elephant.asset.asset.ASSET

class elephant.asset.asset.ASSET(spiketrains_i, spiketrains_j=None, bin_size=array(3.) * ms, t_start_i=None, t_start_j=None, t_stop_i=None, t_stop_j=None, verbose=True)

Analysis of Sequences of Synchronous EvenTs class.

Parameters:

spiketrains_i, spiketrains_jlist of neo.SpikeTrain

Input spike trains for the first and second time dimensions, respectively, to compute the p-values from. If spiketrains_y is None, it’s set to spiketrains.

bin_sizepq.Quantity, optional

The width of the time bins used to compute the probability matrix.

t_start_i, t_start_jpq.Quantity, optional

The start time of the binning for the first and second axes, respectively. If None, the attribute t_start of the spike trains is used (if the same for all spike trains). Default: None

t_stop_i, t_stop_jpq.Quantity, optional

The stop time of the binning for the first and second axes, respectively. If None, the attribute t_stop of the spike trains is used (if the same for all spike trains). Default: None

verbosebool, optional

If True, print messages and show progress bar. Default: True

Raises:

ValueError

If the t_start & t_stop times are not (one of):

perfectly aligned;

fully disjoint.

__init__(spiketrains_i, spiketrains_j=None, bin_size=array(3.) * ms, t_start_i=None, t_start_j=None, t_stop_i=None, t_stop_j=None, verbose=True)

Methods

__init__(spiketrains_i[, spiketrains_j, ...])
cluster_matrix_entries(mask_matrix, ...[, ...])	Given a matrix mask_matrix, replaces its positive elements with integers representing different cluster IDs.
extract_synchronous_events(cmat[, ids])	Given a list of spike trains, a bin size, and a clustered intersection matrix obtained from those spike trains via ASSET analysis, extracts the sequences of synchronous events (SSEs) corresponding to clustered elements in the cluster matrix.
intersection_matrix([normalization])	Generates the intersection matrix from a list of spike trains.
is_symmetric()	Returns:
joint_probability_matrix(pmat, filter_shape, ...)	Map a probability matrix pmat to a joint probability matrix jmat, where jmat[i, j] is the joint p-value of the largest neighbors of pmat[i, j].
mask_matrices(matrices, thresholds)	Given a list of matrices and a list of thresholds, return a boolean matrix B ("mask") such that B[i,j] is True if each input matrix in the list strictly exceeds the corresponding threshold at that position.
probability_matrix_analytical([imat, ...])	Given a list of spike trains, approximates the cumulative probability of each entry in their intersection matrix.
probability_matrix_montecarlo(n_surrogates)	Given a list of parallel spike trains, estimate the cumulative probability of each entry in their intersection matrix by a Monte Carlo approach using surrogate data.

Attributes

x_edges	A Quantity array of n+1 edges of the bins used for the horizontal axis of the intersection matrix, where n is the number of bins that time was discretized in.
y_edges	A Quantity array of n+1 edges of the bins used for the vertical axis of the intersection matrix, where n is the number of bins that time was discretized in.