Handle denormalized numbers when normalizing intensities/codebooks #1371
Conversation
This file contains hidden or bidirectional Unicode text that may be interpreted or compiled differently than what appears below. To review, open the file in an editor that reveals hidden Unicode characters.
Learn more about bidirectional Unicode characters
Codecov Report
@@ Coverage Diff @@
## master #1371 +/- ##
=======================================
Coverage 89.21% 89.21%
=======================================
Files 148 148
Lines 5377 5377
=======================================
Hits 4797 4797
Misses 580 580
Continue to review full report at Codecov. Legend - Click here to learn more |
6029710 to
0de03d9
Compare
When there are intensities that are near-zero (due to numpy generating some denorm-to-zero values instead of zeros), we sometimes get near-zero total intensity. When we normalize that, we get infinite values. Instead of using `fillna`, we replace anything that is not finite with the partitioned value. Test plan: `make fast` and on the linux docker container, I verified that the code snipped posted by @richardque works now. Fixes #1365
Sign up for free
to join this conversation on GitHub.
Already have an account?
Sign in to comment
Add this suggestion to a batch that can be applied as a single commit.
This suggestion is invalid because no changes were made to the code.
Suggestions cannot be applied while the pull request is closed.
Suggestions cannot be applied while viewing a subset of changes.
Only one suggestion per line can be applied in a batch.
Add this suggestion to a batch that can be applied as a single commit.
Applying suggestions on deleted lines is not supported.
You must change the existing code in this line in order to create a valid suggestion.
Outdated suggestions cannot be applied.
This suggestion has been applied or marked resolved.
Suggestions cannot be applied from pending reviews.
Suggestions cannot be applied on multi-line comments.
Suggestions cannot be applied while the pull request is queued to merge.
Suggestion cannot be applied right now. Please check back later.
When there are intensities that are near-zero (due to numpy generating some denorm-to-zero values instead of zeros), we sometimes get near-zero total intensity. When we normalize that, we get infinite values. Instead of using
fillna, we replace anything that is not finite with the partitioned value.Test plan:
make fastand on the linux docker container, I verified that the code snipped posted by @richardque works now.Fixes #1365