Source code for bokeh.plotting._figure
#-----------------------------------------------------------------------------
# Copyright (c) 2012 - 2022, Anaconda, Inc., and Bokeh Contributors.
# All rights reserved.
#
# The full license is in the file LICENSE.txt, distributed with this software.
#-----------------------------------------------------------------------------
#-----------------------------------------------------------------------------
# Boilerplate
#-----------------------------------------------------------------------------
from __future__ import annotations
# Standard library imports
from typing import TYPE_CHECKING
import logging # isort:skip
log = logging.getLogger(__name__)
#-----------------------------------------------------------------------------
# Imports
#-----------------------------------------------------------------------------
# External imports
import numpy as np
# Bokeh imports
from ..core.enums import HorizontalLocation, MarkerType, VerticalLocation
from ..core.properties import (
Auto,
Datetime,
Either,
Enum,
Float,
Instance,
InstanceDefault,
Int,
List,
Nullable,
Object,
Seq,
String,
TextLike,
TimeDelta,
Tuple,
)
from ..models import (
ColumnDataSource,
CoordinateMapping,
DataRange1d,
GraphRenderer,
Plot,
Range,
Scale,
Tool,
)
from ..models.dom import Template
from ..models.tools import (
Drag,
GestureTool,
InspectTool,
Scroll,
Tap,
)
from ..transform import linear_cmap
from ..util.options import Options
from ._graph import get_graph_kwargs
from ._plot import get_range, get_scale, process_axis_and_grid
from ._stack import double_stack, single_stack
from ._tools import process_active_tools, process_tools_arg
from .contour import ContourRenderer, from_contour
from .glyph_api import _MARKER_SHORTCUTS, GlyphAPI
if TYPE_CHECKING:
from numpy.typing import ArrayLike
#-----------------------------------------------------------------------------
# Globals and constants
#-----------------------------------------------------------------------------
#: A default set of tools configured if no configuration is provided
DEFAULT_TOOLS = "pan,wheel_zoom,box_zoom,save,reset,help"
__all__ = (
'figure',
'markers',
)
#-----------------------------------------------------------------------------
# General API
#-----------------------------------------------------------------------------
[docs]class figure(Plot, GlyphAPI):
''' Create a new figure for plotting.
A subclass of |Plot| that simplifies plot creation with default axes, grids,
tools, etc.
Figure objects have many glyph methods that can be used to draw
vectorized graphical glyphs:
.. hlist::
:columns: 3
* :func:`~bokeh.plotting.figure.annular_wedge`
* :func:`~bokeh.plotting.figure.annulus`
* :func:`~bokeh.plotting.figure.arc`
* :func:`~bokeh.plotting.figure.asterisk`
* :func:`~bokeh.plotting.figure.bezier`
* :func:`~bokeh.plotting.figure.circle`
* :func:`~bokeh.plotting.figure.circle_cross`
* :func:`~bokeh.plotting.figure.circle_dot`
* :func:`~bokeh.plotting.figure.circle_x`
* :func:`~bokeh.plotting.figure.circle_y`
* :func:`~bokeh.plotting.figure.cross`
* :func:`~bokeh.plotting.figure.dash`
* :func:`~bokeh.plotting.figure.diamond`
* :func:`~bokeh.plotting.figure.diamond_cross`
* :func:`~bokeh.plotting.figure.diamond_dot`
* :func:`~bokeh.plotting.figure.dot`
* :func:`~bokeh.plotting.figure.ellipse`
* :func:`~bokeh.plotting.figure.harea`
* :func:`~bokeh.plotting.figure.hbar`
* :func:`~bokeh.plotting.figure.hex`
* :func:`~bokeh.plotting.figure.hex_tile`
* :func:`~bokeh.plotting.figure.image`
* :func:`~bokeh.plotting.figure.image_rgba`
* :func:`~bokeh.plotting.figure.image_url`
