def test_get_template_channel_sparsity():
we = WaveformExtractor.load_from_folder('toy_waveforms')
sparsity = get_template_channel_sparsity(we, method='best_channels', outputs='id', num_channels=5)
print(sparsity)
sparsity = get_template_channel_sparsity(we, method='best_channels', outputs='index', num_channels=5)
print(sparsity)
sparsity = get_template_channel_sparsity(we, method='radius', outputs='id', radius_um=50)
print(sparsity)
sparsity = get_template_channel_sparsity(we, method='radius', outputs='index', radius_um=50)
print(sparsity)
sparsity = get_template_channel_sparsity(we, method='threshold', outputs='id', threshold=3)
print(sparsity)
sparsity = get_template_channel_sparsity(we, method='threshold', outputs='index', threshold=3)
print(sparsity)
# load from folder because sorting properties must be loaded
rec = load_extractor('toy_rec')
sort = load_extractor('toy_sort')
we = extract_waveforms(rec, sort, 'toy_waveforms_1')
sparsity = get_template_channel_sparsity(we, method='by_property', outputs='id', by_property="group")
print(sparsity)
sparsity = get_template_channel_sparsity(we, method='by_property', outputs='index', by_property="group")
print(sparsity)
def test_compute_quality_metrics_peak_sign():
rec = load_extractor('toy_rec')
sort = load_extractor('toy_sorting')
# invert recording
rec_inv = scale(rec, gain=-1.)
we = WaveformExtractor.load_from_folder('toy_waveforms')
print(we)
we_inv = WaveformExtractor.create(rec_inv, sort, 'toy_waveforms_inv')
we_inv.set_params(ms_before=3., ms_after=4., max_spikes_per_unit=500)
we_inv.run_extract_waveforms(n_jobs=1, chunk_size=30000)
print(we_inv)
# without PC
metrics = compute_quality_metrics(we,
metric_names=['snr', 'amplitude_cutoff'],
peak_sign="neg")
metrics_inv = compute_quality_metrics(
we_inv, metric_names=['snr', 'amplitude_cutoff'], peak_sign="pos")
assert np.allclose(metrics["snr"].values, metrics_inv["snr"].values)
assert np.allclose(metrics["amplitude_cutoff"].values,
metrics_inv["amplitude_cutoff"].values)
###############################################################################
# A recording can be "dumped" (exported) to:
# * a dict
# * a json file
# * a pickle file
#
# The "dump" operation is lazy, i.e., the traces are not exported.
# Only the information about how to reconstruct the recording are dumped:
from spikeinterface import load_extractor
from pprint import pprint
d = recording2.to_dict()
pprint(d)
recording2_loaded = load_extractor(d)
print(recording2_loaded)
###############################################################################
# The dictionary can also be dumped directly to a JSON file on disk:
recording2.dump('my_recording.json')
recording2_loaded = load_extractor('my_recording.json')
print(recording2_loaded)
###############################################################################
# **IMPORTANT**: the "dump" operation DOES NOT copy the traces to disk!
#
# If you wish to also store the traces in a compact way you need to use the
# :code:`save()` function. This operation is very useful to save traces obtained
###############################################################################
# A sorting can be "dumped" (exported) to:
# * a dict
# * a json file
# * a pickle file
#
# The "dump" operation is lazy, i.e., the spike trains are not exported.
# Only the information about how to reconstruct the sorting are dumped:
from spikeinterface import load_extractor
from pprint import pprint
d = sorting2.to_dict()
pprint(d)
sorting2_loaded = load_extractor(d)
print(sorting2_loaded)
###############################################################################
# The dictionary can also be dumped directly to a JSON file on disk:
sorting2.dump('my_sorting.json')
sorting2_loaded = load_extractor('my_sorting.json')
print(sorting2_loaded)
###############################################################################
# **IMPORTANT**: the "dump" operation DOES NOT copy the spike trains to disk!
#
# If you wish to also store the spike trains in a compact way you need to use the
# :code:`save()` function: