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])
if stopsdata_p.size > 0: # if there are probe trials
trialids_p = np.unique(stopsdata_p[:, 2])
# get mean stops per bin for real and shuffled data
srbin_mean_b, srbin_std_b,shuffled_mean_b, shuffled_std_b = shuffle_analysis_pertrial3(stopsdata_b, trialids_b)
srbin_mean_nb, srbin_std_nb, shuffled_mean_nb, shuffled_std_nb = shuffle_analysis_pertrial3(stopsdata_nb, trialids_nb)
if stopsdata_p.size > 0:
srbin_mean_p, srbin_std_p, shuffled_mean_p, shuffled_std_p = shuffle_analysis_pertrial3(stopsdata_p, trialids_p)
# calculate average speed
speed_beaconed = speed_per_trial(bins,saraharray,trialids_b)
speed_nbeaconed = speed_per_trial(bins,saraharray,trialids_nb)
if stopsdata_p.size>0: # if there are probe trials
speed_probe = speed_per_trial(bins,saraharray,trialids_p)
sd_speed_probe = np.nanstd(speed_probe,axis = 1)
sd_speed_beaconed = np.nanstd(speed_beaconed,axis = 1)
sd_speed_nbeaconed = np.nanstd(speed_nbeaconed,axis = 1)
speed_beaconed = np.nanmean(speed_beaconed,axis = 1)
speed_nbeaconed = np.nanmean(speed_nbeaconed,axis = 1)
if stopsdata_p.size>0: # if there are probe trials
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(
trarray, stops_p) # 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, mcount, :,
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, mcount, :,
1] = srbin_mean # store stops data
if stops_nb.size > 0:
firststopstorenbeac[dcount, mcount, :,
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, mcount, :,
1] = srbin_mean # store stops data
if stops_p.size > 0:
firststopstoreprobe[dcount, mcount, :,
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
# split data by trial type
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_p = extractstops(dailymouse_p)
# filter stops
stops_b = filterstops(stopsdata_b)
stops_p = filterstops(stopsdata_p)
if stops_b.size>0:
trialids_b = np.unique(stops_b[:, 2]) # find trial numbers
srbin_mean, srbin_std, shuffled_mean, shuffled_std= shuffle_analysis_pertrial3(stops_b,trialids_b) # calculate real and shuffled stops along the track
shuff_beac = z_score1(srbin_mean, srbin_std, shuffled_mean, shuffled_std) # calculate zscore for each bin of the track
start_bb_b = shuff_beac[3]; start_rz_b = shuff_beac[9] # black box bin minus the reward zone bin
end_bb_b = shuff_beac[17]; end_rz_b = shuff_beac[11] # black box bin minus the reward zone bin
start_score_b = start_rz_b-start_bb_b # black box bin minus the reward zone bin
end_score_b = end_rz_b-end_bb_b # black box bin minus the reward zone bin
if stops_p.size >0:
trialids_p = np.unique(stops_p[:, 2]) # find trial numbers
srbin_mean, srbin_std, shuffled_mean, shuffled_std= shuffle_analysis_pertrial3(stops_p,trialids_p) # calculate real and shuffled stops along the track
shuff_probe = z_score1(srbin_mean, srbin_std, shuffled_mean, shuffled_std) # calculate zscore for each bin of the track
start_bb_p = shuff_probe[3]; start_rz_p = shuff_probe[9] # black box bin minus the reward zone bin
end_bb_p = shuff_probe[17]; end_rz_p = shuff_probe[11] # black box bin minus the reward zone bin
start_score_p = start_rz_b-start_bb_p # black box bin minus the reward zone bin
end_score_p = end_rz_p-end_bb_p # black box bin minus the reward zone bin
# store data
#extract stops
stopsdata_b = extractstops(dailymouse_b)
stopsdata_nb = extractstops(dailymouse_nb)
stopsdata_p = extractstops(dailymouse_p)
# filter stops
stopsdata_b = filterstops(stopsdata_b)
stopsdata_nb = filterstops(stopsdata_nb)
stopsdata_p = filterstops(stopsdata_p)
# Shuffle stops data & store data
if mcount == 0 or mcount == 3 or mcount == 5 or mcount == 6 or mcount == 7:
if stopsdata_b.size > 0:
trialids_b = np.unique(
stopsdata_b[:, 2]) # get unique trial numbers
srbin_mean, srbin_std, shuffled_mean, shuffled_std = shuffle_analysis_pertrial3(
stopsdata_b, trialids_b) # get real and shuffled stops
zscore_b = z_score1(srbin_mean, srbin_std, shuffled_mean,
shuffled_std) # calculate z-score
firststopstorebeac[dcount, mcount, :] = zscore_b # store data
if stopsdata_nb.size > 0:
trialids_nb = np.unique(
stopsdata_nb[:, 2]) # get unique trial numbers
srbin_mean, srbin_std, shuffled_mean, shuffled_std = shuffle_analysis_pertrial3(
stopsdata_nb, trialids_nb) # get real and shuffled stops
zscore_nb = z_score1(srbin_mean, srbin_std, shuffled_mean,
shuffled_std) # calculate z-score
firststopstorenbeac[dcount,
mcount, :] = zscore_nb # store data
if stopsdata_p.size > 0:
trialids_p = np.unique(
stopsdata_p[:, 2]) # get unique trial numbers
0) # delete all data not on probe tracks
# extract stops
stopsdata_b = extractstops_HUMAN(dailymouse_b)
stopsdata_nb = extractstops_HUMAN(dailymouse_nb)
stopsdata_p = extractstops_HUMAN(dailymouse_p)
# filter stops
stopsdata_b = filterstops(stopsdata_b)
stopsdata_nb = filterstops(stopsdata_nb)
stopsdata_p = filterstops(stopsdata_p)
trialids_b = np.unique(
stopsdata_b[:, 2]) # make array of unique trial numbers
srbin_mean, srbin_std, shuffled_mean, shuffled_std = shuffle_analysis_pertrial3(
stopsdata_b, trialids_b
) # get average real stops & shuffled stops per lcoation bin
zscore_b = z_score1(srbin_mean, srbin_std, shuffled_mean,
shuffled_std) # calculate z-scores
firststopstorebeac[dcount, session_count, :,
1] = zscore_b # store zscores
if stopsdata_nb.size > 0: # if there is non-beaconed data
trialids_nb = np.unique(
stopsdata_nb[:, 2]) # make array of unique trial numbers
srbin_mean, srbin_std, shuffled_mean, shuffled_std = shuffle_analysis_pertrial3(
stopsdata_nb, trialids_nb
) # get average real stops & shuffled stops per lcoation bin
zscore_nb = z_score1(srbin_mean, srbin_std, shuffled_mean,
shuffled_std) # calculate z-scores
firststopstorenbeac[dcount, session_count, :,
def plot_fig1F(session_paths, save_path):
# specify mouse/mice and day/s to analyse
days = [1]
n_bins = 20
# specify mouse/mice and day/s to analyse
days = ['Day' + str(int(x)) for x in np.arange(1)]
# empty arrays for storing data
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
#loop days and mice to collect 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]
trialarray = maketrialarray(saraharray) # make array of trial number same size as saraharray
saraharray[:,9] = trialarray[:,0] # replace trial number because of increment error (see README.py)
# split data by trial type
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_HUMAN(dailymouse_b)
stopsdata_nb = extractstops_HUMAN(dailymouse_nb)
stopsdata_p = extractstops_HUMAN(dailymouse_p)
# filter stops
stopsdata_b = filterstops(stopsdata_b)
stopsdata_nb = filterstops(stopsdata_nb)
stopsdata_p = filterstops(stopsdata_p)
trialids_b = np.unique(stopsdata_b[:, 2]) # make array of unique trial numbers
