#----------------------------------------------------------------------------- # Copyright (c) 2012 - 2019, Anaconda, Inc., and Bokeh Contributors. # All rights reserved. # # The full license is in the file LICENSE.txt, distributed with this software. #----------------------------------------------------------------------------- ''' Represent transformations of data to happen on the client (browser) side. ''' #----------------------------------------------------------------------------- # Boilerplate #----------------------------------------------------------------------------- from __future__ import absolute_import, division, print_function, unicode_literals import logging log = logging.getLogger(__name__) #----------------------------------------------------------------------------- # Imports #----------------------------------------------------------------------------- # Standard library imports from textwrap import dedent from types import FunctionType # External imports # Bokeh imports from ..core.enums import StepMode, JitterRandomDistribution from ..core.has_props import abstract from ..core.properties import Bool, Dict, Either, Enum, Float, Instance, Seq, String, AnyRef from ..model import Model from ..util.compiler import nodejs_compile, CompilationError from ..util.dependencies import import_required from ..util.future import get_param_info, signature from .sources import ColumnarDataSource #----------------------------------------------------------------------------- # Globals and constants #----------------------------------------------------------------------------- __all__ = ( 'CustomJSTransform', 'Dodge', 'Interpolator', 'Jitter', 'LinearInterpolator', 'StepInterpolator', 'Transform', ) #----------------------------------------------------------------------------- # General API #----------------------------------------------------------------------------- @abstract class Transform(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: .. code-block:: coffeescript compute: (x) -> # compute the transform of a single value v_compute: (xs) -> # compute the transform of an array of values ''' pass class CustomJSTransform(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. ''' @classmethod def from_py_func(cls, func, v_func): ''' 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: .. code-block:: python 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) Args: func (function) : a scalar function to transform a single ``x`` value v_func (function) : a vectorized function to transform a vector ``xs`` Returns: CustomJSTransform ''' from bokeh.util.deprecation import deprecated deprecated("'from_py_func' is deprecated and will be removed in an eventual 2.0 release. " "Use CustomJSTransform directly instead.") if not isinstance(func, FunctionType) or not isinstance(v_func, FunctionType): raise ValueError('CustomJSTransform.from_py_func only accepts function objects.') pscript = import_required( 'pscript', dedent("""\ To use Python functions for CustomJSTransform, you need PScript '("conda install -c conda-forge pscript" or "pip install pscript")""") ) def pscript_compile(func): sig = signature(func) all_names, default_values = get_param_info(sig) if len(all_names) - len(default_values) != 0: raise ValueError("Function may only contain keyword arguments.") if default_values and not any(isinstance(value, Model) for value in default_values): raise ValueError("Default value must be a Bokeh Model.") func_kwargs = dict(zip(all_names, default_values)) # Wrap the code attr in a function named `formatter` and call it # with arguments that match the `args` attr code = pscript.py2js(func, 'transformer') + 'return transformer(%s);\n' % ', '.join(all_names) return code, func_kwargs jsfunc, func_kwargs = pscript_compile(func) v_jsfunc, v_func_kwargs = pscript_compile(v_func) # Have to merge the function arguments func_kwargs.update(v_func_kwargs) return cls(func=jsfunc, v_func=v_jsfunc, args=func_kwargs) @classmethod def from_coffeescript(cls, func, v_func, args={}): ''' 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: .. code-block:: coffeescript func = "return Math.cos(x)" v_func = "return [Math.cos(x) for x in xs]" transform = CustomJSTransform.from_coffeescript(func, v_func) Args: 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 ''' compiled = nodejs_compile(func, lang="coffeescript", file="???") if "error" in compiled: raise CompilationError(compiled.error) v_compiled = nodejs_compile(v_func, lang="coffeescript", file="???") if "error" in v_compiled: raise CompilationError(v_compiled.error) return cls(func=compiled.code, v_func=v_compiled.code, args=args) args = Dict(String, AnyRef, help=""" 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 = String(default="", help=""" 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: .. code-block:: javascript func = ''' return Math.floor(x) + 0.5 ''' """) v_func = String(default="", help=""" 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: .. code-block:: javascript 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. """) use_strict = Bool(default=False, help=""" Enables or disables automatic insertion of ``"use strict";`` into ``func`` or ``v_func``. """) class Dodge(Transform): ''' Apply either fixed dodge amount to data. ''' value = Float(default=0, help=""" The amount to dodge the input data. """) range = Instance("bokeh.models.ranges.Range", help=""" When applying ``Dodge`` to categorical data values, the corresponding ``FactorRange`` must be supplied as the ``range`` property. """) class Jitter(Transform): ''' Apply either a uniform or normally sampled random jitter to data. ''' mean = Float(default=0, help=""" The central value for the random sample """) width = Float(default=1, help=""" The width (absolute for uniform distribution and sigma for the normal distribution) of the random sample. """) distribution = Enum(JitterRandomDistribution, default='uniform', help=""" The random distribution upon which to pull the random scatter """) range = Instance("bokeh.models.ranges.Range", help=""" When applying Jitter to Categorical data values, the corresponding ``FactorRange`` must be supplied as the ``range`` property. """) @abstract class Interpolator(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 connecting the two control points. The control point pairs for the interpolators can be specified through either * A literal sequence of values: .. code-block:: python interp = Interpolator(x=[1, 2, 3, 4, 5], y=[2, 5, 10, 12, 16]) * or a pair of columns defined in a ``ColumnDataSource`` object: .. code-block:: python interp = Interpolator(x="year", y="earnings", data=jewlery_prices)) 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 more information on their specific methods of interpolation. ''' x = Either(String, Seq(Float), help=""" Independent coordinate denoting the location of a point. """) y = Either(String, Seq(Float), help=""" Dependant coordinate denoting the value of a point at a location. """) data = Instance(ColumnarDataSource, help=""" Data which defines the source for the named columns if a string is passed to either the ``x`` or ``y`` parameters. """) clip = Bool(True, help=""" 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. """) # Define an initialization routine to do some cross checking of input values def __init__(self, **kwargs): super(Interpolator, self).__init__(**kwargs) class LinearInterpolator(Interpolator): ''' Compute a linear interpolation between the control points provided through the ``x``, ``y``, and ``data`` parameters. ''' pass class StepInterpolator(Interpolator): ''' Compute a step-wise interpolation between the points provided through the ``x``, ``y``, and ``data`` parameters. ''' mode = Enum(StepMode, default="after", help=""" 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. """) #----------------------------------------------------------------------------- # Dev API #----------------------------------------------------------------------------- #----------------------------------------------------------------------------- # Private API #----------------------------------------------------------------------------- #----------------------------------------------------------------------------- # Code #-----------------------------------------------------------------------------