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util.data_manipulation

logger_config​

logger​

consecutive​

def consecutive(data: np.ndarray, stepsize: int = 1) -> List[np.ndarray]

Find consecutive sequences in the data array.

Parameters

  • data (np.ndarray): Input array.
  • stepsize (int, optional): Step size. Defaults to 1.

Returns

  • List[np.ndarray]: List of consecutive sequences.

nan_helper​

def nan_helper(y: np.ndarray) -> Tuple

Identifies indices of NaN values in an array and provides a function to convert them to non-NaN indices.

Parameters

  • y (np.ndarray): Input array containing NaN values.

Returns

  • Tuple[np.ndarray, Union[np.ndarray, None]]: A tuple containing two elements:
  • An array of boolean values indicating the positions of NaN values.
  • A lambda function to convert NaN indices to non-NaN indices.

interpol_first_rows_nans​

def interpol_first_rows_nans(arr: np.ndarray) -> np.ndarray

Interpolates NaN values in the given array.

Parameters

  • arr (np.ndarray): Input array with NaN values.

Returns

  • np.ndarray: Array with interpolated NaN values.

interpolate_nans_with_pandas​

def interpolate_nans_with_pandas(data: np.ndarray) -> np.ndarray

Interpolate NaN values along the time axis of a 3D NumPy array using Pandas.

Parameters

  • data (numpy.ndarray): Input 3D array of shape (time, keypoints, space).

Returns

  • numpy.ndarray:: Array with NaN values interpolated.

crop_and_flip_legacy​

def crop_and_flip_legacy(
        rect: Tuple, src: np.ndarray, points: List[np.ndarray],
        ref_index: Tuple[int, int]) -> Tuple[np.ndarray, List[np.ndarray]]

Crop and flip the image based on the given rectangle and points.

Parameters

  • rect (Tuple): Rectangle coordinates (center, size, theta).
  • src: np.ndarray: Source image.
  • points (List[np.ndarray]): List of points.
  • ref_index (Tuple[int, int]): Reference indices for alignment.

Returns

  • Tuple[np.ndarray, List[np.ndarray]]: Cropped and flipped image, and shifted points.

background​

def background(project_path: str,
               session: str,
               video_path: str,
               num_frames: int = 1000,
               save_background: bool = True) -> np.ndarray

Compute background image from fixed camera.

Parameters

  • project_path (str): Path to the project directory.
  • session (str): Name of the session.
  • video_path (str): Path to the video file.
  • num_frames (int, optional): Number of frames to use for background computation. Defaults to 1000.

Returns

  • np.ndarray: Background image.