tropea_clustering.onion_uni¶
- tropea_clustering.onion_uni(X, bins='auto', number_of_sigmas=2.0)[source]¶
Performs onion clustering on the data array ‘X’.
Returns an array of integer labels, one for each signal sequence. Unclassified sequences are labelled “-1”.
- Parameters:
X (ndarray of shape (n_particles * n_seq, delta_t)) – The data to cluster. Each signal sequence is considered as a single data point.
bins (int, default="auto") – The number of bins used for the construction of the histograms. Can be an integer value, or “auto”. If “auto”, the default of numpy.histogram_bin_edges is used (see https://numpy.org/doc/stable/reference/generated/numpy.histogram_bin_edges.html#numpy.histogram_bin_edges).
number_of_sigmas (float, default=2.0) – Sets the thresholds for classifing a signal sequence inside a state: the sequence is contained in the state if it is entirely contained inside number_of_sigmas * state.sigmas times from state.mean.
- Returns:
states_list (List[StateUni]) – The list of the identified states. Refer to the documentation of StateUni for accessing the information on the states.
labels (ndarray of shape (n_particles * n_seq,)) – Cluster labels for each signal sequence. Unclassified points are given the label “-1”.
- Return type:
Example
import numpy as np from tropea_clustering import onion_uni, helpers # Select time resolution delta_t = 5 # Create random input data np.random.seed(1234) n_particles = 5 n_steps = 1000 input_data = np.random.rand(n_particles, n_steps) # Create input array with the correct shape reshaped_input_data = helpers.reshape_from_nt( input_data, delta_t, ) # Run Onion Clustering state_list, labels = onion_uni(reshaped_input_data)