def test_multicomp_graph(self):
msc = sc.compare_multiple_sorters([self._sorting, self._sorting, self._sorting])
sw.plot_multicomp_graph(msc, edge_cmap='viridis', node_cmap='rainbow', draw_labels=False)
sw.plot_multicomp_agreement(msc)
sw.plot_multicomp_agreement_by_sorter(msc)
fig, axes = plt.subplots(len(msc.sorting_list), 1)
sw.plot_multicomp_agreement_by_sorter(msc, axes=axes)
def compare_sorters(sort1, sort2):
comp_KL_MS4 = sc.compare_two_sorters(sorting1=sort1, sorting2=sort2)
mapped_units = comp_KL_MS4.get_mapped_sorting1().get_mapped_unit_ids()
print('Klusta units:', sort1.get_unit_ids())
print('Mapped Mountainsort4 units:', mapped_units)
comp_multi = sc.compare_multiple_sorters(sorting_list=[sort1, sort2],
name_list=['klusta', 'ms4'])
sorting_agreement = comp_multi.get_agreement_sorting(minimum_matching=2)
print('Units in agreement between Klusta and Mountainsort4:',
sorting_agreement.get_unit_ids())
w_multi = sw.plot_multicomp_graph(comp_multi)
plt.show()
############################################################################# # The multiple sorters comparison internally computes pairwise comparison, # that can be accessed as follows: print(mcmp.comparisons[0].sorting1, mcmp.comparisons[0].sorting2) mcmp.comparisons[0].get_mapped_sorting1().get_mapped_unit_ids() ############################################################################# print(mcmp.comparisons[1].sorting1, mcmp.comparisons[1].sorting2) mcmp.comparisons[0].get_mapped_sorting1().get_mapped_unit_ids() ############################################################################# # The global multi caomparison can be visualized with this graph sw.plot_multicomp_graph(mcmp) ############################################################################# # We can see that there is a better agreement between tridesclous and # mountainsort (5 units matched), while klusta only has two matched units # with tridesclous, and three with mountainsort. ############################################################################# # Consensus-based method # --------------------------- # # We can pull the units in agreement with different sorters using the # :code:`get_agreement_sorting` method. This allows to make spike sorting more # robust by integrating the output of several algorithms. On the other # hand, it might suffer from weak performance of single algorithms. #
comp_multi = sc.compare_multiple_sorters(
sorting_list=[sorting_MS4, sorting_KL], name_list=['klusta', 'ms4'])
##############################################################################
# When comparing with a ground-truth sorting extractor (1), you can get the sorting performance and plot a confusion
# matrix
comp_gt_KL.get_performance()
w_conf = sw.plot_confusion_matrix(comp_gt_KL)
##############################################################################
# When comparing two sorters (2), we can see the matching of units between sorters. For example, this is how to extract
# the unit ids of Mountainsort4 (sorting2) mapped to the units of Klusta (sorting1). Units which are not mapped has -1
# as unit id.
mapped_units = comp_KL_MS4.get_mapped_sorting1().get_mapped_unit_ids()
print('Klusta units:', sorting_KL.get_unit_ids())
print('Mapped Mountainsort4 units:', mapped_units)
##############################################################################
# When comparing multiple sorters (3), you can extract a :code:`SortingExtractor` object with units in agreement
# between sorters. You can also plot a graph showing how the units are matched between the sorters.
sorting_agreement = comp_multi.get_agreement_sorting(minimum_matching=2)
print('Units in agreement between Klusta and Mountainsort4:',
sorting_agreement.get_unit_ids())
w_multi = sw.plot_multicomp_graph(comp_multi)
# example signal-to-noise ratio. Quality metrics can be computed using the :code:`toolkit.validation` submodule
import spikeinterface.toolkit as st
snrs = st.validation.compute_snrs(sorting_true,
recording,
save_as_property=True)
w_perf = sw.plot_sorting_performance(comp_MS4,
property_name='snr',
metric='accuracy')
##############################################################################
# Widgets using MultiSortingComparison
# -------------------------------------
#
# We can also compare all three SortingExtractor objects, obtaining a :code:`MultiSortingComparison` object.
multicomp = sc.compare_multiple_sorters(
[sorting_true, sorting_MS4, sorting_KL])
##############################################################################
# plot_multicomp_graph()
# ~~~~~~~~~~~~~~~~~~~~~~~~~~
w_multi = sw.plot_multicomp_graph(multicomp,
edge_cmap='coolwarm',
node_cmap='viridis',
draw_labels=False,
colorbar=True)
