Apply linting to src/

src/vak/config/dataset.py

@@ -38,18 +38,17 @@ class DatasetConfig:
     name: str | None = field(
         converter=attr.converters.optional(str), default=None
     )
-    params : dict | None = field(
+    params: dict | None = field(
         # we default to an empty dict instead of None
         # so we can still do **['dataset']['params'] everywhere we do when params are specified
-        converter=attr.converters.optional(dict), default={}
+        converter=attr.converters.optional(dict),
+        default={},
     )
 
     @classmethod
     def from_config_dict(cls, config_dict: dict) -> DatasetConfig:
 
-        return cls(
-            **config_dict
-        )
+        return cls(**config_dict)
 
     def asdict(self):
         """Convert this :class:`DatasetConfig` instance

src/vak/datasets/frame_classification/window_dataset.py

@@ -281,7 +281,7 @@ def shape(self):
         tmp_item = self.__getitem__(tmp_x_ind)
         # used by vak functions that need to determine size of window,
         # e.g. when initializing a neural network model
-        return tmp_item['frames'].shape
+        return tmp_item["frames"].shape
 
     def _load_frames(self, frames_path):
         """Helper function that loads "frames",

src/vak/eval/eval_.py

@@ -93,13 +93,13 @@ def eval(
                     f"value for ``{path_name}`` not recognized as a file: {path}"
                 )
 
-    dataset_path = pathlib.Path(dataset_config['path'])
+    dataset_path = pathlib.Path(dataset_config["path"])
     if not dataset_path.exists() or not dataset_path.is_dir():
         raise NotADirectoryError(
             f"`dataset_path` not found or not recognized as a directory: {dataset_path}"
         )
 
-    model_name = model_config['name']
+    model_name = model_config["name"]
     try:
         model_family = models.registry.MODEL_FAMILY_FROM_NAME[model_name]
     except KeyError as e:

src/vak/eval/frame_classification.py

@@ -95,7 +95,7 @@ def eval_frame_classification_model(
                     f"value for ``{path_name}`` not recognized as a file: {path}"
                 )
 
-    dataset_path = pathlib.Path(dataset_config['path'])
+    dataset_path = pathlib.Path(dataset_config["path"])
     if not dataset_path.exists() or not dataset_path.is_dir():
         raise NotADirectoryError(
             f"`dataset_path` not found or not recognized as a directory: {dataset_path}"
@@ -143,7 +143,7 @@ def eval_frame_classification_model(
         )
     transform_params = {
         "spect_standardizer": spect_standardizer,
-        "window_size": window_size
+        "window_size": window_size,
     }
 
     item_transform = transforms.defaults.get_default_transform(

src/vak/learncurve/frame_classification.py

@@ -158,7 +158,9 @@ def learning_curve_for_frame_classification_model(
 
     # ---- main loop that creates "learning curve" ---------------------------------------------------------------------
     logger.info("Starting training for learning curve.")
-    model_name = model_config["name"]  # used below when getting checkpoint path, etc
+    model_name = model_config[
+        "name"
+    ]  # used below when getting checkpoint path, etc
     for train_dur, replicate_num in to_do:
         logger.info(
             f"Training model with training set of size: {train_dur}s, replicate number {replicate_num}.",

src/vak/predict/parametric_umap.py

@@ -105,7 +105,8 @@ def predict_with_parametric_umap_model(
     # TODO: fix this when we build transforms into datasets
     transform_params = {
         "padding": dataset_config["params"].get(
-            "padding", models.convencoder_umap.get_default_padding(metadata.shape)
+            "padding",
+            models.convencoder_umap.get_default_padding(metadata.shape),
         )
     }
     item_transform = transforms.defaults.get_default_transform(

src/vak/train/frame_classification.py

@@ -127,7 +127,7 @@ def train_frame_classification_model(
                     f"value for ``{path_name}`` not recognized as a file: {path}"
                 )
 
-    dataset_path = pathlib.Path(dataset_config['path'])
+    dataset_path = pathlib.Path(dataset_config["path"])
     if not dataset_path.exists() or not dataset_path.is_dir():
         raise NotADirectoryError(
             f"`dataset_path` not found or not recognized as a directory: {dataset_path}"
@@ -209,7 +209,7 @@ def train_frame_classification_model(
         )
         spect_standardizer = None
 
-    model_name = model_config['name']
+    model_name = model_config["name"]
     # TODO: move this into datapipe once each datapipe uses a fixed set of transforms
     # that will require adding `spect_standardizer`` as a parameter to the datapipe,
     # maybe rename to `frames_standardizer`?
@@ -233,7 +233,7 @@ def train_frame_classification_model(
         split="train",
         subset=subset,
         item_transform=train_transform,
-        **dataset_config['params'],
+        **dataset_config["params"],
     )
     logger.info(
         f"Duration of WindowDataset used for training, in seconds: {train_dataset.duration}",

src/vak/train/train_.py

@@ -130,13 +130,13 @@ def train(
                     f"value for ``{path_name}`` not recognized as a file: {path}"
                 )
 
-    dataset_path = pathlib.Path(dataset_config['path'])
+    dataset_path = pathlib.Path(dataset_config["path"])
     if not dataset_path.exists() or not dataset_path.is_dir():
         raise NotADirectoryError(
             f"`dataset_path` not found or not recognized as a directory: {dataset_path}"
         )
 
-    model_name = model_config['name']
+    model_name = model_config["name"]
     try:
         model_family = models.registry.MODEL_FAMILY_FROM_NAME[model_name]
     except KeyError as e: