def make(cls, **kwargs):
"""Create a list of trials.
Each trial is a dict with values for all keys in self.COLUMNS.
"""
# make convenience copies of class defaults
stim_dir = cls.STIM_DIR
columns = cls.COLUMNS
settings = kwargs.copy()
seed = settings.get('seed')
try:
seed = int(seed)
except (TypeError, ValueError):
logging.info('seed was None or couldn\'t be converted to int')
seed = None
prng = random.RandomState(seed)
# Read in the trials (same for all participants)
trials = pd.read_csv(Path(stim_dir, 'propositions.csv'))
# Randomly assign mask trials
trials['mask_type'] = prng.choice(['mask', 'nomask'], size=len(trials))
# Choose a version of each cue at random
all_cues = Path(stim_dir, 'cues').listdir('*.wav', names_only=True)
def pick_cue_file(cue):
options = [c for c in all_cues if c.find(cue) == 0]
return prng.choice(options)
trials['cue_file'] = trials.cue.apply(pick_cue_file)
# Add columns for response variables
for col in ['response', 'rt', 'is_correct']:
trials[col] = ''
# Add practice trials
num_practice = 6
practice_ix = prng.choice(trials.index, num_practice, replace=False)
practice_trials = trials.ix[practice_ix, ]
practice_trials['block'] = 0
practice_trials['block_type'] = 'practice'
trials.drop(practice_ix, inplace=True)
# Finishing touches: block trials and shuffle
num_blocks = 4
trials_per_block = (len(trials)/num_blocks) + 1
trials = add_block(trials, trials_per_block, name='block', start=1,
groupby='cue', seed=seed)
trials = smart_shuffle(trials, col='cue', block='block', seed=seed)
trials['block_type'] = 'test'
# Merge practice trials
trials = pd.concat([practice_trials, trials], ignore_index=True)
# Label trial
trials['trial'] = range(1, len(trials)+1)
# Reorder columns
trials = trials[columns]
return cls(trials.to_dict('record'))
def make(cls, **kwargs):
""" Create a list of trials.
Each trial is a dict with values for all keys in self.COLUMNS.
"""
stim_dir = unipath.Path(cls.STIM_DIR)
# default settings
settings = dict(ratio_prompt_trials=0.75,
ratio_yes_correct_responses=0.75)
settings.update(kwargs)
seed = kwargs.get('seed', '')
try:
seed = int(seed)
except ValueError:
seed = random.randint(100)
print 'no seed given, using', seed
prng = random.RandomState(seed)
# Balance within subject variables
trials = counterbalance({
'feat_type': ['visual', 'nonvisual'],
'mask_type': ['mask', 'nomask']
})
trials = expand(trials,
name='correct_response',
values=['yes', 'no'],
ratio=settings['ratio_yes_correct_responses'],
seed=seed)
trials = expand(trials,
name='response_type',
values=['prompt', 'word'],
ratio=settings['ratio_prompt_trials'],
seed=seed)
# Extend the trials to final length
trials = extend(trials, reps=4)
# Read proposition info
propositions_csv = unipath.Path(stim_dir, 'propositions.csv')
propositions = pandas.read_csv(propositions_csv)
# Add cue
categories = propositions.cue.unique()
trials['cue'] = prng.choice(categories, len(trials), replace=True)
cue_info_csv = unipath.Path(stim_dir, 'cues', '_cue_info.csv')
cues = pandas.read_csv(cue_info_csv)
def determine_cue_file(row):
options = cues.ix[cues.cue == row['cue'], 'cue_file'].tolist()
return prng.choice(options)
trials['cue_file'] = trials.apply(determine_cue_file, axis=1)
# copy propositions to prevent duplicate questions
_propositions = propositions.copy()
def determine_question(row):
is_cue = (_propositions.cue == row['cue'])
is_feat_type = (_propositions.feat_type == row['feat_type'])
is_correct_response = (
_propositions.correct_response == row['correct_response'])
valid_propositions = (is_cue & is_feat_type & is_correct_response)
if valid_propositions.sum() == 0:
# no more propositions for this cue!
# switch to a different cue and try again
# row.name is the index for this row
trials.loc[row.name, 'cue'] = prng.choice(categories)
trials.loc[row.name, 'cue_file'] = determine_cue_file(
trials.ix[row.name])
return determine_question(trials.ix[row.name, ])
options = _propositions.ix[valid_propositions, ]
selected_ix = prng.choice(options.index)
selected_proposition_id = options.ix[selected_ix, 'proposition_id']
_propositions.drop(selected_ix, inplace=True)
return selected_proposition_id
trials['proposition_id'] = trials.apply(determine_question, axis=1)
# Merge in question
trials = trials.merge(propositions)
# Replace proposition on word trials
trials.loc[trials.response_type == 'word', 'question'] = '----------'
for prop_col in ['question_slug', 'feat_type', 'proposition_id']:
trials.loc[trials.response_type == 'word', prop_col] = ''
# Add in word
def determine_word(row):
if row['response_type'] != 'word':
return ''
elif row['correct_response'] == 'yes':
return row['cue']
else:
distractors = list(categories)
distractors.remove(row['cue'])
return prng.choice(distractors)
trials['word'] = trials.apply(determine_word, axis=1)
# for V2, the word is now auditory, so a file must be selected
def determine_word_file(row):
if row['response_type'] == 'prompt':
return ''
options = cues.ix[cues.cue == row['word'], 'cue_file'].tolist()
if row['cue_file'] in options:
options.remove(row['cue_file'])
return prng.choice(options)
trials['word_file'] = trials.apply(determine_word_file, axis=1)
# Add columns for response variables
for col in ['response', 'rt', 'is_correct']:
trials[col] = ''
# Add practice trials
num_practice = 8
practice_ix = prng.choice(trials.index, num_practice)
practice_trials = trials.ix[practice_ix, ]
practice_trials['block'] = 0
practice_trials['block_type'] = 'practice'
trials.drop(practice_ix, inplace=True)
# Separate response type by block
# HARDCODED!!!
prompt_blocks = [1, 3, 5]
word_blocks = [2, 4]
def assign_block(row):
response_type = row['response_type']
if response_type == 'prompt':
return prng.choice(prompt_blocks)
elif response_type == 'word':
return prng.choice(word_blocks)
else:
raise NotImplementedError('response type %s' % response_type)
trials['block'] = trials.apply(assign_block, axis=1)
trials.sort('block', inplace=True)
# Finishing touches
trials = smart_shuffle(trials, col='cue', block='block', seed=seed)
trials['block_type'] = 'test'
# Merge practice trials
trials = pandas.concat([practice_trials, trials])
# Label trial
trials['trial'] = range(len(trials))
# Reorcder columns
trials = trials[cls.COLUMNS]
return cls(trials.to_dict('record'))
def make(cls, **kwargs):
""" Create a list of trials.
Each trial is a dict with values for all keys in self.COLUMNS.
"""
settings = dict(cls.DEFAULTS)
settings.update(kwargs)
seed = settings.get('seed')
prng = pd.np.random.RandomState(seed)
# Balance within subject variables
trials = counterbalance({'feat_type': ['visual', 'nonvisual'],
'mask_type': ['mask', 'nomask']})
trials = expand(trials, name='correct_response', values=['yes', 'no'],
ratio=settings['ratio_yes_correct_responses'],
seed=seed)
trials = expand(trials, name='response_type', values=['prompt', 'pic'],
ratio=settings['ratio_prompt_response_type'],
seed=seed)
# Extend the trials to final length
trials = extend(trials, reps=4)
# Read proposition info
propositions_csv = Path(cls.STIM_DIR, 'propositions.csv')
propositions = pd.read_csv(propositions_csv)
# Add cue
categories = propositions.cue.unique()
trials['cue'] = prng.choice(categories, len(trials), replace=True)
_propositions = propositions.copy()
def determine_question(row):
is_cue = (_propositions.cue == row['cue'])
is_feat_type = (_propositions.feat_type == row['feat_type'])
is_correct_response = (_propositions.correct_response ==
row['correct_response'])
valid_propositions = (is_cue & is_feat_type & is_correct_response)
if valid_propositions.sum() == 0:
trials.ix[row.name, 'cue'] = prng.choice(categories)
return determine_question(trials.ix[row.name, ])
options = _propositions.ix[valid_propositions, ]
selected_ix = prng.choice(options.index)
