def fromFile(filename, encoding='utf-8-sig'):
"""Load data from a psydat, pickle or JSON file.
Parameters
----------
encoding : str
The encoding to use when reading a JSON file. This parameter will be
ignored for any other file type.
"""
filename = pathToString(filename)
if filename.endswith('.psydat'):
with open(filename, 'rb') as f:
try:
contents = pickle.load(f)
except UnicodeDecodeError:
f.seek(0) # reset to start of file to try again
contents = pickle.load(
f, encoding='latin1') # python 2 data files
# if loading an experiment file make sure we don't save further
# copies using __del__
if hasattr(contents, 'abort'):
contents.abort()
return contents
elif filename.endswith('pickle'):
with open(filename, 'rb') as f:
contents = pickle.load(f)
return contents
elif filename.endswith('.json'):
with codecs.open(filename, 'r', encoding=encoding) as f:
contents = json_tricks.load(f)
# Restore RNG if we load a TrialHandler2 object.
# We also need to remove the 'temporary' ._rng_state attribute that
# was saved with it.
from psychopy.data import TrialHandler2
if isinstance(contents, TrialHandler2):
contents._rng = np.random.default_rng()
contents._rng.bit_generator.state = contents._rng_state
del contents._rng_state
return contents
# QuestPlus.
if sys.version_info.major == 3 and sys.version_info.minor >= 6:
from psychopy.data.staircase import QuestPlusHandler
from questplus import QuestPlus
if isinstance(contents, QuestPlusHandler):
# Restore the questplus.QuestPlus object.
contents._qp = QuestPlus.from_json(contents._qp_json)
del contents._qp_json
return contents
# If we haven't returned anything by now, the loaded object is neither
# a TrialHandler2 nor a QuestPlus instance. Return it unchanged.
return contents
else:
msg = "Don't know how to handle this file type, aborting."
raise ValueError(msg)
lower_asymptote_prior = lower_asymptote_prior / lower_asymptote_prior.sum()
slope_prior = scipy.stats.norm.pdf(x=param['slope'], loc=3, scale=1)
slope_prior = slope_prior / slope_prior.sum()
prior = dict(threshold=np.ones(len(param['threshold'])),
slope=slope_prior,
lower_asymptote=lower_asymptote_prior,
lapse_rate=np.ones(len(param['lapse_rate'])))
stim_domain = dict(intensity=intensities)
stim_selection_options = dict(n=3, max_consecutive_reps=2)
q = QuestPlus(stim_domain=stim_domain,
func='weibull',
stim_scale='log10',
param_domain=param,
prior=prior,
outcome_domain=outcome_domain,
stim_selection_method='min_n_entropy',
stim_selection_options=stim_selection_options)
with np.printoptions(precision=3, suppress=True):
print(q.stim_domain)
plot(q)
print(q.next_stim)
for trial_no in range(1, 20 + 1):
if trial_no == 1:
intensity = intensities[
3] # start with a relatively high concentration
else:
intensity = q.next_stim['intensity']