Python Jupyter Notebook：带有小部件的交互式绘图
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Jupyter Notebook: interactive plot with widgets
提问by FLab
I am trying to generate an interactive plot that depends on widgets. The problem I have is that when I change parameters using the slider, a new plot is done after the previous one, instead I would expect only one plot changing according to the parameters.
我正在尝试生成一个依赖于小部件的交互式绘图。我遇到的问题是，当我使用滑块更改参数时，会在前一个绘图之后完成一个新绘图，而不是我希望根据参数只更改一个绘图。
Example:
例子：
from ipywidgets import interact, interactive, fixed, interact_manual
import ipywidgets as widgets
import matplotlib.pyplot as plt
%matplotlib inline
import numpy as np
def plot_func(freq):
x = np.linspace(0, 2*np.pi)
y = np.sin(x * freq)
plt.plot(x, y)
interact(plot_func, freq = widgets.FloatSlider(value=7.5,
min=1,
max=5.0,
step=0.5))
After moving the slider to 4.0, I have:
将滑块移动到 4.0 后，我有：
while I just want one figure to change as I move the slider. How can I achieve this?
而我只想在移动滑块时改变一个数字。我怎样才能做到这一点？
(I am using Python 2.7, matplotlib 2.0 and I have just updated notebook and jupyter to the latest version. let me know if further info is needed.)
（我使用的是 Python 2.7、matplotlib 2.0，我刚刚将 notebook 和 jupyter 更新到了最新版本。如果需要更多信息，请告诉我。）
回答by ImportanceOfBeingErnest
As you want to change the figure, instead of creating a new one, may I suggest the following way:
由于您想更改图形，而不是创建一个新图形，我可以建议以下方式：
- Use an interactive backend;
%matplotlib notebook - Update the line in the plot, instead of drawing new ones.
- 使用交互式后端；
%matplotlib notebook - 更新图中的线，而不是绘制新线。
So the code could look something like this:
所以代码看起来像这样：
%matplotlib notebook
from ipywidgets import *
import numpy as np
import matplotlib.pyplot as plt
x = np.linspace(0, 2 * np.pi)
fig = plt.figure()
ax = fig.add_subplot(1, 1, 1)
line, = ax.plot(x, np.sin(x))
def update(w = 1.0):
line.set_ydata(np.sin(w * x))
fig.canvas.draw_idle()
interact(update);
Alternatively you may use plt.show()as in this answer.
或者，您可以使用plt.show()as in this answer。
回答by Stelios
This is an issue (?) introduced in the last version of jupyter and/or ipywidgets. One workaround I found was to add the line plt.show()at the end of plot_func.
这是 jupyter 和/或 ipywidgets 的最新版本中引入的问题 (?)。我发现的一种解决方法是plt.show()在plot_func.
