3

I'm working on a jupyter based dashboard for data analysis and am planning on using bqplot for plotting the data. Part of the spec for the dashboard is to be able to adjust the axes to be able to zoom in/out on the data. So far I've been able to get this to update dynamically without needing to reload the figure entirely. Is there a way to to do this? If so, how? Below is a snippet of roughly what I mean:

def updateXAxis(values):
    #Update X-axis min/max value here

x_sc = LinearScale(min=float(x_data[0]))
y_sc = LinearScale()

ax_x = Axis(label='X', scale=x_sc, grid_lines='solid', tick_format='0f')
ax_y = Axis(label='Y', scale=y_sc, orientation='vertical', tick_format='0.2f')

m_fig = dict(left=100, top=50, bottom=50, right=100)
fig = Figure(axes=[ax_x, ax_y], marks=data_values, fig_margin=m_fig)

x_range = IntRangeSlider(value=[0,1000],
                        min=0,
                        max=2000,
                        step=1,
                        description="X Axis",
                        disabled=False,
                        continuous_update=False,
                        orientation='horizontal',
                        readout=True)
interactive(updateXAxis, values=x_range)
fig
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2 回答 2

1

这里的问题最终是该interactive功能不是很灵活。相反observe应该使用，下面是一个例子：

def updateXAxis(values):
    if change['type'] == 'change' and change['name'] == 'value':
        x_sc.min = change['new'][0]
        x_sc.max = change['new'][1]

x_sc = LinearScale(min=float(x_data[0]))
y_sc = LinearScale()

ax_x = Axis(label='X', scale=x_sc, grid_lines='solid', tick_format='0f')
ax_y = Axis(label='Y', scale=y_sc, orientation='vertical', tick_format='0.2f')

m_fig = dict(left=100, top=50, bottom=50, right=100)
fig = Figure(axes=[ax_x, ax_y], marks=data_values, fig_margin=m_fig)

x_range = IntRangeSlider(value=[0,1000],
                        min=0,
                        max=2000,
                        step=1,
                        description="X Axis",
                        disabled=False,
                        continuous_update=False,
                        orientation='horizontal',
                        readout=True)

x_range.observe(updateXAxis)
widgets.VBox([fig, x_range])

在我在这里提交的 git 问题上有一个稍微更详细的答案：https ://github.com/bloomberg/bqplot/issues/712

于 2018-08-31T15:06:29.670 回答
0

bqplot 中已经内置了用于平移和缩放图表的交互，我认为您不需要构建自己的交互。看看这个笔记本中的例子。

https://github.com/bloomberg/bqplot/blob/master/examples/Interactions/Interaction%20Layer.ipynb。

您需要添加额外的一行来构建交互。您可以在字典中传递一两个刻度，我在这里将其限制为 x。然后在创建 Figure 时将 PanZoom 对象与交互 kwarg 一起传递。

panzoom = PanZoom(scales={'x': [x_sc]})
fig = Figure(axes=[ax_x, ax_y], marks=[data_values], fig_margin=m_fig, interaction=panzoom)

完整示例：

from bqplot import *
from ipywidgets import *
import numpy as np

x_data = np.linspace(1,101)
y_data = np.linspace(1,101)
x_sc = LinearScale()
y_sc = LinearScale()

ax_x = Axis(label='X', scale=x_sc, grid_lines='solid', tick_format='0f')
ax_y = Axis(label='Y', scale=y_sc, orientation='vertical', tick_format='0.2f')

scatter = Scatter(x=x_data, y=y_data, scales={'x': x_sc, 'y': y_sc})

m_fig = dict(left=100, top=50, bottom=50, right=100)
panzoom = PanZoom(scales={'x': [x_sc]})
fig = Figure(axes=[ax_x, ax_y], marks=[scatter], fig_margin=m_fig, interaction=panzoom)

x_range = IntRangeSlider(value=[0,1000],
                        min=0,
                        max=2000,
                        step=1,
                        description="X Axis",
                        disabled=False,
                        continuous_update=False,
                        orientation='horizontal',
                        readout=True)

fig
于 2018-08-30T14:12:39.547 回答