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from collections.abc import Iterable, Sequence
from contextlib import ExitStack
import functools
import inspect
import logging
from numbers import Real
from operator import attrgetter
import re
import textwrap
import types
import numpy as np
import matplotlib as mpl
from matplotlib import _api, cbook, _docstring, offsetbox
import matplotlib.artist as martist
import matplotlib.axis as maxis
from matplotlib.cbook import _OrderedSet, _check_1d, index_of
import matplotlib.collections as mcoll
import matplotlib.colors as mcolors
import matplotlib.font_manager as font_manager
from matplotlib.gridspec import SubplotSpec
import matplotlib.image as mimage
import matplotlib.lines as mlines
import matplotlib.patches as mpatches
from matplotlib.rcsetup import cycler, validate_axisbelow
import matplotlib.spines as mspines
import matplotlib.table as mtable
import matplotlib.text as mtext
import matplotlib.ticker as mticker
import matplotlib.transforms as mtransforms
_log = logging.getLogger(__name__)
class _axis_method_wrapper:
"""
Helper to generate Axes methods wrapping Axis methods.
After ::
get_foo = _axis_method_wrapper("xaxis", "get_bar")
(in the body of a class) ``get_foo`` is a method that forwards it arguments
to the ``get_bar`` method of the ``xaxis`` attribute, and gets its
signature and docstring from ``Axis.get_bar``.
The docstring of ``get_foo`` is built by replacing "this Axis" by "the
{attr_name}" (i.e., "the xaxis", "the yaxis") in the wrapped method's
dedented docstring; additional replacements can be given in *doc_sub*.
"""
def __init__(self, attr_name, method_name, *, doc_sub=None):
self.attr_name = attr_name
self.method_name = method_name
# Immediately put the docstring in ``self.__doc__`` so that docstring
# manipulations within the class body work as expected.
doc = inspect.getdoc(getattr(maxis.Axis, method_name))
self._missing_subs = []
if doc:
doc_sub = {"this Axis": f"the {self.attr_name}", **(doc_sub or {})}
for k, v in doc_sub.items():
if k not in doc: # Delay raising error until we know qualname.
self._missing_subs.append(k)
doc = doc.replace(k, v)
self.__doc__ = doc
def __set_name__(self, owner, name):
# This is called at the end of the class body as
# ``self.__set_name__(cls, name_under_which_self_is_assigned)``; we
# rely on that to give the wrapper the correct __name__/__qualname__.
get_method = attrgetter(f"{self.attr_name}.{self.method_name}")
def wrapper(self, *args, **kwargs):
return get_method(self)(*args, **kwargs)
wrapper.__module__ = owner.__module__
wrapper.__name__ = name
wrapper.__qualname__ = f"{owner.__qualname__}.{name}"
wrapper.__doc__ = self.__doc__
# Manually copy the signature instead of using functools.wraps because
# displaying the Axis method source when asking for the Axes method
# source would be confusing.
wrapper.__signature__ = inspect.signature(
getattr(maxis.Axis, self.method_name))
if self._missing_subs:
raise ValueError(
"The definition of {} expected that the docstring of Axis.{} "
"contains {!r} as substrings".format(
wrapper.__qualname__, self.method_name,
", ".join(map(repr, self._missing_subs))))
setattr(owner, name, wrapper)
class _TransformedBoundsLocator:
"""
Axes locator for `.Axes.inset_axes` and similarly positioned Axes.
The locator is a callable object used in `.Axes.set_aspect` to compute the
Axes location depending on the renderer.
"""
def __init__(self, bounds, transform):
"""
*bounds* (a ``[l, b, w, h]`` rectangle) and *transform* together
specify the position of the inset Axes.
"""
self._bounds = bounds
self._transform = transform
def __call__(self, ax, renderer):
# Subtracting transSubfigure will typically rely on inverted(),
# freezing the transform; thus, this needs to be delayed until draw
# time as transSubfigure may otherwise change after this is evaluated.
return mtransforms.TransformedBbox(
mtransforms.Bbox.from_bounds(*self._bounds),
self._transform - ax.get_figure(root=False).transSubfigure)
def _process_plot_format(fmt, *, ambiguous_fmt_datakey=False):
"""
Convert a MATLAB style color/line style format string to a (*linestyle*,
*marker*, *color*) tuple.
Example format strings include:
* 'ko': black circles
* '.b': blue dots
* 'r--': red dashed lines
* 'C2--': the third color in the color cycle, dashed lines
The format is absolute in the sense that if a linestyle or marker is not
defined in *fmt*, there is no line or marker. This is expressed by
returning 'None' for the respective quantity.
See Also
--------
matplotlib.Line2D.lineStyles, matplotlib.colors.cnames
All possible styles and color format strings.
"""
linestyle = None
marker = None
color = None
# First check whether fmt is just a colorspec, but specifically exclude the
# grayscale string "1" (not "1.0"), which is interpreted as the tri_down
# marker "1". The grayscale string "0" could be unambiguously understood
# as a color (black) but also excluded for consistency.
if fmt not in ["0", "1"]:
try:
color = mcolors.to_rgba(fmt)
return linestyle, marker, color
except ValueError:
pass
errfmt = ("{!r} is neither a data key nor a valid format string ({})"
if ambiguous_fmt_datakey else
"{!r} is not a valid format string ({})")
i = 0
while i < len(fmt):
c = fmt[i]
if fmt[i:i+2] in mlines.lineStyles: # First, the two-char styles.
if linestyle is not None:
raise ValueError(errfmt.format(fmt, "two linestyle symbols"))
linestyle = fmt[i:i+2]
i += 2
elif c in mlines.lineStyles:
if linestyle is not None:
raise ValueError(errfmt.format(fmt, "two linestyle symbols"))
linestyle = c
i += 1
elif c in mlines.lineMarkers:
if marker is not None:
raise ValueError(errfmt.format(fmt, "two marker symbols"))
marker = c
i += 1
elif c in mcolors.get_named_colors_mapping():
if color is not None:
raise ValueError(errfmt.format(fmt, "two color symbols"))
color = c
i += 1
elif c == "C":
cn_color = re.match(r"C\d+", fmt[i:])
if not cn_color:
raise ValueError(errfmt.format(fmt, "'C' must be followed by a number"))
color = mcolors.to_rgba(cn_color[0])
i += len(cn_color[0])
else:
raise ValueError(errfmt.format(fmt, f"unrecognized character {c!r}"))
if linestyle is None and marker is None:
linestyle = mpl.rcParams['lines.linestyle']
if linestyle is None:
linestyle = 'None'
if marker is None:
marker = 'None'
return linestyle, marker, color
class _PropCycle:
"""
A class that holds the property cycle information.
It expands the iterator-based Cycler into an explicit indexed list to support
conditional advancing.
"""
def __init__(self, cycler):
self._idx = 0
self._cycler_items = [*cycler]
def get_next_color(self):
"""Return the next color in the cycle."""
entry = self._cycler_items[self._idx]
if "color" in entry:
self._idx = (self._idx + 1) % len(self._cycler_items) # Advance cycler.
return entry["color"]
else:
return "k"
def getdefaults(self, kw, ignore=frozenset()):
"""
If some keys in the property cycle (excluding those in the set
*ignore*) are absent or set to None in the dict *kw*, return a copy
of the next entry in the property cycle, excluding keys in *ignore*.
Otherwise, don't advance the property cycle, and return an empty dict.
"""
defaults = self._cycler_items[self._idx]
if any(kw.get(k, None) is None for k in {*defaults} - ignore):
self._idx = (self._idx + 1) % len(self._cycler_items) # Advance cycler.
# Return a new dict to avoid exposing _cycler_items entries to mutation.
return {k: v for k, v in defaults.items() if k not in ignore}
else:
return {}
class _process_plot_var_args:
"""
Process variable length arguments to `~.Axes.plot`, to support ::
plot(t, s)
plot(t1, s1, t2, s2)
plot(t1, s1, 'ko', t2, s2)
plot(t1, s1, 'ko', t2, s2, 'r--', t3, e3)
an arbitrary number of *x*, *y*, *fmt* are allowed
"""
def __init__(self, output='Line2D'):
_api.check_in_list(['Line2D', 'Polygon', 'coordinates'], output=output)
self.output = output
self.set_prop_cycle(None)
def set_prop_cycle(self, cycler):
self._prop_cycle = _PropCycle(mpl._val_or_rc(cycler, 'axes.prop_cycle'))
def __call__(self, axes, *args, data=None, return_kwargs=False, **kwargs):
axes._process_unit_info(kwargs=kwargs)
for pos_only in "xy":
if pos_only in kwargs:
raise _api.kwarg_error(inspect.stack()[1].function, pos_only)
if not args:
return
if data is None: # Process dict views
args = [cbook.sanitize_sequence(a) for a in args]
else: # Process the 'data' kwarg.
replaced = [mpl._replacer(data, arg) for arg in args]
if len(args) == 1:
label_namer_idx = 0
elif len(args) == 2: # Can be x, y or y, c.
# Figure out what the second argument is.
# 1) If the second argument cannot be a format shorthand, the
# second argument is the label_namer.
# 2) Otherwise (it could have been a format shorthand),
# a) if we did perform a substitution, emit a warning, and
# use it as label_namer.
# b) otherwise, it is indeed a format shorthand; use the
# first argument as label_namer.
try:
_process_plot_format(args[1])
except ValueError: # case 1)
label_namer_idx = 1
else:
if replaced[1] is not args[1]: # case 2a)
_api.warn_external(
f"Second argument {args[1]!r} is ambiguous: could "
f"be a format string but is in 'data'; using as "
f"data. If it was intended as data, set the "
f"format string to an empty string to suppress "
f"this warning. If it was intended as a format "
f"string, explicitly pass the x-values as well. "
f"Alternatively, rename the entry in 'data'.",
RuntimeWarning)
label_namer_idx = 1
else: # case 2b)
label_namer_idx = 0
elif len(args) == 3:
label_namer_idx = 1
else:
raise ValueError(
"Using arbitrary long args with data is not supported due "
"to ambiguity of arguments; use multiple plotting calls "
"instead")
if kwargs.get("label") is None:
kwargs["label"] = mpl._label_from_arg(
replaced[label_namer_idx], args[label_namer_idx])
args = replaced
ambiguous_fmt_datakey = data is not None and len(args) == 2
if len(args) >= 4 and not cbook.is_scalar_or_string(
kwargs.get("label")):
raise ValueError("plot() with multiple groups of data (i.e., "
"pairs of x and y) does not support multiple "
"labels")
# Repeatedly grab (x, y) or (x, y, format) from the front of args and
# massage them into arguments to plot() or fill().
while args:
this, args = args[:2], args[2:]
if args and isinstance(args[0], str):
this += args[0],
args = args[1:]
yield from self._plot_args(
axes, this, kwargs, ambiguous_fmt_datakey=ambiguous_fmt_datakey,
return_kwargs=return_kwargs
)
def get_next_color(self):
"""Return the next color in the cycle."""
return self._prop_cycle.get_next_color()
@staticmethod
def _setdefaults(defaults, kw):
"""
Add to the dict *kw* the entries in the dict *default* that are absent
or set to None in *kw*.
"""
for k in defaults:
if kw.get(k, None) is None:
kw[k] = defaults[k]
def _make_line(self, axes, x, y, kw, kwargs):
kw = {**kw, **kwargs} # Don't modify the original kw.
self._setdefaults(self._prop_cycle.getdefaults(kw), kw)
seg = mlines.Line2D(x, y, **kw)
return seg, kw
def _make_coordinates(self, axes, x, y, kw, kwargs):
kw = {**kw, **kwargs} # Don't modify the original kw.
self._setdefaults(self._prop_cycle.getdefaults(kw), kw)
return (x, y), kw
def _make_polygon(self, axes, x, y, kw, kwargs):
# Polygon doesn't directly support unitized inputs.
x = axes.convert_xunits(x)
y = axes.convert_yunits(y)
kw = kw.copy() # Don't modify the original kw.
kwargs = kwargs.copy()
# Ignore 'marker'-related properties as they aren't Polygon
# properties, but they are Line2D properties, and so they are
# likely to appear in the default cycler construction.
# This is done here to the defaults dictionary as opposed to the
# other two dictionaries because we do want to capture when a
# *user* explicitly specifies a marker which should be an error.
# We also want to prevent advancing the cycler if there are no
# defaults needed after ignoring the given properties.
ignores = ({'marker', 'markersize', 'markeredgecolor',
'markerfacecolor', 'markeredgewidth'}
# Also ignore anything provided by *kwargs*.
| {k for k, v in kwargs.items() if v is not None})
# Only using the first dictionary to use as basis
# for getting defaults for back-compat reasons.
# Doing it with both seems to mess things up in
# various places (probably due to logic bugs elsewhere).
default_dict = self._prop_cycle.getdefaults(kw, ignores)
self._setdefaults(default_dict, kw)
# Looks like we don't want "color" to be interpreted to
# mean both facecolor and edgecolor for some reason.
# So the "kw" dictionary is thrown out, and only its
# 'color' value is kept and translated as a 'facecolor'.
# This design should probably be revisited as it increases
# complexity.
facecolor = kw.get('color', None)
# Throw out 'color' as it is now handled as a facecolor
default_dict.pop('color', None)
# To get other properties set from the cycler
# modify the kwargs dictionary.
self._setdefaults(default_dict, kwargs)
seg = mpatches.Polygon(np.column_stack((x, y)),
facecolor=facecolor,
fill=kwargs.get('fill', True),
closed=kw['closed'])
seg.set(**kwargs)
return seg, kwargs
def _plot_args(self, axes, tup, kwargs, *,
return_kwargs=False, ambiguous_fmt_datakey=False):
"""
Process the arguments of ``plot([x], y, [fmt], **kwargs)`` calls.
This processes a single set of ([x], y, [fmt]) parameters; i.e. for
``plot(x, y, x2, y2)`` it will be called twice. Once for (x, y) and
once for (x2, y2).
x and y may be 2D and thus can still represent multiple datasets.
For multiple datasets, if the keyword argument *label* is a list, this
will unpack the list and assign the individual labels to the datasets.
Parameters
----------
tup : tuple
A tuple of the positional parameters. This can be one of
- (y,)
- (x, y)
- (y, fmt)
- (x, y, fmt)
kwargs : dict
The keyword arguments passed to ``plot()``.
return_kwargs : bool
Whether to also return the effective keyword arguments after label
unpacking as well.
ambiguous_fmt_datakey : bool
Whether the format string in *tup* could also have been a
misspelled data key.
Returns
-------
result
If *return_kwargs* is false, a list of Artists representing the
dataset(s).
If *return_kwargs* is true, a list of (Artist, effective_kwargs)
representing the dataset(s). See *return_kwargs*.
The Artist is either `.Line2D` (if called from ``plot()``) or
`.Polygon` otherwise.
"""
if len(tup) > 1 and isinstance(tup[-1], str):
# xy is tup with fmt stripped (could still be (y,) only)
*xy, fmt = tup
linestyle, marker, color = _process_plot_format(
fmt, ambiguous_fmt_datakey=ambiguous_fmt_datakey)
elif len(tup) == 3:
raise ValueError('third arg must be a format string')
else:
xy = tup
linestyle, marker, color = None, None, None
# Don't allow any None value; these would be up-converted to one
# element array of None which causes problems downstream.
if any(v is None for v in tup):
raise ValueError("x, y, and format string must not be None")
kw = {}
for prop_name, val in zip(('linestyle', 'marker', 'color'),
(linestyle, marker, color)):
if val is not None:
# check for conflicts between fmt and kwargs
if (fmt.lower() != 'none'
and prop_name in kwargs
and val != 'None'):
# Technically ``plot(x, y, 'o', ls='--')`` is a conflict
# because 'o' implicitly unsets the linestyle
# (linestyle='None').
# We'll gracefully not warn in this case because an
# explicit set via kwargs can be seen as intention to
# override an implicit unset.
# Note: We don't val.lower() != 'none' because val is not
# necessarily a string (can be a tuple for colors). This
# is safe, because *val* comes from _process_plot_format()
# which only returns 'None'.
_api.warn_external(
f"{prop_name} is redundantly defined by the "
f"'{prop_name}' keyword argument and the fmt string "
f'"{fmt}" (-> {prop_name}={val!r}). The keyword '
f"argument will take precedence.")
