arraydebug 0.1.1

ArrayDebug generates human-friendly debug information for array-like objects.

ArrayDebug

ArrayDebug generates human-friendly debug information for array-like objects.

• Free software: MIT license
• Support python 3.6+

Usage

All you need is to import arraydebug after numpy, torch, pandas, etc.

>>> import numpy as np
>>> import torch
>>> import pandas as pd
>>> ...
>>> import arraydebug # import at last

Then you will get information of the array-like object shown in the debugger, like:

<Tensor: shape=(6, 4), dtype=float32, device='cpu', requires_grad=True>

It works with all debuggers which rely on repr function to display variable information.

• VSCode
• IPython
• pdb
• etc.

VSCode

IPython

In [1]: import torch

In [2]: import arraydebug

In [3]: torch.ones(6, 4, requires_grad=True)
Out[3]:
<Tensor: shape=(6, 4), dtype=float32, device='cpu', requires_grad=True>
tensor([[1., 1., 1., 1.],
        [1., 1., 1., 1.],
        [1., 1., 1., 1.],
        [1., 1., 1., 1.],
        [1., 1., 1., 1.],
        [1., 1., 1., 1.]], requires_grad=True)

pdb

$ python -m pdb examples/example.py
...
...
...
(Pdb) tensor
<Tensor: shape=(6, 4), dtype=float32, device='cpu', requires_grad=True>
tensor([[0.0833, 0.2675, 0.4372, 0.5344],
        [0.9977, 0.6844, 0.1404, 0.2646],
        [0.7211, 0.7529, 0.1239, 0.2511],
        [0.1717, 0.6611, 0.6598, 0.9705],
        [0.5230, 0.3439, 0.0459, 0.9937],
        [0.8603, 0.6598, 0.0652, 0.1235]], requires_grad=True)

Credits

This package was created with Cookiecutter and the audreyr/cookiecutter-pypackage project template.

History

0.1.0 (2022-03-27)

• First release on PyPI.
• Support numpy.ndarray, torch.Tensor.
• Support objects with __array__ method.
• Setup github CI workflow.