def ett(plot_ax="Y", source=False, id_list="final", make_tight=True, print_title=True, linewidth=0.8, fontscale=0.5, isspec=False, make_sem=True):
config = get_config_file(localpath=path.dirname(path.realpath(__file__))+'/')
#IMPORT VARIABLES
if not source:
source = config.get('Source', 'source')
data_path = config.get('Addresses', source)
reaction_times = config.get('Addresses', 'reaction_times')
#END IMPORT VARIABLES
if plot_ax == "X":
plot_axis = 'GazePointX'
direction = "(towards stimulus)"
if plot_ax == "Y":
plot_axis = 'GazePointY'
direction = "(up)"
data_path = path.expanduser(data_path)
rt_path = data_path + reaction_times
files = [lefile for lefile in listdir(rt_path) if lefile.endswith('.tsv')]
ids = [t.split('_',2)[0]+'_'+t.split('_',2)[1] for t in files]
ids = np.unique(ids)
eye_data_total = pd.DataFrame([])
stimulus_datas = pd.DataFrame([])
spec = ['6245247_f']
h_dist = ['1236345_f','6779353_f','7310001_f','7714775_m','7816097_m','7865828_m','7922847_m']
l_dist = ['1975801_m','4724273_f','6268973_m','8963557_f','8286497_m','8963557_m','9651558_m','8240877_m','6887665_m','5559429_f','8582941_f','8582941_m','9302438_f','4276763_f','3878418_m','3537898_f','1247497_f','8717741_m','4744495_f','7117377_m']
best = ['1975801_m','4724273_f','6268973_m','8963557_f','8286497_m','8963557_m','6887665_m','5559429_f','8582941_f','9302438_f','1247497_f','4744495_f','7117377_m']
test = ['chr1_f','chr2_f']
if id_list=="final":
id_list = l_dist
for fileidx, fileid in enumerate(id_list):
ratings = open_csv(rt_path+fileid+'_p')
ratings = pd.DataFrame(ratings[1:], columns=ratings[0], dtype=float)
ratings = ratings.groupby('picture').mean()
sorted_scores = sorted(ratings['score'])
score_top, score_bottom = sorted_scores[-20], sorted_scores [19]
stimulus_data = pd.DataFrame
stimulus_data = stimulus_data.from_csv(rt_path+fileid+'_wm.csv')
stimulus_data['rateL'] = stimulus_data['rateL'].astype(np.float64)
stimulus_data['RTL'] = stimulus_data['RTL'].astype(np.float64)
stimulus_data['orderL'] = stimulus_data['orderL'].astype(np.float64)
stimulus_data['rateR'] = stimulus_data['rateR'].astype(np.float64)
stimulus_data['RTR'] = stimulus_data['RTR'].astype(np.float64)
stimulus_data['orderR'] = stimulus_data['orderR'].astype(np.float64)
stimulus_data['RT'] = stimulus_data['RT'].astype(np.float64)
stimulus_data['session'] = stimulus_data['session'].astype(np.float64)
stimulus_data = stimulus_data[stimulus_data['RT'] >=0]
stimulus_data['block'] = ''
stimulus_data.ix[(stimulus_data['rateL'] >= score_top) & (stimulus_data['rateR'] >= score_top), 'block'] = 'aa'
stimulus_data.ix[(stimulus_data['rateL'] >= score_top) & (stimulus_data['rateR'] <= score_bottom), 'block'] = 'au'
stimulus_data.ix[(stimulus_data['rateL'] <= score_bottom) & (stimulus_data['rateR'] >= score_top), 'block'] = 'ua'
stimulus_data.ix[(stimulus_data['rateL'] <= score_bottom) & (stimulus_data['rateR'] <= score_bottom), 'block'] = 'uu'
aa_trials = list(stimulus_data[(stimulus_data['block'] == 'aa')]['session'])
au_trials = list(stimulus_data[(stimulus_data['block'] == 'au')]['session'])
ua_trials = list(stimulus_data[(stimulus_data['block'] == 'ua')]['session'])
uu_trials = list(stimulus_data[(stimulus_data['block'] == 'uu')]['session'])
stimleft_trials = list(stimulus_data[(stimulus_data['isstimleft'] == True)]['session'])
stimright_trials = list(stimulus_data[(stimulus_data['isstimleft'] == False)]['session'])
stimatt_trials = list(stimulus_data[(stimulus_data['isstimleft'] == True) & (stimulus_data['rateL'] >= score_top) & (stimulus_data['rateR'] <= score_bottom)]['session'])
stimatt_trials = stimatt_trials + list(stimulus_data[(stimulus_data['isstimleft'] == False) & (stimulus_data['rateL'] <= score_bottom) & (stimulus_data['rateR'] >= score_top)]['session'])
stimNatt_trials = list(stimulus_data[(stimulus_data['isstimleft'] == False) & (stimulus_data['rateL'] >= score_top) & (stimulus_data['rateR'] <= score_bottom)]['session'])
stimNatt_trials = stimNatt_trials + list(stimulus_data[(stimulus_data['isstimleft'] == True) & (stimulus_data['rateL'] <= score_bottom) & (stimulus_data['rateR'] >= score_top)]['session'])
pat = 'TimeStamp GazePointXLeft GazePointYLeft ValidityLeft GazePointXRight GazePointYRight ValidityRight GazePointX GazePointY Event'
with open(rt_path+fileid+'_wmet.tsv') as infile:
eye_data = infile.read().split(pat)
eye_data = eye_data[1:] # remove header (describing date etc)
eye_data = [trial.split('\r\n') for trial in eye_data] # split at '\r'
for idx, i in enumerate(eye_data): # crop to 447 ACTUAL time frames - the first one is empty
eye_data[idx] = i[:448]
for idx, trial in enumerate(eye_data):
trial = [row.split('\t') for row in trial]
eye_data[idx] = trial
eye_data = [name[1:] for name in eye_data] # crop the first, empty line
eye_data = np.array(eye_data)
eye_data = eye_data[:,:,[0,3,6,7,8]].astype(np.float64) # convert to float, we don't need separate eye coordinates
eye_data[:,:,3:] = eye_data[:,:,3:] / 2 - 0.5 # the integrated left-right gaze coordinates are the sum of the per-eye screen percentages - divide by 2 (2 eyes) and normalize to: 50% = 0
for a in np.arange(np.shape(eye_data)[0]): # assume that when neither of the eyes is detected the subject looks at the fixation
for i in np.arange(np.shape(eye_data)[1]):
if eye_data[a,i,1] == 4 and eye_data[a,i,2] == 4:
eye_data[a,i,3] = 0
eye_data[a,i,4] = 0
for i in stimleft_trials: # invert stimleft trial coordinates - equates 'right' with 'stimside'
eye_data[i,:,3:] = -eye_data[i,:,3:]
eye_data = eye_data[:,:,[0,3,4]] # we can't work with eye detection indices in the subsequent sumation, discard them here
eye_data_aa = eye_data[aa_trials,:,:]
eye_data_uu = eye_data[uu_trials,:,:]
eye_data_uas = eye_data[stimatt_trials,:,:]
eye_data_aus = eye_data[stimNatt_trials,:,:]
eye_data_aa = np.sum(eye_data_aa, axis=0) / np.shape(eye_data_aa)[0]
eye_data_uu = np.sum(eye_data_uu, axis=0) / np.shape(eye_data_uu)[0]
eye_data_uas = np.sum(eye_data_uas, axis=0) / np.shape(eye_data_uas)[0]
eye_data_aus = np.sum(eye_data_aus, axis=0) / np.shape(eye_data_aus)[0]
eye_data_aa = pd.DataFrame(eye_data_aa, columns=['time','GazePointX','GazePointY'])
eye_data_aa['stimuli'] = 'aa'
eye_data_uu = pd.DataFrame(eye_data_uu, columns=['time','GazePointX','GazePointY'])
eye_data_uu['stimuli'] = 'uu'
eye_data_uas = pd.DataFrame(eye_data_uas, columns=['time','GazePointX','GazePointY'])
eye_data_uas['stimuli'] = 'uas'
eye_data_aus = pd.DataFrame(eye_data_aus, columns=['time','GazePointX','GazePointY'])
eye_data_aus['stimuli'] = 'aus'
eye_data = pd.concat([eye_data_aa, eye_data_uu, eye_data_uas, eye_data_aus])
eye_data['ID'] = fileid
eye_data["timepoint"] = eye_data.index
if fileidx == 0:
eye_data_total = eye_data[["timepoint",'time','GazePointX','GazePointY','stimuli',"ID"]]
else:
eye_data_total = pd.concat([eye_data_total, eye_data[["timepoint",'time','GazePointX','GazePointY','stimuli',"ID"]]])
# load reaction times (to plot as lines) here:
conts = get_dataframes(id_list, rt_path)
sa_reaction_time = conts[(conts['subblock'] == 'uas+sau')]['RT'].mean()*1000
su_reaction_time = conts[(conts['subblock'] == 'aus+sua')]['RT'].mean()*1000
aa_reaction_time = conts[(conts['block'] == 'aa')]['RT'].mean()*1000
uu_reaction_time = conts[(conts['block'] == 'uu')]['RT'].mean()*1000
# end load reaction times
et_means = eye_data_total.groupby(["stimuli","timepoint"]).mean()
if make_sem:
et_sem = eye_data_total.groupby(["stimuli","timepoint"]).aggregate(sem)
fig = figure(figsize=(3, 4), dpi=300, facecolor='#eeeeee', tight_layout=make_tight)
ax1=fig.add_subplot(2,1,1)
matplotlib.rcParams.update({'font.size': 12*fontscale})
ax1.set_xlim(0, et_means['time'].max())
tc = et_means.ix["aa"]['time']
v = et_means.ix["aa"][plot_axis]
ax1.plot(tc, v, color='g')
if make_sem:
se = et_sem.ix["aa"][plot_axis]/2
ax1.fill_between(tc, v+se, v-se, facecolor="g", edgecolor="none", alpha=0.1, zorder=0)
tc = et_means.ix["uu"]['time']
v = et_means.ix["uu"][plot_axis]
ax1.plot(tc, v, color='m')
if make_sem:
se = et_sem.ix["uu"][plot_axis]/2
ax1.fill_between(tc, v+se, v-se, facecolor="m", edgecolor="none", alpha=0.1, zorder=0)
legend(('Attractive - Attractive','Unattractive - Unattractive'), bbox_to_anchor=(0.94, 0.99), shadow=False, frameon=False, prop=FontProperties(size=str(9*fontscale)))
ax1.axhline(0, color='k', alpha = 0.1, linewidth=linewidth)
ax1.axvline(aa_reaction_time, color='g', alpha = 0.3, linewidth=linewidth)
ax1.axvline(uu_reaction_time, color='m', alpha = 0.3, linewidth=linewidth)
ax1.set_ylabel(plot_ax+'-axis % '+direction)
ax1.set_xlabel('Time [ms]')
ax2 = fig.add_subplot(212)
ax2.set_xlim(0, et_means['time'].max())
tc = et_means.ix["uas"]['time']
v = et_means.ix["uas"][plot_axis]
ax2.plot(tc, v, color='g')
if make_sem:
se = et_sem.ix["uas"][plot_axis]/2
ax2.fill_between(tc, v+se, v-se, facecolor="g", edgecolor="none", alpha=0.1, zorder=0)
tc = et_means.ix["aus"]['time']
v = et_means.ix["aus"][plot_axis]
ax2.plot(tc, v, color='m')
if make_sem:
se = et_means.ix["aus"][plot_axis]/2
ax2.fill_between(tc, v+se, v-se, facecolor="m", edgecolor="none", alpha=0.1, zorder=0)
legend(('Attractive on Target Side','Unattractive on Target Side'), bbox_to_anchor=(0.94, 0.99), shadow=False, frameon=False, prop=FontProperties(size=str(9*fontscale)))
ax2.axhline(0, color='k', alpha = 0.1, linewidth=linewidth)
ax2.axvline(sa_reaction_time, color='g', alpha = 0.3, linewidth=linewidth)
ax2.axvline(su_reaction_time, color='m', alpha = 0.3, linewidth=linewidth)
ax2.set_ylabel(plot_ax+'-axis % '+direction)
ax2.set_xlabel('Time [ms]')
def coi(source=False, make_tight=True, print_title = True, elinewidth=3, fontscale=1, isspec = False):
config = get_config_file(localpath=path.dirname(path.realpath(__file__))+'/')
#IMPORT VARIABLES
if not source:
source = config.get('Source', 'source')
data_path = config.get('Addresses', source)
reaction_times = config.get('Addresses', 'reaction_times')
#END IMPORT VARIABLES
data_path = path.expanduser(data_path)
rt_path = data_path + reaction_times
files = [lefile for lefile in listdir(rt_path) if lefile.endswith('.csv')]
ids = [t.split('_',2)[0]+'_'+t.split('_',2)[1] for t in files]
ids = np.unique(ids)
conts = get_dataframes(id_list, rt_path)
if isspec:
spec_conts = get_dataframes(spec, rt_path)
meanscont = spec_conts.groupby('subblock').mean()
print meanscont
cat1 = spec_conts[spec_conts['subblock']=='aus+sua']
cat2 = spec_conts[spec_conts['subblock']=='uas+sau']
print ttest_rel(cat1['RTdiff'], cat2['RTdiff'])
meanscont = conts.groupby(['ID','subblock']).mean()
meanscont = meanscont.reset_index()
ids = sorted(list(set(conts.set_index('ID').index)))
pos_ids = np.arange(len(ids))
sa_means = conts[(conts['subblock'] == 'uas+sau')].groupby('ID')['RTdiff'].mean()
sa_std = conts[(conts['subblock'] == 'uas+sau')].groupby('ID')['RTdiff'].aggregate(sem)
su_means = conts[(conts['subblock'] == 'aus+sua')].groupby('ID')['RTdiff'].mean()
su_std = conts[(conts['subblock'] == 'aus+sua')].groupby('ID')['RTdiff'].aggregate(sem)
sa_means = conts[(conts['subblock'] == 'uas+sau')].groupby('ID')['RTdiff'].mean()
sa_std = conts[(conts['subblock'] == 'uas+sau')].groupby('ID')['RTdiff'].aggregate(sem)
sa_t_means = meanscont[(meanscont['subblock'] == 'uas+sau')]['RTdiff'].mean()
sa_t_std = sem(meanscont[(meanscont['subblock'] == 'uas+sau')]['RTdiff'])
su_means = conts[(conts['subblock'] == 'aus+sua')].groupby('ID')['RTdiff'].mean()
su_std = conts[(conts['subblock'] == 'aus+sua')].groupby('ID')['RTdiff'].aggregate(sem)
su_t_means = meanscont[(meanscont['subblock'] == 'aus+sua')]['RTdiff'].mean()
su_t_std = sem(meanscont[(meanscont['subblock'] == 'aus+sua')]['RTdiff'])
if isspec:
sa_spec_means = spec_conts[(spec_conts['subblock'] == 'uas+sau')]['RTdiff'].mean()
sa_spec_std = sem(spec_conts[(spec_conts['subblock'] == 'uas+sau')]['RTdiff'])
su_spec_means = spec_conts[(spec_conts['subblock'] == 'aus+sua')]['RTdiff'].mean()
su_spec_std = sem(spec_conts[(spec_conts['subblock'] == 'aus+sua')]['RTdiff'])
fig = figure(figsize=(pos_ids.max()*3, 4), dpi=300, facecolor='#eeeeee', tight_layout=make_tight)
ax=fig.add_subplot(1,1,1)
matplotlib.rcParams.update({'font.size': 12*fontscale})
width = 0.3
ax.yaxis.grid(True, linestyle='-', which='major', color='#dddddd',alpha=0.5, zorder=1)
sa_bar = plt.bar(pos_ids, sa_means, width ,color='m', alpha=0.4, zorder=1, linewidth=0)
sa_err = errorbar(pos_ids+(width/2), sa_means, yerr=sa_std, ecolor='0.55', elinewidth=elinewidth, capsize=0, linestyle='None', zorder=2)
sa_t_bar = plt.bar(pos_ids[-1]+1, sa_t_means, width ,color='m', alpha=0.8, zorder=1, linewidth=0)
sa_t_err = errorbar(pos_ids[-1]+1+(width/2), sa_t_means, yerr=sa_t_std, ecolor='0.1', elinewidth=elinewidth, capsize=0, linestyle='None', zorder=2)
su_bar = plt.bar(pos_ids+width, su_means, width ,color='g', alpha=0.4, zorder=1, linewidth=0)
su_err = errorbar(pos_ids+(width*3/2), su_means, yerr=su_std, ecolor='0.55', elinewidth=elinewidth, capsize=0, linestyle='None', zorder=2)
su_t_bar = plt.bar(pos_ids[-1]+1+width, su_t_means, width ,color='g', alpha=0.8, zorder=1, linewidth=0)
su_t_err = errorbar(pos_ids[-1]+1+(width*3/2), su_t_means, yerr=su_t_std, ecolor='0.1', elinewidth=elinewidth, capsize=0, linestyle='None', zorder=2)
if isspec:
sa_spec_bar = plt.bar(pos_ids[-1]+2, sa_spec_means, width ,color='m', alpha=0.4, zorder=1, linewidth=0)
sa_spec_err = errorbar(pos_ids[-1]+2+(width*1/2), sa_spec_means, yerr=sa_spec_std, ecolor='0.55', elinewidth=elinewidth, capsize=0, linestyle='None', zorder=2)
su_spec_bar = plt.bar(pos_ids[-1]+2+width, su_spec_means, width ,color='g', alpha=0.4, zorder=1, linewidth=0)
su_spec_err = errorbar(pos_ids[-1]+2+(width*3/2), su_spec_means, yerr=su_spec_std, ecolor='0.55', elinewidth=elinewidth, capsize=0, linestyle='None', zorder=2)
if isspec:
ids=ids+['total',spec]
else:
ids=ids+['TOTAL '] # blank space at the end so that it doesn't overlap with the x-axis
pos_ids = np.arange(len(ids))
ax.set_xlim(0, pos_ids.max())
ax.set_ylabel(r'$\mathsf{\overline{RT}}$ [s]')
ax.set_xlabel('Participant ID')
ax.set_xticks(pos_ids + width)
ax.set_xticklabels(ids,fontsize=8*fontscale,rotation=90)
for tick in ax.axes.get_xticklines():
tick.set_visible(False)
axis.Axis.zoom(ax.xaxis, -0.5)
legend((sa_t_bar,su_t_bar),('Target flanking attracive face','Target flanking unattractive face'), bbox_to_anchor=(0.92, 0.2), shadow=False, frameon=False, prop=FontProperties(size=str(11*fontscale)))
print meanscont[meanscont['subblock']=='uas+sau']['RTdiff'].mean()-meanscont[meanscont['subblock']=='aus+sua']['RTdiff'].mean()
return meanscont