kw[prop_name] = val
if len(xy) == 2:
x = _check_1d(xy[0])
y = _check_1d(xy[1])
else:
x, y = index_of(xy[-1])
if axes.xaxis is not None:
axes.xaxis.update_units(x)
if axes.yaxis is not None:
axes.yaxis.update_units(y)
if x.shape[0] != y.shape[0]:
raise ValueError(f"x and y must have same first dimension, but "
f"have shapes {x.shape} and {y.shape}")
if x.ndim > 2 or y.ndim > 2:
raise ValueError(f"x and y can be no greater than 2D, but have "
f"shapes {x.shape} and {y.shape}")
if x.ndim == 1:
x = x[:, np.newaxis]
if y.ndim == 1:
y = y[:, np.newaxis]
if self.output == 'Line2D':
make_artist = self._make_line
elif self.output == 'Polygon':
kw['closed'] = kwargs.get('closed', True)
make_artist = self._make_polygon
elif self.output == 'coordinates':
make_artist = self._make_coordinates
else:
_api.check_in_list(['Line2D', 'Polygon', 'coordinates'], output=self.output)
ncx, ncy = x.shape[1], y.shape[1]
if ncx > 1 and ncy > 1 and ncx != ncy:
raise ValueError(f"x has {ncx} columns but y has {ncy} columns")
if ncx == 0 or ncy == 0:
return []
label = kwargs.get('label')
n_datasets = max(ncx, ncy)
if cbook.is_scalar_or_string(label):
labels = [label] * n_datasets
elif len(label) == n_datasets:
labels = label
else:
raise ValueError(
f"label must be scalar or have the same length as the input "
f"data, but found {len(label)} for {n_datasets} datasets.")
result = (make_artist(axes, x[:, j % ncx], y[:, j % ncy], kw,
{**kwargs, 'label': label})
for j, label in enumerate(labels))
if return_kwargs:
return list(result)
else:
return [l[0] for l in result]
@_api.define_aliases({"facecolor": ["fc"]})
class _AxesBase(martist.Artist):
name = "rectilinear"
# axis names are the prefixes for the attributes that contain the
# respective axis; e.g. 'x' <-> self.xaxis, containing an XAxis.
# Note that PolarAxes uses these attributes as well, so that we have
# 'x' <-> self.xaxis, containing a ThetaAxis. In particular we do not
# have 'theta' in _axis_names.
# In practice, this is ('x', 'y') for all 2D Axes and ('x', 'y', 'z')
# for Axes3D.
_axis_names = ("x", "y")
_shared_axes = {name: cbook.Grouper() for name in _axis_names}
_twinned_axes = cbook.Grouper()
_subclass_uses_cla = False
dataLim: mtransforms.Bbox
"""The bounding `.Bbox` enclosing all data displayed in the Axes."""
spines: mspines.Spines
"""
The `.Spines` container for the Axes' spines, i.e. the lines denoting the
data area boundaries.
"""
transData: mtransforms.Transform
"""
The transformation from data coordinates to display coordinates.
See :ref:`transforms_tutorial`.
"""
transAxes: mtransforms.Transform
"""
The transformation from axes coordinates to display coordinates.
See :ref:`transforms_tutorial`.
"""
xaxis: maxis.XAxis
"""
The `.XAxis` instance.
Common axis-related configuration can be achieved through high-level wrapper
methods on Axes; e.g. `ax.set_xticks <.Axes.set_xticks>` is a shortcut for
`ax.xaxis.set_ticks <.Axis.set_ticks>`. The *xaxis* attribute gives direct
direct access to the underlying `~.axis.Axis` if you need more control.
See also
- :ref:`Axis wrapper methods on Axes <axes-api-axis>`
- :doc:`Axis API </api/axis_api>`
"""
yaxis: maxis.YAxis
"""
The `.YAxis` instance.
Common axis-related configuration can be achieved through high-level wrapper
methods on Axes; e.g. `ax.set_yticks <.Axes.set_yticks>` is a shortcut for
`ax.yaxis.set_ticks <.Axis.set_ticks>`. The *yaxis* attribute gives direct
access to the underlying `~.axis.Axis` if you need more control.
See also
- :ref:`Axis wrapper methods on Axes <axes-api-axis>`
- :doc:`Axis API </api/axis_api>`
"""
def __str__(self):
return "{0}({1[0]:g},{1[1]:g};{1[2]:g}x{1[3]:g})".format(
type(self).__name__, self._position.bounds)
def set_zorder(self, level):
super().set_zorder(level)
self._update_twinned_axes_patch_visibility()
def __init__(self, fig,
*args,
facecolor=None, # defaults to rc axes.facecolor
frameon=True,
sharex=None, # use Axes instance's xaxis info
sharey=None, # use Axes instance's yaxis info
label='',
xscale=None,
yscale=None,
box_aspect=None,
forward_navigation_events="auto",
**kwargs
):
"""
Build an Axes in a figure.
