bokeh.models.transforms¶

Represent transformations of data to happen on the client (browser) side.

class CustomJSTransform(**kwargs)[source]¶

Bases: bokeh.models.transforms.Transform

Apply a custom defined transform to data.

Warning

The explicit purpose of this Bokeh Model is to embed raw JavaScript code for a browser to execute. If any part of the code is derived from untrusted user inputs, then you must take appropriate care to sanitize the user input prior to passing to Bokeh.

args¶

property type: Dict ( String , AnyRef )

A mapping of names to Python objects. In particular those can be bokeh’s models. These objects are made available to the transform’ code snippet as the values of named parameters to the callback.

func¶

property type: String

A snippet of JavaScript code to transform a single value. The variable x will contain the untransformed value and can be expected to be present in the function namespace at render time. The snippet will be into the body of a function and therefore requires a return statement.

Example:

func = '''
return Math.floor(x) + 0.5
'''

use_strict¶

property type: Bool

Enables or disables automatic insertion of "use strict"; into func or v_func.

v_func¶

property type: String

A snippet of JavaScript code to transform an array of values. The variable xs will contain the untransformed array and can be expected to be present in the function namespace at render time. The snippet will be into the body of a function and therefore requires a return statement.

Example:

v_func = '''
var new_xs = new Array(xs.length)
for(var i = 0; i < xs.length; i++) {
    new_xs[i] = xs[i] + 0.5
}
return new_xs
'''

Warning

The vectorized function, v_func, must return an array of the same length as the input xs array.

classmethod from_coffeescript(func, v_func, args={})[source]¶

Create a CustomJSTransform instance from a pair of CoffeeScript snippets. The function bodies are translated to JavaScript functions using node and therefore require return statements.

The func snippet namespace will contain the variable x (the untransformed value) at render time. The v_func snippet namespace will contain the variable xs (the untransformed vector) at render time.

Example:

func = "return Math.cos(x)"
v_func = "return [Math.cos(x) for x in xs]"

transform = CustomJSTransform.from_coffeescript(func, v_func)

Parameters:	func (str) – a coffeescript snippet to transform a single x value v_func (str) – a coffeescript snippet function to transform a vector xs
Returns:	CustomJSTransform

classmethod from_py_func(func, v_func)[source]¶

Create a CustomJSTransform instance from a pair of Python functions. The function is translated to JavaScript using PScript.

The python functions must have no positional arguments. It’s possible to pass Bokeh models (e.g. a ColumnDataSource) as keyword arguments to the functions.

The func function namespace will contain the variable x (the untransformed value) at render time. The v_func function namespace will contain the variable xs (the untransformed vector) at render time.

Warning

The vectorized function, v_func, must return an array of the same length as the input xs array.

Example:

def transform():
    from pscript.stubs import Math
    return Math.cos(x)

def v_transform():
    from pscript.stubs import Math
    return [Math.cos(x) for x in xs]

customjs_transform = CustomJSTransform.from_py_func(transform, v_transform)

Parameters:	func (function) – a scalar function to transform a single x value v_func (function) – a vectorized function to transform a vector xs
Returns:	CustomJSTransform

{
  "args": {},
  "func": "",
  "id": "31939",
  "js_event_callbacks": {},
  "js_property_callbacks": {},
  "name": null,
  "subscribed_events": [],
  "tags": [],
  "use_strict": false,
  "v_func": ""
}

class Dodge(**kwargs)[source]¶

Bases: bokeh.models.transforms.Transform

Apply either fixed dodge amount to data.

range¶

property type: Instance ( Range )

When applying Dodge to categorical data values, the corresponding FactorRange must be supplied as the range property.

value¶

property type: Float

The amount to dodge the input data.

{
  "id": "31944",
  "js_event_callbacks": {},
  "js_property_callbacks": {},
  "name": null,
  "range": null,
  "subscribed_events": [],
  "tags": [],
  "value": 0
}

class Interpolator(**kwargs)[source]¶

Bases: bokeh.models.transforms.Transform

Base class for interpolator transforms.

Interpolators return the value of a function which has been evaluated between specified (x, y) pairs of data. As an example, if two control point pairs were provided to the interpolator, a linear interpolaction at a specific value of ‘x’ would result in the value of ‘y’ which existed on the line conneting the two control points.

The control point pairs for the interpolators can be specified through either

• A literal sequence of values:

• or a pair of columns defined in a ColumnDataSource object:

This is the base class and is not intended to end use. Please see the documentation for the final derived classes (Jitter, LineraInterpolator, StepInterpolator) for mor information on their specific methods of interpolation.

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.

clip¶

property type: Bool

Determine if the interpolation should clip the result to include only values inside its predefined range. If this is set to False, it will return the most value of the closest point.

data¶

property type: Instance ( ColumnarDataSource )

Data which defines the source for the named columns if a string is passed to either the x or y parameters.

x¶

property type: Either ( String , Seq ( Float ) )

Independant coordiante denoting the location of a point.

y¶

property type: Either ( String , Seq ( Float ) )

Dependant coordinate denoting the value of a point at a location.

{
  "clip": true,
  "data": null,
  "id": "31947",
  "js_event_callbacks": {},
  "js_property_callbacks": {},
  "name": null,
  "subscribed_events": [],
  "tags": [],
  "x": null,
  "y": null
}

class Jitter(**kwargs)[source]¶

Bases: bokeh.models.transforms.Transform

Apply either a uniform or normally sampled random jitter to data.

distribution¶

property type: Enum ( JitterRandomDistribution )

The random distribution upon which to pull the random scatter

mean¶

property type: Float

The central value for the random sample

range¶

property type: Instance ( Range )

When applying Jitter to Categorical data values, the corresponding FactorRange must be supplied as the range property.

width¶

property type: Float

The width (absolute for uniform distribution and sigma for the normal distribution) of the random sample.

{
  "distribution": "uniform",
  "id": "31952",
  "js_event_callbacks": {},
  "js_property_callbacks": {},
  "mean": 0,
  "name": null,
  "range": null,
  "subscribed_events": [],
  "tags": [],
  "width": 1
}

class LinearInterpolator(**kwargs)[source]¶

Bases: bokeh.models.transforms.Interpolator

Compute a linear interpolation between the control points provided through the x, y, and data parameters.

{
  "clip": true,
  "data": null,
  "id": "31957",
  "js_event_callbacks": {},
  "js_property_callbacks": {},
  "name": null,
  "subscribed_events": [],
  "tags": [],
  "x": null,
  "y": null
}

class StepInterpolator(**kwargs)[source]¶

Bases: bokeh.models.transforms.Interpolator

Compute a step-wise interpolation between the points provided through the x, y, and data parameters.

mode¶

property type: Enum ( StepMode )

Adjust the behavior of the returned value in relation to the control points. The parameter can assume one of three values:

• after (default): Assume the y-value associated with the nearest x-value which is less than or equal to the point to transform.
• before: Assume the y-value associated with the nearest x-value which is greater than the point to transform.
• center: Assume the y-value associated with the nearest x-value to the point to transform.

{
  "clip": true,
  "data": null,
  "id": "31958",
  "js_event_callbacks": {},
  "js_property_callbacks": {},
  "mode": "after",
  "name": null,
  "subscribed_events": [],
  "tags": [],
  "x": null,
  "y": null
}

class Transform(**kwargs)[source]¶

Bases: bokeh.model.Model

Base class for Transform models that represent a computation to be carried out on the client-side.

JavaScript implementations should implement the following methods:

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": "31960",
  "js_event_callbacks": {},
  "js_property_callbacks": {},
  "name": null,
  "subscribed_events": [],
  "tags": []
}