ruff formatter fixes (#12)

ethome/config.py

@@ -2,7 +2,7 @@
 
 # TODO
 # Add support for the user to change these.
- 
+
 global_config = {
     "make_movie__y_offset": 60,
     "make_movie__y_inc": 30,

ethome/features/cnn1d.py

@@ -8,12 +8,12 @@
 
 def build_baseline_model(
     input_dim: tuple,
-    layer_channels: tuple =(512, 256),
-    dropout_rate: float =0.0,
-    learning_rate: float =1e-3,
-    conv_size: int =5,
-    num_classes: int=4,
-    class_weight:tuple = None,
+    layer_channels: tuple = (512, 256),
+    dropout_rate: float = 0.0,
+    learning_rate: float = 1e-3,
+    conv_size: int = 5,
+    num_classes: int = 4,
+    class_weight: tuple = None,
 ):
     if not check_keras():
         raise RuntimeError(
@@ -49,7 +49,7 @@ def add_conv_bn_activate(model, out_dim, activation="relu", conv_size=3, drop=0.
     return model
 
 
-def make_df(pts, colnames: List[str] =None):  # pragma: no cover
+def make_df(pts, colnames: List[str] = None):  # pragma: no cover
     df = []
     for idx in range(len(pts)):
         data = pts[idx].flatten()
@@ -61,7 +61,6 @@ def make_df(pts, colnames: List[str] =None):  # pragma: no cover
 
 
 def features_identity(inputs: np.ndarray):  # pragma: no cover
-
     return inputs, inputs.shape[1:]
 
 
@@ -149,14 +148,14 @@ def __init__(
         dim: tuple,
         use_conv: bool,
         num_classes: int,
-        augment: bool =False,
-        class_to_number: dict =None,
-        past_frames:int=0,
-        future_frames:int=0,
-        frame_gap:int=1,
-        shuffle:bool=False,
-        mode:str="fit",
-        featurize:Callable=features_identity,
+        augment: bool = False,
+        class_to_number: dict = None,
+        past_frames: int = 0,
+        future_frames: int = 0,
+        frame_gap: int = 1,
+        shuffle: bool = False,
+        mode: str = "fit",
+        featurize: Callable = features_identity,
     ):
         self.batch_size = batch_size
         self.featurize = featurize

ethome/features/dl_features.py

@@ -56,7 +56,7 @@
 }
 
 
-def seed_everything(seed:int=2012):
+def seed_everything(seed: int = 2012):
     np.random.seed(seed)
     os.environ["PYTHONHASHSEED"] = str(seed)
 
@@ -72,17 +72,17 @@ def __init__(
         *,
         feature_dim: list,
         num_classes: int,
-        test_data:np.ndarray=None,
-        class_to_number:dict=None,
-        past_frames:int=0,
-        future_frames:int=0,
-        frame_gap:int=1,
-        use_conv:bool=False,
-        build_model:Callable=build_baseline_model,
+        test_data: np.ndarray = None,
+        class_to_number: dict = None,
+        past_frames: int = 0,
+        future_frames: int = 0,
+        frame_gap: int = 1,
+        use_conv: bool = False,
+        build_model: Callable = build_baseline_model,
         Generator=MABe_Generator,
-        use_callbacks:bool=False,
-        learning_decay_freq:int=10,
-        featurizer:Callable=features_identity,
+        use_callbacks: bool = False,
+        learning_decay_freq: int = 10,
+        featurizer: Callable = features_identity,
     ):
         flat_dim = np.prod(feature_dim)
         if use_conv:
@@ -130,7 +130,9 @@ def _set_model(self, model):
         """Set an external, provide initialized and compiled keras model"""
         self.model = model
 
-    def inference(self, model_params: dict, class_weight:dict=None, n_folds:int=5):
+    def inference(
+        self, model_params: dict, class_weight: dict = None, n_folds: int = 5
+    ):
         kwargs = {}
         if class_weight is not None:
             if type(class_weight) is dict:
@@ -188,7 +190,7 @@ def get_test_prediction_probabilities(self):
         return all_test_preds
 
 
-def normalize_data(orig_pose_dictionary:dict):
+def normalize_data(orig_pose_dictionary: dict):
     for key in orig_pose_dictionary:
         X = orig_pose_dictionary[key]["keypoints"]
         X = X.transpose((0, 1, 3, 2))  # last axis is x, y coordinates
@@ -200,16 +202,16 @@ def normalize_data(orig_pose_dictionary:dict):
 
 
 def run_task(
-    vocabulary:dict,
-    test_data:np.ndarray,
-    config_name:str,
-    build_model:Callable,
-    skip_test_prediction:bool=False,
-    seed:int=2021,
+    vocabulary: dict,
+    test_data: np.ndarray,
+    config_name: str,
+    build_model: Callable,
+    skip_test_prediction: bool = False,
+    seed: int = 2021,
     Generator=MABe_Generator,
-    use_callbacks:bool=False,
-    params:dict=None,
-    use_conv:bool=True,
+    use_callbacks: bool = False,
+    params: dict = None,
+    use_conv: bool = True,
 ):
     if params is None:
         if config_name is None:
@@ -279,13 +281,13 @@ def run_task(
     return all_test_probs
 
 
-def lrs(epoch:int, lr:float, freq:int=10):
+def lrs(epoch: int, lr: float, freq: int = 10):
     if (epoch % freq) == 0 and epoch > 0:
         lr /= 3
     return lr
 
 
-def convert_to_mars_format(df:pd.DataFrame, colnames:List[str], animal_setup:dict):
+def convert_to_mars_format(df: pd.DataFrame, colnames: List[str], animal_setup: dict):
     n_animals = len(animal_setup["mouse_ids"])
     n_body_parts = len(animal_setup["bodypart_ids"])
     pose_dict = {}
@@ -301,7 +303,7 @@ def convert_to_mars_format(df:pd.DataFrame, colnames:List[str], animal_setup:dic
 
 
 # Basically, undo the change above
-def convert_to_pandas_df(data, colnames:List[str]=None):
+def convert_to_pandas_df(data, colnames: List[str] = None):
     dfs = []
     for vid in data:
         df = pd.DataFrame(data[vid], columns=colnames)

ethome/features/features.py

@@ -59,7 +59,9 @@ def transform(self, df: pd.DataFrame):
         raise NotImplementedError
 
 
-def feature_class_maker(name:str, compute_function:Callable, required_columns:List[str]=[]):
+def feature_class_maker(
+    name: str, compute_function: Callable, required_columns: List[str] = []
+):
     def __init__(self, required_columns=None, **kwargs):
         """Feature creation object. This houses the feature creation function and the columns that are required to compute the features. Performs some checks on data to make sure has these columns.
 
@@ -72,10 +74,10 @@ def __init__(self, required_columns=None, **kwargs):
             self.required_columns = required_columns
         self.kwargs = kwargs
 
-    def fit(self, edf:pd.DataFrame, **kwargs):  # pragma: no cover
+    def fit(self, edf: pd.DataFrame, **kwargs):  # pragma: no cover
         return
 
-    def transform(self, edf:pd.DataFrame, **kwargs):
+    def transform(self, edf: pd.DataFrame, **kwargs):
         """Make the features. This is called internally by the dataset object when running `add_features`.
 
         Args:

ethome/features/generic_features.py

@@ -4,7 +4,7 @@
 import numpy as np
 
 
-def _diff_within_group(df, sort_key: str, diff_col:str, **kwargs):
+def _diff_within_group(df, sort_key: str, diff_col: str, **kwargs):
     return df.groupby(sort_key)[diff_col].transform(lambda x: x.diff(**kwargs).bfill())
 
 
@@ -45,7 +45,7 @@ def compute_centerofmass_interanimal_distances(
 
 
 def compute_centerofmass_interanimal_speed(
-    df: pd.DataFrame, raw_col_names: list, n_shifts:int=5, **kwargs
+    df: pd.DataFrame, raw_col_names: list, n_shifts: int = 5, **kwargs
 ) -> pd.DataFrame:
     """Speeds between all animals' centroids"""
     animal_setup = df.pose.animal_setup
@@ -123,7 +123,11 @@ def compute_centerofmass(
 
 
 def compute_centerofmass_velocity(
-    df: pd.DataFrame, raw_col_names: list, n_shifts:int=5, bodyparts: list = [], **kwargs
+    df: pd.DataFrame,
+    raw_col_names: list,
+    n_shifts: int = 5,
+    bodyparts: list = [],
+    **kwargs,
 ) -> pd.DataFrame:
     """Velocity of all animals' centroids"""
     animal_setup = df.pose.animal_setup
@@ -161,7 +165,11 @@ def compute_centerofmass_velocity(
 
 
 def compute_part_velocity(
-    df: pd.DataFrame, raw_col_names: list, n_shifts:int=5, bodyparts: list = [], **kwargs
+    df: pd.DataFrame,
+    raw_col_names: list,
+    n_shifts: int = 5,
+    bodyparts: list = [],
+    **kwargs,
 ) -> pd.DataFrame:
     """Velocity of all animals' bodyparts"""
     animal_setup = df.pose.animal_setup
@@ -198,7 +206,11 @@ def compute_part_velocity(
 
 
 def compute_part_speed(
-    df: pd.DataFrame, raw_col_names: list, n_shifts:int=5, bodyparts: list = [], **kwargs
+    df: pd.DataFrame,
+    raw_col_names: list,
+    n_shifts: int = 5,
+    bodyparts: list = [],
+    **kwargs,
 ) -> pd.DataFrame:
     """Speed of all animals' bodyparts"""
     animal_setup = df.pose.animal_setup
@@ -235,7 +247,7 @@ def compute_part_speed(
 
 
 def compute_speed_features(
-    df: pd.DataFrame, raw_col_names: list, n_shifts:int=5, **kwargs
+    df: pd.DataFrame, raw_col_names: list, n_shifts: int = 5, **kwargs
 ) -> pd.DataFrame:
     """Speeds between all body parts pairs (within and between animals)"""
     animal_setup = df.pose.animal_setup