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omnilmm/lib/python3.10/site-packages/matplotlib/cbook/__init__.py

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+"""
+A collection of utility functions and classes.  Originally, many
+(but not all) were from the Python Cookbook -- hence the name cbook.
+
+This module is safe to import from anywhere within Matplotlib;
+it imports Matplotlib only at runtime.
+"""
+
+import collections
+import collections.abc
+import contextlib
+import functools
+import gzip
+import itertools
+import math
+import operator
+import os
+from pathlib import Path
+import shlex
+import subprocess
+import sys
+import time
+import traceback
+import types
+import weakref
+
+import numpy as np
+
+try:
+    from numpy.exceptions import VisibleDeprecationWarning  # numpy >= 1.25
+except ImportError:
+    from numpy import VisibleDeprecationWarning
+
+import matplotlib
+from matplotlib import _api, _c_internal_utils
+
+
+@_api.caching_module_getattr
+class __getattr__:
+    # module-level deprecations
+    MatplotlibDeprecationWarning = _api.deprecated(
+        "3.6", obj_type="",
+        alternative="matplotlib.MatplotlibDeprecationWarning")(
+        property(lambda self: _api.deprecation.MatplotlibDeprecationWarning))
+    mplDeprecation = _api.deprecated(
+        "3.6", obj_type="",
+        alternative="matplotlib.MatplotlibDeprecationWarning")(
+        property(lambda self: _api.deprecation.MatplotlibDeprecationWarning))
+
+
+def _get_running_interactive_framework():
+    """
+    Return the interactive framework whose event loop is currently running, if
+    any, or "headless" if no event loop can be started, or None.
+
+    Returns
+    -------
+    Optional[str]
+        One of the following values: "qt", "gtk3", "gtk4", "wx", "tk",
+        "macosx", "headless", ``None``.
+    """
+    # Use ``sys.modules.get(name)`` rather than ``name in sys.modules`` as
+    # entries can also have been explicitly set to None.
+    QtWidgets = (
+        sys.modules.get("PyQt6.QtWidgets")
+        or sys.modules.get("PySide6.QtWidgets")
+        or sys.modules.get("PyQt5.QtWidgets")
+        or sys.modules.get("PySide2.QtWidgets")
+    )
+    if QtWidgets and QtWidgets.QApplication.instance():
+        return "qt"
+    Gtk = sys.modules.get("gi.repository.Gtk")
+    if Gtk:
+        if Gtk.MAJOR_VERSION == 4:
+            from gi.repository import GLib
+            if GLib.main_depth():
+                return "gtk4"
+        if Gtk.MAJOR_VERSION == 3 and Gtk.main_level():
+            return "gtk3"
+    wx = sys.modules.get("wx")
+    if wx and wx.GetApp():
+        return "wx"
+    tkinter = sys.modules.get("tkinter")
+    if tkinter:
+        codes = {tkinter.mainloop.__code__, tkinter.Misc.mainloop.__code__}
+        for frame in sys._current_frames().values():
+            while frame:
+                if frame.f_code in codes:
+                    return "tk"
+                frame = frame.f_back
+        # premetively break reference cycle between locals and the frame
+        del frame
+    macosx = sys.modules.get("matplotlib.backends._macosx")
+    if macosx and macosx.event_loop_is_running():
+        return "macosx"
+    if not _c_internal_utils.display_is_valid():
+        return "headless"
+    return None
+
+
+def _exception_printer(exc):
+    if _get_running_interactive_framework() in ["headless", None]:
+        raise exc
+    else:
+        traceback.print_exc()
+
+
+class _StrongRef:
+    """
+    Wrapper similar to a weakref, but keeping a strong reference to the object.
+    """
+
+    def __init__(self, obj):
+        self._obj = obj
+
+    def __call__(self):
+        return self._obj
+
+    def __eq__(self, other):
+        return isinstance(other, _StrongRef) and self._obj == other._obj
+
+    def __hash__(self):
+        return hash(self._obj)
+
+
+def _weak_or_strong_ref(func, callback):
+    """
+    Return a `WeakMethod` wrapping *func* if possible, else a `_StrongRef`.
+    """
+    try:
+        return weakref.WeakMethod(func, callback)
+    except TypeError:
+        return _StrongRef(func)
+
+
+class CallbackRegistry:
+    """
+    Handle registering, processing, blocking, and disconnecting
+    for a set of signals and callbacks:
+
+        >>> def oneat(x):
+        ...     print('eat', x)
+        >>> def ondrink(x):
+        ...     print('drink', x)
+
+        >>> from matplotlib.cbook import CallbackRegistry
+        >>> callbacks = CallbackRegistry()
+
+        >>> id_eat = callbacks.connect('eat', oneat)
+        >>> id_drink = callbacks.connect('drink', ondrink)
+
+        >>> callbacks.process('drink', 123)
+        drink 123
+        >>> callbacks.process('eat', 456)
+        eat 456
+        >>> callbacks.process('be merry', 456)   # nothing will be called
+
+        >>> callbacks.disconnect(id_eat)
+        >>> callbacks.process('eat', 456)        # nothing will be called
+
+        >>> with callbacks.blocked(signal='drink'):
+        ...     callbacks.process('drink', 123)  # nothing will be called
+        >>> callbacks.process('drink', 123)
+        drink 123
+
+    In practice, one should always disconnect all callbacks when they are
+    no longer needed to avoid dangling references (and thus memory leaks).
+    However, real code in Matplotlib rarely does so, and due to its design,
+    it is rather difficult to place this kind of code.  To get around this,
+    and prevent this class of memory leaks, we instead store weak references
+    to bound methods only, so when the destination object needs to die, the
+    CallbackRegistry won't keep it alive.
+
+    Parameters
+    ----------
+    exception_handler : callable, optional
+       If not None, *exception_handler* must be a function that takes an
+       `Exception` as single parameter.  It gets called with any `Exception`
+       raised by the callbacks during `CallbackRegistry.process`, and may
+       either re-raise the exception or handle it in another manner.
+
+       The default handler prints the exception (with `traceback.print_exc`) if
+       an interactive event loop is running; it re-raises the exception if no
+       interactive event loop is running.
+
+    signals : list, optional
+        If not None, *signals* is a list of signals that this registry handles:
+        attempting to `process` or to `connect` to a signal not in the list
+        throws a `ValueError`.  The default, None, does not restrict the
+        handled signals.
+    """
+
+    # We maintain two mappings:
+    #   callbacks: signal -> {cid -> weakref-to-callback}
+    #   _func_cid_map: signal -> {weakref-to-callback -> cid}
+
+    def __init__(self, exception_handler=_exception_printer, *, signals=None):
+        self._signals = None if signals is None else list(signals)  # Copy it.
+        self.exception_handler = exception_handler
+        self.callbacks = {}
+        self._cid_gen = itertools.count()
+        self._func_cid_map = {}
+        # A hidden variable that marks cids that need to be pickled.
+        self._pickled_cids = set()
+
+    def __getstate__(self):
+        return {
+            **vars(self),
+            # In general, callbacks may not be pickled, so we just drop them,
+            # unless directed otherwise by self._pickled_cids.
+            "callbacks": {s: {cid: proxy() for cid, proxy in d.items()
+                              if cid in self._pickled_cids}
+                          for s, d in self.callbacks.items()},
+            # It is simpler to reconstruct this from callbacks in __setstate__.
+            "_func_cid_map": None,
+            "_cid_gen": next(self._cid_gen)
+        }
+
+    def __setstate__(self, state):
+        cid_count = state.pop('_cid_gen')
+        vars(self).update(state)
+        self.callbacks = {
+            s: {cid: _weak_or_strong_ref(func, self._remove_proxy)
+                for cid, func in d.items()}
+            for s, d in self.callbacks.items()}
+        self._func_cid_map = {
+            s: {proxy: cid for cid, proxy in d.items()}
+            for s, d in self.callbacks.items()}
+        self._cid_gen = itertools.count(cid_count)
+
+    def connect(self, signal, func):
+        """Register *func* to be called when signal *signal* is generated."""
+        if self._signals is not None:
+            _api.check_in_list(self._signals, signal=signal)
+        self._func_cid_map.setdefault(signal, {})
+        proxy = _weak_or_strong_ref(func, self._remove_proxy)
+        if proxy in self._func_cid_map[signal]:
+            return self._func_cid_map[signal][proxy]
+        cid = next(self._cid_gen)
+        self._func_cid_map[signal][proxy] = cid
+        self.callbacks.setdefault(signal, {})
+        self.callbacks[signal][cid] = proxy
+        return cid
+
+    def _connect_picklable(self, signal, func):
+        """
+        Like `.connect`, but the callback is kept when pickling/unpickling.
+
+        Currently internal-use only.
+        """
+        cid = self.connect(signal, func)
+        self._pickled_cids.add(cid)
+        return cid
+
+    # Keep a reference to sys.is_finalizing, as sys may have been cleared out
+    # at that point.
+    def _remove_proxy(self, proxy, *, _is_finalizing=sys.is_finalizing):
+        if _is_finalizing():
+            # Weakrefs can't be properly torn down at that point anymore.
+            return
+        for signal, proxy_to_cid in list(self._func_cid_map.items()):
+            cid = proxy_to_cid.pop(proxy, None)
+            if cid is not None:
+                del self.callbacks[signal][cid]
+                self._pickled_cids.discard(cid)
+                break
+        else:
+            # Not found
+            return
+        # Clean up empty dicts
+        if len(self.callbacks[signal]) == 0:
+            del self.callbacks[signal]
+            del self._func_cid_map[signal]
+
+    def disconnect(self, cid):
+        """
+        Disconnect the callback registered with callback id *cid*.
+
+        No error is raised if such a callback does not exist.
+        """
+        self._pickled_cids.discard(cid)
+        # Clean up callbacks
+        for signal, cid_to_proxy in list(self.callbacks.items()):
+            proxy = cid_to_proxy.pop(cid, None)
+            if proxy is not None:
+                break
+        else:
+            # Not found
+            return
+
+        proxy_to_cid = self._func_cid_map[signal]
+        for current_proxy, current_cid in list(proxy_to_cid.items()):
+            if current_cid == cid:
+                assert proxy is current_proxy
+                del proxy_to_cid[current_proxy]
+        # Clean up empty dicts
+        if len(self.callbacks[signal]) == 0:
+            del self.callbacks[signal]
+            del self._func_cid_map[signal]
+
+    def process(self, s, *args, **kwargs):
+        """
+        Process signal *s*.
+
+        All of the functions registered to receive callbacks on *s* will be
+        called with ``*args`` and ``**kwargs``.
+        """
+        if self._signals is not None:
+            _api.check_in_list(self._signals, signal=s)
+        for cid, ref in list(self.callbacks.get(s, {}).items()):
+            func = ref()
+            if func is not None:
+                try:
+                    func(*args, **kwargs)
+                # this does not capture KeyboardInterrupt, SystemExit,
+                # and GeneratorExit
+                except Exception as exc:
+                    if self.exception_handler is not None:
+                        self.exception_handler(exc)
+                    else:
+                        raise
+
+    @contextlib.contextmanager
+    def blocked(self, *, signal=None):
+        """
+        Block callback signals from being processed.
+
+        A context manager to temporarily block/disable callback signals
+        from being processed by the registered listeners.
+
+        Parameters
+        ----------
+        signal : str, optional
+            The callback signal to block. The default is to block all signals.
+        """
+        orig = self.callbacks
+        try:
+            if signal is None:
+                # Empty out the callbacks
+                self.callbacks = {}
+            else:
+                # Only remove the specific signal
+                self.callbacks = {k: orig[k] for k in orig if k != signal}
+            yield
+        finally:
+            self.callbacks = orig
+
+
+class silent_list(list):
+    """
+    A list with a short ``repr()``.
+
+    This is meant to be used for a homogeneous list of artists, so that they
+    don't cause long, meaningless output.
+
+    Instead of ::
+
+        [<matplotlib.lines.Line2D object at 0x7f5749fed3c8>,
+         <matplotlib.lines.Line2D object at 0x7f5749fed4e0>,
+         <matplotlib.lines.Line2D object at 0x7f5758016550>]
+
+    one will get ::
+
+        <a list of 3 Line2D objects>
+
+    If ``self.type`` is None, the type name is obtained from the first item in
+    the list (if any).
+    """
+
+    def __init__(self, type, seq=None):
+        self.type = type
+        if seq is not None:
+            self.extend(seq)
+
+    def __repr__(self):
+        if self.type is not None or len(self) != 0:
+            tp = self.type if self.type is not None else type(self[0]).__name__
+            return f"<a list of {len(self)} {tp} objects>"
+        else:
+            return "<an empty list>"
+
+
+def _local_over_kwdict(
+        local_var, kwargs, *keys,
+        warning_cls=_api.MatplotlibDeprecationWarning):
+    out = local_var
+    for key in keys:
+        kwarg_val = kwargs.pop(key, None)
+        if kwarg_val is not None:
+            if out is None:
+                out = kwarg_val
+            else:
+                _api.warn_external(f'"{key}" keyword argument will be ignored',
+                                   warning_cls)
+    return out
+
+
+def strip_math(s):
+    """
+    Remove latex formatting from mathtext.
+
+    Only handles fully math and fully non-math strings.
+    """
+    if len(s) >= 2 and s[0] == s[-1] == "$":
+        s = s[1:-1]
+        for tex, plain in [
+                (r"\times", "x"),  # Specifically for Formatter support.
+                (r"\mathdefault", ""),
+                (r"\rm", ""),
+                (r"\cal", ""),
+                (r"\tt", ""),
+                (r"\it", ""),
+                ("\\", ""),
+                ("{", ""),
+                ("}", ""),
+        ]:
+            s = s.replace(tex, plain)
+    return s
+
+
+def _strip_comment(s):
+    """Strip everything from the first unquoted #."""
+    pos = 0
+    while True:
+        quote_pos = s.find('"', pos)
+        hash_pos = s.find('#', pos)
+        if quote_pos < 0:
+            without_comment = s if hash_pos < 0 else s[:hash_pos]
+            return without_comment.strip()
+        elif 0 <= hash_pos < quote_pos:
+            return s[:hash_pos].strip()
+        else:
+            closing_quote_pos = s.find('"', quote_pos + 1)
+            if closing_quote_pos < 0:
+                raise ValueError(
+                    f"Missing closing quote in: {s!r}. If you need a double-"
+                    'quote inside a string, use escaping: e.g. "the \" char"')
+            pos = closing_quote_pos + 1  # behind closing quote
+
+
+def is_writable_file_like(obj):
+    """Return whether *obj* looks like a file object with a *write* method."""
+    return callable(getattr(obj, 'write', None))
+
+
+def file_requires_unicode(x):
+    """
+    Return whether the given writable file-like object requires Unicode to be
+    written to it.
+    """
+    try:
+        x.write(b'')
+    except TypeError:
+        return True
+    else:
+        return False
+
+
+def to_filehandle(fname, flag='r', return_opened=False, encoding=None):
+    """
+    Convert a path to an open file handle or pass-through a file-like object.
+
+    Consider using `open_file_cm` instead, as it allows one to properly close
+    newly created file objects more easily.
+
+    Parameters
+    ----------
+    fname : str or path-like or file-like
+        If `str` or `os.PathLike`, the file is opened using the flags specified
+        by *flag* and *encoding*.  If a file-like object, it is passed through.
+    flag : str, default: 'r'
+        Passed as the *mode* argument to `open` when *fname* is `str` or
+        `os.PathLike`; ignored if *fname* is file-like.
+    return_opened : bool, default: False
+        If True, return both the file object and a boolean indicating whether
+        this was a new file (that the caller needs to close).  If False, return
+        only the new file.
+    encoding : str or None, default: None
+        Passed as the *mode* argument to `open` when *fname* is `str` or
+        `os.PathLike`; ignored if *fname* is file-like.
+
+    Returns
+    -------
+    fh : file-like
+    opened : bool
+        *opened* is only returned if *return_opened* is True.
+    """
+    if isinstance(fname, os.PathLike):
+        fname = os.fspath(fname)
+    if isinstance(fname, str):
+        if fname.endswith('.gz'):
+            fh = gzip.open(fname, flag)
+        elif fname.endswith('.bz2'):
+            # python may not be compiled with bz2 support,
+            # bury import until we need it
+            import bz2
+            fh = bz2.BZ2File(fname, flag)
+        else:
+            fh = open(fname, flag, encoding=encoding)
+        opened = True
+    elif hasattr(fname, 'seek'):
+        fh = fname
+        opened = False
+    else:
+        raise ValueError('fname must be a PathLike or file handle')
+    if return_opened:
+        return fh, opened
+    return fh
+
+
+def open_file_cm(path_or_file, mode="r", encoding=None):
+    r"""Pass through file objects and context-manage path-likes."""
+    fh, opened = to_filehandle(path_or_file, mode, True, encoding)
+    return fh if opened else contextlib.nullcontext(fh)
+
+
+def is_scalar_or_string(val):
+    """Return whether the given object is a scalar or string like."""
+    return isinstance(val, str) or not np.iterable(val)
+
+
+def get_sample_data(fname, asfileobj=True, *, np_load=False):
+    """
+    Return a sample data file.  *fname* is a path relative to the
+    :file:`mpl-data/sample_data` directory.  If *asfileobj* is `True`
+    return a file object, otherwise just a file path.
+
+    Sample data files are stored in the 'mpl-data/sample_data' directory within
+    the Matplotlib package.
+
+    If the filename ends in .gz, the file is implicitly ungzipped.  If the
+    filename ends with .npy or .npz, *asfileobj* is True, and *np_load* is
+    True, the file is loaded with `numpy.load`.  *np_load* currently defaults
+    to False but will default to True in a future release.
+    """
+    path = _get_data_path('sample_data', fname)
+    if asfileobj:
+        suffix = path.suffix.lower()
+        if suffix == '.gz':
+            return gzip.open(path)
+        elif suffix in ['.npy', '.npz']:
+            if np_load:
+                return np.load(path)
+            else:
+                _api.warn_deprecated(
+                    "3.3", message="In a future release, get_sample_data "
+                    "will automatically load numpy arrays.  Set np_load to "
+                    "True to get the array and suppress this warning.  Set "
+                    "asfileobj to False to get the path to the data file and "
+                    "suppress this warning.")
+                return path.open('rb')
+        elif suffix in ['.csv', '.xrc', '.txt']:
+            return path.open('r')
+        else:
+            return path.open('rb')
+    else:
+        return str(path)
+
+
+def _get_data_path(*args):
+    """
+    Return the `pathlib.Path` to a resource file provided by Matplotlib.
+
+    ``*args`` specify a path relative to the base data path.
+    """
+    return Path(matplotlib.get_data_path(), *args)
+
+
+def flatten(seq, scalarp=is_scalar_or_string):
+    """
+    Return a generator of flattened nested containers.
+
+    For example:
+
+        >>> from matplotlib.cbook import flatten
+        >>> l = (('John', ['Hunter']), (1, 23), [[([42, (5, 23)], )]])
+        >>> print(list(flatten(l)))
+        ['John', 'Hunter', 1, 23, 42, 5, 23]
+
+    By: Composite of Holger Krekel and Luther Blissett
+    From: https://code.activestate.com/recipes/121294/
+    and Recipe 1.12 in cookbook
+    """
+    for item in seq:
+        if scalarp(item) or item is None:
+            yield item
+        else:
+            yield from flatten(item, scalarp)
+
+
+@_api.deprecated("3.6", alternative="functools.lru_cache")
+class maxdict(dict):
+    """
+    A dictionary with a maximum size.
+
+    Notes
+    -----
+    This doesn't override all the relevant methods to constrain the size,
+    just ``__setitem__``, so use with caution.
+    """
+
+    def __init__(self, maxsize):
+        super().__init__()
+        self.maxsize = maxsize
+
+    def __setitem__(self, k, v):
+        super().__setitem__(k, v)
+        while len(self) >= self.maxsize:
+            del self[next(iter(self))]
+
+
+class Stack:
+    """
+    Stack of elements with a movable cursor.
+
+    Mimics home/back/forward in a web browser.
+    """
+
+    def __init__(self, default=None):
+        self.clear()
+        self._default = default
+
+    def __call__(self):
+        """Return the current element, or None."""
+        if not self._elements:
+            return self._default
+        else:
+            return self._elements[self._pos]
+
+    def __len__(self):
+        return len(self._elements)
+
+    def __getitem__(self, ind):
+        return self._elements[ind]
+
+    def forward(self):
+        """Move the position forward and return the current element."""
+        self._pos = min(self._pos + 1, len(self._elements) - 1)
+        return self()
+
+    def back(self):
+        """Move the position back and return the current element."""
+        if self._pos > 0:
+            self._pos -= 1
+        return self()
+
+    def push(self, o):
+        """
+        Push *o* to the stack at current position.  Discard all later elements.
+
+        *o* is returned.
+        """
+        self._elements = self._elements[:self._pos + 1] + [o]
+        self._pos = len(self._elements) - 1
+        return self()
+
+    def home(self):
+        """
+        Push the first element onto the top of the stack.
+
+        The first element is returned.
+        """
+        if not self._elements:
+            return
+        self.push(self._elements[0])
+        return self()
+
+    def empty(self):
+        """Return whether the stack is empty."""
+        return len(self._elements) == 0
+
+    def clear(self):
+        """Empty the stack."""
+        self._pos = -1
+        self._elements = []
+
+    def bubble(self, o):
+        """
+        Raise all references of *o* to the top of the stack, and return it.
+
+        Raises
+        ------
+        ValueError
+            If *o* is not in the stack.
+        """
+        if o not in self._elements:
+            raise ValueError('Given element not contained in the stack')
+        old_elements = self._elements.copy()
+        self.clear()
+        top_elements = []
+        for elem in old_elements:
+            if elem == o:
+                top_elements.append(elem)
+            else:
+                self.push(elem)
+        for _ in top_elements:
+            self.push(o)
+        return o
+
+    def remove(self, o):
+        """
+        Remove *o* from the stack.
+
+        Raises
+        ------
+        ValueError
+            If *o* is not in the stack.
+        """
+        if o not in self._elements:
+            raise ValueError('Given element not contained in the stack')
+        old_elements = self._elements.copy()
+        self.clear()
+        for elem in old_elements:
+            if elem != o:
+                self.push(elem)
+
+
+def safe_masked_invalid(x, copy=False):
+    x = np.array(x, subok=True, copy=copy)
+    if not x.dtype.isnative:
+        # If we have already made a copy, do the byteswap in place, else make a
+        # copy with the byte order swapped.
+        x = x.byteswap(inplace=copy).newbyteorder('N')  # Swap to native order.
+    try:
+        xm = np.ma.masked_invalid(x, copy=False)
+        xm.shrink_mask()
+    except TypeError:
+        return x
+    return xm
+
+
+def print_cycles(objects, outstream=sys.stdout, show_progress=False):
+    """
+    Print loops of cyclic references in the given *objects*.
+
+    It is often useful to pass in ``gc.garbage`` to find the cycles that are
+    preventing some objects from being garbage collected.
+
+    Parameters
+    ----------
+    objects
+        A list of objects to find cycles in.
+    outstream
+        The stream for output.
+    show_progress : bool
+        If True, print the number of objects reached as they are found.
+    """
+    import gc
+
+    def print_path(path):
+        for i, step in enumerate(path):
+            # next "wraps around"
+            next = path[(i + 1) % len(path)]
+
+            outstream.write("   %s -- " % type(step))
+            if isinstance(step, dict):
+                for key, val in step.items():
+                    if val is next:
+                        outstream.write("[{!r}]".format(key))
+                        break
+                    if key is next:
+                        outstream.write("[key] = {!r}".format(val))
+                        break
+            elif isinstance(step, list):
+                outstream.write("[%d]" % step.index(next))
+            elif isinstance(step, tuple):
+                outstream.write("( tuple )")
+            else:
+                outstream.write(repr(step))
+            outstream.write(" ->\n")
+        outstream.write("\n")
+
+    def recurse(obj, start, all, current_path):
+        if show_progress:
+            outstream.write("%d\r" % len(all))
+
+        all[id(obj)] = None
+
+        referents = gc.get_referents(obj)
+        for referent in referents:
+            # If we've found our way back to the start, this is
+            # a cycle, so print it out
+            if referent is start:
+                print_path(current_path)
+
+            # Don't go back through the original list of objects, or
+            # through temporary references to the object, since those
+            # are just an artifact of the cycle detector itself.
+            elif referent is objects or isinstance(referent, types.FrameType):
+                continue
+
+            # We haven't seen this object before, so recurse
+            elif id(referent) not in all:
+                recurse(referent, start, all, current_path + [obj])
+
+    for obj in objects:
+        outstream.write(f"Examining: {obj!r}\n")
+        recurse(obj, obj, {}, [])
+
+
+class Grouper:
+    """
+    A disjoint-set data structure.
+
+    Objects can be joined using :meth:`join`, tested for connectedness
+    using :meth:`joined`, and all disjoint sets can be retrieved by
+    using the object as an iterator.
+
+    The objects being joined must be hashable and weak-referenceable.
+
+    Examples
+    --------
+    >>> from matplotlib.cbook import Grouper
+    >>> class Foo:
+    ...     def __init__(self, s):
+    ...         self.s = s
+    ...     def __repr__(self):
+    ...         return self.s
+    ...
+    >>> a, b, c, d, e, f = [Foo(x) for x in 'abcdef']
+    >>> grp = Grouper()
+    >>> grp.join(a, b)
+    >>> grp.join(b, c)
+    >>> grp.join(d, e)
+    >>> list(grp)
+    [[a, b, c], [d, e]]
+    >>> grp.joined(a, b)
+    True
+    >>> grp.joined(a, c)
+    True
+    >>> grp.joined(a, d)
+    False
+    """
+
+    def __init__(self, init=()):
+        self._mapping = {weakref.ref(x): [weakref.ref(x)] for x in init}
+
+    def __contains__(self, item):
+        return weakref.ref(item) in self._mapping
+
+    def clean(self):
+        """Clean dead weak references from the dictionary."""
+        mapping = self._mapping
+        to_drop = [key for key in mapping if key() is None]
+        for key in to_drop:
+            val = mapping.pop(key)
+            val.remove(key)
+
+    def join(self, a, *args):
+        """
+        Join given arguments into the same set.  Accepts one or more arguments.
+        """
+        mapping = self._mapping
+        set_a = mapping.setdefault(weakref.ref(a), [weakref.ref(a)])
+
+        for arg in args:
+            set_b = mapping.get(weakref.ref(arg), [weakref.ref(arg)])
+            if set_b is not set_a:
+                if len(set_b) > len(set_a):
+                    set_a, set_b = set_b, set_a
+                set_a.extend(set_b)
+                for elem in set_b:
+                    mapping[elem] = set_a
+
+        self.clean()
+
+    def joined(self, a, b):
+        """Return whether *a* and *b* are members of the same set."""
+        self.clean()
+        return (self._mapping.get(weakref.ref(a), object())
+                is self._mapping.get(weakref.ref(b)))
+
+    def remove(self, a):
+        self.clean()
+        set_a = self._mapping.pop(weakref.ref(a), None)
+        if set_a:
+            set_a.remove(weakref.ref(a))
+
+    def __iter__(self):
+        """
+        Iterate over each of the disjoint sets as a list.
+
+        The iterator is invalid if interleaved with calls to join().
+        """
+        self.clean()
+        unique_groups = {id(group): group for group in self._mapping.values()}
+        for group in unique_groups.values():
+            yield [x() for x in group]
+
+    def get_siblings(self, a):
+        """Return all of the items joined with *a*, including itself."""
+        self.clean()
+        siblings = self._mapping.get(weakref.ref(a), [weakref.ref(a)])
+        return [x() for x in siblings]
+
+
+class GrouperView:
+    """Immutable view over a `.Grouper`."""
+
+    def __init__(self, grouper):
+        self._grouper = grouper
+
+    class _GrouperMethodForwarder:
+        def __init__(self, deprecated_kw=None):
+            self._deprecated_kw = deprecated_kw
+
+        def __set_name__(self, owner, name):
+            wrapped = getattr(Grouper, name)
+            forwarder = functools.wraps(wrapped)(
+                lambda self, *args, **kwargs: wrapped(
+                    self._grouper, *args, **kwargs))
+            if self._deprecated_kw:
+                forwarder = _api.deprecated(**self._deprecated_kw)(forwarder)
+            setattr(owner, name, forwarder)
+
+    __contains__ = _GrouperMethodForwarder()
+    __iter__ = _GrouperMethodForwarder()
+    joined = _GrouperMethodForwarder()
+    get_siblings = _GrouperMethodForwarder()
+    clean = _GrouperMethodForwarder(deprecated_kw=dict(since="3.6"))
+    join = _GrouperMethodForwarder(deprecated_kw=dict(since="3.6"))
+    remove = _GrouperMethodForwarder(deprecated_kw=dict(since="3.6"))
+
+
+def simple_linear_interpolation(a, steps):
+    """
+    Resample an array with ``steps - 1`` points between original point pairs.
+
+    Along each column of *a*, ``(steps - 1)`` points are introduced between
+    each original values; the values are linearly interpolated.
+
+    Parameters
+    ----------
+    a : array, shape (n, ...)
+    steps : int
+
+    Returns
+    -------
+    array
+        shape ``((n - 1) * steps + 1, ...)``
+    """
+    fps = a.reshape((len(a), -1))
+    xp = np.arange(len(a)) * steps
+    x = np.arange((len(a) - 1) * steps + 1)
+    return (np.column_stack([np.interp(x, xp, fp) for fp in fps.T])
+            .reshape((len(x),) + a.shape[1:]))
+
+
+def delete_masked_points(*args):
+    """
+    Find all masked and/or non-finite points in a set of arguments,
+    and return the arguments with only the unmasked points remaining.
+
+    Arguments can be in any of 5 categories:
+
+    1) 1-D masked arrays
+    2) 1-D ndarrays
+    3) ndarrays with more than one dimension
+    4) other non-string iterables
+    5) anything else
+
+    The first argument must be in one of the first four categories;
+    any argument with a length differing from that of the first
+    argument (and hence anything in category 5) then will be
+    passed through unchanged.
+
+    Masks are obtained from all arguments of the correct length
+    in categories 1, 2, and 4; a point is bad if masked in a masked
+    array or if it is a nan or inf.  No attempt is made to
+    extract a mask from categories 2, 3, and 4 if `numpy.isfinite`
+    does not yield a Boolean array.
+
+    All input arguments that are not passed unchanged are returned
+    as ndarrays after removing the points or rows corresponding to
+    masks in any of the arguments.
+
+    A vastly simpler version of this function was originally
+    written as a helper for Axes.scatter().
+
+    """
+    if not len(args):
+        return ()
+    if is_scalar_or_string(args[0]):
+        raise ValueError("First argument must be a sequence")
+    nrecs = len(args[0])
+    margs = []
+    seqlist = [False] * len(args)
+    for i, x in enumerate(args):
+        if not isinstance(x, str) and np.iterable(x) and len(x) == nrecs:
+            seqlist[i] = True
+            if isinstance(x, np.ma.MaskedArray):
+                if x.ndim > 1:
+                    raise ValueError("Masked arrays must be 1-D")
+            else:
+                x = np.asarray(x)
+        margs.append(x)
+    masks = []  # List of masks that are True where good.
+    for i, x in enumerate(margs):
+        if seqlist[i]:
+            if x.ndim > 1:
+                continue  # Don't try to get nan locations unless 1-D.
+            if isinstance(x, np.ma.MaskedArray):
+                masks.append(~np.ma.getmaskarray(x))  # invert the mask
+                xd = x.data
+            else:
+                xd = x
+            try:
+                mask = np.isfinite(xd)
+                if isinstance(mask, np.ndarray):
+                    masks.append(mask)
+            except Exception:  # Fixme: put in tuple of possible exceptions?
+                pass
+    if len(masks):
+        mask = np.logical_and.reduce(masks)
+        igood = mask.nonzero()[0]
+        if len(igood) < nrecs:
+            for i, x in enumerate(margs):
+                if seqlist[i]:
+                    margs[i] = x[igood]
+    for i, x in enumerate(margs):
+        if seqlist[i] and isinstance(x, np.ma.MaskedArray):
+            margs[i] = x.filled()
+    return margs
+
+
+def _combine_masks(*args):
+    """
+    Find all masked and/or non-finite points in a set of arguments,
+    and return the arguments as masked arrays with a common mask.
+
+    Arguments can be in any of 5 categories:
+
+    1) 1-D masked arrays
+    2) 1-D ndarrays
+    3) ndarrays with more than one dimension
+    4) other non-string iterables
+    5) anything else
+
+    The first argument must be in one of the first four categories;
+    any argument with a length differing from that of the first
+    argument (and hence anything in category 5) then will be
+    passed through unchanged.
+
+    Masks are obtained from all arguments of the correct length
+    in categories 1, 2, and 4; a point is bad if masked in a masked
+    array or if it is a nan or inf.  No attempt is made to
+    extract a mask from categories 2 and 4 if `numpy.isfinite`
+    does not yield a Boolean array.  Category 3 is included to
+    support RGB or RGBA ndarrays, which are assumed to have only
+    valid values and which are passed through unchanged.
+
+    All input arguments that are not passed unchanged are returned
+    as masked arrays if any masked points are found, otherwise as
+    ndarrays.
+
+    """
+    if not len(args):
+        return ()
+    if is_scalar_or_string(args[0]):
+        raise ValueError("First argument must be a sequence")
+    nrecs = len(args[0])
+    margs = []  # Output args; some may be modified.
+    seqlist = [False] * len(args)  # Flags: True if output will be masked.
+    masks = []    # List of masks.
+    for i, x in enumerate(args):
+        if is_scalar_or_string(x) or len(x) != nrecs:
+            margs.append(x)  # Leave it unmodified.
+        else:
+            if isinstance(x, np.ma.MaskedArray) and x.ndim > 1:
+                raise ValueError("Masked arrays must be 1-D")
+            try:
+                x = np.asanyarray(x)
+            except (VisibleDeprecationWarning, ValueError):
+                # NumPy 1.19 raises a warning about ragged arrays, but we want
+                # to accept basically anything here.
+                x = np.asanyarray(x, dtype=object)
+            if x.ndim == 1:
+                x = safe_masked_invalid(x)
+                seqlist[i] = True
+                if np.ma.is_masked(x):
+                    masks.append(np.ma.getmaskarray(x))
+            margs.append(x)  # Possibly modified.
+    if len(masks):
+        mask = np.logical_or.reduce(masks)
+        for i, x in enumerate(margs):
+            if seqlist[i]:
+                margs[i] = np.ma.array(x, mask=mask)
+    return margs
+
+
+def boxplot_stats(X, whis=1.5, bootstrap=None, labels=None,
+                  autorange=False):
+    r"""
+    Return a list of dictionaries of statistics used to draw a series of box
+    and whisker plots using `~.Axes.bxp`.
+
+    Parameters
+    ----------
+    X : array-like
+        Data that will be represented in the boxplots. Should have 2 or
+        fewer dimensions.
+
+    whis : float or (float, float), default: 1.5
+        The position of the whiskers.
+
+        If a float, the lower whisker is at the lowest datum above
+        ``Q1 - whis*(Q3-Q1)``, and the upper whisker at the highest datum below
+        ``Q3 + whis*(Q3-Q1)``, where Q1 and Q3 are the first and third
+        quartiles.  The default value of ``whis = 1.5`` corresponds to Tukey's
+        original definition of boxplots.
+
+        If a pair of floats, they indicate the percentiles at which to draw the
+        whiskers (e.g., (5, 95)).  In particular, setting this to (0, 100)
+        results in whiskers covering the whole range of the data.
+
+        In the edge case where ``Q1 == Q3``, *whis* is automatically set to
+        (0, 100) (cover the whole range of the data) if *autorange* is True.
+
+        Beyond the whiskers, data are considered outliers and are plotted as
+        individual points.
+
+    bootstrap : int, optional
+        Number of times the confidence intervals around the median
+        should be bootstrapped (percentile method).
+
+    labels : array-like, optional
+        Labels for each dataset. Length must be compatible with
+        dimensions of *X*.
+
+    autorange : bool, optional (False)
+        When `True` and the data are distributed such that the 25th and 75th
+        percentiles are equal, ``whis`` is set to (0, 100) such that the
+        whisker ends are at the minimum and maximum of the data.
+
+    Returns
+    -------
+    list of dict
+        A list of dictionaries containing the results for each column
+        of data. Keys of each dictionary are the following:
+
+        ========   ===================================
+        Key        Value Description
+        ========   ===================================
+        label      tick label for the boxplot
+        mean       arithmetic mean value
+        med        50th percentile
+        q1         first quartile (25th percentile)
+        q3         third quartile (75th percentile)
+        iqr        interquartile range
+        cilo       lower notch around the median
+        cihi       upper notch around the median
+        whislo     end of the lower whisker
+        whishi     end of the upper whisker
+        fliers     outliers
+        ========   ===================================
+
+    Notes
+    -----
+    Non-bootstrapping approach to confidence interval uses Gaussian-based
+    asymptotic approximation:
+
+    .. math::
+
+        \mathrm{med} \pm 1.57 \times \frac{\mathrm{iqr}}{\sqrt{N}}
+
+    General approach from:
+    McGill, R., Tukey, J.W., and Larsen, W.A. (1978) "Variations of
+    Boxplots", The American Statistician, 32:12-16.
+    """
+
+    def _bootstrap_median(data, N=5000):
+        # determine 95% confidence intervals of the median
+        M = len(data)
+        percentiles = [2.5, 97.5]
+
+        bs_index = np.random.randint(M, size=(N, M))
+        bsData = data[bs_index]
+        estimate = np.median(bsData, axis=1, overwrite_input=True)
+
+        CI = np.percentile(estimate, percentiles)
+        return CI
+
+    def _compute_conf_interval(data, med, iqr, bootstrap):
+        if bootstrap is not None:
+            # Do a bootstrap estimate of notch locations.
+            # get conf. intervals around median
+            CI = _bootstrap_median(data, N=bootstrap)
+            notch_min = CI[0]
+            notch_max = CI[1]
+        else:
+
+            N = len(data)
+            notch_min = med - 1.57 * iqr / np.sqrt(N)
+            notch_max = med + 1.57 * iqr / np.sqrt(N)
+
+        return notch_min, notch_max
+
+    # output is a list of dicts
+    bxpstats = []
+
+    # convert X to a list of lists
+    X = _reshape_2D(X, "X")
+
+    ncols = len(X)
+    if labels is None:
+        labels = itertools.repeat(None)
+    elif len(labels) != ncols:
+        raise ValueError("Dimensions of labels and X must be compatible")
+
+    input_whis = whis
+    for ii, (x, label) in enumerate(zip(X, labels)):
+
+        # empty dict
+        stats = {}
+        if label is not None:
+            stats['label'] = label
+
+        # restore whis to the input values in case it got changed in the loop
+        whis = input_whis
+
+        # note tricksiness, append up here and then mutate below
+        bxpstats.append(stats)
+
+        # if empty, bail
+        if len(x) == 0:
+            stats['fliers'] = np.array([])
+            stats['mean'] = np.nan
+            stats['med'] = np.nan
+            stats['q1'] = np.nan
+            stats['q3'] = np.nan
+            stats['iqr'] = np.nan
+            stats['cilo'] = np.nan
+            stats['cihi'] = np.nan
+            stats['whislo'] = np.nan
+            stats['whishi'] = np.nan
+            continue
+
+        # up-convert to an array, just to be safe
+        x = np.asarray(x)
+
+        # arithmetic mean
+        stats['mean'] = np.mean(x)
+
+        # medians and quartiles
+        q1, med, q3 = np.percentile(x, [25, 50, 75])
+
+        # interquartile range
+        stats['iqr'] = q3 - q1
+        if stats['iqr'] == 0 and autorange:
+            whis = (0, 100)
+
+        # conf. interval around median
+        stats['cilo'], stats['cihi'] = _compute_conf_interval(
+            x, med, stats['iqr'], bootstrap
+        )
+
+        # lowest/highest non-outliers
+        if np.iterable(whis) and not isinstance(whis, str):
+            loval, hival = np.percentile(x, whis)
+        elif np.isreal(whis):
+            loval = q1 - whis * stats['iqr']
+            hival = q3 + whis * stats['iqr']
+        else:
+            raise ValueError('whis must be a float or list of percentiles')
+
+        # get high extreme
+        wiskhi = x[x <= hival]
+        if len(wiskhi) == 0 or np.max(wiskhi) < q3:
+            stats['whishi'] = q3
+        else:
+            stats['whishi'] = np.max(wiskhi)
+
+        # get low extreme
+        wisklo = x[x >= loval]
+        if len(wisklo) == 0 or np.min(wisklo) > q1:
+            stats['whislo'] = q1
+        else:
+            stats['whislo'] = np.min(wisklo)
+
+        # compute a single array of outliers
+        stats['fliers'] = np.concatenate([
+            x[x < stats['whislo']],
+            x[x > stats['whishi']],
+        ])
+
+        # add in the remaining stats
+        stats['q1'], stats['med'], stats['q3'] = q1, med, q3
+
+    return bxpstats
+
+
+#: Maps short codes for line style to their full name used by backends.
+ls_mapper = {'-': 'solid', '--': 'dashed', '-.': 'dashdot', ':': 'dotted'}
+#: Maps full names for line styles used by backends to their short codes.
+ls_mapper_r = {v: k for k, v in ls_mapper.items()}
+
+
+def contiguous_regions(mask):
+    """
+    Return a list of (ind0, ind1) such that ``mask[ind0:ind1].all()`` is
+    True and we cover all such regions.
+    """
+    mask = np.asarray(mask, dtype=bool)
+
+    if not mask.size:
+        return []
+
+    # Find the indices of region changes, and correct offset
+    idx, = np.nonzero(mask[:-1] != mask[1:])
+    idx += 1
+
+    # List operations are faster for moderately sized arrays
+    idx = idx.tolist()
+
+    # Add first and/or last index if needed
+    if mask[0]:
+        idx = [0] + idx
+    if mask[-1]:
+        idx.append(len(mask))
+
+    return list(zip(idx[::2], idx[1::2]))
+
+
+def is_math_text(s):
+    """
+    Return whether the string *s* contains math expressions.
+
+    This is done by checking whether *s* contains an even number of
+    non-escaped dollar signs.
+    """
+    s = str(s)
+    dollar_count = s.count(r'$') - s.count(r'\$')
+    even_dollars = (dollar_count > 0 and dollar_count % 2 == 0)
+    return even_dollars
+
+
+def _to_unmasked_float_array(x):
+    """
+    Convert a sequence to a float array; if input was a masked array, masked
+    values are converted to nans.
+    """
+    if hasattr(x, 'mask'):
+        return np.ma.asarray(x, float).filled(np.nan)
+    else:
+        return np.asarray(x, float)
+
+
+def _check_1d(x):
+    """Convert scalars to 1D arrays; pass-through arrays as is."""
+    # Unpack in case of e.g. Pandas or xarray object
+    x = _unpack_to_numpy(x)
+    # plot requires `shape` and `ndim`.  If passed an
+    # object that doesn't provide them, then force to numpy array.
+    # Note this will strip unit information.
+    if (not hasattr(x, 'shape') or
+            not hasattr(x, 'ndim') or
+            len(x.shape) < 1):
+        return np.atleast_1d(x)
+    else:
+        return x
+
+
+def _reshape_2D(X, name):
+    """
+    Use Fortran ordering to convert ndarrays and lists of iterables to lists of
+    1D arrays.
+
+    Lists of iterables are converted by applying `numpy.asanyarray` to each of
+    their elements.  1D ndarrays are returned in a singleton list containing
+    them.  2D ndarrays are converted to the list of their *columns*.
+
+    *name* is used to generate the error message for invalid inputs.
+    """
+
+    # Unpack in case of e.g. Pandas or xarray object
+    X = _unpack_to_numpy(X)
+
+    # Iterate over columns for ndarrays.
+    if isinstance(X, np.ndarray):
+        X = X.T
+
+        if len(X) == 0:
+            return [[]]
+        elif X.ndim == 1 and np.ndim(X[0]) == 0:
+            # 1D array of scalars: directly return it.
+            return [X]
+        elif X.ndim in [1, 2]:
+            # 2D array, or 1D array of iterables: flatten them first.
+            return [np.reshape(x, -1) for x in X]
+        else:
+            raise ValueError(f'{name} must have 2 or fewer dimensions')
+
+    # Iterate over list of iterables.
+    if len(X) == 0:
+        return [[]]
+
+    result = []
+    is_1d = True
+    for xi in X:
+        # check if this is iterable, except for strings which we
+        # treat as singletons.
+        if not isinstance(xi, str):
+            try:
+                iter(xi)
+            except TypeError:
+                pass
+            else:
+                is_1d = False
+        xi = np.asanyarray(xi)
+        nd = np.ndim(xi)
+        if nd > 1:
+            raise ValueError(f'{name} must have 2 or fewer dimensions')
+        result.append(xi.reshape(-1))
+
+    if is_1d:
+        # 1D array of scalars: directly return it.
+        return [np.reshape(result, -1)]
+    else:
+        # 2D array, or 1D array of iterables: use flattened version.
+        return result
+
+
+def violin_stats(X, method, points=100, quantiles=None):
+    """
+    Return a list of dictionaries of data which can be used to draw a series
+    of violin plots.
+
+    See the ``Returns`` section below to view the required keys of the
+    dictionary.
+
+    Users can skip this function and pass a user-defined set of dictionaries
+    with the same keys to `~.axes.Axes.violinplot` instead of using Matplotlib
+    to do the calculations. See the *Returns* section below for the keys
+    that must be present in the dictionaries.
+
+    Parameters
+    ----------
+    X : array-like
+        Sample data that will be used to produce the gaussian kernel density
+        estimates. Must have 2 or fewer dimensions.
+
+    method : callable
+        The method used to calculate the kernel density estimate for each
+        column of data. When called via ``method(v, coords)``, it should
+        return a vector of the values of the KDE evaluated at the values
+        specified in coords.
+
+    points : int, default: 100
+        Defines the number of points to evaluate each of the gaussian kernel
+        density estimates at.
+
+    quantiles : array-like, default: None
+        Defines (if not None) a list of floats in interval [0, 1] for each
+        column of data, which represents the quantiles that will be rendered
+        for that column of data. Must have 2 or fewer dimensions. 1D array will
+        be treated as a singleton list containing them.
+
+    Returns
+    -------
+    list of dict
+        A list of dictionaries containing the results for each column of data.
+        The dictionaries contain at least the following:
+
+        - coords: A list of scalars containing the coordinates this particular
+          kernel density estimate was evaluated at.
+        - vals: A list of scalars containing the values of the kernel density
+          estimate at each of the coordinates given in *coords*.
+        - mean: The mean value for this column of data.
+        - median: The median value for this column of data.
+        - min: The minimum value for this column of data.
+        - max: The maximum value for this column of data.
+        - quantiles: The quantile values for this column of data.
+    """
+
+    # List of dictionaries describing each of the violins.
+    vpstats = []
+
+    # Want X to be a list of data sequences
+    X = _reshape_2D(X, "X")
+
+    # Want quantiles to be as the same shape as data sequences
+    if quantiles is not None and len(quantiles) != 0:
+        quantiles = _reshape_2D(quantiles, "quantiles")
+    # Else, mock quantiles if it's none or empty
+    else:
+        quantiles = [[]] * len(X)
+
+    # quantiles should have the same size as dataset
+    if len(X) != len(quantiles):
+        raise ValueError("List of violinplot statistics and quantiles values"
+                         " must have the same length")
+
+    # Zip x and quantiles
+    for (x, q) in zip(X, quantiles):
+        # Dictionary of results for this distribution
+        stats = {}
+
+        # Calculate basic stats for the distribution
+        min_val = np.min(x)
+        max_val = np.max(x)
+        quantile_val = np.percentile(x, 100 * q)
+
+        # Evaluate the kernel density estimate
+        coords = np.linspace(min_val, max_val, points)
+        stats['vals'] = method(x, coords)
+        stats['coords'] = coords
+
+        # Store additional statistics for this distribution
+        stats['mean'] = np.mean(x)
+        stats['median'] = np.median(x)
+        stats['min'] = min_val
+        stats['max'] = max_val
+        stats['quantiles'] = np.atleast_1d(quantile_val)
+
+        # Append to output
+        vpstats.append(stats)
+
+    return vpstats
+
+
+def pts_to_prestep(x, *args):
+    """
+    Convert continuous line to pre-steps.
+
+    Given a set of ``N`` points, convert to ``2N - 1`` points, which when
+    connected linearly give a step function which changes values at the
+    beginning of the intervals.
+
+    Parameters
+    ----------
+    x : array
+        The x location of the steps. May be empty.
+
+    y1, ..., yp : array
+        y arrays to be turned into steps; all must be the same length as ``x``.
+
+    Returns
+    -------
+    array
+        The x and y values converted to steps in the same order as the input;
+        can be unpacked as ``x_out, y1_out, ..., yp_out``.  If the input is
+        length ``N``, each of these arrays will be length ``2N + 1``. For
+        ``N=0``, the length will be 0.
+
+    Examples
+    --------
+    >>> x_s, y1_s, y2_s = pts_to_prestep(x, y1, y2)
+    """
+    steps = np.zeros((1 + len(args), max(2 * len(x) - 1, 0)))
+    # In all `pts_to_*step` functions, only assign once using *x* and *args*,
+    # as converting to an array may be expensive.
+    steps[0, 0::2] = x
+    steps[0, 1::2] = steps[0, 0:-2:2]
+    steps[1:, 0::2] = args
+    steps[1:, 1::2] = steps[1:, 2::2]
+    return steps
+
+
+def pts_to_poststep(x, *args):
+    """
+    Convert continuous line to post-steps.
+
+    Given a set of ``N`` points convert to ``2N + 1`` points, which when
