments_style, + **pygments_formatter_kwargs or {}, + ) + + self.pygmented = pygmented + self.use_code_qualname = use_code_qualname + self.strip_leading_indent = strip_leading_indent + self.html = html + self.chain = chain + self.options = options + self.collapse_repeated_frames = collapse_repeated_frames + self.show_variables = show_variables + + def format_exception(self, e=None) -> List[dict]: + if e is None: + e = sys.exc_info()[1] + + result = [] + + if self.chain: + if e.__cause__ is not None: + result = self.format_exception(e.__cause__) + result[-1]["tail"] = traceback._cause_message.strip() + elif e.__context__ is not None and not e.__suppress_context__: + result = self.format_exception(e.__context__) + result[-1]["tail"] = traceback._context_message.strip() + + result.append(self.format_traceback_part(e)) + return result + + def format_traceback_part(self, e: BaseException) -> dict: + return dict( + frames=self.format_stack(e.__traceback__ or sys.exc_info()[2]), + exception=dict( + type=type(e).__name__, + message=some_str(e), + ), + tail="", + ) + + def format_stack(self, frame_or_tb=None) -> List[dict]: + if frame_or_tb is None: + frame_or_tb = inspect.currentframe().f_back + + return list( + self.format_stack_data( + FrameInfo.stack_data( + frame_or_tb, + self.options, + collapse_repeated_frames=self.collapse_repeated_frames, + ) + ) + ) + + def format_stack_data( + self, stack: Iterable[Union[FrameInfo, RepeatedFrames]] + ) -> Iterable[dict]: + for item in stack: + if isinstance(item, FrameInfo): + if not self.should_include_frame(item): + continue + yield dict(type="frame", **self.format_frame(item)) + else: + yield dict(type="repeated_frames", **self.format_repeated_frames(item)) + + def format_repeated_frames(self, repeated_frames: RepeatedFrames) -> dict: + counts = sorted( + Counter(repeated_frames.frame_keys).items(), + key=lambda item: (-item[1], item[0][0].co_name), + ) + return dict( + frames=[ + dict( + name=code.co_name, + lineno=lineno, + count=count, + ) + for (code, lineno), count in counts + ] + ) + + def format_frame(self, frame: Union[FrameInfo, FrameType, TracebackType]) -> dict: + if not isinstance(frame, FrameInfo): + frame = FrameInfo(frame, self.options) + + result = dict( + name=( + frame.executing.code_qualname() + if self.use_code_qualname + else frame.code.co_name + ), + filename=frame.filename, + lineno=frame.lineno, + lines=list(self.format_lines(frame.lines)), + ) + if self.show_variables: + result["variables"] = list(self.format_variables(frame)) + return result + + def format_lines(self, lines): + for line in lines: + if isinstance(line, Line): + yield dict(type="line", **self.format_line(line)) + else: + yield dict(type="line_gap") + + def format_line(self, line: Line) -> dict: + return dict( + is_current=line.is_current, + lineno=line.lineno, + text=line.render( + pygmented=self.pygmented, + escape_html=self.html, + strip_leading_indent=self.strip_leading_indent, + ), + ) + + def format_variables(self, frame_info: FrameInfo) -> Iterable[dict]: + try: + for var in sorted(frame_info.variables, key=lambda v: v.name): + yield self.format_variable(var) + except Exception: # pragma: no cover + log.exception("Error in getting frame variables") + + def format_variable(self, var: Variable) -> dict: + return dict( + name=self.format_variable_part(var.name), + value=self.format_variable_part(self.format_variable_value(var.value)), + ) + + def format_variable_part(self, text): + if self.html: + return escape_html(text) + else: + return text + + def format_variable_value(self, value) -> str: + return repr(value) + + def should_include_frame(self, frame_info: FrameInfo) -> bool: + return True # pragma: no cover diff --git a/evalkit_cambrian/lib/python3.10/site-packages/stack_data/utils.py b/evalkit_cambrian/lib/python3.10/site-packages/stack_data/utils.py new file mode 100644 index 0000000000000000000000000000000000000000..ad8cd38dc46b41155cabf4ff264d6d4f4beef8ea --- /dev/null +++ b/evalkit_cambrian/lib/python3.10/site-packages/stack_data/utils.py @@ -0,0 +1,185 @@ +import ast +import itertools +import types +from collections import OrderedDict, Counter, defaultdict +from types import FrameType, TracebackType +from typing import ( + Iterator, List, Tuple, Iterable, Callable, Union, + TypeVar, Mapping, +) + +from asttokens import ASTText + +T = TypeVar('T') +R = TypeVar('R') + + +def truncate(seq, max_length: int, middle): + if len(seq) > max_length: + right = (max_length - len(middle)) // 2 + left = max_length - len(middle) - right + seq = seq[:left] + middle + seq[-right:] + return seq + + +def unique_in_order(it: Iterable[T]) -> List[T]: + return list(OrderedDict.fromkeys(it)) + + +def line_range(atok: ASTText, node: ast.AST) -> Tuple[int, int]: + """ + Returns a pair of numbers representing a half open range + (i.e. suitable as arguments to the `range()` builtin) + of line numbers of the given AST nodes. + """ + if isinstance(node, getattr(ast, "match_case", ())): + start, _end = line_range(atok, node.pattern) + _start, end = line_range(atok, node.body[-1]) + return start, end + else: + (start, _), (end, _) = atok.get_text_positions(node, padded=False) + return start, end + 1 + + +def highlight_unique(lst: List[T]) -> Iterator[Tuple[T, bool]]: + counts = Counter(lst) + + for is_common, group in itertools.groupby(lst, key=lambda x: counts[x] > 3): + if is_common: + group = list(group) + highlighted = [False] * len(group) + + def highlight_index(f): + try: + i = f() + except ValueError: + return None + highlighted[i] = True + return i + + for item in set(group): + first = highlight_index(lambda: group.index(item)) + if first is not None: + highlight_index(lambda: group.index(item, first + 1)) + highlight_index(lambda: -1 - group[::-1].index(item)) + else: + highlighted = itertools.repeat(True) + + yield from zip(group, highlighted) + + +def identity(x: T) -> T: + return x + + +def collapse_repeated(lst, *, collapser, mapper=identity, key=identity): + keyed = list(map(key, lst)) + for is_highlighted, group in itertools.groupby( + zip(lst, highlight_unique(keyed)), + key=lambda t: t[1][1], + ): + original_group, highlighted_group = zip(*group) + if is_highlighted: + yield from map(mapper, original_group) + else: + keyed_group, _ = zip(*highlighted_group) + yield collapser(list(original_group), list(keyed_group)) + + +def is_frame(frame_or_tb: Union[FrameType, TracebackType]) -> bool: + assert_(isinstance(frame_or_tb, (types.FrameType, types.TracebackType))) + return isinstance(frame_or_tb, (types.FrameType,)) + + +def iter_stack(frame_or_tb: Union[FrameType, TracebackType]) -> Iterator[Union[FrameType, TracebackType]]: + current: Union[FrameType, TracebackType, None] = frame_or_tb + while current: + yield current + if is_frame(current): + current = current.f_back + else: + current = current.tb_next + + +def frame_and_lineno(frame_or_tb: Union[FrameType, TracebackType]) -> Tuple[FrameType, int]: + if is_frame(frame_or_tb): + return frame_or_tb, frame_or_tb.f_lineno + else: + return frame_or_tb.tb_frame, frame_or_tb.tb_lineno + + +def group_by_key_func(iterable: Iterable[T], key_func: Callable[[T], R]) -> Mapping[R, List[T]]: + # noinspection PyUnresolvedReferences + """ + Create a dictionary from an iterable such that the keys are the result of evaluating a key function on elements + of the iterable and the values are lists of elements all of which correspond to the key. + + >>> def si(d): return sorted(d.items()) + >>> si(group_by_key_func("a bb ccc d ee fff".split(), len)) + [(1, ['a', 'd']), (2, ['bb', 'ee']), (3, ['ccc', 'fff'])] + >>> si(group_by_key_func([-1, 0, 1, 3, 6, 8, 9, 2], lambda x: x % 2)) + [(0, [0, 6, 8, 2]), (1, [-1, 1, 3, 9])] + """ + result = defaultdict(list) + for item in iterable: + result[key_func(item)].append(item) + return result + + +class cached_property(object): + """ + A property that is only computed once per instance and then replaces itself + with an ordinary attribute. Deleting the attribute resets the property. + + Based on https://github.com/pydanny/cached-property/blob/master/cached_property.py + """ + + def __init__(self, func): + self.__doc__ = func.__doc__ + self.func = func + + def cached_property_wrapper(self, obj, _cls): + if obj is None: + return self + + value = obj.__dict__[self.func.__name__] = self.func(obj) + return value + + __get__ = cached_property_wrapper + + +def _pygmented_with_ranges(formatter, code, ranges): + import pygments + from pygments.lexers import get_lexer_by_name + + class MyLexer(type(get_lexer_by_name("python3"))): + def get_tokens(self, text): + length = 0 + for ttype, value in super().get_tokens(text): + if any(start <= length < end for start, end in ranges): + ttype = ttype.ExecutingNode + length += len(value) + yield ttype, value + + lexer = MyLexer(stripnl=False) + try: + highlighted = pygments.highlight(code, lexer, formatter) + except Exception: + # When pygments fails, prefer code without highlighting over crashing + highlighted = code + return highlighted.splitlines() + + +def assert_(condition, error=""): + if not condition: + if isinstance(error, str): + error = AssertionError(error) + raise error + + +# Copied from the standard traceback module pre-3.11 +def some_str(value): + try: + return str(value) + except: + return '' % type(value).__name__ diff --git a/evalkit_cambrian/lib/python3.10/site-packages/wcwidth-0.2.13.dist-info/METADATA b/evalkit_cambrian/lib/python3.10/site-packages/wcwidth-0.2.13.dist-info/METADATA new file mode 100644 index 0000000000000000000000000000000000000000..f7f7fcdcc852b664672f30f017d14a0688486da8 --- /dev/null +++ b/evalkit_cambrian/lib/python3.10/site-packages/wcwidth-0.2.13.dist-info/METADATA @@ -0,0 +1,410 @@ +Metadata-Version: 2.1 +Name: wcwidth +Version: 0.2.13 +Summary: Measures the displayed width of unicode strings in a terminal +Home-page: https://github.com/jquast/wcwidth +Author: Jeff Quast +Author-email: contact@jeffquast.com +License: MIT +Keywords: cjk,combining,console,eastasian,emoji,emulator,terminal,unicode,wcswidth,wcwidth,xterm +Classifier: Intended Audience :: Developers +Classifier: Natural Language :: English +Classifier: Development Status :: 5 - Production/Stable +Classifier: Environment :: Console +Classifier: License :: OSI Approved :: MIT License +Classifier: Operating System :: POSIX +Classifier: Programming Language :: Python :: 2.7 +Classifier: Programming Language :: Python :: 3.5 +Classifier: Programming Language :: Python :: 3.6 +Classifier: Programming Language :: Python :: 3.7 +Classifier: Programming Language :: Python :: 3.8 +Classifier: Programming Language :: Python :: 3.9 +Classifier: Programming Language :: Python :: 3.10 +Classifier: Programming Language :: Python :: 3.11 +Classifier: Programming Language :: Python :: 3.12 +Classifier: Topic :: Software Development :: Libraries +Classifier: Topic :: Software Development :: Localization +Classifier: Topic :: Software Development :: Internationalization +Classifier: Topic :: Terminals +License-File: LICENSE +Requires-Dist: backports.functools-lru-cache >=1.2.1 ; python_version < "3.2" + +|pypi_downloads| |codecov| |license| + +============ +Introduction +============ + +This library is mainly for CLI programs that carefully produce output for +Terminals, or make pretend to be an emulator. + +**Problem Statement**: The printable length of *most* strings are equal to the +number of cells they occupy on the screen ``1 character : 1 cell``. However, +there are categories of characters that *occupy 2 cells* (full-wide), and +others that *occupy 0* cells (zero-width). + +**Solution**: POSIX.1-2001 and POSIX.1-2008 conforming systems provide +`wcwidth(3)`_ and `wcswidth(3)`_ C functions of which this python module's +functions precisely copy. *These functions return the number of cells a +unicode string is expected to occupy.* + +Installation +------------ + +The stable version of this package is maintained on pypi, install using pip:: + + pip install wcwidth + +Example +------- + +**Problem**: given the following phrase (Japanese), + + >>> text = u'コンニチハ' + +Python **incorrectly** uses the *string length* of 5 codepoints rather than the +*printable length* of 10 cells, so that when using the `rjust` function, the +output length is wrong:: + + >>> print(len('コンニチハ')) + 5 + + >>> print('コンニチハ'.rjust(20, '_')) + _______________コンニチハ + +By defining our own "rjust" function that uses wcwidth, we can correct this:: + + >>> def wc_rjust(text, length, padding=' '): + ... from wcwidth import wcswidth + ... return padding * max(0, (length - wcswidth(text))) + text + ... + +Our **Solution** uses wcswidth to determine the string length correctly:: + + >>> from wcwidth import wcswidth + >>> print(wcswidth('コンニチハ')) + 10 + + >>> print(wc_rjust('コンニチハ', 20, '_')) + __________コンニチハ + + +Choosing a Version +------------------ + +Export an environment variable, ``UNICODE_VERSION``. This should be done by +*terminal emulators* or those developers experimenting with authoring one of +their own, from shell:: + + $ export UNICODE_VERSION=13.0 + +If unspecified, the latest version is used. If your Terminal Emulator does not +export this variable, you can use the `jquast/ucs-detect`_ utility to +automatically detect and export it to your shell. + +wcwidth, wcswidth +----------------- +Use function ``wcwidth()`` to determine the length of a *single unicode +character*, and ``wcswidth()`` to determine the length of many, a *string +of unicode characters*. + +Briefly, return values of function ``wcwidth()`` are: + +``-1`` + Indeterminate (not printable). + +``0`` + Does not advance the cursor, such as NULL or Combining. + +``2`` + Characters of category East Asian Wide (W) or East Asian + Full-width (F) which are displayed using two terminal cells. + +``1`` + All others. + +Function ``wcswidth()`` simply returns the sum of all values for each character +along a string, or ``-1`` when it occurs anywhere along a string. + +Full API Documentation at https://wcwidth.readthedocs.org + +========== +Developing +========== + +Install wcwidth in editable mode:: + + pip install -e . + +Execute unit tests using tox_:: + + tox -e py27,py35,py36,py37,py38,py39,py310,py311,py312 + +Updating Unicode Version +------------------------ + +Regenerate python code tables from latest Unicode Specification data files:: + + tox -e update + +The script is located at ``bin/update-tables.py``, requires Python 3.9 or +later. It is recommended but not necessary to run this script with the newest +Python, because the newest Python has the latest ``unicodedata`` for generating +comments. + +Building Documentation +---------------------- + +This project is using `sphinx`_ 4.5 to build documentation:: + + tox -e sphinx + +The output will be in ``docs/_build/html/``. + +Updating Requirements +--------------------- + +This project is using `pip-tools`_ to manage requirements. + +To upgrade requirements for updating unicode version, run:: + + tox -e update_requirements_update + +To upgrade requirements for testing, run:: + + tox -e update_requirements37,update_requirements39 + +To upgrade requirements for building documentation, run:: + + tox -e update_requirements_docs + +Utilities +--------- + +Supplementary tools for browsing and testing terminals for wide unicode +characters are found in the `bin/`_ of this project's source code. Just ensure +to first ``pip install -r requirements-develop.txt`` from this projects main +folder. For example, an interactive browser for testing:: + + python ./bin/wcwidth-browser.py + +==== +Uses +==== + +This library is used in: + +- `jquast/blessed`_: a thin, practical wrapper around terminal capabilities in + Python. + +- `prompt-toolkit/python-prompt-toolkit`_: a Library for building powerful + interactive command lines in Python. + +- `dbcli/pgcli`_: Postgres CLI with autocompletion and syntax highlighting. + +- `thomasballinger/curtsies`_: a Curses-like terminal wrapper with a display + based on compositing 2d arrays of text. + +- `selectel/pyte`_: Simple VTXXX-compatible linux terminal emulator. + +- `astanin/python-tabulate`_: Pretty-print tabular data in Python, a library + and a command-line utility. + +- `rspeer/python-ftfy`_: Fixes mojibake and other glitches in Unicode + text. + +- `nbedos/termtosvg`_: Terminal recorder that renders sessions as SVG + animations. + +- `peterbrittain/asciimatics`_: Package to help people create full-screen text + UIs. + +- `python-cmd2/cmd2`_: A tool for building interactive command line apps + +- `stratis-storage/stratis-cli`_: CLI for the Stratis project + +- `ihabunek/toot`_: A Mastodon CLI/TUI client + +- `saulpw/visidata`_: Terminal spreadsheet multitool for discovering and + arranging data + +=============== +Other Languages +=============== + +- `timoxley/wcwidth`_: JavaScript +- `janlelis/unicode-display_width`_: Ruby +- `alecrabbit/php-wcwidth`_: PHP +- `Text::CharWidth`_: Perl +- `bluebear94/Terminal-WCWidth`_: Perl 6 +- `mattn/go-runewidth`_: Go +- `grepsuzette/wcwidth`_: Haxe +- `aperezdc/lua-wcwidth`_: Lua +- `joachimschmidt557/zig-wcwidth`_: Zig +- `fumiyas/wcwidth-cjk`_: `LD_PRELOAD` override +- `joshuarubin/wcwidth9`_: Unicode version 9 in C + +======= +History +======= + +0.2.13 *2024-01-06* + * **Bugfix** zero-width support for Hangul Jamo (Korean) + +0.2.12 *2023-11-21* + * re-release to remove .pyi file misplaced in wheel files `Issue #101`_. + +0.2.11 *2023-11-20* + * Include tests files in the source distribution (`PR #98`_, `PR #100`_). + +0.2.10 *2023-11-13* + * **Bugfix** accounting of some kinds of emoji sequences using U+FE0F + Variation Selector 16 (`PR #97`_). + * **Updated** `Specification `_. + +0.2.9 *2023-10-30* + * **Bugfix** zero-width characters used in Emoji ZWJ sequences, Balinese, + Jamo, Devanagari, Tamil, Kannada and others (`PR #91`_). + * **Updated** to include `Specification `_ of + character measurements. + +0.2.8 *2023-09-30* + * Include requirements files in the source distribution (`PR #82`_). + +0.2.7 *2023-09-28* + * **Updated** tables to include Unicode Specification 15.1.0. + * Include ``bin``, ``docs``, and ``tox.ini`` in the source distribution + +0.2.6 *2023-01-14* + * **Updated** tables to include Unicode Specification 14.0.0 and 15.0.0. + * **Changed** developer tools to use pip-compile, and to use jinja2 templates + for code generation in `bin/update-tables.py` to prepare for possible + compiler optimization release. + +0.2.1 .. 0.2.5 *2020-06-23* + * **Repository** changes to update tests and packaging issues, and + begin tagging repository with matching release versions. + +0.2.0 *2020-06-01* + * **Enhancement**: Unicode version may be selected by exporting the + Environment variable ``UNICODE_VERSION``, such as ``13.0``, or ``6.3.0``. + See the `jquast/ucs-detect`_ CLI utility for automatic detection. + * **Enhancement**: + API Documentation is published to readthedocs.org. + * **Updated** tables for *all* Unicode Specifications with files + published in a programmatically consumable format, versions 4.1.0 + through 13.0 + +0.1.9 *2020-03-22* + * **Performance** optimization by `Avram Lubkin`_, `PR #35`_. + * **Updated** tables to Unicode Specification 13.0.0. + +0.1.8 *2020-01-01* + * **Updated** tables to Unicode Specification 12.0.0. (`PR #30`_). + +0.1.7 *2016-07-01* + * **Updated** tables to Unicode Specification 9.0.0. (`PR #18`_). + +0.1.6 *2016-01-08 Production/Stable* + * ``LICENSE`` file now included with distribution. + +0.1.5 *2015-09-13 Alpha* + * **Bugfix**: + Resolution of "combining_ character width" issue, most especially + those that previously returned -1 now often (correctly) return 0. + resolved by `Philip Craig`_ via `PR #11`_. + * **Deprecated**: + The module path ``wcwidth.table_comb`` is no longer available, + it has been superseded by module path ``wcwidth.table_zero``. + +0.1.4 *2014-11-20 Pre-Alpha* + * **Feature**: ``wcswidth()`` now determines printable length + for (most) combining_ characters. The developer's tool + `bin/wcwidth-browser.py`_ is improved to display combining_ + characters when provided the ``--combining`` option + (`Thomas Ballinger`_ and `Leta Montopoli`_ `PR #5`_). + * **Feature**: added static analysis (prospector_) to testing + framework. + +0.1.3 *2014-10-29 Pre-Alpha* + * **Bugfix**: 2nd parameter of wcswidth was not honored. + (`Thomas Ballinger`_, `PR #4`_). + +0.1.2 *2014-10-28 Pre-Alpha* + * **Updated** tables to Unicode Specification 7.0.0. + (`Thomas Ballinger`_, `PR #3`_). + +0.1.1 *2014-05-14 Pre-Alpha* + * Initial release to pypi, Based on Unicode Specification 6.3.0 + +This code was originally derived directly from C code of the same name, +whose latest version is available at +https://www.cl.cam.ac.uk/~mgk25/ucs/wcwidth.c:: + + * Markus Kuhn -- 2007-05-26 (Unicode 5.0) + * + * Permission to use, copy, modify, and distribute this software + * for any purpose and without fee is hereby granted. The author + * disclaims all warranties with regard to this software. + +.. _`Specification_from_pypi`: https://wcwidth.readthedocs.io/en/latest/specs.html +.. _`tox`: https://tox.wiki/en/latest/ +.. _`prospector`: https://github.com/landscapeio/prospector +.. _`combining`: https://en.wikipedia.org/wiki/Combining_character +.. _`bin/`: https://github.com/jquast/wcwidth/tree/master/bin +.. _`bin/wcwidth-browser.py`: https://github.com/jquast/wcwidth/blob/master/bin/wcwidth-browser.py +.. _`Thomas Ballinger`: https://github.com/thomasballinger +.. _`Leta Montopoli`: https://github.com/lmontopo +.. _`Philip Craig`: https://github.com/philipc +.. _`PR #3`: https://github.com/jquast/wcwidth/pull/3 +.. _`PR #4`: https://github.com/jquast/wcwidth/pull/4 +.. _`PR #5`: https://github.com/jquast/wcwidth/pull/5 +.. _`PR #11`: https://github.com/jquast/wcwidth/pull/11 +.. _`PR #18`: https://github.com/jquast/wcwidth/pull/18 +.. _`PR #30`: https://github.com/jquast/wcwidth/pull/30 +.. _`PR #35`: https://github.com/jquast/wcwidth/pull/35 +.. _`PR #82`: https://github.com/jquast/wcwidth/pull/82 +.. _`PR #91`: https://github.com/jquast/wcwidth/pull/91 +.. _`PR #97`: https://github.com/jquast/wcwidth/pull/97 +.. _`PR #98`: https://github.com/jquast/wcwidth/pull/98 +.. _`PR #100`: https://github.com/jquast/wcwidth/pull/100 +.. _`Issue #101`: https://github.com/jquast/wcwidth/issues/101 +.. _`jquast/blessed`: https://github.com/jquast/blessed +.. _`selectel/pyte`: https://github.com/selectel/pyte +.. _`thomasballinger/curtsies`: https://github.com/thomasballinger/curtsies +.. _`dbcli/pgcli`: https://github.com/dbcli/pgcli +.. _`prompt-toolkit/python-prompt-toolkit`: https://github.com/prompt-toolkit/python-prompt-toolkit +.. _`timoxley/wcwidth`: https://github.com/timoxley/wcwidth +.. _`wcwidth(3)`: https://man7.org/linux/man-pages/man3/wcwidth.3.html +.. _`wcswidth(3)`: https://man7.org/linux/man-pages/man3/wcswidth.3.html +.. _`astanin/python-tabulate`: https://github.com/astanin/python-tabulate +.. _`janlelis/unicode-display_width`: https://github.com/janlelis/unicode-display_width +.. _`rspeer/python-ftfy`: https://github.com/rspeer/python-ftfy +.. _`alecrabbit/php-wcwidth`: https://github.com/alecrabbit/php-wcwidth +.. _`Text::CharWidth`: https://metacpan.org/pod/Text::CharWidth +.. _`bluebear94/Terminal-WCWidth`: https://github.com/bluebear94/Terminal-WCWidth +.. _`mattn/go-runewidth`: https://github.com/mattn/go-runewidth +.. _`grepsuzette/wcwidth`: https://github.com/grepsuzette/wcwidth +.. _`jquast/ucs-detect`: https://github.com/jquast/ucs-detect +.. _`Avram Lubkin`: https://github.com/avylove +.. _`nbedos/termtosvg`: https://github.com/nbedos/termtosvg +.. _`peterbrittain/asciimatics`: https://github.com/peterbrittain/asciimatics +.. _`aperezdc/lua-wcwidth`: https://github.com/aperezdc/lua-wcwidth +.. _`joachimschmidt557/zig-wcwidth`: https://github.com/joachimschmidt557/zig-wcwidth +.. _`fumiyas/wcwidth-cjk`: https://github.com/fumiyas/wcwidth-cjk +.. _`joshuarubin/wcwidth9`: https://github.com/joshuarubin/wcwidth9 +.. _`python-cmd2/cmd2`: https://github.com/python-cmd2/cmd2 +.. _`stratis-storage/stratis-cli`: https://github.com/stratis-storage/stratis-cli +.. _`ihabunek/toot`: https://github.com/ihabunek/toot +.. _`saulpw/visidata`: https://github.com/saulpw/visidata +.. _`pip-tools`: https://pip-tools.readthedocs.io/ +.. _`sphinx`: https://www.sphinx-doc.org/ +.. |pypi_downloads| image:: https://img.shields.io/pypi/dm/wcwidth.svg?logo=pypi + :alt: Downloads + :target: https://pypi.org/project/wcwidth/ +.. |codecov| image:: https://codecov.io/gh/jquast/wcwidth/branch/master/graph/badge.svg + :alt: codecov.io Code Coverage + :target: https://app.codecov.io/gh/jquast/wcwidth/ +.. |license| image:: https://img.shields.io/pypi/l/wcwidth.svg + :target: https://pypi.org/project/wcwidth/ + :alt: MIT License diff --git a/janus/lib/python3.10/site-packages/numpy/lib/__init__.py b/janus/lib/python3.10/site-packages/numpy/lib/__init__.py new file mode 100644 index 0000000000000000000000000000000000000000..928121ce8f28bf49ab30283d5901aa8bb0414d29 --- /dev/null +++ b/janus/lib/python3.10/site-packages/numpy/lib/__init__.py @@ -0,0 +1,94 @@ +""" +``numpy.lib`` is mostly a space for implementing functions that don't +belong in core or in another NumPy submodule with a clear purpose +(e.g. ``random``, ``fft``, ``linalg``, ``ma``). + +``numpy.lib``'s private submodules contain basic functions that are used by +other public modules and are useful to have in the main name-space. + +""" + +# Public submodules +# Note: recfunctions and (maybe) format are public too, but not imported +from . import array_utils +from . import introspect +from . import mixins +from . import npyio +from . import scimath +from . import stride_tricks + +# Private submodules +# load module names. See https://github.com/networkx/networkx/issues/5838 +from . import _type_check_impl +from . import _index_tricks_impl +from . import _nanfunctions_impl +from . import _function_base_impl +from . import _stride_tricks_impl +from . import _shape_base_impl +from . import _twodim_base_impl +from . import _ufunclike_impl +from . import _histograms_impl +from . import _utils_impl +from . import _arraysetops_impl +from . import _polynomial_impl +from . import _npyio_impl +from . import _arrayterator_impl +from . import _arraypad_impl +from . import _version + +# numpy.lib namespace members +from ._arrayterator_impl import Arrayterator +from ._version import NumpyVersion +from numpy._core._multiarray_umath import add_docstring, tracemalloc_domain +from numpy._core.function_base import add_newdoc + +__all__ = [ + "Arrayterator", "add_docstring", "add_newdoc", "array_utils", + "introspect", "mixins", "NumpyVersion", "npyio", "scimath", + "stride_tricks", "tracemalloc_domain" +] + +add_newdoc.__module__ = "numpy.lib" + +from numpy._pytesttester import PytestTester +test = PytestTester(__name__) +del PytestTester + +def __getattr__(attr): + # Warn for deprecated/removed aliases + import math + import warnings + + if attr == "math": + warnings.warn( + "`np.lib.math` is a deprecated alias for the standard library " + "`math` module (Deprecated Numpy 1.25). Replace usages of " + "`numpy.lib.math` with `math`", DeprecationWarning, stacklevel=2) + return math + elif attr == "emath": + raise AttributeError( + "numpy.lib.emath was an alias for emath module that was removed " + "in NumPy 2.0. Replace usages of numpy.lib.emath with " + "numpy.emath.", + name=None + ) + elif attr in ( + "histograms", "type_check", "nanfunctions", "function_base", + "arraypad", "arraysetops", "ufunclike", "utils", "twodim_base", + "shape_base", "polynomial", "index_tricks", + ): + raise AttributeError( + f"numpy.lib.{attr} is now private. If you are using a public " + "function, it should be available in the main numpy namespace, " + "otherwise check the NumPy 2.0 migration guide.", + name=None + ) + elif attr == "arrayterator": + raise AttributeError( + "numpy.lib.arrayterator submodule is now private. To access " + "Arrayterator class use numpy.lib.Arrayterator.", + name=None + ) + else: + raise AttributeError("module {!r} has no attribute " + "{!r}".format(__name__, attr)) diff --git a/janus/lib/python3.10/site-packages/numpy/lib/_array_utils_impl.pyi b/janus/lib/python3.10/site-packages/numpy/lib/_array_utils_impl.pyi new file mode 100644 index 0000000000000000000000000000000000000000..11a2aafb8837dd1ac31f048743202933482da4a3 --- /dev/null +++ b/janus/lib/python3.10/site-packages/numpy/lib/_array_utils_impl.pyi @@ -0,0 +1,25 @@ +from typing import Any, Iterable + +from numpy import generic +from numpy.typing import NDArray + +__all__ = ["byte_bounds", "normalize_axis_tuple", "normalize_axis_index"] + +# NOTE: In practice `byte_bounds` can (potentially) take any object +# implementing the `__array_interface__` protocol. The caveat is +# that certain keys, marked as optional in the spec, must be present for +# `byte_bounds`. This concerns `"strides"` and `"data"`. +def byte_bounds(a: generic | NDArray[Any]) -> tuple[int, int]: ... + +def normalize_axis_tuple( + axis: int | Iterable[int], + ndim: int = ..., + argname: None | str = ..., + allow_duplicate: None | bool = ..., +) -> tuple[int, int]: ... + +def normalize_axis_index( + axis: int = ..., + ndim: int = ..., + msg_prefix: None | str = ..., +) -> int: ... diff --git a/janus/lib/python3.10/site-packages/numpy/lib/_arrayterator_impl.py b/janus/lib/python3.10/site-packages/numpy/lib/_arrayterator_impl.py new file mode 100644 index 0000000000000000000000000000000000000000..efc529de5cff5c051e5df5b5847a4317d60d75cb --- /dev/null +++ b/janus/lib/python3.10/site-packages/numpy/lib/_arrayterator_impl.py @@ -0,0 +1,224 @@ +""" +A buffered iterator for big arrays. + +This module solves the problem of iterating over a big file-based array +without having to read it into memory. The `Arrayterator` class wraps +an array object, and when iterated it will return sub-arrays with at most +a user-specified number of elements. + +""" +from operator import mul +from functools import reduce + +__all__ = ['Arrayterator'] + + +class Arrayterator: + """ + Buffered iterator for big arrays. + + `Arrayterator` creates a buffered iterator for reading big arrays in small + contiguous blocks. The class is useful for objects stored in the + file system. It allows iteration over the object *without* reading + everything in memory; instead, small blocks are read and iterated over. + + `Arrayterator` can be used with any object that supports multidimensional + slices. This includes NumPy arrays, but also variables from + Scientific.IO.NetCDF or pynetcdf for example. + + Parameters + ---------- + var : array_like + The object to iterate over. + buf_size : int, optional + The buffer size. If `buf_size` is supplied, the maximum amount of + data that will be read into memory is `buf_size` elements. + Default is None, which will read as many element as possible + into memory. + + Attributes + ---------- + var + buf_size + start + stop + step + shape + flat + + See Also + -------- + numpy.ndenumerate : Multidimensional array iterator. + numpy.flatiter : Flat array iterator. + numpy.memmap : Create a memory-map to an array stored + in a binary file on disk. + + Notes + ----- + The algorithm works by first finding a "running dimension", along which + the blocks will be extracted. Given an array of dimensions + ``(d1, d2, ..., dn)``, e.g. if `buf_size` is smaller than ``d1``, the + first dimension will be used. If, on the other hand, + ``d1 < buf_size < d1*d2`` the second dimension will be used, and so on. + Blocks are extracted along this dimension, and when the last block is + returned the process continues from the next dimension, until all + elements have been read. + + Examples + -------- + >>> import numpy as np + >>> a = np.arange(3 * 4 * 5 * 6).reshape(3, 4, 5, 6) + >>> a_itor = np.lib.Arrayterator(a, 2) + >>> a_itor.shape + (3, 4, 5, 6) + + Now we can iterate over ``a_itor``, and it will return arrays of size + two. Since `buf_size` was smaller than any dimension, the first + dimension will be iterated over first: + + >>> for subarr in a_itor: + ... if not subarr.all(): + ... print(subarr, subarr.shape) # doctest: +SKIP + >>> # [[[[0 1]]]] (1, 1, 1, 2) + + """ + + __module__ = "numpy.lib" + + def __init__(self, var, buf_size=None): + self.var = var + self.buf_size = buf_size + + self.start = [0 for dim in var.shape] + self.stop = list(var.shape) + self.step = [1 for dim in var.shape] + + def __getattr__(self, attr): + return getattr(self.var, attr) + + def __getitem__(self, index): + """ + Return a new arrayterator. + + """ + # Fix index, handling ellipsis and incomplete slices. + if not isinstance(index, tuple): + index = (index,) + fixed = [] + length, dims = len(index), self.ndim + for slice_ in index: + if slice_ is Ellipsis: + fixed.extend([slice(None)] * (dims-length+1)) + length = len(fixed) + elif isinstance(slice_, int): + fixed.append(slice(slice_, slice_+1, 1)) + else: + fixed.append(slice_) + index = tuple(fixed) + if len(index) < dims: + index += (slice(None),) * (dims-len(index)) + + # Return a new arrayterator object. + out = self.__class__(self.var, self.buf_size) + for i, (start, stop, step, slice_) in enumerate( + zip(self.start, self.stop, self.step, index)): + out.start[i] = start + (slice_.start or 0) + out.step[i] = step * (slice_.step or 1) + out.stop[i] = start + (slice_.stop or stop-start) + out.stop[i] = min(stop, out.stop[i]) + return out + + def __array__(self, dtype=None, copy=None): + """ + Return corresponding data. + + """ + slice_ = tuple(slice(*t) for t in zip( + self.start, self.stop, self.step)) + return self.var[slice_] + + @property + def flat(self): + """ + A 1-D flat iterator for Arrayterator objects. + + This iterator returns elements of the array to be iterated over in + `~lib.Arrayterator` one by one. + It is similar to `flatiter`. + + See Also + -------- + lib.Arrayterator + flatiter + + Examples + -------- + >>> a = np.arange(3 * 4 * 5 * 6).reshape(3, 4, 5, 6) + >>> a_itor = np.lib.Arrayterator(a, 2) + + >>> for subarr in a_itor.flat: + ... if not subarr: + ... print(subarr, type(subarr)) + ... + 0 + + """ + for block in self: + yield from block.flat + + @property + def shape(self): + """ + The shape of the array to be iterated over. + + For an example, see `Arrayterator`. + + """ + return tuple(((stop-start-1)//step+1) for start, stop, step in + zip(self.start, self.stop, self.step)) + + def __iter__(self): + # Skip arrays with degenerate dimensions + if [dim for dim in self.shape if dim <= 0]: + return + + start = self.start[:] + stop = self.stop[:] + step = self.step[:] + ndims = self.var.ndim + + while True: + count = self.buf_size or reduce(mul, self.shape) + + # iterate over each dimension, looking for the + # running dimension (ie, the dimension along which + # the blocks will be built from) + rundim = 0 + for i in range(ndims-1, -1, -1): + # if count is zero we ran out of elements to read + # along higher dimensions, so we read only a single position + if count == 0: + stop[i] = start[i]+1 + elif count <= self.shape[i]: + # limit along this dimension + stop[i] = start[i] + count*step[i] + rundim = i + else: + # read everything along this dimension + stop[i] = self.stop[i] + stop[i] = min(self.stop[i], stop[i]) + count = count//self.shape[i] + + # yield a block + slice_ = tuple(slice(*t) for t in zip(start, stop, step)) + yield self.var[slice_] + + # Update start position, taking care of overflow to + # other dimensions + start[rundim] = stop[rundim] # start where we stopped + for i in range(ndims-1, 0, -1): + if start[i] >= self.stop[i]: + start[i] = self.start[i] + start[i-1] += self.step[i-1] + if start[0] >= self.stop[0]: + return diff --git a/janus/lib/python3.10/site-packages/numpy/lib/_function_base_impl.py b/janus/lib/python3.10/site-packages/numpy/lib/_function_base_impl.py new file mode 100644 index 0000000000000000000000000000000000000000..3fa9c5f99d95352ffc29ade5d33531ccc1f90ead --- /dev/null +++ b/janus/lib/python3.10/site-packages/numpy/lib/_function_base_impl.py @@ -0,0 +1,5827 @@ +import builtins +import collections.abc +import functools +import re +import sys +import warnings + +import numpy as np +import numpy._core.numeric as _nx +from numpy._core import transpose, overrides +from numpy._core.numeric import ( + ones, zeros_like, arange, concatenate, array, asarray, asanyarray, empty, + ndarray, take, dot, where, intp, integer, isscalar, absolute + ) +from numpy._core.umath import ( + pi, add, arctan2, frompyfunc, cos, less_equal, sqrt, sin, + mod, exp, not_equal, subtract, minimum + ) +from numpy._core.fromnumeric import ( + ravel, nonzero, partition, mean, any, sum + ) +from numpy._core.numerictypes import typecodes +from numpy.lib._twodim_base_impl import diag +from numpy._core.multiarray import ( + _place, bincount, normalize_axis_index, _monotonicity, + interp as compiled_interp, interp_complex as compiled_interp_complex + ) +from numpy._core._multiarray_umath import _array_converter +from numpy._utils import set_module + +# needed in this module for compatibility +from numpy.lib._histograms_impl import histogram, histogramdd # noqa: F401 + + +array_function_dispatch = functools.partial( + overrides.array_function_dispatch, module='numpy') + + +__all__ = [ + 'select', 'piecewise', 'trim_zeros', 'copy', 'iterable', 'percentile', + 'diff', 'gradient', 'angle', 'unwrap', 'sort_complex', 'flip', + 'rot90', 'extract', 'place', 'vectorize', 'asarray_chkfinite', 'average', + 'bincount', 'digitize', 'cov', 'corrcoef', + 'median', 'sinc', 'hamming', 'hanning', 'bartlett', + 'blackman', 'kaiser', 'trapezoid', 'trapz', 'i0', + 'meshgrid', 'delete', 'insert', 'append', 'interp', + 'quantile' + ] + +# _QuantileMethods is a dictionary listing all the supported methods to +# compute quantile/percentile. +# +# Below virtual_index refers to the index of the element where the percentile +# would be found in the sorted sample. +# When the sample contains exactly the percentile wanted, the virtual_index is +# an integer to the index of this element. +# When the percentile wanted is in between two elements, the virtual_index +# is made of a integer part (a.k.a 'i' or 'left') and a fractional part +# (a.k.a 'g' or 'gamma') +# +# Each method in _QuantileMethods has two properties +# get_virtual_index : Callable +# The function used to compute the virtual_index. +# fix_gamma : Callable +# A function used for discrete methods to force the index to a specific value. +_QuantileMethods = dict( + # --- HYNDMAN and FAN METHODS + # Discrete methods + inverted_cdf=dict( + get_virtual_index=lambda n, quantiles: _inverted_cdf(n, quantiles), + fix_gamma=None, # should never be called + ), + averaged_inverted_cdf=dict( + get_virtual_index=lambda n, quantiles: (n * quantiles) - 1, + fix_gamma=lambda gamma, _: _get_gamma_mask( + shape=gamma.shape, + default_value=1., + conditioned_value=0.5, + where=gamma == 0), + ), + closest_observation=dict( + get_virtual_index=lambda n, quantiles: _closest_observation(n, + quantiles), + fix_gamma=None, # should never be called + ), + # Continuous methods + interpolated_inverted_cdf=dict( + get_virtual_index=lambda n, quantiles: + _compute_virtual_index(n, quantiles, 0, 1), + fix_gamma=lambda gamma, _: gamma, + ), + hazen=dict( + get_virtual_index=lambda n, quantiles: + _compute_virtual_index(n, quantiles, 0.5, 0.5), + fix_gamma=lambda gamma, _: gamma, + ), + weibull=dict( + get_virtual_index=lambda n, quantiles: + _compute_virtual_index(n, quantiles, 0, 0), + fix_gamma=lambda gamma, _: gamma, + ), + # Default method. + # To avoid some rounding issues, `(n-1) * quantiles` is preferred to + # `_compute_virtual_index(n, quantiles, 1, 1)`. + # They are mathematically equivalent. + linear=dict( + get_virtual_index=lambda n, quantiles: (n - 1) * quantiles, + fix_gamma=lambda gamma, _: gamma, + ), + median_unbiased=dict( + get_virtual_index=lambda n, quantiles: + _compute_virtual_index(n, quantiles, 1 / 3.0, 1 / 3.0), + fix_gamma=lambda gamma, _: gamma, + ), + normal_unbiased=dict( + get_virtual_index=lambda n, quantiles: + _compute_virtual_index(n, quantiles, 3 / 8.0, 3 / 8.0), + fix_gamma=lambda gamma, _: gamma, + ), + # --- OTHER METHODS + lower=dict( + get_virtual_index=lambda n, quantiles: np.floor( + (n - 1) * quantiles).astype(np.intp), + fix_gamma=None, # should never be called, index dtype is int + ), + higher=dict( + get_virtual_index=lambda n, quantiles: np.ceil( + (n - 1) * quantiles).astype(np.intp), + fix_gamma=None, # should never be called, index dtype is int + ), + midpoint=dict( + get_virtual_index=lambda n, quantiles: 0.5 * ( + np.floor((n - 1) * quantiles) + + np.ceil((n - 1) * quantiles)), + fix_gamma=lambda gamma, index: _get_gamma_mask( + shape=gamma.shape, + default_value=0.5, + conditioned_value=0., + where=index % 1 == 0), + ), + nearest=dict( + get_virtual_index=lambda n, quantiles: np.around( + (n - 1) * quantiles).astype(np.intp), + fix_gamma=None, + # should never be called, index dtype is int + )) + + +def _rot90_dispatcher(m, k=None, axes=None): + return (m,) + + +@array_function_dispatch(_rot90_dispatcher) +def rot90(m, k=1, axes=(0, 1)): + """ + Rotate an array by 90 degrees in the plane specified by axes. + + Rotation direction is from the first towards the second axis. + This means for a 2D array with the default `k` and `axes`, the + rotation will be counterclockwise. + + Parameters + ---------- + m : array_like + Array of two or more dimensions. + k : integer + Number of times the array is rotated by 90 degrees. + axes : (2,) array_like + The array is rotated in the plane defined by the axes. + Axes must be different. + + Returns + ------- + y : ndarray + A rotated view of `m`. + + See Also + -------- + flip : Reverse the order of elements in an array along the given axis. + fliplr : Flip an array horizontally. + flipud : Flip an array vertically. + + Notes + ----- + ``rot90(m, k=1, axes=(1,0))`` is the reverse of + ``rot90(m, k=1, axes=(0,1))`` + + ``rot90(m, k=1, axes=(1,0))`` is equivalent to + ``rot90(m, k=-1, axes=(0,1))`` + + Examples + -------- + >>> import numpy as np + >>> m = np.array([[1,2],[3,4]], int) + >>> m + array([[1, 2], + [3, 4]]) + >>> np.rot90(m) + array([[2, 4], + [1, 3]]) + >>> np.rot90(m, 2) + array([[4, 3], + [2, 1]]) + >>> m = np.arange(8).reshape((2,2,2)) + >>> np.rot90(m, 1, (1,2)) + array([[[1, 3], + [0, 2]], + [[5, 7], + [4, 6]]]) + + """ + axes = tuple(axes) + if len(axes) != 2: + raise ValueError("len(axes) must be 2.") + + m = asanyarray(m) + + if axes[0] == axes[1] or absolute(axes[0] - axes[1]) == m.ndim: + raise ValueError("Axes must be different.") + + if (axes[0] >= m.ndim or axes[0] < -m.ndim + or axes[1] >= m.ndim or axes[1] < -m.ndim): + raise ValueError("Axes={} out of range for array of ndim={}." + .format(axes, m.ndim)) + + k %= 4 + + if k == 0: + return m[:] + if k == 2: + return flip(flip(m, axes[0]), axes[1]) + + axes_list = arange(0, m.ndim) + (axes_list[axes[0]], axes_list[axes[1]]) = (axes_list[axes[1]], + axes_list[axes[0]]) + + if k == 1: + return transpose(flip(m, axes[1]), axes_list) + else: + # k == 3 + return flip(transpose(m, axes_list), axes[1]) + + +def _flip_dispatcher(m, axis=None): + return (m,) + + +@array_function_dispatch(_flip_dispatcher) +def flip(m, axis=None): + """ + Reverse the order of elements in an array along the given axis. + + The shape of the array is preserved, but the elements are reordered. + + Parameters + ---------- + m : array_like + Input array. + axis : None or int or tuple of ints, optional + Axis or axes along which to flip over. The default, + axis=None, will flip over all of the axes of the input array. + If axis is negative it counts from the last to the first axis. + + If axis is a tuple of ints, flipping is performed on all of the axes + specified in the tuple. + + Returns + ------- + out : array_like + A view of `m` with the entries of axis reversed. Since a view is + returned, this operation is done in constant time. + + See Also + -------- + flipud : Flip an array vertically (axis=0). + fliplr : Flip an array horizontally (axis=1). + + Notes + ----- + flip(m, 0) is equivalent to flipud(m). + + flip(m, 1) is equivalent to fliplr(m). + + flip(m, n) corresponds to ``m[...,::-1,...]`` with ``::-1`` at position n. + + flip(m) corresponds to ``m[::-1,::-1,...,::-1]`` with ``::-1`` at all + positions. + + flip(m, (0, 1)) corresponds to ``m[::-1,::-1,...]