Community-based spike sorting with flexible components: the more the merrier

Here python scripts and jupyter notebooks to reproduces figures from the paper :

Community-based spike sorting with flexible components: the more the merrier

Theses scripts and notebooks are a goood examples for using the internal benchmarks from spikeinterface.

Figure 2 can be reproduce using:

• figure_dataset.ipynb

Figure 3 can be reproduce using:

• detection_method.py : run the script to create and compute the study
• detection_method.ipynb : plot the results

Figure 4 can be reproduce using:

• clustering_drifting.py : run the script to create and compute the study
• clustering_drifting.ipynb : plot the results

Figure 5 can be reproduce using:

• matching_drift.py : run the script to create and compute the study
• matching_drift.ipynb : plot the results

Figure 6 can be reproduce using:

• matching_drift_aware.py : run the script to create and compute the study
• matching_drift_aware.ipynb : plot the results

Figure 7 can be reproduce using:

• sorters_simulation.py : run the script to create and compute the study
• sorters_simulation.ipynb : plot the results

Additional scripts:

• slurm_tools.py : custum machinery to run study in slurm
• dataset.py : parameters to generate the datasets
• configuration.py : local configuration (path, ...)