"""
An object-oriented plotting library.

A procedural interface is provided by the companion pyplot module,
which may be imported directly, e.g.::

    import matplotlib.pyplot as plt

or using ipython::

    ipython

at your terminal, followed by::

    In [1]: %matplotlib
    In [2]: import matplotlib.pyplot as plt

at the ipython shell prompt.

For the most part, direct use of the explicit object-oriented library is
encouraged when programming; the implicit pyplot interface is primarily for
working interactively. The exceptions to this suggestion are the pyplot
functions `.pyplot.figure`, `.pyplot.subplot`, `.pyplot.subplots`, and
`.pyplot.savefig`, which can greatly simplify scripting.  See
:ref:`api_interfaces` for an explanation of the tradeoffs between the implicit
and explicit interfaces.

Modules include:

:mod:`matplotlib.axes`
    The `~.axes.Axes` class.  Most pyplot functions are wrappers for
    `~.axes.Axes` methods.  The axes module is the highest level of OO
    access to the library.

:mod:`matplotlib.figure`
    The `.Figure` class.

:mod:`matplotlib.artist`
    The `.Artist` base class for all classes that draw things.

:mod:`matplotlib.lines`
    The `.Line2D` class for drawing lines and markers.

:mod:`matplotlib.patches`
    Classes for drawing polygons.

:mod:`matplotlib.text`
    The `.Text` and `.Annotation` classes.

:mod:`matplotlib.image`
    The `.AxesImage` and `.FigureImage` classes.

:mod:`matplotlib.collections`
    Classes for efficient drawing of groups of lines or polygons.

:mod:`matplotlib.colors`
    Color specifications and making colormaps.

:mod:`matplotlib.cm`
    Colormaps, and the `.ScalarMappable` mixin class for providing color
    mapping functionality to other classes.

:mod:`matplotlib.ticker`
    Calculation of tick mark locations and formatting of tick labels.

:mod:`matplotlib.backends`
    A subpackage with modules for various GUI libraries and output formats.

The base matplotlib namespace includes:

`~matplotlib.rcParams`
    Default configuration settings; their defaults may be overridden using
    a :file:`matplotlibrc` file.

`~matplotlib.use`
    Setting the Matplotlib backend.  This should be called before any
    figure is created, because it is not possible to switch between
    different GUI backends after that.

The following environment variables can be used to customize the behavior:

:envvar:`MPLBACKEND`
    This optional variable can be set to choose the Matplotlib backend. See
    :ref:`what-is-a-backend`.

:envvar:`MPLCONFIGDIR`
    This is the directory used to store user customizations to
    Matplotlib, as well as some caches to improve performance. If
    :envvar:`MPLCONFIGDIR` is not defined, :file:`{HOME}/.config/matplotlib`
    and :file:`{HOME}/.cache/matplotlib` are used on Linux, and
    :file:`{HOME}/.matplotlib` on other platforms, if they are
    writable. Otherwise, the Python standard library's `tempfile.gettempdir`
    is used to find a base directory in which the :file:`matplotlib`
    subdirectory is created.

Matplotlib was initially written by John D. Hunter (1968-2012) and is now
developed and maintained by a host of others.

Occasionally the internal documentation (python docstrings) will refer
to MATLAB®, a registered trademark of The MathWorks, Inc.

"""

__all__ = [
    "__bibtex__",
    "__version__",
    "__version_info__",
    "set_loglevel",
    "ExecutableNotFoundError",
    "get_configdir",
    "get_cachedir",
    "get_data_path",
    "matplotlib_fname",
    "MatplotlibDeprecationWarning",
    "RcParams",
    "rc_params",
    "rc_params_from_file",
    "rcParamsDefault",
    "rcParams",
    "rcParamsOrig",
    "defaultParams",
    "rc",
    "rcdefaults",
    "rc_file_defaults",
    "rc_file",
    "rc_context",
    "use",
    "get_backend",
    "interactive",
    "is_interactive",
    "colormaps",
    "color_sequences",
]


import atexit
from collections import namedtuple
from collections.abc import MutableMapping
import contextlib
import functools
import importlib
import inspect
from inspect import Parameter
import locale
import logging
import os
from pathlib import Path
import pprint
import re
import shutil
import subprocess
import sys
import tempfile
import warnings

import numpy
from packaging.version import parse as parse_version

# cbook must import matplotlib only within function
# definitions, so it is safe to import from it here.
from . import _api, _version, cbook, _docstring, rcsetup
from matplotlib.cbook import sanitize_sequence
from matplotlib._api import MatplotlibDeprecationWarning
from matplotlib.rcsetup import validate_backend, cycler


_log = logging.getLogger(__name__)

__bibtex__ = r"""@Article{Hunter:2007,
  Author    = {Hunter, J. D.},
  Title     = {Matplotlib: A 2D graphics environment},
  Journal   = {Computing in Science \& Engineering},
  Volume    = {9},
  Number    = {3},
  Pages     = {90--95},
  abstract  = {Matplotlib is a 2D graphics package used for Python
  for application development, interactive scripting, and
  publication-quality image generation across user
  interfaces and operating systems.},
  publisher = {IEEE COMPUTER SOC},
  year      = 2007
}"""

# modelled after sys.version_info
_VersionInfo = namedtuple('_VersionInfo',
                          'major, minor, micro, releaselevel, serial')


def _parse_to_version_info(version_str):
    """
    Parse a version string to a namedtuple analogous to sys.version_info.

    See:
    https://packaging.pypa.io/en/latest/version.html#packaging.version.parse
    https://docs.python.org/3/library/sys.html#sys.version_info
    """
    v = parse_version(version_str)
    if v.pre is None and v.post is None and v.dev is None:
        return _VersionInfo(v.major, v.minor, v.micro, 'final', 0)
    elif v.dev is not None:
        return _VersionInfo(v.major, v.minor, v.micro, 'alpha', v.dev)
    elif v.pre is not None:
        releaselevel = {
            'a': 'alpha',
            'b': 'beta',
            'rc': 'candidate'}.get(v.pre[0], 'alpha')
        return _VersionInfo(v.major, v.minor, v.micro, releaselevel, v.pre[1])
    else:
        # fallback for v.post: guess-next-dev scheme from setuptools_scm
        return _VersionInfo(v.major, v.minor, v.micro + 1, 'alpha', v.post)


def _get_version():
    """Return the version string used for __version__."""
    # Only shell out to a git subprocess if really needed, i.e. when we are in
    # a matplotlib git repo but not in a shallow clone, such as those used by
    # CI, as the latter would trigger a warning from setuptools_scm.
    root = Path(__file__).resolve().parents[2]
    if ((root / ".matplotlib-repo").exists()
            and (root / ".git").exists()
            and not (root / ".git/shallow").exists()):
        import setuptools_scm
        return setuptools_scm.get_version(
            root=root,
            version_scheme="release-branch-semver",
            local_scheme="node-and-date",
            fallback_version=_version.version,
        )
    else:  # Get the version from the _version.py setuptools_scm file.
        return _version.version


@_api.caching_module_getattr
class __getattr__:
    __version__ = property(lambda self: _get_version())
    __version_info__ = property(
        lambda self: _parse_to_version_info(self.__version__))


def _check_versions():

    # Quickfix to ensure Microsoft Visual C++ redistributable
    # DLLs are loaded before importing kiwisolver
    from . import ft2font

    for modname, minver in [
            ("cycler", "0.10"),
            ("dateutil", "2.7"),
            ("kiwisolver", "1.3.1"),
            ("numpy", "1.21"),
            ("pyparsing", "2.3.1"),
    ]:
        module = importlib.import_module(modname)
        if parse_version(module.__version__) < parse_version(minver):
            raise ImportError(f"Matplotlib requires {modname}>={minver}; "
                              f"you have {module.__version__}")


_check_versions()


# The decorator ensures this always returns the same handler (and it is only
# attached once).
@functools.cache
def _ensure_handler():
    """
    The first time this function is called, attach a `StreamHandler` using the
    same format as `logging.basicConfig` to the Matplotlib root logger.

    Return this handler every time this function is called.
    """
    handler = logging.StreamHandler()
    handler.setFormatter(logging.Formatter(logging.BASIC_FORMAT))
    _log.addHandler(handler)
    return handler


def set_loglevel(level):
    """
    Configure Matplotlib's logging levels.

    Matplotlib uses the standard library `logging` framework under the root
    logger 'matplotlib'.  This is a helper function to:

    - set Matplotlib's root logger level
    - set the root logger handler's level, creating the handler
      if it does not exist yet

    Typically, one should call ``set_loglevel("info")`` or
    ``set_loglevel("debug")`` to get additional debugging information.

    Users or applications that are installing their own logging handlers
    may want to directly manipulate ``logging.getLogger('matplotlib')`` rather
    than use this function.

    Parameters
    ----------
    level : {"notset", "debug", "info", "warning", "error", "critical"}
        The log level of the handler.

    Notes
    -----
    The first time this function is called, an additional handler is attached
    to Matplotlib's root handler; this handler is reused every time and this
    function simply manipulates the logger and handler's level.

    """
    _log.setLevel(level.upper())
    _ensure_handler().setLevel(level.upper())


def _logged_cached(fmt, func=None):
    """
    Decorator that logs a function's return value, and memoizes that value.

    After ::

        @_logged_cached(fmt)
        def func(): ...

    the first call to *func* will log its return value at the DEBUG level using
    %-format string *fmt*, and memoize it; later calls to *func* will directly
    return that value.
    """
    if func is None:  # Return the actual decorator.
        return functools.partial(_logged_cached, fmt)

    called = False
    ret = None

    @functools.wraps(func)
    def wrapper(**kwargs):
        nonlocal called, ret
        if not called:
            ret = func(**kwargs)
            called = True
            _log.debug(fmt, ret)
        return ret

    return wrapper


_ExecInfo = namedtuple("_ExecInfo", "executable raw_version version")


class ExecutableNotFoundError(FileNotFoundError):
    """
    Error raised when an executable that Matplotlib optionally
    depends on can't be found.
    """
    pass


@functools.cache
def _get_executable_info(name):
    """
    Get the version of some executable that Matplotlib optionally depends on.

    .. warning::
       The list of executables that this function supports is set according to
       Matplotlib's internal needs, and may change without notice.

    Parameters
    ----------
    name : str
        The executable to query.  The following values are currently supported:
        "dvipng", "gs", "inkscape", "magick", "pdftocairo", "pdftops".  This
        list is subject to change without notice.

    Returns
    -------
    tuple
        A namedtuple with fields ``executable`` (`str`) and ``version``
        (`packaging.Version`, or ``None`` if the version cannot be determined).

    Raises
    ------
    ExecutableNotFoundError
        If the executable is not found or older than the oldest version
        supported by Matplotlib.  For debugging purposes, it is also
        possible to "hide" an executable from Matplotlib by adding it to the
        :envvar:`_MPLHIDEEXECUTABLES` environment variable (a comma-separated
        list), which must be set prior to any calls to this function.
    ValueError
        If the executable is not one that we know how to query.
    """

    def impl(args, regex, min_ver=None, ignore_exit_code=False):
        # Execute the subprocess specified by args; capture stdout and stderr.
        # Search for a regex match in the output; if the match succeeds, the
        # first group of the match is the version.
        # Return an _ExecInfo if the executable exists, and has a version of
        # at least min_ver (if set); else, raise ExecutableNotFoundError.
        try:
            output = subprocess.check_output(
                args, stderr=subprocess.STDOUT,
                text=True, errors="replace")
        except subprocess.CalledProcessError as _cpe:
            if ignore_exit_code:
                output = _cpe.output
            else:
                raise ExecutableNotFoundError(str(_cpe)) from _cpe
        except OSError as _ose:
            raise ExecutableNotFoundError(str(_ose)) from _ose
        match = re.search(regex, output)
        if match:
            raw_version = match.group(1)
            version = parse_version(raw_version)
            if min_ver is not None and version < parse_version(min_ver):
                raise ExecutableNotFoundError(
                    f"You have {args[0]} version {version} but the minimum "
                    f"version supported by Matplotlib is {min_ver}")
            return _ExecInfo(args[0], raw_version, version)
        else:
            raise ExecutableNotFoundError(
                f"Failed to determine the version of {args[0]} from "
                f"{' '.join(args)}, which output {output}")

    if name in os.environ.get("_MPLHIDEEXECUTABLES", "").split(","):
        raise ExecutableNotFoundError(f"{name} was hidden")

    if name == "dvipng":
        return impl(["dvipng", "-version"], "(?m)^dvipng(?: .*)? (.+)", "1.6")
    elif name == "gs":
        execs = (["gswin32c", "gswin64c", "mgs", "gs"]  # "mgs" for miktex.
                 if sys.platform == "win32" else
                 ["gs"])
        for e in execs:
            try:
                return impl([e, "--version"], "(.*)", "9")
            except ExecutableNotFoundError:
                pass
        message = "Failed to find a Ghostscript installation"
        raise ExecutableNotFoundError(message)
    elif name == "inkscape":
        try:
            # Try headless option first (needed for Inkscape version < 1.0):
            return impl(["inkscape", "--without-gui", "-V"],
                        "Inkscape ([^ ]*)")
        except ExecutableNotFoundError:
            pass  # Suppress exception chaining.
        # If --without-gui is not accepted, we may be using Inkscape >= 1.0 so
        # try without it:
        return impl(["inkscape", "-V"], "Inkscape ([^ ]*)")
    elif name == "magick":
        if sys.platform == "win32":
            # Check the registry to avoid confusing ImageMagick's convert with
            # Windows's builtin convert.exe.
            import winreg
            binpath = ""
            for flag in [0, winreg.KEY_WOW64_32KEY, winreg.KEY_WOW64_64KEY]:
                try:
                    with winreg.OpenKeyEx(
                            winreg.HKEY_LOCAL_MACHINE,
                            r"Software\Imagemagick\Current",
                            0, winreg.KEY_QUERY_VALUE | flag) as hkey:
                        binpath = winreg.QueryValueEx(hkey, "BinPath")[0]
                except OSError:
                    pass
            path = None
            if binpath:
                for name in ["convert.exe", "magick.exe"]:
                    candidate = Path(binpath, name)
                    if candidate.exists():
                        path = str(candidate)
                        break
            if path is None:
                raise ExecutableNotFoundError(
                    "Failed to find an ImageMagick installation")
        else:
            path = "convert"
        info = impl([path, "--version"], r"^Version: ImageMagick (\S*)")
        if info.raw_version == "7.0.10-34":
            # https://github.com/ImageMagick/ImageMagick/issues/2720
            raise ExecutableNotFoundError(
                f"You have ImageMagick {info.version}, which is unsupported")
        return info
    elif name == "pdftocairo":
        return impl(["pdftocairo", "-v"], "pdftocairo version (.*)")
    elif name == "pdftops":
        info = impl(["pdftops", "-v"], "^pdftops version (.*)",
                    ignore_exit_code=True)
        if info and not (
                3 <= info.version.major or
                # poppler version numbers.
                parse_version("0.9") <= info.version < parse_version("1.0")):
            raise ExecutableNotFoundError(
                f"You have pdftops version {info.version} but the minimum "
                f"version supported by Matplotlib is 3.0")
        return info
    else:
        raise ValueError(f"Unknown executable: {name!r}")


def _get_xdg_config_dir():
    """
    Return the XDG configuration directory, according to the XDG base
    directory spec:

    https://specifications.freedesktop.org/basedir-spec/basedir-spec-latest.html
    """
    return os.environ.get('XDG_CONFIG_HOME') or str(Path.home() / ".config")


def _get_xdg_cache_dir():
    """
    Return the XDG cache directory, according to the XDG base directory spec:

    https://specifications.freedesktop.org/basedir-spec/basedir-spec-latest.html
    """
    return os.environ.get('XDG_CACHE_HOME') or str(Path.home() / ".cache")


def _get_config_or_cache_dir(xdg_base_getter):
    configdir = os.environ.get('MPLCONFIGDIR')
    if configdir:
        configdir = Path(configdir).resolve()
    elif sys.platform.startswith(('linux', 'freebsd')):
        # Only call _xdg_base_getter here so that MPLCONFIGDIR is tried first,
        # as _xdg_base_getter can throw.
        configdir = Path(xdg_base_getter(), "matplotlib")
    else:
        configdir = Path.home() / ".matplotlib"
    try:
        configdir.mkdir(parents=True, exist_ok=True)
    except OSError:
        pass
    else:
        if os.access(str(configdir), os.W_OK) and configdir.is_dir():
            return str(configdir)
    # If the config or cache directory cannot be created or is not a writable
    # directory, create a temporary one.
    try:
        tmpdir = tempfile.mkdtemp(prefix="matplotlib-")
    except OSError as exc:
        raise OSError(
            f"Matplotlib requires access to a writable cache directory, but the "
            f"default path ({configdir}) is not a writable directory, and a temporary "
            f"directory could not be created; set the MPLCONFIGDIR environment "
            f"variable to a writable directory") from exc
    os.environ["MPLCONFIGDIR"] = tmpdir
    atexit.register(shutil.rmtree, tmpdir)
    _log.warning(
        "Matplotlib created a temporary cache directory at %s because the default path "
        "(%s) is not a writable directory; it is highly recommended to set the "
        "MPLCONFIGDIR environment variable to a writable directory, in particular to "
        "speed up the import of Matplotlib and to better support multiprocessing.",
        tmpdir, configdir)
    return tmpdir


@_logged_cached('CONFIGDIR=%s')
def get_configdir():
    """
    Return the string path of the configuration directory.

    The directory is chosen as follows:

    1. If the MPLCONFIGDIR environment variable is supplied, choose that.
    2. On Linux, follow the XDG specification and look first in
       ``$XDG_CONFIG_HOME``, if defined, or ``$HOME/.config``.  On other
       platforms, choose ``$HOME/.matplotlib``.
    3. If the chosen directory exists and is writable, use that as the
       configuration directory.
    4. Else, create a temporary directory, and use it as the configuration
       directory.
    """
    return _get_config_or_cache_dir(_get_xdg_config_dir)


@_logged_cached('CACHEDIR=%s')
def get_cachedir():
    """
    Return the string path of the cache directory.

    The procedure used to find the directory is the same as for
    `get_configdir`, except using ``$XDG_CACHE_HOME``/``$HOME/.cache`` instead.
    """
    return _get_config_or_cache_dir(_get_xdg_cache_dir)


@_logged_cached('matplotlib data path: %s')
def get_data_path():
    """Return the path to Matplotlib data."""
    return str(Path(__file__).with_name("mpl-data"))


def matplotlib_fname():
    """
    Get the location of the config file.

    The file location is determined in the following order

    - ``$PWD/matplotlibrc``
    - ``$MATPLOTLIBRC`` if it is not a directory
    - ``$MATPLOTLIBRC/matplotlibrc``
    - ``$MPLCONFIGDIR/matplotlibrc``
    - On Linux,
        - ``$XDG_CONFIG_HOME/matplotlib/matplotlibrc`` (if ``$XDG_CONFIG_HOME``
          is defined)
        - or ``$HOME/.config/matplotlib/matplotlibrc`` (if ``$XDG_CONFIG_HOME``
          is not defined)
    - On other platforms,
      - ``$HOME/.matplotlib/matplotlibrc`` if ``$HOME`` is defined
    - Lastly, it looks in ``$MATPLOTLIBDATA/matplotlibrc``, which should always
      exist.
    """

    def gen_candidates():
        # rely on down-stream code to make absolute.  This protects us
        # from having to directly get the current working directory
        # which can fail if the user has ended up with a cwd that is
        # non-existent.
        yield 'matplotlibrc'
        try:
            matplotlibrc = os.environ['MATPLOTLIBRC']
        except KeyError:
            pass
        else:
            yield matplotlibrc
            yield os.path.join(matplotlibrc, 'matplotlibrc')
        yield os.path.join(get_configdir(), 'matplotlibrc')
        yield os.path.join(get_data_path(), 'matplotlibrc')

    for fname in gen_candidates():
        if os.path.exists(fname) and not os.path.isdir(fname):
            return fname

    raise RuntimeError("Could not find matplotlibrc file; your Matplotlib "
                       "install is broken")


# rcParams deprecated and automatically mapped to another key.
# Values are tuples of (version, new_name, f_old2new, f_new2old).
_deprecated_map = {}
# rcParams deprecated; some can manually be mapped to another key.
# Values are tuples of (version, new_name_or_None).
_deprecated_ignore_map = {}
# rcParams deprecated; can use None to suppress warnings; remain actually
# listed in the rcParams.
# Values are tuples of (version,)
_deprecated_remain_as_none = {}


@_docstring.Substitution(
    "\n".join(map("- {}".format, sorted(rcsetup._validators, key=str.lower)))
)
class RcParams(MutableMapping, dict):
    """
    A dict-like key-value store for config parameters, including validation.

    Validating functions are defined and associated with rc parameters in
    :mod:`matplotlib.rcsetup`.

    The list of rcParams is:

    %s

    See Also
    --------
    :ref:`customizing-with-matplotlibrc-files`
    """

    validate = rcsetup._validators

    # validate values on the way in
    def __init__(self, *args, **kwargs):
        self.update(*args, **kwargs)

    def _set(self, key, val):
        """
        Directly write data bypassing deprecation and validation logic.

        Notes
        -----
        As end user or downstream library you almost always should use
        ``rcParams[key] = val`` and not ``_set()``.

        There are only very few special cases that need direct data access.
        These cases previously used ``dict.__setitem__(rcParams, key, val)``,
        which is now deprecated and replaced by ``rcParams._set(key, val)``.

        Even though private, we guarantee API stability for ``rcParams._set``,
        i.e. it is subject to Matplotlib's API and deprecation policy.

        :meta public:
        """
        dict.__setitem__(self, key, val)

    def _get(self, key):
        """
        Directly read data bypassing deprecation, backend and validation
        logic.

        Notes
        -----
        As end user or downstream library you almost always should use
        ``val = rcParams[key]`` and not ``_get()``.

        There are only very few special cases that need direct data access.
        These cases previously used ``dict.__getitem__(rcParams, key, val)``,
        which is now deprecated and replaced by ``rcParams._get(key)``.

        Even though private, we guarantee API stability for ``rcParams._get``,
        i.e. it is subject to Matplotlib's API and deprecation policy.

        :meta public:
        """
        return dict.__getitem__(self, key)

    def __setitem__(self, key, val):
        try:
            if key in _deprecated_map:
                version, alt_key, alt_val, inverse_alt = _deprecated_map[key]
                _api.warn_deprecated(
                    version, name=key, obj_type="rcparam", alternative=alt_key)
                key = alt_key
                val = alt_val(val)
            elif key in _deprecated_remain_as_none and val is not None:
                version, = _deprecated_remain_as_none[key]
                _api.warn_deprecated(version, name=key, obj_type="rcparam")
            elif key in _deprecated_ignore_map:
                version, alt_key = _deprecated_ignore_map[key]
                _api.warn_deprecated(
                    version, name=key, obj_type="rcparam", alternative=alt_key)
                return
            elif key == 'backend':
                if val is rcsetup._auto_backend_sentinel:
                    if 'backend' in self:
                        return
            try:
                cval = self.validate[key](val)
            except ValueError as ve:
                raise ValueError(f"Key {key}: {ve}") from None
            self._set(key, cval)
        except KeyError as err:
            raise KeyError(
                f"{key} is not a valid rc parameter (see rcParams.keys() for "
                f"a list of valid parameters)") from err

    def __getitem__(self, key):
        if key in _deprecated_map:
            version, alt_key, alt_val, inverse_alt = _deprecated_map[key]
            _api.warn_deprecated(
                version, name=key, obj_type="rcparam", alternative=alt_key)
            return inverse_alt(self._get(alt_key))

        elif key in _deprecated_ignore_map:
            version, alt_key = _deprecated_ignore_map[key]
            _api.warn_deprecated(
                version, name=key, obj_type="rcparam", alternative=alt_key)
            return self._get(alt_key) if alt_key else None

        # In theory, this should only ever be used after the global rcParams
        # has been set up, but better be safe e.g. in presence of breakpoints.
        elif key == "backend" and self is globals().get("rcParams"):
            val = self._get(key)
            if val is rcsetup._auto_backend_sentinel:
                from matplotlib import pyplot as plt
                plt.switch_backend(rcsetup._auto_backend_sentinel)

        return self._get(key)

    def _get_backend_or_none(self):
        """Get the requested backend, if any, without triggering resolution."""
        backend = self._get("backend")
        return None if backend is rcsetup._auto_backend_sentinel else backend

    def __repr__(self):
        class_name = self.__class__.__name__
        indent = len(class_name) + 1
        with _api.suppress_matplotlib_deprecation_warning():
            repr_split = pprint.pformat(dict(self), indent=1,
                                        width=80 - indent).split('\n')
        repr_indented = ('\n' + ' ' * indent).join(repr_split)
        return f'{class_name}({repr_indented})'

    def __str__(self):
        return '\n'.join(map('{0[0]}: {0[1]}'.format, sorted(self.items())))

    def __iter__(self):
        """Yield sorted list of keys."""
        with _api.suppress_matplotlib_deprecation_warning():
            yield from sorted(dict.__iter__(self))

    def __len__(self):
        return dict.__len__(self)

    def find_all(self, pattern):
        """
        Return the subset of this RcParams dictionary whose keys match,
        using :func:`re.search`, the given ``pattern``.

        .. note::

            Changes to the returned dictionary are *not* propagated to
            the parent RcParams dictionary.

        """
        pattern_re = re.compile(pattern)
        return RcParams((key, value)
                        for key, value in self.items()
                        if pattern_re.search(key))

    def copy(self):
        """Copy this RcParams instance."""
        rccopy = RcParams()
        for k in self:  # Skip deprecations and revalidation.
            rccopy._set(k, self._get(k))
        return rccopy


def rc_params(fail_on_error=False):
    """Construct a `RcParams` instance from the default Matplotlib rc file."""
    return rc_params_from_file(matplotlib_fname(), fail_on_error)


@functools.cache
def _get_ssl_context():
    try:
        import certifi
    except ImportError:
        _log.debug("Could not import certifi.")
        return None
    import ssl
    return ssl.create_default_context(cafile=certifi.where())


@contextlib.contextmanager
def _open_file_or_url(fname):
    if (isinstance(fname, str)
            and fname.startswith(('http://', 'https://', 'ftp://', 'file:'))):
        import urllib.request
        ssl_ctx = _get_ssl_context()
        if ssl_ctx is None:
            _log.debug(
                "Could not get certifi ssl context, https may not work."
            )
        with urllib.request.urlopen(fname, context=ssl_ctx) as f:
            yield (line.decode('utf-8') for line in f)
    else:
        fname = os.path.expanduser(fname)
        with open(fname, encoding='utf-8') as f:
            yield f


def _rc_params_in_file(fname, transform=lambda x: x, fail_on_error=False):
    """
    Construct a `RcParams` instance from file *fname*.

    Unlike `rc_params_from_file`, the configuration class only contains the
    parameters specified in the file (i.e. default values are not filled in).

    Parameters
    ----------
    fname : path-like
        The loaded file.
    transform : callable, default: the identity function
        A function called on each individual line of the file to transform it,
        before further parsing.
    fail_on_error : bool, default: False
        Whether invalid entries should result in an exception or a warning.
    """
    import matplotlib as mpl
    rc_temp = {}
    with _open_file_or_url(fname) as fd:
        try:
            for line_no, line in enumerate(fd, 1):
                line = transform(line)
                strippedline = cbook._strip_comment(line)
                if not strippedline:
                    continue
                tup = strippedline.split(':', 1)
                if len(tup) != 2:
                    _log.warning('Missing colon in file %r, line %d (%r)',
                                 fname, line_no, line.rstrip('\n'))
                    continue
                key, val = tup
                key = key.strip()
                val = val.strip()
                if val.startswith('"') and val.endswith('"'):
                    val = val[1:-1]  # strip double quotes
                if key in rc_temp:
                    _log.warning('Duplicate key in file %r, line %d (%r)',
                                 fname, line_no, line.rstrip('\n'))
                rc_temp[key] = (val, line, line_no)
        except UnicodeDecodeError:
            _log.warning('Cannot decode configuration file %r as utf-8.',
                         fname)
            raise

    config = RcParams()

    for key, (val, line, line_no) in rc_temp.items():
        if key in rcsetup._validators:
            if fail_on_error:
                config[key] = val  # try to convert to proper type or raise
            else:
                try:
                    config[key] = val  # try to convert to proper type or skip
                except Exception as msg:
                    _log.warning('Bad value in file %r, line %d (%r): %s',
                                 fname, line_no, line.rstrip('\n'), msg)
        elif key in _deprecated_ignore_map:
            version, alt_key = _deprecated_ignore_map[key]
            _api.warn_deprecated(
                version, name=key, alternative=alt_key, obj_type='rcparam',
                addendum="Please update your matplotlibrc.")
        else:
            # __version__ must be looked up as an attribute to trigger the
            # module-level __getattr__.
            version = ('main' if '.post' in mpl.__version__
                       else f'v{mpl.__version__}')
            _log.warning("""
Bad key %(key)s in file %(fname)s, line %(line_no)s (%(line)r)
You probably need to get an updated matplotlibrc file from
https://github.com/matplotlib/matplotlib/blob/%(version)s/lib/matplotlib/mpl-data/matplotlibrc
or from the matplotlib source distribution""",
                         dict(key=key, fname=fname, line_no=line_no,
                              line=line.rstrip('\n'), version=version))
    return config


def rc_params_from_file(fname, fail_on_error=False, use_default_template=True):
    """
    Construct a `RcParams` from file *fname*.