`` with ``::-1`` at + position 0 and position 1. + + Examples + -------- + >>> import numpy as np + >>> A = np.arange(8).reshape((2,2,2)) + >>> A + array([[[0, 1], + [2, 3]], + [[4, 5], + [6, 7]]]) + >>> np.flip(A, 0) + array([[[4, 5], + [6, 7]], + [[0, 1], + [2, 3]]]) + >>> np.flip(A, 1) + array([[[2, 3], + [0, 1]], + [[6, 7], + [4, 5]]]) + >>> np.flip(A) + array([[[7, 6], + [5, 4]], + [[3, 2], + [1, 0]]]) + >>> np.flip(A, (0, 2)) + array([[[5, 4], + [7, 6]], + [[1, 0], + [3, 2]]]) + >>> rng = np.random.default_rng() + >>> A = rng.normal(size=(3,4,5)) + >>> np.all(np.flip(A,2) == A[:,:,::-1,...]) + True + """ + if not hasattr(m, 'ndim'): + m = asarray(m) + if axis is None: + indexer = (np.s_[::-1],) * m.ndim + else: + axis = _nx.normalize_axis_tuple(axis, m.ndim) + indexer = [np.s_[:]] * m.ndim + for ax in axis: + indexer[ax] = np.s_[::-1] + indexer = tuple(indexer) + return m[indexer] + + +@set_module('numpy') +def iterable(y): + """ + Check whether or not an object can be iterated over. + + Parameters + ---------- + y : object + Input object. + + Returns + ------- + b : bool + Return ``True`` if the object has an iterator method or is a + sequence and ``False`` otherwise. + + + Examples + -------- + >>> import numpy as np + >>> np.iterable([1, 2, 3]) + True + >>> np.iterable(2) + False + + Notes + ----- + In most cases, the results of ``np.iterable(obj)`` are consistent with + ``isinstance(obj, collections.abc.Iterable)``. One notable exception is + the treatment of 0-dimensional arrays:: + + >>> from collections.abc import Iterable + >>> a = np.array(1.0) # 0-dimensional numpy array + >>> isinstance(a, Iterable) + True + >>> np.iterable(a) + False + + """ + try: + iter(y) + except TypeError: + return False + return True + + +def _weights_are_valid(weights, a, axis): + """Validate weights array. + + We assume, weights is not None. + """ + wgt = np.asanyarray(weights) + + # Sanity checks + if a.shape != wgt.shape: + if axis is None: + raise TypeError( + "Axis must be specified when shapes of a and weights " + "differ.") + if wgt.shape != tuple(a.shape[ax] for ax in axis): + raise ValueError( + "Shape of weights must be consistent with " + "shape of a along specified axis.") + + # setup wgt to broadcast along axis + wgt = wgt.transpose(np.argsort(axis)) + wgt = wgt.reshape(tuple((s if ax in axis else 1) + for ax, s in enumerate(a.shape))) + return wgt + + +def _average_dispatcher(a, axis=None, weights=None, returned=None, *, + keepdims=None): + return (a, weights) + + +@array_function_dispatch(_average_dispatcher) +def average(a, axis=None, weights=None, returned=False, *, + keepdims=np._NoValue): + """ + Compute the weighted average along the specified axis. + + Parameters + ---------- + a : array_like + Array containing data to be averaged. If `a` is not an array, a + conversion is attempted. + axis : None or int or tuple of ints, optional + Axis or axes along which to average `a`. The default, + `axis=None`, will average over all of the elements of the input array. + If axis is negative it counts from the last to the first axis. + If axis is a tuple of ints, averaging is performed on all of the axes + specified in the tuple instead of a single axis or all the axes as + before. + weights : array_like, optional + An array of weights associated with the values in `a`. Each value in + `a` contributes to the average according to its associated weight. + The array of weights must be the same shape as `a` if no axis is + specified, otherwise the weights must have dimensions and shape + consistent with `a` along the specified axis. + If `weights=None`, then all data in `a` are assumed to have a + weight equal to one. + The calculation is:: + + avg = sum(a * weights) / sum(weights) + + where the sum is over all included elements. + The only constraint on the values of `weights` is that `sum(weights)` + must not be 0. + returned : bool, optional + Default is `False`. If `True`, the tuple (`average`, `sum_of_weights`) + is returned, otherwise only the average is returned. + If `weights=None`, `sum_of_weights` is equivalent to the number of + elements over which the average is taken. + keepdims : bool, optional + If this is set to True, the axes which are reduced are left + in the result as dimensions with size one. With this option, + the result will broadcast correctly against the original `a`. + *Note:* `keepdims` will not work with instances of `numpy.matrix` + or other classes whose methods do not support `keepdims`. + + .. versionadded:: 1.23.0 + + Returns + ------- + retval, [sum_of_weights] : array_type or double + Return the average along the specified axis. When `returned` is `True`, + return a tuple with the average as the first element and the sum + of the weights as the second element. `sum_of_weights` is of the + same type as `retval`. The result dtype follows a general pattern. + If `weights` is None, the result dtype will be that of `a` , or ``float64`` + if `a` is integral. Otherwise, if `weights` is not None and `a` is non- + integral, the result type will be the type of lowest precision capable of + representing values of both `a` and `weights`. If `a` happens to be + integral, the previous rules still applies but the result dtype will + at least be ``float64``. + + Raises + ------ + ZeroDivisionError + When all weights along axis are zero. See `numpy.ma.average` for a + version robust to this type of error. + TypeError + When `weights` does not have the same shape as `a`, and `axis=None`. + ValueError + When `weights` does not have dimensions and shape consistent with `a` + along specified `axis`. + + See Also + -------- + mean + + ma.average : average for masked arrays -- useful if your data contains + "missing" values + numpy.result_type : Returns the type that results from applying the + numpy type promotion rules to the arguments. + + Examples + -------- + >>> import numpy as np + >>> data = np.arange(1, 5) + >>> data + array([1, 2, 3, 4]) + >>> np.average(data) + 2.5 + >>> np.average(np.arange(1, 11), weights=np.arange(10, 0, -1)) + 4.0 + + >>> data = np.arange(6).reshape((3, 2)) + >>> data + array([[0, 1], + [2, 3], + [4, 5]]) + >>> np.average(data, axis=1, weights=[1./4, 3./4]) + array([0.75, 2.75, 4.75]) + >>> np.average(data, weights=[1./4, 3./4]) + Traceback (most recent call last): + ... + TypeError: Axis must be specified when shapes of a and weights differ. + + With ``keepdims=True``, the following result has shape (3, 1). + + >>> np.average(data, axis=1, keepdims=True) + array([[0.5], + [2.5], + [4.5]]) + + >>> data = np.arange(8).reshape((2, 2, 2)) + >>> data + array([[[0, 1], + [2, 3]], + [[4, 5], + [6, 7]]]) + >>> np.average(data, axis=(0, 1), weights=[[1./4, 3./4], [1., 1./2]]) + array([3.4, 4.4]) + >>> np.average(data, axis=0, weights=[[1./4, 3./4], [1., 1./2]]) + Traceback (most recent call last): + ... + ValueError: Shape of weights must be consistent + with shape of a along specified axis. + """ + a = np.asanyarray(a) + + if axis is not None: + axis = _nx.normalize_axis_tuple(axis, a.ndim, argname="axis") + + if keepdims is np._NoValue: + # Don't pass on the keepdims argument if one wasn't given. + keepdims_kw = {} + else: + keepdims_kw = {'keepdims': keepdims} + + if weights is None: + avg = a.mean(axis, **keepdims_kw) + avg_as_array = np.asanyarray(avg) + scl = avg_as_array.dtype.type(a.size/avg_as_array.size) + else: + wgt = _weights_are_valid(weights=weights, a=a, axis=axis) + + if issubclass(a.dtype.type, (np.integer, np.bool)): + result_dtype = np.result_type(a.dtype, wgt.dtype, 'f8') + else: + result_dtype = np.result_type(a.dtype, wgt.dtype) + + scl = wgt.sum(axis=axis, dtype=result_dtype, **keepdims_kw) + if np.any(scl == 0.0): + raise ZeroDivisionError( + "Weights sum to zero, can't be normalized") + + avg = avg_as_array = np.multiply(a, wgt, + dtype=result_dtype).sum(axis, **keepdims_kw) / scl + + if returned: + if scl.shape != avg_as_array.shape: + scl = np.broadcast_to(scl, avg_as_array.shape).copy() + return avg, scl + else: + return avg + + +@set_module('numpy') +def asarray_chkfinite(a, dtype=None, order=None): + """Convert the input to an array, checking for NaNs or Infs. + + Parameters + ---------- + a : array_like + Input data, in any form that can be converted to an array. This + includes lists, lists of tuples, tuples, tuples of tuples, tuples + of lists and ndarrays. Success requires no NaNs or Infs. + dtype : data-type, optional + By default, the data-type is inferred from the input data. + order : {'C', 'F', 'A', 'K'}, optional + Memory layout. 'A' and 'K' depend on the order of input array a. + 'C' row-major (C-style), + 'F' column-major (Fortran-style) memory representation. + 'A' (any) means 'F' if `a` is Fortran contiguous, 'C' otherwise + 'K' (keep) preserve input order + Defaults to 'C'. + + Returns + ------- + out : ndarray + Array interpretation of `a`. No copy is performed if the input + is already an ndarray. If `a` is a subclass of ndarray, a base + class ndarray is returned. + + Raises + ------ + ValueError + Raises ValueError if `a` contains NaN (Not a Number) or Inf (Infinity). + + See Also + -------- + asarray : Create and array. + asanyarray : Similar function which passes through subclasses. + ascontiguousarray : Convert input to a contiguous array. + asfortranarray : Convert input to an ndarray with column-major + memory order. + fromiter : Create an array from an iterator. + fromfunction : Construct an array by executing a function on grid + positions. + + Examples + -------- + >>> import numpy as np + + Convert a list into an array. If all elements are finite, then + ``asarray_chkfinite`` is identical to ``asarray``. + + >>> a = [1, 2] + >>> np.asarray_chkfinite(a, dtype=float) + array([1., 2.]) + + Raises ValueError if array_like contains Nans or Infs. + + >>> a = [1, 2, np.inf] + >>> try: + ... np.asarray_chkfinite(a) + ... except ValueError: + ... print('ValueError') + ... + ValueError + + """ + a = asarray(a, dtype=dtype, order=order) + if a.dtype.char in typecodes['AllFloat'] and not np.isfinite(a).all(): + raise ValueError( + "array must not contain infs or NaNs") + return a + + +def _piecewise_dispatcher(x, condlist, funclist, *args, **kw): + yield x + # support the undocumented behavior of allowing scalars + if np.iterable(condlist): + yield from condlist + + +@array_function_dispatch(_piecewise_dispatcher) +def piecewise(x, condlist, funclist, *args, **kw): + """ + Evaluate a piecewise-defined function. + + Given a set of conditions and corresponding functions, evaluate each + function on the input data wherever its condition is true. + + Parameters + ---------- + x : ndarray or scalar + The input domain. + condlist : list of bool arrays or bool scalars + Each boolean array corresponds to a function in `funclist`. Wherever + `condlist[i]` is True, `funclist[i](x)` is used as the output value. + + Each boolean array in `condlist` selects a piece of `x`, + and should therefore be of the same shape as `x`. + + The length of `condlist` must correspond to that of `funclist`. + If one extra function is given, i.e. if + ``len(funclist) == len(condlist) + 1``, then that extra function + is the default value, used wherever all conditions are false. + funclist : list of callables, f(x,*args,**kw), or scalars + Each function is evaluated over `x` wherever its corresponding + condition is True. It should take a 1d array as input and give an 1d + array or a scalar value as output. If, instead of a callable, + a scalar is provided then a constant function (``lambda x: scalar``) is + assumed. + args : tuple, optional + Any further arguments given to `piecewise` are passed to the functions + upon execution, i.e., if called ``piecewise(..., ..., 1, 'a')``, then + each function is called as ``f(x, 1, 'a')``. + kw : dict, optional + Keyword arguments used in calling `piecewise` are passed to the + functions upon execution, i.e., if called + ``piecewise(..., ..., alpha=1)``, then each function is called as + ``f(x, alpha=1)``. + + Returns + ------- + out : ndarray + The output is the same shape and type as x and is found by + calling the functions in `funclist` on the appropriate portions of `x`, + as defined by the boolean arrays in `condlist`. Portions not covered + by any condition have a default value of 0. + + + See Also + -------- + choose, select, where + + Notes + ----- + This is similar to choose or select, except that functions are + evaluated on elements of `x` that satisfy the corresponding condition from + `condlist`. + + The result is:: + + |-- + |funclist[0](x[condlist[0]]) + out = |funclist[1](x[condlist[1]]) + |... + |funclist[n2](x[condlist[n2]]) + |-- + + Examples + -------- + >>> import numpy as np + + Define the signum function, which is -1 for ``x < 0`` and +1 for ``x >= 0``. + + >>> x = np.linspace(-2.5, 2.5, 6) + >>> np.piecewise(x, [x < 0, x >= 0], [-1, 1]) + array([-1., -1., -1., 1., 1., 1.]) + + Define the absolute value, which is ``-x`` for ``x <0`` and ``x`` for + ``x >= 0``. + + >>> np.piecewise(x, [x < 0, x >= 0], [lambda x: -x, lambda x: x]) + array([2.5, 1.5, 0.5, 0.5, 1.5, 2.5]) + + Apply the same function to a scalar value. + + >>> y = -2 + >>> np.piecewise(y, [y < 0, y >= 0], [lambda x: -x, lambda x: x]) + array(2) + + """ + x = asanyarray(x) + n2 = len(funclist) + + # undocumented: single condition is promoted to a list of one condition + if isscalar(condlist) or ( + not isinstance(condlist[0], (list, ndarray)) and x.ndim != 0): + condlist = [condlist] + + condlist = asarray(condlist, dtype=bool) + n = len(condlist) + + if n == n2 - 1: # compute the "otherwise" condition. + condelse = ~np.any(condlist, axis=0, keepdims=True) + condlist = np.concatenate([condlist, condelse], axis=0) + n += 1 + elif n != n2: + raise ValueError( + "with {} condition(s), either {} or {} functions are expected" + .format(n, n, n+1) + ) + + y = zeros_like(x) + for cond, func in zip(condlist, funclist): + if not isinstance(func, collections.abc.Callable): + y[cond] = func + else: + vals = x[cond] + if vals.size > 0: + y[cond] = func(vals, *args, **kw) + + return y + + +def _select_dispatcher(condlist, choicelist, default=None): + yield from condlist + yield from choicelist + + +@array_function_dispatch(_select_dispatcher) +def select(condlist, choicelist, default=0): + """ + Return an array drawn from elements in choicelist, depending on conditions. + + Parameters + ---------- + condlist : list of bool ndarrays + The list of conditions which determine from which array in `choicelist` + the output elements are taken. When multiple conditions are satisfied, + the first one encountered in `condlist` is used. + choicelist : list of ndarrays + The list of arrays from which the output elements are taken. It has + to be of the same length as `condlist`. + default : scalar, optional + The element inserted in `output` when all conditions evaluate to False. + + Returns + ------- + output : ndarray + The output at position m is the m-th element of the array in + `choicelist` where the m-th element of the corresponding array in + `condlist` is True. + + See Also + -------- + where : Return elements from one of two arrays depending on condition. + take, choose, compress, diag, diagonal + + Examples + -------- + >>> import numpy as np + + Beginning with an array of integers from 0 to 5 (inclusive), + elements less than ``3`` are negated, elements greater than ``3`` + are squared, and elements not meeting either of these conditions + (exactly ``3``) are replaced with a `default` value of ``42``. + + >>> x = np.arange(6) + >>> condlist = [x<3, x>3] + >>> choicelist = [x, x**2] + >>> np.select(condlist, choicelist, 42) + array([ 0, 1, 2, 42, 16, 25]) + + When multiple conditions are satisfied, the first one encountered in + `condlist` is used. + + >>> condlist = [x<=4, x>3] + >>> choicelist = [x, x**2] + >>> np.select(condlist, choicelist, 55) + array([ 0, 1, 2, 3, 4, 25]) + + """ + # Check the size of condlist and choicelist are the same, or abort. + if len(condlist) != len(choicelist): + raise ValueError( + 'list of cases must be same length as list of conditions') + + # Now that the dtype is known, handle the deprecated select([], []) case + if len(condlist) == 0: + raise ValueError("select with an empty condition list is not possible") + + # TODO: This preserves the Python int, float, complex manually to get the + # right `result_type` with NEP 50. Most likely we will grow a better + # way to spell this (and this can be replaced). + choicelist = [ + choice if type(choice) in (int, float, complex) else np.asarray(choice) + for choice in choicelist] + choicelist.append(default if type(default) in (int, float, complex) + else np.asarray(default)) + + try: + dtype = np.result_type(*choicelist) + except TypeError as e: + msg = f'Choicelist and default value do not have a common dtype: {e}' + raise TypeError(msg) from None + + # Convert conditions to arrays and broadcast conditions and choices + # as the shape is needed for the result. Doing it separately optimizes + # for example when all choices are scalars. + condlist = np.broadcast_arrays(*condlist) + choicelist = np.broadcast_arrays(*choicelist) + + # If cond array is not an ndarray in boolean format or scalar bool, abort. + for i, cond in enumerate(condlist): + if cond.dtype.type is not np.bool: + raise TypeError( + 'invalid entry {} in condlist: should be boolean ndarray'.format(i)) + + if choicelist[0].ndim == 0: + # This may be common, so avoid the call. + result_shape = condlist[0].shape + else: + result_shape = np.broadcast_arrays(condlist[0], choicelist[0])[0].shape + + result = np.full(result_shape, choicelist[-1], dtype) + + # Use np.copyto to burn each choicelist array onto result, using the + # corresponding condlist as a boolean mask. This is done in reverse + # order since the first choice should take precedence. + choicelist = choicelist[-2::-1] + condlist = condlist[::-1] + for choice, cond in zip(choicelist, condlist): + np.copyto(result, choice, where=cond) + + return result + + +def _copy_dispatcher(a, order=None, subok=None): + return (a,) + + +@array_function_dispatch(_copy_dispatcher) +def copy(a, order='K', subok=False): + """ + Return an array copy of the given object. + + Parameters + ---------- + a : array_like + Input data. + order : {'C', 'F', 'A', 'K'}, optional + Controls the memory layout of the copy. 'C' means C-order, + 'F' means F-order, 'A' means 'F' if `a` is Fortran contiguous, + 'C' otherwise. 'K' means match the layout of `a` as closely + as possible. (Note that this function and :meth:`ndarray.copy` are very + similar, but have different default values for their order= + arguments.) + subok : bool, optional + If True, then sub-classes will be passed-through, otherwise the + returned array will be forced to be a base-class array (defaults to False). + + Returns + ------- + arr : ndarray + Array interpretation of `a`. + + See Also + -------- + ndarray.copy : Preferred method for creating an array copy + + Notes + ----- + This is equivalent to: + + >>> np.array(a, copy=True) #doctest: +SKIP + + The copy made of the data is shallow, i.e., for arrays with object dtype, + the new array will point to the same objects. + See Examples from `ndarray.copy`. + + Examples + -------- + >>> import numpy as np + + Create an array x, with a reference y and a copy z: + + >>> x = np.array([1, 2, 3]) + >>> y = x + >>> z = np.copy(x) + + Note that, when we modify x, y changes, but not z: + + >>> x[0] = 10 + >>> x[0] == y[0] + True + >>> x[0] == z[0] + False + + Note that, np.copy clears previously set WRITEABLE=False flag. + + >>> a = np.array([1, 2, 3]) + >>> a.flags["WRITEABLE"] = False + >>> b = np.copy(a) + >>> b.flags["WRITEABLE"] + True + >>> b[0] = 3 + >>> b + array([3, 2, 3]) + """ + return array(a, order=order, subok=subok, copy=True) + +# Basic operations + + +def _gradient_dispatcher(f, *varargs, axis=None, edge_order=None): + yield f + yield from varargs + + +@array_function_dispatch(_gradient_dispatcher) +def gradient(f, *varargs, axis=None, edge_order=1): + """ + Return the gradient of an N-dimensional array. + + The gradient is computed using second order accurate central differences + in the interior points and either first or second order accurate one-sides + (forward or backwards) differences at the boundaries. + The returned gradient hence has the same shape as the input array. + + Parameters + ---------- + f : array_like + An N-dimensional array containing samples of a scalar function. + varargs : list of scalar or array, optional + Spacing between f values. Default unitary spacing for all dimensions. + Spacing can be specified using: + + 1. single scalar to specify a sample distance for all dimensions. + 2. N scalars to specify a constant sample distance for each dimension. + i.e. `dx`, `dy`, `dz`, ... + 3. N arrays to specify the coordinates of the values along each + dimension of F. The length of the array must match the size of + the corresponding dimension + 4. Any combination of N scalars/arrays with the meaning of 2. and 3. + + If `axis` is given, the number of varargs must equal the number of axes. + Default: 1. (see Examples below). + + edge_order : {1, 2}, optional + Gradient is calculated using N-th order accurate differences + at the boundaries. Default: 1. + axis : None or int or tuple of ints, optional + Gradient is calculated only along the given axis or axes + The default (axis = None) is to calculate the gradient for all the axes + of the input array. axis may be negative, in which case it counts from + the last to the first axis. + + Returns + ------- + gradient : ndarray or tuple of ndarray + A tuple of ndarrays (or a single ndarray if there is only one + dimension) corresponding to the derivatives of f with respect + to each dimension. Each derivative has the same shape as f. + + Examples + -------- + >>> import numpy as np + >>> f = np.array([1, 2, 4, 7, 11, 16]) + >>> np.gradient(f) + array([1. , 1.5, 2.5, 3.5, 4.5, 5. ]) + >>> np.gradient(f, 2) + array([0.5 , 0.75, 1.25, 1.75, 2.25, 2.5 ]) + + Spacing can be also specified with an array that represents the coordinates + of the values F along the dimensions. + For instance a uniform spacing: + + >>> x = np.arange(f.size) + >>> np.gradient(f, x) + array([1. , 1.5, 2.5, 3.5, 4.5, 5. ]) + + Or a non uniform one: + + >>> x = np.array([0., 1., 1.5, 3.5, 4., 6.]) + >>> np.gradient(f, x) + array([1. , 3. , 3.5, 6.7, 6.9, 2.5]) + + For two dimensional arrays, the return will be two arrays ordered by + axis. In this example the first array stands for the gradient in + rows and the second one in columns direction: + + >>> np.gradient(np.array([[1, 2, 6], [3, 4, 5]])) + (array([[ 2., 2., -1.], + [ 2., 2., -1.]]), + array([[1. , 2.5, 4. ], + [1. , 1. , 1. ]])) + + In this example the spacing is also specified: + uniform for axis=0 and non uniform for axis=1 + + >>> dx = 2. + >>> y = [1., 1.5, 3.5] + >>> np.gradient(np.array([[1, 2, 6], [3, 4, 5]]), dx, y) + (array([[ 1. , 1. , -0.5], + [ 1. , 1. , -0.5]]), + array([[2. , 2. , 2. ], + [2. , 1.7, 0.5]])) + + It is possible to specify how boundaries are treated using `edge_order` + + >>> x = np.array([0, 1, 2, 3, 4]) + >>> f = x**2 + >>> np.gradient(f, edge_order=1) + array([1., 2., 4., 6., 7.]) + >>> np.gradient(f, edge_order=2) + array([0., 2., 4., 6., 8.]) + + The `axis` keyword can be used to specify a subset of axes of which the + gradient is calculated + + >>> np.gradient(np.array([[1, 2, 6], [3, 4, 5]]), axis=0) + array([[ 2., 2., -1.], + [ 2., 2., -1.]]) + + The `varargs` argument defines the spacing between sample points in the + input array. It can take two forms: + + 1. An array, specifying coordinates, which may be unevenly spaced: + + >>> x = np.array([0., 2., 3., 6., 8.]) + >>> y = x ** 2 + >>> np.gradient(y, x, edge_order=2) + array([ 0., 4., 6., 12., 16.]) + + 2. A scalar, representing the fixed sample distance: + + >>> dx = 2 + >>> x = np.array([0., 2., 4., 6., 8.]) + >>> y = x ** 2 + >>> np.gradient(y, dx, edge_order=2) + array([ 0., 4., 8., 12., 16.]) + + It's possible to provide different data for spacing along each dimension. + The number of arguments must match the number of dimensions in the input + data. + + >>> dx = 2 + >>> dy = 3 + >>> x = np.arange(0, 6, dx) + >>> y = np.arange(0, 9, dy) + >>> xs, ys = np.meshgrid(x, y) + >>> zs = xs + 2 * ys + >>> np.gradient(zs, dy, dx) # Passing two scalars + (array([[2., 2., 2.], + [2., 2., 2.], + [2., 2., 2.]]), + array([[1., 1., 1.], + [1., 1., 1.], + [1., 1., 1.]])) + + Mixing scalars and arrays is also allowed: + + >>> np.gradient(zs, y, dx) # Passing one array and one scalar + (array([[2., 2., 2.], + [2., 2., 2.], + [2., 2., 2.]]), + array([[1., 1., 1.], + [1., 1., 1.], + [1., 1., 1.]])) + + Notes + ----- + Assuming that :math:`f\\in C^{3}` (i.e., :math:`f` has at least 3 continuous + derivatives) and let :math:`h_{*}` be a non-homogeneous stepsize, we + minimize the "consistency error" :math:`\\eta_{i}` between the true gradient + and its estimate from a linear combination of the neighboring grid-points: + + .. math:: + + \\eta_{i} = f_{i}^{\\left(1\\right)} - + \\left[ \\alpha f\\left(x_{i}\\right) + + \\beta f\\left(x_{i} + h_{d}\\right) + + \\gamma f\\left(x_{i}-h_{s}\\right) + \\right] + + By substituting :math:`f(x_{i} + h_{d})` and :math:`f(x_{i} - h_{s})` + with their Taylor series expansion, this translates into solving + the following the linear system: + + .. math:: + + \\left\\{ + \\begin{array}{r} + \\alpha+\\beta+\\gamma=0 \\\\ + \\beta h_{d}-\\gamma h_{s}=1 \\\\ + \\beta h_{d}^{2}+\\gamma h_{s}^{2}=0 + \\end{array} + \\right. + + The resulting approximation of :math:`f_{i}^{(1)}` is the following: + + .. math:: + + \\hat f_{i}^{(1)} = + \\frac{ + h_{s}^{2}f\\left(x_{i} + h_{d}\\right) + + \\left(h_{d}^{2} - h_{s}^{2}\\right)f\\left(x_{i}\\right) + - h_{d}^{2}f\\left(x_{i}-h_{s}\\right)} + { h_{s}h_{d}\\left(h_{d} + h_{s}\\right)} + + \\mathcal{O}\\left(\\frac{h_{d}h_{s}^{2} + + h_{s}h_{d}^{2}}{h_{d} + + h_{s}}\\right) + + It is worth noting that if :math:`h_{s}=h_{d}` + (i.e., data are evenly spaced) + we find the standard second order approximation: + + .. math:: + + \\hat f_{i}^{(1)}= + \\frac{f\\left(x_{i+1}\\right) - f\\left(x_{i-1}\\right)}{2h} + + \\mathcal{O}\\left(h^{2}\\right) + + With a similar procedure the forward/backward approximations used for + boundaries can be derived. + + References + ---------- + .. [1] Quarteroni A., Sacco R., Saleri F. (2007) Numerical Mathematics + (Texts in Applied Mathematics). New York: Springer. + .. [2] Durran D. R. (1999) Numerical Methods for Wave Equations + in Geophysical Fluid Dynamics. New York: Springer. + .. [3] Fornberg B. (1988) Generation of Finite Difference Formulas on + Arbitrarily Spaced Grids, + Mathematics of Computation 51, no. 184 : 699-706. + `PDF `_. + """ + f = np.asanyarray(f) + N = f.ndim # number of dimensions + + if axis is None: + axes = tuple(range(N)) + else: + axes = _nx.normalize_axis_tuple(axis, N) + + len_axes = len(axes) + n = len(varargs) + if n == 0: + # no spacing argument - use 1 in all axes + dx = [1.0] * len_axes + elif n == 1 and np.ndim(varargs[0]) == 0: + # single scalar for all axes + dx = varargs * len_axes + elif n == len_axes: + # scalar or 1d array for each axis + dx = list(varargs) + for i, distances in enumerate(dx): + distances = np.asanyarray(distances) + if distances.ndim == 0: + continue + elif distances.ndim != 1: + raise ValueError("distances must be either scalars or 1d") + if len(distances) != f.shape[axes[i]]: + raise ValueError("when 1d, distances must match " + "the length of the corresponding dimension") + if np.issubdtype(distances.dtype, np.integer): + # Convert numpy integer types to float64 to avoid modular + # arithmetic in np.diff(distances). + distances = distances.astype(np.float64) + diffx = np.diff(distances) + # if distances are constant reduce to the scalar case + # since it brings a consistent speedup + if (diffx == diffx[0]).all(): + diffx = diffx[0] + dx[i] = diffx + else: + raise TypeError("invalid number of arguments") + + if edge_order > 2: + raise ValueError("'edge_order' greater than 2 not supported") + + # use central differences on interior and one-sided differences on the + # endpoints. This preserves second order-accuracy over the full domain. + + outvals = [] + + # create slice objects --- initially all are [:, :, ..., :] + slice1 = [slice(None)]*N + slice2 = [slice(None)]*N + slice3 = [slice(None)]*N + slice4 = [slice(None)]*N + + otype = f.dtype + if otype.type is np.datetime64: + # the timedelta dtype with the same unit information + otype = np.dtype(otype.name.replace('datetime', 'timedelta')) + # view as timedelta to allow addition + f = f.view(otype) + elif otype.type is np.timedelta64: + pass + elif np.issubdtype(otype, np.inexact): + pass + else: + # All other types convert to floating point. + # First check if f is a numpy integer type; if so, convert f to float64 + # to avoid modular arithmetic when computing the changes in f. + if np.issubdtype(otype, np.integer): + f = f.astype(np.float64) + otype = np.float64 + + for axis, ax_dx in zip(axes, dx): + if f.shape[axis] < edge_order + 1: + raise ValueError( + "Shape of array too small to calculate a numerical gradient, " + "at least (edge_order + 1) elements are required.") + # result allocation + out = np.empty_like(f, dtype=otype) + + # spacing for the current axis + uniform_spacing = np.ndim(ax_dx) == 0 + + # Numerical differentiation: 2nd order interior + slice1[axis] = slice(1, -1) + slice2[axis] = slice(None, -2) + slice3[axis] = slice(1, -1) + slice4[axis] = slice(2, None) + + if uniform_spacing: + out[tuple(slice1)] = (f[tuple(slice4)] - f[tuple(slice2)]) / (2. * ax_dx) + else: + dx1 = ax_dx[0:-1] + dx2 = ax_dx[1:] + a = -(dx2)/(dx1 * (dx1 + dx2)) + b = (dx2 - dx1) / (dx1 * dx2) + c = dx1 / (dx2 * (dx1 + dx2)) + # fix the shape for broadcasting + shape = np.ones(N, dtype=int) + shape[axis] = -1 + a.shape = b.shape = c.shape = shape + # 1D equivalent -- out[1:-1] = a * f[:-2] + b * f[1:-1] + c * f[2:] + out[tuple(slice1)] = a * f[tuple(slice2)] + b * f[tuple(slice3)] + c * f[tuple(slice4)] + + # Numerical differentiation: 1st order edges + if edge_order == 1: + slice1[axis] = 0 + slice2[axis] = 1 + slice3[axis] = 0 + dx_0 = ax_dx if uniform_spacing else ax_dx[0] + # 1D equivalent -- out[0] = (f[1] - f[0]) / (x[1] - x[0]) + out[tuple(slice1)] = (f[tuple(slice2)] - f[tuple(slice3)]) / dx_0 + + slice1[axis] = -1 + slice2[axis] = -1 + slice3[axis] = -2 + dx_n = ax_dx if uniform_spacing else ax_dx[-1] + # 1D equivalent -- out[-1] = (f[-1] - f[-2]) / (x[-1] - x[-2]) + out[tuple(slice1)] = (f[tuple(slice2)] - f[tuple(slice3)]) / dx_n + + # Numerical differentiation: 2nd order edges + else: + slice1[axis] = 0 + slice2[axis] = 0 + slice3[axis] = 1 + slice4[axis] = 2 + if uniform_spacing: + a = -1.5 / ax_dx + b = 2. / ax_dx + c = -0.5 / ax_dx + else: + dx1 = ax_dx[0] + dx2 = ax_dx[1] + a = -(2. * dx1 + dx2)/(dx1 * (dx1 + dx2)) + b = (dx1 + dx2) / (dx1 * dx2) + c = - dx1 / (dx2 * (dx1 + dx2)) + # 1D equivalent -- out[0] = a * f[0] + b * f[1] + c * f[2] + out[tuple(slice1)] = a * f[tuple(slice2)] + b * f[tuple(slice3)] + c * f[tuple(slice4)] + + slice1[axis] = -1 + slice2[axis] = -3 + slice3[axis] = -2 + slice4[axis] = -1 + if uniform_spacing: + a = 0.5 / ax_dx + b = -2. / ax_dx + c = 1.5 / ax_dx + else: + dx1 = ax_dx[-2] + dx2 = ax_dx[-1] + a = (dx2) / (dx1 * (dx1 + dx2)) + b = - (dx2 + dx1) / (dx1 * dx2) + c = (2. * dx2 + dx1) / (dx2 * (dx1 + dx2)) + # 1D equivalent -- out[-1] = a * f[-3] + b * f[-2] + c * f[-1] + out[tuple(slice1)] = a * f[tuple(slice2)] + b * f[tuple(slice3)] + c * f[tuple(slice4)] + + outvals.append(out) + + # reset the slice object in this dimension to ":" + slice1[axis] = slice(None) + slice2[axis] = slice(None) + slice3[axis] = slice(None) + slice4[axis] = slice(None) + + if len_axes == 1: + return outvals[0] + return tuple(outvals) + + +def _diff_dispatcher(a, n=None, axis=None, prepend=None, append=None): + return (a, prepend, append) + + +@array_function_dispatch(_diff_dispatcher) +def diff(a, n=1, axis=-1, prepend=np._NoValue, append=np._NoValue): + """ + Calculate the n-th discrete difference along the given axis. + + The first difference is given by ``out[i] = a[i+1] - a[i]`` along + the given axis, higher differences are calculated by using `diff` + recursively. + + Parameters + ---------- + a : array_like + Input array + n : int, optional + The number of times values are differenced. If zero, the input + is returned as-is. + axis : int, optional + The axis along which the difference is taken, default is the + last axis. + prepend, append : array_like, optional + Values to prepend or append to `a` along axis prior to + performing the difference. Scalar values are expanded to + arrays with length 1 in the direction of axis and the shape + of the input array in along all other axes. Otherwise the + dimension and shape must match `a` except along axis. + + Returns + ------- + diff : ndarray + The n-th differences. The shape of the output is the same as `a` + except along `axis` where the dimension is smaller by `n`. The + type of the output is the same as the type of the difference + between any two elements of `a`. This is the same as the type of + `a` in most cases. A notable exception is `datetime64`, which + results in a `timedelta64` output array. + + See Also + -------- + gradient, ediff1d, cumsum + + Notes + ----- + Type is preserved for boolean arrays, so the result will contain + `False` when consecutive elements are the same and `True` when they + differ. + + For unsigned integer arrays, the results will also be unsigned. This + should not be surprising, as the result is consistent with + calculating the difference directly: + + >>> u8_arr = np.array([1, 0], dtype=np.uint8) + >>> np.diff(u8_arr) + array([255], dtype=uint8) + >>> u8_arr[1,...] - u8_arr[0,...] + np.uint8(255) + + If this is not desirable, then the array should be cast to a larger + integer type first: + + >>> i16_arr = u8_arr.astype(np.int16) + >>> np.diff(i16_arr) + array([-1], dtype=int16) + + Examples + -------- + >>> import numpy as np + >>> x = np.array([1, 2, 4, 7, 0]) + >>> np.diff(x) + array([ 1, 2, 3, -7]) + >>> np.diff(x, n=2) + array([ 1, 1, -10]) + + >>> x = np.array([[1, 3, 6, 10], [0, 5, 6, 8]]) + >>> np.diff(x) + array([[2, 3, 4], + [5, 1, 2]]) + >>> np.diff(x, axis=0) + array([[-1, 2, 0, -2]]) + + >>> x = np.arange('1066-10-13', '1066-10-16', dtype=np.datetime64) + >>> np.diff(x) + array([1, 1], dtype='timedelta64[D]') + + """ + if n == 0: + return a + if n < 0: + raise ValueError( + "order must be non-negative but got " + repr(n)) + + a = asanyarray(a) + nd = a.ndim + if nd == 0: + raise ValueError("diff requires input that is at least one dimensional") + axis = normalize_axis_index(axis, nd) + + combined = [] + if prepend is not np._NoValue: + prepend = np.asanyarray(prepend) + if prepend.ndim == 0: + shape = list(a.shape) + shape[axis] = 1 + prepend = np.broadcast_to(prepend, tuple(shape)) + combined.append(prepend) + + combined.append(a) + + if append is not np._NoValue: + append = np.asanyarray(append) + if append.ndim == 0: + shape = list(a.shape) + shape[axis] = 1 + append = np.broadcast_to(append, tuple(shape)) + combined.append(append) + + if len(combined) > 1: + a = np.concatenate(combined, axis) + + slice1 = [slice(None)] * nd + slice2 = [slice(None)] * nd + slice1[axis] = slice(1, None) + slice2[axis] = slice(None, -1) + slice1 = tuple(slice1) + slice2 = tuple(slice2) + + op = not_equal if a.dtype == np.bool else subtract + for _ in range(n): + a = op(a[slice1], a[slice2]) + + return a + + +def _interp_dispatcher(x, xp, fp, left=None, right=None, period=None): + return (x, xp, fp) + + +@array_function_dispatch(_interp_dispatcher) +def interp(x, xp, fp, left=None, right=None, period=None): + """ + One-dimensional linear interpolation for monotonically increasing sample points. + + Returns the one-dimensional piecewise linear interpolant to a function + with given discrete data points (`xp`, `fp`), evaluated at `x`. + + Parameters + ---------- + x : array_like + The x-coordinates at which to evaluate the interpolated values. + + xp : 1-D sequence of floats + The x-coordinates of the data points, must be increasing if argument + `period` is not specified. Otherwise, `xp` is internally sorted after + normalizing the periodic boundaries with ``xp = xp % period``. + + fp : 1-D sequence of float or complex + The y-coordinates of the data points, same length as `xp`. + + left : optional float or complex corresponding to fp + Value to return for `x < xp[0]`, default is `fp[0]`. + + right : optional float or complex corresponding to fp + Value to return for `x > xp[-1]`, default is `fp[-1]`. + + period : None or float, optional + A period for the x-coordinates. This parameter allows the proper + interpolation of angular x-coordinates. Parameters `left` and `right` + are ignored if `period` is specified. + + Returns + ------- + y : float or complex (corresponding to fp) or ndarray + The interpolated values, same shape as `x`. + + Raises + ------ + ValueError + If `xp` and `fp` have different length + If `xp` or `fp` are not 1-D sequences + If `period == 0` + + See Also + -------- + scipy.interpolate + + Warnings + -------- + The x-coordinate sequence is expected to be increasing, but this is not + explicitly enforced. However, if the sequence `xp` is non-increasing, + interpolation results are meaningless. + + Note that, since NaN is unsortable, `xp` also cannot contain NaNs. + + A simple check for `xp` being strictly increasing is:: + + np.all(np.diff(xp) > 0) + + Examples + -------- + >>> import numpy as np + >>> xp = [1, 2, 3] + >>> fp = [3, 2, 0] + >>> np.interp(2.5, xp, fp) + 1.0 + >>> np.interp([0, 1, 1.5, 2.72, 3.14], xp, fp) + array([3. , 3. , 2.5 , 0.56, 0. ]) + >>> UNDEF = -99.0 + >>> np.interp(3.14, xp, fp, right=UNDEF) + -99.0 + + Plot an interpolant to the sine function: + + >>> x = np.linspace(0, 2*np.pi, 10) + >>> y = np.sin(x) + >>> xvals = np.linspace(0, 2*np.pi, 50) + >>> yinterp = np.interp(xvals, x, y) + >>> import matplotlib.pyplot as plt + >>> plt.plot(x, y, 'o') + [] + >>> plt.plot(xvals, yinterp, '-x') + [] + >>> plt.show() + + Interpolation with periodic x-coordinates: + + >>> x = [-180, -170, -185, 185, -10, -5, 0, 365] + >>> xp = [190, -190, 350, -350] + >>> fp = [5, 10, 3, 4] + >>> np.interp(x, xp, fp, period=360) + array([7.5 , 5. , 8.75, 6.25, 3. , 3.25, 3.5 , 3.75]) + + Complex interpolation: + + >>> x = [1.5, 4.0] + >>> xp = [2,3,5] + >>> fp = [1.0j, 0, 2+3j] + >>> np.interp(x, xp, fp) + array([0.+1.j , 1.+1.5j]) + + """ + + fp = np.asarray(fp) + + if np.iscomplexobj(fp): + interp_func = compiled_interp_complex + input_dtype = np.complex128 + else: + interp_func = compiled_interp + input_dtype = np.float64 + + if period is not None: + if period == 0: + raise ValueError("period must be a non-zero value") + period = abs(period) + left = None + right = None + + x = np.asarray(x, dtype=np.float64) + xp = np.asarray(xp, dtype=np.float64) + fp = np.asarray(fp, dtype=input_dtype) + + if xp.ndim != 1 or fp.ndim != 1: + raise ValueError("Data points must be 1-D sequences") + if xp.shape[0] != fp.shape[0]: + raise ValueError("fp and xp are not of the same length") + # normalizing periodic boundaries + x = x % period + xp = xp % period + asort_xp = np.argsort(xp) + xp = xp[asort_xp] + fp = fp[asort_xp] + xp = np.concatenate((xp[-1:]-period, xp, xp[0:1]+period)) + fp = np.concatenate((fp[-1:], fp, fp[0:1])) + + return interp_func(x, xp, fp, left, right) + + +def _angle_dispatcher(z, deg=None): + return (z,) + + +@array_function_dispatch(_angle_dispatcher) +def angle(z, deg=False): + """ + Return the angle of the complex argument. + + Parameters + ---------- + z : array_like + A complex number or sequence of complex numbers. + deg : bool, optional + Return angle in degrees if True, radians if False (default). + + Returns + ------- + angle : ndarray or scalar + The counterclockwise angle from the positive real axis on the complex + plane in the range ``(-pi, pi]``, with dtype as numpy.float64. + + See Also + -------- + arctan2 + absolute + + Notes + ----- + This function passes the imaginary and real parts of the argument to + `arctan2` to compute the result; consequently, it follows the convention + of `arctan2` when the magnitude of the argument is zero. See example. + + Examples + -------- + >>> import numpy as np + >>> np.angle([1.0, 1.0j, 1+1j]) # in radians + array([ 0. , 1.57079633, 0.78539816]) # may vary + >>> np.angle(1+1j, deg=True) # in degrees + 45.0 + >>> np.angle([0., -0., complex(0., -0.), complex(-0., -0.)]) # convention + array([ 0. , 3.14159265, -0. , -3.14159265]) + + """ + z = asanyarray(z) + if issubclass(z.dtype.type, _nx.complexfloating): + zimag = z.imag + zreal = z.real + else: + zimag = 0 + zreal = z + + a = arctan2(zimag, zreal) + if deg: + a *= 180/pi + return a + + +def _unwrap_dispatcher(p, discont=None, axis=None, *, period=None): + return (p,) + + +@array_function_dispatch(_unwrap_dispatcher) +def unwrap(p, discont=None, axis=-1, *, period=2*pi): + r""" + Unwrap by taking the complement of large deltas with respect to the period. + + This unwraps a signal `p` by changing elements which have an absolute + difference from their predecessor of more than ``max(discont, period/2)`` + to their `period`-complementary values. + + For the default case where `period` is :math:`2\pi` and `discont` is + :math:`\pi`, this unwraps a radian phase `p` such that adjacent differences + are never greater than :math:`\pi` by adding :math:`2k\pi` for some + integer :math:`k`. + + Parameters + ---------- + p : array_like + Input array. + discont : float, optional + Maximum discontinuity between values, default is ``period/2``. + Values below ``period/2`` are treated as if they were ``period/2``. + To have an effect different from the default, `discont` should be + larger than ``period/2``. + axis : int, optional + Axis along which unwrap will operate, default is the last axis. + period : float, optional + Size of the range over which the input wraps. By default, it is + ``2 pi``. + + .. versionadded:: 1.21.0 + + Returns + ------- + out : ndarray + Output array. + + See Also + -------- + rad2deg, deg2rad + + Notes + ----- + If the discontinuity in `p` is smaller than ``period/2``, + but larger than `discont`, no unwrapping is done because taking + the complement would only make the discontinuity larger. + + Examples + -------- + >>> import numpy as np + >>> phase = np.linspace(0, np.pi, num=5) + >>> phase[3:] += np.pi + >>> phase + array([ 0. , 0.78539816, 1.57079633, 5.49778714, 6.28318531]) # may vary + >>> np.unwrap(phase) + array([ 0. , 0.78539816, 1.57079633, -0.78539816, 0. ]) # may vary + >>> np.unwrap([0, 1, 2, -1, 0], period=4) + array([0, 1, 2, 3, 4]) + >>> np.unwrap([ 1, 2, 3, 4, 5, 6, 1, 2, 3], period=6) + array([1, 2, 3, 4, 5, 6, 7, 8, 9]) + >>> np.unwrap([2, 3, 4, 5, 2, 3, 4, 5], period=4) + array([2, 3, 4, 5, 6, 7, 8, 9]) + >>> phase_deg = np.mod(np.linspace(0 ,720, 19), 360) - 180 + >>> np.unwrap(phase_deg, period=360) + array([-180., -140., -100., -60., -20., 20., 60., 100., 140., + 180., 220., 260., 300., 340., 380., 420., 460., 500., + 540.]) + """ + p = asarray(p) + nd = p.ndim + dd = diff(p, axis=axis) + if discont is None: + discont = period/2 + slice1 = [slice(None, None)]*nd # full slices + slice1[axis] = slice(1, None) + slice1 = tuple(slice1) + dtype = np.result_type(dd, period) + if _nx.issubdtype(dtype, _nx.integer): + interval_high, rem = divmod(period, 2) + boundary_ambiguous = rem == 0 + else: + interval_high = period / 2 + boundary_ambiguous = True + interval_low = -interval_high + ddmod = mod(dd - interval_low, period) + interval_low + if boundary_ambiguous: + # for `mask = (abs(dd) == period/2)`, the above line made + # `ddmod[mask] == -period/2`. correct these such that + # `ddmod[mask] == sign(dd[mask])*period/2`. + _nx.copyto(ddmod, interval_high, + where=(ddmod == interval_low) & (dd > 0)) + ph_correct = ddmod - dd + _nx.copyto(ph_correct, 0, where=abs(dd) < discont) + up = array(p, copy=True, dtype=dtype) + up[slice1] = p[slice1] + ph_correct.cumsum(axis) + return up + + +def _sort_complex(a): + return (a,) + + +@array_function_dispatch(_sort_complex) +def sort_complex(a): + """ + Sort a complex array using the real part first, then the imaginary part. + + Parameters + ---------- + a : array_like + Input array + + Returns + ------- + out : complex ndarray + Always returns a sorted complex array. + + Examples + -------- + >>> import numpy as np + >>> np.sort_complex([5, 3, 6, 2, 1]) + array([1.+0.j, 2.+0.j, 3.+0.j, 5.+0.j, 6.+0.j]) + + >>> np.sort_complex([1 + 2j, 2 - 1j, 3 - 2j, 3 - 3j, 3 + 5j]) + array([1.+2.j, 2.-1.j, 3.-3.j, 3.-2.j, 3.