Annotating licks into bouts

[alexpiet]

• segments licks into bouts based on a threshold of the interlick interval
  • currently ignores whether the licks are left/right
  • Sets the threshold as 700ms, which is simply what I used in a previous project
• updates plot_session_scroller to color licks by the lick bout
• updates plots to take nwb-like objects
• annotates which lick triggered a reward, and whether the lick bout was a rewarded or unrewarded bout
• merge dependency: adding censoring to event triggered response aind-dynamic-foraging-data-utils#39
• merge dependency: adding censor times aind-dynamic-foraging-data-utils#40
• fix import dependency issue
• add unit tests for new code

Add lick annotations to session scroller

from aind_dynamic_foraging_data_utils import nwb_utils as nu
from aind_dynamic_foraging_basic_analysis.plot import plot_foraging_session as pfs

nwb = nu.load_nwb_from_filename(<filepath>)
nwb.fip_df = nu.create_fib_df(nwb, tidy=True)
pfs.plot_session_scroller(nwb, plot_bouts=True)

Compute FIP PSTH aligned to rewarded and unrewarded goCues

from aind_dynamic_foraging_data_utils import nwb_utils as nu
from aind_dynamic_foraging_basic_analysis.plot import plot_fip as pf

nwb = nu.load_nwb_from_filename(<filepath>)
nwb.df_trials = nu.create_df_trials(nwb)
rewarded_go_cues = nwb.df_trials.query('reward == 1')['goCue_start_time_absolute'].values
unrewarded_go_cues = nwb.df_trials.query('reward == 0')['goCue_start_time_absolute'].values
pf.plot_fip_psth_compare_alignments(nwb, {'rewarded goCue':rewarded_go_cues,'unrewarded goCue':unrewarded_go_cues}, 'G_1_preprocessed', censor=True)

Compute FIP PSTH aligned to rewarded and unrewarded lick bout starts

from aind_dynamic_foraging_data_utils import nwb_utils as nu
from aind_dynamic_foraging_basic_analysis.licks import annotation as a
from aind_dynamic_foraging_basic_analysis.plot import plot_fip as pf

nwb = nu.load_nwb_from_filename(<filepath>)
nwb.df_licks = a.annotate_rewards(nwb)
rewarded_bout_start = nwb.df_licks.query('bout_start & bout_rewarded').timestamps.values
unrewarded_bout_start = nwb.df_licks.query('bout_start & (not bout_rewarded)').timestamps.values
pf.plot_fip_psth_compare_alignments(nwb, {'rewarded bout start':rewarded_bout_start,'unrewarded bout start':unrewarded_bout_start}, 'G_1_preprocessed', censor=True)

[alexpiet]

@rachelstephlee @hanhou This is ready for review

[hanhou]

Compute FIP PSTH aligned to rewarded and unrewarded lick bout starts

Haven't checked the code, but a quick question: are bouts independent of trials? In other words, if there are more than one bouts in a trial, all bouts from the second on are considered as "unrewarded bouts" (red circles below)?

We may also need to align PSTH to the first bout after the go cue (essentially the "choice lick").

[alexpiet]

Compute FIP PSTH aligned to rewarded and unrewarded lick bout starts

Haven't checked the code, but a quick question: are bouts independent of trials? In other words, if there are more than one bouts in a trial, all bouts from the second on are considered as "unrewarded bouts" (red circles below)?

We may also need to align PSTH to the first bout after the go cue (essentially the "choice lick").

The concept of a bout here is not connected to a trial. In the example snippet you highlighted the light green bout is rewarded, and all the other bouts are unrewarded.

We could add another annotation of bouts as "first after a go cue" (or whatever we want to call it).

[alexpiet]

@hanhou I went ahead and added a function annotate_cue_response that annotates licks and bouts whether they were the first lick/bout after a go cue

[alexpiet]

@hanhou This does what you suggested

from aind_dynamic_foraging_data_utils import nwb_utils as nu
from aind_dynamic_foraging_basic_analysis.licks import annotation as a
from aind_dynamic_foraging_basic_analysis.plot import plot_fip as pf

nwb = nu.load_nwb_from_filename(<filepath>)
nwb.df_events = nu.create_events_df(nwb)
nwb.df_licks = a.annotate_rewards(nwb)
nwb.df_licks = a.annotate_cue_response(nwb)
rewarded_response_bout_start = nwb.df_licks.query('bout_start & bout_rewarded & bout_cue_response').timestamps.values
unrewarded_response_bout_start = nwb.df_licks.query('bout_start & (not bout_rewarded) & bout_cue_response').timestamps.values
pf.plot_fip_psth_compare_alignments(nwb, {'rewarded response bout start':rewarded_response_bout_start,'unrewarded response bout start':unrewarded_response_bout_start}, 'G_1_preprocessed', censor=True)