Clustering

🅼 oscfar.cluster

def cluster_peaks_p(peak_positions, peak_heights, n, max_e = 0.7, verbose = False):

Cluster peak positions using DBSCAN and select the most prominent peak from each cluster.

Parameters

Parameters:

**peak_positions **: array-like, shape (n_samples, n_features) The positions of the detected peaks to be clustered.
**peak_heights **: array-like, shape (n_samples,) The heights (or intensities) of the detected peaks, used to select the most prominent peak in each cluster.
**n **: int The minimum number of samples in a neighborhood for a point to be considered as a core point in DBSCAN.
**max_e **: float, optional (default=0.7) The maximum distance between two samples for one to be considered as in the neighborhood of the other (epsilon parameter for DBSCAN).
**verbose **: bool, optional (default=False) If True, prints the number of clusters found by DBSCAN.
🅵 oscfar.cluster.cluster_peaks_ph

def cluster_peaks_ph(peak_positions, peak_heights, n, max_e = 0.7, verbose = False):

Clusters peaks based on their positions and heights using DBSCAN.

Parameters:

peak_positions (list or np.ndarray): Positions of the peaks.
peak_heights (list or np.ndarray): Heights of the peaks.
n (int): Minimum number of samples in a cluster.
max_e (float): The maximum distance between two samples for one to be considered as in the neighborhood of the other. Defaults to 0.7.
verbose (bool) (default: False): If True, print the number of clusters found. Defaults to False.

Returns: