AjaxDataSource
Bases: bokeh.models.sources.WebSource
bokeh.models.sources.WebSource
A data source that can populate columns by making Ajax calls to REST endpoints.
The AjaxDataSource can be especially useful if you want to make a standalone document (i.e. not backed by the Bokeh server) that can still dynamically update using an existing REST API.
The response from the REST API should match the .data property of a standard ColumnDataSource, i.e. a JSON dict that maps names to arrays of values:
.data
ColumnDataSource
{ 'x' : [1, 2, 3, ...], 'y' : [9, 3, 2, ...] }
Alternatively, if the REST API returns a different format, a CustomJS callback can be provided to convert the REST response into Bokeh format, via the adapter property of this data source.
CustomJS
adapter
Initial data can be set by specifying the data property directly. This is necessary when used in conjunction with a FactorRange, even if the columns in data` are empty.
data
FactorRange
A full example can be seen at examples/howto/ajax_source.py
property type: Instance ( CustomJS )
Instance
A JavaScript callback to adapt raw JSON responses to Bokeh ColumnDataSource format.
If provided, this callback is executes immediately after the JSON data is received, but before appending or replacing data in the data source. The CustomJS callback will receive the AjaxDataSource as cb_obj and will receive the raw JSON response as cb_data.response. The callback code should return a data object suitable for a Bokeh ColumnDataSource (i.e. a mapping of string column names to arrays of data).
cb_obj
cb_data.response
content_type
property type: String
String
Set the “contentType” parameter for the Ajax request.
property type: ColumnData ( String , Seq ( Any ) )
ColumnData
Seq
Any
Mapping of column names to sequences of data. The columns can be, e.g, Python lists or tuples, NumPy arrays, etc.
The .data attribute can also be set from Pandas DataFrames or GroupBy objects. In these cases, the behaviour is identical to passing the objects to the ColumnDataSource initializer.
data_url
A URL to to fetch data from.
http_headers
property type: Dict ( String , String )
Dict
Specify HTTP headers to set for the Ajax request.
Example:
ajax_source.headers = { 'x-my-custom-header': 'some value' }
if_modified
property type: Bool
Bool
Whether to include an If-Modified-Since header in Ajax requests to the server. If this header is supported by the server, then only new data since the last request will be returned.
If-Modified-Since
js_event_callbacks
property type: Dict ( String , List ( Instance ( CustomJS ) ) )
List
A mapping of event names to lists of CustomJS callbacks.
Typically, rather then modifying this property directly, callbacks should be added using the Model.js_on_event method:
Model.js_on_event
callback = CustomJS(code="console.log('tap event occurred')") plot.js_on_event('tap', callback)
js_property_callbacks
A mapping of attribute names to lists of CustomJS callbacks, to be set up on BokehJS side when the document is created.
Typically, rather then modifying this property directly, callbacks should be added using the Model.js_on_change method:
Model.js_on_change
callback = CustomJS(code="console.log('stuff')") plot.x_range.js_on_change('start', callback)
max_size
property type: Int
Int
Maximum size of the data columns. If a new fetch would result in columns larger than max_size, then earlier data is dropped to make room.
method
property type: Enum ( Enumeration(POST, GET) )
Enum
Specify the HTTP method to use for the Ajax request (GET or POST)
mode
property type: Enum ( Enumeration(replace, append) )
Whether to append new data to existing data (up to max_size), or to replace existing data entirely.
name
An arbitrary, user-supplied name for this model.
This name can be useful when querying the document to retrieve specific Bokeh models.
>>> plot.circle([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.
polling_interval
A polling interval (in milliseconds) for updating data source.
selected
property type: Instance ( Selection )
Selection
An instance of a Selection that indicates selected indices on this DataSource. This is a read-only property. You may only change the attributes of this object to change the selection (e.g., selected.indices).
DataSource
selected.indices
selection_policy
property type: Instance ( SelectionPolicy )
SelectionPolicy
An instance of a SelectionPolicy that determines how selections are set.
subscribed_events
property type: List ( String )
List of events that are subscribed to by Python callbacks. This is the set of events that will be communicated from BokehJS back to Python for this model.
tags
property type: List ( Any )
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.circle([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.
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.
__init__
If called with a single argument that is a dict or pandas.DataFrame, treat that implicitly as the “data” attribute.
pandas.DataFrame
add
Appends a new column of data to the data source.
data (seq) – new data to add
name (str, optional) – column name to use. If not supplied, generate a name of the form “Series ####”
the column name used
str
apply_theme
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 the HasProps instance should modify it).
HasProps
property_values (dict) – theme values to use in place of defaults
None
dataspecs
Collect the names of all DataSpec properties on this class.
DataSpec
This method always traverses the class hierarchy and includes properties defined on any parent classes.
names of DataSpec properties
set[str]
dataspecs_with_props
Collect a dict mapping the names of all DataSpec properties on this class to the associated properties.
mapping of names and DataSpec properties
dict[str, DataSpec]
equals
Structural equality of models.
other (HasProps) – the other instance to compare to
True, if properties are structurally equal, otherwise False
from_df
Create a dict of columns from a Pandas DataFrame, suitable for creating a ColumnDataSource.
dict
DataFrame
data (DataFrame) – data to convert
dict[str, np.array]
from_groupby
Create a dict of columns from a Pandas GroupBy, suitable for creating a ColumnDataSource.
GroupBy
The data generated is the result of running describe on the group.
describe
data (Groupby) – data to convert
js_link
Link two Bokeh model properties using JavaScript.
This is a convenience method that simplifies adding a CustomJS callback to update one Bokeh model property whenever another changes value.
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
other
attr_selector (Union[int, str]) – The index to link an item in a subscriptable attr
attr
Added in version 1.1
ValueError –
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
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:
"change:property_name"
"change:"
# 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:
"stream"
source.js_on_change('streaming', callback)
layout
lookup
Find the PropertyDescriptor for a Bokeh property on a class, given the property name.
PropertyDescriptor
name (str) – name of the property to search for
descriptor for property named name
on_change
Add a callback on this object to trigger when attr changes.
attr (str) – an attribute name on this object
*callbacks (callable) – callback functions to register
widget.on_change('value', callback1, callback2, ..., callback_n)
patch
Efficiently update data source columns at specific locations
If it is only necessary to update a small subset of data in a ColumnDataSource, this method can be used to efficiently update only the subset, instead of requiring the entire data set to be sent.
This method should be passed a dictionary that maps column names to lists of tuples that describe a patch change to apply. To replace individual items in columns entirely, the tuples should be of the form:
(index, new_value) # replace a single column value # or (slice, new_values) # replace several column values
Values at an index or slice will be replaced with the corresponding new values.
In the case of columns whose values are other arrays or lists, (e.g. image or patches glyphs), it is also possible to patch “subregions”. In this case the first item of the tuple should be a whose first element is the index of the array item in the CDS patch, and whose subsequent elements are integer indices or slices into the array item:
# replace the entire 10th column of the 2nd array: +----------------- index of item in column data source | | +--------- row subindex into array item | | | | +- column subindex into array item V V V ([2, slice(None), 10], new_values)
Imagining a list of 2d NumPy arrays, the patch above is roughly equivalent to:
data = [arr1, arr2, ...] # list of 2d arrays data[2][:, 10] = new_data
There are some limitations to the kinds of slices and data that can be accepted.
Negative start, stop, or step values for slices will result in a ValueError.
start
stop
step
ValueError
In a slice, start > stop will result in a ValueError
start > stop
When patching 1d or 2d subitems, the subitems must be NumPy arrays.
New values must be supplied as a flattened one-dimensional array of the appropriate size.
patches (dict[str, list[tuple]]) – lists of patches for each column
The following example shows how to patch entire column elements. In this case,
source = ColumnDataSource(data=dict(foo=[10, 20, 30], bar=[100, 200, 300])) patches = { 'foo' : [ (slice(2), [11, 12]) ], 'bar' : [ (0, 101), (2, 301) ], } source.patch(patches)
After this operation, the value of the source.data will be:
source.data
dict(foo=[11, 12, 30], bar=[101, 200, 301])
For a more comprehensive complete example, see examples/howto/patch_app.py.
properties
Collect the names of properties on this class.
This method optionally traverses the class hierarchy and includes properties defined on any parent classes.
with_bases (bool, optional) – Whether to include properties defined on parent classes in the results. (default: True)
property names
properties_containers
Collect the names of all container properties on this class.
names of container properties
properties_with_refs
Collect the names of all properties on this class that also have references.
names of properties that have references
properties_with_values
Collect a dict mapping property names to their values.
