def test_comparison_not_equal_after_iteration(self):
if _travisTesting:
pytest.skip()
p1 = data.PsiHandler(nTrials=10,
intensRange=[0.1, 10],
alphaRange=[0.1, 10],
betaRange=[0.1, 3],
intensPrecision=0.1,
alphaPrecision=0.1,
betaPrecision=0.1,
delta=0.01)
p2 = data.PsiHandler(nTrials=10,
intensRange=[0.1, 10],
alphaRange=[0.1, 10],
betaRange=[0.1, 3],
intensPrecision=0.1,
alphaPrecision=0.1,
betaPrecision=0.1,
delta=0.001)
p1.__next__()
p2.__next__()
assert p1 != p2
def test_comparison_not_equal(self):
p1 = data.PsiHandler(nTrials=10, intensRange=[0.1, 10],
alphaRange=[0.1, 10], betaRange=[0.1, 3],
intensPrecision=0.1, alphaPrecision=0.1,
betaPrecision=0.1, delta=0.01)
p2 = data.PsiHandler(nTrials=10, intensRange=[0.1, 10],
alphaRange=[0.1, 10], betaRange=[0.1, 3],
intensPrecision=0.1, alphaPrecision=0.1,
betaPrecision=0.1, delta=0.001)
assert p1 != p2
def test_comparison_equals_after_iteration(self):
p1 = data.PsiHandler(nTrials=10, intensRange=[0.1, 10],
alphaRange=[0.1, 10], betaRange=[0.1, 3],
intensPrecision=0.1, alphaPrecision=0.1,
betaPrecision=0.1, delta=0.01)
p2 = data.PsiHandler(nTrials=10, intensRange=[0.1, 10],
alphaRange=[0.1, 10], betaRange=[0.1, 3],
intensPrecision=0.1, alphaPrecision=0.1,
betaPrecision=0.1, delta=0.01)
p1.__next__()
p2.__next__()
assert p1 == p2
def test_json_dump(self):
p = data.PsiHandler(nTrials=10, intensRange=[0.1, 10],
alphaRange=[0.1, 10], betaRange=[0.1, 3],
intensPrecision=1, alphaPrecision=1,
betaPrecision=0.5, delta=0.01)
dump = p.saveAsJson()
p.origin = ''
assert p == json_tricks.np.loads(dump)
def test_json_dump_to_file(self):
_, path = mkstemp(dir=self.tmp_dir, suffix='.json')
p = data.PsiHandler(nTrials=10,
intensRange=[0.1, 10],
alphaRange=[0.1, 10],
betaRange=[0.1, 3],
intensPrecision=1,
alphaPrecision=1,
betaPrecision=0.5,
delta=0.01)
p.saveAsJson(fileName=path, fileCollisionMethod='overwrite')
def test_json_dump_with_data(self):
p = data.PsiHandler(nTrials=10, intensRange=[0.1, 10],
alphaRange=[0.1, 10], betaRange=[0.1, 3],
intensPrecision=1, alphaPrecision=1,
betaPrecision=0.5, delta=0.01)
p.addResponse(1)
p.addOtherData('foo', 'bar')
dump = p.saveAsJson()
p.origin = ''
assert p == json_tricks.np.loads(dump)
def test_comparison_equals(self):
if _travisTesting:
pytest.skip()
p1 = data.PsiHandler(nTrials=10,
intensRange=[0.1, 10],
alphaRange=[0.1, 10],
betaRange=[0.1, 3],
intensPrecision=0.1,
alphaPrecision=0.1,
betaPrecision=0.1,
delta=0.01)
p2 = data.PsiHandler(nTrials=10,
intensRange=[0.1, 10],
alphaRange=[0.1, 10],
betaRange=[0.1, 3],
intensPrecision=0.1,
alphaPrecision=0.1,
betaPrecision=0.1,
delta=0.01)
assert p1 == p2
def test_json_dump_and_reopen_file(self):
p = data.PsiHandler(nTrials=10, intensRange=[0.1, 10],
alphaRange=[0.1, 10], betaRange=[0.1, 3],
intensPrecision=1, alphaPrecision=1,
betaPrecision=0.5, delta=0.01)
p.addResponse(1)
p.addOtherData('foo', 'bar')
p.__next__()
_, path = mkstemp(dir=self.tmp_dir, suffix='.json')
p.saveAsJson(fileName=path, fileCollisionMethod='overwrite')
p.origin = ''
p_loaded = fromFile(path)
assert p == p_loaded
def test_json_dump_after_iteration(self):
if _travisTesting:
pytest.skip()
p = data.PsiHandler(nTrials=10,
intensRange=[0.1, 10],
alphaRange=[0.1, 10],
betaRange=[0.1, 3],
intensPrecision=1,
alphaPrecision=1,
betaPrecision=0.5,
delta=0.01)
p.__next__()
dump = p.saveAsJson()
p.origin = ''
assert p == json_tricks.np.loads(dump)
def run_session(self):
path = os.path.join(self.res_dir, self.subject)
if not os.path.exists(path):
os.makedirs(path)
psydat_path = os.path.join(path, 'psydat')
if not os.path.exists(psydat_path):
os.makedirs(psydat_path)
# welcome
msg = visual.TextStim(self.win,
'Welcome!' + '\n' +
' Press any key to start this session :)',
