ranges (original) (raw)

The end of the range.

name = None#

Type:

An arbitrary, user-supplied name for this model.

This name can be useful when querying the document to retrieve specific Bokeh models.

plot.scatter([1,2,3], [4,5,6], name="temp") plot.select(name="temp") [GlyphRenderer(id='399d53f5-73e9-44d9-9527-544b761c7705', ...)]

Note

No uniqueness guarantees or other conditions are enforced on any names that are provided, nor is the name used directly by Bokeh for any reason.

renderers = []#

Type:

Either(List, Auto)

An explicit list of renderers to autorange against. If unset, defaults to all renderers on a plot.

start = nan#

Type:

The start of the range.

syncable = True#

Type:

Indicates whether this model should be synchronized back to a Bokeh server when updated in a web browser. Setting to False may be useful to reduce network traffic when dealing with frequently updated objects whose updated values we don’t need.

Note

Setting this property to False will prevent any on_change() callbacks on this object from triggering. However, any JS-side callbacks will still work.

tags = []#

Type:

An optional list of arbitrary, user-supplied values to attach to this model.

This data can be useful when querying the document to retrieve specific Bokeh models:

r = plot.scatter([1,2,3], [4,5,6]) r.tags = ["foo", 10] plot.select(tags=['foo', 10]) [GlyphRenderer(id='1de4c3df-a83d-480a-899b-fb263d3d5dd9', ...)]

Or simply a convenient way to attach any necessary metadata to a model that can be accessed by CustomJS callbacks, etc.

Note

No uniqueness guarantees or other conditions are enforced on any tags that are provided, nor are the tags used directly by Bokeh for any reason.

apply_theme(property_values: dict[str, Any]) → None #

Apply a set of theme values which will be used rather than defaults, but will not override application-set values.

The passed-in dictionary may be kept around as-is and shared with other instances to save memory (so neither the caller nor theHasProps instance should modify it).

Parameters:

property_values (dict) – theme values to use in place of defaults

Returns:

None

classmethod clear_extensions() → None #

Clear any currently defined custom extensions.

Serialization calls will result in any currently defined custom extensions being included with the generated Document, whether or not there are utilized. This method can be used to clear out all existing custom extension definitions.

clone(**overrides: Any) → Self#

Duplicate a HasProps object.

This creates a shallow clone of the original model, i.e. any mutable containers or child models will not be duplicated. Allows to override particular properties while cloning.

classmethod dataspecs() → dict[str, DataSpec]#

Collect the names of all DataSpec properties on this class.

This method always traverses the class hierarchy and includes properties defined on any parent classes.

Returns:

names of DataSpec properties

Return type:

classmethod descriptors() → list[PropertyDescriptor[Any]]#

List of property descriptors in the order of definition.

destroy() → None #

Clean up references to the document and property

equals(other: HasProps) → bool #

Structural equality of models.

Parameters:

other (HasProps) – the other instance to compare to

Returns:

True, if properties are structurally equal, otherwise False

js_link(attr: str, other: Model, other_attr: str, attr_selector: int | str | None = None) → None #

Link two Bokeh model properties using JavaScript.

This is a convenience method that simplifies adding aCustomJS callback to update one Bokeh model property whenever another changes value.

Parameters:

attr (str) – The name of a Bokeh property on this model
other (Model) – A Bokeh model to link to self.attr
other_attr (str) – The property on other to link together
attr_selector (int | str) – The index to link an item in a subscriptable attr

Added in version 1.1

Raises:

Examples

This code with js_link:

select.js_link('value', plot, 'sizing_mode')

is equivalent to the following:

from bokeh.models import CustomJS select.js_on_change('value', CustomJS(args=dict(other=plot), code="other.sizing_mode = this.value" ) )

Additionally, to use attr_selector to attach the left side of a range slider to a plot’s x_range:

range_slider.js_link('value', plot.x_range, 'start', attr_selector=0)

which is equivalent to:

from bokeh.models import CustomJS range_slider.js_on_change('value', CustomJS(args=dict(other=plot.x_range), code="other.start = this.value[0]" ) )

js_on_change(event: str, *callbacks: JSChangeCallback) → None #

Attach a CustomJS callback to an arbitrary BokehJS model event.

On the BokehJS side, change events for model properties have the form "change:property_name". As a convenience, if the event name passed to this method is also the name of a property on the model, then it will be prefixed with "change:" automatically:

these two are equivalent

source.js_on_change('data', callback) source.js_on_change('change:data', callback)

However, there are other kinds of events that can be useful to respond to, in addition to property change events. For example to run a callback whenever data is streamed to a ColumnDataSource, use the"stream" event on the source:

source.js_on_change('streaming', callback)

classmethod lookup(name: str, *, raises: bool = True) → PropertyDescriptor[Any] | None #

Find the PropertyDescriptor for a Bokeh property on a class, given the property name.

Parameters:

name (str) – name of the property to search for
raises (bool) – whether to raise or return None if missing

Returns:

descriptor for property named name

Return type:

on_change(attr: str, *callbacks: PropertyCallback) → None #

Add a callback on this object to trigger when attr changes.

Parameters:

attr (str) – an attribute name on this object
*callbacks (callable) – callback functions to register

Returns:

None

Examples

widget.on_change('value', callback1, callback2, ..., callback_n)

on_event(event: str | type[Event], *callbacks: Callable[[Event], None] | Callable[[], None]) → None #

Run callbacks when the specified event occurs on this Model

Not all Events are supported for all Models. See specific Events in bokeh.events for more information on which Models are able to trigger them.

classmethod parameters() → list[Parameter]#

Generate Python Parameter values suitable for functions that are derived from the glyph.

Returns:

list(Parameter)

classmethod properties(*, _with_props: bool = False) → set[str] | dict[str, Property[Any]]#

Collect the names of properties on this class.

Warning

In a future version of Bokeh, this method will return a dictionary mapping property names to property objects. To future-proof this current usage of this method, wrap the return value in list.

Returns:

property names

classmethod properties_with_refs() → dict[str, Property[Any]]#

Collect the names of all properties on this class that also have references.

This method always traverses the class hierarchy and includes properties defined on any parent classes.

Returns:

names of properties that have references

Return type:

properties_with_values(*, include_defaults: bool = True, include_undefined: bool = False) → dict[str, Any]#

Collect a dict mapping property names to their values.

This method always traverses the class hierarchy and includes properties defined on any parent classes.

