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#!/usr/bin/env python3
"""
The second-level axes subclass used for all ultraplot figures.
Implements plotting method overrides.
"""
import contextlib
import inspect
import itertools
import re
import sys
from collections.abc import Callable, Iterable
from numbers import Integral, Number
from typing import Any, Iterable, Mapping, Optional, Sequence, TypeAlias, Union
import matplotlib as mpl
import matplotlib.artist as martist
import matplotlib.axes as maxes
import matplotlib.cbook as cbook
import matplotlib.cm as mcm
import matplotlib.collections as mcollections
import matplotlib.colors as mcolors
import matplotlib.container as mcontainer
import matplotlib.contour as mcontour
import matplotlib.image as mimage
import matplotlib.lines as mlines
import matplotlib.patches as mpatches
import matplotlib.pyplot as mplt
import matplotlib.ticker as mticker
import numpy as np
import numpy.ma as ma
from numpy.typing import ArrayLike
from packaging import version
from .. import colors as pcolors
from .. import constructor, utils
from ..config import rc
from ..internals import (
_get_aliases,
_not_none,
_pop_kwargs,
_pop_params,
_pop_props,
_version_mpl,
context,
docstring,
guides,
ic, # noqa: F401
inputs,
warnings,
)
from ..utils import units
from . import base
try:
from cartopy.crs import PlateCarree
except ModuleNotFoundError:
PlateCarree = object
__all__ = ["PlotAxes"]
# Constants
# NOTE: Increased from native linewidth of 0.25 matplotlib uses for grid box edges.
# This is half of rc['patch.linewidth'] of 0.6. Half seems like a nice default.
EDGEWIDTH = 0.3
DataInput: TypeAlias = ArrayLike
ColorTupleRGB: TypeAlias = tuple[float, float, float]
ColorTupleRGBA: TypeAlias = tuple[float, float, float, float]
ColorInput: TypeAlias = DataInput | str | ColorTupleRGB | ColorTupleRGBA | None
ParsedColor: TypeAlias = DataInput | list[str] | str | None
# Data argument docstrings
_args_1d_docstring = """
*args : {y} or {x}, {y}
The data passed as positional or keyword arguments. Interpreted as follows:
* If only `{y}` coordinates are passed, try to infer the `{x}` coordinates
from the `~pandas.Series` or :class:`~pandas.DataFrame` indices or the
:class:`~xarray.DataArray` coordinates. Otherwise, the `{x}` coordinates
are ``np.arange(0, {y}.shape[0])``.
* If the `{y}` coordinates are a 2D array, plot each column of data in succession
(except where each column of data represents a statistical distribution, as with
``boxplot``, ``violinplot``, or when using ``means=True`` or ``medians=True``).
* If any arguments are `pint.Quantity`, auto-add the pint unit registry
to matplotlib's unit registry using `~pint.UnitRegistry.setup_matplotlib`.
A `pint.Quantity` embedded in an `xarray.DataArray` is also supported.
"""
_args_1d_multi_docstring = """
*args : {y}2 or {x}, {y}2, or {x}, {y}1, {y}2
The data passed as positional or keyword arguments. Interpreted as follows:
* If only `{y}` coordinates are passed, try to infer the `{x}` coordinates from
the `~pandas.Series` or :class:`~pandas.DataFrame` indices or the :class:`~xarray.DataArray`
coordinates. Otherwise, the `{x}` coordinates are ``np.arange(0, {y}2.shape[0])``.
* If only `{x}` and `{y}2` coordinates are passed, set the `{y}1` coordinates
to zero. This draws elements originating from the zero line.
* If both `{y}1` and `{y}2` are provided, draw elements between these points. If
either are 2D, draw elements by iterating over each column.
* If any arguments are `pint.Quantity`, auto-add the pint unit registry
to matplotlib's unit registry using `~pint.UnitRegistry.setup_matplotlib`.
A `pint.Quantity` embedded in an `xarray.DataArray` is also supported.
"""
_args_2d_docstring = """
*args : {z} or x, y, {z}
The data passed as positional or keyword arguments. Interpreted as follows:
* If only {zvar} coordinates are passed, try to infer the `x` and `y` coordinates
from the :class:`~pandas.DataFrame` indices and columns or the :class:`~xarray.DataArray`
coordinates. Otherwise, the `y` coordinates are ``np.arange(0, y.shape[0])``
and the `x` coordinates are ``np.arange(0, y.shape[1])``.
* For ``pcolor`` and ``pcolormesh``, calculate coordinate *edges* using
`~ultraplot.utils.edges` or `:func:`~ultraplot.utils.edges2d`` if *centers* were provided.
For all other methods, calculate coordinate *centers* if *edges* were provided.
* If the `x` or `y` coordinates are `pint.Quantity`, auto-add the pint unit registry
to matplotlib's unit registry using `~pint.UnitRegistry.setup_matplotlib`. If the
{zvar} coordinates are `pint.Quantity`, pass the magnitude to the plotting
command. A `pint.Quantity` embedded in an `xarray.DataArray` is also supported.
