pipeline
logger_config
logger
VAMEPipeline Objects
class VAMEPipeline()
VAME pipeline class.
__init__
def __init__(project_name: str,
videos: List[str],
poses_estimations: List[str],
source_software: Literal["DeepLabCut", "SLEAP", "LightningPose"],
working_directory: str = ".",
video_type: str = ".mp4",
fps: int | None = None,
copy_videos: bool = False,
paths_to_pose_nwb_series_data: Optional[str] = None,
config_kwargs: Optional[dict] = None) -> None
Initializes the VAME pipeline.
Parameters
- project_name (
str
): Project name. - videos (
List[str]
): List of video files. - poses_estimations (
List[str]
): List of pose estimation files. - source_software (
Literal["DeepLabCut", "SLEAP", "LightningPose"]
): Source software used for pose estimation. - working_directory (
str, optional
): Working directory, by default ".". - video_type (
str, optional
): Video file type, by default ".mp4". - fps (
int, optional
): Sampling rate of the videos. If not passed, it will be estimated from the video file. By default None. - copy_videos (
bool, optional
): Copy videos, by default False. - paths_to_pose_nwb_series_data (
Optional[str], optional
): Path to pose NWB series data, by default None. - config_kwargs (
Optional[dict], optional
): Additional configuration keyword arguments, by default None.
Returns
None
get_states
def get_states(summary: bool = True) -> dict
Returns the pipeline states.
Returns
dict
: Pipeline states.
get_sessions
def get_sessions() -> List[str]
Returns a list of session names.
Returns
List[str]
: Session names.
get_raw_datasets
def get_raw_datasets() -> xr.Dataset
Returns a xarray dataset which combines all the raw data from the project.
Returns
- dss (
xarray.Dataset
): Combined raw dataset.
preprocessing
def preprocessing(centered_reference_keypoint: str = "snout",
orientation_reference_keypoint: str = "tailbase") -> None
Preprocesses the data.
Parameters
- centered_reference_keypoint (
str, optional
): Key point to center the data, by default "snout". - orientation_reference_keypoint (
str, optional
): Key point to orient the data, by default "tailbase".
Returns
None
create_training_set
def create_training_set() -> None
Creates the training set.
Returns
None
train_model
def train_model() -> None
Trains the model.
Returns
None
evaluate_model
def evaluate_model() -> None
Evaluates the model.
Returns
None
run_segmentation
def run_segmentation() -> None
Runs the pose estimation segmentation into motifs.
Returns
None
run_community_clustering
def run_community_clustering() -> None
Runs the community clustering.
Returns
None
generate_motif_videos
def generate_motif_videos(
video_type: str = ".mp4",
segmentation_algorithm: Literal["hmm", "kmeans"] = "hmm") -> None
Generates motif videos.
Parameters
- video_type (
str, optional
): Video type, by default ".mp4". - segmentation_algorithm (
Literal["hmm", "kmeans"], optional
): Segmentation algorithm, by default "hmm".
Returns
None
generate_community_videos
def generate_community_videos(
video_type: str = ".mp4",
segmentation_algorithm: Literal["hmm", "kmeans"] = "hmm") -> None
Generates community videos.
Parameters
- video_type (
str, optional
): Video type, by default ".mp4". - segmentation_algorithm (
Literal["hmm", "kmeans"], optional
): Segmentation algorithm, by default "hmm".
Returns
None
generate_videos
def generate_videos(
video_type: str = ".mp4",
segmentation_algorithm: Literal["hmm", "kmeans"] = "hmm") -> None
Generates motif and community videos.
Parameters
- video_type (
str, optional
): Video type, by default ".mp4". - segmentation_algorithm (
Literal["hmm", "kmeans"], optional
): Segmentation algorithm, by default "hmm".
Returns
None
visualize_preprocessing
def visualize_preprocessing(scatter: bool = True,
timeseries: bool = True,
show_figure: bool = False,
save_to_file: bool = True) -> None
Visualizes the preprocessing results.
Parameters
- scatter (
bool, optional
): Visualize scatter plot, by default True. - timeseries (
bool, optional
): Visualize timeseries plot, by default True. - show_figure (
bool, optional
): Show the figure, by default False. - save_to_file (
bool, optional
): Save the figure to file, by default True.
Returns
None
visualize_model_losses
def visualize_model_losses(save_to_file: bool = True,
show_figure: bool = True) -> None
Visualizes the model losses.
Parameters
- save_to_file (
bool, optional
): Save the figure to file, by default False. - show_figure (
bool, optional
): Show the figure, by default True.
Returns
None
visualize_motif_tree
def visualize_motif_tree(
segmentation_algorithm: Literal["hmm", "kmeans"]) -> None
Visualizes the motif tree.
Parameters
- segmentation_algorithm (
Literal["hmm", "kmeans"]
): Segmentation algorithm.
Returns
None
visualize_umap
def visualize_umap(label: Literal["community", "motif"] = "community",
segmentation_algorithm: Literal["hmm", "kmeans"] = "hmm",
show_figure: bool = False,
save_to_file: bool = True) -> None
Visualizes the UMAP plot.
Parameters
- label (
Literal["community", "motif"], optional
): Label to visualize, by default "community". - segmentation_algorithm (
Literal["hmm", "kmeans"], optional
): Segmentation algorithm, by default "hmm".
Returns
None
report
def report(segmentation_algorithm: Literal["hmm", "kmeans"] = "hmm") -> None
Generates the project report.
Parameters
- segmentation_algorithm (
Literal["hmm", "kmeans"], optional
): Segmentation algorithm, by default "hmm".
Returns
None
run_pipeline
def run_pipeline(from_step: int = 0, preprocessing_kwargs: dict = {}) -> None
Runs the pipeline.
Parameters
- from_step (
int, optional
): Start from step, by default 0. - preprocessing_kwargs (
dict, optional
): Preprocessing keyword arguments, by default .
Returns
None
unique_in_order
def unique_in_order(sequence)