Non-serializable properties are skipped and property values are in “serialized” format which may be slightly different from the values you would normally read from the properties; the intent of this method is to return the information needed to losslessly reconstitute the object instance.

Parameters:

include_defaults (bool, optional) – Whether to include properties that haven’t been explicitly set since the object was created. (default: True)

Returns:

mapping from property names to their values

Return type:

query_properties_with_values(query: Callable[[PropertyDescriptor[Any]], bool], *, include_defaults: bool = True, include_undefined: bool = False) → dict[str, Any]#

Query the properties values of HasProps instances with a predicate.

Parameters:

query (callable) – A callable that accepts property descriptors and returns True or False
include_defaults (bool, optional) – Whether to include properties that have not been explicitly set by a user (default: True)

Returns:

mapping of property names and values for matching properties

Return type:

references() → set[Model]#

Returns all Models that this object has references to.

remove_on_change(attr: str, *callbacks: Callable[[str, Any, Any], None]) → None #

Remove a callback from this object

select(selector: SelectorType) → Iterable[Model]#

Query this object and all of its references for objects that match the given selector.

Parameters:

selector (JSON-like)

Returns:

seq[Model]

select_one(selector: SelectorType) → Model | None #

Query this object and all of its references for objects that match the given selector. Raises an error if more than one object is found. Returns single matching object, or None if nothing is found :param selector: :type selector: JSON-like

Returns:

Model

set_from_json(name: str, value: Any, *, setter: Setter | None = None) → None #

Set a property value on this object from JSON.

Parameters:

name (str) – name of the attribute to set
value (JSON-value) – value to set to the attribute to
setter (ClientSession or ServerSession or None , optional) –
This is used to prevent “boomerang” updates to Bokeh apps.
In the context of a Bokeh server application, incoming updates to properties will be annotated with the session that is doing the updating. This value is propagated through any subsequent change notifications that the update triggers. The session can compare the event setter to itself, and suppress any updates that originate from itself.

Returns:

None

set_select(selector: type[Model] | SelectorType, updates: dict[str, Any]) → None #

Update objects that match a given selector with the specified attribute/value updates.

Parameters:

selector (JSON-like)
updates (dict)

Returns:

None

themed_values() → dict[str, Any] | None #

Get any theme-provided overrides.

Results are returned as a dict from property name to value, orNone if no theme overrides any values for this instance.

Returns:

dict or None

to_serializable(serializer: Serializer) → ObjectRefRep#

Converts this object to a serializable representation.

trigger(attr: str, old: Any, new: Any, hint: DocumentPatchedEvent | None = None, setter: Setter | None = None) → None #

unapply_theme() → None #

Remove any themed values and restore defaults.

Returns:

None

update(**kwargs: Any) → None #

Updates the object’s properties from the given keyword arguments.

Returns:

None

Examples

The following are equivalent:

from bokeh.models import Range1d

r = Range1d

set properties individually:

r.start = 10 r.end = 20

update properties together:

r.update(start=10, end=20)

property document_: Document | None_#

The Document this model is attached to (can be None)

class DataRange1d(*args: Any, id: ID | None = None, **kwargs: Any)[source]#

Bases: DataRange

An auto-fitting range in a continuous scalar dimension.

By default, the start and end of the range automatically assume min and max values of the data for associated renderers.

{ "bounds": null, "default_span": 2.0, "end": { "type": "number", "value": "nan" }, "flipped": false, "follow": null, "follow_interval": null, "id": "p64088", "js_event_callbacks": { "type": "map" }, "js_property_callbacks": { "type": "map" }, "max_interval": null, "min_interval": null, "name": null, "only_visible": false, "range_padding": 0.1, "range_padding_units": "percent", "renderers": [], "start": { "type": "number", "value": "nan" }, "subscribed_events": { "type": "set" }, "syncable": true, "tags": [] }

bounds = None#

Type:

Nullable(MinMaxBounds)

The bounds that the range is allowed to go to. Typically used to prevent the user from panning/zooming/etc away from the data.

By default, the bounds will be None, allowing your plot to pan/zoom as far as you want. If bounds are ‘auto’ they will be computed to be the same as the start and end of the DataRange1d.

Bounds are provided as a tuple of (min, max) so regardless of whether your range is increasing or decreasing, the first item should be the minimum value of the range and the second item should be the maximum. Setting min > max will result in a ValueError.

If you only want to constrain one end of the plot, you can set min ormax to None e.g. DataRange1d(bounds=(None, 12))

default_span = 2.0#

Type:

Either(Float, TimeDelta)

A default width for the interval, in case start is equal to end(if used with a log axis, default_span is in powers of 10).

end = nan#

Type:

The end of the range.

flipped = False#

Type:

Whether the range should be “flipped” from its normal direction when auto-ranging.

follow = None#

Type:

Nullable(Enum(StartEnd))

Configure the data to follow one or the other data extreme, with a maximum range size of follow_interval.

If set to "start" then the range will adjust so that start always corresponds to the minimum data value (or maximum, if flipped isTrue).

If set to "end" then the range will adjust so that end always corresponds to the maximum data value (or minimum, if flipped isTrue).

If set to None (default), then auto-ranging does not follow, and the range will encompass both the minimum and maximum data values.

follow cannot be used with bounds, and if set, bounds will be set toNone.

follow_interval = None#

Type:

Nullable(Either(Float, TimeDelta))

If follow is set to "start" or "end" then the range will always be constrained to that:

abs(r.start - r.end) <= follow_interval

is maintained.

max_interval = None#

Type:

The level that the range is allowed to zoom out, expressed as the maximum visible interval. Note that bounds can impose an implicit constraint on the maximum interval as well.

min_interval = None#

Type:

The level that the range is allowed to zoom in, expressed as the minimum visible interval. If set to None (default), the minimum interval is not bound.

name = None#

Type:

An arbitrary, user-supplied name for this model.

This name can be useful when querying the document to retrieve specific Bokeh models.

plot.scatter([1,2,3], [4,5,6], name="temp") plot.select(name="temp") [GlyphRenderer(id='399d53f5-73e9-44d9-9527-544b761c7705', ...)]

Note

No uniqueness guarantees or other conditions are enforced on any names that are provided, nor is the name used directly by Bokeh for any reason.

only_visible = False#

Type:

If True, renderers that that are not visible will be excluded from automatic bounds computations.

range_padding = 0.1#

Type:

Either(Float, TimeDelta)

How much padding to add around the computed data bounds.