Parameters
----------
fig : `~matplotlib.figure.Figure`
The Axes is built in the `.Figure` *fig*.
*args
``*args`` can be a single ``(left, bottom, width, height)``
rectangle or a single `.Bbox`. This specifies the rectangle (in
figure coordinates) where the Axes is positioned.
``*args`` can also consist of three numbers or a single three-digit
number; in the latter case, the digits are considered as
independent numbers. The numbers are interpreted as ``(nrows,
ncols, index)``: ``(nrows, ncols)`` specifies the size of an array
of subplots, and ``index`` is the 1-based index of the subplot
being created. Finally, ``*args`` can also directly be a
`.SubplotSpec` instance.
sharex, sharey : `~matplotlib.axes.Axes`, optional
The x- or y-`~.matplotlib.axis` is shared with the x- or y-axis in
the input `~.axes.Axes`. Note that it is not possible to unshare
axes.
frameon : bool, default: True
Whether the Axes frame is visible.
box_aspect : float, optional
Set a fixed aspect for the Axes box, i.e. the ratio of height to
width. See `~.axes.Axes.set_box_aspect` for details.
forward_navigation_events : bool or "auto", default: "auto"
Control whether pan/zoom events are passed through to Axes below
this one. "auto" is *True* for axes with an invisible patch and
*False* otherwise.
**kwargs
Other optional keyword arguments:
%(Axes:kwdoc)s
Returns
-------
`~.axes.Axes`
The new `~.axes.Axes` object.
"""
super().__init__()
if "rect" in kwargs:
if args:
raise TypeError(
"'rect' cannot be used together with positional arguments")
rect = kwargs.pop("rect")
_api.check_isinstance((mtransforms.Bbox, Iterable), rect=rect)
args = (rect,)
subplotspec = None
if len(args) == 1 and isinstance(args[0], mtransforms.Bbox):
self._position = args[0].frozen()
elif len(args) == 1 and np.iterable(args[0]):
self._position = mtransforms.Bbox.from_bounds(*args[0])
else:
self._position = self._originalPosition = mtransforms.Bbox.unit()
subplotspec = SubplotSpec._from_subplot_args(fig, args)
if self._position.width < 0 or self._position.height < 0:
raise ValueError('Width and height specified must be non-negative')
self._originalPosition = self._position.frozen()
self.axes = self
self._aspect = 'auto'
self._adjustable = 'box'
self._anchor = 'C'
self._stale_viewlims = dict.fromkeys(self._axis_names, False)
self._forward_navigation_events = forward_navigation_events
self._sharex = sharex
self._sharey = sharey
self.set_label(label)
self.set_figure(fig)
# The subplotspec needs to be set after the figure (so that
# figure-level subplotpars are taken into account), but the figure
# needs to be set after self._position is initialized.
if subplotspec:
self.set_subplotspec(subplotspec)
else:
self._subplotspec = None
self.set_box_aspect(box_aspect)
self._axes_locator = None # Optionally set via update(kwargs).
self._children = []
# placeholder for any colorbars added that use this Axes.
# (see colorbar.py):
self._colorbars = []
self.spines = mspines.Spines.from_dict(self._gen_axes_spines())
self.xaxis = None # will be populated in _init_axis()
self.yaxis = None # will be populated in _init_axis()
self._init_axis() # this call may differ for non-sep axes, e.g., polar
self._axis_map = {
name: getattr(self, f"{name}axis") for name in self._axis_names
} # A mapping of axis names, e.g. 'x', to `Axis` instances.
self._facecolor = mpl._val_or_rc(facecolor, 'axes.facecolor')
self._frameon = frameon
self.set_axisbelow(mpl.rcParams['axes.axisbelow'])
self._rasterization_zorder = None
self.clear()
# funcs used to format x and y - fall back on major formatters
self.fmt_xdata = None
self.fmt_ydata = None
self.set_navigate(True)
if xscale:
self.set_xscale(xscale)
if yscale:
self.set_yscale(yscale)
self._internal_update(kwargs)
for name, axis in self._axis_map.items():
axis.callbacks._connect_picklable(
'units', self._unit_change_handler(name))
rcParams = mpl.rcParams
self.tick_params(
top=rcParams['xtick.top'] and rcParams['xtick.minor.top'],
bottom=rcParams['xtick.bottom'] and rcParams['xtick.minor.bottom'],
labeltop=(rcParams['xtick.labeltop'] and
rcParams['xtick.minor.top']),
labelbottom=(rcParams['xtick.labelbottom'] and
rcParams['xtick.minor.bottom']),
left=rcParams['ytick.left'] and rcParams['ytick.minor.left'],
right=rcParams['ytick.right'] and rcParams['ytick.minor.right'],
labelleft=(rcParams['ytick.labelleft'] and
rcParams['ytick.minor.left']),
labelright=(rcParams['ytick.labelright'] and
rcParams['ytick.minor.right']),
which='minor')
self.tick_params(
top=rcParams['xtick.top'] and rcParams['xtick.major.top'],
bottom=rcParams['xtick.bottom'] and rcParams['xtick.major.bottom'],
labeltop=(rcParams['xtick.labeltop'] and
rcParams['xtick.major.top']),
labelbottom=(rcParams['xtick.labelbottom'] and
rcParams['xtick.major.bottom']),
left=rcParams['ytick.left'] and rcParams['ytick.major.left'],
right=rcParams['ytick.right'] and rcParams['ytick.major.right'],
labelleft=(rcParams['ytick.labelleft'] and
rcParams['ytick.major.left']),
labelright=(rcParams['ytick.labelright'] and
rcParams['ytick.major.right']),
which='major')
def __init_subclass__(cls, **kwargs):
parent_uses_cla = super(cls, cls)._subclass_uses_cla
if 'cla' in cls.__dict__:
_api.warn_deprecated(
'3.6',
pending=True,
message=f'Overriding `Axes.cla` in {cls.__qualname__} is '
'pending deprecation in %(since)s and will be fully '
'deprecated in favor of `Axes.clear` in the future. '
'Please report '
f'this to the {cls.__module__!r} author.')
cls._subclass_uses_cla = 'cla' in cls.__dict__ or parent_uses_cla
super().__init_subclass__(**kwargs)
def __getstate__(self):
state = super().__getstate__()
# Prune the sharing & twinning info to only contain the current group.
state["_shared_axes"] = {
name: self._shared_axes[name].get_siblings(self)
for name in self._axis_names if self in self._shared_axes[name]}
state["_twinned_axes"] = (self._twinned_axes.get_siblings(self)
if self in self._twinned_axes else None)
return state
def __setstate__(self, state):
# Merge the grouping info back into the global groupers.
shared_axes = state.pop("_shared_axes")
for name, shared_siblings in shared_axes.items():
self._shared_axes[name].join(*shared_siblings)
twinned_siblings = state.pop("_twinned_axes")
if twinned_siblings:
self._twinned_axes.join(*twinned_siblings)
self.__dict__ = state
self._stale = True
def __repr__(self):
fields = []
if self.get_label():
fields += [f"label={self.get_label()!r}"]
if hasattr(self, "get_title"):
titles = {}
for k in ["left", "center", "right"]:
title = self.get_title(loc=k)
if title:
titles[k] = title
if titles:
fields += [f"title={titles}"]
for name, axis in self._axis_map.items():
if axis.label and axis.label.get_text():
fields += [f"{name}label={axis.label.get_text()!r}"]
return f"<{self.__class__.__name__}: " + ", ".join(fields) + ">"
def get_subplotspec(self):
"""Return the `.SubplotSpec` associated with the subplot, or None."""
return self._subplotspec
def set_subplotspec(self, subplotspec):
"""Set the `.SubplotSpec`. associated with the subplot."""
self._subplotspec = subplotspec
self._set_position(subplotspec.get_position(self.get_figure(root=False)))
def get_gridspec(self):
"""Return the `.GridSpec` associated with the subplot, or None."""
return self._subplotspec.get_gridspec() if self._subplotspec else None
def get_window_extent(self, renderer=None):
"""
Return the Axes bounding box in display space.
