# make array of trial number for each row in dataset
trialarray = maketrialarray(
saraharray) # write array of trial per row in datafile
saraharray[:,
9] = trialarray[:,
0] # replace trial column in dataset *see README for why this is done*
# split data by trial type
dailymouse_b = np.delete(saraharray, np.where(saraharray[:, 8] > 0), 0)
dailymouse_nb = np.delete(saraharray, np.where(saraharray[:, 8] != 10),
0)
dailymouse_p = np.delete(saraharray, np.where(saraharray[:, 8] != 20),
0)
# get stops
stops_b = extractstops(dailymouse_b)
stops_nb = extractstops(dailymouse_nb)
stops_p = extractstops(dailymouse_p)
#filterstops
stops_b = filterstops(stops_b)
stops_nb = filterstops(stops_nb)
stops_p = filterstops(stops_p)
# get first stop for each trial
trarray = np.arange(np.min(saraharray[:, 9]),
np.max(saraharray[:, 9] + 0.1),
1) # array of trial numbers
beac = []
nbeac = []
probe = [] # make empty arrays to store data
except KeyError: # if there is no datafile, skip that mouse & day
print ('Error, no file')
continue
trialno = np.max(saraharray[:,9]) # total number of trials for that day and mouse (used later for defining y axis max)
# make array of trial number for each row in dataset
trialarray = maketrialarray(saraharray)
saraharray[:,9] = trialarray[:,0] # replace trial column in dataset *see README for why this is done*
# Extract data for beaconed, non-beaconed, probe
dailymouse_b = np.delete(saraharray, np.where(saraharray[:, 8] > 0), 0) # delete all data not on beaconed tracks
dailymouse_nb = np.delete(saraharray, np.where(saraharray[:, 8] != 10), 0)# delete all data not on non beaconed tracks
dailymouse_p = np.delete(saraharray, np.where(saraharray[:, 8] != 20), 0)# delete all data not on probe tracks
#extract stops
stopsdata_b = extractstops(dailymouse_b)
stopsdata_nb = extractstops(dailymouse_nb)
stopsdata_p = extractstops(dailymouse_p)
#get location and trial number of rewards
reward_beac = extractrewards(dailymouse_b)
reward_nbeac = extractrewards(dailymouse_nb)
# filter stops (removes stops 0.5 cm after a stop)
stopsdata_b = filterstops(stopsdata_b)
stopsdata_nb = filterstops(stopsdata_nb)
stopsdata_p = filterstops(stopsdata_p)
# extract trial numbers from data (only unique ones)
trialids_b = np.unique(stopsdata_b[:, 2])
trialids_nb = np.unique(stopsdata_nb[:, 2])
trialno = np.amax(
stopsdata[:, 9]) # find max trial number of current session
stopsdata[:, 9] += max_trialno
max_trialno += trialno
stopsdata_p = np.vstack((stopsdata_p, stopsdata))
dcount += 1
if stopsdata_p.size > 0:
if mcount == 3 or mcount == 5 or mcount == 7 or mcount == 8:
# get trials
trialids = np.unique(stopsdata_p[:, 9])
# for plotting stops against time
timedata = timetostop(
stopsdata_p,
trialids) # add time from bb (replaces apsolute time)
timedata = extractstops(timedata) # extract stops
timedata = filterstops(timedata) # filter stops
timestore = np.vstack(
(timestore, timedata)) # store data for all mice
if mcount == 3:
time1 = timedata
if mcount == 5:
time2 = timedata
if mcount == 6:
time3 = timedata
if mcount == 7:
time4 = timedata
if mcount == 8:
time5 = timedata
# for splitting data based on time to RZ
trialids = np.unique(stopsdata_p[:,