    Parameters
    ----------
    fname : str or path-like
        A file with Matplotlib rc settings.
    fail_on_error : bool
        If True, raise an error when the parser fails to convert a parameter.
    use_default_template : bool
        If True, initialize with default parameters before updating with those
        in the given file. If False, the configuration class only contains the
        parameters specified in the file. (Useful for updating dicts.)
    """
    config_from_file = _rc_params_in_file(fname, fail_on_error=fail_on_error)

    if not use_default_template:
        return config_from_file

    with _api.suppress_matplotlib_deprecation_warning():
        config = RcParams({**rcParamsDefault, **config_from_file})

    if "".join(config['text.latex.preamble']):
        _log.info("""
*****************************************************************
You have the following UNSUPPORTED LaTeX preamble customizations:
%s
Please do not ask for support with these customizations active.
*****************************************************************
""", '\n'.join(config['text.latex.preamble']))
    _log.debug('loaded rc file %s', fname)

    return config


# When constructing the global instances, we need to perform certain updates
# by explicitly calling the superclass (dict.update, dict.items) to avoid
# triggering resolution of _auto_backend_sentinel.
rcParamsDefault = _rc_params_in_file(
    cbook._get_data_path("matplotlibrc"),
    # Strip leading comment.
    transform=lambda line: line[1:] if line.startswith("#") else line,
    fail_on_error=True)
dict.update(rcParamsDefault, rcsetup._hardcoded_defaults)
# Normally, the default matplotlibrc file contains *no* entry for backend (the
# corresponding line starts with ##, not #; we fill on _auto_backend_sentinel
# in that case.  However, packagers can set a different default backend
# (resulting in a normal `#backend: foo` line) in which case we should *not*
# fill in _auto_backend_sentinel.
dict.setdefault(rcParamsDefault, "backend", rcsetup._auto_backend_sentinel)
rcParams = RcParams()  # The global instance.
dict.update(rcParams, dict.items(rcParamsDefault))
dict.update(rcParams, _rc_params_in_file(matplotlib_fname()))
rcParamsOrig = rcParams.copy()
with _api.suppress_matplotlib_deprecation_warning():
    # This also checks that all rcParams are indeed listed in the template.
    # Assigning to rcsetup.defaultParams is left only for backcompat.
    defaultParams = rcsetup.defaultParams = {
        # We want to resolve deprecated rcParams, but not backend...
        key: [(rcsetup._auto_backend_sentinel if key == "backend" else
               rcParamsDefault[key]),
              validator]
        for key, validator in rcsetup._validators.items()}
if rcParams['axes.formatter.use_locale']:
    locale.setlocale(locale.LC_ALL, '')


def rc(group, **kwargs):
    """
    Set the current `.rcParams`.  *group* is the grouping for the rc, e.g.,
    for ``lines.linewidth`` the group is ``lines``, for
    ``axes.facecolor``, the group is ``axes``, and so on.  Group may
    also be a list or tuple of group names, e.g., (*xtick*, *ytick*).
    *kwargs* is a dictionary attribute name/value pairs, e.g.,::

      rc('lines', linewidth=2, color='r')

    sets the current `.rcParams` and is equivalent to::

      rcParams['lines.linewidth'] = 2
      rcParams['lines.color'] = 'r'

    The following aliases are available to save typing for interactive users:

    =====   =================
    Alias   Property
    =====   =================
    'lw'    'linewidth'
    'ls'    'linestyle'
    'c'     'color'
    'fc'    'facecolor'
    'ec'    'edgecolor'
    'mew'   'markeredgewidth'
    'aa'    'antialiased'
    =====   =================

    Thus you could abbreviate the above call as::

          rc('lines', lw=2, c='r')

    Note you can use python's kwargs dictionary facility to store
    dictionaries of default parameters.  e.g., you can customize the
    font rc as follows::

      font = {'family' : 'monospace',
              'weight' : 'bold',
              'size'   : 'larger'}
      rc('font', **font)  # pass in the font dict as kwargs

    This enables you to easily switch between several configurations.  Use
    ``matplotlib.style.use('default')`` or :func:`~matplotlib.rcdefaults` to
    restore the default `.rcParams` after changes.

    Notes
    -----
    Similar functionality is available by using the normal dict interface, i.e.
    ``rcParams.update({"lines.linewidth": 2, ...})`` (but ``rcParams.update``
    does not support abbreviations or grouping).
    """

    aliases = {
        'lw':  'linewidth',
        'ls':  'linestyle',
        'c':   'color',
        'fc':  'facecolor',
        'ec':  'edgecolor',
        'mew': 'markeredgewidth',
        'aa':  'antialiased',
        }

    if isinstance(group, str):
        group = (group,)
    for g in group:
        for k, v in kwargs.items():
            name = aliases.get(k) or k
            key = f'{g}.{name}'
            try:
                rcParams[key] = v
            except KeyError as err:
                raise KeyError(('Unrecognized key "%s" for group "%s" and '
                                'name "%s"') % (key, g, name)) from err


def rcdefaults():
    """
    Restore the `.rcParams` from Matplotlib's internal default style.

    Style-blacklisted `.rcParams` (defined in
    ``matplotlib.style.core.STYLE_BLACKLIST``) are not updated.

    See Also
    --------
    matplotlib.rc_file_defaults
        Restore the `.rcParams` from the rc file originally loaded by
        Matplotlib.
    matplotlib.style.use
        Use a specific style file.  Call ``style.use('default')`` to restore
        the default style.
    """
    # Deprecation warnings were already handled when creating rcParamsDefault,
    # no need to reemit them here.
    with _api.suppress_matplotlib_deprecation_warning():
        from .style.core import STYLE_BLACKLIST
        rcParams.clear()
        rcParams.update({k: v for k, v in rcParamsDefault.items()
                         if k not in STYLE_BLACKLIST})


def rc_file_defaults():
    """
    Restore the `.rcParams` from the original rc file loaded by Matplotlib.

    Style-blacklisted `.rcParams` (defined in
    ``matplotlib.style.core.STYLE_BLACKLIST``) are not updated.
    """
    # Deprecation warnings were already handled when creating rcParamsOrig, no
    # need to reemit them here.
    with _api.suppress_matplotlib_deprecation_warning():
        from .style.core import STYLE_BLACKLIST
        rcParams.update({k: rcParamsOrig[k] for k in rcParamsOrig
                         if k not in STYLE_BLACKLIST})


def rc_file(fname, *, use_default_template=True):
    """
    Update `.rcParams` from file.

