feat: support np.random.Generator by flying-sheep · Pull Request #3983 · scverse/scanpy

flying-sheep · 2026-02-23T15:27:34Z

Closes Switch to numpy.random.Generator #3371
Tests included or not required because:

Release notes not necessary because:

The idea is to make the code behave as closely as possible to how it did, but make it read like modern code:

add decorator that converts random_state into rng argument (deduplicating them and using 0 by default)
add helpers that allow old behavior (e.g. for APIs that only take RandomState)
after this PR: make feat: presets #3653 change the default behavior when a preset is passed.

TODO:

add the decorator
add helpers for restarting (e.g. if 0 was passed, it’d be reused by the functions called in function body)
for the functions that called it: re-add the np.random.seed calls (maybe if isinstance(rng, _FakeRandomGen): gen = legacy_numpy_gen(rng) or so?)
- partially done, finish the work
add spawn to _FakeRandomGen (that does nothing) and use spawn for a tree structure
figure out how to handle persistence for new RNG (random_state in adata.uns[thing]["params")

codecov · 2026-02-24T13:00:56Z

❌ 3 Tests Failed:

Tests completed	Failed	Passed	Skipped
2397	3	2394	349

View the top 3 failed test(s) by shortest run time

tests/test_scrublet.py::test_scrublet_simulate_doublets

Stack Traces | 0.061s run time

#x1B[0m#x1B[94mdef#x1B[39;49;00m#x1B[90m #x1B[39;49;00m#x1B[92mtest_scrublet_simulate_doublets#x1B[39;49;00m():#x1B[90m#x1B[39;49;00m
    #x1B[90m    #x1B[39;49;00m#x1B[33m"""Check that doublet simulation runs and simulates some doublets."""#x1B[39;49;00m#x1B[90m#x1B[39;49;00m
        adata_obs = pbmc200()#x1B[90m#x1B[39;49;00m
        sc.pp.filter_genes(adata_obs, min_cells=#x1B[94m3#x1B[39;49;00m)#x1B[90m#x1B[39;49;00m
        sc.pp.filter_cells(adata_obs, min_genes=#x1B[94m3#x1B[39;49;00m)#x1B[90m#x1B[39;49;00m
        adata_obs.layers[#x1B[33m"#x1B[39;49;00m#x1B[33mraw#x1B[39;49;00m#x1B[33m"#x1B[39;49;00m] = adata_obs.X#x1B[90m#x1B[39;49;00m
        sc.pp.normalize_total(adata_obs)#x1B[90m#x1B[39;49;00m
        logged = sc.pp.log1p(adata_obs, copy=#x1B[94mTrue#x1B[39;49;00m)#x1B[90m#x1B[39;49;00m
    #x1B[90m#x1B[39;49;00m
        _ = sc.pp.highly_variable_genes(logged)#x1B[90m#x1B[39;49;00m
        adata_obs = adata_obs[:, logged.var[#x1B[33m"#x1B[39;49;00m#x1B[33mhighly_variable#x1B[39;49;00m#x1B[33m"#x1B[39;49;00m]]#x1B[90m#x1B[39;49;00m
    #x1B[90m#x1B[39;49;00m
        adata_sim = sc.pp.scrublet_simulate_doublets(#x1B[90m#x1B[39;49;00m
            adata_obs, sim_doublet_ratio=#x1B[94m0.02#x1B[39;49;00m, layer=#x1B[33m"#x1B[39;49;00m#x1B[33mraw#x1B[39;49;00m#x1B[33m"#x1B[39;49;00m#x1B[90m#x1B[39;49;00m
        )#x1B[90m#x1B[39;49;00m
    #x1B[90m#x1B[39;49;00m
>       assert_array_equal(#x1B[90m#x1B[39;49;00m
            adata_sim.obsm[#x1B[33m"#x1B[39;49;00m#x1B[33mdoublet_parents#x1B[39;49;00m#x1B[33m"#x1B[39;49;00m],#x1B[90m#x1B[39;49;00m
            np.array([[#x1B[94m13#x1B[39;49;00m, #x1B[94m132#x1B[39;49;00m], [#x1B[94m106#x1B[39;49;00m, #x1B[94m43#x1B[39;49;00m], [#x1B[94m152#x1B[39;49;00m, #x1B[94m3#x1B[39;49;00m], [#x1B[94m160#x1B[39;49;00m, #x1B[94m103#x1B[39;49;00m]]),#x1B[90m#x1B[39;49;00m
        )#x1B[90m#x1B[39;49;00m
#x1B[1m#x1B[31mE       AssertionError: #x1B[0m
#x1B[1m#x1B[31mE       Arrays are not equal#x1B[0m
#x1B[1m#x1B[31mE       #x1B[0m
#x1B[1m#x1B[31mE       Mismatched elements: 8 / 8 (100%)#x1B[0m
#x1B[1m#x1B[31mE       First 5 mismatches are at indices:#x1B[0m
#x1B[1m#x1B[31mE        [0, 0]: 175 (ACTUAL), 13 (DESIRED)#x1B[0m
#x1B[1m#x1B[31mE        [0, 1]: 58 (ACTUAL), 132 (DESIRED)#x1B[0m
#x1B[1m#x1B[31mE        [1, 0]: 94 (ACTUAL), 106 (DESIRED)#x1B[0m
#x1B[1m#x1B[31mE        [1, 1]: 71 (ACTUAL), 43 (DESIRED)#x1B[0m
#x1B[1m#x1B[31mE        [2, 0]: 18 (ACTUAL), 152 (DESIRED)#x1B[0m
#x1B[1m#x1B[31mE       Max absolute difference among violations: 192#x1B[0m
#x1B[1m#x1B[31mE       Max relative difference among violations: 64.#x1B[0m
#x1B[1m#x1B[31mE        ACTUAL: array([[175,  58],#x1B[0m
#x1B[1m#x1B[31mE              [ 94,  71],#x1B[0m
#x1B[1m#x1B[31mE              [ 18, 195],#x1B[0m
#x1B[1m#x1B[31mE              [ 97,  47]])#x1B[0m
#x1B[1m#x1B[31mE        DESIRED: array([[ 13, 132],#x1B[0m
#x1B[1m#x1B[31mE              [106,  43],#x1B[0m
#x1B[1m#x1B[31mE              [152,   3],#x1B[0m
#x1B[1m#x1B[31mE              [160, 103]])#x1B[0m

