def get_SessionDf(LogDf, metrics, trial_entry_event="TRIAL_AVAILABLE_STATE", trial_exit_event="ITI_STATE"):
TrialSpans = bhv.get_spans_from_names(LogDf, trial_entry_event, trial_exit_event)
TrialDfs = []
for i, row in tqdm(TrialSpans.iterrows(),position=0, leave=True):
TrialDfs.append(bhv.time_slice(LogDf, row['t_on'], row['t_off']))
SessionDf = bhv.parse_trials(TrialDfs, metrics)
return SessionDf, TrialDfs
def reaches_during_delay_across_sess(animal_fd_path, tasks_names,
init_day_idx):
SessionsDf = utils.get_sessions(animal_fd_path)
animal_meta = pd.read_csv(animal_fd_path / 'animal_meta.csv')
# Filter sessions to the ones of the task we want to see
FilteredSessionsDf = pd.concat(
[SessionsDf.groupby('task').get_group(name) for name in tasks_names])
log_paths = [
Path(path) / 'arduino_log.txt' for path in FilteredSessionsDf['path']
]
fig, axes = plt.subplots(ncols=2, figsize=[6, 4], sharey=True, sharex=True)
colors = sns.color_palette(palette='turbo', n_colors=len(log_paths))
for j, log_path in enumerate(log_paths[init_day_idx:]):
LogDf = bhv.get_LogDf_from_path(log_path)
# ADD SINGLE GO_CUE_EVENT
LogDf = bhv.add_go_cue_LogDf(LogDf)
TrialSpans = bhv.get_spans_from_names(LogDf, "TRIAL_ENTRY_STATE",
"ITI_STATE")
TrialDfs = []
for i, row in tqdm(TrialSpans.iterrows(), position=0, leave=True):
TrialDfs.append(bhv.time_slice(LogDf, row['t_on'], row['t_off']))
metrics = (met.get_start, met.get_stop, met.get_correct_side,
met.get_outcome, met.get_interval_category,
met.get_chosen_side, met.has_reach_left,
met.has_reach_right)
SessionDf = bhv.parse_trials(TrialDfs, metrics)
CDF_of_reaches_during_delay(SessionDf,
TrialDfs,
axes=axes,
color=colors[j],
alpha=0.75,
label='day ' + str(j + 1))
fig.suptitle('CDF of first reach split on trial type \n' +
animal_meta['value'][5] + '-' + animal_meta['value'][0])
axes[0].set_ylabel('Fraction of trials')
axes[0].legend(frameon=False, fontsize='x-small')
fig.tight_layout()
return axes
def grasp_dur_across_sess(animal_fd_path, tasks_names):
SessionsDf = utils.get_sessions(animal_fd_path)
animal_meta = pd.read_csv(animal_fd_path / 'animal_meta.csv')
# Filter sessions to the ones of the task we want to see
FilteredSessionsDf = pd.concat(
[SessionsDf.groupby('task').get_group(name) for name in tasks_names])
log_paths = [
Path(path) / 'arduino_log.txt' for path in FilteredSessionsDf['path']
]
fig, axes = plt.subplots(ncols=2, figsize=[8, 4], sharey=True, sharex=True)
sides = ['LEFT', 'RIGHT']
Df = []
# gather data
for day, log_path in enumerate(log_paths):
LogDf = bhv.get_LogDf_from_path(log_path)
TrialSpans = bhv.get_spans_from_names(LogDf, "TRIAL_ENTRY_STATE",
"ITI_STATE")
TrialDfs = []
for i, row in tqdm(TrialSpans.iterrows(), position=0, leave=True):
TrialDfs.append(bhv.time_slice(LogDf, row['t_on'], row['t_off']))
metrics = (met.get_start, met.get_stop, met.get_correct_side,
met.get_outcome, met.get_interval_category,
met.get_chosen_side, met.has_reach_left,
met.has_reach_right)
SessionDf = bhv.parse_trials(TrialDfs, metrics)
for side in sides:
event_on, event_off = 'REACH_' + str(side) + '_ON', 'REACH_' + str(
side) + '_OFF' # event names
# reaches
grasp_spansDf = bhv.get_spans_from_names(LogDf, event_on,
event_off)
reach_durs = np.array(grasp_spansDf['dt'].values, dtype=object)
Df.append([reach_durs], columns=['durs'], ignore_index=True)
# grasps
choiceDf = bhv.groupby_dict(
SessionDf, dict(has_choice=True, chosen_side=side.lower()))
grasp_durs = choiceDf[~choiceDf['grasp_dur'].isna(
)]['grasp_dur'].values # filter out Nans
Df.append([grasp_durs, 'g', side],
columns=['durs', 'type', 'side'],
ignore_index=True)
sns.violinplot(data=Df[Df['type'] == 'r'],
x=day,
y='durs',
hue='side',
split=True,
cut=0,
legend='reaches')
sns.violinplot(data=Df[Df['type'] == 'g'],
x=day,
y='durs',
hue='side',
split=True,
cut=0,
legend='grasps')
fig.suptitle('CDF of first reach split on trial type \n' +
animal_meta['value'][5] + '-' + animal_meta['value'][0])
axes.legend
return axes
log_paths = [Path(path)/'arduino_log.txt' for path in FilteredSessionsDf['path']]
for k,log_path in enumerate(tqdm(log_paths[:no_sessions_to_analyze], position=0, leave=True, desc=animal)):
LogDf = bhv.get_LogDf_from_path(log_path)
# Getting metrics
TrialSpans = bhv.get_spans_from_names(LogDf, "TRIAL_ENTRY_STATE", "ITI_STATE")
TrialDfs = []
for i, row in TrialSpans.iterrows():
TrialDfs.append(bhv.time_slice(LogDf, row['t_on'], row['t_off']))
metrics = ( met.get_start, met.get_stop, met.get_correct_side, met.get_outcome, met.get_chosen_side, met.has_choice,
met.rew_collected)
SessionDf = bhv.parse_trials(TrialDfs, metrics)
# Session metrics
n_rews_collected[j,k] = SessionDf['rew_collect'].sum()
n_anticipatory[j,k] = SessionDf['has_choice'].sum()
no_trials[j,k] = len(SessionDf)
session_length [j,k] = (LogDf['t'].iloc[-1]-LogDf['t'].iloc[0])/(1000*60) # convert msec. -> sec.-> min.
# %%
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