srbin_mean, srbin_std, shuffled_mean, shuffled_std = shuffle_analysis_pertrial3(stopsdata_b,trialids_b) # get average real stops & shuffled stops per lcoation bin
zscore_b = z_score1(srbin_mean, srbin_std, shuffled_mean, shuffled_std) # calculate z-scores
firststopstorebeac[dcount, session_count,:,1] = zscore_b # store zscores
if stopsdata_nb.size > 0: # if there is non-beaconed data
trialids_nb = np.unique(stopsdata_nb[:, 2]) # make array of unique trial numbers
srbin_mean, srbin_std, shuffled_mean, shuffled_std = shuffle_analysis_pertrial3(stopsdata_nb, trialids_nb) # get average real stops & shuffled stops per lcoation bin
zscore_nb = z_score1(srbin_mean, srbin_std, shuffled_mean, shuffled_std) # calculate z-scores
firststopstorenbeac[dcount, session_count,:,1] = zscore_nb # store zscores
if stopsdata_p.size > 0: # if there is probe data
trialids_p = np.unique(stopsdata_p[:, 2]) # make array of unique trial numbers
srbin_mean, srbin_std, shuffled_mean, shuffled_std = shuffle_analysis_pertrial3(stopsdata_p, trialids_p) # get average real stops & shuffled stops per lcoation bin
zscore_p = z_score1(srbin_mean, srbin_std, shuffled_mean, shuffled_std) # calculate z-scores
firststopstoreprobe[dcount, session_count,:,1] = zscore_p # store zscores
session_count += 1
# AVERAGE DATA FOR PLOTS
# stack experiments then average over days then mice
con_b1 = np.nanmean(((firststopstorebeac[0, :, :,1])), axis=0)
con_nb1 = np.nanmean(((firststopstorenbeac[0, :, :,1])), axis=0)
con_p1 = np.nanmean(((firststopstoreprobe[0,:, :, 1])), axis=0)
sdcon_b1 = np.nanstd(((firststopstorebeac[0, :, :,1])), axis=0)/math.sqrt(session_count)
sdcon_nb1 = np.nanstd(((firststopstorenbeac[0, :, :,1])), axis=0)/ math.sqrt(session_count)
sdcon_p1 = np.nanstd(((firststopstoreprobe[0, :, :,1])), axis=0)/ math.sqrt(session_count)
# PLOT GRAPHS
bins = np.arange(0.5,n_bins+0.5,1) # track bins
fig = plt.figure(figsize = (12,4))
ax = fig.add_subplot(1,3,1)
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(-10, linewidth = 3, color = 'black') # bold line on the x axis
ax.axhline(0, linewidth = 1,ls='--', color = 'black') # bold line on the x axis
#ax.plot(bins*5,con_b,color = 'red',label = 'AAV-fl-GFP', linewidth = 2) #plot becaoned trials
#ax.fill_between(bins*5,con_b-sdcon_b,con_b+sdcon_b, facecolor = 'red', alpha = 0.3)
ax.plot(bins,con_b1,'',color = 'blue',label = 'AAV-fl-GFP', linewidth = 2) #plot becaoned trials
ax.fill_between(bins,con_b1-sdcon_b1,con_b1+sdcon_b1, facecolor = 'blue', 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(-10,35)
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=5) # set number of ticks on y axis
ax.set_xticklabels(['0', '10', '20'])
ax.set_yticklabels(['-10','0','10','20', '30'])
ax = plt.ylabel('Stops (Zscore)', 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(-10, linewidth = 3, color = 'black') # bold line on the x axis
ax.axhline(0, linewidth = 1,ls='--', color = 'black') # bold line on the x axis
#ax.plot(bins*5,con_nb,color = 'red', linewidth = 2) #plot becaoned trials
#ax.fill_between(bins*5,con_nb-sdcon_nb,con_nb+sdcon_nb, facecolor = 'red', alpha = 0.3)
ax.plot(bins,con_nb1,color = 'blue', linewidth = 2) #plot becaoned trials
ax.fill_between(bins,con_nb1-sdcon_nb1,con_nb1+sdcon_nb1, facecolor = 'blue', 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(-10,35)
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=5) # set number of ticks on y axis
ax.set_xticklabels(['0', '10', '20'])
