grouping

Module for handling performing Agglomerative Hierarchical Clustering Algorithm.

Based on ‘Detection of Intensity Change Points in Time-Resolved Single-Molecule Measurements’ from Watkins nad Yang, J. Phys. Chem. B 2005, 109, 617-628 (http://pubs.acs.org/doi/abs/10.1021/jp0467548)

Joshua Botha University of Pretoria 2018

Module Contents

Classes

Group
ClusteringStep
AHCA	Class for executing Agglomerative Hierarchical Clustering Algorithm and storing the results.
GlobalLevel

Functions

max_jm(array)

Finds the j and m index for the max value.

Attributes

logger

grouping.logger = "'grouping'"

grouping.max_jm(array: numpy.ndarray)

Finds the j and m index for the max value.

class grouping.Group(lvls_inds: List[int_p_s] = None, particle: smsh5.Particle = None, group_ind: int = None)

property num_photons: int_p_s

property dwell_time_s: float

property int_p_s: float

property group_times_ns

property group_microtimes

lvls_inds = 'None'

group_ind = 'None'

lvls = 'None'

histogram = 'None'

class grouping.ClusteringStep(particle: smsh5.Particle, first: bool = False, seed_groups: List[Group] = None, single_level: bool = False)

property group_ints: List[float]

property group_level_dwelltimes

property group_total_dwelltime

property group_num_levels

property group_level_ints

_particle: smsh5.Particle

first = 'False'

single_level = 'False'

last = 'False'

_log_l_em = 'None'

_em_p_mj = 'None'

_em_log_l = 'None'

_ahc_p_mj = 'None'

_ahc_groups = 'None'

groups = 'None'

bic = 'None'

num_groups = 'None'

level_group_ind = 'None'

group_levels = 'None'

_num_prev_groups

groups_have_hists = 'False'

calc_int_bounds(order: str = 'descending') → List[Tuple[float, float]]

Calculates the bounds between the groups.

Parameters:

order (str) – Option are ‘descending’ and ‘ascending’

ahc()

Agglomerative Hierarchical Clustering

emc()

Expectation Maximisation clustering

calc_bic()

group_2_levels()

setup_next_step() → ClusteringStep

class grouping.AHCA(particle)

Class for executing Agglomerative Hierarchical Clustering Algorithm and storing the results.

property selected_step: ClusteringStep

property best_step: ClusteringStep

property steps_num_groups: List[int]

backup = 'None'

has_groups = 'False'

_particle

uuid

steps = 'None'

best_step_ind = 'None'

bics = 'None'

selected_step_ind = 'None'

num_steps = 'None'

plots_need_to_be_updated = 'False'

use_roi_for_grouping = 'True'

grouped_with_roi = 'False'

clear_and_backup_results() → None

restore_and_delete_backup() → None

run_grouping(feedback_queue: multiprocessing.JoinableQueue = None)

Run grouping

set_selected_step(step_ind: int)

reset_selected_step()

class grouping.GlobalLevel(global_particle: smsh5.Particle | smsh5.GlobalParticle, parent_particle_dataset_ind: int, particle_levels: List[change_point.Level | GlobalLevel], int_p_s: float, group_ind: int, start_time_offset_ns: int, dwell_time_ns: float, num_photons: int)

property times_s

property dwell_time_s

is_global_level = 'True'

particle

parent_particle_dataset_ind

start_time_offset_ns

times_ns

int_p_s

group_ind

dwell_time_ns

num_photons

microtimes_particle_inds_pairs = '()'