Closed dondi closed 6 months ago
This will be the next one that @ceciliazaragoza and @akaiap will tackle this week.
I also found repetitive code in scripts for generate_network.py
, generate_new_network_version.py
and generate_sgd_network_from_yeastract_network.py
I found a lingering /* eslint-disable max-len */
in line 1 at the top of server/dals/protein-dal.js
, so removed the comment, fixed multiline string in the file, and ran lint and test with all tests passing.
I also found repetitive code in scripts for
generate_network.py
,generate_new_network_version.py
andgenerate_sgd_network_from_yeastract_network.py
A'Kaia and I are working on this issue that Maika brought up, and for the second comment shouldn't the print string be print(f'Creating REGULATORS TO REGULATORS MATRIX\n')
?
A'Kaia and I worked on the issue that Maika suggested in generate_network.py, and lint and test are all passing.
I also added the createMatrix method to generate_new_network_version.py
and removed the redundancy in if result != False
, changing to if result
@ceciliazaragoza will issue a PR to merge with beta
for these changes. In addition, @kdahlquist pointed out that we need to reinstate the practice of doing QA on our pull requests in accordance with the https://github.com/dondi/GRNsight/wiki/Pull-Request-Checklist
So for this PR, @ceciliazaragoza will copy/paste the checklist on this wiki page on the pull request, and the team will work through the checklist concurrently on their respective local builds
If any checklist item regressions are found, the team will:
For @nchun2, her work in GRNmap sets her up as the “import/export” specialist between GRNmap and GRNsight. She can focus on items that ensure smooth file-based data flow between the two systems. @kdahlquist will write a specific issue
A number of smaller style issues where noted in #996—this can be good onboarding work for new team members in the spring. Go to the closed pull requests list, find #996, then go over the comments from the last review, changing the code accordingly without incurring regressions