在python中实现一个完整的三维散点图与散景

Question

我目前正在使用Bokeh来输出一个三维交互散点图的项目。我想根据2或3个类别对点进行着色，并且我想在悬停它之后显示与点对应的基因。我意识到了Bokeh没有完全实现3D图的事实，并且我找到了下面的脚本，它允许用python生成这样的3D图（original code）。

尽管原始代码产生了一个3D表面，但有些读数为documentation，我已经设法产生了一个3D图。我也设法根据类别对点进行着色。但是，当我尝试生成工具提示时，其信息将被编码到python（或任何其他）的“额外”变量中，因此我无法生成该信息。我没有JS的知识，所以我只是试图调整变量来看看会发生什么。

我公司生产的代码是这一个：

from __future__ import division 
from bokeh.core.properties import Instance, String 
from bokeh.models import ColumnDataSource, LayoutDOM 
from bokeh.io import show 
import numpy as np 


JS_CODE = """ 
# This file contains the JavaScript (CoffeeScript) implementation 
# for a Bokeh custom extension. The "surface3d.py" contains the 
# python counterpart. 
# 
# This custom model wraps one part of the third-party vis.js library: 
# 
#  http://visjs.org/index.html 
# 
# Making it easy to hook up python data analytics tools (NumPy, SciPy, 
# Pandas, etc.) to web presentations using the Bokeh server. 

# These "require" lines are similar to python "import" statements 
import * as p from "core/properties" 
import {LayoutDOM, LayoutDOMView} from "models/layouts/layout_dom" 

# This defines some default options for the Graph3d feature of vis.js 
# See: http://visjs.org/graph3d_examples.html for more details. 
OPTIONS = 
    width: '700px' 
    height: '700px' 
    style: 'dot-color' 
    showPerspective: true 
    showGrid: true 
    keepAspectRatio: true 
    verticalRatio: 1.0 
    showLegend: false 
    cameraPosition: 
    horizontal: -0.35 
    vertical: 0.22 
    distance: 1.8 

    dotSizeRatio: 0.01 

    tooltip: true #(point) -> return 'value: <b>' + point.z + '</b><br>' + point.data.extra 




# To create custom model extensions that will render on to the HTML canvas 
# or into the DOM, we must create a View subclass for the model. Currently 
# Bokeh models and views are based on BackBone. More information about 
# using Backbone can be found here: 
# 
#  http://backbonejs.org/ 
# 
# In this case we will subclass from the existing BokehJS ``LayoutDOMView``, 
# corresponding to our 
export class Surface3dView extends LayoutDOMView 

    initialize: (options) -> 
    super(options) 

    url = "https://cdnjs.cloudflare.com/ajax/libs/vis/4.16.1/vis.min.js" 

    script = document.createElement('script') 
    script.src = url 
    script.async = false 
    script.onreadystatechange = script.onload =() => @_init() 
    document.querySelector("head").appendChild(script) 

    _init:() -> 
    # Create a new Graph3s using the vis.js API. This assumes the vis.js has 
    # already been loaded (e.g. in a custom app template). In the future Bokeh 
    # models will be able to specify and load external scripts automatically. 
    # 
    # Backbone Views create <div> elements by default, accessible as @el. Many 
    # Bokeh views ignore this default <div>, and instead do things like draw 
    # to the HTML canvas. In this case though, we use the <div> to attach a 
    # Graph3d to the DOM. 
    @_graph = new vis.Graph3d(@el, @get_data(), OPTIONS) 

    # Set Backbone listener so that when the Bokeh data source has a change 
    # event, we can process the new data 
    @connect(@model.data_source.change,() => 
     @_graph.setData(@get_data()) 
    ) 

    # This is the callback executed when the Bokeh data has an change. Its basic 
    # function is to adapt the Bokeh data source to the vis.js DataSet format. 
    get_data:() -> 
    data = new vis.DataSet() 
    source = @model.data_source 
    for i in [0...source.get_length()] 
     data.add({ 
     x:  source.get_column(@model.x)[i] 
     y:  source.get_column(@model.y)[i] 
     z:  source.get_column(@model.z)[i] 
     extra: source.get_column(@model.extra)[i] 
     style: source.get_column(@model.color)[i] 
     }) 
    return data 

# We must also create a corresponding JavaScript Backbone model sublcass to 
# correspond to the python Bokeh model subclass. In this case, since we want 
# an element that can position itself in the DOM according to a Bokeh layout, 
# we subclass from ``LayoutDOM`` 
export class Surface3d extends LayoutDOM 

    # This is usually boilerplate. In some cases there may not be a view. 
    default_view: Surface3dView 

    # The ``type`` class attribute should generally match exactly the name 
    # of the corresponding Python class. 
    type: "Surface3d" 

    # The @define block adds corresponding "properties" to the JS model. These 
    # should basically line up 1-1 with the Python model class. Most property 
    # types have counterparts, e.g. ``bokeh.core.properties.String`` will be 
    # ``p.String`` in the JS implementatin. Where the JS type system is not yet 
    # as rich, you can use ``p.Any`` as a "wildcard" property type. 
    @define { 
    x:   [ p.String   ] 
    y:   [ p.String   ] 
    z:   [ p.String   ] 
    color:  [ p.String   ] 
    extra:  [ p.String   ] 
    data_source: [ p.Instance   ] 
    } 
""" 

# This custom extension model will have a DOM view that should layout-able in 
# Bokeh layouts, so use ``LayoutDOM`` as the base class. If you wanted to create 
# a custom tool, you could inherit from ``Tool``, or from ``Glyph`` if you 
# wanted to create a custom glyph, etc. 
class Surface3d(LayoutDOM): 

    # The special class attribute ``__implementation__`` should contain a string 
    # of JavaScript (or CoffeeScript) code that implements the JavaScript side 
    # of the custom extension model. 
    __implementation__ = JS_CODE 

    # Below are all the "properties" for this model. Bokeh properties are 
    # class attributes that define the fields (and their types) that can be 
    # communicated automatically between Python and the browser. Properties 
    # also support type validation. More information about properties in 
    # can be found here: 
    # 
    # https://bokeh.pydata.org/en/latest/docs/reference/core.html#bokeh-core-properties 

    # This is a Bokeh ColumnDataSource that can be updated in the Bokeh 
    # server by Python code 
    data_source = Instance(ColumnDataSource) 

    # The vis.js library that we are wrapping expects data for x, y, z, and 
    # color. The data will actually be stored in the ColumnDataSource, but 
    # these properties let us specify the *name* of the column that should 
    # be used for each field. 
    x = String 
    y = String 
    z = String 
    extra = String 
    color = String 




X_data = np.random.normal(0,10,100) 
Y_data = np.random.normal(0,5,100) 
Z_data = np.random.normal(0,3,100) 
color = np.asarray([0 for x in range(50)]+[1 for x in range(50)]) 
extra = np.asarray(['a' for x in range(50)]+['b' for x in range(50)]) 


source = ColumnDataSource(data=dict(x=X_data, y=Y_data, z=Z_data, color = color, extra=extra)) 

surface = Surface3d(x="x", y="y", z="z", extra="extra", color="color", data_source=source) 

show(surface) 

鉴于此，我从项目理想化的输出应该是：

1. 产生正确的提示，与对应值的基因。
2. 补充地，在工具提示中添加点所属的类别（如果可以完成，我不会在做这个）。
3. 不知何故，删除我不需要的颜色条（图例）。将showLegend值设置为false时不会消失。

在此先感谢您。

Answer 1

所以有两个小的调整，以达到你想要的。

我认为最重要的是使用visjs的版本。

URL更改为url = "http://visjs.org/dist/vis.js"

其次，提示函数声明应改为：

tooltip: (point) -> return 'value: <b>' + point.z + '</b><br>' + 'extra: <b>' + point.data.extra 

不是CoffeeScript的用户，但似乎更正语法使用自定义工具提示的html 。

这里是更新的例子，如果需要的话。

from __future__ import division 
from bokeh.core.properties import Instance, String 
from bokeh.models import ColumnDataSource, LayoutDOM 
from bokeh.io import show 
import numpy as np 


JS_CODE = """ 
# This file contains the JavaScript (CoffeeScript) implementation 
# for a Bokeh custom extension. The "surface3d.py" contains the 
# python counterpart. 
# 
# This custom model wraps one part of the third-party vis.js library: 
# 
#  http://visjs.org/index.html 
# 
# Making it easy to hook up python data analytics tools (NumPy, SciPy, 
# Pandas, etc.) to web presentations using the Bokeh server. 

# These "require" lines are similar to python "import" statements 
import * as p from "core/properties" 
import {LayoutDOM, LayoutDOMView} from "models/layouts/layout_dom" 

# This defines some default options for the Graph3d feature of vis.js 
# See: http://visjs.org/graph3d_examples.html for more details. 
OPTIONS = 
    width: '700px' 
    height: '700px' 
    style: 'dot-color' 
    showPerspective: true 
    showGrid: true 
    keepAspectRatio: true 
    verticalRatio: 1.0 
    showLegend: false 
    cameraPosition: 
    horizontal: -0.35 
    vertical: 0.22 
    distance: 1.8 
    dotSizeRatio: 0.01 
    tooltip: (point) -> return 'value: <b>' + point.z + '</b><br>' + 'extra: <b>' + point.data.extra 




