from typing import Optional, List
from highcharts_core.options.series.bar import BarSeries
from highcharts_core.options.series.data.boxplot import BoxPlotData, BoxPlotDataCollection
from highcharts_core.options.series.data.range import RangeData, RangeDataCollection
from highcharts_core.options.plot_options.boxplot import BoxPlotOptions
from highcharts_core.utility_functions import mro__to_untrimmed_dict, is_ndarray
[docs]class BoxPlotSeries(BarSeries, BoxPlotOptions):
"""Options to configure a Box Plot series.
A box plot is a convenient way of depicting groups of data through their
five-number summaries:
* the smallest observation (sample minimum),
* lower quartile (Q1),
* median (Q2),
* upper quartile (Q3), and
* largest observation (sample maximum).
.. figure:: ../../../_static/boxplot-example.png
:alt: Box Plot Example Chart
:align: center
"""
def __init__(self, **kwargs):
super().__init__(**kwargs)
@classmethod
def _data_collection_class(cls):
"""Returns the class object used for the data collection.
:rtype: :class:`DataPointCollection <highcharts_core.options.series.data.collections.DataPointCollection>`
descendent
"""
return BoxPlotDataCollection
@classmethod
def _data_point_class(cls):
"""Returns the class object used for individual data points.
:rtype: :class:`DataBase <highcharts_core.options.series.data.base.DataBase>`
descendent
"""
return BoxPlotData
@property
def data(self) -> Optional[List[BoxPlotData] | BoxPlotDataCollection]:
"""Collection of data that represents the series. Defaults to
:obj:`None <python:None>`.
While the series type returns a collection of :class:`BoxPlotData` instances,
it accepts as input different types of data:
.. tabs::
.. tab:: 5D Collection
.. code-block::
series = BoxPlotSeries()
series.data = [
[3, 0, 10, 3, 5],
[7, 8, 7, 2, 9],
[6, 9, 5, 1, 3]
]
A collection of five-dimensional numerical values. Each member of the
collection will be interpreted as :meth:`low <BoxPlotData.low>`,
:meth:`q1 <BoxPlotData.q1>`, :meth:`median <BoxPlotData.median>`,
:meth:`q3 <BoxPlotData.q3>`, and :meth:`high <BoxPlotData.high>`,
respectively.
The :meth:`x <BoxPlotData.x>` value will be automatically inferred. If
:meth:`BoxPlotSeries.point_start` is :obj:`None <python:None>`, ``x`` values
will begin at ``0``. Otherwise, they will start at ``point_start``.
If :meth:`BoxPlotSeries.point_interval` is :obj:`None <python:None>`, ``x``
values will be incremented by ``1``. Otherwise, they will be incremented
by the value of ``point_interval``.
.. tab:: 2D Collection
.. code-block::
series = BoxPlotSeries()
# Categorical X-axis
series.data = [
['Category A', 3, 0, 10, 3, 5],
['Category B', 7, 8, 7, 2, 9],
['Category C', 6, 9, 5, 1, 3]
]
# Numerical X-axis
series.data = [
[0, 3, 0, 10, 3, 5],
[1, 7, 8, 7, 2, 9],
[2, 6, 9, 5, 1, 3]
]
A collection of six-dimensional numerical values. Each member of the
collection will be interpreted as :meth:`x <BoxPlotData.x>`,
:meth:`low <BoxPlotData.low>`, :meth:`q1 <BoxPlotData.q1>`,
:meth:`median <BoxPlotData.median>`, :meth:`q3 <BoxPlotData.q3>`, and
:meth:`high <BoxPlotData.high>`, respectively.
The ``x`` value can be a
:class:`str <python:str>`, :class:`date <python:datetime.date>`,
:class:`datetime <python:datetime.datetime>`, or numeric value.
.. note::
If the ``x`` value is a :class:`str <python:str>`, it will be interpreted
as the name of the data point.
.. tab:: Object Collection
A one-dimensional collection of :class:`BoxPlotData` objects.
:rtype: :class:`list <python:list>` of :class:`BoxPlotData` or
:class:`BoxPlotDataCollection` or
:obj:`None <python:None>`
"""
return self._data
@data.setter
def data(self, value):
if not is_ndarray(value) and not value:
self._data = None
else:
self._data = BoxPlotData.from_array(value)
@classmethod
def _get_kwargs_from_dict(cls, as_dict):
kwargs = {
'accessibility': as_dict.get('accessibility', None),
'allow_point_select': as_dict.get('allowPointSelect', None),
'animation': as_dict.get('animation', None),
'class_name': as_dict.get('className', None),
'clip': as_dict.get('clip', None),
'color': as_dict.get('color', None),
'cursor': as_dict.get('cursor', None),
'custom': as_dict.get('custom', None),
'dash_style': as_dict.get('dashStyle', None),
'data_labels': as_dict.get('dataLabels', None),
'description': as_dict.get('description', None),
'enable_mouse_tracking': as_dict.get('enableMouseTracking', None),
'events': as_dict.get('events', None),
'include_in_data_export': as_dict.get('includeInDataExport', None),
'keys': as_dict.get('keys', None),
'label': as_dict.get('label', None),
'legend_symbol': as_dict.get('legendSymbol', None),
'linked_to': as_dict.get('linkedTo', None),
'marker': as_dict.get('marker', None),
'on_point': as_dict.get('onPoint', None),
'opacity': as_dict.get('opacity', None),
'point': as_dict.get('point', None),
'point_description_formatter': as_dict.get('pointDescriptionFormatter', None),
'selected': as_dict.get('selected', None),
'show_checkbox': as_dict.get('showCheckbox', None),
'show_in_legend': as_dict.get('showInLegend', None),
'skip_keyboard_navigation': as_dict.get('skipKeyboardNavigation', None),
'sonification': as_dict.get('sonification', None),
'states': as_dict.get('states', None),
'sticky_tracking': as_dict.get('stickyTracking', None),
'threshold': as_dict.get('threshold', None),
'tooltip': as_dict.get('tooltip', None),
'turbo_threshold': as_dict.get('turboThreshold', None),
'visible': as_dict.get('visible', None),
'animation_limit': as_dict.get('animationLimit', None),
'boost_blending': as_dict.get('boostBlending', None),
'boost_threshold': as_dict.get('boostThreshold', None),
'color_axis': as_dict.get('colorAxis', None),
'color_index': as_dict.get('colorIndex', None),
'color_key': as_dict.get('colorKey', None),
'connect_ends': as_dict.get('connectEnds', None),
'connect_nulls': as_dict.get('connectNulls', None),
'crisp': as_dict.get('crisp', None),
'crop_threshold': as_dict.get('cropThreshold', None),
'data_sorting': as_dict.get('dataSorting', None),
'drag_drop': as_dict.get('dragDrop', None),
'fill_color': as_dict.get('fillColor', None),
'fill_opacity': as_dict.get('fillOpacity', None),
'find_nearest_point_by': as_dict.get('findNearestPointBy', None),
'get_extremes_from_all': as_dict.get('getExtremesFromAll', None),
'inactive_other_points': as_dict.get('inactiveOtherPoints', None),
'linecap': as_dict.get('linecap', None),
'line_color': as_dict.get('lineColor', None),
'line_width': as_dict.get('lineWidth', None),
'negative_color': as_dict.get('negativeColor', None),
'negative_fill_color': as_dict.get('negativeFillColor', None),
'point_interval': as_dict.get('pointInterval', None),
'point_interval_unit': as_dict.get('pointIntervalUnit', None),
'point_placement': as_dict.get('pointPlacement', None),
'point_start': as_dict.get('pointStart', None),
'relative_x_value': as_dict.get('relativeXValue', None),
'shadow': as_dict.get('shadow', None),
'soft_threshold': as_dict.get('softThreshold', None),
'stacking': as_dict.get('stacking', None),
'step': as_dict.get('step', None),
'track_by_area': as_dict.get('trackByArea', None),
'zone_axis': as_dict.get('zoneAxis', None),
'zones': as_dict.get('zones', None),
'border_color': as_dict.get('borderColor', None),
'border_radius': as_dict.get('borderRadius', None),
'border_width': as_dict.get('borderWidth', None),
'center_in_category': as_dict.get('centerInCategory', None),
'color_by_point': as_dict.get('colorByPoint', None),
'colors': as_dict.get('colors', None),
'grouping': as_dict.get('grouping', None),
'group_padding': as_dict.get('groupPadding', None),
'max_point_width': as_dict.get('maxPointWidth', None),
