Don't generate duplicate cluster_id columns if generate_and_save_cluster_masks run twice

src/ark/phenotyping/pixie_preprocessing.py

@@ -427,13 +427,8 @@ def create_pixel_matrix(fovs, channels, base_dir, tiff_dir, seg_dir,
             quant_dat_fov.index.name = "channel"
 
             # update the file with the newly processed fov quantile values
-            quant_dat_all = quant_dat_all.merge(
-                quant_dat_fov,
-                how="outer",
-                left_index=True,
-                right_index=True
-            )
-
+            quant_dat_all = quant_dat_all.merge(quant_dat_fov, how="outer",
+                                                left_index=True, right_index=True)
             quant_dat_all.to_csv(quantile_path)
 
             # update number of fovs processed

src/ark/utils/data_utils.py

@@ -446,6 +446,9 @@ def generate_and_save_cell_cluster_masks(
     cluster_map = cmd.mapping.filter([cmd.cluster_column, cmd.cluster_id_column])
     cluster_map = cluster_map.drop_duplicates()
 
+    # drop the cluster_id column from updated_cluster_map if it already exists, otherwise do nothing
+    gui_map = gui_map.drop(columns="cluster_id", errors="ignore")
+
     # add a cluster_id column corresponding to the new mask integers
     updated_cluster_map = gui_map.merge(cluster_map, on=[cmd.cluster_column], how="left")
     updated_cluster_map.to_csv(cluster_id_to_name_path, index=False)

src/ark/utils/example_dataset.py

@@ -180,6 +180,7 @@ def get_example_dataset(dataset: str, save_dir: Union[str, pathlib.Path],
                 * `"LDA_training_inference"`
                 * `"neighborhood_analysis"`
                 * `"pairwise_spatial_enrichment"`
+                * `"ez_seg_data"`
         save_dir (Union[str, pathlib.Path]): The path to save the dataset files in.
         overwrite_existing (bool): The option to overwrite existing configs of the `dataset`
             downloaded. Defaults to True.

tests/utils/data_utils_test.py

@@ -359,33 +359,35 @@ def test_generate_and_save_cell_cluster_masks(tmp_path: pathlib.Path, sub_dir, n
     cluster_mapping.to_csv(os.path.join(tmp_path, 'cluster_mapping.csv'), index=False)
 
     # test various batch_sizes, no sub_dir, name_suffix = ''.
-    data_utils.generate_and_save_cell_cluster_masks(
-        fovs=fovs,
-        save_dir=os.path.join(tmp_path, 'cell_masks'),
-        seg_dir=tmp_path,
-        cell_data=consensus_data_som,
-        cluster_id_to_name_path=mapping_file_path,
-        fov_col=settings.FOV_ID,
-        label_col=settings.CELL_LABEL,
-        cell_cluster_col='cell_som_cluster',
-        seg_suffix='_whole_cell.tiff',
-        sub_dir=sub_dir,
-        name_suffix=name_suffix
-    )
+    # NOTE: test is run twice to ensure that results are same even if existing cluster_id found
+    for i in np.arange(2):
+        data_utils.generate_and_save_cell_cluster_masks(
+            fovs=fovs,
+            save_dir=os.path.join(tmp_path, 'cell_masks'),
+            seg_dir=tmp_path,
+            cell_data=consensus_data_som,
+            cluster_id_to_name_path=mapping_file_path,
+            fov_col=settings.FOV_ID,
+            label_col=settings.CELL_LABEL,
+            cell_cluster_col='cell_som_cluster',
+            seg_suffix='_whole_cell.tiff',
+            sub_dir=sub_dir,
+            name_suffix=name_suffix
+        )
 
-    # open each cell mask and make sure the shape and values are valid
-    if sub_dir is None:
-        sub_dir = ''
+        # open each cell mask and make sure the shape and values are valid
+        if sub_dir is None:
+            sub_dir = ''
 
-    for i, fov in enumerate(fovs):
-        fov_name = fov + name_suffix + ".tiff"
-        cell_mask = io.imread(os.path.join(tmp_path, 'cell_masks', sub_dir, fov_name))
-        actual_img_dims = (40, 40) if i < fov_size_split else (20, 20)
-        assert cell_mask.shape == actual_img_dims
-        assert np.all(cell_mask <= 5)
+        for i, fov in enumerate(fovs):
+            fov_name = fov + name_suffix + ".tiff"
+            cell_mask = io.imread(os.path.join(tmp_path, 'cell_masks', sub_dir, fov_name))
+            actual_img_dims = (40, 40) if i < fov_size_split else (20, 20)
+            assert cell_mask.shape == actual_img_dims
+            assert np.all(cell_mask <= 5)
 
-    new_cluster_mapping = pd.read_csv(mapping_file_path)
-    assert "cluster_id" in new_cluster_mapping.columns
+        new_cluster_mapping = pd.read_csv(mapping_file_path)
+        assert "cluster_id" in new_cluster_mapping.columns
 
 
 def test_generate_pixel_cluster_mask():
@@ -851,35 +853,35 @@ def test_convert_to_adata(self):
             assert pathlib.Path(fov_adata_path).exists()
 
