def main(experiment=False,
source=False,
prepixelation='not specified',
elinewidth=2,
ecolor='0.3',
make_tight=True):
data_all = get_and_filter_results(experiment=experiment,
source=source,
prepixelation=prepixelation,
make_CoI=True)
ids = sorted(list(set(data_all['ID'])))
pos_ids = np.arange(len(ids))
fig = figure(figsize=(pos_ids.max() * 4, 5),
dpi=300,
facecolor='#eeeeee',
tight_layout=make_tight)
ax = fig.add_subplot(1, 1, 1)
width = 0.1
ax.yaxis.grid(True,
linestyle='-',
which='major',
color='#dddddd',
alpha=0.6,
zorder=0)
ax.set_axisbelow(True)
scrambling_list = set(data_all['scrambling'])
errors_index = range(len(set(data_all['ID'])))
errors_columns = ['ID', 'correct', 'incorrect']
total_errors_index = range(
len(set(data_all['ID'])) * (len(scrambling_list) + 1)
) # number of error entries - per ID: one for each scrambling step, and TWO for 0
total_errors_columns = ['ID', 'ER', 'scrambling', 'intensity', 'CoI']
total_errors = pd.DataFrame(
index=total_errors_index, columns=total_errors_columns
) # empty container frame for concatenating input from multiple files
for id_ix, le_id in enumerate(set(data_all['ID'])):
id_ix = id_ix * (
len(scrambling_list) + 1
) # number of errors per ID: one for each scrambling step, and TWO for 0
data_for_id = data_all[data_all['ID'] == le_id]
strong_em_errors = len(data_for_id[
(data_for_id['scrambling'] == list(scrambling_list)[0])
& (data_for_id['correct answer'] != data_for_id['keypress']) &
(data_for_id['intensity'] == 100)].index) / len(data_for_id[
(data_for_id['scrambling'] == list(scrambling_list)[0])
& (data_for_id['intensity'] == 100)].index)
weak_em_errors = len(data_for_id[
(data_for_id['scrambling'] == list(scrambling_list)[0])
& (data_for_id['correct answer'] != data_for_id['keypress']) &
(data_for_id['intensity'] == 40)].index) / len(data_for_id[
(data_for_id['scrambling'] == list(scrambling_list)[0])
& (data_for_id['intensity'] == 40)].index)
total_errors.ix[id_ix]['ID'] = le_id
total_errors.ix[id_ix]['ER'] = strong_em_errors
total_errors.ix[id_ix]['scrambling'] = list(scrambling_list)[0]
total_errors.ix[id_ix]['intensity'] = 100
total_errors.ix[id_ix + 1]['ID'] = le_id
total_errors.ix[id_ix + 1]['ER'] = weak_em_errors
total_errors.ix[id_ix + 1]['scrambling'] = list(scrambling_list)[0]
total_errors.ix[id_ix + 1]['intensity'] = 40
for scrambling_id, scrambling in enumerate(
list(scrambling_list)[1:]): #all scrambling steps except 0
errors = len(data_for_id[
(data_for_id['scrambling'] == scrambling)
& (data_for_id['correct answer'] != data_for_id['keypress'])].
index) / len(data_for_id[(data_for_id['scrambling']
== scrambling)].index)
total_errors.ix[id_ix + 2 + scrambling_id]['ID'] = le_id
total_errors.ix[id_ix + 2 + scrambling_id]['ER'] = errors
total_errors.ix[id_ix + 2 +
scrambling_id]['scrambling'] = scrambling
total_errors.ix[id_ix + 2 + scrambling_id]['intensity'] = 100
total_errors = categories_of_interest(
total_errors, scrambling_list) #make categories of interest
total_errors_just_for_plotting = total_errors.copy(
) # please DO NOT USE THE DATA FROM THIS NEW VARIABLE for anything BUT plotting
total_errors_just_for_plotting.ix[(
total_errors_just_for_plotting['ER'] == 0
), 'ER'] = 0.002 # this is a hack to make 0-height bins visible when plotting
#START DRAWING GRAPHIC ELEMENTS
for scrambling_id, scrambling in enumerate(
set(total_errors_just_for_plotting['scrambling'])):
if scrambling == 0:
#below this: per-participant graphs
plot_em_strong = plt.bar(
pos_ids - width,
total_errors_just_for_plotting[
(total_errors_just_for_plotting['scrambling'] == scrambling
) & (total_errors_just_for_plotting['intensity'] == 100)]
['ER'],
width,
color='m',
alpha=0.7,
zorder=1,
linewidth=0)
plot_em_weak = plt.bar(
pos_ids,
total_errors_just_for_plotting[(
total_errors_just_for_plotting['scrambling'] == scrambling
) & (total_errors_just_for_plotting['intensity'] == 40)]['ER'],
width,
color='m',
alpha=0.4,
zorder=1,
linewidth=0)
#below this: total graphs
tot_plot_em_strong = plt.bar(
pos_ids[-1] + 1 - width,
total_errors_just_for_plotting[
(total_errors_just_for_plotting['scrambling'] == scrambling
) & (total_errors_just_for_plotting['intensity'] == 100)]
['ER'].mean(),
width,
color='m',
alpha=0.7,
zorder=1,
linewidth=0)
tot_err_em_strong = errorbar(
pos_ids[-1] + 1 - (width / 2),
total_errors_just_for_plotting[
(total_errors_just_for_plotting['scrambling'] == scrambling
) & (total_errors_just_for_plotting['intensity'] == 100)]
['ER'].mean(),
yerr=sem(total_errors_just_for_plotting[
(total_errors_just_for_plotting['scrambling'] == scrambling
) & (total_errors_just_for_plotting['intensity'] == 100)]
['ER']),
ecolor=ecolor,
elinewidth=elinewidth,
capsize=0,
linestyle='None',
zorder=2)
tot_plot_em_weak = plt.bar(
pos_ids[-1] + 1,
total_errors_just_for_plotting[
(total_errors_just_for_plotting['scrambling'] == scrambling
) & (total_errors_just_for_plotting['intensity'] == 40)]
['ER'].mean(),
width,
color='m',
alpha=0.4,
zorder=1,
linewidth=0)
tot_err_em_weak = errorbar(
pos_ids[-1] + 1 + (width / 2) + width * scrambling_id,
total_errors_just_for_plotting[
(total_errors_just_for_plotting['scrambling'] == scrambling
) & (total_errors_just_for_plotting['intensity'] == 40)]
['ER'].mean(),
yerr=sem(total_errors_just_for_plotting[
(total_errors_just_for_plotting['scrambling'] == scrambling
) & (total_errors_just_for_plotting['intensity'] == 40)]
['ER']),
ecolor=ecolor,
elinewidth=elinewidth,
capsize=0,
linestyle='None',
zorder=2)
else:
#below this: per-participant graphs
errors_part_n0 = total_errors_just_for_plotting[(
total_errors_just_for_plotting['scrambling'] == scrambling
)]['ER']
plot_sc = plt.bar(
pos_ids + width * scrambling_id,
total_errors_just_for_plotting[(
total_errors_just_for_plotting['scrambling'] == scrambling
)]['ER'],
width,
color='g',
alpha=0.2 + 0.12 * scrambling_id,
zorder=1,
linewidth=0)
#below this: total graphs
errors_total_n0 = total_errors_just_for_plotting[(
total_errors_just_for_plotting['scrambling'] == scrambling
)]['ER'].mean()
plot_sc = plt.bar(pos_ids[-1] + 1 + width * scrambling_id,
errors_total_n0,
width,
color='g',
alpha=0.2 + 0.12 * scrambling_id,
zorder=1,
linewidth=0)
errorbar(
pos_ids[-1] + 1 + (width / 2) + width * scrambling_id,
errors_total_n0,
yerr=sem(total_errors_just_for_plotting[(
total_errors_just_for_plotting['scrambling'] == scrambling
)]['ER']),
ecolor=ecolor,
elinewidth=elinewidth,
capsize=0,
linestyle='None',
zorder=2)
width_multiplier = 15 / np.shape(data_all[
(data_all['scrambling'] == scrambling)].groupby('ID')['RT'].mean())[0]
plt.axvline(pos_ids[-1] + 1 - width * width_multiplier, color='0.2')
ids = ids + ['ALL']
pos_ids = np.arange(len(ids))
ax.set_xlim(0, pos_ids[-1] + width * 5)
ax.set_ylim(0, total_errors_just_for_plotting['ER'].max() * 1.2)
ax.set_ylabel(r'$\mathsf{\Sigma_{wrong} / \Sigma_{all}}$', fontsize=13)
ax.set_xlabel('Participant', fontsize=11)
ax.set_xticks(pos_ids + width * 3)
ax.set_xticklabels(
ids,
fontsize=9) # add rotation=30 if labels are too long and need rotating
for tick in ax.axes.get_xticklines():
tick.set_visible(False)
axis.Axis.zoom(ax.xaxis, -0.5)
scrambling_list = [str(i) for i in scrambling_list if i != 0]
legend((plot_em_strong, plot_em_weak, plot_sc),
('Strong Emotion', 'Weak Emotion',
'Scrambled ' + ', '.join(scrambling_list)),
loc='upper center',
bbox_to_anchor=(0.5, 1.065),
ncol=3,
fancybox=False,
shadow=False,
prop=FontProperties(size='9'))
#END DRAWING GRAPHIC ELEMENTS
total_errors['ER'] = total_errors['ER'].astype(
float
) # apparently some other scripts (using R linear models) cannot deal with the output unless we do this.
return total_errors
def main(experiment=False,
source=False,
prepixelation='not specified',
elinewidth=2,
ecolor='0.3',
make_tight=True,
total='means',
make_std=False,
make_sem=True,
fontscale=1):
data_all = get_and_filter_results(experiment,
source,
prepixelation,
mismeasurement='fix',
apply_correct_values=True,
make_CoI=True)
scrambling_list = set(data_all['scrambling'])
data_all = categories_of_interest(data_all, scrambling_list)
ids = sorted(list(set(data_all['ID'])))
pos_ids = np.arange(len(ids))
fig = figure(figsize=(pos_ids.max() * 5, 4),
dpi=300,
facecolor='#eeeeee',
tight_layout=make_tight)
ax = fig.add_subplot(1, 1, 1)
ax.yaxis.grid(True,
linestyle='-',
which='major',
color='#dddddd',
alpha=0.6,
zorder=0)
ax.set_axisbelow(True)
width = 0.1
for scrambling_id, scrambling in enumerate(set(data_all['scrambling'])):
if scrambling == 0:
#below this: per-participant graphs
plot_em_strong = plt.bar(
pos_ids - width,
data_all[(data_all['scrambling'] == scrambling) & (
data_all['intensity'] == 100)].groupby('ID')['RT'].mean(),
width,
color='m',
alpha=0.7,
zorder=1,
linewidth=0)
plot_em_weak = plt.bar(
pos_ids,
data_all[(data_all['scrambling'] == scrambling) & (
data_all['intensity'] == 40)].groupby('ID')['RT'].mean(),
width,
color='m',
alpha=0.4,
zorder=1,
linewidth=0)
if make_std:
errorbar(pos_ids - (width / 2),
data_all[(data_all['scrambling'] == scrambling)
& (data_all['intensity'] == 100)].groupby(
'ID')['RT'].mean(),
yerr=data_all[(data_all['scrambling'] == scrambling
)].groupby('ID')['RT'].aggregate(
np.std),
ecolor=str(float(ecolor) + 0.25),
elinewidth=elinewidth,
capsize=0,
linestyle='None',
zorder=2)
errorbar(pos_ids + (width / 2) + width * scrambling_id,
data_all[(data_all['scrambling'] == scrambling)
& (data_all['intensity'] == 40)].groupby(
'ID')['RT'].mean(),
yerr=data_all[(data_all['scrambling'] == scrambling
)].groupby('ID')['RT'].aggregate(
np.std),
ecolor=str(float(ecolor) + 0.25),
elinewidth=elinewidth,
capsize=0,
linestyle='None',
zorder=2)
if make_sem:
errorbar(pos_ids - (width / 2),
data_all[(data_all['scrambling'] == scrambling)
& (data_all['intensity'] == 100)].groupby(
'ID')['RT'].mean(),
yerr=data_all[(data_all['scrambling'] == scrambling
)].groupby('ID')['RT'].aggregate(sem),
ecolor=ecolor,
elinewidth=elinewidth,
capsize=0,
linestyle='None',
zorder=2)
errorbar(pos_ids + (width / 2) + width * scrambling_id,
data_all[(data_all['scrambling'] == scrambling)
& (data_all['intensity'] == 40)].groupby(
'ID')['RT'].mean(),
yerr=data_all[(data_all['scrambling'] == scrambling
)].groupby('ID')['RT'].aggregate(sem),
ecolor=ecolor,
elinewidth=elinewidth,
capsize=0,
linestyle='None',
zorder=2)
#below this: total graphs
if total == 'all':
plt.bar(
pos_ids[-1] + 1 - width,
data_all[(data_all['scrambling'] == scrambling)
& (data_all['intensity'] == 100)]['RT'].mean(),
width,
color='m',
alpha=0.7,
zorder=1,
linewidth=0)
plt.bar(pos_ids[-1] + 1,
data_all[(data_all['scrambling'] == scrambling)
& (data_all['intensity'] == 40)]['RT'].mean(),
width,
color='m',
alpha=0.4,
zorder=1,
linewidth=0)
if make_std:
errorbar(
pos_ids[-1] + 1 - (width / 2),
data_all[(data_all['scrambling'] == scrambling) &
(data_all['intensity'] == 100)]['RT'].mean(),
yerr=np.std(
data_all[(data_all['scrambling'] == scrambling)
& (data_all['intensity'] == 100)]['RT']),
ecolor=str(float(ecolor) + 0.25),
elinewidth=elinewidth,
capsize=0,
linestyle='None',
zorder=2)
errorbar(
pos_ids[-1] + 1 + (width / 2) + width * scrambling_id,
data_all[(data_all['scrambling'] == scrambling)
& (data_all['intensity'] == 40)]['RT'].mean(),
yerr=np.std(
data_all[(data_all['scrambling'] == scrambling)
& (data_all['intensity'] == 40)]['RT']),
ecolor=str(float(ecolor) + 0.25),
elinewidth=elinewidth,
capsize=0,
linestyle='None',
zorder=2)
if make_sem:
errorbar(pos_ids[-1] + 1 - (width / 2),
data_all[(data_all['scrambling'] == scrambling)
& (data_all['intensity'] == 100)]
['RT'].mean(),
yerr=sem(data_all[(
data_all['scrambling'] == scrambling)]['RT']),
ecolor=ecolor,
elinewidth=elinewidth,
capsize=0,
linestyle='None',
zorder=2)
errorbar(
pos_ids[-1] + 1 + (width / 2) + width * scrambling_id,
data_all[(data_all['scrambling'] == scrambling)
& (data_all['intensity'] == 40)]['RT'].mean(),
yerr=sem(data_all[(
data_all['scrambling'] == scrambling)]['RT']),
ecolor=ecolor,
elinewidth=elinewidth,
capsize=0,
linestyle='None',
zorder=2)
elif total == 'means':
plt.bar(
pos_ids[-1] + 1 - width,
data_all[(data_all['scrambling'] == scrambling)
& (data_all['intensity'] == 100)]['RT'].mean(),
width,
color='m',
alpha=0.7,
zorder=1,
linewidth=0)
plt.bar(pos_ids[-1] + 1,
data_all[(data_all['scrambling'] == scrambling)
& (data_all['intensity'] == 40)]['RT'].mean(),
width,
color='m',
alpha=0.4,
zorder=1,
linewidth=0)
if make_std:
errorbar(
pos_ids[-1] + 1 - (width / 2),
data_all[(data_all['scrambling'] == scrambling) &
(data_all['intensity'] == 100)]['RT'].mean(),
yerr=np.std(
data_all[(data_all['scrambling'] == scrambling)
& (data_all['intensity'] == 100)].groupby(
'ID')['RT'].mean()),
ecolor=str(float(ecolor) + 0.25),
elinewidth=elinewidth,
capsize=0,
linestyle='None',
zorder=2)
errorbar(
pos_ids[-1] + 1 + (width / 2) + width * scrambling_id,
data_all[(data_all['scrambling'] == scrambling)
& (data_all['intensity'] == 40)]['RT'].mean(),
yerr=np.std(
data_all[(data_all['scrambling'] == scrambling)
& (data_all['intensity'] == 40)].groupby(
'ID')['RT'].mean()),
ecolor=str(float(ecolor) + 0.25),
elinewidth=elinewidth,
capsize=0,
linestyle='None',
zorder=2)
if make_sem:
errorbar(
pos_ids[-1] + 1 - (width / 2),
data_all[(data_all['scrambling'] == scrambling) &
(data_all['intensity'] == 100)]['RT'].mean(),
yerr=sem(
data_all[(data_all['scrambling'] == scrambling)
& (data_all['intensity'] == 100)].groupby(
'ID')['RT'].mean()),
ecolor=ecolor,
elinewidth=elinewidth,
capsize=0,
linestyle='None',
zorder=2)
errorbar(
pos_ids[-1] + 1 + (width / 2) + width * scrambling_id,
data_all[(data_all['scrambling'] == scrambling)
& (data_all['intensity'] == 40)]['RT'].mean(),
yerr=sem(
data_all[(data_all['scrambling'] == scrambling)
& (data_all['intensity'] == 40)].groupby(
'ID')['RT'].mean()),
ecolor=ecolor,
elinewidth=elinewidth,
capsize=0,
linestyle='None',
zorder=2)
else:
#below this: per-participant graphs
plot_sc = plt.bar(pos_ids + width * scrambling_id,
data_all[(data_all['scrambling'] == scrambling
)].groupby('ID')['RT'].mean(),
width,
color='g',
alpha=0.2 + 0.12 * scrambling_id,
zorder=1,
linewidth=0)
if make_std:
errorbar(pos_ids + (width / 2) + width * scrambling_id,
data_all[(data_all['scrambling'] == scrambling
)].groupby('ID')['RT'].mean(),
yerr=data_all[(data_all['scrambling'] == scrambling
)].groupby('ID')['RT'].aggregate(
np.std),
ecolor=str(float(ecolor) + 0.25),
elinewidth=elinewidth,
capsize=0,
linestyle='None',
zorder=2)
if make_sem:
errorbar(pos_ids + (width / 2) + width * scrambling_id,
data_all[(data_all['scrambling'] == scrambling
)].groupby('ID')['RT'].mean(),
yerr=data_all[(data_all['scrambling'] == scrambling
)].groupby('ID')['RT'].aggregate(sem),
ecolor=ecolor,
elinewidth=elinewidth,
capsize=0,
linestyle='None',
zorder=2)
#below this: total graphs
if total == 'all':
plot_sc = plt.bar(
pos_ids[-1] + 1 + width * scrambling_id,
data_all[(
data_all['scrambling'] == scrambling)]['RT'].mean(),
width,
color='g',
alpha=0.2 + 0.12 * scrambling_id,
zorder=1,
linewidth=0)
if make_std:
errorbar(pos_ids[-1] + 1 + (width / 2) +
width * scrambling_id,
data_all[(data_all['scrambling'] == scrambling
)]['RT'].mean(),
yerr=np.std(data_all[(
data_all['scrambling'] == scrambling)]['RT']),
ecolor=str(float(ecolor) + 0.25),
elinewidth=elinewidth,
capsize=0,
linestyle='None',
zorder=2)
if make_sem:
errorbar(pos_ids[-1] + 1 + (width / 2) +
width * scrambling_id,
data_all[(data_all['scrambling'] == scrambling
)]['RT'].mean(),
yerr=sem(data_all[(
data_all['scrambling'] == scrambling)]['RT']),
ecolor=ecolor,
elinewidth=elinewidth,
capsize=0,
linestyle='None',
zorder=2)
elif total == 'means':
plot_sc = plt.bar(
pos_ids[-1] + 1 + width * scrambling_id,
data_all[(
data_all['scrambling'] == scrambling)]['RT'].mean(),
width,
color='g',
alpha=0.2 + 0.12 * scrambling_id,
zorder=1,
linewidth=0)
if make_std:
errorbar(pos_ids[-1] + 1 + (width / 2) +
width * scrambling_id,
data_all[(data_all['scrambling'] == scrambling
)]['RT'].mean(),
yerr=np.std(
data_all[(data_all['scrambling'] == scrambling
)].groupby('ID')['RT'].mean()),
ecolor=str(float(ecolor) + 0.25),
elinewidth=elinewidth,
capsize=0,
linestyle='None',
zorder=2)
if make_sem:
errorbar(
pos_ids[-1] + 1 + (width / 2) + width * scrambling_id,
data_all[(data_all['scrambling'] == scrambling
)]['RT'].mean(),
yerr=sem(data_all[(data_all['scrambling'] == scrambling
)].groupby('ID')['RT'].mean()),
ecolor=ecolor,
elinewidth=elinewidth,
capsize=0,
linestyle='None',
zorder=2)
width_multiplier = 15 / np.shape(
data_all[(data_all['scrambling'] == scrambling)].groupby(
'ID')['RT'].mean())[0] #(fine tune) separator positioning
plt.axvline(pos_ids[-1] + 1 - width * width_multiplier,
color='0.2') #separator - per-person/total
scrambling_list = [str(i) for i in scrambling_list
if i != 0] #format as string for legend
ids = ids + ['ALL']
pos_ids = np.arange(len(ids))
ax.set_ylabel(r'$\mathsf{\overline{RT}}$ [s]', fontsize=11 * fontscale)
ax.set_xlabel('Participant', fontsize=11 * fontscale)
ax.set_xticks(pos_ids + width * 3)
ax.set_xticklabels(ids, fontsize=9 *
fontscale) # add rotation=30 if things get too crowded
for tick in ax.axes.get_xticklines():
tick.set_visible(False)
ax.set_xlim(0, pos_ids[-1] +
width * 5) # before scaling to add padding in front of zero
axis.Axis.zoom(
ax.xaxis, -0.5
) # sets x margins further apart from the content proportional to its length
axis.Axis.zoom(
ax.yaxis, -0.5
) # sets y margins further apart from the content proportional to its length
ax.set_ylim(bottom=0) # after scaling to disregard padding unerneath zero.
legend((plot_em_strong, plot_em_weak, plot_sc),
('Strong Emotion', 'Weak Emotion',
'Scrambled ' + ', '.join(scrambling_list)),
loc='upper center',
bbox_to_anchor=(0.5, 1.065),
ncol=3,
fancybox=False,
shadow=False,
prop=FontProperties(size=str(9 * fontscale)))
return data_all
def main(experiment=False, source=False, prepixelation='not specified', elinewidth=2, ecolor='0.3', make_tight=True, total='means', make_std=False, make_sem=True, fontscale=1):
data_all = get_and_filter_results(experiment, source, prepixelation, mismeasurement='fix', apply_correct_values=True, make_CoI=True)
scrambling_list = set(data_all['scrambling'])
data_all = categories_of_interest(data_all, scrambling_list)
ids = sorted(list(set(data_all['ID'])))
pos_ids = np.arange(len(ids))
fig = figure(figsize=(pos_ids.max()*5, 4), dpi=300,facecolor='#eeeeee', tight_layout=make_tight)
ax=fig.add_subplot(1,1,1)
ax.yaxis.grid(True, linestyle='-', which='major', color='#dddddd',alpha=0.6, zorder = 0)
ax.set_axisbelow(True)
width = 0.1
for scrambling_id, scrambling in enumerate(set(data_all['scrambling'])):
if scrambling == 0:
#below this: per-participant graphs
plot_em_strong = plt.bar(pos_ids-width, data_all[(data_all['scrambling'] == scrambling) & (data_all['intensity'] == 100)].groupby('ID')['RT'].mean(), width ,color='m', alpha=0.7, zorder = 1, linewidth=0)
plot_em_weak = plt.bar(pos_ids, data_all[(data_all['scrambling'] == scrambling) & (data_all['intensity'] == 40)].groupby('ID')['RT'].mean(), width ,color='m', alpha=0.4, zorder = 1, linewidth=0)
if make_std:
errorbar(pos_ids-(width/2), data_all[(data_all['scrambling'] == scrambling) & (data_all['intensity'] == 100)].groupby('ID')['RT'].mean(), yerr=data_all[(data_all['scrambling'] == scrambling)].groupby('ID')['RT'].aggregate(np.std), ecolor=str(float(ecolor)+0.25), elinewidth=elinewidth, capsize=0, linestyle='None', zorder = 2)
errorbar(pos_ids+(width/2)+width*scrambling_id, data_all[(data_all['scrambling'] == scrambling) & (data_all['intensity'] == 40)].groupby('ID')['RT'].mean(), yerr=data_all[(data_all['scrambling'] == scrambling)].groupby('ID')['RT'].aggregate(np.std), ecolor=str(float(ecolor)+0.25), elinewidth=elinewidth, capsize=0, linestyle='None', zorder = 2)
if make_sem:
errorbar(pos_ids-(width/2), data_all[(data_all['scrambling'] == scrambling) & (data_all['intensity'] == 100)].groupby('ID')['RT'].mean(), yerr=data_all[(data_all['scrambling'] == scrambling)].groupby('ID')['RT'].aggregate(sem), ecolor=ecolor, elinewidth=elinewidth, capsize=0, linestyle='None', zorder = 2)
errorbar(pos_ids+(width/2)+width*scrambling_id, data_all[(data_all['scrambling'] == scrambling) & (data_all['intensity'] == 40)].groupby('ID')['RT'].mean(), yerr=data_all[(data_all['scrambling'] == scrambling)].groupby('ID')['RT'].aggregate(sem), ecolor=ecolor, elinewidth=elinewidth, capsize=0, linestyle='None', zorder = 2)
#below this: total graphs
if total == 'all':
plt.bar(pos_ids[-1]+1-width, data_all[(data_all['scrambling'] == scrambling) & (data_all['intensity'] == 100)]['RT'].mean(), width ,color='m', alpha=0.7, zorder = 1, linewidth=0)
plt.bar(pos_ids[-1]+1, data_all[(data_all['scrambling'] == scrambling) & (data_all['intensity'] == 40)]['RT'].mean(), width ,color='m', alpha=0.4, zorder = 1, linewidth=0)
if make_std:
errorbar(pos_ids[-1]+1-(width/2), data_all[(data_all['scrambling'] == scrambling) & (data_all['intensity'] == 100)]['RT'].mean(), yerr=np.std(data_all[(data_all['scrambling'] == scrambling) & (data_all['intensity'] == 100)]['RT']), ecolor=str(float(ecolor)+0.25), elinewidth=elinewidth, capsize=0, linestyle='None', zorder = 2)
errorbar(pos_ids[-1]+1+(width/2)+width*scrambling_id, data_all[(data_all['scrambling'] == scrambling) & (data_all['intensity'] == 40)]['RT'].mean(), yerr=np.std(data_all[(data_all['scrambling'] == scrambling) & (data_all['intensity'] == 40)]['RT']), ecolor=str(float(ecolor)+0.25), elinewidth=elinewidth, capsize=0, linestyle='None', zorder = 2)
if make_sem:
errorbar(pos_ids[-1]+1-(width/2), data_all[(data_all['scrambling'] == scrambling) & (data_all['intensity'] == 100)]['RT'].mean(), yerr=sem(data_all[(data_all['scrambling'] == scrambling)]['RT']), ecolor=ecolor, elinewidth=elinewidth, capsize=0, linestyle='None', zorder = 2)
errorbar(pos_ids[-1]+1+(width/2)+width*scrambling_id, data_all[(data_all['scrambling'] == scrambling) & (data_all['intensity'] == 40)]['RT'].mean(), yerr=sem(data_all[(data_all['scrambling'] == scrambling)]['RT']), ecolor=ecolor, elinewidth=elinewidth, capsize=0, linestyle='None', zorder = 2)
elif total == 'means':
plt.bar(pos_ids[-1]+1-width, data_all[(data_all['scrambling'] == scrambling) & (data_all['intensity'] == 100)]['RT'].mean(), width ,color='m', alpha=0.7, zorder = 1, linewidth=0)
plt.bar(pos_ids[-1]+1, data_all[(data_all['scrambling'] == scrambling) & (data_all['intensity'] == 40)]['RT'].mean(), width ,color='m', alpha=0.4, zorder = 1, linewidth=0)
if make_std:
errorbar(pos_ids[-1]+1-(width/2), data_all[(data_all['scrambling'] == scrambling) & (data_all['intensity'] == 100)]['RT'].mean(), yerr=np.std(data_all[(data_all['scrambling'] == scrambling) & (data_all['intensity'] == 100)].groupby('ID')['RT'].mean()), ecolor=str(float(ecolor)+0.25), elinewidth=elinewidth, capsize=0, linestyle='None', zorder = 2)
errorbar(pos_ids[-1]+1+(width/2)+width*scrambling_id, data_all[(data_all['scrambling'] == scrambling) & (data_all['intensity'] == 40)]['RT'].mean(), yerr=np.std(data_all[(data_all['scrambling'] == scrambling) & (data_all['intensity'] == 40)].groupby('ID')['RT'].mean()), ecolor=str(float(ecolor)+0.25), elinewidth=elinewidth, capsize=0, linestyle='None', zorder = 2)
if make_sem:
errorbar(pos_ids[-1]+1-(width/2), data_all[(data_all['scrambling'] == scrambling) & (data_all['intensity'] == 100)]['RT'].mean(), yerr=sem(data_all[(data_all['scrambling'] == scrambling) & (data_all['intensity'] == 100)].groupby('ID')['RT'].mean()), ecolor=ecolor, elinewidth=elinewidth, capsize=0, linestyle='None', zorder = 2)
errorbar(pos_ids[-1]+1+(width/2)+width*scrambling_id, data_all[(data_all['scrambling'] == scrambling) & (data_all['intensity'] == 40)]['RT'].mean(), yerr=sem(data_all[(data_all['scrambling'] == scrambling) & (data_all['intensity'] == 40)].groupby('ID')['RT'].mean()), ecolor=ecolor, elinewidth=elinewidth, capsize=0, linestyle='None', zorder = 2)
else:
#below this: per-participant graphs
plot_sc = plt.bar(pos_ids+width*scrambling_id, data_all[(data_all['scrambling'] == scrambling)].groupby('ID')['RT'].mean(), width ,color='g', alpha=0.2+0.12*scrambling_id, zorder = 1, linewidth=0)
if make_std:
errorbar(pos_ids+(width/2)+width*scrambling_id, data_all[(data_all['scrambling'] == scrambling)].groupby('ID')['RT'].mean(), yerr=data_all[(data_all['scrambling'] == scrambling)].groupby('ID')['RT'].aggregate(np.std), ecolor=str(float(ecolor)+0.25), elinewidth=elinewidth, capsize=0, linestyle='None', zorder = 2)
if make_sem:
errorbar(pos_ids+(width/2)+width*scrambling_id, data_all[(data_all['scrambling'] == scrambling)].groupby('ID')['RT'].mean(), yerr=data_all[(data_all['scrambling'] == scrambling)].groupby('ID')['RT'].aggregate(sem), ecolor=ecolor, elinewidth=elinewidth, capsize=0, linestyle='None', zorder = 2)
#below this: total graphs
if total == 'all':
plot_sc = plt.bar(pos_ids[-1]+1+width*scrambling_id, data_all[(data_all['scrambling'] == scrambling)]['RT'].mean(), width ,color='g', alpha=0.2+0.12*scrambling_id, zorder = 1, linewidth=0)
if make_std:
errorbar(pos_ids[-1]+1+(width/2)+width*scrambling_id, data_all[(data_all['scrambling'] == scrambling)]['RT'].mean(), yerr=np.std(data_all[(data_all['scrambling'] == scrambling)]['RT']), ecolor=str(float(ecolor)+0.25), elinewidth=elinewidth, capsize=0, linestyle='None', zorder = 2)
if make_sem:
errorbar(pos_ids[-1]+1+(width/2)+width*scrambling_id, data_all[(data_all['scrambling'] == scrambling)]['RT'].mean(), yerr=sem(data_all[(data_all['scrambling'] == scrambling)]['RT']), ecolor=ecolor, elinewidth=elinewidth, capsize=0, linestyle='None', zorder = 2)
elif total == 'means':
plot_sc = plt.bar(pos_ids[-1]+1+width*scrambling_id, data_all[(data_all['scrambling'] == scrambling)]['RT'].mean(), width ,color='g', alpha=0.2+0.12*scrambling_id, zorder = 1, linewidth=0)
if make_std:
errorbar(pos_ids[-1]+1+(width/2)+width*scrambling_id, data_all[(data_all['scrambling'] == scrambling)]['RT'].mean(), yerr=np.std(data_all[(data_all['scrambling'] == scrambling)].groupby('ID')['RT'].mean()), ecolor=str(float(ecolor)+0.25), elinewidth=elinewidth, capsize=0, linestyle='None', zorder = 2)
if make_sem:
errorbar(pos_ids[-1]+1+(width/2)+width*scrambling_id, data_all[(data_all['scrambling'] == scrambling)]['RT'].mean(), yerr=sem(data_all[(data_all['scrambling'] == scrambling)].groupby('ID')['RT'].mean()), ecolor=ecolor, elinewidth=elinewidth, capsize=0, linestyle='None', zorder = 2)