ax.set_yticklabels(['','','','', ''])
ax = plt.xlabel('Location (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.axhline(0, linewidth = 1,ls='--', color = 'black') # bold line on the x axis
ax.axvline(0, linewidth = 3, color = 'black') # bold line on the y axis
ax.axhline(-10, linewidth = 3, color = 'black') # bold line on the x axis
#ax.plot(bins*5,con_p,color = 'red', label = 'Beaconed', linewidth = 2) #plot becaoned trials
#ax.fill_between(bins*5,con_p-sdcon_p,con_p+sdcon_p, facecolor = 'red', alpha = 0.3)
ax.plot(bins,con_p1,color = 'blue', label = 'Beaconed', linewidth = 2) #plot becaoned trials
ax.fill_between(bins,con_p1-sdcon_p1,con_p1+sdcon_p1, facecolor = 'blue', 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(-10,35)
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=5) # set number of ticks on y axis
ax.set_xticklabels(['0', '10', '20'])
ax.set_yticklabels(['','','','', ''])
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()
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()
# filter stops
stopsdata_b = filterstops(stopsdata_b)
stopsdata_nb = filterstops(stopsdata_nb)
stopsdata_p = filterstops(stopsdata_p)
# get array of trial numbers
trialids_b = np.unique(stopsdata_b[:, 2])
trialids_nb = np.unique(stopsdata_nb[:, 2])
trialids_p = np.unique(stopsdata_p[:, 2])
#get location and trial number of rewards
reward_beac = extractrewards(dailymouse_b)
reward_nbeac = extractrewards(dailymouse_nb)
# real and shuffled stop average
srbin_mean_b, srbin_std_b, shuffled_mean_b, shuffled_std_b = shuffle_analysis_pertrial3(
stopsdata_b, trialids_b) # get average real and shuffled stops
srbin_mean_nb, srbin_std_nb, shuffled_mean_nb, shuffled_std_nb = shuffle_analysis_pertrial3(
stopsdata_nb, trialids_nb) # get average real and shuffled stops
if stopsdata_p.size > 0:
srbin_mean_p, srbin_std_p, shuffled_mean_p, shuffled_std_p = shuffle_analysis_pertrial_tracks(
stopsdata_p, trialids_p,
tracklength / 10) # get average real and shuffled stops
# calculate average speed
speed_beaconed = speed_per_trial(bins, saraharray, trialids_b)
speed_nbeaconed = speed_per_trial(bins, saraharray, trialids_nb)
if stopsdata_p.size > 0:
speed_probe = speed_per_trial(bins, saraharray, trialids_p)
sd_speed_probe = np.nanstd(speed_probe, axis=1)
sd_speed_beaconed = np.nanstd(speed_beaconed, axis=1)
sd_speed_nbeaconed = np.nanstd(speed_nbeaconed, axis=1)
def plot_fig1G (session_paths, save_path):
days = [1] #
number_of_trials = 1000 # arbitrarily large amount of trials so we can catch all the trials
# Arrays for storing data (output)
firststopstorebeac = np.zeros(((number_of_trials), len(session_paths)))
firststopstorenbeac = np.zeros(((number_of_trials), len(session_paths)))
firststopstoreprobe = np.zeros(((number_of_trials), len(session_paths)))
firststopstorebeac[:,:] = np.nan
firststopstorenbeac[:,:] = np.nan
firststopstoreprobe[:,:] = np.nan
sd_con_firststopstorebeac = np.zeros(((number_of_trials), len(session_paths)))
sd_con_firststopstorenbeac = np.zeros(((number_of_trials), len(session_paths)))
sd_con_firststopstoreprobe = np.zeros(((number_of_trials), len(session_paths)))
sd_con_firststopstorebeac[:,:] = np.nan
sd_con_firststopstorenbeac[:,:] = np.nan
sd_con_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*
# get stops and trial arrays
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
