def run_behaving(
rs=None,
output_dir="/media/dendrite",
all_channels_file="../DO1_ALL_CHANNELS",
channel_groups_file="../DO1_CHANNEL_GROUPS",
analog_channels_file="../ANALOG_7_8",
ns5_filename=None,
remove_from_TC=None,
soft_time_limits=(-2.0, 2.0),
hard_time_limits=(-0.04, 0.15),
do_add_bcontrol=True,
bcontrol_folder="/media/hippocampus/TRWINRIG_DATA/Data/chris/CR20B",
do_timestamps=True,
do_add_btrial_numbers=True,
force_put_neural_data=False,
do_avg_plots=True,
do_extract_spikes=True,
CAR=True,
save_to_klusters=True,
do_MUA_grand_plot=True,
do_MUA_plot_by_stimulus=True,
do_run_klustakwik=True,
do_SUA_plot_by_stimulus=True,
group_multiplier=100,
psth_time_limits=(None, None),
lfp_time_limits="hard",
**kwargs
):
if len(kwargs) > 0:
print "unexpected kwargs"
print kwargs
# Make session
if rs is None:
printnow("creating recording session %s" % ns5_filename)
rsm = rswrap.RecordingSessionMaker(
data_analysis_dir=output_dir,
all_channels_file=all_channels_file,
channel_groups_file=channel_groups_file,
analog_channels_file=analog_channels_file,
)
if remove_from_TC is None:
remove_from_TC = []
rs = rsm.make_session(ns5_filename=ns5_filename, remove_from_TC=remove_from_TC)
rs.write_time_limits(soft_time_limits, hard_time_limits)
rs.group_multiplier = group_multiplier
printnow("RS %s" % rs.full_path)
# add bcontrol
if do_add_bcontrol:
printnow("adding bcontrol")
rswrap.add_bcontrol_data_to_session(bcontrol_folder, rs, verbose=True)
# add timestamps
if do_timestamps:
printnow("adding timestamps")
# write timestamps to directory
times, numbers = rswrap.add_timestamps_to_session(rs, verbose=True, meth="digital_trial_number")
# put in neural db (does nothing if exists unless forced)
printnow("putting neural data")
rs.put_neural_data_into_db(verbose=True, force=force_put_neural_data)
# adding btrial numbers
if do_add_btrial_numbers:
printnow("adding btrial numbers")
if len(numbers) == 0:
print "warning: no numbers?"
numbers = None # will auto-determine if all is well
rswrap.add_behavioral_trial_numbers2(rs, known_trial_numbers=numbers, trial_number_channel=16, verbose=True)
# plot averages
if do_avg_plots:
printnow("avg plots")
if lfp_time_limits == "hard":
lfp_time_limits = rs.read_time_limits()[1]
rswrap.plot_avg_lfp(rs, savefig=True, t_start=lfp_time_limits[0], t_stop=lfp_time_limits[1])
rswrap.plot_avg_audio(rs, savefig=True)
# spike extract
if do_extract_spikes:
printnow("extracting spikes")
rs.generate_spike_block(CAR=CAR, smooth_spikes=False, verbose=True)
rs.run_spikesorter(save_to_db=True, save_to_klusters=save_to_klusters)
rs.spiketime_dump()
# plot MUA stuff
if do_MUA_grand_plot:
printnow("mua grand psths")
rswrap.plot_all_spike_psths(rs, savefig=True)
# plot MUA by stim
if do_MUA_plot_by_stimulus:
printnow("mua by stimulus")
t_start, t_stop = psth_time_limits
rswrap.plot_MUA_by_stim(rs, savefig=True, t_start=t_start, t_stop=t_stop)
# spike sort
if do_run_klustakwik:
printnow("running klustakwik")
rs.group_multiplier = group_multiplier
rs.run_klustakwik()
rs.prep_for_klusters(verbose=True)
# SUA by stim
if do_SUA_plot_by_stimulus:
printnow("SUA plot by stimulus")
t_start, t_stop = psth_time_limits
rswrap.plot_all_spike_psths_by_stim(
rs, savefig=True, t_start=t_start, t_stop=t_stop, skipNoScore=False, override_path=rs.last_klusters_dir()
)
return rs
