Javascript Jupyter（IPython）笔记本中的交互式绘图，带有可拖动点，拖动点时调用Python代码

我想在Jupyter笔记本中制作一些交互式绘图，其中用户可以拖动绘图中的某些点。然后，这些点的位置应作为Python函数（在笔记本中）的输入，用于更新绘图

这里已经完成了类似的工作：

但是回调是针对Javascript函数的。在某些情况下，更新绘图的代码需要非常复杂，并且需要很长时间才能用Javascript重写。如果需要的话，我愿意用Javascript指定可拖动的点，但是可以调用Python来更新绘图吗

我想知道像Bokeh或Plotly这样的工具是否可以提供这种功能；博士

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为此，您需要知道：

• 如何从Jupyter的Javascript与IPython内核交互 前端。现在是通过

  Jupyter.Kernel.execute

  （）
• 足够舒服了。（与屏幕到绘图坐标转换类似。）
• D3Via Python库是您选择的。对于这个例子

mpld3有它的特性和功能，可用于。但是现在有,；维护人员说，现在最好的方法是在更新时删除并重新绘制整个绘图，否则就要深入javascript

正如你所说的（据我所知），Ipywidgets是一种在使用IPython内核时将HTML

input

元素链接到Jupyter笔记本绘图的方法，因此不是你想要的。但比我提议的要容易一千倍。IPyWidget在其示例套件中获取github repo到的链接

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关于Jupyter笔记本电脑与IPython内核的直接交互的最佳博客文章来自2013年。这是给伊皮顿的你试过了吗？

Scatter

有一个

enable\u move

参数，当您设置为

True

时，它们允许拖动点。此外，拖动时，您可以观察到

Scatter

或

Label

的

x

或

y

值的变化，并通过该值触发python函数，从而生成新的绘图。他们在笔记本上这样做

Jupyter笔记本代码：

# Let's begin by importing some libraries we'll need
import numpy as np
from __future__ import print_function # So that this notebook becomes both Python 2 and Python 3 compatible

# And creating some random data
size = 10
np.random.seed(0)
x_data = np.arange(size)
y_data = np.cumsum(np.random.randn(size)  * 100.0)

from bqplot import pyplot as plt

# Creating a new Figure and setting it's title
plt.figure(title='My Second Chart')
# Let's assign the scatter plot to a variable
scatter_plot = plt.scatter(x_data, y_data)

# Let's show the plot
plt.show()

# then enable modification and attach a callback function:

def foo(change):
    print('This is a trait change. Foo was called by the fact that we moved the Scatter')
    print('In fact, the Scatter plot sent us all the new data: ')
    print('To access the data, try modifying the function and printing the data variable')
    global pdata 
    pdata = [scatter_plot.x,scatter_plot.y]

# First, we hook up our function `foo` to the colors attribute (or Trait) of the scatter plot
scatter_plot.observe(foo, ['y','x'])

scatter_plot.enable_move = True

---

您可能想使用bokeh而不是matplotlib@MaxNoe如果你能提供一个bokeh的工作示例，我会接受这个答案。哇，谢谢你，我从来没有听说过mpld3。改变一切。这有用吗@slushy谢谢，但该示例只使用了常用的小部件。它不会对鼠标单击绘图做出响应。

javascriptCallback = function(out) {
  // Error checking omitted for brevity.
  output = out.content.user_expressions.out1;
  res = output.data["text/plain"];
  newValue = JSON.parse(res);  // If necessary
  //
  // Use newValue to do something now.
  //
}

var kernel = Jupyter.notebook.kernel;
var callbacks = {shell: {reply: javascriptCallback }};
kernel.execute(
  "print('only the success/fail status of this code is reported')",
  callbacks,
  {user_expressions:
    {out1: "python_callback(" + 10 + ")"}  // function call as a string
  }
);

import matplotlib as mpl
import mpld3

class DragPlugin(mpld3.plugins.PluginBase):
    JAVASCRIPT = r"""
    // Beginning content unchanged, and removed for brevity.

    DragPlugin.prototype.draw = function(){
        var obj = mpld3.get_element(this.props.id);

        var drag = d3.behavior.drag()
            .origin(function(d) { return {x:obj.ax.x(d[0]),
                                          y:obj.ax.y(d[1])}; })
            .on("dragstart", dragstarted)
            .on("drag", dragged)
            .on("dragend", dragended);

        // Additional content unchanged, and removed for brevity

        obj.elements()
           .data(obj.offsets)
           .style("cursor", "default")
           .attr("name", "redrawable")  // DIFFERENT
           .call(drag);

        // Also modify the 'dragstarted' function to store
        // the starting position, and the 'dragended' function
        // to initiate the exchange with the IPython kernel
        // that will update the plot.
    };
    """

    def __init__(self, points):
        if isinstance(points, mpl.lines.Line2D):
            suffix = "pts"
        else:
            suffix = None

    self.dict_ = {"type": "drag",
                  "id": mpld3.utils.get_id(points, suffix)}

# Let's begin by importing some libraries we'll need
import numpy as np
from __future__ import print_function # So that this notebook becomes both Python 2 and Python 3 compatible

# And creating some random data
size = 10
np.random.seed(0)
x_data = np.arange(size)
y_data = np.cumsum(np.random.randn(size)  * 100.0)

from bqplot import pyplot as plt

# Creating a new Figure and setting it's title
plt.figure(title='My Second Chart')
# Let's assign the scatter plot to a variable
scatter_plot = plt.scatter(x_data, y_data)

# Let's show the plot
plt.show()

# then enable modification and attach a callback function:

def foo(change):
    print('This is a trait change. Foo was called by the fact that we moved the Scatter')
    print('In fact, the Scatter plot sent us all the new data: ')
    print('To access the data, try modifying the function and printing the data variable')
    global pdata 
    pdata = [scatter_plot.x,scatter_plot.y]

# First, we hook up our function `foo` to the colors attribute (or Trait) of the scatter plot
scatter_plot.observe(foo, ['y','x'])

scatter_plot.enable_move = True