# get stops
stops_b = extractstops_HUMAN(dailymouse_b)
stops_nb = extractstops_HUMAN(dailymouse_nb)
stops_p= extractstops_HUMAN(dailymouse_p)
# filter stops
stops_b = filterstops(stops_b)
stops_nb = filterstops(stops_nb)
stops_p = filterstops(stops_p)
if stops_b.size > 0:
trialids_b = np.unique(stops_b[:, 2]) # make array of unique trial numbers
for i in range(len(stops_b)):
srbin_mean, srbin_std, shuffled_mean, shuffled_std = shuffle_analysis_pertrial3(stops_b,trialids_b) # get average real stops & shuffled stops per lcoation bin
shuff_beac = z_score1(srbin_mean, srbin_std, shuffled_mean, shuffled_std) # calculate z-scores
bb = shuff_beac[3]; rz = shuff_beac[9] # black box - reward zone zscore
score = rz - bb
if stops_nb.size > 0:
trialids_nb = np.unique(stops_nb[:, 2]) # make array of unique trial numbers
srbin_mean, srbin_std, shuffled_mean, shuffled_std = shuffle_analysis_pertrial3(stops_nb,trialids_nb) # get average real stops & shuffled stops per lcoation bin
shuff_nbeac = z_score1(srbin_mean, srbin_std, shuffled_mean, shuffled_std) # calculate z-scores
bb = shuff_nbeac[3];
rz = shuff_nbeac[9] # black box - reward zone zscore
score = rz - bb
for i in range(len(stops_nb)):
firststopstorebeac[i, session_count] = score[i][0]
if stops_p.size > 0:
trialids_p = np.unique(stops_p[:, 2]) # make array of unique trial numbers
srbin_mean, srbin_std, shuffled_mean, shuffled_std = shuffle_analysis_pertrial3(stops_p,trialids_p) # get average real stops & shuffled stops per lcoation bin
shuff_probe = z_score1(srbin_mean, srbin_std, shuffled_mean, shuffled_std) # calculate z-scores
bb = shuff_probe[3];
rz = shuff_probe[9] # black box - reward zone zscore
score = rz - bb
firststopstoreprobe[dcount, mcount] = score # store data
mcount += 1
# get average real stops & shuffled stops per location bin
zscore_b = z_score1(srbin_mean, srbin_std, shuffled_mean, shuffled_std)
for i in range(len(stops_b)):
firststopstorebeac[i, session_count] = zscore_b[i][0]
if stops_nb.size>0:
trialids_nb = np.unique(stops_nb[:, 2])
srbin_mean, srbin_std, shuffled_mean, shuffled_std = shuffle_analysis_pertrial3(stops_nb,trialids_nb) # get average real stops & shuffled stops per location bin
zscore_nb = z_score1(srbin_mean, srbin_std, shuffled_mean, shuffled_std)
for i in range(len(stops_nb)):
firststopstorebeac[i, session_count] = zscore_nb[i][0]
if stops_p.nb.size>0:
trialids_p = np.unique(stops_p[:, 2])
srbin_mean, srbin_std, shuffled_mean, shuffled_std = shuffle_analysis_pertrial3(stops_p,trialids_p) # get average real stops & shuffled stops per location bin
zscore_p = z_score1(srbin_mean, srbin_std, shuffled_mean, shuffled_std)
for i in range(len(stops_p)):
firststopstorebeac[i, session_count] = zscore_p[i][0]
session_count +=1
# stack experiments then average over days for each mouse
con_b = np.nanmean(((firststopstorebeac)), axis = 1)
con_b = con_b[~np.isnan(con_b)]
con_nb = np.nanmean(((firststopstorenbeac)), axis =1)
con_nb = con_nb[~np.isnan(con_nb)]
con_p = np.nanmean(((firststopstoreprobe)), axis = 1)
con_p = con_p[~np.isnan(con_nb)]
sd_con_b = np.nanstd(((firststopstorebeac)), axis=1)/math.sqrt(session_count)
sd_con_b = sd_con_b[~np.isnan(sd_con_b)]
sd_con_nb = np.nanstd(((firststopstorenbeac)), axis=1)/math.sqrt(session_count)
sd_con_nb = sd_con_nb[~np.isnan(sd_con_nb)]
sd_con_p = np.nanstd(((firststopstoreprobe)), axis=1)/math.sqrt(session_count)
sd_con_p = sd_con_p[~np.isnan(sd_con_p)]
# PLOT GRAPHS
bins = np.arange(0.5,21.5+1e-6,1) # track bins
fig = plt.figure(figsize = (12,3))
ax = fig.add_subplot(1,3,1)
ax.axvline(0, linewidth = 3, color = 'black') # bold line on the y axis
ax.axhline(-10, linewidth = 3, color = 'black') # bold line on the x axis
ax.axhline(0, linewidth = 1,ls='--', color = 'black') # bold line on the x axis
ax.plot(bins,con_b,'o',color = 'Black',label = 'AAV-fl-GFP', linewidth = 2, markersize = 6, markeredgecolor = 'black') #plot becaoned trials
ax.errorbar(bins,con_b,sd_con_b, fmt = 'o', color = 'black', capsize = 2, capthick = 1, markersize = 4, elinewidth = 1.5)
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(0,22)
ax.set_ylim(-10,20)
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 = plt.ylabel('Training day)', fontsize=16, labelpad = 18)
ax = fig.add_subplot(1,3,2) #stops per trial
ax.axvline(0, linewidth = 3, color = 'black') # bold line on the y axis
ax.axhline(-10, linewidth = 3, color = 'black') # bold line on the x axis
ax.axhline(0, linewidth = 1,ls='--', color = 'black') # bold line on the x axis
ax.plot(bins,con_nb,'o', color = 'Black', linewidth = 2, markersize = 6, markeredgecolor = 'black') #plot becaoned trials
ax.errorbar(bins,con_nb,sd_con_nb, fmt = 'o', color = 'black', capsize = 2, capthick = 1, markersize = 4, elinewidth = 1.5)
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(0,22)
ax.set_ylim(-10,20)
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 = plt.xlabel('Training day', fontsize=16, labelpad = 18)
ax = fig.add_subplot(1,3,3) #stops per trial
ax.axhline(0, linewidth = 1,ls='--', color = 'black') # bold line on the x axis
ax.axvline(0, linewidth = 3, color = 'black') # bold line on the y axis
ax.axhline(-10, linewidth = 3, color = 'black') # bold line on the x axis
ax.plot(bins,con_p, 'o', color = 'Black',label = 'Beaconed', linewidth = 2, markersize = 6, markeredgecolor = 'black') #plot becaoned trials
ax.errorbar(bins,con_p,sd_con_p, fmt = 'o', color = 'black', capsize = 2, capthick = 1, markersize = 4, elinewidth = 1.5)
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(0,22)
ax.set_ylim(-10,20)
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 = plt.xlabel('Training day', fontsize=16, labelpad = 18)
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()
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
#calculate zscores
srbin_mean, srbin_std, shuffled_mean, shuffled_std = shuffle_analysis_pertrial3(high_p, h_trials_p )
zscore_p = z_score1(srbin_mean, srbin_std, shuffled_mean, shuffled_std)
Z_high_probe[mcount,:,0] = zscore_p
srbin_mean, srbin_std, shuffled_mean, shuffled_std = shuffle_analysis_pertrial3( low_p, l_trials_p )
zscore_p = z_score1(srbin_mean, srbin_std, shuffled_mean, shuffled_std)
Z_low_probe[mcount,:,0] = zscore_p
srbin_mean, srbin_std, shuffled_mean, shuffled_std = shuffle_analysis_pertrial3( upper_p, h2_trials_p )
zscore_p = z_score1(srbin_mean, srbin_std, shuffled_mean, shuffled_std)
Z_high_probe[mcount,:,1] = zscore_p
srbin_mean, srbin_std, shuffled_mean, shuffled_std = shuffle_analysis_pertrial3( lower_p, l2_trials_p )
zscore_p = z_score1(srbin_mean, srbin_std, shuffled_mean, shuffled_std)
Z_low_probe[mcount,:,1] = zscore_p
# find preferred stopping location & time to RZ for each quartile
upper = (np.argmax(avg_upper_stops_p[3:17]))+3
high = (np.argmax(avg_high_stops_p[3:17]))+3
