Verifying bugs and bug fixes in NumPy#

In this how-to you will learn how to:

Verify the existence of a bug in NumPy
Verify the fix, if any, made for the bug

While you walk through the verification process, you will learn how to:

Set up a Python virtual environment (using virtualenv)
Install appropriate versions of NumPy, first to see the bug in action, then to verify its fix

Issue 16354 is used as an example.

This issue was:

Title: np.polymul return type is np.float64 or np.complex128 when given an all-zero argument

np.polymul returns an object with type np.float64 when one argument is all zero, and both arguments have type np.int64 or np.float32. Something similar happens with all zero np.complex64 giving result type np.complex128.

This doesn’t happen with non-zero arguments; there the result is as expected.

This bug isn’t present in np.convolve.

Reproducing code example:
>>> import numpy as np
>>> np.__version__
'1.18.4'
>>> a = np.array([1,2,3])
>>> z = np.array([0,0,0])
>>> np.polymul(a.astype(np.int64), a.astype(np.int64)).dtype
dtype('int64')
>>> np.polymul(a.astype(np.int64), z.astype(np.int64)).dtype
dtype('float64')
>>> np.polymul(a.astype(np.float32), z.astype(np.float32)).dtype
dtype('float64')
>>> np.polymul(a.astype(np.complex64), z.astype(np.complex64)).dtype
dtype('complex128')
Numpy/Python version information:
>>> import sys, numpy; print(numpy.__version__, sys.version)
1.18.4 3.7.5 (default, Nov  7 2019, 10:50:52) [GCC 8.3.0]

1. Set up a virtual environment#

Create a new directory, enter into it, and set up a virtual environment using your preferred method. For example, this is how to do it using virtualenv on linux or macOS:

virtualenv venv_np_bug
source venv_np_bug/bin/activate

This ensures the system/global/default Python/NumPy installation will not be altered.

2. Install the NumPy version in which the bug was reported#

The report references NumPy version 1.18.4, so that is the version you need to install in this case.

Since this bug is tied to a release and not a specific commit, a pre-built wheel installed in your virtual environment via pip will suffice:

pip install numpy==1.18.4

Some bugs may require you to build the NumPy version referenced in the issue report. To learn how to do that, visit Building from source.

3. Reproduce the bug#

The issue reported in #16354 is that the wrong dtype is returned if one of the inputs of the method numpy.polymul is a zero array.

To reproduce the bug, start a Python terminal, enter the code snippet shown in the bug report, and ensure that the results match those in the issue:

>>> import numpy as np
>>> np.__version__
'...' # 1.18.4
>>> a = np.array([1,2,3])
>>> z = np.array([0,0,0])
>>> np.polymul(a.astype(np.int64), a.astype(np.int64)).dtype
dtype('int64')
>>> np.polymul(a.astype(np.int64), z.astype(np.int64)).dtype
dtype('...') # float64
>>> np.polymul(a.astype(np.float32), z.astype(np.float32)).dtype
dtype('...') # float64
>>> np.polymul(a.astype(np.complex64), z.astype(np.complex64)).dtype
dtype('...') # complex128

As reported, whenever the zero array, z in the example above, is one of the arguments to numpy.polymul, an incorrect dtype is returned.

4. Check for fixes in the latest version of NumPy#

If the issue report for your bug has not yet been resolved, further action or patches need to be submitted.

In this case, however, the issue was resolved by PR 17577 and is now closed. So you can try to verify the fix.

To verify the fix:

Uninstall the version of NumPy in which the bug still exists:
```
pip uninstall numpy
```
Install the latest version of NumPy:
```
pip install numpy
```

In your Python terminal, run the reported code snippet you used to verify the existence of the bug and confirm that the issue has been resolved:

>>> import numpy as np
>>> np.__version__
'...' # 1.18.4
>>> a = np.array([1,2,3])
>>> z = np.array([0,0,0])
>>> np.polymul(a.astype(np.int64), a.astype(np.int64)).dtype
dtype('int64')
>>> np.polymul(a.astype(np.int64), z.astype(np.int64)).dtype
dtype('int64')
>>> np.polymul(a.astype(np.float32), z.astype(np.float32)).dtype
dtype('float32')
>>> np.polymul(a.astype(np.complex64), z.astype(np.complex64)).dtype
dtype('complex64')

Note that the correct dtype is now returned even when a zero array is one of the arguments to numpy.polymul.

5. Support NumPy development by verifying and fixing bugs#

Go to the NumPy GitHub issues page and see if you can confirm the existence of any other bugs which have not been confirmed yet. In particular, it is useful for the developers to know if a bug can be reproduced on a newer version of NumPy.

Comments verifying the existence of bugs alert the NumPy developers that more than one user can reproduce the issue.