+    connected linearly give a step function which changes values at the end of
+    the intervals.
+
+    Parameters
+    ----------
+    x : array
+        The x location of the steps. May be empty.
+
+    y1, ..., yp : array
+        y arrays to be turned into steps; all must be the same length as ``x``.
+
+    Returns
+    -------
+    array
+        The x and y values converted to steps in the same order as the input;
+        can be unpacked as ``x_out, y1_out, ..., yp_out``.  If the input is
+        length ``N``, each of these arrays will be length ``2N + 1``. For
+        ``N=0``, the length will be 0.
+
+    Examples
+    --------
+    >>> x_s, y1_s, y2_s = pts_to_poststep(x, y1, y2)
+    """
+    steps = np.zeros((1 + len(args), max(2 * len(x) - 1, 0)))
+    steps[0, 0::2] = x
+    steps[0, 1::2] = steps[0, 2::2]
+    steps[1:, 0::2] = args
+    steps[1:, 1::2] = steps[1:, 0:-2:2]
+    return steps
+
+
+def pts_to_midstep(x, *args):
+    """
+    Convert continuous line to mid-steps.
+
+    Given a set of ``N`` points convert to ``2N`` points which when connected
+    linearly give a step function which changes values at the middle of the
+    intervals.
+
+    Parameters
+    ----------
+    x : array
+        The x location of the steps. May be empty.
+
+    y1, ..., yp : array
+        y arrays to be turned into steps; all must be the same length as
+        ``x``.
+
+    Returns
+    -------
+    array
+        The x and y values converted to steps in the same order as the input;
+        can be unpacked as ``x_out, y1_out, ..., yp_out``.  If the input is
+        length ``N``, each of these arrays will be length ``2N``.
+
+    Examples
+    --------
+    >>> x_s, y1_s, y2_s = pts_to_midstep(x, y1, y2)
+    """
+    steps = np.zeros((1 + len(args), 2 * len(x)))
+    x = np.asanyarray(x)
+    steps[0, 1:-1:2] = steps[0, 2::2] = (x[:-1] + x[1:]) / 2
+    steps[0, :1] = x[:1]  # Also works for zero-sized input.
+    steps[0, -1:] = x[-1:]
+    steps[1:, 0::2] = args
+    steps[1:, 1::2] = steps[1:, 0::2]
+    return steps
+
+
+STEP_LOOKUP_MAP = {'default': lambda x, y: (x, y),
+                   'steps': pts_to_prestep,
+                   'steps-pre': pts_to_prestep,
+                   'steps-post': pts_to_poststep,
+                   'steps-mid': pts_to_midstep}
+
+
+def index_of(y):
+    """
+    A helper function to create reasonable x values for the given *y*.
+
+    This is used for plotting (x, y) if x values are not explicitly given.
+
+    First try ``y.index`` (assuming *y* is a `pandas.Series`), if that
+    fails, use ``range(len(y))``.
+
+    This will be extended in the future to deal with more types of
+    labeled data.
+
+    Parameters
+    ----------
+    y : float or array-like
+
+    Returns
+    -------
+    x, y : ndarray
+       The x and y values to plot.
+    """
+    try:
+        return y.index.to_numpy(), y.to_numpy()
+    except AttributeError:
+        pass
+    try:
+        y = _check_1d(y)
+    except (VisibleDeprecationWarning, ValueError):
+        # NumPy 1.19 will warn on ragged input, and we can't actually use it.
+        pass
+    else:
+        return np.arange(y.shape[0], dtype=float), y
+    raise ValueError('Input could not be cast to an at-least-1D NumPy array')
+
+
+def safe_first_element(obj):
+    """
+    Return the first element in *obj*.
+
+    This is a type-independent way of obtaining the first element,
+    supporting both index access and the iterator protocol.
+    """
+    return _safe_first_finite(obj, skip_nonfinite=False)
+
+
+def _safe_first_finite(obj, *, skip_nonfinite=True):
+    """
+    Return the first finite element in *obj* if one is available and skip_nonfinite is
+    True. Otherwise return the first element.
+
+    This is a method for internal use.
+
+    This is a type-independent way of obtaining the first finite element, supporting
+    both index access and the iterator protocol.
+    """
+    def safe_isfinite(val):
+        if val is None:
+            return False
+        try:
+            return np.isfinite(val) if np.isscalar(val) else True
+        except TypeError:
+            # This is something that numpy can not make heads or tails
+            # of, assume "finite"
+            return True
+    if skip_nonfinite is False:
+        if isinstance(obj, collections.abc.Iterator):
+            # needed to accept `array.flat` as input.
+            # np.flatiter reports as an instance of collections.Iterator
+            # but can still be indexed via [].
+            # This has the side effect of re-setting the iterator, but
+            # that is acceptable.
+            try:
+                return obj[0]
+            except TypeError:
+                pass
+            raise RuntimeError("matplotlib does not support generators "
+                               "as input")
+        return next(iter(obj))
+    elif isinstance(obj, np.flatiter):
+        # TODO do the finite filtering on this
+        return obj[0]
+    elif isinstance(obj, collections.abc.Iterator):
+        raise RuntimeError("matplotlib does not "
+                           "support generators as input")
+    else:
+        return next((val for val in obj if safe_isfinite(val)), safe_first_element(obj))
+
+
+def sanitize_sequence(data):
+    """
+    Convert dictview objects to list. Other inputs are returned unchanged.
+    """
+    return (list(data) if isinstance(data, collections.abc.MappingView)
+            else data)
+
+
+def normalize_kwargs(kw, alias_mapping=None):
+    """
+    Helper function to normalize kwarg inputs.
+
+    Parameters
+    ----------
+    kw : dict or None
+        A dict of keyword arguments.  None is explicitly supported and treated
+        as an empty dict, to support functions with an optional parameter of
+        the form ``props=None``.
+
+    alias_mapping : dict or Artist subclass or Artist instance, optional
+        A mapping between a canonical name to a list of aliases, in order of
+        precedence from lowest to highest.
+
+        If the canonical value is not in the list it is assumed to have the
+        highest priority.
+
+        If an Artist subclass or instance is passed, use its properties alias
+        mapping.
+
+    Raises
+    ------
+    TypeError
+        To match what Python raises if invalid arguments/keyword arguments are
+        passed to a callable.
+    """
+    from matplotlib.artist import Artist
+
+    if kw is None:
+        return {}
+
+    # deal with default value of alias_mapping
+    if alias_mapping is None:
+        alias_mapping = dict()
+    elif (isinstance(alias_mapping, type) and issubclass(alias_mapping, Artist)
+          or isinstance(alias_mapping, Artist)):
+        alias_mapping = getattr(alias_mapping, "_alias_map", {})
+
+    to_canonical = {alias: canonical
+                    for canonical, alias_list in alias_mapping.items()
+                    for alias in alias_list}
+    canonical_to_seen = {}
+    ret = {}  # output dictionary
+
+    for k, v in kw.items():
+        canonical = to_canonical.get(k, k)
+        if canonical in canonical_to_seen:
+            raise TypeError(f"Got both {canonical_to_seen[canonical]!r} and "
+                            f"{k!r}, which are aliases of one another")
+        canonical_to_seen[canonical] = k
+        ret[canonical] = v
+
+    return ret
+
+
+@contextlib.contextmanager
+def _lock_path(path):
+    """
+    Context manager for locking a path.
+
+    Usage::
+
+        with _lock_path(path):
+            ...
+
+    Another thread or process that attempts to lock the same path will wait
+    until this context manager is exited.
+
+    The lock is implemented by creating a temporary file in the parent
+    directory, so that directory must exist and be writable.
+    """
+    path = Path(path)
+    lock_path = path.with_name(path.name + ".matplotlib-lock")
+    retries = 50
+    sleeptime = 0.1
+    for _ in range(retries):
+        try:
+            with lock_path.open("xb"):
+                break
+        except FileExistsError:
+            time.sleep(sleeptime)
+    else:
+        raise TimeoutError("""\
+Lock error: Matplotlib failed to acquire the following lock file:
+    {}
+This maybe due to another process holding this lock file.  If you are sure no
+other Matplotlib process is running, remove this file and try again.""".format(
+            lock_path))
+    try:
+        yield
+    finally:
+        lock_path.unlink()
+
+
+def _topmost_artist(
+        artists,
+        _cached_max=functools.partial(max, key=operator.attrgetter("zorder"))):
+    """
+    Get the topmost artist of a list.
+
+    In case of a tie, return the *last* of the tied artists, as it will be
+    drawn on top of the others. `max` returns the first maximum in case of
+    ties, so we need to iterate over the list in reverse order.
+    """
+    return _cached_max(reversed(artists))
+
+
+def _str_equal(obj, s):
+    """
+    Return whether *obj* is a string equal to string *s*.
+
+    This helper solely exists to handle the case where *obj* is a numpy array,
+    because in such cases, a naive ``obj == s`` would yield an array, which
+    cannot be used in a boolean context.
+    """
+    return isinstance(obj, str) and obj == s
+
+
+def _str_lower_equal(obj, s):
+    """
+    Return whether *obj* is a string equal, when lowercased, to string *s*.
+
+    This helper solely exists to handle the case where *obj* is a numpy array,
+    because in such cases, a naive ``obj == s`` would yield an array, which
+    cannot be used in a boolean context.
+    """
+    return isinstance(obj, str) and obj.lower() == s
+
+
+def _array_perimeter(arr):
+    """
+    Get the elements on the perimeter of *arr*.
+
+    Parameters
+    ----------
+    arr : ndarray, shape (M, N)
+        The input array.
+
+    Returns
+    -------
+    ndarray, shape (2*(M - 1) + 2*(N - 1),)
+        The elements on the perimeter of the array::
+
+           [arr[0, 0], ..., arr[0, -1], ..., arr[-1, -1], ..., arr[-1, 0], ...]
+
+    Examples
+    --------
+    >>> i, j = np.ogrid[:3, :4]
+    >>> a = i*10 + j
+    >>> a
+    array([[ 0,  1,  2,  3],
+           [10, 11, 12, 13],
+           [20, 21, 22, 23]])
+    >>> _array_perimeter(a)
+    array([ 0,  1,  2,  3, 13, 23, 22, 21, 20, 10])
+    """
+    # note we use Python's half-open ranges to avoid repeating
+    # the corners
+    forward = np.s_[0:-1]      # [0 ... -1)
+    backward = np.s_[-1:0:-1]  # [-1 ... 0)
+    return np.concatenate((
+        arr[0, forward],
+        arr[forward, -1],
+        arr[-1, backward],
+        arr[backward, 0],
+    ))
+
+
+def _unfold(arr, axis, size, step):
+    """
+    Append an extra dimension containing sliding windows along *axis*.
+
+    All windows are of size *size* and begin with every *step* elements.
+
+    Parameters
+    ----------
+    arr : ndarray, shape (N_1, ..., N_k)
+        The input array
+    axis : int
+        Axis along which the windows are extracted
+    size : int
+        Size of the windows
+    step : int
+        Stride between first elements of subsequent windows.
+
+    Returns
+    -------
+    ndarray, shape (N_1, ..., 1 + (N_axis-size)/step, ..., N_k, size)
+
+    Examples
+    --------
+    >>> i, j = np.ogrid[:3, :7]
+    >>> a = i*10 + j
+    >>> a
+    array([[ 0,  1,  2,  3,  4,  5,  6],
+           [10, 11, 12, 13, 14, 15, 16],
+           [20, 21, 22, 23, 24, 25, 26]])
+    >>> _unfold(a, axis=1, size=3, step=2)
+    array([[[ 0,  1,  2],
+            [ 2,  3,  4],
+            [ 4,  5,  6]],
+           [[10, 11, 12],
+            [12, 13, 14],
+            [14, 15, 16]],
+           [[20, 21, 22],
+            [22, 23, 24],
+            [24, 25, 26]]])
+    """
+    new_shape = [*arr.shape, size]
+    new_strides = [*arr.strides, arr.strides[axis]]
+    new_shape[axis] = (new_shape[axis] - size) // step + 1
+    new_strides[axis] = new_strides[axis] * step
+    return np.lib.stride_tricks.as_strided(arr,
+                                           shape=new_shape,
+                                           strides=new_strides,
+                                           writeable=False)
+
+
+def _array_patch_perimeters(x, rstride, cstride):
+    """
+    Extract perimeters of patches from *arr*.
+
+    Extracted patches are of size (*rstride* + 1) x (*cstride* + 1) and
+    share perimeters with their neighbors. The ordering of the vertices matches
+    that returned by ``_array_perimeter``.
+
+    Parameters
+    ----------
+    x : ndarray, shape (N, M)
+        Input array
+    rstride : int
+        Vertical (row) stride between corresponding elements of each patch
+    cstride : int
+        Horizontal (column) stride between corresponding elements of each patch
+
+    Returns
+    -------
+    ndarray, shape (N/rstride * M/cstride, 2 * (rstride + cstride))
+    """
+    assert rstride > 0 and cstride > 0
+    assert (x.shape[0] - 1) % rstride == 0
+    assert (x.shape[1] - 1) % cstride == 0
+    # We build up each perimeter from four half-open intervals. Here is an
+    # illustrated explanation for rstride == cstride == 3
+    #
+    #       T T T R
+    #       L     R
+    #       L     R
+    #       L B B B
+    #
+    # where T means that this element will be in the top array, R for right,
+    # B for bottom and L for left. Each of the arrays below has a shape of:
+    #
+    #    (number of perimeters that can be extracted vertically,
+    #     number of perimeters that can be extracted horizontally,
+    #     cstride for top and bottom and rstride for left and right)
+    #
+    # Note that _unfold doesn't incur any memory copies, so the only costly
+    # operation here is the np.concatenate.
+    top = _unfold(x[:-1:rstride, :-1], 1, cstride, cstride)
+    bottom = _unfold(x[rstride::rstride, 1:], 1, cstride, cstride)[..., ::-1]
+    right = _unfold(x[:-1, cstride::cstride], 0, rstride, rstride)
+    left = _unfold(x[1:, :-1:cstride], 0, rstride, rstride)[..., ::-1]
+    return (np.concatenate((top, right, bottom, left), axis=2)
+              .reshape(-1, 2 * (rstride + cstride)))
+
+
+@contextlib.contextmanager
+def _setattr_cm(obj, **kwargs):
+    """
+    Temporarily set some attributes; restore original state at context exit.
+    """
+    sentinel = object()
+    origs = {}
+    for attr in kwargs:
+        orig = getattr(obj, attr, sentinel)
+        if attr in obj.__dict__ or orig is sentinel:
+            # if we are pulling from the instance dict or the object
+            # does not have this attribute we can trust the above
+            origs[attr] = orig
+        else:
+            # if the attribute is not in the instance dict it must be
+            # from the class level
+            cls_orig = getattr(type(obj), attr)
+            # if we are dealing with a property (but not a general descriptor)
+            # we want to set the original value back.
+            if isinstance(cls_orig, property):
+                origs[attr] = orig
+            # otherwise this is _something_ we are going to shadow at
+            # the instance dict level from higher up in the MRO.  We
+            # are going to assume we can delattr(obj, attr) to clean
+            # up after ourselves.  It is possible that this code will
+            # fail if used with a non-property custom descriptor which
+            # implements __set__ (and __delete__ does not act like a
+            # stack).  However, this is an internal tool and we do not
+            # currently have any custom descriptors.
+            else:
+                origs[attr] = sentinel
+
+    try:
+        for attr, val in kwargs.items():
+            setattr(obj, attr, val)
+        yield
+    finally:
+        for attr, orig in origs.items():
+            if orig is sentinel:
+                delattr(obj, attr)
+            else:
+                setattr(obj, attr, orig)
+
+
+class _OrderedSet(collections.abc.MutableSet):
+    def __init__(self):
+        self._od = collections.OrderedDict()
+
+    def __contains__(self, key):
+        return key in self._od
+
+    def __iter__(self):
+        return iter(self._od)
+
+    def __len__(self):
+        return len(self._od)
+
+    def add(self, key):
+        self._od.pop(key, None)
+        self._od[key] = None
+
+    def discard(self, key):
+        self._od.pop(key, None)
+
+
+# Agg's buffers are unmultiplied RGBA8888, which neither PyQt<=5.1 nor cairo
+# support; however, both do support premultiplied ARGB32.
+
+
+def _premultiplied_argb32_to_unmultiplied_rgba8888(buf):
+    """
+    Convert a premultiplied ARGB32 buffer to an unmultiplied RGBA8888 buffer.
+    """
+    rgba = np.take(  # .take() ensures C-contiguity of the result.
+        buf,
+        [2, 1, 0, 3] if sys.byteorder == "little" else [1, 2, 3, 0], axis=2)
+    rgb = rgba[..., :-1]
+    alpha = rgba[..., -1]
+    # Un-premultiply alpha.  The formula is the same as in cairo-png.c.
+    mask = alpha != 0
+    for channel in np.rollaxis(rgb, -1):
+        channel[mask] = (
+            (channel[mask].astype(int) * 255 + alpha[mask] // 2)
+            // alpha[mask])
+    return rgba
+
+
+def _unmultiplied_rgba8888_to_premultiplied_argb32(rgba8888):
+    """
+    Convert an unmultiplied RGBA8888 buffer to a premultiplied ARGB32 buffer.
+    """
+    if sys.byteorder == "little":
+        argb32 = np.take(rgba8888, [2, 1, 0, 3], axis=2)
+        rgb24 = argb32[..., :-1]
+        alpha8 = argb32[..., -1:]
+    else:
+        argb32 = np.take(rgba8888, [3, 0, 1, 2], axis=2)
+        alpha8 = argb32[..., :1]
+        rgb24 = argb32[..., 1:]
+    # Only bother premultiplying when the alpha channel is not fully opaque,
+    # as the cost is not negligible.  The unsafe cast is needed to do the
+    # multiplication in-place in an integer buffer.
+    if alpha8.min() != 0xff:
+        np.multiply(rgb24, alpha8 / 0xff, out=rgb24, casting="unsafe")
+    return argb32
+
+
+def _get_nonzero_slices(buf):
+    """
+    Return the bounds of the nonzero region of a 2D array as a pair of slices.
+
+    ``buf[_get_nonzero_slices(buf)]`` is the smallest sub-rectangle in *buf*
+    that encloses all non-zero entries in *buf*.  If *buf* is fully zero, then
+    ``(slice(0, 0), slice(0, 0))`` is returned.
+    """
+    x_nz, = buf.any(axis=0).nonzero()
+    y_nz, = buf.any(axis=1).nonzero()
+    if len(x_nz) and len(y_nz):
+        l, r = x_nz[[0, -1]]
+        b, t = y_nz[[0, -1]]
+        return slice(b, t + 1), slice(l, r + 1)
+    else:
+        return slice(0, 0), slice(0, 0)
+
+
+def _pformat_subprocess(command):
+    """Pretty-format a subprocess command for printing/logging purposes."""
+    return (command if isinstance(command, str)
+            else " ".join(shlex.quote(os.fspath(arg)) for arg in command))
+
+
+def _check_and_log_subprocess(command, logger, **kwargs):
+    """
+    Run *command*, returning its stdout output if it succeeds.
+
+    If it fails (exits with nonzero return code), raise an exception whose text
+    includes the failed command and captured stdout and stderr output.
+
+    Regardless of the return code, the command is logged at DEBUG level on
+    *logger*.  In case of success, the output is likewise logged.
+    """
+    logger.debug('%s', _pformat_subprocess(command))
+    proc = subprocess.run(
+        command, stdout=subprocess.PIPE, stderr=subprocess.PIPE, **kwargs)
+    if proc.returncode:
+        stdout = proc.stdout
+        if isinstance(stdout, bytes):
+            stdout = stdout.decode()
+        stderr = proc.stderr
+        if isinstance(stderr, bytes):
+            stderr = stderr.decode()
+        raise RuntimeError(
+            f"The command\n"
+            f"    {_pformat_subprocess(command)}\n"
+            f"failed and generated the following output:\n"
+            f"{stdout}\n"
+            f"and the following error:\n"
+            f"{stderr}")
+    if proc.stdout:
+        logger.debug("stdout:\n%s", proc.stdout)
+    if proc.stderr:
+        logger.debug("stderr:\n%s", proc.stderr)
+    return proc.stdout
+
+
+def _backend_module_name(name):
+    """
+    Convert a backend name (either a standard backend -- "Agg", "TkAgg", ... --
+    or a custom backend -- "module://...") to the corresponding module name).
+    """
+    return (name[9:] if name.startswith("module://")
+            else "matplotlib.backends.backend_{}".format(name.lower()))
+
+
+def _setup_new_guiapp():
+    """
+    Perform OS-dependent setup when Matplotlib creates a new GUI application.
+    """
+    # Windows: If not explicit app user model id has been set yet (so we're not
+    # already embedded), then set it to "matplotlib", so that taskbar icons are
+    # correct.
+    try:
+        _c_internal_utils.Win32_GetCurrentProcessExplicitAppUserModelID()
+    except OSError:
+        _c_internal_utils.Win32_SetCurrentProcessExplicitAppUserModelID(
+            "matplotlib")
+
+
+def _format_approx(number, precision):
+    """
+    Format the number with at most the number of decimals given as precision.
+    Remove trailing zeros and possibly the decimal point.
+    """
+    return f'{number:.{precision}f}'.rstrip('0').rstrip('.') or '0'
+
+
+def _g_sig_digits(value, delta):
+    """
+    Return the number of significant digits to %g-format *value*, assuming that
+    it is known with an error of *delta*.
+    """
+    if delta == 0:
+        # delta = 0 may occur when trying to format values over a tiny range;
+        # in that case, replace it by the distance to the closest float.
+        delta = abs(np.spacing(value))
+    # If e.g. value = 45.67 and delta = 0.02, then we want to round to 2 digits
+    # after the decimal point (floor(log10(0.02)) = -2); 45.67 contributes 2
+    # digits before the decimal point (floor(log10(45.67)) + 1 = 2): the total
+    # is 4 significant digits.  A value of 0 contributes 1 "digit" before the
+    # decimal point.
+    # For inf or nan, the precision doesn't matter.
+    return max(
+        0,
+        (math.floor(math.log10(abs(value))) + 1 if value else 1)
+        - math.floor(math.log10(delta))) if math.isfinite(value) else 0
+
+
+def _unikey_or_keysym_to_mplkey(unikey, keysym):
+    """
+    Convert a Unicode key or X keysym to a Matplotlib key name.
+
+    The Unicode key is checked first; this avoids having to list most printable
+    keysyms such as ``EuroSign``.
+    """
+    # For non-printable characters, gtk3 passes "\0" whereas tk passes an "".
+    if unikey and unikey.isprintable():
+        return unikey
+    key = keysym.lower()
+    if key.startswith("kp_"):  # keypad_x (including kp_enter).
+        key = key[3:]
+    if key.startswith("page_"):  # page_{up,down}
+        key = key.replace("page_", "page")
+    if key.endswith(("_l", "_r")):  # alt_l, ctrl_l, shift_l.
+        key = key[:-2]
+    key = {
+        "return": "enter",
+        "prior": "pageup",  # Used by tk.
+        "next": "pagedown",  # Used by tk.
+    }.get(key, key)
+    return key
+
+
+@functools.lru_cache(None)
+def _make_class_factory(mixin_class, fmt, attr_name=None):
+    """
+    Return a function that creates picklable classes inheriting from a mixin.
+
+    After ::
+
+        factory = _make_class_factory(FooMixin, fmt, attr_name)
+        FooAxes = factory(Axes)
+
+    ``Foo`` is a class that inherits from ``FooMixin`` and ``Axes`` and **is
+    picklable** (picklability is what differentiates this from a plain call to
+    `type`).  Its ``__name__`` is set to ``fmt.format(Axes.__name__)`` and the
+    base class is stored in the ``attr_name`` attribute, if not None.
+
+    Moreover, the return value of ``factory`` is memoized: calls with the same
+    ``Axes`` class always return the same subclass.
+    """
+
+    @functools.lru_cache(None)
+    def class_factory(axes_class):
+        # if we have already wrapped this class, declare victory!
+        if issubclass(axes_class, mixin_class):
+            return axes_class
+
+        # The parameter is named "axes_class" for backcompat but is really just
+        # a base class; no axes semantics are used.
+        base_class = axes_class
+
+        class subcls(mixin_class, base_class):
+            # Better approximation than __module__ = "matplotlib.cbook".
+            __module__ = mixin_class.__module__
+
+            def __reduce__(self):
+                return (_picklable_class_constructor,
+                        (mixin_class, fmt, attr_name, base_class),
+                        self.__getstate__())
+
+        subcls.__name__ = subcls.__qualname__ = fmt.format(base_class.__name__)
+        if attr_name is not None:
+            setattr(subcls, attr_name, base_class)
+        return subcls
+
+    class_factory.__module__ = mixin_class.__module__
+    return class_factory
+
+
+def _picklable_class_constructor(mixin_class, fmt, attr_name, base_class):
+    """Internal helper for _make_class_factory."""
+    factory = _make_class_factory(mixin_class, fmt, attr_name)
+    cls = factory(base_class)
+    return cls.__new__(cls)
+
+
+def _unpack_to_numpy(x):
+    """Internal helper to extract data from e.g. pandas and xarray objects."""
+    if isinstance(x, np.ndarray):
+        # If numpy, return directly
+        return x
+    if hasattr(x, 'to_numpy'):
+        # Assume that any function to_numpy() do actually return a numpy array
+        return x.to_numpy()
+    if hasattr(x, 'values'):
+        xtmp = x.values
+        # For example a dict has a 'values' attribute, but it is not a property
+        # so in this case we do not want to return a function
+        if isinstance(xtmp, np.ndarray):
+            return xtmp
+    return x
+
+
+def _auto_format_str(fmt, value):
+    """
+    Apply *value* to the format string *fmt*.
+
+    This works both with unnamed %-style formatting and
+    unnamed {}-style formatting. %-style formatting has priority.
+    If *fmt* is %-style formattable that will be used. Otherwise,
+    {}-formatting is applied. Strings without formatting placeholders
+    are passed through as is.
+
+    Examples
+    --------
+    >>> _auto_format_str('%.2f m', 0.2)
+    '0.20 m'
+    >>> _auto_format_str('{} m', 0.2)
+    '0.2 m'
+    >>> _auto_format_str('const', 0.2)
+    'const'
+    >>> _auto_format_str('%d or {}', 0.2)
+    '0 or {}'
+    """
+    try:
+        return fmt % (value,)
+    except (TypeError, ValueError):
+        return fmt.format(value)

omnilmm/lib/python3.10/site-packages/matplotlib/mpl-data/kpsewhich.lua

@@ -0,0 +1,3 @@
+-- see dviread._LuatexKpsewhich
+kpse.set_program_name("latex")
+while true do print(kpse.lookup(io.read():gsub("\r", ""))); io.flush(); end

omnilmm/lib/python3.10/site-packages/matplotlib/mpl-data/matplotlibrc

@@ -0,0 +1,788 @@
+#### MATPLOTLIBRC FORMAT
+
+## NOTE FOR END USERS: DO NOT EDIT THIS FILE!
+##
+## This is a sample Matplotlib configuration file - you can find a copy
+## of it on your system in site-packages/matplotlib/mpl-data/matplotlibrc
+## (relative to your Python installation location).
+## DO NOT EDIT IT!
+##
+## If you wish to change your default style, copy this file to one of the
+## following locations:
+##     Unix/Linux:
+##         $HOME/.config/matplotlib/matplotlibrc OR
+##         $XDG_CONFIG_HOME/matplotlib/matplotlibrc (if $XDG_CONFIG_HOME is set)
+##     Other platforms:
+##         $HOME/.matplotlib/matplotlibrc
+## and edit that copy.
+##
+## See https://matplotlib.org/stable/tutorials/introductory/customizing.html#customizing-with-matplotlibrc-files
+## for more details on the paths which are checked for the configuration file.
+##
+## Blank lines, or lines starting with a comment symbol, are ignored, as are
+## trailing comments.  Other lines must have the format:
+##     key: val  # optional comment
+##
+## Formatting: Use PEP8-like style (as enforced in the rest of the codebase).
+## All lines start with an additional '#', so that removing all leading '#'s
+## yields a valid style file.
+##
+## Colors: for the color values below, you can either use
+##     - a Matplotlib color string, such as r, k, or b
+##     - an RGB tuple, such as (1.0, 0.5, 0.0)
+##     - a double-quoted hex string, such as "#ff00ff".
+##       The unquoted string ff00ff is also supported for backward
+##       compatibility, but is discouraged.
+##     - a scalar grayscale intensity such as 0.75
+##     - a legal html color name, e.g., red, blue, darkslategray
+##
+## String values may optionally be enclosed in double quotes, which allows
+## using the comment character # in the string.
+##
+## This file (and other style files) must be encoded as utf-8.
+##
+## Matplotlib configuration are currently divided into following parts:
+##     - BACKENDS
+##     - LINES
+##     - PATCHES
+##     - HATCHES
+##     - BOXPLOT
+##     - FONT
+##     - TEXT
+##     - LaTeX
+##     - AXES
+##     - DATES
+##     - TICKS
+##     - GRIDS
+##     - LEGEND
+##     - FIGURE
+##     - IMAGES
+##     - CONTOUR PLOTS
+##     - ERRORBAR PLOTS
+##     - HISTOGRAM PLOTS
+##     - SCATTER PLOTS
+##     - AGG RENDERING
+##     - PATHS
+##     - SAVING FIGURES
+##     - INTERACTIVE KEYMAPS
+##     - ANIMATION
+
+##### CONFIGURATION BEGINS HERE
+
+
+## ***************************************************************************
+## * BACKENDS                                                                *
+## ***************************************************************************
+## The default backend.  If you omit this parameter, the first working
+## backend from the following list is used:
+##     MacOSX QtAgg Gtk4Agg Gtk3Agg TkAgg WxAgg Agg
+## Other choices include:
+##     QtCairo GTK4Cairo GTK3Cairo TkCairo WxCairo Cairo
+##     Qt5Agg Qt5Cairo Wx  # deprecated.
+##     PS PDF SVG Template
+## You can also deploy your own backend outside of Matplotlib by referring to
+## the module name (which must be in the PYTHONPATH) as 'module://my_backend'.
+##backend: Agg
+
+## The port to use for the web server in the WebAgg backend.
+#webagg.port: 8988
+
+## The address on which the WebAgg web server should be reachable
+#webagg.address: 127.0.0.1
+
+## If webagg.port is unavailable, a number of other random ports will
+## be tried until one that is available is found.
+#webagg.port_retries: 50
+
+## When True, open the web browser to the plot that is shown
+#webagg.open_in_browser: True
+
+## If you are running pyplot inside a GUI and your backend choice
+## conflicts, we will automatically try to find a compatible one for
+## you if backend_fallback is True
+#backend_fallback: True
+
+#interactive: False
+#figure.hooks:          # list of dotted.module.name:dotted.callable.name
+#toolbar:     toolbar2  # {None, toolbar2, toolmanager}
+#timezone:    UTC       # a pytz timezone string, e.g., US/Central or Europe/Paris
+
+
+## ***************************************************************************
+## * LINES                                                                   *
+## ***************************************************************************
+## See https://matplotlib.org/stable/api/artist_api.html#module-matplotlib.lines
+## for more information on line properties.
+#lines.linewidth: 1.5               # line width in points
+#lines.linestyle: -                 # solid line
+#lines.color:     C0                # has no affect on plot(); see axes.prop_cycle
+#lines.marker:          None        # the default marker
+#lines.markerfacecolor: auto        # the default marker face color
+#lines.markeredgecolor: auto        # the default marker edge color
+#lines.markeredgewidth: 1.0         # the line width around the marker symbol
+#lines.markersize:      6           # marker size, in points
+#lines.dash_joinstyle:  round       # {miter, round, bevel}
+#lines.dash_capstyle:   butt        # {butt, round, projecting}
+#lines.solid_joinstyle: round       # {miter, round, bevel}
+#lines.solid_capstyle:  projecting  # {butt, round, projecting}
+#lines.antialiased: True            # render lines in antialiased (no jaggies)
+
+## The three standard dash patterns.  These are scaled by the linewidth.
+#lines.dashed_pattern: 3.7, 1.6
+#lines.dashdot_pattern: 6.4, 1.6, 1, 1.6
+#lines.dotted_pattern: 1, 1.65
+#lines.scale_dashes: True
+
+#markers.fillstyle: full  # {full, left, right, bottom, top, none}
+
+#pcolor.shading: auto
+#pcolormesh.snap: True  # Whether to snap the mesh to pixel boundaries. This is
+                        # provided solely to allow old test images to remain
+                        # unchanged. Set to False to obtain the previous behavior.
+
+## ***************************************************************************
+## * PATCHES                                                                 *
+## ***************************************************************************
+## Patches are graphical objects that fill 2D space, like polygons or circles.
+## See https://matplotlib.org/stable/api/artist_api.html#module-matplotlib.patches
+## for more information on patch properties.
+#patch.linewidth:       1.0    # edge width in points.
+#patch.facecolor:       C0
+#patch.edgecolor:       black  # if forced, or patch is not filled
+#patch.force_edgecolor: False  # True to always use edgecolor
+#patch.antialiased:     True   # render patches in antialiased (no jaggies)
+
+
+## ***************************************************************************
+## * HATCHES                                                                 *
+## ***************************************************************************
+#hatch.color:     black
+#hatch.linewidth: 1.0
+
+
+## ***************************************************************************
+## * BOXPLOT                                                                 *
+## ***************************************************************************
+#boxplot.notch:       False
+#boxplot.vertical:    True
+#boxplot.whiskers:    1.5
+#boxplot.bootstrap:   None
+#boxplot.patchartist: False
+#boxplot.showmeans:   False
+#boxplot.showcaps:    True
+#boxplot.showbox:     True
+#boxplot.showfliers:  True
+#boxplot.meanline:    False
+
+#boxplot.flierprops.color:           black
+#boxplot.flierprops.marker:          o
+#boxplot.flierprops.markerfacecolor: none
+#boxplot.flierprops.markeredgecolor: black
+#boxplot.flierprops.markeredgewidth: 1.0
+#boxplot.flierprops.markersize:      6
+#boxplot.flierprops.linestyle:       none
+#boxplot.flierprops.linewidth:       1.0
+
+#boxplot.boxprops.color:     black
+#boxplot.boxprops.linewidth: 1.0
+#boxplot.boxprops.linestyle: -
+
+#boxplot.whiskerprops.color:     black
+#boxplot.whiskerprops.linewidth: 1.0
+#boxplot.whiskerprops.linestyle: -
+
+#boxplot.capprops.color:     black
+#boxplot.capprops.linewidth: 1.0
+#boxplot.capprops.linestyle: -
+
+#boxplot.medianprops.color:     C1
+#boxplot.medianprops.linewidth: 1.0
+#boxplot.medianprops.linestyle: -
+
+#boxplot.meanprops.color:           C2
+#boxplot.meanprops.marker:          ^
+#boxplot.meanprops.markerfacecolor: C2
+#boxplot.meanprops.markeredgecolor: C2
+#boxplot.meanprops.markersize:       6
+#boxplot.meanprops.linestyle:       --
+#boxplot.meanprops.linewidth:       1.0
+
+
+## ***************************************************************************
+## * FONT                                                                    *
+## ***************************************************************************
+## The font properties used by `text.Text`.
+## See https://matplotlib.org/stable/api/font_manager_api.html for more information
+## on font properties.  The 6 font properties used for font matching are
+## given below with their default values.
+##
+## The font.family property can take either a single or multiple entries of any
+## combination of concrete font names (not supported when rendering text with
+## usetex) or the following five generic values:
+##     - 'serif' (e.g., Times),
+##     - 'sans-serif' (e.g., Helvetica),
+##     - 'cursive' (e.g., Zapf-Chancery),
+##     - 'fantasy' (e.g., Western), and
+##     - 'monospace' (e.g., Courier).
+## Each of these values has a corresponding default list of font names
+## (font.serif, etc.); the first available font in the list is used.  Note that
+## for font.serif, font.sans-serif, and font.monospace, the first element of
+## the list (a DejaVu font) will always be used because DejaVu is shipped with
+## Matplotlib and is thus guaranteed to be available; the other entries are
+## left as examples of other possible values.
+##
+## The font.style property has three values: normal (or roman), italic
+## or oblique.  The oblique style will be used for italic, if it is not
+## present.
+##
+## The font.variant property has two values: normal or small-caps.  For
+## TrueType fonts, which are scalable fonts, small-caps is equivalent
+## to using a font size of 'smaller', or about 83 % of the current font
+## size.
+##
+## The font.weight property has effectively 13 values: normal, bold,
+## bolder, lighter, 100, 200, 300, ..., 900.  Normal is the same as
+## 400, and bold is 700.  bolder and lighter are relative values with
+## respect to the current weight.
+##
+## The font.stretch property has 11 values: ultra-condensed,
+## extra-condensed, condensed, semi-condensed, normal, semi-expanded,
+## expanded, extra-expanded, ultra-expanded, wider, and narrower.  This
+## property is not currently implemented.
+##
+## The font.size property is the default font size for text, given in points.
+## 10 pt is the standard value.
+##
+## Note that font.size controls default text sizes.  To configure
+## special text sizes tick labels, axes, labels, title, etc., see the rc
+## settings for axes and ticks.  Special text sizes can be defined
+## relative to font.size, using the following values: xx-small, x-small,
+## small, medium, large, x-large, xx-large, larger, or smaller
+
+#font.family:  sans-serif
+#font.style:   normal
+#font.variant: normal
+#font.weight:  normal
+#font.stretch: normal
+#font.size:    10.0
+
+#font.serif:      DejaVu Serif, Bitstream Vera Serif, Computer Modern Roman, New Century Schoolbook, Century Schoolbook L, Utopia, ITC Bookman, Bookman, Nimbus Roman No9 L, Times New Roman, Times, Palatino, Charter, serif
+#font.sans-serif: DejaVu Sans, Bitstream Vera Sans, Computer Modern Sans Serif, Lucida Grande, Verdana, Geneva, Lucid, Arial, Helvetica, Avant Garde, sans-serif
+#font.cursive:    Apple Chancery, Textile, Zapf Chancery, Sand, Script MT, Felipa, Comic Neue, Comic Sans MS, cursive
+#font.fantasy:    Chicago, Charcoal, Impact, Western, Humor Sans, xkcd, fantasy
+#font.monospace:  DejaVu Sans Mono, Bitstream Vera Sans Mono, Computer Modern Typewriter, Andale Mono, Nimbus Mono L, Courier New, Courier, Fixed, Terminal, monospace
+
+
+## ***************************************************************************
+## * TEXT                                                                    *
+## ***************************************************************************
+## The text properties used by `text.Text`.
+## See https://matplotlib.org/stable/api/artist_api.html#module-matplotlib.text
+## for more information on text properties
+#text.color: black
+
+## FreeType hinting flag ("foo" corresponds to FT_LOAD_FOO); may be one of the
+## following (Proprietary Matplotlib-specific synonyms are given in parentheses,
+## but their use is discouraged):
+## - default: Use the font's native hinter if possible, else FreeType's auto-hinter.
+##            ("either" is a synonym).
+## - no_autohint: Use the font's native hinter if possible, else don't hint.
+##                ("native" is a synonym.)
+## - force_autohint: Use FreeType's auto-hinter.  ("auto" is a synonym.)
+## - no_hinting: Disable hinting.  ("none" is a synonym.)
+#text.hinting: force_autohint
+
+#text.hinting_factor: 8  # Specifies the amount of softness for hinting in the
+                         # horizontal direction.  A value of 1 will hint to full
+                         # pixels.  A value of 2 will hint to half pixels etc.
+#text.kerning_factor: 0  # Specifies the scaling factor for kerning values.  This
+                         # is provided solely to allow old test images to remain
+                         # unchanged.  Set to 6 to obtain previous behavior.
+                         # Values  other than 0 or 6 have no defined meaning.
+#text.antialiased: True  # If True (default), the text will be antialiased.
+                         # This only affects raster outputs.
+#text.parse_math: True  # Use mathtext if there is an even number of unescaped
+                        # dollar signs.
+
+
+## ***************************************************************************
+## * LaTeX                                                                   *
+## ***************************************************************************
+## For more information on LaTeX properties, see
+## https://matplotlib.org/stable/tutorials/text/usetex.html
+#text.usetex: False  # use latex for all text handling. The following fonts
+                     # are supported through the usual rc parameter settings:
+                     # new century schoolbook, bookman, times, palatino,
+                     # zapf chancery, charter, serif, sans-serif, helvetica,
+                     # avant garde, courier, monospace, computer modern roman,
+                     # computer modern sans serif, computer modern typewriter
+#text.latex.preamble:   # IMPROPER USE OF THIS FEATURE WILL LEAD TO LATEX FAILURES
+                        # AND IS THEREFORE UNSUPPORTED. PLEASE DO NOT ASK FOR HELP
+                        # IF THIS FEATURE DOES NOT DO WHAT YOU EXPECT IT TO.
+                        # text.latex.preamble is a single line of LaTeX code that
+                        # will be passed on to the LaTeX system. It may contain
+                        # any code that is valid for the LaTeX "preamble", i.e.
+                        # between the "\documentclass" and "\begin{document}"
+                        # statements.
+                        # Note that it has to be put on a single line, which may
+                        # become quite long.
+                        # The following packages are always loaded with usetex,
+                        # so beware of package collisions:
+                        #   geometry, inputenc, type1cm.
+                        # PostScript (PSNFSS) font packages may also be
+                        # loaded, depending on your font settings.
+
+## The following settings allow you to select the fonts in math mode.
+#mathtext.fontset: dejavusans  # Should be 'dejavusans' (default),
+                               # 'dejavuserif', 'cm' (Computer Modern), 'stix',
+                               # 'stixsans' or 'custom'
+## "mathtext.fontset: custom" is defined by the mathtext.bf, .cal, .it, ...
+## settings which map a TeX font name to a fontconfig font pattern.  (These
+## settings are not used for other font sets.)
+#mathtext.bf:  sans:bold
+#mathtext.cal: cursive
+#mathtext.it:  sans:italic
+#mathtext.rm:  sans
+#mathtext.sf:  sans
+#mathtext.tt:  monospace
+#mathtext.fallback: cm  # Select fallback font from ['cm' (Computer Modern), 'stix'
+                        # 'stixsans'] when a symbol can not be found in one of the
+                        # custom math fonts. Select 'None' to not perform fallback
+                        # and replace the missing character by a dummy symbol.
+#mathtext.default: it  # The default font to use for math.
+                       # Can be any of the LaTeX font names, including
+                       # the special name "regular" for the same font
+                       # used in regular text.
+
+
+## ***************************************************************************
+## * AXES                                                                    *
+## ***************************************************************************
+## Following are default face and edge colors, default tick sizes,
+## default font sizes for tick labels, and so on.  See
+## https://matplotlib.org/stable/api/axes_api.html#module-matplotlib.axes
+#axes.facecolor:     white   # axes background color
+#axes.edgecolor:     black   # axes edge color
+#axes.linewidth:     0.8     # edge line width
+#axes.grid:          False   # display grid or not
+#axes.grid.axis:     both    # which axis the grid should apply to
+#axes.grid.which:    major   # grid lines at {major, minor, both} ticks
+#axes.titlelocation: center  # alignment of the title: {left, right, center}
+#axes.titlesize:     large   # font size of the axes title
+#axes.titleweight:   normal  # font weight of title
+#axes.titlecolor:    auto    # color of the axes title, auto falls back to
+                             # text.color as default value
+#axes.titley:        None    # position title (axes relative units).  None implies auto
+#axes.titlepad:      6.0     # pad between axes and title in points
+#axes.labelsize:     medium  # font size of the x and y labels
+#axes.labelpad:      4.0     # space between label and axis
+#axes.labelweight:   normal  # weight of the x and y labels
+#axes.labelcolor:    black
+#axes.axisbelow:     line    # draw axis gridlines and ticks:
+                             #     - below patches (True)
+                             #     - above patches but below lines ('line')
+                             #     - above all (False)
+
+#axes.formatter.limits: -5, 6  # use scientific notation if log10
+                               # of the axis range is smaller than the
+                               # first or larger than the second
+#axes.formatter.use_locale: False  # When True, format tick labels
+                                   # according to the user's locale.
+                                   # For example, use ',' as a decimal
+                                   # separator in the fr_FR locale.
+#axes.formatter.use_mathtext: False  # When True, use mathtext for scientific
+                                     # notation.
+#axes.formatter.min_exponent: 0  # minimum exponent to format in scientific notation
+#axes.formatter.useoffset: True  # If True, the tick label formatter
+                                 # will default to labeling ticks relative
+                                 # to an offset when the data range is
+                                 # small compared to the minimum absolute
+                                 # value of the data.
+#axes.formatter.offset_threshold: 4  # When useoffset is True, the offset
+                                     # will be used when it can remove
+                                     # at least this number of significant
+                                     # digits from tick labels.
+
+#axes.spines.left:   True  # display axis spines
+#axes.spines.bottom: True
+#axes.spines.top:    True
+#axes.spines.right:  True
+
+#axes.unicode_minus: True  # use Unicode for the minus symbol rather than hyphen.  See
+                           # https://en.wikipedia.org/wiki/Plus_and_minus_signs#Character_codes
+#axes.prop_cycle: cycler('color', ['1f77b4', 'ff7f0e', '2ca02c', 'd62728', '9467bd', '8c564b', 'e377c2', '7f7f7f', 'bcbd22', '17becf'])
+                  # color cycle for plot lines as list of string color specs:
+                  # single letter, long name, or web-style hex
+                  # As opposed to all other parameters in this file, the color
+                  # values must be enclosed in quotes for this parameter,
+                  # e.g. '1f77b4', instead of 1f77b4.
+                  # See also https://matplotlib.org/stable/tutorials/intermediate/color_cycle.html
+                  # for more details on prop_cycle usage.
+#axes.xmargin:   .05  # x margin.  See `axes.Axes.margins`
+#axes.ymargin:   .05  # y margin.  See `axes.Axes.margins`
+#axes.zmargin:   .05  # z margin.  See `axes.Axes.margins`
+#axes.autolimit_mode: data  # If "data", use axes.xmargin and axes.ymargin as is.
+                            # If "round_numbers", after application of margins, axis
+                            # limits are further expanded to the nearest "round" number.
+#polaraxes.grid: True  # display grid on polar axes
+#axes3d.grid:    True  # display grid on 3D axes
+
+#axes3d.xaxis.panecolor:    (0.95, 0.95, 0.95, 0.5)  # background pane on 3D axes
+#axes3d.yaxis.panecolor:    (0.90, 0.90, 0.90, 0.5)  # background pane on 3D axes
+#axes3d.zaxis.panecolor:    (0.925, 0.925, 0.925, 0.5)  # background pane on 3D axes
+
+## ***************************************************************************
+## * AXIS                                                                    *
+## ***************************************************************************
+#xaxis.labellocation: center  # alignment of the xaxis label: {left, right, center}
+#yaxis.labellocation: center  # alignment of the yaxis label: {bottom, top, center}
+
+
+## ***************************************************************************
+## * DATES                                                                   *
+## ***************************************************************************
+## These control the default format strings used in AutoDateFormatter.
+## Any valid format datetime format string can be used (see the python
+## `datetime` for details).  For example, by using:
+##     - '%x' will use the locale date representation
+##     - '%X' will use the locale time representation
+##     - '%c' will use the full locale datetime representation
+## These values map to the scales:
+##     {'year': 365, 'month': 30, 'day': 1, 'hour': 1/24, 'minute': 1 / (24 * 60)}
+
+#date.autoformatter.year:        %Y
+#date.autoformatter.month:       %Y-%m
+#date.autoformatter.day:         %Y-%m-%d
+#date.autoformatter.hour:        %m-%d %H
+#date.autoformatter.minute:      %d %H:%M
+#date.autoformatter.second:      %H:%M:%S
+#date.autoformatter.microsecond: %M:%S.%f
+## The reference date for Matplotlib's internal date representation
+## See https://matplotlib.org/stable/gallery/ticks/date_precision_and_epochs.html
+#date.epoch: 1970-01-01T00:00:00
+## 'auto', 'concise':
+#date.converter:                  auto
+## For auto converter whether to use interval_multiples:
+#date.interval_multiples:         True
+
+## ***************************************************************************
+## * TICKS                                                                   *
+## ***************************************************************************
+## See https://matplotlib.org/stable/api/axis_api.html#matplotlib.axis.Tick
+#xtick.top:           False   # draw ticks on the top side
+#xtick.bottom:        True    # draw ticks on the bottom side
+#xtick.labeltop:      False   # draw label on the top
+#xtick.labelbottom:   True    # draw label on the bottom
+#xtick.major.size:    3.5     # major tick size in points
+#xtick.minor.size:    2       # minor tick size in points
+#xtick.major.width:   0.8     # major tick width in points
+#xtick.minor.width:   0.6     # minor tick width in points
+#xtick.major.pad:     3.5     # distance to major tick label in points
+#xtick.minor.pad:     3.4     # distance to the minor tick label in points
+#xtick.color:         black   # color of the ticks
+#xtick.labelcolor:    inherit # color of the tick labels or inherit from xtick.color
+#xtick.labelsize:     medium  # font size of the tick labels
+#xtick.direction:     out     # direction: {in, out, inout}
+#xtick.minor.visible: False   # visibility of minor ticks on x-axis
+#xtick.major.top:     True    # draw x axis top major ticks
+#xtick.major.bottom:  True    # draw x axis bottom major ticks
+#xtick.minor.top:     True    # draw x axis top minor ticks
+#xtick.minor.bottom:  True    # draw x axis bottom minor ticks
+#xtick.alignment:     center  # alignment of xticks
+
+#ytick.left:          True    # draw ticks on the left side
+#ytick.right:         False   # draw ticks on the right side
+#ytick.labelleft:     True    # draw tick labels on the left side
+#ytick.labelright:    False   # draw tick labels on the right side
+#ytick.major.size:    3.5     # major tick size in points
+#ytick.minor.size:    2       # minor tick size in points
+#ytick.major.width:   0.8     # major tick width in points
+#ytick.minor.width:   0.6     # minor tick width in points
+#ytick.major.pad:     3.5     # distance to major tick label in points
+#ytick.minor.pad:     3.4     # distance to the minor tick label in points
+#ytick.color:         black   # color of the ticks
+#ytick.labelcolor:    inherit # color of the tick labels or inherit from ytick.color
+#ytick.labelsize:     medium  # font size of the tick labels
+#ytick.direction:     out     # direction: {in, out, inout}
+#ytick.minor.visible: False   # visibility of minor ticks on y-axis
+#ytick.major.left:    True    # draw y axis left major ticks
+#ytick.major.right:   True    # draw y axis right major ticks
+#ytick.minor.left:    True    # draw y axis left minor ticks
+#ytick.minor.right:   True    # draw y axis right minor ticks
+#ytick.alignment:     center_baseline  # alignment of yticks
+
+
+## ***************************************************************************
+## * GRIDS                                                                   *
+## ***************************************************************************
+#grid.color:     "#b0b0b0"  # grid color
+#grid.linestyle: -          # solid
+#grid.linewidth: 0.8        # in points
+#grid.alpha:     1.0        # transparency, between 0.0 and 1.0
+
+
+## ***************************************************************************
+## * LEGEND                                                                  *
+## ***************************************************************************
+#legend.loc:           best
+#legend.frameon:       True     # if True, draw the legend on a background patch
+#legend.framealpha:    0.8      # legend patch transparency
+#legend.facecolor:     inherit  # inherit from axes.facecolor; or color spec
+#legend.edgecolor:     0.8      # background patch boundary color
+#legend.fancybox:      True     # if True, use a rounded box for the
+                                # legend background, else a rectangle
+#legend.shadow:        False    # if True, give background a shadow effect
+#legend.numpoints:     1        # the number of marker points in the legend line
+#legend.scatterpoints: 1        # number of scatter points
+#legend.markerscale:   1.0      # the relative size of legend markers vs. original
+#legend.fontsize:      medium
+#legend.labelcolor:    None
+#legend.title_fontsize: None    # None sets to the same as the default axes.
+
+## Dimensions as fraction of font size:
+#legend.borderpad:     0.4  # border whitespace
+#legend.labelspacing:  0.5  # the vertical space between the legend entries
+#legend.handlelength:  2.0  # the length of the legend lines
+#legend.handleheight:  0.7  # the height of the legend handle
+#legend.handletextpad: 0.8  # the space between the legend line and legend text
+#legend.borderaxespad: 0.5  # the border between the axes and legend edge
+#legend.columnspacing: 2.0  # column separation
+
+
+## ***************************************************************************
+## * FIGURE                                                                  *
+## ***************************************************************************
+## See https://matplotlib.org/stable/api/figure_api.html#matplotlib.figure.Figure
+#figure.titlesize:   large     # size of the figure title (``Figure.suptitle()``)
+#figure.titleweight: normal    # weight of the figure title
+#figure.labelsize:   large     # size of the figure label (``Figure.sup[x|y]label()``)
+#figure.labelweight: normal    # weight of the figure label
+#figure.figsize:     6.4, 4.8  # figure size in inches
+#figure.dpi:         100       # figure dots per inch
+#figure.facecolor:   white     # figure face color
+#figure.edgecolor:   white     # figure edge color
+#figure.frameon:     True      # enable figure frame
+#figure.max_open_warning: 20   # The maximum number of figures to open through
+                               # the pyplot interface before emitting a warning.
+                               # If less than one this feature is disabled.
+#figure.raise_window : True    # Raise the GUI window to front when show() is called.
+
+## The figure subplot parameters.  All dimensions are a fraction of the figure width and height.
+#figure.subplot.left:   0.125  # the left side of the subplots of the figure
+#figure.subplot.right:  0.9    # the right side of the subplots of the figure
+#figure.subplot.bottom: 0.11   # the bottom of the subplots of the figure
+#figure.subplot.top:    0.88   # the top of the subplots of the figure
+#figure.subplot.wspace: 0.2    # the amount of width reserved for space between subplots,
+                               # expressed as a fraction of the average axis width
+#figure.subplot.hspace: 0.2    # the amount of height reserved for space between subplots,
+                               # expressed as a fraction of the average axis height
+
+## Figure layout
+#figure.autolayout: False  # When True, automatically adjust subplot
+                           # parameters to make the plot fit the figure
+                           # using `tight_layout`
+#figure.constrained_layout.use: False  # When True, automatically make plot
+                                       # elements fit on the figure. (Not
+                                       # compatible with `autolayout`, above).
+#figure.constrained_layout.h_pad:  0.04167  # Padding around axes objects. Float representing
+#figure.constrained_layout.w_pad:  0.04167  # inches. Default is 3/72 inches (3 points)
+#figure.constrained_layout.hspace: 0.02     # Space between subplot groups. Float representing
+#figure.constrained_layout.wspace: 0.02     # a fraction of the subplot widths being separated.
+
+
+## ***************************************************************************
+## * IMAGES                                                                  *
+## ***************************************************************************
+#image.aspect:          equal        # {equal, auto} or a number
+#image.interpolation:   antialiased  # see help(imshow) for options
+#image.cmap:            viridis      # A colormap name (plasma, magma, etc.)
+#image.lut:             256          # the size of the colormap lookup table
+#image.origin:          upper        # {lower, upper}
+#image.resample:        True
+#image.composite_image: True  # When True, all the images on a set of axes are
+                              # combined into a single composite image before
+                              # saving a figure as a vector graphics file,
+                              # such as a PDF.
+
+
+## ***************************************************************************
+## * CONTOUR PLOTS                                                           *
+## ***************************************************************************
+#contour.negative_linestyle: dashed  # string or on-off ink sequence
+#contour.corner_mask:        True    # {True, False}
+#contour.linewidth:          None    # {float, None} Size of the contour line
+                                     # widths. If set to None, it falls back to
+                                     # `line.linewidth`.
+#contour.algorithm:          mpl2014 # {mpl2005, mpl2014, serial, threaded}
+
+
+## ***************************************************************************
+## * ERRORBAR PLOTS                                                          *
+## ***************************************************************************
+#errorbar.capsize: 0  # length of end cap on error bars in pixels
+
+
+## ***************************************************************************
+## * HISTOGRAM PLOTS                                                         *
+## ***************************************************************************
+#hist.bins: 10  # The default number of histogram bins or 'auto'.
+
+
+## ***************************************************************************
+## * SCATTER PLOTS                                                           *
+## ***************************************************************************
+#scatter.marker: o         # The default marker type for scatter plots.
+#scatter.edgecolors: face  # The default edge colors for scatter plots.
+
+
+## ***************************************************************************
+## * AGG RENDERING                                                           *
+## ***************************************************************************
+## Warning: experimental, 2008/10/10
+#agg.path.chunksize: 0  # 0 to disable; values in the range
+                        # 10000 to 100000 can improve speed slightly
+                        # and prevent an Agg rendering failure
+                        # when plotting very large data sets,
+                        # especially if they are very gappy.
+                        # It may cause minor artifacts, though.
+                        # A value of 20000 is probably a good
+                        # starting point.
+
+
+## ***************************************************************************
+## * PATHS                                                                   *
+## ***************************************************************************
+#path.simplify: True  # When True, simplify paths by removing "invisible"
+                      # points to reduce file size and increase rendering
+                      # speed
+#path.simplify_threshold: 0.111111111111  # The threshold of similarity below
+                                          # which vertices will be removed in
+                                          # the simplification process.
+#path.snap: True  # When True, rectilinear axis-aligned paths will be snapped
+                  # to the nearest pixel when certain criteria are met.
+                  # When False, paths will never be snapped.
+#path.sketch: None  # May be None, or a 3-tuple of the form:
+                    # (scale, length, randomness).
+                    #     - *scale* is the amplitude of the wiggle
+                    #         perpendicular to the line (in pixels).
+                    #     - *length* is the length of the wiggle along the
+                    #         line (in pixels).
+                    #     - *randomness* is the factor by which the length is
+                    #         randomly scaled.
+#path.effects:
+
+
+## ***************************************************************************
+## * SAVING FIGURES                                                          *
+## ***************************************************************************
+## The default savefig parameters can be different from the display parameters
+## e.g., you may want a higher resolution, or to make the figure
+## background white
+#savefig.dpi:       figure      # figure dots per inch or 'figure'
+#savefig.facecolor: auto        # figure face color when saving
+#savefig.edgecolor: auto        # figure edge color when saving
+#savefig.format:    png         # {png, ps, pdf, svg}
+#savefig.bbox:      standard    # {tight, standard}
+                                # 'tight' is incompatible with pipe-based animation
+                                # backends (e.g. 'ffmpeg') but will work with those
+                                # based on temporary files (e.g. 'ffmpeg_file')
+#savefig.pad_inches:  0.1       # padding to be used, when bbox is set to 'tight'
+#savefig.directory:   ~         # default directory in savefig dialog, gets updated after
+                                # interactive saves, unless set to the empty string (i.e.
+                                # the current directory); use '.' to start at the current
+                                # directory but update after interactive saves
+#savefig.transparent: False     # whether figures are saved with a transparent
+                                # background by default
+#savefig.orientation: portrait  # orientation of saved figure, for PostScript output only
+
+### tk backend params
+#tk.window_focus:   False  # Maintain shell focus for TkAgg
+
+### ps backend params
+#ps.papersize:      letter  # {auto, letter, legal, ledger, A0-A10, B0-B10}
+#ps.useafm:         False   # use of AFM fonts, results in small files
+#ps.usedistiller:   False   # {ghostscript, xpdf, None}
+                            # Experimental: may produce smaller files.
+                            # xpdf intended for production of publication quality files,
+                            # but requires ghostscript, xpdf and ps2eps
+#ps.distiller.res:  6000    # dpi
+#ps.fonttype:       3       # Output Type 3 (Type3) or Type 42 (TrueType)
+
+### PDF backend params
+#pdf.compression:    6  # integer from 0 to 9
+                        # 0 disables compression (good for debugging)
+#pdf.fonttype:       3  # Output Type 3 (Type3) or Type 42 (TrueType)
+#pdf.use14corefonts: False
+#pdf.inheritcolor:   False
+
+### SVG backend params
+#svg.image_inline: True  # Write raster image data directly into the SVG file
+#svg.fonttype: path      # How to handle SVG fonts:
+                         #     path: Embed characters as paths -- supported
+                         #           by most SVG renderers
+                         #     None: Assume fonts are installed on the
+                         #           machine where the SVG will be viewed.
+#svg.hashsalt: None      # If not None, use this string as hash salt instead of uuid4
+
+### pgf parameter
+## See https://matplotlib.org/stable/tutorials/text/pgf.html for more information.
+#pgf.rcfonts: True
+#pgf.preamble:  # See text.latex.preamble for documentation
+#pgf.texsystem: xelatex
+
+### docstring params
+#docstring.hardcopy: False  # set this when you want to generate hardcopy docstring
+
+
+## ***************************************************************************
+## * INTERACTIVE KEYMAPS                                                     *
+## ***************************************************************************
+## Event keys to interact with figures/plots via keyboard.
+## See https://matplotlib.org/stable/users/explain/interactive.html for more
+## details on interactive navigation.  Customize these settings according to
+## your needs. Leave the field(s) empty if you don't need a key-map. (i.e.,
+## fullscreen : '')
+#keymap.fullscreen: f, ctrl+f   # toggling
+#keymap.home: h, r, home        # home or reset mnemonic
+#keymap.back: left, c, backspace, MouseButton.BACK  # forward / backward keys
+#keymap.forward: right, v, MouseButton.FORWARD      # for quick navigation
+#keymap.pan: p                  # pan mnemonic
+#keymap.zoom: o                 # zoom mnemonic
+#keymap.save: s, ctrl+s         # saving current figure
+#keymap.help: f1                # display help about active tools
+#keymap.quit: ctrl+w, cmd+w, q  # close the current figure
+#keymap.quit_all:               # close all figures
+#keymap.grid: g                 # switching on/off major grids in current axes
+#keymap.grid_minor: G           # switching on/off minor grids in current axes
+#keymap.yscale: l               # toggle scaling of y-axes ('log'/'linear')
+#keymap.xscale: k, L            # toggle scaling of x-axes ('log'/'linear')
+#keymap.copy: ctrl+c, cmd+c     # copy figure to clipboard
+
+
+## ***************************************************************************
+## * ANIMATION                                                               *
+## ***************************************************************************
+#animation.html: none  # How to display the animation as HTML in
+                       # the IPython notebook:
+                       #     - 'html5' uses HTML5 video tag
+                       #     - 'jshtml' creates a JavaScript animation
+#animation.writer:  ffmpeg        # MovieWriter 'backend' to use
+#animation.codec:   h264          # Codec to use for writing movie
+#animation.bitrate: -1            # Controls size/quality trade-off for movie.
+                                  # -1 implies let utility auto-determine
+#animation.frame_format: png      # Controls frame format used by temp files
+
+## Path to ffmpeg binary.  Unqualified paths are resolved by subprocess.Popen.
+#animation.ffmpeg_path:  ffmpeg
+## Additional arguments to pass to ffmpeg.
+#animation.ffmpeg_args:
+
+## Path to ImageMagick's convert binary.  Unqualified paths are resolved by
+## subprocess.Popen, except that on Windows, we look up an install of
+## ImageMagick in the registry (as convert is also the name of a system tool).
+#animation.convert_path: convert
+## Additional arguments to pass to convert.
+#animation.convert_args: -layers, OptimizePlus
+#
+#animation.embed_limit:  20.0     # Limit, in MB, of size of base64 encoded
+                                  # animation in HTML (i.e. IPython notebook)