 
-@pytest.fixture(scope="module")
+@pytest.fixture(scope="function")
 def testing_anndatas(
         tmp_path_factory: pytest.TempPathFactory
-) -> Callable[[int, pathlib.Path], Tuple[List[str], AnnCollection]]:
-    def create_adatas(n_fovs, save_dir: pathlib.Path):
-        fov_names, ann_collection = ark_test_utils.generate_anncollection(
-            fovs=n_fovs,
-            n_vars=10,
-            n_obs=100,
-            obs_properties=4,
-            obs_categorical_properties=2,
-            random_n_obs=True,
-            join_obs="inner",
-            join_obsm="inner"
-        )
+) -> Iterator[Tuple[List[str], AnnCollection, pathlib.Path]]:
+
+    fov_names, ann_collection = ark_test_utils.generate_anncollection(
+        fovs=5,
+        n_vars=10,
+        n_obs=100,
+        obs_properties=4,
+        obs_categorical_properties=2,
+        random_n_obs=True,
+        join_obs="inner",
+        join_obsm="inner",
+    )
 
-        for fov_name, fov_adata in zip(fov_names, ann_collection.adatas):
-            fov_adata.write_zarr(os.path.join(save_dir, f"{fov_name}.zarr"))
-        return fov_names, ann_collection
+    save_dir = tmp_path_factory.mktemp("anndatas")
 
-    yield create_adatas
+    for fov_name, fov_adata in zip(fov_names, ann_collection.adatas):
+        fov_adata.write_zarr(os.path.join(save_dir, f"{fov_name}.zarr"))
+
+    yield fov_names, ann_collection, save_dir
 
 
-def test_load_anndatas(testing_anndatas, tmp_path_factory):
-    ann_collection_path = tmp_path_factory.mktemp("anndatas")
+def test_load_anndatas(testing_anndatas):
 
-    fov_names, ann_collection = testing_anndatas(n_fovs=5, save_dir=ann_collection_path)
+    fov_names, ann_collection, anndata_dir = testing_anndatas
 
-    ac = data_utils.load_anndatas(ann_collection_path, join_obs="inner", join_obsm="inner")
+    ac = data_utils.load_anndatas(anndata_dir, join_obs="inner", join_obsm="inner")
 
     assert isinstance(ac, AnnCollection)
     assert len(ac.adatas) == len(fov_names)
@@ -888,21 +890,24 @@ def test_load_anndatas(testing_anndatas, tmp_path_factory):
     # Assert that each AnnData component of an AnnCollection is the same as the one on disk.
     for fov_name, fov_adata in zip(fov_names, ann_collection.adatas):
         anndata.tests.helpers.assert_adata_equal(
-            a=read_zarr(ann_collection_path / f"{fov_name}.zarr"),
+            a=read_zarr(anndata_dir / f"{fov_name}.zarr"),
             b=fov_adata
         )
 
 
-def test_AnnDataIterDataPipe(testing_anndatas, tmp_path_factory):
-    ann_collection_path = tmp_path_factory.mktemp("anndatas")
+def test_AnnDataIterDataPipe(testing_anndatas):
 
-    _ = testing_anndatas(n_fovs=5, save_dir=ann_collection_path)
-    ac = data_utils.load_anndatas(ann_collection_path, join_obs="inner", join_obsm="inner")
+    fov_names, _, anndata_dir = testing_anndatas
+    ac = data_utils.load_anndatas(anndata_dir, join_obs="inner", join_obsm="inner")
 
     a_idp = data_utils.AnnDataIterDataPipe(fovs=ac)
 
     from torchdata.datapipes.iter import IterDataPipe
     assert isinstance(a_idp, IterDataPipe)
 
-    for fov in a_idp:
-        assert isinstance(fov, AnnData)
+    for fov_name, fov_adata in zip(fov_names, a_idp):
+        assert isinstance(fov_adata, AnnData)
+        anndata.tests.helpers.assert_adata_equal(
+            a=read_zarr(anndata_dir / f"{fov_name}.zarr"),
+            b=fov_adata
+        )

tests/utils/example_dataset_test.py

@@ -183,11 +183,14 @@ def test_download_example_dataset(self, dataset_download: ExampleDataset):
         Args:
             dataset_download (ExampleDataset): Fixture for the dataset, respective to each
         """
+        import os
         dataset_names = list(
-            dataset_download.dataset_paths[dataset_download.dataset].keys())
+            dataset_download.dataset_paths[dataset_download.dataset].keys()
+        )
         for ds_n in dataset_names:
             dataset_cache_path = pathlib.Path(
-                dataset_download.dataset_paths[dataset_download.dataset][ds_n])
+                dataset_download.dataset_paths[dataset_download.dataset][ds_n]
+            )
             self.dataset_test_fns[ds_n](dir_p=dataset_cache_path)
 
     @pytest.mark.parametrize("_overwrite_existing", [True, False])