selected_proposition_id = options.ix[selected_ix, 'proposition_id']
_propositions.drop(selected_ix, inplace=True)
return selected_proposition_id
trials['proposition_id'] = trials.apply(determine_question, axis=1)
# Merge in question
trials = trials.merge(propositions)
# Add in picture
def determine_pic(row):
if row['response_type'] != 'pic':
return ''
elif row['correct_response'] == 'yes':
return row['cue']
else:
distractors = list(categories)
distractors.remove(row['cue'])
return prng.choice(distractors)
trials['pic'] = trials.apply(determine_pic, axis=1)
# Add columns for response variables
for col in ['response', 'rt', 'is_correct']:
trials[col] = ''
# Add practice trials
num_practice = 8
practice_ix = prng.choice(trials.index, num_practice)
practice_trials = trials.ix[practice_ix, ]
practice_trials['block'] = 0
practice_trials['block_type'] = 'practice'
trials.drop(practice_ix, inplace=True)
# Finishing touches
trials = add_block(trials, 50, name='block', start=1, groupby='cue',
seed=seed)
trials = smart_shuffle(trials, col='cue', block='block', seed=seed)
trials['block_type'] = 'test'
# Merge practice trials
trials = pd.concat([practice_trials, trials])
# Label trial
trials['trial'] = range(len(trials))
# Reorcder columns
trials = trials[cls.COLUMNS]
return cls(trials.to_dict('record'))
def make(cls, **kwargs):
""" Create a list of trials.
Each trial is a dict with values for all keys in self.COLUMNS.
"""
settings = dict(cls.DEFAULTS)
settings.update(kwargs)
seed = settings.get('seed')
prng = pd.np.random.RandomState(seed)
# Balance within subject variables
trials = counterbalance({
'feat_type': ['visual', 'nonvisual'],
'mask_type': ['mask', 'nomask']
})
trials = expand(trials,
name='correct_response',
values=['yes', 'no'],
ratio=settings['ratio_yes_correct_responses'],
seed=seed)
trials = expand(trials,
name='response_type',
values=['prompt', 'pic'],
ratio=settings['ratio_prompt_response_type'],
seed=seed)
# Extend the trials to final length
trials = extend(trials, reps=4)
# Read proposition info
propositions_csv = Path(cls.STIM_DIR, 'propositions.csv')
propositions = pd.read_csv(propositions_csv)
# Add cue
categories = propositions.cue.unique()
trials['cue'] = prng.choice(categories, len(trials), replace=True)
_propositions = propositions.copy()
def determine_question(row):
is_cue = (_propositions.cue == row['cue'])
is_feat_type = (_propositions.feat_type == row['feat_type'])
is_correct_response = (
_propositions.correct_response == row['correct_response'])
valid_propositions = (is_cue & is_feat_type & is_correct_response)
if valid_propositions.sum() == 0:
trials.ix[row.name, 'cue'] = prng.choice(categories)
return determine_question(trials.ix[row.name, ])
options = _propositions.ix[valid_propositions, ]
selected_ix = prng.choice(options.index)
selected_proposition_id = options.ix[selected_ix, 'proposition_id']
_propositions.drop(selected_ix, inplace=True)
return selected_proposition_id
trials['proposition_id'] = trials.apply(determine_question, axis=1)
# Merge in question
trials = trials.merge(propositions)
# Add in picture
def determine_pic(row):
if row['response_type'] != 'pic':
return ''
elif row['correct_response'] == 'yes':
return row['cue']
else:
distractors = list(categories)
distractors.remove(row['cue'])
return prng.choice(distractors)
trials['pic'] = trials.apply(determine_pic, axis=1)
# Add columns for response variables
for col in ['response', 'rt', 'is_correct']:
trials[col] = ''
# Add practice trials
num_practice = 8
practice_ix = prng.choice(trials.index, num_practice)
practice_trials = trials.ix[practice_ix, ]
practice_trials['block'] = 0
practice_trials['block_type'] = 'practice'
trials.drop(practice_ix, inplace=True)
# Finishing touches
trials = add_block(trials,
50,
name='block',
start=1,
groupby='cue',
seed=seed)
trials = smart_shuffle(trials, col='cue', block='block', seed=seed)
trials['block_type'] = 'test'
# Merge practice trials
trials = pd.concat([practice_trials, trials])
# Label trial
trials['trial'] = range(len(trials))
# Reorcder columns
trials = trials[cls.COLUMNS]
return cls(trials.to_dict('record'))