Filter Operations on Label2DModel and Shape

[selmanozleyen]

Hi @timtreis @ilan-gold ,

I am working on this PR also and thought maybe it will be smart to have the code I use there supported here because they seem useful at first glance. So I wrote and tested subset_sdata_by_table_mask in this PR.

@LucaMarconato I can't ask for reviews other than @ilan-gold do you know why?

---

Some notes

• For PointsModel (GeoDataFrame) it assumes the index is instance_id always
• For Label2DModel the image is assumed to have the instance_ids as values themselves
• For Label2DModel when the element is a xr.DataTree it assumes the keys are the different scales
• I didn't use the relational operations to get the shapes and points for this reason because they assume the merge is on the element indices themselves
• I didn't add support for AnnData because we would have to also document the return types and stuff in the case of AnnData input which doesn't seem reasonable to me if its going to make it 1-1 same as scanpy.pp.filter_cells

Code excerpt from tests to demonstrate the usage:

sdata = concatenate(
        {
            "labels": blobs_annotating_element("blobs_labels"),
            "shapes": blobs_annotating_element("blobs_circles"),
            "points": blobs_annotating_element("blobs_points"),
            "multiscale_labels": blobs_annotating_element("blobs_multiscale_labels"),
        },
        concatenate_tables=True,
    )
    third_elems = sdata.tables["table"].obs["instance_id"] == 3
    subset_sdata = subset_sdata_by_table_mask(sdata, "table", third_elems)

    labels_remaining_ids = set(np.unique(subset_sdata.labels["blobs_labels-labels"].data.compute())) - {0}
    assert labels_remaining_ids == {3}

    for scale in subset_sdata.labels["blobs_multiscale_labels-multiscale_labels"]:
        ms_labels_remaining_ids = set(
            np.unique(subset_sdata.labels["blobs_multiscale_labels-multiscale_labels"][scale].image.compute())
        ) - {0}
        assert ms_labels_remaining_ids == {3}

    points_remaining_ids = set(np.unique(subset_sdata.points["blobs_points-points"]["instance_id"].compute())) - {0}
    assert points_remaining_ids == {3}

    shapes_remaining_ids = set(np.unique(subset_sdata.shapes["blobs_circles-shapes"].index)) - {0}
    assert shapes_remaining_ids == {3}

[codecov]

Codecov Report

✅ All modified and coverable lines are covered by tests.
✅ Project coverage is 92.17%. Comparing base (79cf8c9) to head (631fe2a).

Additional details and impacted files

@@            Coverage Diff             @@
##             main     #946      +/-   ##
==========================================
+ Coverage   92.08%   92.17%   +0.08%     
==========================================
  Files          48       48              
  Lines        7446     7500      +54     
==========================================
+ Hits         6857     6913      +56     
+ Misses        589      587       -2     

Files with missing lines	Coverage Δ
src/spatialdata/__init__.py	96.42% <ø> (ø)
src/spatialdata/_core/query/relational_query.py	92.78% <100.00%> (+1.28%)	⬆️

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[timtreis]

Can this PR make use of

spatialdata/src/spatialdata/_core/query/relational_query.py

Line 787 in 7604a3d

def match_sdata_to_table(

and

spatialdata/src/spatialdata/_core/query/relational_query.py

Line 757 in 7604a3d

def match_element_to_table(

?

[timtreis]

Parametrise, don't loop over inputs

[selmanozleyen]

the loop here is for the multiple scales. Or did you mean something else?

[selmanozleyen]

@timtreis About using def match_sdata_to_table( and the other. I noticed for shapes the index is assumed to be the instance_id but this doesn't match how the blobs are filled and would fail in the tests. It was documented that way so I assumed this was intentional but match_sdata_to_table wouldn't (and didn't) pass the current tests because it assumed the element index was the instance_id.

[selmanozleyen]

@timtreis now I make use of match_element_to_table. Other than one comment all seems resolved