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analysis.videowriter

logger_config​

logger​

create_cluster_videos​

def create_cluster_videos(
        config: dict,
        path_to_file: str,
        session: str,
        n_clusters: int,
        video_type: str,
        flag: str,
        segmentation_algorithm: SegmentationAlgorithms,
        cohort: bool = True,
        output_video_type: str = ".mp4",
        tqdm_logger_stream: Union[TqdmToLogger, None] = None) -> None

Generate cluster videos and save them to filesystem on project folder.

Parameters

  • config (dict): Configuration parameters.
  • path_to_file (str): Path to the file.
  • session (str): Name of the session.
  • n_clusters (int): Number of clusters.
  • video_type (str): Type of input video.
  • flag (str): Flag indicating the type of video (motif or community).
  • segmentation_algorithm (SegmentationAlgorithms): Which segmentation algorithm to use. Options are 'hmm' or 'kmeans'.
  • cohort (bool, optional): Flag indicating cohort analysis. Defaults to True.
  • output_video_type (str, optional): Type of output video. Default is '.mp4'.
  • tqdm_logger_stream (TqdmToLogger, optional): Tqdm logger stream. Default is None.

Returns

  • None

motif_videos​

@save_state(model=MotifVideosFunctionSchema)
def motif_videos(config: dict,
                 segmentation_algorithm: SegmentationAlgorithms,
                 video_type: str = ".mp4",
                 output_video_type: str = ".mp4",
                 save_logs: bool = False) -> None

Generate motif videos and save them to filesystem. Fills in the values in the "motif_videos" key of the states.json file. Files are saved at:

  • project_name/
    • results/
      • session/
        • model_name/
          • segmentation_algorithm-n_clusters/
            • cluster_videos/
              • session-motif_0.mp4
              • session-motif_1.mp4
              • ...

Parameters

  • config (dict): Configuration parameters.
  • segmentation_algorithm (SegmentationAlgorithms): Which segmentation algorithm to use. Options are 'hmm' or 'kmeans'.
  • video_type (str, optional): Type of video. Default is '.mp4'.
  • output_video_type (str, optional): Type of output video. Default is '.mp4'.
  • save_logs (bool, optional): Save logs to filesystem. Default is False.

Returns

  • None

community_videos​

@save_state(model=CommunityVideosFunctionSchema)
def community_videos(config: dict,
                     segmentation_algorithm: SegmentationAlgorithms,
                     cohort: bool = True,
                     video_type: str = ".mp4",
                     save_logs: bool = False,
                     output_video_type: str = ".mp4") -> None

Generate community videos and save them to filesystem on project community_videos folder. Fills in the values in the "community_videos" key of the states.json file. Files are saved at:

  1. If cohort is True: TODO: Add cohort analysis

  2. If cohort is False:

  • project_name/
    • results/
      • file_name/
        • model_name/
          • segmentation_algorithm-n_clusters/
            • community_videos/
              • file_name-community_0.mp4
              • file_name-community_1.mp4
              • ...

Parameters

  • config (dict): Configuration parameters.
  • segmentation_algorithm (SegmentationAlgorithms): Which segmentation algorithm to use. Options are 'hmm' or 'kmeans'.
  • cohort (bool, optional): Flag indicating cohort analysis. Defaults to True.
  • video_type (str, optional): Type of video. Default is '.mp4'.
  • save_logs (bool, optional): Save logs to filesystem. Default is False.
  • output_video_type (str, optional): Type of output video. Default is '.mp4'.

Returns

  • None