shap

simba/mixins/circular_statistics.py

@@ -1019,9 +1019,9 @@ def circular_range(data: np.ndarray) -> float:
         :rtype: np.ndarray
 
         :example:
-        >>> CircularStatisticsMixin().circular_range([350, 20, 60, 100])
+        >>> CircularStatisticsMixin().circular_range(np.ndarray([350, 20, 60, 100]))
         >>> 110.0
-        >>> CircularStatisticsMixin().circular_range([110, 20, 60, 100])
+        >>> CircularStatisticsMixin().circular_range(np.ndarray([110, 20, 60, 100]))
         >>> 90.0
         """
 

simba/mixins/timeseries_features_mixin.py

@@ -864,11 +864,11 @@ def sliding_variance(data: np.ndarray, window_sizes: np.ndarray, sample_rate: in
 
     @staticmethod
     @njit(
-        "(float32[:], float64[:], int64, types.ListType(types.unicode_type))",
+        "(float32[:], float64[:], float64, types.ListType(types.unicode_type))",
         fastmath=True,
         cache=True,
     )
-    def sliding_descriptive_statistics(data: np.ndarray, window_sizes: np.ndarray, sample_rate: int, statistics: Literal["var", "max", "min", "std", "median", "mean", "mad", "sum", "mac", "rms", "absenergy"]) -> np.ndarray:
+    def sliding_descriptive_statistics(data: np.ndarray, window_sizes: np.ndarray, sample_rate: float, statistics: Literal["var", "max", "min", "std", "median", "mean", "mad", "sum", "mac", "rms", "absenergy"]) -> np.ndarray:
         """
         Jitted compute of descriptive statistics over sliding windows in 1D data array.
 

simba/plotting/pose_plotter_mp.py

@@ -163,11 +163,11 @@ def run(self):
                                               circle_size=video_circle_size,
                                               video_save_dir=self.temp_folder)
                 for cnt, result in enumerate(pool.imap(constants, pose_lst, chunksize=self.config.multiprocess_chunksize)):
-                    print(f"Image {max(len(pose_df), obs_per_split*(cnt+1))}/{len(pose_df)}, Video {file_cnt+1}/{len(list(self.data.keys()))}...")
+                    print(f"Image {min(len(pose_df), obs_per_split*(cnt+1))}/{len(pose_df)}, Video {file_cnt+1}/{len(list(self.data.keys()))}...")
             pool.terminate()
             pool.join()
             print(f"Joining {video_name} multi-processed video...")
-            concatenate_videos_in_folder(in_folder=self.temp_folder, save_path=save_video_path, remove_splits=True, gpu=self.gpu)
+            concatenate_videos_in_folder(in_folder=self.temp_folder, save_path=save_video_path, remove_splits=True, gpu=False)
             video_timer.stop_timer()
             stdout_success(msg=f"Pose video {video_name} complete and saved at {save_video_path}", elapsed_time=video_timer.elapsed_time_str, source=self.__class__.__name__)
         self.config.timer.stop_timer()

simba/third_party_label_appenders/converters.py

@@ -14,7 +14,7 @@
 
 import cv2
 import numpy as np
-from pycocotools import mask
+#from pycocotools import mask
 from shapely.geometry import Polygon
 from skimage.draw import polygon
 
@@ -27,25 +27,25 @@
 from simba.utils.printing import SimbaTimer, stdout_success
 
 
-def geometry_to_rle(geometry: Union[np.ndarray, Polygon], img_size: Tuple[int, int]):
-    """
-    Converts a geometry (polygon or NumPy array) into a Run-Length Encoding (RLE) mask, suitable for object detection or segmentation tasks.
-
-    :param geometry: The geometry to be converted into an RLE. It can be either a shapely Polygon or a (n, 2) np.ndarray with vertices.
-    :param img_size:  A tuple `(height, width)` representing the size of the image in which the geometry is to be encoded. This defines the dimensions of the output binary mask.
-    :return:
-    """
-    check_instance(source=geometry_to_rle.__name__, instance=geometry, accepted_types=(Polygon, np.ndarray))
-    if isinstance(geometry, (Polygon,)):
-        geometry = geometry.exterior.coords
-    else:
-        check_valid_array(data=geometry, source=geometry_to_rle.__name__, accepted_ndims=[(2,)], accepted_dtypes=Formats.NUMERIC_DTYPES.value)
-    binary_mask = np.zeros(img_size, dtype=np.uint8)
-    rr, cc = polygon(geometry[:, 0].flatten(), geometry[:, 1].flatten(), img_size)
-    binary_mask[rr, cc] = 1
-    rle = mask.encode(np.asfortranarray(binary_mask))
-    rle['counts'] = rle['counts'].decode('utf-8')
-    return rle
+# def geometry_to_rle(geometry: Union[np.ndarray, Polygon], img_size: Tuple[int, int]):
+#     """
+#     Converts a geometry (polygon or NumPy array) into a Run-Length Encoding (RLE) mask, suitable for object detection or segmentation tasks.
+#
+#     :param geometry: The geometry to be converted into an RLE. It can be either a shapely Polygon or a (n, 2) np.ndarray with vertices.
+#     :param img_size:  A tuple `(height, width)` representing the size of the image in which the geometry is to be encoded. This defines the dimensions of the output binary mask.
+#     :return:
+#     """
+#     check_instance(source=geometry_to_rle.__name__, instance=geometry, accepted_types=(Polygon, np.ndarray))
+#     if isinstance(geometry, (Polygon,)):
+#         geometry = geometry.exterior.coords
+#     else:
+#         check_valid_array(data=geometry, source=geometry_to_rle.__name__, accepted_ndims=[(2,)], accepted_dtypes=Formats.NUMERIC_DTYPES.value)
+#     binary_mask = np.zeros(img_size, dtype=np.uint8)
+#     rr, cc = polygon(geometry[:, 0].flatten(), geometry[:, 1].flatten(), img_size)
+#     binary_mask[rr, cc] = 1
+#     rle = mask.encode(np.asfortranarray(binary_mask))
+#     rle['counts'] = rle['counts'].decode('utf-8')
+#     return rle
 
 def geometries_to_coco(geometries: Dict[str, np.ndarray],
                        video_path: Union[str, os.PathLike],

simba/third_party_label_appenders/third_party_appender.py

@@ -59,7 +59,7 @@ class ThirdPartyLabelAppender(ConfigReader):
        `Third-party import tutorials <https://github.com/sgoldenlab/simba/blob/master/docs/third_party_annot.md>`__.
 
        `BENTO: expected input <https://github.com/sgoldenlab/simba/blob/master/misc/bento_example.annot`__.
-       `BORIS: expected input <https://github.com/sgoldenlab/simba/blob/master/misc/boris_example.csv>`__..
+       `BORIS: expected input <https://github.com/sgoldenlab/simba/blob/master/misc/boris_example.csv>`__.
        `DEEPETHOGRAM: expected input <https://github.com/sgoldenlab/simba/blob/master/misc/deep_ethogram_labels.csv>`__.
        `ETHOVISION: expected input <https://github.com/sgoldenlab/simba/blob/master/misc/ethovision_example.xlsx>`__.
        `OBSERVER: expected input I <https://github.com/sgoldenlab/simba/blob/master/misc/Observer_example_1.xlsx>`__.