+5.j]) + + """ + b = array(a, copy=True) + b.sort() + if not issubclass(b.dtype.type, _nx.complexfloating): + if b.dtype.char in 'bhBH': + return b.astype('F') + elif b.dtype.char == 'g': + return b.astype('G') + else: + return b.astype('D') + else: + return b + + +def _arg_trim_zeros(filt): + """Return indices of the first and last non-zero element. + + Parameters + ---------- + filt : array_like + Input array. + + Returns + ------- + start, stop : ndarray + Two arrays containing the indices of the first and last non-zero + element in each dimension. + + See also + -------- + trim_zeros + + Examples + -------- + >>> import numpy as np + >>> _arg_trim_zeros(np.array([0, 0, 1, 1, 0])) + (array([2]), array([3])) + """ + nonzero = ( + np.argwhere(filt) + if filt.dtype != np.object_ + # Historically, `trim_zeros` treats `None` in an object array + # as non-zero while argwhere doesn't, account for that + else np.argwhere(filt != 0) + ) + if nonzero.size == 0: + start = stop = np.array([], dtype=np.intp) + else: + start = nonzero.min(axis=0) + stop = nonzero.max(axis=0) + return start, stop + + +def _trim_zeros(filt, trim=None, axis=None): + return (filt,) + + +@array_function_dispatch(_trim_zeros) +def trim_zeros(filt, trim='fb', axis=None): + """Remove values along a dimension which are zero along all other. + + Parameters + ---------- + filt : array_like + Input array. + trim : {"fb", "f", "b"}, optional + A string with 'f' representing trim from front and 'b' to trim from + back. By default, zeros are trimmed on both sides. + Front and back refer to the edges of a dimension, with "front" refering + to the side with the lowest index 0, and "back" refering to the highest + index (or index -1). + axis : int or sequence, optional + If None, `filt` is cropped such, that the smallest bounding box is + returned that still contains all values which are not zero. + If an axis is specified, `filt` will be sliced in that dimension only + on the sides specified by `trim`. The remaining area will be the + smallest that still contains all values wich are not zero. + + Returns + ------- + trimmed : ndarray or sequence + The result of trimming the input. The number of dimensions and the + input data type are preserved. + + Notes + ----- + For all-zero arrays, the first axis is trimmed first. + + Examples + -------- + >>> import numpy as np + >>> a = np.array((0, 0, 0, 1, 2, 3, 0, 2, 1, 0)) + >>> np.trim_zeros(a) + array([1, 2, 3, 0, 2, 1]) + + >>> np.trim_zeros(a, trim='b') + array([0, 0, 0, ..., 0, 2, 1]) + + Multiple dimensions are supported. + + >>> b = np.array([[0, 0, 2, 3, 0, 0], + ... [0, 1, 0, 3, 0, 0], + ... [0, 0, 0, 0, 0, 0]]) + >>> np.trim_zeros(b) + array([[0, 2, 3], + [1, 0, 3]]) + + >>> np.trim_zeros(b, axis=-1) + array([[0, 2, 3], + [1, 0, 3], + [0, 0, 0]]) + + The input data type is preserved, list/tuple in means list/tuple out. + + >>> np.trim_zeros([0, 1, 2, 0]) + [1, 2] + + """ + filt_ = np.asarray(filt) + + trim = trim.lower() + if trim not in {"fb", "bf", "f", "b"}: + raise ValueError(f"unexpected character(s) in `trim`: {trim!r}") + + start, stop = _arg_trim_zeros(filt_) + stop += 1 # Adjust for slicing + + if start.size == 0: + # filt is all-zero -> assign same values to start and stop so that + # resulting slice will be empty + start = stop = np.zeros(filt_.ndim, dtype=np.intp) + else: + if 'f' not in trim: + start = (None,) * filt_.ndim + if 'b' not in trim: + stop = (None,) * filt_.ndim + + if len(start) == 1: + # filt is 1D -> don't use multi-dimensional slicing to preserve + # non-array input types + sl = slice(start[0], stop[0]) + elif axis is None: + # trim all axes + sl = tuple(slice(*x) for x in zip(start, stop)) + else: + # only trim single axis + axis = normalize_axis_index(axis, filt_.ndim) + sl = (slice(None),) * axis + (slice(start[axis], stop[axis]),) + (...,) + + trimmed = filt[sl] + return trimmed + + + +def _extract_dispatcher(condition, arr): + return (condition, arr) + + +@array_function_dispatch(_extract_dispatcher) +def extract(condition, arr): + """ + Return the elements of an array that satisfy some condition. + + This is equivalent to ``np.compress(ravel(condition), ravel(arr))``. If + `condition` is boolean ``np.extract`` is equivalent to ``arr[condition]``. + + Note that `place` does the exact opposite of `extract`. + + Parameters + ---------- + condition : array_like + An array whose nonzero or True entries indicate the elements of `arr` + to extract. + arr : array_like + Input array of the same size as `condition`. + + Returns + ------- + extract : ndarray + Rank 1 array of values from `arr` where `condition` is True. + + See Also + -------- + take, put, copyto, compress, place + + Examples + -------- + >>> import numpy as np + >>> arr = np.arange(12).reshape((3, 4)) + >>> arr + array([[ 0, 1, 2, 3], + [ 4, 5, 6, 7], + [ 8, 9, 10, 11]]) + >>> condition = np.mod(arr, 3)==0 + >>> condition + array([[ True, False, False, True], + [False, False, True, False], + [False, True, False, False]]) + >>> np.extract(condition, arr) + array([0, 3, 6, 9]) + + + If `condition` is boolean: + + >>> arr[condition] + array([0, 3, 6, 9]) + + """ + return _nx.take(ravel(arr), nonzero(ravel(condition))[0]) + + +def _place_dispatcher(arr, mask, vals): + return (arr, mask, vals) + + +@array_function_dispatch(_place_dispatcher) +def place(arr, mask, vals): + """ + Change elements of an array based on conditional and input values. + + Similar to ``np.copyto(arr, vals, where=mask)``, the difference is that + `place` uses the first N elements of `vals`, where N is the number of + True values in `mask`, while `copyto` uses the elements where `mask` + is True. + + Note that `extract` does the exact opposite of `place`. + + Parameters + ---------- + arr : ndarray + Array to put data into. + mask : array_like + Boolean mask array. Must have the same size as `a`. + vals : 1-D sequence + Values to put into `a`. Only the first N elements are used, where + N is the number of True values in `mask`. If `vals` is smaller + than N, it will be repeated, and if elements of `a` are to be masked, + this sequence must be non-empty. + + See Also + -------- + copyto, put, take, extract + + Examples + -------- + >>> import numpy as np + >>> arr = np.arange(6).reshape(2, 3) + >>> np.place(arr, arr>2, [44, 55]) + >>> arr + array([[ 0, 1, 2], + [44, 55, 44]]) + + """ + return _place(arr, mask, vals) + + +def disp(mesg, device=None, linefeed=True): + """ + Display a message on a device. + + .. deprecated:: 2.0 + Use your own printing function instead. + + Parameters + ---------- + mesg : str + Message to display. + device : object + Device to write message. If None, defaults to ``sys.stdout`` which is + very similar to ``print``. `device` needs to have ``write()`` and + ``flush()`` methods. + linefeed : bool, optional + Option whether to print a line feed or not. Defaults to True. + + Raises + ------ + AttributeError + If `device` does not have a ``write()`` or ``flush()`` method. + + Examples + -------- + >>> import numpy as np + + Besides ``sys.stdout``, a file-like object can also be used as it has + both required methods: + + >>> from io import StringIO + >>> buf = StringIO() + >>> np.disp('"Display" in a file', device=buf) + >>> buf.getvalue() + '"Display" in a file\\n' + + """ + + # Deprecated in NumPy 2.0, 2023-07-11 + warnings.warn( + "`disp` is deprecated, " + "use your own printing function instead. " + "(deprecated in NumPy 2.0)", + DeprecationWarning, + stacklevel=2 + ) + + if device is None: + device = sys.stdout + if linefeed: + device.write('%s\n' % mesg) + else: + device.write('%s' % mesg) + device.flush() + return + + +# See https://docs.scipy.org/doc/numpy/reference/c-api.generalized-ufuncs.html +_DIMENSION_NAME = r'\w+' +_CORE_DIMENSION_LIST = '(?:{0:}(?:,{0:})*)?'.format(_DIMENSION_NAME) +_ARGUMENT = r'\({}\)'.format(_CORE_DIMENSION_LIST) +_ARGUMENT_LIST = '{0:}(?:,{0:})*'.format(_ARGUMENT) +_SIGNATURE = '^{0:}->{0:}$'.format(_ARGUMENT_LIST) + + +def _parse_gufunc_signature(signature): + """ + Parse string signatures for a generalized universal function. + + Arguments + --------- + signature : string + Generalized universal function signature, e.g., ``(m,n),(n,p)->(m,p)`` + for ``np.matmul``. + + Returns + ------- + Tuple of input and output core dimensions parsed from the signature, each + of the form List[Tuple[str, ...]]. + """ + signature = re.sub(r'\s+', '', signature) + + if not re.match(_SIGNATURE, signature): + raise ValueError( + 'not a valid gufunc signature: {}'.format(signature)) + return tuple([tuple(re.findall(_DIMENSION_NAME, arg)) + for arg in re.findall(_ARGUMENT, arg_list)] + for arg_list in signature.split('->')) + + +def _update_dim_sizes(dim_sizes, arg, core_dims): + """ + Incrementally check and update core dimension sizes for a single argument. + + Arguments + --------- + dim_sizes : Dict[str, int] + Sizes of existing core dimensions. Will be updated in-place. + arg : ndarray + Argument to examine. + core_dims : Tuple[str, ...] + Core dimensions for this argument. + """ + if not core_dims: + return + + num_core_dims = len(core_dims) + if arg.ndim < num_core_dims: + raise ValueError( + '%d-dimensional argument does not have enough ' + 'dimensions for all core dimensions %r' + % (arg.ndim, core_dims)) + + core_shape = arg.shape[-num_core_dims:] + for dim, size in zip(core_dims, core_shape): + if dim in dim_sizes: + if size != dim_sizes[dim]: + raise ValueError( + 'inconsistent size for core dimension %r: %r vs %r' + % (dim, size, dim_sizes[dim])) + else: + dim_sizes[dim] = size + + +def _parse_input_dimensions(args, input_core_dims): + """ + Parse broadcast and core dimensions for vectorize with a signature. + + Arguments + --------- + args : Tuple[ndarray, ...] + Tuple of input arguments to examine. + input_core_dims : List[Tuple[str, ...]] + List of core dimensions corresponding to each input. + + Returns + ------- + broadcast_shape : Tuple[int, ...] + Common shape to broadcast all non-core dimensions to. + dim_sizes : Dict[str, int] + Common sizes for named core dimensions. + """ + broadcast_args = [] + dim_sizes = {} + for arg, core_dims in zip(args, input_core_dims): + _update_dim_sizes(dim_sizes, arg, core_dims) + ndim = arg.ndim - len(core_dims) + dummy_array = np.lib.stride_tricks.as_strided(0, arg.shape[:ndim]) + broadcast_args.append(dummy_array) + broadcast_shape = np.lib._stride_tricks_impl._broadcast_shape( + *broadcast_args + ) + return broadcast_shape, dim_sizes + + +def _calculate_shapes(broadcast_shape, dim_sizes, list_of_core_dims): + """Helper for calculating broadcast shapes with core dimensions.""" + return [broadcast_shape + tuple(dim_sizes[dim] for dim in core_dims) + for core_dims in list_of_core_dims] + + +def _create_arrays(broadcast_shape, dim_sizes, list_of_core_dims, dtypes, + results=None): + """Helper for creating output arrays in vectorize.""" + shapes = _calculate_shapes(broadcast_shape, dim_sizes, list_of_core_dims) + if dtypes is None: + dtypes = [None] * len(shapes) + if results is None: + arrays = tuple(np.empty(shape=shape, dtype=dtype) + for shape, dtype in zip(shapes, dtypes)) + else: + arrays = tuple(np.empty_like(result, shape=shape, dtype=dtype) + for result, shape, dtype + in zip(results, shapes, dtypes)) + return arrays + + +def _get_vectorize_dtype(dtype): + if dtype.char in "SU": + return dtype.char + return dtype + + +@set_module('numpy') +class vectorize: + """ + vectorize(pyfunc=np._NoValue, otypes=None, doc=None, excluded=None, + cache=False, signature=None) + + Returns an object that acts like pyfunc, but takes arrays as input. + + Define a vectorized function which takes a nested sequence of objects or + numpy arrays as inputs and returns a single numpy array or a tuple of numpy + arrays. The vectorized function evaluates `pyfunc` over successive tuples + of the input arrays like the python map function, except it uses the + broadcasting rules of numpy. + + The data type of the output of `vectorized` is determined by calling + the function with the first element of the input. This can be avoided + by specifying the `otypes` argument. + + Parameters + ---------- + pyfunc : callable, optional + A python function or method. + Can be omitted to produce a decorator with keyword arguments. + otypes : str or list of dtypes, optional + The output data type. It must be specified as either a string of + typecode characters or a list of data type specifiers. There should + be one data type specifier for each output. + doc : str, optional + The docstring for the function. If None, the docstring will be the + ``pyfunc.__doc__``. + excluded : set, optional + Set of strings or integers representing the positional or keyword + arguments for which the function will not be vectorized. These will be + passed directly to `pyfunc` unmodified. + + cache : bool, optional + If `True`, then cache the first function call that determines the number + of outputs if `otypes` is not provided. + + signature : string, optional + Generalized universal function signature, e.g., ``(m,n),(n)->(m)`` for + vectorized matrix-vector multiplication. If provided, ``pyfunc`` will + be called with (and expected to return) arrays with shapes given by the + size of corresponding core dimensions. By default, ``pyfunc`` is + assumed to take scalars as input and output. + + Returns + ------- + out : callable + A vectorized function if ``pyfunc`` was provided, + a decorator otherwise. + + See Also + -------- + frompyfunc : Takes an arbitrary Python function and returns a ufunc + + Notes + ----- + The `vectorize` function is provided primarily for convenience, not for + performance. The implementation is essentially a for loop. + + If `otypes` is not specified, then a call to the function with the + first argument will be used to determine the number of outputs. The + results of this call will be cached if `cache` is `True` to prevent + calling the function twice. However, to implement the cache, the + original function must be wrapped which will slow down subsequent + calls, so only do this if your function is expensive. + + The new keyword argument interface and `excluded` argument support + further degrades performance. + + References + ---------- + .. [1] :doc:`/reference/c-api/generalized-ufuncs` + + Examples + -------- + >>> import numpy as np + >>> def myfunc(a, b): + ... "Return a-b if a>b, otherwise return a+b" + ... if a > b: + ... return a - b + ... else: + ... return a + b + + >>> vfunc = np.vectorize(myfunc) + >>> vfunc([1, 2, 3, 4], 2) + array([3, 4, 1, 2]) + + The docstring is taken from the input function to `vectorize` unless it + is specified: + + >>> vfunc.__doc__ + 'Return a-b if a>b, otherwise return a+b' + >>> vfunc = np.vectorize(myfunc, doc='Vectorized `myfunc`') + >>> vfunc.__doc__ + 'Vectorized `myfunc`' + + The output type is determined by evaluating the first element of the input, + unless it is specified: + + >>> out = vfunc([1, 2, 3, 4], 2) + >>> type(out[0]) + + >>> vfunc = np.vectorize(myfunc, otypes=[float]) + >>> out = vfunc([1, 2, 3, 4], 2) + >>> type(out[0]) + + + The `excluded` argument can be used to prevent vectorizing over certain + arguments. This can be useful for array-like arguments of a fixed length + such as the coefficients for a polynomial as in `polyval`: + + >>> def mypolyval(p, x): + ... _p = list(p) + ... res = _p.pop(0) + ... while _p: + ... res = res*x + _p.pop(0) + ... return res + + Here, we exclude the zeroth argument from vectorization whether it is + passed by position or keyword. + + >>> vpolyval = np.vectorize(mypolyval, excluded={0, 'p'}) + >>> vpolyval([1, 2, 3], x=[0, 1]) + array([3, 6]) + >>> vpolyval(p=[1, 2, 3], x=[0, 1]) + array([3, 6]) + + The `signature` argument allows for vectorizing functions that act on + non-scalar arrays of fixed length. For example, you can use it for a + vectorized calculation of Pearson correlation coefficient and its p-value: + + >>> import scipy.stats + >>> pearsonr = np.vectorize(scipy.stats.pearsonr, + ... signature='(n),(n)->(),()') + >>> pearsonr([[0, 1, 2, 3]], [[1, 2, 3, 4], [4, 3, 2, 1]]) + (array([ 1., -1.]), array([ 0., 0.])) + + Or for a vectorized convolution: + + >>> convolve = np.vectorize(np.convolve, signature='(n),(m)->(k)') + >>> convolve(np.eye(4), [1, 2, 1]) + array([[1., 2., 1., 0., 0., 0.], + [0., 1., 2., 1., 0., 0.], + [0., 0., 1., 2., 1., 0.], + [0., 0., 0., 1., 2., 1.]]) + + Decorator syntax is supported. The decorator can be called as + a function to provide keyword arguments: + + >>> @np.vectorize + ... def identity(x): + ... return x + ... + >>> identity([0, 1, 2]) + array([0, 1, 2]) + >>> @np.vectorize(otypes=[float]) + ... def as_float(x): + ... return x + ... + >>> as_float([0, 1, 2]) + array([0., 1., 2.]) + """ + def __init__(self, pyfunc=np._NoValue, otypes=None, doc=None, + excluded=None, cache=False, signature=None): + + if (pyfunc != np._NoValue) and (not callable(pyfunc)): + #Splitting the error message to keep + #the length below 79 characters. + part1 = "When used as a decorator, " + part2 = "only accepts keyword arguments." + raise TypeError(part1 + part2) + + self.pyfunc = pyfunc + self.cache = cache + self.signature = signature + if pyfunc != np._NoValue and hasattr(pyfunc, '__name__'): + self.__name__ = pyfunc.__name__ + + self._ufunc = {} # Caching to improve default performance + self._doc = None + self.__doc__ = doc + if doc is None and hasattr(pyfunc, '__doc__'): + self.__doc__ = pyfunc.__doc__ + else: + self._doc = doc + + if isinstance(otypes, str): + for char in otypes: + if char not in typecodes['All']: + raise ValueError("Invalid otype specified: %s" % (char,)) + elif iterable(otypes): + otypes = [_get_vectorize_dtype(_nx.dtype(x)) for x in otypes] + elif otypes is not None: + raise ValueError("Invalid otype specification") + self.otypes = otypes + + # Excluded variable support + if excluded is None: + excluded = set() + self.excluded = set(excluded) + + if signature is not None: + self._in_and_out_core_dims = _parse_gufunc_signature(signature) + else: + self._in_and_out_core_dims = None + + def _init_stage_2(self, pyfunc, *args, **kwargs): + self.__name__ = pyfunc.__name__ + self.pyfunc = pyfunc + if self._doc is None: + self.__doc__ = pyfunc.__doc__ + else: + self.__doc__ = self._doc + + def _call_as_normal(self, *args, **kwargs): + """ + Return arrays with the results of `pyfunc` broadcast (vectorized) over + `args` and `kwargs` not in `excluded`. + """ + excluded = self.excluded + if not kwargs and not excluded: + func = self.pyfunc + vargs = args + else: + # The wrapper accepts only positional arguments: we use `names` and + # `inds` to mutate `the_args` and `kwargs` to pass to the original + # function. + nargs = len(args) + + names = [_n for _n in kwargs if _n not in excluded] + inds = [_i for _i in range(nargs) if _i not in excluded] + the_args = list(args) + + def func(*vargs): + for _n, _i in enumerate(inds): + the_args[_i] = vargs[_n] + kwargs.update(zip(names, vargs[len(inds):])) + return self.pyfunc(*the_args, **kwargs) + + vargs = [args[_i] for _i in inds] + vargs.extend([kwargs[_n] for _n in names]) + + return self._vectorize_call(func=func, args=vargs) + + def __call__(self, *args, **kwargs): + if self.pyfunc is np._NoValue: + self._init_stage_2(*args, **kwargs) + return self + + return self._call_as_normal(*args, **kwargs) + + def _get_ufunc_and_otypes(self, func, args): + """Return (ufunc, otypes).""" + # frompyfunc will fail if args is empty + if not args: + raise ValueError('args can not be empty') + + if self.otypes is not None: + otypes = self.otypes + + # self._ufunc is a dictionary whose keys are the number of + # arguments (i.e. len(args)) and whose values are ufuncs created + # by frompyfunc. len(args) can be different for different calls if + # self.pyfunc has parameters with default values. We only use the + # cache when func is self.pyfunc, which occurs when the call uses + # only positional arguments and no arguments are excluded. + + nin = len(args) + nout = len(self.otypes) + if func is not self.pyfunc or nin not in self._ufunc: + ufunc = frompyfunc(func, nin, nout) + else: + ufunc = None # We'll get it from self._ufunc + if func is self.pyfunc: + ufunc = self._ufunc.setdefault(nin, ufunc) + else: + # Get number of outputs and output types by calling the function on + # the first entries of args. We also cache the result to prevent + # the subsequent call when the ufunc is evaluated. + # Assumes that ufunc first evaluates the 0th elements in the input + # arrays (the input values are not checked to ensure this) + args = [asarray(arg) for arg in args] + if builtins.any(arg.size == 0 for arg in args): + raise ValueError('cannot call `vectorize` on size 0 inputs ' + 'unless `otypes` is set') + + inputs = [arg.flat[0] for arg in args] + outputs = func(*inputs) + + # Performance note: profiling indicates that -- for simple + # functions at least -- this wrapping can almost double the + # execution time. + # Hence we make it optional. + if self.cache: + _cache = [outputs] + + def _func(*vargs): + if _cache: + return _cache.pop() + else: + return func(*vargs) + else: + _func = func + + if isinstance(outputs, tuple): + nout = len(outputs) + else: + nout = 1 + outputs = (outputs,) + + otypes = ''.join([asarray(outputs[_k]).dtype.char + for _k in range(nout)]) + + # Performance note: profiling indicates that creating the ufunc is + # not a significant cost compared with wrapping so it seems not + # worth trying to cache this. + ufunc = frompyfunc(_func, len(args), nout) + + return ufunc, otypes + + def _vectorize_call(self, func, args): + """Vectorized call to `func` over positional `args`.""" + if self.signature is not None: + res = self._vectorize_call_with_signature(func, args) + elif not args: + res = func() + else: + ufunc, otypes = self._get_ufunc_and_otypes(func=func, args=args) + + # Convert args to object arrays first + inputs = [asanyarray(a, dtype=object) for a in args] + + outputs = ufunc(*inputs) + + if ufunc.nout == 1: + res = asanyarray(outputs, dtype=otypes[0]) + else: + res = tuple([asanyarray(x, dtype=t) + for x, t in zip(outputs, otypes)]) + return res + + def _vectorize_call_with_signature(self, func, args): + """Vectorized call over positional arguments with a signature.""" + input_core_dims, output_core_dims = self._in_and_out_core_dims + + if len(args) != len(input_core_dims): + raise TypeError('wrong number of positional arguments: ' + 'expected %r, got %r' + % (len(input_core_dims), len(args))) + args = tuple(asanyarray(arg) for arg in args) + + broadcast_shape, dim_sizes = _parse_input_dimensions( + args, input_core_dims) + input_shapes = _calculate_shapes(broadcast_shape, dim_sizes, + input_core_dims) + args = [np.broadcast_to(arg, shape, subok=True) + for arg, shape in zip(args, input_shapes)] + + outputs = None + otypes = self.otypes + nout = len(output_core_dims) + + for index in np.ndindex(*broadcast_shape): + results = func(*(arg[index] for arg in args)) + + n_results = len(results) if isinstance(results, tuple) else 1 + + if nout != n_results: + raise ValueError( + 'wrong number of outputs from pyfunc: expected %r, got %r' + % (nout, n_results)) + + if nout == 1: + results = (results,) + + if outputs is None: + for result, core_dims in zip(results, output_core_dims): + _update_dim_sizes(dim_sizes, result, core_dims) + + outputs = _create_arrays(broadcast_shape, dim_sizes, + output_core_dims, otypes, results) + + for output, result in zip(outputs, results): + output[index] = result + + if outputs is None: + # did not call the function even once + if otypes is None: + raise ValueError('cannot call `vectorize` on size 0 inputs ' + 'unless `otypes` is set') + if builtins.any(dim not in dim_sizes + for dims in output_core_dims + for dim in dims): + raise ValueError('cannot call `vectorize` with a signature ' + 'including new output dimensions on size 0 ' + 'inputs') + outputs = _create_arrays(broadcast_shape, dim_sizes, + output_core_dims, otypes) + + return outputs[0] if nout == 1 else outputs + + +def _cov_dispatcher(m, y=None, rowvar=None, bias=None, ddof=None, + fweights=None, aweights=None, *, dtype=None): + return (m, y, fweights, aweights) + + +@array_function_dispatch(_cov_dispatcher) +def cov(m, y=None, rowvar=True, bias=False, ddof=None, fweights=None, + aweights=None, *, dtype=None): + """ + Estimate a covariance matrix, given data and weights. + + Covariance indicates the level to which two variables vary together. + If we examine N-dimensional samples, :math:`X = [x_1, x_2, ... x_N]^T`, + then the covariance matrix element :math:`C_{ij}` is the covariance of + :math:`x_i` and :math:`x_j`. The element :math:`C_{ii}` is the variance + of :math:`x_i`. + + See the notes for an outline of the algorithm. + + Parameters + ---------- + m : array_like + A 1-D or 2-D array containing multiple variables and observations. + Each row of `m` represents a variable, and each column a single + observation of all those variables. Also see `rowvar` below. + y : array_like, optional + An additional set of variables and observations. `y` has the same form + as that of `m`. + rowvar : bool, optional + If `rowvar` is True (default), then each row represents a + variable, with observations in the columns. Otherwise, the relationship + is transposed: each column represents a variable, while the rows + contain observations. + bias : bool, optional + Default normalization (False) is by ``(N - 1)``, where ``N`` is the + number of observations given (unbiased estimate). If `bias` is True, + then normalization is by ``N``. These values can be overridden by using + the keyword ``ddof`` in numpy versions >= 1.5. + ddof : int, optional + If not ``None`` the default value implied by `bias` is overridden. + Note that ``ddof=1`` will return the unbiased estimate, even if both + `fweights` and `aweights` are specified, and ``ddof=0`` will return + the simple average. See the notes for the details. The default value + is ``None``. + fweights : array_like, int, optional + 1-D array of integer frequency weights; the number of times each + observation vector should be repeated. + aweights : array_like, optional + 1-D array of observation vector weights. These relative weights are + typically large for observations considered "important" and smaller for + observations considered less "important". If ``ddof=0`` the array of + weights can be used to assign probabilities to observation vectors. + dtype : data-type, optional + Data-type of the result. By default, the return data-type will have + at least `numpy.float64` precision. + + .. versionadded:: 1.20 + + Returns + ------- + out : ndarray + The covariance matrix of the variables. + + See Also + -------- + corrcoef : Normalized covariance matrix + + Notes + ----- + Assume that the observations are in the columns of the observation + array `m` and let ``f = fweights`` and ``a = aweights`` for brevity. The + steps to compute the weighted covariance are as follows:: + + >>> m = np.arange(10, dtype=np.float64) + >>> f = np.arange(10) * 2 + >>> a = np.arange(10) ** 2. + >>> ddof = 1 + >>> w = f * a + >>> v1 = np.sum(w) + >>> v2 = np.sum(w * a) + >>> m -= np.sum(m * w, axis=None, keepdims=True) / v1 + >>> cov = np.dot(m * w, m.T) * v1 / (v1**2 - ddof * v2) + + Note that when ``a == 1``, the normalization factor + ``v1 / (v1**2 - ddof * v2)`` goes over to ``1 / (np.sum(f) - ddof)`` + as it should. + + Examples + -------- + >>> import numpy as np + + Consider two variables, :math:`x_0` and :math:`x_1`, which + correlate perfectly, but in opposite directions: + + >>> x = np.array([[0, 2], [1, 1], [2, 0]]).T + >>> x + array([[0, 1, 2], + [2, 1, 0]]) + + Note how :math:`x_0` increases while :math:`x_1` decreases. The covariance + matrix shows this clearly: + + >>> np.cov(x) + array([[ 1., -1.], + [-1., 1.]]) + + Note that element :math:`C_{0,1}`, which shows the correlation between + :math:`x_0` and :math:`x_1`, is negative. + + Further, note how `x` and `y` are combined: + + >>> x = [-2.1, -1, 4.3] + >>> y = [3, 1.1, 0.12] + >>> X = np.stack((x, y), axis=0) + >>> np.cov(X) + array([[11.71 , -4.286 ], # may vary + [-4.286 , 2.144133]]) + >>> np.cov(x, y) + array([[11.71 , -4.286 ], # may vary + [-4.286 , 2.144133]]) + >>> np.cov(x) + array(11.71) + + """ + # Check inputs + if ddof is not None and ddof != int(ddof): + raise ValueError( + "ddof must be integer") + + # Handles complex arrays too + m = np.asarray(m) + if m.ndim > 2: + raise ValueError("m has more than 2 dimensions") + + if y is not None: + y = np.asarray(y) + if y.ndim > 2: + raise ValueError("y has more than 2 dimensions") + + if dtype is None: + if y is None: + dtype = np.result_type(m, np.float64) + else: + dtype = np.result_type(m, y, np.float64) + + X = array(m, ndmin=2, dtype=dtype) + if not rowvar and m.ndim != 1: + X = X.T + if X.shape[0] == 0: + return np.array([]).reshape(0, 0) + if y is not None: + y = array(y, copy=None, ndmin=2, dtype=dtype) + if not rowvar and y.shape[0] != 1: + y = y.T + X = np.concatenate((X, y), axis=0) + + if ddof is None: + if bias == 0: + ddof = 1 + else: + ddof = 0 + + # Get the product of frequencies and weights + w = None + if fweights is not None: + fweights = np.asarray(fweights, dtype=float) + if not np.all(fweights == np.around(fweights)): + raise TypeError( + "fweights must be integer") + if fweights.ndim > 1: + raise RuntimeError( + "cannot handle multidimensional fweights") + if fweights.shape[0] != X.shape[1]: + raise RuntimeError( + "incompatible numbers of samples and fweights") + if any(fweights < 0): + raise ValueError( + "fweights cannot be negative") + w = fweights + if aweights is not None: + aweights = np.asarray(aweights, dtype=float) + if aweights.ndim > 1: + raise RuntimeError( + "cannot handle multidimensional aweights") + if aweights.shape[0] != X.shape[1]: + raise RuntimeError( + "incompatible numbers of samples and aweights") + if any(aweights < 0): + raise ValueError( + "aweights cannot be negative") + if w is None: + w = aweights + else: + w *= aweights + + avg, w_sum = average(X, axis=1, weights=w, returned=True) + w_sum = w_sum[0] + + # Determine the normalization + if w is None: + fact = X.shape[1] - ddof + elif ddof == 0: + fact = w_sum + elif aweights is None: + fact = w_sum - ddof + else: + fact = w_sum - ddof*sum(w*aweights)/w_sum + + if fact <= 0: + warnings.warn("Degrees of freedom <= 0 for slice", + RuntimeWarning, stacklevel=2) + fact = 0.0 + + X -= avg[:, None] + if w is None: + X_T = X.T + else: + X_T = (X*w).T + c = dot(X, X_T.conj()) + c *= np.true_divide(1, fact) + return c.squeeze() + + +def _corrcoef_dispatcher(x, y=None, rowvar=None, bias=None, ddof=None, *, + dtype=None): + return (x, y) + + +@array_function_dispatch(_corrcoef_dispatcher) +def corrcoef(x, y=None, rowvar=True, bias=np._NoValue, ddof=np._NoValue, *, + dtype=None): + """ + Return Pearson product-moment correlation coefficients. + + Please refer to the documentation for `cov` for more detail. The + relationship between the correlation coefficient matrix, `R`, and the + covariance matrix, `C`, is + + .. math:: R_{ij} = \\frac{ C_{ij} } { \\sqrt{ C_{ii} C_{jj} } } + + The values of `R` are between -1 and 1, inclusive. + + Parameters + ---------- + x : array_like + A 1-D or 2-D array containing multiple variables and observations. + Each row of `x` represents a variable, and each column a single + observation of all those variables. Also see `rowvar` below. + y : array_like, optional + An additional set of variables and observations. `y` has the same + shape as `x`. + rowvar : bool, optional + If `rowvar` is True (default), then each row represents a + variable, with observations in the columns. Otherwise, the relationship + is transposed: each column represents a variable, while the rows + contain observations. + bias : _NoValue, optional + Has no effect, do not use. + + .. deprecated:: 1.10.0 + ddof : _NoValue, optional + Has no effect, do not use. + + .. deprecated:: 1.10.0 + dtype : data-type, optional + Data-type of the result. By default, the return data-type will have + at least `numpy.float64` precision. + + .. versionadded:: 1.20 + + Returns + ------- + R : ndarray + The correlation coefficient matrix of the variables. + + See Also + -------- + cov : Covariance matrix + + Notes + ----- + Due to floating point rounding the resulting array may not be Hermitian, + the diagonal elements may not be 1, and the elements may not satisfy the + inequality abs(a) <= 1. The real and imaginary parts are clipped to the + interval [-1, 1] in an attempt to improve on that situation but is not + much help in the complex case. + + This function accepts but discards arguments `bias` and `ddof`. This is + for backwards compatibility with previous versions of this function. These + arguments had no effect on the return values of the function and can be + safely ignored in this and previous versions of numpy. + + Examples + -------- + >>> import numpy as np + + In this example we generate two random arrays, ``xarr`` and ``yarr``, and + compute the row-wise and column-wise Pearson correlation coefficients, + ``R``. Since ``rowvar`` is true by default, we first find the row-wise + Pearson correlation coefficients between the variables of ``xarr``. + + >>> import numpy as np + >>> rng = np.random.default_rng(seed=42) + >>> xarr = rng.random((3, 3)) + >>> xarr + array([[0.77395605, 0.43887844, 0.85859792], + [0.69736803, 0.09417735, 0.97562235], + [0.7611397 , 0.78606431, 0.12811363]]) + >>> R1 = np.corrcoef(xarr) + >>> R1 + array([[ 1. , 0.99256089, -0.68080986], + [ 0.99256089, 1. , -0.76492172], + [-0.68080986, -0.76492172, 1. ]]) + + If we add another set of variables and observations ``yarr``, we can + compute the row-wise Pearson correlation coefficients between the + variables in ``xarr`` and ``yarr``. + + >>> yarr = rng.random((3, 3)) + >>> yarr + array([[0.45038594, 0.37079802, 0.92676499], + [0.64386512, 0.82276161, 0.4434142 ], + [0.22723872, 0.55458479, 0.06381726]]) + >>> R2 = np.corrcoef(xarr, yarr) + >>> R2 + array([[ 1. , 0.99256089, -0.68080986, 0.75008178, -0.934284 , + -0.99004057], + [ 0.99256089, 1. , -0.76492172, 0.82502011, -0.97074098, + -0.99981569], + [-0.68080986, -0.76492172, 1. , -0.99507202, 0.89721355, + 0.77714685], + [ 0.75008178, 0.82502011, -0.99507202, 1. , -0.93657855, + -0.83571711], + [-0.934284 , -0.97074098, 0.89721355, -0.93657855, 1. , + 0.97517215], + [-0.99004057, -0.99981569, 0.77714685, -0.83571711, 0.97517215, + 1. ]]) + + Finally if we use the option ``rowvar=False``, the columns are now + being treated as the variables and we will find the column-wise Pearson + correlation coefficients between variables in ``xarr`` and ``yarr``. + + >>> R3 = np.corrcoef(xarr, yarr, rowvar=False) + >>> R3 + array([[ 1. , 0.77598074, -0.47458546, -0.75078643, -0.9665554 , + 0.22423734], + [ 0.77598074, 1. , -0.92346708, -0.99923895, -0.58826587, + -0.44069024], + [-0.47458546, -0.92346708, 1. , 0.93773029, 0.23297648, + 0.75137473], + [-0.75078643, -0.99923895, 0.93773029, 1. , 0.55627469, + 0.47536961], + [-0.9665554 , -0.58826587, 0.23297648, 0.55627469, 1. , + -0.46666491], + [ 0.22423734, -0.44069024, 0.75137473, 0.47536961, -0.46666491, + 1. ]]) + + """ + if bias is not np._NoValue or ddof is not np._NoValue: + # 2015-03-15, 1.10 + warnings.warn('bias and ddof have no effect and are deprecated', + DeprecationWarning, stacklevel=2) + c = cov(x, y, rowvar, dtype=dtype) + try: + d = diag(c) + except ValueError: + # scalar covariance + # nan if incorrect value (nan, inf, 0), 1 otherwise + return c / c + stddev = sqrt(d.real) + c /= stddev[:, None] + c /= stddev[None, :] + + # Clip real and imaginary parts to [-1, 1]. This does not guarantee + # abs(a[i,j]) <= 1 for complex arrays, but is the best we can do without + # excessive work. + np.clip(c.real, -1, 1, out=c.real) + if np.iscomplexobj(c): + np.clip(c.imag, -1, 1, out=c.imag) + + return c + + +@set_module('numpy') +def blackman(M): + """ + Return the Blackman window. + + The Blackman window is a taper formed by using the first three + terms of a summation of cosines. It was designed to have close to the + minimal leakage possible. It is close to optimal, only slightly worse + than a Kaiser window. + + Parameters + ---------- + M : int + Number of points in the output window. If zero or less, an empty + array is returned. + + Returns + ------- + out : ndarray + The window, with the maximum value normalized to one (the value one + appears only if the number of samples is odd). + + See Also + -------- + bartlett, hamming, hanning, kaiser + + Notes + ----- + The Blackman window is defined as + + .. math:: w(n) = 0.42 - 0.5 \\cos(2\\pi n/M) + 0.08 \\cos(4\\pi n/M) + + Most references to the Blackman window come from the signal processing + literature, where it is used as one of many windowing functions for + smoothing values. It is also known as an apodization (which means + "removing the foot", i.e. smoothing discontinuities at the beginning + and end of the sampled signal) or tapering function. It is known as a + "near optimal" tapering function, almost as good (by some measures) + as the kaiser window. + + References + ---------- + Blackman, R.B. and Tukey, J.W., (1958) The measurement of power spectra, + Dover Publications, New York. + + Oppenheim, A.V., and R.W. Schafer. Discrete-Time Signal Processing. + Upper Saddle River, NJ: Prentice-Hall, 1999, pp. 468-471. + + Examples + -------- + >>> import numpy as np + >>> import matplotlib.pyplot as plt + >>> np.blackman(12) + array([-1.38777878e-17, 3.26064346e-02, 1.59903635e-01, # may vary + 4.14397981e-01, 7.36045180e-01, 9.67046769e-01, + 9.67046769e-01, 7.36045180e-01, 4.14397981e-01, + 1.59903635e-01, 3.26064346e-02, -1.38777878e-17]) + + Plot the window and the frequency response. + + .. plot:: + :include-source: + + import matplotlib.pyplot as plt + from numpy.fft import fft, fftshift + window = np.blackman(51) + plt.plot(window) + plt.title("Blackman window") + plt.ylabel("Amplitude") + plt.xlabel("Sample") + plt.show() # doctest: +SKIP + + plt.figure() + A = fft(window, 2048) / 25.5 + mag = np.abs(fftshift(A)) + freq = np.linspace(-0.5, 0.5, len(A)) + with np.errstate(divide='ignore', invalid='ignore'): + response = 20 * np.log10(mag) + response = np.clip(response, -100, 100) + plt.plot(freq, response) + plt.title("Frequency response of Blackman window") + plt.ylabel("Magnitude [dB]") + plt.xlabel("Normalized frequency [cycles per sample]") + plt.axis('tight') + plt.show() + + """ + # Ensures at least float64 via 0.0. M should be an integer, but conversion + # to double is safe for a range. + values = np.array([0.0, M]) + M = values[1] + + if M < 1: + return array([], dtype=values.dtype) + if M == 1: + return ones(1, dtype=values.dtype) + n = arange(1-M, M, 2) + return 0.42 + 0.5*cos(pi*n/(M-1)) + 0.08*cos(2.0*pi*n/(M-1)) + + +@set_module('numpy') +def bartlett(M): + """ + Return the Bartlett window. + + The Bartlett window is very similar to a triangular window, except + that the end points are at zero. It is often used in signal + processing for tapering a signal, without generating too much + ripple in the frequency domain. + + Parameters + ---------- + M : int + Number of points in the output window. If zero or less, an + empty array is returned. + + Returns + ------- + out : array + The triangular window, with the maximum value normalized to one + (the value one appears only if the number of samples is odd), with + the first and last samples equal to zero. + + See Also + -------- + blackman, hamming, hanning, kaiser + + Notes + ----- + The Bartlett window is defined as + + .. math:: w(n) = \\frac{2}{M-1} \\left( + \\frac{M-1}{2} - \\left|n - \\frac{M-1}{2}\\right| + \\right) + + Most references to the Bartlett window come from the signal processing + literature, where it is used as one of many windowing functions for + smoothing values. Note that convolution with this window produces linear + interpolation. It is also known as an apodization (which means "removing + the foot", i.e. smoothing discontinuities at the beginning and end of the + sampled signal) or tapering function. The Fourier transform of the + Bartlett window is the product of two sinc functions. Note the excellent + discussion in Kanasewich [2]_. + + References + ---------- + .. [1] M.S. Bartlett, "Periodogram Analysis and Continuous Spectra", + Biometrika 37, 1-16, 1950. + .. [2] E.R. Kanasewich, "Time Sequence Analysis in Geophysics", + The University of Alberta Press, 1975, pp. 109-110. + .. [3] A.V. Oppenheim and R.W. Schafer, "Discrete-Time Signal + Processing", Prentice-Hall, 1999, pp. 468-471. + .. [4] Wikipedia, "Window function", + https://en.wikipedia.org/wiki/Window_function + .. [5] W.H. Press, B.P. Flannery, S.A. Teukolsky, and W.T. Vetterling, + "Numerical Recipes", Cambridge University Press, 1986, page 429. + + Examples + -------- + >>> import numpy as np + >>> import matplotlib.pyplot as plt + >>> np.bartlett(12) + array([ 0. , 0.18181818, 0.36363636, 0.54545455, 0.72727273, # may vary + 0.90909091, 0.90909091, 0.72727273, 0.54545455, 0.36363636, + 0.18181818, 0. ]) + + Plot the window and its frequency response (requires SciPy and matplotlib). + + .. plot:: + :include-source: + + import matplotlib.pyplot as plt + from numpy.fft import fft, fftshift + window = np.bartlett(51) + plt.plot(window) + plt.title("Bartlett window") + plt.ylabel("Amplitude") + plt.xlabel("Sample") + plt.show() + plt.figure() + A = fft(window, 2048) / 25.5 + mag = np.abs(fftshift(A)) + freq = np.linspace(-0.5, 0.5, len(A)) + with np.errstate(divide='ignore', invalid='ignore'): + response = 20 * np.log10(mag) + response = np.clip(response, -100, 100) + plt.plot(freq, response) + plt.title("Frequency response of Bartlett window") + plt.ylabel("Magnitude [dB]") + plt.xlabel("Normalized frequency [cycles per sample]") + plt.axis('tight') + plt.show() + + """ + # Ensures at least float64 via 0.0. M should be an integer, but conversion + # to double is safe for a range. + values = np.array([0.0, M]) + M = values[1] + + if M < 1: + return array([], dtype=values.dtype) + if M == 1: + return ones(1, dtype=values.dtype) + n = arange(1-M, M, 2) + return where(less_equal(n, 0), 1 + n/(M-1), 1 - n/(M-1)) + + +@set_module('numpy') +def hanning(M): + """ + Return the Hanning window. + + The Hanning window is a taper formed by using a weighted cosine. + + Parameters + ---------- + M : int + Number of points in the output window. If zero or less, an + empty array is returned. + + Returns + ------- + out : ndarray, shape(M,) + The window, with the maximum value normalized to one (the value + one appears only if `M` is odd). + + See Also + -------- + bartlett, blackman, hamming, kaiser + + Notes + ----- + The Hanning window is defined as + + .. math:: w(n) = 0.5 - 0.5\\cos\\left(\\frac{2\\pi{n}}{M-1}\\right) + \\qquad 0 \\leq n \\leq M-1 + + The Hanning was named for Julius von Hann, an Austrian meteorologist. + It is also known as the Cosine Bell. Some authors prefer that it be + called a Hann window, to help avoid confusion with the very similar + Hamming window. + + Most references to the Hanning window come from the signal processing + literature, where it is used as one of many windowing functions for + smoothing values. It is also known as an apodization (which means + "removing the foot", i.e. smoothing discontinuities at the beginning + and end of the sampled signal) or tapering function. + + References + ---------- + .. [1] Blackman, R.B. and Tukey, J.W., (1958) The measurement of power + spectra, Dover Publications, New York. + .. [2] E.R. Kanasewich, "Time Sequence Analysis in Geophysics", + The University of Alberta Press, 1975, pp. 106-108. + .. [3] Wikipedia, "Window function", + https://en.wikipedia.org/wiki/Window_function + .. [4] W.H. Press, B.P. Flannery, S.A. Teukolsky, and W.T. Vetterling, + "Numerical Recipes", Cambridge University Press, 1986, page 425. + + Examples + -------- + >>> import numpy as np + >>> np.hanning(12) + array([0. , 0.07937323, 0.29229249, 0.57115742, 0.82743037, + 0.97974649, 0.97974649, 0.82743037, 0.57115742, 0.29229249, + 0.07937323, 0. ]) + + Plot the window and its frequency response. + + .. plot:: + :include-source: + + import matplotlib.pyplot as plt + from numpy.fft import fft, fftshift + window = np.hanning(51) + plt.plot(window) + plt.title("Hann window") + plt.ylabel("Amplitude") + plt.xlabel("Sample") + plt.show() + + plt.figure() + A = fft(window, 2048) / 25.5 + mag = np.abs(fftshift(A)) + freq = np.linspace(-0.5, 0.5, len(A)) + with np.errstate(divide='ignore', invalid='ignore'): + response = 20 * np.log10(mag) + response = np.clip(response, -100, 100) + plt.plot(freq, response) + plt.title("Frequency response of the Hann window") + plt.ylabel("Magnitude [dB]") + plt.xlabel("Normalized frequency [cycles per sample]") + plt.axis('tight') + plt.show() + + """ + # Ensures at least float64 via 0.0. M should be an integer, but conversion + # to double is safe for a range. + values = np.array([0.0, M]) + M = values[1] + + if M < 1: + return array([], dtype=values.dtype) + if M == 1: + return ones(1, dtype=values.dtype) + n = arange(1-M, M, 2) + return 0.5 + 0.5*cos(pi*n/(M-1)) + + +@set_module('numpy') +def hamming(M): + """ + Return the Hamming window. + + The Hamming window is a taper formed by using a weighted cosine. + + Parameters + ---------- + M : int + Number of points in the output window. If zero or less, an + empty array is returned. + + Returns + ------- + out : ndarray + The window, with the maximum value normalized to one (the value + one appears only if the number of samples is odd). + + See Also + -------- + bartlett, blackman, hanning, kaiser + + Notes + ----- + The Hamming window is defined as + + .. math:: w(n) = 0.54 - 0.46\\cos\\left(\\frac{2\\pi{n}}{M-1}\\right) + \\qquad 0 \\leq n \\leq M-1 + + The Hamming was named for R. W. Hamming, an associate of J. W. Tukey + and is described in Blackman and Tukey. It was recommended for + smoothing the truncated autocovariance function in the time domain. + Most references to the Hamming window come from the signal processing + literature, where it is used as one of many windowing functions for + smoothing values. It is also known as an apodization (which means + "removing the foot", i.e. smoothing discontinuities at the beginning + and end of the sampled signal) or tapering function. + + References + ---------- + .. [1] Blackman, R.B. and Tukey, J.W., (1958) The measurement of power + spectra, Dover Publications, New York. + .. [2] E.R. Kanasewich, "Time Sequence Analysis in Geophysics", The + University of Alberta Press, 1975, pp. 109-110. + .. [3] Wikipedia, "Window function", + https://en.wikipedia.org/wiki/Window_function + .. [4] W.H. Press, B.P. Flannery, S.A. Teukolsky, and W.T. Vetterling, + "Numerical Recipes", Cambridge University Press, 1986, page 425. + + Examples + -------- + >>> import numpy as np + >>> np.hamming(12) + array([ 0.08 , 0.15302337, 0.34890909, 0.60546483, 0.84123594, # may vary + 0.98136677, 0.98136677, 0.84123594, 0.60546483, 0.34890909, + 0.15302337, 0.08 ]) + + Plot the window and the frequency response. + + .. plot:: + :include-source: + + import matplotlib.pyplot as plt + from numpy.fft import fft, fftshift + window = np.hamming(51) + plt.plot(window) + plt.title("Hamming window") + plt.ylabel("Amplitude") + plt.xlabel("Sample") + plt.show() + + plt.figure() + A = fft(window, 2048) / 25.5 + mag = np.abs(fftshift(A)) + freq = np.linspace(-0.5, 0.5, len(A)) + response = 20 * np.log10(mag) + response = np.clip(response, -100, 100) + plt.plot(freq, response) + plt.title("Frequency response of Hamming window") + plt.ylabel("Magnitude [dB]") + plt.xlabel("Normalized frequency [cycles per sample]") + plt.axis('tight') + plt.show() + + """ + # Ensures at least float64 via 0.0. M should be an integer, but conversion + # to double is safe for a range. + values = np.array([0.0, M]) + M = values[1] + + if M < 1: + return array([], dtype=values.dtype) + if M == 1: + return ones(1, dtype=values.dtype) + n = arange(1-M, M, 2) + return 0.54 + 0.46*cos(pi*n/(M-1)) + + +## Code from cephes for i0 + +_i0A = [ + -4.41534164647933937950E-18, + 3.33079451882223809783E-17, + -2.43127984654795469359E-16, + 1.71539128555513303061E-15, + -1.16853328779934516808E-14, + 7.67618549860493561688E-14, + -4.85644678311192946090E-13, + 2.95505266312963983461E-12, + -1.72682629144155570723E-11, + 9.67580903537323691224E-11, + -5.18979560163526290666E-10, + 2.65982372468238665035E-9, + -1.30002500998624804212E-8, + 6.04699502254191894932E-8, + -2.67079385394061173391E-7, + 1.11738753912010371815E-6, + -4.41673835845875056359E-6, + 1.64484480707288970893E-5, + -5.75419501008210370398E-5, + 1.88502885095841655729E-4, + -5.76375574538582365885E-4, + 1.63947561694133579842E-3, + -4.32430999505057594430E-3, + 1.05464603945949983183E-2, + -2.37374148058994688156E-2, + 4.93052842396707084878E-2, + -9.49010970480476444210E-2, + 1.71620901522208775349E-1, + -3.04682672343198398683E-1, + 6.76795274409476084995E-1 + ] + +_i0B = [ + -7.23318048787475395456E-18, + -4.83050448594418207126E-18, + 4.46562142029675999901E-17, + 3.46122286769746109310E-17, + -2.82762398051658348494E-16, + -3.42548561967721913462E-16, + 1.77256013305652638360E-15, + 3.81168066935262242075E-15, + -9.55484669882830764870E-15, + -4.15056934728722208663E-14, + 1.54008621752140982691E-14, + 3.85277838274214270114E-13, + 7.18012445138366623367E-13, + -1.79417853150680611778E-12, + -1.32158118404477131188E-11, + -3.14991652796324136454E-11, + 1.18891471078464383424E-11, + 4.94060238822496958910E-10, + 3.39623202570838634515E-9, + 2.26666899049817806459E-8, + 2.04891858946906374183E-7, + 2.89137052083475648297E-6, + 6.88975834691682398426E-5, + 3.36911647825569408990E-3, + 8.04490411014108831608E-1 + ] + + +def _chbevl(x, vals): + b0 = vals[0] + b1 = 0.0 + + for i in range(1, len(vals)): + b2 = b1 + b1 = b0 + b0 = x*b1 - b2 + vals[i] + + return 0.5*(b0 - b2) + + +def _i0_1(x): + return exp(x) * _chbevl(x/2.0-2, _i0A) + + +def _i0_2(x): + return exp(x) * _chbevl(32.0/x - 2.0, _i0B) / sqrt(x) + + +def _i0_dispatcher(x): + return (x,) + + +@array_function_dispatch(_i0_dispatcher) +def i0(x): + """ + Modified Bessel function of the first kind, order 0. + + Usually denoted :math:`I_0`. + + Parameters + ---------- + x : array_like of float + Argument of