omnilmm/lib/python3.10/site-packages/matplotlib/mpl-data/plot_directive/plot_directive.css

@@ -0,0 +1,16 @@
+/*
+ * plot_directive.css
+ * ~~~~~~~~~~~~
+ *
+ * Stylesheet controlling images created using the `plot` directive within
+ * Sphinx.
+ *
+ * :copyright: Copyright 2020-* by the Matplotlib development team.
+ * :license: Matplotlib, see LICENSE for details.
+ *
+ */
+
+img.plot-directive {
+    border: 0;
+    max-width: 100%;
+}

omnilmm/lib/python3.10/site-packages/matplotlib/mpl-data/sample_data/README.txt

@@ -0,0 +1,2 @@
+This is the sample data needed for some of matplotlib's examples and
+docs.  See matplotlib.cbook.get_sample_data for more info.

omnilmm/lib/python3.10/site-packages/matplotlib/mpl-data/sample_data/Stocks.csv

@@ -0,0 +1,526 @@
+# Data source: https://finance.yahoo.com
+Date,IBM,AAPL,MSFT,XRX,AMZN,DELL,GOOGL,ADBE,^GSPC,^IXIC
+1990-01-01,10.970438003540039,0.24251236021518707,0.40375930070877075,11.202081680297852,,,,1.379060983657837,329.0799865722656,415.79998779296875
+1990-02-01,11.554415702819824,0.24251236021518707,0.43104037642478943,10.39472484588623,,,,1.7790844440460205,331.8900146484375,425.79998779296875
+1990-02-05,,,,,,,,,,
+1990-03-01,11.951693534851074,0.28801724314689636,0.4834197461605072,11.394058227539062,,,,2.2348830699920654,339.94000244140625,435.5
+1990-04-01,12.275476455688477,0.2817564308643341,0.5063362717628479,10.139430046081543,,,,2.2531447410583496,330.79998779296875,420.1000061035156
+1990-05-01,13.514284133911133,0.295173317193985,0.6372847557067871,9.704153060913086,,,,2.0985164642333984,361.2300109863281,459.0
+1990-05-04,,,,,,,,,,
+1990-06-01,13.380948066711426,0.3211067020893097,0.6634747385978699,9.749448776245117,,,,2.164785146713257,358.0199890136719,462.29998779296875
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omnilmm/lib/python3.10/site-packages/matplotlib/mpl-data/sample_data/embedding_in_wx3.xrc

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+1996,38.96977475,35.9264854,58.6,53.78988011,48.6473926,58.7,27.1,75.03519921,16.7,66.43777883,69.7,81.3,46.4,37.3,73.9,79.8,48.7
+1997,40.68568483,35.10193413,58.7,54.99946903,48.56105033,60,26.8,75.1637013,17,66.78635548,70,81.9,47,38.3,74.4,81,49.2
+1998,41.91240333,37.59854457,59.1,56.35124789,49.2585152,60,27,75.48616027,17.8,67.2554484,70.1,82.1,48.3,39.7,75.1,81.3,50.5
+1999,42.88720191,38.63152919,59.2,58.22882288,49.81020815,61.2,28.1,75.83816206,18.6,67.82022113,70.9,83.5,47.8,40.2,76.5,81.1,51.2
+2000,45.05776637,40.02358491,59.2,59.38985737,49.80361649,61.9,27.7,76.69214284,18.4,68.36599498,70.9,83.5,48.2,41,77.5,81.1,51.8
+2001,45.86601517,40.69028156,59.4,60.71233149,50.27514494,63,27.6,77.37522931,19,68.57852029,71.2,85.1,47,42.2,77.5,80.9,51.7
+2002,47.13465821,41.13295053,60.9,61.8951284,50.5523346,63.7,27,78.64424394,18.7,68.82995959,70.5,85.8,45.7,41.1,77.7,81.3,51.5
+2003,47.93518721,42.75854266,61.1,62.1694558,50.34559774,64.6,25.1,78.54494815,18.8,68.89448726,70.6,86.5,46,41.7,77.8,81.5,50.9
+2004,47.88714025,43.46649345,61.3,61.91458697,49.95089449,64.2,22.2,78.65074774,18.2,68.45473436,70.8,86.5,44.7,42.1,77.8,80.7,50.5
+2005,47.67275409,43.10036784,61.4,61.50098432,49.79185139,63.4,20.6,79.06712173,17.9,68.57122114,69.9,86,45.1,41.6,77.5,81.2,50
+2006,46.79029957,44.49933107,61.6,60.17284465,49.21091439,63,18.6,78.68630551,16.8,68.29759443,69.6,85.9,44.1,40.8,77.4,81.2,49.8
+2007,47.60502633,43.10045895,61.4,59.41199314,49.00045935,62.5,17.6,78.72141311,16.8,67.87492278,70.2,85.4,44.1,40.7,77.1,82.1,49.3
+2008,47.570834,42.71173041,60.7,59.30576517,48.88802678,62.4,17.8,79.19632674,16.5,67.59402834,70.2,85.2,43.3,40.7,77.2,81.7,49.4
+2009,48.66722357,43.34892051,61,58.48958333,48.84047414,62.8,18.1,79.5329087,16.8,67.96979204,69.3,85.1,43.3,40.7,77.1,82,49.4
+2010,48.73004227,42.06672091,61.3,59.01025521,48.75798769,62.5,17.6,79.61862451,17.2,67.92810557,69,85,43.1,40.2,77,81.7,49.3
+2011,50.03718193,42.7734375,61.2,58.7423969,48.18041792,62.2,18.2,79.43281184,17.5,68.42673015,69.5,84.8,43.1,40.1,76.7,81.9,49.2

omnilmm/lib/python3.10/site-packages/matplotlib/mpl-data/sample_data/s1045.ima.gz

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+version https://git-lfs.github.com/spec/v1
+oid sha256:32b424d64f62b7e71cb24d29fd53938ad5664d608055a67ab2b2af4369f8b89e
+size 33229

omnilmm/lib/python3.10/site-packages/matplotlib/mpl-data/sample_data/topobathy.npz

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+version https://git-lfs.github.com/spec/v1
+oid sha256:0244e03291702df45024dcb5cacbc4f3d4cb30d72dfa7fd371c4ac61c42b4fbf
+size 45224

omnilmm/lib/python3.10/site-packages/matplotlib/mpl-data/stylelib/Solarize_Light2.mplstyle

@@ -0,0 +1,53 @@
+# Solarized color palette taken from https://ethanschoonover.com/solarized/
+# Inspired by, and copied from ggthemes https://github.com/jrnold/ggthemes
+
+#TODO:
+#    1. Padding to title from face
+#    2. Remove top & right ticks
+#    3. Give Title a Magenta Color(?)
+
+#base00     ='#657b83'
+#base01     ='#93a1a1'
+#base2      ='#eee8d5'
+#base3      ='#fdf6e3'
+#base01     ='#586e75'
+#Magenta    ='#d33682'
+#Blue       ='#268bd2'
+#cyan       ='#2aa198'
+#violet     ='#6c71c4'
+#green      ='#859900'
+#orange     ='#cb4b16'
+
+figure.facecolor    : FDF6E3
+
+patch.antialiased   : True
+
+lines.linewidth     : 2.0
+lines.solid_capstyle: butt
+
+axes.titlesize      : 16
+axes.labelsize      : 12
+axes.labelcolor     : 657b83
+axes.facecolor      : eee8d5
+axes.edgecolor      : eee8d5
+axes.axisbelow      : True
+axes.prop_cycle    : cycler('color', ['268BD2', '2AA198', '859900', 'B58900', 'CB4B16', 'DC322F', 'D33682', '6C71C4'])
+# Blue
+# Cyan
+# Green
+# Yellow
+# Orange
+# Red
+# Magenta
+# Violet
+axes.grid           : True
+grid.color          : fdf6e3    # grid color
+grid.linestyle      : -         # line
+grid.linewidth      : 1         # in points
+
+### TICKS
+xtick.color         : 657b83
+xtick.direction     : out
+
+ytick.color         : 657b83
+ytick.direction     : out

omnilmm/lib/python3.10/site-packages/matplotlib/mpl-data/stylelib/_classic_test_patch.mplstyle

@@ -0,0 +1,6 @@
+# This patch should go on top of the "classic" style and exists solely to avoid
+# changing baseline images.
+
+text.kerning_factor : 6
+
+ytick.alignment: center_baseline

omnilmm/lib/python3.10/site-packages/matplotlib/mpl-data/stylelib/_mpl-gallery-nogrid.mplstyle

@@ -0,0 +1,19 @@
+# This style is used for the plot_types gallery. It is considered private.
+
+axes.grid: False
+axes.axisbelow: True
+
+figure.figsize: 2, 2
+# make it so the axes labels don't show up.  Obviously 
+# not good style for any quantitative analysis:
+figure.subplot.left: 0.01
+figure.subplot.right: 0.99
+figure.subplot.bottom: 0.01
+figure.subplot.top: 0.99
+
+xtick.major.size: 0.0
+ytick.major.size: 0.0
+
+# colors:
+image.cmap    : Blues
+axes.prop_cycle: cycler('color', ['1f77b4', '82bbdb', 'ccdff1'])

omnilmm/lib/python3.10/site-packages/matplotlib/mpl-data/stylelib/_mpl-gallery.mplstyle

@@ -0,0 +1,19 @@
+# This style is used for the plot_types gallery. It is considered part of the private API.
+
+axes.grid: True
+axes.axisbelow: True
+
+figure.figsize: 2, 2
+# make it so the axes labels don't show up.  Obviously 
+# not good style for any quantitative analysis:
+figure.subplot.left: 0.01
+figure.subplot.right: 0.99
+figure.subplot.bottom: 0.01
+figure.subplot.top: 0.99
+
+xtick.major.size: 0.0
+ytick.major.size: 0.0
+
+# colors:
+image.cmap    : Blues
+axes.prop_cycle: cycler('color', ['1f77b4', '58a1cf', 'abd0e6'])

omnilmm/lib/python3.10/site-packages/matplotlib/mpl-data/stylelib/bmh.mplstyle

@@ -0,0 +1,29 @@
+#Author: Cameron Davidson-Pilon, original styles from Bayesian Methods for Hackers
+# https://github.com/CamDavidsonPilon/Probabilistic-Programming-and-Bayesian-Methods-for-Hackers/
+
+lines.linewidth : 2.0
+
+patch.linewidth: 0.5
+patch.facecolor: blue
+patch.edgecolor: eeeeee
+patch.antialiased: True
+
+text.hinting_factor : 8
+
+mathtext.fontset : cm
+
+axes.facecolor: eeeeee
+axes.edgecolor: bcbcbc
+axes.grid : True
+axes.titlesize: x-large
+axes.labelsize: large
+axes.prop_cycle: cycler('color', ['348ABD', 'A60628', '7A68A6', '467821', 'D55E00', 'CC79A7', '56B4E9', '009E73', 'F0E442', '0072B2'])
+
+grid.color: b2b2b2
+grid.linestyle: --
+grid.linewidth: 0.5
+
+legend.fancybox: True
+
+xtick.direction: in
+ytick.direction: in

omnilmm/lib/python3.10/site-packages/matplotlib/mpl-data/stylelib/classic.mplstyle

@@ -0,0 +1,492 @@
+### Classic matplotlib plotting style as of v1.5
+
+
+### LINES
+# See https://matplotlib.org/api/artist_api.html#module-matplotlib.lines for more
+# information on line properties.
+lines.linewidth   : 1.0     # line width in points
+lines.linestyle   : -       # solid line
+lines.color       : b       # has no affect on plot(); see axes.prop_cycle
+lines.marker      : None    # the default marker
+lines.markerfacecolor  : auto    # the default markerfacecolor
+lines.markeredgecolor  : auto    # the default markeredgecolor
+lines.markeredgewidth  : 0.5     # the line width around the marker symbol
+lines.markersize  : 6            # markersize, in points
+lines.dash_joinstyle : round        # miter|round|bevel
+lines.dash_capstyle : butt          # butt|round|projecting
+lines.solid_joinstyle : round       # miter|round|bevel
+lines.solid_capstyle : projecting   # butt|round|projecting
+lines.antialiased : True         # render lines in antialiased (no jaggies)
+lines.dashed_pattern : 6, 6
+lines.dashdot_pattern : 3, 5, 1, 5
+lines.dotted_pattern : 1, 3
+lines.scale_dashes: False
+
+### Marker props
+markers.fillstyle: full
+
+### PATCHES
+# Patches are graphical objects that fill 2D space, like polygons or
+# circles.  See
+# https://matplotlib.org/api/artist_api.html#module-matplotlib.patches
+# information on patch properties
+patch.linewidth        : 1.0     # edge width in points
+patch.facecolor        : b
+patch.force_edgecolor  : True
+patch.edgecolor        : k
+patch.antialiased      : True    # render patches in antialiased (no jaggies)
+
+hatch.color            : k
+hatch.linewidth        : 1.0
+
+hist.bins              : 10
+
+### FONT
+#
+# font properties used by text.Text.  See
+# https://matplotlib.org/api/font_manager_api.html for more
+# information on font properties.  The 6 font properties used for font
+# matching are given below with their default values.
+#
+# The font.family property has five values: 'serif' (e.g., Times),
+# 'sans-serif' (e.g., Helvetica), 'cursive' (e.g., Zapf-Chancery),
+# 'fantasy' (e.g., Western), and 'monospace' (e.g., Courier).  Each of
+# these font families has a default list of font names in decreasing
+# order of priority associated with them.  When text.usetex is False,
+# font.family may also be one or more concrete font names.
+#
+# The font.style property has three values: normal (or roman), italic
+# or oblique.  The oblique style will be used for italic, if it is not
+# present.
+#
+# The font.variant property has two values: normal or small-caps.  For
+# TrueType fonts, which are scalable fonts, small-caps is equivalent
+# to using a font size of 'smaller', or about 83% of the current font
+# size.
+#
+# The font.weight property has effectively 13 values: normal, bold,
+# bolder, lighter, 100, 200, 300, ..., 900.  Normal is the same as
+# 400, and bold is 700.  bolder and lighter are relative values with
+# respect to the current weight.
+#
+# The font.stretch property has 11 values: ultra-condensed,
+# extra-condensed, condensed, semi-condensed, normal, semi-expanded,
+# expanded, extra-expanded, ultra-expanded, wider, and narrower.  This
+# property is not currently implemented.
+#
+# The font.size property is the default font size for text, given in pts.
+# 12pt is the standard value.
+#
+font.family         : sans-serif
+font.style          : normal
+font.variant        : normal
+font.weight         : normal
+font.stretch        : normal
+# note that font.size controls default text sizes.  To configure
+# special text sizes tick labels, axes, labels, title, etc, see the rc
+# settings for axes and ticks. Special text sizes can be defined
+# relative to font.size, using the following values: xx-small, x-small,
+# small, medium, large, x-large, xx-large, larger, or smaller
+font.size           : 12.0
+font.serif     : DejaVu Serif, New Century Schoolbook, Century Schoolbook L, Utopia, ITC Bookman, Bookman, Nimbus Roman No9 L, Times New Roman, Times, Palatino, Charter, serif
+font.sans-serif: DejaVu Sans, Lucida Grande, Verdana, Geneva, Lucid, Arial, Helvetica, Avant Garde, sans-serif
+font.cursive   : Apple Chancery, Textile, Zapf Chancery, Sand, Script MT, Felipa, cursive
+font.fantasy   : Comic Sans MS, Chicago, Charcoal, ImpactWestern, Humor Sans, fantasy
+font.monospace : DejaVu Sans Mono, Andale Mono, Nimbus Mono L, Courier New, Courier, Fixed, Terminal, monospace
+
+### TEXT
+# text properties used by text.Text.  See
+# https://matplotlib.org/api/artist_api.html#module-matplotlib.text for more
+# information on text properties
+
+text.color          : k
+
+### LaTeX customizations. See http://www.scipy.org/Wiki/Cookbook/Matplotlib/UsingTex
+text.usetex         : False  # use latex for all text handling. The following fonts
+                             # are supported through the usual rc parameter settings:
+                             # new century schoolbook, bookman, times, palatino,
+                             # zapf chancery, charter, serif, sans-serif, helvetica,
+                             # avant garde, courier, monospace, computer modern roman,
+                             # computer modern sans serif, computer modern typewriter
+                             # If another font is desired which can loaded using the
+                             # LaTeX \usepackage command, please inquire at the
+                             # matplotlib mailing list
+text.latex.preamble :  # IMPROPER USE OF THIS FEATURE WILL LEAD TO LATEX FAILURES
+                       # AND IS THEREFORE UNSUPPORTED. PLEASE DO NOT ASK FOR HELP
+                       # IF THIS FEATURE DOES NOT DO WHAT YOU EXPECT IT TO.
+                       # text.latex.preamble is a single line of LaTeX code that
+                       # will be passed on to the LaTeX system. It may contain
+                       # any code that is valid for the LaTeX "preamble", i.e.
+                       # between the "\documentclass" and "\begin{document}"
+                       # statements.
+                       # Note that it has to be put on a single line, which may
+                       # become quite long.
+                       # The following packages are always loaded with usetex, so
+                       # beware of package collisions: color, geometry, graphicx,
+                       # type1cm, textcomp.
+                       # Adobe Postscript (PSSNFS) font packages may also be
+                       # loaded, depending on your font settings.
+
+text.hinting : auto   # May be one of the following:
+                      #   'none': Perform no hinting
+                      #   'auto': Use freetype's autohinter
+                      #   'native': Use the hinting information in the
+                      #             font file, if available, and if your
+                      #             freetype library supports it
+                      #   'either': Use the native hinting information,
+                      #             or the autohinter if none is available.
+                      # For backward compatibility, this value may also be
+                      # True === 'auto' or False === 'none'.
+text.hinting_factor : 8 # Specifies the amount of softness for hinting in the
+                        # horizontal direction.  A value of 1 will hint to full
+                        # pixels.  A value of 2 will hint to half pixels etc.
+
+text.antialiased : True # If True (default), the text will be antialiased.
+                        # This only affects the Agg backend.
+
+# The following settings allow you to select the fonts in math mode.
+# They map from a TeX font name to a fontconfig font pattern.
+# These settings are only used if mathtext.fontset is 'custom'.
+# Note that this "custom" mode is unsupported and may go away in the
+# future.
+mathtext.cal : cursive
+mathtext.rm  : serif
+mathtext.tt  : monospace
+mathtext.it  : serif:italic
+mathtext.bf  : serif:bold
+mathtext.sf  : sans\-serif
+mathtext.fontset : cm # Should be 'cm' (Computer Modern), 'stix',
+                      # 'stixsans' or 'custom'
+mathtext.fallback: cm  # Select fallback font from ['cm' (Computer Modern), 'stix'
+                       # 'stixsans'] when a symbol can not be found in one of the
+                       # custom math fonts. Select 'None' to not perform fallback
+                       # and replace the missing character by a dummy.
+
+mathtext.default : it # The default font to use for math.
+                      # Can be any of the LaTeX font names, including
+                      # the special name "regular" for the same font
+                      # used in regular text.
+
+### AXES
+# default face and edge color, default tick sizes,
+# default fontsizes for ticklabels, and so on.  See
+# https://matplotlib.org/api/axes_api.html#module-matplotlib.axes
+axes.facecolor      : w       # axes background color
+axes.edgecolor      : k       # axes edge color
+axes.linewidth      : 1.0     # edge linewidth
+axes.grid           : False   # display grid or not
+axes.grid.which     : major
+axes.grid.axis      : both
+axes.titlesize      : large   # fontsize of the axes title
+axes.titley         : 1.0     # at the top, no autopositioning.
+axes.titlepad       : 5.0     # pad between axes and title in points
+axes.titleweight    : normal  # font weight for axes title
+axes.labelsize      : medium  # fontsize of the x any y labels
+axes.labelpad       : 5.0     # space between label and axis
+axes.labelweight    : normal  # weight of the x and y labels
+axes.labelcolor     : k
+axes.axisbelow      : False   # whether axis gridlines and ticks are below
+                              # the axes elements (lines, text, etc)
+
+axes.formatter.limits : -7, 7 # use scientific notation if log10
+                              # of the axis range is smaller than the
+                              # first or larger than the second
+axes.formatter.use_locale : False # When True, format tick labels
+                                  # according to the user's locale.
+                                  # For example, use ',' as a decimal
+                                  # separator in the fr_FR locale.
+axes.formatter.use_mathtext : False # When True, use mathtext for scientific
+                                    # notation.
+axes.formatter.useoffset      : True    # If True, the tick label formatter
+                                        # will default to labeling ticks relative
+                                        # to an offset when the data range is very
+                                        # small compared to the minimum absolute
+                                        # value of the data.
+axes.formatter.offset_threshold : 2      # When useoffset is True, the offset
+                                         # will be used when it can remove
+                                         # at least this number of significant
+                                         # digits from tick labels.
+
+axes.unicode_minus  : True    # use Unicode for the minus symbol
+                              # rather than hyphen.  See
+                              # https://en.wikipedia.org/wiki/Plus_and_minus_signs#Character_codes
+axes.prop_cycle    : cycler('color', 'bgrcmyk')
+                                           # color cycle for plot lines
+                                           # as list of string colorspecs:
+                                           # single letter, long name, or
+                                           # web-style hex
+axes.autolimit_mode : round_numbers
+axes.xmargin        : 0  # x margin.  See `axes.Axes.margins`
+axes.ymargin        : 0  # y margin See `axes.Axes.margins`
+axes.spines.bottom  : True
+axes.spines.left    : True
+axes.spines.right   : True
+axes.spines.top     : True
+polaraxes.grid      : True    # display grid on polar axes
+axes3d.grid         : True    # display grid on 3d axes
+
+date.autoformatter.year   : %Y
+date.autoformatter.month  : %b %Y
+date.autoformatter.day    : %b %d %Y
+date.autoformatter.hour   : %H:%M:%S
+date.autoformatter.minute : %H:%M:%S.%f
+date.autoformatter.second : %H:%M:%S.%f
+date.autoformatter.microsecond : %H:%M:%S.%f
+date.converter:                  auto  # 'auto', 'concise'
+
+### TICKS
+# see https://matplotlib.org/api/axis_api.html#matplotlib.axis.Tick
+
+xtick.top            : True   # draw ticks on the top side
+xtick.bottom         : True   # draw ticks on the bottom side
+xtick.major.size     : 4      # major tick size in points
+xtick.minor.size     : 2      # minor tick size in points
+xtick.minor.visible  : False
+xtick.major.width    : 0.5    # major tick width in points
+xtick.minor.width    : 0.5    # minor tick width in points
+xtick.major.pad      : 4      # distance to major tick label in points
+xtick.minor.pad      : 4      # distance to the minor tick label in points
+xtick.color          : k      # color of the tick labels
+xtick.labelsize      : medium # fontsize of the tick labels
+xtick.direction      : in     # direction: in, out, or inout
+xtick.major.top      : True   # draw x axis top major ticks
+xtick.major.bottom   : True   # draw x axis bottom major ticks
+xtick.minor.top      : True   # draw x axis top minor ticks
+xtick.minor.bottom   : True   # draw x axis bottom minor ticks
+xtick.alignment : center
+
+ytick.left           : True   # draw ticks on the left side
+ytick.right          : True   # draw ticks on the right side
+ytick.major.size     : 4      # major tick size in points
+ytick.minor.size     : 2      # minor tick size in points
+ytick.minor.visible  : False
+ytick.major.width    : 0.5    # major tick width in points
+ytick.minor.width    : 0.5    # minor tick width in points
+ytick.major.pad      : 4      # distance to major tick label in points
+ytick.minor.pad      : 4      # distance to the minor tick label in points
+ytick.color          : k      # color of the tick labels
+ytick.labelsize      : medium # fontsize of the tick labels
+ytick.direction      : in     # direction: in, out, or inout
+ytick.major.left     : True   # draw y axis left major ticks
+ytick.major.right    : True   # draw y axis right major ticks
+ytick.minor.left     : True   # draw y axis left minor ticks
+ytick.minor.right    : True   # draw y axis right minor ticks
+ytick.alignment      : center
+
+### GRIDS
+grid.color       :   k       # grid color
+grid.linestyle   :   :       # dotted
+grid.linewidth   :   0.5     # in points
+grid.alpha       :   1.0     # transparency, between 0.0 and 1.0
+
+### Legend
+legend.fancybox      : False  # if True, use a rounded box for the
+                              # legend, else a rectangle
+legend.loc           : upper right
+legend.numpoints     : 2      # the number of points in the legend line
+legend.fontsize      : large
+legend.borderpad     : 0.4    # border whitespace in fontsize units
+legend.markerscale   : 1.0    # the relative size of legend markers vs. original
+# the following dimensions are in axes coords
+legend.labelspacing  : 0.5    # the vertical space between the legend entries in fraction of fontsize
+legend.handlelength  : 2.     # the length of the legend lines in fraction of fontsize
+legend.handleheight  : 0.7     # the height of the legend handle in fraction of fontsize
+legend.handletextpad : 0.8    # the space between the legend line and legend text in fraction of fontsize
+legend.borderaxespad : 0.5   # the border between the axes and legend edge in fraction of fontsize
+legend.columnspacing : 2.    # the border between the axes and legend edge in fraction of fontsize
+legend.shadow        : False
+legend.frameon       : True   # whether or not to draw a frame around legend
+legend.framealpha    : None    # opacity of legend frame
+legend.scatterpoints : 3 # number of scatter points
+legend.facecolor     : inherit   # legend background color (when 'inherit' uses axes.facecolor)
+legend.edgecolor     : inherit   # legend edge color (when 'inherit' uses axes.edgecolor)
+
+
+
+### FIGURE
+# See https://matplotlib.org/api/figure_api.html#matplotlib.figure.Figure
+figure.titlesize : medium     # size of the figure title
+figure.titleweight : normal   # weight of the figure title
+figure.labelsize:   medium    # size of the figure label
+figure.labelweight: normal    # weight of the figure label
+figure.figsize   : 8, 6    # figure size in inches
+figure.dpi       : 80      # figure dots per inch
+figure.facecolor : 0.75    # figure facecolor; 0.75 is scalar gray
+figure.edgecolor : w       # figure edgecolor
+figure.autolayout : False  # When True, automatically adjust subplot
+                           # parameters to make the plot fit the figure
+figure.frameon : True
+
+# The figure subplot parameters.  All dimensions are a fraction of the
+# figure width or height
+figure.subplot.left    : 0.125  # the left side of the subplots of the figure
+figure.subplot.right   : 0.9    # the right side of the subplots of the figure
+figure.subplot.bottom  : 0.1    # the bottom of the subplots of the figure
+figure.subplot.top     : 0.9    # the top of the subplots of the figure
+figure.subplot.wspace  : 0.2    # the amount of width reserved for space between subplots,
+                                # expressed as a fraction of the average axis width
+figure.subplot.hspace  : 0.2    # the amount of height reserved for space between subplots,
+                                # expressed as a fraction of the average axis height
+
+### IMAGES
+image.aspect : equal             # equal | auto | a number
+image.interpolation  : bilinear  # see help(imshow) for options
+image.cmap   : jet               # gray | jet | ...
+image.lut    : 256               # the size of the colormap lookup table
+image.origin : upper             # lower | upper
+image.resample  : False
+image.composite_image : True
+
+### CONTOUR PLOTS
+contour.negative_linestyle :  dashed # dashed | solid
+contour.corner_mask : True
+
+# errorbar props
+errorbar.capsize: 3
+
+# scatter props
+scatter.marker: o
+
+### Boxplots
+boxplot.bootstrap: None
+boxplot.boxprops.color: b
+boxplot.boxprops.linestyle: -
+boxplot.boxprops.linewidth: 1.0
+boxplot.capprops.color: k
+boxplot.capprops.linestyle: -
+boxplot.capprops.linewidth: 1.0
+boxplot.flierprops.color: b
+boxplot.flierprops.linestyle: none
+boxplot.flierprops.linewidth: 1.0
+boxplot.flierprops.marker: +
+boxplot.flierprops.markeredgecolor: k
+boxplot.flierprops.markerfacecolor: auto
+boxplot.flierprops.markersize: 6.0
+boxplot.meanline: False
+boxplot.meanprops.color: r
+boxplot.meanprops.linestyle: -
+boxplot.meanprops.linewidth: 1.0
+boxplot.medianprops.color: r
+boxplot.meanprops.marker: s
+boxplot.meanprops.markerfacecolor: r
+boxplot.meanprops.markeredgecolor: k
+boxplot.meanprops.markersize: 6.0
+boxplot.medianprops.linestyle: -
+boxplot.medianprops.linewidth: 1.0
+boxplot.notch: False
+boxplot.patchartist: False
+boxplot.showbox: True
+boxplot.showcaps: True
+boxplot.showfliers: True
+boxplot.showmeans: False
+boxplot.vertical: True
+boxplot.whiskerprops.color: b
+boxplot.whiskerprops.linestyle: --
+boxplot.whiskerprops.linewidth: 1.0
+boxplot.whiskers: 1.5
+
+### Agg rendering
+### Warning: experimental, 2008/10/10
+agg.path.chunksize : 0           # 0 to disable; values in the range
+                                 # 10000 to 100000 can improve speed slightly
+                                 # and prevent an Agg rendering failure
+                                 # when plotting very large data sets,
+                                 # especially if they are very gappy.
+                                 # It may cause minor artifacts, though.
+                                 # A value of 20000 is probably a good
+                                 # starting point.
+### SAVING FIGURES
+path.simplify : True   # When True, simplify paths by removing "invisible"
+                       # points to reduce file size and increase rendering
+                       # speed
+path.simplify_threshold : 0.1111111111111111
+                               # The threshold of similarity below which
+                               # vertices will be removed in the simplification
+                               # process
+path.snap : True # When True, rectilinear axis-aligned paths will be snapped to
+                 # the nearest pixel when certain criteria are met.  When False,
+                 # paths will never be snapped.
+path.sketch : None # May be none, or a 3-tuple of the form (scale, length,
+                   # randomness).
+                   # *scale* is the amplitude of the wiggle
+                   # perpendicular to the line (in pixels).  *length*
+                   # is the length of the wiggle along the line (in
+                   # pixels).  *randomness* is the factor by which
+                   # the length is randomly scaled.
+
+# the default savefig params can be different from the display params
+# e.g., you may want a higher resolution, or to make the figure
+# background white
+savefig.dpi         : 100      # figure dots per inch
+savefig.facecolor   : w        # figure facecolor when saving
+savefig.edgecolor   : w        # figure edgecolor when saving
+savefig.format      : png      # png, ps, pdf, svg
+savefig.bbox        : standard # 'tight' or 'standard'.
+                               # 'tight' is incompatible with pipe-based animation
+                               # backends (e.g. 'ffmpeg') but will work with those
+                               # based on temporary files (e.g. 'ffmpeg_file')
+savefig.pad_inches  : 0.1      # Padding to be used when bbox is set to 'tight'
+savefig.transparent : False    # setting that controls whether figures are saved with a
+                               # transparent background by default
+savefig.orientation : portrait
+
+# ps backend params
+ps.papersize      : letter   # auto, letter, legal, ledger, A0-A10, B0-B10
+ps.useafm         : False    # use of afm fonts, results in small files
+ps.usedistiller   : False    # can be: None, ghostscript or xpdf
+                                          # Experimental: may produce smaller files.
+                                          # xpdf intended for production of publication quality files,
+                                          # but requires ghostscript, xpdf and ps2eps
+ps.distiller.res  : 6000      # dpi
+ps.fonttype       : 3         # Output Type 3 (Type3) or Type 42 (TrueType)
+
+# pdf backend params
+pdf.compression   : 6 # integer from 0 to 9
+                      # 0 disables compression (good for debugging)
+pdf.fonttype       : 3         # Output Type 3 (Type3) or Type 42 (TrueType)
+pdf.inheritcolor   : False
+pdf.use14corefonts : False
+
+# pgf backend params
+pgf.texsystem       : xelatex
+pgf.rcfonts         : True
+pgf.preamble        :
+
+# svg backend params
+svg.image_inline : True       # write raster image data directly into the svg file
+svg.fonttype : path            # How to handle SVG fonts:
+#    'none': Assume fonts are installed on the machine where the SVG will be viewed.
+#    'path': Embed characters as paths -- supported by most SVG renderers
+
+# Event keys to interact with figures/plots via keyboard.
+# Customize these settings according to your needs.
+# Leave the field(s) empty if you don't need a key-map. (i.e., fullscreen : '')
+
+keymap.fullscreen : f, ctrl+f       # toggling
+keymap.home : h, r, home            # home or reset mnemonic
+keymap.back : left, c, backspace    # forward / backward keys to enable
+keymap.forward : right, v           #   left handed quick navigation
+keymap.pan : p                      # pan mnemonic
+keymap.zoom : o                     # zoom mnemonic
+keymap.save : s, ctrl+s             # saving current figure
+keymap.quit : ctrl+w, cmd+w         # close the current figure
+keymap.grid : g                     # switching on/off a grid in current axes
+keymap.yscale : l                   # toggle scaling of y-axes ('log'/'linear')
+keymap.xscale : k, L                # toggle scaling of x-axes ('log'/'linear')
+
+###ANIMATION settings
+animation.writer : ffmpeg         # MovieWriter 'backend' to use
+animation.codec : mpeg4           # Codec to use for writing movie
+animation.bitrate: -1             # Controls size/quality tradeoff for movie.
+                                  # -1 implies let utility auto-determine
+animation.frame_format: png       # Controls frame format used by temp files
+animation.ffmpeg_path: ffmpeg     # Path to ffmpeg binary. Without full path
+                                  # $PATH is searched
+animation.ffmpeg_args:            # Additional arguments to pass to ffmpeg
+animation.convert_path: convert   # Path to ImageMagick's convert binary.
+                                  # On Windows use the full path since convert
+                                  # is also the name of a system tool.
+animation.convert_args:
+animation.html: none
+
+_internal.classic_mode: True

omnilmm/lib/python3.10/site-packages/matplotlib/mpl-data/stylelib/dark_background.mplstyle

@@ -0,0 +1,29 @@
+# Set black background default line colors to white.
+
+lines.color: white
+patch.edgecolor: white
+
+text.color: white
+
+axes.facecolor: black
+axes.edgecolor: white
+axes.labelcolor: white
+axes.prop_cycle: cycler('color', ['8dd3c7', 'feffb3', 'bfbbd9', 'fa8174', '81b1d2', 'fdb462', 'b3de69', 'bc82bd', 'ccebc4', 'ffed6f'])
+
+xtick.color: white
+ytick.color: white
+
+grid.color: white
+
+figure.facecolor: black
+figure.edgecolor: black
+
+savefig.facecolor: black
+savefig.edgecolor: black
+
+### Boxplots
+boxplot.boxprops.color: white
+boxplot.capprops.color: white
+boxplot.flierprops.color: white
+boxplot.flierprops.markeredgecolor: white
+boxplot.whiskerprops.color: white

omnilmm/lib/python3.10/site-packages/matplotlib/mpl-data/stylelib/fast.mplstyle

@@ -0,0 +1,11 @@
+# a small set of changes that will make your plotting FAST (1).
+#
+# (1) in some cases
+
+# Maximally simplify lines.
+path.simplify: True
+path.simplify_threshold: 1.0
+
+# chunk up large lines into smaller lines!
+# simple trick to avoid those pesky O(>n) algorithms!
+agg.path.chunksize: 10000

omnilmm/lib/python3.10/site-packages/matplotlib/mpl-data/stylelib/fivethirtyeight.mplstyle

@@ -0,0 +1,40 @@
+#Author: Cameron Davidson-Pilon, replicated styles from FiveThirtyEight.com
+# See https://www.dataorigami.net/blogs/fivethirtyeight-mpl
+
+lines.linewidth: 4
+lines.solid_capstyle: butt
+
+legend.fancybox: true
+
+axes.prop_cycle: cycler('color', ['008fd5', 'fc4f30', 'e5ae38', '6d904f', '8b8b8b', '810f7c'])
+axes.facecolor: f0f0f0
+axes.labelsize: large
+axes.axisbelow: true
+axes.grid: true
+axes.edgecolor: f0f0f0
+axes.linewidth: 3.0
+axes.titlesize: x-large
+
+patch.edgecolor: f0f0f0
+patch.linewidth: 0.5
+
+svg.fonttype: path
+
+grid.linestyle: -
+grid.linewidth: 1.0
+grid.color: cbcbcb
+
+xtick.major.size: 0
+xtick.minor.size: 0
+ytick.major.size: 0
+ytick.minor.size: 0
+
+font.size:14.0
+
+savefig.edgecolor: f0f0f0
+savefig.facecolor: f0f0f0
+
+figure.subplot.left: 0.08
+figure.subplot.right: 0.95
+figure.subplot.bottom: 0.07
+figure.facecolor: f0f0f0

omnilmm/lib/python3.10/site-packages/matplotlib/mpl-data/stylelib/ggplot.mplstyle

@@ -0,0 +1,39 @@
+# from https://everyhue.me/posts/sane-color-scheme-for-matplotlib/
+
+patch.linewidth: 0.5
+patch.facecolor: 348ABD  # blue
+patch.edgecolor: EEEEEE
+patch.antialiased: True
+
+font.size: 10.0
+
+axes.facecolor: E5E5E5
+axes.edgecolor: white
+axes.linewidth: 1
+axes.grid: True
+axes.titlesize: x-large
+axes.labelsize: large
+axes.labelcolor: 555555
+axes.axisbelow: True       # grid/ticks are below elements (e.g., lines, text)
+
+axes.prop_cycle: cycler('color', ['E24A33', '348ABD', '988ED5', '777777', 'FBC15E', '8EBA42', 'FFB5B8'])
+                   # E24A33 : red
+                   # 348ABD : blue
+                   # 988ED5 : purple
+                   # 777777 : gray
+                   # FBC15E : yellow
+                   # 8EBA42 : green
+                   # FFB5B8 : pink
+
+xtick.color: 555555
+xtick.direction: out
+
+ytick.color: 555555
+ytick.direction: out
+
+grid.color: white
+grid.linestyle: -    # solid line
+
+figure.facecolor: white
+figure.edgecolor: 0.50
+

omnilmm/lib/python3.10/site-packages/matplotlib/mpl-data/stylelib/grayscale.mplstyle

@@ -0,0 +1,29 @@
+# Set all colors to grayscale
+# Note: strings of float values are interpreted by matplotlib as gray values.
+
+
+lines.color: black
+patch.facecolor: gray
+patch.edgecolor: black
+
+text.color: black
+
+axes.facecolor: white
+axes.edgecolor: black
+axes.labelcolor: black
+# black to light gray
+axes.prop_cycle: cycler('color', ['0.00', '0.40', '0.60', '0.70'])
+
+xtick.color: black
+ytick.color: black
+
+grid.color: black
+
+figure.facecolor: 0.75
+figure.edgecolor: white
+
+image.cmap: gray
+
+savefig.facecolor: white
+savefig.edgecolor: white
+

omnilmm/lib/python3.10/site-packages/matplotlib/mpl-data/stylelib/seaborn-v0_8-bright.mplstyle

@@ -0,0 +1,3 @@
+# Seaborn bright palette
+axes.prop_cycle: cycler('color', ['003FFF', '03ED3A', 'E8000B', '8A2BE2', 'FFC400', '00D7FF'])
+patch.facecolor: 003FFF

omnilmm/lib/python3.10/site-packages/matplotlib/mpl-data/stylelib/seaborn-v0_8-colorblind.mplstyle

@@ -0,0 +1,3 @@
+# Seaborn colorblind palette
+axes.prop_cycle: cycler('color', ['0072B2', '009E73', 'D55E00', 'CC79A7', 'F0E442', '56B4E9'])
+patch.facecolor: 0072B2

omnilmm/lib/python3.10/site-packages/matplotlib/mpl-data/stylelib/seaborn-v0_8-dark-palette.mplstyle

@@ -0,0 +1,3 @@
+# Seaborn dark palette
+axes.prop_cycle: cycler('color', ['001C7F', '017517', '8C0900', '7600A1', 'B8860B', '006374'])
+patch.facecolor: 001C7F

omnilmm/lib/python3.10/site-packages/matplotlib/mpl-data/stylelib/seaborn-v0_8-dark.mplstyle

@@ -0,0 +1,30 @@
+# Seaborn common parameters
+# .15 = dark_gray
+# .8 = light_gray
+figure.facecolor: white
+text.color: .15
+axes.labelcolor: .15
+legend.frameon: False
+legend.numpoints: 1
+legend.scatterpoints: 1
+xtick.direction: out
+ytick.direction: out
+xtick.color: .15
+ytick.color: .15
+axes.axisbelow: True
+image.cmap: Greys
+font.family: sans-serif
+font.sans-serif: Arial, Liberation Sans, DejaVu Sans, Bitstream Vera Sans, sans-serif
+grid.linestyle: -
+lines.solid_capstyle: round
+
+# Seaborn dark parameters
+axes.grid: False
+axes.facecolor: EAEAF2
+axes.edgecolor: white
+axes.linewidth: 0
+grid.color: white
+xtick.major.size: 0
+ytick.major.size: 0
+xtick.minor.size: 0
+ytick.minor.size: 0

omnilmm/lib/python3.10/site-packages/matplotlib/mpl-data/stylelib/seaborn-v0_8-darkgrid.mplstyle

@@ -0,0 +1,30 @@
+# Seaborn common parameters
+# .15 = dark_gray
+# .8 = light_gray
+figure.facecolor: white
+text.color: .15
+axes.labelcolor: .15
+legend.frameon: False
+legend.numpoints: 1
+legend.scatterpoints: 1
+xtick.direction: out
+ytick.direction: out
+xtick.color: .15
+ytick.color: .15
+axes.axisbelow: True
+image.cmap: Greys
+font.family: sans-serif
+font.sans-serif: Arial, Liberation Sans, DejaVu Sans, Bitstream Vera Sans, sans-serif
+grid.linestyle: -
+lines.solid_capstyle: round
+
+# Seaborn darkgrid parameters
+axes.grid: True
+axes.facecolor: EAEAF2
+axes.edgecolor: white
+axes.linewidth: 0
+grid.color: white
+xtick.major.size: 0
+ytick.major.size: 0
+xtick.minor.size: 0
+ytick.minor.size: 0

omnilmm/lib/python3.10/site-packages/matplotlib/mpl-data/stylelib/seaborn-v0_8-deep.mplstyle

@@ -0,0 +1,3 @@
+# Seaborn deep palette
+axes.prop_cycle: cycler('color', ['4C72B0', '55A868', 'C44E52', '8172B2', 'CCB974', '64B5CD'])
+patch.facecolor: 4C72B0

omnilmm/lib/python3.10/site-packages/matplotlib/mpl-data/stylelib/seaborn-v0_8-muted.mplstyle

@@ -0,0 +1,3 @@
+# Seaborn muted palette
+axes.prop_cycle: cycler('color', ['4878CF', '6ACC65', 'D65F5F', 'B47CC7', 'C4AD66', '77BEDB'])
+patch.facecolor: 4878CF

omnilmm/lib/python3.10/site-packages/matplotlib/mpl-data/stylelib/seaborn-v0_8-notebook.mplstyle

@@ -0,0 +1,21 @@
+# Seaborn notebook context
+figure.figsize: 8.0, 5.5
+axes.labelsize: 11
+axes.titlesize: 12
+xtick.labelsize: 10
+ytick.labelsize: 10
+legend.fontsize: 10
+
+grid.linewidth: 1
+lines.linewidth: 1.75
+patch.linewidth: .3
+lines.markersize: 7
+lines.markeredgewidth: 0
+
+xtick.major.width: 1
+ytick.major.width: 1
+xtick.minor.width: .5
+ytick.minor.width: .5
+
+xtick.major.pad: 7
+ytick.major.pad: 7

omnilmm/lib/python3.10/site-packages/matplotlib/mpl-data/stylelib/seaborn-v0_8-paper.mplstyle

@@ -0,0 +1,21 @@
+# Seaborn paper context
+figure.figsize: 6.4, 4.4
+axes.labelsize: 8.8
+axes.titlesize: 9.6
+xtick.labelsize: 8
+ytick.labelsize: 8
+legend.fontsize: 8
+
+grid.linewidth: 0.8
+lines.linewidth: 1.4
+patch.linewidth: 0.24
+lines.markersize: 5.6
+lines.markeredgewidth: 0
+
+xtick.major.width: 0.8
+ytick.major.width: 0.8
+xtick.minor.width: 0.4
+ytick.minor.width: 0.4
+
+xtick.major.pad: 5.6
+ytick.major.pad: 5.6

omnilmm/lib/python3.10/site-packages/matplotlib/mpl-data/stylelib/seaborn-v0_8-pastel.mplstyle

@@ -0,0 +1,3 @@
+# Seaborn pastel palette
+axes.prop_cycle: cycler('color', ['92C6FF', '97F0AA', 'FF9F9A', 'D0BBFF', 'FFFEA3', 'B0E0E6'])
+patch.facecolor: 92C6FF

omnilmm/lib/python3.10/site-packages/matplotlib/mpl-data/stylelib/seaborn-v0_8-poster.mplstyle

@@ -0,0 +1,21 @@
+# Seaborn poster context
+figure.figsize: 12.8, 8.8
+axes.labelsize: 17.6
+axes.titlesize: 19.2
+xtick.labelsize: 16
+ytick.labelsize: 16
+legend.fontsize: 16
+
+grid.linewidth: 1.6
+lines.linewidth: 2.8
+patch.linewidth: 0.48
+lines.markersize: 11.2
+lines.markeredgewidth: 0
+
+xtick.major.width: 1.6
+ytick.major.width: 1.6
+xtick.minor.width: 0.8
+ytick.minor.width: 0.8
+
+xtick.major.pad: 11.2
+ytick.major.pad: 11.2

omnilmm/lib/python3.10/site-packages/matplotlib/mpl-data/stylelib/seaborn-v0_8-talk.mplstyle

@@ -0,0 +1,21 @@
+# Seaborn talk context
+figure.figsize: 10.4, 7.15
+axes.labelsize: 14.3
+axes.titlesize: 15.6
+xtick.labelsize: 13
+ytick.labelsize: 13
+legend.fontsize: 13
+
+grid.linewidth: 1.3
+lines.linewidth: 2.275
+patch.linewidth: 0.39
+lines.markersize: 9.1
+lines.markeredgewidth: 0
+
+xtick.major.width: 1.3
+ytick.major.width: 1.3
+xtick.minor.width: 0.65
+ytick.minor.width: 0.65
+
+xtick.major.pad: 9.1
+ytick.major.pad: 9.1

omnilmm/lib/python3.10/site-packages/matplotlib/mpl-data/stylelib/seaborn-v0_8-ticks.mplstyle

@@ -0,0 +1,30 @@
+# Seaborn common parameters
+# .15 = dark_gray
+# .8 = light_gray
+figure.facecolor: white
+text.color: .15
+axes.labelcolor: .15
+legend.frameon: False
+legend.numpoints: 1
+legend.scatterpoints: 1
+xtick.direction: out
+ytick.direction: out
+xtick.color: .15
+ytick.color: .15
+axes.axisbelow: True
+image.cmap: Greys
+font.family: sans-serif
+font.sans-serif: Arial, Liberation Sans, DejaVu Sans, Bitstream Vera Sans, sans-serif
+grid.linestyle: -
+lines.solid_capstyle: round
+
+# Seaborn white parameters
+axes.grid: False
+axes.facecolor: white
+axes.edgecolor: .15
+axes.linewidth: 1.25
+grid.color: .8
+xtick.major.size: 6
+ytick.major.size: 6
+xtick.minor.size: 3
+ytick.minor.size: 3

omnilmm/lib/python3.10/site-packages/matplotlib/mpl-data/stylelib/seaborn-v0_8-white.mplstyle

@@ -0,0 +1,30 @@
+# Seaborn common parameters
+# .15 = dark_gray
+# .8 = light_gray
+figure.facecolor: white
+text.color: .15
+axes.labelcolor: .15
+legend.frameon: False
+legend.numpoints: 1
+legend.scatterpoints: 1
+xtick.direction: out
+ytick.direction: out
+xtick.color: .15
+ytick.color: .15
+axes.axisbelow: True
+image.cmap: Greys
+font.family: sans-serif
+font.sans-serif: Arial, Liberation Sans, DejaVu Sans, Bitstream Vera Sans, sans-serif
+grid.linestyle: -
+lines.solid_capstyle: round
+
+# Seaborn white parameters
+axes.grid: False
+axes.facecolor: white
+axes.edgecolor: .15
+axes.linewidth: 1.25
+grid.color: .8
+xtick.major.size: 0
+ytick.major.size: 0
+xtick.minor.size: 0
+ytick.minor.size: 0

omnilmm/lib/python3.10/site-packages/matplotlib/mpl-data/stylelib/seaborn-v0_8-whitegrid.mplstyle

@@ -0,0 +1,30 @@
+# Seaborn common parameters
+# .15 = dark_gray
+# .8 = light_gray
+figure.facecolor: white
+text.color: .15
+axes.labelcolor: .15
+legend.frameon: False
+legend.numpoints: 1
+legend.scatterpoints: 1
+xtick.direction: out
+ytick.direction: out
+xtick.color: .15
+ytick.color: .15
+axes.axisbelow: True
+image.cmap: Greys
+font.family: sans-serif
+font.sans-serif: Arial, Liberation Sans, DejaVu Sans, Bitstream Vera Sans, sans-serif
+grid.linestyle: -
+lines.solid_capstyle: round
+
+# Seaborn whitegrid parameters
+axes.grid: True
+axes.facecolor: white
+axes.edgecolor: .8
+axes.linewidth: 1
+grid.color: .8
+xtick.major.size: 0
+ytick.major.size: 0
+xtick.minor.size: 0
+ytick.minor.size: 0

omnilmm/lib/python3.10/site-packages/matplotlib/mpl-data/stylelib/seaborn-v0_8.mplstyle

@@ -0,0 +1,57 @@
+# default seaborn aesthetic
+# darkgrid + deep palette + notebook context
+
+axes.axisbelow: True
+axes.edgecolor: white
+axes.facecolor: EAEAF2
+axes.grid: True
+axes.labelcolor: .15
+axes.labelsize: 11
+axes.linewidth: 0
+axes.prop_cycle: cycler('color', ['4C72B0', '55A868', 'C44E52', '8172B2', 'CCB974', '64B5CD'])
+axes.titlesize: 12
+
+figure.facecolor: white
+figure.figsize: 8.0, 5.5
+
+font.family: sans-serif
+font.sans-serif: Arial, Liberation Sans, DejaVu Sans, Bitstream Vera Sans, sans-serif
+
+grid.color: white
+grid.linestyle: -
+grid.linewidth: 1
+
+image.cmap: Greys
+
+legend.fontsize: 10
+legend.frameon: False
+legend.numpoints: 1
+legend.scatterpoints: 1
+
+lines.linewidth: 1.75
+lines.markeredgewidth: 0
+lines.markersize: 7
+lines.solid_capstyle: round
+
+patch.facecolor: 4C72B0
+patch.linewidth: .3
+
+text.color: .15
+
+xtick.color: .15
+xtick.direction: out
+xtick.labelsize: 10
+xtick.major.pad: 7
+xtick.major.size: 0
+xtick.major.width: 1
+xtick.minor.size: 0
+xtick.minor.width: .5
+
+ytick.color: .15
+ytick.direction: out
+ytick.labelsize: 10
+ytick.major.pad: 7
+ytick.major.size: 0
+ytick.major.width: 1
+ytick.minor.size: 0
+ytick.minor.width: .5

omnilmm/lib/python3.10/site-packages/matplotlib/mpl-data/stylelib/tableau-colorblind10.mplstyle

@@ -0,0 +1,3 @@
+# Tableau colorblind 10 palette
+axes.prop_cycle: cycler('color', ['006BA4', 'FF800E', 'ABABAB', '595959', '5F9ED1', 'C85200', '898989', 'A2C8EC', 'FFBC79', 'CFCFCF'])
+patch.facecolor: 006BA4