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 filter_ophys_experiments(self, experiments,
ids=None,
experiment_container_ids=None,
targeted_structures=None,
imaging_depths=None,
transgenic_lines=None,
stimuli=None,
session_types=None):
# re-using the code from above
experiments = self.filter_experiment_containers(experiments,
ids=ids,
targeted_structures=targeted_structures,
imaging_depths=imaging_depths,
transgenic_lines=transgenic_lines)
if experiment_container_ids is not None:
experiments = [e for e in experiments if e[
'experiment_container_id'] in experiment_container_ids]
if session_types is not None:
experiments = [e for e in experiments if e[
'stimulus_name'] in session_types]
if stimuli is not None:
experiments = [e for e in experiments
if len(set(stimuli) & set(stimulus_info.stimuli_in_session(e['stimulus_name']))) > 0]
return experiments
def get_ophys_experiment_stimuli(self, experiment_id):
""" For a single experiment, return the list of stimuli present in that experiment. """
exps = self.get_ophys_experiments(ids=[experiment_id])
if len(exps) == 0:
return None
return stim_info.stimuli_in_session(exps[0]['session_type'])
def get_ophys_experiment_stimuli(self, experiment_id):
""" For a single experiment, return the list of stimuli present in that experiment. """
exps = self.get_ophys_experiments(ids=[experiment_id])
if len(exps) == 0:
return None
return stim_info.stimuli_in_session(exps[0]['session_type'])
def filter_ophys_experiments(self,
experiments,
ids=None,
experiment_container_ids=None,
targeted_structures=None,
imaging_depths=None,
cre_lines=None,
reporter_lines=None,
transgenic_lines=None,
stimuli=None,
session_types=None,
include_failed=False,
require_eye_tracking=False,
simple=False):
experiments = self.filter_experiments_and_containers(
experiments,
ids=ids,
targeted_structures=targeted_structures,
imaging_depths=imaging_depths,
cre_lines=cre_lines,
reporter_lines=reporter_lines,
transgenic_lines=transgenic_lines)
if require_eye_tracking:
experiments = [
e for e in experiments
if e.get('fail_eye_tracking', None) is False
]
if not include_failed:
experiments = [
e for e in experiments
if not e.get('experiment_container', {}).get('failed', False)
]
if experiment_container_ids is not None:
experiments = [
e for e in experiments
if e['experiment_container_id'] in experiment_container_ids
]
if session_types is not None:
experiments = [
e for e in experiments if e['stimulus_name'] in session_types
]
if stimuli is not None:
experiments = [
e for e in experiments if len(
set(stimuli)
& set(stimulus_info.stimuli_in_session(e['stimulus_name']))
) > 0
]
if simple:
experiments = self.simplify_ophys_experiments(experiments)
return experiments
def filter_ophys_experiments(self, experiments,
ids=None,
experiment_container_ids=None,
targeted_structures=None,
imaging_depths=None,
cre_lines=None,
reporter_lines=None,
transgenic_lines=None,
stimuli=None,
session_types=None,
include_failed=False,
require_eye_tracking=False,
simple=False):
experiments = self.filter_experiments_and_containers(experiments,
ids=ids,
targeted_structures=targeted_structures,
imaging_depths=imaging_depths,
cre_lines=cre_lines,
reporter_lines=reporter_lines,
transgenic_lines=transgenic_lines)
if require_eye_tracking:
experiments = [e for e in experiments
if e.get('fail_eye_tracking', None) is False]
if not include_failed:
experiments = [e for e in experiments
if not e.get('experiment_container',{}).get('failed', False)]
if experiment_container_ids is not None:
experiments = [e for e in experiments if e[
'experiment_container_id'] in experiment_container_ids]
if session_types is not None:
experiments = [e for e in experiments if e[
'stimulus_name'] in session_types]
if stimuli is not None:
experiments = [e for e in experiments
if len(set(stimuli) & set(stimulus_info.stimuli_in_session(e['stimulus_name']))) > 0]
if simple:
experiments = self.simplify_ophys_experiments(experiments)
return experiments
def _add_stim_epochs(trace,ax,data_set):
stim_colors_dict = {'locally_sparse_noise':'green','drifting_gratings':'yellow','natural_movie_one':'magenta','natural_movie_two':'magenta','natural_movie_three':'red','natural_scenes':'orange','spontaneous':'grey','static_gratings':'blue'}
from allensdk.brain_observatory.stimulus_info import stimuli_in_session
stim_types = stimuli_in_session(data_set.get_metadata()['session_type'])
for stim in stim_types:
#print(stim)
stim_table = data_set.get_stimulus_table(stim)
window_list = _get_epoch_windows(stim_table)
for w in window_list:
#ax.fill_betweenx(np.arange(trace.shape[0]),w[0],w[1],facecolor=stim_colors_dict[stim],alpha=0.2)
#ax.axvspan(w[0],w[1],np.min(trace),np.max(trace),facecolor=stim_colors_dict[stim],alpha=0.2)
ax.axvspan(w[0],w[1],facecolor=stim_colors_dict[stim],alpha=0.2)
ax.set_ylim([np.min(trace),np.max(trace)])
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()