def test_natural_scenes(ns, nwb_b, analysis_b_new):
ns_new = NaturalScenes.from_analysis_file(BODS(nwb_b), analysis_b_new)
#assert np.allclose(ns.sweep_response, ns_new.sweep_response)
assert np.allclose(ns.mean_sweep_response, ns_new.mean_sweep_response, equal_nan=True)
assert np.allclose(ns.noise_correlation, ns_new.noise_correlation, equal_nan=True)
assert np.allclose(ns.signal_correlation, ns_new.signal_correlation, equal_nan=True)
assert np.allclose(ns.representational_similarity, ns_new.representational_similarity, equal_nan=True)
def test_natural_scenes(ns, nwb_b, analysis_b_new):
ns_new = NaturalScenes.from_analysis_file(BODS(nwb_b), analysis_b_new)
#assert np.allclose(ns.sweep_response, ns_new.sweep_response)
assert np.allclose(ns.mean_sweep_response, ns_new.mean_sweep_response, equal_nan=True)
assert np.allclose(ns.noise_correlation, ns_new.noise_correlation, equal_nan=True)
assert np.allclose(ns.signal_correlation, ns_new.signal_correlation, equal_nan=True)
assert np.allclose(ns.representational_similarity, ns_new.representational_similarity, equal_nan=True)
def build_type(nwb_file, data_file, configs, output_dir, type_name):
data_set = BrainObservatoryNwbDataSet(nwb_file)
try:
if type_name == "dg":
dga = DriftingGratings.from_analysis_file(data_set, data_file)
build_drifting_gratings(dga, configs, output_dir)
elif type_name == "sg":
sga = StaticGratings.from_analysis_file(data_set, data_file)
build_static_gratings(sga, configs, output_dir)
elif type_name == "nm1":
nma = NaturalMovie.from_analysis_file(data_set, data_file, si.NATURAL_MOVIE_ONE)
build_natural_movie(nma, configs, output_dir, si.NATURAL_MOVIE_ONE)
elif type_name == "nm2":
nma = NaturalMovie.from_analysis_file(data_set, data_file, si.NATURAL_MOVIE_TWO)
build_natural_movie(nma, configs, output_dir, si.NATURAL_MOVIE_TWO)
elif type_name == "nm3":
nma = NaturalMovie.from_analysis_file(data_set, data_file, si.NATURAL_MOVIE_THREE)
build_natural_movie(nma, configs, output_dir, si.NATURAL_MOVIE_THREE)
elif type_name == "ns":
nsa = NaturalScenes.from_analysis_file(data_set, data_file)
build_natural_scenes(nsa, configs, output_dir)
elif type_name == "sp":
nma = NaturalMovie.from_analysis_file(data_set, data_file, si.NATURAL_MOVIE_ONE)
build_speed_tuning(nma, configs, output_dir)
elif type_name == "lsn_on":
lsna = lsna_check_hvas(data_set, data_file)
build_locally_sparse_noise(lsna, configs, output_dir, True)
elif type_name == "lsn_off":
lsna = lsna_check_hvas(data_set, data_file)
build_locally_sparse_noise(lsna, configs, output_dir, False)
elif type_name == "rf":
lsna = lsna_check_hvas(data_set, data_file)
build_receptive_field(lsna, configs, output_dir)
elif type_name == "corr":
build_correlation_plots(data_set, data_file, configs, output_dir)
elif type_name == "eye":
build_eye_tracking_plots(data_set, configs, output_dir)
except MissingStimulusException as e:
logging.warning("could not load stimulus (%s)", type_name)
except Exception as e:
traceback.print_exc()
logging.critical("error running stimulus (%s)", type_name)
raise e
def ns(nwb_b, analysis_b):
return NaturalScenes.from_analysis_file(BODS(nwb_b), analysis_b)
def build_correlation_plots(data_set, analysis_file, configs, output_dir):
sig_corrs = []
noise_corrs = []
avail_stims = si.stimuli_in_session(data_set.get_session_type())
ans = []
labels = []
colors = []
if si.DRIFTING_GRATINGS in avail_stims:
dg = DriftingGratings.from_analysis_file(data_set, analysis_file)
if hasattr(dg, 'representational_similarity'):
ans.append(dg)
labels.append(si.DRIFTING_GRATINGS_SHORT)
colors.append(si.DRIFTING_GRATINGS_COLOR)
setups = [ ( [configs['large']], True ), ( [configs['small']], False )]
for cfgs, show_labels in setups:
for fn in build_plots("drifting_gratings_representational_similarity", 1.0, cfgs, output_dir):
oplots.plot_representational_similarity(dg.representational_similarity,
dims=[dg.orivals, dg.tfvals[1:]],
dim_labels=["dir", "tf"],
dim_order=[1,0],
colors=['r','b'],
labels=show_labels)
if si.STATIC_GRATINGS in avail_stims:
sg = StaticGratings.from_analysis_file(data_set, analysis_file)
if hasattr(sg, 'representational_similarity'):
ans.append(sg)
labels.append(si.STATIC_GRATINGS_SHORT)
colors.append(si.STATIC_GRATINGS_COLOR)
setups = [ ( [configs['large']], True ), ( [configs['small']], False )]
for cfgs, show_labels in setups:
for fn in build_plots("static_gratings_representational_similarity", 1.0, cfgs, output_dir):
oplots.plot_representational_similarity(sg.representational_similarity,
dims=[sg.orivals, sg.sfvals[1:], sg.phasevals],
dim_labels=["ori", "sf", "ph"],
dim_order=[1,0,2],
colors=['r','g','b'],
labels=show_labels)
if si.NATURAL_SCENES in avail_stims:
ns = NaturalScenes.from_analysis_file(data_set, analysis_file)
if hasattr(ns, 'representational_similarity'):
ans.append(ns)
labels.append(si.NATURAL_SCENES_SHORT)
colors.append(si.NATURAL_SCENES_COLOR)
setups = [ ( [configs['large']], True ), ( [configs['small']], False )]
for cfgs, show_labels in setups:
for fn in build_plots("natural_scenes_representational_similarity", 1.0, cfgs, output_dir):
oplots.plot_representational_similarity(ns.representational_similarity, labels=show_labels)
if len(ans):
for an in ans:
sig_corrs.append(an.signal_correlation)
extra_dims = range(2,len(an.noise_correlation.shape))
noise_corrs.append(an.noise_correlation.mean(axis=tuple(extra_dims)))
for fn in build_plots("correlation", 1.0, [configs['large'], configs['svg']], output_dir):
oplots.population_correlation_scatter(sig_corrs, noise_corrs, labels, colors, scale=16.0)
oplots.finalize_with_axes()
for fn in build_plots("correlation", 1.0, [configs['small']], output_dir):
oplots.population_correlation_scatter(sig_corrs, noise_corrs, labels, colors, scale=4.0)
oplots.finalize_no_labels()
csids = ans[0].data_set.get_cell_specimen_ids()
for fn, csid, i in build_cell_plots(csids, "signal_correlation", 1.0, [configs['large']], output_dir):
row = ans[0].row_from_cell_id(csid, i)
oplots.plot_cell_correlation([ np.delete(sig_corr[row],i) for sig_corr in sig_corrs ],
labels, colors)
oplots.finalize_with_axes()
for fn, csid, i in build_cell_plots(csids, "signal_correlation", 1.0, [configs['small']], output_dir):
row = ans[0].row_from_cell_id(csid, i)
oplots.plot_cell_correlation([ np.delete(sig_corr[row],i) for sig_corr in sig_corrs ],
labels, colors)
oplots.finalize_no_labels()
def ns(nwb_b, analysis_b):
return NaturalScenes.from_analysis_file(BODS(nwb_b), analysis_b)