the Bessel function. + + Returns + ------- + out : ndarray, shape = x.shape, dtype = float + The modified Bessel function evaluated at each of the elements of `x`. + + See Also + -------- + scipy.special.i0, scipy.special.iv, scipy.special.ive + + Notes + ----- + The scipy implementation is recommended over this function: it is a + proper ufunc written in C, and more than an order of magnitude faster. + + We use the algorithm published by Clenshaw [1]_ and referenced by + Abramowitz and Stegun [2]_, for which the function domain is + partitioned into the two intervals [0,8] and (8,inf), and Chebyshev + polynomial expansions are employed in each interval. Relative error on + the domain [0,30] using IEEE arithmetic is documented [3]_ as having a + peak of 5.8e-16 with an rms of 1.4e-16 (n = 30000). + + References + ---------- + .. [1] C. W. Clenshaw, "Chebyshev series for mathematical functions", in + *National Physical Laboratory Mathematical Tables*, vol. 5, London: + Her Majesty's Stationery Office, 1962. + .. [2] M. Abramowitz and I. A. Stegun, *Handbook of Mathematical + Functions*, 10th printing, New York: Dover, 1964, pp. 379. + https://personal.math.ubc.ca/~cbm/aands/page_379.htm + .. [3] https://metacpan.org/pod/distribution/Math-Cephes/lib/Math/Cephes.pod#i0:-Modified-Bessel-function-of-order-zero + + Examples + -------- + >>> import numpy as np + >>> np.i0(0.) + array(1.0) + >>> np.i0([0, 1, 2, 3]) + array([1. , 1.26606588, 2.2795853 , 4.88079259]) + + """ + x = np.asanyarray(x) + if x.dtype.kind == 'c': + raise TypeError("i0 not supported for complex values") + if x.dtype.kind != 'f': + x = x.astype(float) + x = np.abs(x) + return piecewise(x, [x <= 8.0], [_i0_1, _i0_2]) + +## End of cephes code for i0 + + +@set_module('numpy') +def kaiser(M, beta): + """ + Return the Kaiser window. + + The Kaiser window is a taper formed by using a Bessel function. + + Parameters + ---------- + M : int + Number of points in the output window. If zero or less, an + empty array is returned. + beta : float + Shape parameter for window. + + Returns + ------- + out : array + The window, with the maximum value normalized to one (the value + one appears only if the number of samples is odd). + + See Also + -------- + bartlett, blackman, hamming, hanning + + Notes + ----- + The Kaiser window is defined as + + .. math:: w(n) = I_0\\left( \\beta \\sqrt{1-\\frac{4n^2}{(M-1)^2}} + \\right)/I_0(\\beta) + + with + + .. math:: \\quad -\\frac{M-1}{2} \\leq n \\leq \\frac{M-1}{2}, + + where :math:`I_0` is the modified zeroth-order Bessel function. + + The Kaiser was named for Jim Kaiser, who discovered a simple + approximation to the DPSS window based on Bessel functions. The Kaiser + window is a very good approximation to the Digital Prolate Spheroidal + Sequence, or Slepian window, which is the transform which maximizes the + energy in the main lobe of the window relative to total energy. + + The Kaiser can approximate many other windows by varying the beta + parameter. + + ==== ======================= + beta Window shape + ==== ======================= + 0 Rectangular + 5 Similar to a Hamming + 6 Similar to a Hanning + 8.6 Similar to a Blackman + ==== ======================= + + A beta value of 14 is probably a good starting point. Note that as beta + gets large, the window narrows, and so the number of samples needs to be + large enough to sample the increasingly narrow spike, otherwise NaNs will + get returned. + + Most references to the Kaiser window come from the signal processing + literature, where it is used as one of many windowing functions for + smoothing values. It is also known as an apodization (which means + "removing the foot", i.e. smoothing discontinuities at the beginning + and end of the sampled signal) or tapering function. + + References + ---------- + .. [1] J. F. Kaiser, "Digital Filters" - Ch 7 in "Systems analysis by + digital computer", Editors: F.F. Kuo and J.F. Kaiser, p 218-285. + John Wiley and Sons, New York, (1966). + .. [2] E.R. Kanasewich, "Time Sequence Analysis in Geophysics", The + University of Alberta Press, 1975, pp. 177-178. + .. [3] Wikipedia, "Window function", + https://en.wikipedia.org/wiki/Window_function + + Examples + -------- + >>> import numpy as np + >>> import matplotlib.pyplot as plt + >>> np.kaiser(12, 14) + array([7.72686684e-06, 3.46009194e-03, 4.65200189e-02, # may vary + 2.29737120e-01, 5.99885316e-01, 9.45674898e-01, + 9.45674898e-01, 5.99885316e-01, 2.29737120e-01, + 4.65200189e-02, 3.46009194e-03, 7.72686684e-06]) + + + Plot the window and the frequency response. + + .. plot:: + :include-source: + + import matplotlib.pyplot as plt + from numpy.fft import fft, fftshift + window = np.kaiser(51, 14) + plt.plot(window) + plt.title("Kaiser window") + plt.ylabel("Amplitude") + plt.xlabel("Sample") + plt.show() + + plt.figure() + A = fft(window, 2048) / 25.5 + mag = np.abs(fftshift(A)) + freq = np.linspace(-0.5, 0.5, len(A)) + response = 20 * np.log10(mag) + response = np.clip(response, -100, 100) + plt.plot(freq, response) + plt.title("Frequency response of Kaiser window") + plt.ylabel("Magnitude [dB]") + plt.xlabel("Normalized frequency [cycles per sample]") + plt.axis('tight') + plt.show() + + """ + # Ensures at least float64 via 0.0. M should be an integer, but conversion + # to double is safe for a range. (Simplified result_type with 0.0 + # strongly typed. result-type is not/less order sensitive, but that mainly + # matters for integers anyway.) + values = np.array([0.0, M, beta]) + M = values[1] + beta = values[2] + + if M == 1: + return np.ones(1, dtype=values.dtype) + n = arange(0, M) + alpha = (M-1)/2.0 + return i0(beta * sqrt(1-((n-alpha)/alpha)**2.0))/i0(beta) + + +def _sinc_dispatcher(x): + return (x,) + + +@array_function_dispatch(_sinc_dispatcher) +def sinc(x): + r""" + Return the normalized sinc function. + + The sinc function is equal to :math:`\sin(\pi x)/(\pi x)` for any argument + :math:`x\ne 0`. ``sinc(0)`` takes the limit value 1, making ``sinc`` not + only everywhere continuous but also infinitely differentiable. + + .. note:: + + Note the normalization factor of ``pi`` used in the definition. + This is the most commonly used definition in signal processing. + Use ``sinc(x / np.pi)`` to obtain the unnormalized sinc function + :math:`\sin(x)/x` that is more common in mathematics. + + Parameters + ---------- + x : ndarray + Array (possibly multi-dimensional) of values for which to calculate + ``sinc(x)``. + + Returns + ------- + out : ndarray + ``sinc(x)``, which has the same shape as the input. + + Notes + ----- + The name sinc is short for "sine cardinal" or "sinus cardinalis". + + The sinc function is used in various signal processing applications, + including in anti-aliasing, in the construction of a Lanczos resampling + filter, and in interpolation. + + For bandlimited interpolation of discrete-time signals, the ideal + interpolation kernel is proportional to the sinc function. + + References + ---------- + .. [1] Weisstein, Eric W. "Sinc Function." From MathWorld--A Wolfram Web + Resource. https://mathworld.wolfram.com/SincFunction.html + .. [2] Wikipedia, "Sinc function", + https://en.wikipedia.org/wiki/Sinc_function + + Examples + -------- + >>> import numpy as np + >>> import matplotlib.pyplot as plt + >>> x = np.linspace(-4, 4, 41) + >>> np.sinc(x) + array([-3.89804309e-17, -4.92362781e-02, -8.40918587e-02, # may vary + -8.90384387e-02, -5.84680802e-02, 3.89804309e-17, + 6.68206631e-02, 1.16434881e-01, 1.26137788e-01, + 8.50444803e-02, -3.89804309e-17, -1.03943254e-01, + -1.89206682e-01, -2.16236208e-01, -1.55914881e-01, + 3.89804309e-17, 2.33872321e-01, 5.04551152e-01, + 7.56826729e-01, 9.35489284e-01, 1.00000000e+00, + 9.35489284e-01, 7.56826729e-01, 5.04551152e-01, + 2.33872321e-01, 3.89804309e-17, -1.55914881e-01, + -2.16236208e-01, -1.89206682e-01, -1.03943254e-01, + -3.89804309e-17, 8.50444803e-02, 1.26137788e-01, + 1.16434881e-01, 6.68206631e-02, 3.89804309e-17, + -5.84680802e-02, -8.90384387e-02, -8.40918587e-02, + -4.92362781e-02, -3.89804309e-17]) + + >>> plt.plot(x, np.sinc(x)) + [] + >>> plt.title("Sinc Function") + Text(0.5, 1.0, 'Sinc Function') + >>> plt.ylabel("Amplitude") + Text(0, 0.5, 'Amplitude') + >>> plt.xlabel("X") + Text(0.5, 0, 'X') + >>> plt.show() + + """ + x = np.asanyarray(x) + y = pi * where(x == 0, 1.0e-20, x) + return sin(y)/y + + +def _ureduce(a, func, keepdims=False, **kwargs): + """ + Internal Function. + Call `func` with `a` as first argument swapping the axes to use extended + axis on functions that don't support it natively. + + Returns result and a.shape with axis dims set to 1. + + Parameters + ---------- + a : array_like + Input array or object that can be converted to an array. + func : callable + Reduction function capable of receiving a single axis argument. + It is called with `a` as first argument followed by `kwargs`. + kwargs : keyword arguments + additional keyword arguments to pass to `func`. + + Returns + ------- + result : tuple + Result of func(a, **kwargs) and a.shape with axis dims set to 1 + which can be used to reshape the result to the same shape a ufunc with + keepdims=True would produce. + + """ + a = np.asanyarray(a) + axis = kwargs.get('axis', None) + out = kwargs.get('out', None) + + if keepdims is np._NoValue: + keepdims = False + + nd = a.ndim + if axis is not None: + axis = _nx.normalize_axis_tuple(axis, nd) + + if keepdims: + if out is not None: + index_out = tuple( + 0 if i in axis else slice(None) for i in range(nd)) + kwargs['out'] = out[(Ellipsis, ) + index_out] + + if len(axis) == 1: + kwargs['axis'] = axis[0] + else: + keep = set(range(nd)) - set(axis) + nkeep = len(keep) + # swap axis that should not be reduced to front + for i, s in enumerate(sorted(keep)): + a = a.swapaxes(i, s) + # merge reduced axis + a = a.reshape(a.shape[:nkeep] + (-1,)) + kwargs['axis'] = -1 + else: + if keepdims: + if out is not None: + index_out = (0, ) * nd + kwargs['out'] = out[(Ellipsis, ) + index_out] + + r = func(a, **kwargs) + + if out is not None: + return out + + if keepdims: + if axis is None: + index_r = (np.newaxis, ) * nd + else: + index_r = tuple( + np.newaxis if i in axis else slice(None) + for i in range(nd)) + r = r[(Ellipsis, ) + index_r] + + return r + + +def _median_dispatcher( + a, axis=None, out=None, overwrite_input=None, keepdims=None): + return (a, out) + + +@array_function_dispatch(_median_dispatcher) +def median(a, axis=None, out=None, overwrite_input=False, keepdims=False): + """ + Compute the median along the specified axis. + + Returns the median of the array elements. + + Parameters + ---------- + a : array_like + Input array or object that can be converted to an array. + axis : {int, sequence of int, None}, optional + Axis or axes along which the medians are computed. The default, + axis=None, will compute the median along a flattened version of + the array. If a sequence of axes, the array is first flattened + along the given axes, then the median is computed along the + resulting flattened axis. + out : ndarray, optional + Alternative output array in which to place the result. It must + have the same shape and buffer length as the expected output, + but the type (of the output) will be cast if necessary. + overwrite_input : bool, optional + If True, then allow use of memory of input array `a` for + calculations. The input array will be modified by the call to + `median`. This will save memory when you do not need to preserve + the contents of the input array. Treat the input as undefined, + but it will probably be fully or partially sorted. Default is + False. If `overwrite_input` is ``True`` and `a` is not already an + `ndarray`, an error will be raised. + keepdims : bool, optional + If this is set to True, the axes which are reduced are left + in the result as dimensions with size one. With this option, + the result will broadcast correctly against the original `arr`. + + Returns + ------- + median : ndarray + A new array holding the result. If the input contains integers + or floats smaller than ``float64``, then the output data-type is + ``np.float64``. Otherwise, the data-type of the output is the + same as that of the input. If `out` is specified, that array is + returned instead. + + See Also + -------- + mean, percentile + + Notes + ----- + Given a vector ``V`` of length ``N``, the median of ``V`` is the + middle value of a sorted copy of ``V``, ``V_sorted`` - i + e., ``V_sorted[(N-1)/2]``, when ``N`` is odd, and the average of the + two middle values of ``V_sorted`` when ``N`` is even. + + Examples + -------- + >>> import numpy as np + >>> a = np.array([[10, 7, 4], [3, 2, 1]]) + >>> a + array([[10, 7, 4], + [ 3, 2, 1]]) + >>> np.median(a) + np.float64(3.5) + >>> np.median(a, axis=0) + array([6.5, 4.5, 2.5]) + >>> np.median(a, axis=1) + array([7., 2.]) + >>> np.median(a, axis=(0, 1)) + np.float64(3.5) + >>> m = np.median(a, axis=0) + >>> out = np.zeros_like(m) + >>> np.median(a, axis=0, out=m) + array([6.5, 4.5, 2.5]) + >>> m + array([6.5, 4.5, 2.5]) + >>> b = a.copy() + >>> np.median(b, axis=1, overwrite_input=True) + array([7., 2.]) + >>> assert not np.all(a==b) + >>> b = a.copy() + >>> np.median(b, axis=None, overwrite_input=True) + np.float64(3.5) + >>> assert not np.all(a==b) + + """ + return _ureduce(a, func=_median, keepdims=keepdims, axis=axis, out=out, + overwrite_input=overwrite_input) + + +def _median(a, axis=None, out=None, overwrite_input=False): + # can't be reasonably be implemented in terms of percentile as we have to + # call mean to not break astropy + a = np.asanyarray(a) + + # Set the partition indexes + if axis is None: + sz = a.size + else: + sz = a.shape[axis] + if sz % 2 == 0: + szh = sz // 2 + kth = [szh - 1, szh] + else: + kth = [(sz - 1) // 2] + + # We have to check for NaNs (as of writing 'M' doesn't actually work). + supports_nans = np.issubdtype(a.dtype, np.inexact) or a.dtype.kind in 'Mm' + if supports_nans: + kth.append(-1) + + if overwrite_input: + if axis is None: + part = a.ravel() + part.partition(kth) + else: + a.partition(kth, axis=axis) + part = a + else: + part = partition(a, kth, axis=axis) + + if part.shape == (): + # make 0-D arrays work + return part.item() + if axis is None: + axis = 0 + + indexer = [slice(None)] * part.ndim + index = part.shape[axis] // 2 + if part.shape[axis] % 2 == 1: + # index with slice to allow mean (below) to work + indexer[axis] = slice(index, index+1) + else: + indexer[axis] = slice(index-1, index+1) + indexer = tuple(indexer) + + # Use mean in both odd and even case to coerce data type, + # using out array if needed. + rout = mean(part[indexer], axis=axis, out=out) + if supports_nans and sz > 0: + # If nans are possible, warn and replace by nans like mean would. + rout = np.lib._utils_impl._median_nancheck(part, rout, axis) + + return rout + + +def _percentile_dispatcher(a, q, axis=None, out=None, overwrite_input=None, + method=None, keepdims=None, *, weights=None, + interpolation=None): + return (a, q, out, weights) + + +@array_function_dispatch(_percentile_dispatcher) +def percentile(a, + q, + axis=None, + out=None, + overwrite_input=False, + method="linear", + keepdims=False, + *, + weights=None, + interpolation=None): + """ + Compute the q-th percentile of the data along the specified axis. + + Returns the q-th percentile(s) of the array elements. + + Parameters + ---------- + a : array_like of real numbers + Input array or object that can be converted to an array. + q : array_like of float + Percentage or sequence of percentages for the percentiles to compute. + Values must be between 0 and 100 inclusive. + axis : {int, tuple of int, None}, optional + Axis or axes along which the percentiles are computed. The + default is to compute the percentile(s) along a flattened + version of the array. + out : ndarray, optional + Alternative output array in which to place the result. It must + have the same shape and buffer length as the expected output, + but the type (of the output) will be cast if necessary. + overwrite_input : bool, optional + If True, then allow the input array `a` to be modified by intermediate + calculations, to save memory. In this case, the contents of the input + `a` after this function completes is undefined. + method : str, optional + This parameter specifies the method to use for estimating the + percentile. There are many different methods, some unique to NumPy. + See the notes for explanation. The options sorted by their R type + as summarized in the H&F paper [1]_ are: + + 1. 'inverted_cdf' + 2. 'averaged_inverted_cdf' + 3. 'closest_observation' + 4. 'interpolated_inverted_cdf' + 5. 'hazen' + 6. 'weibull' + 7. 'linear' (default) + 8. 'median_unbiased' + 9. 'normal_unbiased' + + The first three methods are discontinuous. NumPy further defines the + following discontinuous variations of the default 'linear' (7.) option: + + * 'lower' + * 'higher', + * 'midpoint' + * 'nearest' + + .. versionchanged:: 1.22.0 + This argument was previously called "interpolation" and only + offered the "linear" default and last four options. + + keepdims : bool, optional + If this is set to True, the axes which are reduced are left in + the result as dimensions with size one. With this option, the + result will broadcast correctly against the original array `a`. + + weights : array_like, optional + An array of weights associated with the values in `a`. Each value in + `a` contributes to the percentile according to its associated weight. + The weights array can either be 1-D (in which case its length must be + the size of `a` along the given axis) or of the same shape as `a`. + If `weights=None`, then all data in `a` are assumed to have a + weight equal to one. + Only `method="inverted_cdf"` supports weights. + See the notes for more details. + + .. versionadded:: 2.0.0 + + interpolation : str, optional + Deprecated name for the method keyword argument. + + .. deprecated:: 1.22.0 + + Returns + ------- + percentile : scalar or ndarray + If `q` is a single percentile and `axis=None`, then the result + is a scalar. If multiple percentiles are given, first axis of + the result corresponds to the percentiles. The other axes are + the axes that remain after the reduction of `a`. If the input + contains integers or floats smaller than ``float64``, the output + data-type is ``float64``. Otherwise, the output data-type is the + same as that of the input. If `out` is specified, that array is + returned instead. + + See Also + -------- + mean + median : equivalent to ``percentile(..., 50)`` + nanpercentile + quantile : equivalent to percentile, except q in the range [0, 1]. + + Notes + ----- + The behavior of `numpy.percentile` with percentage `q` is + that of `numpy.quantile` with argument ``q/100``. + For more information, please see `numpy.quantile`. + + Examples + -------- + >>> import numpy as np + >>> a = np.array([[10, 7, 4], [3, 2, 1]]) + >>> a + array([[10, 7, 4], + [ 3, 2, 1]]) + >>> np.percentile(a, 50) + 3.5 + >>> np.percentile(a, 50, axis=0) + array([6.5, 4.5, 2.5]) + >>> np.percentile(a, 50, axis=1) + array([7., 2.]) + >>> np.percentile(a, 50, axis=1, keepdims=True) + array([[7.], + [2.]]) + + >>> m = np.percentile(a, 50, axis=0) + >>> out = np.zeros_like(m) + >>> np.percentile(a, 50, axis=0, out=out) + array([6.5, 4.5, 2.5]) + >>> m + array([6.5, 4.5, 2.5]) + + >>> b = a.copy() + >>> np.percentile(b, 50, axis=1, overwrite_input=True) + array([7., 2.]) + >>> assert not np.all(a == b) + + The different methods can be visualized graphically: + + .. plot:: + + import matplotlib.pyplot as plt + + a = np.arange(4) + p = np.linspace(0, 100, 6001) + ax = plt.gca() + lines = [ + ('linear', '-', 'C0'), + ('inverted_cdf', ':', 'C1'), + # Almost the same as `inverted_cdf`: + ('averaged_inverted_cdf', '-.', 'C1'), + ('closest_observation', ':', 'C2'), + ('interpolated_inverted_cdf', '--', 'C1'), + ('hazen', '--', 'C3'), + ('weibull', '-.', 'C4'), + ('median_unbiased', '--', 'C5'), + ('normal_unbiased', '-.', 'C6'), + ] + for method, style, color in lines: + ax.plot( + p, np.percentile(a, p, method=method), + label=method, linestyle=style, color=color) + ax.set( + title='Percentiles for different methods and data: ' + str(a), + xlabel='Percentile', + ylabel='Estimated percentile value', + yticks=a) + ax.legend(bbox_to_anchor=(1.03, 1)) + plt.tight_layout() + plt.show() + + References + ---------- + .. [1] R. J. Hyndman and Y. Fan, + "Sample quantiles in statistical packages," + The American Statistician, 50(4), pp. 361-365, 1996 + + """ + if interpolation is not None: + method = _check_interpolation_as_method( + method, interpolation, "percentile") + + a = np.asanyarray(a) + if a.dtype.kind == "c": + raise TypeError("a must be an array of real numbers") + + # Use dtype of array if possible (e.g., if q is a python int or float) + # by making the divisor have the dtype of the data array. + q = np.true_divide(q, a.dtype.type(100) if a.dtype.kind == "f" else 100) + q = asanyarray(q) # undo any decay that the ufunc performed (see gh-13105) + if not _quantile_is_valid(q): + raise ValueError("Percentiles must be in the range [0, 100]") + + if weights is not None: + if method != "inverted_cdf": + msg = ("Only method 'inverted_cdf' supports weights. " + f"Got: {method}.") + raise ValueError(msg) + if axis is not None: + axis = _nx.normalize_axis_tuple(axis, a.ndim, argname="axis") + weights = _weights_are_valid(weights=weights, a=a, axis=axis) + if np.any(weights < 0): + raise ValueError("Weights must be non-negative.") + + return _quantile_unchecked( + a, q, axis, out, overwrite_input, method, keepdims, weights) + + +def _quantile_dispatcher(a, q, axis=None, out=None, overwrite_input=None, + method=None, keepdims=None, *, weights=None, + interpolation=None): + return (a, q, out, weights) + + +@array_function_dispatch(_quantile_dispatcher) +def quantile(a, + q, + axis=None, + out=None, + overwrite_input=False, + method="linear", + keepdims=False, + *, + weights=None, + interpolation=None): + """ + Compute the q-th quantile of the data along the specified axis. + + Parameters + ---------- + a : array_like of real numbers + Input array or object that can be converted to an array. + q : array_like of float + Probability or sequence of probabilities of the quantiles to compute. + Values must be between 0 and 1 inclusive. + axis : {int, tuple of int, None}, optional + Axis or axes along which the quantiles are computed. The default is + to compute the quantile(s) along a flattened version of the array. + out : ndarray, optional + Alternative output array in which to place the result. It must have + the same shape and buffer length as the expected output, but the + type (of the output) will be cast if necessary. + overwrite_input : bool, optional + If True, then allow the input array `a` to be modified by + intermediate calculations, to save memory. In this case, the + contents of the input `a` after this function completes is + undefined. + method : str, optional + This parameter specifies the method to use for estimating the + quantile. There are many different methods, some unique to NumPy. + The recommended options, numbered as they appear in [1]_, are: + + 1. 'inverted_cdf' + 2. 'averaged_inverted_cdf' + 3. 'closest_observation' + 4. 'interpolated_inverted_cdf' + 5. 'hazen' + 6. 'weibull' + 7. 'linear' (default) + 8. 'median_unbiased' + 9. 'normal_unbiased' + + The first three methods are discontinuous. For backward compatibility + with previous versions of NumPy, the following discontinuous variations + of the default 'linear' (7.) option are available: + + * 'lower' + * 'higher', + * 'midpoint' + * 'nearest' + + See Notes for details. + + .. versionchanged:: 1.22.0 + This argument was previously called "interpolation" and only + offered the "linear" default and last four options. + + keepdims : bool, optional + If this is set to True, the axes which are reduced are left in + the result as dimensions with size one. With this option, the + result will broadcast correctly against the original array `a`. + + weights : array_like, optional + An array of weights associated with the values in `a`. Each value in + `a` contributes to the quantile according to its associated weight. + The weights array can either be 1-D (in which case its length must be + the size of `a` along the given axis) or of the same shape as `a`. + If `weights=None`, then all data in `a` are assumed to have a + weight equal to one. + Only `method="inverted_cdf"` supports weights. + See the notes for more details. + + .. versionadded:: 2.0.0 + + interpolation : str, optional + Deprecated name for the method keyword argument. + + .. deprecated:: 1.22.0 + + Returns + ------- + quantile : scalar or ndarray + If `q` is a single probability and `axis=None`, then the result + is a scalar. If multiple probability levels are given, first axis + of the result corresponds to the quantiles. The other axes are + the axes that remain after the reduction of `a`. If the input + contains integers or floats smaller than ``float64``, the output + data-type is ``float64``. Otherwise, the output data-type is the + same as that of the input. If `out` is specified, that array is + returned instead. + + See Also + -------- + mean + percentile : equivalent to quantile, but with q in the range [0, 100]. + median : equivalent to ``quantile(..., 0.5)`` + nanquantile + + Notes + ----- + Given a sample `a` from an underlying distribution, `quantile` provides a + nonparametric estimate of the inverse cumulative distribution function. + + By default, this is done by interpolating between adjacent elements in + ``y``, a sorted copy of `a`:: + + (1-g)*y[j] + g*y[j+1] + + where the index ``j`` and coefficient ``g`` are the integral and + fractional components of ``q * (n-1)``, and ``n`` is the number of + elements in the sample. + + This is a special case of Equation 1 of H&F [1]_. More generally, + + - ``j = (q*n + m - 1) // 1``, and + - ``g = (q*n + m - 1) % 1``, + + where ``m`` may be defined according to several different conventions. + The preferred convention may be selected using the ``method`` parameter: + + =============================== =============== =============== + ``method`` number in H&F ``m`` + =============================== =============== =============== + ``interpolated_inverted_cdf`` 4 ``0`` + ``hazen`` 5 ``1/2`` + ``weibull`` 6 ``q`` + ``linear`` (default) 7 ``1 - q`` + ``median_unbiased`` 8 ``q/3 + 1/3`` + ``normal_unbiased`` 9 ``q/4 + 3/8`` + =============================== =============== =============== + + Note that indices ``j`` and ``j + 1`` are clipped to the range ``0`` to + ``n - 1`` when the results of the formula would be outside the allowed + range of non-negative indices. The ``- 1`` in the formulas for ``j`` and + ``g`` accounts for Python's 0-based indexing. + + The table above includes only the estimators from H&F that are continuous + functions of probability `q` (estimators 4-9). NumPy also provides the + three discontinuous estimators from H&F (estimators 1-3), where ``j`` is + defined as above, ``m`` is defined as follows, and ``g`` is a function + of the real-valued ``index = q*n + m - 1`` and ``j``. + + 1. ``inverted_cdf``: ``m = 0`` and ``g = int(index - j > 0)`` + 2. ``averaged_inverted_cdf``: ``m = 0`` and + ``g = (1 + int(index - j > 0)) / 2`` + 3. ``closest_observation``: ``m = -1/2`` and + ``g = 1 - int((index == j) & (j%2 == 1))`` + + For backward compatibility with previous versions of NumPy, `quantile` + provides four additional discontinuous estimators. Like + ``method='linear'``, all have ``m = 1 - q`` so that ``j = q*(n-1) // 1``, + but ``g`` is defined as follows. + + - ``lower``: ``g = 0`` + - ``midpoint``: ``g = 0.5`` + - ``higher``: ``g = 1`` + - ``nearest``: ``g = (q*(n-1) % 1) > 0.5`` + + **Weighted quantiles:** + More formally, the quantile at probability level :math:`q` of a cumulative + distribution function :math:`F(y)=P(Y \\leq y)` with probability measure + :math:`P` is defined as any number :math:`x` that fulfills the + *coverage conditions* + + .. math:: P(Y < x) \\leq q \\quad\\text{and}\\quad P(Y \\leq x) \\geq q + + with random variable :math:`Y\\sim P`. + Sample quantiles, the result of `quantile`, provide nonparametric + estimation of the underlying population counterparts, represented by the + unknown :math:`F`, given a data vector `a` of length ``n``. + + Some of the estimators above arise when one considers :math:`F` as the + empirical distribution function of the data, i.e. + :math:`F(y) = \\frac{1}{n} \\sum_i 1_{a_i \\leq y}`. + Then, different methods correspond to different choices of :math:`x` that + fulfill the above coverage conditions. Methods that follow this approach + are ``inverted_cdf`` and ``averaged_inverted_cdf``. + + For weighted quantiles, the coverage conditions still hold. The + empirical cumulative distribution is simply replaced by its weighted + version, i.e. + :math:`P(Y \\leq t) = \\frac{1}{\\sum_i w_i} \\sum_i w_i 1_{x_i \\leq t}`. + Only ``method="inverted_cdf"`` supports weights. + + Examples + -------- + >>> import numpy as np + >>> a = np.array([[10, 7, 4], [3, 2, 1]]) + >>> a + array([[10, 7, 4], + [ 3, 2, 1]]) + >>> np.quantile(a, 0.5) + 3.5 + >>> np.quantile(a, 0.5, axis=0) + array([6.5, 4.5, 2.5]) + >>> np.quantile(a, 0.5, axis=1) + array([7., 2.]) + >>> np.quantile(a, 0.5, axis=1, keepdims=True) + array([[7.], + [2.]]) + >>> m = np.quantile(a, 0.5, axis=0) + >>> out = np.zeros_like(m) + >>> np.quantile(a, 0.5, axis=0, out=out) + array([6.5, 4.5, 2.5]) + >>> m + array([6.5, 4.5, 2.5]) + >>> b = a.copy() + >>> np.quantile(b, 0.5, axis=1, overwrite_input=True) + array([7., 2.]) + >>> assert not np.all(a == b) + + See also `numpy.percentile` for a visualization of most methods. + + References + ---------- + .. [1] R. J. Hyndman and Y. Fan, + "Sample quantiles in statistical packages," + The American Statistician, 50(4), pp. 361-365, 1996 + + """ + if interpolation is not None: + method = _check_interpolation_as_method( + method, interpolation, "quantile") + + a = np.asanyarray(a) + if a.dtype.kind == "c": + raise TypeError("a must be an array of real numbers") + + # Use dtype of array if possible (e.g., if q is a python int or float). + if isinstance(q, (int, float)) and a.dtype.kind == "f": + q = np.asanyarray(q, dtype=a.dtype) + else: + q = np.asanyarray(q) + + if not _quantile_is_valid(q): + raise ValueError("Quantiles must be in the range [0, 1]") + + if weights is not None: + if method != "inverted_cdf": + msg = ("Only method 'inverted_cdf' supports weights. " + f"Got: {method}.") + raise ValueError(msg) + if axis is not None: + axis = _nx.normalize_axis_tuple(axis, a.ndim, argname="axis") + weights = _weights_are_valid(weights=weights, a=a, axis=axis) + if np.any(weights < 0): + raise ValueError("Weights must be non-negative.") + + return _quantile_unchecked( + a, q, axis, out, overwrite_input, method, keepdims, weights) + + +def _quantile_unchecked(a, + q, + axis=None, + out=None, + overwrite_input=False, + method="linear", + keepdims=False, + weights=None): + """Assumes that q is in [0, 1], and is an ndarray""" + return _ureduce(a, + func=_quantile_ureduce_func, + q=q, + weights=weights, + keepdims=keepdims, + axis=axis, + out=out, + overwrite_input=overwrite_input, + method=method) + + +def _quantile_is_valid(q): + # avoid expensive reductions, relevant for arrays with < O(1000) elements + if q.ndim == 1 and q.size < 10: + for i in range(q.size): + if not (0.0 <= q[i] <= 1.0): + return False + else: + if not (q.min() >= 0 and q.max() <= 1): + return False + return True + + +def _check_interpolation_as_method(method, interpolation, fname): + # Deprecated NumPy 1.22, 2021-11-08 + warnings.warn( + f"the `interpolation=` argument to {fname} was renamed to " + "`method=`, which has additional options.\n" + "Users of the modes 'nearest', 'lower', 'higher', or " + "'midpoint' are encouraged to review the method they used. " + "(Deprecated NumPy 1.22)", + DeprecationWarning, stacklevel=4) + if method != "linear": + # sanity check, we assume this basically never happens + raise TypeError( + "You shall not pass both `method` and `interpolation`!\n" + "(`interpolation` is Deprecated in favor of `method`)") + return interpolation + + +def _compute_virtual_index(n, quantiles, alpha: float, beta: float): + """ + Compute the floating point indexes of an array for the linear + interpolation of quantiles. + n : array_like + The sample sizes. + quantiles : array_like + The quantiles values. + alpha : float + A constant used to correct the index computed. + beta : float + A constant used to correct the index computed. + + alpha and beta values depend on the chosen method + (see quantile documentation) + + Reference: + Hyndman&Fan paper "Sample Quantiles in Statistical Packages", + DOI: 10.1080/00031305.1996.10473566 + """ + return n * quantiles + ( + alpha + quantiles * (1 - alpha - beta) + ) - 1 + + +def _get_gamma(virtual_indexes, previous_indexes, method): + """ + Compute gamma (a.k.a 'm' or 'weight') for the linear interpolation + of quantiles. + + virtual_indexes : array_like + The indexes where the percentile is supposed to be found in the sorted + sample. + previous_indexes : array_like + The floor values of virtual_indexes. + interpolation : dict + The interpolation method chosen, which may have a specific rule + modifying gamma. + + gamma is usually the fractional part of virtual_indexes but can be modified + by the interpolation method. + """ + gamma = np.asanyarray(virtual_indexes - previous_indexes) + gamma = method["fix_gamma"](gamma, virtual_indexes) + # Ensure both that we have an array, and that we keep the dtype + # (which may have been matched to the input array). + return np.asanyarray(gamma, dtype=virtual_indexes.dtype) + + +def _lerp(a, b, t, out=None): + """ + Compute the linear interpolation weighted by gamma on each point of + two same shape array. + + a : array_like + Left bound. + b : array_like + Right bound. + t : array_like + The interpolation weight. + out : array_like + Output array. + """ + diff_b_a = subtract(b, a) + # asanyarray is a stop-gap until gh-13105 + lerp_interpolation = asanyarray(add(a, diff_b_a * t, out=out)) + subtract(b, diff_b_a * (1 - t), out=lerp_interpolation, where=t >= 0.5, + casting='unsafe', dtype=type(lerp_interpolation.dtype)) + if lerp_interpolation.ndim == 0 and out is None: + lerp_interpolation = lerp_interpolation[()] # unpack 0d arrays + return lerp_interpolation + + +def _get_gamma_mask(shape, default_value, conditioned_value, where): + out = np.full(shape, default_value) + np.copyto(out, conditioned_value, where=where, casting="unsafe") + return out + + +def _discrete_interpolation_to_boundaries(index, gamma_condition_fun): + previous = np.floor(index) + next = previous + 1 + gamma = index - previous + res = _get_gamma_mask(shape=index.shape, + default_value=next, + conditioned_value=previous, + where=gamma_condition_fun(gamma, index) + ).astype(np.intp) + # Some methods can lead to out-of-bound integers, clip them: + res[res < 0] = 0 + return res + + +def _closest_observation(n, quantiles): + # "choose the nearest even order statistic at g=0" (H&F (1996) pp. 362). + # Order is 1-based so for zero-based indexing round to nearest odd index. + gamma_fun = lambda gamma, index: (gamma == 0) & (np.floor(index) % 2 == 1) + return _discrete_interpolation_to_boundaries((n * quantiles) - 1 - 0.5, + gamma_fun) + + +def _inverted_cdf(n, quantiles): + gamma_fun = lambda gamma, _: (gamma == 0) + return _discrete_interpolation_to_boundaries((n * quantiles) - 1, + gamma_fun) + + +def _quantile_ureduce_func( + a: np.array, + q: np.array, + weights: np.array, + axis: int | None = None, + out=None, + overwrite_input: bool = False, + method="linear", +) -> np.array: + if q.ndim > 2: + # The code below works fine for nd, but it might not have useful + # semantics. For now, keep the supported dimensions the same as it was + # before. + raise ValueError("q must be a scalar or 1d") + if overwrite_input: + if axis is None: + axis = 0 + arr = a.ravel() + wgt = None if weights is None else weights.ravel() + else: + arr = a + wgt = weights + else: + if axis is None: + axis = 0 + arr = a.flatten() + wgt = None if weights is None else weights.flatten() + else: + arr = a.copy() + wgt = weights + result = _quantile(arr, + quantiles=q, + axis=axis, + method=method, + out=out, + weights=wgt) + return result + + +def _get_indexes(arr, virtual_indexes, valid_values_count): + """ + Get the valid indexes of arr neighbouring virtual_indexes. + Note + This is a companion function to linear interpolation of + Quantiles + + Returns + ------- + (previous_indexes, next_indexes): Tuple + A Tuple of virtual_indexes neighbouring indexes + """ + previous_indexes = np.asanyarray(np.floor(virtual_indexes)) + next_indexes = np.asanyarray(previous_indexes + 1) + indexes_above_bounds = virtual_indexes >= valid_values_count - 1 + # When indexes is above max index, take the max value of the array + if indexes_above_bounds.any(): + previous_indexes[indexes_above_bounds] = -1 + next_indexes[indexes_above_bounds] = -1 + # When indexes is below min index, take the min value of the array + indexes_below_bounds = virtual_indexes < 0 + if indexes_below_bounds.any(): + previous_indexes[indexes_below_bounds] = 0 + next_indexes[indexes_below_bounds] = 0 + if np.issubdtype(arr.dtype, np.inexact): + # After the sort, slices having NaNs will have for last element a NaN + virtual_indexes_nans = np.isnan(virtual_indexes) + if virtual_indexes_nans.any(): + previous_indexes[virtual_indexes_nans] = -1 + next_indexes[virtual_indexes_nans] = -1 + previous_indexes = previous_indexes.astype(np.intp) + next_indexes = next_indexes.astype(np.intp) + return previous_indexes, next_indexes + + +def _quantile( + arr: np.array, + quantiles: np.array, + axis: int = -1, + method="linear", + out=None, + weights=None, +): + """ + Private function that doesn't support extended axis or keepdims. + These methods are extended to this function using _ureduce + See nanpercentile for parameter usage + It computes the quantiles of the array for the given axis. + A linear interpolation is performed based on the `interpolation`. + + By default, the method is "linear" where alpha == beta == 1 which + performs the 7th method of Hyndman&Fan. + With "median_unbiased" we get alpha == beta == 1/3 + thus the 8th method of Hyndman&Fan. + """ + # --- Setup + arr = np.asanyarray(arr) + values_count = arr.shape[axis] + # The dimensions of `q` are prepended to the output shape, so we need the + # axis being sampled from `arr` to be last. + if axis != 0: # But moveaxis is slow, so only call it if necessary. + arr = np.moveaxis(arr, axis, destination=0) + supports_nans = ( + np.issubdtype(arr.dtype, np.inexact) or arr.dtype.kind in 'Mm' + ) + + if weights is None: + # --- Computation of indexes + # Index where to find the value in the sorted array. + # Virtual because it is a floating point value, not an valid index. + # The nearest neighbours are used for interpolation + try: + method_props = _QuantileMethods[method] + except KeyError: + raise ValueError( + f"{method!r} is not a valid method. Use one of: " + f"{_QuantileMethods.keys()}") from None + virtual_indexes = method_props["get_virtual_index"](values_count, + quantiles) + virtual_indexes = np.asanyarray(virtual_indexes) + + if method_props["fix_gamma"] is None: + supports_integers = True + else: + int_virtual_indices = np.issubdtype(virtual_indexes.dtype, + np.integer) + supports_integers = method == 'linear' and int_virtual_indices + + if supports_integers: + # No interpolation needed, take the points along axis + if supports_nans: + # may contain nan, which would sort to the end + arr.partition( + concatenate((virtual_indexes.ravel(), [-1])), axis=0, + ) + slices_having_nans = np.isnan(arr[-1, ...]) + else: + # cannot contain nan + arr.partition(virtual_indexes.ravel(), axis=0) + slices_having_nans = np.array(False, dtype=bool) + result = take(arr, virtual_indexes, axis=0, out=out) + else: + previous_indexes, next_indexes = _get_indexes(arr, + virtual_indexes, + values_count) + # --- Sorting + arr.partition( + np.unique(np.concatenate(([0, -1], + previous_indexes.ravel(), + next_indexes.ravel(), + ))), + axis=0) + if supports_nans: + slices_having_nans = np.isnan(arr[-1, ...]) + else: + slices_having_nans = None + # --- Get values from indexes + previous = arr[previous_indexes] + next = arr[next_indexes] + # --- Linear interpolation + gamma = _get_gamma(virtual_indexes, previous_indexes, method_props) + result_shape = virtual_indexes.shape + (1,) * (arr.ndim - 1) + gamma = gamma.reshape(result_shape) + result = _lerp(previous, + next, + gamma, + out=out) + else: + # Weighted case + # This implements method="inverted_cdf", the only supported weighted + # method, which needs to sort anyway. + weights = np.asanyarray(weights) + if axis != 0: + weights = np.moveaxis(weights, axis, destination=0) + index_array = np.argsort(arr, axis=0, kind="stable") + + # arr = arr[index_array, ...] # but this adds trailing dimensions of + # 1. + arr = np.take_along_axis(arr, index_array, axis=0) + if weights.shape == arr.shape: + weights = np.take_along_axis(weights, index_array, axis=0) + else: + # weights is 1d + weights = weights.reshape(-1)[index_array, ...] + + if supports_nans: + # may contain nan, which would sort to the end + slices_having_nans = np.isnan(arr[-1, ...]) + else: + # cannot contain nan + slices_having_nans = np.array(False, dtype=bool) + + # We use the weights to calculate the empirical cumulative + # distribution function cdf + cdf = weights.cumsum(axis=0, dtype=np.float64) + cdf /= cdf[-1, ...] # normalization to 1 + # Search index i such that + # sum(weights[j], j=0..i-1) < quantile <= sum(weights[j], j=0..i) + # is then equivalent to + # cdf[i-1] < quantile <= cdf[i] + # Unfortunately, searchsorted only accepts 1-d arrays as first + # argument, so we will need to iterate over dimensions. + + # Without the following cast, searchsorted can return surprising + # results, e.g. + # np.searchsorted(np.array([0.2, 0.4, 0.6, 0.8, 1.]), + # np.array(0.4, dtype=np.float32), side="left") + # returns 2 instead of 1 because 0.4 is not binary representable. + if quantiles.dtype.kind == "f": + cdf = cdf.astype(quantiles.dtype) + # Weights must be non-negative, so we might have zero weights at the + # beginning leading to some leading zeros in cdf. The call to + # np.searchsorted for quantiles=0 will then pick the first element, + # but should pick the first one larger than zero. We + # therefore simply set 0 values in cdf to -1. + if np.any(cdf[0, ...] == 0): + cdf[cdf == 0] = -1 + + def find_cdf_1d(arr, cdf): + indices = np.searchsorted(cdf, quantiles, side="left") + # We might have reached the maximum with i = len(arr), e.g. for + # quantiles = 1, and need to cut it to len(arr) - 1. + indices = minimum(indices, values_count - 1) + result = take(arr, indices, axis=0) + return result + + r_shape = arr.shape[1:] + if quantiles.ndim > 0: + r_shape = quantiles.shape + r_shape + if out is None: + result = np.empty_like(arr, shape=r_shape) + else: + if out.shape != r_shape: + msg = (f"Wrong shape of argument 'out', shape={r_shape} is " + f"required; got shape={out.shape}.") + raise ValueError(msg) + result = out + + # See apply_along_axis, which we do for axis=0. Note that Ni = (,) + # always, so we remove it here. + Nk = arr.shape[1:] + for kk in np.ndindex(Nk): + result[(...,) + kk] = find_cdf_1d( + arr[np.s_[:, ] + kk], cdf[np.s_[:, ] + kk] + ) + + # Make result the same as in unweighted inverted_cdf. + if result.shape == () and result.dtype == np.dtype("O"): + result = result.item() + + if np.any(slices_having_nans): + if result.ndim == 0 and out is None: + # can't write to a scalar, but indexing will be correct + result = arr[-1] + else: + np.copyto(result, arr[-1, ...], where=slices_having_nans) + return result + + +def _trapezoid_dispatcher(y, x=None, dx=None, axis=None): + return (y, x) + + +@array_function_dispatch(_trapezoid_dispatcher) +def trapezoid(y, x=None, dx=1.0, axis=-1): + r""" + Integrate along the given axis using the composite trapezoidal rule. + + If `x` is provided, the integration happens in sequence along its + elements - they are not sorted. + + Integrate `y` (`x`) along each 1d slice on the given axis, compute + :math:`\int y(x) dx`. + When `x` is specified, this integrates along the parametric curve, + computing :math:`\int_t y(t) dt = + \int_t y(t) \left.\frac{dx}{dt}\right|_{x=x(t)} dt`. + + .. versionadded:: 2.0.0 + + Parameters + ---------- + y : array_like + Input array to integrate. + x : array_like, optional + The sample points corresponding to the `y` values. If `x` is None, + the sample points are assumed to be evenly spaced `dx` apart. The + default is None. + dx : scalar, optional + The spacing between sample points when `x` is None. The default is 1. + axis : int, optional + The axis along which to integrate. + + Returns + ------- + trapezoid : float or ndarray + Definite integral of `y` = n-dimensional array as approximated along + a single axis by the trapezoidal rule. If `y` is a 1-dimensional array, + then the result is a float. If `n` is greater than 1, then the result + is an `n`-1 dimensional array. + + See Also + -------- + sum, cumsum + + Notes + ----- + Image [2]_ illustrates trapezoidal rule -- y-axis locations of points + will be taken from `y` array, by default x-axis distances between + points will be 1.0, alternatively they can be provided with `x` array + or with `dx` scalar. Return value will be equal to combined area under + the red lines. + + + References + ---------- + .. [1] Wikipedia page: https://en.wikipedia.org/wiki/Trapezoidal_rule + + .. [2] Illustration image: + https://en.wikipedia.org/wiki/File:Composite_trapezoidal_rule_illustration.png + + Examples + -------- + >>> import numpy as np + + Use the trapezoidal rule on evenly spaced points: + + >>> np.trapezoid([1, 2, 3]) + 4.0 + + The spacing between sample points can be selected by either the + ``x`` or ``dx`` arguments: + + >>> np.trapezoid([1, 2, 3], x=[4, 6, 8]) + 8.0 + >>> np.trapezoid([1, 2, 3], dx=2) + 8.0 + + Using a decreasing ``x`` corresponds to integrating in reverse: + + >>> np.trapezoid([1, 2, 3], x=[8, 6, 4]) + -8.0 + + More generally ``x`` is used to integrate along a parametric curve. We can + estimate the integral :math:`\int_0^1 x^2 = 1/3` using: + + >>> x = np.linspace(0, 1, num=50) + >>> y = x**2 + >>> np.trapezoid(y, x) + 0.33340274885464394 + + Or estimate the area of a circle, noting we repeat the sample which closes + the curve: + + >>> theta = np.linspace(0, 2 * np.pi, num=1000, endpoint=True) + >>> np.trapezoid(np.cos(theta), x=np.sin(theta)) + 3.141571941375841 + + ``np.trapezoid`` can be applied along a specified axis to do multiple + computations in one call: + + >>> a = np.arange(6).reshape(2, 3) + >>> a + array([[0, 1, 2], + [3, 4, 5]]) + >>> np.trapezoid(a, axis=0) + array([1.5, 2.5, 3.5]) + >>> np.trapezoid(a, axis=1) + array([2., 8.]) + """ + + y = asanyarray(y) + if x is None: + d = dx + else: + x = asanyarray(x) + if x.ndim == 1: + d = diff(x) + # reshape to correct shape + shape = [1]*y.ndim + shape[axis] = d.shape[0] + d = d.reshape(shape) + else: + d = diff(x, axis=axis) + nd = y.ndim + slice1 = [slice(None)]*nd + slice2 = [slice(None)]*nd + slice1[axis] = slice(1, None) + slice2[axis] = slice(None, -1) + try: + ret = (d * (y[tuple(slice1)] + y[tuple(slice2)]) / 2.0).sum(axis) + except ValueError: + # Operations didn't work, cast to ndarray + d = np.asarray(d) + y = np.asarray(y) + ret = add.reduce(d * (y[tuple(slice1)]+y[tuple(slice2)])/2.0, axis) + return ret + + +@set_module('numpy') +def trapz(y, x=None, dx=1.0, axis=-1): + """ + `trapz` is deprecated in NumPy 2.0. + + Please use `trapezoid` instead, or one of the numerical integration + functions in `scipy.integrate`. + """ + # Deprecated in NumPy 2.0, 2023-08-18 + warnings.warn( + "`trapz` is deprecated. Use `trapezoid` instead, or one of the " + "numerical integration functions in `scipy.integrate`.", + DeprecationWarning, + stacklevel=2 + ) + return trapezoid(y, x=x, dx=dx, axis=axis) + + +def _meshgrid_dispatcher(*xi, copy=None, sparse=None, indexing=None): + return xi + + +# Based on scitools meshgrid +@array_function_dispatch(_meshgrid_dispatcher) +def meshgrid(*xi, copy=True, sparse=False, indexing='xy'): + """ + Return a tuple of coordinate matrices from coordinate vectors. + + Make N-D coordinate arrays for vectorized evaluations of + N-D scalar/vector fields over N-D grids, given + one-dimensional coordinate arrays x1, x2,..., xn. + + Parameters + ---------- + x1, x2,..., xn : array_like + 1-D arrays representing the coordinates of a grid. + indexing : {'xy', 'ij'}, optional + Cartesian ('xy', default) or matrix ('ij') indexing of output. + See Notes for more details. + sparse : bool, optional + If True the shape of the returned coordinate array for dimension *i* + is reduced from ``(N1, ..., Ni, ... Nn)`` to + ``(1, ..., 1, Ni, 1, ..., 1)``. These sparse coordinate grids are + intended to be use with :ref:`basics.broadcasting`. When all + coordinates are used in an expression, broadcasting still leads to a + fully-dimensonal result array. + + Default is False. + + copy : bool, optional + If False, a view into the original arrays are returned in order to + conserve memory. Default is True. Please note that + ``sparse=False, copy=False`` will likely return non-contiguous + arrays. Furthermore, more than one element of a broadcast array + may refer to a single memory location. If you need to write to the + arrays, make copies first. + + Returns + ------- + X1, X2,..., XN : tuple of ndarrays + For vectors `x1`, `x2`,..., `xn` with lengths ``Ni=len(xi)``, + returns ``(N1, N2, N3,..., Nn)`` shaped arrays if indexing='ij' + or ``(N2, N1, N3,..., Nn)`` shaped arrays if indexing='xy' + with the elements of `xi` repeated to fill the matrix along + the first dimension for `x1`, the second for `x2` and so on. + + Notes + ----- + This function supports both indexing conventions through the indexing + keyword argument. Giving the string 'ij' returns a meshgrid with + matrix indexing, while 'xy' returns a meshgrid with Cartesian indexing. + In the 2-D case with inputs of length M and N, the outputs are of shape + (N, M) for 'xy' indexing and (M, N) for 'ij' indexing. In the 3-D case + with inputs of length M, N and P, outputs are of shape (N, M, P) for + 'xy' indexing and (M, N, P) for 'ij' indexing. The difference is + illustrated by the following code snippet:: + + xv, yv = np.meshgrid(x, y, indexing='ij') + for i in range(nx): + for j in range(ny): + # treat xv[i,j], yv[i,j] + + xv, yv = np.meshgrid(x, y, indexing='xy') + for i in range(nx): + for j in range(ny): + # treat xv[j,i], yv[j,i] + + In the 1-D and 0-D case, the indexing and sparse keywords have no effect. + + See Also + -------- + mgrid : Construct a multi-dimensional "meshgrid" using indexing notation. + ogrid : Construct an open multi-dimensional "meshgrid" using indexing + notation. + :ref:`how-to-index` + + Examples + -------- + >>> import numpy as np + >>> nx, ny = (3, 2) + >>> x = np.linspace(0, 1, nx) + >>> y = np.linspace(0, 1, ny) + >>> xv, yv = np.meshgrid(x, y) + >>> xv + array([[0. , 0.5, 1. ], + [0. , 0.5, 1. ]]) + >>> yv + array([[0., 0., 0.], + [1., 1., 1.]]) + + The result of `meshgrid` is a coordinate grid: + + >>> import matplotlib.pyplot as plt + >>> plt.plot(xv, yv, marker='o', color='k', linestyle='none') + >>> plt.show() + + You can create sparse output arrays to save memory and computation time. + + >>> xv, yv = np.meshgrid(x, y, sparse=True) + >>> xv + array([[0. , 0.5, 1. ]]) + >>> yv + array([[0.], + [1.]]) + + `meshgrid` is very useful to evaluate functions on a grid. If the + function depends on all coordinates, both dense and sparse outputs can be + used. + + >>> x = np.linspace(-5, 5, 101) + >>> y = np.linspace(-5, 5, 101) + >>> # full coordinate arrays + >>> xx, yy = np.meshgrid(x, y) + >>> zz = np.sqrt(xx**2 + yy**2) + >>> xx.shape, yy.shape, zz.shape + ((101, 101), (101, 101), (101, 101)) + >>> # sparse coordinate arrays + >>> xs, ys = np.meshgrid(x, y, sparse=True) + >>> zs = np.sqrt(xs**2 + ys**2) + >>> xs.shape, ys.shape, zs.shape + ((1, 101), (101, 1), (101, 101)) + >>> np.array_equal(zz, zs) + True + + >>> h = plt.contourf(x, y, zs) + >>> plt.axis('scaled') + >>> plt.colorbar() + >>> plt.show() + """ + ndim = len(xi) + + if indexing not in ['xy', 'ij']: + raise ValueError( + "Valid values for `indexing` are 'xy' and 'ij'.") + + s0 = (1,) * ndim + output = [np.asanyarray(x).reshape(s0[:i] + (-1,) + s0[i + 1:]) + for i, x in enumerate(xi)] + + if indexing == 'xy' and ndim > 1: + # switch first and second axis + output[0].shape = (1, -1) + s0[2:] + output[1].shape = (-1, 1) + s0[2:] + + if not sparse: + # Return the full N-D matrix (not only the 1-D vector) + output = np.broadcast_arrays(*output, subok=True) + + if copy: + output = tuple(x.copy() for x in output) + + return output + + +def _delete_dispatcher(arr, obj, axis=None): + return (arr, obj) + + +@array_function_dispatch(_delete_dispatcher) +def delete(arr, obj, axis=None): + """ + Return a new array with sub-arrays along an axis deleted. For a one + dimensional array, this returns those entries not returned by + `arr[obj]`. + + Parameters + ---------- + arr : array_like + Input array. + obj : slice, int, array-like of ints or bools + Indicate indices of sub-arrays to remove along the specified axis. + + .. versionchanged:: 1.19.0 + Boolean indices are now treated as a mask of elements to remove, + rather than being cast to the integers 0 and 1. + + axis : int, optional + The axis along which to delete the subarray defined by `obj`. + If `axis` is None, `obj` is applied to the flattened array. + + Returns + ------- + out : ndarray + A copy of `arr` with the elements specified by `obj` removed. Note + that `delete` does not occur in-place. If `axis` is None, `out` is + a flattened array. + + See Also + -------- + insert : Insert elements into an array. + append : Append elements at the end of an array. + + Notes + ----- + Often it is preferable to use a boolean mask. For example: + + >>> arr = np.arange(12) + 1 + >>> mask = np.ones(len(arr), dtype=bool) + >>> mask[[0,2,4]] = False + >>> result = arr[mask,...] + + Is equivalent to ``np.delete(arr, [0,2,4], axis=0)``, but allows further + use of `mask`. + + Examples + -------- + >>> import numpy as np + >>> arr = np.array([[1,2,3,4], [5,6,7,8], [9,10,11,12]]) + >>> arr + array([[ 1, 2, 3, 4], + [ 5, 6, 7, 8], + [ 9, 10, 11, 12]]) + >>> np.delete(arr, 1, 0) + array([[ 1, 2, 3, 4], + [ 9, 10, 11, 12]]) + + >>> np.delete(arr, np.s_[::2], 1) + array([[ 2, 4], + [ 6, 8], + [10, 12]]) + >>> np.delete(arr, [1,3,5], None) + array([ 1, 3, 5, 7, 8, 9, 10, 11, 12]) + + """ + conv = _array_converter(arr) + arr, = conv.as_arrays(subok=False) + + ndim = arr.ndim + arrorder = 'F' if arr.flags.fnc else 'C' + if axis is None: + if ndim != 1: + arr = arr.ravel() + # needed for np.matrix, which is still not 1d after being ravelled + ndim = arr.ndim + axis = ndim - 1 + else: + axis = normalize_axis_index(axis, ndim) + + slobj = [slice(None)]*ndim + N = arr.shape[axis] + newshape = list(arr.shape) + + if isinstance(obj, slice): + start, stop, step = obj.indices(N) + xr = range(start, stop, step) + numtodel = len(xr) + + if numtodel <= 0: + return conv.wrap(arr.copy(order=arrorder), to_scalar=False) + + # Invert if step is negative: + if step < 0: + step = -step + start = xr[-1] + stop = xr[0] + 1 + + newshape[axis] -= numtodel + new = empty(newshape, arr.dtype, arrorder) + # copy initial chunk + if start == 0: + pass + else: + slobj[axis] = slice(None, start) + new[tuple(slobj)] = arr[tuple(slobj)] + # copy end chunk + if stop == N: + pass + else: + slobj[axis] = slice(stop-numtodel, None) + slobj2 = [slice(None)]*ndim + slobj2[axis] = slice(stop, None) + new[tuple(slobj)] = arr[tuple(slobj2)] + # copy middle pieces + if step == 1: + pass + else: # use array indexing. + keep = ones(stop-start, dtype=bool) + keep[:stop-start:step] = False + slobj[axis] = slice(start, stop-numtodel) + slobj2 = [slice(None)]*ndim + slobj2[axis] = slice(start, stop) + arr = arr[tuple(slobj2)] + slobj2[axis] = keep + new[tuple(slobj)] = arr[tuple(slobj2)] + + return conv.wrap(new, to_scalar=False) + + if isinstance(obj, (int, integer)) and not isinstance(obj, bool): + single_value = True + else: + single_value = False + _obj = obj + obj = np.asarray(obj) + # `size == 0` to allow empty lists similar to indexing, but (as there) + # is really too generic: + if obj.size == 0 and not isinstance(_obj, np.ndarray): + obj = obj.astype(intp) + elif obj.size == 1 and obj.dtype.kind in "ui": + # For a size 1 integer array we can use the single-value path + # (most dtypes, except boolean, should just fail later). + obj = obj.item() + single_value = True + + if single_value: + # optimization for a single value + if (obj < -N or obj >= N): + raise IndexError( + "index %i is out of bounds for axis %i with " + "size %i" % (obj, axis, N)) + if (obj < 0): + obj += N + newshape[axis] -= 1 + new = empty(newshape, arr.dtype, arrorder) + slobj[axis] = slice(None, obj) + new[tuple(slobj)] = arr[tuple(slobj)] + slobj[axis] = slice(obj, None) + slobj2 = [slice(None)]*ndim + slobj2[axis] = slice(obj+1, None) + new[tuple(slobj)] = arr[tuple(slobj2)] + else: + if obj.dtype == bool: + if obj.shape != (N,): + raise ValueError('boolean array argument obj to delete ' + 'must be one dimensional and match the axis ' + 'length of {}'.format(N)) + + # optimization, the other branch is slower + keep = ~obj + else: + keep = ones(N, dtype=bool) + keep[obj,] = False + + slobj[axis] = keep + new = arr[tuple(slobj)] + + return conv.wrap(new, to_scalar=False) + + +def _insert_dispatcher(arr, obj, values, axis=None): + return (arr, obj, values) + + +@array_function_dispatch(_insert_dispatcher) +def insert(arr, obj, values, axis=None): + """ + Insert values along the given axis before the given indices. + + Parameters + ---------- + arr : array_like + Input array. + obj : slice, int, array-like of ints or bools + Object that defines the index or indices before which `values` is + inserted. + + .. versionchanged:: 2.1.2 + Boolean indices are now treated as a mask of elements to insert, + rather than being cast to the integers 0 and 1. + + Support for multiple insertions when `obj` is a single scalar or a + sequence with one element (similar to calling insert multiple + times). + values : array_like + Values to insert into `arr`. If the type of `values` is different + from that of `arr`, `values` is converted to the type of `arr`. + `values` should be shaped so that ``arr[...,obj,...] = values`` + is legal. + axis : int, optional + Axis along which to insert `values`. If `axis` is None then `arr` + is flattened first. + + Returns + ------- + out : ndarray + A copy of `arr` with `values` inserted. Note that `insert` + does not occur in-place: a new array is returned. If + `axis` is None, `out` is a flattened array. + + See Also + -------- + append : Append elements at the end of an array. + concatenate : Join a sequence of arrays along an existing axis. + delete : Delete elements from an array. + + Notes + ----- + Note that for higher dimensional inserts ``obj=0`` behaves very different + from ``obj=[0]`` just like ``arr[:,0,:] = values`` is different from + ``arr[:,[0],:] = values``. This is because of the difference between basic + and advanced :ref:`indexing `. + + Examples + -------- + >>> import numpy as np + >>> a = np.arange(6).reshape(3, 2) + >>> a + array([[0, 1], + [2, 3], + [4, 5]]) + >>> np.insert(a, 1, 6) + array([0, 6, 1, 2, 3, 4, 5]) + >>> np.insert(a, 1, 6, axis=1) + array([[0, 6, 1], + [2, 6, 3], + [4, 6, 5]]) + + Difference between sequence and scalars, + showing how ``obj=[1]`` behaves different from ``obj=1``: + + >>> np.insert(a, [1], [[7],[8],[9]], axis=1) + array([[0, 7, 1], + [2, 8, 3], + [4, 9, 5]]) + >>> np.insert(a, 1, [[7],[8],[9]], axis=1) + array([[0, 7, 8, 9, 1], + [2, 7, 8, 9, 3], + [4, 7, 8, 9, 5]]) + >>> np.array_equal(np.insert(a, 1, [7, 8, 9], axis=1), + ... np.insert(a, [1], [[7],[8],[9]], axis=1)) + True + + >>> b = a.flatten() + >>> b + array([0, 1, 2, 3, 4, 5]) + >>> np.insert(b, [2, 2], [6, 7]) + array([0, 1, 6, 7, 2, 3, 4, 5]) + + >>> np.insert(b, slice(2, 4), [7, 8]) + array([0, 1, 7, 2, 8, 3, 4, 5]) + + >>> np.insert(b, [2, 2], [7.13, False]) # type casting + array([0, 1, 7, 0, 2, 3, 4, 5]) + + >>> x = np.arange(8).reshape(2, 4) + >>> idx = (1, 3) + >>> np.insert(x, idx, 999, axis=1) + array([[ 0, 999, 1, 2, 999, 3], + [ 4, 999, 5, 6, 999, 7]]) + + """ + conv = _array_converter(arr) + arr, = conv.as_arrays(subok=False) + + ndim = arr.ndim + arrorder = 'F' if arr.flags.fnc else 'C' + if axis is None: + if ndim != 1: + arr = arr.ravel() + # needed for np.matrix, which is still not 1d after being ravelled + ndim = arr.ndim + axis = ndim - 1 + else: + axis = normalize_axis_index(axis, ndim) + slobj = [slice(None)]*ndim + N = arr.shape[axis] + newshape = list(arr.shape) + + if isinstance(obj, slice): + # turn it into a range object + indices = arange(*obj.indices(N), dtype=intp) + else: + # need to copy obj, because indices will be changed in-place + indices = np.array(obj) + if indices.dtype == bool: + if obj.ndim != 1: + raise ValueError('boolean array argument obj to insert ' + 'must be one dimensional') + indices = np.flatnonzero(obj) + elif indices.ndim > 1: + raise ValueError( + "index array argument obj to insert must be one dimensional " + "or scalar") + if indices.size == 1: + index = indices.item() + if index < -N or index > N: + raise IndexError(f"index {obj} is out of bounds for axis {axis} " + f"with size {N}") + if (index < 0): + index += N + + # There are some object array corner cases here, but we cannot avoid + # that: + values = array(values, copy=None, ndmin=arr.ndim, dtype=arr.dtype) + if indices.ndim == 0: + # broadcasting is very different here, since a[:,0,:] = ... behaves + # very different from a[:,[0],:] = ...! This changes values so that + # it works likes the second case. (here a[:,0:1,:]) + values = np.moveaxis(values, 0, axis) + numnew = values.shape[axis] + newshape[axis] += numnew + new = empty(newshape, arr.dtype, arrorder) + slobj[axis] = slice(None, index) + new[tuple(slobj)] = arr[tuple(slobj)] + slobj[axis] = slice(index, index+numnew) + new[tuple(slobj)] = values + slobj[axis] = slice(index+numnew, None) + slobj2 = [slice(None)] * ndim + slobj2[axis] = slice(index, None) + new[tuple(slobj)] = arr[tuple(slobj2)] + + return conv.wrap(new, to_scalar=False) + + elif indices.size == 0 and not isinstance(obj, np.ndarray): + # Can safely cast the empty list to intp + indices = indices.astype(intp) + + indices[indices < 0] += N + + numnew = len(indices) + order = indices.argsort(kind='mergesort') # stable sort + indices[order] += np.arange(numnew) + + newshape[axis] += numnew + old_mask = ones(newshape[axis], dtype=bool) + old_mask[indices] = False + + new = empty(newshape, arr.dtype, arrorder) + slobj2 = [slice(None)]*ndim + slobj[axis] = indices + slobj2[axis] = old_mask + new[tuple(slobj)] = values + new[tuple(slobj2)] = arr + + return conv.wrap(new, to_scalar=False) + + +def _append_dispatcher(arr, values, axis=None): + return (arr, values) + + +@array_function_dispatch(_append_dispatcher) +def append(arr, values, axis=None): + """ + Append values to the end of an array. + + Parameters + ---------- + arr : array_like + Values are appended to a copy of this array. + values : array_like + These values are appended to a copy of `arr`. It must be of the + correct shape (the same shape as `arr`, excluding `axis`). If + `axis` is not specified, `values` can be any shape and will be + flattened before use. + axis : int, optional + The axis along which `values` are appended. If `axis` is not + given, both `arr` and `values` are flattened before use. + + Returns + ------- + append : ndarray + A copy of `arr` with `values` appended to `axis`. Note that + `append` does not occur in-place: a new array is allocated and + filled. If `axis` is None, `out` is a flattened array. + + See Also + -------- + insert : Insert elements into an array. + delete : Delete elements from an array. + + Examples + -------- + >>> import numpy as np + >>> np.append([1, 2, 3], [[4, 5, 6], [7, 8, 9]]) + array([1, 2, 3, ..., 7, 8, 9]) + + When `axis` is specified, `values` must have the correct shape. + + >>> np.append([[1, 2, 3], [4, 5, 6]], [[7, 8, 9]], axis=0) + array([[1, 2, 3], + [4, 5, 6], + [7, 8, 9]]) + + >>> np.append([[1, 2, 3], [4, 5, 6]], [7, 8, 9], axis=0) + Traceback (most recent call last): + ... + ValueError: all the input arrays must have same number of dimensions, but + the array at index 0 has 2 dimension(s) and the array at index 1 has 1 + dimension(s) + + >>> a = np.array([1, 2], dtype=int) + >>> c = np.append(a, []) + >>> c + array([1., 2.]) + >>> c.dtype + float64 + + Default dtype for empty ndarrays is `float64` thus making the output of dtype + `float64` when appended with dtype `int64` + + """ + arr = asanyarray(arr) + if axis is None: + if arr.ndim != 1: + arr = arr.ravel() + values = ravel(values) + axis = arr.ndim-1 + return concatenate((arr, values), axis=axis) + + +def _digitize_dispatcher(x, bins, right=None): + return (x, bins) + + +@array_function_dispatch(_digitize_dispatcher) +def digitize(x, bins, right=False): + """ + Return the indices of the bins to which each value in input array belongs. + + ========= ============= ============================ + `right` order of bins returned index `i` satisfies + ========= ============= ============================ + ``False`` increasing ``bins[i-1] <= x < bins[i]`` + ``True`` increasing ``bins[i-1] < x <= bins[i]`` + ``False`` decreasing ``bins[i-1] > x >= bins[i]`` + ``True`` decreasing ``bins[i-1] >= x > bins[i]`` + ========= ============= ============================ + + If values in `x` are beyond the bounds of `bins`, 0 or ``len(bins)`` is + returned as appropriate. + + Parameters + ---------- + x : array_like + Input array to be binned. Prior to NumPy 1.10.0, this array had to + be 1-dimensional, but can now have any shape. + bins : array_like + Array of bins. It has to be 1-dimensional and monotonic. + right : bool, optional + Indicating whether the intervals include the right or the left bin + edge. Default behavior is (right==False) indicating that the interval + does not include the right edge. The left bin end is open in this + case, i.e., bins[i-1] <= x < bins[i] is the default behavior for + monotonically increasing bins. + + Returns + ------- + indices : ndarray of ints + Output array of indices, of same shape as `x`. + + Raises + ------ + ValueError + If `bins` is not monotonic. + TypeError + If the type of the input is complex. + + See Also + -------- + bincount, histogram, unique, searchsorted + + Notes + ----- + If values in `x` are such that they fall outside the bin range, + attempting to index `bins` with the indices that `digitize` returns + will result in an IndexError. + + .. versionadded:: 1.10.0 + + `numpy.digitize` is implemented in terms of `numpy.searchsorted`. + This means that a binary search is used to bin the values, which scales + much better for larger number of bins than the previous linear search. + It also removes the requirement for the input array to be 1-dimensional. + + For monotonically *increasing* `bins`, the following are equivalent:: + + np.digitize(x, bins, right=True) + np.searchsorted(bins, x, side='left') + + Note that as the order of the arguments are reversed, the side must be too. + The `searchsorted` call is marginally faster, as it does not do any + monotonicity checks. Perhaps more importantly, it supports all dtypes. + + Examples + -------- + >>> import numpy as np + >>> x = np.array([0.2, 6.4, 3.0, 1.6]) + >>> bins = np.array([0.0, 1.0, 2.5, 4.0, 10.0]) + >>> inds = np.digitize(x, bins) + >>> inds + array([1, 4, 3, 2]) + >>> for n in range(x.size): + ... print(bins[inds[n]-1], "<=", x[n], "<", bins[inds[n]]) + ... + 0.0 <= 0.2 < 1.0 + 4.0 <= 6.4 < 10.0 + 2.5 <= 3.0 < 4.0 + 1.0 <= 1.6 < 2.5 + + >>> x = np.array([1.2, 10.0, 12.4, 15.5, 20.]) + >>> bins = np.array([0, 5, 10, 15, 20]) + >>> np.digitize(x,bins,right=True) + array([1, 2, 3, 4, 4]) + >>> np.digitize(x,bins,right=False) + array([1, 3, 3, 4, 5]) + """ + x = _nx.asarray(x) + bins = _nx.asarray(bins) + + # here for compatibility, searchsorted below is happy to take this + if np.issubdtype(x.dtype, _nx.complexfloating): + raise TypeError("x may not be complex") + + mono = _monotonicity(bins) + if mono == 0: + raise ValueError("bins must be monotonically increasing or decreasing") + + # this is backwards because the arguments below are swapped + side = 'left' if right else 'right' + if mono == -1: + # reverse the bins, and invert the results + return len(bins) - _nx.searchsorted(bins[::-1], x, side=side) + else: + return _nx.searchsorted(bins, x, side=side) diff --git a/janus/lib/python3.10/site-packages/numpy/lib/_histograms_impl.py b/janus/lib/python3.10/site-packages/numpy/lib/_histograms_impl.py new file mode 100644 index 0000000000000000000000000000000000000000..b361bb4f91ac0723f65399cd6b73048649179c2b --- /dev/null +++ b/janus/lib/python3.10/site-packages/numpy/lib/_histograms_impl.py @@ -0,0 +1,1090 @@ +""" +Histogram-related functions +""" +import contextlib +import functools +import operator +import warnings + +import numpy as np +from numpy._core import overrides + +__all__ = ['histogram', 'histogramdd', 'histogram_bin_edges'] + +array_function_dispatch = functools.partial( + overrides.array_function_dispatch, module='numpy') + +# range is a keyword argument to many functions, so save the builtin so they can +# use it. +_range = range + + +def _ptp(x): + """Peak-to-peak value of x. + + This implementation avoids the problem of signed integer arrays having a + peak-to-peak value that cannot be represented with the array's data type. + This function returns an unsigned value for signed integer arrays. + """ + return _unsigned_subtract(x.max(), x.min()) + + +def _hist_bin_sqrt(x, range): + """ + Square root histogram bin estimator. + + Bin width is inversely proportional to the data size. Used by many + programs for its simplicity. + + Parameters + ---------- + x : array_like + Input data that is to be histogrammed, trimmed to range. May not + be empty. + + Returns + ------- + h : An estimate of the optimal bin width for the given data. + """ + del range # unused + return _ptp(x) / np.sqrt(x.size) + + +def _hist_bin_sturges(x, range): + """ + Sturges histogram bin estimator. + + A very simplistic estimator based on the assumption of normality of + the data. This estimator has poor performance for non-normal data, + which becomes especially obvious for large data sets. The estimate + depends only on size of the data. + + Parameters + ---------- + x : array_like + Input data that is to be histogrammed, trimmed to range. May not + be empty. + + Returns + ------- + h : An estimate of the optimal bin width for the given data. + """ + del range # unused + return _ptp(x) / (np.log2(x.size) + 1.0) + + +def _hist_bin_rice(x, range): + """ + Rice histogram bin estimator. + + Another simple estimator with no normality assumption. It has better + performance for large data than Sturges, but tends to overestimate + the number of bins. The number of bins is proportional to the cube + root of data size (asymptotically optimal). The estimate depends + only on size of the data. + + Parameters + ---------- + x : array_like + Input data that is to be histogrammed, trimmed to range. May not + be empty. + + Returns + ------- + h : An estimate of the optimal bin width for the given data. + """ + del range # unused + return _ptp(x) / (2.0 * x.size ** (1.0 / 3)) + + +def _hist_bin_scott(x, range): + """ + Scott histogram bin estimator. + + The binwidth is proportional to the standard deviation of the data + and inversely proportional to the cube root of data size + (asymptotically optimal). + + Parameters + ---------- + x : array_like + Input data that is to be histogrammed, trimmed to range. May not + be empty. + + Returns + ------- + h : An estimate of the optimal bin width for the given data. + """ + del range # unused + return (24.0 * np.pi**0.5 / x.size)**(1.0 / 3.0) * np.std(x) + + +def _hist_bin_stone(x, range): + """ + Histogram bin estimator based on minimizing the estimated integrated squared error (ISE). + + The number of bins is chosen by minimizing the estimated ISE against the unknown true distribution. + The ISE is estimated using cross-validation and can be regarded as a generalization of Scott's rule. + https://en.wikipedia.org/wiki/Histogram#Scott.27s_normal_reference_rule + + This paper by Stone appears to be the origination of this rule. + https://digitalassets.lib.berkeley.edu/sdtr/ucb/text/34.pdf + + Parameters + ---------- + x : array_like + Input data that is to be histogrammed, trimmed to range. May not + be empty. + range : (float, float) + The lower and upper range of the bins. + + Returns + ------- + h : An estimate of the optimal bin width for the given data. + """ + + n = x.size + ptp_x = _ptp(x) + if n <= 1 or ptp_x == 0: + return 0 + + def jhat(nbins): + hh = ptp_x / nbins + p_k = np.histogram(x, bins=nbins, range=range)[0] / n + return (2 - (n + 1) * p_k.dot(p_k)) / hh + + nbins_upper_bound = max(100, int(np.sqrt(n))) + nbins = min(_range(1, nbins_upper_bound + 1), key=jhat) + if nbins == nbins_upper_bound: + warnings.warn("The number of bins estimated may be suboptimal.", + RuntimeWarning, stacklevel=3) + return ptp_x / nbins + + +def _hist_bin_doane(x, range): + """ + Doane's histogram bin estimator. + + Improved version of Sturges' formula which works better for + non-normal data. See + stats.stackexchange.com/questions/55134/doanes-formula-for-histogram-binning + + Parameters + ---------- + x : array_like + Input data that is to be histogrammed, trimmed to range. May not + be empty. + + Returns + ------- + h : An estimate of the optimal bin width for the given data. + """ + del range # unused + if x.size > 2: + sg1 = np.sqrt(6.0 * (x.size - 2) / ((x.size + 1.0) * (x.size + 3))) + sigma = np.std(x) + if sigma > 0.0: + # These three operations add up to + # g1 = np.mean(((x - np.mean(x)) / sigma)**3) + # but use only one temp array instead of three + temp = x - np.mean(x) + np.true_divide(temp, sigma, temp) + np.power(temp, 3, temp) + g1 = np.mean(temp) + return _ptp(x) / (1.0 + np.log2(x.size) + + np.log2(1.0 + np.absolute(g1) / sg1)) + return 0.0 + + +def _hist_bin_fd(x, range): + """ + The Freedman-Diaconis histogram bin estimator. + + The Freedman-Diaconis rule uses interquartile range (IQR) to + estimate binwidth. It is considered a variation of the Scott rule + with more robustness as the IQR is less affected by outliers than + the standard deviation. However, the IQR depends on fewer points + than the standard deviation, so it is less accurate, especially for + long tailed distributions. + + If the IQR is 0, this function returns 0 for the bin width. + Binwidth is inversely proportional to the cube root of data size + (asymptotically optimal). + + Parameters + ---------- + x : array_like + Input data that is to be histogrammed, trimmed to range. May not + be empty. + + Returns + ------- + h : An estimate of the optimal bin width for the given data. + """ + del range # unused + iqr = np.subtract(*np.percentile(x, [75, 25])) + return 2.0 * iqr * x.size ** (-1.0 / 3.0) + + +def _hist_bin_auto(x, range): + """ + Histogram bin estimator that uses the minimum width of the + Freedman-Diaconis and Sturges estimators if the FD bin width is non-zero. + If the bin width from the FD estimator is 0, the Sturges estimator is used. + + The FD estimator is usually the most robust method, but its width + estimate tends to be too large for small `x` and bad for data with limited + variance. The Sturges estimator is quite good for small (<1000) datasets + and is the default in the R language. This method gives good off-the-shelf + behaviour. + + If there is limited variance the IQR can be 0, which results in the + FD bin width being 0 too. This is not a valid bin width, so + ``np.histogram_bin_edges`` chooses 1 bin instead, which may not be optimal. + If the IQR is 0, it's unlikely any variance-based estimators will be of + use, so we revert to the Sturges estimator, which only uses the size of the + dataset in its calculation. + + Parameters + ---------- + x : array_like + Input data that is to be histogrammed, trimmed to range. May not + be empty. + + Returns + ------- + h : An estimate of the optimal bin width for the given data. + + See Also + -------- + _hist_bin_fd, _hist_bin_sturges + """ + fd_bw = _hist_bin_fd(x, range) + sturges_bw = _hist_bin_sturges(x, range) + del range # unused + if fd_bw: + return min(fd_bw, sturges_bw) + else: + # limited variance, so we return a len dependent bw estimator + return sturges_bw + +# Private dict initialized at module load time +_hist_bin_selectors = {'stone': _hist_bin_stone, + 'auto': _hist_bin_auto, + 'doane': _hist_bin_doane, + 'fd': _hist_bin_fd, + 'rice': _hist_bin_rice, + 'scott': _hist_bin_scott, + 'sqrt': _hist_bin_sqrt, + 'sturges': _hist_bin_sturges} + + +def _ravel_and_check_weights(a, weights): + """ Check a and weights have matching shapes, and ravel both """ + a = np.asarray(a) + + # Ensure that the array is a "subtractable" dtype + if a.dtype == np.bool: + warnings.warn("Converting input from {} to {} for compatibility." + .format(a.dtype, np.uint8), + RuntimeWarning, stacklevel=3) + a = a.astype(np.uint8) + + if weights is not None: + weights = np.asarray(weights) + if weights.shape != a.shape: + raise ValueError( + 'weights should have the same shape as a.') + weights = weights.ravel() + a = a.ravel() + return a, weights + + +def _get_outer_edges(a, range): + """ + Determine the outer bin edges to use, from either the data or the range + argument + """ + if range is not None: + first_edge, last_edge = range + if first_edge > last_edge: + raise ValueError( + 'max must be larger than min in range parameter.') + if not (np.isfinite(first_edge) and np.isfinite(last_edge)): + raise ValueError( + "supplied range of [{}, {}] is not finite".format(first_edge, last_edge)) + elif a.size == 0: + # handle empty arrays. Can't determine range, so use 0-1. + first_edge, last_edge = 0, 1 + else: + first_edge, last_edge = a.min(), a.max() + if not (np.isfinite(first_edge) and np.isfinite(last_edge)): + raise ValueError( + "autodetected range of [{}, {}] is not finite".format(first_edge, last_edge)) + + # expand empty range to avoid divide by zero + if first_edge == last_edge: + first_edge = first_edge - 0.5 + last_edge = last_edge + 0.5 + + return first_edge, last_edge + + +def _unsigned_subtract(a, b): + """ + Subtract two values where a >= b, and produce an unsigned result + + This is needed when finding the difference between the upper and lower + bound of an int16 histogram + """ + # coerce to a single type + signed_to_unsigned = { + np.byte: np.ubyte, + np.short: np.ushort, + np.intc: np.uintc, + np.int_: np.uint, + np.longlong: np.ulonglong + } + dt = np.result_type(a, b) + try: + unsigned_dt = signed_to_unsigned[dt.type] + except KeyError: + return np.subtract(a, b, dtype=dt) + else: + # we know the inputs are integers, and we are deliberately casting + # signed to unsigned. The input may be negative python integers so + # ensure we pass in arrays with the initial dtype (related to NEP 50). + return np.subtract(np.asarray(a, dtype=dt), np.asarray(b, dtype=dt), + casting='unsafe', dtype=unsigned_dt) + + +def _get_bin_edges(a, bins, range, weights): + """ + Computes the bins used internally by `histogram`. + + Parameters + ========== + a : ndarray + Ravelled data array + bins, range + Forwarded arguments from `histogram`. + weights : ndarray, optional + Ravelled weights array, or None + + Returns + ======= + bin_edges : ndarray + Array of bin edges + uniform_bins : (Number, Number, int): + The upper bound, lowerbound, and number of bins, used in the optimized + implementation of `histogram` that works on uniform bins. + """ + # parse the overloaded bins argument + n_equal_bins = None + bin_edges = None + + if isinstance(bins, str): + bin_name = bins + # if `bins` is a string for an automatic method, + # this will replace it with the number of bins calculated + if bin_name not in _hist_bin_selectors: + raise ValueError( + "{!r} is not a valid estimator for `bins`".format(bin_name)) + if weights is not None: + raise TypeError("Automated estimation of the number of " + "bins is not supported for weighted data") + + first_edge, last_edge = _get_outer_edges(a, range) + + # truncate the range if needed + if range is not None: + keep = (a >= first_edge) + keep &= (a <= last_edge) + if not np.logical_and.reduce(keep): + a = a[keep] + + if a.size == 0: + n_equal_bins = 1 + else: + # Do not call selectors on empty arrays + width = _hist_bin_selectors[bin_name](a, (first_edge, last_edge)) + if width: + if np.issubdtype(a.dtype, np.integer) and width < 1: + width = 1 + n_equal_bins = int(np.ceil(_unsigned_subtract(last_edge, first_edge) / width)) + else: + # Width can be zero for some estimators, e.g. FD when + # the IQR of the data is zero. + n_equal_bins = 1 + + elif np.ndim(bins) == 0: + try: + n_equal_bins = operator.index(bins) + except TypeError as e: + raise TypeError( + '`bins` must be an integer, a string, or an array') from e + if n_equal_bins < 1: + raise ValueError('`bins` must be positive, when an integer') + + first_edge, last_edge = _get_outer_edges(a, range) + + elif np.ndim(bins) == 1: + bin_edges = np.asarray(bins) + if np.any(bin_edges[:-1] > bin_edges[1:]): + raise ValueError( + '`bins` must increase monotonically, when an array') + + else: + raise ValueError('`bins` must be 1d, when an array') + + if n_equal_bins is not None: + # gh-10322 means that type resolution rules are dependent on array + # shapes. To avoid this causing problems, we pick a type now and stick + # with it throughout. + bin_type = np.result_type(first_edge, last_edge, a) + if np.issubdtype(bin_type, np.integer): + bin_type = np.result_type(bin_type, float) + + # bin edges must be computed + bin_edges = np.linspace( + first_edge, last_edge, n_equal_bins + 1, + endpoint=True, dtype=bin_type) + if np.any(bin_edges[:-1] >= bin_edges[1:]): + raise ValueError( + f'Too many bins for data range. Cannot create {n_equal_bins} ' + f'finite-sized bins.') + return bin_edges, (first_edge, last_edge, n_equal_bins) + else: + return bin_edges, None + + +def _search_sorted_inclusive(a, v): + """ + Like `searchsorted`, but where the last item in `v` is placed on the right. + + In the context of a histogram, this makes the last bin edge inclusive + """ + return np.concatenate(( + a.searchsorted(v[:-1], 'left'), + a.searchsorted(v[-1:], 'right') + )) + + +def _histogram_bin_edges_dispatcher(a, bins=None, range=None, weights=None): + return (a, bins, weights) + + +@array_function_dispatch(_histogram_bin_edges_dispatcher) +def histogram_bin_edges(a, bins=10, range=None, weights=None): + r""" + Function to calculate only the edges of the bins used by the `histogram` + function. + + Parameters + ---------- + a : array_like + Input data. The histogram is computed over the flattened array. + bins : int or sequence of scalars or str, optional + If `bins` is an int, it defines the number of equal-width + bins in the given range (10, by default). If `bins` is a + sequence, it defines the bin edges, including the rightmost + edge, allowing for non-uniform bin widths. + + If `bins` is a string from the list below, `histogram_bin_edges` will + use the method chosen to calculate the optimal bin width and + consequently the number of bins (see the Notes section for more detail + on the estimators) from the data that falls within the requested range. + While the bin width will be optimal for the actual data + in the range, the number of bins will be computed to fill the + entire range, including the empty portions. For visualisation, + using the 'auto' option is suggested. Weighted data is not + supported for automated bin size selection. + + 'auto' + Minimum bin width between the 'sturges' and 'fd' estimators. + Provides good all-around performance. + + 'fd' (Freedman Diaconis Estimator) + Robust (resilient to outliers) estimator that takes into + account data variability and data size. + + 'doane' + An improved version of Sturges' estimator that works better + with non-normal datasets. + + 'scott' + Less robust estimator that takes into account data variability + and data size. + + 'stone' + Estimator based on leave-one-out cross-validation estimate of + the integrated squared error. Can be regarded as a generalization + of Scott's rule. + + 'rice' + Estimator does not take variability into account, only data + size. Commonly overestimates number of bins required. + + 'sturges' + R's default method, only accounts for data size. Only + optimal for gaussian data and underestimates number of bins + for large non-gaussian datasets. + + 'sqrt' + Square root (of data size) estimator, used by Excel and + other programs for its speed and simplicity. + + range : (float, float), optional + The lower and upper range of the bins. If not provided, range + is simply ``(a.min(), a.max())``. Values outside the range are + ignored. The first element of the range must be less than or + equal to the second. `range` affects the automatic bin + computation as well. While bin width is computed to be optimal + based on the actual data within `range`, the bin count will fill + the entire range including portions containing no data. + + weights : array_like, optional + An array of weights, of the same shape as `a`. Each value in + `a` only contributes its associated weight towards the bin count + (instead of 1). This is currently not used by any of the bin estimators, + but may be in the future. + + Returns + ------- + bin_edges : array of dtype float + The edges to pass into `histogram` + + See Also + -------- + histogram + + Notes + ----- + The methods to estimate the optimal number of bins are well founded + in literature, and are inspired by the choices R provides for + histogram visualisation. Note that having the number of bins + proportional to :math:`n^{1/3}` is asymptotically optimal, which is + why it appears in most estimators. These are simply plug-in methods + that give good starting points for number of bins. In the equations + below, :math:`h` is the binwidth and :math:`n_h` is the number of + bins. All estimators that compute bin counts are recast to bin width + using the `ptp` of the data. The final bin count is obtained from + ``np.round(np.ceil(range / h))``. The final bin width is often less + than what is returned by the estimators below. + + 'auto' (minimum bin width of the 'sturges' and 'fd' estimators) + A compromise to get a good value. For small datasets the Sturges + value will usually be chosen, while larger datasets will usually + default to FD. Avoids the overly conservative behaviour of FD + and Sturges for small and large datasets respectively. + Switchover point is usually :math:`a.size \approx 1000`. + + 'fd' (Freedman Diaconis Estimator) + .. math:: h = 2 \frac{IQR}{n^{1/3}} + + The binwidth is proportional to the interquartile range (IQR) + and inversely proportional to cube root of a.size. Can be too + conservative for small datasets, but is quite good for large + datasets. The IQR is very robust to outliers. + + 'scott' + .. math:: h = \sigma \sqrt[3]{\frac{24 \sqrt{\pi}}{n}} + + The binwidth is proportional to the standard deviation of the + data and inversely proportional to cube root of ``x.size``. Can + be too conservative for small datasets, but is quite good for + large datasets. The standard deviation is not very robust to + outliers. Values are very similar to the Freedman-Diaconis + estimator in the absence of outliers. + + 'rice' + .. math:: n_h = 2n^{1/3} + + The number of bins is only proportional to cube root of + ``a.size``. It tends to overestimate the number of bins and it + does not take into account data variability. + + 'sturges' + .. math:: n_h = \log _{2}(n) + 1 + + The number of bins is the base 2 log of ``a.size``. This + estimator assumes normality of data and is too conservative for + larger, non-normal datasets. This is the default method in R's + ``hist`` method. + + 'doane' + .. math:: n_h = 1 + \log_{2}(n) + + \log_{2}\left(1 + \frac{|g_1|}{\sigma_{g_1}}\right) + + g_1 = mean\left[\left(\frac{x - \mu}{\sigma}\right)^3\right] + + \sigma_{g_1} = \sqrt{\frac{6(n - 2)}{(n + 1)(n + 3)}} + + An improved version of Sturges' formula that produces better + estimates for non-normal datasets. This estimator attempts to + account for the skew of the data. + + 'sqrt' + .. math:: n_h = \sqrt n + + The simplest and fastest estimator. Only takes into account the + data size. + + Additionally, if the data is of integer dtype, then the binwidth will never + be less than 1. + + Examples + -------- + >>> import numpy as np + >>> arr = np.array([0, 0, 0, 1, 2, 3, 3, 4, 5]) + >>> np.histogram_bin_edges(arr, bins='auto', range=(0, 1)) + array([0. , 0.25, 0.5 , 0.75, 1. ]) + >>> np.histogram_bin_edges(arr, bins=2) + array([0. , 2.5, 5. ]) + + For consistency with histogram, an array of pre-computed bins is + passed through unmodified: + + >>> np.histogram_bin_edges(arr, [1, 2]) + array([1, 2]) + + This function allows one set of bins to be computed, and reused across + multiple histograms: + + >>> shared_bins = np.histogram_bin_edges(arr, bins='auto') + >>> shared_bins + array([0., 1., 2., 3., 4., 5.]) + + >>> group_id = np.array([0, 1, 1, 0, 1, 1, 0, 1, 1]) + >>> hist_0, _ = np.histogram(arr[group_id == 0], bins=shared_bins) + >>> hist_1, _ = np.histogram(arr[group_id == 1], bins=shared_bins) + + >>> hist_0; hist_1 + array([1, 1, 0, 1, 0]) + array([2, 0, 1, 1, 2]) + + Which gives more easily comparable results than using separate bins for + each histogram: + + >>> hist_0, bins_0 = np.histogram(arr[group_id == 0], bins='auto') + >>> hist_1, bins_1 = np.histogram(arr[group_id == 1], bins='auto') + >>> hist_0; hist_1 + array([1, 1, 1]) + array([2, 1, 1, 2]) + >>> bins_0; bins_1 + array([0., 1., 2., 3.]) + array([0. , 1.25, 2.5 , 3.75, 5. ]) + + """ + a, weights = _ravel_and_check_weights(a, weights) + bin_edges, _ = _get_bin_edges(a, bins, range, weights) + return bin_edges + + +def _histogram_dispatcher( + a, bins=None, range=None, density=None, weights=None): + return (a, bins, weights) + + +@array_function_dispatch(_histogram_dispatcher) +def histogram(a, bins=10, range=None, density=None, weights=None): + r""" + Compute the histogram of a dataset. + + Parameters + ---------- + a : array_like + Input data. The histogram is computed over the flattened array. + bins : int or sequence of scalars or str, optional + If `bins` is an int, it defines the number of equal-width + bins in the given range (10, by default). If `bins` is a + sequence, it defines a monotonically increasing array of bin edges, + including the rightmost edge, allowing for non-uniform bin widths. + + If `bins` is a string, it defines the method used to calculate the + optimal bin width, as defined by `histogram_bin_edges`. + + range : (float, float), optional + The lower and upper range of the bins. If not provided, range + is simply ``(a.min(), a.max())``. Values outside the range are + ignored. The first element of the range must be less than or + equal to the second. `range` affects the automatic bin + computation as well. While bin width is computed to be optimal + based on the actual data within `range`, the bin count will fill + the entire range including portions containing no data. + weights : array_like, optional + An array of weights, of the same shape as `a`. Each value in + `a` only contributes its associated weight towards the bin count + (instead of 1). If `density` is True, the weights are + normalized, so that the integral of the density over the range + remains 1. + Please note that the ``dtype`` of `weights` will also become the + ``dtype`` of the returned accumulator (`hist`), so it must be + large enough to hold accumulated values as well. + density : bool, optional + If ``False``, the result will contain the number of samples in + each bin. If ``True``, the result is the value of the + probability *density* function at the bin, normalized such that + the *integral* over the range is 1. Note that the sum of the + histogram values will not be equal to 1 unless bins of unity + width are chosen; it is not a probability *mass* function. + + Returns + ------- + hist : array + The values of the histogram. See `density` and `weights` for a + description of the possible semantics. If `weights` are given, + ``hist.dtype`` will be taken from `weights`. + bin_edges : array of dtype float + Return the bin edges ``(length(hist)+1)``. + + + See Also + -------- + histogramdd, bincount, searchsorted, digitize, histogram_bin_edges + + Notes + ----- + All but the last (righthand-most) bin is half-open. In other words, + if `bins` is:: + + [1, 2, 3, 4] + + then the first bin is ``[1, 2)`` (including 1, but excluding 2) and + the second ``[2, 3)``. The last bin, however, is ``[3, 4]``, which + *includes* 4. + + + Examples + -------- + >>> import numpy as np + >>> np.histogram([1, 2, 1], bins=[0, 1, 2, 3]) + (array([0, 2, 1]), array([0, 1, 2, 3])) + >>> np.histogram(np.arange(4), bins=np.arange(5), density=True) + (array([0.25, 0.25, 0.25, 0.25]), array([0, 1, 2, 3, 4])) + >>> np.histogram([[1, 2, 1], [1, 0, 1]], bins=[0,1,2,3]) + (array([1, 4, 1]), array([0, 1, 2, 3])) + + >>> a = np.arange(5) + >>> hist, bin_edges = np.histogram(a, density=True) + >>> hist + array([0.5, 0. , 0.5, 0. , 0. , 0.5, 0. , 0.5, 0. , 0.5]) + >>> hist.sum() + 2.4999999999999996 + >>> np.sum(hist * np.diff(bin_edges)) + 1.0 + + Automated Bin Selection Methods example, using 2 peak random data + with 2000 points. + + .. plot:: + :include-source: + + import matplotlib.pyplot as plt + import numpy as np + + rng = np.random.RandomState(10) # deterministic random data + a = np.hstack((rng.normal(size=1000), + rng.normal(loc=5, scale=2, size=1000))) + plt.hist(a, bins='auto') # arguments are passed to np.histogram + plt.title("Histogram with 'auto' bins") + plt.show() + + """ + a, weights = _ravel_and_check_weights(a, weights) + + bin_edges, uniform_bins = _get_bin_edges(a, bins, range, weights) + + # Histogram is an integer or a float array depending on the weights. + if weights is None: + ntype = np.dtype(np.intp) + else: + ntype = weights.dtype + + # We set a block size, as this allows us to iterate over chunks when + # computing histograms, to minimize memory usage. + BLOCK = 65536 + + # The fast path uses bincount, but that only works for certain types + # of weight + simple_weights = ( + weights is None or + np.can_cast(weights.dtype, np.double) or + np.can_cast(weights.dtype, complex) + ) + + if uniform_bins is not None and simple_weights: + # Fast algorithm for equal bins + # We now convert values of a to bin indices, under the assumption of + # equal bin widths (which is valid here). + first_edge, last_edge, n_equal_bins = uniform_bins + + # Initialize empty histogram + n = np.zeros(n_equal_bins, ntype) + + # Pre-compute histogram scaling factor + norm_numerator = n_equal_bins + norm_denom = _unsigned_subtract(last_edge, first_edge) + + # We iterate over blocks here for two reasons: the first is that for + # large arrays, it is actually faster (for example for a 10^8 array it + # is 2x as fast) and it results in a memory footprint 3x lower in the + # limit of large arrays. + for i in _range(0, len(a), BLOCK): + tmp_a = a[i:i+BLOCK] + if weights is None: + tmp_w = None + else: + tmp_w = weights[i:i + BLOCK] + + # Only include values in the right range + keep = (tmp_a >= first_edge) + keep &= (tmp_a <= last_edge) + if not np.logical_and.reduce(keep): + tmp_a = tmp_a[keep] + if tmp_w is not None: + tmp_w = tmp_w[keep] + + # This cast ensures no type promotions occur below, which gh-10322 + # make unpredictable. Getting it wrong leads to precision errors + # like gh-8123. + tmp_a = tmp_a.astype(bin_edges.dtype, copy=False) + + # Compute the bin indices, and for values that lie exactly on + # last_edge we need to subtract one + f_indices = ((_unsigned_subtract(tmp_a, first_edge) / norm_denom) + * norm_numerator) + indices = f_indices.astype(np.intp) + indices[indices == n_equal_bins] -= 1 + + # The index computation is not guaranteed to give exactly + # consistent results within ~1 ULP of the bin edges. + decrement = tmp_a < bin_edges[indices] + indices[decrement] -= 1 + # The last bin includes the right edge. The other bins do not. + increment = ((tmp_a >= bin_edges[indices + 1]) + & (indices != n_equal_bins - 1)) + indices[increment] += 1 + + # We now compute the histogram using bincount + if ntype.kind == 'c': + n.real += np.bincount(indices, weights=tmp_w.real, + minlength=n_equal_bins) + n.imag += np.bincount(indices, weights=tmp_w.imag, + minlength=n_equal_bins) + else: + n += np.bincount(indices, weights=tmp_w, + minlength=n_equal_bins).astype(ntype) + else: + # Compute via cumulative histogram + cum_n = np.zeros(bin_edges.shape, ntype) + if weights is None: + for i in _range(0, len(a), BLOCK): + sa = np.sort(a[i:i+BLOCK]) + cum_n += _search_sorted_inclusive(sa, bin_edges) + else: + zero = np.zeros(1, dtype=ntype) + for i in _range(0, len(a), BLOCK): + tmp_a = a[i:i+BLOCK] + tmp_w = weights[i:i+BLOCK] + sorting_index = np.argsort(tmp_a) + sa = tmp_a[sorting_index] + sw = tmp_w[sorting_index] + cw = np.concatenate((zero, sw.cumsum())) + bin_index = _search_sorted_inclusive(sa, bin_edges) + cum_n += cw[bin_index] + + n = np.diff(cum_n) + + if density: + db = np.array(np.diff(bin_edges), float) + return n/db/n.sum(), bin_edges + + return n, bin_edges + + +def _histogramdd_dispatcher(sample, bins=None, range=None, density=None, + weights=None): + if hasattr(sample, 'shape'): # same condition as used in histogramdd + yield sample + else: + yield from sample + with contextlib.suppress(TypeError): + yield from bins + yield weights + + +@array_function_dispatch(_histogramdd_dispatcher) +def histogramdd(sample, bins=10, range=None, density=None, weights=None): + """ + Compute the multidimensional histogram of some data. + + Parameters + ---------- + sample : (N, D) array, or (N, D) array_like + The data to be histogrammed. + + Note the unusual interpretation of sample when an array_like: + + * When an array, each row is a coordinate in a D-dimensional space - + such as ``histogramdd(np.array([p1, p2, p3]))``. + * When an array_like, each element is the list of values for single + coordinate - such as ``histogramdd((X, Y, Z))``. + + The first form should be preferred. + + bins : sequence or int, optional + The bin specification: + + * A sequence of arrays describing the monotonically increasing bin + edges along each dimension. + * The number of bins for each dimension (nx, ny, ... =bins) + * The number of bins for all dimensions (nx=ny=...