python）でボケを使用して完全な3D散布図を実装する

Question

現在、Bokehを使用して3Dインタラクティブ散布図を出力したいプロジェクトに取り組んでいます。 2つまたは3つのカテゴリに基づいてドットに色を付け、ドットに対応する遺伝子をホバーした後に表示したいと思います。 Bokehが3Dプロットを完全に実装していないという事実を認識しており、次のスクリプトを見つけました。これにより、python（ 元のコード ）でこのような3Dプロットを作成できます。 ）。

元のコードは3Dサーフェスを生成しますが、 documentation を少し読んで、3Dプロットを生成することができました。また、カテゴリに基づいてドットに色を付けることもできました。ただし、情報がpython（またはその他）の 'extra'変数にエンコードされるツールチップを作成しようとすると、その情報を作成できません。知識がありません。 JSなので、変数を微調整して何が起こるかを確認しようとしています。

私が作成したコードは次のとおりです。

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://docs.bokeh.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)

これを考えると、プロジェクトからの私の理想的な出力は次のようになります。

1. 値に対応する遺伝子を使用して、正しいツールチップを作成します。
2. 補足として、ドットが属するカテゴリをツールチップに追加します（1.が実行できる場合は、これを実行しても問題ありません）。
3. どういうわけか、私が必要としないカラーバー（凡例）を削除します。 showLegend値をfalseに設定しても、消えることはありません。

前もって感謝します。

Answer 1

したがって、必要なものを実現するために2つの小さな調整が必要です。

最も重要なのは、使用されているvisjsのバージョンだと思います。

URLをurl = "http://visjs.org/dist/vis.js"に変更します

次に、ツールチップの関数宣言を次のように変更する必要があります。

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

Coffeescriptユーザーではありませんが、カスタムツールチップhtmlを使用するための構文が修正されているようです。

必要に応じて、更新された例を次に示します。

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://docs.bokeh.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)

Answer 2

最良の答えではありませんが、完全に実装された3Dのスキャッターをplotlyを使用して取得できるようになりました。

import plotly.plotly as py
import plotly.graph_objs as go

import numpy as np

x, y, z = np.random.multivariate_normal(np.array([0,0,0]), np.eye(3), 200).transpose()
trace1 = go.Scatter3d(
    x=x,
    y=y,
    z=z,
    mode='markers',
    marker=dict(
        size=12,
        line=dict(
            color='rgba(217, 217, 217, 0.14)',
            width=0.5
        ),
        opacity=0.8
    )
)

x2, y2, z2 = np.random.multivariate_normal(np.array([0,0,0]), np.eye(3), 200).transpose()
trace2 = go.Scatter3d(
    x=x2,
    y=y2,
    z=z2,
    mode='markers',
    marker=dict(
        color='rgb(127, 127, 127)',
        size=12,
        symbol='circle',
        line=dict(
            color='rgb(204, 204, 204)',
            width=1
        ),
        opacity=0.9
    )
)
data = [trace1, trace2]
layout = go.Layout(
    margin=dict(
        l=0,
        r=0,
        b=0,
        t=0
    )
)
fig = go.Figure(data=data, layout=layout)
py.plot(fig, filename='simple-3d-scatter')

コードはhtmlファイルを生成し、完全なパーソナライズを可能にします。