Matplotlib Notebook Interactive Plot: Cell Must Be Stopped to Display

If you are using Matplotlib Notebook to create interactive plots, you may encounter a situation where the plot does not display until the cell is stopped. This can be frustrating, especially if you are expecting to see the plot in real-time as you run your code. In this article, we will explore why this happens and how you can resolve this issue.

Matplotlib is a popular data visualization library in Python that allows you to create a wide range of charts and plots. Matplotlib Notebook is an interactive environment that integrates Matplotlib with Jupyter Notebook, providing a convenient way to create and explore plots.

When you create a plot using Matplotlib Notebook, the plot is typically displayed immediately after the code cell is executed. However, there are cases where the plot does not appear until the cell is stopped. This behavior is due to the way Matplotlib Notebook handles the rendering of plots.

By default, Matplotlib Notebook uses a backend called "inline" to render plots. This means that the plots are rendered directly in the notebook, within the output cell, using the HTML5 canvas element. The inline backend is convenient because it does not require any additional setup or configuration.

However, the inline backend has some limitations. One of these limitations is that it does not support the dynamic updating of plots. When you create an interactive plot that updates in real-time, the inline backend may not be able to render the plot until the code cell is finished executing.

To understand why this happens, let's consider an example. Suppose you have a code cell that creates a plot and updates it every second. If the inline backend were to render the plot immediately, it would need to continuously update the HTML5 canvas element in the output cell. This can be computationally expensive and may cause the notebook to become unresponsive.

To avoid this issue, Matplotlib Notebook defers the rendering of plots until the code cell is finished executing. This means that the plot will not be displayed until the cell is stopped or the execution is complete. While this behavior may seem counterintuitive, it is necessary to ensure the smooth execution of the notebook.

So, how can you resolve this issue and force the plot to display immediately? One solution is to change the backend used by Matplotlib Notebook. Instead of using the inline backend, you can switch to a different backend that supports the dynamic updating of plots.

One popular backend that supports dynamic updating is the "nbagg" backend. To switch to the "nbagg" backend, you can add the following code at the beginning of your notebook:

This code sets the backend to "nbagg" and enables the interactive mode. Once you have switched to the "nbagg" backend, you can create interactive plots that update in real-time. The plot will be displayed immediately, without the need to stop the cell.

It is important to note that switching to the "nbagg" backend may require some additional setup. You may need to install additional dependencies or enable certain settings in your Jupyter Notebook environment. Make sure to consult the Matplotlib documentation or the documentation of your Jupyter Notebook distribution for more information.

In conclusion, if you are using Matplotlib Notebook and your plots are not displaying until the cell is stopped, it is likely due to the limitations of the inline backend. To resolve this issue, you can switch to a different backend that supports the dynamic updating of plots, such as the "nbagg" backend. By making this change, you can create interactive plots that update in real-time and are displayed immediately.

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