"""
docstring._snippet_manager["plot.args_1d_y"] = _args_1d_docstring.format(x="x", y="y")
docstring._snippet_manager["plot.args_1d_x"] = _args_1d_docstring.format(x="y", y="x")
docstring._snippet_manager["plot.args_1d_multiy"] = _args_1d_multi_docstring.format(
x="x", y="y"
) # noqa: E501
docstring._snippet_manager["plot.args_1d_multix"] = _args_1d_multi_docstring.format(
x="y", y="x"
) # noqa: E501
docstring._snippet_manager["plot.args_2d"] = _args_2d_docstring.format(
z="z", zvar="`z`"
) # noqa: E501
docstring._snippet_manager["plot.args_2d_flow"] = _args_2d_docstring.format(
z="u, v", zvar="`u` and `v`"
) # noqa: E501
# Shared docstrings
_args_1d_shared_docstring = """
data : dict-like, optional
A dict-like dataset container (e.g., :class:`~pandas.DataFrame` or
`~xarray.Dataset`). If passed, each data argument can optionally
be a string `key` and the arrays used for plotting are retrieved
with ``data[key]``. This is a `native matplotlib feature
<https://matplotlib.org/stable/gallery/misc/keyword_plotting.html>`__.
autoformat : bool, default: :rc:`autoformat`
Whether the `x` axis labels, `y` axis labels, axis formatters, axes titles,
legend titles, and colorbar labels are automatically configured when a
`~pandas.Series`, :class:`~pandas.DataFrame`, :class:`~xarray.DataArray`, or `~pint.Quantity`
is passed to the plotting command. Formatting of `pint.Quantity`
unit strings is controlled by :rc:`unitformat`.
"""
_args_2d_shared_docstring = """
%(plot.args_1d_shared)s
transpose : bool, default: False
Whether to transpose the input data. This should be used when
passing datasets with column-major dimension order ``(x, y)``.
Otherwise row-major dimension order ``(y, x)`` is expected.
order : {'C', 'F'}, default: 'C'
Alternative to `transpose`. ``'C'`` corresponds to the default C-cyle
row-major ordering (equivalent to ``transpose=False``). ``'F'`` corresponds
to Fortran-style column-major ordering (equivalent to ``transpose=True``).
globe : bool, default: False
For `ultraplot.axes.GeoAxes` only. Whether to enforce global
coverage. When set to ``True`` this does the following:
#. Interpolates input data to the North and South poles by setting the data
values at the poles to the mean from latitudes nearest each pole.
#. Makes meridional coverage "circular", i.e. the last longitude coordinate
equals the first longitude coordinate plus 360\N{DEGREE SIGN}.
#. When basemap is the backend, cycles 1D longitude vectors to fit within
the map edges. For example, if the central longitude is 90\N{DEGREE SIGN},
the data is shifted so that it spans -90\N{DEGREE SIGN} to 270\N{DEGREE SIGN}.
"""
docstring._snippet_manager["plot.args_1d_shared"] = _args_1d_shared_docstring
docstring._snippet_manager["plot.args_2d_shared"] = _args_2d_shared_docstring
_curved_quiver_docstring = """
Draws curved vector field arrows (streamlines with arrows) for 2D vector fields.
Parameters
----------
x, y : 1D or 2D arrays
Grid coordinates.
u, v : 2D arrays
Vector components.
color : color or 2D array, optional
Streamline color.
density : float or (float, float), optional
Controls the closeness of streamlines.
grains : int or (int, int), optional
Number of seed points in x and y.
linewidth : float or 2D array, optional
Width of streamlines.
cmap, norm : optional
Colormap and normalization for array colors.
arrowsize : float, optional
Arrow size scaling.
arrowstyle : str, optional
Arrow style specification.
transform : optional
Matplotlib transform.
zorder : float, optional
Z-order for lines/arrows.
start_points : (N, 2) array, optional
Starting points for streamlines.
Returns
-------
CurvedQuiverSet
Container with attributes:
- lines: LineCollection of streamlines
- arrows: PatchCollection of arrows
"""
docstring._snippet_manager["plot.curved_quiver"] = _curved_quiver_docstring
_sankey_docstring = """
Draw a Sankey diagram.
Parameters
----------
flows : sequence of float or flow tuples
If a numeric sequence, use Matplotlib's Sankey implementation.
Otherwise, expect flow tuples or dicts describing (source, target, value).
nodes : sequence or dict, optional
Node identifiers or dicts with ``id``/``label``/``color`` keys. If omitted,
nodes are inferred from flow sources/targets.
labels : sequence of str, optional
Labels for each flow in Matplotlib's Sankey mode.
orientations : sequence of int, optional
Flow orientations (-1: down, 0: right, 1: up) for Matplotlib's Sankey.
pathlengths : float or sequence of float, optional
Path lengths for each flow in Matplotlib's Sankey. Defaults to
:rc:`sankey.pathlengths` when omitted.
trunklength : float, optional
Length of the trunk between the input and output flows. Defaults to
:rc:`sankey.trunklength` when omitted.
patchlabel : str, optional
Label for the main patch in Matplotlib's Sankey mode. Defaults to
:rc:`sankey.pathlabel` when omitted.
scale, unit, format, gap, radius, shoulder, offset, head_angle, margin, tolerance : optional
Passed to `matplotlib.sankey.Sankey`.
