def test_plot_units_depth_vs_amplitude(self):
sw.plot_units_depth_vs_amplitude(self._we)
sw.plot_unit_probe_map(we, unit_ids=unit_ids) ############################################################################## # plot_unit_waveform_density_map() # ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~ # # This is your best friend to check over merge unit_ids = sorting.unit_ids[:4] sw.plot_unit_waveform_density_map(we, unit_ids=unit_ids, max_channels=5) ############################################################################## # plot_amplitudes_distribution() # ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~ sw.plot_amplitudes_distribution(we) ############################################################################## # plot_amplitudes_timeseres() # ~~~~~~~~~~~~~~~~~~~~~~~~~~~ sw.plot_amplitudes_timeseries(we) ############################################################################## # plot_units_depth_vs_amplitude() # ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~ sw.plot_units_depth_vs_amplitude(we) plt.show()
def export_report(waveform_extractor,
output_folder,
remove_if_exists=False,
format="png",
show_figures=False,
peak_sign='neg',
**job_kwargs):
"""
Exports a SI spike sorting report. The report includes summary figures of the spike sorting output
(e.g. amplitude distributions, unit localization and depth VS amplitude) as well as unit-specific reports,
that include waveforms, templates, template maps, ISI distributions, and more.
Parameters
----------
waveform_extractor: a WaveformExtractor or None
If WaveformExtractor is provide then the compute is faster otherwise
output_folder: str
The output folder where the report files are saved
remove_if_exists: bool
If True and the output folder exists, it is removed
format: str
'png' (default) or 'pdf' or any format handled by matplotlib
peak_sign: 'neg' or 'pos'
used to compute amplitudes and metrics
show_figures: bool
If True, figures are shown. If False (default), figures are closed after saving.
{}
"""
we = waveform_extractor
sorting = we.sorting
unit_ids = sorting.unit_ids
# lets matplotlib do this check svg is also cool
# assert format in ["png", "pdf"], "'format' can be 'png' or 'pdf'"
if we.is_extension('spike_amplitudes'):
sac = we.load_extension('spike_amplitudes')
amplitudes = sac.get_amplitudes(outputs='by_unit')
else:
amplitudes = st.compute_spike_amplitudes(we,
peak_sign=peak_sign,
outputs='by_unit',
**job_kwargs)
output_folder = Path(output_folder).absolute()
if output_folder.is_dir():
if remove_if_exists:
shutil.rmtree(output_folder)
else:
raise FileExistsError(f'{output_folder} already exists')
output_folder.mkdir(parents=True, exist_ok=True)
# unit list
units = pd.DataFrame(
index=unit_ids) # , columns=['max_on_channel_id', 'amplitude'])
units.index.name = 'unit_id'
units['max_on_channel_id'] = pd.Series(
st.get_template_extremum_channel(we, peak_sign='neg', outputs='id'))
units['amplitude'] = pd.Series(
st.get_template_extremum_amplitude(we, peak_sign='neg'))
units.to_csv(output_folder / 'unit list.csv', sep='\t')
# metrics
if we.is_extension('quality_metrics'):
qmc = we.load_extension('quality_metrics')
metrics = qmc._metrics
else:
# compute principal_components if not done
if not we.is_extension('principal_components'):
pca = st.compute_principal_components(we,
load_if_exists=True,
n_components=5,
mode='by_channel_local')
metrics = st.compute_quality_metrics(we)
metrics.to_csv(output_folder / 'quality metrics.csv')
unit_colors = sw.get_unit_colors(sorting)
# global figures
fig = plt.figure(figsize=(20, 10))
w = sw.plot_unit_localization(we, figure=fig, unit_colors=unit_colors)
fig.savefig(output_folder / f'unit_localization.{format}')
if not show_figures:
plt.close(fig)
fig, ax = plt.subplots(figsize=(20, 10))
sw.plot_units_depth_vs_amplitude(we, ax=ax, unit_colors=unit_colors)
fig.savefig(output_folder / f'units_depth_vs_amplitude.{format}')
if not show_figures:
plt.close(fig)
fig = plt.figure(figsize=(20, 10))
sw.plot_amplitudes_distribution(we, figure=fig, unit_colors=unit_colors)
fig.savefig(output_folder / f'amplitudes_distribution.{format}')
if not show_figures:
plt.close(fig)
# units
units_folder = output_folder / 'units'
units_folder.mkdir()
for unit_id in unit_ids:
fig = plt.figure(
constrained_layout=False,
figsize=(15, 7),
)
sw.plot_unit_summary(we, unit_id, figure=fig)
fig.suptitle(f'unit {unit_id}')
fig.savefig(units_folder / f'{unit_id}.{format}')
if not show_figures:
plt.close(fig)