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bqplot.py
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bqplot.py
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import datetime
import typing
from typing import Any, Dict, Union
import bqplot
import ipywidgets
import numpy as np
from numpy import ndarray
import reacton
from reacton.core import ContainerAdder, Element, _get_render_context
from . import ipywidgets as w
from .ipywidgets import Layout
from .utils import implements
class FigureElement(Element[bqplot.Figure]):
def __enter__(self):
rc = _get_render_context()
ca = ContainerAdder[bqplot.Figure](self, "marks")
rc.container_adders.append(ca)
return self
if __name__ == "__main__":
from . import generate
class CodeGen(generate.CodeGen):
element_classes = {bqplot.Figure: FigureElement}
ignore_props = "domain_class".split() + generate.CodeGen.ignore_props
def get_extra_argument(self, cls):
return {ipywidgets.Button: [("on_click", None, typing.Callable[[], Any])]}.get(cls, [])
current_module = __import__(__name__)
CodeGen([bqplot]).generate(__file__)
# generated code:
def _Albers(
allow_padding: bool = True,
center: tuple = (0, 60),
parallels: tuple = (29.5, 45.5),
precision: float = 0.1,
reverse: bool = False,
rotate: tuple = (96, 0),
scale_factor: float = 250,
on_allow_padding: typing.Callable[[bool], Any] = None,
on_center: typing.Callable[[tuple], Any] = None,
on_parallels: typing.Callable[[tuple], Any] = None,
on_precision: typing.Callable[[float], Any] = None,
on_reverse: typing.Callable[[bool], Any] = None,
on_rotate: typing.Callable[[tuple], Any] = None,
on_scale_factor: typing.Callable[[float], Any] = None,
) -> Element[bqplot.scales.Albers]:
"""A geographical scale which is an alias for a conic equal area projection.
The Albers projection is a conic equal area map. It does not preserve scale
or shape, though it is recommended for chloropleths since it preserves the
relative areas of geographic features. Default values are US-centric.
Attributes
----------
scale_factor: float (default: 250)
Specifies the scale value for the projection
rotate: tuple (default: (96, 0))
Degree of rotation in each axis.
parallels: tuple (default: (29.5, 45.5))
Sets the two parallels for the conic projection.
center: tuple (default: (0, 60))
Specifies the longitude and latitude where the map is centered.
precision: float (default: 0.1)
Specifies the threshold for the projections adaptive resampling to the
specified value in pixels.
rtype: (Number, Number) (class-level attribute)
This attribute should not be modified. The range type of a geo
scale is a tuple.
dtype: type (class-level attribute)
the associated data type / domain type
"""
...
@implements(_Albers)
def Albers(**kwargs):
widget_cls = bqplot.scales.Albers
comp = reacton.core.ComponentWidget(widget=widget_cls)
return Element(comp, kwargs=kwargs)
del _Albers
def _AlbersUSA(
allow_padding: bool = True,
reverse: bool = False,
scale_factor: float = 1200,
translate: tuple = (600, 490),
on_allow_padding: typing.Callable[[bool], Any] = None,
on_reverse: typing.Callable[[bool], Any] = None,
on_scale_factor: typing.Callable[[float], Any] = None,
on_translate: typing.Callable[[tuple], Any] = None,
) -> Element[bqplot.scales.AlbersUSA]:
"""A composite projection of four Albers projections meant specifically for
the United States.
Attributes
----------
scale_factor: float (default: 1200)
Specifies the scale value for the projection
translate: tuple (default: (600, 490))
rtype: (Number, Number) (class-level attribute)
This attribute should not be modified. The range type of a geo
scale is a tuple.
dtype: type (class-level attribute)
the associated data type / domain type
"""
...
@implements(_AlbersUSA)
def AlbersUSA(**kwargs):
widget_cls = bqplot.scales.AlbersUSA
comp = reacton.core.ComponentWidget(widget=widget_cls)
return Element(comp, kwargs=kwargs)
del _AlbersUSA
def _Axis(
color: str = None,
grid_color: str = None,
grid_lines: str = "solid",
label: str = "",
label_color: str = None,
label_location: str = "middle",
label_offset: str = None,
num_ticks: int = None,
offset: dict = {},
orientation: str = "horizontal",
scale: Element[bqplot.scales.Scale] = None,
side: str = None,
tick_format: str = None,
tick_rotate: int = 0,
tick_style: dict = {},
tick_values: ndarray = None,
visible: bool = True,
on_color: typing.Callable[[str], Any] = None,
on_grid_color: typing.Callable[[str], Any] = None,
on_grid_lines: typing.Callable[[str], Any] = None,
on_label: typing.Callable[[str], Any] = None,
on_label_color: typing.Callable[[str], Any] = None,
on_label_location: typing.Callable[[str], Any] = None,
on_label_offset: typing.Callable[[str], Any] = None,
on_num_ticks: typing.Callable[[int], Any] = None,
on_offset: typing.Callable[[dict], Any] = None,
on_orientation: typing.Callable[[str], Any] = None,
on_scale: typing.Callable[[Element[bqplot.scales.Scale]], Any] = None,
on_side: typing.Callable[[str], Any] = None,
on_tick_format: typing.Callable[[str], Any] = None,
on_tick_rotate: typing.Callable[[int], Any] = None,
on_tick_style: typing.Callable[[dict], Any] = None,
on_tick_values: typing.Callable[[ndarray], Any] = None,
on_visible: typing.Callable[[bool], Any] = None,
) -> Element[bqplot.axes.Axis]:
"""A line axis.
A line axis is the visual representation of a numerical or date scale.
Attributes
----------
icon: string (class-level attribute)
The font-awesome icon name for this object.
axis_types: dict (class-level attribute)
A registry of existing axis types.
orientation: {'horizontal', 'vertical'}
The orientation of the axis, either vertical or horizontal
side: {'bottom', 'top', 'left', 'right'} or None (default: None)
The side of the axis, either bottom, top, left or right.
label: string (default: '')
The axis label
tick_format: string or None (default: '')
The tick format for the axis, for dates use d3 string formatting.
scale: Scale
The scale represented by the axis
num_ticks: int or None (default: None)
If tick_values is None, number of ticks
tick_values: numpy.ndarray or None (default: None)
Tick values for the axis
offset: dict (default: {})
Contains a scale and a value {'scale': scale or None,
'value': value of the offset}
If offset['scale'] is None, the corresponding figure scale is used
instead.
label_location: {'middle', 'start', 'end'}
The location of the label along the axis, one of 'start', 'end' or
'middle'
label_color: Color or None (default: None)
The color of the axis label
grid_lines: {'none', 'solid', 'dashed'}
The display of the grid lines
grid_color: Color or None (default: None)
The color of the grid lines
color: Color or None (default: None)
The color of the line
label_offset: string or None (default: None)
Label displacement from the axis line. Units allowed are 'em', 'px'
and 'ex'. Positive values are away from the figure and negative
values are towards the figure with respect to the axis line.
visible: bool (default: True)
A visibility toggle for the axis
tick_style: Dict (default: {})
Dictionary containing the CSS-style of the text for the ticks.
For example: font-size of the text can be changed by passing
`{'font-size': 14}`
tick_rotate: int (default: 0)
Degrees to rotate tick labels by.
"""
...
@implements(_Axis)
def Axis(**kwargs):
widget_cls = bqplot.axes.Axis
comp = reacton.core.ComponentWidget(widget=widget_cls)
return Element(comp, kwargs=kwargs)
del _Axis
def _Bars(
align: str = "center",
apply_clip: bool = True,
base: float = 0.0,
color: ndarray = None,
color_mode: str = "auto",
colors: list = ["steelblue"],
display_legend: bool = False,
enable_hover: bool = True,
fill: bool = True,
interactions: dict = {"hover": "tooltip"},
label_display: bool = False,
label_display_format: str = ".2f",
label_display_horizontal_offset: float = 0.0,
label_display_vertical_offset: float = 0.0,
label_font_style: dict = {},
labels: list = [],
opacities: list = [],
opacity_mode: str = "auto",
orientation: str = "vertical",
padding: float = 0.05,
preserve_domain: dict = {},
scales: dict = {},
scales_metadata: dict = {
"x": {"orientation": "horizontal", "dimension": "x"},
"y": {"orientation": "vertical", "dimension": "y"},
"color": {"dimension": "color"},
},
selected: ndarray = None,
selected_style: dict = {},
stroke: str = None,
stroke_width: float = 1.0,
tooltip: Element[ipywidgets.widgets.domwidget.DOMWidget] = None,
tooltip_location: str = "mouse",
tooltip_style: dict = {"opacity": 0.9},
type: str = "stacked",
unselected_style: dict = {},
visible: bool = True,
x: ndarray = np.array([]),
y: ndarray = np.array([]),
on_align: typing.Callable[[str], Any] = None,
on_apply_clip: typing.Callable[[bool], Any] = None,
on_base: typing.Callable[[float], Any] = None,
on_color: typing.Callable[[ndarray], Any] = None,
on_color_mode: typing.Callable[[str], Any] = None,
on_colors: typing.Callable[[list], Any] = None,
on_display_legend: typing.Callable[[bool], Any] = None,
on_enable_hover: typing.Callable[[bool], Any] = None,
on_fill: typing.Callable[[bool], Any] = None,
on_interactions: typing.Callable[[dict], Any] = None,
on_label_display: typing.Callable[[bool], Any] = None,
on_label_display_format: typing.Callable[[str], Any] = None,
on_label_display_horizontal_offset: typing.Callable[[float], Any] = None,
on_label_display_vertical_offset: typing.Callable[[float], Any] = None,
on_label_font_style: typing.Callable[[dict], Any] = None,
on_labels: typing.Callable[[list], Any] = None,
on_opacities: typing.Callable[[list], Any] = None,
on_opacity_mode: typing.Callable[[str], Any] = None,
on_orientation: typing.Callable[[str], Any] = None,
on_padding: typing.Callable[[float], Any] = None,
on_preserve_domain: typing.Callable[[dict], Any] = None,
on_scales: typing.Callable[[dict], Any] = None,
on_scales_metadata: typing.Callable[[dict], Any] = None,
on_selected: typing.Callable[[ndarray], Any] = None,
on_selected_style: typing.Callable[[dict], Any] = None,
on_stroke: typing.Callable[[str], Any] = None,
on_stroke_width: typing.Callable[[float], Any] = None,
on_tooltip: typing.Callable[[Element[ipywidgets.widgets.domwidget.DOMWidget]], Any] = None,
on_tooltip_location: typing.Callable[[str], Any] = None,
on_tooltip_style: typing.Callable[[dict], Any] = None,
on_type: typing.Callable[[str], Any] = None,
on_unselected_style: typing.Callable[[dict], Any] = None,
on_visible: typing.Callable[[bool], Any] = None,
on_x: typing.Callable[[ndarray], Any] = None,
on_y: typing.Callable[[ndarray], Any] = None,
) -> Element[bqplot.marks.Bars]:
"""Bar mark.
In the case of the Bars mark, scales for 'x' and 'y' MUST be provided.
The scales of other data attributes are optional. In the case where another
data attribute than 'x' or 'y' is provided but the corresponding scale is
missing, the data attribute is ignored.
Attributes
----------
icon: string (class-level attribute)
font-awesome icon for that mark
name: string (class-level attribute)
user-friendly name of the mark
color_mode: {'auto', 'group', 'element', 'no_group'}
Specify how default colors are applied to bars.
The 'group' mode means colors are assigned per group. If the list
of colors is shorter than the number of groups, colors are reused.
The 'element' mode means colors are assigned per group element. If the list
of colors is shorter than the number of bars in a group, colors are reused.
The 'no_group' mode means colors are assigned per bar, discarding the fact
that there are groups or stacks. If the list of colors is shorter than the
total number of bars, colors are reused.
opacity_mode: {'auto', 'group', 'element', 'no_group'}
Same as the `color_mode` attribute, but for the opacity.
type: {'stacked', 'grouped'}
whether 2-dimensional bar charts should appear grouped or stacked.
colors: list of colors (default: ['steelblue'])
list of colors for the bars.
orientation: {'horizontal', 'vertical'}
Specifies whether the bar chart is drawn horizontally or vertically.
If a horizontal bar chart is drawn, the x data is drawn vertically.
padding: float (default: 0.05)
Attribute to control the spacing between the bars value is specified
as a percentage of the width of the bar
fill: Bool (default: True)
Whether to fill the bars or not
stroke: Color or None (default: None)
Stroke color for the bars
stroke_width: Float (default: 0.)
Stroke width of the bars
opacities: list of floats (default: [])
Opacities for the bars. Defaults to 1 when the list is too
short, or the element of the list is set to None.
base: float (default: 0.0)
reference value from which the bars are drawn. defaults to 0.0
align: {'center', 'left', 'right'}
alignment of bars with respect to the tick value
label_display: bool (default: False)
whether or not to display bar data labels
label_display_format: string (default: .2f)
format for displaying values.
label_font_style: dict
CSS style for the text of each cell
label_display_vertical_offset: float
vertical offset value for the label display
label_display_horizontal_offset: float
horizontal offset value for the label display
Data Attributes
x: numpy.ndarray (default: [])
abscissas of the data points (1d array)
y: numpy.ndarray (default: [])
ordinates of the values for the data points
color: numpy.ndarray or None (default: None)
color of the data points (1d array). Defaults to default_color when not
provided or when a value is NaN
Notes
-----
The fields which can be passed to the default tooltip are:
All the data attributes
index: index of the bar being hovered on
sub_index: if data is two dimensional, this is the minor index
"""
...
@implements(_Bars)
def Bars(**kwargs):
widget_cls = bqplot.marks.Bars
comp = reacton.core.ComponentWidget(widget=widget_cls)
return Element(comp, kwargs=kwargs)
del _Bars
def _BaseAxis() -> Element[bqplot.axes.BaseAxis]:
""" """
...
@implements(_BaseAxis)
def BaseAxis(**kwargs):
widget_cls = bqplot.axes.BaseAxis
comp = reacton.core.ComponentWidget(widget=widget_cls)
return Element(comp, kwargs=kwargs)
del _BaseAxis
def _Bins(
align: str = "center",
apply_clip: bool = True,
base: float = 0.0,
bins: typing.Union[int, list, str] = 10,
color: ndarray = None,
color_mode: str = "auto",
colors: list = ["steelblue"],
density: bool = False,
display_legend: bool = False,
enable_hover: bool = True,
fill: bool = True,
interactions: dict = {"hover": "tooltip"},
label_display: bool = False,
label_display_format: str = ".2f",
label_display_horizontal_offset: float = 0.0,
label_display_vertical_offset: float = 0.0,
label_font_style: dict = {},
labels: list = [],
max: float = None,
min: float = None,
opacities: list = [],
opacity_mode: str = "auto",
orientation: str = "vertical",
padding: float = 0.05,
preserve_domain: dict = {},
sample: ndarray = np.array([]),
scales: dict = {},
scales_metadata: dict = {
"x": {"orientation": "horizontal", "dimension": "x"},
"y": {"orientation": "vertical", "dimension": "y"},
"color": {"dimension": "color"},
},
selected: ndarray = None,
selected_style: dict = {},
stroke: str = None,
stroke_width: float = 1.0,
tooltip: Element[ipywidgets.widgets.domwidget.DOMWidget] = None,
tooltip_location: str = "mouse",
tooltip_style: dict = {"opacity": 0.9},
type: str = "stacked",
unselected_style: dict = {},
visible: bool = True,
x: ndarray = np.array([]),
y: ndarray = np.array([]),
on_align: typing.Callable[[str], Any] = None,
on_apply_clip: typing.Callable[[bool], Any] = None,
on_base: typing.Callable[[float], Any] = None,
on_bins: typing.Callable[[typing.Union[int, list, str]], Any] = None,
on_color: typing.Callable[[ndarray], Any] = None,
on_color_mode: typing.Callable[[str], Any] = None,
on_colors: typing.Callable[[list], Any] = None,
on_density: typing.Callable[[bool], Any] = None,
on_display_legend: typing.Callable[[bool], Any] = None,
on_enable_hover: typing.Callable[[bool], Any] = None,
on_fill: typing.Callable[[bool], Any] = None,
on_interactions: typing.Callable[[dict], Any] = None,
on_label_display: typing.Callable[[bool], Any] = None,
on_label_display_format: typing.Callable[[str], Any] = None,
on_label_display_horizontal_offset: typing.Callable[[float], Any] = None,
on_label_display_vertical_offset: typing.Callable[[float], Any] = None,
on_label_font_style: typing.Callable[[dict], Any] = None,
on_labels: typing.Callable[[list], Any] = None,
on_max: typing.Callable[[float], Any] = None,
on_min: typing.Callable[[float], Any] = None,
on_opacities: typing.Callable[[list], Any] = None,
on_opacity_mode: typing.Callable[[str], Any] = None,
on_orientation: typing.Callable[[str], Any] = None,
on_padding: typing.Callable[[float], Any] = None,
on_preserve_domain: typing.Callable[[dict], Any] = None,
on_sample: typing.Callable[[ndarray], Any] = None,
on_scales: typing.Callable[[dict], Any] = None,
on_scales_metadata: typing.Callable[[dict], Any] = None,
on_selected: typing.Callable[[ndarray], Any] = None,
on_selected_style: typing.Callable[[dict], Any] = None,
on_stroke: typing.Callable[[str], Any] = None,
on_stroke_width: typing.Callable[[float], Any] = None,
on_tooltip: typing.Callable[[Element[ipywidgets.widgets.domwidget.DOMWidget]], Any] = None,
on_tooltip_location: typing.Callable[[str], Any] = None,
on_tooltip_style: typing.Callable[[dict], Any] = None,
on_type: typing.Callable[[str], Any] = None,
on_unselected_style: typing.Callable[[dict], Any] = None,
on_visible: typing.Callable[[bool], Any] = None,
on_x: typing.Callable[[ndarray], Any] = None,
on_y: typing.Callable[[ndarray], Any] = None,
) -> Element[bqplot.marks.Bins]:
"""Backend histogram mark.
A `Bars` instance that bins sample data.
It is very similar in purpose to the `Hist` mark, the difference being that
the binning is done in the backend (python), which avoids large amounts of
data being shipped back and forth to the frontend. It should therefore be
preferred for large data.
The binning method is the numpy `histogram` method.
The following documentation is in part taken from the numpy documentation.
Attributes
----------
icon: string (class-level attribute)
font-awesome icon for that mark
name: string (class-level attribute)
user-friendly name of the mark
bins: nonnegative int (default: 10)
or {'auto', 'fd', 'doane', 'scott', 'rice', 'sturges', 'sqrt'}
If `bins` is an int, it defines the number of equal-width
bins in the given range (10, by default).
If `bins` is a string (method name), `histogram` will use
the method chosen to calculate the optimal bin width and
consequently the number of bins (see `Notes` for more detail on
the estimators) from the data that falls within the requested
range.
density : bool (default: `False`)
If `False`, the height of each bin is the number of samples in it.
If `True`, the height of each bin is the value of the
probability *density* function at the bin, normalized such that
the *integral* over the range is 1. Note that the sum of the
histogram values will not be equal to 1 unless bins of unity
width are chosen; it is not a probability *mass* function.
min : float (default: None)
The lower range of the bins. If not provided, lower range
is simply `x.min()`.
max : float (default: None)
The upper range of the bins. If not provided, lower range
is simply `x.max()`.
Data Attributes
sample: numpy.ndarray (default: [])
sample of which the histogram must be computed.
Notes
-----
The fields which can be passed to the default tooltip are:
All the `Bars` data attributes (`x`, `y`, `color`)
index: index of the bin
"""
...
@implements(_Bins)
def Bins(**kwargs):
widget_cls = bqplot.marks.Bins
comp = reacton.core.ComponentWidget(widget=widget_cls)
return Element(comp, kwargs=kwargs)
del _Bins
def _Boxplot(
apply_clip: bool = True,
auto_detect_outliers: bool = True,
box_fill_color: str = "steelblue",
box_width: int = None,
display_legend: bool = False,
enable_hover: bool = True,
interactions: dict = {"hover": "tooltip"},
labels: list = [],
opacities: list = [],
outlier_fill_color: str = "gray",
preserve_domain: dict = {},
scales: dict = {},
scales_metadata: dict = {"x": {"orientation": "horizontal", "dimension": "x"}, "y": {"orientation": "vertical", "dimension": "y"}},
selected: ndarray = None,
selected_style: dict = {},
stroke: str = None,
tooltip: Element[ipywidgets.widgets.domwidget.DOMWidget] = None,
tooltip_location: str = "mouse",
tooltip_style: dict = {"opacity": 0.9},
unselected_style: dict = {},
visible: bool = True,
x: ndarray = np.array([]),
y: ndarray = np.array([[]]),
on_apply_clip: typing.Callable[[bool], Any] = None,
on_auto_detect_outliers: typing.Callable[[bool], Any] = None,
on_box_fill_color: typing.Callable[[str], Any] = None,
on_box_width: typing.Callable[[int], Any] = None,
on_display_legend: typing.Callable[[bool], Any] = None,
on_enable_hover: typing.Callable[[bool], Any] = None,
on_interactions: typing.Callable[[dict], Any] = None,
on_labels: typing.Callable[[list], Any] = None,
on_opacities: typing.Callable[[list], Any] = None,
on_outlier_fill_color: typing.Callable[[str], Any] = None,
on_preserve_domain: typing.Callable[[dict], Any] = None,
on_scales: typing.Callable[[dict], Any] = None,
on_scales_metadata: typing.Callable[[dict], Any] = None,
on_selected: typing.Callable[[ndarray], Any] = None,
on_selected_style: typing.Callable[[dict], Any] = None,
on_stroke: typing.Callable[[str], Any] = None,
on_tooltip: typing.Callable[[Element[ipywidgets.widgets.domwidget.DOMWidget]], Any] = None,
on_tooltip_location: typing.Callable[[str], Any] = None,
on_tooltip_style: typing.Callable[[dict], Any] = None,
on_unselected_style: typing.Callable[[dict], Any] = None,
on_visible: typing.Callable[[bool], Any] = None,
on_x: typing.Callable[[ndarray], Any] = None,
on_y: typing.Callable[[ndarray], Any] = None,
) -> Element[bqplot.marks.Boxplot]:
"""Boxplot marks.
Attributes
----------
stroke: Color or None
stroke color of the marker
color: Color
fill color of the box
opacities: list of floats (default: [])
Opacities for the markers of the boxplot. Defaults to 1 when the
list is too short, or the element of the list is set to None.
outlier-color: color
color for the outlier
box_width: int (default: None)
width of the box in pixels. The minimum value is 5.
If set to None, box_with is auto calculated
auto_detect_outliers: bool (default: True)
Flag to toggle outlier auto-detection
Data Attributes
x: numpy.ndarray (default: [])
abscissas of the data points (1d array)
y: numpy.ndarray (default: [[]])
Sample data points (2d array)
"""
...
@implements(_Boxplot)
def Boxplot(**kwargs):
widget_cls = bqplot.marks.Boxplot
comp = reacton.core.ComponentWidget(widget=widget_cls)
return Element(comp, kwargs=kwargs)
del _Boxplot
def _ColorAxis(
color: str = None,
grid_color: str = None,
grid_lines: str = "solid",
label: str = "",
label_color: str = None,
label_location: str = "middle",
label_offset: str = None,
num_ticks: int = None,
offset: dict = {},
orientation: str = "horizontal",
scale: Element[bqplot.scales.ColorScale] = None,
side: str = "bottom",
tick_format: str = None,
tick_rotate: int = 0,
tick_style: dict = {},
tick_values: ndarray = None,
visible: bool = True,
on_color: typing.Callable[[str], Any] = None,
on_grid_color: typing.Callable[[str], Any] = None,
on_grid_lines: typing.Callable[[str], Any] = None,
on_label: typing.Callable[[str], Any] = None,
on_label_color: typing.Callable[[str], Any] = None,
on_label_location: typing.Callable[[str], Any] = None,
on_label_offset: typing.Callable[[str], Any] = None,
on_num_ticks: typing.Callable[[int], Any] = None,
on_offset: typing.Callable[[dict], Any] = None,
on_orientation: typing.Callable[[str], Any] = None,
on_scale: typing.Callable[[Element[bqplot.scales.ColorScale]], Any] = None,
on_side: typing.Callable[[str], Any] = None,
on_tick_format: typing.Callable[[str], Any] = None,
on_tick_rotate: typing.Callable[[int], Any] = None,
on_tick_style: typing.Callable[[dict], Any] = None,
on_tick_values: typing.Callable[[ndarray], Any] = None,
on_visible: typing.Callable[[bool], Any] = None,
) -> Element[bqplot.axes.ColorAxis]:
"""A colorbar axis.
A color axis is the visual representation of a color scale.
Attributes
----------
scale: ColorScale
The scale represented by the axis
"""
...
@implements(_ColorAxis)
def ColorAxis(**kwargs):
widget_cls = bqplot.axes.ColorAxis
comp = reacton.core.ComponentWidget(widget=widget_cls)
return Element(comp, kwargs=kwargs)
del _ColorAxis
def _ColorScale(
allow_padding: bool = True,
colors: list = [],
extrapolation: str = "constant",
max: float = None,
mid: float = None,
min: float = None,
reverse: bool = False,
scale_type: str = "linear",
scheme: str = "RdYlGn",
on_allow_padding: typing.Callable[[bool], Any] = None,
on_colors: typing.Callable[[list], Any] = None,
on_extrapolation: typing.Callable[[str], Any] = None,
on_max: typing.Callable[[float], Any] = None,
on_mid: typing.Callable[[float], Any] = None,
on_min: typing.Callable[[float], Any] = None,
on_reverse: typing.Callable[[bool], Any] = None,
on_scale_type: typing.Callable[[str], Any] = None,
on_scheme: typing.Callable[[str], Any] = None,
) -> Element[bqplot.scales.ColorScale]:
"""A color scale.
A mapping from numbers to colors. The relation is affine by part.
Attributes
----------
scale_type: {'linear'}
scale type
colors: list of colors (default: [])
list of colors
min: float or None (default: None)
if not None, min is the minimal value of the domain
max: float or None (default: None)
if not None, max is the maximal value of the domain
mid: float or None (default: None)
if not None, mid is the value corresponding to the mid color.
scheme: string (default: 'RdYlGn')
Colorbrewer color scheme of the color scale.
extrapolation: {'constant', 'linear'} (default: 'constant')
How to extrapolate values outside the [min, max] domain.
rtype: string (class-level attribute)
The range type of a color scale is 'Color'. This should not be modified.
dtype: type (class-level attribute)
the associated data type / domain type
"""
...
@implements(_ColorScale)
def ColorScale(**kwargs):
widget_cls = bqplot.scales.ColorScale
comp = reacton.core.ComponentWidget(widget=widget_cls)
return Element(comp, kwargs=kwargs)
del _ColorScale
def _DOMWidget(
layout: Union[Dict[str, Any], Element[ipywidgets.widgets.widget_layout.Layout]] = {},
on_layout: typing.Callable[[Union[Dict[str, Any], Element[ipywidgets.widgets.widget_layout.Layout]]], Any] = None,
) -> Element[ipywidgets.widgets.domwidget.DOMWidget]:
"""Widget that can be inserted into the DOM"""
...
@implements(_DOMWidget)
def DOMWidget(**kwargs):
if isinstance(kwargs.get("layout"), dict):
kwargs["layout"] = Layout(**kwargs["layout"])
widget_cls = ipywidgets.widgets.domwidget.DOMWidget
comp = reacton.core.ComponentWidget(widget=widget_cls)
return Element(comp, kwargs=kwargs)
del _DOMWidget
def _DateColorScale(
allow_padding: bool = True,
colors: list = [],
extrapolation: str = "constant",
max: datetime.datetime = None,
mid: datetime.datetime = None,
min: datetime.datetime = None,
reverse: bool = False,
scale_type: str = "linear",
scheme: str = "RdYlGn",
on_allow_padding: typing.Callable[[bool], Any] = None,
on_colors: typing.Callable[[list], Any] = None,
on_extrapolation: typing.Callable[[str], Any] = None,
on_max: typing.Callable[[datetime.datetime], Any] = None,
on_mid: typing.Callable[[datetime.datetime], Any] = None,
on_min: typing.Callable[[datetime.datetime], Any] = None,
on_reverse: typing.Callable[[bool], Any] = None,
on_scale_type: typing.Callable[[str], Any] = None,
on_scheme: typing.Callable[[str], Any] = None,
) -> Element[bqplot.scales.DateColorScale]:
"""A date color scale.
A mapping from dates to a numerical domain.
Attributes
----------
min: Date or None (default: None)
if not None, min is the minimal value of the domain
max: Date or None (default: None)
if not None, max is the maximal value of the domain
mid: Date or None (default: None)
if not None, mid is the value corresponding to the mid color.
rtype: string (class-level attribute)
This attribute should not be modified by the user.
The range type of a color scale is 'Color'.
dtype: type (class-level attribute)
the associated data type / domain type
"""
...
@implements(_DateColorScale)
def DateColorScale(**kwargs):
widget_cls = bqplot.scales.DateColorScale
comp = reacton.core.ComponentWidget(widget=widget_cls)
return Element(comp, kwargs=kwargs)
del _DateColorScale
def _DateScale(
allow_padding: bool = True,
max: datetime.datetime = None,
min: datetime.datetime = None,
reverse: bool = False,
on_allow_padding: typing.Callable[[bool], Any] = None,
on_max: typing.Callable[[datetime.datetime], Any] = None,
on_min: typing.Callable[[datetime.datetime], Any] = None,
on_reverse: typing.Callable[[bool], Any] = None,
) -> Element[bqplot.scales.DateScale]:
"""A date scale, with customizable formatting.
An affine mapping from dates to a numerical range.
Attributes
----------
min: Date or None (default: None)
if not None, min is the minimal value of the domain
max: Date (default: None)
if not None, max is the maximal value of the domain
domain_class: type (default: Date)
traitlet type used to validate values in of the domain of the scale.
rtype: string (class-level attribute)
This attribute should not be modified by the user.
The range type of a linear scale is numerical.
dtype: type (class-level attribute)
the associated data type / domain type
"""
...
@implements(_DateScale)
def DateScale(**kwargs):
widget_cls = bqplot.scales.DateScale
comp = reacton.core.ComponentWidget(widget=widget_cls)
return Element(comp, kwargs=kwargs)
del _DateScale
def _EquiRectangular(
allow_padding: bool = True,
center: tuple = (0, 60),
reverse: bool = False,
scale_factor: float = 145.0,
on_allow_padding: typing.Callable[[bool], Any] = None,
on_center: typing.Callable[[tuple], Any] = None,
on_reverse: typing.Callable[[bool], Any] = None,
on_scale_factor: typing.Callable[[float], Any] = None,
) -> Element[bqplot.scales.EquiRectangular]:
"""An elementary projection that uses the identity function.
The projection is neither equal-area nor conformal.
Attributes
----------
scale_factor: float (default: 145)
Specifies the scale value for the projection
center: tuple (default: (0, 60))
Specifies the longitude and latitude where the map is centered.
"""
...
@implements(_EquiRectangular)
def EquiRectangular(**kwargs):
widget_cls = bqplot.scales.EquiRectangular
comp = reacton.core.ComponentWidget(widget=widget_cls)
return Element(comp, kwargs=kwargs)
del _EquiRectangular
def _Figure(
animation_duration: int = 0,
axes: list = [],
background_style: dict = {},
fig_margin: dict = {"top": 60, "bottom": 60, "left": 60, "right": 60},
interaction: Element[bqplot.interacts.Interaction] = None,
layout: Union[Dict[str, Any], Element[ipywidgets.widgets.widget_layout.Layout]] = {},
legend_location: str = "top-right",
legend_style: dict = {},
legend_text: dict = {},
marks: list = [],
max_aspect_ratio: float = 100,
min_aspect_ratio: float = 0.01,
padding_x: float = 0.0,
padding_y: float = 0.025,
pixel_ratio: float = None,
scale_x: Element[bqplot.scales.Scale] = None,
scale_y: Element[bqplot.scales.Scale] = None,
theme: str = "classic",
title: str = "",
title_style: dict = {},
on_animation_duration: typing.Callable[[int], Any] = None,
on_axes: typing.Callable[[list], Any] = None,
on_background_style: typing.Callable[[dict], Any] = None,
on_fig_margin: typing.Callable[[dict], Any] = None,
on_interaction: typing.Callable[[Element[bqplot.interacts.Interaction]], Any] = None,
on_layout: typing.Callable[[Union[Dict[str, Any], Element[ipywidgets.widgets.widget_layout.Layout]]], Any] = None,
on_legend_location: typing.Callable[[str], Any] = None,
on_legend_style: typing.Callable[[dict], Any] = None,
on_legend_text: typing.Callable[[dict], Any] = None,
on_marks: typing.Callable[[list], Any] = None,
on_max_aspect_ratio: typing.Callable[[float], Any] = None,
on_min_aspect_ratio: typing.Callable[[float], Any] = None,
on_padding_x: typing.Callable[[float], Any] = None,
on_padding_y: typing.Callable[[float], Any] = None,
on_pixel_ratio: typing.Callable[[float], Any] = None,
on_scale_x: typing.Callable[[Element[bqplot.scales.Scale]], Any] = None,
on_scale_y: typing.Callable[[Element[bqplot.scales.Scale]], Any] = None,
on_theme: typing.Callable[[str], Any] = None,
on_title: typing.Callable[[str], Any] = None,
on_title_style: typing.Callable[[dict], Any] = None,
) -> Element[bqplot.figure.Figure]:
"""Main canvas for drawing a chart.
The Figure object holds the list of Marks and Axes. It also holds an
optional Interaction object that is responsible for figure-level mouse
interactions, the "interaction layer".
Besides, the Figure object has two reference scales, for positioning items
in an absolute fashion in the figure canvas.
Attributes
----------