prior : int, optional
Index of a prior diagram to connect to.
connect : (int, int), optional
Flow indices for the prior and current diagram connection. Defaults to
:rc:`sankey.connect` when omitted.
rotation : float, optional
Rotation angle in degrees. Defaults to :rc:`sankey.rotation` when omitted.
node_kw, flow_kw, label_kw : dict-like, optional
Style dictionaries for the layered Sankey renderer.
node_label_kw, flow_label_kw : dict-like, optional
Label style dictionaries for node and flow labels in layered mode.
node_label_box : bool or dict-like, optional
If ``True``, draw a rounded box behind node labels. If dict-like, used as
the ``bbox`` argument for node label styling.
style : {'budget', 'pastel', 'mono'}, optional
Built-in styling presets for layered mode.
node_order : sequence, optional
Explicit node ordering for layered mode.
layer_order : sequence, optional
Explicit layer ordering for layered mode.
group_cycle : sequence, optional
Cycle for flow group colors (defaults to flow cycle).
flow_other : float, optional
Aggregate flows below this threshold into a single ``other_label``.
other_label : str, optional
Label for the aggregated flow target. Defaults to :rc:`sankey.other_label`
when omitted.
value_format : str or callable, optional
Formatter for flow labels when not explicitly provided.
node_label_outside : {'auto', True, False}, optional
Place node labels outside narrow nodes. Defaults to
:rc:`sankey.node_label_outside` when omitted.
node_label_offset : float, optional
Offset for outside node labels (axes-relative units). Defaults to
:rc:`sankey.node_label_offset` when omitted.
flow_sort : bool, optional
Whether to sort flows by target position to reduce crossings. Defaults to
:rc:`sankey.flow_sort` when omitted.
flow_label_pos : float, optional
Horizontal placement for single flow labels (0 to 1 along the ribbon).
Defaults to :rc:`sankey.flow_label_pos` when omitted.
When flow labels overlap, positions are redistributed between 0.25 and 0.75.
node_labels, flow_labels : bool, optional
Whether to draw node or flow labels in layered mode. Defaults to
:rc:`sankey.node_labels` and :rc:`sankey.flow_labels` when omitted.
align : {'center', 'top', 'bottom'}, optional
Vertical alignment for nodes within each layer in layered mode. Defaults to
:rc:`sankey.align` when omitted.
layers : dict-like, optional
Manual layer assignments for nodes in layered mode.
**kwargs
Patch properties passed to `matplotlib.sankey.Sankey.add` in Matplotlib mode.
Layered defaults
----------------
Layered mode uses :rc:`sankey.nodepad`, :rc:`sankey.nodewidth`,
:rc:`sankey.margin`, :rc:`sankey.flow.alpha`, :rc:`sankey.flow.curvature`,
and :rc:`sankey.node.facecolor` when not set explicitly.
Returns
-------
matplotlib.sankey.Sankey or list or SankeyDiagram
The Sankey diagram instance, or a list for multi-diagram usage. For layered
mode, returns a `~ultraplot.axes.plot_types.sankey.SankeyDiagram`.
"""
docstring._snippet_manager["plot.sankey"] = _sankey_docstring
_chord_docstring = """
Draw a chord diagram using pyCirclize.
Parameters
----------
matrix : str, Path, pandas.DataFrame, or Matrix
Input matrix for the chord diagram.
start, end : float, optional
Plot start and end degrees (-360 <= start < end <= 360).
space : float or sequence of float, optional
Space degrees between sectors.
endspace : bool, optional
If True, insert space after the final sector.
r_lim : 2-tuple of float, optional
Outer track radius limits (0 to 100).
cmap : str or dict, optional
Colormap name or name-to-color mapping for sectors and links. If omitted,
UltraPlot's color cycle is used.
link_cmap : list of (from, to, color), optional
Override link colors.
ticks_interval : int, optional
Tick interval for sector tracks. If None, no ticks are shown.
order : {'asc', 'desc'} or list, optional
Node ordering strategy or explicit node order.
label_kw, ticks_kw, link_kw : dict-like, optional
Keyword arguments passed to pyCirclize for labels, ticks, and links.
link_kw_handler : callable, optional
Callback to customize per-link keyword arguments.
tooltip : bool, optional
Enable interactive tooltips (requires ipympl).
Returns
-------
pycirclize.Circos
The underlying Circos instance.
"""
docstring._snippet_manager["plot.chord_diagram"] = _chord_docstring
_radar_docstring = """
Draw a radar chart using pyCirclize.
Parameters
----------
table : str, Path, pandas.DataFrame, or RadarTable
Input table for the radar chart.
r_lim : 2-tuple of float, optional
Radar chart radius limits (0 to 100).
vmin, vmax : float, optional
Value range for the radar chart.
fill : bool, optional
Whether to fill the radar polygons.
marker_size : int, optional
Marker size for radar points.
bg_color : color-spec or None, optional
Background fill color.
circular : bool, optional
Whether to draw circular grid lines.
cmap : str or dict, optional
Colormap name or row-name-to-color mapping. If omitted, UltraPlot's
color cycle is used.
show_grid_label : bool, optional
Whether to show radial grid labels.
grid_interval_ratio : float or None, optional
Grid interval ratio (0 to 1).
grid_line_kw, grid_label_kw : dict-like, optional
Keyword arguments passed to pyCirclize for grid lines and labels.
grid_label_formatter : callable, optional
Formatter for grid label values.
label_kw_handler, line_kw_handler, marker_kw_handler : callable, optional
Per-series styling callbacks passed to pyCirclize.
