def test_brain_observatory_experiment_containers_notebook(boc):
targeted_structures = boc.get_all_targeted_structures()
visp_ecs = boc.get_experiment_containers(targeted_structures=['VISp'])
depths = boc.get_all_imaging_depths()
stims = boc.get_all_stimuli()
cre_lines = boc.get_all_cre_lines()
cux2_ecs = boc.get_experiment_containers(cre_lines=['Cux2-CreERT2'])
cux2_ec_id = cux2_ecs[-1]['id']
exps = boc.get_ophys_experiments(experiment_container_ids=[cux2_ec_id])
exp = boc.get_ophys_experiments(experiment_container_ids=[cux2_ec_id],
stimuli=[stim_info.STATIC_GRATINGS])[0]
exp = boc.get_ophys_experiment_data(exp['id'])
assert set(depths) == set([
175, 185, 195, 200, 205, 225, 250, 265, 275, 276, 285, 300, 320, 325,
335, 350, 365, 375, 390, 400, 550, 570, 625
])
expected_stimuli = [
'drifting_gratings', 'locally_sparse_noise',
'locally_sparse_noise_4deg', 'locally_sparse_noise_8deg',
'natural_movie_one', 'natural_movie_three', 'natural_movie_two',
'natural_scenes', 'spontaneous', 'static_gratings'
]
assert set(stims) == set(expected_stimuli)
expected_cre_lines = [
u'Cux2-CreERT2', u'Emx1-IRES-Cre', u'Fezf2-CreER', u'Nr5a1-Cre',
u'Ntsr1-Cre_GN220', u'Pvalb-IRES-Cre', u'Rbp4-Cre_KL100',
u'Rorb-IRES2-Cre', u'Scnn1a-Tg3-Cre', u'Slc17a7-IRES2-Cre',
u'Sst-IRES-Cre', u'Tlx3-Cre_PL56', u'Vip-IRES-Cre'
]
assert set(cre_lines) == set(expected_cre_lines)
cells = boc.get_cell_specimens()
cells = pd.DataFrame.from_records(cells)
# find direction selective cells in VISp
visp_ec_ids = [ec['id'] for ec in visp_ecs]
visp_cells = cells[cells['experiment_container_id'].isin(visp_ec_ids)]
# significant response to drifting gratings stimulus
sig_cells = visp_cells[visp_cells['p_dg'] < 0.05]
# direction selective cells
dsi_cells = sig_cells[(sig_cells['dsi_dg'] > 0.5)
& (sig_cells['dsi_dg'] < 1.5)]
#assert len(cells) == 27124
assert len(cells) > 0
#assert len(visp_cells) == 16031
assert len(visp_cells) > 0
#assert len(sig_cells) == 8669
assert len(sig_cells) > 0
#assert len(dsi_cells) == 4943
assert len(dsi_cells) > 0
# find experiment containers for those cells
dsi_ec_ids = dsi_cells['experiment_container_id'].unique()
# Download the ophys experiments containing the drifting gratings stimulus for VISp experiment containers
dsi_exps = boc.get_ophys_experiments(experiment_container_ids=dsi_ec_ids,
stimuli=[stim_info.DRIFTING_GRATINGS])
# pick a direction-selective cell and find its NWB file
dsi_cell = dsi_cells.iloc[0]
# figure out which ophys experiment has the drifting gratings stimulus for the cell's experiment container
cell_exp = boc.get_ophys_experiments(
experiment_container_ids=[dsi_cell['experiment_container_id']],
stimuli=[stim_info.DRIFTING_GRATINGS])[0]
data_set = boc.get_ophys_experiment_data(cell_exp['id'])
# Fluorescence
dsi_cell_id = dsi_cell['cell_specimen_id']
time, raw_traces = data_set.get_fluorescence_traces(
cell_specimen_ids=[dsi_cell_id])
_, demixed_traces = data_set.get_demixed_traces(
cell_specimen_ids=[dsi_cell_id])
_, neuropil_traces = data_set.get_neuropil_traces(
cell_specimen_ids=[dsi_cell_id])
_, corrected_traces = data_set.get_corrected_fluorescence_traces(
cell_specimen_ids=[dsi_cell_id])
_, dff_traces = data_set.get_dff_traces(cell_specimen_ids=[dsi_cell_id])
# ROI Masks
data_set = boc.get_ophys_experiment_data(510221121)
# get the specimen IDs for a few cells
cids = data_set.get_cell_specimen_ids()[:15:5]
# get masks for specific cells
roi_mask_list = data_set.get_roi_mask(cell_specimen_ids=cids)
# make a mask of all ROIs in the experiment
all_roi_masks = data_set.get_roi_mask_array()
combined_mask = all_roi_masks.max(axis=0)
max_projection = data_set.get_max_projection()
# ROI Analysis
# example loading drifing grating data
data_set = boc.get_ophys_experiment_data(512326618)
dg = DriftingGratings(data_set)
# filter for visually responding, selective cells
vis_cells = (dg.peak.ptest_dg < 0.05) & (dg.peak.peak_dff_dg > 3)
osi_cells = vis_cells & (dg.peak.osi_dg > 0.5) & (dg.peak.osi_dg <= 1.5)
dsi_cells = vis_cells & (dg.peak.dsi_dg > 0.5) & (dg.peak.dsi_dg <= 1.5)
# 2-d tf vs. ori histogram
# tfval = 0 is used for the blank sweep, so we are ignoring it here
os = np.zeros((len(dg.orivals), len(dg.tfvals) - 1))
ds = np.zeros((len(dg.orivals), len(dg.tfvals) - 1))
for i, trial in dg.peak[osi_cells].iterrows():
os[trial.ori_dg, trial.tf_dg - 1] += 1
for i, trial in dg.peak[dsi_cells].iterrows():
ds[trial.ori_dg, trial.tf_dg - 1] += 1
max_count = max(os.max(), ds.max())
# Neuropil correction
data_set = boc.get_ophys_experiment_data(569407590)
csid = data_set.get_cell_specimen_ids()[0]
time, demixed_traces = data_set.get_demixed_traces(
cell_specimen_ids=[csid])
_, neuropil_traces = data_set.get_neuropil_traces(cell_specimen_ids=[csid])
results = estimate_contamination_ratios(demixed_traces[0],
neuropil_traces[0])
correction = demixed_traces[0] - results['r'] * neuropil_traces[0]
_, corrected_traces = data_set.get_corrected_fluorescence_traces(
cell_specimen_ids=[csid])
# Running Speed and Motion Correction
data_set = boc.get_ophys_experiment_data(512326618)
dxcm, dxtime = data_set.get_running_speed()
mc = data_set.get_motion_correction()
assert True
def shrink(self):
return self.resize(os.max(self.size()-1, 0))