Closed d-schindler closed 2 years ago
Merging #52 (2a61121) into master (f00fd8e) will increase coverage by
8.14%
. The diff coverage is60.30%
.
@@ Coverage Diff @@
## master #52 +/- ##
==========================================
+ Coverage 41.85% 50.00% +8.14%
==========================================
Files 8 8
Lines 571 586 +15
==========================================
+ Hits 239 293 +54
+ Misses 332 293 -39
Flag | Coverage Δ | |
---|---|---|
pytest | 50.00% <60.30%> (+8.14%) |
:arrow_up: |
Flags with carried forward coverage won't be shown. Click here to find out more.
Impacted Files | Coverage Δ | |
---|---|---|
src/pygenstability/plotting.py | 0.00% <0.00%> (ø) |
|
src/pygenstability/optimal_scales.py | 50.72% <50.72%> (ø) |
|
src/pygenstability/constructors.py | 100.00% <100.00%> (+9.40%) |
:arrow_up: |
src/pygenstability/pygenstability.py | 100.00% <100.00%> (+9.30%) |
:arrow_up: |
:mega: We’re building smart automated test selection to slash your CI/CD build times. Learn more
@d-schindler , I'm not sure this works, on the multiscale example it does not as compared to version on master branch. I'll let you fix it before we can merge
I made a small modification and it should work on our multiscale example. But it would be good to test it on other graphs and compare to the version of @peach-lucien
as scale selection has become a more central aspect of the whole package, I moved the file from contrib to pygenstability
@d-schindler , if this goes into the main package, could you add tests to it? thank you!
ah sorry, as it's called in the main function, it is tested, except the plotting, which we'll add later once the package it more stable, we can merge then!
I realised that the current implementation of the scale selection criterion does not recover all local minima of the criterion and that the criterion looks different to my original implementation. Therefore, I replaced the code with my original implementation. In particular, the moving mean is computed with pandas using a triangular window and this seems to be different to the numpy method used previously. However, we should get rid off pandas later.