#x1B[1m#x1B[31mtests/test_scrublet.py#x1B[0m:227: AssertionError

tests/test_plotting.py::test_heatmap_var_as_dict

Stack Traces | 0.262s run time

image_comparer = <function image_comparer.<locals>.save_and_compare at 0x7fa21011ab90>

    #x1B[0m#x1B[37m@needs#x1B[39;49;00m.leidenalg#x1B[90m#x1B[39;49;00m
    #x1B[94mdef#x1B[39;49;00m#x1B[90m #x1B[39;49;00m#x1B[92mtest_heatmap_var_as_dict#x1B[39;49;00m(image_comparer) -> #x1B[94mNone#x1B[39;49;00m:#x1B[90m#x1B[39;49;00m
        save_and_compare_images = partial(image_comparer, ROOT, tol=#x1B[94m15#x1B[39;49;00m)#x1B[90m#x1B[39;49;00m
    #x1B[90m#x1B[39;49;00m
        pbmc = pbmc68k_reduced()#x1B[90m#x1B[39;49;00m
        sc.tl.leiden(#x1B[90m#x1B[39;49;00m
            pbmc,#x1B[90m#x1B[39;49;00m
            key_added=#x1B[33m"#x1B[39;49;00m#x1B[33mclusters#x1B[39;49;00m#x1B[33m"#x1B[39;49;00m,#x1B[90m#x1B[39;49;00m
            resolution=#x1B[94m0.5#x1B[39;49;00m,#x1B[90m#x1B[39;49;00m
            flavor=#x1B[33m"#x1B[39;49;00m#x1B[33migraph#x1B[39;49;00m#x1B[33m"#x1B[39;49;00m,#x1B[90m#x1B[39;49;00m
            n_iterations=#x1B[94m2#x1B[39;49;00m,#x1B[90m#x1B[39;49;00m
            directed=#x1B[94mFalse#x1B[39;49;00m,#x1B[90m#x1B[39;49;00m
        )#x1B[90m#x1B[39;49;00m
        #x1B[90m# call umap to trigger colors for the clusters#x1B[39;49;00m#x1B[90m#x1B[39;49;00m
        sc.pl.umap(pbmc, color=#x1B[33m"#x1B[39;49;00m#x1B[33mclusters#x1B[39;49;00m#x1B[33m"#x1B[39;49;00m, show=#x1B[94mFalse#x1B[39;49;00m)#x1B[90m#x1B[39;49;00m
        marker_genes_dict = {#x1B[90m#x1B[39;49;00m
            #x1B[33m"#x1B[39;49;00m#x1B[33m3#x1B[39;49;00m#x1B[33m"#x1B[39;49;00m: [#x1B[33m"#x1B[39;49;00m#x1B[33mGNLY#x1B[39;49;00m#x1B[33m"#x1B[39;49;00m, #x1B[33m"#x1B[39;49;00m#x1B[33mNKG7#x1B[39;49;00m#x1B[33m"#x1B[39;49;00m],#x1B[90m#x1B[39;49;00m
            #x1B[33m"#x1B[39;49;00m#x1B[33m1#x1B[39;49;00m#x1B[33m"#x1B[39;49;00m: [#x1B[33m"#x1B[39;49;00m#x1B[33mFCER1A#x1B[39;49;00m#x1B[33m"#x1B[39;49;00m],#x1B[90m#x1B[39;49;00m
            #x1B[33m"#x1B[39;49;00m#x1B[33m2#x1B[39;49;00m#x1B[33m"#x1B[39;49;00m: [#x1B[33m"#x1B[39;49;00m#x1B[33mCD3D#x1B[39;49;00m#x1B[33m"#x1B[39;49;00m],#x1B[90m#x1B[39;49;00m
            #x1B[33m"#x1B[39;49;00m#x1B[33m0#x1B[39;49;00m#x1B[33m"#x1B[39;49;00m: [#x1B[33m"#x1B[39;49;00m#x1B[33mFCGR3A#x1B[39;49;00m#x1B[33m"#x1B[39;49;00m],#x1B[90m#x1B[39;49;00m
            #x1B[33m"#x1B[39;49;00m#x1B[33m4#x1B[39;49;00m#x1B[33m"#x1B[39;49;00m: [#x1B[33m"#x1B[39;49;00m#x1B[33mCD79A#x1B[39;49;00m#x1B[33m"#x1B[39;49;00m, #x1B[33m"#x1B[39;49;00m#x1B[33mMS4A1#x1B[39;49;00m#x1B[33m"#x1B[39;49;00m],#x1B[90m#x1B[39;49;00m
        }#x1B[90m#x1B[39;49;00m
        sc.pl.heatmap(#x1B[90m#x1B[39;49;00m
            adata=pbmc,#x1B[90m#x1B[39;49;00m
            var_names=marker_genes_dict,#x1B[90m#x1B[39;49;00m
            groupby=#x1B[33m"#x1B[39;49;00m#x1B[33mclusters#x1B[39;49;00m#x1B[33m"#x1B[39;49;00m,#x1B[90m#x1B[39;49;00m
            vmin=-#x1B[94m2#x1B[39;49;00m,#x1B[90m#x1B[39;49;00m
            vmax=#x1B[94m2#x1B[39;49;00m,#x1B[90m#x1B[39;49;00m
            cmap=#x1B[33m"#x1B[39;49;00m#x1B[33mRdBu_r#x1B[39;49;00m#x1B[33m"#x1B[39;49;00m,#x1B[90m#x1B[39;49;00m
            dendrogram=#x1B[94mTrue#x1B[39;49;00m,#x1B[90m#x1B[39;49;00m
            swap_axes=#x1B[94mTrue#x1B[39;49;00m,#x1B[90m#x1B[39;49;00m
            show=#x1B[94mFalse#x1B[39;49;00m,#x1B[90m#x1B[39;49;00m
        )#x1B[90m#x1B[39;49;00m
>       save_and_compare_images(#x1B[33m"#x1B[39;49;00m#x1B[33mheatmap_var_as_dict#x1B[39;49;00m#x1B[33m"#x1B[39;49;00m)#x1B[90m#x1B[39;49;00m
#x1B[1m#x1B[31mE       AssertionError: Image files did not match.#x1B[0m
#x1B[1m#x1B[31mE       RMS Value:  25.151928203164957#x1B[0m
#x1B[1m#x1B[31mE       Expected:   file:.../_images/heatmap_var_as_dict/expected.png#x1B[0m
#x1B[1m#x1B[31mE       Actual:     file:.../_images/heatmap_var_as_dict/actual.png#x1B[0m
#x1B[1m#x1B[31mE       Difference: file:.../_images/heatmap_var_as_dict/actual-failed-diff.png#x1B[0m
#x1B[1m#x1B[31mE       Tolerance:  15#x1B[0m