Returns
-------
pycirclize.Circos
The underlying Circos instance.
"""
docstring._snippet_manager["plot.radar_chart"] = _radar_docstring
_circos_docstring = """
Create a Circos instance using pyCirclize.
Parameters
----------
sectors : mapping
Sector name and size (or range) mapping.
start, end : float, optional
Plot start and end degrees (-360 <= start < end <= 360).
space : float or sequence of float, optional
Space degrees between sectors.
endspace : bool, optional
If True, insert space after the final sector.
sector2clockwise : dict, optional
Override clockwise settings per sector.
show_axis_for_debug : bool, optional
Show the polar axis for debug layout.
plot : bool, optional
If True, immediately render the circos figure on this axes.
tooltip : bool, optional
Enable interactive tooltips (requires ipympl).
Returns
-------
pycirclize.Circos
The underlying Circos instance.
"""
docstring._snippet_manager["plot.circos"] = _circos_docstring
_phylogeny_docstring = """
Draw a phylogenetic tree using pyCirclize.
Parameters
----------
tree_data : str, Path, or Tree
Tree data (file, URL, Tree object, or tree string).
start, end : float, optional
Plot start and end degrees (-360 <= start < end <= 360).
r_lim : 2-tuple of float, optional
Tree track radius limits (0 to 100).
format : str, optional
Tree format (`newick`, `phyloxml`, `nexus`, `nexml`, `cdao`).
outer : bool, optional
If True, plot tree on the outer side.
align_leaf_label : bool, optional
If True, align leaf labels.
ignore_branch_length : bool, optional
Ignore branch lengths when plotting.
leaf_label_size : float, optional
Leaf label size.
leaf_label_rmargin : float, optional
Leaf label radius margin.
reverse : bool, optional
Reverse tree direction.
ladderize : bool, optional
Ladderize tree.
line_kw, align_line_kw : dict-like, optional
Keyword arguments for tree line styling.
label_formatter : callable, optional
Formatter for leaf labels.
tooltip : bool, optional
Enable interactive tooltips (requires ipympl).
Returns
-------
pycirclize.Circos, pycirclize.TreeViz
The Circos instance and TreeViz helper.
"""
docstring._snippet_manager["plot.phylogeny"] = _phylogeny_docstring
_circos_bed_docstring = """
Create a Circos instance from a BED file using pyCirclize.
Parameters
----------
bed_file : str or Path
BED file describing chromosome ranges.
start, end : float, optional
Plot start and end degrees (-360 <= start < end <= 360).
space : float or sequence of float, optional
Space degrees between sectors.
endspace : bool, optional
If True, insert space after the final sector.
sector2clockwise : dict, optional
Override clockwise settings per sector.
plot : bool, optional
If True, immediately render the circos figure on this axes.
tooltip : bool, optional
Enable interactive tooltips (requires ipympl).
Returns
-------
pycirclize.Circos
The underlying Circos instance.
"""
docstring._snippet_manager["plot.circos_bed"] = _circos_bed_docstring
# Auto colorbar and legend docstring
_guide_docstring = """
colorbar : bool, int, or str, optional
If not ``None``, this is a location specifying where to draw an
*inset* or *outer* colorbar from the resulting object(s). If ``True``,
the default :rc:`colorbar.loc` is used. If the same location is
used in successive plotting calls, object(s) will be added to the
existing colorbar in that location (valid for colorbars built from lists
of artists). Valid locations are shown in in `~ultraplot.axes.Axes.colorbar`.
colorbar_kw : dict-like, optional
Extra keyword args for the call to `~ultraplot.axes.Axes.colorbar`.
legend : bool, int, or str, optional
Location specifying where to draw an *inset* or *outer* legend from the
resulting object(s). If ``True``, the default :rc:`legend.loc` is used.
If the same location is used in successive plotting calls, object(s)
will be added to existing legend in that location. Valid locations
are shown in :meth:`~ultraplot.axes.Axes.legend`.
legend_kw : dict-like, optional
Extra keyword args for the call to :class:`~ultraplot.axes.Axes.legend`.
"""
docstring._snippet_manager["plot.guide"] = _guide_docstring
# Misc shared 1D plotting docstrings
_inbounds_docstring = """
inbounds : bool, default: :rc:`axes.inbounds`
Whether to restrict the default `y` (`x`) axis limits to account for only
in-bounds data when the `x` (`y`) axis limits have been locked.
See also :rcraw:`axes.inbounds` and :rcraw:`cmap.inbounds`.
"""
_error_means_docstring = """
mean, means : bool, default: False
Whether to plot the means of each column for 2D `{y}` coordinates. Means
are calculated with `numpy.nanmean`. If no other arguments are specified,
this also sets ``barstd=True`` (and ``boxstd=True`` for violin plots).
median, medians : bool, default: False
Whether to plot the medians of each column for 2D `{y}` coordinates. Medians
are calculated with `numpy.nanmedian`. If no other arguments arguments are
specified, this also sets ``barstd=True`` (and ``boxstd=True`` for violin plots).
