def test_locally_sparse_noise(lsn, nwb_c, analysis_c_new):
ds = BODS(nwb_c)
session_type = ds.get_metadata()['session_type']
logging.debug(session_type)
if session_type == si.THREE_SESSION_C:
lsn_new = LocallySparseNoise.from_analysis_file(ds, analysis_c_new, si.LOCALLY_SPARSE_NOISE)
elif session_type == si.THREE_SESSION_C2:
lsn_new = LocallySparseNoise.from_analysis_file(ds, analysis_c_new, si.LOCALLY_SPARSE_NOISE_4DEG)
#assert np.allclose(lsn.sweep_response, lsn_new.sweep_response)
assert np.allclose(lsn.mean_sweep_response, lsn_new.mean_sweep_response, equal_nan=True)
def test_locally_sparse_noise(lsn, nwb_c, analysis_c_new):
ds = BODS(nwb_c)
session_type = ds.get_metadata()['session_type']
logging.debug(session_type)
if session_type == si.THREE_SESSION_C:
lsn_new = LocallySparseNoise.from_analysis_file(ds, analysis_c_new, si.LOCALLY_SPARSE_NOISE)
elif session_type == si.THREE_SESSION_C2:
lsn_new = LocallySparseNoise.from_analysis_file(ds, analysis_c_new, si.LOCALLY_SPARSE_NOISE_4DEG)
#assert np.allclose(lsn.sweep_response, lsn_new.sweep_response)
assert np.allclose(lsn.mean_sweep_response, lsn_new.mean_sweep_response, equal_nan=True)
def lsn(nwb_c, analysis_c):
# in order to work around 2/3 unicode compatibility, separate files are specified for python 2 and 3
# we need to look up a different key depending on python version
key = si.LOCALLY_SPARSE_NOISE_4DEG if sys.version_info < (
3, ) else si.LOCALLY_SPARSE_NOISE
return LocallySparseNoise.from_analysis_file(BODS(nwb_c), analysis_c, key)
def write_rfs(dataset, write_dir):
'''
https://allensdk.readthedocs.io/en/latest/_static/examples/nb/brain_observatory_analysis.html
'''
cell_ids = dataset.get_cell_specimen_ids()
session_type = dataset.get_session_type()
if 'locally_sparse_noise' in dataset.list_stimuli():
logging.info('WRITING RECEPTIVE FIELDS FOR EXPERIMENT {}'.format(
dataset.get_metadata()['ophys_experiment_id']))
on_dir = os.path.join(write_dir, 'ReceptiveFields', 'on', session_type)
off_dir = os.path.join(write_dir, 'ReceptiveFields', 'off',
session_type)
os.makedirs(on_dir, exist_ok=True)
os.makedirs(off_dir, exist_ok=True)
lsn = LocallySparseNoise(dataset)
rfs = lsn.receptive_field
rfs[np.isnan(rfs)] = 0
for ind, cell_id in enumerate(cell_ids):
rf = rfs[:, :, ind, :]
rf = max_min_scale(rf) * 255
on, off = rf.transpose([2, 0, 1])
fname = str(cell_id) + '.png'
cv2.imwrite(os.path.join(on_dir, fname), on)
cv2.imwrite(os.path.join(off_dir, fname), off)
def lsna_check_hvas(data_set, data_file):
avail_stims = si.stimuli_in_session(data_set.get_session_type())
targeted_structure = data_set.get_metadata()['targeted_structure']
stim = None
if targeted_structure == "VISp":
if si.LOCALLY_SPARSE_NOISE_4DEG in avail_stims:
stim = si.LOCALLY_SPARSE_NOISE_4DEG
elif si.LOCALLY_SPARSE_NOISE in avail_stims:
stim = si.LOCALLY_SPARSE_NOISE
else:
if si.LOCALLY_SPARSE_NOISE_8DEG in avail_stims:
stim = si.LOCALLY_SPARSE_NOISE_8DEG
elif si.LOCALLY_SPARSE_NOISE in avail_stims:
stim = si.LOCALLY_SPARSE_NOISE
if stim is None:
raise MissingStimulusException("Could not find appropriate LSN stimulus for session %s",
data_set.get_session_type())
else:
logging.debug("in structure %s, using %s stimulus for plots", targeted_structure, stim)
return LocallySparseNoise.from_analysis_file(data_set, data_file, stim)
def test_brain_observatory_locally_sparse_noise_notebook(boc):
specimen_id = 517410165
cell = boc.get_cell_specimens(ids=[specimen_id])[0]
exp = boc.get_ophys_experiments(experiment_container_ids=[cell['experiment_container_id']],
stimuli=[stim_info.LOCALLY_SPARSE_NOISE])[0]
data_set = boc.get_ophys_experiment_data(exp['id'])
lsn = LocallySparseNoise(data_set)
