cogpy.burst.blob_detection

Functions in this module are used to detect blobs in n-dimensional data arrays.

detect_bursts: Detects bursts in a n-dimensional data array using the h-maxima transform. get_coords_fs_dict: Gets the sampling rate for each dimension in dataarray. set_sigma_dict: Sets the sigma values for the blob detection. separate_min_max_sigma_dict: Separates the sigma dictionary into min and max sigma dictionaries. get_blobs_df: Gets the blobs in a data array and returns a dataframe with their coordinates and amplitudes.

dependencies between functions:

get_coords_fs_dict, separate_min_max_sigma_dict -> set_sigma_dict set_sigma_dict -> get_blobs_df detect_bursts -> get_blobs_df

Functions

detect_blobs(datax[, num_sigma, ...])	Get the blobs (Laplace of Gaussian blobs) in a data array and return a dataframe with their coordinates and amplitudes. :param datax: The input data array with dimensions and coordinates. :type datax: xarray.DataArray :param num_sigma: The number of sigma values to use for the blob detection. Default is 10. :type num_sigma: int :param sigma_dict_raw: A dictionary with the sigma values for each dimension. If None, the function will use the default values of (1/fs, 5/fs) for each dimension, where fs is the sampling rate of the dimension. Default is None. :type sigma_dict_raw: dict, optional.
detect_hmaxima(datax[, h_quantile, h, footprint])	Detect bursts (h-maxima) in a n-dimensional data array using the h-maxima transform.
get_coo_df(datax, h_maxima)
get_coords_fs_dict(datax)	Get the sampling rate for each dimension in dataarray
separate_min_max_sigma_dict(sigma_dict_raw)	Separate the sigma dictionary into min and max sigma dictionaries. :param sigma_dict_raw: A dictionary with the sigma values for each dimension. The values should be tuples of (min sigma, max sigma) for each dimension. :type sigma_dict_raw: dict.
set_sigma_dict(sigma_dict, datax[, return_tuple])	Set the sigma values for the blob detection.