Merge branch 'result_storage' of https://github.com/felixpetschko/scirpy

 into result_storage

src/scirpy/pl/_clonotypes.py

@@ -414,7 +414,7 @@ def _plot_clonotype_network_panel(
     color_by_n_cells,
 ):
     cell_indices = read_cell_indices(cell_indices)
-    
+
     colorbar_title = "mean per dot"
     pie_colors = None
     cat_colors = None

src/scirpy/tests/test_ir_query.py

@@ -5,8 +5,6 @@
 import pytest
 from mudata import MuData
 
-from scirpy.util import read_cell_indices
-
 from scirpy.pp import ir_dist
 from scirpy.tl._ir_query import (
     _reduce_json,
@@ -16,6 +14,7 @@
     ir_query_annotate,
     ir_query_annotate_df,
 )
+from scirpy.util import read_cell_indices
 
 
 @pytest.mark.parametrize("metric", ["identity", "levenshtein"])
@@ -34,10 +33,10 @@ def test_ir_query(adata_cdr3, adata_cdr3_2, metric, key1, key2):
 
     tmp_key2 = f"ir_query_TESTDB_aa_{metric}" if key2 is None else key2
     tmp_ad = adata_cdr3.mod["airr"] if isinstance(adata_cdr3, MuData) else adata_cdr3
-    
+
     cell_indices = read_cell_indices(tmp_ad.uns[tmp_key2]["cell_indices"])
     cell_indices_reference = read_cell_indices(tmp_ad.uns[tmp_key2]["cell_indices_reference"])
-    
+
     assert tmp_ad.uns[tmp_key2]["distances"].shape == (4, 3)
     assert len(cell_indices) == 4
     assert len(cell_indices_reference) == 3

src/scirpy/tl/_clonotypes.py

@@ -1,4 +1,5 @@
 import itertools
+import json
 import random
 from collections.abc import Sequence
 from typing import Literal, cast
@@ -9,7 +10,6 @@
 import scipy.sparse as sp
 from anndata import AnnData
 from scanpy import logging
-import json
 
 from scirpy.ir_dist import MetricType, _get_metric_key
 from scirpy.ir_dist._clonotype_neighbors import ClonotypeNeighbors
@@ -604,9 +604,7 @@ def _graph_from_coordinates(adata: AnnData, clonotype_key: str, basis: str) -> t
     # map the cell-id to the corresponding row/col in the clonotype distance matrix
     cell_indices = read_cell_indices(clonotype_res["cell_indices"])
     dist_idx, obs_names = zip(
-        *itertools.chain.from_iterable(
-            zip(itertools.repeat(i), obs_names) for i, obs_names in cell_indices.items()
-        ),
+        *itertools.chain.from_iterable(zip(itertools.repeat(i), obs_names) for i, obs_names in cell_indices.items()),
         strict=False,
     )
     dist_idx_lookup = pd.DataFrame(index=obs_names, data=dist_idx, columns=["dist_idx"])

src/scirpy/tl/_ir_query.py

@@ -7,11 +7,10 @@
 import numpy as np
 import pandas as pd
 from scanpy import logging
-import json
 
 from scirpy.ir_dist import MetricType, _get_metric_key
 from scirpy.ir_dist._clonotype_neighbors import ClonotypeNeighbors
-from scirpy.util import DataHandler, _is_na, tqdm, read_cell_indices
+from scirpy.util import DataHandler, _is_na, read_cell_indices, tqdm
 
 from ._clonotypes import _common_doc, _common_doc_parallelism, _doc_clonotype_definition, _validate_parameters
 

src/scirpy/util/__init__.py

@@ -1,9 +1,10 @@
 import contextlib
+import json
 import os
 import warnings
 from collections.abc import Callable, Mapping, Sequence
 from textwrap import dedent
-from typing import Any, Optional, Union, cast, overload
+from typing import Any, Literal, Optional, Union, cast, overload
 
 import awkward as ak
 import numpy as np
@@ -16,9 +17,6 @@
 from scipy.sparse import issparse
 from tqdm.auto import tqdm
 
-from typing import Literal
-import json
-
 # reexport tqdm (here was previously a workaround for https://github.com/tqdm/tqdm/issues/1082)
 __all__ = ["tqdm"]
 
@@ -609,17 +607,20 @@ def _get_usable_cpus(n_jobs: int = 0, use_numba: bool = False):
 
     return usable_cpus
 
-def read_cell_indices(cell_indices: Union[dict[str, np.ndarray[str]], str]) -> dict[str,list[str]]:
+
+def read_cell_indices(cell_indices: dict[str, np.ndarray[str]] | str) -> dict[str, list[str]]:
     """
     The datatype of the cell_indices Mapping (clonotype_id -> cell_ids) that is stored to the anndata.uns
     attribute after the ´define_clonotype_clusters´ function has changed from dict[str, np.ndarray[str] to
     str (json) due to performance considerations regarding the writing speed of the anndata object. But we still
     want that older anndata objects with the dict[str, np.ndarray[str] datatype can be used. So we use this function
     to read the cell_indices from the anndata object to support both formats.
     """
-    if(isinstance(cell_indices, str)): # new format
+    if isinstance(cell_indices, str):  # new format
         return json.loads(cell_indices)
-    elif(isinstance(cell_indices, dict)): # old format
+    elif isinstance(cell_indices, dict):  # old format
         return {k: v.tolist() for k, v in cell_indices.items()}
-    else: # unsupported format
-        raise TypeError(f"Unsupported type for cell_indices: {type(cell_indices)}. Expected str (json) or dict[str, np.ndarray[str]].")
+    else:  # unsupported format
+        raise TypeError(
+            f"Unsupported type for cell_indices: {type(cell_indices)}. Expected str (json) or dict[str, np.ndarray[str]]."
+        )