def shuffle_analysis(stopsdata, trialids):
# Calculate stop rate for each section of the track
srbin = create_srdata(stopsdata, trialids) # Array(BINNR, trialnum)
srbin_mean = np.mean(srbin, axis=0) # Array(BINNR)
srbin_std = np.std(srbin, axis=0) # Array(BINNR)
return srbin_mean, srbin_std
#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
trialids_b = np.unique(stops_b[:, 2]) # find unique trial numbers
stops_f_b = FirstStops(
trarray, stops_b) # get locations of first stop for each trial
stops_f_b = create_srdata(stops_f_b, trialids_b) # bin first stop data
beac = np.nanmean(
stops_f_b, axis=0) # average times mouse stops first in each bin
if stops_nb.size > 0:
trialids_nb = np.unique(stops_nb[:, 2])
stops_f_nb = FirstStops(
trarray,
stops_nb) # get locations of first stop for each trial
stops_f_nb = create_srdata(stops_f_nb,
trialids_nb) # bin first stop data
nbeac = np.nanmean(
stops_f_nb,
axis=0) # average times mouse stops first in each bin
if stops_p.size > 0:
trialids_p = np.unique(stops_p[:, 2])
stops_f_p = FirstStops(
#reassign trial number
if dcount >0:
trialno_b =np.amax(stops_b[:, 2])
stops_b[:, 2] += b
b +=trialno_b
stopsdata_b = np.vstack((stopsdata_b, stops_b))
if stops_p.size >0:
trialno_p =np.amax(stops_p[:, 2])
stops_p[:, 2] += p
p +=trialno_p
stopsdata_p = np.vstack((stopsdata_p, stops_p))
dcount +=1
trialids_b = np.unique(stops_b[:, 2])
data_b = create_srdata( stops_b, trialids_b )
beac = np.nansum(data_b, axis = 0)/trialids_b.shape[0]
if stops_p.size>0:
trialids_p = np.unique(stops_p[:, 2])
# get stopping data
data_p = create_srdata( stops_p, trialids_p )
probe = np.nansum(data_p, axis = 0)/trialids_p.shape[0]
print('##...', mcount,day, '...##')
# store data
if mcount == 0 or mcount == 2 or mcount == 3 or mcount == 9:
loc = np.argmax(beac[3:17])
control_b[mcount] = loc+3
loc = np.argmax(probe[3:17])
control_p[mcount] = loc+3
print(loc, 'loc')
mean = (np.nanmean(timedata[:, 0])) - 3
SD = np.nanstd(timedata[:, 0])
CV = SD / mean
var[mcount, 0] = CV
var[mcount, 1] = mean
var[mcount, 2] = SD
mean = np.nanmean(timedata[:, 1])
SD = np.nanstd(timedata[:, 1])
CV = SD / mean
var[mcount, 3] = CV
var[mcount, 4] = mean
var[mcount, 5] = SD
#data for example plots
if mcount == 5:
m5 = datastore
stops = create_srdata(datastore, trialids)
avg_stops = np.nanmean(stops, axis=0)
allstops[mcount, :] = avg_stops
mcount += 1
# TASK 12
# IMPORT DATA
filename = 'Data_Input/Behaviour_DataFiles/Task12_0600.h5'
# SPECIFY MICE
days = ['Day' + str(int(x)) for x in np.arange(15, 27.1)]
mice = ['M' + str(int(x)) for x in np.arange(1, 9.1)] # choose specific day/s
# Stores
quartiles1 = np.zeros((len(mice), 4, 2))
def plot_fig1D(session_paths, save_path):
days = [1]
n_bins = 20
# specify mouse/mice and day/s to analyse
days = ['Day' + str(int(x)) for x in np.arange(1)]
# Arrays for storing data (output)
firststopstorebeac = np.zeros((len(days), len(session_paths), n_bins, 2))
firststopstorenbeac = np.zeros((len(days), len(session_paths), n_bins, 2))
firststopstoreprobe = np.zeros((len(days), len(session_paths), n_bins, 2))
firststopstorebeac[:, :, :, :] = np.nan
firststopstorenbeac[:, :, :, :] = np.nan
firststopstoreprobe[:, :, :, :] = np.nan
# For each day and mouse, pull raw data, calculate first stops and store data
for dcount, day in enumerate(days):
session_count = 0
for session_path in session_paths:
saraharray, track_start, track_end = translate_to_legacy_format(
session_path)
rz_start = saraharray[0, 11]
rz_end = saraharray[0, 12]
# 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_HUMAN(dailymouse_b)
stops_nb = extractstops_HUMAN(dailymouse_nb)
stops_p = extractstops_HUMAN(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
trialids_b = np.unique(stops_b[:, 2]) # find unique trial numbers
stops_f_b = FirstStops_humans(
trarray, stops_b, track_start,
track_end) # get locations of first stop for each trial
stops_f_b = create_srdata(stops_f_b,
trialids_b) # bin first stop data
beac = np.nanmean(
stops_f_b,
axis=0) # average times mouse stops first in each bin
if stops_nb.size > 0:
trialids_nb = np.unique(stops_nb[:, 2])
stops_f_nb = FirstStops_humans(
trarray, stops_nb, track_start,
track_end) # get locations of first stop for each trial
stops_f_nb = create_srdata(stops_f_nb,
trialids_nb) # bin first stop data
nbeac = np.nanmean(
stops_f_nb,
axis=0) # average times mouse stops first in each bin
if stops_p.size > 0:
trialids_p = np.unique(stops_p[:, 2])
stops_f_p = FirstStops_humans(
trarray, stops_p, track_start,
track_end) # get locations of first stop for each trial
stops_f_p = create_srdata(stops_f_p,
trialids_p) # bin first stop data
probe = np.nanmean(
stops_f_p,
axis=0) # average times mouse stops first in each bin
# store data
#if mcount == 3 or mcount == 5 or mcount == 6 or mcount == 7 or mcount == 8:
firststopstorebeac[dcount, session_count, :,
0] = beac # store first stop data
srbin_mean, srbin_std, shuffled_mean, shuffled_std = shuffle_analysis_pertrial3(
stops_b, trialids_b) # get average stops per location bin
firststopstorebeac[dcount, session_count, :,
1] = srbin_mean # store stops data
if stops_nb.size > 0:
firststopstorenbeac[dcount, session_count, :,
0] = nbeac # store first stop data
srbin_mean, srbin_std, shuffled_mean, shuffled_std = shuffle_analysis_pertrial3(
stops_nb,
trialids_nb) # get average stops per location bin
firststopstorenbeac[dcount, session_count, :,
1] = srbin_mean # store stops data
if stops_p.size > 0:
firststopstoreprobe[dcount, session_count, :,
0] = probe # store first stop data
srbin_mean, srbin_std, shuffled_mean, shuffled_std = shuffle_analysis_pertrial3(
stops_p, trialids_p) # get average stops per location bin
firststopstoreprobe[dcount, session_count, :,
1] = srbin_mean # store stops data
session_count += 1
# Average over days for all mice
# week 1 (first stop)
con_beac_w1 = np.nanmean(((firststopstorebeac[0, :, :, 0])), axis=0)
con_nbeac_w1 = np.nanmean(((firststopstorenbeac[0, :, :, 0])), axis=0)
con_probe_w1 = np.nanmean(((firststopstoreprobe[0, :, :, 0])), axis=0)
sd_con_beac_w1 = np.nanstd(
((firststopstorebeac[0, :, :, 0])), axis=0) / math.sqrt(session_count)
sd_con_nbeac_w1 = np.nanstd(
((firststopstorenbeac[0, :, :, 0])), axis=0) / math.sqrt(session_count)
sd_con_probe_w1 = np.nanstd(
((firststopstoreprobe[0, :, :, 0])), axis=0) / math.sqrt(session_count)
# week 1 (all stops)
con_beac1_w1 = np.nanmean(((firststopstorebeac[0, :, :, 1])), axis=0)
con_nbeac1_w1 = np.nanmean(((firststopstorenbeac[0, :, :, 1])), axis=0)
con_probe1_w1 = np.nanmean(((firststopstoreprobe[0, :, :, 1])), axis=0)
sd_con_beac1_w1 = np.nanstd(
((firststopstorebeac[0, :, :, 1])), axis=0) / math.sqrt(session_count)
sd_con_nbeac1_w1 = np.nanstd(
((firststopstorenbeac[0, :, :, 1])), axis=0) / math.sqrt(session_count)
sd_con_probe1_w1 = np.nanstd(
((firststopstoreprobe[0, :, :, 1])), axis=0) / math.sqrt(session_count)
'''
# week 4 (first stop)
con_beac_w4 = np.nanmean(np.nanmean(((firststopstorebeac[18:22,:,:,0])), axis = 0), axis = 0)
con_nbeac_w4 = np.nanmean(np.nanmean(((firststopstorenbeac[18:22,:,:,0])), axis =0), axis = 0)
con_probe_w4 = np.nanmean(np.nanmean(((firststopstoreprobe[18:22,:,:,0])), axis = 0), axis = 0)
sd_con_beac_w4 = np.nanstd(np.nanmean(((firststopstorebeac[18:22,:,:,0])), axis = 0), axis = 0)/math.sqrt(8)
sd_con_nbeac_w4 = np.nanstd(np.nanmean(((firststopstorenbeac[18:22,:,:,0])), axis =0), axis = 0)/math.sqrt(8)
sd_con_probe_w4 = np.nanstd(np.nanmean(((firststopstoreprobe[18:22,:,:,0])), axis = 0), axis = 0)/math.sqrt(8)
# week 4 (all stops)
con_beac1_w4 = np.nanmean(np.nanmean(((firststopstorebeac[18:22,:,:,1])), axis = 0), axis = 0)
con_nbeac1_w4 = np.nanmean(np.nanmean(((firststopstorenbeac[18:22,:,:,1])), axis =0), axis = 0)
con_probe1_w4 = np.nanmean(np.nanmean(((firststopstoreprobe[18:22,:,:,1])), axis = 0), axis = 0)
sd_con_beac1_w4 = np.nanstd(np.nanmean(((firststopstorebeac[18:22,:,:,1])), axis = 0), axis = 0)/math.sqrt(8)
sd_con_nbeac1_w4 = np.nanstd(np.nanmean(((firststopstorenbeac[18:22,:,:,1])), axis =0), axis = 0)/math.sqrt(8)
sd_con_probe1_w4 = np.nanstd(np.nanmean(((firststopstoreprobe[18:22,:,:,1])), axis = 0), axis = 0)/math.sqrt(8)
'''
# PLOT GRAPHS
bins = np.arange(0.5, n_bins + 0.5, 1)
# first stop histogram
fig = plt.figure(figsize=(12, 4))
ax = fig.add_subplot(1, 3, 1) #stops per trial
ax.set_title('Beaconed',
fontsize=20,
verticalalignment='bottom',
style='italic') # title
ax.axvspan(
rz_start, rz_end, facecolor='g', alpha=0.25, hatch='/',
linewidth=0) # green box spanning the rewardzone - to mark reward zone
ax.axvspan(0,
track_start,
facecolor='k',
alpha=0.15,
hatch='/',
linewidth=0) # black box
ax.axvspan(track_end,
20,
facecolor='k',
alpha=0.15,
hatch='/',
linewidth=0) # black box
ax.axvline(0, linewidth=3, color='black') # bold line on the y axis
ax.axhline(0, linewidth=3, color='black') # bold line on the x axis
ax.plot(bins, con_beac_w1, color='blue', label='Beaconed',
linewidth=2) #plot becaoned trials
ax.fill_between(bins,
con_beac_w1 - sd_con_beac_w1,
con_beac_w1 + sd_con_beac_w1,
facecolor='blue',
alpha=0.3)
#ax.plot(bins,con_beac_w4,color = 'red',label = 'Beaconed', linewidth = 2) #plot becaoned trials
#ax.fill_between(bins,con_beac_w4-sd_con_beac_w4,con_beac_w4+sd_con_beac_w4, facecolor = 'red', alpha = 0.3)
ax.tick_params(axis='x',
pad=10,
top='off',
right='off',
direction='out',
width=2,
length=7,
labelsize=16)
ax.tick_params(axis='y',
pad=10,
top='off',
right='off',
direction='out',
width=2,
length=7,
labelsize=16)
ax.set_xlim(0, 20)
ax.set_ylim(0, 0.95)
adjust_spines(ax, ['left', 'bottom']) # removes top and right spines
ax.locator_params(axis='x', nbins=3) # set number of ticks on x axis
ax.locator_params(axis='y', nbins=4) # set number of ticks on y axis
ax.set_xticklabels(['0', '10', '20'])
ax.set_yticklabels(['0', '0.3', '0.6', '0.9'])
ax.set_ylabel('1st stop probability', fontsize=16, labelpad=18)
ax = fig.add_subplot(1, 3, 2) #stops per trial
ax.set_title('Non-Beaconed',
fontsize=20,
verticalalignment='bottom',
style='italic') # title
ax.axvspan(
rz_start, rz_end, facecolor='g', alpha=0.25, hatch='/',
linewidth=0) # green box spanning the rewardzone - to mark reward zone
ax.axvspan(0,
track_start,
facecolor='k',
alpha=0.15,
hatch='/',
linewidth=0) # black box
ax.axvspan(track_end,
20,
facecolor='k',
alpha=0.15,
hatch='/',
linewidth=0) # black box
ax.axvline(0, linewidth=3, color='black') # bold line on the y axis
ax.axhline(0, linewidth=3, color='black') # bold line on the x axis
ax.plot(bins, con_nbeac_w1, color='blue',
linewidth=2) #plot becaoned trials
ax.fill_between(bins,
con_nbeac_w1 - sd_con_nbeac_w1,
con_nbeac_w1 + sd_con_nbeac_w1,
facecolor='blue',
alpha=0.3)
#ax.plot(bins,con_nbeac_w4,color = 'red', linewidth = 2) #plot becaoned trials
#ax.fill_between(bins,con_nbeac_w4-sd_con_nbeac_w4,con_nbeac_w4+sd_con_nbeac_w4, facecolor = 'red', alpha = 0.3)
ax.tick_params(axis='x',
pad=10,
