Fix computation of degree matrix (from normal matrix to its adjacency)

[smoia]

Closes #

Fix an error for which degree matrices were computed from the original input matrix, not the adjacency matrix. This is not a problem if the input matrix and the adjacency are the same, but if by any reason they are not, the degree matrix would be wrong.

Proposed Changes

Change Type

• bugfix (+0.0.1)
• minor (+0.1.0)
• major (+1.0.0)
• refactoring (no version update)
• test (no version update)
• infrastructure (no version update)
• documentation (no version update)
• other

Checklist before review

• I added everything I wanted to add to this PR.
• [Code or tests only] I wrote/updated the necessary docstrings.
• [Code or tests only] I ran and passed tests locally.
• [Documentation only] I built the docs locally.
• My contribution is harmonious with the rest of the code: I'm not introducing repetitions.
• My code respects the adopted style, especially linting conventions.
• The title of this PR is explanatory on its own, enough to be understood as part of a changelog.
• I added or indicated the right labels.

• I added information regarding the timeline of completion for this PR.

• Please, comment on my PR while it's a draft and give me feedback on the development!

[codecov]

Codecov Report

Merging #67 (2f841be) into master (059532f) will increase coverage by 0.31%.
The diff coverage is 100.00%.

@@            Coverage Diff             @@
##           master      #67      +/-   ##
==========================================
+ Coverage   90.61%   90.93%   +0.31%     
==========================================
  Files          14       14              
  Lines        1236     1235       -1     
==========================================
+ Hits         1120     1123       +3     
+ Misses        116      112       -4     

Files Changed	Coverage Δ
nigsp/operations/laplacian.py	95.78% <100.00%> (+4.12%)	⬆️

[smoia]

🚀 PR was released in 0.18.1 🚀

[mscheltienne]

Have a look at assert_allclose from the numpy.testing module:
https://numpy.org/doc/stable/reference/generated/numpy.testing.assert_allclose.html

Compared to np.allclose it has a saner atol=0 and more informative message on failure. I got that tip from a trusted Scipy maintainer ;)

[smoia]

Nice! Do you want to open a quick pr to change it?