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.
include_defaults (bool, optional) – Whether to include properties that haven’t been explicitly set since the object was created. (default: True)
mapping from property names to their values
query_properties_with_values
Query the properties values of HasProps instances with a predicate.
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)
mapping of property names and values for matching properties
references
Returns all Models that this object has references to.
Models
remove
Remove a column of data.
name (str) – name of the column to remove
If the column name does not exist, a warning is issued.
remove_on_change
Remove a callback from this object
select
Query this object and all of its references for objects that match the given selector.
selector (JSON-like) –
seq[Model]
select_one
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
Model
set_from_json
Set a property value on this object from JSON.
name – (str) : name of the attribute to set
json – (JSON-value) : value to set to the attribute to
models (dict or None, optional) –
Mapping of model ids to models (default: None)
This is needed in cases where the attributes to update also have values that have references.
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.
set_select
Update objects that match a given selector with the specified attribute/value updates.
updates (dict) –
stream
Efficiently update data source columns with new append-only data.
In cases where it is necessary to update data columns in, this method can efficiently send only the new data, instead of requiring the entire data set to be re-sent.
new_data (dict[str, seq]) –
a mapping of column names to sequences of new data to append to each column.
All columns of the data source must be present in new_data, with identical-length append data.
new_data
rollover (int, optional) – A maximum column size, above which data from the start of the column begins to be discarded. If None, then columns will continue to grow unbounded (default: None)
source = ColumnDataSource(data=dict(foo=[], bar=[])) # has new, identical-length updates for all columns in source new_data = { 'foo' : [10, 20], 'bar' : [100, 200], } source.stream(new_data)
themed_values
Get any theme-provided overrides.
Results are returned as a dict from property name to value, or None if no theme overrides any values for this instance.
dict or None
to_df
Convert this data source to pandas DataFrame.
to_json
Returns a dictionary of the attributes of this object, containing only “JSON types” (string, number, boolean, none, dict, list).
References to other objects are serialized as “refs” (just the object ID and type info), so the deserializer will need to separately have the full attributes of those other objects.
There’s no corresponding from_json() because to deserialize an object is normally done in the context of a Document (since the Document can resolve references).
from_json()
For most purposes it’s best to serialize and deserialize entire documents.
include_defaults (bool) – whether to include attributes that haven’t been changed from the default
to_json_string
Returns a JSON string encoding the attributes of this object.
References to other objects are serialized as references (just the object ID and type info), so the deserializer will need to separately have the full attributes of those other objects.
There’s no corresponding from_json_string() because to deserialize an object is normally done in the context of a Document (since the Document can resolve references).
from_json_string()
trigger
unapply_theme
Remove any themed values and restore defaults.
update
Updates the object’s properties from the given keyword arguments.
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)
update_from_json
Updates the object’s properties from a JSON attributes dictionary.
json_attributes – (JSON-dict) : attributes and values to update
column_names
A list of the column names in this data source.
document
The Document this model is attached to (can be None)
Document
struct
A Bokeh protocol “structure” of this model, i.e. a dict of the form:
{ 'type' : << view model name >> 'id' : << unique model id >> }
Additionally there may be a subtype field if this model is a subtype.
{ "adapter": null, "content_type": "application/json", "data": {}, "data_url": null, "http_headers": {}, "id": "17282", "if_modified": false, "js_event_callbacks": {}, "js_property_callbacks": {}, "max_size": null, "method": "POST", "mode": "replace", "name": null, "polling_interval": null, "selected": { "id": "17284" }, "selection_policy": { "id": "17283" }, "subscribed_events": [], "tags": [] }
CDSView
Bases: bokeh.model.Model
bokeh.model.Model
A view into a ColumnDataSource that represents a row-wise subset.
filters
property type: List ( Instance ( Filter ) )
Filter
List of filters that the view comprises.
source
property type: Instance ( ColumnarDataSource )
ColumnarDataSource
The ColumnDataSource associated with this view. Used to determine the length of the columns.
{ "filters": [], "id": "17302", "js_event_callbacks": {}, "js_property_callbacks": {}, "name": null, "source": null, "subscribed_events": [], "tags": [] }
Bases: bokeh.models.sources.DataSource
bokeh.models.sources.DataSource
A base class for data source types, which can be mapped onto a columnar format.
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": "17310", "js_event_callbacks": {}, "js_property_callbacks": {}, "name": null, "selected": { "id": "17312" }, "selection_policy": { "id": "17311" }, "subscribed_events": [], "tags": [] }
Bases: bokeh.models.sources.ColumnarDataSource
bokeh.models.sources.ColumnarDataSource
Maps names of columns to sequences or arrays.
The ColumnDataSource is a fundamental data structure of Bokeh. Most plots, data tables, etc. will be driven by a ColumnDataSource.
If the ColumnDataSource initializer is called with a single argument that can be any of the following:
A Python dict that maps string names to sequences of values, e.g. lists, arrays, etc.
data = {'x': [1,2,3,4], 'y': np.ndarray([10.0, 20.0, 30.0, 40.0])} source = ColumnDataSource(data)
ColumnDataSource only creates a shallow copy of data. Use e.g. ColumnDataSource(copy.deepcopy(data)) if initializing from another ColumnDataSource.data object that you want to keep independent.
ColumnDataSource(copy.deepcopy(data))
ColumnDataSource.data
A Pandas DataFrame object
source = ColumnDataSource(df)
In this case the CDS will have columns corresponding to the columns of the DataFrame. If the DataFrame columns have multiple levels, they will be flattened using an underscore (e.g. level_0_col_level_1_col). The index of the DataFrame will be flattened to an Index of tuples if it’s a MultiIndex, and then reset using reset_index. The result will be a column with the same name if the index was named, or level_0_name_level_1_name if it was a named MultiIndex. If the Index did not have a name or the MultiIndex name could not be flattened/determined, the reset_index function will name the index column index, or level_0 if the name index is not available.
Index
MultiIndex
reset_index
index
level_0
A Pandas GroupBy object
group = df.groupby(('colA', 'ColB'))
In this case the CDS will have columns corresponding to the result of calling group.describe(). The describe method generates columns for statistical measures such as mean and count for all the non-grouped original columns. The CDS columns are formed by joining original column names with the computed measure. For example, if a DataFrame has columns 'year' and 'mpg'. Then passing df.groupby('year') to a CDS will result in columns such as 'mpg_mean'
group.describe()
mean
count
'year'
'mpg'
df.groupby('year')
'mpg_mean'
If the GroupBy.describe result has a named index column, then CDS will also have a column with this name. However, if the index name (or any subname of a MultiIndex) is None, then the CDS will have a column generically named index for the index.
GroupBy.describe
Note this capability to adapt GroupBy objects may only work with Pandas >=0.20.0.
>=0.20.0
There is an implicit assumption that all the columns in a given ColumnDataSource all have the same length at all times. For this reason, it is usually preferable to update the .data property of a data source “all at once”.
{ "data": {}, "id": "17320", "js_event_callbacks": {}, "js_property_callbacks": {}, "name": null, "selected": { "id": "17322" }, "selection_policy": { "id": "17321" }, "subscribed_events": [], "tags": [] }
A base class for data source types.
{ "id": "17331", "js_event_callbacks": {}, "js_property_callbacks": {}, "name": null, "selected": { "id": "17332" }, "subscribed_events": [], "tags": [] }
GeoJSONDataSource
geojson
property type: JSON
JSON
GeoJSON that contains features for plotting. Currently GeoJSONDataSource can only process a FeatureCollection or GeometryCollection.
FeatureCollection
GeometryCollection
{ "geojson": null, "id": "17339", "js_event_callbacks": {}, "js_property_callbacks": {}, "name": null, "selected": { "id": "17341" }, "selection_policy": { "id": "17340" }, "subscribed_events": [], "tags": [] }
ServerSentDataSource
A data source that can populate columns by receiving server sent events endpoints.
{ "adapter": null, "data": {}, "data_url": null, "id": "17350", "js_event_callbacks": {}, "js_property_callbacks": {}, "max_size": null, "mode": "replace", "name": null, "selected": { "id": "17352" }, "selection_policy": { "id": "17351" }, "subscribed_events": [], "tags": [] }
WebSource
Bases: bokeh.models.sources.ColumnDataSource
bokeh.models.sources.ColumnDataSource
Base class for web column data sources that can update from data URLs.
This base class is typically not useful to instantiate on its own.
{ "adapter": null, "data": {}, "data_url": null, "id": "17365", "js_event_callbacks": {}, "js_property_callbacks": {}, "max_size": null, "mode": "replace", "name": null, "selected": { "id": "17367" }, "selection_policy": { "id": "17366" }, "subscribed_events": [], "tags": [] }