""" 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. """ # start delvewheel patch def _delvewheel_patch_1_5_4(): import os libs_dir = os.path.abspath(os.path.join(os.path.dirname(__file__), os.pardir, 'matplotlib.libs')) if os.path.isdir(libs_dir): os.add_dll_directory(libs_dir) _delvewheel_patch_1_5_4() del _delvewheel_patch_1_5_4 # end delvewheel patch __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 = '2.6.1' 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