#x1B[1m#x1B[31mtests/test_plotting.py#x1B[0m:147: AssertionError

tests/notebooks/test_pbmc3k.py::test_pbmc3k

Stack Traces | 4.61s run time

subtests = <_pytest.subtests.Subtests object at 0x7f8389d8f770>
image_comparer = <function image_comparer.<locals>.save_and_compare at 0x7f83896a7950>

    #x1B[0m#x1B[37m@needs#x1B[39;49;00m.leidenalg#x1B[90m#x1B[39;49;00m
    #x1B[90m# https://github..../pandas/issues/61928#x1B[39;49;00m#x1B[90m#x1B[39;49;00m
    #x1B[37m@pytest#x1B[39;49;00m.mark.filterwarnings(#x1B[33m"#x1B[39;49;00m#x1B[33mignore:invalid value encountered in cast:RuntimeWarning#x1B[39;49;00m#x1B[33m"#x1B[39;49;00m)#x1B[90m#x1B[39;49;00m
    #x1B[94mdef#x1B[39;49;00m#x1B[90m #x1B[39;49;00m#x1B[92mtest_pbmc3k#x1B[39;49;00m(subtests: pytest.Subtests, image_comparer) -> #x1B[94mNone#x1B[39;49;00m:  #x1B[90m# noqa: PLR0915#x1B[39;49;00m#x1B[90m#x1B[39;49;00m
        #x1B[90m# ensure violin plots and other non-determinstic plots have deterministic behavior#x1B[39;49;00m#x1B[90m#x1B[39;49;00m
        np.random.seed(#x1B[94m0#x1B[39;49;00m)#x1B[90m#x1B[39;49;00m
        save_and_compare_images = partial(image_comparer, ROOT, tol=#x1B[94m20#x1B[39;49;00m)#x1B[90m#x1B[39;49;00m
        adata = sc.datasets.pbmc3k()#x1B[90m#x1B[39;49;00m
    #x1B[90m#x1B[39;49;00m
        #x1B[90m# Preprocessing#x1B[39;49;00m#x1B[90m#x1B[39;49;00m
    #x1B[90m#x1B[39;49;00m
        sc.pl.highest_expr_genes(adata, n_top=#x1B[94m20#x1B[39;49;00m, show=#x1B[94mFalse#x1B[39;49;00m)#x1B[90m#x1B[39;49;00m
        save_and_compare_images(#x1B[33m"#x1B[39;49;00m#x1B[33mhighest_expr_genes#x1B[39;49;00m#x1B[33m"#x1B[39;49;00m)#x1B[90m#x1B[39;49;00m
    #x1B[90m#x1B[39;49;00m
        sc.pp.filter_cells(adata, min_genes=#x1B[94m200#x1B[39;49;00m)#x1B[90m#x1B[39;49;00m
        sc.pp.filter_genes(adata, min_cells=#x1B[94m3#x1B[39;49;00m)#x1B[90m#x1B[39;49;00m
    #x1B[90m#x1B[39;49;00m
        mito_genes = [name #x1B[94mfor#x1B[39;49;00m name #x1B[95min#x1B[39;49;00m adata.var_names #x1B[94mif#x1B[39;49;00m name.startswith(#x1B[33m"#x1B[39;49;00m#x1B[33mMT-#x1B[39;49;00m#x1B[33m"#x1B[39;49;00m)]#x1B[90m#x1B[39;49;00m
        #x1B[90m# for each cell compute fraction of counts in mito genes vs. all genes#x1B[39;49;00m#x1B[90m#x1B[39;49;00m
        #x1B[90m# the `.A1` is only necessary as X is sparse to transform to a dense array after summing#x1B[39;49;00m#x1B[90m#x1B[39;49;00m
        adata.obs[#x1B[33m"#x1B[39;49;00m#x1B[33mpercent_mito#x1B[39;49;00m#x1B[33m"#x1B[39;49;00m] = (#x1B[90m#x1B[39;49;00m
            np.sum(adata[:, mito_genes].X, axis=#x1B[94m1#x1B[39;49;00m).A1 / np.sum(adata.X, axis=#x1B[94m1#x1B[39;49;00m).A1#x1B[90m#x1B[39;49;00m
        )#x1B[90m#x1B[39;49;00m
        #x1B[90m# add the total counts per cell as observations-annotation to adata#x1B[39;49;00m#x1B[90m#x1B[39;49;00m
        adata.obs[#x1B[33m"#x1B[39;49;00m#x1B[33mn_counts#x1B[39;49;00m#x1B[33m"#x1B[39;49;00m] = adata.X.sum(axis=#x1B[94m1#x1B[39;49;00m).A1#x1B[90m#x1B[39;49;00m
    #x1B[90m#x1B[39;49;00m
        #x1B[94mwith#x1B[39;49;00m subtests.test(#x1B[33m"#x1B[39;49;00m#x1B[33mviolin#x1B[39;49;00m#x1B[33m"#x1B[39;49;00m):#x1B[90m#x1B[39;49;00m
            sc.pl.violin(#x1B[90m#x1B[39;49;00m
                adata,#x1B[90m#x1B[39;49;00m
                [#x1B[33m"#x1B[39;49;00m#x1B[33mn_genes#x1B[39;49;00m#x1B[33m"#x1B[39;49;00m, #x1B[33m"#x1B[39;49;00m#x1B[33mn_counts#x1B[39;49;00m#x1B[33m"#x1B[39;49;00m, #x1B[33m"#x1B[39;49;00m#x1B[33mpercent_mito#x1B[39;49;00m#x1B[33m"#x1B[39;49;00m],#x1B[90m#x1B[39;49;00m
                jitter=#x1B[94mFalse#x1B[39;49;00m,#x1B[90m#x1B[39;49;00m
                multi_panel=#x1B[94mTrue#x1B[39;49;00m,#x1B[90m#x1B[39;49;00m
                show=#x1B[94mFalse#x1B[39;49;00m,#x1B[90m#x1B[39;49;00m
            )#x1B[90m#x1B[39;49;00m
            save_and_compare_images(#x1B[33m"#x1B[39;49;00m#x1B[33mviolin#x1B[39;49;00m#x1B[33m"#x1B[39;49;00m)#x1B[90m#x1B[39;49;00m
    #x1B[90m#x1B[39;49;00m
        #x1B[94mwith#x1B[39;49;00m subtests.test(#x1B[33m"#x1B[39;49;00m#x1B[33mscatter_1#x1B[39;49;00m#x1B[33m"#x1B[39;49;00m):#x1B[90m#x1B[39;49;00m
            sc.pl.scatter(adata, x=#x1B[33m"#x1B[39;49;00m#x1B[33mn_counts#x1B[39;49;00m#x1B[33m"#x1B[39;49;00m, y=#x1B[33m"#x1B[39;49;00m#x1B[33mpercent_mito#x1B[39;49;00m#x1B[33m"#x1B[39;49;00m, show=#x1B[94mFalse#x1B[39;49;00m)#x1B[90m#x1B[39;49;00m
            save_and_compare_images(#x1B[33m"#x1B[39;49;00m#x1B[33mscatter_1#x1B[39;49;00m#x1B[33m"#x1B[39;49;00m)#x1B[90m#x1B[39;49;00m
        #x1B[94mwith#x1B[39;49;00m subtests.test(#x1B[33m"#x1B[39;49;00m#x1B[33mscatter_2#x1B[39;49;00m#x1B[33m"#x1B[39;49;00m):#x1B[90m#x1B[39;49;00m
            sc.pl.scatter(adata, x=#x1B[33m"#x1B[39;49;00m#x1B[33mn_counts#x1B[39;49;00m#x1B[33m"#x1B[39;49;00m, y=#x1B[33m"#x1B[39;49;00m#x1B[33mn_genes#x1B[39;49;00m#x1B[33m"#x1B[39;49;00m, show=#x1B[94mFalse#x1B[39;49;00m)#x1B[90m#x1B[39;49;00m
            save_and_compare_images(#x1B[33m"#x1B[39;49;00m#x1B[33mscatter_2#x1B[39;49;00m#x1B[33m"#x1B[39;49;00m)#x1B[90m#x1B[39;49;00m
    #x1B[90m#x1B[39;49;00m
        adata = adata[adata.obs[#x1B[33m"#x1B[39;49;00m#x1B[33mn_genes#x1B[39;49;00m#x1B[33m"#x1B[39;49;00m] < #x1B[94m2500#x1B[39;49;00m, :]#x1B[90m#x1B[39;49;00m
        adata = adata[adata.obs[#x1B[33m"#x1B[39;49;00m#x1B[33mpercent_mito#x1B[39;49;00m#x1B[33m"#x1B[39;49;00m] < #x1B[94m0.05#x1B[39;49;00m, :]#x1B[90m#x1B[39;49;00m
    #x1B[90m#x1B[39;49;00m
        adata.raw = sc.pp.log1p(adata, copy=#x1B[94mTrue#x1B[39;49;00m)#x1B[90m#x1B[39;49;00m
    #x1B[90m#x1B[39;49;00m
        #x1B[94mwith#x1B[39;49;00m pytest.warns(#x1B[96mFutureWarning#x1B[39;49;00m, match=#x1B[33mr#x1B[39;49;00m#x1B[33m"#x1B[39;49;00m#x1B[33msc#x1B[39;49;00m#x1B[33m\#x1B[39;49;00m#x1B[33m.pp#x1B[39;49;00m#x1B[33m\#x1B[39;49;00m#x1B[33m.normalize_total#x1B[39;49;00m#x1B[33m"#x1B[39;49;00m):#x1B[90m#x1B[39;49;00m
            sc.pp.normalize_per_cell(adata, counts_per_cell_after=#x1B[94m1e4#x1B[39;49;00m)#x1B[90m#x1B[39;49;00m
    #x1B[90m#x1B[39;49;00m
        #x1B[94mwith#x1B[39;49;00m pytest.warns(#x1B[96mFutureWarning#x1B[39;49;00m, match=#x1B[33mr#x1B[39;49;00m#x1B[33m"#x1B[39;49;00m#x1B[33msc#x1B[39;49;00m#x1B[33m\#x1B[39;49;00m#x1B[33m.pp#x1B[39;49;00m#x1B[33m\#x1B[39;49;00m#x1B[33m.highly_variable_genes#x1B[39;49;00m#x1B[33m"#x1B[39;49;00m):#x1B[90m#x1B[39;49;00m
            filter_result = sc.pp.filter_genes_dispersion(#x1B[90m#x1B[39;49;00m
                adata.X,#x1B[90m#x1B[39;49;00m
                min_mean=#x1B[94m0.0125#x1B[39;49;00m,#x1B[90m#x1B[39;49;00m
                max_mean=#x1B[94m3#x1B[39;49;00m,#x1B[90m#x1B[39;49;00m
                min_disp=#x1B[94m0.5#x1B[39;49;00m,#x1B[90m#x1B[39;49;00m
            )#x1B[90m#x1B[39;49;00m
    #x1B[90m#x1B[39;49;00m
        #x1B[94mwith#x1B[39;49;00m subtests.test(#x1B[33m"#x1B[39;49;00m#x1B[33mfilter_genes_dispersion#x1B[39;49;00m#x1B[33m"#x1B[39;49;00m):#x1B[90m#x1B[39;49;00m
            #x1B[94mwith#x1B[39;49;00m pytest.warns(#x1B[96mFutureWarning#x1B[39;49;00m, match=#x1B[33mr#x1B[39;49;00m#x1B[33m"#x1B[39;49;00m#x1B[33msc#x1B[39;49;00m#x1B[33m\#x1B[39;49;00m#x1B[33m.pl#x1B[39;49;00m#x1B[33m\#x1B[39;49;00m#x1B[33m.highly_variable_genes#x1B[39;49;00m#x1B[33m"#x1B[39;49;00m):#x1B[90m#x1B[39;49;00m
                sc.pl.filter_genes_dispersion(filter_result, show=#x1B[94mFalse#x1B[39;49;00m)#x1B[90m#x1B[39;49;00m
            save_and_compare_images(#x1B[33m"#x1B[39;49;00m#x1B[33mfilter_genes_dispersion#x1B[39;49;00m#x1B[33m"#x1B[39;49;00m)#x1B[90m#x1B[39;49;00m
    #x1B[90m#x1B[39;49;00m
        adata = adata[:, filter_result.gene_subset].copy()#x1B[90m#x1B[39;49;00m
        sc.pp.log1p(adata)#x1B[90m#x1B[39;49;00m
        sc.pp.regress_out(adata, [#x1B[33m"#x1B[39;49;00m#x1B[33mn_counts#x1B[39;49;00m#x1B[33m"#x1B[39;49;00m, #x1B[33m"#x1B[39;49;00m#x1B[33mpercent_mito#x1B[39;49;00m#x1B[33m"#x1B[39;49;00m])#x1B[90m#x1B[39;49;00m
        sc.pp.scale(adata, max_value=#x1B[94m10#x1B[39;49;00m)#x1B[90m#x1B[39;49;00m
    #x1B[90m#x1B[39;49;00m
        #x1B[90m# PCA#x1B[39;49;00m#x1B[90m#x1B[39;49;00m
    #x1B[90m#x1B[39;49;00m
        sc.pp.pca(adata, svd_solver=#x1B[33m"#x1B[39;49;00m#x1B[33marpack#x1B[39;49;00m#x1B[33m"#x1B[39;49;00m)#x1B[90m#x1B[39;49;00m
        #x1B[94mwith#x1B[39;49;00m subtests.test(#x1B[33m"#x1B[39;49;00m#x1B[33mpca#x1B[39;49;00m#x1B[33m"#x1B[39;49;00m):#x1B[90m#x1B[39;49;00m
            sc.pl.pca(adata, color=#x1B[33m"#x1B[39;49;00m#x1B[33mCST3#x1B[39;49;00m#x1B[33m"#x1B[39;49;00m, show=#x1B[94mFalse#x1B[39;49;00m)#x1B[90m#x1B[39;49;00m
            save_and_compare_images(#x1B[33m"#x1B[39;49;00m#x1B[33mpca#x1B[39;49;00m#x1B[33m"#x1B[39;49;00m)#x1B[90m#x1B[39;49;00m
    #x1B[90m#x1B[39;49;00m
        #x1B[94mwith#x1B[39;49;00m subtests.test(#x1B[33m"#x1B[39;49;00m#x1B[33mpca_variance_ratio#x1B[39;49;00m#x1B[33m"#x1B[39;49;00m):#x1B[90m#x1B[39;49;00m
            sc.pl.pca_variance_ratio(adata, log=#x1B[94mTrue#x1B[39;49;00m, show=#x1B[94mFalse#x1B[39;49;00m)#x1B[90m#x1B[39;49;00m
            save_and_compare_images(#x1B[33m"#x1B[39;49;00m#x1B[33mpca_variance_ratio#x1B[39;49;00m#x1B[33m"#x1B[39;49;00m)#x1B[90m#x1B[39;49;00m
    #x1B[90m#x1B[39;49;00m
        #x1B[90m# Neighbors#x1B[39;49;00m#x1B[90m#x1B[39;49;00m
    #x1B[90m#x1B[39;49;00m
        sc.pp.neighbors(adata, n_neighbors=#x1B[94m10#x1B[39;49;00m, n_pcs=#x1B[94m40#x1B[39;49;00m)#x1B[90m#x1B[39;49;00m
    #x1B[90m#x1B[39;49;00m
        #x1B[90m# Clustering the graph#x1B[39;49;00m#x1B[90m#x1B[39;49;00m
    #x1B[90m#x1B[39;49;00m
        sc.tl.leiden(#x1B[90m#x1B[39;49;00m
            adata,#x1B[90m#x1B[39;49;00m
            resolution=#x1B[94m0.9#x1B[39;49;00m,#x1B[90m#x1B[39;49;00m
            random_state=#x1B[94m1#x1B[39;49;00m,#x1B[90m#x1B[39;49;00m
            directed=#x1B[94mFalse#x1B[39;49;00m,#x1B[90m#x1B[39;49;00m
            n_iterations=#x1B[94m2#x1B[39;49;00m,#x1B[90m#x1B[39;49;00m
            flavor=#x1B[33m"#x1B[39;49;00m#x1B[33migraph#x1B[39;49;00m#x1B[33m"#x1B[39;49;00m,#x1B[90m#x1B[39;49;00m
        )#x1B[90m#x1B[39;49;00m
    #x1B[90m#x1B[39;49;00m
        #x1B[94mwith#x1B[39;49;00m subtests.test(#x1B[33m"#x1B[39;49;00m#x1B[33mscatter_3#x1B[39;49;00m#x1B[33m"#x1B[39;49;00m):#x1B[90m#x1B[39;49;00m
            sc.pl.scatter(adata, #x1B[33m"#x1B[39;49;00m#x1B[33mCST3#x1B[39;49;00m#x1B[33m"#x1B[39;49;00m, #x1B[33m"#x1B[39;49;00m#x1B[33mNKG7#x1B[39;49;00m#x1B[33m"#x1B[39;49;00m, color=#x1B[33m"#x1B[39;49;00m#x1B[33mleiden#x1B[39;49;00m#x1B[33m"#x1B[39;49;00m, show=#x1B[94mFalse#x1B[39;49;00m)#x1B[90m#x1B[39;49;00m
            save_and_compare_images(#x1B[33m"#x1B[39;49;00m#x1B[33mscatter_3#x1B[39;49;00m#x1B[33m"#x1B[39;49;00m)#x1B[90m#x1B[39;49;00m
    #x1B[90m#x1B[39;49;00m
        #x1B[90m# Finding marker genes#x1B[39;49;00m#x1B[90m#x1B[39;49;00m
        #x1B[90m# Due to incosistency with our test runner vs local, these clusters need to#x1B[39;49;00m#x1B[90m#x1B[39;49;00m
        #x1B[90m# be pre-annotated as the numbers for each cluster are not consistent.#x1B[39;49;00m#x1B[90m#x1B[39;49;00m
        marker_genes = [#x1B[90m#x1B[39;49;00m
            *[#x1B[33m"#x1B[39;49;00m#x1B[33mRP11-18H21.1#x1B[39;49;00m#x1B[33m"#x1B[39;49;00m, #x1B[33m"#x1B[39;49;00m#x1B[33mGZMK#x1B[39;49;00m#x1B[33m"#x1B[39;49;00m, #x1B[33m"#x1B[39;49;00m#x1B[33mCD79A#x1B[39;49;00m#x1B[33m"#x1B[39;49;00m, #x1B[33m"#x1B[39;49;00m#x1B[33mFCGR3A#x1B[39;49;00m#x1B[33m"#x1B[39;49;00m],#x1B[90m#x1B[39;49;00m
            *[#x1B[33m"#x1B[39;49;00m#x1B[33mGNLY#x1B[39;49;00m#x1B[33m"#x1B[39;49;00m, #x1B[33m"#x1B[39;49;00m#x1B[33mS100A8#x1B[39;49;00m#x1B[33m"#x1B[39;49;00m, #x1B[33m"#x1B[39;49;00m#x1B[33mFCER1A#x1B[39;49;00m#x1B[33m"#x1B[39;49;00m, #x1B[33m"#x1B[39;49;00m#x1B[33mPPBP#x1B[39;49;00m#x1B[33m"#x1B[39;49;00m],#x1B[90m#x1B[39;49;00m
        ]#x1B[90m#x1B[39;49;00m
        data_df = adata[:, marker_genes].to_df()#x1B[90m#x1B[39;49;00m
        data_df[#x1B[33m"#x1B[39;49;00m#x1B[33mleiden#x1B[39;49;00m#x1B[33m"#x1B[39;49;00m] = adata.obs[#x1B[33m"#x1B[39;49;00m#x1B[33mleiden#x1B[39;49;00m#x1B[33m"#x1B[39;49;00m]#x1B[90m#x1B[39;49;00m
        max_idxs = data_df.groupby(#x1B[33m"#x1B[39;49;00m#x1B[33mleiden#x1B[39;49;00m#x1B[33m"#x1B[39;49;00m, observed=#x1B[94mTrue#x1B[39;49;00m).mean().idxmax()#x1B[90m#x1B[39;49;00m
        #x1B[94mwith#x1B[39;49;00m subtests.test(#x1B[33m"#x1B[39;49;00m#x1B[33mmarker_genes_unique#x1B[39;49;00m#x1B[33m"#x1B[39;49;00m):#x1B[90m#x1B[39;49;00m
            #x1B[94massert#x1B[39;49;00m #x1B[95mnot#x1B[39;49;00m max_idxs[marker_genes][#x1B[90m#x1B[39;49;00m
                max_idxs[marker_genes].duplicated(keep=#x1B[94mFalse#x1B[39;49;00m)#x1B[90m#x1B[39;49;00m
            ].tolist(), #x1B[33m"#x1B[39;49;00m#x1B[33mNot all marker genes are unique per cluster#x1B[39;49;00m#x1B[33m"#x1B[39;49;00m#x1B[90m#x1B[39;49;00m
        leiden_relabel = {#x1B[90m#x1B[39;49;00m
            max_idxs[marker_gene]: #x1B[96mstr#x1B[39;49;00m(i) #x1B[94mfor#x1B[39;49;00m i, marker_gene #x1B[95min#x1B[39;49;00m #x1B[96menumerate#x1B[39;49;00m(marker_genes)#x1B[90m#x1B[39;49;00m
        }#x1B[90m#x1B[39;49;00m
        adata.obs[#x1B[33m"#x1B[39;49;00m#x1B[33mleiden_old#x1B[39;49;00m#x1B[33m"#x1B[39;49;00m] = adata.obs[#x1B[33m"#x1B[39;49;00m#x1B[33mleiden#x1B[39;49;00m#x1B[33m"#x1B[39;49;00m].copy()#x1B[90m#x1B[39;49;00m
>       adata.rename_categories(#x1B[90m#x1B[39;49;00m
            #x1B[33m"#x1B[39;49;00m#x1B[33mleiden#x1B[39;49;00m#x1B[33m"#x1B[39;49;00m, [leiden_relabel[key] #x1B[94mfor#x1B[39;49;00m key #x1B[95min#x1B[39;49;00m #x1B[96msorted#x1B[39;49;00m(leiden_relabel.keys())]#x1B[90m#x1B[39;49;00m
        )#x1B[90m#x1B[39;49;00m

#x1B[1m#x1B[31mtests/notebooks/test_pbmc3k.py#x1B[0m:148: 
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 
#x1B[1m#x1B[31m../../../..../scanpy/B9PcT7QG/hatch-test.stable/lib/python3.14.../anndata/_core/anndata.py#x1B[0m:1129: in rename_categories
    #x1B[0m#x1B[96mself#x1B[39;49;00m.obs[key] = #x1B[96mself#x1B[39;49;00m.obs[key].cat.rename_categories(categories)#x1B[90m#x1B[39;49;00m
                    ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^#x1B[90m#x1B[39;49;00m
#x1B[1m#x1B[31m../../../..../scanpy/B9PcT7QG/hatch-test.stable/lib/python3.14.../pandas/core/accessor.py#x1B[0m:112: in f
    #x1B[0m#x1B[94mreturn#x1B[39;49;00m #x1B[96mself#x1B[39;49;00m._delegate_method(name, *args, **kwargs)#x1B[90m#x1B[39;49;00m
           ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^#x1B[90m#x1B[39;49;00m
#x1B[1m#x1B[31m../../../..../scanpy/B9PcT7QG/hatch-test.stable/lib/python3.14.../core/arrays/categorical.py#x1B[0m:2982: in _delegate_method
    #x1B[0mres = method(*args, **kwargs)#x1B[90m#x1B[39;49;00m
          ^^^^^^^^^^^^^^^^^^^^^^^#x1B[90m#x1B[39;49;00m
#x1B[1m#x1B[31m../../../..../scanpy/B9PcT7QG/hatch-test.stable/lib/python3.14.../core/arrays/categorical.py#x1B[0m:1206: in rename_categories
    #x1B[0mcat._set_categories(new_categories)#x1B[90m#x1B[39;49;00m
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 

self = ['0', '1', '0', '2', '4', ..., '6', '1', '1', '1', '0']
Length: 2638
Categories (8, object): ['0', '1', '2', '3', '4', '5', '6', '7']
categories = ['0', '2', '5', '4', '1', '6', ...], fastpath = False

    #x1B[0m#x1B[94mdef#x1B[39;49;00m#x1B[90m #x1B[39;49;00m#x1B[92m_set_categories#x1B[39;49;00m(#x1B[96mself#x1B[39;49;00m, categories, fastpath: #x1B[96mbool#x1B[39;49;00m = #x1B[94mFalse#x1B[39;49;00m) -> #x1B[94mNone#x1B[39;49;00m:#x1B[90m#x1B[39;49;00m
    #x1B[90m    #x1B[39;49;00m#x1B[33m"""#x1B[39;49;00m
    #x1B[33m    Sets new categories inplace#x1B[39;49;00m
    #x1B[33m#x1B[39;49;00m
    #x1B[33m    Parameters#x1B[39;49;00m
    #x1B[33m    ----------#x1B[39;49;00m
    #x1B[33m    fastpath : bool, default False#x1B[39;49;00m
    #x1B[33m       Don't perform validation of the categories for uniqueness or nulls#x1B[39;49;00m
    #x1B[33m#x1B[39;49;00m
    #x1B[33m    Examples#x1B[39;49;00m
    #x1B[33m    --------#x1B[39;49;00m
    #x1B[33m    >>> c = pd.Categorical(['a', 'b'])#x1B[39;49;00m
    #x1B[33m    >>> c#x1B[39;49;00m
    #x1B[33m    ['a', 'b']#x1B[39;49;00m
    #x1B[33m    Categories (2, object): ['a', 'b']#x1B[39;49;00m
    #x1B[33m#x1B[39;49;00m
    #x1B[33m    >>> c._set_categories(pd.Index(['a', 'c']))#x1B[39;49;00m
    #x1B[33m    >>> c#x1B[39;49;00m
    #x1B[33m    ['a', 'c']#x1B[39;49;00m
    #x1B[33m    Categories (2, object): ['a', 'c']#x1B[39;49;00m
    #x1B[33m    """#x1B[39;49;00m#x1B[90m#x1B[39;49;00m
        #x1B[94mif#x1B[39;49;00m fastpath:#x1B[90m#x1B[39;49;00m
            new_dtype = CategoricalDtype._from_fastpath(categories, #x1B[96mself#x1B[39;49;00m.ordered)#x1B[90m#x1B[39;49;00m
        #x1B[94melse#x1B[39;49;00m:#x1B[90m#x1B[39;49;00m
            new_dtype = CategoricalDtype(categories, ordered=#x1B[96mself#x1B[39;49;00m.ordered)#x1B[90m#x1B[39;49;00m
        #x1B[94mif#x1B[39;49;00m (#x1B[90m#x1B[39;49;00m
            #x1B[95mnot#x1B[39;49;00m fastpath#x1B[90m#x1B[39;49;00m
            #x1B[95mand#x1B[39;49;00m #x1B[96mself#x1B[39;49;00m.dtype.categories #x1B[95mis#x1B[39;49;00m #x1B[95mnot#x1B[39;49;00m #x1B[94mNone#x1B[39;49;00m#x1B[90m#x1B[39;49;00m
            #x1B[95mand#x1B[39;49;00m #x1B[96mlen#x1B[39;49;00m(new_dtype.categories) != #x1B[96mlen#x1B[39;49;00m(#x1B[96mself#x1B[39;49;00m.dtype.categories)#x1B[90m#x1B[39;49;00m
        ):#x1B[90m#x1B[39;49;00m
>           #x1B[94mraise#x1B[39;49;00m #x1B[96mValueError#x1B[39;49;00m(#x1B[90m#x1B[39;49;00m
                #x1B[33m"#x1B[39;49;00m#x1B[33mnew categories need to have the same number of #x1B[39;49;00m#x1B[33m"#x1B[39;49;00m#x1B[90m#x1B[39;49;00m
                #x1B[33m"#x1B[39;49;00m#x1B[33mitems as the old categories!#x1B[39;49;00m#x1B[33m"#x1B[39;49;00m#x1B[90m#x1B[39;49;00m
            )#x1B[90m#x1B[39;49;00m
#x1B[1m#x1B[31mE           ValueError: new categories need to have the same number of items as the old categories!#x1B[0m

#x1B[1m#x1B[31m../../../..../scanpy/B9PcT7QG/hatch-test.stable/lib/python3.14.../core/arrays/categorical.py#x1B[0m:931: ValueError

To view more test analytics, go to the Test Analytics Dashboard
_{📋 Got 3 mins? Take this short survey to help us improve Test Analytics.}

flying-sheep added 2 commits February 23, 2026 16:26

feat: support np.random.Generator

de9c481

add decorator

8ab6661

flying-sheep added 2 commits February 24, 2026 14:19

scrublet

1ef8780

almost done

5308a1a

Provide feedback

Saved searches

Use saved searches to filter your results more quickly

Comments

feat: support np.random.Generator#3983

feat: support np.random.Generator#3983
flying-sheep wants to merge 4 commits intomainfrom
pa/rng

flying-sheep commented Feb 23, 2026 •

edited

Loading

Uh oh!

codecov bot commented Feb 24, 2026 •

edited

Loading

Uh oh!

Reviewers

Assignees

Labels

Projects

Milestone

Development

Uh oh!

1 participant

Comments

Conversation

flying-sheep commented Feb 23, 2026 • edited Loading Uh oh! There was an error while loading. Please reload this page.

Uh oh!

Uh oh!

codecov bot commented Feb 24, 2026 • edited Loading Uh oh! There was an error while loading. Please reload this page.

Uh oh!

❌ 3 Tests Failed:

Uh oh!

Reviewers

Assignees

Labels

Projects

Milestone

Development

Uh oh!

1 participant

flying-sheep commented Feb 23, 2026 •

edited

Loading

codecov bot commented Feb 24, 2026 •

edited

Loading