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Add float_datatype parameter for SingleCells compartment load and merge performance flexibility #248
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Codecov Report
@@ Coverage Diff @@
## master #248 +/- ##
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- Coverage 95.74% 95.71% -0.03%
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Files 53 53
Lines 2822 2826 +4
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+ Hits 2702 2705 +3
- Misses 120 121 +1
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Looks good @d33bs ! This should help @axiomcura in WayScience/cell-health-data#10 (I think this was the right PR to tag here, but it will also likely help many others as well!)
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Looks good @d33bs ! Only two minor comments but then I think we're good to merge.
Looks good! I'm going to merge. Thanks @d33bs |
Description
This change adds a float_datatype parameter for use with SingleCells compartment load and merge operations to assist with performance considerations (as cited within WayScience/cell-health-data#10 and other areas). In testing this locally using
np.float32
instead ofnp.float64
I found ~45% reduction in peak memory consumption while runningmerge_single_cells
on small testing datasets (this reduction may vary contingent on dataset).This change is also related to #198 and #227 (if only to notate datatype considerations).
What is the nature of your change?
Checklist
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