fix(back): Fix formatting for new stable version of black (pixano#64)

* fix(back): Fix formatting for new stable version of black

* fix(ci): Specify black formatter version

.github/workflows/format.yml

@@ -103,17 +103,20 @@ jobs:
         with:
           options: "--check --verbose --diff --color"
           src: "./pixano"
+          version: "~= 24.0"
 
       - name: Format backend tests with black
         uses: psf/black@stable
         with:
           options: "--check --verbose --diff --color"
           src: "./tests"
           jupyter: true
+          version: "~= 24.0"
 
       - name: Format Jupyter notebooks with black
         uses: psf/black@stable
         with:
           options: "--check --verbose --diff --color"
           src: "./notebooks"
           jupyter: true
+          version: "~= 24.0"

pixano/data/dataset/dataset.py

@@ -67,9 +67,11 @@ def __init__(
             path=path,
             info=DatasetInfo.from_json(info_file),
             stats=DatasetStat.from_json(stats_file) if stats_file.is_file() else None,
-            thumbnail=Image(uri=thumb_file.absolute().as_uri()).url
-            if thumb_file.is_file()
-            else None,
+            thumbnail=(
+                Image(uri=thumb_file.absolute().as_uri()).url
+                if thumb_file.is_file()
+                else None
+            ),
         )
 
     @property
@@ -288,9 +290,11 @@ def update_table(
                 for feat in new_feats:
                     # None is not supported for booleans yet (should be fixed in pylance 0.9.1)
                     feat_array = pa.array(
-                        [False] * len(table)
-                        if feat.dtype == "bool"
-                        else [None] * len(table),
+                        (
+                            [False] * len(table)
+                            if feat.dtype == "bool"
+                            else [None] * len(table)
+                        ),
                         type=field_to_pyarrow(feat.dtype),
                     )
                     new_feats_table = new_feats_table.append_column(

pixano/data/exporters/coco_exporter.py

@@ -233,9 +233,9 @@ def _export_object(
         # Category
         category = {
             "id": None,
-            "name": obj.features["category"].value
-            if "category" in obj.features
-            else None,
+            "name": (
+                obj.features["category"].value if "category" in obj.features else None
+            ),
         }
         if category["name"] is not None:
             for cat in self.dataset.info.categories:

pixano/data/importers/coco_importer.py

@@ -152,16 +152,20 @@ def import_rows(self) -> Iterator:
                                 "original_id": str(ann["id"]),
                                 "item_id": item_id,
                                 "view_id": "image",
-                                "bbox": BBox.from_xywh(ann["bbox"])
-                                .normalize(im["height"], im["width"])
-                                .to_dict()
-                                if ann["bbox"]
-                                else None,
-                                "mask": CompressedRLE.encode(
-                                    ann["segmentation"], im["height"], im["width"]
-                                ).to_dict()
-                                if ann["segmentation"]
-                                else None,
+                                "bbox": (
+                                    BBox.from_xywh(ann["bbox"])
+                                    .normalize(im["height"], im["width"])
+                                    .to_dict()
+                                    if ann["bbox"]
+                                    else None
+                                ),
+                                "mask": (
+                                    CompressedRLE.encode(
+                                        ann["segmentation"], im["height"], im["width"]
+                                    ).to_dict()
+                                    if ann["segmentation"]
+                                    else None
+                                ),
                                 "category": str(categories[ann["category_id"]]),
                             }
                             for ann in im_anns

pixano/data/item/item_object.py

@@ -110,9 +110,7 @@ def to_pyarrow(self, mask: ItemURLE = None) -> BBox:
         return (
             BBox.from_dict(self.model_dump())
             if self.coords != [0.0, 0.0, 0.0, 0.0]
-            else BBox.from_rle(mask.to_pyarrow())
-            if mask
-            else None
+            else BBox.from_rle(mask.to_pyarrow()) if mask else None
         )
 
 

pixano/models/inference_model.py

@@ -243,11 +243,11 @@ def process_dataset(
                     save_batch.extend(
                         self.preannotate(process_batch, views, uri_prefix, threshold)
                         if process_type == "obj"
-                        else self.precompute_embeddings(
-                            process_batch, views, uri_prefix
+                        else (
+                            self.precompute_embeddings(process_batch, views, uri_prefix)
+                            if process_type in ["segment_emb", "search_emb"]
+                            else []
                         )
-                        if process_type in ["segment_emb", "search_emb"]
-                        else []
                     )
                     progress.update(batch_size)