Below is a notional diagram that shows many of the most common kinds of models that comprise the Bokeh object system. To create Bokeh plots, these objects are created and assembled, and then this object graph is serialized to JSON. This JSON representation is consumed by the BokehJS client library, which uses it to render the plot.
Where space permits, the attributes of the model are show inline. Not all objects are shown below; see the Reference Guide for full details.
Models and Properties¶
The primary components of the low-level API are models, which are objects that have attributes that can be automatically serialized in a way that lets them be reconstituted as BokehJS models. Technically, models are classes that inherit from HasProps at some point:
from bokeh.core.properties import HasProps, Int class Whatever(HasProps): """ `Whatever` model. """
class Another(Whatever, LineProps): """ `Another` model. """
Models contain properties, which are class attributes of type
class IntProps(HasFields): prop1 = Int prop2 = Int() prop3 = Int(10)
The IntProps model represents objects that have three integer values,
prop3, that can be automatically serialized
from python, and unserialized by BokehJS.
prop1 isn’t an instance of
HasFields uses a
metaclass that automatically instantiates Property classes when necessary,
prop2 are equivalent (though independent) properties.
This is useful for readability; if you don’t need to pass any arguments to
property’s constructor then prefer the former over the later.
There is wide variety of property types, ranging from primitive types such as:
As well as container-like properties, that take other Properties as parameters:
List— for a list of one type of objects:
Dict— for a mapping between two type:
and finally some specialized types like
Instance— to hold a reference to another model:
Enum— to represent enumerated values:
Enum("foo", "bar", "baz")
Either— to create a union type:
Range— to restrict values to a given range:
The primary benefit of these property types is that validation can be performed and meaningful error reporting can occur when an attempt is made to assign an invalid type or value.
There is an
Any that is the super-type of all other
types, and will accept any type of value. Since this circumvents all type validation,
make sure to use it sparingly, if at all.
See bokeh.core.properties for full details.
An example of a more complex, realistic model might look like this:
class Sample(HasProps, FillProps): """ `Sample` model. """ prop1 = Int(127) prop2 = Either(Int, List(Int), Dict(String, List(Int))) prop3 = Enum("x", "y", "z") prop4 = Range(Float, 0.0, 1.0) prop5 = List(Instance(Range1d))
There is a special property-like type named
that make it simpler to mix in in properties from a mixin using a prefix, e.g.:
class Includes(HasProps): """ `Includes` model. """ some_props = Include(FillProps)
In this case there is a placeholder property some_props, that will be removed
and automatically replaced with all the properties from
each with some_ appended as a prefix.
The prefix can be a valid identifier. If it ends with
will be removed. Adding
_props isn’t necessary, but can be useful if a
some already exists in parallel (see
Plot.title as an example).
Include is equivalent to writing:
class ExplicitIncludes(HasProps): """ `ExplicitIncludes` model. """ some_fill_color = ColorSpec(default="gray") some_fill_alpha = DataSpec(default=1.0)
Note that you could inherit from
FillProps in this
case, as well:
class IncludesExtends(HasProps, FillProps): """ `IncludesExtends` model. """ some = String some_props = Include(FillProps)
but note that this is equivalent to:
class ExplicitIncludesExtends(HasProps): """ `ExplicitIncludesExtends` model. """ fill_color = ColorSpec(default="gray") fill_alpha = DataSpec(default=1.0) some = String some_fill_color = ColorSpec(default="gray") some_fill_alpha = DataSpec(default=1.0)