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README.md

@@ -7,7 +7,7 @@
 
 # PopV
 
-PopV uses popular vote of a variety of cell-type transfer tools to classify cell-types in a query dataset based on a test dataset. 
+PopV uses popular vote of a variety of cell-type transfer tools to classify cell-types in a query dataset based on a test dataset.
 Using this variety of algorithms, we compute the agreement between those algorithms and use this agreement to predict which cell-types are with a high likelihood the same cell-types observed in the reference.
 
 ## Algorithms

docs/notebooks/tabula_sapiens_tutorial.ipynb

@@ -1254,4 +1254,4 @@
   },
   "nbformat": 4,
   "nbformat_minor": 5
-}
+}

tabula_sapiens_tutorial.ipynb

@@ -1582,4 +1582,4 @@
   },
   "nbformat": 4,
   "nbformat_minor": 5
-}
+}

tests/core/test_models.py

@@ -10,7 +10,7 @@
 from popv.reproducibility import _accuracy
 
 
-def _get_test_anndata(cl_obo_folder="resources/ontology/", mode='retrain'):
+def _get_test_anndata(cl_obo_folder="resources/ontology/", mode="retrain"):
     print("UUU", os.getcwd())
     save_folder = "tests/tmp_testing/popv_test_results/"
     fn = save_folder + "annotated_query.h5ad"
@@ -180,41 +180,39 @@ def test_celltypist():
 def test_annotation():
     """Test Annotation and Plotting pipeline."""
     adata = _get_test_anndata().adata
-    popv.annotation.annotate_data(
-        adata, methods=["svm", "rf"],
-        save_path="tests/tmp_testing/popv_test_results/")
+    popv.annotation.annotate_data(adata, methods=["svm", "rf"], save_path="tests/tmp_testing/popv_test_results/")
     popv.visualization.agreement_score_bar_plot(adata)
     popv.visualization.prediction_score_bar_plot(adata)
     popv.visualization.make_agreement_plots(adata, prediction_keys=adata.uns["prediction_keys"], show=False)
     popv.visualization.celltype_ratio_bar_plot(adata)
     obo_fn = "resources/ontology/cl.obo"
-    _accuracy._ontology_accuracy(adata[adata.obs['_dataset']=='ref'], obofile=obo_fn, gt_key='cell_ontology_class', pred_key='popv_prediction')
-    _accuracy._fine_ontology_sibling_accuracy(adata[adata.obs['_dataset']=='ref'], obofile=obo_fn, gt_key='cell_ontology_class', pred_key='popv_prediction')
+    _accuracy._ontology_accuracy(
+        adata[adata.obs["_dataset"] == "ref"], obofile=obo_fn, gt_key="cell_ontology_class", pred_key="popv_prediction"
+    )
+    _accuracy._fine_ontology_sibling_accuracy(
+        adata[adata.obs["_dataset"] == "ref"], obofile=obo_fn, gt_key="cell_ontology_class", pred_key="popv_prediction"
+    )
 
     assert "popv_majority_vote_prediction" in adata.obs.columns
     assert not adata.obs["popv_majority_vote_prediction"].isnull().any()
 
-    adata = _get_test_anndata(mode='inference').adata
-    popv.annotation.annotate_data(
-        adata, save_path="tests/tmp_testing/popv_test_results/")
+    adata = _get_test_anndata(mode="inference").adata
+    popv.annotation.annotate_data(adata, save_path="tests/tmp_testing/popv_test_results/")
 
-    adata = _get_test_anndata(mode='fast').adata
-    popv.annotation.annotate_data(
-        adata, save_path="tests/tmp_testing/popv_test_results/")
+    adata = _get_test_anndata(mode="fast").adata
+    popv.annotation.annotate_data(adata, save_path="tests/tmp_testing/popv_test_results/")
 
 
 def test_annotation_no_ontology():
     """Test Annotation and Plotting pipeline without ontology."""
     adata = _get_test_anndata(cl_obo_folder=False).adata
-    popv.annotation.annotate_data(
-        adata, methods=["svm", "rf"],
-        save_path="tests/tmp_testing/popv_test_results/")
+    popv.annotation.annotate_data(adata, methods=["svm", "rf"], save_path="tests/tmp_testing/popv_test_results/")
     popv.visualization.agreement_score_bar_plot(adata)
     popv.visualization.prediction_score_bar_plot(adata)
     popv.visualization.make_agreement_plots(adata, prediction_keys=adata.uns["prediction_keys"])
     popv.visualization.celltype_ratio_bar_plot(adata, save_folder="tests/tmp_testing/popv_test_results/")
     popv.visualization.celltype_ratio_bar_plot(adata, normalize=False)
-    adata.obs['empty_columns'] = 'a'
+    adata.obs["empty_columns"] = "a"
     input_data = adata.obs[["empty_columns", "popv_rf_prediction"]].values.tolist()
     popv.reproducibility._alluvial.plot(input_data)