In order to have a better feedback on what the algorithm is doing, and especially the clustering phase, the code can produce sanity plots that may be helpful to troubleshoot. This is the flag
make_plots in the
clustering section of the parameter files (see the configuration section documentation on MPI). All plots will be stored in the folder
If you do not care about those plots, you can set to
make_plots entries in the configuration file, and this will speed up the algorithm
View of the activity¶
The best way to visualize the activity on your electrodes, and to see if data are properly loaded or if results are making any sense is to use the devoted python GUI and the preview mode (see the visualization section on Python GUI)
Views of the Clusters¶
During the clustering phase, the algorithm will save files names
cluster_i where i is the number of the electrode. A typical plot will look like that
On the two plots in the left column, you can see the rho vs delta plots (see [Rodriguez et Laio, 2014]). Top plots shows the centroids that have been selected, and bottom plots shows in red all the putative centers that were considered by the algorithm.
On the 4 plots on the rights, this is a 3D projection of all the spikes collected by that electrode, projected along different axes: x vs y, y vs z and x vs z.
If, in those plots, you see clusters that you would have rather split, and that do not have different color, then this is likely that the clustering algorithm had wrong parameters. Remember that in the configuration file
max_clusters controls the maximal number of clusters per electrodes that will be searched (so you may want to increase it if clustering is not accurate enough), and that
sim_same_elec will control how much similar clusters will be merged. So again, decrease it if you think you are losing some obvious clusters.
Views of the Local Merges¶
During the clustering, if you set the parameter
True in the
clustering section, the code will produce (since 0.8.4) sanity plots for the local merges, to show you groups of clusters that were merged together. The method used to compute the distances between cluster can be distance (normalized distance between clusters, assuming they are Gaussian), dip-test of unimodality, folding test, or the Bhattacharyya distance.
Views of the waveforms¶
At the end of the clustering phase, the algorithm will save files names
waveform_i where i is the number of the electrode. A typical plot will look like that
On this plot, you should get an insight on the templates that have been computed out of the clustering phase. For all the clusters detected on that given electrode, you should see all the waveforms peaking on that particular electrode, and the template, in red (in blue, this is the min and max amplitudes allowed during the fitting procedure). Note that if template is not aligned with the waveforms, this is normal. The templates are aligned on the electrodes were they have an absolute min. Here you are just looking at them on a particular electrode. The key point is that, as you can see, templates should all go below threshold on that particular electrode (dash-dotted line). When the template is flat, it means that it has been removed from the dictionary, because of time shifting and duplication elsewhere.