emd.cycles.Cycles

class emd.cycles.Cycles(IP, phase_step=4.71238898038469, phase_edge=0.2617993877991494, min_len=2, compute_timings=False, mode='cycle', use_cache=True)

Find, store and analyse single cycles [1].

Methods

add_cycle_metric(name, cycle_vals[, dtype])	Add an externally computed per-cycle metric.
compute_chain_metric(name, vals, func[, dtype])	Compute a metric for each chain and store the result in the cycle object.
compute_chain_timings()	Compute some standard chain timing metrics.
compute_cycle_metric(name, vals, func[, ...])	Compute a statistic for all cycles.
compute_cycle_timings()	Compute some standard cycle timing metrics.
compute_position_in_chain()	Compute where in a sequence a cycle occurs.
get_cycle_vector(ii[, mode])	Create cycle-vector representation of cycle timings.
get_inds_of_cycle(ii[, mode])	Find indices of specified cycle.
get_matching_cycles(conditions[, ret_separate])	Find subset of cycles matching specified conditions.
get_metric_dataframe([subset, conditions])	Return pandas dataframe containing cycle metrics.
iterate([through, conditions, mode])	Iterate through some or all cycles.
pick_cycle_subset(conditions)	Set conditions to define subsets + chains.

References

[1]

Andrew J. Quinn, Vitor Lopes-dos-Santos, Norden Huang, Wei-Kuang Liang, Chi-Hung Juan, Jia-Rong Yeh, Anna C. Nobre, David Dupret, & Mark W. Woolrich (2021). Within-cycle instantaneous frequency profiles report oscillatory waveform dynamics. bioRxiv, 2021.04.12.439547. https://doi.org/10.1101/2021.04.12.439547

__init__(IP, phase_step=4.71238898038469, phase_edge=0.2617993877991494, min_len=2, compute_timings=False, mode='cycle', use_cache=True)

Class storing and manipulating singl cycles.

Methods

__init__(IP[, phase_step, phase_edge, ...])	Class storing and manipulating singl cycles.
add_cycle_metric(name, cycle_vals[, dtype])	Add an externally computed per-cycle metric.
compute_chain_metric(name, vals, func[, dtype])	Compute a metric for each chain and store the result in the cycle object.
compute_chain_timings()	Compute some standard chain timing metrics.
compute_cycle_metric(name, vals, func[, ...])	Compute a statistic for all cycles.
compute_cycle_timings()	Compute some standard cycle timing metrics.
compute_position_in_chain()	Compute where in a sequence a cycle occurs.
get_cycle_vector(ii[, mode])	Create cycle-vector representation of cycle timings.
get_inds_of_cycle(ii[, mode])	Find indices of specified cycle.
get_matching_cycles(conditions[, ret_separate])	Find subset of cycles matching specified conditions.
get_metric_dataframe([subset, conditions])	Return pandas dataframe containing cycle metrics.
iterate([through, conditions, mode])	Iterate through some or all cycles.
pick_cycle_subset(conditions)	Set conditions to define subsets + chains.