=bins). + + range : sequence, optional + A sequence of length D, each an optional (lower, upper) tuple giving + the outer bin edges to be used if the edges are not given explicitly in + `bins`. + An entry of None in the sequence results in the minimum and maximum + values being used for the corresponding dimension. + The default, None, is equivalent to passing a tuple of D None values. + density : bool, optional + If False, the default, returns the number of samples in each bin. + If True, returns the probability *density* function at the bin, + ``bin_count / sample_count / bin_volume``. + weights : (N,) array_like, optional + An array of values `w_i` weighing each sample `(x_i, y_i, z_i, ...)`. + Weights are normalized to 1 if density is True. If density is False, + the values of the returned histogram are equal to the sum of the + weights belonging to the samples falling into each bin. + + Returns + ------- + H : ndarray + The multidimensional histogram of sample x. See density and weights + for the different possible semantics. + edges : tuple of ndarrays + A tuple of D arrays describing the bin edges for each dimension. + + See Also + -------- + histogram: 1-D histogram + histogram2d: 2-D histogram + + Examples + -------- + >>> import numpy as np + >>> rng = np.random.default_rng() + >>> r = rng.normal(size=(100,3)) + >>> H, edges = np.histogramdd(r, bins = (5, 8, 4)) + >>> H.shape, edges[0].size, edges[1].size, edges[2].size + ((5, 8, 4), 6, 9, 5) + + """ + + try: + # Sample is an ND-array. + N, D = sample.shape + except (AttributeError, ValueError): + # Sample is a sequence of 1D arrays. + sample = np.atleast_2d(sample).T + N, D = sample.shape + + nbin = np.empty(D, np.intp) + edges = D*[None] + dedges = D*[None] + if weights is not None: + weights = np.asarray(weights) + + try: + M = len(bins) + if M != D: + raise ValueError( + 'The dimension of bins must be equal to the dimension of the ' + 'sample x.') + except TypeError: + # bins is an integer + bins = D*[bins] + + # normalize the range argument + if range is None: + range = (None,) * D + elif len(range) != D: + raise ValueError('range argument must have one entry per dimension') + + # Create edge arrays + for i in _range(D): + if np.ndim(bins[i]) == 0: + if bins[i] < 1: + raise ValueError( + '`bins[{}]` must be positive, when an integer'.format(i)) + smin, smax = _get_outer_edges(sample[:,i], range[i]) + try: + n = operator.index(bins[i]) + + except TypeError as e: + raise TypeError( + "`bins[{}]` must be an integer, when a scalar".format(i) + ) from e + + edges[i] = np.linspace(smin, smax, n + 1) + elif np.ndim(bins[i]) == 1: + edges[i] = np.asarray(bins[i]) + if np.any(edges[i][:-1] > edges[i][1:]): + raise ValueError( + '`bins[{}]` must be monotonically increasing, when an array' + .format(i)) + else: + raise ValueError( + '`bins[{}]` must be a scalar or 1d array'.format(i)) + + nbin[i] = len(edges[i]) + 1 # includes an outlier on each end + dedges[i] = np.diff(edges[i]) + + # Compute the bin number each sample falls into. + Ncount = tuple( + # avoid np.digitize to work around gh-11022 + np.searchsorted(edges[i], sample[:, i], side='right') + for i in _range(D) + ) + + # Using digitize, values that fall on an edge are put in the right bin. + # For the rightmost bin, we want values equal to the right edge to be + # counted in the last bin, and not as an outlier. + for i in _range(D): + # Find which points are on the rightmost edge. + on_edge = (sample[:, i] == edges[i][-1]) + # Shift these points one bin to the left. + Ncount[i][on_edge] -= 1 + + # Compute the sample indices in the flattened histogram matrix. + # This raises an error if the array is too large. + xy = np.ravel_multi_index(Ncount, nbin) + + # Compute the number of repetitions in xy and assign it to the + # flattened histmat. + hist = np.bincount(xy, weights, minlength=nbin.prod()) + + # Shape into a proper matrix + hist = hist.reshape(nbin) + + # This preserves the (bad) behavior observed in gh-7845, for now. + hist = hist.astype(float, casting='safe') + + # Remove outliers (indices 0 and -1 for each dimension). + core = D*(slice(1, -1),) + hist = hist[core] + + if density: + # calculate the probability density function + s = hist.sum() + for i in _range(D): + shape = np.ones(D, int) + shape[i] = nbin[i] - 2 + hist = hist / dedges[i].reshape(shape) + hist /= s + + if (hist.shape != nbin - 2).any(): + raise RuntimeError( + "Internal Shape Error") + return hist, edges diff --git a/janus/lib/python3.10/site-packages/numpy/lib/_index_tricks_impl.py b/janus/lib/python3.10/site-packages/numpy/lib/_index_tricks_impl.py new file mode 100644 index 0000000000000000000000000000000000000000..da8fbedc8072b4d73ebc9b2e25229fff761ff073 --- /dev/null +++ b/janus/lib/python3.10/site-packages/numpy/lib/_index_tricks_impl.py @@ -0,0 +1,1069 @@ +import functools +import sys +import math +import warnings + +import numpy as np +from .._utils import set_module +import numpy._core.numeric as _nx +from numpy._core.numeric import ScalarType, array +from numpy._core.numerictypes import issubdtype + +import numpy.matrixlib as matrixlib +from numpy._core.multiarray import ravel_multi_index, unravel_index +from numpy._core import overrides, linspace +from numpy.lib.stride_tricks import as_strided +from numpy.lib._function_base_impl import diff + + +array_function_dispatch = functools.partial( + overrides.array_function_dispatch, module='numpy') + + +__all__ = [ + 'ravel_multi_index', 'unravel_index', 'mgrid', 'ogrid', 'r_', 'c_', + 's_', 'index_exp', 'ix_', 'ndenumerate', 'ndindex', 'fill_diagonal', + 'diag_indices', 'diag_indices_from' +] + + +def _ix__dispatcher(*args): + return args + + +@array_function_dispatch(_ix__dispatcher) +def ix_(*args): + """ + Construct an open mesh from multiple sequences. + + This function takes N 1-D sequences and returns N outputs with N + dimensions each, such that the shape is 1 in all but one dimension + and the dimension with the non-unit shape value cycles through all + N dimensions. + + Using `ix_` one can quickly construct index arrays that will index + the cross product. ``a[np.ix_([1,3],[2,5])]`` returns the array + ``[[a[1,2] a[1,5]], [a[3,2] a[3,5]]]``. + + Parameters + ---------- + args : 1-D sequences + Each sequence should be of integer or boolean type. + Boolean sequences will be interpreted as boolean masks for the + corresponding dimension (equivalent to passing in + ``np.nonzero(boolean_sequence)``). + + Returns + ------- + out : tuple of ndarrays + N arrays with N dimensions each, with N the number of input + sequences. Together these arrays form an open mesh. + + See Also + -------- + ogrid, mgrid, meshgrid + + Examples + -------- + >>> import numpy as np + >>> a = np.arange(10).reshape(2, 5) + >>> a + array([[0, 1, 2, 3, 4], + [5, 6, 7, 8, 9]]) + >>> ixgrid = np.ix_([0, 1], [2, 4]) + >>> ixgrid + (array([[0], + [1]]), array([[2, 4]])) + >>> ixgrid[0].shape, ixgrid[1].shape + ((2, 1), (1, 2)) + >>> a[ixgrid] + array([[2, 4], + [7, 9]]) + + >>> ixgrid = np.ix_([True, True], [2, 4]) + >>> a[ixgrid] + array([[2, 4], + [7, 9]]) + >>> ixgrid = np.ix_([True, True], [False, False, True, False, True]) + >>> a[ixgrid] + array([[2, 4], + [7, 9]]) + + """ + out = [] + nd = len(args) + for k, new in enumerate(args): + if not isinstance(new, _nx.ndarray): + new = np.asarray(new) + if new.size == 0: + # Explicitly type empty arrays to avoid float default + new = new.astype(_nx.intp) + if new.ndim != 1: + raise ValueError("Cross index must be 1 dimensional") + if issubdtype(new.dtype, _nx.bool): + new, = new.nonzero() + new = new.reshape((1,)*k + (new.size,) + (1,)*(nd-k-1)) + out.append(new) + return tuple(out) + + +class nd_grid: + """ + Construct a multi-dimensional "meshgrid". + + ``grid = nd_grid()`` creates an instance which will return a mesh-grid + when indexed. The dimension and number of the output arrays are equal + to the number of indexing dimensions. If the step length is not a + complex number, then the stop is not inclusive. + + However, if the step length is a **complex number** (e.g. 5j), then the + integer part of its magnitude is interpreted as specifying the + number of points to create between the start and stop values, where + the stop value **is inclusive**. + + If instantiated with an argument of ``sparse=True``, the mesh-grid is + open (or not fleshed out) so that only one-dimension of each returned + argument is greater than 1. + + Parameters + ---------- + sparse : bool, optional + Whether the grid is sparse or not. Default is False. + + Notes + ----- + Two instances of `nd_grid` are made available in the NumPy namespace, + `mgrid` and `ogrid`, approximately defined as:: + + mgrid = nd_grid(sparse=False) + ogrid = nd_grid(sparse=True) + + Users should use these pre-defined instances instead of using `nd_grid` + directly. + """ + __slots__ = ('sparse',) + + def __init__(self, sparse=False): + self.sparse = sparse + + def __getitem__(self, key): + try: + size = [] + # Mimic the behavior of `np.arange` and use a data type + # which is at least as large as `np.int_` + num_list = [0] + for k in range(len(key)): + step = key[k].step + start = key[k].start + stop = key[k].stop + if start is None: + start = 0 + if step is None: + step = 1 + if isinstance(step, (_nx.complexfloating, complex)): + step = abs(step) + size.append(int(step)) + else: + size.append( + int(math.ceil((stop - start) / (step*1.0)))) + num_list += [start, stop, step] + typ = _nx.result_type(*num_list) + if self.sparse: + nn = [_nx.arange(_x, dtype=_t) + for _x, _t in zip(size, (typ,)*len(size))] + else: + nn = _nx.indices(size, typ) + for k, kk in enumerate(key): + step = kk.step + start = kk.start + if start is None: + start = 0 + if step is None: + step = 1 + if isinstance(step, (_nx.complexfloating, complex)): + step = int(abs(step)) + if step != 1: + step = (kk.stop - start) / float(step - 1) + nn[k] = (nn[k]*step+start) + if self.sparse: + slobj = [_nx.newaxis]*len(size) + for k in range(len(size)): + slobj[k] = slice(None, None) + nn[k] = nn[k][tuple(slobj)] + slobj[k] = _nx.newaxis + return tuple(nn) # ogrid -> tuple of arrays + return nn # mgrid -> ndarray + except (IndexError, TypeError): + step = key.step + stop = key.stop + start = key.start + if start is None: + start = 0 + if isinstance(step, (_nx.complexfloating, complex)): + # Prevent the (potential) creation of integer arrays + step_float = abs(step) + step = length = int(step_float) + if step != 1: + step = (key.stop-start)/float(step-1) + typ = _nx.result_type(start, stop, step_float) + return _nx.arange(0, length, 1, dtype=typ)*step + start + else: + return _nx.arange(start, stop, step) + + +class MGridClass(nd_grid): + """ + An instance which returns a dense multi-dimensional "meshgrid". + + An instance which returns a dense (or fleshed out) mesh-grid + when indexed, so that each returned argument has the same shape. + The dimensions and number of the output arrays are equal to the + number of indexing dimensions. If the step length is not a complex + number, then the stop is not inclusive. + + However, if the step length is a **complex number** (e.g. 5j), then + the integer part of its magnitude is interpreted as specifying the + number of points to create between the start and stop values, where + the stop value **is inclusive**. + + Returns + ------- + mesh-grid : ndarray + A single array, containing a set of `ndarray`\\ s all of the same + dimensions. stacked along the first axis. + + See Also + -------- + ogrid : like `mgrid` but returns open (not fleshed out) mesh grids + meshgrid: return coordinate matrices from coordinate vectors + r_ : array concatenator + :ref:`how-to-partition` + + Examples + -------- + >>> import numpy as np + >>> np.mgrid[0:5, 0:5] + array([[[0, 0, 0, 0, 0], + [1, 1, 1, 1, 1], + [2, 2, 2, 2, 2], + [3, 3, 3, 3, 3], + [4, 4, 4, 4, 4]], + [[0, 1, 2, 3, 4], + [0, 1, 2, 3, 4], + [0, 1, 2, 3, 4], + [0, 1, 2, 3, 4], + [0, 1, 2, 3, 4]]]) + >>> np.mgrid[-1:1:5j] + array([-1. , -0.5, 0. , 0.5, 1. ]) + + >>> np.mgrid[0:4].shape + (4,) + >>> np.mgrid[0:4, 0:5].shape + (2, 4, 5) + >>> np.mgrid[0:4, 0:5, 0:6].shape + (3, 4, 5, 6) + + """ + __slots__ = () + + def __init__(self): + super().__init__(sparse=False) + + +mgrid = MGridClass() + + +class OGridClass(nd_grid): + """ + An instance which returns an open multi-dimensional "meshgrid". + + An instance which returns an open (i.e. not fleshed out) mesh-grid + when indexed, so that only one dimension of each returned array is + greater than 1. The dimension and number of the output arrays are + equal to the number of indexing dimensions. If the step length is + not a complex number, then the stop is not inclusive. + + However, if the step length is a **complex number** (e.g. 5j), then + the integer part of its magnitude is interpreted as specifying the + number of points to create between the start and stop values, where + the stop value **is inclusive**. + + Returns + ------- + mesh-grid : ndarray or tuple of ndarrays + If the input is a single slice, returns an array. + If the input is multiple slices, returns a tuple of arrays, with + only one dimension not equal to 1. + + See Also + -------- + mgrid : like `ogrid` but returns dense (or fleshed out) mesh grids + meshgrid: return coordinate matrices from coordinate vectors + r_ : array concatenator + :ref:`how-to-partition` + + Examples + -------- + >>> from numpy import ogrid + >>> ogrid[-1:1:5j] + array([-1. , -0.5, 0. , 0.5, 1. ]) + >>> ogrid[0:5, 0:5] + (array([[0], + [1], + [2], + [3], + [4]]), + array([[0, 1, 2, 3, 4]])) + + """ + __slots__ = () + + def __init__(self): + super().__init__(sparse=True) + + +ogrid = OGridClass() + + +class AxisConcatenator: + """ + Translates slice objects to concatenation along an axis. + + For detailed documentation on usage, see `r_`. + """ + __slots__ = ('axis', 'matrix', 'trans1d', 'ndmin') + + # allow ma.mr_ to override this + concatenate = staticmethod(_nx.concatenate) + makemat = staticmethod(matrixlib.matrix) + + def __init__(self, axis=0, matrix=False, ndmin=1, trans1d=-1): + self.axis = axis + self.matrix = matrix + self.trans1d = trans1d + self.ndmin = ndmin + + def __getitem__(self, key): + # handle matrix builder syntax + if isinstance(key, str): + frame = sys._getframe().f_back + mymat = matrixlib.bmat(key, frame.f_globals, frame.f_locals) + return mymat + + if not isinstance(key, tuple): + key = (key,) + + # copy attributes, since they can be overridden in the first argument + trans1d = self.trans1d + ndmin = self.ndmin + matrix = self.matrix + axis = self.axis + + objs = [] + # dtypes or scalars for weak scalar handling in result_type + result_type_objs = [] + + for k, item in enumerate(key): + scalar = False + if isinstance(item, slice): + step = item.step + start = item.start + stop = item.stop + if start is None: + start = 0 + if step is None: + step = 1 + if isinstance(step, (_nx.complexfloating, complex)): + size = int(abs(step)) + newobj = linspace(start, stop, num=size) + else: + newobj = _nx.arange(start, stop, step) + if ndmin > 1: + newobj = array(newobj, copy=None, ndmin=ndmin) + if trans1d != -1: + newobj = newobj.swapaxes(-1, trans1d) + elif isinstance(item, str): + if k != 0: + raise ValueError("special directives must be the " + "first entry.") + if item in ('r', 'c'): + matrix = True + col = (item == 'c') + continue + if ',' in item: + vec = item.split(',') + try: + axis, ndmin = [int(x) for x in vec[:2]] + if len(vec) == 3: + trans1d = int(vec[2]) + continue + except Exception as e: + raise ValueError( + "unknown special directive {!r}".format(item) + ) from e + try: + axis = int(item) + continue + except (ValueError, TypeError) as e: + raise ValueError("unknown special directive") from e + elif type(item) in ScalarType: + scalar = True + newobj = item + else: + item_ndim = np.ndim(item) + newobj = array(item, copy=None, subok=True, ndmin=ndmin) + if trans1d != -1 and item_ndim < ndmin: + k2 = ndmin - item_ndim + k1 = trans1d + if k1 < 0: + k1 += k2 + 1 + defaxes = list(range(ndmin)) + axes = defaxes[:k1] + defaxes[k2:] + defaxes[k1:k2] + newobj = newobj.transpose(axes) + + objs.append(newobj) + if scalar: + result_type_objs.append(item) + else: + result_type_objs.append(newobj.dtype) + + # Ensure that scalars won't up-cast unless warranted, for 0, drops + # through to error in concatenate. + if len(result_type_objs) != 0: + final_dtype = _nx.result_type(*result_type_objs) + # concatenate could do cast, but that can be overridden: + objs = [array(obj, copy=None, subok=True, + ndmin=ndmin, dtype=final_dtype) for obj in objs] + + res = self.concatenate(tuple(objs), axis=axis) + + if matrix: + oldndim = res.ndim + res = self.makemat(res) + if oldndim == 1 and col: + res = res.T + return res + + def __len__(self): + return 0 + +# separate classes are used here instead of just making r_ = concatenator(0), +# etc. because otherwise we couldn't get the doc string to come out right +# in help(r_) + + +class RClass(AxisConcatenator): + """ + Translates slice objects to concatenation along the first axis. + + This is a simple way to build up arrays quickly. There are two use cases. + + 1. If the index expression contains comma separated arrays, then stack + them along their first axis. + 2. If the index expression contains slice notation or scalars then create + a 1-D array with a range indicated by the slice notation. + + If slice notation is used, the syntax ``start:stop:step`` is equivalent + to ``np.arange(start, stop, step)`` inside of the brackets. However, if + ``step`` is an imaginary number (i.e. 100j) then its integer portion is + interpreted as a number-of-points desired and the start and stop are + inclusive. In other words ``start:stop:stepj`` is interpreted as + ``np.linspace(start, stop, step, endpoint=1)`` inside of the brackets. + After expansion of slice notation, all comma separated sequences are + concatenated together. + + Optional character strings placed as the first element of the index + expression can be used to change the output. The strings 'r' or 'c' result + in matrix output. If the result is 1-D and 'r' is specified a 1 x N (row) + matrix is produced. If the result is 1-D and 'c' is specified, then a N x 1 + (column) matrix is produced. If the result is 2-D then both provide the + same matrix result. + + A string integer specifies which axis to stack multiple comma separated + arrays along. A string of two comma-separated integers allows indication + of the minimum number of dimensions to force each entry into as the + second integer (the axis to concatenate along is still the first integer). + + A string with three comma-separated integers allows specification of the + axis to concatenate along, the minimum number of dimensions to force the + entries to, and which axis should contain the start of the arrays which + are less than the specified number of dimensions. In other words the third + integer allows you to specify where the 1's should be placed in the shape + of the arrays that have their shapes upgraded. By default, they are placed + in the front of the shape tuple. The third argument allows you to specify + where the start of the array should be instead. Thus, a third argument of + '0' would place the 1's at the end of the array shape. Negative integers + specify where in the new shape tuple the last dimension of upgraded arrays + should be placed, so the default is '-1'. + + Parameters + ---------- + Not a function, so takes no parameters + + + Returns + ------- + A concatenated ndarray or matrix. + + See Also + -------- + concatenate : Join a sequence of arrays along an existing axis. + c_ : Translates slice objects to concatenation along the second axis. + + Examples + -------- + >>> import numpy as np + >>> np.r_[np.array([1,2,3]), 0, 0, np.array([4,5,6])] + array([1, 2, 3, ..., 4, 5, 6]) + >>> np.r_[-1:1:6j, [0]*3, 5, 6] + array([-1. , -0.6, -0.2, 0.2, 0.6, 1. , 0. , 0. , 0. , 5. , 6. ]) + + String integers specify the axis to concatenate along or the minimum + number of dimensions to force entries into. + + >>> a = np.array([[0, 1, 2], [3, 4, 5]]) + >>> np.r_['-1', a, a] # concatenate along last axis + array([[0, 1, 2, 0, 1, 2], + [3, 4, 5, 3, 4, 5]]) + >>> np.r_['0,2', [1,2,3], [4,5,6]] # concatenate along first axis, dim>=2 + array([[1, 2, 3], + [4, 5, 6]]) + + >>> np.r_['0,2,0', [1,2,3], [4,5,6]] + array([[1], + [2], + [3], + [4], + [5], + [6]]) + >>> np.r_['1,2,0', [1,2,3], [4,5,6]] + array([[1, 4], + [2, 5], + [3, 6]]) + + Using 'r' or 'c' as a first string argument creates a matrix. + + >>> np.r_['r',[1,2,3], [4,5,6]] + matrix([[1, 2, 3, 4, 5, 6]]) + + """ + __slots__ = () + + def __init__(self): + AxisConcatenator.__init__(self, 0) + + +r_ = RClass() + + +class CClass(AxisConcatenator): + """ + Translates slice objects to concatenation along the second axis. + + This is short-hand for ``np.r_['-1,2,0', index expression]``, which is + useful because of its common occurrence. In particular, arrays will be + stacked along their last axis after being upgraded to at least 2-D with + 1's post-pended to the shape (column vectors made out of 1-D arrays). + + See Also + -------- + column_stack : Stack 1-D arrays as columns into a 2-D array. + r_ : For more detailed documentation. + + Examples + -------- + >>> import numpy as np + >>> np.c_[np.array([1,2,3]), np.array([4,5,6])] + array([[1, 4], + [2, 5], + [3, 6]]) + >>> np.c_[np.array([[1,2,3]]), 0, 0, np.array([[4,5,6]])] + array([[1, 2, 3, ..., 4, 5, 6]]) + + """ + __slots__ = () + + def __init__(self): + AxisConcatenator.__init__(self, -1, ndmin=2, trans1d=0) + + +c_ = CClass() + + +@set_module('numpy') +class ndenumerate: + """ + Multidimensional index iterator. + + Return an iterator yielding pairs of array coordinates and values. + + Parameters + ---------- + arr : ndarray + Input array. + + See Also + -------- + ndindex, flatiter + + Examples + -------- + >>> import numpy as np + >>> a = np.array([[1, 2], [3, 4]]) + >>> for index, x in np.ndenumerate(a): + ... print(index, x) + (0, 0) 1 + (0, 1) 2 + (1, 0) 3 + (1, 1) 4 + + """ + + def __init__(self, arr): + self.iter = np.asarray(arr).flat + + def __next__(self): + """ + Standard iterator method, returns the index tuple and array value. + + Returns + ------- + coords : tuple of ints + The indices of the current iteration. + val : scalar + The array element of the current iteration. + + """ + return self.iter.coords, next(self.iter) + + def __iter__(self): + return self + + +@set_module('numpy') +class ndindex: + """ + An N-dimensional iterator object to index arrays. + + Given the shape of an array, an `ndindex` instance iterates over + the N-dimensional index of the array. At each iteration a tuple + of indices is returned, the last dimension is iterated over first. + + Parameters + ---------- + shape : ints, or a single tuple of ints + The size of each dimension of the array can be passed as + individual parameters or as the elements of a tuple. + + See Also + -------- + ndenumerate, flatiter + + Examples + -------- + >>> import numpy as np + + Dimensions as individual arguments + + >>> for index in np.ndindex(3, 2, 1): + ... print(index) + (0, 0, 0) + (0, 1, 0) + (1, 0, 0) + (1, 1, 0) + (2, 0, 0) + (2, 1, 0) + + Same dimensions - but in a tuple ``(3, 2, 1)`` + + >>> for index in np.ndindex((3, 2, 1)): + ... print(index) + (0, 0, 0) + (0, 1, 0) + (1, 0, 0) + (1, 1, 0) + (2, 0, 0) + (2, 1, 0) + + """ + + def __init__(self, *shape): + if len(shape) == 1 and isinstance(shape[0], tuple): + shape = shape[0] + x = as_strided(_nx.zeros(1), shape=shape, + strides=_nx.zeros_like(shape)) + self._it = _nx.nditer(x, flags=['multi_index', 'zerosize_ok'], + order='C') + + def __iter__(self): + return self + + def ndincr(self): + """ + Increment the multi-dimensional index by one. + + This method is for backward compatibility only: do not use. + + .. deprecated:: 1.20.0 + This method has been advised against since numpy 1.8.0, but only + started emitting DeprecationWarning as of this version. + """ + # NumPy 1.20.0, 2020-09-08 + warnings.warn( + "`ndindex.ndincr()` is deprecated, use `next(ndindex)` instead", + DeprecationWarning, stacklevel=2) + next(self) + + def __next__(self): + """ + Standard iterator method, updates the index and returns the index + tuple. + + Returns + ------- + val : tuple of ints + Returns a tuple containing the indices of the current + iteration. + + """ + next(self._it) + return self._it.multi_index + + +# You can do all this with slice() plus a few special objects, +# but there's a lot to remember. This version is simpler because +# it uses the standard array indexing syntax. +# +# Written by Konrad Hinsen +# last revision: 1999-7-23 +# +# Cosmetic changes by T. Oliphant 2001 +# +# + +class IndexExpression: + """ + A nicer way to build up index tuples for arrays. + + .. note:: + Use one of the two predefined instances ``index_exp`` or `s_` + rather than directly using `IndexExpression`. + + For any index combination, including slicing and axis insertion, + ``a[indices]`` is the same as ``a[np.index_exp[indices]]`` for any + array `a`. However, ``np.index_exp[indices]`` can be used anywhere + in Python code and returns a tuple of slice objects that can be + used in the construction of complex index expressions. + + Parameters + ---------- + maketuple : bool + If True, always returns a tuple. + + See Also + -------- + s_ : Predefined instance without tuple conversion: + `s_ = IndexExpression(maketuple=False)`. + The ``index_exp`` is another predefined instance that + always returns a tuple: + `index_exp = IndexExpression(maketuple=True)`. + + Notes + ----- + You can do all this with :class:`slice` plus a few special objects, + but there's a lot to remember and this version is simpler because + it uses the standard array indexing syntax. + + Examples + -------- + >>> import numpy as np + >>> np.s_[2::2] + slice(2, None, 2) + >>> np.index_exp[2::2] + (slice(2, None, 2),) + + >>> np.array([0, 1, 2, 3, 4])[np.s_[2::2]] + array([2, 4]) + + """ + __slots__ = ('maketuple',) + + def __init__(self, maketuple): + self.maketuple = maketuple + + def __getitem__(self, item): + if self.maketuple and not isinstance(item, tuple): + return (item,) + else: + return item + + +index_exp = IndexExpression(maketuple=True) +s_ = IndexExpression(maketuple=False) + +# End contribution from Konrad. + + +# The following functions complement those in twodim_base, but are +# applicable to N-dimensions. + + +def _fill_diagonal_dispatcher(a, val, wrap=None): + return (a,) + + +@array_function_dispatch(_fill_diagonal_dispatcher) +def fill_diagonal(a, val, wrap=False): + """Fill the main diagonal of the given array of any dimensionality. + + For an array `a` with ``a.ndim >= 2``, the diagonal is the list of + values ``a[i, ..., i]`` with indices ``i`` all identical. This function + modifies the input array in-place without returning a value. + + Parameters + ---------- + a : array, at least 2-D. + Array whose diagonal is to be filled in-place. + val : scalar or array_like + Value(s) to write on the diagonal. If `val` is scalar, the value is + written along the diagonal. If array-like, the flattened `val` is + written along the diagonal, repeating if necessary to fill all + diagonal entries. + + wrap : bool + For tall matrices in NumPy version up to 1.6.2, the + diagonal "wrapped" after N columns. You can have this behavior + with this option. This affects only tall matrices. + + See also + -------- + diag_indices, diag_indices_from + + Notes + ----- + This functionality can be obtained via `diag_indices`, but internally + this version uses a much faster implementation that never constructs the + indices and uses simple slicing. + + Examples + -------- + >>> import numpy as np + >>> a = np.zeros((3, 3), int) + >>> np.fill_diagonal(a, 5) + >>> a + array([[5, 0, 0], + [0, 5, 0], + [0, 0, 5]]) + + The same function can operate on a 4-D array: + + >>> a = np.zeros((3, 3, 3, 3), int) + >>> np.fill_diagonal(a, 4) + + We only show a few blocks for clarity: + + >>> a[0, 0] + array([[4, 0, 0], + [0, 0, 0], + [0, 0, 0]]) + >>> a[1, 1] + array([[0, 0, 0], + [0, 4, 0], + [0, 0, 0]]) + >>> a[2, 2] + array([[0, 0, 0], + [0, 0, 0], + [0, 0, 4]]) + + The wrap option affects only tall matrices: + + >>> # tall matrices no wrap + >>> a = np.zeros((5, 3), int) + >>> np.fill_diagonal(a, 4) + >>> a + array([[4, 0, 0], + [0, 4, 0], + [0, 0, 4], + [0, 0, 0], + [0, 0, 0]]) + + >>> # tall matrices wrap + >>> a = np.zeros((5, 3), int) + >>> np.fill_diagonal(a, 4, wrap=True) + >>> a + array([[4, 0, 0], + [0, 4, 0], + [0, 0, 4], + [0, 0, 0], + [4, 0, 0]]) + + >>> # wide matrices + >>> a = np.zeros((3, 5), int) + >>> np.fill_diagonal(a, 4, wrap=True) + >>> a + array([[4, 0, 0, 0, 0], + [0, 4, 0, 0, 0], + [0, 0, 4, 0, 0]]) + + The anti-diagonal can be filled by reversing the order of elements + using either `numpy.flipud` or `numpy.fliplr`. + + >>> a = np.zeros((3, 3), int); + >>> np.fill_diagonal(np.fliplr(a), [1,2,3]) # Horizontal flip + >>> a + array([[0, 0, 1], + [0, 2, 0], + [3, 0, 0]]) + >>> np.fill_diagonal(np.flipud(a), [1,2,3]) # Vertical flip + >>> a + array([[0, 0, 3], + [0, 2, 0], + [1, 0, 0]]) + + Note that the order in which the diagonal is filled varies depending + on the flip function. + """ + if a.ndim < 2: + raise ValueError("array must be at least 2-d") + end = None + if a.ndim == 2: + # Explicit, fast formula for the common case. For 2-d arrays, we + # accept rectangular ones. + step = a.shape[1] + 1 + # This is needed to don't have tall matrix have the diagonal wrap. + if not wrap: + end = a.shape[1] * a.shape[1] + else: + # For more than d=2, the strided formula is only valid for arrays with + # all dimensions equal, so we check first. + if not np.all(diff(a.shape) == 0): + raise ValueError("All dimensions of input must be of equal length") + step = 1 + (np.cumprod(a.shape[:-1])).sum() + + # Write the value out into the diagonal. + a.flat[:end:step] = val + + +@set_module('numpy') +def diag_indices(n, ndim=2): + """ + Return the indices to access the main diagonal of an array. + + This returns a tuple of indices that can be used to access the main + diagonal of an array `a` with ``a.ndim >= 2`` dimensions and shape + (n, n, ..., n). For ``a.ndim = 2`` this is the usual diagonal, for + ``a.ndim > 2`` this is the set of indices to access ``a[i, i, ..., i]`` + for ``i = [0..n-1]``. + + Parameters + ---------- + n : int + The size, along each dimension, of the arrays for which the returned + indices can be used. + + ndim : int, optional + The number of dimensions. + + See Also + -------- + diag_indices_from + + Examples + -------- + >>> import numpy as np + + Create a set of indices to access the diagonal of a (4, 4) array: + + >>> di = np.diag_indices(4) + >>> di + (array([0, 1, 2, 3]), array([0, 1, 2, 3])) + >>> a = np.arange(16).reshape(4, 4) + >>> a + array([[ 0, 1, 2, 3], + [ 4, 5, 6, 7], + [ 8, 9, 10, 11], + [12, 13, 14, 15]]) + >>> a[di] = 100 + >>> a + array([[100, 1, 2, 3], + [ 4, 100, 6, 7], + [ 8, 9, 100, 11], + [ 12, 13, 14, 100]]) + + Now, we create indices to manipulate a 3-D array: + + >>> d3 = np.diag_indices(2, 3) + >>> d3 + (array([0, 1]), array([0, 1]), array([0, 1])) + + And use it to set the diagonal of an array of zeros to 1: + + >>> a = np.zeros((2, 2, 2), dtype=int) + >>> a[d3] = 1 + >>> a + array([[[1, 0], + [0, 0]], + [[0, 0], + [0, 1]]]) + + """ + idx = np.arange(n) + return (idx,) * ndim + + +def _diag_indices_from(arr): + return (arr,) + + +@array_function_dispatch(_diag_indices_from) +def diag_indices_from(arr): + """ + Return the indices to access the main diagonal of an n-dimensional array. + + See `diag_indices` for full details. + + Parameters + ---------- + arr : array, at least 2-D + + See Also + -------- + diag_indices + + Examples + -------- + >>> import numpy as np + + Create a 4 by 4 array. + + >>> a = np.arange(16).reshape(4, 4) + >>> a + array([[ 0, 1, 2, 3], + [ 4, 5, 6, 7], + [ 8, 9, 10, 11], + [12, 13, 14, 15]]) + + Get the indices of the diagonal elements. + + >>> di = np.diag_indices_from(a) + >>> di + (array([0, 1, 2, 3]), array([0, 1, 2, 3])) + + >>> a[di] + array([ 0, 5, 10, 15]) + + This is simply syntactic sugar for diag_indices. + + >>> np.diag_indices(a.shape[0]) + (array([0, 1, 2, 3]), array([0, 1, 2, 3])) + + """ + + if not arr.ndim >= 2: + raise ValueError("input array must be at least 2-d") + # For more than d=2, the strided formula is only valid for arrays with + # all dimensions equal, so we check first. + if not np.all(diff(arr.shape) == 0): + raise ValueError("All dimensions of input must be of equal length") + + return diag_indices(arr.shape[0], arr.ndim) diff --git a/janus/lib/python3.10/site-packages/numpy/lib/_polynomial_impl.pyi b/janus/lib/python3.10/site-packages/numpy/lib/_polynomial_impl.pyi new file mode 100644 index 0000000000000000000000000000000000000000..112ec33d2520426fdad8a5c034184c268fe68ea1 --- /dev/null +++ b/janus/lib/python3.10/site-packages/numpy/lib/_polynomial_impl.pyi @@ -0,0 +1,315 @@ +from typing import ( + Literal as L, + TypeAlias, + overload, + Any, + SupportsInt, + SupportsIndex, + TypeVar, + NoReturn, +) + +import numpy as np +from numpy import ( + poly1d, + unsignedinteger, + signedinteger, + floating, + complexfloating, + int32, + int64, + float64, + complex128, + object_, +) + +from numpy._typing import ( + NDArray, + ArrayLike, + _ArrayLikeBool_co, + _ArrayLikeUInt_co, + _ArrayLikeInt_co, + _ArrayLikeFloat_co, + _ArrayLikeComplex_co, + _ArrayLikeObject_co, +) + +_T = TypeVar("_T") + +_2Tup: TypeAlias = tuple[_T, _T] +_5Tup: TypeAlias = tuple[ + _T, + NDArray[float64], + NDArray[int32], + NDArray[float64], + NDArray[float64], +] + +__all__ = [ + "poly", + "roots", + "polyint", + "polyder", + "polyadd", + "polysub", + "polymul", + "polydiv", + "polyval", + "poly1d", + "polyfit", +] + +def poly(seq_of_zeros: ArrayLike) -> NDArray[floating[Any]]: ... + +# Returns either a float or complex array depending on the input values. +# See `np.linalg.eigvals`. +def roots(p: ArrayLike) -> NDArray[complexfloating[Any, Any]] | NDArray[floating[Any]]: ... + +@overload +def polyint( + p: poly1d, + m: SupportsInt | SupportsIndex = ..., + k: None | _ArrayLikeComplex_co | _ArrayLikeObject_co = ..., +) -> poly1d: ... +@overload +def polyint( + p: _ArrayLikeFloat_co, + m: SupportsInt | SupportsIndex = ..., + k: None | _ArrayLikeFloat_co = ..., +) -> NDArray[floating[Any]]: ... +@overload +def polyint( + p: _ArrayLikeComplex_co, + m: SupportsInt | SupportsIndex = ..., + k: None | _ArrayLikeComplex_co = ..., +) -> NDArray[complexfloating[Any, Any]]: ... +@overload +def polyint( + p: _ArrayLikeObject_co, + m: SupportsInt | SupportsIndex = ..., + k: None | _ArrayLikeObject_co = ..., +) -> NDArray[object_]: ... + +@overload +def polyder( + p: poly1d, + m: SupportsInt | SupportsIndex = ..., +) -> poly1d: ... +@overload +def polyder( + p: _ArrayLikeFloat_co, + m: SupportsInt | SupportsIndex = ..., +) -> NDArray[floating[Any]]: ... +@overload +def polyder( + p: _ArrayLikeComplex_co, + m: SupportsInt | SupportsIndex = ..., +) -> NDArray[complexfloating[Any, Any]]: ... +@overload +def polyder( + p: _ArrayLikeObject_co, + m: SupportsInt | SupportsIndex = ..., +) -> NDArray[object_]: ... + +@overload +def polyfit( + x: _ArrayLikeFloat_co, + y: _ArrayLikeFloat_co, + deg: SupportsIndex | SupportsInt, + rcond: None | float = ..., + full: L[False] = ..., + w: None | _ArrayLikeFloat_co = ..., + cov: L[False] = ..., +) -> NDArray[float64]: ... +@overload +def polyfit( + x: _ArrayLikeComplex_co, + y: _ArrayLikeComplex_co, + deg: SupportsIndex | SupportsInt, + rcond: None | float = ..., + full: L[False] = ..., + w: None | _ArrayLikeFloat_co = ..., + cov: L[False] = ..., +) -> NDArray[complex128]: ... +@overload +def polyfit( + x: _ArrayLikeFloat_co, + y: _ArrayLikeFloat_co, + deg: SupportsIndex | SupportsInt, + rcond: None | float = ..., + full: L[False] = ..., + w: None | _ArrayLikeFloat_co = ..., + cov: L[True, "unscaled"] = ..., +) -> _2Tup[NDArray[float64]]: ... +@overload +def polyfit( + x: _ArrayLikeComplex_co, + y: _ArrayLikeComplex_co, + deg: SupportsIndex | SupportsInt, + rcond: None | float = ..., + full: L[False] = ..., + w: None | _ArrayLikeFloat_co = ..., + cov: L[True, "unscaled"] = ..., +) -> _2Tup[NDArray[complex128]]: ... +@overload +def polyfit( + x: _ArrayLikeFloat_co, + y: _ArrayLikeFloat_co, + deg: SupportsIndex | SupportsInt, + rcond: None | float = ..., + full: L[True] = ..., + w: None | _ArrayLikeFloat_co = ..., + cov: bool | L["unscaled"] = ..., +) -> _5Tup[NDArray[float64]]: ... +@overload +def polyfit( + x: _ArrayLikeComplex_co, + y: _ArrayLikeComplex_co, + deg: SupportsIndex | SupportsInt, + rcond: None | float = ..., + full: L[True] = ..., + w: None | _ArrayLikeFloat_co = ..., + cov: bool | L["unscaled"] = ..., +) -> _5Tup[NDArray[complex128]]: ... + +@overload +def polyval( + p: _ArrayLikeBool_co, + x: _ArrayLikeBool_co, +) -> NDArray[int64]: ... +@overload +def polyval( + p: _ArrayLikeUInt_co, + x: _ArrayLikeUInt_co, +) -> NDArray[unsignedinteger[Any]]: ... +@overload +def polyval( + p: _ArrayLikeInt_co, + x: _ArrayLikeInt_co, +) -> NDArray[signedinteger[Any]]: ... +@overload +def polyval( + p: _ArrayLikeFloat_co, + x: _ArrayLikeFloat_co, +) -> NDArray[floating[Any]]: ... +@overload +def polyval( + p: _ArrayLikeComplex_co, + x: _ArrayLikeComplex_co, +) -> NDArray[complexfloating[Any, Any]]: ... +@overload +def polyval( + p: _ArrayLikeObject_co, + x: _ArrayLikeObject_co, +) -> NDArray[object_]: ... + +@overload +def polyadd( + a1: poly1d, + a2: _ArrayLikeComplex_co | _ArrayLikeObject_co, +) -> poly1d: ... +@overload +def polyadd( + a1: _ArrayLikeComplex_co | _ArrayLikeObject_co, + a2: poly1d, +) -> poly1d: ... +@overload +def polyadd( + a1: _ArrayLikeBool_co, + a2: _ArrayLikeBool_co, +) -> NDArray[np.bool]: ... +@overload +def polyadd( + a1: _ArrayLikeUInt_co, + a2: _ArrayLikeUInt_co, +) -> NDArray[unsignedinteger[Any]]: ... +@overload +def polyadd( + a1: _ArrayLikeInt_co, + a2: _ArrayLikeInt_co, +) -> NDArray[signedinteger[Any]]: ... +@overload +def polyadd( + a1: _ArrayLikeFloat_co, + a2: _ArrayLikeFloat_co, +) -> NDArray[floating[Any]]: ... +@overload +def polyadd( + a1: _ArrayLikeComplex_co, + a2: _ArrayLikeComplex_co, +) -> NDArray[complexfloating[Any, Any]]: ... +@overload +def polyadd( + a1: _ArrayLikeObject_co, + a2: _ArrayLikeObject_co, +) -> NDArray[object_]: ... + +@overload +def polysub( + a1: poly1d, + a2: _ArrayLikeComplex_co | _ArrayLikeObject_co, +) -> poly1d: ... +@overload +def polysub( + a1: _ArrayLikeComplex_co | _ArrayLikeObject_co, + a2: poly1d, +) -> poly1d: ... +@overload +def polysub( + a1: _ArrayLikeBool_co, + a2: _ArrayLikeBool_co, +) -> NoReturn: ... +@overload +def polysub( + a1: _ArrayLikeUInt_co, + a2: _ArrayLikeUInt_co, +) -> NDArray[unsignedinteger[Any]]: ... +@overload +def polysub( + a1: _ArrayLikeInt_co, + a2: _ArrayLikeInt_co, +) -> NDArray[signedinteger[Any]]: ... +@overload +def polysub( + a1: _ArrayLikeFloat_co, + a2: _ArrayLikeFloat_co, +) -> NDArray[floating[Any]]: ... +@overload +def polysub( + a1: _ArrayLikeComplex_co, + a2: _ArrayLikeComplex_co, +) -> NDArray[complexfloating[Any, Any]]: ... +@overload +def polysub( + a1: _ArrayLikeObject_co, + a2: _ArrayLikeObject_co, +) -> NDArray[object_]: ... + +# NOTE: Not an alias, but they do have the same signature (that we can reuse) +polymul = polyadd + +@overload +def polydiv( + u: poly1d, + v: _ArrayLikeComplex_co | _ArrayLikeObject_co, +) -> _2Tup[poly1d]: ... +@overload +def polydiv( + u: _ArrayLikeComplex_co | _ArrayLikeObject_co, + v: poly1d, +) -> _2Tup[poly1d]: ... +@overload +def polydiv( + u: _ArrayLikeFloat_co, + v: _ArrayLikeFloat_co, +) -> _2Tup[NDArray[floating[Any]]]: ... +@overload +def polydiv( + u: _ArrayLikeComplex_co, + v: _ArrayLikeComplex_co, +) -> _2Tup[NDArray[complexfloating[Any, Any]]]: ... +@overload +def polydiv( + u: _ArrayLikeObject_co, + v: _ArrayLikeObject_co, +) -> _2Tup[NDArray[Any]]: ... diff --git a/janus/lib/python3.10/site-packages/numpy/lib/_scimath_impl.pyi b/janus/lib/python3.10/site-packages/numpy/lib/_scimath_impl.pyi new file mode 100644 index 0000000000000000000000000000000000000000..43b7110b2923453110edce9942a0659508c03eb6 --- /dev/null +++ b/janus/lib/python3.10/site-packages/numpy/lib/_scimath_impl.pyi @@ -0,0 +1,94 @@ +from typing import overload, Any + +from numpy import complexfloating + +from numpy._typing import ( + NDArray, + _ArrayLikeFloat_co, + _ArrayLikeComplex_co, + _ComplexLike_co, + _FloatLike_co, +) + +__all__ = ["sqrt", "log", "log2", "logn", "log10", "power", "arccos", "arcsin", "arctanh"] + +@overload +def sqrt(x: _FloatLike_co) -> Any: ... +@overload +def sqrt(x: _ComplexLike_co) -> complexfloating[Any, Any]: ... +@overload +def sqrt(x: _ArrayLikeFloat_co) -> NDArray[Any]: ... +@overload +def sqrt(x: _ArrayLikeComplex_co) -> NDArray[complexfloating[Any, Any]]: ... + +@overload +def log(x: _FloatLike_co) -> Any: ... +@overload +def log(x: _ComplexLike_co) -> complexfloating[Any, Any]: ... +@overload +def log(x: _ArrayLikeFloat_co) -> NDArray[Any]: ... +@overload +def log(x: _ArrayLikeComplex_co) -> NDArray[complexfloating[Any, Any]]: ... + +@overload +def log10(x: _FloatLike_co) -> Any: ... +@overload +def log10(x: _ComplexLike_co) -> complexfloating[Any, Any]: ... +@overload +def log10(x: _ArrayLikeFloat_co) -> NDArray[Any]: ... +@overload +def log10(x: _ArrayLikeComplex_co) -> NDArray[complexfloating[Any, Any]]: ... + +@overload +def log2(x: _FloatLike_co) -> Any: ... +@overload +def log2(x: _ComplexLike_co) -> complexfloating[Any, Any]: ... +@overload +def log2(x: _ArrayLikeFloat_co) -> NDArray[Any]: ... +@overload +def log2(x: _ArrayLikeComplex_co) -> NDArray[complexfloating[Any, Any]]: ... + +@overload +def logn(n: _FloatLike_co, x: _FloatLike_co) -> Any: ... +@overload +def logn(n: _ComplexLike_co, x: _ComplexLike_co) -> complexfloating[Any, Any]: ... +@overload +def logn(n: _ArrayLikeFloat_co, x: _ArrayLikeFloat_co) -> NDArray[Any]: ... +@overload +def logn(n: _ArrayLikeComplex_co, x: _ArrayLikeComplex_co) -> NDArray[complexfloating[Any, Any]]: ... + +@overload +def power(x: _FloatLike_co, p: _FloatLike_co) -> Any: ... +@overload +def power(x: _ComplexLike_co, p: _ComplexLike_co) -> complexfloating[Any, Any]: ... +@overload +def power(x: _ArrayLikeFloat_co, p: _ArrayLikeFloat_co) -> NDArray[Any]: ... +@overload +def power(x: _ArrayLikeComplex_co, p: _ArrayLikeComplex_co) -> NDArray[complexfloating[Any, Any]]: ... + +@overload +def arccos(x: _FloatLike_co) -> Any: ... +@overload +def arccos(x: _ComplexLike_co) -> complexfloating[Any, Any]: ... +@overload +def arccos(x: _ArrayLikeFloat_co) -> NDArray[Any]: ... +@overload +def arccos(x: _ArrayLikeComplex_co) -> NDArray[complexfloating[Any, Any]]: ... + +@overload +def arcsin(x: _FloatLike_co) -> Any: ... +@overload +def arcsin(x: _ComplexLike_co) -> complexfloating[Any, Any]: ... +@overload +def arcsin(x: _ArrayLikeFloat_co) -> NDArray[Any]: ... +@overload +def arcsin(x: _ArrayLikeComplex_co) -> NDArray[complexfloating[Any, Any]]: ... + +@overload +def arctanh(x: _FloatLike_co) -> Any: ... +@overload +def arctanh(x: _ComplexLike_co) -> complexfloating[Any, Any]: ... +@overload +def arctanh(x: _ArrayLikeFloat_co) -> NDArray[Any]: ... +@overload +def arctanh(x: _ArrayLikeComplex_co) -> NDArray[complexfloating[Any, Any]]: ... diff --git a/janus/lib/python3.10/site-packages/numpy/lib/_twodim_base_impl.py b/janus/lib/python3.10/site-packages/numpy/lib/_twodim_base_impl.py new file mode 100644 index 0000000000000000000000000000000000000000..e8815bede8919c5658243146bd06f4d325126051 --- /dev/null +++ b/janus/lib/python3.10/site-packages/numpy/lib/_twodim_base_impl.py @@ -0,0 +1,1188 @@ +""" Basic functions for manipulating 2d arrays + +""" +import functools +import operator + +from numpy._core._multiarray_umath import _array_converter +from numpy._core.numeric import ( + asanyarray, arange, zeros, greater_equal, multiply, ones, + asarray, where, int8, int16, int32, int64, intp, empty, promote_types, + diagonal, nonzero, indices + ) +from numpy._core.overrides import finalize_array_function_like, set_module +from numpy._core import overrides +from numpy._core import iinfo +from numpy.lib._stride_tricks_impl import broadcast_to + + +__all__ = [ + 'diag', 'diagflat', 'eye', 'fliplr', 'flipud', 'tri', 'triu', + 'tril', 'vander', 'histogram2d', 'mask_indices', 'tril_indices', + 'tril_indices_from', 'triu_indices', 'triu_indices_from', ] + + +array_function_dispatch = functools.partial( + overrides.array_function_dispatch, module='numpy') + + +i1 = iinfo(int8) +i2 = iinfo(int16) +i4 = iinfo(int32) + + +def _min_int(low, high): + """ get small int that fits the range """ + if high <= i1.max and low >= i1.min: + return int8 + if high <= i2.max and low >= i2.min: + return int16 + if high <= i4.max and low >= i4.min: + return int32 + return int64 + + +def _flip_dispatcher(m): + return (m,) + + +@array_function_dispatch(_flip_dispatcher) +def fliplr(m): + """ + Reverse the order of elements along axis 1 (left/right). + + For a 2-D array, this flips the entries in each row in the left/right + direction. Columns are preserved, but appear in a different order than + before. + + Parameters + ---------- + m : array_like + Input array, must be at least 2-D. + + Returns + ------- + f : ndarray + A view of `m` with the columns reversed. Since a view + is returned, this operation is :math:`\\mathcal O(1)`. + + See Also + -------- + flipud : Flip array in the up/down direction. + flip : Flip array in one or more dimensions. + rot90 : Rotate array counterclockwise. + + Notes + ----- + Equivalent to ``m[:,::-1]`` or ``np.flip(m, axis=1)``. + Requires the array to be at least 2-D. + + Examples + -------- + >>> import numpy as np + >>> A = np.diag([1.,2.,3.]) + >>> A + array([[1., 0., 0.], + [0., 2., 0.], + [0., 0., 3.]]) + >>> np.fliplr(A) + array([[0., 0., 1.], + [0., 2., 0.], + [3., 0., 0.]]) + + >>> rng = np.random.default_rng() + >>> A = rng.normal(size=(2,3,5)) + >>> np.all(np.fliplr(A) == A[:,::-1,...]) + True + + """ + m = asanyarray(m) + if m.ndim < 2: + raise ValueError("Input must be >= 2-d.") + return m[:, ::-1] + + +@array_function_dispatch(_flip_dispatcher) +def flipud(m): + """ + Reverse the order of elements along axis 0 (up/down). + + For a 2-D array, this flips the entries in each column in the up/down + direction. Rows are preserved, but appear in a different order than before. + + Parameters + ---------- + m : array_like + Input array. + + Returns + ------- + out : array_like + A view of `m` with the rows reversed. Since a view is + returned, this operation is :math:`\\mathcal O(1)`. + + See Also + -------- + fliplr : Flip array in the left/right direction. + flip : Flip array in one or more dimensions. + rot90 : Rotate array counterclockwise. + + Notes + ----- + Equivalent to ``m[::-1, ...]`` or ``np.flip(m, axis=0)``. + Requires the array to be at least 1-D. + + Examples + -------- + >>> import numpy as np + >>> A = np.diag([1.0, 2, 3]) + >>> A + array([[1., 0., 0.], + [0., 2., 0.], + [0., 0., 3.]]) + >>> np.flipud(A) + array([[0., 0., 3.], + [0., 2., 0.], + [1., 0., 0.]]) + + >>> rng = np.random.default_rng() + >>> A = rng.normal(size=(2,3,5)) + >>> np.all(np.flipud(A) == A[::-1,...]) + True + + >>> np.flipud([1,2]) + array([2, 1]) + + """ + m = asanyarray(m) + if m.ndim < 1: + raise ValueError("Input must be >= 1-d.") + return m[::-1, ...] + + +@finalize_array_function_like +@set_module('numpy') +def eye(N, M=None, k=0, dtype=float, order='C', *, device=None, like=None): + """ + Return a 2-D array with ones on the diagonal and zeros elsewhere. + + Parameters + ---------- + N : int + Number of rows in the output. + M : int, optional + Number of columns in the output. If None, defaults to `N`. + k : int, optional + Index of the diagonal: 0 (the default) refers to the main diagonal, + a positive value refers to an upper diagonal, and a negative value + to a lower diagonal. + dtype : data-type, optional + Data-type of the returned array. + order : {'C', 'F'}, optional + Whether the output should be stored in row-major (C-style) or + column-major (Fortran-style) order in memory. + device : str, optional + The device on which to place the created array. Default: None. + For Array-API interoperability only, so must be ``"cpu"`` if passed. + + .. versionadded:: 2.0.0 + ${ARRAY_FUNCTION_LIKE} + + .. versionadded:: 1.20.0 + + Returns + ------- + I : ndarray of shape (N,M) + An array where all elements are equal to zero, except for the `k`-th + diagonal, whose values are equal to one. + + See Also + -------- + identity : (almost) equivalent function + diag : diagonal 2-D array from a 1-D array specified by the user. + + Examples + -------- + >>> import numpy as np + >>> np.eye(2, dtype=int) + array([[1, 0], + [0, 1]]) + >>> np.eye(3, k=1) + array([[0., 1., 0.], + [0., 0., 1.], + [0., 0., 0.]]) + + """ + if like is not None: + return _eye_with_like( + like, N, M=M, k=k, dtype=dtype, order=order, device=device + ) + if M is None: + M = N + m = zeros((N, M), dtype=dtype, order=order, device=device) + if k >= M: + return m + # Ensure M and k are integers, so we don't get any surprise casting + # results in the expressions `M-k` and `M+1` used below. This avoids + # a problem with inputs with type (for example) np.uint64. + M = operator.index(M) + k = operator.index(k) + if k >= 0: + i = k + else: + i = (-k) * M + m[:M-k].flat[i::M+1] = 1 + return m + + +_eye_with_like = array_function_dispatch()(eye) + + +def _diag_dispatcher(v, k=None): + return (v,) + + +@array_function_dispatch(_diag_dispatcher) +def diag(v, k=0): + """ + Extract a diagonal or construct a diagonal array. + + See the more detailed documentation for ``numpy.diagonal`` if you use this + function to extract a diagonal and wish to write to the resulting array; + whether it returns a copy or a view depends on what version of numpy you + are using. + + Parameters + ---------- + v : array_like + If `v` is a 2-D array, return a copy of its `k`-th diagonal. + If `v` is a 1-D array, return a 2-D array with `v` on the `k`-th + diagonal. + k : int, optional + Diagonal in question. The default is 0. Use `k>0` for diagonals + above the main diagonal, and `k<0` for diagonals below the main + diagonal. + + Returns + ------- + out : ndarray + The extracted diagonal or constructed diagonal array. + + See Also + -------- + diagonal : Return specified diagonals. + diagflat : Create a 2-D array with the flattened input as a diagonal. + trace : Sum along diagonals. + triu : Upper triangle of an array. + tril : Lower triangle of an array. + + Examples + -------- + >>> import numpy as np + >>> x = np.arange(9).reshape((3,3)) + >>> x + array([[0, 1, 2], + [3, 4, 5], + [6, 7, 8]]) + + >>> np.diag(x) + array([0, 4, 8]) + >>> np.diag(x, k=1) + array([1, 5]) + >>> np.diag(x, k=-1) + array([3, 7]) + + >>> np.diag(np.diag(x)) + array([[0, 0, 0], + [0, 4, 0], + [0, 0, 8]]) + + """ + v = asanyarray(v) + s = v.shape + if len(s) == 1: + n = s[0]+abs(k) + res = zeros((n, n), v.dtype) + if k >= 0: + i = k + else: + i = (-k) * n + res[:n-k].flat[i::n+1] = v + return res + elif len(s) == 2: + return diagonal(v, k) + else: + raise ValueError("Input must be 1- or 2-d.") + + +@array_function_dispatch(_diag_dispatcher) +def diagflat(v, k=0): + """ + Create a two-dimensional array with the flattened input as a diagonal. + + Parameters + ---------- + v : array_like + Input data, which is flattened and set as the `k`-th + diagonal of the output. + k : int, optional + Diagonal to set; 0, the default, corresponds to the "main" diagonal, + a positive (negative) `k` giving the number of the diagonal above + (below) the main. + + Returns + ------- + out : ndarray + The 2-D output array. + + See Also + -------- + diag : MATLAB work-alike for 1-D and 2-D arrays. + diagonal : Return specified diagonals. + trace : Sum along diagonals. + + Examples + -------- + >>> import numpy as np + >>> np.diagflat([[1,2], [3,4]]) + array([[1, 0, 0, 0], + [0, 2, 0, 0], + [0, 0, 3, 0], + [0, 0, 0, 4]]) + + >>> np.diagflat([1,2], 1) + array([[0, 1, 0], + [0, 0, 2], + [0, 0, 0]]) + + """ + conv = _array_converter(v) + v, = conv.as_arrays(subok=False) + v = v.ravel() + s = len(v) + n = s + abs(k) + res = zeros((n, n), v.dtype) + if (k >= 0): + i = arange(0, n-k, dtype=intp) + fi = i+k+i*n + else: + i = arange(0, n+k, dtype=intp) + fi = i+(i-k)*n + res.flat[fi] = v + + return conv.wrap(res) + + +@finalize_array_function_like +@set_module('numpy') +def tri(N, M=None, k=0, dtype=float, *, like=None): + """ + An array with ones at and below the given diagonal and zeros elsewhere. + + Parameters + ---------- + N : int + Number of rows in the array. + M : int, optional + Number of columns in the array. + By default, `M` is taken equal to `N`. + k : int, optional + The sub-diagonal at and below which the array is filled. + `k` = 0 is the main diagonal, while `k` < 0 is below it, + and `k` > 0 is above. The default is 0. + dtype : dtype, optional + Data type of the returned array. The default is float. + ${ARRAY_FUNCTION_LIKE} + + .. versionadded:: 1.20.0 + + Returns + ------- + tri : ndarray of shape (N, M) + Array with its lower triangle filled with ones and zero elsewhere; + in other words ``T[i,j] == 1`` for ``j <= i + k``, 0 otherwise. + + Examples + -------- + >>> import numpy as np + >>> np.tri(3, 5, 2, dtype=int) + array([[1, 1, 1, 0, 0], + [1, 1, 1, 1, 0], + [1, 1, 1, 1, 1]]) + + >>> np.tri(3, 5, -1) + array([[0., 0., 0., 0., 0.], + [1., 0., 0., 0., 0.], + [1., 1., 0., 0., 0.]]) + + """ + if like is not None: + return _tri_with_like(like, N, M=M, k=k, dtype=dtype) + + if M is None: + M = N + + m = greater_equal.outer(arange(N, dtype=_min_int(0, N)), + arange(-k, M-k, dtype=_min_int(-k, M - k))) + + # Avoid making a copy if the requested type is already bool + m = m.astype(dtype, copy=False) + + return m + + +_tri_with_like = array_function_dispatch()(tri) + + +def _trilu_dispatcher(m, k=None): + return (m,) + + +@array_function_dispatch(_trilu_dispatcher) +def tril(m, k=0): + """ + Lower triangle of an array. + + Return a copy of an array with elements above the `k`-th diagonal zeroed. + For arrays with ``ndim`` exceeding 2, `tril` will apply to the final two + axes. + + Parameters + ---------- + m : array_like, shape (..., M, N) + Input array. + k : int, optional + Diagonal above which to zero elements. `k = 0` (the default) is the + main diagonal, `k < 0` is below it and `k > 0` is above. + + Returns + ------- + tril : ndarray, shape (..., M, N) + Lower triangle of `m`, of same shape and data-type as `m`. + + See Also + -------- + triu : same thing, only for the upper triangle + + Examples + -------- + >>> import numpy as np + >>> np.tril([[1,2,3],[4,5,6],[7,8,9],[10,11,12]], -1) + array([[ 0, 0, 0], + [ 4, 0, 0], + [ 7, 8, 0], + [10, 11, 12]]) + + >>> np.tril(np.arange(3*4*5).reshape(3, 4, 5)) + array([[[ 0, 0, 0, 0, 0], + [ 5, 6, 0, 0, 0], + [10, 11, 12, 0, 0], + [15, 16, 17, 18, 0]], + [[20, 0, 0, 0, 0], + [25, 26, 0, 0, 0], + [30, 31, 32, 0, 0], + [35, 36, 37, 38, 0]], + [[40, 0, 0, 0, 0], + [45, 46, 0, 0, 0], + [50, 51, 52, 0, 0], + [55, 56, 57, 58, 0]]]) + + """ + m = asanyarray(m) + mask = tri(*m.shape[-2:], k=k, dtype=bool) + + return where(mask, m, zeros(1, m.dtype)) + + +@array_function_dispatch(_trilu_dispatcher) +def triu(m, k=0): + """ + Upper triangle of an array. + + Return a copy of an array with the elements below the `k`-th diagonal + zeroed. For arrays with ``ndim`` exceeding 2, `triu` will apply to the + final two axes. + + Please refer to the documentation for `tril` for further details. + + See Also + -------- + tril : lower triangle of an array + + Examples + -------- + >>> import numpy as np + >>> np.triu([[1,2,3],[4,5,6],[7,8,9],[10,11,12]], -1) + array([[ 1, 2, 3], + [ 4, 5, 6], + [ 0, 8, 9], + [ 0, 0, 12]]) + + >>> np.triu(np.arange(3*4*5).reshape(3, 4, 5)) + array([[[ 0, 1, 2, 3, 4], + [ 0, 6, 7, 8, 9], + [ 0, 0, 12, 13, 14], + [ 0, 0, 0, 18, 19]], + [[20, 21, 22, 23, 24], + [ 0, 26, 27, 28, 29], + [ 0, 0, 32, 33, 34], + [ 0, 0, 0, 38, 39]], + [[40, 41, 42, 43, 44], + [ 0, 46, 47, 48, 49], + [ 0, 0, 52, 53, 54], + [ 0, 0, 0, 58, 59]]]) + + """ + m = asanyarray(m) + mask = tri(*m.shape[-2:], k=k-1, dtype=bool) + + return where(mask, zeros(1, m.dtype), m) + + +def _vander_dispatcher(x, N=None, increasing=None): + return (x,) + + +# Originally borrowed from John Hunter and matplotlib +@array_function_dispatch(_vander_dispatcher) +def vander(x, N=None, increasing=False): + """ + Generate a Vandermonde matrix. + + The columns of the output matrix are powers of the input vector. The + order of the powers is determined by the `increasing` boolean argument. + Specifically, when `increasing` is False, the `i`-th output column is + the input vector raised element-wise to the power of ``N - i - 1``. Such + a matrix with a geometric progression in each row is named for Alexandre- + Theophile Vandermonde. + + Parameters + ---------- + x : array_like + 1-D input array. + N : int, optional + Number of columns in the output. If `N` is not specified, a square + array is returned (``N = len(x)``). + increasing : bool, optional + Order of the powers of the columns. If True, the powers increase + from left to right, if False (the default) they are reversed. + + Returns + ------- + out : ndarray + Vandermonde matrix. If `increasing` is False, the first column is + ``x^(N-1)``, the second ``x^(N-2)`` and so forth. If `increasing` is + True, the columns are ``x^0, x^1, ..., x^(N-1)``. + + See Also + -------- + polynomial.polynomial.polyvander + + Examples + -------- + >>> import numpy as np + >>> x = np.array([1, 2, 3, 5]) + >>> N = 3 + >>> np.vander(x, N) + array([[ 1, 1, 1], + [ 4, 2, 1], + [ 9, 3, 1], + [25, 5, 1]]) + + >>> np.column_stack([x**(N-1-i) for i in range(N)]) + array([[ 1, 1, 1], + [ 4, 2, 1], + [ 9, 3, 1], + [25, 5, 1]]) + + >>> x = np.array([1, 2, 3, 5]) + >>> np.vander(x) + array([[ 1, 1, 1, 1], + [ 8, 4, 2, 1], + [ 27, 9, 3, 1], + [125, 25, 5, 1]]) + >>> np.vander(x, increasing=True) + array([[ 1, 1, 1, 1], + [ 1, 2, 4, 8], + [ 1, 3, 9, 27], + [ 1, 5, 25, 125]]) + + The determinant of a square Vandermonde matrix is the product + of the differences between the values of the input vector: + + >>> np.linalg.det(np.vander(x)) + 48.000000000000043 # may vary + >>> (5-3)*(5-2)*(5-1)*(3-2)*(3-1)*(2-1) + 48 + + """ + x = asarray(x) + if x.ndim != 1: + raise ValueError("x must be a one-dimensional array or sequence.") + if N is None: + N = len(x) + + v = empty((len(x), N), dtype=promote_types(x.dtype, int)) + tmp = v[:, ::-1] if not increasing else v + + if N > 0: + tmp[:, 0] = 1 + if N > 1: + tmp[:, 1:] = x[:, None] + multiply.accumulate(tmp[:, 1:], out=tmp[:, 1:], axis=1) + + return v + + +def _histogram2d_dispatcher(x, y, bins=None, range=None, density=None, + weights=None): + yield x + yield y + + # This terrible logic is adapted from the checks in histogram2d + try: + N = len(bins) + except TypeError: + N = 1 + if N == 2: + yield from bins # bins=[x, y] + else: + yield bins + + yield weights + + +@array_function_dispatch(_histogram2d_dispatcher) +def histogram2d(x, y, bins=10, range=None, density=None, weights=None): + """ + Compute the bi-dimensional histogram of two data samples. + + Parameters + ---------- + x : array_like, shape (N,) + An array containing the x coordinates of the points to be + histogrammed. + y : array_like, shape (N,) + An array containing the y coordinates of the points to be + histogrammed. + bins : int or array_like or [int, int] or [array, array], optional + The bin specification: + + * If int, the number of bins for the two dimensions (nx=ny=bins). + * If array_like, the bin edges for the two dimensions + (x_edges=y_edges=bins). + * If [int, int], the number of bins in each dimension + (nx, ny = bins). + * If [array, array], the bin edges in each dimension + (x_edges, y_edges = bins). + * A combination [int, array] or [array, int], where int + is the number of bins and array is the bin edges. + + range : array_like, shape(2,2), optional + The leftmost and rightmost edges of the bins along each dimension + (if not specified explicitly in the `bins` parameters): + ``[[xmin, xmax], [ymin, ymax]]``. All values outside of this range + will be considered outliers and not tallied in the histogram. + density : bool, optional + If False, the default, returns the number of samples in each bin. + If True, returns the probability *density* function at the bin, + ``bin_count / sample_count / bin_area``. + weights : array_like, shape(N,), optional + An array of values ``w_i`` weighing each sample ``(x_i, y_i)``. + Weights are normalized to 1 if `density` is True. If `density` is + False, the values of the returned histogram are equal to the sum of + the weights belonging to the samples falling into each bin. + + Returns + ------- + H : ndarray, shape(nx, ny) + The bi-dimensional histogram of samples `x` and `y`. Values in `x` + are histogrammed along the first dimension and values in `y` are + histogrammed along the second dimension. + xedges : ndarray, shape(nx+1,) + The bin edges along the first dimension. + yedges : ndarray, shape(ny+1,) + The bin edges along the second dimension. + + See Also + -------- + histogram : 1D histogram + histogramdd : Multidimensional histogram + + Notes + ----- + When `density` is True, then the returned histogram is the sample + density, defined such that the sum over bins of the product + ``bin_value * bin_area`` is 1. + + Please note that the histogram does not follow the Cartesian convention + where `x` values are on the abscissa and `y` values on the ordinate + axis. Rather, `x` is histogrammed along the first dimension of the + array (vertical), and `y` along the second dimension of the array + (horizontal). This ensures compatibility with `histogramdd`. + + Examples + -------- + >>> import numpy as np + >>> from matplotlib.image import NonUniformImage + >>> import matplotlib.pyplot as plt + + Construct a 2-D histogram with variable bin width. First define the bin + edges: + + >>> xedges = [0, 1, 3, 5] + >>> yedges = [0, 2, 3, 4, 6] + + Next we create a histogram H with random bin content: + + >>> x = np.random.normal(2, 1, 100) + >>> y = np.random.normal(1, 1, 100) + >>> H, xedges, yedges = np.histogram2d(x, y, bins=(xedges, yedges)) + >>> # Histogram does not follow Cartesian convention (see Notes), + >>> # therefore transpose H for visualization purposes. + >>> H = H.T + + :func:`imshow ` can only display square bins: + + >>> fig = plt.figure(figsize=(7, 3)) + >>> ax = fig.add_subplot(131, title='imshow: square bins') + >>> plt.imshow(H, interpolation='nearest', origin='lower', + ... extent=[xedges[0], xedges[-1], yedges[0], yedges[-1]]) + + + :func:`pcolormesh ` can display actual edges: + + >>> ax = fig.add_subplot(132, title='pcolormesh: actual edges', + ... aspect='equal') + >>> X, Y = np.meshgrid(xedges, yedges) + >>> ax.pcolormesh(X, Y, H) + + + :class:`NonUniformImage ` can be used to + display actual bin edges with interpolation: + + >>> ax = fig.add_subplot(133, title='NonUniformImage: interpolated', + ... aspect='equal', xlim=xedges[[0, -1]], ylim=yedges[[0, -1]]) + >>> im = NonUniformImage(ax, interpolation='bilinear') + >>> xcenters = (xedges[:-1] + xedges[1:]) / 2 + >>> ycenters = (yedges[:-1] + yedges[1:]) / 2 + >>> im.set_data(xcenters, ycenters, H) + >>> ax.add_image(im) + >>> plt.show() + + It is also possible to construct a 2-D histogram without specifying bin + edges: + + >>> # Generate non-symmetric test data + >>> n = 10000 + >>> x = np.linspace(1, 100, n) + >>> y = 2*np.log(x) + np.random.rand(n) - 0.5 + >>> # Compute 2d histogram. Note the order of x/y and xedges/yedges + >>> H, yedges, xedges = np.histogram2d(y, x, bins=20) + + Now we can plot the histogram using + :func:`pcolormesh `, and a + :func:`hexbin ` for comparison. + + >>> # Plot histogram using pcolormesh + >>> fig, (ax1, ax2) = plt.subplots(ncols=2, sharey=True) + >>> ax1.pcolormesh(xedges, yedges, H, cmap='rainbow') + >>> ax1.plot(x, 2*np.log(x), 'k-') + >>> ax1.set_xlim(x.min(), x.max()) + >>> ax1.set_ylim(y.min(), y.max()) + >>> ax1.set_xlabel('x') + >>> ax1.set_ylabel('y') + >>> ax1.set_title('histogram2d') + >>> ax1.grid() + + >>> # Create hexbin plot for comparison + >>> ax2.hexbin(x, y, gridsize=20, cmap='rainbow') + >>> ax2.plot(x, 2*np.log(x), 'k-') + >>> ax2.set_title('hexbin') + >>> ax2.set_xlim(x.min(), x.max()) + >>> ax2.set_xlabel('x') + >>> ax2.grid() + + >>> plt.show() + """ + from numpy import histogramdd + + if len(x) != len(y): + raise ValueError('x and y must have the same length.') + + try: + N = len(bins) + except TypeError: + N = 1 + + if N != 1 and N != 2: + xedges = yedges = asarray(bins) + bins = [xedges, yedges] + hist, edges = histogramdd([x, y], bins, range, density, weights) + return hist, edges[0], edges[1] + + +@set_module('numpy') +def mask_indices(n, mask_func, k=0): + """ + Return the indices to access (n, n) arrays, given a masking function. + + Assume `mask_func` is a function that, for a square array a of size + ``(n, n)`` with a possible offset argument `k`, when called as + ``mask_func(a, k)`` returns a new array with zeros in certain locations + (functions like `triu` or `tril` do precisely this). Then this function + returns the indices where the non-zero values would be located. + + Parameters + ---------- + n : int + The returned indices will be valid to access arrays of shape (n, n). + mask_func : callable + A function whose call signature is similar to that of `triu`, `tril`. + That is, ``mask_func(x, k)`` returns a boolean array, shaped like `x`. + `k` is an optional argument to the function. + k : scalar + An optional argument which is passed through to `mask_func`. Functions + like `triu`, `tril` take a second argument that is interpreted as an + offset. + + Returns + ------- + indices : tuple of arrays. + The `n` arrays of indices corresponding to the locations where + ``mask_func(np.ones((n, n)), k)`` is True. + + See Also + -------- + triu, tril, triu_indices, tril_indices + + Examples + -------- + >>> import numpy as np + + These are the indices that would allow you to access the upper triangular + part of any 3x3 array: + + >>> iu = np.mask_indices(3, np.triu) + + For example, if `a` is a 3x3 array: + + >>> a = np.arange(9).reshape(3, 3) + >>> a + array([[0, 1, 2], + [3, 4, 5], + [6, 7, 8]]) + >>> a[iu] + array([0, 1, 2, 4, 5, 8]) + + An offset can be passed also to the masking function. This gets us the + indices starting on the first diagonal right of the main one: + + >>> iu1 = np.mask_indices(3, np.triu, 1) + + with which we now extract only three elements: + + >>> a[iu1] + array([1, 2, 5]) + + """ + m = ones((n, n), int) + a = mask_func(m, k) + return nonzero(a != 0) + + +@set_module('numpy') +def tril_indices(n, k=0, m=None): + """ + Return the indices for the lower-triangle of an (n, m) array. + + Parameters + ---------- + n : int + The row dimension of the arrays for which the returned + indices will be valid. + k : int, optional + Diagonal offset (see `tril` for details). + m : int, optional + The column dimension of the arrays for which the returned + arrays will be valid. + By default `m` is taken equal to `n`. + + + Returns + ------- + inds : tuple of arrays + The row and column indices, respectively. The row indices are sorted + in non-decreasing order, and the correspdonding column indices are + strictly increasing for each row. + + See also + -------- + triu_indices : similar function, for upper-triangular. + mask_indices : generic function accepting an arbitrary mask function. + tril, triu + + Examples + -------- + >>> import numpy as np + + Compute two different sets of indices to access 4x4 arrays, one for the + lower triangular part starting at the main diagonal, and one starting two + diagonals further right: + + >>> il1 = np.tril_indices(4) + >>> il1 + (array([0, 1, 1, 2, 2, 2, 3, 3, 3, 3]), array([0, 0, 1, 0, 1, 2, 0, 1, 2, 3])) + + Note that row indices (first array) are non-decreasing, and the corresponding + column indices (second array) are strictly increasing for each row. + Here is how they can be used with a sample array: + + >>> a = np.arange(16).reshape(4, 4) + >>> a + array([[ 0, 1, 2, 3], + [ 4, 5, 6, 7], + [ 8, 9, 10, 11], + [12, 13, 14, 15]]) + + Both for indexing: + + >>> a[il1] + array([ 0, 4, 5, ..., 13, 14, 15]) + + And for assigning values: + + >>> a[il1] = -1 + >>> a + array([[-1, 1, 2, 3], + [-1, -1, 6, 7], + [-1, -1, -1, 11], + [-1, -1, -1, -1]]) + + These cover almost the whole array (two diagonals right of the main one): + + >>> il2 = np.tril_indices(4, 2) + >>> a[il2] = -10 + >>> a + array([[-10, -10, -10, 3], + [-10, -10, -10, -10], + [-10, -10, -10, -10], + [-10, -10, -10, -10]]) + + """ + tri_ = tri(n, m, k=k, dtype=bool) + + return tuple(broadcast_to(inds, tri_.shape)[tri_] + for inds in indices(tri_.shape, sparse=True)) + + +def _trilu_indices_form_dispatcher(arr, k=None): + return (arr,) + + +@array_function_dispatch(_trilu_indices_form_dispatcher) +def tril_indices_from(arr, k=0): + """ + Return the indices for the lower-triangle of arr. + + See `tril_indices` for full details. + + Parameters + ---------- + arr : array_like + The indices will be valid for square arrays whose dimensions are + the same as arr. + k : int, optional + Diagonal offset (see `tril` for details). + + Examples + -------- + >>> import numpy as np + + Create a 4 by 4 array + + >>> a = np.arange(16).reshape(4, 4) + >>> a + array([[ 0, 1, 2, 3], + [ 4, 5, 6, 7], + [ 8, 9, 10, 11], + [12, 13, 14, 15]]) + + Pass the array to get the indices of the lower triangular elements. + + >>> trili = np.tril_indices_from(a) + >>> trili + (array([0, 1, 1, 2, 2, 2, 3, 3, 3, 3]), array([0, 0, 1, 0, 1, 2, 0, 1, 2, 3])) + + >>> a[trili] + array([ 0, 4, 5, 8, 9, 10, 12, 13, 14, 15]) + + This is syntactic sugar for tril_indices(). + + >>> np.tril_indices(a.shape[0]) + (array([0, 1, 1, 2, 2, 2, 3, 3, 3, 3]), array([0, 0, 1, 0, 1, 2, 0, 1, 2, 3])) + + Use the `k` parameter to return the indices for the lower triangular array + up to the k-th diagonal. + + >>> trili1 = np.tril_indices_from(a, k=1) + >>> a[trili1] + array([ 0, 1, 4, 5, 6, 8, 9, 10, 11, 12, 13, 14, 15]) + + See Also + -------- + tril_indices, tril, triu_indices_from + """ + if arr.ndim != 2: + raise ValueError("input array must be 2-d") + return tril_indices(arr.shape[-2], k=k, m=arr.shape[-1]) + + +@set_module('numpy') +def triu_indices(n, k=0, m=None): + """ + Return the indices for the upper-triangle of an (n, m) array. + + Parameters + ---------- + n : int + The size of the arrays for which the returned indices will + be valid. + k : int, optional + Diagonal offset (see `triu` for details). + m : int, optional + The column dimension of the arrays for which the returned + arrays will be valid. + By default `m` is taken equal to `n`. + + + Returns + ------- + inds : tuple, shape(2) of ndarrays, shape(`n`) + The row and column indices, respectively. The row indices are sorted + in non-decreasing order, and the correspdonding column indices are + strictly increasing for each row. + + See also + -------- + tril_indices : similar function, for lower-triangular. + mask_indices : generic function accepting an arbitrary mask function. + triu, tril + + Examples + -------- + >>> import numpy as np + + Compute two different sets of indices to access 4x4 arrays, one for the + upper triangular part starting at the main diagonal, and one starting two + diagonals further right: + + >>> iu1 = np.triu_indices(4) + >>> iu1 + (array([0, 0, 0, 0, 1, 1, 1, 2, 2, 3]), array([0, 1, 2, 3, 1, 2, 3, 2, 3, 3])) + + Note that row indices (first array) are non-decreasing, and the corresponding + column indices (second array) are strictly increasing for each row. + + Here is how they can be used with a sample array: + + >>> a = np.arange(16).reshape(4, 4) + >>> a + array([[ 0, 1, 2, 3], + [ 4, 5, 6, 7], + [ 8, 9, 10, 11], + [12, 13, 14, 15]]) + + Both for indexing: + + >>> a[iu1] + array([ 0, 1, 2, ..., 10, 11, 15]) + + And for assigning values: + + >>> a[iu1] = -1 + >>> a + array([[-1, -1, -1, -1], + [ 4, -1, -1, -1], + [ 8, 9, -1, -1], + [12, 13, 14, -1]]) + + These cover only a small part of the whole array (two diagonals right + of the main one): + + >>> iu2 = np.triu_indices(4, 2) + >>> a[iu2] = -10 + >>> a + array([[ -1, -1, -10, -10], + [ 4, -1, -1, -10], + [ 8, 9, -1, -1], + [ 12, 13, 14, -1]]) + + """ + tri_ = ~tri(n, m, k=k - 1, dtype=bool) + + return tuple(broadcast_to(inds, tri_.shape)[tri_] + for inds in indices(tri_.shape, sparse=True)) + + +@array_function_dispatch(_trilu_indices_form_dispatcher) +def triu_indices_from(arr, k=0): + """ + Return the indices for the upper-triangle of arr. + + See `triu_indices` for full details. + + Parameters + ---------- + arr : ndarray, shape(N, N) + The indices will be valid for square arrays. + k : int, optional + Diagonal offset (see `triu` for details). + + Returns + ------- + triu_indices_from : tuple, shape(2) of ndarray, shape(N) + Indices for the upper-triangle of `arr`. + + Examples + -------- + >>> import numpy as np + + Create a 4 by 4 array + + >>> a = np.arange(16).reshape(4, 4) + >>> a + array([[ 0, 1, 2, 3], + [ 4, 5, 6, 7], + [ 8, 9, 10, 11], + [12, 13, 14, 15]]) + + Pass the array to get the indices of the upper triangular elements. + + >>> triui = np.triu_indices_from(a) + >>> triui + (array([0, 0, 0, 0, 1, 1, 1, 2, 2, 3]), array([0, 1, 2, 3, 1, 2, 3, 2, 3, 3])) + + >>> a[triui] + array([ 0, 1, 2, 3, 5, 6, 7, 10, 11, 15]) + + This is syntactic sugar for triu_indices(). + + >>> np.triu_indices(a.shape[0]) + (array([0, 0, 0, 0, 1, 1, 1, 2, 2, 3]), array([0, 1, 2, 3, 1, 2, 3, 2, 3, 3])) + + Use the `k` parameter to return the indices for the upper triangular array + from the k-th diagonal. + + >>> triuim1 = np.triu_indices_from(a, k=1) + >>> a[triuim1] + array([ 1, 2, 3, 6, 7, 11]) + + + See Also + -------- + triu_indices, triu, tril_indices_from + """ + if arr.ndim != 2: + raise ValueError("input array must be 2-d") + return triu_indices(arr.shape[-2], k=k, m=arr.shape[-1]) diff --git a/janus/lib/python3.10/site-packages/numpy/lib/_type_check_impl.py b/janus/lib/python3.10/site-packages/numpy/lib/_type_check_impl.py new file mode 100644 index 0000000000000000000000000000000000000000..e5c9ffbbb8d41049c17f65d56f5afe23a5d5bbcd --- /dev/null +++ b/janus/lib/python3.10/site-packages/numpy/lib/_type_check_impl.py @@ -0,0 +1,699 @@ +"""Automatically adapted for numpy Sep 19, 2005 by convertcode.py + +""" +import functools + +__all__ = ['iscomplexobj', 'isrealobj', 'imag', 'iscomplex', + 'isreal', 'nan_to_num', 'real', 'real_if_close', + 'typename', 'mintypecode', + 'common_type'] + +from .._utils import set_module +import numpy._core.numeric as _nx +from numpy._core.numeric import asarray, asanyarray, isnan, zeros +from numpy._core import overrides, getlimits +from ._ufunclike_impl import isneginf, isposinf + + +array_function_dispatch = functools.partial( + overrides.array_function_dispatch, module='numpy') + + +_typecodes_by_elsize = 'GDFgdfQqLlIiHhBb?' + + +@set_module('numpy') +def mintypecode(typechars, typeset='GDFgdf', default='d'): + """ + Return the character for the minimum-size type to which given types can + be safely cast. + + The returned type character must represent the smallest size dtype such + that an array of the returned type can handle the data from an array of + all types in `typechars` (or if `typechars` is an array, then its + dtype.char). + + Parameters + ---------- + typechars : list of str or array_like + If a list of strings, each string should represent a dtype. + If array_like, the character representation of the array dtype is used. + typeset : str or list of str, optional + The set of characters that the returned character is chosen from. + The default set is 'GDFgdf'. + default : str, optional + The default character, this is returned if none of the characters in + `typechars` matches a character in `typeset`. + + Returns + ------- + typechar : str + The character representing the minimum-size type that was found. + + See Also + -------- + dtype + + Examples + -------- + >>> import numpy as np + >>> np.mintypecode(['d', 'f', 'S']) + 'd' + >>> x = np.array([1.1, 2-3.j]) + >>> np.mintypecode(x) + 'D' + + >>> np.mintypecode('abceh', default='G') + 'G' + + """ + typecodes = ((isinstance(t, str) and t) or asarray(t).dtype.char + for t in typechars) + intersection = set(t for t in typecodes if t in typeset) + if not intersection: + return default + if 'F' in intersection and 'd' in intersection: + return 'D' + return min(intersection, key=_typecodes_by_elsize.index) + + +def _real_dispatcher(val): + return (val,) + + +@array_function_dispatch(_real_dispatcher) +def real(val): + """ + Return the real part of the complex argument. + + Parameters + ---------- + val : array_like + Input array. + + Returns + ------- + out : ndarray or scalar + The real component of the complex argument. If `val` is real, the type + of `val` is used for the output. If `val` has complex elements, the + returned type is float. + + See Also + -------- + real_if_close, imag, angle + + Examples + -------- + >>> import numpy as np + >>> a = np.array([1+2j, 3+4j, 5+6j]) + >>> a.real + array([1., 3., 5.]) + >>> a.real = 9 + >>> a + array([9.+2.j, 9.+4.j, 9.+6.j]) + >>> a.real = np.array([9, 8, 7]) + >>> a + array([9.+2.j, 8.+4.j, 7.+6.j]) + >>> np.real(1 + 1j) + 1.0 + + """ + try: + return val.real + except AttributeError: + return asanyarray(val).real + + +def _imag_dispatcher(val): + return (val,) + + +@array_function_dispatch(_imag_dispatcher) +def imag(val): + """ + Return the imaginary part of the complex argument. + + Parameters + ---------- + val : array_like + Input array. + + Returns + ------- + out : ndarray or scalar + The imaginary component of the complex argument. If `val` is real, + the type of `val` is used for the output. If `val` has complex + elements, the returned type is float. + + See Also + -------- + real, angle, real_if_close + + Examples + -------- + >>> import numpy as np + >>> a = np.array([1+2j, 3+4j, 5+6j]) + >>> a.imag + array([2., 4., 6.]) + >>> a.imag = np.array([8, 10, 12]) + >>> a + array([1. +8.j, 3.+10.j, 5.+12.j]) + >>> np.imag(1 + 1j) + 1.0 + + """ + try: + return val.imag + except AttributeError: + return asanyarray(val).imag + + +def _is_type_dispatcher(x): + return (x,) + + +@array_function_dispatch(_is_type_dispatcher) +def iscomplex(x): + """ + Returns a bool array, where True if input element is complex. + + What is tested is whether the input has a non-zero imaginary part, not if + the input type is complex. + + Parameters + ---------- + x : array_like + Input array. + + Returns + ------- + out : ndarray of bools + Output array. + + See Also + -------- + isreal + iscomplexobj : Return True if x is a complex type or an array of complex + numbers. + + Examples + -------- + >>> import numpy as np + >>> np.iscomplex([1+1j, 1+0j, 4.5, 3, 2, 2j]) + array([ True, False, False, False, False, True]) + + """ + ax = asanyarray(x) + if issubclass(ax.dtype.type, _nx.complexfloating): + return ax.imag != 0 + res = zeros(ax.shape, bool) + return res[()] # convert to scalar if needed + + +@array_function_dispatch(_is_type_dispatcher) +def isreal(x): + """ + Returns a bool array, where True if input element is real. + + If element has complex type with zero imaginary part, the return value + for that element is True. + + Parameters + ---------- + x : array_like + Input array. + + Returns + ------- + out : ndarray, bool + Boolean array of same shape as `x`. + + Notes + ----- + `isreal` may behave unexpectedly for string or object arrays (see examples) + + See Also + -------- + iscomplex + isrealobj : Return True if x is not a complex type. + + Examples + -------- + >>> import numpy as np + >>> a = np.array([1+1j, 1+0j, 4.5, 3, 2, 2j], dtype=complex) + >>> np.isreal(a) + array([False, True, True, True, True, False]) + + The function does not work on string arrays. + + >>> a = np.array([2j, "a"], dtype="U") + >>> np.isreal(a) # Warns about non-elementwise comparison + False + + Returns True for all elements in input array of ``dtype=object`` even if + any of the elements is complex. + + >>> a = np.array([1, "2", 3+4j], dtype=object) + >>> np.isreal(a) + array([ True, True, True]) + + isreal should not be used with object arrays + + >>> a = np.array([1+2j, 2+1j], dtype=object) + >>> np.isreal(a) + array([ True, True]) + + """ + return imag(x) == 0 + + +@array_function_dispatch(_is_type_dispatcher) +def iscomplexobj(x): + """ + Check for a complex type or an array of complex numbers. + + The type of the input is checked, not the value. Even if the input + has an imaginary part equal to zero, `iscomplexobj` evaluates to True. + + Parameters + ---------- + x : any + The input can be of any type and shape. + + Returns + ------- + iscomplexobj : bool + The return value, True if `x` is of a complex type or has at least + one complex element. + + See Also + -------- + isrealobj, iscomplex + + Examples + -------- + >>> import numpy as np + >>> np.iscomplexobj(1) + False + >>> np.iscomplexobj(1+0j) + True + >>> np.iscomplexobj([3, 1+0j, True]) + True + + """ + try: + dtype = x.dtype + type_ = dtype.type + except AttributeError: + type_ = asarray(x).dtype.type + return issubclass(type_, _nx.complexfloating) + + +@array_function_dispatch(_is_type_dispatcher) +def isrealobj(x): + """ + Return True if x is a not complex type or an array of complex numbers. + + The type of the input is checked, not the value. So even if the input + has an imaginary part equal to zero, `isrealobj` evaluates to False + if the data type is complex. + + Parameters + ---------- + x : any + The input can be of any type and shape. + + Returns + ------- + y : bool + The return value, False if `x` is of a complex type. + + See Also + -------- + iscomplexobj, isreal + + Notes + ----- + The function is only meant for arrays with numerical values but it + accepts all other objects. Since it assumes array input, the return + value of other objects may be True. + + >>> np.isrealobj('A string') + True + >>> np.isrealobj(False) + True + >>> np.isrealobj(None) + True + + Examples + -------- + >>> import numpy as np + >>> np.isrealobj(1) + True + >>> np.isrealobj(1+0j) + False + >>> np.isrealobj([3, 1+0j, True]) + False + + """ + return not iscomplexobj(x) + +#----------------------------------------------------------------------------- + +def _getmaxmin(t): + from numpy._core import getlimits + f = getlimits.finfo(t) + return f.max, f.min + + +def _nan_to_num_dispatcher(x, copy=None, nan=None, posinf=None, neginf=None): + return (x,) + + +@array_function_dispatch(_nan_to_num_dispatcher) +def nan_to_num(x, copy=True, nan=0.0, posinf=None, neginf=None): + """ + Replace NaN with zero and infinity with large finite numbers (default + behaviour) or with the numbers defined by the user using the `nan`, + `posinf` and/or `neginf` keywords. + + If `x` is inexact, NaN is replaced by zero or by the user defined value in + `nan` keyword, infinity is replaced by the largest finite floating point + values representable by ``x.dtype`` or by the user defined value in + `posinf` keyword and -infinity is replaced by the most negative finite + floating point values representable by ``x.dtype`` or by the user defined + value in `neginf` keyword. + + For complex dtypes, the above is applied to each of the real and + imaginary components of `x` separately. + + If `x` is not inexact, then no replacements are made. + + Parameters + ---------- + x : scalar or array_like + Input data. + copy : bool, optional + Whether to create a copy of `x` (True) or to replace values + in-place (False). The in-place operation only occurs if + casting to an array does not require a copy. + Default is True. + nan : int, float, optional + Value to be used to fill NaN values. If no value is passed + then NaN values will be replaced with 0.0. + posinf : int, float, optional + Value to be used to fill positive infinity values. If no value is + passed then positive infinity values will be replaced with a very + large number. + neginf : int, float, optional + Value to be used to fill negative infinity values. If no value is + passed then negative infinity values will be replaced with a very + small (or negative) number. + + Returns + ------- + out : ndarray + `x`, with the non-finite values replaced. If `copy` is False, this may + be `x` itself. + + See Also + -------- + isinf : Shows which elements are positive or negative infinity. + isneginf : Shows which elements are negative infinity. + isposinf : Shows which elements are positive infinity. + isnan : Shows which elements are Not a Number (NaN). + isfinite : Shows which elements are finite (not NaN, not infinity) + + Notes + ----- + NumPy uses the IEEE Standard for Binary Floating-Point for Arithmetic + (IEEE 754). This means that Not a Number is not equivalent to infinity. + + Examples + -------- + >>> import numpy as np + >>> np.nan_to_num(np.inf) + 1.7976931348623157e+308 + >>> np.nan_to_num(-np.inf) + -1.7976931348623157e+308 + >>> np.nan_to_num(np.nan) + 0.0 + >>> x = np.array([np.inf, -np.inf, np.nan, -128, 128]) + >>> np.nan_to_num(x) + array([ 1.79769313e+308, -1.79769313e+308, 0.00000000e+000, # may vary + -1.28000000e+002, 1.28000000e+002]) + >>> np.nan_to_num(x, nan=-9999, posinf=33333333, neginf=33333333) + array([ 3.3333333e+07, 3.3333333e+07, -9.9990000e+03, + -1.2800000e+02, 1.2800000e+02]) + >>> y = np.array([complex(np.inf, np.nan), np.nan, complex(np.nan, np.inf)]) + array([ 1.79769313e+308, -1.79769313e+308, 0.00000000e+000, # may vary + -1.28000000e+002, 1.28000000e+002]) + >>> np.nan_to_num(y) + array([ 1.79769313e+308 +0.00000000e+000j, # may vary + 0.00000000e+000 +0.00000000e+000j, + 0.00000000e+000 +1.79769313e+308j]) + >>> np.nan_to_num(y, nan=111111, posinf=222222) + array([222222.+111111.j, 111111. +0.j, 111111.+222222.j]) + """ + x = _nx.array(x, subok=True, copy=copy) + xtype = x.dtype.type + + isscalar = (x.ndim == 0) + + if not issubclass(xtype, _nx.inexact): + return x[()] if isscalar else x + + iscomplex = issubclass(xtype, _nx.complexfloating) + + dest = (x.real, x.imag) if iscomplex else (x,) + maxf, minf = _getmaxmin(x.real.dtype) + if posinf is not None: + maxf = posinf + if neginf is not None: + minf = neginf + for d in dest: + idx_nan = isnan(d) + idx_posinf = isposinf(d) + idx_neginf = isneginf(d) + _nx.copyto(d, nan, where=idx_nan) + _nx.copyto(d, maxf, where=idx_posinf) + _nx.copyto(d, minf, where=idx_neginf) + return x[()] if isscalar else x + +#----------------------------------------------------------------------------- + +def _real_if_close_dispatcher(a, tol=None): + return (a,) + + +@array_function_dispatch(_real_if_close_dispatcher) +def real_if_close(a, tol=100): + """ + If input is complex with all imaginary parts close to zero, return + real parts. + + "Close to zero" is defined as `tol` * (machine epsilon of the type for + `a`). + + Parameters + ---------- + a : array_like + Input array. + tol : float + Tolerance in machine epsilons for the complex part of the elements + in the array. If the tolerance is <=1, then the absolute tolerance + is used. + + Returns + ------- + out : ndarray + If `a` is real, the type of `a` is used for the output. If `a` + has complex elements, the returned type is float. + + See Also + -------- + real, imag, angle + + Notes + ----- + Machine epsilon varies from machine to machine and between data types + but Python floats on most platforms have a machine epsilon equal to + 2.2204460492503131e-16. You can use 'np.finfo(float).eps' to print + out the machine epsilon for floats. + + Examples + -------- + >>> import numpy as np + >>> np.finfo(float).eps + 2.2204460492503131e-16 # may vary + + >>> np.real_if_close([2.1 + 4e-14j, 5.2 + 3e-15j], tol=1000) + array([2.1, 5.2]) + >>> np.real_if_close([2.1 + 4e-13j, 5.2 + 3e-15j], tol=1000) + array([2.1+4.e-13j, 5.2 + 3e-15j]) + + """ + a = asanyarray(a) + type_ = a.dtype.type + if not issubclass(type_, _nx.complexfloating): + return a + if tol > 1: + f = getlimits.finfo(type_) + tol = f.eps * tol + if _nx.all(_nx.absolute(a.imag) < tol): + a = a.real + return a + + +#----------------------------------------------------------------------------- + +_namefromtype = {'S1': 'character', + '?': 'bool', + 'b': 'signed char', + 'B': 'unsigned char', + 'h': 'short', + 'H': 'unsigned short', + 'i': 'integer', + 'I': 'unsigned integer', + 'l': 'long integer', + 'L': 'unsigned long integer', + 'q': 'long long integer', + 'Q': 'unsigned long long integer', + 'f': 'single precision', + 'd': 'double precision', + 'g': 'long precision', + 'F': 'complex single precision', + 'D': 'complex double precision', + 'G': 'complex long double precision', + 'S': 'string', + 'U': 'unicode', + 'V': 'void', + 'O': 'object' + } + +@set_module('numpy') +def typename(char): + """ + Return a description for the given data type code. + + Parameters + ---------- + char : str + Data type code. + + Returns + ------- + out : str + Description of the input data type code. + + See Also + -------- + dtype + + Examples + -------- + >>> import numpy as np + >>> typechars = ['S1', '?', 'B', 'D', 'G', 'F', 'I', 'H', 'L', 'O', 'Q', + ... 'S', 'U', 'V', 'b', 'd', 'g', 'f', 'i', 'h', 'l', 'q'] + >>> for typechar in typechars: + ... print(typechar, ' : ', np.typename(typechar)) + ... + S1 : character + ? : bool + B : unsigned char + D : complex double precision + G : complex long double precision + F : complex single precision + I : unsigned integer + H : unsigned short + L : unsigned long integer + O : object + Q : unsigned long long integer + S : string + U : unicode + V : void + b : signed char + d : double precision + g : long precision + f : single precision + i : integer + h : short + l : long integer + q : long long integer + + """ + return _namefromtype[char] + +#----------------------------------------------------------------------------- + + +#determine the "minimum common type" for a group of arrays. +array_type = [[_nx.float16, _nx.float32, _nx.float64, _nx.longdouble], + [None, _nx.complex64, _nx.complex128, _nx.clongdouble]] +array_precision = {_nx.float16: 0, + _nx.float32: 1, + _nx.float64: 2, + _nx.longdouble: 3, + _nx.complex64: 1, + _nx.complex128: 2, + _nx.clongdouble: 3} + + +def _common_type_dispatcher(*arrays): + return arrays + + +@array_function_dispatch(_common_type_dispatcher) +def common_type(*arrays): + """ + Return a scalar type which is common to the input arrays. + + The return type will always be an inexact (i.e. floating point) scalar + type, even if all the arrays are integer arrays. If one of the inputs is + an integer array, the minimum precision type that is returned is a + 64-bit floating point dtype. + + All input arrays except int64 and uint64 can be safely cast to the + returned dtype without loss of information. + + Parameters + ---------- + array1, array2, ... : ndarrays + Input arrays. + + Returns + ------- + out : data type code + Data type code. + + See Also + -------- + dtype, mintypecode + + Examples + -------- + >>> np.common_type(np.arange(2, dtype=np.float32)) + + >>> np.common_type(np.arange(2, dtype=np.float32), np.arange(2)) + + >>> np.common_type(np.arange(4), np.array([45, 6.j]), np.array([45.0])) + + + """ + is_complex = False + precision = 0 + for a in arrays: + t = a.dtype.type + if iscomplexobj(a): + is_complex = True + if issubclass(t, _nx.integer): + p = 2 # array_precision[_nx.double] + else: + p = array_precision.get(t) + if p is None: + raise TypeError("can't get common type for non-numeric array") + precision = max(precision, p) + if is_complex: + return array_type[1][precision] + else: + return array_type[0][precision] diff --git a/janus/lib/python3.10/site-packages/numpy/lib/_version.pyi b/janus/lib/python3.10/site-packages/numpy/lib/_version.pyi new file mode 100644 index 0000000000000000000000000000000000000000..c53ef795f9266f5233d8c86e696bd8e1f3699557 --- /dev/null +++ b/janus/lib/python3.10/site-packages/numpy/lib/_version.pyi @@ -0,0 +1,17 @@ +__all__ = ["NumpyVersion"] + +class NumpyVersion: + vstring: str + version: str + major: int + minor: int + bugfix: int + pre_release: str + is_devversion: bool + def __init__(self, vstring: str) -> None: ... + def __lt__(self, other: str | NumpyVersion) -> bool: ... + def __le__(self, other: str | NumpyVersion) -> bool: ... + def __eq__(self, other: str | NumpyVersion) -> bool: ... # type: ignore[override] + def __ne__(self, other: str | NumpyVersion) -> bool: ... # type: ignore[override] + def __gt__(self, other: str | NumpyVersion) -> bool: ... + def __ge__(self, other: str | NumpyVersion) -> bool: ... diff --git a/janus/lib/python3.10/site-packages/numpy/lib/array_utils.py b/janus/lib/python3.10/site-packages/numpy/lib/array_utils.py new file mode 100644 index 0000000000000000000000000000000000000000..b4e7976131d268b307d8ac3bb13034d9e425f6e6 --- /dev/null +++ b/janus/lib/python3.10/site-packages/numpy/lib/array_utils.py @@ -0,0 +1,7 @@ +from ._array_utils_impl import ( + __all__, + __doc__, + byte_bounds, + normalize_axis_index, + normalize_axis_tuple, +) diff --git a/janus/lib/python3.10/site-packages/numpy/lib/array_utils.pyi b/janus/lib/python3.10/site-packages/numpy/lib/array_utils.pyi new file mode 100644 index 0000000000000000000000000000000000000000..4b9ebe334a1f211a21aac0cfd1605166ca909d1b --- /dev/null +++ b/janus/lib/python3.10/site-packages/numpy/lib/array_utils.pyi @@ -0,0 +1,6 @@ +from ._array_utils_impl import ( + __all__ as __all__, + byte_bounds as byte_bounds, + normalize_axis_index as normalize_axis_index, + normalize_axis_tuple as normalize_axis_tuple, +) diff --git a/janus/lib/python3.10/site-packages/numpy/lib/format.py b/janus/lib/python3.10/site-packages/numpy/lib/format.py new file mode 100644 index 0000000000000000000000000000000000000000..a22c096b246ce7109ca7b39f7735e4e38ab1e036 --- /dev/null +++ b/janus/lib/python3.10/site-packages/numpy/lib/format.py @@ -0,0 +1,1008 @@ +""" +Binary serialization + +NPY format +========== + +A simple format for saving numpy arrays to disk with the full +information about them. + +The ``.npy`` format is the standard binary file format in NumPy for +persisting a *single* arbitrary NumPy array on disk. The format stores all +of the shape and dtype information necessary to reconstruct the array +correctly even on another machine with a different architecture. +The format is designed to be as simple as possible while achieving +its limited goals. + +The ``.npz`` format is the standard format for persisting *multiple* NumPy +arrays on disk. A ``.npz`` file is a zip file containing multiple ``.npy`` +files, one for each array. + +Capabilities +------------ + +- Can represent all NumPy arrays including nested record arrays and + object arrays. + +- Represents the data in its native binary form. + +- Supports Fortran-contiguous arrays directly. + +- Stores all of the necessary information to reconstruct the array + including shape and dtype on a machine of a different + architecture. Both little-endian and big-endian arrays are + supported, and a file with little-endian numbers will yield + a little-endian array on any machine reading the file. The + types are described in terms of their actual sizes. For example, + if a machine with a 64-bit C "long int" writes out an array with + "long ints", a reading machine with 32-bit C "long ints" will yield + an array with 64-bit integers. + +- Is straightforward to reverse engineer. Datasets often live longer than + the programs that created them. A competent developer should be + able to create a solution in their preferred programming language to + read most ``.npy`` files that they have been given without much + documentation. + +- Allows memory-mapping of the data. See `open_memmap`. + +- Can be read from a filelike stream object instead of an actual file. + +- Stores object arrays, i.e. arrays containing elements that are arbitrary + Python objects. Files with object arrays are not to be mmapable, but + can be read and written to disk. + +Limitations +----------- + +- Arbitrary subclasses of numpy.ndarray are not completely preserved. + Subclasses will be accepted for writing, but only the array data will + be written out. A regular numpy.ndarray object will be created + upon reading the file. + +.. warning:: + + Due to limitations in the interpretation of structured dtypes, dtypes + with fields with empty names will have the names replaced by 'f0', 'f1', + etc. Such arrays will not round-trip through the format entirely + accurately. The data is intact; only the field names will differ. We are + working on a fix for this. This fix will not require a change in the + file format. The arrays with such structures can still be saved and + restored, and the correct dtype may be restored by using the + ``loadedarray.view(correct_dtype)`` method. + +File extensions +--------------- + +We recommend using the ``.npy`` and ``.npz`` extensions for files saved +in this format. This is by no means a requirement; applications may wish +to use these file formats but use an extension specific to the +application. In the absence of an obvious alternative, however, +we suggest using ``.npy`` and ``.npz``. + +Version numbering +----------------- + +The version numbering of these formats is independent of NumPy version +numbering. If the format is upgraded, the code in `numpy.io` will still +be able to read and write Version 1.0 files. + +Format Version 1.0 +------------------ + +The first 6 bytes are a magic string: exactly ``\\x93NUMPY``. + +The next 1 byte is an unsigned byte: the major version number of the file +format, e.g. ``\\x01``. + +The next 1 byte is an unsigned byte: the minor version number of the file +format, e.g. ``\\x00``. Note: the version of the file format is not tied +to the version of the numpy package. + +The next 2 bytes form a little-endian unsigned short int: the length of +the header data HEADER_LEN. + +The next HEADER_LEN bytes form the header data describing the array's +format. It is an ASCII string which contains a Python literal expression +of a dictionary. It is terminated by a newline (``\\n``) and padded with +spaces (``\\x20``) to make the total of +``len(magic string) + 2 + len(length) + HEADER_LEN`` be evenly divisible +by 64 for alignment purposes. + +The dictionary contains three keys: + + "descr" : dtype.descr + An object that can be passed as an argument to the `numpy.dtype` + constructor to create the array's dtype. + "fortran_order" : bool + Whether the array data is Fortran-contiguous or not. Since + Fortran-contiguous arrays are a common form of non-C-contiguity, + we allow them to be written directly to disk for efficiency. + "shape" : tuple of int + The shape of the array. + +For repeatability and readability, the dictionary keys are sorted in +alphabetic order. This is for convenience only. A writer SHOULD implement +this if possible. A reader MUST NOT depend on this. + +Following the header comes the array data. If the dtype contains Python +objects (i.e. ``dtype.hasobject is True``), then the data is a Python +pickle of the array. Otherwise the data is the contiguous (either C- +or Fortran-, depending on ``fortran_order``) bytes of the array. +Consumers can figure out the number of bytes by multiplying the number +of elements given by the shape (noting that ``shape=()`` means there is +1 element) by ``dtype.itemsize``. + +Format Version 2.0 +------------------ + +The version 1.0 format only allowed the array header to have a total size of +65535 bytes. This can be exceeded by structured arrays with a large number of +columns. The version 2.0 format extends the header size to 4 GiB. +`numpy.save` will automatically save in 2.0 format if the data requires it, +else it will always use the more compatible 1.0 format. + +The description of the fourth element of the header therefore has become: +"The next 4 bytes form a little-endian unsigned int: the length of the header +data HEADER_LEN." + +Format Version 3.0 +------------------ + +This version replaces the ASCII string (which in practice was latin1) with +a utf8-encoded string, so supports structured types with any unicode field +names. + +Notes +----- +The ``.npy`` format, including motivation for creating it and a comparison of +alternatives, is described in the +:doc:`"npy-format" NEP `, however details have +evolved with time and this document is more current. + +""" +import io +import os +import pickle +import warnings + +import numpy +from numpy.lib._utils_impl import drop_metadata + + +__all__ = [] + +drop_metadata.__module__ = "numpy.lib.format" + +EXPECTED_KEYS = {'descr', 'fortran_order', 'shape'} +MAGIC_PREFIX = b'\x93NUMPY' +MAGIC_LEN = len(MAGIC_PREFIX) + 2 +ARRAY_ALIGN = 64 # plausible values are powers of 2 between 16 and 4096 +BUFFER_SIZE = 2**18 # size of buffer for reading npz files in bytes +# allow growth within the address space of a 64 bit machine along one axis +GROWTH_AXIS_MAX_DIGITS = 21 # = len(str(8*2**64-1)) hypothetical int1 dtype + +# difference between version 1.0 and 2.0 is a 4 byte (I) header length +# instead of 2 bytes (H) allowing storage of large structured arrays +_header_size_info = { + (1, 0): (' 255: + raise ValueError("major version must be 0 <= major < 256") + if minor < 0 or minor > 255: + raise ValueError("minor version must be 0 <= minor < 256") + return MAGIC_PREFIX + bytes([major, minor]) + +def read_magic(fp): + """ Read the magic string to get the version of the file format. + + Parameters + ---------- + fp : filelike object + + Returns + ------- + major : int + minor : int + """ + magic_str = _read_bytes(fp, MAGIC_LEN, "magic string") + if magic_str[:-2] != MAGIC_PREFIX: + msg = "the magic string is not correct; expected %r, got %r" + raise ValueError(msg % (MAGIC_PREFIX, magic_str[:-2])) + major, minor = magic_str[-2:] + return major, minor + + +def dtype_to_descr(dtype): + """ + Get a serializable descriptor from the dtype. + + The .descr attribute of a dtype object cannot be round-tripped through + the dtype() constructor. Simple types, like dtype('float32'), have + a descr which looks like a record array with one field with '' as + a name. The dtype() constructor interprets this as a request to give + a default name. Instead, we construct descriptor that can be passed to + dtype(). + + Parameters + ---------- + dtype : dtype + The dtype of the array that will be written to disk. + + Returns + ------- + descr : object + An object that can be passed to `numpy.dtype()` in order to + replicate the input dtype. + + """ + # NOTE: that drop_metadata may not return the right dtype e.g. for user + # dtypes. In that case our code below would fail the same, though. + new_dtype = drop_metadata(dtype) + if new_dtype is not dtype: + warnings.warn("metadata on a dtype is not saved to an npy/npz. " + "Use another format (such as pickle) to store it.", + UserWarning, stacklevel=2) + dtype = new_dtype + + if dtype.names is not None: + # This is a record array. The .descr is fine. XXX: parts of the + # record array with an empty name, like padding bytes, still get + # fiddled with. This needs to be fixed in the C implementation of + # dtype(). + return dtype.descr + elif not type(dtype)._legacy: + # this must be a user-defined dtype since numpy does not yet expose any + # non-legacy dtypes in the public API + # + # non-legacy dtypes don't yet have __array_interface__ + # support. Instead, as a hack, we use pickle to save the array, and lie + # that the dtype is object. When the array is loaded, the descriptor is + # unpickled with the array and the object dtype in the header is + # discarded. + # + # a future NEP should define a way to serialize user-defined + # descriptors and ideally work out the possible security implications + warnings.warn("Custom dtypes are saved as python objects using the " + "pickle protocol. Loading this file requires " + "allow_pickle=True to be set.", + UserWarning, stacklevel=2) + return "|O" + else: + return dtype.str + +def descr_to_dtype(descr): + """ + Returns a dtype based off the given description. + + This is essentially the reverse of `~lib.format.dtype_to_descr`. It will + remove the valueless padding fields created by, i.e. simple fields like + dtype('float32'), and then convert the description to its corresponding + dtype. + + Parameters + ---------- + descr : object + The object retrieved by dtype.descr. Can be passed to + `numpy.dtype` in order to replicate the input dtype. + + Returns + ------- + dtype : dtype + The dtype constructed by the description. + + """ + if isinstance(descr, str): + # No padding removal needed + return numpy.dtype(descr) + elif isinstance(descr, tuple): + # subtype, will always have a shape descr[1] + dt = descr_to_dtype(descr[0]) + return numpy.dtype((dt, descr[1])) + + titles = [] + names = [] + formats = [] + offsets = [] + offset = 0 + for field in descr: + if len(field) == 2: + name, descr_str = field + dt = descr_to_dtype(descr_str) + else: + name, descr_str, shape = field + dt = numpy.dtype((descr_to_dtype(descr_str), shape)) + + # Ignore padding bytes, which will be void bytes with '' as name + # Once support for blank names is removed, only "if name == ''" needed) + is_pad = (name == '' and dt.type is numpy.void and dt.names is None) + if not is_pad: + title, name = name if isinstance(name, tuple) else (None, name) + titles.append(title) + names.append(name) + formats.append(dt) + offsets.append(offset) + offset += dt.itemsize + + return numpy.dtype({'names': names, 'formats': formats, 'titles': titles, + 'offsets': offsets, 'itemsize': offset}) + +def header_data_from_array_1_0(array): + """ Get the dictionary of header metadata from a numpy.ndarray. + + Parameters + ---------- + array : numpy.ndarray + + Returns + ------- + d : dict + This has the appropriate entries for writing its string representation + to the header of the file. + """ + d = {'shape': array.shape} + if array.flags.c_contiguous: + d['fortran_order'] = False + elif array.flags.f_contiguous: + d['fortran_order'] = True + else: + # Totally non-contiguous data. We will have to make it C-contiguous + # before writing. Note that we need to test for C_CONTIGUOUS first + # because a 1-D array is both C_CONTIGUOUS and F_CONTIGUOUS. + d['fortran_order'] = False + + d['descr'] = dtype_to_descr(array.dtype) + return d + + +def _wrap_header(header, version): + """ + Takes a stringified header, and attaches the prefix and padding to it + """ + import struct + assert version is not None + fmt, encoding = _header_size_info[version] + header = header.encode(encoding) + hlen = len(header) + 1 + padlen = ARRAY_ALIGN - ((MAGIC_LEN + struct.calcsize(fmt) + hlen) % ARRAY_ALIGN) + try: + header_prefix = magic(*version) + struct.pack(fmt, hlen + padlen) + except struct.error: + msg = "Header length {} too big for version={}".format(hlen, version) + raise ValueError(msg) from None + + # Pad the header with spaces and a final newline such that the magic + # string, the header-length short and the header are aligned on a + # ARRAY_ALIGN byte boundary. This supports memory mapping of dtypes + # aligned up to ARRAY_ALIGN on systems like Linux where mmap() + # offset must be page-aligned (i.e. the beginning of the file). + return header_prefix + header + b' '*padlen + b'\n' + + +def _wrap_header_guess_version(header): + """ + Like `_wrap_header`, but chooses an appropriate version given the contents + """ + try: + return _wrap_header(header, (1, 0)) + except ValueError: + pass + + try: + ret = _wrap_header(header, (2, 0)) + except UnicodeEncodeError: + pass + else: + warnings.warn("Stored array in format 2.0. It can only be" + "read by NumPy >= 1.9", UserWarning, stacklevel=2) + return ret + + header = _wrap_header(header, (3, 0)) + warnings.warn("Stored array in format 3.0. It can only be " + "read by NumPy >= 1.17", UserWarning, stacklevel=2) + return header + + +def _write_array_header(fp, d, version=None): + """ Write the header for an array and returns the version used + + Parameters + ---------- + fp : filelike object + d : dict + This has the appropriate entries for writing its string representation + to the header of the file. + version : tuple or None + None means use oldest that works. Providing an explicit version will + raise a ValueError if the format does not allow saving this data. + Default: None + """ + header = ["{"] + for key, value in sorted(d.items()): + # Need to use repr here, since we eval these when reading + header.append("'%s': %s, " % (key, repr(value))) + header.append("}") + header = "".join(header) + + # Add some spare space so that the array header can be modified in-place + # when changing the array size, e.g. when growing it by appending data at + # the end. + shape = d['shape'] + header += " " * ((GROWTH_AXIS_MAX_DIGITS - len(repr( + shape[-1 if d['fortran_order'] else 0] + ))) if len(shape) > 0 else 0) + + if version is None: + header = _wrap_header_guess_version(header) + else: + header = _wrap_header(header, version) + fp.write(header) + +def write_array_header_1_0(fp, d): + """ Write the header for an array using the 1.0 format. + + Parameters + ---------- + fp : filelike object + d : dict + This has the appropriate entries for writing its string + representation to the header of the file. + """ + _write_array_header(fp, d, (1, 0)) + + +def write_array_header_2_0(fp, d): + """ Write the header for an array using the 2.0 format. + The 2.0 format allows storing very large structured arrays. + + Parameters + ---------- + fp : filelike object + d : dict + This has the appropriate entries for writing its string + representation to the header of the file. + """ + _write_array_header(fp, d, (2, 0)) + +def read_array_header_1_0(fp, max_header_size=_MAX_HEADER_SIZE): + """ + Read an array header from a filelike object using the 1.0 file format + version. + + This will leave the file object located just after the header. + + Parameters + ---------- + fp : filelike object + A file object or something with a `.read()` method like a file. + + Returns + ------- + shape : tuple of int + The shape of the array. + fortran_order : bool + The array data will be written out directly if it is either + C-contiguous or Fortran-contiguous. Otherwise, it will be made + contiguous before writing it out. + dtype : dtype + The dtype of the file's data. + max_header_size : int, optional + Maximum allowed size of the header. Large headers may not be safe + to load securely and thus require explicitly passing a larger value. + See :py:func:`ast.literal_eval()` for details. + + Raises + ------ + ValueError + If the data is invalid. + + """ + return _read_array_header( + fp, version=(1, 0), max_header_size=max_header_size) + +def read_array_header_2_0(fp, max_header_size=_MAX_HEADER_SIZE): + """ + Read an array header from a filelike object using the 2.0 file format + version. + + This will leave the file object located just after the header. + + Parameters + ---------- + fp : filelike object + A file object or something with a `.read()` method like a file. + max_header_size : int, optional + Maximum allowed size of the header. Large headers may not be safe + to load securely and thus require explicitly passing a larger value. + See :py:func:`ast.literal_eval()` for details. + + Returns + ------- + shape : tuple of int + The shape of the array. + fortran_order : bool + The array data will be written out directly if it is either + C-contiguous or Fortran-contiguous. Otherwise, it will be made + contiguous before writing it out. + dtype : dtype + The dtype of the file's data. + + Raises + ------ + ValueError + If the data is invalid. + + """ + return _read_array_header( + fp, version=(2, 0), max_header_size=max_header_size) + + +def _filter_header(s): + """Clean up 'L' in npz header ints. + + Cleans up the 'L' in strings representing integers. Needed to allow npz + headers produced in Python2 to be read in Python3. + + Parameters + ---------- + s : string + Npy file header. + + Returns + ------- + header : str + Cleaned up header. + + """ + import tokenize + from io import StringIO + + tokens = [] + last_token_was_number = False + for token in tokenize.generate_tokens(StringIO(s).readline): + token_type = token[0] + token_string = token[1] + if (last_token_was_number and + token_type == tokenize.NAME and + token_string == "L"): + continue + else: + tokens.append(token) + last_token_was_number = (token_type == tokenize.NUMBER) + return tokenize.untokenize(tokens) + + +def _read_array_header(fp, version, max_header_size=_MAX_HEADER_SIZE): + """ + see read_array_header_1_0 + """ + # Read an unsigned, little-endian short int which has the length of the + # header. + import ast + import struct + hinfo = _header_size_info.get(version) + if hinfo is None: + raise ValueError("Invalid version {!r}".format(version)) + hlength_type, encoding = hinfo + + hlength_str = _read_bytes(fp, struct.calcsize(hlength_type), "array header length") + header_length = struct.unpack(hlength_type, hlength_str)[0] + header = _read_bytes(fp, header_length, "array header") + header = header.decode(encoding) + if len(header) > max_header_size: + raise ValueError( + f"Header info length ({len(header)}) is large and may not be safe " + "to load securely.\n" + "To allow loading, adjust `max_header_size` or fully trust " + "the `.npy` file using `allow_pickle=True`.\n" + "For safety against large resource use or crashes, sandboxing " + "may be necessary.") + + # The header is a pretty-printed string representation of a literal + # Python dictionary with trailing newlines padded to a ARRAY_ALIGN byte + # boundary. The keys are strings. + # "shape" : tuple of int + # "fortran_order" : bool + # "descr" : dtype.descr + # Versions (2, 0) and (1, 0) could have been created by a Python 2 + # implementation before header filtering was implemented. + # + # For performance reasons, we try without _filter_header first though + try: + d = ast.literal_eval(header) + except SyntaxError as e: + if version <= (2, 0): + header = _filter_header(header) + try: + d = ast.literal_eval(header) + except SyntaxError as e2: + msg = "Cannot parse header: {!r}" + raise ValueError(msg.format(header)) from e2 + else: + warnings.warn( + "Reading `.npy` or `.npz` file required additional " + "header parsing as it was created on Python 2. Save the " + "file again to speed up loading and avoid this warning.", + UserWarning, stacklevel=4) + else: + msg = "Cannot parse header: {!r}" + raise ValueError(msg.format(header)) from e + if not isinstance(d, dict): + msg = "Header is not a dictionary: {!r}" + raise ValueError(msg.format(d)) + + if EXPECTED_KEYS != d.keys(): + keys = sorted(d.keys()) + msg = "Header does not contain the correct keys: {!r}" + raise ValueError(msg.format(keys)) + + # Sanity-check the values. + if (not isinstance(d['shape'], tuple) or + not all(isinstance(x, int) for x in d['shape'])): + msg = "shape is not valid: {!r}" + raise ValueError(msg.format(d['shape'])) + if not isinstance(d['fortran_order'], bool): + msg = "fortran_order is not a valid bool: {!r}" + raise ValueError(msg.format(d['fortran_order'])) + try: + dtype = descr_to_dtype(d['descr']) + except TypeError as e: + msg = "descr is not a valid dtype descriptor: {!r}" + raise ValueError(msg.format(d['descr'])) from e + + return d['shape'], d['fortran_order'], dtype + +def write_array(fp, array, version=None, allow_pickle=True, pickle_kwargs=None): + """ + Write an array to an NPY file, including a header. + + If the array is neither C-contiguous nor Fortran-contiguous AND the + file_like object is not a real file object, this function will have to + copy data in memory. + + Parameters + ---------- + fp : file_like object + An open, writable file object, or similar object with a + ``.write()`` method. + array : ndarray + The array to write to disk. + version : (int, int) or None, optional + The version number of the format. None means use the oldest + supported version that is able to store the data. Default: None + allow_pickle : bool, optional + Whether to allow writing pickled data. Default: True + pickle_kwargs : dict, optional + Additional keyword arguments to pass to pickle.dump, excluding + 'protocol'. These are only useful when pickling objects in object + arrays on Python 3 to Python 2 compatible format. + + Raises + ------ + ValueError + If the array cannot be persisted. This includes the case of + allow_pickle=False and array being an object array. + Various other errors + If the array contains Python objects as part of its dtype, the + process of pickling them may raise various errors if the objects + are not picklable. + + """ + _check_version(version) + _write_array_header(fp, header_data_from_array_1_0(array), version) + + if array.itemsize == 0: + buffersize = 0 + else: + # Set buffer size to 16 MiB to hide the Python loop overhead. + buffersize = max(16 * 1024 ** 2 // array.itemsize, 1) + + dtype_class = type(array.dtype) + + if array.dtype.hasobject or not dtype_class._legacy: + # We contain Python objects so we cannot write out the data + # directly. Instead, we will pickle it out + if not allow_pickle: + if array.dtype.hasobject: + raise ValueError("Object arrays cannot be saved when " + "allow_pickle=False") + if not dtype_class._legacy: + raise ValueError("User-defined dtypes cannot be saved " + "when allow_pickle=False") + if pickle_kwargs is None: + pickle_kwargs = {} + pickle.dump(array, fp, protocol=4, **pickle_kwargs) + elif array.flags.f_contiguous and not array.flags.c_contiguous: + if isfileobj(fp): + array.T.tofile(fp) + else: + for chunk in numpy.nditer( + array, flags=['external_loop', 'buffered', 'zerosize_ok'], + buffersize=buffersize, order='F'): + fp.write(chunk.tobytes('C')) + else: + if isfileobj(fp): + array.tofile(fp) + else: + for chunk in numpy.nditer( + array, flags=['external_loop', 'buffered', 'zerosize_ok'], + buffersize=buffersize, order='C'): + fp.write(chunk.tobytes('C')) + + +def read_array(fp, allow_pickle=False, pickle_kwargs=None, *, + max_header_size=_MAX_HEADER_SIZE): + """ + Read an array from an NPY file. + + Parameters + ---------- + fp : file_like object + If this is not a real file object, then this may take extra memory + and time. + allow_pickle : bool, optional + Whether to allow writing pickled data. Default: False + pickle_kwargs : dict + Additional keyword arguments to pass to pickle.load. These are only + useful when loading object arrays saved on Python 2 when using + Python 3. + max_header_size : int, optional + Maximum allowed size of the header. Large headers may not be safe + to load securely and thus require explicitly passing a larger value. + See :py:func:`ast.literal_eval()` for details. + This option is ignored when `allow_pickle` is passed. In that case + the file is by definition trusted and the limit is unnecessary. + + Returns + ------- + array : ndarray + The array from the data on disk. + + Raises + ------ + ValueError + If the data is invalid, or allow_pickle=False and the file contains + an object array. + + """ + if allow_pickle: + # Effectively ignore max_header_size, since `allow_pickle` indicates + # that the input is fully trusted. + max_header_size = 2**64 + + version = read_magic(fp) + _check_version(version) + shape, fortran_order, dtype = _read_array_header( + fp, version, max_header_size=max_header_size) + if len(shape) == 0: + count = 1 + else: + count = numpy.multiply.reduce(shape, dtype=numpy.int64) + + # Now read the actual data. + if dtype.hasobject: + # The array contained Python objects. We need to unpickle the data. + if not allow_pickle: + raise ValueError("Object arrays cannot be loaded when " + "allow_pickle=False") + if pickle_kwargs is None: + pickle_kwargs = {} + try: + array = pickle.load(fp, **pickle_kwargs) + except UnicodeError as err: + # Friendlier error message + raise UnicodeError("Unpickling a python object failed: %r\n" + "You may need to pass the encoding= option " + "to numpy.load" % (err,)) from err + else: + if isfileobj(fp): + # We can use the fast fromfile() function. + array = numpy.fromfile(fp, dtype=dtype, count=count) + else: + # This is not a real file. We have to read it the + # memory-intensive way. + # crc32 module fails on reads greater than 2 ** 32 bytes, + # breaking large reads from gzip streams. Chunk reads to + # BUFFER_SIZE bytes to avoid issue and reduce memory overhead + # of the read. In non-chunked case count < max_read_count, so + # only one read is performed. + + # Use np.ndarray instead of np.empty since the latter does + # not correctly instantiate zero-width string dtypes; see + # https://github.com/numpy/numpy/pull/6430 + array = numpy.ndarray(count, dtype=dtype) + + if dtype.itemsize > 0: + # If dtype.itemsize == 0 then there's nothing more to read + max_read_count = BUFFER_SIZE // min(BUFFER_SIZE, dtype.itemsize) + + for i in range(0, count, max_read_count): + read_count = min(max_read_count, count - i) + read_size = int(read_count * dtype.itemsize) + data = _read_bytes(fp, read_size, "array data") + array[i:i+read_count] = numpy.frombuffer(data, dtype=dtype, + count=read_count) + + if fortran_order: + array.shape = shape[::-1] + array = array.transpose() + else: + array.shape = shape + + return array + + +def open_memmap(filename, mode='r+', dtype=None, shape=None, + fortran_order=False, version=None, *, + max_header_size=_MAX_HEADER_SIZE): + """ + Open a .npy file as a memory-mapped array. + + This may be used to read an existing file or create a new one. + + Parameters + ---------- + filename : str or path-like + The name of the file on disk. This may *not* be a file-like + object. + mode : str, optional + The mode in which to open the file; the default is 'r+'. In + addition to the standard file modes, 'c' is also accepted to mean + "copy on write." See `memmap` for the available mode strings. + dtype : data-type, optional + The data type of the array if we are creating a new file in "write" + mode, if not, `dtype` is ignored. The default value is None, which + results in a data-type of `float64`. + shape : tuple of int + The shape of the array if we are creating a new file in "write" + mode, in which case this parameter is required. Otherwise, this + parameter is ignored and is thus optional. + fortran_order : bool, optional + Whether the array should be Fortran-contiguous (True) or + C-contiguous (False, the default) if we are creating a new file in + "write" mode. + version : tuple of int (major, minor) or None + If the mode is a "write" mode, then this is the version of the file + format used to create the file. None means use the oldest + supported version that is able to store the data. Default: None + max_header_size : int, optional + Maximum allowed size of the header. Large headers may not be safe + to load securely and thus require explicitly passing a larger value. + See :py:func:`ast.literal_eval()` for details. + + Returns + ------- + marray : memmap + The memory-mapped array. + + Raises + ------ + ValueError + If the data or the mode is invalid. + OSError + If the file is not found or cannot be opened correctly. + + See Also + -------- + numpy.memmap + + """ + if isfileobj(filename): + raise ValueError("Filename must be a string or a path-like object." + " Memmap cannot use existing file handles.") + + if 'w' in mode: + # We are creating the file, not reading it. + # Check if we ought to create the file. + _check_version(version) + # Ensure that the given dtype is an authentic dtype object rather + # than just something that can be interpreted as a dtype object. + dtype = numpy.dtype(dtype) + if dtype.hasobject: + msg = "Array can't be memory-mapped: Python objects in dtype." + raise ValueError(msg) + d = dict( + descr=dtype_to_descr(dtype), + fortran_order=fortran_order, + shape=shape, + ) + # If we got here, then it should be safe to create the file. + with open(os.fspath(filename), mode+'b') as fp: + _write_array_header(fp, d, version) + offset = fp.tell() + else: + # Read the header of the file first. + with open(os.fspath(filename), 'rb') as fp: + version = read_magic(fp) + _check_version(version) + + shape, fortran_order, dtype = _read_array_header( + fp, version, max_header_size=max_header_size) + if dtype.hasobject: + msg = "Array can't be memory-mapped: Python objects in dtype." + raise ValueError(msg) + offset = fp.tell() + + if fortran_order: + order = 'F' + else: + order = 'C' + + # We need to change a write-only mode to a read-write mode since we've + # already written data to the file. + if mode == 'w+': + mode = 'r+' + + marray = numpy.memmap(filename, dtype=dtype, shape=shape, order=order, + mode=mode, offset=offset) + + return marray + + +def _read_bytes(fp, size, error_template="ran out of data"): + """ + Read from file-like object until size bytes are read. + Raises ValueError if not EOF is encountered before size bytes are read. + Non-blocking objects only supported if they derive from io objects. + + Required as e.g. ZipExtFile in python 2.6 can return less data than + requested. + """ + data = bytes() + while True: + # io files (default in python3) return None or raise on + # would-block, python2 file will truncate, probably nothing can be + # done about that. note that regular files can't be non-blocking + try: + r = fp.read(size - len(data)) + data += r + if len(r) == 0 or len(data) == size: + break + except BlockingIOError: + pass + if len(data) != size: + msg = "EOF: reading %s, expected %d bytes got %d" + raise ValueError(msg % (error_template, size, len(data))) + else: + return data + + +def isfileobj(f): + if not isinstance(f, (io.FileIO, io.BufferedReader, io.BufferedWriter)): + return False + try: + # BufferedReader/Writer may raise OSError when + # fetching `fileno()` (e.g. when wrapping BytesIO). + f.fileno() + return True + except OSError: + return False diff --git a/janus/lib/python3.10/site-packages/numpy/lib/mixins.py b/janus/lib/python3.10/site-packages/numpy/lib/mixins.py new file mode 100644 index 0000000000000000000000000000000000000000..5e78ac0990b34c9926f7f5a819d9652605d5a0ca --- /dev/null +++ b/janus/lib/python3.10/site-packages/numpy/lib/mixins.py @@ -0,0 +1,182 @@ +""" +Mixin classes for custom array types that don't inherit from ndarray. +""" +from numpy._core import umath as um + + +__all__ = ['NDArrayOperatorsMixin'] + + +def _disables_array_ufunc(obj): + """True when __array_ufunc__ is set to None.""" + try: + return obj.__array_ufunc__ is None + except AttributeError: + return False + + +def _binary_method(ufunc, name): + """Implement a forward binary method with a ufunc, e.g., __add__.""" + def func(self, other): + if _disables_array_ufunc(other): + return NotImplemented + return ufunc(self, other) + func.__name__ = '__{}__'.format(name) + return func + + +def _reflected_binary_method(ufunc, name): + """Implement a reflected binary method with a ufunc, e.g., __radd__.""" + def func(self, other): + if _disables_array_ufunc(other): + return NotImplemented + return ufunc(other, self) + func.__name__ = '__r{}__'.format(name) + return func + + +def _inplace_binary_method(ufunc, name): + """Implement an in-place binary method with a ufunc, e.g., __iadd__.""" + def func(self, other): + return ufunc(self, other, out=(self,)) + func.__name__ = '__i{}__'.format(name) + return func + + +def _numeric_methods(ufunc, name): + """Implement forward, reflected and inplace binary methods with a ufunc.""" + return (_binary_method(ufunc, name), + _reflected_binary_method(ufunc, name), + _inplace_binary_method(ufunc, name)) + + +def _unary_method(ufunc, name): + """Implement a unary special method with a ufunc.""" + def func(self): + return ufunc(self) + func.__name__ = '__{}__'.format(name) + return func + + +class NDArrayOperatorsMixin: + """Mixin defining all operator special methods using __array_ufunc__. + + This class implements the special methods for almost all of Python's + builtin operators defined in the `operator` module, including comparisons + (``==``, ``>``, etc.) and arithmetic (``+``, ``*``, ``-``, etc.), by + deferring to the ``__array_ufunc__`` method, which subclasses must + implement. + + It is useful for writing classes that do not inherit from `numpy.ndarray`, + but that should support arithmetic and numpy universal functions like + arrays as described in `A Mechanism for Overriding Ufuncs + `_. + + As an trivial example, consider this implementation of an ``ArrayLike`` + class that simply wraps a NumPy array and ensures that the result of any + arithmetic operation is also an ``ArrayLike`` object: + + >>> import numbers + >>> class ArrayLike(np.lib.mixins.NDArrayOperatorsMixin): + ... def __init__(self, value): + ... self.value = np.asarray(value) + ... + ... # One might also consider adding the built-in list type to this + ... # list, to support operations like np.add(array_like, list) + ... _HANDLED_TYPES = (np.ndarray, numbers.Number) + ... + ... def __array_ufunc__(self, ufunc, method, *inputs, **kwargs): + ... out = kwargs.get('out', ()) + ... for x in inputs + out: + ... # Only support operations with instances of + ... # _HANDLED_TYPES. Use ArrayLike instead of type(self) + ... # for isinstance to allow subclasses that don't + ... # override __array_ufunc__ to handle ArrayLike objects. + ... if not isinstance( + ... x, self._HANDLED_TYPES + (ArrayLike,) + ... ): + ... return NotImplemented + ... + ... # Defer to the implementation of the ufunc + ... # on unwrapped values. + ... inputs = tuple(x.value if isinstance(x, ArrayLike) else x + ... for x in inputs) + ... if out: + ... kwargs['out'] = tuple( + ... x.value if isinstance(x, ArrayLike) else x + ... for x in out) + ... result = getattr(ufunc, method)(*inputs, **kwargs) + ... + ... if type(result) is tuple: + ... # multiple return values + ... return tuple(type(self)(x) for x in result) + ... elif method == 'at': + ... # no return value + ... return None + ... else: + ... # one return value + ... return type(self)(result) + ... + ... def __repr__(self): + ... return '%s(%r)' % (type(self).__name__, self.value) + + In interactions between ``ArrayLike`` objects and numbers or numpy arrays, + the result is always another ``ArrayLike``: + + >>> x = ArrayLike([1, 2, 3]) + >>> x - 1 + ArrayLike(array([0, 1, 2])) + >>> 1 - x + ArrayLike(array([ 0, -1, -2])) + >>> np.arange(3) - x + ArrayLike(array([-1, -1, -1])) + >>> x - np.arange(3) + ArrayLike(array([1, 1, 1])) + + Note that unlike ``numpy.ndarray``, ``ArrayLike`` does not allow operations + with arbitrary, unrecognized types. This ensures that interactions with + ArrayLike preserve a well-defined casting hierarchy. + + """ + __slots__ = () + # Like np.ndarray, this mixin class implements "Option 1" from the ufunc + # overrides NEP. + + # comparisons don't have reflected and in-place versions + __lt__ = _binary_method(um.less, 'lt') + __le__ = _binary_method(um.less_equal, 'le') + __eq__ = _binary_method(um.equal, 'eq') + __ne__ = _binary_method(um.not_equal, 'ne') + __gt__ = _binary_method(um.greater, 'gt') + __ge__ = _binary_method(um.greater_equal, 'ge') + + # numeric methods + __add__, __radd__, __iadd__ = _numeric_methods(um.add, 'add') + __sub__, __rsub__, __isub__ = _numeric_methods(um.subtract, 'sub') + __mul__, __rmul__, __imul__ = _numeric_methods(um.multiply, 'mul') + __matmul__, __rmatmul__, __imatmul__ = _numeric_methods( + um.matmul, 'matmul') + # Python 3 does not use __div__, __rdiv__, or __idiv__ + __truediv__, __rtruediv__, __itruediv__ = _numeric_methods( + um.true_divide, 'truediv') + __floordiv__, __rfloordiv__, __ifloordiv__ = _numeric_methods( + um.floor_divide, 'floordiv') + __mod__, __rmod__, __imod__ = _numeric_methods(um.remainder, 'mod') + __divmod__ = _binary_method(um.divmod, 'divmod') + __rdivmod__ = _reflected_binary_method(um.divmod, 'divmod') + # __idivmod__ does not exist + # TODO: handle the optional third argument for __pow__? + __pow__, __rpow__, __ipow__ = _numeric_methods(um.power, 'pow') + __lshift__, __rlshift__, __ilshift__ = _numeric_methods( + um.left_shift, 'lshift') + __rshift__, __rrshift__, __irshift__ = _numeric_methods( + um.right_shift, 'rshift') + __and__, __rand__, __iand__ = _numeric_methods(um.bitwise_and, 'and') + __xor__, __rxor__, __ixor__ = _numeric_methods(um.bitwise_xor, 'xor') + __or__, __ror__, __ior__ = _numeric_methods(um.bitwise_or, 'or') + + # unary methods + __neg__ = _unary_method(um.negative, 'neg') + __pos__ = _unary_method(um.positive, 'pos') + __abs__ = _unary_method(um.absolute, 'abs') + __invert__ = _unary_method(um.invert, 'invert') diff --git a/janus/lib/python3.10/site-packages/numpy/lib/npyio.py b/janus/lib/python3.10/site-packages/numpy/lib/npyio.py new file mode 100644 index 0000000000000000000000000000000000000000..1003ef5be4b1940ddf6943a90d1bee786677c55e --- /dev/null +++ b/janus/lib/python3.10/site-packages/numpy/lib/npyio.py @@ -0,0 +1,3 @@ +from ._npyio_impl import ( + __doc__, DataSource, NpzFile +) diff --git a/janus/lib/python3.10/site-packages/numpy/lib/stride_tricks.pyi b/janus/lib/python3.10/site-packages/numpy/lib/stride_tricks.pyi new file mode 100644 index 0000000000000000000000000000000000000000..eb46f28ae5f43e92c7c14df31edb154cf6d1f60b --- /dev/null +++ b/janus/lib/python3.10/site-packages/numpy/lib/stride_tricks.pyi @@ -0,0 +1,4 @@ +from numpy.lib._stride_tricks_impl import ( + as_strided as as_strided, + sliding_window_view as sliding_window_view, +)