specimen_ids = data_set.get_cell_specimen_ids()
cell_loc = np.argwhere(specimen_ids==specimen_id)[0][0]
receptive_field = lsn.receptive_field[:,:,cell_loc,0]
assert True
def session_c(self, plot_flag=False, save_flag=True):
lsn = LocallySparseNoise(self.nwb)
nm2 = NaturalMovie(self.nwb, 'natural_movie_two')
nm1 = NaturalMovie(self.nwb, 'natural_movie_one', speed_tuning=True)
SessionAnalysis._log.info("Session C analyzed")
peak = multi_dataframe_merge([nm1.peak_run, nm1.peak, nm2.peak])
self.append_metadata(peak)
self.metrics_c["roi_id"] = nm1.roi_id
if save_flag:
self.save_session_c(lsn, nm1, nm2, peak)
if plot_flag:
cp._plot_3sc(lsn, nm1, nm2, self.save_dir)
cp.plot_lsn_traces(lsn, self.save_dir)
def test_harness(dataset, stimulus, trigger):
with patch(
'allensdk.brain_observatory.stimulus_analysis.StimulusAnalysis.get_speed_tuning',
mock_speed_tuning()) as get_speed_tuning:
lsn = LocallySparseNoise(dataset, stimulus)
assert lsn._stim_table is StimulusAnalysis._PRELOAD
assert lsn._LSN is StimulusAnalysis._PRELOAD
assert lsn._LSN_mask is StimulusAnalysis._PRELOAD
assert lsn._sweeplength is StimulusAnalysis._PRELOAD
assert lsn._interlength is StimulusAnalysis._PRELOAD
assert lsn._extralength is StimulusAnalysis._PRELOAD
assert lsn._sweep_response is StimulusAnalysis._PRELOAD
assert lsn._mean_sweep_response is StimulusAnalysis._PRELOAD
assert lsn._pval is StimulusAnalysis._PRELOAD
assert lsn._receptive_field is StimulusAnalysis._PRELOAD
if trigger == 1:
print(lsn.stim_table)
print(lsn.sweep_response)
print(lsn.receptive_field)
elif trigger == 2:
print(lsn.LSN)
print(lsn.mean_sweep_response)
print(lsn.receptive_field)
elif trigger == 3:
print(lsn.LSN_mask)
print(lsn.pval)
print(lsn.receptive_field)
elif trigger == 4:
print(lsn.sweeplength)
print(lsn.sweep_response)
print(lsn.receptive_field)
elif trigger == 5:
print(lsn.interlength)
print(lsn.mean_sweep_response)
print(lsn.receptive_field)
elif trigger == 6:
print(lsn.extralength)
print(lsn.pval)
print(lsn.receptive_field)
assert lsn._stim_table is not StimulusAnalysis._PRELOAD
assert lsn._LSN is not StimulusAnalysis._PRELOAD
assert lsn._LSN_mask is not StimulusAnalysis._PRELOAD
assert lsn._sweeplength is not StimulusAnalysis._PRELOAD
assert lsn._interlength is not StimulusAnalysis._PRELOAD
assert lsn._extralength is not StimulusAnalysis._PRELOAD
assert lsn._sweep_response is not StimulusAnalysis._PRELOAD
assert lsn._mean_sweep_response is not StimulusAnalysis._PRELOAD
assert lsn._pval is not StimulusAnalysis._PRELOAD
assert lsn._receptive_field is not StimulusAnalysis._PRELOAD
# verify super class members weren't preloaded
assert not dataset.get_corrected_fluorescence_traces.called
assert lsn._timestamps is StimulusAnalysis._PRELOAD
assert lsn._celltraces is StimulusAnalysis._PRELOAD
assert lsn._numbercells is StimulusAnalysis._PRELOAD
assert not dataset.get_roi_ids.called
assert lsn._roi_id is StimulusAnalysis._PRELOAD
assert not dataset.get_cell_specimen_ids.called
assert lsn._cell_id is StimulusAnalysis._PRELOAD
assert not dataset.get_dff_traces.called
assert lsn._dfftraces is StimulusAnalysis._PRELOAD
assert lsn._dxcm is StimulusAnalysis._PRELOAD
assert lsn._dxtime is StimulusAnalysis._PRELOAD
def lsn(nwb_c, analysis_c):
return LocallySparseNoise.from_analysis_file(BODS(nwb_c), analysis_c,
si.LOCALLY_SPARSE_NOISE_8DEG)
def lsn(nwb_c, analysis_c):
# in order to work around 2/3 unicode compatibility, separate files are specified for python 2 and 3
# we need to look up a different key depending on python version
key = si.LOCALLY_SPARSE_NOISE_4DEG if sys.version_info < (3,) else si.LOCALLY_SPARSE_NOISE
return LocallySparseNoise.from_analysis_file(BODS(nwb_c), analysis_c, key)