When range_padding_units is set to "percent", the span of the range span is expanded to make the range range_padding percent larger.

When range_padding_units is set to "absolute", the start and end of the range span are extended by the amount range_padding.

range_padding_units = 'percent'#

Type:

Enum(PaddingUnits)

Whether the range_padding should be interpreted as a percentage, or as an absolute quantity. (default: "percent")

renderers = []#

Type:

Either(List, Auto)

An explicit list of renderers to autorange against. If unset, defaults to all renderers on a plot.

start = nan#

Type:

The start of the range.

syncable = True#

Type:

Note

Setting this property to False will prevent any on_change() callbacks on this object from triggering. However, any JS-side callbacks will still work.

tags = []#

Type:

An optional list of arbitrary, user-supplied values to attach to this model.

This data can be useful when querying the document to retrieve specific Bokeh models:

r = plot.scatter([1,2,3], [4,5,6]) r.tags = ["foo", 10] plot.select(tags=['foo', 10]) [GlyphRenderer(id='1de4c3df-a83d-480a-899b-fb263d3d5dd9', ...)]

Or simply a convenient way to attach any necessary metadata to a model that can be accessed by CustomJS callbacks, etc.

Note

No uniqueness guarantees or other conditions are enforced on any tags that are provided, nor are the tags used directly by Bokeh for any reason.

apply_theme(property_values: dict[str, Any]) → None #

Apply a set of theme values which will be used rather than defaults, but will not override application-set values.

The passed-in dictionary may be kept around as-is and shared with other instances to save memory (so neither the caller nor theHasProps instance should modify it).

Parameters:

property_values (dict) – theme values to use in place of defaults

Returns:

None

classmethod clear_extensions() → None #

Clear any currently defined custom extensions.

clone(**overrides: Any) → Self#

Duplicate a HasProps object.

This creates a shallow clone of the original model, i.e. any mutable containers or child models will not be duplicated. Allows to override particular properties while cloning.

classmethod dataspecs() → dict[str, DataSpec]#

Collect the names of all DataSpec properties on this class.

This method always traverses the class hierarchy and includes properties defined on any parent classes.

Returns:

names of DataSpec properties

Return type:

classmethod descriptors() → list[PropertyDescriptor[Any]]#

List of property descriptors in the order of definition.

destroy() → None #

Clean up references to the document and property

equals(other: HasProps) → bool #

Structural equality of models.

Parameters:

other (HasProps) – the other instance to compare to

Returns:

True, if properties are structurally equal, otherwise False

js_link(attr: str, other: Model, other_attr: str, attr_selector: int | str | None = None) → None #

Link two Bokeh model properties using JavaScript.

This is a convenience method that simplifies adding aCustomJS callback to update one Bokeh model property whenever another changes value.

Parameters:

attr (str) – The name of a Bokeh property on this model
other (Model) – A Bokeh model to link to self.attr
other_attr (str) – The property on other to link together
attr_selector (int | str) – The index to link an item in a subscriptable attr

Added in version 1.1

Raises:

Examples

This code with js_link:

select.js_link('value', plot, 'sizing_mode')

is equivalent to the following:

from bokeh.models import CustomJS select.js_on_change('value', CustomJS(args=dict(other=plot), code="other.sizing_mode = this.value" ) )

Additionally, to use attr_selector to attach the left side of a range slider to a plot’s x_range:

range_slider.js_link('value', plot.x_range, 'start', attr_selector=0)

which is equivalent to:

from bokeh.models import CustomJS range_slider.js_on_change('value', CustomJS(args=dict(other=plot.x_range), code="other.start = this.value[0]" ) )

js_on_change(event: str, *callbacks: JSChangeCallback) → None #

Attach a CustomJS callback to an arbitrary BokehJS model event.

these two are equivalent

source.js_on_change('data', callback) source.js_on_change('change:data', callback)

source.js_on_change('streaming', callback)

classmethod lookup(name: str, *, raises: bool = True) → PropertyDescriptor[Any] | None #

Find the PropertyDescriptor for a Bokeh property on a class, given the property name.

Parameters:

name (str) – name of the property to search for
raises (bool) – whether to raise or return None if missing

Returns:

descriptor for property named name

Return type:

on_change(attr: str, *callbacks: PropertyCallback) → None #

Add a callback on this object to trigger when attr changes.

Parameters:

attr (str) – an attribute name on this object
*callbacks (callable) – callback functions to register

Returns:

None

Examples

widget.on_change('value', callback1, callback2, ..., callback_n)

on_event(event: str | type[Event], *callbacks: Callable[[Event], None] | Callable[[], None]) → None #

Run callbacks when the specified event occurs on this Model

Not all Events are supported for all Models. See specific Events in bokeh.events for more information on which Models are able to trigger them.

classmethod parameters() → list[Parameter]#

Generate Python Parameter values suitable for functions that are derived from the glyph.

Returns:

list(Parameter)

classmethod properties(*, _with_props: bool = False) → set[str] | dict[str, Property[Any]]#

Collect the names of properties on this class.

Warning

In a future version of Bokeh, this method will return a dictionary mapping property names to property objects. To future-proof this current usage of this method, wrap the return value in list.

Returns:

property names

classmethod properties_with_refs() → dict[str, Property[Any]]#

Collect the names of all properties on this class that also have references.

This method always traverses the class hierarchy and includes properties defined on any parent classes.

Returns:

names of properties that have references

Return type:

properties_with_values(*, include_defaults: bool = True, include_undefined: bool = False) → dict[str, Any]#

Collect a dict mapping property names to their values.

This method always traverses the class hierarchy and includes properties defined on any parent classes.

Parameters:

include_defaults (bool, optional) – Whether to include properties that haven’t been explicitly set since the object was created. (default: True)

Returns:

mapping from property names to their values

Return type:

query_properties_with_values(query: Callable[[PropertyDescriptor[Any]], bool], *, include_defaults: bool = True, include_undefined: bool = False) → dict[str, Any]#

Query the properties values of HasProps instances with a predicate.

Parameters:

query (callable) – A callable that accepts property descriptors and returns True or False
include_defaults (bool, optional) – Whether to include properties that have not been explicitly set by a user (default: True)

Returns:

mapping of property names and values for matching properties

Return type:

references() → set[Model]#

Returns all Models that this object has references to.

remove_on_change(attr: str, *callbacks: Callable[[str, Any, Any], None]) → None #

Remove a callback from this object

select(selector: SelectorType) → Iterable[Model]#

Query this object and all of its references for objects that match the given selector.