'min_point_length': as_dict.get('minPointLength', None),
'point_padding': as_dict.get('pointPadding', None),
'point_range': as_dict.get('pointRange', None),
'point_width': as_dict.get('pointWidth', None),
'depth': as_dict.get('depth', None),
'edge_color': as_dict.get('edgeColor', None),
'edge_width': as_dict.get('edgeWidth', None),
'group_z_padding': as_dict.get('groupZPadding', None),
'box_dash_style': as_dict.get('boxDashStyle', None),
'median_color': as_dict.get('medianColor', None),
'median_dash_style': as_dict.get('medianDashStyle', None),
'median_width': as_dict.get('medianWidth', None),
'stem_dash_style': as_dict.get('stemDashStyle', None),
'stem_width': as_dict.get('stemWidth', None),
'whisker_color': as_dict.get('whiskerColor', None),
'whisker_dash_style': as_dict.get('whiskerDashStyle', None),
'whisker_length': as_dict.get('whiskerLength', None),
'whisker_width': as_dict.get('whiskerWidth', None),
'data': as_dict.get('data', None),
'id': as_dict.get('id', None),
'index': as_dict.get('index', None),
'legend_index': as_dict.get('legendIndex', None),
'name': as_dict.get('name', None),
'stack': as_dict.get('stack', None),
'x_axis': as_dict.get('xAxis', None),
'y_axis': as_dict.get('yAxis', None),
'z_index': as_dict.get('zIndex', None),
}
return kwargs
def _to_untrimmed_dict(self, in_cls = None) -> dict:
untrimmed = mro__to_untrimmed_dict(self, in_cls = in_cls)
return untrimmed
[docs]class ErrorBarSeries(BoxPlotSeries):
"""Options to configure an Error Bar series.
Error bars are a graphical representation of the variability of data and are used
on graphs to indicate the error, or uncertainty in a reported measurement.
.. figure:: ../../../_static/errorbar-example.png
:alt: Error Bar Example Chart
:align: center
"""
@classmethod
def _data_collection_class(cls):
"""Returns the class object used for the data collection.
:rtype: :class:`DataPointCollection <highcharts_core.options.series.data.collections.DataPointCollection>`
descendent
"""
return RangeDataCollection
@classmethod
def _data_point_class(cls):
"""Returns the class object used for individual data points.
:rtype: :class:`DataBase <highcharts_core.options.series.data.base.DataBase>`
descendent
"""
return RangeData
@property
def data(self) -> Optional[List[RangeData] | RangeDataCollection]:
"""Collection of data that represents the series. Defaults to
:obj:`None <python:None>`.
While the series type returns a collection of :class:`RangeData` instances,
it accepts as input two different types of data:
.. tabs::
.. tab:: 3D Collection
.. code-block::
series = ErrorBarSeries()
# Category X-axis
series.data = [
['Category A', 8, 3],
['Category B', 1, 1],
['Category C', 6, 8]
]
# Numerical X-axis
series.data = [
[0, 8, 3],
[1, 1, 1],
[2, 6, 8]
]
A three-dimensional collection of numerical values. Each member of the
collection will be interpreted as an ``x`` value, a ``low`` value, and a
``high`` value.
The ``x`` value can be a :class:`str <python:str>`,
:class:`date <python:datetime.date>`,
:class:`datetime <python:datetime.datetime>`, or numeric value.
.. note::
If the ``x`` value is a :class:`str <python:str>`, it will be interpreted
as the name of the data point.
.. tab:: 2D Collection
.. code-block::
series = ErrorBarSeries()
series.data = [
[8, 3],
[1, 1],
[6, 8]
]
A two-dimensional collection of values. Each member of the collection will be
interpreted as an ``low`` and ``high`` value. The ``x`` values are
automatically inferred:
If :meth:`ErrorBarSeries.point_start` is :obj:`None <python:None>`, ``x``
values will begin at ``0``. Otherwise, they will start at ``point_start``.
If :meth:`ErrorBarSeries.point_interval` is :obj:`None <python:None>`, ``x``
values will be incremented by ``1``. Otherwise, they will be incremented
by the value of ``point_interval``.
.. tab:: Object Collection
A one-dimensional collection of :class:`RangeData` objects.
:rtype: :class:`list <python:list>` of :class:`RangeData` or
:class:`RangeDataCollection` or
:obj:`None <python:None>`
"""
return self._data
@data.setter
def data(self, value):
if not is_ndarray(value) and not value:
self._data = None
else:
self._data = RangeData.from_array(value)