"""
_error_bars_docstring = """
bars : bool, default: None
Shorthand for `barstd`, `barstds`.
barstd, barstds : bool, float, or 2-tuple of float, optional
Valid only if `mean` or `median` is ``True``. Standard deviation multiples for
*thin error bars* with optional whiskers (i.e., caps). If scalar, then +/- that
multiple is used. If ``True``, the default standard deviation range of +/-3 is used.
barpctile, barpctiles : bool, float, or 2-tuple of float, optional
Valid only if `mean` or `median` is ``True``. As with `barstd`, but instead
using percentiles for the error bars. If scalar, that percentile range is
used (e.g., ``90`` shows the 5th to 95th percentiles). If ``True``, the default
percentile range of 0 to 100 is used.
bardata : array-like, optional
Valid only if `mean` and `median` are ``False``. If shape is 2 x N, these
are the lower and upper bounds for the thin error bars. If shape is N, these
are the absolute, symmetric deviations from the central points.
boxes : bool, default: None
Shorthand for `boxstd`, `boxstds`.
boxstd, boxstds, boxpctile, boxpctiles, boxdata : optional
As with `barstd`, `barpctile`, and `bardata`, but for *thicker error bars*
representing a smaller interval than the thin error bars. If `boxstds` is
``True``, the default standard deviation range of +/-1 is used. If `boxpctiles`
is ``True``, the default percentile range of 25 to 75 is used (i.e., the
interquartile range). When "boxes" and "bars" are combined, this has the
effect of drawing miniature box-and-whisker plots.
capsize : float, default: :rc:`errorbar.capsize`
The cap size for thin error bars in points.
barz, barzorder, boxz, boxzorder : float, default: 2.5
The "zorder" for the thin and thick error bars.
barc, barcolor, boxc, boxcolor \
: color-spec, default: :rc:`boxplot.whiskerprops.color`
Colors for the thin and thick error bars.
barlw, barlinewidth, boxlw, boxlinewidth \
: float, default: :rc:`boxplot.whiskerprops.linewidth`
Line widths for the thin and thick error bars, in points. The default for boxes
is 4 times :rcraw:`boxplot.whiskerprops.linewidth`.
boxm, boxmarker : bool or marker-spec, default: 'o'
Whether to draw a small marker in the middle of the box denoting
the mean or median position. Ignored if `boxes` is ``False``.
boxms, boxmarkersize : size-spec, default: ``(2 * boxlinewidth) ** 2``
The marker size for the `boxmarker` marker in points ** 2.
boxmc, boxmarkercolor, boxmec, boxmarkeredgecolor : color-spec, default: 'w'
Color, face color, and edge color for the `boxmarker` marker.
"""
_error_shading_docstring = """
shade : bool, default: None
Shorthand for `shadestd`.
shadestd, shadestds, shadepctile, shadepctiles, shadedata : optional
As with `barstd`, `barpctile`, and `bardata`, but using *shading* to indicate
the error range. If `shadestds` is ``True``, the default standard deviation
range of +/-2 is used. If `shadepctiles` is ``True``, the default
percentile range of 10 to 90 is used.
fade : bool, default: None
Shorthand for `fadestd`.
fadestd, fadestds, fadepctile, fadepctiles, fadedata : optional
As with `shadestd`, `shadepctile`, and `shadedata`, but for an additional,
more faded, *secondary* shaded region. If `fadestds` is ``True``, the default
standard deviation range of +/-3 is used. If `fadepctiles` is ``True``,
the default percentile range of 0 to 100 is used.
shadec, shadecolor, fadec, fadecolor : color-spec, default: None
Colors for the different shaded regions. The parent artist color is used by default.
shadez, shadezorder, fadez, fadezorder : float, default: 1.5
The "zorder" for the different shaded regions.
shadea, shadealpha, fadea, fadealpha : float, default: 0.4, 0.2
The opacity for the different shaded regions.
shadelw, shadelinewidth, fadelw, fadelinewidth : float, default: :rc:`patch.linewidth`.
The edge line width for the shading patches.
shdeec, shadeedgecolor, fadeec, fadeedgecolor : float, default: 'none'
The edge color for the shading patches.
shadelabel, fadelabel : bool or str, optional
Labels for the shaded regions to be used as separate legend entries. To toggle
labels "on" and apply a *default* label, use e.g. ``shadelabel=True``. To apply
a *custom* label, use e.g. ``shadelabel='label'``. Otherwise, the shading is
drawn underneath the line and/or marker in the legend entry.
"""
docstring._snippet_manager["plot.inbounds"] = _inbounds_docstring
docstring._snippet_manager["plot.error_means_y"] = _error_means_docstring.format(y="y")
docstring._snippet_manager["plot.error_means_x"] = _error_means_docstring.format(y="x")
docstring._snippet_manager["plot.error_bars"] = _error_bars_docstring
docstring._snippet_manager["plot.error_shading"] = _error_shading_docstring
# Color docstrings
_cycle_docstring = """
cycle : cycle-spec, optional
The cycle specifer, passed to the `~ultraplot.constructor.Cycle` constructor.
If the returned cycler is unchanged from the current cycler, the axes
cycler will not be reset to its first position. To disable property cycling
and just use black for the default color, use ``cycle=False``, ``cycle='none'``,
or ``cycle=()`` (analogous to disabling ticks with e.g. ``xformatter='none'``).
To restore the default property cycler, use ``cycle=True``.
cycle_kw : dict-like, optional
Passed to `~ultraplot.constructor.Cycle`.
"""
_cmap_norm_docstring = """
cmap : colormap-spec, default: \
:rc:`cmap.sequential` or :rc:`cmap.diverging`
The colormap specifer, passed to the :class:`~ultraplot.constructor.Colormap` constructor
function. If :rcraw:`cmap.autodiverging` is ``True`` and the normalization
range contains negative and positive values then :rcraw:`cmap.diverging` is used.
Otherwise :rcraw:`cmap.sequential` is used.
cmap_kw : dict-like, optional
Passed to :class:`~ultraplot.constructor.Colormap`.
c, color, colors : color-spec or sequence of color-spec, optional
The color(s) used to create a :class:`~ultraplot.colors.DiscreteColormap`.
If not passed, `cmap` is used.
norm : norm-spec, default: \
`~matplotlib.colors.Normalize` or `~ultraplot.colors.DivergingNorm`
The data value normalizer, passed to the `~ultraplot.constructor.Norm`
constructor function. If `discrete` is ``True`` then 1) this affects the default
level-generation algorithm (e.g. ``norm='log'`` builds levels in log-space) and
2) this is passed to `~ultraplot.colors.DiscreteNorm` to scale the colors before they
are discretized (if `norm` is not already a `~ultraplot.colors.DiscreteNorm`).
If :rcraw:`cmap.autodiverging` is ``True`` and the normalization range contains
negative and positive values then `~ultraplot.colors.DivergingNorm` is used.
Otherwise `~matplotlib.colors.Normalize` is used.
norm_kw : dict-like, optional
Passed to `~ultraplot.constructor.Norm`.
extend : {'neither', 'both', 'min', 'max'}, default: 'neither'
Direction for drawing colorbar "extensions" indicating
out-of-bounds data on the end of the colorbar.
discrete : bool, default: :rc:`cmap.discrete`
If ``False``, then `~ultraplot.colors.DiscreteNorm` is not applied to the
colormap. Instead, for non-contour plots, the number of levels will be
roughly controlled by :rcraw:`cmap.lut`. This has a similar effect to
using `levels=large_number` but it may improve rendering speed. Default is
``True`` only for contouring commands like `~ultraplot.axes.Axes.contourf`
and pseudocolor commands like `~ultraplot.axes.Axes.pcolor`.
sequential, diverging, cyclic, qualitative : bool, default: None
Boolean arguments used if `cmap` is not passed. Set these to ``True``
to use the default :rcraw:`cmap.sequential`, :rcraw:`cmap.diverging`,
:rcraw:`cmap.cyclic`, and :rcraw:`cmap.qualitative` colormaps.
The `diverging` option also applies `~ultraplot.colors.DivergingNorm`
as the default continuous normalizer.
"""
docstring._snippet_manager["plot.cycle"] = _cycle_docstring
docstring._snippet_manager["plot.cmap_norm"] = _cmap_norm_docstring
_log_doc = """
Plot {kind}
UltraPlot is optimized for visualizing logarithmic scales by default. For cases with large differences in magnitude,
we recommend setting `rc["formatter.log"] = True` to enhance axis label formatting.
{matplotlib_doc}
"""
docstring._snippet_manager["plot.loglog"] = _log_doc.format(
kind="loglog", matplotlib_doc=mplt.loglog.__doc__
)
docstring._snippet_manager["plot.semilogy"] = _log_doc.format(
kind="semilogy", matplotlib_doc=mplt.semilogy.__doc__
)
docstring._snippet_manager["plot.semilogx"] = _log_doc.format(
kind="semilogx", matplotlib_doc=mplt.semilogx.__doc__
)
# Levels docstrings
# NOTE: In some functions we only need some components
_vmin_vmax_docstring = """
vmin, vmax : float, optional
The minimum and maximum color scale values used with the `norm` normalizer.
If `discrete` is ``False`` these are the absolute limits, and if `discrete`
is ``True`` these are the approximate limits used to automatically determine
`levels` or `values` lists at "nice" intervals. If `levels` or `values` were
already passed as lists, these are ignored, and `vmin` and `vmax` are set to
the minimum and maximum of the lists. If `robust` was passed, the default `vmin`
and `vmax` are some percentile range of the data values. Otherwise, the default
`vmin` and `vmax` are the minimum and maximum of the data values.
"""
_manual_levels_docstring = """
N
Shorthand for `levels`.
levels : int or sequence of float, default: :rc:`cmap.levels`
The number of level edges or a sequence of level edges. If the former, `locator`
is used to generate this many level edges at "nice" intervals. If the latter,
the levels should be monotonically increasing or decreasing (note decreasing
levels fail with ``contour`` plots).
values : int or sequence of float, default: None
The number of level centers or a sequence of level centers. If the former,
`locator` is used to generate this many level centers at "nice" intervals.
