Source code for viziphant.asset

Analysis of Sequences of Synchronous EvenTs (ASSET) plots
Visualizes the output of :class:`elephant.asset.ASSET` analysis.

.. autosummary::
    :toctree: toctree/asset


# Copyright 2017-2022 by the Viziphant team, see `doc/authors.rst`.
# License: Modified BSD, see LICENSE.txt for details.

import numpy as np
import warnings

from viziphant.rasterplot import rasterplot

[docs]def plot_synchronous_events(spiketrains, sse, title=None, **kwargs): """ Reorder and plot the `spiketrains` according to a series of synchronous events `sse` obtained with the ASSET analysis. Spike trains that do not participate in the chosen group will be shown at the top in a different color. Parameters ---------- spiketrains : list of neo.SpikeTrain ASSET input spiketrains. sse : dict One entry of the output dict from :meth:`elephant.asset.ASSET.extract_synchronous_events`. title : str or None, optional User-defined title string. If None, it'll be set to an automatic description. Default: None **kwargs Additional arguments to :func:`viziphant.rasterplot.rasterplot` Returns ------- axes : matplotlib.Axes.axes See Also -------- viziphant.patterns.plot_patterns : plot patterns repeated in time Examples -------- In this example we * simulate two noisy synfire chains; * shuffle the neurons to destroy visual appearance; * run ASSET analysis to recover the original neurons arrangement. .. plot:: :include-source: import neo import numpy as np import quantities as pq import matplotlib.pyplot as plt import viziphant from elephant import asset np.random.seed(10) spiketrain = np.linspace(0, 50, num=10) np.random.shuffle(spiketrain) spiketrains = np.c_[spiketrain, spiketrain + 100] spiketrains += np.random.random_sample(spiketrains.shape) * 5 spiketrains = [neo.SpikeTrain(st, units='ms', t_stop=1 * pq.s) for st in spiketrains] asset_obj = asset.ASSET(spiketrains, bin_size=3 * imat = asset_obj.intersection_matrix() pmat = asset_obj.probability_matrix_analytical(imat, kernel_width=50 * jmat = asset_obj.joint_probability_matrix(pmat, filter_shape=(5, 1), n_largest=3) mmat = asset_obj.mask_matrices([pmat, jmat], thresholds=.9) cmat = asset_obj.cluster_matrix_entries(mmat, max_distance=11, min_neighbors=3, stretch=5) sses = asset_obj.extract_synchronous_events(cmat) viziphant.asset.plot_synchronous_events(spiketrains, sse=sses[1], s=10) Refer to `ASSET tutorial <>`_ for real-case scenario. """ if len(sse) == 0: warnings.warn("Passed an empty synchronous event dict.") cluster_chain = [] for chain in sse.values(): cluster_chain.extend(chain) _, indices_pattern = np.unique(cluster_chain, return_index=True) indices_pattern = np.take(cluster_chain, np.sort(indices_pattern)) indices_left = set(range(len(spiketrains))).difference(indices_pattern) reordered_sts = [spiketrains[idx] for idx in indices_pattern] sts_not_a_pattern = [spiketrains[idx] for idx in sorted(indices_left)] if title is None: title = "Neurons ordering reconstructed with ASSET" axes = rasterplot([reordered_sts, sts_not_a_pattern], title=title, **kwargs) axes.set_ylabel('reordered neurons') axes.yaxis.set_label_coords(-0.01, 0.5) return axes