color='black',
units='deg',
pos=(0, 0),
height=0.8)
msg.draw()
self.win.mouseVisible = False
self.win.flip()
event.waitKeys()
# read staircase parameters
conditions = [
dict({'stimulus': key}, **value)
for key, value in self.param.items() if key.startswith('stimulus')
]
if conditions[0]['stairType'] == 'simple':
stairs = data.MultiStairHandler(stairType='simple',
conditions=conditions,
nTrials=self.trial_nmb,
method='sequential')
elif conditions[0]['stairType'] == 'quest':
stairs = []
for cond in conditions:
if self.priors_file_path:
prior_file = self.priors_file_path + cond[
'label'] + '.psydat'
print(prior_file)
prior_handler = misc.fromFile(prior_file)
else:
prior_handler = None
cur_handler = data.QuestHandler(cond['startVal'],
cond['startValSd'],
pThreshold=cond['pThreshold'],
nTrials=self.trial_nmb,
minVal=cond['min_val'],
maxVal=cond['max_val'],
staircase=prior_handler,
extraInfo=cond,
grain=0.02)
stairs.append(cur_handler)
elif conditions[0]['stairType'] == 'psi':
stairs = []
for cond in conditions:
if self.priors_file_path:
prior_file = self.priors_file_path + cond['label'] + '.npy'
else:
prior_file = None
print(prior_file)
cur_handler = data.PsiHandler(nTrials=self.trial_nmb,
intensRange=[1, 10],
alphaRange=[1, 10],
betaRange=[0.01, 10],
intensPrecision=0.1,
alphaPrecision=0.1,
betaPrecision=0.01,
delta=0.01,
extraInfo=cond,
prior=prior_file,
fromFile=(prior_file
is not None))
stairs.append(cur_handler)
# write configuration files
xpp = config_tools.WriteXpp(self.subject, self.idx)
xpp_file = xpp.head(self.cfg_file, self.par_file)
config_tools.write_xrl(self.subject,
cfg_file=self.cfg_file,
par_file=self.par_file,
xpp_file=xpp_file)
xlsname = path + '/' + self.idx + self.param[
'noise_condition'] + '.xlsx'
""" running staircase """
if isinstance(stairs, data.MultiStairHandler):
# start running the staircase using the MultiStairHandler for the up-down method
count = 0
for rot, cond in stairs:
count += 1
direction = (-1)**(cond['label'].endswith('m')
) # direction as -1 if for minus stim
rot = rot * direction # rotation for this trial
judge, react_time, trial_time_start = self.run_trial(
rot, cond, cond['std'], count)
# check whether the theta is valid - if not, the rotation given by staircase should be corrected by
# realizable values
valid_theta = np.round(np.load(self.hue_list), decimals=1)
disp_standard = self.take_closest(
valid_theta, cond['standard']) # theta actually displayed
stair_test = cond[
'standard'] + stairs._nextIntensity * direction
if stair_test < 0:
stair_test += 360
disp_test = self.take_closest(valid_theta, stair_test)
disp_intensity = disp_test - disp_standard
if disp_intensity > 300:
disp_intensity = (disp_test + disp_standard) - 360
stairs.addResponse(judge, abs(disp_intensity))
xpp.task(count, cond, rot, float(disp_intensity), judge,
react_time, trial_time_start)
if 'escape' in event.waitKeys():
config_tools.write_xrl(self.subject,
break_info='userbreak')
core.quit()
config_tools.write_xrl(self.subject, xls_file=xlsname)
stairs.saveAsExcel(xlsname) # save results
misc.toFile(
os.path.join(
psydat_path,
self.idx + self.param['noise_condition'] + '.psydat'),
stairs)
elif isinstance(stairs, list):
# start running the staircase using custom interleaving stairs for the quest and psi methods
count = 0
rot_all = []
rot_all_disp = []
judge_all = []
estimates = {s.extraInfo['label']: [] for s in stairs}
for trial_n in range(self.trial_nmb):
for handler_idx, cur_handler in enumerate(stairs):
count += 1
direction = (-1)**(
cur_handler.extraInfo['label'].endswith('m')
) # direction as -1 if for minus stim
if cur_handler._nextIntensity >= 10.0:
sys.exit(
"Hue difference is out of range! Please enlarge the testing range or take more training!"
)
rot = cur_handler._nextIntensity * direction # rotation for this trial
if trial_n >= 5: # avoid repeating an intensity more than 3 times
last_rots = [
np.round(r, decimals=1) for r in [
rot_all_disp[handler_idx][trial_n - 1],
rot_all_disp[handler_idx][trial_n - 2],
rot_all_disp[handler_idx][trial_n - 3]
]
]
last_resp = [
judge_all[handler_idx][trial_n - 1],
judge_all[handler_idx][trial_n - 2],
judge_all[handler_idx][trial_n - 3]
]
if last_rots[0] == last_rots[1] == last_rots[2] \
and last_resp[0] == last_resp[1] == last_resp[2]:
if cur_handler._nextIntensity > 0.5:
rot = (cur_handler._nextIntensity -
0.5) * direction
print('Intensity decreases by 0.5!')
if cur_handler._nextIntensity <= 0.5:
rot = (cur_handler._nextIntensity +
0.5) * direction
print('Intensity increases by 0.5!')
cond = cur_handler.extraInfo
judge, react_time, trial_time_start = self.run_trial(
rot, cond, cond['std'], count)
if len(rot_all) <= handler_idx:
rot_all.append([])
rot_all[handler_idx].append(rot)
if len(judge_all) <= handler_idx:
judge_all.append([])
judge_all[handler_idx].append(judge)
valid_theta = np.round(np.load(self.hue_list), decimals=1)
disp_standard = self.take_closest(valid_theta,
cond['standard'])
stair_test = cond[
'standard'] + rot # calculated test hue for this trial
if stair_test < 0:
stair_test += 360
disp_test = self.take_closest(
valid_theta,
stair_test) # actual displayed test hue for this trial
disp_intensity = disp_test - disp_standard # actual displayed hue difference
if disp_intensity > 300:
disp_intensity = (disp_test + disp_standard) - 360
cur_handler.addResponse(
judge, abs(disp_intensity)
) # only positive number is accepted by addResponse
if len(rot_all_disp
) <= handler_idx: # add displayed intensities
rot_all_disp.append([])
rot_all_disp[handler_idx].append(disp_intensity)
if isinstance(cur_handler, data.PsiHandler):
estimates[cur_handler.extraInfo['label']].append([
cur_handler.estimateLambda()[0], # location
cur_handler.estimateLambda768()[1], # slope
cur_handler.estimateThreshold(0.75)
])
elif isinstance(cur_handler, data.QuestHandler):
estimates[cur_handler.extraInfo['label']].append([
cur_handler.mean(),
cur_handler.mode(),
cur_handler.quantile(0.5)
])
xpp.task(count, cond, rot, disp_intensity, judge,
react_time, trial_time_start)
if 'escape' in event.waitKeys():
config_tools.write_xrl(self.subject,
break_info='userbreak')
core.quit()
config_tools.write_xrl(self.subject, xls_file=xlsname)
# save results in xls-file
workbook = xlsxwriter.Workbook(xlsname)
for handler_idx, cur_handler in enumerate(stairs):
worksheet = workbook.add_worksheet(
cur_handler.extraInfo['label'])
worksheet.write('A1', 'Reversal Intensities')
worksheet.write('B1', 'Reversal Indices')
worksheet.write('C1', 'All Intensities')
worksheet.write('D1', 'All Responses')
for i in range(len(rot_all[handler_idx])):
# worksheet.write('C' + str(i + 2), rot_all[handler_idx][i])
worksheet.write('C' + str(i + 2),
rot_all_disp[handler_idx][i])
worksheet.write('D' + str(i + 2),
judge_all[handler_idx][i])
workbook.close()
# print resulting parameters and estimates for each step
res_file_path = os.path.join(path, self.idx + '_estimates.csv')
res_writer = csv.writer(open(res_file_path, 'w'))
for res_stim, res_vals in estimates.items():
for res_val_id, res_val in enumerate(res_vals):
res_writer.writerow([
res_stim, res_val_id, res_val[0], res_val[1],
res_val[2]
])
# save each handler into a psydat-file and save posterior into a numpy-file
for cur_handler in stairs:
file_name = os.path.join(
psydat_path, self.idx + self.param['noise_condition'] +
cur_handler.extraInfo['label'])
misc.toFile(file_name + '.psydat', cur_handler)
if isinstance(cur_handler, data.PsiHandler):
cur_handler.savePosterior(file_name + '.npy')
def run_session(self):
path = os.path.join(self.res_dir, self.subject)
if not os.path.exists(path):
os.makedirs(path)
# welcome
msg = visual.TextStim(self.win,
'Welcome!' + '\n' +
' Press any key to start this session :)',
color='black',
units='deg',
pos=(0, 0),
height=0.8)
msg.draw()
self.win.mouseVisible = False
self.win.flip()
event.waitKeys()
# read staircase parameters
conditions = [
dict({'stimulus': key}, **value)
for key, value in self.param.items() if key.startswith('stimulus')
]
if conditions[0]['stairType'] == 'simple':
stairs = data.MultiStairHandler(stairType='simple',
conditions=conditions,
nTrials=self.trial_nmb,
method='sequential')
elif conditions[0]['stairType'] == 'quest':
stairs = []
for cond in conditions:
if self.priors_file_path:
prior_file = self.priors_file_path + cond[
'label'] + '.psydat'
print(prior_file)
prior_handler = misc.fromFile(prior_file)
else:
prior_handler = None
cur_handler = data.QuestHandler(cond['startVal'],
cond['startValSd'],
pThreshold=cond['pThreshold'],
nTrials=self.trial_nmb,
minVal=cond['min_val'],
maxVal=cond['max_val'],
staircase=prior_handler,
extraInfo=cond)
stairs.append(cur_handler)
elif conditions[0]['stairType'] == 'psi':
stairs = []
for cond in conditions:
if self.priors_file_path:
prior_file = self.priors_file_path + cond['label'] + '.npy'
else:
prior_file = None
print(prior_file)
cur_handler = data.PsiHandler(nTrials=self.trial_nmb,
intensRange=[1, 10],
alphaRange=[1, 10],
betaRange=[0.01, 10],
intensPrecision=0.1,
alphaPrecision=0.1,
betaPrecision=0.01,
delta=0.01,
extraInfo=cond,
prior=prior_file,
fromFile=(prior_file
is not None))
stairs.append(cur_handler)
# write configuration files
xpp = config_tools.WriteXpp(self.subject, self.idx)
xpp_file = xpp.head(self.cfg_file, self.par_file)
config_tools.write_xrl(self.subject,
cfg_file=self.cfg_file,
par_file=self.par_file,
xpp_file=xpp_file)
xlsname = path + '/' + self.idx + self.param[
'noise_condition'] + '.xlsx'
""" running staircase """
if isinstance(stairs, data.MultiStairHandler):
# start running the staircase using the MultiStairHandler for the up-down method
count = 0
for rot, cond in stairs:
count += 1
judge, thiskey, trial_time = self.run_trial(rot, cond, count)
# check whether the theta is valid - if not, the rotation given by staircase should be corrected by
# realizable values
valid_theta = np.round(np.load(self.hue_list), decimals=1)
disp_standard = self.take_closest(
valid_theta, cond['standard']) # theta actually displayed
stair_test = cond['standard'] + stairs._nextIntensity * (-1)**(
cond['label'].endswith('m')) # theta for staircase
if stair_test < 0:
stair_test += 360
disp_test = self.take_closest(valid_theta, stair_test)
disp_intensity = abs(disp_test - disp_standard)
if disp_intensity > 300:
disp_intensity = 360 - (disp_test + disp_standard)
stairs.addResponse(judge, disp_intensity)
xpp.task(count, cond, rot, float(disp_intensity), judge,
trial_time)
event.waitKeys(keyList=[
thiskey
]) # press the response key again to start the next trial
config_tools.write_xrl(self.subject, xls_file=xlsname)
stairs.saveAsExcel(xlsname) # save results
psydat_file_path = os.path.join(
path, "psydat", self.idx + self.param['condition'] +
'.psydat') # save the handler into a psydat-file
misc.toFile(psydat_file_path, stairs)
elif isinstance(stairs, list):
# start running the staircase using custom interleaving stairs for the quest and psi methods
count = 0
rot_all = []
rot_all_disp = []
judge_all = []
estimates = {s.extraInfo['label']: [] for s in stairs}
for trial_n in range(self.trial_nmb):
for handler_idx, cur_handler in enumerate(stairs):
count += 1
rot = next(cur_handler)
if len(rot_all) <= handler_idx:
rot_all.append([])
rot_all[handler_idx].append(rot)
cond = cur_handler.extraInfo
judge, thiskey, trial_time = self.run_trial(
rot, cond, count)
if len(judge_all) <= handler_idx:
judge_all.append([])
judge_all[handler_idx].append(judge)
# cur_handler.addResponse(judge) # to the next trial
valid_theta = np.round(np.load(self.hue_list), decimals=1)
disp_standard = self.take_closest(
valid_theta,
cond['standard']) # theta actually displayed
stair_test = cond[
'standard'] + cur_handler._nextIntensity * (-1)**(
cond['label'].endswith('m')) # theta for staircase
if stair_test < 0:
stair_test += 360
disp_test = self.take_closest(valid_theta, stair_test)
disp_intensity = abs(disp_test - disp_standard)
if disp_intensity > 300:
disp_intensity = 360 - (disp_test + disp_standard)
cur_handler.addResponse(judge, disp_intensity)
if len(rot_all_disp
) <= handler_idx: # add displayed intensities
rot_all_disp.append([])
rot_all_disp[handler_idx].append(disp_intensity)
print('stair test: ' + str(stair_test) + ', ' +
'disp_test:' + str(disp_test))
if isinstance(cur_handler, data.PsiHandler):
estimates[cur_handler.extraInfo['label']].append([
cur_handler.estimateLambda()[0], # location
cur_handler.estimateLambda768()[1], # slope
cur_handler.estimateThreshold(0.75)
])
elif isinstance(cur_handler, data.QuestHandler):
estimates[cur_handler.extraInfo['label']].append([
cur_handler.mean(),
cur_handler.mode(),
cur_handler.quantile(0.5)
])
xpp.task(count, cond, rot, disp_intensity, judge,
trial_time)
event.waitKeys(keyList=[
thiskey
]) # press the response key again to start the next trial
config_tools.write_xrl(self.subject, xls_file=xlsname)
# save results in xls-file
workbook = xlsxwriter.Workbook(xlsname)
for handler_idx, cur_handler in enumerate(stairs):
worksheet = workbook.add_worksheet(
cur_handler.extraInfo['label'])
worksheet.write('A1', 'Reversal Intensities')
worksheet.write('B1', 'Reversal Indices')
worksheet.write('C1', 'All Intensities')
worksheet.write('D1', 'All Responses')
for i in range(len(rot_all[handler_idx])):
# worksheet.write('C' + str(i + 2), rot_all[handler_idx][i])
worksheet.write('C' + str(i + 2),
rot_all_disp[handler_idx][i])
worksheet.write('D' + str(i + 2),
judge_all[handler_idx][i])
workbook.close()
# print resulting parameters and estimates for each step
res_file_path = os.path.join(path, self.idx + '_estimates.csv')
res_writer = csv.writer(open(res_file_path, 'w'))
for res_stim, res_vals in estimates.items():
for res_val_id, res_val in enumerate(res_vals):
res_writer.writerow([
res_stim, res_val_id, res_val[0], res_val[1],
res_val[2]
])
# save each handler into a psydat-file and save posterior into a numpy-file
for cur_handler in stairs:
file_name = os.path.join(
path, self.idx + self.param['noise_condition'] +
cur_handler.extraInfo['label'])
misc.toFile(file_name + '.psydat', cur_handler)
if isinstance(cur_handler, data.PsiHandler):
cur_handler.savePosterior(file_name + '.npy')