If the latter, levels are inferred using `~ultraplot.utils.edges`.
This will override any `levels` input.
center_levels : bool, default False
If set to true, the discrete color bar bins will be centered on the level values
instead of using the level values as the edges of the discrete bins. This option
can be used for diverging, discrete color bars with both positive and negative
data to ensure data near zero is properly represented.
"""
_auto_levels_docstring = """
robust : bool, float, or 2-tuple, default: :rc:`cmap.robust`
If ``True`` and `vmin` or `vmax` were not provided, they are
determined from the 2nd and 98th data percentiles rather than the
minimum and maximum. If float, this percentile range is used (for example,
``90`` corresponds to the 5th to 95th percentiles). If 2-tuple of float,
these specific percentiles should be used. This feature is useful
when your data has large outliers.
inbounds : bool, default: :rc:`cmap.inbounds`
If ``True`` and `vmin` or `vmax` were not provided, when axis limits
have been explicitly restricted with :func:`~matplotlib.axes.Axes.set_xlim`
or :func:`~matplotlib.axes.Axes.set_ylim`, out-of-bounds data is ignored.
See also :rcraw:`cmap.inbounds` and :rcraw:`axes.inbounds`.
locator : locator-spec, default: `matplotlib.ticker.MaxNLocator`
The locator used to determine level locations if `levels` or `values` were not
already passed as lists. Passed to the `~ultraplot.constructor.Locator` constructor.
Default is `~matplotlib.ticker.MaxNLocator` with `levels` integer levels.
locator_kw : dict-like, optional
Keyword arguments passed to `matplotlib.ticker.Locator` class.
symmetric : bool, default: False
If ``True``, the normalization range or discrete colormap levels are
symmetric about zero.
positive : bool, default: False
If ``True``, the normalization range or discrete colormap levels are
positive with a minimum at zero.
negative : bool, default: False
If ``True``, the normaliation range or discrete colormap levels are
negative with a minimum at zero.
nozero : bool, default: False
If ``True``, ``0`` is removed from the level list. This is mainly useful for
single-color `~matplotlib.axes.Axes.contour` plots.
"""
docstring._snippet_manager["plot.vmin_vmax"] = _vmin_vmax_docstring
docstring._snippet_manager["plot.levels_manual"] = _manual_levels_docstring
docstring._snippet_manager["plot.levels_auto"] = _auto_levels_docstring
# Labels docstrings
_label_docstring = """
label, value : float or str, optional
The single legend label or colorbar coordinate to be used for
this plotted element. Can be numeric or string. This is generally
used with 1D positional arguments.
"""
_labels_1d_docstring = """
%(plot.label)s
labels, values : sequence of float or sequence of str, optional
The legend labels or colorbar coordinates used for each plotted element.
Can be numeric or string, and must match the number of plotted elements.
This is generally used with 2D positional arguments.
"""
_labels_2d_docstring = """
label : str, optional
The legend label to be used for this object. In the case of
contours, this is paired with the the central artist in the artist
list returned by `matplotlib.contour.ContourSet.legend_elements`.
labels : bool, optional
Whether to apply labels to contours and grid boxes. The text will be
white when the luminance of the underlying filled contour or grid box
is less than 50 and black otherwise.
labels_kw : dict-like, optional
Ignored if `labels` is ``False``. Extra keyword args for the labels.
For contour plots, this is passed to `~matplotlib.axes.Axes.clabel`.
Otherwise, this is passed to `~matplotlib.axes.Axes.text`.
formatter, fmt : formatter-spec, optional
The `~matplotlib.ticker.Formatter` used to format number labels.
Passed to the `~ultraplot.constructor.Formatter` constructor.
formatter_kw : dict-like, optional
Keyword arguments passed to `matplotlib.ticker.Formatter` class.
precision : int, optional
The maximum number of decimal places for number labels generated
with the default formatter `~ultraplot.ticker.Simpleformatter`.
"""
docstring._snippet_manager["plot.label"] = _label_docstring
docstring._snippet_manager["plot.labels_1d"] = _labels_1d_docstring
docstring._snippet_manager["plot.labels_2d"] = _labels_2d_docstring
# Negative-positive colors
_negpos_docstring = """
negpos : bool, default: False
Whether to shade {objects} where ``{pos}`` with `poscolor`
and where ``{neg}`` with `negcolor`. If ``True`` this
function will return a length-2 silent list of handles.
negcolor, poscolor : color-spec, default: :rc:`negcolor`, :rc:`poscolor`
Colors to use for the negative and positive {objects}. Ignored if
`negpos` is ``False``.
"""
docstring._snippet_manager["plot.negpos_fill"] = _negpos_docstring.format(
objects="patches", neg="y2 < y1", pos="y2 >= y1"
)
docstring._snippet_manager["plot.negpos_lines"] = _negpos_docstring.format(
objects="lines", neg="ymax < ymin", pos="ymax >= ymin"
)
docstring._snippet_manager["plot.negpos_bar"] = _negpos_docstring.format(
objects="bars", neg="height < 0", pos="height >= 0"
)
# Plot docstring
_plot_docstring = """
Plot standard lines.
Parameters
----------
%(plot.args_1d_{y})s
%(plot.args_1d_shared)s
Other parameters
----------------
%(plot.cycle)s
%(artist.line)s
%(plot.error_means_{y})s
%(plot.error_bars)s
%(plot.error_shading)s
%(plot.inbounds)s
%(plot.labels_1d)s
%(plot.guide)s
**kwargs
Passed to :func:`~matplotlib.axes.Axes.plot`.
See also
--------
PlotAxes.plot
PlotAxes.plotx
matplotlib.axes.Axes.plot
"""
docstring._snippet_manager["plot.plot"] = _plot_docstring.format(y="y")
docstring._snippet_manager["plot.plotx"] = _plot_docstring.format(y="x")
# Step docstring
# NOTE: Internally matplotlib implements step with thin wrapper of plot
_step_docstring = """
Plot step lines.
Parameters
----------
%(plot.args_1d_{y})s
%(plot.args_1d_shared)s
Other parameters
----------------
%(plot.cycle)s
%(artist.line)s
%(plot.inbounds)s
%(plot.labels_1d)s
%(plot.guide)s
**kwargs
Passed to `~matplotlib.axes.Axes.step`.
See also
--------
PlotAxes.step
PlotAxes.stepx
matplotlib.axes.Axes.step
"""
docstring._snippet_manager["plot.step"] = _step_docstring.format(y="y")
docstring._snippet_manager["plot.stepx"] = _step_docstring.format(y="x")
# Stem docstring
_stem_docstring = """
Plot stem lines.
Parameters
----------
%(plot.args_1d_{y})s
%(plot.args_1d_shared)s
Other parameters
----------------
%(plot.cycle)s
%(plot.inbounds)s
%(plot.guide)s
**kwargs
Passed to `~matplotlib.axes.Axes.stem`.
"""
docstring._snippet_manager["plot.stem"] = _stem_docstring.format(y="x")
docstring._snippet_manager["plot.stemx"] = _stem_docstring.format(y="x")
# Lines docstrings
_lines_docstring = """
Plot {orientation} lines.
Parameters
----------
%(plot.args_1d_multi{y})s
%(plot.args_1d_shared)s
Other parameters
----------------
stack, stacked : bool, default: False
Whether to "stack" lines from successive columns of {y} data
or plot lines on top of each other.
%(plot.cycle)s
%(artist.line)s
%(plot.negpos_lines)s
%(plot.inbounds)s
%(plot.labels_1d)s
%(plot.guide)s
**kwargs
Passed to `~matplotlib.axes.Axes.{prefix}lines`.
See also
--------
PlotAxes.vlines
PlotAxes.hlines
matplotlib.axes.Axes.vlines
matplotlib.axes.Axes.hlines
"""
docstring._snippet_manager["plot.vlines"] = _lines_docstring.format(
y="y", prefix="v", orientation="vertical"
)
docstring._snippet_manager["plot.hlines"] = _lines_docstring.format(
y="x", prefix="h", orientation="horizontal"
)
# Scatter docstring
_parametric_docstring = """
Plot a parametric line.
Parameters
----------
%(plot.args_1d_y)s
c, color, colors, values, labels : sequence of float, str, or color-spec, optional
The parametric coordinate(s). These can be passed as a third positional
argument or as a keyword argument. If they are float, the colors will be
determined from `norm` and `cmap`. If they are strings, the color values
will be ``np.arange(len(colors))`` and eventual colorbar ticks will
be labeled with the strings. If they are colors, they are used for the
line segments and `cmap` is ignored -- for example, ``colors='blue'``
makes a monochromatic "parametric" line.
interp : int, default: 0
Interpolate to this many additional points between the parametric
coordinates. This can be increased to make the color gradations
between a small number of coordinates appear "smooth".
%(plot.args_1d_shared)s
Other parameters
----------------
%(plot.cmap_norm)s
%(plot.vmin_vmax)s
%(plot.inbounds)s
scalex, scaley : bool, optional
Whether the view limits are adapted to the data limits. The values are
passed on to `~matplotlib.axes.Axes.autoscale_view`.
%(plot.label)s
%(plot.guide)s
**kwargs
Valid :class:`~matplotlib.collections.LineCollection` properties.
Returns
-------
:class:`~matplotlib.collections.LineCollection`
The parametric line. See `this matplotlib example \
<https://matplotlib.org/stable/gallery/lines_bars_and_markers/multicolored_line>`__.
See also
--------
PlotAxes.plot
PlotAxes.plotx
matplotlib.collections.LineCollection
"""
docstring._snippet_manager["plot.parametric"] = _parametric_docstring
# Scatter function docstring
_scatter_docstring = """
Plot markers with flexible keyword arguments.
Parameters
----------
%(plot.args_1d_{y})s
s, size, ms, markersize : float or array-like or unit-spec, optional
The marker size area(s). If this is an array matching the shape of `x` and `y`,
the units are scaled by `smin` and `smax`. If this contains unit string(s), it
is processed by `~ultraplot.utils.units` and represents the width rather than area.
c, color, colors, mc, markercolor, markercolors, fc, facecolor, facecolors \
: array-like or color-spec, optional