# To create custom model extensions that will render on to the HTML canvas 
# or into the DOM, we must create a View subclass for the model. Currently 
# Bokeh models and views are based on BackBone. More information about 
# using Backbone can be found here: 
# 
#  http://backbonejs.org/ 
# 
# In this case we will subclass from the existing BokehJS ``LayoutDOMView``, 
# corresponding to our 
export class Surface3dView extends LayoutDOMView 

    initialize: (options) -> 
    super(options) 

    url = "http://visjs.org/dist/vis.js" 

    script = document.createElement('script') 
    script.src = url 
    script.async = false 
    script.onreadystatechange = script.onload =() => @_init() 
    document.querySelector("head").appendChild(script) 

    _init:() -> 
    # Create a new Graph3s using the vis.js API. This assumes the vis.js has 
    # already been loaded (e.g. in a custom app template). In the future Bokeh 
    # models will be able to specify and load external scripts automatically. 
    # 
    # Backbone Views create <div> elements by default, accessible as @el. Many 
    # Bokeh views ignore this default <div>, and instead do things like draw 
    # to the HTML canvas. In this case though, we use the <div> to attach a 
    # Graph3d to the DOM. 
    @_graph = new vis.Graph3d(@el, @get_data(), OPTIONS) 

    # Set Backbone listener so that when the Bokeh data source has a change 
    # event, we can process the new data 
    @connect(@model.data_source.change,() => 
     @_graph.setData(@get_data()) 
    ) 

    # This is the callback executed when the Bokeh data has an change. Its basic 
    # function is to adapt the Bokeh data source to the vis.js DataSet format. 
    get_data:() -> 
    data = new vis.DataSet() 
    source = @model.data_source 
    for i in [0...source.get_length()] 
     data.add({ 
     x:  source.get_column(@model.x)[i] 
     y:  source.get_column(@model.y)[i] 
     z:  source.get_column(@model.z)[i] 
     extra: source.get_column(@model.extra)[i] 
     style: source.get_column(@model.color)[i] 
     }) 
    return data 

# We must also create a corresponding JavaScript Backbone model sublcass to 
# correspond to the python Bokeh model subclass. In this case, since we want 
# an element that can position itself in the DOM according to a Bokeh layout, 
# we subclass from ``LayoutDOM`` 
export class Surface3d extends LayoutDOM 

    # This is usually boilerplate. In some cases there may not be a view. 
    default_view: Surface3dView 

    # The ``type`` class attribute should generally match exactly the name 
    # of the corresponding Python class. 
    type: "Surface3d" 

    # The @define block adds corresponding "properties" to the JS model. These 
    # should basically line up 1-1 with the Python model class. Most property 
    # types have counterparts, e.g. ``bokeh.core.properties.String`` will be 
    # ``p.String`` in the JS implementatin. Where the JS type system is not yet 
    # as rich, you can use ``p.Any`` as a "wildcard" property type. 
    @define { 
    x:   [ p.String   ] 
    y:   [ p.String   ] 
    z:   [ p.String   ] 
    color:  [ p.String   ] 
    extra:  [ p.String   ] 
    data_source: [ p.Instance   ] 
    } 
""" 

# This custom extension model will have a DOM view that should layout-able in 
# Bokeh layouts, so use ``LayoutDOM`` as the base class. If you wanted to create 
# a custom tool, you could inherit from ``Tool``, or from ``Glyph`` if you 
# wanted to create a custom glyph, etc. 
class Surface3d(LayoutDOM): 

    # The special class attribute ``__implementation__`` should contain a string 
    # of JavaScript (or CoffeeScript) code that implements the JavaScript side 
    # of the custom extension model. 
    __implementation__ = JS_CODE 

    # Below are all the "properties" for this model. Bokeh properties are 
    # class attributes that define the fields (and their types) that can be 
    # communicated automatically between Python and the browser. Properties 
    # also support type validation. More information about properties in 
    # can be found here: 
    # 
    # https://bokeh.pydata.org/en/latest/docs/reference/core.html#bokeh-core-properties 

    # This is a Bokeh ColumnDataSource that can be updated in the Bokeh 
    # server by Python code 
    data_source = Instance(ColumnDataSource) 

    # The vis.js library that we are wrapping expects data for x, y, z, and 
    # color. The data will actually be stored in the ColumnDataSource, but 
    # these properties let us specify the *name* of the column that should 
    # be used for each field. 
    x = String 
    y = String 
    z = String 
    extra = String 
    color = String 




X_data = np.random.normal(0,10,100) 
Y_data = np.random.normal(0,5,100) 
Z_data = np.random.normal(0,3,100) 
color = np.asarray([0 for x in range(50)]+[1 for x in range(50)]) 
extra = np.asarray(['a' for x in range(50)]+['b' for x in range(50)]) 


source = ColumnDataSource(data=dict(x=X_data, y=Y_data, z=Z_data, color = color, extra=extra)) 

surface = Surface3d(x="x", y="y", z="z", extra="extra", color="color", data_source=source) 

show(surface)