perf: chan's parallel mean-var algorithm for dask by ilan-gold · Pull Request #4123 · scverse/scanpy

ilan-gold · 2026-05-15T09:20:29Z

See https://en.wikipedia.org/wiki/Algorithms_for_calculating_variance#Parallel_algorithm

This has two benefits - it allows us to calculate mean/var in one pass instead of effectively two (square sum and sum squared) and gets rid of a numerical instability issue that @zboldyga found the solution to (see removed comment)

Closes #
Tests included or not required because:

Release notes not necessary because:

for more information, see https://pre-commit.ci

codecov · 2026-05-15T09:26:50Z

❌ 17 Tests Failed:

Tests completed	Failed	Passed	Skipped
2617	17	2600	160

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

tests/test_highly_variable_genes.py::test_subset_inplace_consistency[None-scipy_csr_mat-seurat_v3]

Stack Traces | 0.013s run time

subtests = <_pytest.subtests.Subtests object at 0x7f57c0d2f710>
flavor = 'seurat_v3', array_type = <class 'scipy.sparse._csr.csr_matrix'>
batch_key = None

    #x1B[0m#x1B[37m@pytest#x1B[39;49;00m.mark.parametrize(#x1B[90m#x1B[39;49;00m
        #x1B[33m"#x1B[39;49;00m#x1B[33mflavor#x1B[39;49;00m#x1B[33m"#x1B[39;49;00m,#x1B[90m#x1B[39;49;00m
        [#x1B[90m#x1B[39;49;00m
            #x1B[33m"#x1B[39;49;00m#x1B[33mseurat#x1B[39;49;00m#x1B[33m"#x1B[39;49;00m,#x1B[90m#x1B[39;49;00m
            #x1B[33m"#x1B[39;49;00m#x1B[33mcell_ranger#x1B[39;49;00m#x1B[33m"#x1B[39;49;00m,#x1B[90m#x1B[39;49;00m
            pytest.param(#x1B[33m"#x1B[39;49;00m#x1B[33mseurat_v3#x1B[39;49;00m#x1B[33m"#x1B[39;49;00m, marks=needs.skmisc),#x1B[90m#x1B[39;49;00m
            pytest.param(#x1B[33m"#x1B[39;49;00m#x1B[33mseurat_v3_paper#x1B[39;49;00m#x1B[33m"#x1B[39;49;00m, marks=needs.skmisc),#x1B[90m#x1B[39;49;00m
        ],#x1B[90m#x1B[39;49;00m
    )#x1B[90m#x1B[39;49;00m
    #x1B[37m@pytest#x1B[39;49;00m.mark.parametrize(#x1B[90m#x1B[39;49;00m
        #x1B[33m"#x1B[39;49;00m#x1B[33marray_type#x1B[39;49;00m#x1B[33m"#x1B[39;49;00m,#x1B[90m#x1B[39;49;00m
        [#x1B[90m#x1B[39;49;00m
            p#x1B[90m#x1B[39;49;00m
            #x1B[94mfor#x1B[39;49;00m p #x1B[95min#x1B[39;49;00m ARRAY_TYPES#x1B[90m#x1B[39;49;00m
            #x1B[94mif#x1B[39;49;00m #x1B[33m"#x1B[39;49;00m#x1B[33mdask#x1B[39;49;00m#x1B[33m"#x1B[39;49;00m #x1B[95mnot#x1B[39;49;00m #x1B[95min#x1B[39;49;00m p.id #x1B[95mor#x1B[39;49;00m (#x1B[33m"#x1B[39;49;00m#x1B[33m1d_chunked#x1B[39;49;00m#x1B[33m"#x1B[39;49;00m #x1B[95min#x1B[39;49;00m p.id #x1B[95mand#x1B[39;49;00m #x1B[33m"#x1B[39;49;00m#x1B[33mcsr#x1B[39;49;00m#x1B[33m"#x1B[39;49;00m #x1B[95min#x1B[39;49;00m p.id)#x1B[90m#x1B[39;49;00m
        ],#x1B[90m#x1B[39;49;00m
    )#x1B[90m#x1B[39;49;00m
    #x1B[37m@pytest#x1B[39;49;00m.mark.parametrize(#x1B[33m"#x1B[39;49;00m#x1B[33mbatch_key#x1B[39;49;00m#x1B[33m"#x1B[39;49;00m, [#x1B[94mNone#x1B[39;49;00m, #x1B[33m"#x1B[39;49;00m#x1B[33mbatch#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_subset_inplace_consistency#x1B[39;49;00m(#x1B[90m#x1B[39;49;00m
        subtests: pytest.Subtests,#x1B[90m#x1B[39;49;00m
        flavor: Literal[#x1B[33m"#x1B[39;49;00m#x1B[33mseurat#x1B[39;49;00m#x1B[33m"#x1B[39;49;00m, #x1B[33m"#x1B[39;49;00m#x1B[33mcell_ranger#x1B[39;49;00m#x1B[33m"#x1B[39;49;00m, #x1B[33m"#x1B[39;49;00m#x1B[33mseurat_v3#x1B[39;49;00m#x1B[33m"#x1B[39;49;00m, #x1B[33m"#x1B[39;49;00m#x1B[33mseurat_v3_paper#x1B[39;49;00m#x1B[33m"#x1B[39;49;00m],#x1B[90m#x1B[39;49;00m
        array_type,#x1B[90m#x1B[39;49;00m
        batch_key: Literal[#x1B[33m"#x1B[39;49;00m#x1B[33mbatch#x1B[39;49;00m#x1B[33m"#x1B[39;49;00m] | #x1B[94mNone#x1B[39;49;00m,#x1B[90m#x1B[39;49;00m
    ) -> #x1B[94mNone#x1B[39;49;00m:#x1B[90m#x1B[39;49;00m
    #x1B[90m    #x1B[39;49;00m#x1B[33m"""Tests `n_top_genes=n`.#x1B[39;49;00m
    #x1B[33m#x1B[39;49;00m
    #x1B[33m    - if `inplace` and `subset` interact correctly#x1B[39;49;00m
    #x1B[33m    - for both the `seurat` and `cell_ranger` flavors#x1B[39;49;00m
    #x1B[33m    - for dask arrays and non-dask arrays#x1B[39;49;00m
    #x1B[33m    - for both with and without batch_key#x1B[39;49;00m
    #x1B[33m    """#x1B[39;49;00m#x1B[90m#x1B[39;49;00m
        rng = np.random.default_rng(#x1B[94m0#x1B[39;49;00m)#x1B[90m#x1B[39;49;00m
        adata = (#x1B[90m#x1B[39;49;00m
            sc.datasets.blobs(n_observations=#x1B[94m20#x1B[39;49;00m, n_variables=#x1B[94m80#x1B[39;49;00m, rng=rng)#x1B[90m#x1B[39;49;00m
            #x1B[94mif#x1B[39;49;00m #x1B[33m"#x1B[39;49;00m#x1B[33mseurat_v3#x1B[39;49;00m#x1B[33m"#x1B[39;49;00m #x1B[95mnot#x1B[39;49;00m #x1B[95min#x1B[39;49;00m flavor#x1B[90m#x1B[39;49;00m
            #x1B[94melse#x1B[39;49;00m pbmc3k()[:#x1B[94m1500#x1B[39;49;00m, :#x1B[94m1000#x1B[39;49;00m].copy()#x1B[90m#x1B[39;49;00m
        )#x1B[90m#x1B[39;49;00m
        adata.obs[#x1B[33m"#x1B[39;49;00m#x1B[33mbatch#x1B[39;49;00m#x1B[33m"#x1B[39;49;00m] = rng.choice([#x1B[33m"#x1B[39;49;00m#x1B[33ma#x1B[39;49;00m#x1B[33m"#x1B[39;49;00m, #x1B[33m"#x1B[39;49;00m#x1B[33mb#x1B[39;49;00m#x1B[33m"#x1B[39;49;00m], adata.shape[#x1B[94m0#x1B[39;49;00m])#x1B[90m#x1B[39;49;00m
        adata.X = array_type(np.abs(adata.X).astype(#x1B[96mint#x1B[39;49;00m))#x1B[90m#x1B[39;49;00m
    #x1B[90m#x1B[39;49;00m
        #x1B[94mif#x1B[39;49;00m flavor #x1B[95min#x1B[39;49;00m {#x1B[33m"#x1B[39;49;00m#x1B[33mseurat#x1B[39;49;00m#x1B[33m"#x1B[39;49;00m, #x1B[33m"#x1B[39;49;00m#x1B[33mcell_ranger#x1B[39;49;00m#x1B[33m"#x1B[39;49;00m}:#x1B[90m#x1B[39;49;00m
            sc.pp.normalize_total(adata, target_sum=#x1B[94m1e4#x1B[39;49;00m)#x1B[90m#x1B[39;49;00m
            sc.pp.log1p(adata)#x1B[90m#x1B[39;49;00m
    #x1B[90m#x1B[39;49;00m
        n_genes = adata.shape[#x1B[94m1#x1B[39;49;00m]#x1B[90m#x1B[39;49;00m
    #x1B[90m#x1B[39;49;00m
        adatas: #x1B[96mdict#x1B[39;49;00m[#x1B[96mbool#x1B[39;49;00m, AnnData] = {}#x1B[90m#x1B[39;49;00m
        dfs: #x1B[96mdict#x1B[39;49;00m[#x1B[96mbool#x1B[39;49;00m, pd.DataFrame] = {}#x1B[90m#x1B[39;49;00m
        #x1B[90m# for loops instead of parametrization to compare between settings#x1B[39;49;00m#x1B[90m#x1B[39;49;00m
        #x1B[94mfor#x1B[39;49;00m subset, inplace #x1B[95min#x1B[39;49;00m itertools.product([#x1B[94mTrue#x1B[39;49;00m, #x1B[94mFalse#x1B[39;49;00m], repeat=#x1B[94m2#x1B[39;49;00m):#x1B[90m#x1B[39;49;00m
            adata_copy = adata.copy()#x1B[90m#x1B[39;49;00m
    #x1B[90m#x1B[39;49;00m
>           output_df = sc.pp.highly_variable_genes(#x1B[90m#x1B[39;49;00m
                adata_copy,#x1B[90m#x1B[39;49;00m
                flavor=flavor,#x1B[90m#x1B[39;49;00m
                n_top_genes=#x1B[94m15#x1B[39;49;00m,#x1B[90m#x1B[39;49;00m
                batch_key=batch_key,#x1B[90m#x1B[39;49;00m
                subset=subset,#x1B[90m#x1B[39;49;00m
                inplace=inplace,#x1B[90m#x1B[39;49;00m
            )#x1B[90m#x1B[39;49;00m

#x1B[1m#x1B[31mtests/test_highly_variable_genes.py#x1B[0m:686: 
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 
#x1B[1m#x1B[.../scanpy/preprocessing/_highly_variable_genes.py#x1B[0m:780: in highly_variable_genes
    #x1B[0m#x1B[94mreturn#x1B[39;49;00m _highly_variable_genes_seurat_v3(#x1B[90m#x1B[39;49;00m
#x1B[1m#x1B[.../scanpy/preprocessing/_highly_variable_genes.py#x1B[0m:196: in _highly_variable_genes_seurat_v3
    #x1B[0maggregated_mean_var = ad.AnnData(#x1B[90m#x1B[39;49;00m
#x1B[1m#x1B[31m../../../..../scanpy/B9PcT7QG/hatch-test.stable/lib/python3.14.../anndata/_core/anndata.py#x1B[0m:254: in __init__
    #x1B[0m#x1B[96mself#x1B[39;49;00m._init_as_actual(#x1B[90m#x1B[39;49;00m
#x1B[1m#x1B[31m../../../..../scanpy/B9PcT7QG/hatch-test.stable/lib/python3.14.../anndata/_core/anndata.py#x1B[0m:448: in _init_as_actual
    #x1B[0m#x1B[96mself#x1B[39;49;00m._obs = _gen_dataframe(#x1B[90m#x1B[39;49;00m
#x1B[1m#x1B[31m../...../_temp/uv-python-dir/cpython-3.14.5-linux-x86_64-gnu/lib/python3.14/functools.py#x1B[0m:982: in wrapper
    #x1B[0m#x1B[94mreturn#x1B[39;49;00m dispatch(args[#x1B[94m0#x1B[39;49;00m].#x1B[91m__class__#x1B[39;49;00m)(*args, **kw)#x1B[90m#x1B[39;49;00m
           ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^#x1B[90m#x1B[39;49;00m
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 

anno =    __hvg_v3_batch_info__
0                      0
index_names = ['obs_names', 'row_names']

    #x1B[0m#x1B[37m@_gen_dataframe#x1B[39;49;00m.register(pd.DataFrame)#x1B[90m#x1B[39;49;00m
    #x1B[94mdef#x1B[39;49;00m#x1B[90m #x1B[39;49;00m#x1B[92m_gen_dataframe_df#x1B[39;49;00m(#x1B[90m#x1B[39;49;00m
        anno: pd.DataFrame,#x1B[90m#x1B[39;49;00m
        index_names: Iterable[#x1B[96mstr#x1B[39;49;00m],#x1B[90m#x1B[39;49;00m
        *,#x1B[90m#x1B[39;49;00m
        source: Literal[#x1B[33m"#x1B[39;49;00m#x1B[33mX#x1B[39;49;00m#x1B[33m"#x1B[39;49;00m, #x1B[33m"#x1B[39;49;00m#x1B[33mshape#x1B[39;49;00m#x1B[33m"#x1B[39;49;00m],#x1B[90m#x1B[39;49;00m
        attr: Literal[#x1B[33m"#x1B[39;49;00m#x1B[33mobs#x1B[39;49;00m#x1B[33m"#x1B[39;49;00m, #x1B[33m"#x1B[39;49;00m#x1B[33mvar#x1B[39;49;00m#x1B[33m"#x1B[39;49;00m],#x1B[90m#x1B[39;49;00m
        length: #x1B[96mint#x1B[39;49;00m | #x1B[94mNone#x1B[39;49;00m = #x1B[94mNone#x1B[39;49;00m,#x1B[90m#x1B[39;49;00m
    ):#x1B[90m#x1B[39;49;00m
        #x1B[94mif#x1B[39;49;00m #x1B[96misinstance#x1B[39;49;00m(anno.index, pd.MultiIndex):#x1B[90m#x1B[39;49;00m
            msg = (#x1B[90m#x1B[39;49;00m
                #x1B[33m"#x1B[39;49;00m#x1B[33mpandas.MultiIndex not supported as index for obs or var on declaration.#x1B[39;49;00m#x1B[33m\n#x1B[39;49;00m#x1B[33m\#x1B[39;49;00m
    #x1B[33m            You can set `obs_names` manually although most operations after will error or convert to str.#x1B[39;49;00m#x1B[33m\n#x1B[39;49;00m#x1B[33m\#x1B[39;49;00m
    #x1B[33m            This behavior will likely be clarified in a future breaking release.#x1B[39;49;00m#x1B[33m"#x1B[39;49;00m#x1B[90m#x1B[39;49;00m
            )#x1B[90m#x1B[39;49;00m
            #x1B[94mraise#x1B[39;49;00m #x1B[96mValueError#x1B[39;49;00m(msg)#x1B[90m#x1B[39;49;00m
        #x1B[94mif#x1B[39;49;00m length #x1B[95mis#x1B[39;49;00m #x1B[95mnot#x1B[39;49;00m #x1B[94mNone#x1B[39;49;00m #x1B[95mand#x1B[39;49;00m length != #x1B[96mlen#x1B[39;49;00m(anno):#x1B[90m#x1B[39;49;00m
            #x1B[94mraise#x1B[39;49;00m _mk_df_error(source, attr, length, #x1B[96mlen#x1B[39;49;00m(anno))#x1B[90m#x1B[39;49;00m
        anno = anno.copy(deep=#x1B[94mFalse#x1B[39;49;00m)#x1B[90m#x1B[39;49;00m
        #x1B[94mif#x1B[39;49;00m #x1B[95mnot#x1B[39;49;00m is_string_dtype(anno.index[~anno.index.isna()]):#x1B[90m#x1B[39;49;00m
            msg = #x1B[33m"#x1B[39;49;00m#x1B[33mTransforming to str index.#x1B[39;49;00m#x1B[33m"#x1B[39;49;00m#x1B[90m#x1B[39;49;00m
>           warnings.warn(msg, ImplicitModificationWarning, stacklevel=#x1B[94m2#x1B[39;49;00m)#x1B[90m#x1B[39;49;00m
#x1B[1m#x1B[31mE           anndata._warnings.ImplicitModificationWarning: Transforming to str index.#x1B[0m

#x1B[1m#x1B[31m../../../..../scanpy/B9PcT7QG/hatch-test.stable/lib/python3.14.../anndata/_core/aligned_df.py#x1B[0m:93: ImplicitModificationWarning

tests/test_highly_variable_genes.py::test_subset_inplace_consistency[None-scipy_csr_arr-seurat_v3]

Stack Traces | 0.014s run time

subtests = <_pytest.subtests.Subtests object at 0x7f57c083e8d0>
flavor = 'seurat_v3', array_type = <class 'scipy.sparse._csr.csr_array'>
batch_key = None

    #x1B[0m#x1B[37m@pytest#x1B[39;49;00m.mark.parametrize(#x1B[90m#x1B[39;49;00m
        #x1B[33m"#x1B[39;49;00m#x1B[33mflavor#x1B[39;49;00m#x1B[33m"#x1B[39;49;00m,#x1B[90m#x1B[39;49;00m
        [#x1B[90m#x1B[39;49;00m
            #x1B[33m"#x1B[39;49;00m#x1B[33mseurat#x1B[39;49;00m#x1B[33m"#x1B[39;49;00m,#x1B[90m#x1B[39;49;00m
            #x1B[33m"#x1B[39;49;00m#x1B[33mcell_ranger#x1B[39;49;00m#x1B[33m"#x1B[39;49;00m,#x1B[90m#x1B[39;49;00m
            pytest.param(#x1B[33m"#x1B[39;49;00m#x1B[33mseurat_v3#x1B[39;49;00m#x1B[33m"#x1B[39;49;00m, marks=needs.skmisc),#x1B[90m#x1B[39;49;00m
            pytest.param(#x1B[33m"#x1B[39;49;00m#x1B[33mseurat_v3_paper#x1B[39;49;00m#x1B[33m"#x1B[39;49;00m, marks=needs.skmisc),#x1B[90m#x1B[39;49;00m
        ],#x1B[90m#x1B[39;49;00m
    )#x1B[90m#x1B[39;49;00m
    #x1B[37m@pytest#x1B[39;49;00m.mark.parametrize(#x1B[90m#x1B[39;49;00m
        #x1B[33m"#x1B[39;49;00m#x1B[33marray_type#x1B[39;49;00m#x1B[33m"#x1B[39;49;00m,#x1B[90m#x1B[39;49;00m
        [#x1B[90m#x1B[39;49;00m
            p#x1B[90m#x1B[39;49;00m
            #x1B[94mfor#x1B[39;49;00m p #x1B[95min#x1B[39;49;00m ARRAY_TYPES#x1B[90m#x1B[39;49;00m
            #x1B[94mif#x1B[39;49;00m #x1B[33m"#x1B[39;49;00m#x1B[33mdask#x1B[39;49;00m#x1B[33m"#x1B[39;49;00m #x1B[95mnot#x1B[39;49;00m #x1B[95min#x1B[39;49;00m p.id #x1B[95mor#x1B[39;49;00m (#x1B[33m"#x1B[39;49;00m#x1B[33m1d_chunked#x1B[39;49;00m#x1B[33m"#x1B[39;49;00m #x1B[95min#x1B[39;49;00m p.id #x1B[95mand#x1B[39;49;00m #x1B[33m"#x1B[39;49;00m#x1B[33mcsr#x1B[39;49;00m#x1B[33m"#x1B[39;49;00m #x1B[95min#x1B[39;49;00m p.id)#x1B[90m#x1B[39;49;00m
        ],#x1B[90m#x1B[39;49;00m
    )#x1B[90m#x1B[39;49;00m
    #x1B[37m@pytest#x1B[39;49;00m.mark.parametrize(#x1B[33m"#x1B[39;49;00m#x1B[33mbatch_key#x1B[39;49;00m#x1B[33m"#x1B[39;49;00m, [#x1B[94mNone#x1B[39;49;00m, #x1B[33m"#x1B[39;49;00m#x1B[33mbatch#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_subset_inplace_consistency#x1B[39;49;00m(#x1B[90m#x1B[39;49;00m
        subtests: pytest.Subtests,#x1B[90m#x1B[39;49;00m
        flavor: Literal[#x1B[33m"#x1B[39;49;00m#x1B[33mseurat#x1B[39;49;00m#x1B[33m"#x1B[39;49;00m, #x1B[33m"#x1B[39;49;00m#x1B[33mcell_ranger#x1B[39;49;00m#x1B[33m"#x1B[39;49;00m, #x1B[33m"#x1B[39;49;00m#x1B[33mseurat_v3#x1B[39;49;00m#x1B[33m"#x1B[39;49;00m, #x1B[33m"#x1B[39;49;00m#x1B[33mseurat_v3_paper#x1B[39;49;00m#x1B[33m"#x1B[39;49;00m],#x1B[90m#x1B[39;49;00m
        array_type,#x1B[90m#x1B[39;49;00m
        batch_key: Literal[#x1B[33m"#x1B[39;49;00m#x1B[33mbatch#x1B[39;49;00m#x1B[33m"#x1B[39;49;00m] | #x1B[94mNone#x1B[39;49;00m,#x1B[90m#x1B[39;49;00m
    ) -> #x1B[94mNone#x1B[39;49;00m:#x1B[90m#x1B[39;49;00m
    #x1B[90m    #x1B[39;49;00m#x1B[33m"""Tests `n_top_genes=n`.#x1B[39;49;00m
    #x1B[33m#x1B[39;49;00m
    #x1B[33m    - if `inplace` and `subset` interact correctly#x1B[39;49;00m
    #x1B[33m    - for both the `seurat` and `cell_ranger` flavors#x1B[39;49;00m
    #x1B[33m    - for dask arrays and non-dask arrays#x1B[39;49;00m
    #x1B[33m    - for both with and without batch_key#x1B[39;49;00m
    #x1B[33m    """#x1B[39;49;00m#x1B[90m#x1B[39;49;00m
        rng = np.random.default_rng(#x1B[94m0#x1B[39;49;00m)#x1B[90m#x1B[39;49;00m
        adata = (#x1B[90m#x1B[39;49;00m
            sc.datasets.blobs(n_observations=#x1B[94m20#x1B[39;49;00m, n_variables=#x1B[94m80#x1B[39;49;00m, rng=rng)#x1B[90m#x1B[39;49;00m
            #x1B[94mif#x1B[39;49;00m #x1B[33m"#x1B[39;49;00m#x1B[33mseurat_v3#x1B[39;49;00m#x1B[33m"#x1B[39;49;00m #x1B[95mnot#x1B[39;49;00m #x1B[95min#x1B[39;49;00m flavor#x1B[90m#x1B[39;49;00m
            #x1B[94melse#x1B[39;49;00m pbmc3k()[:#x1B[94m1500#x1B[39;49;00m, :#x1B[94m1000#x1B[39;49;00m].copy()#x1B[90m#x1B[39;49;00m
        )#x1B[90m#x1B[39;49;00m
        adata.obs[#x1B[33m"#x1B[39;49;00m#x1B[33mbatch#x1B[39;49;00m#x1B[33m"#x1B[39;49;00m] = rng.choice([#x1B[33m"#x1B[39;49;00m#x1B[33ma#x1B[39;49;00m#x1B[33m"#x1B[39;49;00m, #x1B[33m"#x1B[39;49;00m#x1B[33mb#x1B[39;49;00m#x1B[33m"#x1B[39;49;00m], adata.shape[#x1B[94m0#x1B[39;49;00m])#x1B[90m#x1B[39;49;00m
        adata.X = array_type(np.abs(adata.X).astype(#x1B[96mint#x1B[39;49;00m))#x1B[90m#x1B[39;49;00m
    #x1B[90m#x1B[39;49;00m
        #x1B[94mif#x1B[39;49;00m flavor #x1B[95min#x1B[39;49;00m {#x1B[33m"#x1B[39;49;00m#x1B[33mseurat#x1B[39;49;00m#x1B[33m"#x1B[39;49;00m, #x1B[33m"#x1B[39;49;00m#x1B[33mcell_ranger#x1B[39;49;00m#x1B[33m"#x1B[39;49;00m}:#x1B[90m#x1B[39;49;00m
            sc.pp.normalize_total(adata, target_sum=#x1B[94m1e4#x1B[39;49;00m)#x1B[90m#x1B[39;49;00m
            sc.pp.log1p(adata)#x1B[90m#x1B[39;49;00m
    #x1B[90m#x1B[39;49;00m
        n_genes = adata.shape[#x1B[94m1#x1B[39;49;00m]#x1B[90m#x1B[39;49;00m
    #x1B[90m#x1B[39;49;00m
        adatas: #x1B[96mdict#x1B[39;49;00m[#x1B[96mbool#x1B[39;49;00m, AnnData] = {}#x1B[90m#x1B[39;49;00m
        dfs: #x1B[96mdict#x1B[39;49;00m[#x1B[96mbool#x1B[39;49;00m, pd.DataFrame] = {}#x1B[90m#x1B[39;49;00m
        #x1B[90m# for loops instead of parametrization to compare between settings#x1B[39;49;00m#x1B[90m#x1B[39;49;00m
        #x1B[94mfor#x1B[39;49;00m subset, inplace #x1B[95min#x1B[39;49;00m itertools.product([#x1B[94mTrue#x1B[39;49;00m, #x1B[94mFalse#x1B[39;49;00m], repeat=#x1B[94m2#x1B[39;49;00m):#x1B[90m#x1B[39;49;00m
            adata_copy = adata.copy()#x1B[90m#x1B[39;49;00m
    #x1B[90m#x1B[39;49;00m
>           output_df = sc.pp.highly_variable_genes(#x1B[90m#x1B[39;49;00m
                adata_copy,#x1B[90m#x1B[39;49;00m
                flavor=flavor,#x1B[90m#x1B[39;49;00m
                n_top_genes=#x1B[94m15#x1B[39;49;00m,#x1B[90m#x1B[39;49;00m
                batch_key=batch_key,#x1B[90m#x1B[39;49;00m
                subset=subset,#x1B[90m#x1B[39;49;00m
                inplace=inplace,#x1B[90m#x1B[39;49;00m
            )#x1B[90m#x1B[39;49;00m

#x1B[1m#x1B[31mtests/test_highly_variable_genes.py#x1B[0m:686: 
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 
#x1B[1m#x1B[.../scanpy/preprocessing/_highly_variable_genes.py#x1B[0m:780: in highly_variable_genes
    #x1B[0m#x1B[94mreturn#x1B[39;49;00m _highly_variable_genes_seurat_v3(#x1B[90m#x1B[39;49;00m
#x1B[1m#x1B[.../scanpy/preprocessing/_highly_variable_genes.py#x1B[0m:196: in _highly_variable_genes_seurat_v3
    #x1B[0maggregated_mean_var = ad.AnnData(#x1B[90m#x1B[39;49;00m
#x1B[1m#x1B[31m../../../..../scanpy/B9PcT7QG/hatch-test.stable/lib/python3.14.../anndata/_core/anndata.py#x1B[0m:254: in __init__
    #x1B[0m#x1B[96mself#x1B[39;49;00m._init_as_actual(#x1B[90m#x1B[39;49;00m
#x1B[1m#x1B[31m../../../..../scanpy/B9PcT7QG/hatch-test.stable/lib/python3.14.../anndata/_core/anndata.py#x1B[0m:448: in _init_as_actual
    #x1B[0m#x1B[96mself#x1B[39;49;00m._obs = _gen_dataframe(#x1B[90m#x1B[39;49;00m
#x1B[1m#x1B[31m../...../_temp/uv-python-dir/cpython-3.14.5-linux-x86_64-gnu/lib/python3.14/functools.py#x1B[0m:982: in wrapper
    #x1B[0m#x1B[94mreturn#x1B[39;49;00m dispatch(args[#x1B[94m0#x1B[39;49;00m].#x1B[91m__class__#x1B[39;49;00m)(*args, **kw)#x1B[90m#x1B[39;49;00m
           ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^#x1B[90m#x1B[39;49;00m
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 

anno =    __hvg_v3_batch_info__
0                      0
index_names = ['obs_names', 'row_names']

    #x1B[0m#x1B[37m@_gen_dataframe#x1B[39;49;00m.register(pd.DataFrame)#x1B[90m#x1B[39;49;00m
    #x1B[94mdef#x1B[39;49;00m#x1B[90m #x1B[39;49;00m#x1B[92m_gen_dataframe_df#x1B[39;49;00m(#x1B[90m#x1B[39;49;00m
        anno: pd.DataFrame,#x1B[90m#x1B[39;49;00m
        index_names: Iterable[#x1B[96mstr#x1B[39;49;00m],#x1B[90m#x1B[39;49;00m
        *,#x1B[90m#x1B[39;49;00m
        source: Literal[#x1B[33m"#x1B[39;49;00m#x1B[33mX#x1B[39;49;00m#x1B[33m"#x1B[39;49;00m, #x1B[33m"#x1B[39;49;00m#x1B[33mshape#x1B[39;49;00m#x1B[33m"#x1B[39;49;00m],#x1B[90m#x1B[39;49;00m
        attr: Literal[#x1B[33m"#x1B[39;49;00m#x1B[33mobs#x1B[39;49;00m#x1B[33m"#x1B[39;49;00m, #x1B[33m"#x1B[39;49;00m#x1B[33mvar#x1B[39;49;00m#x1B[33m"#x1B[39;49;00m],#x1B[90m#x1B[39;49;00m
        length: #x1B[96mint#x1B[39;49;00m | #x1B[94mNone#x1B[39;49;00m = #x1B[94mNone#x1B[39;49;00m,#x1B[90m#x1B[39;49;00m
    ):#x1B[90m#x1B[39;49;00m
        #x1B[94mif#x1B[39;49;00m #x1B[96misinstance#x1B[39;49;00m(anno.index, pd.MultiIndex):#x1B[90m#x1B[39;49;00m
            msg = (#x1B[90m#x1B[39;49;00m
                #x1B[33m"#x1B[39;49;00m#x1B[33mpandas.MultiIndex not supported as index for obs or var on declaration.#x1B[39;49;00m#x1B[33m\n#x1B[39;49;00m#x1B[33m\#x1B[39;49;00m
    #x1B[33m            You can set `obs_names` manually although most operations after will error or convert to str.#x1B[39;49;00m#x1B[33m\n#x1B[39;49;00m#x1B[33m\#x1B[39;49;00m
    #x1B[33m            This behavior will likely be clarified in a future breaking release.#x1B[39;49;00m#x1B[33m"#x1B[39;49;00m#x1B[90m#x1B[39;49;00m
            )#x1B[90m#x1B[39;49;00m
            #x1B[94mraise#x1B[39;49;00m #x1B[96mValueError#x1B[39;49;00m(msg)#x1B[90m#x1B[39;49;00m
        #x1B[94mif#x1B[39;49;00m length #x1B[95mis#x1B[39;49;00m #x1B[95mnot#x1B[39;49;00m #x1B[94mNone#x1B[39;49;00m #x1B[95mand#x1B[39;49;00m length != #x1B[96mlen#x1B[39;49;00m(anno):#x1B[90m#x1B[39;49;00m
            #x1B[94mraise#x1B[39;49;00m _mk_df_error(source, attr, length, #x1B[96mlen#x1B[39;49;00m(anno))#x1B[90m#x1B[39;49;00m
        anno = anno.copy(deep=#x1B[94mFalse#x1B[39;49;00m)#x1B[90m#x1B[39;49;00m
        #x1B[94mif#x1B[39;49;00m #x1B[95mnot#x1B[39;49;00m is_string_dtype(anno.index[~anno.index.isna()]):#x1B[90m#x1B[39;49;00m
            msg = #x1B[33m"#x1B[39;49;00m#x1B[33mTransforming to str index.#x1B[39;49;00m#x1B[33m"#x1B[39;49;00m#x1B[90m#x1B[39;49;00m
>           warnings.warn(msg, ImplicitModificationWarning, stacklevel=#x1B[94m2#x1B[39;49;00m)#x1B[90m#x1B[39;49;00m
#x1B[1m#x1B[31mE           anndata._warnings.ImplicitModificationWarning: Transforming to str index.#x1B[0m

#x1B[1m#x1B[31m../../../..../scanpy/B9PcT7QG/hatch-test.stable/lib/python3.14.../anndata/_core/aligned_df.py#x1B[0m:93: ImplicitModificationWarning

tests/test_highly_variable_genes.py::test_subset_inplace_consistency[None-scipy_csr_arr-seurat_v3_paper]

Stack Traces | 0.015s run time

subtests = <_pytest.subtests.Subtests object at 0x7f57c82b0a70>
flavor = 'seurat_v3_paper', array_type = <class 'scipy.sparse._csr.csr_array'>
batch_key = None

    #x1B[0m#x1B[37m@pytest#x1B[39;49;00m.mark.parametrize(#x1B[90m#x1B[39;49;00m
        #x1B[33m"#x1B[39;49;00m#x1B[33mflavor#x1B[39;49;00m#x1B[33m"#x1B[39;49;00m,#x1B[90m#x1B[39;49;00m
        [#x1B[90m#x1B[39;49;00m
            #x1B[33m"#x1B[39;49;00m#x1B[33mseurat#x1B[39;49;00m#x1B[33m"#x1B[39;49;00m,#x1B[90m#x1B[39;49;00m
            #x1B[33m"#x1B[39;49;00m#x1B[33mcell_ranger#x1B[39;49;00m#x1B[33m"#x1B[39;49;00m,#x1B[90m#x1B[39;49;00m
            pytest.param(#x1B[33m"#x1B[39;49;00m#x1B[33mseurat_v3#x1B[39;49;00m#x1B[33m"#x1B[39;49;00m, marks=needs.skmisc),#x1B[90m#x1B[39;49;00m
            pytest.param(#x1B[33m"#x1B[39;49;00m#x1B[33mseurat_v3_paper#x1B[39;49;00m#x1B[33m"#x1B[39;49;00m, marks=needs.skmisc),#x1B[90m#x1B[39;49;00m
        ],#x1B[90m#x1B[39;49;00m
    )#x1B[90m#x1B[39;49;00m
    #x1B[37m@pytest#x1B[39;49;00m.mark.parametrize(#x1B[90m#x1B[39;49;00m
        #x1B[33m"#x1B[39;49;00m#x1B[33marray_type#x1B[39;49;00m#x1B[33m"#x1B[39;49;00m,#x1B[90m#x1B[39;49;00m
        [#x1B[90m#x1B[39;49;00m
            p#x1B[90m#x1B[39;49;00m
            #x1B[94mfor#x1B[39;49;00m p #x1B[95min#x1B[39;49;00m ARRAY_TYPES#x1B[90m#x1B[39;49;00m
            #x1B[94mif#x1B[39;49;00m #x1B[33m"#x1B[39;49;00m#x1B[33mdask#x1B[39;49;00m#x1B[33m"#x1B[39;49;00m #x1B[95mnot#x1B[39;49;00m #x1B[95min#x1B[39;49;00m p.id #x1B[95mor#x1B[39;49;00m (#x1B[33m"#x1B[39;49;00m#x1B[33m1d_chunked#x1B[39;49;00m#x1B[33m"#x1B[39;49;00m #x1B[95min#x1B[39;49;00m p.id #x1B[95mand#x1B[39;49;00m #x1B[33m"#x1B[39;49;00m#x1B[33mcsr#x1B[39;49;00m#x1B[33m"#x1B[39;49;00m #x1B[95min#x1B[39;49;00m p.id)#x1B[90m#x1B[39;49;00m
        ],#x1B[90m#x1B[39;49;00m
    )#x1B[90m#x1B[39;49;00m
    #x1B[37m@pytest#x1B[39;49;00m.mark.parametrize(#x1B[33m"#x1B[39;49;00m#x1B[33mbatch_key#x1B[39;49;00m#x1B[33m"#x1B[39;49;00m, [#x1B[94mNone#x1B[39;49;00m, #x1B[33m"#x1B[39;49;00m#x1B[33mbatch#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_subset_inplace_consistency#x1B[39;49;00m(#x1B[90m#x1B[39;49;00m
        subtests: pytest.Subtests,#x1B[90m#x1B[39;49;00m
        flavor: Literal[#x1B[33m"#x1B[39;49;00m#x1B[33mseurat#x1B[39;49;00m#x1B[33m"#x1B[39;49;00m, #x1B[33m"#x1B[39;49;00m#x1B[33mcell_ranger#x1B[39;49;00m#x1B[33m"#x1B[39;49;00m, #x1B[33m"#x1B[39;49;00m#x1B[33mseurat_v3#x1B[39;49;00m#x1B[33m"#x1B[39;49;00m, #x1B[33m"#x1B[39;49;00m#x1B[33mseurat_v3_paper#x1B[39;49;00m#x1B[33m"#x1B[39;49;00m],#x1B[90m#x1B[39;49;00m
        array_type,#x1B[90m#x1B[39;49;00m
        batch_key: Literal[#x1B[33m"#x1B[39;49;00m#x1B[33mbatch#x1B[39;49;00m#x1B[33m"#x1B[39;49;00m] | #x1B[94mNone#x1B[39;49;00m,#x1B[90m#x1B[39;49;00m
    ) -> #x1B[94mNone#x1B[39;49;00m:#x1B[90m#x1B[39;49;00m
    #x1B[90m    #x1B[39;49;00m#x1B[33m"""Tests `n_top_genes=n`.#x1B[39;49;00m
    #x1B[33m#x1B[39;49;00m
    #x1B[33m    - if `inplace` and `subset` interact correctly#x1B[39;49;00m
    #x1B[33m    - for both the `seurat` and `cell_ranger` flavors#x1B[39;49;00m
    #x1B[33m    - for dask arrays and non-dask arrays#x1B[39;49;00m
    #x1B[33m    - for both with and without batch_key#x1B[39;49;00m
    #x1B[33m    """#x1B[39;49;00m#x1B[90m#x1B[39;49;00m
        rng = np.random.default_rng(#x1B[94m0#x1B[39;49;00m)#x1B[90m#x1B[39;49;00m
        adata = (#x1B[90m#x1B[39;49;00m
            sc.datasets.blobs(n_observations=#x1B[94m20#x1B[39;49;00m, n_variables=#x1B[94m80#x1B[39;49;00m, rng=rng)#x1B[90m#x1B[39;49;00m
            #x1B[94mif#x1B[39;49;00m #x1B[33m"#x1B[39;49;00m#x1B[33mseurat_v3#x1B[39;49;00m#x1B[33m"#x1B[39;49;00m #x1B[95mnot#x1B[39;49;00m #x1B[95min#x1B[39;49;00m flavor#x1B[90m#x1B[39;49;00m
            #x1B[94melse#x1B[39;49;00m pbmc3k()[:#x1B[94m1500#x1B[39;49;00m, :#x1B[94m1000#x1B[39;49;00m].copy()#x1B[90m#x1B[39;49;00m
        )#x1B[90m#x1B[39;49;00m
        adata.obs[#x1B[33m"#x1B[39;49;00m#x1B[33mbatch#x1B[39;49;00m#x1B[33m"#x1B[39;49;00m] = rng.choice([#x1B[33m"#x1B[39;49;00m#x1B[33ma#x1B[39;49;00m#x1B[33m"#x1B[39;49;00m, #x1B[33m"#x1B[39;49;00m#x1B[33mb#x1B[39;49;00m#x1B[33m"#x1B[39;49;00m], adata.shape[#x1B[94m0#x1B[39;49;00m])#x1B[90m#x1B[39;49;00m
        adata.X = array_type(np.abs(adata.X).astype(#x1B[96mint#x1B[39;49;00m))#x1B[90m#x1B[39;49;00m
    #x1B[90m#x1B[39;49;00m
        #x1B[94mif#x1B[39;49;00m flavor #x1B[95min#x1B[39;49;00m {#x1B[33m"#x1B[39;49;00m#x1B[33mseurat#x1B[39;49;00m#x1B[33m"#x1B[39;49;00m, #x1B[33m"#x1B[39;49;00m#x1B[33mcell_ranger#x1B[39;49;00m#x1B[33m"#x1B[39;49;00m}:#x1B[90m#x1B[39;49;00m
            sc.pp.normalize_total(adata, target_sum=#x1B[94m1e4#x1B[39;49;00m)#x1B[90m#x1B[39;49;00m
            sc.pp.log1p(adata)#x1B[90m#x1B[39;49;00m
    #x1B[90m#x1B[39;49;00m
        n_genes = adata.shape[#x1B[94m1#x1B[39;49;00m]#x1B[90m#x1B[39;49;00m
    #x1B[90m#x1B[39;49;00m
        adatas: #x1B[96mdict#x1B[39;49;00m[#x1B[96mbool#x1B[39;49;00m, AnnData] = {}#x1B[90m#x1B[39;49;00m
        dfs: #x1B[96mdict#x1B[39;49;00m[#x1B[96mbool#x1B[39;49;00m, pd.DataFrame] = {}#x1B[90m#x1B[39;49;00m
        #x1B[90m# for loops instead of parametrization to compare between settings#x1B[39;49;00m#x1B[90m#x1B[39;49;00m
        #x1B[94mfor#x1B[39;49;00m subset, inplace #x1B[95min#x1B[39;49;00m itertools.product([#x1B[94mTrue#x1B[39;49;00m, #x1B[94mFalse#x1B[39;49;00m], repeat=#x1B[94m2#x1B[39;49;00m):#x1B[90m#x1B[39;49;00m
            adata_copy = adata.copy()#x1B[90m#x1B[39;49;00m
    #x1B[90m#x1B[39;49;00m
>           output_df = sc.pp.highly_variable_genes(#x1B[90m#x1B[39;49;00m
                adata_copy,#x1B[90m#x1B[39;49;00m
                flavor=flavor,#x1B[90m#x1B[39;49;00m
                n_top_genes=#x1B[94m15#x1B[39;49;00m,#x1B[90m#x1B[39;49;00m
                batch_key=batch_key,#x1B[90m#x1B[39;49;00m
                subset=subset,#x1B[90m#x1B[39;49;00m
                inplace=inplace,#x1B[90m#x1B[39;49;00m
            )#x1B[90m#x1B[39;49;00m

#x1B[1m#x1B[31mtests/test_highly_variable_genes.py#x1B[0m:686: 
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 
#x1B[1m#x1B[.../scanpy/preprocessing/_highly_variable_genes.py#x1B[0m:780: in highly_variable_genes
    #x1B[0m#x1B[94mreturn#x1B[39;49;00m _highly_variable_genes_seurat_v3(#x1B[90m#x1B[39;49;00m
#x1B[1m#x1B[.../scanpy/preprocessing/_highly_variable_genes.py#x1B[0m:196: in _highly_variable_genes_seurat_v3
    #x1B[0maggregated_mean_var = ad.AnnData(#x1B[90m#x1B[39;49;00m
#x1B[1m#x1B[31m../../../..../scanpy/B9PcT7QG/hatch-test.stable/lib/python3.14.../anndata/_core/anndata.py#x1B[0m:254: in __init__
    #x1B[0m#x1B[96mself#x1B[39;49;00m._init_as_actual(#x1B[90m#x1B[39;49;00m
#x1B[1m#x1B[31m../../../..../scanpy/B9PcT7QG/hatch-test.stable/lib/python3.14.../anndata/_core/anndata.py#x1B[0m:448: in _init_as_actual
    #x1B[0m#x1B[96mself#x1B[39;49;00m._obs = _gen_dataframe(#x1B[90m#x1B[39;49;00m
#x1B[1m#x1B[31m../...../_temp/uv-python-dir/cpython-3.14.5-linux-x86_64-gnu/lib/python3.14/functools.py#x1B[0m:982: in wrapper
    #x1B[0m#x1B[94mreturn#x1B[39;49;00m dispatch(args[#x1B[94m0#x1B[39;49;00m].#x1B[91m__class__#x1B[39;49;00m)(*args, **kw)#x1B[90m#x1B[39;49;00m
           ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^#x1B[90m#x1B[39;49;00m
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 

anno =    __hvg_v3_batch_info__
0                      0
index_names = ['obs_names', 'row_names']

    #x1B[0m#x1B[37m@_gen_dataframe#x1B[39;49;00m.register(pd.DataFrame)#x1B[90m#x1B[39;49;00m
    #x1B[94mdef#x1B[39;49;00m#x1B[90m #x1B[39;49;00m#x1B[92m_gen_dataframe_df#x1B[39;49;00m(#x1B[90m#x1B[39;49;00m
        anno: pd.DataFrame,#x1B[90m#x1B[39;49;00m
        index_names: Iterable[#x1B[96mstr#x1B[39;49;00m],#x1B[90m#x1B[39;49;00m
        *,#x1B[90m#x1B[39;49;00m
        source: Literal[#x1B[33m"#x1B[39;49;00m#x1B[33mX#x1B[39;49;00m#x1B[33m"#x1B[39;49;00m, #x1B[33m"#x1B[39;49;00m#x1B[33mshape#x1B[39;49;00m#x1B[33m"#x1B[39;49;00m],#x1B[90m#x1B[39;49;00m
        attr: Literal[#x1B[33m"#x1B[39;49;00m#x1B[33mobs#x1B[39;49;00m#x1B[33m"#x1B[39;49;00m, #x1B[33m"#x1B[39;49;00m#x1B[33mvar#x1B[39;49;00m#x1B[33m"#x1B[39;49;00m],#x1B[90m#x1B[39;49;00m
        length: #x1B[96mint#x1B[39;49;00m | #x1B[94mNone#x1B[39;49;00m = #x1B[94mNone#x1B[39;49;00m,#x1B[90m#x1B[39;49;00m
    ):#x1B[90m#x1B[39;49;00m
        #x1B[94mif#x1B[39;49;00m #x1B[96misinstance#x1B[39;49;00m(anno.index, pd.MultiIndex):#x1B[90m#x1B[39;49;00m
            msg = (#x1B[90m#x1B[39;49;00m
                #x1B[33m"#x1B[39;49;00m#x1B[33mpandas.MultiIndex not supported as index for obs or var on declaration.#x1B[39;49;00m#x1B[33m\n#x1B[39;49;00m#x1B[33m\#x1B[39;49;00m
    #x1B[33m            You can set `obs_names` manually although most operations after will error or convert to str.#x1B[39;49;00m#x1B[33m\n#x1B[39;49;00m#x1B[33m\#x1B[39;49;00m
    #x1B[33m            This behavior will likely be clarified in a future breaking release.#x1B[39;49;00m#x1B[33m"#x1B[39;49;00m#x1B[90m#x1B[39;49;00m
            )#x1B[90m#x1B[39;49;00m
            #x1B[94mraise#x1B[39;49;00m #x1B[96mValueError#x1B[39;49;00m(msg)#x1B[90m#x1B[39;49;00m
        #x1B[94mif#x1B[39;49;00m length #x1B[95mis#x1B[39;49;00m #x1B[95mnot#x1B[39;49;00m #x1B[94mNone#x1B[39;49;00m #x1B[95mand#x1B[39;49;00m length != #x1B[96mlen#x1B[39;49;00m(anno):#x1B[90m#x1B[39;49;00m
            #x1B[94mraise#x1B[39;49;00m _mk_df_error(source, attr, length, #x1B[96mlen#x1B[39;49;00m(anno))#x1B[90m#x1B[39;49;00m
        anno = anno.copy(deep=#x1B[94mFalse#x1B[39;49;00m)#x1B[90m#x1B[39;49;00m
        #x1B[94mif#x1B[39;49;00m #x1B[95mnot#x1B[39;49;00m is_string_dtype(anno.index[~anno.index.isna()]):#x1B[90m#x1B[39;49;00m
            msg = #x1B[33m"#x1B[39;49;00m#x1B[33mTransforming to str index.#x1B[39;49;00m#x1B[33m"#x1B[39;49;00m#x1B[90m#x1B[39;49;00m
>           warnings.warn(msg, ImplicitModificationWarning, stacklevel=#x1B[94m2#x1B[39;49;00m)#x1B[90m#x1B[39;49;00m
#x1B[1m#x1B[31mE           anndata._warnings.ImplicitModificationWarning: Transforming to str index.#x1B[0m

#x1B[1m#x1B[31m../../../..../scanpy/B9PcT7QG/hatch-test.stable/lib/python3.14.../anndata/_core/aligned_df.py#x1B[0m:93: ImplicitModificationWarning

tests/test_highly_variable_genes.py::test_subset_inplace_consistency[None-scipy_csr_mat-seurat_v3_paper]

Stack Traces | 0.015s run time

subtests = <_pytest.subtests.Subtests object at 0x7f57c0ef9df0>
flavor = 'seurat_v3_paper', array_type = <class 'scipy.sparse._csr.csr_matrix'>
batch_key = None

    #x1B[0m#x1B[37m@pytest#x1B[39;49;00m.mark.parametrize(#x1B[90m#x1B[39;49;00m
        #x1B[33m"#x1B[39;49;00m#x1B[33mflavor#x1B[39;49;00m#x1B[33m"#x1B[39;49;00m,#x1B[90m#x1B[39;49;00m
        [#x1B[90m#x1B[39;49;00m
            #x1B[33m"#x1B[39;49;00m#x1B[33mseurat#x1B[39;49;00m#x1B[33m"#x1B[39;49;00m,#x1B[90m#x1B[39;49;00m
            #x1B[33m"#x1B[39;49;00m#x1B[33mcell_ranger#x1B[39;49;00m#x1B[33m"#x1B[39;49;00m,#x1B[90m#x1B[39;49;00m
            pytest.param(#x1B[33m"#x1B[39;49;00m#x1B[33mseurat_v3#x1B[39;49;00m#x1B[33m"#x1B[39;49;00m, marks=needs.skmisc),#x1B[90m#x1B[39;49;00m
            pytest.param(#x1B[33m"#x1B[39;49;00m#x1B[33mseurat_v3_paper#x1B[39;49;00m#x1B[33m"#x1B[39;49;00m, marks=needs.skmisc),#x1B[90m#x1B[39;49;00m
        ],#x1B[90m#x1B[39;49;00m
    )#x1B[90m#x1B[39;49;00m
    #x1B[37m@pytest#x1B[39;49;00m.mark.parametrize(#x1B[90m#x1B[39;49;00m
        #x1B[33m"#x1B[39;49;00m#x1B[33marray_type#x1B[39;49;00m#x1B[33m"#x1B[39;49;00m,#x1B[90m#x1B[39;49;00m
        [#x1B[90m#x1B[39;49;00m
            p#x1B[90m#x1B[39;49;00m
            #x1B[94mfor#x1B[39;49;00m p #x1B[95min#x1B[39;49;00m ARRAY_TYPES#x1B[90m#x1B[39;49;00m
            #x1B[94mif#x1B[39;49;00m #x1B[33m"#x1B[39;49;00m#x1B[33mdask#x1B[39;49;00m#x1B[33m"#x1B[39;49;00m #x1B[95mnot#x1B[39;49;00m #x1B[95min#x1B[39;49;00m p.id #x1B[95mor#x1B[39;49;00m (#x1B[33m"#x1B[39;49;00m#x1B[33m1d_chunked#x1B[39;49;00m#x1B[33m"#x1B[39;49;00m #x1B[95min#x1B[39;49;00m p.id #x1B[95mand#x1B[39;49;00m #x1B[33m"#x1B[39;49;00m#x1B[33mcsr#x1B[39;49;00m#x1B[33m"#x1B[39;49;00m #x1B[95min#x1B[39;49;00m p.id)#x1B[90m#x1B[39;49;00m
        ],#x1B[90m#x1B[39;49;00m
    )#x1B[90m#x1B[39;49;00m
    #x1B[37m@pytest#x1B[39;49;00m.mark.parametrize(#x1B[33m"#x1B[39;49;00m#x1B[33mbatch_key#x1B[39;49;00m#x1B[33m"#x1B[39;49;00m, [#x1B[94mNone#x1B[39;49;00m, #x1B[33m"#x1B[39;49;00m#x1B[33mbatch#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_subset_inplace_consistency#x1B[39;49;00m(#x1B[90m#x1B[39;49;00m
        subtests: pytest.Subtests,#x1B[90m#x1B[39;49;00m
        flavor: Literal[#x1B[33m"#x1B[39;49;00m#x1B[33mseurat#x1B[39;49;00m#x1B[33m"#x1B[39;49;00m, #x1B[33m"#x1B[39;49;00m#x1B[33mcell_ranger#x1B[39;49;00m#x1B[33m"#x1B[39;49;00m, #x1B[33m"#x1B[39;49;00m#x1B[33mseurat_v3#x1B[39;49;00m#x1B[33m"#x1B[39;49;00m, #x1B[33m"#x1B[39;49;00m#x1B[33mseurat_v3_paper#x1B[39;49;00m#x1B[33m"#x1B[39;49;00m],#x1B[90m#x1B[39;49;00m
        array_type,#x1B[90m#x1B[39;49;00m
        batch_key: Literal[#x1B[33m"#x1B[39;49;00m#x1B[33mbatch#x1B[39;49;00m#x1B[33m"#x1B[39;49;00m] | #x1B[94mNone#x1B[39;49;00m,#x1B[90m#x1B[39;49;00m
    ) -> #x1B[94mNone#x1B[39;49;00m:#x1B[90m#x1B[39;49;00m
    #x1B[90m    #x1B[39;49;00m#x1B[33m"""Tests `n_top_genes=n`.#x1B[39;49;00m
    #x1B[33m#x1B[39;49;00m
    #x1B[33m    - if `inplace` and `subset` interact correctly#x1B[39;49;00m
    #x1B[33m    - for both the `seurat` and `cell_ranger` flavors#x1B[39;49;00m
    #x1B[33m    - for dask arrays and non-dask arrays#x1B[39;49;00m
    #x1B[33m    - for both with and without batch_key#x1B[39;49;00m
    #x1B[33m    """#x1B[39;49;00m#x1B[90m#x1B[39;49;00m
        rng = np.random.default_rng(#x1B[94m0#x1B[39;49;00m)#x1B[90m#x1B[39;49;00m
        adata = (#x1B[90m#x1B[39;49;00m
            sc.datasets.blobs(n_observations=#x1B[94m20#x1B[39;49;00m, n_variables=#x1B[94m80#x1B[39;49;00m, rng=rng)#x1B[90m#x1B[39;49;00m
            #x1B[94mif#x1B[39;49;00m #x1B[33m"#x1B[39;49;00m#x1B[33mseurat_v3#x1B[39;49;00m#x1B[33m"#x1B[39;49;00m #x1B[95mnot#x1B[39;49;00m #x1B[95min#x1B[39;49;00m flavor#x1B[90m#x1B[39;49;00m
            #x1B[94melse#x1B[39;49;00m pbmc3k()[:#x1B[94m1500#x1B[39;49;00m, :#x1B[94m1000#x1B[39;49;00m].copy()#x1B[90m#x1B[39;49;00m
        )#x1B[90m#x1B[39;49;00m
        adata.obs[#x1B[33m"#x1B[39;49;00m#x1B[33mbatch#x1B[39;49;00m#x1B[33m"#x1B[39;49;00m] = rng.choice([#x1B[33m"#x1B[39;49;00m#x1B[33ma#x1B[39;49;00m#x1B[33m"#x1B[39;49;00m, #x1B[33m"#x1B[39;49;00m#x1B[33mb#x1B[39;49;00m#x1B[33m"#x1B[39;49;00m], adata.shape[#x1B[94m0#x1B[39;49;00m])#x1B[90m#x1B[39;49;00m
        adata.X = array_type(np.abs(adata.X).astype(#x1B[96mint#x1B[39;49;00m))#x1B[90m#x1B[39;49;00m
    #x1B[90m#x1B[39;49;00m
        #x1B[94mif#x1B[39;49;00m flavor #x1B[95min#x1B[39;49;00m {#x1B[33m"#x1B[39;49;00m#x1B[33mseurat#x1B[39;49;00m#x1B[33m"#x1B[39;49;00m, #x1B[33m"#x1B[39;49;00m#x1B[33mcell_ranger#x1B[39;49;00m#x1B[33m"#x1B[39;49;00m}:#x1B[90m#x1B[39;49;00m
            sc.pp.normalize_total(adata, target_sum=#x1B[94m1e4#x1B[39;49;00m)#x1B[90m#x1B[39;49;00m
            sc.pp.log1p(adata)#x1B[90m#x1B[39;49;00m
    #x1B[90m#x1B[39;49;00m
        n_genes = adata.shape[#x1B[94m1#x1B[39;49;00m]#x1B[90m#x1B[39;49;00m
    #x1B[90m#x1B[39;49;00m
        adatas: #x1B[96mdict#x1B[39;49;00m[#x1B[96mbool#x1B[39;49;00m, AnnData] = {}#x1B[90m#x1B[39;49;00m
        dfs: #x1B[96mdict#x1B[39;49;00m[#x1B[96mbool#x1B[39;49;00m, pd.DataFrame] = {}#x1B[90m#x1B[39;49;00m
        #x1B[90m# for loops instead of parametrization to compare between settings#x1B[39;49;00m#x1B[90m#x1B[39;49;00m
        #x1B[94mfor#x1B[39;49;00m subset, inplace #x1B[95min#x1B[39;49;00m itertools.product([#x1B[94mTrue#x1B[39;49;00m, #x1B[94mFalse#x1B[39;49;00m], repeat=#x1B[94m2#x1B[39;49;00m):#x1B[90m#x1B[39;49;00m
            adata_copy = adata.copy()#x1B[90m#x1B[39;49;00m
    #x1B[90m#x1B[39;49;00m
>           output_df = sc.pp.highly_variable_genes(#x1B[90m#x1B[39;49;00m
                adata_copy,#x1B[90m#x1B[39;49;00m
                flavor=flavor,#x1B[90m#x1B[39;49;00m
                n_top_genes=#x1B[94m15#x1B[39;49;00m,#x1B[90m#x1B[39;49;00m
                batch_key=batch_key,#x1B[90m#x1B[39;49;00m
                subset=subset,#x1B[90m#x1B[39;49;00m
                inplace=inplace,#x1B[90m#x1B[39;49;00m
            )#x1B[90m#x1B[39;49;00m

#x1B[1m#x1B[31mtests/test_highly_variable_genes.py#x1B[0m:686: 
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 
#x1B[1m#x1B[.../scanpy/preprocessing/_highly_variable_genes.py#x1B[0m:780: in highly_variable_genes
    #x1B[0m#x1B[94mreturn#x1B[39;49;00m _highly_variable_genes_seurat_v3(#x1B[90m#x1B[39;49;00m
#x1B[1m#x1B[.../scanpy/preprocessing/_highly_variable_genes.py#x1B[0m:196: in _highly_variable_genes_seurat_v3
    #x1B[0maggregated_mean_var = ad.AnnData(#x1B[90m#x1B[39;49;00m
#x1B[1m#x1B[31m../../../..../scanpy/B9PcT7QG/hatch-test.stable/lib/python3.14.../anndata/_core/anndata.py#x1B[0m:254: in __init__
    #x1B[0m#x1B[96mself#x1B[39;49;00m._init_as_actual(#x1B[90m#x1B[39;49;00m
#x1B[1m#x1B[31m../../../..../scanpy/B9PcT7QG/hatch-test.stable/lib/python3.14.../anndata/_core/anndata.py#x1B[0m:448: in _init_as_actual
    #x1B[0m#x1B[96mself#x1B[39;49;00m._obs = _gen_dataframe(#x1B[90m#x1B[39;49;00m
#x1B[1m#x1B[31m../...../_temp/uv-python-dir/cpython-3.14.5-linux-x86_64-gnu/lib/python3.14/functools.py#x1B[0m:982: in wrapper
    #x1B[0m#x1B[94mreturn#x1B[39;49;00m dispatch(args[#x1B[94m0#x1B[39;49;00m].#x1B[91m__class__#x1B[39;49;00m)(*args, **kw)#x1B[90m#x1B[39;49;00m
           ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^#x1B[90m#x1B[39;49;00m
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 

anno =    __hvg_v3_batch_info__
0                      0
index_names = ['obs_names', 'row_names']

    #x1B[0m#x1B[37m@_gen_dataframe#x1B[39;49;00m.register(pd.DataFrame)#x1B[90m#x1B[39;49;00m
    #x1B[94mdef#x1B[39;49;00m#x1B[90m #x1B[39;49;00m#x1B[92m_gen_dataframe_df#x1B[39;49;00m(#x1B[90m#x1B[39;49;00m
        anno: pd.DataFrame,#x1B[90m#x1B[39;49;00m
        index_names: Iterable[#x1B[96mstr#x1B[39;49;00m],#x1B[90m#x1B[39;49;00m
        *,#x1B[90m#x1B[39;49;00m
        source: Literal[#x1B[33m"#x1B[39;49;00m#x1B[33mX#x1B[39;49;00m#x1B[33m"#x1B[39;49;00m, #x1B[33m"#x1B[39;49;00m#x1B[33mshape#x1B[39;49;00m#x1B[33m"#x1B[39;49;00m],#x1B[90m#x1B[39;49;00m
        attr: Literal[#x1B[33m"#x1B[39;49;00m#x1B[33mobs#x1B[39;49;00m#x1B[33m"#x1B[39;49;00m, #x1B[33m"#x1B[39;49;00m#x1B[33mvar#x1B[39;49;00m#x1B[33m"#x1B[39;49;00m],#x1B[90m#x1B[39;49;00m
        length: #x1B[96mint#x1B[39;49;00m | #x1B[94mNone#x1B[39;49;00m = #x1B[94mNone#x1B[39;49;00m,#x1B[90m#x1B[39;49;00m
    ):#x1B[90m#x1B[39;49;00m
        #x1B[94mif#x1B[39;49;00m #x1B[96misinstance#x1B[39;49;00m(anno.index, pd.MultiIndex):#x1B[90m#x1B[39;49;00m
            msg = (#x1B[90m#x1B[39;49;00m
                #x1B[33m"#x1B[39;49;00m#x1B[33mpandas.MultiIndex not supported as index for obs or var on declaration.#x1B[39;49;00m#x1B[33m\n#x1B[39;49;00m#x1B[33m\#x1B[39;49;00m
    #x1B[33m            You can set `obs_names` manually although most operations after will error or convert to str.#x1B[39;49;00m#x1B[33m\n#x1B[39;49;00m#x1B[33m\#x1B[39;49;00m
    #x1B[33m            This behavior will likely be clarified in a future breaking release.#x1B[39;49;00m#x1B[33m"#x1B[39;49;00m#x1B[90m#x1B[39;49;00m
            )#x1B[90m#x1B[39;49;00m
            #x1B[94mraise#x1B[39;49;00m #x1B[96mValueError#x1B[39;49;00m(msg)#x1B[90m#x1B[39;49;00m
        #x1B[94mif#x1B[39;49;00m length #x1B[95mis#x1B[39;49;00m #x1B[95mnot#x1B[39;49;00m #x1B[94mNone#x1B[39;49;00m #x1B[95mand#x1B[39;49;00m length != #x1B[96mlen#x1B[39;49;00m(anno):#x1B[90m#x1B[39;49;00m
            #x1B[94mraise#x1B[39;49;00m _mk_df_error(source, attr, length, #x1B[96mlen#x1B[39;49;00m(anno))#x1B[90m#x1B[39;49;00m
        anno = anno.copy(deep=#x1B[94mFalse#x1B[39;49;00m)#x1B[90m#x1B[39;49;00m
        #x1B[94mif#x1B[39;49;00m #x1B[95mnot#x1B[39;49;00m is_string_dtype(anno.index[~anno.index.isna()]):#x1B[90m#x1B[39;49;00m
            msg = #x1B[33m"#x1B[39;49;00m#x1B[33mTransforming to str index.#x1B[39;49;00m#x1B[33m"#x1B[39;49;00m#x1B[90m#x1B[39;49;00m
>           warnings.warn(msg, ImplicitModificationWarning, stacklevel=#x1B[94m2#x1B[39;49;00m)#x1B[90m#x1B[39;49;00m
#x1B[1m#x1B[31mE           anndata._warnings.ImplicitModificationWarning: Transforming to str index.#x1B[0m

#x1B[1m#x1B[31m../../../..../scanpy/B9PcT7QG/hatch-test.stable/lib/python3.14.../anndata/_core/aligned_df.py#x1B[0m:93: ImplicitModificationWarning

tests/test_highly_variable_genes.py::test_dask_consistency[dask_array_sparse-1d_chunked-csr_matrix-batched-seurat_v3]

Stack Traces | 0.016s run time

adata = AnnData object with n_obs × n_vars = 1500 × 1000
    obs: 'batch'
    var: 'gene_ids'
flavor = 'seurat_v3', batch_key = 'batch'
to_dask = <function gen_csr_csc_params_wrapper.<locals>.wrapper at 0x7f57df075380>

    #x1B[0m#x1B[37m@pytest#x1B[39;49;00m.mark.parametrize(#x1B[90m#x1B[39;49;00m
        #x1B[33m"#x1B[39;49;00m#x1B[33mflavor#x1B[39;49;00m#x1B[33m"#x1B[39;49;00m,#x1B[90m#x1B[39;49;00m
        [#x1B[90m#x1B[39;49;00m
            #x1B[33m"#x1B[39;49;00m#x1B[33mseurat#x1B[39;49;00m#x1B[33m"#x1B[39;49;00m,#x1B[90m#x1B[39;49;00m
            #x1B[33m"#x1B[39;49;00m#x1B[33mcell_ranger#x1B[39;49;00m#x1B[33m"#x1B[39;49;00m,#x1B[90m#x1B[39;49;00m
            pytest.param(#x1B[33m"#x1B[39;49;00m#x1B[33mseurat_v3#x1B[39;49;00m#x1B[33m"#x1B[39;49;00m, marks=needs.skmisc),#x1B[90m#x1B[39;49;00m
            pytest.param(#x1B[33m"#x1B[39;49;00m#x1B[33mseurat_v3_paper#x1B[39;49;00m#x1B[33m"#x1B[39;49;00m, marks=needs.skmisc),#x1B[90m#x1B[39;49;00m
        ],#x1B[90m#x1B[39;49;00m
    )#x1B[90m#x1B[39;49;00m
    #x1B[37m@pytest#x1B[39;49;00m.mark.parametrize(#x1B[33m"#x1B[39;49;00m#x1B[33mbatch_key#x1B[39;49;00m#x1B[33m"#x1B[39;49;00m, [#x1B[94mNone#x1B[39;49;00m, #x1B[33m"#x1B[39;49;00m#x1B[33mbatch#x1B[39;49;00m#x1B[33m"#x1B[39;49;00m], ids=[#x1B[33m"#x1B[39;49;00m#x1B[33msingle#x1B[39;49;00m#x1B[33m"#x1B[39;49;00m, #x1B[33m"#x1B[39;49;00m#x1B[33mbatched#x1B[39;49;00m#x1B[33m"#x1B[39;49;00m])#x1B[90m#x1B[39;49;00m
    #x1B[37m@pytest#x1B[39;49;00m.mark.parametrize(#x1B[90m#x1B[39;49;00m
        #x1B[33m"#x1B[39;49;00m#x1B[33mto_dask#x1B[39;49;00m#x1B[33m"#x1B[39;49;00m, [p #x1B[94mfor#x1B[39;49;00m p #x1B[95min#x1B[39;49;00m ARRAY_TYPES #x1B[94mif#x1B[39;49;00m #x1B[33m"#x1B[39;49;00m#x1B[33m1d_chunked#x1B[39;49;00m#x1B[33m"#x1B[39;49;00m #x1B[95min#x1B[39;49;00m p.id #x1B[95mand#x1B[39;49;00m #x1B[33m"#x1B[39;49;00m#x1B[33mcsr#x1B[39;49;00m#x1B[33m"#x1B[39;49;00m #x1B[95min#x1B[39;49;00m p.id]#x1B[90m#x1B[39;49;00m
    )#x1B[90m#x1B[39;49;00m
    #x1B[94mdef#x1B[39;49;00m#x1B[90m #x1B[39;49;00m#x1B[92mtest_dask_consistency#x1B[39;49;00m(adata: AnnData, flavor, batch_key, to_dask):#x1B[90m#x1B[39;49;00m
        #x1B[90m# current blob produces singularities in loess....maybe a bad sign of the data?#x1B[39;49;00m#x1B[90m#x1B[39;49;00m
        #x1B[94mif#x1B[39;49;00m #x1B[33m"#x1B[39;49;00m#x1B[33mseurat_v3#x1B[39;49;00m#x1B[33m"#x1B[39;49;00m #x1B[95min#x1B[39;49;00m flavor:#x1B[90m#x1B[39;49;00m
            adata = pbmc3k()[:#x1B[94m1500#x1B[39;49;00m, :#x1B[94m1000#x1B[39;49;00m].copy()#x1B[90m#x1B[39;49;00m
        adata.X = np.abs(adata.X).astype(#x1B[96mint#x1B[39;49;00m)#x1B[90m#x1B[39;49;00m
        #x1B[94mif#x1B[39;49;00m batch_key #x1B[95mis#x1B[39;49;00m #x1B[95mnot#x1B[39;49;00m #x1B[94mNone#x1B[39;49;00m:#x1B[90m#x1B[39;49;00m
            adata.obs[batch_key] = np.tile([#x1B[33m"#x1B[39;49;00m#x1B[33ma#x1B[39;49;00m#x1B[33m"#x1B[39;49;00m, #x1B[33m"#x1B[39;49;00m#x1B[33mb#x1B[39;49;00m#x1B[33m"#x1B[39;49;00m], adata.shape[#x1B[94m0#x1B[39;49;00m] // #x1B[94m2#x1B[39;49;00m)#x1B[90m#x1B[39;49;00m
        #x1B[90m# seurat_v3 expects counts#x1B[39;49;00m#x1B[90m#x1B[39;49;00m
        #x1B[94mif#x1B[39;49;00m #x1B[33m"#x1B[39;49;00m#x1B[33mseurat_v3#x1B[39;49;00m#x1B[33m"#x1B[39;49;00m #x1B[95mnot#x1B[39;49;00m #x1B[95min#x1B[39;49;00m flavor:#x1B[90m#x1B[39;49;00m
            sc.pp.normalize_total(adata, target_sum=#x1B[94m1e4#x1B[39;49;00m)#x1B[90m#x1B[39;49;00m
            sc.pp.log1p(adata)#x1B[90m#x1B[39;49;00m
        adata_dask = adata.copy()#x1B[90m#x1B[39;49;00m
        adata_dask.X = to_dask(adata_dask.X)#x1B[90m#x1B[39;49;00m
    #x1B[90m#x1B[39;49;00m
        #x1B[94mwith#x1B[39;49;00m (#x1B[90m#x1B[39;49;00m
            pytest.warns(#x1B[96mUserWarning#x1B[39;49;00m, match=#x1B[33m"#x1B[39;49;00m#x1B[33mn_top_genes.*normalized dispersions#x1B[39;49;00m#x1B[33m"#x1B[39;49;00m)#x1B[90m#x1B[39;49;00m
            #x1B[94mif#x1B[39;49;00m flavor == #x1B[33m"#x1B[39;49;00m#x1B[33mcell_ranger#x1B[39;49;00m#x1B[33m"#x1B[39;49;00m#x1B[90m#x1B[39;49;00m
            #x1B[94melse#x1B[39;49;00m nullcontext()#x1B[90m#x1B[39;49;00m
        ):#x1B[90m#x1B[39;49;00m
>           output_mem, output_dask = (#x1B[90m#x1B[39;49;00m
            ^^^^^^^^^^^^^^^^^^^^^^^#x1B[90m#x1B[39;49;00m
                sc.pp.highly_variable_genes(#x1B[90m#x1B[39;49;00m
                    ad, flavor=flavor, n_top_genes=#x1B[94m15#x1B[39;49;00m, inplace=#x1B[94mFalse#x1B[39;49;00m#x1B[90m#x1B[39;49;00m
                )#x1B[90m#x1B[39;49;00m
                #x1B[94mfor#x1B[39;49;00m ad #x1B[95min#x1B[39;49;00m [adata, adata_dask]#x1B[90m#x1B[39;49;00m
            )#x1B[90m#x1B[39;49;00m

#x1B[1m#x1B[31mtests/test_highly_variable_genes.py#x1B[0m:758: 
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 
#x1B[1m#x1B[31mtests/test_highly_variable_genes.py#x1B[0m:759: in <genexpr>
    #x1B[0msc.pp.highly_variable_genes(#x1B[90m#x1B[39;49;00m
#x1B[1m#x1B[.../scanpy/preprocessing/_highly_variable_genes.py#x1B[0m:780: in highly_variable_genes
    #x1B[0m#x1B[94mreturn#x1B[39;49;00m _highly_variable_genes_seurat_v3(#x1B[90m#x1B[39;49;00m
#x1B[1m#x1B[.../scanpy/preprocessing/_highly_variable_genes.py#x1B[0m:196: in _highly_variable_genes_seurat_v3
    #x1B[0maggregated_mean_var = ad.AnnData(#x1B[90m#x1B[39;49;00m
#x1B[1m#x1B[31m../../../..../scanpy/B9PcT7QG/hatch-test.stable/lib/python3.14.../anndata/_core/anndata.py#x1B[0m:254: in __init__
    #x1B[0m#x1B[96mself#x1B[39;49;00m._init_as_actual(#x1B[90m#x1B[39;49;00m
#x1B[1m#x1B[31m../../../..../scanpy/B9PcT7QG/hatch-test.stable/lib/python3.14.../anndata/_core/anndata.py#x1B[0m:448: in _init_as_actual
    #x1B[0m#x1B[96mself#x1B[39;49;00m._obs = _gen_dataframe(#x1B[90m#x1B[39;49;00m
#x1B[1m#x1B[31m../...../_temp/uv-python-dir/cpython-3.14.5-linux-x86_64-gnu/lib/python3.14/functools.py#x1B[0m:982: in wrapper
    #x1B[0m#x1B[94mreturn#x1B[39;49;00m dispatch(args[#x1B[94m0#x1B[39;49;00m].#x1B[91m__class__#x1B[39;49;00m)(*args, **kw)#x1B[90m#x1B[39;49;00m
           ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^#x1B[90m#x1B[39;49;00m
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 

anno =    __hvg_v3_batch_info__
0                      0
index_names = ['obs_names', 'row_names']

    #x1B[0m#x1B[37m@_gen_dataframe#x1B[39;49;00m.register(pd.DataFrame)#x1B[90m#x1B[39;49;00m
    #x1B[94mdef#x1B[39;49;00m#x1B[90m #x1B[39;49;00m#x1B[92m_gen_dataframe_df#x1B[39;49;00m(#x1B[90m#x1B[39;49;00m
        anno: pd.DataFrame,#x1B[90m#x1B[39;49;00m
        index_names: Iterable[#x1B[96mstr#x1B[39;49;00m],#x1B[90m#x1B[39;49;00m
        *,#x1B[90m#x1B[39;49;00m
        source: Literal[#x1B[33m"#x1B[39;49;00m#x1B[33mX#x1B[39;49;00m#x1B[33m"#x1B[39;49;00m, #x1B[33m"#x1B[39;49;00m#x1B[33mshape#x1B[39;49;00m#x1B[33m"#x1B[39;49;00m],#x1B[90m#x1B[39;49;00m
        attr: Literal[#x1B[33m"#x1B[39;49;00m#x1B[33mobs#x1B[39;49;00m#x1B[33m"#x1B[39;49;00m, #x1B[33m"#x1B[39;49;00m#x1B[33mvar#x1B[39;49;00m#x1B[33m"#x1B[39;49;00m],#x1B[90m#x1B[39;49;00m
        length: #x1B[96mint#x1B[39;49;00m | #x1B[94mNone#x1B[39;49;00m = #x1B[94mNone#x1B[39;49;00m,#x1B[90m#x1B[39;49;00m
    ):#x1B[90m#x1B[39;49;00m
        #x1B[94mif#x1B[39;49;00m #x1B[96misinstance#x1B[39;49;00m(anno.index, pd.MultiIndex):#x1B[90m#x1B[39;49;00m
            msg = (#x1B[90m#x1B[39;49;00m
                #x1B[33m"#x1B[39;49;00m#x1B[33mpandas.MultiIndex not supported as index for obs or var on declaration.#x1B[39;49;00m#x1B[33m\n#x1B[39;49;00m#x1B[33m\#x1B[39;49;00m
    #x1B[33m            You can set `obs_names` manually although most operations after will error or convert to str.#x1B[39;49;00m#x1B[33m\n#x1B[39;49;00m#x1B[33m\#x1B[39;49;00m
    #x1B[33m            This behavior will likely be clarified in a future breaking release.#x1B[39;49;00m#x1B[33m"#x1B[39;49;00m#x1B[90m#x1B[39;49;00m
            )#x1B[90m#x1B[39;49;00m
            #x1B[94mraise#x1B[39;49;00m #x1B[96mValueError#x1B[39;49;00m(msg)#x1B[90m#x1B[39;49;00m
        #x1B[94mif#x1B[39;49;00m length #x1B[95mis#x1B[39;49;00m #x1B[95mnot#x1B[39;49;00m #x1B[94mNone#x1B[39;49;00m #x1B[95mand#x1B[39;49;00m length != #x1B[96mlen#x1B[39;49;00m(anno):#x1B[90m#x1B[39;49;00m
            #x1B[94mraise#x1B[39;49;00m _mk_df_error(source, attr, length, #x1B[96mlen#x1B[39;49;00m(anno))#x1B[90m#x1B[39;49;00m
        anno = anno.copy(deep=#x1B[94mFalse#x1B[39;49;00m)#x1B[90m#x1B[39;49;00m
        #x1B[94mif#x1B[39;49;00m #x1B[95mnot#x1B[39;49;00m is_string_dtype(anno.index[~anno.index.isna()]):#x1B[90m#x1B[39;49;00m
            msg = #x1B[33m"#x1B[39;49;00m#x1B[33mTransforming to str index.#x1B[39;49;00m#x1B[33m"#x1B[39;49;00m#x1B[90m#x1B[39;49;00m
>           warnings.warn(msg, ImplicitModificationWarning, stacklevel=#x1B[94m2#x1B[39;49;00m)#x1B[90m#x1B[39;49;00m
#x1B[1m#x1B[31mE           anndata._warnings.ImplicitModificationWarning: Transforming to str index.#x1B[0m

#x1B[1m#x1B[31m../../../..../scanpy/B9PcT7QG/hatch-test.stable/lib/python3.14.../anndata/_core/aligned_df.py#x1B[0m:93: ImplicitModificationWarning

tests/test_highly_variable_genes.py::test_dask_consistency[dask_array_sparse-1d_chunked-csr_matrix-single-seurat_v3]

Stack Traces | 0.016s run time

adata = AnnData object with n_obs × n_vars = 1500 × 1000
    var: 'gene_ids'
flavor = 'seurat_v3', batch_key = None
to_dask = <function gen_csr_csc_params_wrapper.<locals>.wrapper at 0x7f57df075380>

    #x1B[0m#x1B[37m@pytest#x1B[39;49;00m.mark.parametrize(#x1B[90m#x1B[39;49;00m
        #x1B[33m"#x1B[39;49;00m#x1B[33mflavor#x1B[39;49;00m#x1B[33m"#x1B[39;49;00m,#x1B[90m#x1B[39;49;00m
        [#x1B[90m#x1B[39;49;00m
            #x1B[33m"#x1B[39;49;00m#x1B[33mseurat#x1B[39;49;00m#x1B[33m"#x1B[39;49;00m,#x1B[90m#x1B[39;49;00m
            #x1B[33m"#x1B[39;49;00m#x1B[33mcell_ranger#x1B[39;49;00m#x1B[33m"#x1B[39;49;00m,#x1B[90m#x1B[39;49;00m
            pytest.param(#x1B[33m"#x1B[39;49;00m#x1B[33mseurat_v3#x1B[39;49;00m#x1B[33m"#x1B[39;49;00m, marks=needs.skmisc),#x1B[90m#x1B[39;49;00m
            pytest.param(#x1B[33m"#x1B[39;49;00m#x1B[33mseurat_v3_paper#x1B[39;49;00m#x1B[33m"#x1B[39;49;00m, marks=needs.skmisc),#x1B[90m#x1B[39;49;00m
        ],#x1B[90m#x1B[39;49;00m
    )#x1B[90m#x1B[39;49;00m
    #x1B[37m@pytest#x1B[39;49;00m.mark.parametrize(#x1B[33m"#x1B[39;49;00m#x1B[33mbatch_key#x1B[39;49;00m#x1B[33m"#x1B[39;49;00m, [#x1B[94mNone#x1B[39;49;00m, #x1B[33m"#x1B[39;49;00m#x1B[33mbatch#x1B[39;49;00m#x1B[33m"#x1B[39;49;00m], ids=[#x1B[33m"#x1B[39;49;00m#x1B[33msingle#x1B[39;49;00m#x1B[33m"#x1B[39;49;00m, #x1B[33m"#x1B[39;49;00m#x1B[33mbatched#x1B[39;49;00m#x1B[33m"#x1B[39;49;00m])#x1B[90m#x1B[39;49;00m
    #x1B[37m@pytest#x1B[39;49;00m.mark.parametrize(#x1B[90m#x1B[39;49;00m
        #x1B[33m"#x1B[39;49;00m#x1B[33mto_dask#x1B[39;49;00m#x1B[33m"#x1B[39;49;00m, [p #x1B[94mfor#x1B[39;49;00m p #x1B[95min#x1B[39;49;00m ARRAY_TYPES #x1B[94mif#x1B[39;49;00m #x1B[33m"#x1B[39;49;00m#x1B[33m1d_chunked#x1B[39;49;00m#x1B[33m"#x1B[39;49;00m #x1B[95min#x1B[39;49;00m p.id #x1B[95mand#x1B[39;49;00m #x1B[33m"#x1B[39;49;00m#x1B[33mcsr#x1B[39;49;00m#x1B[33m"#x1B[39;49;00m #x1B[95min#x1B[39;49;00m p.id]#x1B[90m#x1B[39;49;00m
    )#x1B[90m#x1B[39;49;00m
    #x1B[94mdef#x1B[39;49;00m#x1B[90m #x1B[39;49;00m#x1B[92mtest_dask_consistency#x1B[39;49;00m(adata: AnnData, flavor, batch_key, to_dask):#x1B[90m#x1B[39;49;00m
        #x1B[90m# current blob produces singularities in loess....maybe a bad sign of the data?#x1B[39;49;00m#x1B[90m#x1B[39;49;00m
        #x1B[94mif#x1B[39;49;00m #x1B[33m"#x1B[39;49;00m#x1B[33mseurat_v3#x1B[39;49;00m#x1B[33m"#x1B[39;49;00m #x1B[95min#x1B[39;49;00m flavor:#x1B[90m#x1B[39;49;00m
            adata = pbmc3k()[:#x1B[94m1500#x1B[39;49;00m, :#x1B[94m1000#x1B[39;49;00m].copy()#x1B[90m#x1B[39;49;00m
        adata.X = np.abs(adata.X).astype(#x1B[96mint#x1B[39;49;00m)#x1B[90m#x1B[39;49;00m
        #x1B[94mif#x1B[39;49;00m batch_key #x1B[95mis#x1B[39;49;00m #x1B[95mnot#x1B[39;49;00m #x1B[94mNone#x1B[39;49;00m:#x1B[90m#x1B[39;49;00m
            adata.obs[batch_key] = np.tile([#x1B[33m"#x1B[39;49;00m#x1B[33ma#x1B[39;49;00m#x1B[33m"#x1B[39;49;00m, #x1B[33m"#x1B[39;49;00m#x1B[33mb#x1B[39;49;00m#x1B[33m"#x1B[39;49;00m], adata.shape[#x1B[94m0#x1B[39;49;00m] // #x1B[94m2#x1B[39;49;00m)#x1B[90m#x1B[39;49;00m
        #x1B[90m# seurat_v3 expects counts#x1B[39;49;00m#x1B[90m#x1B[39;49;00m
        #x1B[94mif#x1B[39;49;00m #x1B[33m"#x1B[39;49;00m#x1B[33mseurat_v3#x1B[39;49;00m#x1B[33m"#x1B[39;49;00m #x1B[95mnot#x1B[39;49;00m #x1B[95min#x1B[39;49;00m flavor:#x1B[90m#x1B[39;49;00m
            sc.pp.normalize_total(adata, target_sum=#x1B[94m1e4#x1B[39;49;00m)#x1B[90m#x1B[39;49;00m
            sc.pp.log1p(adata)#x1B[90m#x1B[39;49;00m
        adata_dask = adata.copy()#x1B[90m#x1B[39;49;00m
        adata_dask.X = to_dask(adata_dask.X)#x1B[90m#x1B[39;49;00m
    #x1B[90m#x1B[39;49;00m
        #x1B[94mwith#x1B[39;49;00m (#x1B[90m#x1B[39;49;00m
            pytest.warns(#x1B[96mUserWarning#x1B[39;49;00m, match=#x1B[33m"#x1B[39;49;00m#x1B[33mn_top_genes.*normalized dispersions#x1B[39;49;00m#x1B[33m"#x1B[39;49;00m)#x1B[90m#x1B[39;49;00m
            #x1B[94mif#x1B[39;49;00m flavor == #x1B[33m"#x1B[39;49;00m#x1B[33mcell_ranger#x1B[39;49;00m#x1B[33m"#x1B[39;49;00m#x1B[90m#x1B[39;49;00m
            #x1B[94melse#x1B[39;49;00m nullcontext()#x1B[90m#x1B[39;49;00m
        ):#x1B[90m#x1B[39;49;00m
>           output_mem, output_dask = (#x1B[90m#x1B[39;49;00m
            ^^^^^^^^^^^^^^^^^^^^^^^#x1B[90m#x1B[39;49;00m
                sc.pp.highly_variable_genes(#x1B[90m#x1B[39;49;00m
                    ad, flavor=flavor, n_top_genes=#x1B[94m15#x1B[39;49;00m, inplace=#x1B[94mFalse#x1B[39;49;00m#x1B[90m#x1B[39;49;00m
                )#x1B[90m#x1B[39;49;00m
                #x1B[94mfor#x1B[39;49;00m ad #x1B[95min#x1B[39;49;00m [adata, adata_dask]#x1B[90m#x1B[39;49;00m
            )#x1B[90m#x1B[39;49;00m

#x1B[1m#x1B[31mtests/test_highly_variable_genes.py#x1B[0m:758: 
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 
#x1B[1m#x1B[31mtests/test_highly_variable_genes.py#x1B[0m:759: in <genexpr>
    #x1B[0msc.pp.highly_variable_genes(#x1B[90m#x1B[39;49;00m
#x1B[1m#x1B[.../scanpy/preprocessing/_highly_variable_genes.py#x1B[0m:780: in highly_variable_genes
    #x1B[0m#x1B[94mreturn#x1B[39;49;00m _highly_variable_genes_seurat_v3(#x1B[90m#x1B[39;49;00m
#x1B[1m#x1B[.../scanpy/preprocessing/_highly_variable_genes.py#x1B[0m:196: in _highly_variable_genes_seurat_v3
    #x1B[0maggregated_mean_var = ad.AnnData(#x1B[90m#x1B[39;49;00m
#x1B[1m#x1B[31m../../../..../scanpy/B9PcT7QG/hatch-test.stable/lib/python3.14.../anndata/_core/anndata.py#x1B[0m:254: in __init__
    #x1B[0m#x1B[96mself#x1B[39;49;00m._init_as_actual(#x1B[90m#x1B[39;49;00m
#x1B[1m#x1B[31m../../../..../scanpy/B9PcT7QG/hatch-test.stable/lib/python3.14.../anndata/_core/anndata.py#x1B[0m:448: in _init_as_actual
    #x1B[0m#x1B[96mself#x1B[39;49;00m._obs = _gen_dataframe(#x1B[90m#x1B[39;49;00m
#x1B[1m#x1B[31m../...../_temp/uv-python-dir/cpython-3.14.5-linux-x86_64-gnu/lib/python3.14/functools.py#x1B[0m:982: in wrapper
    #x1B[0m#x1B[94mreturn#x1B[39;49;00m dispatch(args[#x1B[94m0#x1B[39;49;00m].#x1B[91m__class__#x1B[39;49;00m)(*args, **kw)#x1B[90m#x1B[39;49;00m
           ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^#x1B[90m#x1B[39;49;00m
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 

anno =    __hvg_v3_batch_info__
0                      0
index_names = ['obs_names', 'row_names']

    #x1B[0m#x1B[37m@_gen_dataframe#x1B[39;49;00m.register(pd.DataFrame)#x1B[90m#x1B[39;49;00m
    #x1B[94mdef#x1B[39;49;00m#x1B[90m #x1B[39;49;00m#x1B[92m_gen_dataframe_df#x1B[39;49;00m(#x1B[90m#x1B[39;49;00m
        anno: pd.DataFrame,#x1B[90m#x1B[39;49;00m
        index_names: Iterable[#x1B[96mstr#x1B[39;49;00m],#x1B[90m#x1B[39;49;00m
        *,#x1B[90m#x1B[39;49;00m
        source: Literal[#x1B[33m"#x1B[39;49;00m#x1B[33mX#x1B[39;49;00m#x1B[33m"#x1B[39;49;00m, #x1B[33m"#x1B[39;49;00m#x1B[33mshape#x1B[39;49;00m#x1B[33m"#x1B[39;49;00m],#x1B[90m#x1B[39;49;00m
        attr: Literal[#x1B[33m"#x1B[39;49;00m#x1B[33mobs#x1B[39;49;00m#x1B[33m"#x1B[39;49;00m, #x1B[33m"#x1B[39;49;00m#x1B[33mvar#x1B[39;49;00m#x1B[33m"#x1B[39;49;00m],#x1B[90m#x1B[39;49;00m
        length: #x1B[96mint#x1B[39;49;00m | #x1B[94mNone#x1B[39;49;00m = #x1B[94mNone#x1B[39;49;00m,#x1B[90m#x1B[39;49;00m
    ):#x1B[90m#x1B[39;49;00m
        #x1B[94mif#x1B[39;49;00m #x1B[96misinstance#x1B[39;49;00m(anno.index, pd.MultiIndex):#x1B[90m#x1B[39;49;00m
            msg = (#x1B[90m#x1B[39;49;00m
                #x1B[33m"#x1B[39;49;00m#x1B[33mpandas.MultiIndex not supported as index for obs or var on declaration.#x1B[39;49;00m#x1B[33m\n#x1B[39;49;00m#x1B[33m\#x1B[39;49;00m
    #x1B[33m            You can set `obs_names` manually although most operations after will error or convert to str.#x1B[39;49;00m#x1B[33m\n#x1B[39;49;00m#x1B[33m\#x1B[39;49;00m
    #x1B[33m            This behavior will likely be clarified in a future breaking release.#x1B[39;49;00m#x1B[33m"#x1B[39;49;00m#x1B[90m#x1B[39;49;00m
            )#x1B[90m#x1B[39;49;00m
            #x1B[94mraise#x1B[39;49;00m #x1B[96mValueError#x1B[39;49;00m(msg)#x1B[90m#x1B[39;49;00m
        #x1B[94mif#x1B[39;49;00m length #x1B[95mis#x1B[39;49;00m #x1B[95mnot#x1B[39;49;00m #x1B[94mNone#x1B[39;49;00m #x1B[95mand#x1B[39;49;00m length != #x1B[96mlen#x1B[39;49;00m(anno):#x1B[90m#x1B[39;49;00m
            #x1B[94mraise#x1B[39;49;00m _mk_df_error(source, attr, length, #x1B[96mlen#x1B[39;49;00m(anno))#x1B[90m#x1B[39;49;00m
        anno = anno.copy(deep=#x1B[94mFalse#x1B[39;49;00m)#x1B[90m#x1B[39;49;00m
        #x1B[94mif#x1B[39;49;00m #x1B[95mnot#x1B[39;49;00m is_string_dtype(anno.index[~anno.index.isna()]):#x1B[90m#x1B[39;49;00m
            msg = #x1B[33m"#x1B[39;49;00m#x1B[33mTransforming to str index.#x1B[39;49;00m#x1B[33m"#x1B[39;49;00m#x1B[90m#x1B[39;49;00m
>           warnings.warn(msg, ImplicitModificationWarning, stacklevel=#x1B[94m2#x1B[39;49;00m)#x1B[90m#x1B[39;49;00m
#x1B[1m#x1B[31mE           anndata._warnings.ImplicitModificationWarning: Transforming to str index.#x1B[0m

#x1B[1m#x1B[31m../../../..../scanpy/B9PcT7QG/hatch-test.stable/lib/python3.14.../anndata/_core/aligned_df.py#x1B[0m:93: ImplicitModificationWarning

tests/test_highly_variable_genes.py::test_dask_consistency[dask_array_sparse-1d_chunked-csr_matrix-single-seurat_v3_paper]

Stack Traces | 0.016s run time

adata = AnnData object with n_obs × n_vars = 1500 × 1000
    var: 'gene_ids'
flavor = 'seurat_v3_paper', batch_key = None
to_dask = <function gen_csr_csc_params_wrapper.<locals>.wrapper at 0x7f57df075380>

    #x1B[0m#x1B[37m@pytest#x1B[39;49;00m.mark.parametrize(#x1B[90m#x1B[39;49;00m
        #x1B[33m"#x1B[39;49;00m#x1B[33mflavor#x1B[39;49;00m#x1B[33m"#x1B[39;49;00m,#x1B[90m#x1B[39;49;00m
        [#x1B[90m#x1B[39;49;00m
            #x1B[33m"#x1B[39;49;00m#x1B[33mseurat#x1B[39;49;00m#x1B[33m"#x1B[39;49;00m,#x1B[90m#x1B[39;49;00m
            #x1B[33m"#x1B[39;49;00m#x1B[33mcell_ranger#x1B[39;49;00m#x1B[33m"#x1B[39;49;00m,#x1B[90m#x1B[39;49;00m
            pytest.param(#x1B[33m"#x1B[39;49;00m#x1B[33mseurat_v3#x1B[39;49;00m#x1B[33m"#x1B[39;49;00m, marks=needs.skmisc),#x1B[90m#x1B[39;49;00m
            pytest.param(#x1B[33m"#x1B[39;49;00m#x1B[33mseurat_v3_paper#x1B[39;49;00m#x1B[33m"#x1B[39;49;00m, marks=needs.skmisc),#x1B[90m#x1B[39;49;00m
        ],#x1B[90m#x1B[39;49;00m
    )#x1B[90m#x1B[39;49;00m
    #x1B[37m@pytest#x1B[39;49;00m.mark.parametrize(#x1B[33m"#x1B[39;49;00m#x1B[33mbatch_key#x1B[39;49;00m#x1B[33m"#x1B[39;49;00m, [#x1B[94mNone#x1B[39;49;00m, #x1B[33m"#x1B[39;49;00m#x1B[33mbatch#x1B[39;49;00m#x1B[33m"#x1B[39;49;00m], ids=[#x1B[33m"#x1B[39;49;00m#x1B[33msingle#x1B[39;49;00m#x1B[33m"#x1B[39;49;00m, #x1B[33m"#x1B[39;49;00m#x1B[33mbatched#x1B[39;49;00m#x1B[33m"#x1B[39;49;00m])#x1B[90m#x1B[39;49;00m
    #x1B[37m@pytest#x1B[39;49;00m.mark.parametrize(#x1B[90m#x1B[39;49;00m
        #x1B[33m"#x1B[39;49;00m#x1B[33mto_dask#x1B[39;49;00m#x1B[33m"#x1B[39;49;00m, [p #x1B[94mfor#x1B[39;49;00m p #x1B[95min#x1B[39;49;00m ARRAY_TYPES #x1B[94mif#x1B[39;49;00m #x1B[33m"#x1B[39;49;00m#x1B[33m1d_chunked#x1B[39;49;00m#x1B[33m"#x1B[39;49;00m #x1B[95min#x1B[39;49;00m p.id #x1B[95mand#x1B[39;49;00m #x1B[33m"#x1B[39;49;00m#x1B[33mcsr#x1B[39;49;00m#x1B[33m"#x1B[39;49;00m #x1B[95min#x1B[39;49;00m p.id]#x1B[90m#x1B[39;49;00m
    )#x1B[90m#x1B[39;49;00m
    #x1B[94mdef#x1B[39;49;00m#x1B[90m #x1B[39;49;00m#x1B[92mtest_dask_consistency#x1B[39;49;00m(adata: AnnData, flavor, batch_key, to_dask):#x1B[90m#x1B[39;49;00m
        #x1B[90m# current blob produces singularities in loess....maybe a bad sign of the data?#x1B[39;49;00m#x1B[90m#x1B[39;49;00m
        #x1B[94mif#x1B[39;49;00m #x1B[33m"#x1B[39;49;00m#x1B[33mseurat_v3#x1B[39;49;00m#x1B[33m"#x1B[39;49;00m #x1B[95min#x1B[39;49;00m flavor:#x1B[90m#x1B[39;49;00m
            adata = pbmc3k()[:#x1B[94m1500#x1B[39;49;00m, :#x1B[94m1000#x1B[39;49;00m].copy()#x1B[90m#x1B[39;49;00m
        adata.X = np.abs(adata.X).astype(#x1B[96mint#x1B[39;49;00m)#x1B[90m#x1B[39;49;00m
        #x1B[94mif#x1B[39;49;00m batch_key #x1B[95mis#x1B[39;49;00m #x1B[95mnot#x1B[39;49;00m #x1B[94mNone#x1B[39;49;00m:#x1B[90m#x1B[39;49;00m
            adata.obs[batch_key] = np.tile([#x1B[33m"#x1B[39;49;00m#x1B[33ma#x1B[39;49;00m#x1B[33m"#x1B[39;49;00m, #x1B[33m"#x1B[39;49;00m#x1B[33mb#x1B[39;49;00m#x1B[33m"#x1B[39;49;00m], adata.shape[#x1B[94m0#x1B[39;49;00m] // #x1B[94m2#x1B[39;49;00m)#x1B[90m#x1B[39;49;00m
        #x1B[90m# seurat_v3 expects counts#x1B[39;49;00m#x1B[90m#x1B[39;49;00m
        #x1B[94mif#x1B[39;49;00m #x1B[33m"#x1B[39;49;00m#x1B[33mseurat_v3#x1B[39;49;00m#x1B[33m"#x1B[39;49;00m #x1B[95mnot#x1B[39;49;00m #x1B[95min#x1B[39;49;00m flavor:#x1B[90m#x1B[39;49;00m
            sc.pp.normalize_total(adata, target_sum=#x1B[94m1e4#x1B[39;49;00m)#x1B[90m#x1B[39;49;00m
            sc.pp.log1p(adata)#x1B[90m#x1B[39;49;00m
        adata_dask = adata.copy()#x1B[90m#x1B[39;49;00m
        adata_dask.X = to_dask(adata_dask.X)#x1B[90m#x1B[39;49;00m
    #x1B[90m#x1B[39;49;00m
        #x1B[94mwith#x1B[39;49;00m (#x1B[90m#x1B[39;49;00m
            pytest.warns(#x1B[96mUserWarning#x1B[39;49;00m, match=#x1B[33m"#x1B[39;49;00m#x1B[33mn_top_genes.*normalized dispersions#x1B[39;49;00m#x1B[33m"#x1B[39;49;00m)#x1B[90m#x1B[39;49;00m
            #x1B[94mif#x1B[39;49;00m flavor == #x1B[33m"#x1B[39;49;00m#x1B[33mcell_ranger#x1B[39;49;00m#x1B[33m"#x1B[39;49;00m#x1B[90m#x1B[39;49;00m
            #x1B[94melse#x1B[39;49;00m nullcontext()#x1B[90m#x1B[39;49;00m
        ):#x1B[90m#x1B[39;49;00m
>           output_mem, output_dask = (#x1B[90m#x1B[39;49;00m
            ^^^^^^^^^^^^^^^^^^^^^^^#x1B[90m#x1B[39;49;00m
                sc.pp.highly_variable_genes(#x1B[90m#x1B[39;49;00m
                    ad, flavor=flavor, n_top_genes=#x1B[94m15#x1B[39;49;00m, inplace=#x1B[94mFalse#x1B[39;49;00m#x1B[90m#x1B[39;49;00m
                )#x1B[90m#x1B[39;49;00m
                #x1B[94mfor#x1B[39;49;00m ad #x1B[95min#x1B[39;49;00m [adata, adata_dask]#x1B[90m#x1B[39;49;00m
            )#x1B[90m#x1B[39;49;00m

#x1B[1m#x1B[31mtests/test_highly_variable_genes.py#x1B[0m:758: 
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 
#x1B[1m#x1B[31mtests/test_highly_variable_genes.py#x1B[0m:759: in <genexpr>
    #x1B[0msc.pp.highly_variable_genes(#x1B[90m#x1B[39;49;00m
#x1B[1m#x1B[.../scanpy/preprocessing/_highly_variable_genes.py#x1B[0m:780: in highly_variable_genes
    #x1B[0m#x1B[94mreturn#x1B[39;49;00m _highly_variable_genes_seurat_v3(#x1B[90m#x1B[39;49;00m
#x1B[1m#x1B[.../scanpy/preprocessing/_highly_variable_genes.py#x1B[0m:196: in _highly_variable_genes_seurat_v3
    #x1B[0maggregated_mean_var = ad.AnnData(#x1B[90m#x1B[39;49;00m
#x1B[1m#x1B[31m../../../..../scanpy/B9PcT7QG/hatch-test.stable/lib/python3.14.../anndata/_core/anndata.py#x1B[0m:254: in __init__
    #x1B[0m#x1B[96mself#x1B[39;49;00m._init_as_actual(#x1B[90m#x1B[39;49;00m
#x1B[1m#x1B[31m../../../..../scanpy/B9PcT7QG/hatch-test.stable/lib/python3.14.../anndata/_core/anndata.py#x1B[0m:448: in _init_as_actual
    #x1B[0m#x1B[96mself#x1B[39;49;00m._obs = _gen_dataframe(#x1B[90m#x1B[39;49;00m
#x1B[1m#x1B[31m../...../_temp/uv-python-dir/cpython-3.14.5-linux-x86_64-gnu/lib/python3.14/functools.py#x1B[0m:982: in wrapper
    #x1B[0m#x1B[94mreturn#x1B[39;49;00m dispatch(args[#x1B[94m0#x1B[39;49;00m].#x1B[91m__class__#x1B[39;49;00m)(*args, **kw)#x1B[90m#x1B[39;49;00m
           ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^#x1B[90m#x1B[39;49;00m
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 

anno =    __hvg_v3_batch_info__
0                      0
index_names = ['obs_names', 'row_names']

    #x1B[0m#x1B[37m@_gen_dataframe#x1B[39;49;00m.register(pd.DataFrame)#x1B[90m#x1B[39;49;00m
    #x1B[94mdef#x1B[39;49;00m#x1B[90m #x1B[39;49;00m#x1B[92m_gen_dataframe_df#x1B[39;49;00m(#x1B[90m#x1B[39;49;00m
        anno: pd.DataFrame,#x1B[90m#x1B[39;49;00m
        index_names: Iterable[#x1B[96mstr#x1B[39;49;00m],#x1B[90m#x1B[39;49;00m
        *,#x1B[90m#x1B[39;49;00m
        source: Literal[#x1B[33m"#x1B[39;49;00m#x1B[33mX#x1B[39;49;00m#x1B[33m"#x1B[39;49;00m, #x1B[33m"#x1B[39;49;00m#x1B[33mshape#x1B[39;49;00m#x1B[33m"#x1B[39;49;00m],#x1B[90m#x1B[39;49;00m
        attr: Literal[#x1B[33m"#x1B[39;49;00m#x1B[33mobs#x1B[39;49;00m#x1B[33m"#x1B[39;49;00m, #x1B[33m"#x1B[39;49;00m#x1B[33mvar#x1B[39;49;00m#x1B[33m"#x1B[39;49;00m],#x1B[90m#x1B[39;49;00m
        length: #x1B[96mint#x1B[39;49;00m | #x1B[94mNone#x1B[39;49;00m = #x1B[94mNone#x1B[39;49;00m,#x1B[90m#x1B[39;49;00m
    ):#x1B[90m#x1B[39;49;00m
        #x1B[94mif#x1B[39;49;00m #x1B[96misinstance#x1B[39;49;00m(anno.index, pd.MultiIndex):#x1B[90m#x1B[39;49;00m
            msg = (#x1B[90m#x1B[39;49;00m
                #x1B[33m"#x1B[39;49;00m#x1B[33mpandas.MultiIndex not supported as index for obs or var on declaration.#x1B[39;49;00m#x1B[33m\n#x1B[39;49;00m#x1B[33m\#x1B[39;49;00m
    #x1B[33m            You can set `obs_names` manually although most operations after will error or convert to str.#x1B[39;49;00m#x1B[33m\n#x1B[39;49;00m#x1B[33m\#x1B[39;49;00m
    #x1B[33m            This behavior will likely be clarified in a future breaking release.#x1B[39;49;00m#x1B[33m"#x1B[39;49;00m#x1B[90m#x1B[39;49;00m
            )#x1B[90m#x1B[39;49;00m
            #x1B[94mraise#x1B[39;49;00m #x1B[96mValueError#x1B[39;49;00m(msg)#x1B[90m#x1B[39;49;00m
        #x1B[94mif#x1B[39;49;00m length #x1B[95mis#x1B[39;49;00m #x1B[95mnot#x1B[39;49;00m #x1B[94mNone#x1B[39;49;00m #x1B[95mand#x1B[39;49;00m length != #x1B[96mlen#x1B[39;49;00m(anno):#x1B[90m#x1B[39;49;00m
            #x1B[94mraise#x1B[39;49;00m _mk_df_error(source, attr, length, #x1B[96mlen#x1B[39;49;00m(anno))#x1B[90m#x1B[39;49;00m
        anno = anno.copy(deep=#x1B[94mFalse#x1B[39;49;00m)#x1B[90m#x1B[39;49;00m
        #x1B[94mif#x1B[39;49;00m #x1B[95mnot#x1B[39;49;00m is_string_dtype(anno.index[~anno.index.isna()]):#x1B[90m#x1B[39;49;00m
            msg = #x1B[33m"#x1B[39;49;00m#x1B[33mTransforming to str index.#x1B[39;49;00m#x1B[33m"#x1B[39;49;00m#x1B[90m#x1B[39;49;00m
>           warnings.warn(msg, ImplicitModificationWarning, stacklevel=#x1B[94m2#x1B[39;49;00m)#x1B[90m#x1B[39;49;00m
#x1B[1m#x1B[31mE           anndata._warnings.ImplicitModificationWarning: Transforming to str index.#x1B[0m

#x1B[1m#x1B[31m../../../..../scanpy/B9PcT7QG/hatch-test.stable/lib/python3.14.../anndata/_core/aligned_df.py#x1B[0m:93: ImplicitModificationWarning

tests/test_highly_variable_genes.py::test_subset_inplace_consistency[None-numpy_ndarray-seurat_v3_paper]

Stack Traces | 0.018s run time

subtests = <_pytest.subtests.Subtests object at 0x7f57c0727470>
flavor = 'seurat_v3_paper', array_type = <function asarray at 0x7f57e43833d0>
batch_key = None

    #x1B[0m#x1B[37m@pytest#x1B[39;49;00m.mark.parametrize(#x1B[90m#x1B[39;49;00m
        #x1B[33m"#x1B[39;49;00m#x1B[33mflavor#x1B[39;49;00m#x1B[33m"#x1B[39;49;00m,#x1B[90m#x1B[39;49;00m
        [#x1B[90m#x1B[39;49;00m
            #x1B[33m"#x1B[39;49;00m#x1B[33mseurat#x1B[39;49;00m#x1B[33m"#x1B[39;49;00m,#x1B[90m#x1B[39;49;00m
            #x1B[33m"#x1B[39;49;00m#x1B[33mcell_ranger#x1B[39;49;00m#x1B[33m"#x1B[39;49;00m,#x1B[90m#x1B[39;49;00m
            pytest.param(#x1B[33m"#x1B[39;49;00m#x1B[33mseurat_v3#x1B[39;49;00m#x1B[33m"#x1B[39;49;00m, marks=needs.skmisc),#x1B[90m#x1B[39;49;00m
            pytest.param(#x1B[33m"#x1B[39;49;00m#x1B[33mseurat_v3_paper#x1B[39;49;00m#x1B[33m"#x1B[39;49;00m, marks=needs.skmisc),#x1B[90m#x1B[39;49;00m
        ],#x1B[90m#x1B[39;49;00m
    )#x1B[90m#x1B[39;49;00m
    #x1B[37m@pytest#x1B[39;49;00m.mark.parametrize(#x1B[90m#x1B[39;49;00m
        #x1B[33m"#x1B[39;49;00m#x1B[33marray_type#x1B[39;49;00m#x1B[33m"#x1B[39;49;00m,#x1B[90m#x1B[39;49;00m
        [#x1B[90m#x1B[39;49;00m
            p#x1B[90m#x1B[39;49;00m
            #x1B[94mfor#x1B[39;49;00m p #x1B[95min#x1B[39;49;00m ARRAY_TYPES#x1B[90m#x1B[39;49;00m
            #x1B[94mif#x1B[39;49;00m #x1B[33m"#x1B[39;49;00m#x1B[33mdask#x1B[39;49;00m#x1B[33m"#x1B[39;49;00m #x1B[95mnot#x1B[39;49;00m #x1B[95min#x1B[39;49;00m p.id #x1B[95mor#x1B[39;49;00m (#x1B[33m"#x1B[39;49;00m#x1B[33m1d_chunked#x1B[39;49;00m#x1B[33m"#x1B[39;49;00m #x1B[95min#x1B[39;49;00m p.id #x1B[95mand#x1B[39;49;00m #x1B[33m"#x1B[39;49;00m#x1B[33mcsr#x1B[39;49;00m#x1B[33m"#x1B[39;49;00m #x1B[95min#x1B[39;49;00m p.id)#x1B[90m#x1B[39;49;00m
        ],#x1B[90m#x1B[39;49;00m
    )#x1B[90m#x1B[39;49;00m
    #x1B[37m@pytest#x1B[39;49;00m.mark.parametrize(#x1B[33m"#x1B[39;49;00m#x1B[33mbatch_key#x1B[39;49;00m#x1B[33m"#x1B[39;49;00m, [#x1B[94mNone#x1B[39;49;00m, #x1B[33m"#x1B[39;49;00m#x1B[33mbatch#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_subset_inplace_consistency#x1B[39;49;00m(#x1B[90m#x1B[39;49;00m
        subtests: pytest.Subtests,#x1B[90m#x1B[39;49;00m
        flavor: Literal[#x1B[33m"#x1B[39;49;00m#x1B[33mseurat#x1B[39;49;00m#x1B[33m"#x1B[39;49;00m, #x1B[33m"#x1B[39;49;00m#x1B[33mcell_ranger#x1B[39;49;00m#x1B[33m"#x1B[39;49;00m, #x1B[33m"#x1B[39;49;00m#x1B[33mseurat_v3#x1B[39;49;00m#x1B[33m"#x1B[39;49;00m, #x1B[33m"#x1B[39;49;00m#x1B[33mseurat_v3_paper#x1B[39;49;00m#x1B[33m"#x1B[39;49;00m],#x1B[90m#x1B[39;49;00m
        array_type,#x1B[90m#x1B[39;49;00m
        batch_key: Literal[#x1B[33m"#x1B[39;49;00m#x1B[33mbatch#x1B[39;49;00m#x1B[33m"#x1B[39;49;00m] | #x1B[94mNone#x1B[39;49;00m,#x1B[90m#x1B[39;49;00m
    ) -> #x1B[94mNone#x1B[39;49;00m:#x1B[90m#x1B[39;49;00m
    #x1B[90m    #x1B[39;49;00m#x1B[33m"""Tests `n_top_genes=n`.#x1B[39;49;00m
    #x1B[33m#x1B[39;49;00m
    #x1B[33m    - if `inplace` and `subset` interact correctly#x1B[39;49;00m
    #x1B[33m    - for both the `seurat` and `cell_ranger` flavors#x1B[39;49;00m
    #x1B[33m    - for dask arrays and non-dask arrays#x1B[39;49;00m
    #x1B[33m    - for both with and without batch_key#x1B[39;49;00m
    #x1B[33m    """#x1B[39;49;00m#x1B[90m#x1B[39;49;00m
        rng = np.random.default_rng(#x1B[94m0#x1B[39;49;00m)#x1B[90m#x1B[39;49;00m
        adata = (#x1B[90m#x1B[39;49;00m
            sc.datasets.blobs(n_observations=#x1B[94m20#x1B[39;49;00m, n_variables=#x1B[94m80#x1B[39;49;00m, rng=rng)#x1B[90m#x1B[39;49;00m
            #x1B[94mif#x1B[39;49;00m #x1B[33m"#x1B[39;49;00m#x1B[33mseurat_v3#x1B[39;49;00m#x1B[33m"#x1B[39;49;00m #x1B[95mnot#x1B[39;49;00m #x1B[95min#x1B[39;49;00m flavor#x1B[90m#x1B[39;49;00m
            #x1B[94melse#x1B[39;49;00m pbmc3k()[:#x1B[94m1500#x1B[39;49;00m, :#x1B[94m1000#x1B[39;49;00m].copy()#x1B[90m#x1B[39;49;00m
        )#x1B[90m#x1B[39;49;00m
        adata.obs[#x1B[33m"#x1B[39;49;00m#x1B[33mbatch#x1B[39;49;00m#x1B[33m"#x1B[39;49;00m] = rng.choice([#x1B[33m"#x1B[39;49;00m#x1B[33ma#x1B[39;49;00m#x1B[33m"#x1B[39;49;00m, #x1B[33m"#x1B[39;49;00m#x1B[33mb#x1B[39;49;00m#x1B[33m"#x1B[39;49;00m], adata.shape[#x1B[94m0#x1B[39;49;00m])#x1B[90m#x1B[39;49;00m
        adata.X = array_type(np.abs(adata.X).astype(#x1B[96mint#x1B[39;49;00m))#x1B[90m#x1B[39;49;00m
    #x1B[90m#x1B[39;49;00m
        #x1B[94mif#x1B[39;49;00m flavor #x1B[95min#x1B[39;49;00m {#x1B[33m"#x1B[39;49;00m#x1B[33mseurat#x1B[39;49;00m#x1B[33m"#x1B[39;49;00m, #x1B[33m"#x1B[39;49;00m#x1B[33mcell_ranger#x1B[39;49;00m#x1B[33m"#x1B[39;49;00m}:#x1B[90m#x1B[39;49;00m
            sc.pp.normalize_total(adata, target_sum=#x1B[94m1e4#x1B[39;49;00m)#x1B[90m#x1B[39;49;00m
            sc.pp.log1p(adata)#x1B[90m#x1B[39;49;00m
    #x1B[90m#x1B[39;49;00m
        n_genes = adata.shape[#x1B[94m1#x1B[39;49;00m]#x1B[90m#x1B[39;49;00m
    #x1B[90m#x1B[39;49;00m
        adatas: #x1B[96mdict#x1B[39;49;00m[#x1B[96mbool#x1B[39;49;00m, AnnData] = {}#x1B[90m#x1B[39;49;00m
        dfs: #x1B[96mdict#x1B[39;49;00m[#x1B[96mbool#x1B[39;49;00m, pd.DataFrame] = {}#x1B[90m#x1B[39;49;00m
        #x1B[90m# for loops instead of parametrization to compare between settings#x1B[39;49;00m#x1B[90m#x1B[39;49;00m
        #x1B[94mfor#x1B[39;49;00m subset, inplace #x1B[95min#x1B[39;49;00m itertools.product([#x1B[94mTrue#x1B[39;49;00m, #x1B[94mFalse#x1B[39;49;00m], repeat=#x1B[94m2#x1B[39;49;00m):#x1B[90m#x1B[39;49;00m
            adata_copy = adata.copy()#x1B[90m#x1B[39;49;00m
    #x1B[90m#x1B[39;49;00m
>           output_df = sc.pp.highly_variable_genes(#x1B[90m#x1B[39;49;00m
                adata_copy,#x1B[90m#x1B[39;49;00m
                flavor=flavor,#x1B[90m#x1B[39;49;00m
                n_top_genes=#x1B[94m15#x1B[39;49;00m,#x1B[90m#x1B[39;49;00m
                batch_key=batch_key,#x1B[90m#x1B[39;49;00m
                subset=subset,#x1B[90m#x1B[39;49;00m
                inplace=inplace,#x1B[90m#x1B[39;49;00m
            )#x1B[90m#x1B[39;49;00m

#x1B[1m#x1B[31mtests/test_highly_variable_genes.py#x1B[0m:686: 
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 
#x1B[1m#x1B[.../scanpy/preprocessing/_highly_variable_genes.py#x1B[0m:780: in highly_variable_genes
    #x1B[0m#x1B[94mreturn#x1B[39;49;00m _highly_variable_genes_seurat_v3(#x1B[90m#x1B[39;49;00m
#x1B[1m#x1B[.../scanpy/preprocessing/_highly_variable_genes.py#x1B[0m:196: in _highly_variable_genes_seurat_v3
    #x1B[0maggregated_mean_var = ad.AnnData(#x1B[90m#x1B[39;49;00m
#x1B[1m#x1B[31m../../../..../scanpy/B9PcT7QG/hatch-test.stable/lib/python3.14.../anndata/_core/anndata.py#x1B[0m:254: in __init__
    #x1B[0m#x1B[96mself#x1B[39;49;00m._init_as_actual(#x1B[90m#x1B[39;49;00m
#x1B[1m#x1B[31m../../../..../scanpy/B9PcT7QG/hatch-test.stable/lib/python3.14.../anndata/_core/anndata.py#x1B[0m:448: in _init_as_actual
    #x1B[0m#x1B[96mself#x1B[39;49;00m._obs = _gen_dataframe(#x1B[90m#x1B[39;49;00m
#x1B[1m#x1B[31m../...../_temp/uv-python-dir/cpython-3.14.5-linux-x86_64-gnu/lib/python3.14/functools.py#x1B[0m:982: in wrapper
    #x1B[0m#x1B[94mreturn#x1B[39;49;00m dispatch(args[#x1B[94m0#x1B[39;49;00m].#x1B[91m__class__#x1B[39;49;00m)(*args, **kw)#x1B[90m#x1B[39;49;00m
           ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^#x1B[90m#x1B[39;49;00m
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 

anno =    __hvg_v3_batch_info__
0                      0
index_names = ['obs_names', 'row_names']

    #x1B[0m#x1B[37m@_gen_dataframe#x1B[39;49;00m.register(pd.DataFrame)#x1B[90m#x1B[39;49;00m
    #x1B[94mdef#x1B[39;49;00m#x1B[90m #x1B[39;49;00m#x1B[92m_gen_dataframe_df#x1B[39;49;00m(#x1B[90m#x1B[39;49;00m
        anno: pd.DataFrame,#x1B[90m#x1B[39;49;00m
        index_names: Iterable[#x1B[96mstr#x1B[39;49;00m],#x1B[90m#x1B[39;49;00m
        *,#x1B[90m#x1B[39;49;00m
        source: Literal[#x1B[33m"#x1B[39;49;00m#x1B[33mX#x1B[39;49;00m#x1B[33m"#x1B[39;49;00m, #x1B[33m"#x1B[39;49;00m#x1B[33mshape#x1B[39;49;00m#x1B[33m"#x1B[39;49;00m],#x1B[90m#x1B[39;49;00m
        attr: Literal[#x1B[33m"#x1B[39;49;00m#x1B[33mobs#x1B[39;49;00m#x1B[33m"#x1B[39;49;00m, #x1B[33m"#x1B[39;49;00m#x1B[33mvar#x1B[39;49;00m#x1B[33m"#x1B[39;49;00m],#x1B[90m#x1B[39;49;00m
        length: #x1B[96mint#x1B[39;49;00m | #x1B[94mNone#x1B[39;49;00m = #x1B[94mNone#x1B[39;49;00m,#x1B[90m#x1B[39;49;00m
    ):#x1B[90m#x1B[39;49;00m
        #x1B[94mif#x1B[39;49;00m #x1B[96misinstance#x1B[39;49;00m(anno.index, pd.MultiIndex):#x1B[90m#x1B[39;49;00m
            msg = (#x1B[90m#x1B[39;49;00m
                #x1B[33m"#x1B[39;49;00m#x1B[33mpandas.MultiIndex not supported as index for obs or var on declaration.#x1B[39;49;00m#x1B[33m\n#x1B[39;49;00m#x1B[33m\#x1B[39;49;00m
    #x1B[33m            You can set `obs_names` manually although most operations after will error or convert to str.#x1B[39;49;00m#x1B[33m\n#x1B[39;49;00m#x1B[33m\#x1B[39;49;00m
    #x1B[33m            This behavior will likely be clarified in a future breaking release.#x1B[39;49;00m#x1B[33m"#x1B[39;49;00m#x1B[90m#x1B[39;49;00m
            )#x1B[90m#x1B[39;49;00m
            #x1B[94mraise#x1B[39;49;00m #x1B[96mValueError#x1B[39;49;00m(msg)#x1B[90m#x1B[39;49;00m
        #x1B[94mif#x1B[39;49;00m length #x1B[95mis#x1B[39;49;00m #x1B[95mnot#x1B[39;49;00m #x1B[94mNone#x1B[39;49;00m #x1B[95mand#x1B[39;49;00m length != #x1B[96mlen#x1B[39;49;00m(anno):#x1B[90m#x1B[39;49;00m
            #x1B[94mraise#x1B[39;49;00m _mk_df_error(source, attr, length, #x1B[96mlen#x1B[39;49;00m(anno))#x1B[90m#x1B[39;49;00m
        anno = anno.copy(deep=#x1B[94mFalse#x1B[39;49;00m)#x1B[90m#x1B[39;49;00m
        #x1B[94mif#x1B[39;49;00m #x1B[95mnot#x1B[39;49;00m is_string_dtype(anno.index[~anno.index.isna()]):#x1B[90m#x1B[39;49;00m
            msg = #x1B[33m"#x1B[39;49;00m#x1B[33mTransforming to str index.#x1B[39;49;00m#x1B[33m"#x1B[39;49;00m#x1B[90m#x1B[39;49;00m
>           warnings.warn(msg, ImplicitModificationWarning, stacklevel=#x1B[94m2#x1B[39;49;00m)#x1B[90m#x1B[39;49;00m
#x1B[1m#x1B[31mE           anndata._warnings.ImplicitModificationWarning: Transforming to str index.#x1B[0m

#x1B[1m#x1B[31m../../../..../scanpy/B9PcT7QG/hatch-test.stable/lib/python3.14.../anndata/_core/aligned_df.py#x1B[0m:93: ImplicitModificationWarning

tests/test_highly_variable_genes.py::test_subset_inplace_consistency[None-numpy_ndarray-seurat_v3]

Stack Traces | 0.019s run time

subtests = <_pytest.subtests.Subtests object at 0x7f57c0c90b90>
flavor = 'seurat_v3', array_type = <function asarray at 0x7f57e43833d0>
batch_key = None

    #x1B[0m#x1B[37m@pytest#x1B[39;49;00m.mark.parametrize(#x1B[90m#x1B[39;49;00m
        #x1B[33m"#x1B[39;49;00m#x1B[33mflavor#x1B[39;49;00m#x1B[33m"#x1B[39;49;00m,#x1B[90m#x1B[39;49;00m
        [#x1B[90m#x1B[39;49;00m
            #x1B[33m"#x1B[39;49;00m#x1B[33mseurat#x1B[39;49;00m#x1B[33m"#x1B[39;49;00m,#x1B[90m#x1B[39;49;00m
            #x1B[33m"#x1B[39;49;00m#x1B[33mcell_ranger#x1B[39;49;00m#x1B[33m"#x1B[39;49;00m,#x1B[90m#x1B[39;49;00m
            pytest.param(#x1B[33m"#x1B[39;49;00m#x1B[33mseurat_v3#x1B[39;49;00m#x1B[33m"#x1B[39;49;00m, marks=needs.skmisc),#x1B[90m#x1B[39;49;00m
            pytest.param(#x1B[33m"#x1B[39;49;00m#x1B[33mseurat_v3_paper#x1B[39;49;00m#x1B[33m"#x1B[39;49;00m, marks=needs.skmisc),#x1B[90m#x1B[39;49;00m
        ],#x1B[90m#x1B[39;49;00m
    )#x1B[90m#x1B[39;49;00m
    #x1B[37m@pytest#x1B[39;49;00m.mark.parametrize(#x1B[90m#x1B[39;49;00m
        #x1B[33m"#x1B[39;49;00m#x1B[33marray_type#x1B[39;49;00m#x1B[33m"#x1B[39;49;00m,#x1B[90m#x1B[39;49;00m
        [#x1B[90m#x1B[39;49;00m
            p#x1B[90m#x1B[39;49;00m
            #x1B[94mfor#x1B[39;49;00m p #x1B[95min#x1B[39;49;00m ARRAY_TYPES#x1B[90m#x1B[39;49;00m
            #x1B[94mif#x1B[39;49;00m #x1B[33m"#x1B[39;49;00m#x1B[33mdask#x1B[39;49;00m#x1B[33m"#x1B[39;49;00m #x1B[95mnot#x1B[39;49;00m #x1B[95min#x1B[39;49;00m p.id #x1B[95mor#x1B[39;49;00m (#x1B[33m"#x1B[39;49;00m#x1B[33m1d_chunked#x1B[39;49;00m#x1B[33m"#x1B[39;49;00m #x1B[95min#x1B[39;49;00m p.id #x1B[95mand#x1B[39;49;00m #x1B[33m"#x1B[39;49;00m#x1B[33mcsr#x1B[39;49;00m#x1B[33m"#x1B[39;49;00m #x1B[95min#x1B[39;49;00m p.id)#x1B[90m#x1B[39;49;00m
        ],#x1B[90m#x1B[39;49;00m
    )#x1B[90m#x1B[39;49;00m
    #x1B[37m@pytest#x1B[39;49;00m.mark.parametrize(#x1B[33m"#x1B[39;49;00m#x1B[33mbatch_key#x1B[39;49;00m#x1B[33m"#x1B[39;49;00m, [#x1B[94mNone#x1B[39;49;00m, #x1B[33m"#x1B[39;49;00m#x1B[33mbatch#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_subset_inplace_consistency#x1B[39;49;00m(#x1B[90m#x1B[39;49;00m
        subtests: pytest.Subtests,#x1B[90m#x1B[39;49;00m
        flavor: Literal[#x1B[33m"#x1B[39;49;00m#x1B[33mseurat#x1B[39;49;00m#x1B[33m"#x1B[39;49;00m, #x1B[33m"#x1B[39;49;00m#x1B[33mcell_ranger#x1B[39;49;00m#x1B[33m"#x1B[39;49;00m, #x1B[33m"#x1B[39;49;00m#x1B[33mseurat_v3#x1B[39;49;00m#x1B[33m"#x1B[39;49;00m, #x1B[33m"#x1B[39;49;00m#x1B[33mseurat_v3_paper#x1B[39;49;00m#x1B[33m"#x1B[39;49;00m],#x1B[90m#x1B[39;49;00m
        array_type,#x1B[90m#x1B[39;49;00m
        batch_key: Literal[#x1B[33m"#x1B[39;49;00m#x1B[33mbatch#x1B[39;49;00m#x1B[33m"#x1B[39;49;00m] | #x1B[94mNone#x1B[39;49;00m,#x1B[90m#x1B[39;49;00m
    ) -> #x1B[94mNone#x1B[39;49;00m:#x1B[90m#x1B[39;49;00m
    #x1B[90m    #x1B[39;49;00m#x1B[33m"""Tests `n_top_genes=n`.#x1B[39;49;00m
    #x1B[33m#x1B[39;49;00m
    #x1B[33m    - if `inplace` and `subset` interact correctly#x1B[39;49;00m
    #x1B[33m    - for both the `seurat` and `cell_ranger` flavors#x1B[39;49;00m
    #x1B[33m    - for dask arrays and non-dask arrays#x1B[39;49;00m
    #x1B[33m    - for both with and without batch_key#x1B[39;49;00m
    #x1B[33m    """#x1B[39;49;00m#x1B[90m#x1B[39;49;00m
        rng = np.random.default_rng(#x1B[94m0#x1B[39;49;00m)#x1B[90m#x1B[39;49;00m
        adata = (#x1B[90m#x1B[39;49;00m
            sc.datasets.blobs(n_observations=#x1B[94m20#x1B[39;49;00m, n_variables=#x1B[94m80#x1B[39;49;00m, rng=rng)#x1B[90m#x1B[39;49;00m
            #x1B[94mif#x1B[39;49;00m #x1B[33m"#x1B[39;49;00m#x1B[33mseurat_v3#x1B[39;49;00m#x1B[33m"#x1B[39;49;00m #x1B[95mnot#x1B[39;49;00m #x1B[95min#x1B[39;49;00m flavor#x1B[90m#x1B[39;49;00m
            #x1B[94melse#x1B[39;49;00m pbmc3k()[:#x1B[94m1500#x1B[39;49;00m, :#x1B[94m1000#x1B[39;49;00m].copy()#x1B[90m#x1B[39;49;00m
        )#x1B[90m#x1B[39;49;00m
        adata.obs[#x1B[33m"#x1B[39;49;00m#x1B[33mbatch#x1B[39;49;00m#x1B[33m"#x1B[39;49;00m] = rng.choice([#x1B[33m"#x1B[39;49;00m#x1B[33ma#x1B[39;49;00m#x1B[33m"#x1B[39;49;00m, #x1B[33m"#x1B[39;49;00m#x1B[33mb#x1B[39;49;00m#x1B[33m"#x1B[39;49;00m], adata.shape[#x1B[94m0#x1B[39;49;00m])#x1B[90m#x1B[39;49;00m
        adata.X = array_type(np.abs(adata.X).astype(#x1B[96mint#x1B[39;49;00m))#x1B[90m#x1B[39;49;00m
    #x1B[90m#x1B[39;49;00m
        #x1B[94mif#x1B[39;49;00m flavor #x1B[95min#x1B[39;49;00m {#x1B[33m"#x1B[39;49;00m#x1B[33mseurat#x1B[39;49;00m#x1B[33m"#x1B[39;49;00m, #x1B[33m"#x1B[39;49;00m#x1B[33mcell_ranger#x1B[39;49;00m#x1B[33m"#x1B[39;49;00m}:#x1B[90m#x1B[39;49;00m
            sc.pp.normalize_total(adata, target_sum=#x1B[94m1e4#x1B[39;49;00m)#x1B[90m#x1B[39;49;00m
            sc.pp.log1p(adata)#x1B[90m#x1B[39;49;00m
    #x1B[90m#x1B[39;49;00m
        n_genes = adata.shape[#x1B[94m1#x1B[39;49;00m]#x1B[90m#x1B[39;49;00m
    #x1B[90m#x1B[39;49;00m
        adatas: #x1B[96mdict#x1B[39;49;00m[#x1B[96mbool#x1B[39;49;00m, AnnData] = {}#x1B[90m#x1B[39;49;00m
        dfs: #x1B[96mdict#x1B[39;49;00m[#x1B[96mbool#x1B[39;49;00m, pd.DataFrame] = {}#x1B[90m#x1B[39;49;00m
        #x1B[90m# for loops instead of parametrization to compare between settings#x1B[39;49;00m#x1B[90m#x1B[39;49;00m
        #x1B[94mfor#x1B[39;49;00m subset, inplace #x1B[95min#x1B[39;49;00m itertools.product([#x1B[94mTrue#x1B[39;49;00m, #x1B[94mFalse#x1B[39;49;00m], repeat=#x1B[94m2#x1B[39;49;00m):#x1B[90m#x1B[39;49;00m
            adata_copy = adata.copy()#x1B[90m#x1B[39;49;00m
    #x1B[90m#x1B[39;49;00m
>           output_df = sc.pp.highly_variable_genes(#x1B[90m#x1B[39;49;00m
                adata_copy,#x1B[90m#x1B[39;49;00m
                flavor=flavor,#x1B[90m#x1B[39;49;00m
                n_top_genes=#x1B[94m15#x1B[39;49;00m,#x1B[90m#x1B[39;49;00m
                batch_key=batch_key,#x1B[90m#x1B[39;49;00m
                subset=subset,#x1B[90m#x1B[39;49;00m
                inplace=inplace,#x1B[90m#x1B[39;49;00m
            )#x1B[90m#x1B[39;49;00m

#x1B[1m#x1B[31mtests/test_highly_variable_genes.py#x1B[0m:686: 
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 
#x1B[1m#x1B[.../scanpy/preprocessing/_highly_variable_genes.py#x1B[0m:780: in highly_variable_genes
    #x1B[0m#x1B[94mreturn#x1B[39;49;00m _highly_variable_genes_seurat_v3(#x1B[90m#x1B[39;49;00m
#x1B[1m#x1B[.../scanpy/preprocessing/_highly_variable_genes.py#x1B[0m:196: in _highly_variable_genes_seurat_v3
    #x1B[0maggregated_mean_var = ad.AnnData(#x1B[90m#x1B[39;49;00m
#x1B[1m#x1B[31m../../../..../scanpy/B9PcT7QG/hatch-test.stable/lib/python3.14.../anndata/_core/anndata.py#x1B[0m:254: in __init__
    #x1B[0m#x1B[96mself#x1B[39;49;00m._init_as_actual(#x1B[90m#x1B[39;49;00m
#x1B[1m#x1B[31m../../../..../scanpy/B9PcT7QG/hatch-test.stable/lib/python3.14.../anndata/_core/anndata.py#x1B[0m:448: in _init_as_actual
    #x1B[0m#x1B[96mself#x1B[39;49;00m._obs = _gen_dataframe(#x1B[90m#x1B[39;49;00m
#x1B[1m#x1B[31m../...../_temp/uv-python-dir/cpython-3.14.5-linux-x86_64-gnu/lib/python3.14/functools.py#x1B[0m:982: in wrapper
    #x1B[0m#x1B[94mreturn#x1B[39;49;00m dispatch(args[#x1B[94m0#x1B[39;49;00m].#x1B[91m__class__#x1B[39;49;00m)(*args, **kw)#x1B[90m#x1B[39;49;00m
           ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^#x1B[90m#x1B[39;49;00m
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 

anno =    __hvg_v3_batch_info__
0                      0
index_names = ['obs_names', 'row_names']

    #x1B[0m#x1B[37m@_gen_dataframe#x1B[39;49;00m.register(pd.DataFrame)#x1B[90m#x1B[39;49;00m
    #x1B[94mdef#x1B[39;49;00m#x1B[90m #x1B[39;49;00m#x1B[92m_gen_dataframe_df#x1B[39;49;00m(#x1B[90m#x1B[39;49;00m
        anno: pd.DataFrame,#x1B[90m#x1B[39;49;00m
        index_names: Iterable[#x1B[96mstr#x1B[39;49;00m],#x1B[90m#x1B[39;49;00m
        *,#x1B[90m#x1B[39;49;00m
        source: Literal[#x1B[33m"#x1B[39;49;00m#x1B[33mX#x1B[39;49;00m#x1B[33m"#x1B[39;49;00m, #x1B[33m"#x1B[39;49;00m#x1B[33mshape#x1B[39;49;00m#x1B[33m"#x1B[39;49;00m],#x1B[90m#x1B[39;49;00m
        attr: Literal[#x1B[33m"#x1B[39;49;00m#x1B[33mobs#x1B[39;49;00m#x1B[33m"#x1B[39;49;00m, #x1B[33m"#x1B[39;49;00m#x1B[33mvar#x1B[39;49;00m#x1B[33m"#x1B[39;49;00m],#x1B[90m#x1B[39;49;00m
        length: #x1B[96mint#x1B[39;49;00m | #x1B[94mNone#x1B[39;49;00m = #x1B[94mNone#x1B[39;49;00m,#x1B[90m#x1B[39;49;00m
    ):#x1B[90m#x1B[39;49;00m
        #x1B[94mif#x1B[39;49;00m #x1B[96misinstance#x1B[39;49;00m(anno.index, pd.MultiIndex):#x1B[90m#x1B[39;49;00m
            msg = (#x1B[90m#x1B[39;49;00m
                #x1B[33m"#x1B[39;49;00m#x1B[33mpandas.MultiIndex not supported as index for obs or var on declaration.#x1B[39;49;00m#x1B[33m\n#x1B[39;49;00m#x1B[33m\#x1B[39;49;00m
    #x1B[33m            You can set `obs_names` manually although most operations after will error or convert to str.#x1B[39;49;00m#x1B[33m\n#x1B[39;49;00m#x1B[33m\#x1B[39;49;00m
    #x1B[33m            This behavior will likely be clarified in a future breaking release.#x1B[39;49;00m#x1B[33m"#x1B[39;49;00m#x1B[90m#x1B[39;49;00m
            )#x1B[90m#x1B[39;49;00m
            #x1B[94mraise#x1B[39;49;00m #x1B[96mValueError#x1B[39;49;00m(msg)#x1B[90m#x1B[39;49;00m
        #x1B[94mif#x1B[39;49;00m length #x1B[95mis#x1B[39;49;00m #x1B[95mnot#x1B[39;49;00m #x1B[94mNone#x1B[39;49;00m #x1B[95mand#x1B[39;49;00m length != #x1B[96mlen#x1B[39;49;00m(anno):#x1B[90m#x1B[39;49;00m
            #x1B[94mraise#x1B[39;49;00m _mk_df_error(source, attr, length, #x1B[96mlen#x1B[39;49;00m(anno))#x1B[90m#x1B[39;49;00m
        anno = anno.copy(deep=#x1B[94mFalse#x1B[39;49;00m)#x1B[90m#x1B[39;49;00m
        #x1B[94mif#x1B[39;49;00m #x1B[95mnot#x1B[39;49;00m is_string_dtype(anno.index[~anno.index.isna()]):#x1B[90m#x1B[39;49;00m
            msg = #x1B[33m"#x1B[39;49;00m#x1B[33mTransforming to str index.#x1B[39;49;00m#x1B[33m"#x1B[39;49;00m#x1B[90m#x1B[39;49;00m
>           warnings.warn(msg, ImplicitModificationWarning, stacklevel=#x1B[94m2#x1B[39;49;00m)#x1B[90m#x1B[39;49;00m
#x1B[1m#x1B[31mE           anndata._warnings.ImplicitModificationWarning: Transforming to str index.#x1B[0m

#x1B[1m#x1B[31m../../../..../scanpy/B9PcT7QG/hatch-test.stable/lib/python3.14.../anndata/_core/aligned_df.py#x1B[0m:93: ImplicitModificationWarning

tests/test_highly_variable_genes.py::test_dask_consistency[dask_array_sparse-1d_chunked-csr_matrix-batched-seurat_v3_paper]

Stack Traces | 0.022s run time

adata = AnnData object with n_obs × n_vars = 1500 × 1000
    obs: 'batch'
    var: 'gene_ids'
flavor = 'seurat_v3_paper', batch_key = 'batch'
to_dask = <function gen_csr_csc_params_wrapper.<locals>.wrapper at 0x7f57df075380>

    #x1B[0m#x1B[37m@pytest#x1B[39;49;00m.mark.parametrize(#x1B[90m#x1B[39;49;00m
        #x1B[33m"#x1B[39;49;00m#x1B[33mflavor#x1B[39;49;00m#x1B[33m"#x1B[39;49;00m,#x1B[90m#x1B[39;49;00m
        [#x1B[90m#x1B[39;49;00m
            #x1B[33m"#x1B[39;49;00m#x1B[33mseurat#x1B[39;49;00m#x1B[33m"#x1B[39;49;00m,#x1B[90m#x1B[39;49;00m
            #x1B[33m"#x1B[39;49;00m#x1B[33mcell_ranger#x1B[39;49;00m#x1B[33m"#x1B[39;49;00m,#x1B[90m#x1B[39;49;00m
            pytest.param(#x1B[33m"#x1B[39;49;00m#x1B[33mseurat_v3#x1B[39;49;00m#x1B[33m"#x1B[39;49;00m, marks=needs.skmisc),#x1B[90m#x1B[39;49;00m
            pytest.param(#x1B[33m"#x1B[39;49;00m#x1B[33mseurat_v3_paper#x1B[39;49;00m#x1B[33m"#x1B[39;49;00m, marks=needs.skmisc),#x1B[90m#x1B[39;49;00m
        ],#x1B[90m#x1B[39;49;00m
    )#x1B[90m#x1B[39;49;00m
    #x1B[37m@pytest#x1B[39;49;00m.mark.parametrize(#x1B[33m"#x1B[39;49;00m#x1B[33mbatch_key#x1B[39;49;00m#x1B[33m"#x1B[39;49;00m, [#x1B[94mNone#x1B[39;49;00m, #x1B[33m"#x1B[39;49;00m#x1B[33mbatch#x1B[39;49;00m#x1B[33m"#x1B[39;49;00m], ids=[#x1B[33m"#x1B[39;49;00m#x1B[33msingle#x1B[39;49;00m#x1B[33m"#x1B[39;49;00m, #x1B[33m"#x1B[39;49;00m#x1B[33mbatched#x1B[39;49;00m#x1B[33m"#x1B[39;49;00m])#x1B[90m#x1B[39;49;00m
    #x1B[37m@pytest#x1B[39;49;00m.mark.parametrize(#x1B[90m#x1B[39;49;00m
        #x1B[33m"#x1B[39;49;00m#x1B[33mto_dask#x1B[39;49;00m#x1B[33m"#x1B[39;49;00m, [p #x1B[94mfor#x1B[39;49;00m p #x1B[95min#x1B[39;49;00m ARRAY_TYPES #x1B[94mif#x1B[39;49;00m #x1B[33m"#x1B[39;49;00m#x1B[33m1d_chunked#x1B[39;49;00m#x1B[33m"#x1B[39;49;00m #x1B[95min#x1B[39;49;00m p.id #x1B[95mand#x1B[39;49;00m #x1B[33m"#x1B[39;49;00m#x1B[33mcsr#x1B[39;49;00m#x1B[33m"#x1B[39;49;00m #x1B[95min#x1B[39;49;00m p.id]#x1B[90m#x1B[39;49;00m
    )#x1B[90m#x1B[39;49;00m
    #x1B[94mdef#x1B[39;49;00m#x1B[90m #x1B[39;49;00m#x1B[92mtest_dask_consistency#x1B[39;49;00m(adata: AnnData, flavor, batch_key, to_dask):#x1B[90m#x1B[39;49;00m
        #x1B[90m# current blob produces singularities in loess....maybe a bad sign of the data?#x1B[39;49;00m#x1B[90m#x1B[39;49;00m
        #x1B[94mif#x1B[39;49;00m #x1B[33m"#x1B[39;49;00m#x1B[33mseurat_v3#x1B[39;49;00m#x1B[33m"#x1B[39;49;00m #x1B[95min#x1B[39;49;00m flavor:#x1B[90m#x1B[39;49;00m
            adata = pbmc3k()[:#x1B[94m1500#x1B[39;49;00m, :#x1B[94m1000#x1B[39;49;00m].copy()#x1B[90m#x1B[39;49;00m
        adata.X = np.abs(adata.X).astype(#x1B[96mint#x1B[39;49;00m)#x1B[90m#x1B[39;49;00m
        #x1B[94mif#x1B[39;49;00m batch_key #x1B[95mis#x1B[39;49;00m #x1B[95mnot#x1B[39;49;00m #x1B[94mNone#x1B[39;49;00m:#x1B[90m#x1B[39;49;00m
            adata.obs[batch_key] = np.tile([#x1B[33m"#x1B[39;49;00m#x1B[33ma#x1B[39;49;00m#x1B[33m"#x1B[39;49;00m, #x1B[33m"#x1B[39;49;00m#x1B[33mb#x1B[39;49;00m#x1B[33m"#x1B[39;49;00m], adata.shape[#x1B[94m0#x1B[39;49;00m] // #x1B[94m2#x1B[39;49;00m)#x1B[90m#x1B[39;49;00m
        #x1B[90m# seurat_v3 expects counts#x1B[39;49;00m#x1B[90m#x1B[39;49;00m
        #x1B[94mif#x1B[39;49;00m #x1B[33m"#x1B[39;49;00m#x1B[33mseurat_v3#x1B[39;49;00m#x1B[33m"#x1B[39;49;00m #x1B[95mnot#x1B[39;49;00m #x1B[95min#x1B[39;49;00m flavor:#x1B[90m#x1B[39;49;00m
            sc.pp.normalize_total(adata, target_sum=#x1B[94m1e4#x1B[39;49;00m)#x1B[90m#x1B[39;49;00m
            sc.pp.log1p(adata)#x1B[90m#x1B[39;49;00m
        adata_dask = adata.copy()#x1B[90m#x1B[39;49;00m
        adata_dask.X = to_dask(adata_dask.X)#x1B[90m#x1B[39;49;00m
    #x1B[90m#x1B[39;49;00m
        #x1B[94mwith#x1B[39;49;00m (#x1B[90m#x1B[39;49;00m
            pytest.warns(#x1B[96mUserWarning#x1B[39;49;00m, match=#x1B[33m"#x1B[39;49;00m#x1B[33mn_top_genes.*normalized dispersions#x1B[39;49;00m#x1B[33m"#x1B[39;49;00m)#x1B[90m#x1B[39;49;00m
            #x1B[94mif#x1B[39;49;00m flavor == #x1B[33m"#x1B[39;49;00m#x1B[33mcell_ranger#x1B[39;49;00m#x1B[33m"#x1B[39;49;00m#x1B[90m#x1B[39;49;00m
            #x1B[94melse#x1B[39;49;00m nullcontext()#x1B[90m#x1B[39;49;00m
        ):#x1B[90m#x1B[39;49;00m
>           output_mem, output_dask = (#x1B[90m#x1B[39;49;00m
            ^^^^^^^^^^^^^^^^^^^^^^^#x1B[90m#x1B[39;49;00m
                sc.pp.highly_variable_genes(#x1B[90m#x1B[39;49;00m
                    ad, flavor=flavor, n_top_genes=#x1B[94m15#x1B[39;49;00m, inplace=#x1B[94mFalse#x1B[39;49;00m#x1B[90m#x1B[39;49;00m
                )#x1B[90m#x1B[39;49;00m
                #x1B[94mfor#x1B[39;49;00m ad #x1B[95min#x1B[39;49;00m [adata, adata_dask]#x1B[90m#x1B[39;49;00m
            )#x1B[90m#x1B[39;49;00m

#x1B[1m#x1B[31mtests/test_highly_variable_genes.py#x1B[0m:758: 
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 
#x1B[1m#x1B[31mtests/test_highly_variable_genes.py#x1B[0m:759: in <genexpr>
    #x1B[0msc.pp.highly_variable_genes(#x1B[90m#x1B[39;49;00m
#x1B[1m#x1B[.../scanpy/preprocessing/_highly_variable_genes.py#x1B[0m:780: in highly_variable_genes
    #x1B[0m#x1B[94mreturn#x1B[39;49;00m _highly_variable_genes_seurat_v3(#x1B[90m#x1B[39;49;00m
#x1B[1m#x1B[.../scanpy/preprocessing/_highly_variable_genes.py#x1B[0m:196: in _highly_variable_genes_seurat_v3
    #x1B[0maggregated_mean_var = ad.AnnData(#x1B[90m#x1B[39;49;00m
#x1B[1m#x1B[31m../../../..../scanpy/B9PcT7QG/hatch-test.stable/lib/python3.14.../anndata/_core/anndata.py#x1B[0m:254: in __init__
    #x1B[0m#x1B[96mself#x1B[39;49;00m._init_as_actual(#x1B[90m#x1B[39;49;00m
#x1B[1m#x1B[31m../../../..../scanpy/B9PcT7QG/hatch-test.stable/lib/python3.14.../anndata/_core/anndata.py#x1B[0m:448: in _init_as_actual
    #x1B[0m#x1B[96mself#x1B[39;49;00m._obs = _gen_dataframe(#x1B[90m#x1B[39;49;00m
#x1B[1m#x1B[31m../...../_temp/uv-python-dir/cpython-3.14.5-linux-x86_64-gnu/lib/python3.14/functools.py#x1B[0m:982: in wrapper
    #x1B[0m#x1B[94mreturn#x1B[39;49;00m dispatch(args[#x1B[94m0#x1B[39;49;00m].#x1B[91m__class__#x1B[39;49;00m)(*args, **kw)#x1B[90m#x1B[39;49;00m
           ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^#x1B[90m#x1B[39;49;00m
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 

anno =    __hvg_v3_batch_info__
0                      0
index_names = ['obs_names', 'row_names']

    #x1B[0m#x1B[37m@_gen_dataframe#x1B[39;49;00m.register(pd.DataFrame)#x1B[90m#x1B[39;49;00m
    #x1B[94mdef#x1B[39;49;00m#x1B[90m #x1B[39;49;00m#x1B[92m_gen_dataframe_df#x1B[39;49;00m(#x1B[90m#x1B[39;49;00m
        anno: pd.DataFrame,#x1B[90m#x1B[39;49;00m
        index_names: Iterable[#x1B[96mstr#x1B[39;49;00m],#x1B[90m#x1B[39;49;00m
        *,#x1B[90m#x1B[39;49;00m
        source: Literal[#x1B[33m"#x1B[39;49;00m#x1B[33mX#x1B[39;49;00m#x1B[33m"#x1B[39;49;00m, #x1B[33m"#x1B[39;49;00m#x1B[33mshape#x1B[39;49;00m#x1B[33m"#x1B[39;49;00m],#x1B[90m#x1B[39;49;00m
        attr: Literal[#x1B[33m"#x1B[39;49;00m#x1B[33mobs#x1B[39;49;00m#x1B[33m"#x1B[39;49;00m, #x1B[33m"#x1B[39;49;00m#x1B[33mvar#x1B[39;49;00m#x1B[33m"#x1B[39;49;00m],#x1B[90m#x1B[39;49;00m
        length: #x1B[96mint#x1B[39;49;00m | #x1B[94mNone#x1B[39;49;00m = #x1B[94mNone#x1B[39;49;00m,#x1B[90m#x1B[39;49;00m
    ):#x1B[90m#x1B[39;49;00m
        #x1B[94mif#x1B[39;49;00m #x1B[96misinstance#x1B[39;49;00m(anno.index, pd.MultiIndex):#x1B[90m#x1B[39;49;00m
            msg = (#x1B[90m#x1B[39;49;00m
                #x1B[33m"#x1B[39;49;00m#x1B[33mpandas.MultiIndex not supported as index for obs or var on declaration.#x1B[39;49;00m#x1B[33m\n#x1B[39;49;00m#x1B[33m\#x1B[39;49;00m
    #x1B[33m            You can set `obs_names` manually although most operations after will error or convert to str.#x1B[39;49;00m#x1B[33m\n#x1B[39;49;00m#x1B[33m\#x1B[39;49;00m
    #x1B[33m            This behavior will likely be clarified in a future breaking release.#x1B[39;49;00m#x1B[33m"#x1B[39;49;00m#x1B[90m#x1B[39;49;00m
            )#x1B[90m#x1B[39;49;00m
            #x1B[94mraise#x1B[39;49;00m #x1B[96mValueError#x1B[39;49;00m(msg)#x1B[90m#x1B[39;49;00m
        #x1B[94mif#x1B[39;49;00m length #x1B[95mis#x1B[39;49;00m #x1B[95mnot#x1B[39;49;00m #x1B[94mNone#x1B[39;49;00m #x1B[95mand#x1B[39;49;00m length != #x1B[96mlen#x1B[39;49;00m(anno):#x1B[90m#x1B[39;49;00m
            #x1B[94mraise#x1B[39;49;00m _mk_df_error(source, attr, length, #x1B[96mlen#x1B[39;49;00m(anno))#x1B[90m#x1B[39;49;00m
        anno = anno.copy(deep=#x1B[94mFalse#x1B[39;49;00m)#x1B[90m#x1B[39;49;00m
        #x1B[94mif#x1B[39;49;00m #x1B[95mnot#x1B[39;49;00m is_string_dtype(anno.index[~anno.index.isna()]):#x1B[90m#x1B[39;49;00m
            msg = #x1B[33m"#x1B[39;49;00m#x1B[33mTransforming to str index.#x1B[39;49;00m#x1B[33m"#x1B[39;49;00m#x1B[90m#x1B[39;49;00m
>           warnings.warn(msg, ImplicitModificationWarning, stacklevel=#x1B[94m2#x1B[39;49;00m)#x1B[90m#x1B[39;49;00m
#x1B[1m#x1B[31mE           anndata._warnings.ImplicitModificationWarning: Transforming to str index.#x1B[0m

#x1B[1m#x1B[31m../../../..../scanpy/B9PcT7QG/hatch-test.stable/lib/python3.14.../anndata/_core/aligned_df.py#x1B[0m:93: ImplicitModificationWarning

tests/test_highly_variable_genes.py::test_subset_inplace_consistency[None-scipy_csc_mat-seurat_v3]

Stack Traces | 0.023s run time

subtests = <_pytest.subtests.Subtests object at 0x7f57c0ea47d0>
flavor = 'seurat_v3', array_type = <class 'scipy.sparse._csc.csc_matrix'>
batch_key = None

    #x1B[0m#x1B[37m@pytest#x1B[39;49;00m.mark.parametrize(#x1B[90m#x1B[39;49;00m
        #x1B[33m"#x1B[39;49;00m#x1B[33mflavor#x1B[39;49;00m#x1B[33m"#x1B[39;49;00m,#x1B[90m#x1B[39;49;00m
        [#x1B[90m#x1B[39;49;00m
            #x1B[33m"#x1B[39;49;00m#x1B[33mseurat#x1B[39;49;00m#x1B[33m"#x1B[39;49;00m,#x1B[90m#x1B[39;49;00m
            #x1B[33m"#x1B[39;49;00m#x1B[33mcell_ranger#x1B[39;49;00m#x1B[33m"#x1B[39;49;00m,#x1B[90m#x1B[39;49;00m
            pytest.param(#x1B[33m"#x1B[39;49;00m#x1B[33mseurat_v3#x1B[39;49;00m#x1B[33m"#x1B[39;49;00m, marks=needs.skmisc),#x1B[90m#x1B[39;49;00m
            pytest.param(#x1B[33m"#x1B[39;49;00m#x1B[33mseurat_v3_paper#x1B[39;49;00m#x1B[33m"#x1B[39;49;00m, marks=needs.skmisc),#x1B[90m#x1B[39;49;00m
        ],#x1B[90m#x1B[39;49;00m
    )#x1B[90m#x1B[39;49;00m
    #x1B[37m@pytest#x1B[39;49;00m.mark.parametrize(#x1B[90m#x1B[39;49;00m
        #x1B[33m"#x1B[39;49;00m#x1B[33marray_type#x1B[39;49;00m#x1B[33m"#x1B[39;49;00m,#x1B[90m#x1B[39;49;00m
        [#x1B[90m#x1B[39;49;00m
            p#x1B[90m#x1B[39;49;00m
            #x1B[94mfor#x1B[39;49;00m p #x1B[95min#x1B[39;49;00m ARRAY_TYPES#x1B[90m#x1B[39;49;00m
            #x1B[94mif#x1B[39;49;00m #x1B[33m"#x1B[39;49;00m#x1B[33mdask#x1B[39;49;00m#x1B[33m"#x1B[39;49;00m #x1B[95mnot#x1B[39;49;00m #x1B[95min#x1B[39;49;00m p.id #x1B[95mor#x1B[39;49;00m (#x1B[33m"#x1B[39;49;00m#x1B[33m1d_chunked#x1B[39;49;00m#x1B[33m"#x1B[39;49;00m #x1B[95min#x1B[39;49;00m p.id #x1B[95mand#x1B[39;49;00m #x1B[33m"#x1B[39;49;00m#x1B[33mcsr#x1B[39;49;00m#x1B[33m"#x1B[39;49;00m #x1B[95min#x1B[39;49;00m p.id)#x1B[90m#x1B[39;49;00m
        ],#x1B[90m#x1B[39;49;00m
    )#x1B[90m#x1B[39;49;00m
    #x1B[37m@pytest#x1B[39;49;00m.mark.parametrize(#x1B[33m"#x1B[39;49;00m#x1B[33mbatch_key#x1B[39;49;00m#x1B[33m"#x1B[39;49;00m, [#x1B[94mNone#x1B[39;49;00m, #x1B[33m"#x1B[39;49;00m#x1B[33mbatch#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_subset_inplace_consistency#x1B[39;49;00m(#x1B[90m#x1B[39;49;00m
        subtests: pytest.Subtests,#x1B[90m#x1B[39;49;00m
        flavor: Literal[#x1B[33m"#x1B[39;49;00m#x1B[33mseurat#x1B[39;49;00m#x1B[33m"#x1B[39;49;00m, #x1B[33m"#x1B[39;49;00m#x1B[33mcell_ranger#x1B[39;49;00m#x1B[33m"#x1B[39;49;00m, #x1B[33m"#x1B[39;49;00m#x1B[33mseurat_v3#x1B[39;49;00m#x1B[33m"#x1B[39;49;00m, #x1B[33m"#x1B[39;49;00m#x1B[33mseurat_v3_paper#x1B[39;49;00m#x1B[33m"#x1B[39;49;00m],#x1B[90m#x1B[39;49;00m
        array_type,#x1B[90m#x1B[39;49;00m
        batch_key: Literal[#x1B[33m"#x1B[39;49;00m#x1B[33mbatch#x1B[39;49;00m#x1B[33m"#x1B[39;49;00m] | #x1B[94mNone#x1B[39;49;00m,#x1B[90m#x1B[39;49;00m
    ) -> #x1B[94mNone#x1B[39;49;00m:#x1B[90m#x1B[39;49;00m
    #x1B[90m    #x1B[39;49;00m#x1B[33m"""Tests `n_top_genes=n`.#x1B[39;49;00m
    #x1B[33m#x1B[39;49;00m
    #x1B[33m    - if `inplace` and `subset` interact correctly#x1B[39;49;00m
    #x1B[33m    - for both the `seurat` and `cell_ranger` flavors#x1B[39;49;00m
    #x1B[33m    - for dask arrays and non-dask arrays#x1B[39;49;00m
    #x1B[33m    - for both with and without batch_key#x1B[39;49;00m
    #x1B[33m    """#x1B[39;49;00m#x1B[90m#x1B[39;49;00m
        rng = np.random.default_rng(#x1B[94m0#x1B[39;49;00m)#x1B[90m#x1B[39;49;00m
        adata = (#x1B[90m#x1B[39;49;00m
            sc.datasets.blobs(n_observations=#x1B[94m20#x1B[39;49;00m, n_variables=#x1B[94m80#x1B[39;49;00m, rng=rng)#x1B[90m#x1B[39;49;00m
            #x1B[94mif#x1B[39;49;00m #x1B[33m"#x1B[39;49;00m#x1B[33mseurat_v3#x1B[39;49;00m#x1B[33m"#x1B[39;49;00m #x1B[95mnot#x1B[39;49;00m #x1B[95min#x1B[39;49;00m flavor#x1B[90m#x1B[39;49;00m
            #x1B[94melse#x1B[39;49;00m pbmc3k()[:#x1B[94m1500#x1B[39;49;00m, :#x1B[94m1000#x1B[39;49;00m].copy()#x1B[90m#x1B[39;49;00m
        )#x1B[90m#x1B[39;49;00m
        adata.obs[#x1B[33m"#x1B[39;49;00m#x1B[33mbatch#x1B[39;49;00m#x1B[33m"#x1B[39;49;00m] = rng.choice([#x1B[33m"#x1B[39;49;00m#x1B[33ma#x1B[39;49;00m#x1B[33m"#x1B[39;49;00m, #x1B[33m"#x1B[39;49;00m#x1B[33mb#x1B[39;49;00m#x1B[33m"#x1B[39;49;00m], adata.shape[#x1B[94m0#x1B[39;49;00m])#x1B[90m#x1B[39;49;00m
        adata.X = array_type(np.abs(adata.X).astype(#x1B[96mint#x1B[39;49;00m))#x1B[90m#x1B[39;49;00m
    #x1B[90m#x1B[39;49;00m
        #x1B[94mif#x1B[39;49;00m flavor #x1B[95min#x1B[39;49;00m {#x1B[33m"#x1B[39;49;00m#x1B[33mseurat#x1B[39;49;00m#x1B[33m"#x1B[39;49;00m, #x1B[33m"#x1B[39;49;00m#x1B[33mcell_ranger#x1B[39;49;00m#x1B[33m"#x1B[39;49;00m}:#x1B[90m#x1B[39;49;00m
            sc.pp.normalize_total(adata, target_sum=#x1B[94m1e4#x1B[39;49;00m)#x1B[90m#x1B[39;49;00m
            sc.pp.log1p(adata)#x1B[90m#x1B[39;49;00m
    #x1B[90m#x1B[39;49;00m
        n_genes = adata.shape[#x1B[94m1#x1B[39;49;00m]#x1B[90m#x1B[39;49;00m
    #x1B[90m#x1B[39;49;00m
        adatas: #x1B[96mdict#x1B[39;49;00m[#x1B[96mbool#x1B[39;49;00m, AnnData] = {}#x1B[90m#x1B[39;49;00m
        dfs: #x1B[96mdict#x1B[39;49;00m[#x1B[96mbool#x1B[39;49;00m, pd.DataFrame] = {}#x1B[90m#x1B[39;49;00m
        #x1B[90m# for loops instead of parametrization to compare between settings#x1B[39;49;00m#x1B[90m#x1B[39;49;00m
        #x1B[94mfor#x1B[39;49;00m subset, inplace #x1B[95min#x1B[39;49;00m itertools.product([#x1B[94mTrue#x1B[39;49;00m, #x1B[94mFalse#x1B[39;49;00m], repeat=#x1B[94m2#x1B[39;49;00m):#x1B[90m#x1B[39;49;00m
            adata_copy = adata.copy()#x1B[90m#x1B[39;49;00m
    #x1B[90m#x1B[39;49;00m
>           output_df = sc.pp.highly_variable_genes(#x1B[90m#x1B[39;49;00m
                adata_copy,#x1B[90m#x1B[39;49;00m
                flavor=flavor,#x1B[90m#x1B[39;49;00m
                n_top_genes=#x1B[94m15#x1B[39;49;00m,#x1B[90m#x1B[39;49;00m
                batch_key=batch_key,#x1B[90m#x1B[39;49;00m
                subset=subset,#x1B[90m#x1B[39;49;00m
                inplace=inplace,#x1B[90m#x1B[39;49;00m
            )#x1B[90m#x1B[39;49;00m

#x1B[1m#x1B[31mtests/test_highly_variable_genes.py#x1B[0m:686: 
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 
#x1B[1m#x1B[.../scanpy/preprocessing/_highly_variable_genes.py#x1B[0m:780: in highly_variable_genes
    #x1B[0m#x1B[94mreturn#x1B[39;49;00m _highly_variable_genes_seurat_v3(#x1B[90m#x1B[39;49;00m
#x1B[1m#x1B[.../scanpy/preprocessing/_highly_variable_genes.py#x1B[0m:196: in _highly_variable_genes_seurat_v3
    #x1B[0maggregated_mean_var = ad.AnnData(#x1B[90m#x1B[39;49;00m
#x1B[1m#x1B[31m../../../..../scanpy/B9PcT7QG/hatch-test.stable/lib/python3.14.../anndata/_core/anndata.py#x1B[0m:254: in __init__
    #x1B[0m#x1B[96mself#x1B[39;49;00m._init_as_actual(#x1B[90m#x1B[39;49;00m
#x1B[1m#x1B[31m../../../..../scanpy/B9PcT7QG/hatch-test.stable/lib/python3.14.../anndata/_core/anndata.py#x1B[0m:448: in _init_as_actual
    #x1B[0m#x1B[96mself#x1B[39;49;00m._obs = _gen_dataframe(#x1B[90m#x1B[39;49;00m
#x1B[1m#x1B[31m../...../_temp/uv-python-dir/cpython-3.14.5-linux-x86_64-gnu/lib/python3.14/functools.py#x1B[0m:982: in wrapper
    #x1B[0m#x1B[94mreturn#x1B[39;49;00m dispatch(args[#x1B[94m0#x1B[39;49;00m].#x1B[91m__class__#x1B[39;49;00m)(*args, **kw)#x1B[90m#x1B[39;49;00m
           ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^#x1B[90m#x1B[39;49;00m
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 

anno =    __hvg_v3_batch_info__
0                      0
index_names = ['obs_names', 'row_names']

    #x1B[0m#x1B[37m@_gen_dataframe#x1B[39;49;00m.register(pd.DataFrame)#x1B[90m#x1B[39;49;00m
    #x1B[94mdef#x1B[39;49;00m#x1B[90m #x1B[39;49;00m#x1B[92m_gen_dataframe_df#x1B[39;49;00m(#x1B[90m#x1B[39;49;00m
        anno: pd.DataFrame,#x1B[90m#x1B[39;49;00m
        index_names: Iterable[#x1B[96mstr#x1B[39;49;00m],#x1B[90m#x1B[39;49;00m
        *,#x1B[90m#x1B[39;49;00m
        source: Literal[#x1B[33m"#x1B[39;49;00m#x1B[33mX#x1B[39;49;00m#x1B[33m"#x1B[39;49;00m, #x1B[33m"#x1B[39;49;00m#x1B[33mshape#x1B[39;49;00m#x1B[33m"#x1B[39;49;00m],#x1B[90m#x1B[39;49;00m
        attr: Literal[#x1B[33m"#x1B[39;49;00m#x1B[33mobs#x1B[39;49;00m#x1B[33m"#x1B[39;49;00m, #x1B[33m"#x1B[39;49;00m#x1B[33mvar#x1B[39;49;00m#x1B[33m"#x1B[39;49;00m],#x1B[90m#x1B[39;49;00m
        length: #x1B[96mint#x1B[39;49;00m | #x1B[94mNone#x1B[39;49;00m = #x1B[94mNone#x1B[39;49;00m,#x1B[90m#x1B[39;49;00m
    ):#x1B[90m#x1B[39;49;00m
        #x1B[94mif#x1B[39;49;00m #x1B[96misinstance#x1B[39;49;00m(anno.index, pd.MultiIndex):#x1B[90m#x1B[39;49;00m
            msg = (#x1B[90m#x1B[39;49;00m
                #x1B[33m"#x1B[39;49;00m#x1B[33mpandas.MultiIndex not supported as index for obs or var on declaration.#x1B[39;49;00m#x1B[33m\n#x1B[39;49;00m#x1B[33m\#x1B[39;49;00m
    #x1B[33m            You can set `obs_names` manually although most operations after will error or convert to str.#x1B[39;49;00m#x1B[33m\n#x1B[39;49;00m#x1B[33m\#x1B[39;49;00m
    #x1B[33m            This behavior will likely be clarified in a future breaking release.#x1B[39;49;00m#x1B[33m"#x1B[39;49;00m#x1B[90m#x1B[39;49;00m
            )#x1B[90m#x1B[39;49;00m
            #x1B[94mraise#x1B[39;49;00m #x1B[96mValueError#x1B[39;49;00m(msg)#x1B[90m#x1B[39;49;00m
        #x1B[94mif#x1B[39;49;00m length #x1B[95mis#x1B[39;49;00m #x1B[95mnot#x1B[39;49;00m #x1B[94mNone#x1B[39;49;00m #x1B[95mand#x1B[39;49;00m length != #x1B[96mlen#x1B[39;49;00m(anno):#x1B[90m#x1B[39;49;00m
            #x1B[94mraise#x1B[39;49;00m _mk_df_error(source, attr, length, #x1B[96mlen#x1B[39;49;00m(anno))#x1B[90m#x1B[39;49;00m
        anno = anno.copy(deep=#x1B[94mFalse#x1B[39;49;00m)#x1B[90m#x1B[39;49;00m
        #x1B[94mif#x1B[39;49;00m #x1B[95mnot#x1B[39;49;00m is_string_dtype(anno.index[~anno.index.isna()]):#x1B[90m#x1B[39;49;00m
            msg = #x1B[33m"#x1B[39;49;00m#x1B[33mTransforming to str index.#x1B[39;49;00m#x1B[33m"#x1B[39;49;00m#x1B[90m#x1B[39;49;00m
>           warnings.warn(msg, ImplicitModificationWarning, stacklevel=#x1B[94m2#x1B[39;49;00m)#x1B[90m#x1B[39;49;00m
#x1B[1m#x1B[31mE           anndata._warnings.ImplicitModificationWarning: Transforming to str index.#x1B[0m

#x1B[1m#x1B[31m../../../..../scanpy/B9PcT7QG/hatch-test.stable/lib/python3.14.../anndata/_core/aligned_df.py#x1B[0m:93: ImplicitModificationWarning

tests/test_highly_variable_genes.py::test_seurat_v3_degenerate

Stack Traces | 0.025s run time

#x1B[0m#x1B[37m@needs#x1B[39;49;00m.skmisc#x1B[90m#x1B[39;49;00m
    #x1B[94mdef#x1B[39;49;00m#x1B[90m #x1B[39;49;00m#x1B[92mtest_seurat_v3_degenerate#x1B[39;49;00m() -> #x1B[94mNone#x1B[39;49;00m:#x1B[90m#x1B[39;49;00m
    #x1B[90m    #x1B[39;49;00m#x1B[33m"""Tests that the flavor handles all-zero genes."""#x1B[39;49;00m#x1B[90m#x1B[39;49;00m
        adata = AnnData(sps.random(#x1B[94m10#x1B[39;49;00m, #x1B[94m1000#x1B[39;49;00m, density=#x1B[94m0.001#x1B[39;49;00m, #x1B[96mformat#x1B[39;49;00m=#x1B[33m"#x1B[39;49;00m#x1B[33mcsr#x1B[39;49;00m#x1B[33m"#x1B[39;49;00m, dtype=#x1B[33m"#x1B[39;49;00m#x1B[33mint#x1B[39;49;00m#x1B[33m"#x1B[39;49;00m))#x1B[90m#x1B[39;49;00m
        adata.X.data = np.abs(adata.X.data)#x1B[90m#x1B[39;49;00m
    #x1B[90m#x1B[39;49;00m
>       sc.pp.highly_variable_genes(adata, flavor=#x1B[33m"#x1B[39;49;00m#x1B[33mseurat_v3#x1B[39;49;00m#x1B[33m"#x1B[39;49;00m)#x1B[90m#x1B[39;49;00m

#x1B[1m#x1B[31mtests/test_highly_variable_genes.py#x1B[0m:510: 
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 
#x1B[1m#x1B[.../scanpy/preprocessing/_highly_variable_genes.py#x1B[0m:780: in highly_variable_genes
    #x1B[0m#x1B[94mreturn#x1B[39;49;00m _highly_variable_genes_seurat_v3(#x1B[90m#x1B[39;49;00m
#x1B[1m#x1B[.../scanpy/preprocessing/_highly_variable_genes.py#x1B[0m:196: in _highly_variable_genes_seurat_v3
    #x1B[0maggregated_mean_var = ad.AnnData(#x1B[90m#x1B[39;49;00m
#x1B[1m#x1B[31m../../../..../scanpy/B9PcT7QG/hatch-test.stable/lib/python3.14.../anndata/_core/anndata.py#x1B[0m:254: in __init__
    #x1B[0m#x1B[96mself#x1B[39;49;00m._init_as_actual(#x1B[90m#x1B[39;49;00m
#x1B[1m#x1B[31m../../../..../scanpy/B9PcT7QG/hatch-test.stable/lib/python3.14.../anndata/_core/anndata.py#x1B[0m:448: in _init_as_actual
    #x1B[0m#x1B[96mself#x1B[39;49;00m._obs = _gen_dataframe(#x1B[90m#x1B[39;49;00m
#x1B[1m#x1B[31m../...../_temp/uv-python-dir/cpython-3.14.5-linux-x86_64-gnu/lib/python3.14/functools.py#x1B[0m:982: in wrapper
    #x1B[0m#x1B[94mreturn#x1B[39;49;00m dispatch(args[#x1B[94m0#x1B[39;49;00m].#x1B[91m__class__#x1B[39;49;00m)(*args, **kw)#x1B[90m#x1B[39;49;00m
           ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^#x1B[90m#x1B[39;49;00m
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 

anno =    __hvg_v3_batch_info__
0                      0
index_names = ['obs_names', 'row_names']

    #x1B[0m#x1B[37m@_gen_dataframe#x1B[39;49;00m.register(pd.DataFrame)#x1B[90m#x1B[39;49;00m
    #x1B[94mdef#x1B[39;49;00m#x1B[90m #x1B[39;49;00m#x1B[92m_gen_dataframe_df#x1B[39;49;00m(#x1B[90m#x1B[39;49;00m
        anno: pd.DataFrame,#x1B[90m#x1B[39;49;00m
        index_names: Iterable[#x1B[96mstr#x1B[39;49;00m],#x1B[90m#x1B[39;49;00m
        *,#x1B[90m#x1B[39;49;00m
        source: Literal[#x1B[33m"#x1B[39;49;00m#x1B[33mX#x1B[39;49;00m#x1B[33m"#x1B[39;49;00m, #x1B[33m"#x1B[39;49;00m#x1B[33mshape#x1B[39;49;00m#x1B[33m"#x1B[39;49;00m],#x1B[90m#x1B[39;49;00m
        attr: Literal[#x1B[33m"#x1B[39;49;00m#x1B[33mobs#x1B[39;49;00m#x1B[33m"#x1B[39;49;00m, #x1B[33m"#x1B[39;49;00m#x1B[33mvar#x1B[39;49;00m#x1B[33m"#x1B[39;49;00m],#x1B[90m#x1B[39;49;00m
        length: #x1B[96mint#x1B[39;49;00m | #x1B[94mNone#x1B[39;49;00m = #x1B[94mNone#x1B[39;49;00m,#x1B[90m#x1B[39;49;00m
    ):#x1B[90m#x1B[39;49;00m
        #x1B[94mif#x1B[39;49;00m #x1B[96misinstance#x1B[39;49;00m(anno.index, pd.MultiIndex):#x1B[90m#x1B[39;49;00m
            msg = (#x1B[90m#x1B[39;49;00m
                #x1B[33m"#x1B[39;49;00m#x1B[33mpandas.MultiIndex not supported as index for obs or var on declaration.#x1B[39;49;00m#x1B[33m\n#x1B[39;49;00m#x1B[33m\#x1B[39;49;00m
    #x1B[33m            You can set `obs_names` manually although most operations after will error or convert to str.#x1B[39;49;00m#x1B[33m\n#x1B[39;49;00m#x1B[33m\#x1B[39;49;00m
    #x1B[33m            This behavior will likely be clarified in a future breaking release.#x1B[39;49;00m#x1B[33m"#x1B[39;49;00m#x1B[90m#x1B[39;49;00m
            )#x1B[90m#x1B[39;49;00m
            #x1B[94mraise#x1B[39;49;00m #x1B[96mValueError#x1B[39;49;00m(msg)#x1B[90m#x1B[39;49;00m
        #x1B[94mif#x1B[39;49;00m length #x1B[95mis#x1B[39;49;00m #x1B[95mnot#x1B[39;49;00m #x1B[94mNone#x1B[39;49;00m #x1B[95mand#x1B[39;49;00m length != #x1B[96mlen#x1B[39;49;00m(anno):#x1B[90m#x1B[39;49;00m
            #x1B[94mraise#x1B[39;49;00m _mk_df_error(source, attr, length, #x1B[96mlen#x1B[39;49;00m(anno))#x1B[90m#x1B[39;49;00m
        anno = anno.copy(deep=#x1B[94mFalse#x1B[39;49;00m)#x1B[90m#x1B[39;49;00m
        #x1B[94mif#x1B[39;49;00m #x1B[95mnot#x1B[39;49;00m is_string_dtype(anno.index[~anno.index.isna()]):#x1B[90m#x1B[39;49;00m
            msg = #x1B[33m"#x1B[39;49;00m#x1B[33mTransforming to str index.#x1B[39;49;00m#x1B[33m"#x1B[39;49;00m#x1B[90m#x1B[39;49;00m
>           warnings.warn(msg, ImplicitModificationWarning, stacklevel=#x1B[94m2#x1B[39;49;00m)#x1B[90m#x1B[39;49;00m
#x1B[1m#x1B[31mE           anndata._warnings.ImplicitModificationWarning: Transforming to str index.#x1B[0m

#x1B[1m#x1B[31m../../../..../scanpy/B9PcT7QG/hatch-test.stable/lib/python3.14.../anndata/_core/aligned_df.py#x1B[0m:93: ImplicitModificationWarning

tests/test_highly_variable_genes.py::test_subset_inplace_consistency[None-scipy_csc_mat-seurat_v3_paper]

Stack Traces | 0.029s run time

subtests = <_pytest.subtests.Subtests object at 0x7f57c8129eb0>
flavor = 'seurat_v3_paper', array_type = <class 'scipy.sparse._csc.csc_matrix'>
batch_key = None

    #x1B[0m#x1B[37m@pytest#x1B[39;49;00m.mark.parametrize(#x1B[90m#x1B[39;49;00m
        #x1B[33m"#x1B[39;49;00m#x1B[33mflavor#x1B[39;49;00m#x1B[33m"#x1B[39;49;00m,#x1B[90m#x1B[39;49;00m
        [#x1B[90m#x1B[39;49;00m
            #x1B[33m"#x1B[39;49;00m#x1B[33mseurat#x1B[39;49;00m#x1B[33m"#x1B[39;49;00m,#x1B[90m#x1B[39;49;00m
            #x1B[33m"#x1B[39;49;00m#x1B[33mcell_ranger#x1B[39;49;00m#x1B[33m"#x1B[39;49;00m,#x1B[90m#x1B[39;49;00m
            pytest.param(#x1B[33m"#x1B[39;49;00m#x1B[33mseurat_v3#x1B[39;49;00m#x1B[33m"#x1B[39;49;00m, marks=needs.skmisc),#x1B[90m#x1B[39;49;00m
            pytest.param(#x1B[33m"#x1B[39;49;00m#x1B[33mseurat_v3_paper#x1B[39;49;00m#x1B[33m"#x1B[39;49;00m, marks=needs.skmisc),#x1B[90m#x1B[39;49;00m
        ],#x1B[90m#x1B[39;49;00m
    )#x1B[90m#x1B[39;49;00m
    #x1B[37m@pytest#x1B[39;49;00m.mark.parametrize(#x1B[90m#x1B[39;49;00m
        #x1B[33m"#x1B[39;49;00m#x1B[33marray_type#x1B[39;49;00m#x1B[33m"#x1B[39;49;00m,#x1B[90m#x1B[39;49;00m
        [#x1B[90m#x1B[39;49;00m
            p#x1B[90m#x1B[39;49;00m
            #x1B[94mfor#x1B[39;49;00m p #x1B[95min#x1B[39;49;00m ARRAY_TYPES#x1B[90m#x1B[39;49;00m
            #x1B[94mif#x1B[39;49;00m #x1B[33m"#x1B[39;49;00m#x1B[33mdask#x1B[39;49;00m#x1B[33m"#x1B[39;49;00m #x1B[95mnot#x1B[39;49;00m #x1B[95min#x1B[39;49;00m p.id #x1B[95mor#x1B[39;49;00m (#x1B[33m"#x1B[39;49;00m#x1B[33m1d_chunked#x1B[39;49;00m#x1B[33m"#x1B[39;49;00m #x1B[95min#x1B[39;49;00m p.id #x1B[95mand#x1B[39;49;00m #x1B[33m"#x1B[39;49;00m#x1B[33mcsr#x1B[39;49;00m#x1B[33m"#x1B[39;49;00m #x1B[95min#x1B[39;49;00m p.id)#x1B[90m#x1B[39;49;00m
        ],#x1B[90m#x1B[39;49;00m
    )#x1B[90m#x1B[39;49;00m
    #x1B[37m@pytest#x1B[39;49;00m.mark.parametrize(#x1B[33m"#x1B[39;49;00m#x1B[33mbatch_key#x1B[39;49;00m#x1B[33m"#x1B[39;49;00m, [#x1B[94mNone#x1B[39;49;00m, #x1B[33m"#x1B[39;49;00m#x1B[33mbatch#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_subset_inplace_consistency#x1B[39;49;00m(#x1B[90m#x1B[39;49;00m
        subtests: pytest.Subtests,#x1B[90m#x1B[39;49;00m
        flavor: Literal[#x1B[33m"#x1B[39;49;00m#x1B[33mseurat#x1B[39;49;00m#x1B[33m"#x1B[39;49;00m, #x1B[33m"#x1B[39;49;00m#x1B[33mcell_ranger#x1B[39;49;00m#x1B[33m"#x1B[39;49;00m, #x1B[33m"#x1B[39;49;00m#x1B[33mseurat_v3#x1B[39;49;00m#x1B[33m"#x1B[39;49;00m, #x1B[33m"#x1B[39;49;00m#x1B[33mseurat_v3_paper#x1B[39;49;00m#x1B[33m"#x1B[39;49;00m],#x1B[90m#x1B[39;49;00m
        array_type,#x1B[90m#x1B[39;49;00m
        batch_key: Literal[#x1B[33m"#x1B[39;49;00m#x1B[33mbatch#x1B[39;49;00m#x1B[33m"#x1B[39;49;00m] | #x1B[94mNone#x1B[39;49;00m,#x1B[90m#x1B[39;49;00m
    ) -> #x1B[94mNone#x1B[39;49;00m:#x1B[90m#x1B[39;49;00m
    #x1B[90m    #x1B[39;49;00m#x1B[33m"""Tests `n_top_genes=n`.#x1B[39;49;00m
    #x1B[33m#x1B[39;49;00m
    #x1B[33m    - if `inplace` and `subset` interact correctly#x1B[39;49;00m
    #x1B[33m    - for both the `seurat` and `cell_ranger` flavors#x1B[39;49;00m
    #x1B[33m    - for dask arrays and non-dask arrays#x1B[39;49;00m
    #x1B[33m    - for both with and without batch_key#x1B[39;49;00m
    #x1B[33m    """#x1B[39;49;00m#x1B[90m#x1B[39;49;00m
        rng = np.random.default_rng(#x1B[94m0#x1B[39;49;00m)#x1B[90m#x1B[39;49;00m
        adata = (#x1B[90m#x1B[39;49;00m
            sc.datasets.blobs(n_observations=#x1B[94m20#x1B[39;49;00m, n_variables=#x1B[94m80#x1B[39;49;00m, rng=rng)#x1B[90m#x1B[39;49;00m
            #x1B[94mif#x1B[39;49;00m #x1B[33m"#x1B[39;49;00m#x1B[33mseurat_v3#x1B[39;49;00m#x1B[33m"#x1B[39;49;00m #x1B[95mnot#x1B[39;49;00m #x1B[95min#x1B[39;49;00m flavor#x1B[90m#x1B[39;49;00m
            #x1B[94melse#x1B[39;49;00m pbmc3k()[:#x1B[94m1500#x1B[39;49;00m, :#x1B[94m1000#x1B[39;49;00m].copy()#x1B[90m#x1B[39;49;00m
        )#x1B[90m#x1B[39;49;00m
        adata.obs[#x1B[33m"#x1B[39;49;00m#x1B[33mbatch#x1B[39;49;00m#x1B[33m"#x1B[39;49;00m] = rng.choice([#x1B[33m"#x1B[39;49;00m#x1B[33ma#x1B[39;49;00m#x1B[33m"#x1B[39;49;00m, #x1B[33m"#x1B[39;49;00m#x1B[33mb#x1B[39;49;00m#x1B[33m"#x1B[39;49;00m], adata.shape[#x1B[94m0#x1B[39;49;00m])#x1B[90m#x1B[39;49;00m
        adata.X = array_type(np.abs(adata.X).astype(#x1B[96mint#x1B[39;49;00m))#x1B[90m#x1B[39;49;00m
    #x1B[90m#x1B[39;49;00m
        #x1B[94mif#x1B[39;49;00m flavor #x1B[95min#x1B[39;49;00m {#x1B[33m"#x1B[39;49;00m#x1B[33mseurat#x1B[39;49;00m#x1B[33m"#x1B[39;49;00m, #x1B[33m"#x1B[39;49;00m#x1B[33mcell_ranger#x1B[39;49;00m#x1B[33m"#x1B[39;49;00m}:#x1B[90m#x1B[39;49;00m
            sc.pp.normalize_total(adata, target_sum=#x1B[94m1e4#x1B[39;49;00m)#x1B[90m#x1B[39;49;00m
            sc.pp.log1p(adata)#x1B[90m#x1B[39;49;00m
    #x1B[90m#x1B[39;49;00m
        n_genes = adata.shape[#x1B[94m1#x1B[39;49;00m]#x1B[90m#x1B[39;49;00m
    #x1B[90m#x1B[39;49;00m
        adatas: #x1B[96mdict#x1B[39;49;00m[#x1B[96mbool#x1B[39;49;00m, AnnData] = {}#x1B[90m#x1B[39;49;00m
        dfs: #x1B[96mdict#x1B[39;49;00m[#x1B[96mbool#x1B[39;49;00m, pd.DataFrame] = {}#x1B[90m#x1B[39;49;00m
        #x1B[90m# for loops instead of parametrization to compare between settings#x1B[39;49;00m#x1B[90m#x1B[39;49;00m
        #x1B[94mfor#x1B[39;49;00m subset, inplace #x1B[95min#x1B[39;49;00m itertools.product([#x1B[94mTrue#x1B[39;49;00m, #x1B[94mFalse#x1B[39;49;00m], repeat=#x1B[94m2#x1B[39;49;00m):#x1B[90m#x1B[39;49;00m
            adata_copy = adata.copy()#x1B[90m#x1B[39;49;00m
    #x1B[90m#x1B[39;49;00m
>           output_df = sc.pp.highly_variable_genes(#x1B[90m#x1B[39;49;00m
                adata_copy,#x1B[90m#x1B[39;49;00m
                flavor=flavor,#x1B[90m#x1B[39;49;00m
                n_top_genes=#x1B[94m15#x1B[39;49;00m,#x1B[90m#x1B[39;49;00m
                batch_key=batch_key,#x1B[90m#x1B[39;49;00m
                subset=subset,#x1B[90m#x1B[39;49;00m
                inplace=inplace,#x1B[90m#x1B[39;49;00m
            )#x1B[90m#x1B[39;49;00m

#x1B[1m#x1B[31mtests/test_highly_variable_genes.py#x1B[0m:686: 
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 
#x1B[1m#x1B[.../scanpy/preprocessing/_highly_variable_genes.py#x1B[0m:780: in highly_variable_genes
    #x1B[0m#x1B[94mreturn#x1B[39;49;00m _highly_variable_genes_seurat_v3(#x1B[90m#x1B[39;49;00m
#x1B[1m#x1B[.../scanpy/preprocessing/_highly_variable_genes.py#x1B[0m:196: in _highly_variable_genes_seurat_v3
    #x1B[0maggregated_mean_var = ad.AnnData(#x1B[90m#x1B[39;49;00m
#x1B[1m#x1B[31m../../../..../scanpy/B9PcT7QG/hatch-test.stable/lib/python3.14.../anndata/_core/anndata.py#x1B[0m:254: in __init__
    #x1B[0m#x1B[96mself#x1B[39;49;00m._init_as_actual(#x1B[90m#x1B[39;49;00m
#x1B[1m#x1B[31m../../../..../scanpy/B9PcT7QG/hatch-test.stable/lib/python3.14.../anndata/_core/anndata.py#x1B[0m:448: in _init_as_actual
    #x1B[0m#x1B[96mself#x1B[39;49;00m._obs = _gen_dataframe(#x1B[90m#x1B[39;49;00m
#x1B[1m#x1B[31m../...../_temp/uv-python-dir/cpython-3.14.5-linux-x86_64-gnu/lib/python3.14/functools.py#x1B[0m:982: in wrapper
    #x1B[0m#x1B[94mreturn#x1B[39;49;00m dispatch(args[#x1B[94m0#x1B[39;49;00m].#x1B[91m__class__#x1B[39;49;00m)(*args, **kw)#x1B[90m#x1B[39;49;00m
           ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^#x1B[90m#x1B[39;49;00m
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 

anno =    __hvg_v3_batch_info__
0                      0
index_names = ['obs_names', 'row_names']

    #x1B[0m#x1B[37m@_gen_dataframe#x1B[39;49;00m.register(pd.DataFrame)#x1B[90m#x1B[39;49;00m
    #x1B[94mdef#x1B[39;49;00m#x1B[90m #x1B[39;49;00m#x1B[92m_gen_dataframe_df#x1B[39;49;00m(#x1B[90m#x1B[39;49;00m
        anno: pd.DataFrame,#x1B[90m#x1B[39;49;00m
        index_names: Iterable[#x1B[96mstr#x1B[39;49;00m],#x1B[90m#x1B[39;49;00m
        *,#x1B[90m#x1B[39;49;00m
        source: Literal[#x1B[33m"#x1B[39;49;00m#x1B[33mX#x1B[39;49;00m#x1B[33m"#x1B[39;49;00m, #x1B[33m"#x1B[39;49;00m#x1B[33mshape#x1B[39;49;00m#x1B[33m"#x1B[39;49;00m],#x1B[90m#x1B[39;49;00m
        attr: Literal[#x1B[33m"#x1B[39;49;00m#x1B[33mobs#x1B[39;49;00m#x1B[33m"#x1B[39;49;00m, #x1B[33m"#x1B[39;49;00m#x1B[33mvar#x1B[39;49;00m#x1B[33m"#x1B[39;49;00m],#x1B[90m#x1B[39;49;00m
        length: #x1B[96mint#x1B[39;49;00m | #x1B[94mNone#x1B[39;49;00m = #x1B[94mNone#x1B[39;49;00m,#x1B[90m#x1B[39;49;00m
    ):#x1B[90m#x1B[39;49;00m
        #x1B[94mif#x1B[39;49;00m #x1B[96misinstance#x1B[39;49;00m(anno.index, pd.MultiIndex):#x1B[90m#x1B[39;49;00m
            msg = (#x1B[90m#x1B[39;49;00m
                #x1B[33m"#x1B[39;49;00m#x1B[33mpandas.MultiIndex not supported as index for obs or var on declaration.#x1B[39;49;00m#x1B[33m\n#x1B[39;49;00m#x1B[33m\#x1B[39;49;00m
    #x1B[33m            You can set `obs_names` manually although most operations after will error or convert to str.#x1B[39;49;00m#x1B[33m\n#x1B[39;49;00m#x1B[33m\#x1B[39;49;00m
    #x1B[33m            This behavior will likely be clarified in a future breaking release.#x1B[39;49;00m#x1B[33m"#x1B[39;49;00m#x1B[90m#x1B[39;49;00m
            )#x1B[90m#x1B[39;49;00m
            #x1B[94mraise#x1B[39;49;00m #x1B[96mValueError#x1B[39;49;00m(msg)#x1B[90m#x1B[39;49;00m
        #x1B[94mif#x1B[39;49;00m length #x1B[95mis#x1B[39;49;00m #x1B[95mnot#x1B[39;49;00m #x1B[94mNone#x1B[39;49;00m #x1B[95mand#x1B[39;49;00m length != #x1B[96mlen#x1B[39;49;00m(anno):#x1B[90m#x1B[39;49;00m
            #x1B[94mraise#x1B[39;49;00m _mk_df_error(source, attr, length, #x1B[96mlen#x1B[39;49;00m(anno))#x1B[90m#x1B[39;49;00m
        anno = anno.copy(deep=#x1B[94mFalse#x1B[39;49;00m)#x1B[90m#x1B[39;49;00m
        #x1B[94mif#x1B[39;49;00m #x1B[95mnot#x1B[39;49;00m is_string_dtype(anno.index[~anno.index.isna()]):#x1B[90m#x1B[39;49;00m
            msg = #x1B[33m"#x1B[39;49;00m#x1B[33mTransforming to str index.#x1B[39;49;00m#x1B[33m"#x1B[39;49;00m#x1B[90m#x1B[39;49;00m
>           warnings.warn(msg, ImplicitModificationWarning, stacklevel=#x1B[94m2#x1B[39;49;00m)#x1B[90m#x1B[39;49;00m
#x1B[1m#x1B[31mE           anndata._warnings.ImplicitModificationWarning: Transforming to str index.#x1B[0m

#x1B[1m#x1B[31m../../../..../scanpy/B9PcT7QG/hatch-test.stable/lib/python3.14.../anndata/_core/aligned_df.py#x1B[0m:93: ImplicitModificationWarning

tests/test_highly_variable_genes.py::test_subset_inplace_consistency[None-dask_array_sparse-1d_chunked-csr_matrix-seurat_v3_paper]

Stack Traces | 0.04s run time

subtests = <_pytest.subtests.Subtests object at 0x7f57c0b04ad0>
flavor = 'seurat_v3_paper'
array_type = <function gen_csr_csc_params_wrapper.<locals>.wrapper at 0x7f57df075380>
batch_key = None

    #x1B[0m#x1B[37m@pytest#x1B[39;49;00m.mark.parametrize(#x1B[90m#x1B[39;49;00m
        #x1B[33m"#x1B[39;49;00m#x1B[33mflavor#x1B[39;49;00m#x1B[33m"#x1B[39;49;00m,#x1B[90m#x1B[39;49;00m
        [#x1B[90m#x1B[39;49;00m
            #x1B[33m"#x1B[39;49;00m#x1B[33mseurat#x1B[39;49;00m#x1B[33m"#x1B[39;49;00m,#x1B[90m#x1B[39;49;00m
            #x1B[33m"#x1B[39;49;00m#x1B[33mcell_ranger#x1B[39;49;00m#x1B[33m"#x1B[39;49;00m,#x1B[90m#x1B[39;49;00m
            pytest.param(#x1B[33m"#x1B[39;49;00m#x1B[33mseurat_v3#x1B[39;49;00m#x1B[33m"#x1B[39;49;00m, marks=needs.skmisc),#x1B[90m#x1B[39;49;00m
            pytest.param(#x1B[33m"#x1B[39;49;00m#x1B[33mseurat_v3_paper#x1B[39;49;00m#x1B[33m"#x1B[39;49;00m, marks=needs.skmisc),#x1B[90m#x1B[39;49;00m
        ],#x1B[90m#x1B[39;49;00m
    )#x1B[90m#x1B[39;49;00m
    #x1B[37m@pytest#x1B[39;49;00m.mark.parametrize(#x1B[90m#x1B[39;49;00m
        #x1B[33m"#x1B[39;49;00m#x1B[33marray_type#x1B[39;49;00m#x1B[33m"#x1B[39;49;00m,#x1B[90m#x1B[39;49;00m
        [#x1B[90m#x1B[39;49;00m
            p#x1B[90m#x1B[39;49;00m
            #x1B[94mfor#x1B[39;49;00m p #x1B[95min#x1B[39;49;00m ARRAY_TYPES#x1B[90m#x1B[39;49;00m
            #x1B[94mif#x1B[39;49;00m #x1B[33m"#x1B[39;49;00m#x1B[33mdask#x1B[39;49;00m#x1B[33m"#x1B[39;49;00m #x1B[95mnot#x1B[39;49;00m #x1B[95min#x1B[39;49;00m p.id #x1B[95mor#x1B[39;49;00m (#x1B[33m"#x1B[39;49;00m#x1B[33m1d_chunked#x1B[39;49;00m#x1B[33m"#x1B[39;49;00m #x1B[95min#x1B[39;49;00m p.id #x1B[95mand#x1B[39;49;00m #x1B[33m"#x1B[39;49;00m#x1B[33mcsr#x1B[39;49;00m#x1B[33m"#x1B[39;49;00m #x1B[95min#x1B[39;49;00m p.id)#x1B[90m#x1B[39;49;00m
        ],#x1B[90m#x1B[39;49;00m
    )#x1B[90m#x1B[39;49;00m
    #x1B[37m@pytest#x1B[39;49;00m.mark.parametrize(#x1B[33m"#x1B[39;49;00m#x1B[33mbatch_key#x1B[39;49;00m#x1B[33m"#x1B[39;49;00m, [#x1B[94mNone#x1B[39;49;00m, #x1B[33m"#x1B[39;49;00m#x1B[33mbatch#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_subset_inplace_consistency#x1B[39;49;00m(#x1B[90m#x1B[39;49;00m
        subtests: pytest.Subtests,#x1B[90m#x1B[39;49;00m
        flavor: Literal[#x1B[33m"#x1B[39;49;00m#x1B[33mseurat#x1B[39;49;00m#x1B[33m"#x1B[39;49;00m, #x1B[33m"#x1B[39;49;00m#x1B[33mcell_ranger#x1B[39;49;00m#x1B[33m"#x1B[39;49;00m, #x1B[33m"#x1B[39;49;00m#x1B[33mseurat_v3#x1B[39;49;00m#x1B[33m"#x1B[39;49;00m, #x1B[33m"#x1B[39;49;00m#x1B[33mseurat_v3_paper#x1B[39;49;00m#x1B[33m"#x1B[39;49;00m],#x1B[90m#x1B[39;49;00m
        array_type,#x1B[90m#x1B[39;49;00m
        batch_key: Literal[#x1B[33m"#x1B[39;49;00m#x1B[33mbatch#x1B[39;49;00m#x1B[33m"#x1B[39;49;00m] | #x1B[94mNone#x1B[39;49;00m,#x1B[90m#x1B[39;49;00m
    ) -> #x1B[94mNone#x1B[39;49;00m:#x1B[90m#x1B[39;49;00m
    #x1B[90m    #x1B[39;49;00m#x1B[33m"""Tests `n_top_genes=n`.#x1B[39;49;00m
    #x1B[33m#x1B[39;49;00m
    #x1B[33m    - if `inplace` and `subset` interact correctly#x1B[39;49;00m
    #x1B[33m    - for both the `seurat` and `cell_ranger` flavors#x1B[39;49;00m
    #x1B[33m    - for dask arrays and non-dask arrays#x1B[39;49;00m
    #x1B[33m    - for both with and without batch_key#x1B[39;49;00m
    #x1B[33m    """#x1B[39;49;00m#x1B[90m#x1B[39;49;00m
        rng = np.random.default_rng(#x1B[94m0#x1B[39;49;00m)#x1B[90m#x1B[39;49;00m
        adata = (#x1B[90m#x1B[39;49;00m
            sc.datasets.blobs(n_observations=#x1B[94m20#x1B[39;49;00m, n_variables=#x1B[94m80#x1B[39;49;00m, rng=rng)#x1B[90m#x1B[39;49;00m
            #x1B[94mif#x1B[39;49;00m #x1B[33m"#x1B[39;49;00m#x1B[33mseurat_v3#x1B[39;49;00m#x1B[33m"#x1B[39;49;00m #x1B[95mnot#x1B[39;49;00m #x1B[95min#x1B[39;49;00m flavor#x1B[90m#x1B[39;49;00m
            #x1B[94melse#x1B[39;49;00m pbmc3k()[:#x1B[94m1500#x1B[39;49;00m, :#x1B[94m1000#x1B[39;49;00m].copy()#x1B[90m#x1B[39;49;00m
        )#x1B[90m#x1B[39;49;00m
        adata.obs[#x1B[33m"#x1B[39;49;00m#x1B[33mbatch#x1B[39;49;00m#x1B[33m"#x1B[39;49;00m] = rng.choice([#x1B[33m"#x1B[39;49;00m#x1B[33ma#x1B[39;49;00m#x1B[33m"#x1B[39;49;00m, #x1B[33m"#x1B[39;49;00m#x1B[33mb#x1B[39;49;00m#x1B[33m"#x1B[39;49;00m], adata.shape[#x1B[94m0#x1B[39;49;00m])#x1B[90m#x1B[39;49;00m
        adata.X = array_type(np.abs(adata.X).astype(#x1B[96mint#x1B[39;49;00m))#x1B[90m#x1B[39;49;00m
    #x1B[90m#x1B[39;49;00m
        #x1B[94mif#x1B[39;49;00m flavor #x1B[95min#x1B[39;49;00m {#x1B[33m"#x1B[39;49;00m#x1B[33mseurat#x1B[39;49;00m#x1B[33m"#x1B[39;49;00m, #x1B[33m"#x1B[39;49;00m#x1B[33mcell_ranger#x1B[39;49;00m#x1B[33m"#x1B[39;49;00m}:#x1B[90m#x1B[39;49;00m
            sc.pp.normalize_total(adata, target_sum=#x1B[94m1e4#x1B[39;49;00m)#x1B[90m#x1B[39;49;00m
            sc.pp.log1p(adata)#x1B[90m#x1B[39;49;00m
    #x1B[90m#x1B[39;49;00m
        n_genes = adata.shape[#x1B[94m1#x1B[39;49;00m]#x1B[90m#x1B[39;49;00m
    #x1B[90m#x1B[39;49;00m
        adatas: #x1B[96mdict#x1B[39;49;00m[#x1B[96mbool#x1B[39;49;00m, AnnData] = {}#x1B[90m#x1B[39;49;00m
        dfs: #x1B[96mdict#x1B[39;49;00m[#x1B[96mbool#x1B[39;49;00m, pd.DataFrame] = {}#x1B[90m#x1B[39;49;00m
        #x1B[90m# for loops instead of parametrization to compare between settings#x1B[39;49;00m#x1B[90m#x1B[39;49;00m
        #x1B[94mfor#x1B[39;49;00m subset, inplace #x1B[95min#x1B[39;49;00m itertools.product([#x1B[94mTrue#x1B[39;49;00m, #x1B[94mFalse#x1B[39;49;00m], repeat=#x1B[94m2#x1B[39;49;00m):#x1B[90m#x1B[39;49;00m
            adata_copy = adata.copy()#x1B[90m#x1B[39;49;00m
    #x1B[90m#x1B[39;49;00m
>           output_df = sc.pp.highly_variable_genes(#x1B[90m#x1B[39;49;00m
                adata_copy,#x1B[90m#x1B[39;49;00m
                flavor=flavor,#x1B[90m#x1B[39;49;00m
                n_top_genes=#x1B[94m15#x1B[39;49;00m,#x1B[90m#x1B[39;49;00m
                batch_key=batch_key,#x1B[90m#x1B[39;49;00m
                subset=subset,#x1B[90m#x1B[39;49;00m
                inplace=inplace,#x1B[90m#x1B[39;49;00m
            )#x1B[90m#x1B[39;49;00m

#x1B[1m#x1B[31mtests/test_highly_variable_genes.py#x1B[0m:686: 
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 
#x1B[1m#x1B[.../scanpy/preprocessing/_highly_variable_genes.py#x1B[0m:780: in highly_variable_genes
    #x1B[0m#x1B[94mreturn#x1B[39;49;00m _highly_variable_genes_seurat_v3(#x1B[90m#x1B[39;49;00m
#x1B[1m#x1B[.../scanpy/preprocessing/_highly_variable_genes.py#x1B[0m:196: in _highly_variable_genes_seurat_v3
    #x1B[0maggregated_mean_var = ad.AnnData(#x1B[90m#x1B[39;49;00m
#x1B[1m#x1B[31m../../../..../scanpy/B9PcT7QG/hatch-test.stable/lib/python3.14.../anndata/_core/anndata.py#x1B[0m:254: in __init__
    #x1B[0m#x1B[96mself#x1B[39;49;00m._init_as_actual(#x1B[90m#x1B[39;49;00m
#x1B[1m#x1B[31m../../../..../scanpy/B9PcT7QG/hatch-test.stable/lib/python3.14.../anndata/_core/anndata.py#x1B[0m:448: in _init_as_actual
    #x1B[0m#x1B[96mself#x1B[39;49;00m._obs = _gen_dataframe(#x1B[90m#x1B[39;49;00m
#x1B[1m#x1B[31m../...../_temp/uv-python-dir/cpython-3.14.5-linux-x86_64-gnu/lib/python3.14/functools.py#x1B[0m:982: in wrapper
    #x1B[0m#x1B[94mreturn#x1B[39;49;00m dispatch(args[#x1B[94m0#x1B[39;49;00m].#x1B[91m__class__#x1B[39;49;00m)(*args, **kw)#x1B[90m#x1B[39;49;00m
           ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^#x1B[90m#x1B[39;49;00m
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 

anno =    __hvg_v3_batch_info__
0                      0
index_names = ['obs_names', 'row_names']

    #x1B[0m#x1B[37m@_gen_dataframe#x1B[39;49;00m.register(pd.DataFrame)#x1B[90m#x1B[39;49;00m
    #x1B[94mdef#x1B[39;49;00m#x1B[90m #x1B[39;49;00m#x1B[92m_gen_dataframe_df#x1B[39;49;00m(#x1B[90m#x1B[39;49;00m
        anno: pd.DataFrame,#x1B[90m#x1B[39;49;00m
        index_names: Iterable[#x1B[96mstr#x1B[39;49;00m],#x1B[90m#x1B[39;49;00m
        *,#x1B[90m#x1B[39;49;00m
        source: Literal[#x1B[33m"#x1B[39;49;00m#x1B[33mX#x1B[39;49;00m#x1B[33m"#x1B[39;49;00m, #x1B[33m"#x1B[39;49;00m#x1B[33mshape#x1B[39;49;00m#x1B[33m"#x1B[39;49;00m],#x1B[90m#x1B[39;49;00m
        attr: Literal[#x1B[33m"#x1B[39;49;00m#x1B[33mobs#x1B[39;49;00m#x1B[33m"#x1B[39;49;00m, #x1B[33m"#x1B[39;49;00m#x1B[33mvar#x1B[39;49;00m#x1B[33m"#x1B[39;49;00m],#x1B[90m#x1B[39;49;00m
        length: #x1B[96mint#x1B[39;49;00m | #x1B[94mNone#x1B[39;49;00m = #x1B[94mNone#x1B[39;49;00m,#x1B[90m#x1B[39;49;00m
    ):#x1B[90m#x1B[39;49;00m
        #x1B[94mif#x1B[39;49;00m #x1B[96misinstance#x1B[39;49;00m(anno.index, pd.MultiIndex):#x1B[90m#x1B[39;49;00m
            msg = (#x1B[90m#x1B[39;49;00m
                #x1B[33m"#x1B[39;49;00m#x1B[33mpandas.MultiIndex not supported as index for obs or var on declaration.#x1B[39;49;00m#x1B[33m\n#x1B[39;49;00m#x1B[33m\#x1B[39;49;00m
    #x1B[33m            You can set `obs_names` manually although most operations after will error or convert to str.#x1B[39;49;00m#x1B[33m\n#x1B[39;49;00m#x1B[33m\#x1B[39;49;00m
    #x1B[33m            This behavior will likely be clarified in a future breaking release.#x1B[39;49;00m#x1B[33m"#x1B[39;49;00m#x1B[90m#x1B[39;49;00m
            )#x1B[90m#x1B[39;49;00m
            #x1B[94mraise#x1B[39;49;00m #x1B[96mValueError#x1B[39;49;00m(msg)#x1B[90m#x1B[39;49;00m
        #x1B[94mif#x1B[39;49;00m length #x1B[95mis#x1B[39;49;00m #x1B[95mnot#x1B[39;49;00m #x1B[94mNone#x1B[39;49;00m #x1B[95mand#x1B[39;49;00m length != #x1B[96mlen#x1B[39;49;00m(anno):#x1B[90m#x1B[39;49;00m
            #x1B[94mraise#x1B[39;49;00m _mk_df_error(source, attr, length, #x1B[96mlen#x1B[39;49;00m(anno))#x1B[90m#x1B[39;49;00m
        anno = anno.copy(deep=#x1B[94mFalse#x1B[39;49;00m)#x1B[90m#x1B[39;49;00m
        #x1B[94mif#x1B[39;49;00m #x1B[95mnot#x1B[39;49;00m is_string_dtype(anno.index[~anno.index.isna()]):#x1B[90m#x1B[39;49;00m
            msg = #x1B[33m"#x1B[39;49;00m#x1B[33mTransforming to str index.#x1B[39;49;00m#x1B[33m"#x1B[39;49;00m#x1B[90m#x1B[39;49;00m
>           warnings.warn(msg, ImplicitModificationWarning, stacklevel=#x1B[94m2#x1B[39;49;00m)#x1B[90m#x1B[39;49;00m
#x1B[1m#x1B[31mE           anndata._warnings.ImplicitModificationWarning: Transforming to str index.#x1B[0m

#x1B[1m#x1B[31m../../../..../scanpy/B9PcT7QG/hatch-test.stable/lib/python3.14.../anndata/_core/aligned_df.py#x1B[0m:93: ImplicitModificationWarning

tests/test_highly_variable_genes.py::test_subset_inplace_consistency[None-dask_array_sparse-1d_chunked-csr_matrix-seurat_v3]

Stack Traces | 0.044s run time

subtests = <_pytest.subtests.Subtests object at 0x7f57c82b2cf0>
flavor = 'seurat_v3'
array_type = <function gen_csr_csc_params_wrapper.<locals>.wrapper at 0x7f57df075380>
batch_key = None

    #x1B[0m#x1B[37m@pytest#x1B[39;49;00m.mark.parametrize(#x1B[90m#x1B[39;49;00m
        #x1B[33m"#x1B[39;49;00m#x1B[33mflavor#x1B[39;49;00m#x1B[33m"#x1B[39;49;00m,#x1B[90m#x1B[39;49;00m
        [#x1B[90m#x1B[39;49;00m
            #x1B[33m"#x1B[39;49;00m#x1B[33mseurat#x1B[39;49;00m#x1B[33m"#x1B[39;49;00m,#x1B[90m#x1B[39;49;00m
            #x1B[33m"#x1B[39;49;00m#x1B[33mcell_ranger#x1B[39;49;00m#x1B[33m"#x1B[39;49;00m,#x1B[90m#x1B[39;49;00m
            pytest.param(#x1B[33m"#x1B[39;49;00m#x1B[33mseurat_v3#x1B[39;49;00m#x1B[33m"#x1B[39;49;00m, marks=needs.skmisc),#x1B[90m#x1B[39;49;00m
            pytest.param(#x1B[33m"#x1B[39;49;00m#x1B[33mseurat_v3_paper#x1B[39;49;00m#x1B[33m"#x1B[39;49;00m, marks=needs.skmisc),#x1B[90m#x1B[39;49;00m
        ],#x1B[90m#x1B[39;49;00m
    )#x1B[90m#x1B[39;49;00m
    #x1B[37m@pytest#x1B[39;49;00m.mark.parametrize(#x1B[90m#x1B[39;49;00m
        #x1B[33m"#x1B[39;49;00m#x1B[33marray_type#x1B[39;49;00m#x1B[33m"#x1B[39;49;00m,#x1B[90m#x1B[39;49;00m
        [#x1B[90m#x1B[39;49;00m
            p#x1B[90m#x1B[39;49;00m
            #x1B[94mfor#x1B[39;49;00m p #x1B[95min#x1B[39;49;00m ARRAY_TYPES#x1B[90m#x1B[39;49;00m
            #x1B[94mif#x1B[39;49;00m #x1B[33m"#x1B[39;49;00m#x1B[33mdask#x1B[39;49;00m#x1B[33m"#x1B[39;49;00m #x1B[95mnot#x1B[39;49;00m #x1B[95min#x1B[39;49;00m p.id #x1B[95mor#x1B[39;49;00m (#x1B[33m"#x1B[39;49;00m#x1B[33m1d_chunked#x1B[39;49;00m#x1B[33m"#x1B[39;49;00m #x1B[95min#x1B[39;49;00m p.id #x1B[95mand#x1B[39;49;00m #x1B[33m"#x1B[39;49;00m#x1B[33mcsr#x1B[39;49;00m#x1B[33m"#x1B[39;49;00m #x1B[95min#x1B[39;49;00m p.id)#x1B[90m#x1B[39;49;00m
        ],#x1B[90m#x1B[39;49;00m
    )#x1B[90m#x1B[39;49;00m
    #x1B[37m@pytest#x1B[39;49;00m.mark.parametrize(#x1B[33m"#x1B[39;49;00m#x1B[33mbatch_key#x1B[39;49;00m#x1B[33m"#x1B[39;49;00m, [#x1B[94mNone#x1B[39;49;00m, #x1B[33m"#x1B[39;49;00m#x1B[33mbatch#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_subset_inplace_consistency#x1B[39;49;00m(#x1B[90m#x1B[39;49;00m
        subtests: pytest.Subtests,#x1B[90m#x1B[39;49;00m
        flavor: Literal[#x1B[33m"#x1B[39;49;00m#x1B[33mseurat#x1B[39;49;00m#x1B[33m"#x1B[39;49;00m, #x1B[33m"#x1B[39;49;00m#x1B[33mcell_ranger#x1B[39;49;00m#x1B[33m"#x1B[39;49;00m, #x1B[33m"#x1B[39;49;00m#x1B[33mseurat_v3#x1B[39;49;00m#x1B[33m"#x1B[39;49;00m, #x1B[33m"#x1B[39;49;00m#x1B[33mseurat_v3_paper#x1B[39;49;00m#x1B[33m"#x1B[39;49;00m],#x1B[90m#x1B[39;49;00m
        array_type,#x1B[90m#x1B[39;49;00m
        batch_key: Literal[#x1B[33m"#x1B[39;49;00m#x1B[33mbatch#x1B[39;49;00m#x1B[33m"#x1B[39;49;00m] | #x1B[94mNone#x1B[39;49;00m,#x1B[90m#x1B[39;49;00m
    ) -> #x1B[94mNone#x1B[39;49;00m:#x1B[90m#x1B[39;49;00m
    #x1B[90m    #x1B[39;49;00m#x1B[33m"""Tests `n_top_genes=n`.#x1B[39;49;00m
    #x1B[33m#x1B[39;49;00m
    #x1B[33m    - if `inplace` and `subset` interact correctly#x1B[39;49;00m
    #x1B[33m    - for both the `seurat` and `cell_ranger` flavors#x1B[39;49;00m
    #x1B[33m    - for dask arrays and non-dask arrays#x1B[39;49;00m
    #x1B[33m    - for both with and without batch_key#x1B[39;49;00m
    #x1B[33m    """#x1B[39;49;00m#x1B[90m#x1B[39;49;00m
        rng = np.random.default_rng(#x1B[94m0#x1B[39;49;00m)#x1B[90m#x1B[39;49;00m
        adata = (#x1B[90m#x1B[39;49;00m
            sc.datasets.blobs(n_observations=#x1B[94m20#x1B[39;49;00m, n_variables=#x1B[94m80#x1B[39;49;00m, rng=rng)#x1B[90m#x1B[39;49;00m
            #x1B[94mif#x1B[39;49;00m #x1B[33m"#x1B[39;49;00m#x1B[33mseurat_v3#x1B[39;49;00m#x1B[33m"#x1B[39;49;00m #x1B[95mnot#x1B[39;49;00m #x1B[95min#x1B[39;49;00m flavor#x1B[90m#x1B[39;49;00m
            #x1B[94melse#x1B[39;49;00m pbmc3k()[:#x1B[94m1500#x1B[39;49;00m, :#x1B[94m1000#x1B[39;49;00m].copy()#x1B[90m#x1B[39;49;00m
        )#x1B[90m#x1B[39;49;00m
        adata.obs[#x1B[33m"#x1B[39;49;00m#x1B[33mbatch#x1B[39;49;00m#x1B[33m"#x1B[39;49;00m] = rng.choice([#x1B[33m"#x1B[39;49;00m#x1B[33ma#x1B[39;49;00m#x1B[33m"#x1B[39;49;00m, #x1B[33m"#x1B[39;49;00m#x1B[33mb#x1B[39;49;00m#x1B[33m"#x1B[39;49;00m], adata.shape[#x1B[94m0#x1B[39;49;00m])#x1B[90m#x1B[39;49;00m
        adata.X = array_type(np.abs(adata.X).astype(#x1B[96mint#x1B[39;49;00m))#x1B[90m#x1B[39;49;00m
    #x1B[90m#x1B[39;49;00m
        #x1B[94mif#x1B[39;49;00m flavor #x1B[95min#x1B[39;49;00m {#x1B[33m"#x1B[39;49;00m#x1B[33mseurat#x1B[39;49;00m#x1B[33m"#x1B[39;49;00m, #x1B[33m"#x1B[39;49;00m#x1B[33mcell_ranger#x1B[39;49;00m#x1B[33m"#x1B[39;49;00m}:#x1B[90m#x1B[39;49;00m
            sc.pp.normalize_total(adata, target_sum=#x1B[94m1e4#x1B[39;49;00m)#x1B[90m#x1B[39;49;00m
            sc.pp.log1p(adata)#x1B[90m#x1B[39;49;00m
    #x1B[90m#x1B[39;49;00m
        n_genes = adata.shape[#x1B[94m1#x1B[39;49;00m]#x1B[90m#x1B[39;49;00m
    #x1B[90m#x1B[39;49;00m
        adatas: #x1B[96mdict#x1B[39;49;00m[#x1B[96mbool#x1B[39;49;00m, AnnData] = {}#x1B[90m#x1B[39;49;00m
        dfs: #x1B[96mdict#x1B[39;49;00m[#x1B[96mbool#x1B[39;49;00m, pd.DataFrame] = {}#x1B[90m#x1B[39;49;00m
        #x1B[90m# for loops instead of parametrization to compare between settings#x1B[39;49;00m#x1B[90m#x1B[39;49;00m
        #x1B[94mfor#x1B[39;49;00m subset, inplace #x1B[95min#x1B[39;49;00m itertools.product([#x1B[94mTrue#x1B[39;49;00m, #x1B[94mFalse#x1B[39;49;00m], repeat=#x1B[94m2#x1B[39;49;00m):#x1B[90m#x1B[39;49;00m
            adata_copy = adata.copy()#x1B[90m#x1B[39;49;00m
    #x1B[90m#x1B[39;49;00m
>           output_df = sc.pp.highly_variable_genes(#x1B[90m#x1B[39;49;00m
                adata_copy,#x1B[90m#x1B[39;49;00m
                flavor=flavor,#x1B[90m#x1B[39;49;00m
                n_top_genes=#x1B[94m15#x1B[39;49;00m,#x1B[90m#x1B[39;49;00m
                batch_key=batch_key,#x1B[90m#x1B[39;49;00m
                subset=subset,#x1B[90m#x1B[39;49;00m
                inplace=inplace,#x1B[90m#x1B[39;49;00m
            )#x1B[90m#x1B[39;49;00m

#x1B[1m#x1B[31mtests/test_highly_variable_genes.py#x1B[0m:686: 
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 
#x1B[1m#x1B[.../scanpy/preprocessing/_highly_variable_genes.py#x1B[0m:780: in highly_variable_genes
    #x1B[0m#x1B[94mreturn#x1B[39;49;00m _highly_variable_genes_seurat_v3(#x1B[90m#x1B[39;49;00m
#x1B[1m#x1B[.../scanpy/preprocessing/_highly_variable_genes.py#x1B[0m:196: in _highly_variable_genes_seurat_v3
    #x1B[0maggregated_mean_var = ad.AnnData(#x1B[90m#x1B[39;49;00m
#x1B[1m#x1B[31m../../../..../scanpy/B9PcT7QG/hatch-test.stable/lib/python3.14.../anndata/_core/anndata.py#x1B[0m:254: in __init__
    #x1B[0m#x1B[96mself#x1B[39;49;00m._init_as_actual(#x1B[90m#x1B[39;49;00m
#x1B[1m#x1B[31m../../../..../scanpy/B9PcT7QG/hatch-test.stable/lib/python3.14.../anndata/_core/anndata.py#x1B[0m:448: in _init_as_actual
    #x1B[0m#x1B[96mself#x1B[39;49;00m._obs = _gen_dataframe(#x1B[90m#x1B[39;49;00m
#x1B[1m#x1B[31m../...../_temp/uv-python-dir/cpython-3.14.5-linux-x86_64-gnu/lib/python3.14/functools.py#x1B[0m:982: in wrapper
    #x1B[0m#x1B[94mreturn#x1B[39;49;00m dispatch(args[#x1B[94m0#x1B[39;49;00m].#x1B[91m__class__#x1B[39;49;00m)(*args, **kw)#x1B[90m#x1B[39;49;00m
           ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^#x1B[90m#x1B[39;49;00m
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 

anno =    __hvg_v3_batch_info__
0                      0
index_names = ['obs_names', 'row_names']

    #x1B[0m#x1B[37m@_gen_dataframe#x1B[39;49;00m.register(pd.DataFrame)#x1B[90m#x1B[39;49;00m
    #x1B[94mdef#x1B[39;49;00m#x1B[90m #x1B[39;49;00m#x1B[92m_gen_dataframe_df#x1B[39;49;00m(#x1B[90m#x1B[39;49;00m
        anno: pd.DataFrame,#x1B[90m#x1B[39;49;00m
        index_names: Iterable[#x1B[96mstr#x1B[39;49;00m],#x1B[90m#x1B[39;49;00m
        *,#x1B[90m#x1B[39;49;00m
        source: Literal[#x1B[33m"#x1B[39;49;00m#x1B[33mX#x1B[39;49;00m#x1B[33m"#x1B[39;49;00m, #x1B[33m"#x1B[39;49;00m#x1B[33mshape#x1B[39;49;00m#x1B[33m"#x1B[39;49;00m],#x1B[90m#x1B[39;49;00m
        attr: Literal[#x1B[33m"#x1B[39;49;00m#x1B[33mobs#x1B[39;49;00m#x1B[33m"#x1B[39;49;00m, #x1B[33m"#x1B[39;49;00m#x1B[33mvar#x1B[39;49;00m#x1B[33m"#x1B[39;49;00m],#x1B[90m#x1B[39;49;00m
        length: #x1B[96mint#x1B[39;49;00m | #x1B[94mNone#x1B[39;49;00m = #x1B[94mNone#x1B[39;49;00m,#x1B[90m#x1B[39;49;00m
    ):#x1B[90m#x1B[39;49;00m
        #x1B[94mif#x1B[39;49;00m #x1B[96misinstance#x1B[39;49;00m(anno.index, pd.MultiIndex):#x1B[90m#x1B[39;49;00m
            msg = (#x1B[90m#x1B[39;49;00m
                #x1B[33m"#x1B[39;49;00m#x1B[33mpandas.MultiIndex not supported as index for obs or var on declaration.#x1B[39;49;00m#x1B[33m\n#x1B[39;49;00m#x1B[33m\#x1B[39;49;00m
    #x1B[33m            You can set `obs_names` manually although most operations after will error or convert to str.#x1B[39;49;00m#x1B[33m\n#x1B[39;49;00m#x1B[33m\#x1B[39;49;00m
    #x1B[33m            This behavior will likely be clarified in a future breaking release.#x1B[39;49;00m#x1B[33m"#x1B[39;49;00m#x1B[90m#x1B[39;49;00m
            )#x1B[90m#x1B[39;49;00m
            #x1B[94mraise#x1B[39;49;00m #x1B[96mValueError#x1B[39;49;00m(msg)#x1B[90m#x1B[39;49;00m
        #x1B[94mif#x1B[39;49;00m length #x1B[95mis#x1B[39;49;00m #x1B[95mnot#x1B[39;49;00m #x1B[94mNone#x1B[39;49;00m #x1B[95mand#x1B[39;49;00m length != #x1B[96mlen#x1B[39;49;00m(anno):#x1B[90m#x1B[39;49;00m
            #x1B[94mraise#x1B[39;49;00m _mk_df_error(source, attr, length, #x1B[96mlen#x1B[39;49;00m(anno))#x1B[90m#x1B[39;49;00m
        anno = anno.copy(deep=#x1B[94mFalse#x1B[39;49;00m)#x1B[90m#x1B[39;49;00m
        #x1B[94mif#x1B[39;49;00m #x1B[95mnot#x1B[39;49;00m is_string_dtype(anno.index[~anno.index.isna()]):#x1B[90m#x1B[39;49;00m
            msg = #x1B[33m"#x1B[39;49;00m#x1B[33mTransforming to str index.#x1B[39;49;00m#x1B[33m"#x1B[39;49;00m#x1B[90m#x1B[39;49;00m
>           warnings.warn(msg, ImplicitModificationWarning, stacklevel=#x1B[94m2#x1B[39;49;00m)#x1B[90m#x1B[39;49;00m
#x1B[1m#x1B[31mE           anndata._warnings.ImplicitModificationWarning: Transforming to str index.#x1B[0m

#x1B[1m#x1B[31m../../../..../scanpy/B9PcT7QG/hatch-test.stable/lib/python3.14.../anndata/_core/aligned_df.py#x1B[0m:93: ImplicitModificationWarning

tests/test_highly_variable_genes.py::test_seurat_v3_warning

Stack Traces | 0.06s run time

#x1B[0m#x1B[37m@needs#x1B[39;49;00m.skmisc#x1B[90m#x1B[39;49;00m
    #x1B[94mdef#x1B[39;49;00m#x1B[90m #x1B[39;49;00m#x1B[92mtest_seurat_v3_warning#x1B[39;49;00m():#x1B[90m#x1B[39;49;00m
        pbmc = pbmc3k()[:#x1B[94m200#x1B[39;49;00m].copy()#x1B[90m#x1B[39;49;00m
        sc.pp.log1p(pbmc)#x1B[90m#x1B[39;49;00m
>       #x1B[94mwith#x1B[39;49;00m pytest.warns(#x1B[90m#x1B[39;49;00m
            #x1B[96mUserWarning#x1B[39;49;00m,#x1B[90m#x1B[39;49;00m
            match=#x1B[33m"#x1B[39;49;00m#x1B[33m`flavor=#x1B[39;49;00m#x1B[33m'#x1B[39;49;00m#x1B[33mseurat_v3#x1B[39;49;00m#x1B[33m'#x1B[39;49;00m#x1B[33m` expects raw count data, but non-integers were found.#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       anndata._warnings.ImplicitModificationWarning: Transforming to str index.#x1B[0m

#x1B[1m#x1B[31mtests/test_highly_variable_genes.py#x1B[0m:497: ImplicitModificationWarning

tests/test_highly_variable_genes.py::test_compare_to_seurat_v3

Stack Traces | 0.119s run time

#x1B[0m#x1B[37m@needs#x1B[39;49;00m.skmisc#x1B[90m#x1B[39;49;00m
    #x1B[94mdef#x1B[39;49;00m#x1B[90m #x1B[39;49;00m#x1B[92mtest_compare_to_seurat_v3#x1B[39;49;00m():#x1B[90m#x1B[39;49;00m
        #x1B[90m### test without batch#x1B[39;49;00m#x1B[90m#x1B[39;49;00m
        seurat_hvg_info = pd.read_csv(FILE_V3)#x1B[90m#x1B[39;49;00m
    #x1B[90m#x1B[39;49;00m
        pbmc = pbmc3k()#x1B[90m#x1B[39;49;00m
        sc.pp.filter_cells(pbmc, min_genes=#x1B[94m200#x1B[39;49;00m)  #x1B[90m# this doesnt do anything btw#x1B[39;49;00m#x1B[90m#x1B[39;49;00m
        sc.pp.filter_genes(pbmc, min_cells=#x1B[94m3#x1B[39;49;00m)#x1B[90m#x1B[39;49;00m
    #x1B[90m#x1B[39;49;00m
        pbmc_dense = pbmc.copy()#x1B[90m#x1B[39;49;00m
        pbmc_dense.X = pbmc_dense.X.toarray()#x1B[90m#x1B[39;49;00m
    #x1B[90m#x1B[39;49;00m
>       sc.pp.highly_variable_genes(pbmc, n_top_genes=#x1B[94m1000#x1B[39;49;00m, flavor=#x1B[33m"#x1B[39;49;00m#x1B[33mseurat_v3#x1B[39;49;00m#x1B[33m"#x1B[39;49;00m)#x1B[90m#x1B[39;49;00m

#x1B[1m#x1B[31mtests/test_highly_variable_genes.py#x1B[0m:436: 
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 
#x1B[1m#x1B[.../scanpy/preprocessing/_highly_variable_genes.py#x1B[0m:780: in highly_variable_genes
    #x1B[0m#x1B[94mreturn#x1B[39;49;00m _highly_variable_genes_seurat_v3(#x1B[90m#x1B[39;49;00m
#x1B[1m#x1B[.../scanpy/preprocessing/_highly_variable_genes.py#x1B[0m:196: in _highly_variable_genes_seurat_v3
    #x1B[0maggregated_mean_var = ad.AnnData(#x1B[90m#x1B[39;49;00m
#x1B[1m#x1B[31m../../../..../scanpy/B9PcT7QG/hatch-test.stable/lib/python3.14.../anndata/_core/anndata.py#x1B[0m:254: in __init__
    #x1B[0m#x1B[96mself#x1B[39;49;00m._init_as_actual(#x1B[90m#x1B[39;49;00m
#x1B[1m#x1B[31m../../../..../scanpy/B9PcT7QG/hatch-test.stable/lib/python3.14.../anndata/_core/anndata.py#x1B[0m:448: in _init_as_actual
    #x1B[0m#x1B[96mself#x1B[39;49;00m._obs = _gen_dataframe(#x1B[90m#x1B[39;49;00m
#x1B[1m#x1B[31m../...../_temp/uv-python-dir/cpython-3.14.5-linux-x86_64-gnu/lib/python3.14/functools.py#x1B[0m:982: in wrapper
    #x1B[0m#x1B[94mreturn#x1B[39;49;00m dispatch(args[#x1B[94m0#x1B[39;49;00m].#x1B[91m__class__#x1B[39;49;00m)(*args, **kw)#x1B[90m#x1B[39;49;00m
           ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^#x1B[90m#x1B[39;49;00m
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 

anno =    __hvg_v3_batch_info__
0                      0
index_names = ['obs_names', 'row_names']

    #x1B[0m#x1B[37m@_gen_dataframe#x1B[39;49;00m.register(pd.DataFrame)#x1B[90m#x1B[39;49;00m
    #x1B[94mdef#x1B[39;49;00m#x1B[90m #x1B[39;49;00m#x1B[92m_gen_dataframe_df#x1B[39;49;00m(#x1B[90m#x1B[39;49;00m
        anno: pd.DataFrame,#x1B[90m#x1B[39;49;00m
        index_names: Iterable[#x1B[96mstr#x1B[39;49;00m],#x1B[90m#x1B[39;49;00m
        *,#x1B[90m#x1B[39;49;00m
        source: Literal[#x1B[33m"#x1B[39;49;00m#x1B[33mX#x1B[39;49;00m#x1B[33m"#x1B[39;49;00m, #x1B[33m"#x1B[39;49;00m#x1B[33mshape#x1B[39;49;00m#x1B[33m"#x1B[39;49;00m],#x1B[90m#x1B[39;49;00m
        attr: Literal[#x1B[33m"#x1B[39;49;00m#x1B[33mobs#x1B[39;49;00m#x1B[33m"#x1B[39;49;00m, #x1B[33m"#x1B[39;49;00m#x1B[33mvar#x1B[39;49;00m#x1B[33m"#x1B[39;49;00m],#x1B[90m#x1B[39;49;00m
        length: #x1B[96mint#x1B[39;49;00m | #x1B[94mNone#x1B[39;49;00m = #x1B[94mNone#x1B[39;49;00m,#x1B[90m#x1B[39;49;00m
    ):#x1B[90m#x1B[39;49;00m
        #x1B[94mif#x1B[39;49;00m #x1B[96misinstance#x1B[39;49;00m(anno.index, pd.MultiIndex):#x1B[90m#x1B[39;49;00m
            msg = (#x1B[90m#x1B[39;49;00m
                #x1B[33m"#x1B[39;49;00m#x1B[33mpandas.MultiIndex not supported as index for obs or var on declaration.#x1B[39;49;00m#x1B[33m\n#x1B[39;49;00m#x1B[33m\#x1B[39;49;00m
    #x1B[33m            You can set `obs_names` manually although most operations after will error or convert to str.#x1B[39;49;00m#x1B[33m\n#x1B[39;49;00m#x1B[33m\#x1B[39;49;00m
    #x1B[33m            This behavior will likely be clarified in a future breaking release.#x1B[39;49;00m#x1B[33m"#x1B[39;49;00m#x1B[90m#x1B[39;49;00m
            )#x1B[90m#x1B[39;49;00m
            #x1B[94mraise#x1B[39;49;00m #x1B[96mValueError#x1B[39;49;00m(msg)#x1B[90m#x1B[39;49;00m
        #x1B[94mif#x1B[39;49;00m length #x1B[95mis#x1B[39;49;00m #x1B[95mnot#x1B[39;49;00m #x1B[94mNone#x1B[39;49;00m #x1B[95mand#x1B[39;49;00m length != #x1B[96mlen#x1B[39;49;00m(anno):#x1B[90m#x1B[39;49;00m
            #x1B[94mraise#x1B[39;49;00m _mk_df_error(source, attr, length, #x1B[96mlen#x1B[39;49;00m(anno))#x1B[90m#x1B[39;49;00m
        anno = anno.copy(deep=#x1B[94mFalse#x1B[39;49;00m)#x1B[90m#x1B[39;49;00m
        #x1B[94mif#x1B[39;49;00m #x1B[95mnot#x1B[39;49;00m is_string_dtype(anno.index[~anno.index.isna()]):#x1B[90m#x1B[39;49;00m
            msg = #x1B[33m"#x1B[39;49;00m#x1B[33mTransforming to str index.#x1B[39;49;00m#x1B[33m"#x1B[39;49;00m#x1B[90m#x1B[39;49;00m
>           warnings.warn(msg, ImplicitModificationWarning, stacklevel=#x1B[94m2#x1B[39;49;00m)#x1B[90m#x1B[39;49;00m
#x1B[1m#x1B[31mE           anndata._warnings.ImplicitModificationWarning: Transforming to str index.#x1B[0m

#x1B[1m#x1B[31m../../../..../scanpy/B9PcT7QG/hatch-test.stable/lib/python3.14.../anndata/_core/aligned_df.py#x1B[0m:93: ImplicitModificationWarning

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ilan-gold and others added 5 commits May 14, 2026 00:41

perf: chan mean-var

b1b7b38

Merge branch 'ig/two_pass_hvg_v3' into ig/chan_mean_var

fd097dc

fix: one pass

204ba32

Merge branch 'ig/two_pass_hvg_v3' into ig/chan_mean_var

016531b

[pre-commit.ci] auto fixes from pre-commit.com hooks

c3b75b3

for more information, see https://pre-commit.ci

ilan-gold changed the title ~~Ig/chan mean var~~ perf: chan's parallel mean-var algorithm May 15, 2026

ilan-gold mentioned this pull request May 15, 2026

fix float32 precision loss in aggregate variance: cast to float64 before squaring #4122

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ilan-gold changed the title ~~perf: chan's parallel mean-var algorithm~~ perf: chan's parallel mean-var algorithm for dask May 15, 2026

Merge branch 'ig/two_pass_hvg_v3' into ig/chan_mean_var

a0bfc37

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perf: chan's parallel mean-var algorithm for dask#4123

perf: chan's parallel mean-var algorithm for dask#4123
ilan-gold wants to merge 6 commits into
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ilan-gold commented May 15, 2026 •

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