def behavior(self):
### Behavior Analysis ###
behavior_plot_dir = os.path.join(self.FIG_SAVE_DIR, 'behavior')
behavior_analysis.plot_behavior(self.trials, behavior_plot_dir, prefix=self.figure_prefix)
trial_types, counts = behavior_analysis.get_trial_counts(self.trials)
behavior_analysis.plot_trial_type_pie(counts, trial_types, behavior_plot_dir, prefix=self.figure_prefix)
def behavior(self):
### Behavior Analysis ###
behavior_plot_dir = os.path.join(self.FIG_SAVE_DIR, 'behavior')
behavior_analysis.plot_behavior(self.trials,
behavior_plot_dir,
prefix=self.figure_prefix)
behavior_analysis.plot_trial_licks(self.trials,
self.vsync_times,
self.behavior_start_frame,
behavior_plot_dir,
prefix=self.figure_prefix)
trial_types, counts = behavior_analysis.get_trial_counts(self.trials)
behavior_analysis.plot_trial_type_pie(counts,
trial_types,
behavior_plot_dir,
prefix=self.figure_prefix)
pkl_list = [
getattr(self, pd)
for pd in ['behavior_data', 'mapping_data', 'replay_data']
if hasattr(self, pd)
]
analysis.plot_running_wheel(pkl_list,
behavior_plot_dir,
save_plotly=False,
prefix=self.figure_prefix)
def run_qc(exp_id, save_root):
identifier = exp_id
if identifier.find('_') >= 0:
d = data_getters.local_data_getter(base_dir=identifier)
else:
d = data_getters.lims_data_getter(exp_id=identifier)
paths = d.data_dict
FIG_SAVE_DIR = os.path.join(
save_root, paths['es_id'] + '_' + paths['external_specimen_name'] +
'_' + paths['datestring'])
if not os.path.exists(FIG_SAVE_DIR):
os.mkdir(FIG_SAVE_DIR)
figure_prefix = paths['external_specimen_name'] + '_' + paths[
'datestring'] + '_'
### GET FILE PATHS TO SYNC AND PKL FILES ###
SYNC_FILE = paths['sync_file']
BEHAVIOR_PKL = paths['behavior_pkl']
REPLAY_PKL = paths['replay_pkl']
MAPPING_PKL = paths['mapping_pkl']
for f, s in zip([SYNC_FILE, BEHAVIOR_PKL, REPLAY_PKL, MAPPING_PKL],
['sync: ', 'behavior: ', 'replay: ', 'mapping: ']):
print(s + f)
### GET MAIN DATA STREAMS ###
syncDataset = sync_dataset(SYNC_FILE)
behavior_data = pd.read_pickle(BEHAVIOR_PKL)
mapping_data = pd.read_pickle(MAPPING_PKL)
replay_data = pd.read_pickle(REPLAY_PKL)
### Behavior Analysis ###
behavior_plot_dir = os.path.join(FIG_SAVE_DIR, 'behavior')
trials = behavior_analysis.get_trials_df(behavior_data)
behavior_analysis.plot_behavior(trials,
behavior_plot_dir,
prefix=figure_prefix)
trial_types, counts = behavior_analysis.get_trial_counts(trials)
behavior_analysis.plot_trial_type_pie(counts,
trial_types,
behavior_plot_dir,
prefix=figure_prefix)
### CHECK FRAME COUNTS ###
vr, vf = probeSync.get_sync_line_data(syncDataset, channel=2)
behavior_frame_count = behavior_data['items']['behavior'][
'intervalsms'].size + 1
mapping_frame_count = mapping_data['intervalsms'].size + 1
replay_frame_count = replay_data['intervalsms'].size + 1
total_pkl_frames = (behavior_frame_count + mapping_frame_count +
replay_frame_count)
### CHECK THAT NO FRAMES WERE DROPPED FROM SYNC ###
print('frames in pkl files: {}'.format(total_pkl_frames))
print('frames in sync file: {}'.format(len(vf)))
#assert(total_pkl_frames==len(vf))
### CHECK THAT REPLAY AND BEHAVIOR HAVE SAME FRAME COUNT ###
print('frames in behavior stim: {}'.format(behavior_frame_count))
print('frames in replay stim: {}'.format(replay_frame_count))
#assert(behavior_frame_count==replay_frame_count)
# look for potential frame offsets from aborted stims
(behavior_start_frame,
mapping_start_frame, replay_start_frame) = probeSync.get_frame_offsets(
syncDataset,
[behavior_frame_count, mapping_frame_count, replay_frame_count])
behavior_end_frame = behavior_start_frame + behavior_frame_count - 1
mapping_end_frame = mapping_start_frame + mapping_frame_count - 1
replay_end_frame = replay_start_frame + replay_frame_count - 1
MONITOR_LAG = 0.036 #TO DO: don't hardcode this...
FRAME_APPEAR_TIMES = vf + MONITOR_LAG
behavior_start_time, mapping_start_time, replay_start_time = [
FRAME_APPEAR_TIMES[f] for f in
[behavior_start_frame, mapping_start_frame, replay_start_frame]
]
behavior_end_time, mapping_end_time, replay_end_time = [
FRAME_APPEAR_TIMES[f]
for f in [behavior_end_frame, mapping_end_frame, replay_end_frame]
]
### Plot vsync info ###
vsync_save_dir = os.path.join(FIG_SAVE_DIR, 'vsyncs')
analysis.plot_frame_intervals(vf,
behavior_frame_count,
mapping_frame_count,
behavior_start_frame,
mapping_start_frame,
replay_start_frame,
vsync_save_dir,
prefix=figure_prefix)
analysis.plot_vsync_interval_histogram(vf,
vsync_save_dir,
prefix=figure_prefix)
analysis.vsync_report(vf,
total_pkl_frames,
vsync_save_dir,
prefix=figure_prefix)
### BUILD UNIT TABLE ####
probe_dict = probeSync.build_unit_table(paths['data_probes'], paths,
syncDataset)
### Plot Probe Yield QC ###
probe_yield_dir = os.path.join(FIG_SAVE_DIR, 'probe_yield')
probe_dirs = [paths['probe' + pid] for pid in paths['data_probes']]
analysis.plot_unit_quality_hist(probe_dict,
probe_yield_dir,
prefix=figure_prefix)
analysis.plot_unit_distribution_along_probe(probe_dict,
probe_yield_dir,
prefix=figure_prefix)
analysis.plot_all_spike_hist(probe_dict,
probe_yield_dir,
prefix=figure_prefix + 'good')
analysis.copy_probe_depth_images(paths,
probe_yield_dir,
prefix=figure_prefix)
### Unit Metrics ###
unit_metrics_dir = os.path.join(FIG_SAVE_DIR, 'unit_metrics')
analysis.plot_unit_metrics(paths, unit_metrics_dir, prefix=figure_prefix)
### Probe/Sync alignment
probeSyncDir = os.path.join(FIG_SAVE_DIR, 'probeSyncAlignment')
analysis.plot_barcode_interval_hist(probe_dirs,
syncDataset,
probeSyncDir,
prefix=figure_prefix)
analysis.plot_barcode_intervals(probe_dirs,
syncDataset,
probeSyncDir,
prefix=figure_prefix)
analysis.probe_sync_report(probe_dirs,
syncDataset,
probeSyncDir,
prefix=figure_prefix)
analysis.plot_barcode_matches(probe_dirs,
syncDataset,
probeSyncDir,
prefix=figure_prefix)
### Plot visual responses
get_RFs(probe_dict,
mapping_data,
mapping_start_frame,
FRAME_APPEAR_TIMES,
os.path.join(FIG_SAVE_DIR, 'receptive_fields'),
prefix=figure_prefix)
analysis.plot_population_change_response(probe_dict,
behavior_frame_count,
mapping_frame_count,
trials,
FRAME_APPEAR_TIMES,
os.path.join(
FIG_SAVE_DIR,
'change_response'),
ctx_units_percentile=66,
prefix=figure_prefix)
### Plot running ###
analysis.plot_running_wheel(behavior_data,
mapping_data,
replay_data,
behavior_plot_dir,
prefix=figure_prefix)
### LFP ###
lfp_save_dir = os.path.join(FIG_SAVE_DIR, 'LFP')
lick_times = analysis.get_rewarded_lick_times(
probeSync.get_lick_times(syncDataset),
FRAME_APPEAR_TIMES,
trials,
min_inter_lick_time=0.5)
lfp_dict = probeSync.build_lfp_dict(probe_dirs, syncDataset)
analysis.plot_lick_triggered_LFP(lfp_dict,
lick_times,
lfp_save_dir,
prefix=figure_prefix,
agarChRange=None,
num_licks=20,
windowBefore=0.5,
windowAfter=1.5,
min_inter_lick_time=0.5,
behavior_duration=3600)
### VIDEOS ###
video_dir = os.path.join(FIG_SAVE_DIR, 'videos')
analysis.lost_camera_frame_report(paths, video_dir, prefix=figure_prefix)
analysis.camera_frame_grabs(
paths,
syncDataset,
video_dir,
[behavior_start_time, mapping_start_time, replay_start_time],
[behavior_end_time, mapping_end_time, replay_end_time],
epoch_frame_nums=[2, 2, 2],
prefix=figure_prefix)
REPLAY_PKL = paths['replay_pkl']
MAPPING_PKL = paths['mapping_pkl']
for f, s in zip([SYNC_FILE, BEHAVIOR_PKL, REPLAY_PKL, MAPPING_PKL],
['sync: ', 'behavior: ', 'replay: ', 'mapping: ']):
print(s + f)
### GET MAIN DATA STREAMS ###
syncDataset = sync_dataset(SYNC_FILE)
behavior_data = pd.read_pickle(BEHAVIOR_PKL)
mapping_data = pd.read_pickle(MAPPING_PKL)
replay_data = pd.read_pickle(REPLAY_PKL)
### Behavior Analysis ###
trials = behavior_analysis.get_trials_df(behavior_data)
behavior_analysis.plot_behavior(trials, FIG_SAVE_DIR)
trial_types, counts = behavior_analysis.get_trial_counts(trials)
behavior_analysis.plot_trial_type_pie(counts, trial_types, FIG_SAVE_DIR)
### CHECK FRAME COUNTS ###
vr, vf = probeSync.get_sync_line_data(syncDataset, channel=2)
behavior_frame_count = behavior_data['items']['behavior'][
'intervalsms'].size + 1
mapping_frame_count = mapping_data['intervalsms'].size + 1
replay_frame_count = replay_data['intervalsms'].size + 1
total_pkl_frames = (behavior_frame_count + mapping_frame_count +
replay_frame_count)