def test_run_study_sorters():
study = GroundTruthStudy(study_folder)
sorter_list = [
'tridesclous',
]
print(
f"\n#################################\nINSTALLED SORTERS\n#################################\n"
f"{installed_sorters()}")
study.run_sorters(sorter_list)
def _setup_comparison_study():
rec0, gt_sorting0 = toy_example(num_channels=4, duration=30, seed=0, num_segments=1)
rec1, gt_sorting1 = toy_example(num_channels=32, duration=30, seed=0, num_segments=1)
gt_dict = {
'toy_tetrode': (rec0, gt_sorting0),
'toy_probe32': (rec1, gt_sorting1),
}
study = GroundTruthStudy.create(study_folder, gt_dict)
##############################################################################
# Setup study folder and run all sorters
# --------------------------------------
#
# We first generate the folder.
# this can take some time because recordings are copied inside the folder.
rec0, gt_sorting0 = se.toy_example(num_channels=4, duration=10, seed=10, num_segments=1)
rec1, gt_sorting1 = se.toy_example(num_channels=4, duration=10, seed=0, num_segments=1)
gt_dict = {
'rec0': (rec0, gt_sorting0),
'rec1': (rec1, gt_sorting1),
}
study_folder = 'a_study_folder'
study = GroundTruthStudy.create(study_folder, gt_dict)
##############################################################################
# Then just run all sorters on all recordings in one functions.
# sorter_list = st.sorters.available_sorters() # this get all sorters.
sorter_list = ['herdingspikes', 'tridesclous', ]
study.run_sorters(sorter_list, mode_if_folder_exists="keep")
##############################################################################
# You can re run **run_study_sorters** as many time as you want.
# By default **mode='keep'** so only uncomputed sorter are rerun.
# For instance, so just remove the "sorter_folders/rec1/herdingspikes" to re-run
# only one sorter on one recording.
#
# Then we copy the spike sorting outputs into a separate subfolder.
def test_extract_sortings():
study = GroundTruthStudy(study_folder)
study.copy_sortings()
for rec_name in study.rec_names:
gt_sorting = study.get_ground_truth(rec_name)
for rec_name in study.rec_names:
metrics = study.get_metrics(rec_name=rec_name)
snr = study.get_units_snr(rec_name=rec_name)
study.copy_sortings()
run_times = study.aggregate_run_times()
study.run_comparisons(exhaustive_gt=True)
perf = study.aggregate_performance_by_units()
count_units = study.aggregate_count_units()
dataframes = study.aggregate_dataframes()
print(dataframes)