    Style-blacklisted `.rcParams` (defined in
    ``matplotlib.style.core.STYLE_BLACKLIST``) are not updated.

    Parameters
    ----------
    fname : str or path-like
        A file with Matplotlib rc settings.

    use_default_template : bool
        If True, initialize with default parameters before updating with those
        in the given file. If False, the current configuration persists
        and only the parameters specified in the file are updated.
    """
    # Deprecation warnings were already handled in rc_params_from_file, no need
    # to reemit them here.
    with _api.suppress_matplotlib_deprecation_warning():
        from .style.core import STYLE_BLACKLIST
        rc_from_file = rc_params_from_file(
            fname, use_default_template=use_default_template)
        rcParams.update({k: rc_from_file[k] for k in rc_from_file
                         if k not in STYLE_BLACKLIST})


@contextlib.contextmanager
def rc_context(rc=None, fname=None):
    """
    Return a context manager for temporarily changing rcParams.

    The :rc:`backend` will not be reset by the context manager.

    rcParams changed both through the context manager invocation and
    in the body of the context will be reset on context exit.

    Parameters
    ----------
    rc : dict
        The rcParams to temporarily set.
    fname : str or path-like
        A file with Matplotlib rc settings. If both *fname* and *rc* are given,
        settings from *rc* take precedence.

    See Also
    --------
    :ref:`customizing-with-matplotlibrc-files`

    Examples
    --------
    Passing explicit values via a dict::

        with mpl.rc_context({'interactive': False}):
            fig, ax = plt.subplots()
            ax.plot(range(3), range(3))
            fig.savefig('example.png')
            plt.close(fig)

    Loading settings from a file::

         with mpl.rc_context(fname='print.rc'):
             plt.plot(x, y)  # uses 'print.rc'

    Setting in the context body::

        with mpl.rc_context():
            # will be reset
            mpl.rcParams['lines.linewidth'] = 5
            plt.plot(x, y)

    """
    orig = dict(rcParams.copy())
    del orig['backend']
    try:
        if fname:
            rc_file(fname)
        if rc:
            rcParams.update(rc)
        yield
    finally:
        dict.update(rcParams, orig)  # Revert to the original rcs.


def use(backend, *, force=True):
    """
    Select the backend used for rendering and GUI integration.

    If pyplot is already imported, `~matplotlib.pyplot.switch_backend` is used
    and if the new backend is different than the current backend, all Figures
    will be closed.

    Parameters
    ----------
    backend : str
        The backend to switch to.  This can either be one of the standard
        backend names, which are case-insensitive:

        - interactive backends:
          GTK3Agg, GTK3Cairo, GTK4Agg, GTK4Cairo, MacOSX, nbAgg, QtAgg,
          QtCairo, TkAgg, TkCairo, WebAgg, WX, WXAgg, WXCairo, Qt5Agg, Qt5Cairo

        - non-interactive backends:
          agg, cairo, pdf, pgf, ps, svg, template

        or a string of the form: ``module://my.module.name``.

        Switching to an interactive backend is not possible if an unrelated
        event loop has already been started (e.g., switching to GTK3Agg if a
        TkAgg window has already been opened).  Switching to a non-interactive
        backend is always possible.

    force : bool, default: True
        If True (the default), raise an `ImportError` if the backend cannot be
        set up (either because it fails to import, or because an incompatible
        GUI interactive framework is already running); if False, silently
        ignore the failure.

    See Also
    --------
    :ref:`backends`
    matplotlib.get_backend
    matplotlib.pyplot.switch_backend

    """
    name = validate_backend(backend)
    # don't (prematurely) resolve the "auto" backend setting
    if rcParams._get_backend_or_none() == name:
        # Nothing to do if the requested backend is already set
        pass
    else:
        # if pyplot is not already imported, do not import it.  Doing
        # so may trigger a `plt.switch_backend` to the _default_ backend
        # before we get a chance to change to the one the user just requested
        plt = sys.modules.get('matplotlib.pyplot')
        # if pyplot is imported, then try to change backends
        if plt is not None:
            try:
                # we need this import check here to re-raise if the
                # user does not have the libraries to support their
                # chosen backend installed.
                plt.switch_backend(name)
            except ImportError:
                if force:
                    raise
        # if we have not imported pyplot, then we can set the rcParam
        # value which will be respected when the user finally imports
        # pyplot
        else:
            rcParams['backend'] = backend
    # if the user has asked for a given backend, do not helpfully
    # fallback
    rcParams['backend_fallback'] = False


if os.environ.get('MPLBACKEND'):
    rcParams['backend'] = os.environ.get('MPLBACKEND')


def get_backend():
    """
    Return the name of the current backend.

    See Also
    --------
    matplotlib.use
    """
    return rcParams['backend']


def interactive(b):
    """
    Set whether to redraw after every plotting command (e.g. `.pyplot.xlabel`).
    """
    rcParams['interactive'] = b


def is_interactive():
    """
    Return whether to redraw after every plotting command.

    .. note::

        This function is only intended for use in backends. End users should
        use `.pyplot.isinteractive` instead.
    """
    return rcParams['interactive']


def _val_or_rc(val, rc_name):
    """
    If *val* is None, return ``mpl.rcParams[rc_name]``, otherwise return val.
    """
    return val if val is not None else rcParams[rc_name]


def _init_tests():
    # The version of FreeType to install locally for running the
    # tests.  This must match the value in `setupext.py`
    LOCAL_FREETYPE_VERSION = '2.12.1'

    from matplotlib import ft2font
    if (ft2font.__freetype_version__ != LOCAL_FREETYPE_VERSION or
            ft2font.__freetype_build_type__ != 'local'):
        _log.warning(
            f"Matplotlib is not built with the correct FreeType version to "
            f"run tests.  Rebuild without setting system_freetype=1 in "
            f"mplsetup.cfg.  Expect many image comparison failures below.  "
            f"Expected freetype version {LOCAL_FREETYPE_VERSION}.  "
            f"Found freetype version {ft2font.__freetype_version__}.  "
            "Freetype build type is {}local".format(
                "" if ft2font.__freetype_build_type__ == 'local' else "not "))


def _replacer(data, value):
    """
    Either returns ``data[value]`` or passes ``data`` back, converts either to
    a sequence.
    """
    try:
        # if key isn't a string don't bother
        if isinstance(value, str):
            # try to use __getitem__
            value = data[value]
    except Exception:
        # key does not exist, silently fall back to key
        pass
    return sanitize_sequence(value)


def _label_from_arg(y, default_name):
    try:
        return y.name
    except AttributeError:
        if isinstance(default_name, str):
            return default_name
    return None


def _add_data_doc(docstring, replace_names):
    """
    Add documentation for a *data* field to the given docstring.

    Parameters
    ----------
    docstring : str
        The input docstring.
    replace_names : list of str or None
        The list of parameter names which arguments should be replaced by
        ``data[name]`` (if ``data[name]`` does not throw an exception).  If
        None, replacement is attempted for all arguments.

    Returns
    -------
    str
        The augmented docstring.
    """
    if (docstring is None
            or replace_names is not None and len(replace_names) == 0):
        return docstring
    docstring = inspect.cleandoc(docstring)

    data_doc = ("""\
    If given, all parameters also accept a string ``s``, which is
    interpreted as ``data[s]`` (unless this raises an exception)."""
                if replace_names is None else f"""\
    If given, the following parameters also accept a string ``s``, which is
    interpreted as ``data[s]`` (unless this raises an exception):

    {', '.join(map('*{}*'.format, replace_names))}""")
    # using string replacement instead of formatting has the advantages
    # 1) simpler indent handling
    # 2) prevent problems with formatting characters '{', '%' in the docstring
    if _log.level <= logging.DEBUG:
        # test_data_parameter_replacement() tests against these log messages
        # make sure to keep message and test in sync
        if "data : indexable object, optional" not in docstring:
            _log.debug("data parameter docstring error: no data parameter")
        if 'DATA_PARAMETER_PLACEHOLDER' not in docstring:
            _log.debug("data parameter docstring error: missing placeholder")
    return docstring.replace('    DATA_PARAMETER_PLACEHOLDER', data_doc)


def _preprocess_data(func=None, *, replace_names=None, label_namer=None):
    """
    A decorator to add a 'data' kwarg to a function.

    When applied::

        @_preprocess_data()
        def func(ax, *args, **kwargs): ...

    the signature is modified to ``decorated(ax, *args, data=None, **kwargs)``
    with the following behavior:

    - if called with ``data=None``, forward the other arguments to ``func``;
    - otherwise, *data* must be a mapping; for any argument passed in as a
      string ``name``, replace the argument by ``data[name]`` (if this does not
      throw an exception), then forward the arguments to ``func``.

    In either case, any argument that is a `MappingView` is also converted to a
    list.

    Parameters
    ----------
    replace_names : list of str or None, default: None
        The list of parameter names for which lookup into *data* should be
        attempted. If None, replacement is attempted for all arguments.
    label_namer : str, default: None
        If set e.g. to "namer" (which must be a kwarg in the function's
        signature -- not as ``**kwargs``), if the *namer* argument passed in is
        a (string) key of *data* and no *label* kwarg is passed, then use the
        (string) value of the *namer* as *label*. ::

            @_preprocess_data(label_namer="foo")
            def func(foo, label=None): ...

            func("key", data={"key": value})
            # is equivalent to
            func.__wrapped__(value, label="key")
    """

    if func is None:  # Return the actual decorator.
        return functools.partial(
            _preprocess_data,
            replace_names=replace_names, label_namer=label_namer)

    sig = inspect.signature(func)
    varargs_name = None
    varkwargs_name = None
    arg_names = []
    params = list(sig.parameters.values())
    for p in params:
        if p.kind is Parameter.VAR_POSITIONAL:
            varargs_name = p.name
        elif p.kind is Parameter.VAR_KEYWORD:
            varkwargs_name = p.name
        else:
            arg_names.append(p.name)
    data_param = Parameter("data", Parameter.KEYWORD_ONLY, default=None)
    if varkwargs_name:
        params.insert(-1, data_param)
    else:
        params.append(data_param)
    new_sig = sig.replace(parameters=params)
    arg_names = arg_names[1:]  # remove the first "ax" / self arg

    assert {*arg_names}.issuperset(replace_names or []) or varkwargs_name, (
        "Matplotlib internal error: invalid replace_names "
        f"({replace_names!r}) for {func.__name__!r}")
    assert label_namer is None or label_namer in arg_names, (
        "Matplotlib internal error: invalid label_namer "
        f"({label_namer!r}) for {func.__name__!r}")

    @functools.wraps(func)
    def inner(ax, *args, data=None, **kwargs):
        if data is None:
            return func(ax, *map(sanitize_sequence, args), **kwargs)

        bound = new_sig.bind(ax, *args, **kwargs)
        auto_label = (bound.arguments.get(label_namer)
                      or bound.kwargs.get(label_namer))

        for k, v in bound.arguments.items():
            if k == varkwargs_name:
                for k1, v1 in v.items():
                    if replace_names is None or k1 in replace_names:
                        v[k1] = _replacer(data, v1)
            elif k == varargs_name:
                if replace_names is None:
                    bound.arguments[k] = tuple(_replacer(data, v1) for v1 in v)
            else:
                if replace_names is None or k in replace_names:
                    bound.arguments[k] = _replacer(data, v)

        new_args = bound.args
        new_kwargs = bound.kwargs

        args_and_kwargs = {**bound.arguments, **bound.kwargs}
        if label_namer and "label" not in args_and_kwargs:
            new_kwargs["label"] = _label_from_arg(
                args_and_kwargs.get(label_namer), auto_label)

        return func(*new_args, **new_kwargs)

    inner.__doc__ = _add_data_doc(inner.__doc__, replace_names)
    inner.__signature__ = new_sig
    return inner


_log.debug('interactive is %s', is_interactive())
_log.debug('platform is %s', sys.platform)


# workaround: we must defer colormaps import to after loading rcParams, because
# colormap creation depends on rcParams
from matplotlib.cm import _colormaps as colormaps
from matplotlib.colors import _color_sequences as color_sequences
