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add 5th and 95th percentile #317

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Merged
merged 4 commits into from
Oct 17, 2025
Merged

add 5th and 95th percentile #317

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6336578
add 5th and 95th percentile
ritvikvasan Oct 2, 2025
ed936f2
ruff format
ritvikvasan Oct 2, 2025
6ef1829
optimize sorting and test
ziw-liu Oct 17, 2025
5acd8bd
remove scipy
ziw-liu Oct 17, 2025
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20 changes: 20 additions & 0 deletions tests/preprocessing/test_norm_stats.py
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Original file line number Diff line number Diff line change
@@ -0,0 +1,20 @@
import numpy as np
from viscy.utils.mp_utils import get_val_stats


def test_get_val_stats():
sample_values = np.arange(0, 101)
stats = get_val_stats(sample_values)
expected_stats = {
"mean": 50.0,
"std": np.std(sample_values),
"median": 50.0,
"iqr": 50.0,
"p1": 1.0,
"p5": 5.0,
"p95": 95.0,
"p99": 99.0,
"p99_p1": 98.0,
"p95_p5": 90.0,
}
assert stats == expected_stats
10 changes: 9 additions & 1 deletion viscy/utils/meta_utils.py
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Original file line number Diff line number Diff line change
Expand Up @@ -31,7 +31,15 @@ def write_meta_field(position: ngff.Position, metadata, field_name, subfield_nam
"""
if field_name in position.zattrs:
if subfield_name in position.zattrs[field_name]:
position.zattrs[field_name][subfield_name].update(metadata)
# Need to create a new dict and reassign to trigger zarr write
updated_subfield = {
**position.zattrs[field_name][subfield_name],
**metadata,
}
position.zattrs[field_name] = {
**position.zattrs[field_name],
subfield_name: updated_subfield,
}
else:
D1 = position.zattrs[field_name]
field_metadata = {
Expand Down
17 changes: 13 additions & 4 deletions viscy/utils/mp_utils.py
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Original file line number Diff line number Diff line change
Expand Up @@ -2,7 +2,6 @@

import iohub.ngff as ngff
import numpy as np
import scipy.stats

import viscy.utils.image_utils as image_utils
import viscy.utils.masks as mask_utils
Expand Down Expand Up @@ -260,11 +259,21 @@ def get_val_stats(sample_values):
indices
:return dict meta_row: Dict with intensity data for image
"""

percentiles = [1, 5, 25, 50, 75, 95, 99]
percentile_values = {
k: float(v)
for k, v in zip(percentiles, np.nanpercentile(sample_values, percentiles))
}
meta_row = {
"mean": float(np.nanmean(sample_values)),
"std": float(np.nanstd(sample_values)),
"median": float(np.nanmedian(sample_values)),
"iqr": float(scipy.stats.iqr(sample_values)),
"median": percentile_values[50],
"iqr": percentile_values[75] - percentile_values[25],
"p5": percentile_values[5],
"p95": percentile_values[95],
"p95_p5": percentile_values[95] - percentile_values[5],
"p1": percentile_values[1],
"p99": percentile_values[99],
"p99_p1": percentile_values[99] - percentile_values[1],
}
return meta_row