top='off',
right='off',
direction='out',
width=2,
length=7,
labelsize=16)
ax.tick_params(axis='y',
pad=10,
top='off',
right='off',
direction='out',
width=2,
length=7,
labelsize=16)
ax.set_xlim(0, 20)
ax.set_ylim(0, 0.95)
adjust_spines(ax, ['left', 'bottom']) # re;moves top and right spines
ax.locator_params(axis='x', nbins=3) # set number of ticks on x axis
ax.locator_params(axis='y', nbins=4) # set number of ticks on y axis
ax.set_xticklabels(['0', '10', '20'])
ax.set_yticklabels(['', '', '', ''])
ax.set_xlabel('Distance (VU)', fontsize=16, labelpad=18)
ax = fig.add_subplot(1, 3, 3) #stops per trial
ax.set_title('Probe',
fontsize=20,
verticalalignment='bottom',
style='italic') # title
ax.axvspan(
rz_start, rz_end, facecolor='g', alpha=0.25, hatch='/',
linewidth=0) # green box spanning the rewardzone - to mark reward zone
ax.axvspan(0,
track_start,
facecolor='k',
alpha=0.15,
hatch='/',
linewidth=0) # black box
ax.axvspan(track_end,
20,
facecolor='k',
alpha=0.15,
hatch='/',
linewidth=0) # black box
ax.axvline(0, linewidth=3, color='black') # bold line on the y axis
ax.axhline(0, linewidth=3, color='black') # bold line on the x axis
ax.plot(bins, con_probe_w1, color='blue', label='Beaconed',
linewidth=2) #plot becaoned trials
ax.fill_between(bins,
con_probe_w1 - sd_con_probe_w1,
con_probe_w1 + sd_con_probe_w1,
facecolor='blue',
alpha=0.3)
#ax.plot(bins,con_probe_w4,color = 'red', label = 'Beaconed', linewidth = 2) #plot becaoned trials
#ax.fill_between(bins,con_probe_w4-sd_con_probe_w4,con_probe_w4+sd_con_probe_w4, facecolor = 'red', alpha = 0.3)
ax.tick_params(axis='x',
pad=10,
top='off',
right='off',
direction='out',
width=2,
length=7,
labelsize=16)
ax.tick_params(axis='y',
pad=10,
top='off',
right='off',
direction='out',
width=2,
length=7,
labelsize=16)
ax.set_xlim(0, 20)
ax.set_ylim(0, 0.95)
adjust_spines(ax, ['left', 'bottom']) # removes top and right spines
ax.locator_params(axis='x', nbins=3) # set number of ticks on x axis
ax.locator_params(axis='y', nbins=4) # set number of ticks on y axis
ax.set_yticklabels(['', '', '', ''])
ax.set_xticklabels(['0', '10', '20'])
plt.subplots_adjust(hspace=.35,
wspace=.35,
bottom=0.25,
left=0.15,
right=0.82,
top=0.85)
fig.savefig(save_path, dpi=200)
plt.close()
u = np.percentile(ut[:], 75) m = np.percentile(ut[:], 50) # #split trials based on time low1 = np.delete(datastore, np.where(datastore[:, 1] > m), 0) high1 = np.delete(datastore, np.where(datastore[:, 1] < m), 0) lower_p = np.delete(low1, np.where(low1[:, 1] > l), 0) upper_p = np.delete(high1, np.where(high1[:, 1] < u), 0) low_p = np.delete(low1, np.where(low1[:, 1] < l), 0) high_p = np.delete(high1, np.where(high1[:, 1] > u), 0) # get trial numbers for each of the quartiles h_trials_p = np.unique(high_p[:,2]) l_trials_p = np.unique(low_p[:,2]) h2_trials_p = np.unique(upper_p[:,2]) l2_trials_p = np.unique(lower_p[:,2]) #calculate average stops high_stops_p = create_srdata(high_p, h_trials_p) avg_high_stops_p = np.nanmean(high_stops_p, axis=0) low_stops_p = create_srdata(low_p, l_trials_p) avg_low_stops_p = np.nanmean(low_stops_p, axis=0) upper_stops_p = create_srdata(upper_p, h2_trials_p) avg_upper_stops_p = np.nanmean(upper_stops_p, axis=0) lower_stops_p = create_srdata(lower_p, l2_trials_p) avg_lower_stops_p = np.nanmean(lower_stops_p, axis=0) #store average stops s2_con_high_probe[mcount,:,0] = avg_high_stops_p s2_con_low_probe[mcount,:,0] = avg_low_stops_p s2_con_high_probe[mcount,:,1] = avg_upper_stops_p s2_con_low_probe[mcount,:,1] = avg_lower_stops_p stops = create_srdata(datastore, trialids) avg_stops= np.nanmean(stops, axis=0) allstops[mcount,:] = avg_stops