def test_export_to_phy():
repo = 'https://gin.g-node.org/NeuralEnsemble/ephy_testing_data'
remote_path = 'mearec/mearec_test_10s.h5'
local_path = download_dataset(repo=repo,
remote_path=remote_path,
local_folder=None)
recording = se.MEArecRecordingExtractor(local_path)
sorting = se.MEArecSortingExtractor(local_path)
waveform_folder = Path('waveforms')
output_folder = Path('phy_output')
for f in (waveform_folder, output_folder):
if f.is_dir():
shutil.rmtree(f)
waveform_extractor = extract_waveforms(recording, sorting, waveform_folder)
export_to_phy(waveform_extractor,
output_folder,
compute_pc_features=True,
compute_amplitudes=True,
max_channels_per_template=8,
n_jobs=1,
chunk_size=10000,
progress_bar=True)
def test_export_to_phy_by_property():
num_units = 4
recording, sorting = se.toy_example(num_channels=8,
duration=10,
num_units=num_units,
num_segments=1)
recording.set_channel_groups([0, 0, 0, 0, 1, 1, 1, 1])
sorting.set_property("group", [0, 0, 1, 1])
waveform_folder = Path('waveforms')
waveform_folder_rm = Path('waveforms_rm')
output_folder = Path('phy_output')
output_folder_rm = Path('phy_output_rm')
rec_folder = Path("rec")
sort_folder = Path("sort")
for f in (waveform_folder, waveform_folder_rm, output_folder,
output_folder_rm, rec_folder, sort_folder):
if f.is_dir():
shutil.rmtree(f)
recording = recording.save(folder=rec_folder)
sorting = sorting.save(folder=sort_folder)
waveform_extractor = extract_waveforms(recording, sorting, waveform_folder)
export_to_phy(waveform_extractor,
output_folder,
compute_pc_features=True,
compute_amplitudes=True,
max_channels_per_template=8,
sparsity_dict=dict(method="by_property",
by_property="group"),
n_jobs=1,
chunk_size=10000,
progress_bar=True)
template_inds = np.load(output_folder / "template_ind.npy")
assert template_inds.shape == (num_units, 4)
# Remove one channel
recording_rm = recording.channel_slice([0, 2, 3, 4, 5, 6, 7])
waveform_extractor_rm = extract_waveforms(recording_rm, sorting,
waveform_folder_rm)
export_to_phy(waveform_extractor_rm,
output_folder_rm,
compute_pc_features=True,
compute_amplitudes=True,
max_channels_per_template=8,
sparsity_dict=dict(method="by_property",
by_property="group"),
n_jobs=1,
chunk_size=10000,
progress_bar=True)
template_inds = np.load(output_folder_rm / "template_ind.npy")
assert template_inds.shape == (num_units, 4)
assert len(np.where(template_inds == -1)[0]) > 0
def test_export_to_phy_by_sparsity():
repo = 'https://gin.g-node.org/NeuralEnsemble/ephy_testing_data'
remote_path = 'mearec/mearec_test_10s.h5'
local_path = download_dataset(repo=repo,
remote_path=remote_path,
local_folder=None)
recording = se.MEArecRecordingExtractor(local_path)
sorting = se.MEArecSortingExtractor(local_path)
waveform_folder = Path('waveforms')
output_folder_radius = Path('phy_output_radius')
output_folder_thr = Path('phy_output_thr')
for f in (waveform_folder, output_folder_radius, output_folder_thr):
if f.is_dir():
shutil.rmtree(f)
waveform_extractor = extract_waveforms(recording, sorting, waveform_folder)
export_to_phy(waveform_extractor,
output_folder_radius,
compute_pc_features=True,
compute_amplitudes=True,
max_channels_per_template=None,
sparsity_dict=dict(method="radius", radius_um=50),
n_jobs=1,
chunk_size=10000,
progress_bar=True)
template_ind = np.load(output_folder_radius / "template_ind.npy")
# templates have different shapes!
assert -1 in template_ind
export_to_phy(waveform_extractor,
output_folder_thr,
compute_pc_features=True,
compute_amplitudes=True,
max_channels_per_template=None,
sparsity_dict=dict(method="threshold", threshold=2),
n_jobs=1,
chunk_size=10000,
progress_bar=True)
template_ind = np.load(output_folder_thr / "template_ind.npy")
# templates have different shapes!
assert -1 in template_ind
print(wavefroms.shape)
template = we_TDC.get_template(unit_id0)
print(template.shape)
##############################################################################
# Once we have the `WaveformExtractor` object
# we can post-process, validate, and curate the results. With
# the :code:`toolkit.postprocessing` submodule, one can, for example,
# get waveforms, templates, maximum channels, PCA scores, or export the data
# to Phy. `Phy <https://github.com/cortex-lab/phy>`_ is a GUI for manual
# curation of the spike sorting output. To export to phy you can run:
from spikeinterface.exporters import export_to_phy
export_to_phy(we_TDC, './phy_folder_for_TDC',
compute_pc_features=False, compute_amplitudes=True)
##############################################################################
# Then you can run the template-gui with: :code:`phy template-gui phy/params.py`
# and manually curate the results.
##############################################################################
# Quality metrics for the spike sorting output are very important to asses the spike sorting performance.
# The :code:`spikeinterface.toolkit.qualitymetrics` module implements several quality metrics
# to assess the goodness of sorted units. Among those, for example,
# are signal-to-noise ratio, ISI violation ratio, isolation distance, and many more.
# Theses metrics are built on top of WaveformExtractor class and return a dictionary with the unit ids as keys:
snrs = st.compute_snrs(we_TDC)
print(snrs)