Parameters:

selector (JSON-like)

Returns:

seq[Model]

select_one(selector: SelectorType) → Model | None #

Returns:

Model

set_from_json(name: str, value: Any, *, setter: Setter | None = None) → None #

Set a property value on this object from JSON.

Parameters:

name (str) – name of the attribute to set
value (JSON-value) – value to set to the attribute to
setter (ClientSession or ServerSession or None , optional) –
This is used to prevent “boomerang” updates to Bokeh apps.
In the context of a Bokeh server application, incoming updates to properties will be annotated with the session that is doing the updating. This value is propagated through any subsequent change notifications that the update triggers. The session can compare the event setter to itself, and suppress any updates that originate from itself.

Returns:

None

set_select(selector: type[Model] | SelectorType, updates: dict[str, Any]) → None #

Update objects that match a given selector with the specified attribute/value updates.

Parameters:

selector (JSON-like)
updates (dict)

Returns:

None

themed_values() → dict[str, Any] | None #

Get any theme-provided overrides.

Results are returned as a dict from property name to value, orNone if no theme overrides any values for this instance.

Returns:

dict or None

to_serializable(serializer: Serializer) → ObjectRefRep#

Converts this object to a serializable representation.

trigger(attr: str, old: Any, new: Any, hint: DocumentPatchedEvent | None = None, setter: Setter | None = None) → None #

unapply_theme() → None #

Remove any themed values and restore defaults.

Returns:

None

update(**kwargs: Any) → None #

Updates the object’s properties from the given keyword arguments.

Returns:

None

Examples

The following are equivalent:

from bokeh.models import Range1d

r = Range1d

set properties individually:

r.start = 10 r.end = 20

update properties together:

r.update(start=10, end=20)

property document_: Document | None_#

The Document this model is attached to (can be None)

class FactorRange(*args: Any, id: ID | None = None, **kwargs: Any)[source]#

Bases: Range

A Range of values for a categorical dimension.

In addition to supplying factors as a keyword argument to theFactorRange initializer, you may also instantiate with a sequence of positional arguments:

FactorRange("foo", "bar") # equivalent to FactorRange(factors=["foo", "bar"])

Users will normally supply categorical values directly:

p.scatter(x=["foo", "bar"], ...)

BokehJS will create a mapping from "foo" and "bar" to a numerical coordinate system called synthetic coordinates. In the simplest cases, factors are separated by a distance of 1.0 in synthetic coordinates, however the exact mapping from factors to synthetic coordinates is affected by the padding properties as well as whether the number of levels the factors have.

Users typically do not need to worry about the details of this mapping, however it can be useful to fine tune positions by adding offsets. When supplying factors as coordinates or values, it is possible to add an offset in the synthetic coordinate space by adding a final number value to a factor tuple. For example:

p.scatter(x=[("foo", 0.3), ...], ...)

will position the first circle at an x position that is offset by adding 0.3 to the synthetic coordinate for "foo".

{ "bounds": null, "end": 0, "factor_padding": 0.0, "factors": [], "group_padding": 1.4, "id": "p64105", "js_event_callbacks": { "type": "map" }, "js_property_callbacks": { "type": "map" }, "max_interval": null, "min_interval": null, "name": null, "range_padding": 0, "range_padding_units": "percent", "start": 0, "subgroup_padding": 0.8, "subscribed_events": { "type": "set" }, "syncable": true, "tags": [] }

bounds = None#

Type:

Nullable(MinMaxBounds)

The bounds (in synthetic coordinates) that the range is allowed to go to. Typically used to prevent the user from panning/zooming/etc away from the data.

Note

Synthetic coordinates are only computed in the browser, based on the factors and various padding properties. Some experimentation may be required to arrive at bounds suitable for specific situations.

By default, the bounds will be None, allowing your plot to pan/zoom as far as you want. If bounds are ‘auto’ they will be computed to be the same as the start and end of the FactorRange.

end = 0#

Type:

Readonly

The end of the range, in synthetic coordinates.

Note

Synthetic coordinates are only computed in the browser, based on the factors and various padding properties. The value of end will only be available in situations where bidirectional communication is available (e.g. server, notebook).

factor_padding = 0.0#

Type:

How much padding to add in between all lowest-level factors. Whenfactor_padding is non-zero, every factor in every group will have the padding value applied.

factors = []#

Type:

FactorSeq

A sequence of factors to define this categorical range.

Factors may have 1, 2, or 3 levels. For 1-level factors, each factor is simply a string. For example:

FactorRange(factors=["sales", "marketing", "engineering"])

defines a range with three simple factors that might represent different units of a business.

For 2- and 3- level factors, each factor is a tuple of strings:

FactorRange(factors=[ ["2016", "sales"], ["2016", "marketing"], ["2016", "engineering"], ["2017", "sales"], ["2017", "marketing"], ["2017", "engineering"], ])

defines a range with six 2-level factors that might represent the three business units, grouped by year.

Note that factors and sub-factors may only be strings.

group_padding = 1.4#

Type:

How much padding to add in between top-level groups of factors. This property only applies when the overall range factors have either two or three levels. For example, with:

FactorRange(factors=[["foo", "1"], ["foo", "2"], ["bar", "1"]])

The top level groups correspond to "foo" and "bar", and the group padding will be applied between the factors ["foo", "2"] and["bar", "1"]

max_interval = None#

Type:

Nullable(Float)

The level that the range is allowed to zoom out, expressed as the maximum visible interval in synthetic coordinates.. Note that boundscan impose an implicit constraint on the maximum interval as well.

The default “width” of a category is 1.0 in synthetic coordinates. However, the distance between factors is affected by the various padding properties and whether or not factors are grouped.

min_interval = None#

Type:

Nullable(Float)

The level that the range is allowed to zoom in, expressed as the minimum visible interval in synthetic coordinates. If set to None(default), the minimum interval is not bounded.

The default “width” of a category is 1.0 in synthetic coordinates. However, the distance between factors is affected by the various padding properties and whether or not factors are grouped.

name = None#

Type:

An arbitrary, user-supplied name for this model.

This name can be useful when querying the document to retrieve specific Bokeh models.

plot.scatter([1,2,3], [4,5,6], name="temp") plot.select(name="temp") [GlyphRenderer(id='399d53f5-73e9-44d9-9527-544b761c7705', ...)]

Note

No uniqueness guarantees or other conditions are enforced on any names that are provided, nor is the name used directly by Bokeh for any reason.

range_padding = 0#

Type:

How much padding to add around the outside of computed range bounds.

When range_padding_units is set to "percent", the span of the range span is expanded to make the range range_padding percent larger.

When range_padding_units is set to "absolute", the start and end of the range span are extended by the amount range_padding.

range_padding_units = 'percent'#

Type:

Enum(PaddingUnits)

Whether the range_padding should be interpreted as a percentage, or as an absolute quantity. (default: "percent")

start = 0#

Type:

Readonly

The start of the range, in synthetic coordinates.

Note

Synthetic coordinates are only computed in the browser, based on the factors and various padding properties. The value of start will only be available in situations where bidirectional communication is available (e.g. server, notebook).

subgroup_padding = 0.8#

Type:

How much padding to add in between mid-level groups of factors. This property only applies when the overall factors have three levels. For example with:

FactorRange(factors=[ ['foo', 'A', '1'], ['foo', 'A', '2'], ['foo', 'A', '3'], ['foo', 'B', '2'], ['bar', 'A', '1'], ['bar', 'A', '2'] ])

This property dictates how much padding to add between the three factors in the [‘foo’, ‘A’] group, and between the two factors in the [bar]

syncable = True#

Type:

Note

Setting this property to False will prevent any on_change() callbacks on this object from triggering. However, any JS-side callbacks will still work.

tags = []#

Type:

An optional list of arbitrary, user-supplied values to attach to this model.

This data can be useful when querying the document to retrieve specific Bokeh models:

r = plot.scatter([1,2,3], [4,5,6]) r.tags = ["foo", 10] plot.select(tags=['foo', 10]) [GlyphRenderer(id='1de4c3df-a83d-480a-899b-fb263d3d5dd9', ...)]

Or simply a convenient way to attach any necessary metadata to a model that can be accessed by CustomJS callbacks, etc.

Note

No uniqueness guarantees or other conditions are enforced on any tags that are provided, nor are the tags used directly by Bokeh for any reason.

apply_theme(property_values: dict[str, Any]) → None #

Apply a set of theme values which will be used rather than defaults, but will not override application-set values.

The passed-in dictionary may be kept around as-is and shared with other instances to save memory (so neither the caller nor theHasProps instance should modify it).

Parameters:

property_values (dict) – theme values to use in place of defaults

Returns:

None

classmethod clear_extensions() → None #

Clear any currently defined custom extensions.

clone(**overrides: Any) → Self#

Duplicate a HasProps object.

This creates a shallow clone of the original model, i.e. any mutable containers or child models will not be duplicated. Allows to override particular properties while cloning.

classmethod dataspecs() → dict[str, DataSpec]#

Collect the names of all DataSpec properties on this class.

This method always traverses the class hierarchy and includes properties defined on any parent classes.

Returns:

names of DataSpec properties

Return type:

classmethod descriptors() → list[PropertyDescriptor[Any]]#

List of property descriptors in the order of definition.

destroy() → None #

Clean up references to the document and property

equals(other: HasProps) → bool #

Structural equality of models.

Parameters:

other (HasProps) – the other instance to compare to

Returns:

True, if properties are structurally equal, otherwise False

js_link(attr: str, other: Model, other_attr: str, attr_selector: int | str | None = None) → None #

Link two Bokeh model properties using JavaScript.

This is a convenience method that simplifies adding aCustomJS callback to update one Bokeh model property whenever another changes value.

Parameters:

attr (str) – The name of a Bokeh property on this model
other (Model) – A Bokeh model to link to self.attr
other_attr (str) – The property on other to link together
attr_selector (int | str) – The index to link an item in a subscriptable attr

Added in version 1.1

Raises:

Examples

This code with js_link:

select.js_link('value', plot, 'sizing_mode')

is equivalent to the following:

from bokeh.models import CustomJS select.js_on_change('value', CustomJS(args=dict(other=plot), code="other.sizing_mode = this.value" ) )

Additionally, to use attr_selector to attach the left side of a range slider to a plot’s x_range:

range_slider.js_link('value', plot.x_range, 'start', attr_selector=0)

which is equivalent to:

from bokeh.models import CustomJS range_slider.js_on_change('value', CustomJS(args=dict(other=plot.x_range), code="other.start = this.value[0]" ) )

js_on_change(event: str, *callbacks: JSChangeCallback) → None #

Attach a CustomJS callback to an arbitrary BokehJS model event.

these two are equivalent

source.js_on_change('data', callback) source.js_on_change('change:data', callback)

source.js_on_change('streaming', callback)

classmethod lookup(name: str, *, raises: bool = True) → PropertyDescriptor[Any] | None #

Find the PropertyDescriptor for a Bokeh property on a class, given the property name.

Parameters:

name (str) – name of the property to search for
raises (bool) – whether to raise or return None if missing

Returns:

descriptor for property named name

Return type:

on_change(attr: str, *callbacks: PropertyCallback) → None #

Add a callback on this object to trigger when attr changes.

Parameters:

attr (str) – an attribute name on this object
*callbacks (callable) – callback functions to register

Returns:

None

Examples

widget.on_change('value', callback1, callback2, ..., callback_n)

on_event(event: str | type[Event], *callbacks: Callable[[Event], None] | Callable[[], None]) → None #

Run callbacks when the specified event occurs on this Model

Not all Events are supported for all Models. See specific Events in bokeh.events for more information on which Models are able to trigger them.

classmethod parameters() → list[Parameter]#

Generate Python Parameter values suitable for functions that are derived from the glyph.

Returns:

list(Parameter)

classmethod properties(*, _with_props: bool = False) → set[str] | dict[str, Property[Any]]#

Collect the names of properties on this class.

Warning

In a future version of Bokeh, this method will return a dictionary mapping property names to property objects. To future-proof this current usage of this method, wrap the return value in list.

Returns:

property names

classmethod properties_with_refs() → dict[str, Property[Any]]#

Collect the names of all properties on this class that also have references.

This method always traverses the class hierarchy and includes properties defined on any parent classes.

Returns:

names of properties that have references

Return type:

properties_with_values(*, include_defaults: bool = True, include_undefined: bool = False) → dict[str, Any]#

Collect a dict mapping property names to their values.

This method always traverses the class hierarchy and includes properties defined on any parent classes.

Parameters:

include_defaults (bool, optional) – Whether to include properties that haven’t been explicitly set since the object was created. (default: True)

Returns:

mapping from property names to their values

Return type:

query_properties_with_values(query: Callable[[PropertyDescriptor[Any]], bool], *, include_defaults: bool = True, include_undefined: bool = False) → dict[str, Any]#

Query the properties values of HasProps instances with a predicate.

Parameters:

query (callable) – A callable that accepts property descriptors and returns True or False
include_defaults (bool, optional) – Whether to include properties that have not been explicitly set by a user (default: True)

Returns:

mapping of property names and values for matching properties

Return type:

references() → set[Model]#

Returns all Models that this object has references to.

remove_on_change(attr: str, *callbacks: Callable[[str, Any, Any], None]) → None #

Remove a callback from this object

select(selector: SelectorType) → Iterable[Model]#

Query this object and all of its references for objects that match the given selector.

Parameters:

selector (JSON-like)

Returns:

seq[Model]

select_one(selector: SelectorType) → Model | None #

Returns:

Model

set_from_json(name: str, value: Any, *, setter: Setter | None = None) → None #

Set a property value on this object from JSON.

Parameters:

name (str) – name of the attribute to set
value (JSON-value) – value to set to the attribute to
setter (ClientSession or ServerSession or None , optional) –
This is used to prevent “boomerang” updates to Bokeh apps.
In the context of a Bokeh server application, incoming updates to properties will be annotated with the session that is doing the updating. This value is propagated through any subsequent change notifications that the update triggers. The session can compare the event setter to itself, and suppress any updates that originate from itself.

Returns:

None

set_select(selector: type[Model] | SelectorType, updates: dict[str, Any]) → None #

Update objects that match a given selector with the specified attribute/value updates.

Parameters:

selector (JSON-like)
updates (dict)

Returns:

None

themed_values() → dict[str, Any] | None #

Get any theme-provided overrides.

Results are returned as a dict from property name to value, orNone if no theme overrides any values for this instance.

Returns:

dict or None

to_serializable(serializer: Serializer) → ObjectRefRep#

Converts this object to a serializable representation.

trigger(attr: str, old: Any, new: Any, hint: DocumentPatchedEvent | None = None, setter: Setter | None = None) → None #

unapply_theme() → None #

Remove any themed values and restore defaults.

Returns:

None

update(**kwargs: Any) → None #

Updates the object’s properties from the given keyword arguments.

Returns:

None

Examples

The following are equivalent:

from bokeh.models import Range1d

r = Range1d

set properties individually:

r.start = 10 r.end = 20

update properties together:

r.update(start=10, end=20)

property document_: Document | None_#

The Document this model is attached to (can be None)

class Range(*args: Any, id: ID | None = None, **kwargs: Any)[source]#

Bases: Model

A base class for all range types.

Note

This is an abstract base class used to help organize the hierarchy of Bokeh model types. It is not useful to instantiate on its own.

{ "id": "p64120", "js_event_callbacks": { "type": "map" }, "js_property_callbacks": { "type": "map" }, "name": null, "subscribed_events": { "type": "set" }, "syncable": true, "tags": [] }

name = None#

Type:

An arbitrary, user-supplied name for this model.

This name can be useful when querying the document to retrieve specific Bokeh models.

plot.scatter([1,2,3], [4,5,6], name="temp") plot.select(name="temp") [GlyphRenderer(id='399d53f5-73e9-44d9-9527-544b761c7705', ...)]

Note

No uniqueness guarantees or other conditions are enforced on any names that are provided, nor is the name used directly by Bokeh for any reason.

syncable = True#

Type:

Note

Setting this property to False will prevent any on_change() callbacks on this object from triggering. However, any JS-side callbacks will still work.

tags = []#

Type:

An optional list of arbitrary, user-supplied values to attach to this model.

This data can be useful when querying the document to retrieve specific Bokeh models:

r = plot.scatter([1,2,3], [4,5,6]) r.tags = ["foo", 10] plot.select(tags=['foo', 10]) [GlyphRenderer(id='1de4c3df-a83d-480a-899b-fb263d3d5dd9', ...)]

Or simply a convenient way to attach any necessary metadata to a model that can be accessed by CustomJS callbacks, etc.

Note

No uniqueness guarantees or other conditions are enforced on any tags that are provided, nor are the tags used directly by Bokeh for any reason.

apply_theme(property_values: dict[str, Any]) → None #

Apply a set of theme values which will be used rather than defaults, but will not override application-set values.

The passed-in dictionary may be kept around as-is and shared with other instances to save memory (so neither the caller nor theHasProps instance should modify it).

Parameters:

property_values (dict) – theme values to use in place of defaults

Returns:

None

classmethod clear_extensions() → None #

Clear any currently defined custom extensions.

clone(**overrides: Any) → Self#

Duplicate a HasProps object.

This creates a shallow clone of the original model, i.e. any mutable containers or child models will not be duplicated. Allows to override particular properties while cloning.

classmethod dataspecs() → dict[str, DataSpec]#

Collect the names of all DataSpec properties on this class.

This method always traverses the class hierarchy and includes properties defined on any parent classes.

Returns:

names of DataSpec properties

Return type:

classmethod descriptors() → list[PropertyDescriptor[Any]]#

List of property descriptors in the order of definition.

destroy() → None #

Clean up references to the document and property

equals(other: HasProps) → bool #

Structural equality of models.

Parameters:

other (HasProps) – the other instance to compare to

Returns:

True, if properties are structurally equal, otherwise False

js_link(attr: str, other: Model, other_attr: str, attr_selector: int | str | None = None) → None #

Link two Bokeh model properties using JavaScript.

This is a convenience method that simplifies adding aCustomJS callback to update one Bokeh model property whenever another changes value.

Parameters:

attr (str) – The name of a Bokeh property on this model
other (Model) – A Bokeh model to link to self.attr
other_attr (str) – The property on other to link together
attr_selector (int | str) – The index to link an item in a subscriptable attr

Added in version 1.1

Raises:

Examples

This code with js_link:

select.js_link('value', plot, 'sizing_mode')

is equivalent to the following:

from bokeh.models import CustomJS select.js_on_change('value', CustomJS(args=dict(other=plot), code="other.sizing_mode = this.value" ) )

Additionally, to use attr_selector to attach the left side of a range slider to a plot’s x_range:

range_slider.js_link('value', plot.x_range, 'start', attr_selector=0)

which is equivalent to:

from bokeh.models import CustomJS range_slider.js_on_change('value', CustomJS(args=dict(other=plot.x_range), code="other.start = this.value[0]" ) )

js_on_change(event: str, *callbacks: JSChangeCallback) → None #

Attach a CustomJS callback to an arbitrary BokehJS model event.

these two are equivalent

source.js_on_change('data', callback) source.js_on_change('change:data', callback)

source.js_on_change('streaming', callback)

classmethod lookup(name: str, *, raises: bool = True) → PropertyDescriptor[Any] | None #

Find the PropertyDescriptor for a Bokeh property on a class, given the property name.

Parameters:

name (str) – name of the property to search for
raises (bool) – whether to raise or return None if missing

Returns:

descriptor for property named name

Return type:

on_change(attr: str, *callbacks: PropertyCallback) → None #

Add a callback on this object to trigger when attr changes.

Parameters:

attr (str) – an attribute name on this object
*callbacks (callable) – callback functions to register

Returns:

None

Examples

widget.on_change('value', callback1, callback2, ..., callback_n)

on_event(event: str | type[Event], *callbacks: Callable[[Event], None] | Callable[[], None]) → None #

Run callbacks when the specified event occurs on this Model

Not all Events are supported for all Models. See specific Events in bokeh.events for more information on which Models are able to trigger them.

classmethod parameters() → list[Parameter]#

Generate Python Parameter values suitable for functions that are derived from the glyph.

Returns:

list(Parameter)

classmethod properties(*, _with_props: bool = False) → set[str] | dict[str, Property[Any]]#

Collect the names of properties on this class.

Warning

In a future version of Bokeh, this method will return a dictionary mapping property names to property objects. To future-proof this current usage of this method, wrap the return value in list.

Returns:

property names

classmethod properties_with_refs() → dict[str, Property[Any]]#

Collect the names of all properties on this class that also have references.

This method always traverses the class hierarchy and includes properties defined on any parent classes.

Returns:

names of properties that have references

Return type:

properties_with_values(*, include_defaults: bool = True, include_undefined: bool = False) → dict[str, Any]#

Collect a dict mapping property names to their values.

This method always traverses the class hierarchy and includes properties defined on any parent classes.

Parameters:

include_defaults (bool, optional) – Whether to include properties that haven’t been explicitly set since the object was created. (default: True)

Returns:

mapping from property names to their values

Return type:

query_properties_with_values(query: Callable[[PropertyDescriptor[Any]], bool], *, include_defaults: bool = True, include_undefined: bool = False) → dict[str, Any]#

Query the properties values of HasProps instances with a predicate.

Parameters:

query (callable) – A callable that accepts property descriptors and returns True or False
include_defaults (bool, optional) – Whether to include properties that have not been explicitly set by a user (default: True)

Returns:

mapping of property names and values for matching properties

Return type:

references() → set[Model]#

Returns all Models that this object has references to.

remove_on_change(attr: str, *callbacks: Callable[[str, Any, Any], None]) → None #

Remove a callback from this object

select(selector: SelectorType) → Iterable[Model]#

Query this object and all of its references for objects that match the given selector.

Parameters:

selector (JSON-like)

Returns:

seq[Model]

select_one(selector: SelectorType) → Model | None #

Returns:

Model

set_from_json(name: str, value: Any, *, setter: Setter | None = None) → None #

Set a property value on this object from JSON.

Parameters:

name (str) – name of the attribute to set
value (JSON-value) – value to set to the attribute to
setter (ClientSession or ServerSession or None , optional) –
This is used to prevent “boomerang” updates to Bokeh apps.
In the context of a Bokeh server application, incoming updates to properties will be annotated with the session that is doing the updating. This value is propagated through any subsequent change notifications that the update triggers. The session can compare the event setter to itself, and suppress any updates that originate from itself.

Returns:

None

set_select(selector: type[Model] | SelectorType, updates: dict[str, Any]) → None #

Update objects that match a given selector with the specified attribute/value updates.

Parameters:

selector (JSON-like)
updates (dict)

Returns:

None

themed_values() → dict[str, Any] | None #

Get any theme-provided overrides.

Results are returned as a dict from property name to value, orNone if no theme overrides any values for this instance.

Returns:

dict or None

to_serializable(serializer: Serializer) → ObjectRefRep#

Converts this object to a serializable representation.

trigger(attr: str, old: Any, new: Any, hint: DocumentPatchedEvent | None = None, setter: Setter | None = None) → None #

unapply_theme() → None #

Remove any themed values and restore defaults.

Returns:

None

update(**kwargs: Any) → None #

Updates the object’s properties from the given keyword arguments.

Returns:

None

Examples

The following are equivalent:

from bokeh.models import Range1d

r = Range1d

set properties individually:

r.start = 10 r.end = 20

update properties together:

r.update(start=10, end=20)

property document_: Document | None_#

The Document this model is attached to (can be None)

class Range1d(*args: Any, id: ID | None = None, **kwargs: Any)[source]#

Bases: NumericalRange

A fixed, closed range [start, end] in a continuous scalar dimension.

In addition to supplying start and end keyword arguments to the Range1d initializer, you can also instantiate with the convenience syntax:

Range(0, 10) # equivalent to Range(start=0, end=10)

{ "bounds": null, "end": 1, "id": "p64124", "js_event_callbacks": { "type": "map" }, "js_property_callbacks": { "type": "map" }, "max_interval": null, "min_interval": null, "name": null, "reset_end": null, "reset_start": null, "start": 0, "subscribed_events": { "type": "set" }, "syncable": true, "tags": [] }

bounds = None#

Type:

Nullable(MinMaxBounds)

The bounds that the range is allowed to go to. Typically used to prevent the user from panning/zooming/etc away from the data.

If set to 'auto', the bounds will be computed to the start and end of the Range.

By default, bounds are None and your plot to pan/zoom as far as you want. If you only want to constrain one end of the plot, you can set min or max to None.

Examples:

Range1d(0, 1, bounds='auto') # Auto-bounded to 0 and 1 (Default behavior) Range1d(start=0, end=1, bounds=(0, None)) # Maximum is unbounded, minimum bounded to 0

end = 1#

Type:

The end of the range.

max_interval = None#

Type:

The level that the range is allowed to zoom out, expressed as the maximum visible interval. Can be a TimeDelta. Note that bounds can impose an implicit constraint on the maximum interval as well.

min_interval = None#

Type:

The level that the range is allowed to zoom in, expressed as the minimum visible interval. If set to None (default), the minimum interval is not bound. Can be a TimeDelta.

name = None#

Type:

An arbitrary, user-supplied name for this model.

This name can be useful when querying the document to retrieve specific Bokeh models.

plot.scatter([1,2,3], [4,5,6], name="temp") plot.select(name="temp") [GlyphRenderer(id='399d53f5-73e9-44d9-9527-544b761c7705', ...)]

Note

No uniqueness guarantees or other conditions are enforced on any names that are provided, nor is the name used directly by Bokeh for any reason.

reset_end = None#

Type:

Either(Null, Float, Datetime, TimeDelta)

The end of the range to apply when resetting. If set to None defaults to the end value during initialization.

reset_start = None#

Type:

Either(Null, Float, Datetime, TimeDelta)

The start of the range to apply after reset. If set to None defaults to the start value during initialization.

start = 0#

Type:

The start of the range.

syncable = True#

Type:

Note

Setting this property to False will prevent any on_change() callbacks on this object from triggering. However, any JS-side callbacks will still work.

tags = []#

Type:

An optional list of arbitrary, user-supplied values to attach to this model.

This data can be useful when querying the document to retrieve specific Bokeh models:

r = plot.scatter([1,2,3], [4,5,6]) r.tags = ["foo", 10] plot.select(tags=['foo', 10]) [GlyphRenderer(id='1de4c3df-a83d-480a-899b-fb263d3d5dd9', ...)]

Or simply a convenient way to attach any necessary metadata to a model that can be accessed by CustomJS callbacks, etc.

Note

No uniqueness guarantees or other conditions are enforced on any tags that are provided, nor are the tags used directly by Bokeh for any reason.

apply_theme(property_values: dict[str, Any]) → None #

Apply a set of theme values which will be used rather than defaults, but will not override application-set values.

The passed-in dictionary may be kept around as-is and shared with other instances to save memory (so neither the caller nor theHasProps instance should modify it).

Parameters:

property_values (dict) – theme values to use in place of defaults

Returns:

None

classmethod clear_extensions() → None #

Clear any currently defined custom extensions.

clone(**overrides: Any) → Self#

Duplicate a HasProps object.

This creates a shallow clone of the original model, i.e. any mutable containers or child models will not be duplicated. Allows to override particular properties while cloning.

classmethod dataspecs() → dict[str, DataSpec]#

Collect the names of all DataSpec properties on this class.

This method always traverses the class hierarchy and includes properties defined on any parent classes.

Returns:

names of DataSpec properties

Return type:

classmethod descriptors() → list[PropertyDescriptor[Any]]#

List of property descriptors in the order of definition.

destroy() → None #

Clean up references to the document and property

equals(other: HasProps) → bool #

Structural equality of models.

Parameters:

other (HasProps) – the other instance to compare to

Returns:

True, if properties are structurally equal, otherwise False

js_link(attr: str, other: Model, other_attr: str, attr_selector: int | str | None = None) → None #

Link two Bokeh model properties using JavaScript.

This is a convenience method that simplifies adding aCustomJS callback to update one Bokeh model property whenever another changes value.

Parameters:

attr (str) – The name of a Bokeh property on this model
other (Model) – A Bokeh model to link to self.attr
other_attr (str) – The property on other to link together
attr_selector (int | str) – The index to link an item in a subscriptable attr

Added in version 1.1

Raises:

Examples

This code with js_link:

select.js_link('value', plot, 'sizing_mode')

is equivalent to the following:

from bokeh.models import CustomJS select.js_on_change('value', CustomJS(args=dict(other=plot), code="other.sizing_mode = this.value" ) )

Additionally, to use attr_selector to attach the left side of a range slider to a plot’s x_range:

range_slider.js_link('value', plot.x_range, 'start', attr_selector=0)

which is equivalent to:

from bokeh.models import CustomJS range_slider.js_on_change('value', CustomJS(args=dict(other=plot.x_range), code="other.start = this.value[0]" ) )

js_on_change(event: str, *callbacks: JSChangeCallback) → None #

Attach a CustomJS callback to an arbitrary BokehJS model event.

these two are equivalent

source.js_on_change('data', callback) source.js_on_change('change:data', callback)

source.js_on_change('streaming', callback)

classmethod lookup(name: str, *, raises: bool = True) → PropertyDescriptor[Any] | None #

Find the PropertyDescriptor for a Bokeh property on a class, given the property name.

Parameters:

name (str) – name of the property to search for
raises (bool) – whether to raise or return None if missing

Returns:

descriptor for property named name

Return type:

on_change(attr: str, *callbacks: PropertyCallback) → None #

Add a callback on this object to trigger when attr changes.

Parameters:

attr (str) – an attribute name on this object
*callbacks (callable) – callback functions to register

Returns:

None

Examples

widget.on_change('value', callback1, callback2, ..., callback_n)

on_event(event: str | type[Event], *callbacks: Callable[[Event], None] | Callable[[], None]) → None #

Run callbacks when the specified event occurs on this Model

Not all Events are supported for all Models. See specific Events in bokeh.events for more information on which Models are able to trigger them.

classmethod parameters() → list[Parameter]#

Generate Python Parameter values suitable for functions that are derived from the glyph.

Returns:

list(Parameter)

classmethod properties(*, _with_props: bool = False) → set[str] | dict[str, Property[Any]]#

Collect the names of properties on this class.

Warning

In a future version of Bokeh, this method will return a dictionary mapping property names to property objects. To future-proof this current usage of this method, wrap the return value in list.

Returns:

property names

classmethod properties_with_refs() → dict[str, Property[Any]]#

Collect the names of all properties on this class that also have references.

This method always traverses the class hierarchy and includes properties defined on any parent classes.

Returns:

names of properties that have references

Return type:

properties_with_values(*, include_defaults: bool = True, include_undefined: bool = False) → dict[str, Any]#

Collect a dict mapping property names to their values.

This method always traverses the class hierarchy and includes properties defined on any parent classes.

Parameters:

include_defaults (bool, optional) – Whether to include properties that haven’t been explicitly set since the object was created. (default: True)

Returns:

mapping from property names to their values

Return type:

query_properties_with_values(query: Callable[[PropertyDescriptor[Any]], bool], *, include_defaults: bool = True, include_undefined: bool = False) → dict[str, Any]#

Query the properties values of HasProps instances with a predicate.

Parameters:

query (callable) – A callable that accepts property descriptors and returns True or False
include_defaults (bool, optional) – Whether to include properties that have not been explicitly set by a user (default: True)

Returns:

mapping of property names and values for matching properties

Return type: