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Discover how to fix `mpmath` not working properly with high precision settings in Jupyter Notebook for Python.
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This video is based on the question https://stackoverflow.com/q/76815348/ asked by the user 'ShoutOutAndCalculate' ( https://stackoverflow.com/u/11141816/ ) and on the answer https://stackoverflow.com/a/76815394/ provided by the user 'Tim Peters' ( https://stackoverflow.com/u/2705542/ ) at 'Stack Overflow' website. Thanks to these great users and Stackexchange community for their contributions.

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Understanding mpmath High Precision Issues in Jupyter Notebook

If you've been working with the mpmath library in Python for high precision calculations, you may have encountered a frustrating issue: despite setting the desired precision, the calculations seem to revert back to a default level. This problem can arise when working within Jupyter Notebook, especially if you've recently reinstalled libraries or environments.

In this post, we'll explore why you might be facing this issue and how to effectively resolve it, ensuring your precise calculations function as intended.

The Problem

The user reported attempting high precision division using mpmath as follows:

[[See Video to Reveal this Text or Code Snippet]]

However, the output returned was:

[[See Video to Reveal this Text or Code Snippet]]

This indicates that they were unable to achieve the expected high precision despite setting mp.dps (digits of precision) to 530. Other similar attempts, like using mp.nprint(mp.mpf(1) / mp.mpf(6), 50), yielded unexpected results as well.

Diagnosis of the Issue

Upon closer examination, the core issue arises from misconfiguring the precision settings.

The Mistake:

The user was modifying the dps attribute at the module level instead of the context level of mpmath.

Why This Matters:

Setting mp.dps = 530 does not affect the context of the library directly. Instead, it results in the module’s default behaviors that often do not align with the high precision intentions.

The Solution

To resolve this issue, it's imperative to correctly set the precision in the mpmath context. Here’s how to do it properly:

Correct Code Implementation:

Import the mpmath module:

[[See Video to Reveal this Text or Code Snippet]]

Set the context precision:
Instead of modifying mp.dps, you should use:

[[See Video to Reveal this Text or Code Snippet]]

Perform your high precision calculation:

[[See Video to Reveal this Text or Code Snippet]]

Example Output:

With the correct setup, you can expect an output like:

[[See Video to Reveal this Text or Code Snippet]]

This demonstrates a calculation in line with the precision you intended to set.

Best Practices

Avoid Import Aliasing: When using mpmath, refrain from using import mpmath as mp. This can lead to confusion between the module itself and the context object. It’s best to use the full module name to eliminate any ambiguity.

Check Your Setup: If issues persist, ensure your mpmath library is properly installed and updated. A clean installation often resolves underlying issues.

Conclusion

Using the mpmath library for high precision calculations in Python can be incredibly powerful — provided it's configured correctly. By setting the precision at the context level of the module, you can ensure that your calculations reflect the desired level of accuracy. Always remember the distinction between the module and its context attributes to avoid common pitfalls like those described above.

Now, get back to coding with the confidence that your mpmath functionality is back on track!