def worker(arguments):
dbname, event_labels, Tpre, Tpost, freqs, normfun = arguments[:6]
dtup = arguments[6:]
print(dtup)
lfp = load_and_preprocess(dbname, dtup)
# get events
evt = dbio.fetch(dbname, 'events', *dtup)
evtdict = {}
evtdict['stops'] = evt['banked'].dropna()
evtdict['pops'] = evt['popped'].dropna()
evtdict['starts'] = evt['start inflating']
if 'is_control' in evt.columns:
evtdict['stops_free'] = evt.query('is_control == False')['banked'].dropna()
evtdict['stops_control'] = evt.query('is_control == True')['banked'].dropna()
evtdict['stops_rewarded'] = evt.query('trial_type != 4')['banked'].dropna()
evtdict['stops_unrewarded'] = evt.query('trial_type == 4')['banked'].dropna()
else:
evtdict['stops_free'] = evtdict['stops']
evtdict['stops_control'] = None
evtdict['stops_rewarded'] = evtdict['stops']
evtdict['stops_unrewarded'] = None
this_spectra = []
this_labels = []
taxis = None
faxis = None
if evtdict[event_labels[0]] is None:
print("Dataset {} has no events of type {}".format(dtup, event_labels[0]))
elif evtdict[event_labels[1]] is None:
print("Dataset {} has no events of type {}".format(dtup, event_labels[1]))
else:
this_spectra, this_labels, taxis, faxis = avg_time_freq_arrays(lfp,
[evtdict[event_labels[0]], evtdict[event_labels[1]]],
Tpre, Tpost,
method='wav', normfun=normfun, freqs=freqs)
return this_spectra, this_labels, taxis, faxis
lfpset = lfpset.decimate(5) # instantaneous power lfpset = lfpset.instpwr() # remove censored regions lfpset = lfpset.censor() # rolling smooth lfpset = lfpset.smooth(0.075) # convert to dB lfpset = lfpset.apply(lambda x: 10 * np.log10(x)) # get events evt = dbio.fetch(dbname, "events", *dtup[:-1]) t_evt = evt[["start inflating", "banked", "popped"]] # calculate duration of inflation didpop = ~np.isnan(t_evt["popped"]) stops = t_evt["banked"].copy() stops[didpop] = t_evt["popped"][didpop] t_evt["didpop"] = didpop t_evt["stop inflating"] = stops t_evt["inf_dur"] = t_evt["stop inflating"] - t_evt["start inflating"] max_inf = t_evt.inf_dur.max() # define pre and post time intervals Tpre = -1 Tpost = np.ceil(max_inf) + 1
# set a random seed
np.random.seed(12345)
# name of database to use
dbname = os.path.expanduser("~/data/bartc/plexdata/bartc.hdf5")
# first, get a list of lfp channels
setlist = pd.read_hdf(dbname, "/meta/lfplist")
# group by (patient, dataset) entries:
groups = setlist.groupby(["patient", "dataset"])
# for name, grp in groups:
name = (18, 1)
print name
print "Fetching LFP data..."
lfp = dbio.fetch_all_such_LFP(dbname, *name)
# fetch events
evt = dbio.fetch(dbname, "events", *name)
stops = evt[["stop inflating"]].dropna().values
# prepare to do some plotting
Tpre = -2
Tpost = 0
median_of_corr = corr_then_med(lfp, stops, Tpre, Tpost)
corr_of_median = med_then_corr(lfp, stops, Tpre, Tpost)
