完全无法为多个跟踪器返回所选数据点的信息 [英] Plotly failed to return information for selected data points for multiple tracers

问题描述

在此示例中，他们将所有内容绘制在单个go.Scatter跟踪器中，然后他们可以使用selection_fn获取所选点的信息.

In this example, they plot everything in a single go.Scatter tracer and then they can use the selection_fn to get the information for the selected points.

我想对包含3个聚类的数据集做类似的事情.为了使群集更容易看到，我为一个类使用了一个跟踪器.因此，我尝试修改示例代码以使其适应我的数据集，如下所示.

I want to do similar thing with my dataset with consists of 3 clusters. In order to make the clusters easier to be seen, I use one tracer for one class. Therefore, I try to modify the example code to adapt to my dataset as shown below.

import plotly.plotly as py
import plotly.graph_objs as go
from plotly.tools import set_credentials_file
import plotly.offline as py

import pandas as pd
import numpy as np
from ipywidgets import interactive, HBox, VBox

from sklearn.datasets import make_blobs

X, y = make_blobs(30,random_state=101)

py.init_notebook_mode()

f = go.FigureWidget([go.Scatter(y = X[y==0][:,1], x = X[y==0][:,0], mode = 'markers'), 
                     go.Scatter(y = X[y==1][:,1], x = X[y==1][:,0], mode = 'markers'),
                     go.Scatter(y = X[y==2][:,1], x = X[y==2][:,0], mode = 'markers')])
scatter = f.data[0]
N = len(X)

# Create a table FigureWidget that updates on selection from points in the scatter plot of f
t = go.FigureWidget([go.Table(
    header=dict(values=['x','y','class'],
                fill = dict(color='#C2D4FF'),
                align = ['left'] * 5),
    cells=dict(values=[X[:,0], X[:,1], y],
               fill = dict(color='#F5F8FF'),
               align = ['left'] * 5))])

def selection_fn(trace,points,selector):
    print(points.point_inds)
    t.data[0].cells.values = [X[points.point_inds,0], X[points.point_inds,1], y[points.point_inds]]

scatter.on_selection(selection_fn)

# Put everything together
VBox((HBox(),f,t))

错误行为1:返回错误信息

从trace 0中选择两个数据点时，它确实向我返回了2条信息，但这是错误的.

Wrong Behaviors 1: Wrong information returned

When selecting two data points from trace 0, it does return 2 information to me, yet it's wrong.

从跟踪器1和2选择数据点时，它甚至不返回信息

When selecting data points from tracer 1 and 2, it doesn't even return the information

经过简短的调试后，我注意到每个跟踪器和完整数据集的索引都不匹配.该代码只能从跟踪器0返回索引，但是，当将索引传递到完整数据集时，它会为您提供错误的点信息.从示踪剂1和2选择点时，它甚至无法返回索引，因此无法提取任何信息.

After a brief debugging, I notices that there is mismatch in the index for each tracer and the complete dataset. This code can return the index from tracer 0 only, however, when it passes the index to the full dataset, it gives you the mis-matached information for the points. When selecting points from tracer 1 and 2, it can't even return the index, thus no information can be extracted.

尽管我了解问题所在，但由于我仍然不熟悉Plotly，所以我不知道如何修改代码.

Although I understand the problem, I don't know how to modify the code since I am still new to plotly.

推荐答案

尝试了几天之后，我想出了一种实现方法. (也许有人仍然可以提供更好的方法?)

After trying it for several days, I have figured out a hack to achieve it. (Maybe someone can still provide a better way?)

技巧是为表中的每一列创建3个列表，并将所选点的数据附加到列表中，并最后更新表.

The trick is to create 3 lists for each of the column in the table, and the append the data of the selected points to the list, and update the table in the end.

这是完整的代码.

X, y = make_blobs(30,random_state=101)

py.init_notebook_mode()

f = go.FigureWidget([go.Scatter(y = X[y==0][:,1], x = X[y==0][:,0], text=y[y==0], mode = 'markers', name='class 0'), 
                     go.Scatter(y = X[y==1][:,1], x = X[y==1][:,0], text=y[y==1], mode = 'markers', name='class 1'),
                     go.Scatter(y = X[y==2][:,1], x = X[y==2][:,0], text=y[y==2], mode = 'markers', name='class 2')])


# Create a table FigureWidget that updates on selection from points in the scatter plot of f
t = go.FigureWidget([go.Table(
    header=dict(values=['x','y', 'class'],
                fill = dict(color='#C2D4FF'),
                align = ['left'] * 5),
    cells=dict(values=[X[:,0], X[:,1], y],
               fill = dict(color='#F5F8FF'),
               align = ['left'] * 5))])

# def data_append(trace,points,selector):
#     X1 = []
#     X2 = []
#     c = []


X1 = []
X2 = []
data_cluster = []
num_called = 0
def selection_fn(trace,points,selector):
    global num_called
    global X1, X2, data_cluster
    if num_called == 3: # number of scatters
        num_called = 0
        X1 = []
        X2 = []
        data_cluster = []
    X1.extend(trace['x'][points.point_inds])
    X2.extend(trace['y'][points.point_inds])
    data_cluster.extend(trace['text'][points.point_inds])
    t.data[0].cells.values = [X1, X2,data_cluster]
    num_called +=1
for scatter in f.data:
    scatter.on_selection(selection_fn)

# Put everything together
VBox((HBox(),f,t))

代码输出

如您所见，该表将准确返回三个选定数据点的信息.

As you can see, the table return exactly the information for the three selected data points.

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