width_multiplier = 15/np.shape(data_all[(data_all['scrambling'] == scrambling)].groupby('ID')['RT'].mean())[0] #(fine tune) separator positioning
plt.axvline(pos_ids[-1]+1-width*width_multiplier, color='0.2') #separator - per-person/total
scrambling_list = [str(i) for i in scrambling_list if i != 0] #format as string for legend
ids=ids+['ALL']
pos_ids = np.arange(len(ids))
ax.set_ylabel(r'$\mathsf{\overline{RT}}$ [s]', fontsize=11*fontscale)
ax.set_xlabel('Participant', fontsize=11*fontscale)
ax.set_xticks(pos_ids + width*3)
ax.set_xticklabels(ids,fontsize=9*fontscale) # add rotation=30 if things get too crowded
for tick in ax.axes.get_xticklines():
tick.set_visible(False)
ax.set_xlim(0, pos_ids[-1]+width*5) # before scaling to add padding in front of zero
axis.Axis.zoom(ax.xaxis, -0.5) # sets x margins further apart from the content proportional to its length
axis.Axis.zoom(ax.yaxis, -0.5) # sets y margins further apart from the content proportional to its length
ax.set_ylim(bottom=0) # after scaling to disregard padding unerneath zero.
legend((plot_em_strong,plot_em_weak, plot_sc),('Strong Emotion','Weak Emotion', 'Scrambled '+', '.join(scrambling_list)),loc='upper center', bbox_to_anchor=(0.5, 1.065), ncol=3, fancybox=False, shadow=False, prop=FontProperties(size=str(9*fontscale)))
return data_all
def main(experiment=False, source=False, prepixelation='not specified', elinewidth=2, ecolor='0.3', make_tight=True):
data_all = get_and_filter_results(experiment=experiment, source=source, prepixelation=prepixelation, make_CoI=True)
ids = sorted(list(set(data_all['ID'])))
pos_ids = np.arange(len(ids))
fig = figure(figsize=(pos_ids.max()*4, 5), dpi=300,facecolor='#eeeeee', tight_layout=make_tight)
ax=fig.add_subplot(1,1,1)
width = 0.1
ax.yaxis.grid(True, linestyle='-', which='major', color='#dddddd',alpha=0.6, zorder = 0)
ax.set_axisbelow(True)
scrambling_list = set(data_all['scrambling'])
errors_index = range(len(set(data_all['ID'])))
errors_columns = ['ID', 'correct', 'incorrect']
total_errors_index = range(len(set(data_all['ID']))*(len(scrambling_list)+1)) # number of error entries - per ID: one for each scrambling step, and TWO for 0
total_errors_columns = ['ID', 'ER', 'scrambling', 'intensity', 'CoI']
total_errors = pd.DataFrame(index=total_errors_index, columns=total_errors_columns) # empty container frame for concatenating input from multiple files
for id_ix, le_id in enumerate(set(data_all['ID'])):
id_ix = id_ix*(len(scrambling_list)+1) # number of errors per ID: one for each scrambling step, and TWO for 0
data_for_id = data_all[data_all['ID'] == le_id]
strong_em_errors = len(data_for_id[(data_for_id['scrambling'] == list(scrambling_list)[0]) & (data_for_id['correct answer'] != data_for_id['keypress']) & (data_for_id['intensity'] == 100)].index)/len(data_for_id[(data_for_id['scrambling'] == list(scrambling_list)[0]) & (data_for_id['intensity'] == 100)].index)
weak_em_errors = len(data_for_id[(data_for_id['scrambling'] == list(scrambling_list)[0]) & (data_for_id['correct answer'] != data_for_id['keypress']) & (data_for_id['intensity'] == 40)].index)/len(data_for_id[(data_for_id['scrambling'] == list(scrambling_list)[0]) & (data_for_id['intensity'] == 40)].index)
total_errors.ix[id_ix]['ID'] = le_id
total_errors.ix[id_ix]['ER'] = strong_em_errors
total_errors.ix[id_ix]['scrambling'] = list(scrambling_list)[0]
total_errors.ix[id_ix]['intensity'] = 100
total_errors.ix[id_ix+1]['ID'] = le_id
total_errors.ix[id_ix+1]['ER'] = weak_em_errors
total_errors.ix[id_ix+1]['scrambling'] = list(scrambling_list)[0]
total_errors.ix[id_ix+1]['intensity'] = 40
for scrambling_id, scrambling in enumerate(list(scrambling_list)[1:]): #all scrambling steps except 0
errors = len(data_for_id[(data_for_id['scrambling'] == scrambling) & (data_for_id['correct answer'] != data_for_id['keypress'])].index)/len(data_for_id[(data_for_id['scrambling'] == scrambling)].index)
total_errors.ix[id_ix+2+scrambling_id]['ID'] = le_id
total_errors.ix[id_ix+2+scrambling_id]['ER'] = errors
total_errors.ix[id_ix+2+scrambling_id]['scrambling'] = scrambling
total_errors.ix[id_ix+2+scrambling_id]['intensity'] = 100
total_errors = categories_of_interest(total_errors, scrambling_list) #make categories of interest
total_errors_just_for_plotting = total_errors.copy() # please DO NOT USE THE DATA FROM THIS NEW VARIABLE for anything BUT plotting
total_errors_just_for_plotting.ix[(total_errors_just_for_plotting['ER'] == 0), 'ER'] = 0.002 # this is a hack to make 0-height bins visible when plotting
#START DRAWING GRAPHIC ELEMENTS
for scrambling_id, scrambling in enumerate(set(total_errors_just_for_plotting['scrambling'])):
if scrambling == 0:
#below this: per-participant graphs
plot_em_strong = plt.bar(pos_ids-width, total_errors_just_for_plotting[(total_errors_just_for_plotting['scrambling'] == scrambling) & (total_errors_just_for_plotting['intensity'] == 100)]['ER'], width ,color='m', alpha=0.7, zorder = 1, linewidth=0)
plot_em_weak = plt.bar(pos_ids, total_errors_just_for_plotting[(total_errors_just_for_plotting['scrambling'] == scrambling) & (total_errors_just_for_plotting['intensity'] == 40)]['ER'], width ,color='m', alpha=0.4, zorder = 1, linewidth=0)
#below this: total graphs
tot_plot_em_strong = plt.bar(pos_ids[-1]+1-width, total_errors_just_for_plotting[(total_errors_just_for_plotting['scrambling'] == scrambling) & (total_errors_just_for_plotting['intensity'] == 100)]['ER'].mean(), width ,color='m', alpha=0.7, zorder = 1, linewidth=0)
tot_err_em_strong = errorbar(pos_ids[-1]+1-(width/2), total_errors_just_for_plotting[(total_errors_just_for_plotting['scrambling'] == scrambling) & (total_errors_just_for_plotting['intensity'] == 100)]['ER'].mean(), yerr=sem(total_errors_just_for_plotting[(total_errors_just_for_plotting['scrambling'] == scrambling) & (total_errors_just_for_plotting['intensity'] == 100)]['ER']), ecolor=ecolor, elinewidth=elinewidth, capsize=0, linestyle='None', zorder = 2)
tot_plot_em_weak = plt.bar(pos_ids[-1]+1, total_errors_just_for_plotting[(total_errors_just_for_plotting['scrambling'] == scrambling) & (total_errors_just_for_plotting['intensity'] == 40)]['ER'].mean(), width ,color='m', alpha=0.4, zorder = 1, linewidth=0)
tot_err_em_weak = errorbar(pos_ids[-1]+1+(width/2)+width*scrambling_id, total_errors_just_for_plotting[(total_errors_just_for_plotting['scrambling'] == scrambling) & (total_errors_just_for_plotting['intensity'] == 40)]['ER'].mean(), yerr=sem(total_errors_just_for_plotting[(total_errors_just_for_plotting['scrambling'] == scrambling) & (total_errors_just_for_plotting['intensity'] == 40)]['ER']), ecolor=ecolor, elinewidth=elinewidth, capsize=0, linestyle='None', zorder = 2)
else:
#below this: per-participant graphs
errors_part_n0 = total_errors_just_for_plotting[(total_errors_just_for_plotting['scrambling'] == scrambling)]['ER']
plot_sc = plt.bar(pos_ids+width*scrambling_id, total_errors_just_for_plotting[(total_errors_just_for_plotting['scrambling'] == scrambling)]['ER'], width ,color='g', alpha=0.2+0.12*scrambling_id, zorder = 1, linewidth=0)
#below this: total graphs
errors_total_n0 = total_errors_just_for_plotting[(total_errors_just_for_plotting['scrambling'] == scrambling)]['ER'].mean()
plot_sc = plt.bar(pos_ids[-1]+1+width*scrambling_id, errors_total_n0, width ,color='g', alpha=0.2+0.12*scrambling_id, zorder = 1,linewidth=0)
errorbar(pos_ids[-1]+1+(width/2)+width*scrambling_id, errors_total_n0, yerr=sem(total_errors_just_for_plotting[(total_errors_just_for_plotting['scrambling'] == scrambling)]['ER']), ecolor=ecolor, elinewidth=elinewidth, capsize=0, linestyle='None', zorder = 2)
width_multiplier = 15/np.shape(data_all[(data_all['scrambling'] == scrambling)].groupby('ID')['RT'].mean())[0]
plt.axvline(pos_ids[-1]+1-width*width_multiplier, color='0.2')
ids=ids+['ALL']
pos_ids = np.arange(len(ids))
ax.set_xlim(0, pos_ids[-1]+width*5)
ax.set_ylim(0, total_errors_just_for_plotting['ER'].max()*1.2)
ax.set_ylabel(r'$\mathsf{\Sigma_{wrong} / \Sigma_{all}}$', fontsize=13)
ax.set_xlabel('Participant', fontsize=11)
ax.set_xticks(pos_ids + width*3)
ax.set_xticklabels(ids,fontsize=9) # add rotation=30 if labels are too long and need rotating
for tick in ax.axes.get_xticklines():
tick.set_visible(False)
axis.Axis.zoom(ax.xaxis, -0.5)
scrambling_list = [str(i) for i in scrambling_list if i != 0]
legend((plot_em_strong,plot_em_weak, plot_sc),('Strong Emotion','Weak Emotion', 'Scrambled '+', '.join(scrambling_list)),loc='upper center', bbox_to_anchor=(0.5, 1.065), ncol=3, fancybox=False, shadow=False,prop= FontProperties(size='9'))
#END DRAWING GRAPHIC ELEMENTS
total_errors['ER'] = total_errors['ER'].astype(float) # apparently some other scripts (using R linear models) cannot deal with the output unless we do this.
return total_errors