* :func:`~bokeh.plotting.figure.inverted_triangle`
* :func:`~bokeh.plotting.figure.line`
* :func:`~bokeh.plotting.figure.multi_line`
* :func:`~bokeh.plotting.figure.multi_polygons`
* :func:`~bokeh.plotting.figure.oval`
* :func:`~bokeh.plotting.figure.patch`
* :func:`~bokeh.plotting.figure.patches`
* :func:`~bokeh.plotting.figure.plus`
* :func:`~bokeh.plotting.figure.quad`
* :func:`~bokeh.plotting.figure.quadratic`
* :func:`~bokeh.plotting.figure.ray`
* :func:`~bokeh.plotting.figure.rect`
* :func:`~bokeh.plotting.figure.segment`
* :func:`~bokeh.plotting.figure.square`
* :func:`~bokeh.plotting.figure.square_cross`
* :func:`~bokeh.plotting.figure.square_dot`
* :func:`~bokeh.plotting.figure.square_pin`
* :func:`~bokeh.plotting.figure.square_x`
* :func:`~bokeh.plotting.figure.star`
* :func:`~bokeh.plotting.figure.star_dot`
* :func:`~bokeh.plotting.figure.step`
* :func:`~bokeh.plotting.figure.text`
* :func:`~bokeh.plotting.figure.triangle`
* :func:`~bokeh.plotting.figure.triangle_dot`
* :func:`~bokeh.plotting.figure.triangle_pin`
* :func:`~bokeh.plotting.figure.varea`
* :func:`~bokeh.plotting.figure.vbar`
* :func:`~bokeh.plotting.figure.wedge`
* :func:`~bokeh.plotting.figure.x`
* :func:`~bokeh.plotting.figure.y`
There is a scatter function that can be parameterized by marker type:
* :func:`~bokeh.plotting.figure.scatter`
There are also specialized methods for stacking bars:
* bars: :func:`~bokeh.plotting.figure.hbar_stack`, :func:`~bokeh.plotting.figure.vbar_stack`
* lines: :func:`~bokeh.plotting.figure.hline_stack`, :func:`~bokeh.plotting.figure.vline_stack`
* areas: :func:`~bokeh.plotting.figure.harea_stack`, :func:`~bokeh.plotting.figure.varea_stack`
As well as one specialized method for making simple hexbin plots:
* :func:`~bokeh.plotting.figure.hexbin`
In addition to all the ``figure`` property attributes, the following
options are also accepted:
.. bokeh-options:: FigureOptions
:module: bokeh.plotting._figure
'''
__view_model__ = "Figure"
def __init__(self, *arg, **kw) -> None:
opts = FigureOptions(kw)
names = self.properties()
for name in kw.keys():
if name not in names:
self._raise_attribute_error_with_matches(name, names | opts.properties())
super().__init__(*arg, **kw)
self.x_range = get_range(opts.x_range)
self.y_range = get_range(opts.y_range)
self.x_scale = get_scale(self.x_range, opts.x_axis_type)
self.y_scale = get_scale(self.y_range, opts.y_axis_type)
process_axis_and_grid(self, opts.x_axis_type, opts.x_axis_location, opts.x_minor_ticks, opts.x_axis_label, self.x_range, 0)
process_axis_and_grid(self, opts.y_axis_type, opts.y_axis_location, opts.y_minor_ticks, opts.y_axis_label, self.y_range, 1)
tool_objs, tool_map = process_tools_arg(self, opts.tools, opts.tooltips)
self.add_tools(*tool_objs)
process_active_tools(
self.toolbar,
tool_map,
opts.active_drag,
opts.active_inspect,
opts.active_scroll,
opts.active_tap,
opts.active_multi,
)
@property
def plot(self):
return self
@property
def coordinates(self):
return None
[docs] def subplot(self,
*,
x_source: Range | None = None, y_source: Range | None = None,
x_scale: Scale | None = None, y_scale: Scale | None = None,
x_target: Range, y_target: Range,
) -> GlyphAPI:
""" Create a new sub-coordinate system and expose a plotting API. """
coordinates = CoordinateMapping(x_source=x_source, y_source=y_source, x_target=x_target, y_target=y_target)
return GlyphAPI(self, coordinates)
[docs] def hexbin(self, x, y, size, orientation="pointytop", palette="Viridis256", line_color=None, fill_color=None, aspect_scale=1, **kwargs):
''' Perform a simple equal-weight hexagonal binning.
A :class:`~bokeh.models.glyphs.HexTile` glyph will be added to display
the binning. The :class:`~bokeh.models.sources.ColumnDataSource` for
the glyph will have columns ``q``, ``r``, and ``count``, where ``q``
and ``r`` are `axial coordinates`_ for a tile, and ``count`` is the
associated bin count.
It is often useful to set ``match_aspect=True`` on the associated plot,
so that hexagonal tiles are all regular (i.e. not "stretched") in
screen space.
For more sophisticated use-cases, e.g. weighted binning or individually
scaling hex tiles, use :func:`hex_tile` directly, or consider a higher
level library such as HoloViews.
Args:
x (array[float]) :
A NumPy array of x-coordinates to bin into hexagonal tiles.
y (array[float]) :
A NumPy array of y-coordinates to bin into hexagonal tiles
size (float) :
The size of the hexagonal tiling to use. The size is defined as
distance from the center of a hexagon to a corner.
In case the aspect scaling is not 1-1, then specifically `size`
is the distance from the center to the "top" corner with the
`"pointytop"` orientation, and the distance from the center to
a "side" corner with the "flattop" orientation.
orientation ("pointytop" or "flattop", optional) :
Whether the hexagonal tiles should be oriented with a pointed
corner on top, or a flat side on top. (default: "pointytop")
palette (str or seq[color], optional) :
A palette (or palette name) to use to colormap the bins according
to count. (default: 'Viridis256')
If ``fill_color`` is supplied, it overrides this value.
line_color (color, optional) :
The outline color for hex tiles, or None (default: None)
fill_color (color, optional) :
An optional fill color for hex tiles, or None. If None, then
the ``palette`` will be used to color map the tiles by
count. (default: None)
aspect_scale (float) :
Match a plot's aspect ratio scaling.
When working with a plot with ``aspect_scale != 1``, this
parameter can be set to match the plot, in order to draw
regular hexagons (instead of "stretched" ones).
This is roughly equivalent to binning in "screen space", and
it may be better to use axis-aligned rectangular bins when
plot aspect scales are not one.
Any additional keyword arguments are passed to :func:`hex_tile`.
Returns
(Glyphrender, DataFrame)
A tuple with the ``HexTile`` renderer generated to display the
binning, and a Pandas ``DataFrame`` with columns ``q``, ``r``,
and ``count``, where ``q`` and ``r`` are `axial coordinates`_
for a tile, and ``count`` is the associated bin count.
Example:
.. bokeh-plot::
:source-position: above
import numpy as np
from bokeh.models import HoverTool
from bokeh.plotting import figure, show
x = 2 + 2*np.random.standard_normal(500)
y = 2 + 2*np.random.standard_normal(500)
p = figure(match_aspect=True, tools="wheel_zoom,reset")
p.background_fill_color = '#440154'
p.grid.visible = False
p.hexbin(x, y, size=0.5, hover_color="pink", hover_alpha=0.8)
hover = HoverTool(tooltips=[("count", "@c"), ("(q,r)", "(@q, @r)")])
p.add_tools(hover)
show(p)
.. _axial coordinates: https://www.redblobgames.com/grids/hexagons/#coordinates-axial
'''
from ..util.hex import hexbin
bins = hexbin(x, y, size, orientation, aspect_scale=aspect_scale)
if fill_color is None:
fill_color = linear_cmap('c', palette, 0, max(bins.counts))
source = ColumnDataSource(data=dict(q=bins.q, r=bins.r, c=bins.counts))
r = self.hex_tile(q="q", r="r", size=size, orientation=orientation, aspect_scale=aspect_scale,
source=source, line_color=line_color, fill_color=fill_color, **kwargs)
return (r, bins)
[docs] def harea_stack(self, stackers, **kw):
''' Generate multiple ``HArea`` renderers for levels stacked left
to right.
Args:
stackers (seq[str]) : a list of data source field names to stack
successively for ``x1`` and ``x2`` harea coordinates.
Additionally, the ``name`` of the renderer will be set to
the value of each successive stacker (this is useful with the
special hover variable ``$name``)
Any additional keyword arguments are passed to each call to ``harea``.
If a keyword value is a list or tuple, then each call will get one
value from the sequence.
Returns:
list[GlyphRenderer]
Examples:
Assuming a ``ColumnDataSource`` named ``source`` with columns
*2016* and *2017*, then the following call to ``harea_stack`` will
will create two ``HArea`` renderers that stack:
.. code-block:: python
p.harea_stack(['2016', '2017'], y='y', color=['blue', 'red'], source=source)
This is equivalent to the following two separate calls:
.. code-block:: python
p.harea(x1=stack(), x2=stack('2016'), y='y', color='blue', source=source, name='2016')
p.harea(x1=stack('2016'), x2=stack('2016', '2017'), y='y', color='red', source=source, name='2017')
'''
result = []
for kw in double_stack(stackers, "x1", "x2", **kw):
result.append(self.harea(**kw))
return result
[docs] def hbar_stack(self, stackers, **kw):
''' Generate multiple ``HBar`` renderers for levels stacked left to right.
Args:
stackers (seq[str]) : a list of data source field names to stack
successively for ``left`` and ``right`` bar coordinates.
Additionally, the ``name`` of the renderer will be set to
the value of each successive stacker (this is useful with the
special hover variable ``$name``)
Any additional keyword arguments are passed to each call to ``hbar``.
If a keyword value is a list or tuple, then each call will get one
value from the sequence.
Returns:
list[GlyphRenderer]
Examples:
Assuming a ``ColumnDataSource`` named ``source`` with columns
*2016* and *2017*, then the following call to ``hbar_stack`` will
will create two ``HBar`` renderers that stack:
.. code-block:: python
p.hbar_stack(['2016', '2017'], y=10, width=0.9, color=['blue', 'red'], source=source)
This is equivalent to the following two separate calls:
.. code-block:: python
p.hbar(bottom=stack(), top=stack('2016'), y=10, width=0.9, color='blue', source=source, name='2016')
p.hbar(bottom=stack('2016'), top=stack('2016', '2017'), y=10, width=0.9, color='red', source=source, name='2017')
'''
result = []
for kw in double_stack(stackers, "left", "right", **kw):
result.append(self.hbar(**kw))
return result
def _line_stack(self, x, y, **kw):
''' Generate multiple ``Line`` renderers for lines stacked vertically
or horizontally.
Args:
x (seq[str]) :
y (seq[str]) :
Additionally, the ``name`` of the renderer will be set to
the value of each successive stacker (this is useful with the
special hover variable ``$name``)
Any additional keyword arguments are passed to each call to ``hbar``.
If a keyword value is a list or tuple, then each call will get one
value from the sequence.
Returns:
list[GlyphRenderer]
Examples:
Assuming a ``ColumnDataSource`` named ``source`` with columns
*2016* and *2017*, then the following call to ``line_stack`` with
stackers for the y-coordinates will will create two ``Line``
renderers that stack:
.. code-block:: python
p.line_stack(['2016', '2017'], x='x', color=['blue', 'red'], source=source)
This is equivalent to the following two separate calls:
.. code-block:: python
p.line(y=stack('2016'), x='x', color='blue', source=source, name='2016')
p.line(y=stack('2016', '2017'), x='x', color='red', source=source, name='2017')
'''
if all(isinstance(val, (list, tuple)) for val in (x,y)):
raise ValueError("Only one of x or y may be a list of stackers")
result = []
if isinstance(y, (list, tuple)):
kw['x'] = x
for kw in single_stack(y, "y", **kw):
result.append(self.line(**kw))
return result
if isinstance(x, (list, tuple)):
kw['y'] = y
for kw in single_stack(x, "x", **kw):
result.append(self.line(**kw))
return result
return [self.line(x, y, **kw)]
[docs] def hline_stack(self, stackers, **kw):
''' Generate multiple ``Line`` renderers for lines stacked horizontally.
Args:
stackers (seq[str]) : a list of data source field names to stack
successively for ``x`` line coordinates.
Additionally, the ``name`` of the renderer will be set to
the value of each successive stacker (this is useful with the
special hover variable ``$name``)
Any additional keyword arguments are passed to each call to ``line``.
If a keyword value is a list or tuple, then each call will get one
value from the sequence.
Returns:
list[GlyphRenderer]
Examples:
Assuming a ``ColumnDataSource`` named ``source`` with columns
*2016* and *2017*, then the following call to ``hline_stack`` with
stackers for the x-coordinates will will create two ``Line``
renderers that stack:
.. code-block:: python
p.hline_stack(['2016', '2017'], y='y', color=['blue', 'red'], source=source)
This is equivalent to the following two separate calls:
.. code-block:: python
p.line(x=stack('2016'), y='y', color='blue', source=source, name='2016')
p.line(x=stack('2016', '2017'), y='y', color='red', source=source, name='2017')
'''
return self._line_stack(x=stackers, **kw)
[docs] def varea_stack(self, stackers, **kw):
''' Generate multiple ``VArea`` renderers for levels stacked bottom
to top.
Args:
stackers (seq[str]) : a list of data source field names to stack
successively for ``y1`` and ``y1`` varea coordinates.
Additionally, the ``name`` of the renderer will be set to
the value of each successive stacker (this is useful with the
special hover variable ``$name``)
Any additional keyword arguments are passed to each call to ``varea``.
If a keyword value is a list or tuple, then each call will get one
value from the sequence.
Returns:
list[GlyphRenderer]
Examples:
Assuming a ``ColumnDataSource`` named ``source`` with columns
*2016* and *2017*, then the following call to ``varea_stack`` will
will create two ``VArea`` renderers that stack:
.. code-block:: python
p.varea_stack(['2016', '2017'], x='x', color=['blue', 'red'], source=source)
This is equivalent to the following two separate calls:
.. code-block:: python
p.varea(y1=stack(), y2=stack('2016'), x='x', color='blue', source=source, name='2016')
p.varea(y1=stack('2016'), y2=stack('2016', '2017'), x='x', color='red', source=source, name='2017')
'''
result = []
for kw in double_stack(stackers, "y1", "y2", **kw):
result.append(self.varea(**kw))
return result
[docs] def vbar_stack(self, stackers, **kw):
''' Generate multiple ``VBar`` renderers for levels stacked bottom
to top.
Args:
stackers (seq[str]) : a list of data source field names to stack
successively for ``left`` and ``right`` bar coordinates.
Additionally, the ``name`` of the renderer will be set to
the value of each successive stacker (this is useful with the
special hover variable ``$name``)
Any additional keyword arguments are passed to each call to ``vbar``.
If a keyword value is a list or tuple, then each call will get one
value from the sequence.
Returns:
list[GlyphRenderer]
Examples:
Assuming a ``ColumnDataSource`` named ``source`` with columns
*2016* and *2017*, then the following call to ``vbar_stack`` will
will create two ``VBar`` renderers that stack:
.. code-block:: python
p.vbar_stack(['2016', '2017'], x=10, width=0.9, color=['blue', 'red'], source=source)
This is equivalent to the following two separate calls:
.. code-block:: python
p.vbar(bottom=stack(), top=stack('2016'), x=10, width=0.9, color='blue', source=source, name='2016')
p.vbar(bottom=stack('2016'), top=stack('2016', '2017'), x=10, width=0.9, color='red', source=source, name='2017')
'''
result = []
for kw in double_stack(stackers, "bottom", "top", **kw):
result.append(self.vbar(**kw))
return result
[docs] def vline_stack(self, stackers, **kw):
''' Generate multiple ``Line`` renderers for lines stacked vertically.
Args:
stackers (seq[str]) : a list of data source field names to stack
successively for ``y`` line coordinates.
Additionally, the ``name`` of the renderer will be set to
the value of each successive stacker (this is useful with the
special hover variable ``$name``)
Any additional keyword arguments are passed to each call to ``line``.
If a keyword value is a list or tuple, then each call will get one
value from the sequence.
Returns:
list[GlyphRenderer]
Examples:
Assuming a ``ColumnDataSource`` named ``source`` with columns
*2016* and *2017*, then the following call to ``vline_stack`` with
stackers for the y-coordinates will will create two ``Line``
renderers that stack:
.. code-block:: python
p.vline_stack(['2016', '2017'], x='x', color=['blue', 'red'], source=source)
This is equivalent to the following two separate calls:
.. code-block:: python
p.line(y=stack('2016'), x='x', color='blue', source=source, name='2016')
p.line(y=stack('2016', '2017'), x='x', color='red', source=source, name='2017')
'''
return self._line_stack(y=stackers, **kw)
[docs] def graph(self, node_source, edge_source, layout_provider, **kwargs):
''' Creates a network graph using the given node, edge and layout provider.
Args:
node_source (:class:`~bokeh.models.sources.ColumnDataSource`) : a user-supplied data source
for the graph nodes. An attempt will be made to convert the object to
:class:`~bokeh.models.sources.ColumnDataSource` if needed. If none is supplied, one is created
for the user automatically.
edge_source (:class:`~bokeh.models.sources.ColumnDataSource`) : a user-supplied data source
for the graph edges. An attempt will be made to convert the object to
:class:`~bokeh.models.sources.ColumnDataSource` if needed. If none is supplied, one is created
for the user automatically.
layout_provider (:class:`~bokeh.models.graphs.LayoutProvider`) : a ``LayoutProvider`` instance to
provide the graph coordinates in Cartesian space.
**kwargs: |line properties| and |fill properties|
'''
kw = get_graph_kwargs(node_source, edge_source, **kwargs)
graph_renderer = GraphRenderer(layout_provider=layout_provider, **kw)
self.renderers.append(graph_renderer)
return graph_renderer
[docs] def contour(
self,
x: ArrayLike | None = None,
y: ArrayLike | None = None,
z: ArrayLike | np.ma.MaskedArray | None = None,
levels: ArrayLike | None = None,
**visuals,
) -> ContourRenderer:
''' Creates a contour plot of filled polygons and/or contour lines.
Filled contour polygons are calculated if ``fill_color`` is set,
contour lines if ``line_color`` is set.
Args:
x (array-like[float] of shape (ny, nx) or (nx,), optional) :
The x-coordinates of the ``z`` values. May be 2D with the same
shape as ``z.shape``, or 1D with length ``nx = z.shape[1]``.
If not specified are assumed to be ``np.arange(nx)``. Must be
ordered monotonically.
y (array-like[float] of shape (ny, nx) or (ny,), optional) :
The y-coordinates of the ``z`` values. May be 2D with the same
shape as ``z.shape``, or 1D with length ``ny = z.shape[0]``.
If not specified are assumed to be ``np.arange(ny)``. Must be
ordered monotonically.
z (array-like[float] of shape (ny, nx)) :
A 2D NumPy array of gridded values to calculate the contours
of. May be a masked array, and any invalid values (``np.inf``
or ``np.nan``) will also be masked out.
levels (array-like[float]) :
The z-levels to calculate the contours at, must be increasing.
Contour lines are calculated at each level and filled contours
are calculated between each adjacent pair of levels so the
number of sets of contour lines is ``len(levels)`` and the
number of sets of filled contour polygons is ``len(levels)-1``.
**visuals: |fill properties|, |hatch properties| and |line properties|
Fill and hatch properties are used for filled contours, line
properties for line contours. If using vectorized properties
then the correct number must be used, ``len(levels)`` for line
properties and ``len(levels)-1`` for fill and hatch properties.
``fill_color`` and ``line_color`` are more flexible in that
they will accept longer sequences and interpolate them to the
required number using :func:`~bokeh.palettes.linear_palette`,
and also accept palette collections (dictionaries mapping from
integer length to color sequence) such as
`bokeh.palettes.Cividis`.
'''
contour_renderer = from_contour(x, y, z, levels, **visuals)
self.renderers.append(contour_renderer)
return contour_renderer
[docs]def markers():
''' Prints a list of valid marker types for scatter()
Returns:
None
'''
print("Available markers: \n\n - " + "\n - ".join(list(MarkerType)))
print()
print("Shortcuts: \n\n" + "\n".join(" %r: %s" % item for item in _MARKER_SHORTCUTS.items()))
#-----------------------------------------------------------------------------
# Dev API
#-----------------------------------------------------------------------------
# This class itself is intentionally undocumented (it is used to generate
# documentation elsewhere)
class BaseFigureOptions(Options):
tools = Either(String, Seq(Either(String, Instance(Tool))), default=DEFAULT_TOOLS, help="""
Tools the plot should start with.
""")
x_minor_ticks = Either(Auto, Int, default="auto", help="""
Number of minor ticks between adjacent x-axis major ticks.
""")
y_minor_ticks = Either(Auto, Int, default="auto", help="""
Number of minor ticks between adjacent y-axis major ticks.
""")
x_axis_location = Nullable(Enum(VerticalLocation), default="below", help="""
Where the x-axis should be located.
""")
y_axis_location = Nullable(Enum(HorizontalLocation), default="left", help="""
Where the y-axis should be located.
""")
x_axis_label = Nullable(TextLike, default="", help="""
A label for the x-axis.
""")
y_axis_label = Nullable(TextLike, default="", help="""
A label for the y-axis.
""")
active_drag = Nullable(Either(Auto, String, Instance(Drag)), default="auto", help="""
Which drag tool should initially be active.
""")
active_inspect = Nullable(Either(Auto, String, Instance(InspectTool), Seq(Instance(InspectTool))), default="auto", help="""
Which drag tool should initially be active.
""")
active_scroll = Nullable(Either(Auto, String, Instance(Scroll)), default="auto", help="""
Which scroll tool should initially be active.
""")
active_tap = Nullable(Either(Auto, String, Instance(Tap)), default="auto", help="""
Which tap tool should initially be active.
""")
active_multi = Nullable(Either(Auto, String, Instance(GestureTool)), default="auto", help="""
Specify an active multi-gesture tool, for instance an edit tool or a range tool.
""")
tooltips = Nullable(Either(Instance(Template), String, List(Tuple(String, String))), help="""
An optional argument to configure tooltips for the Figure. This argument
accepts the same values as the ``HoverTool.tooltips`` property. If a hover
tool is specified in the ``tools`` argument, this value will override that
hover tools ``tooltips`` value. If no hover tool is specified in the
``tools`` argument, then passing tooltips here will cause one to be created
and added.
""")
RangeLike = Either(
Instance(Range),
Either(
Tuple(Float, Float),
Tuple(Datetime, Datetime),
Tuple(TimeDelta, TimeDelta),
),
Seq(String),
Object("pandas.Series"),
Object("pandas.core.groupby.GroupBy"),
)
AxisType = Nullable(Either(Auto, Enum("linear", "log", "datetime", "mercator")))
class FigureOptions(BaseFigureOptions):
x_range = RangeLike(default=InstanceDefault(DataRange1d), help="""
Customize the x-range of the plot.
""")
y_range = RangeLike(default=InstanceDefault(DataRange1d), help="""
Customize the y-range of the plot.
""")
x_axis_type = AxisType(default="auto", help="""
The type of the x-axis.
""")
y_axis_type = AxisType(default="auto", help="""
The type of the y-axis.
""")
#-----------------------------------------------------------------------------
# Private API
#-----------------------------------------------------------------------------
_color_fields = {"color", "fill_color", "line_color"}
_alpha_fields = {"alpha", "fill_alpha", "line_alpha"}
#-----------------------------------------------------------------------------
# Code
#-----------------------------------------------------------------------------