This bounding box does not include the spines, ticks, ticklabels,
or other labels. For a bounding box including these elements use
`~matplotlib.axes.Axes.get_tightbbox`.
See Also
--------
matplotlib.axes.Axes.get_tightbbox
matplotlib.axis.Axis.get_tightbbox
matplotlib.spines.Spine.get_window_extent
"""
return self.bbox
def _init_axis(self):
# This is moved out of __init__ because non-separable axes don't use it
self.xaxis = maxis.XAxis(self, clear=False)
self.spines.bottom.register_axis(self.xaxis)
self.spines.top.register_axis(self.xaxis)
self.yaxis = maxis.YAxis(self, clear=False)
self.spines.left.register_axis(self.yaxis)
self.spines.right.register_axis(self.yaxis)
def set_figure(self, fig):
# docstring inherited
super().set_figure(fig)
self.bbox = mtransforms.TransformedBbox(self._position,
fig.transSubfigure)
# these will be updated later as data is added
self.dataLim = mtransforms.Bbox.null()
self._viewLim = mtransforms.Bbox.unit()
self.transScale = mtransforms.TransformWrapper(
mtransforms.IdentityTransform())
self._set_lim_and_transforms()
def _unstale_viewLim(self):
# We should arrange to store this information once per share-group
# instead of on every axis.
need_scale = {
name: any(ax._stale_viewlims[name]
for ax in self._shared_axes[name].get_siblings(self))
for name in self._axis_names}
if any(need_scale.values()):
for name in need_scale:
for ax in self._shared_axes[name].get_siblings(self):
ax._stale_viewlims[name] = False
self.autoscale_view(**{f"scale{name}": scale
for name, scale in need_scale.items()})
@property
def viewLim(self):
"""The view limits as `.Bbox` in data coordinates."""
self._unstale_viewLim()
return self._viewLim
def _request_autoscale_view(self, axis="all", tight=None):
"""
Mark a single axis, or all of them, as stale wrt. autoscaling.
No computation is performed until the next autoscaling; thus, separate
calls to control individual `Axis`s incur negligible performance cost.
Parameters
----------
axis : str, default: "all"
Either an element of ``self._axis_names``, or "all".
tight : bool or None, default: None
"""
axis_names = _api.getitem_checked(
{**{k: [k] for k in self._axis_names}, "all": self._axis_names},
axis=axis)
for name in axis_names:
self._stale_viewlims[name] = True
if tight is not None:
self._tight = tight
def _set_lim_and_transforms(self):
"""
Set the *_xaxis_transform*, *_yaxis_transform*, *transScale*,
*transData*, *transLimits* and *transAxes* transformations.
.. note::
This method is primarily used by rectilinear projections of the
`~matplotlib.axes.Axes` class, and is meant to be overridden by
new kinds of projection Axes that need different transformations
and limits. (See `~matplotlib.projections.polar.PolarAxes` for an
example.)
"""
self.transAxes = mtransforms.BboxTransformTo(self.bbox)
# Transforms the x and y axis separately by a scale factor.
# It is assumed that this part will have non-linear components
# (e.g., for a log scale).
self.transScale = mtransforms.TransformWrapper(
mtransforms.IdentityTransform())
# An affine transformation on the data, generally to limit the
# range of the axes
self.transLimits = mtransforms.BboxTransformFrom(
mtransforms.TransformedBbox(self._viewLim, self.transScale))
# The parentheses are important for efficiency here -- they
# group the last two (which are usually affines) separately
# from the first (which, with log-scaling can be non-affine).
self.transData = self.transScale + (self.transLimits + self.transAxes)
self._xaxis_transform = mtransforms.blended_transform_factory(
self.transData, self.transAxes)
self._yaxis_transform = mtransforms.blended_transform_factory(
self.transAxes, self.transData)
def get_xaxis_transform(self, which='grid'):
"""
Get the transformation used for drawing x-axis labels, ticks
and gridlines. The x-direction is in data coordinates and the
y-direction is in axis coordinates.
.. note::
This transformation is primarily used by the
`~matplotlib.axis.Axis` class, and is meant to be
overridden by new kinds of projections that may need to
place axis elements in different locations.
Parameters
----------
which : {'grid', 'tick1', 'tick2'}
"""
if which == 'grid':