def prepare_experiment(args, store_node, create_child=True):
'''
Given the arguments passed in via the command-line, configure the
Experiment, Controller, Data and Paradigm class accordingly and return an
instance of the model and controller class.
'''
# The HDF5 file that is used for the data
store_file = store_node._v_file
# If the user did not specify a list of parameters that the data should be
# grouped into before analysis (i.e. for computing the hit and false alarm
# fractions), then use the parameters specified via rove as the analysis
# parameters.
if len(args.analyze) == 0:
args.analyze = args.rove[:]
# Load the experiment from the launchers folder. args.type should be the
# name of the corresponding file in the launchers folder (without the .py
# extension)
module = get_experiment(args.type)
# Pull out the classes
paradigm_class = module.Paradigm
experiment_class = module.Experiment
controller_class = module.Controller
data_class = module.Data
node_name = module.node_name
# Create the experiment and data nodes. Hint! This is where you would
# change the default pathname for the experiment if you wished.
if create_child:
name = node_name + '_' + datetime.now().strftime(time_fmt)
exp_node = store_file.createGroup(store_node, name)
else:
exp_node = store_node
# Where the data is stored
data_node = store_file.createGroup(exp_node, 'data')
# Configure the TrialSetting/trial_setting_editor objects to contain the
# parameters we wish to control in the experiment
trial_setting.add_parameters(args.rove, paradigm_class, args.repeats)
if args.att:
# The user wants to specify values in terms of dB attenuation rather
# than a calibrated dB SPL standard. Prepare the calibration
# accordingly.
cal1 = calibration.Attenuation()
cal2 = calibration.Attenuation()
else:
if args.cal is not None:
cal1_filename, cal2_filename = args.cal
else:
cal1_filename = get_config('CAL_PRIMARY')
cal2_filename = get_config('CAL_SECONDARY')
if cal1_filename is None:
raise IOError, 'Unable to find a calibration file for the ' \
'primary speaker'
if cal2_filename is None:
raise IOError, 'Unable to find a calibration file for the ' \
'secondary speaker'
cal1 = calibration.load_mat_cal(cal1_filename, args.equalized)
log.debug('Loaded calibration file %s for primary', cal1_filename)
cal2 = calibration.load_mat_cal(cal2_filename, args.equalized)
log.debug('Loaded calibration file %s for secondary', cal2_filename)
controller_args = {
'cal_primary': cal1,
'cal_secondary': cal2,
'address': args.address,
}
log.debug('store_node: %s', store_node)
log.debug('data_node: %s', data_node)
log.debug('exp_node: %s', exp_node)
# Prepare the classes. This really is a lot of boilerplate to link up
# parameters with paradigms, etc, to facilitate analysis
paradigm = paradigm_class()
data = data_class(store_node=data_node,
save_microphone=args.save_microphone)
data.parameters = args.analyze
model = experiment_class(
store_node=store_node,
experiment_node=exp_node,
data_node=data_node,
data=data,
paradigm=paradigm,
spool_physiology=args.physiology,
)
if args.analyze:
model.plot_index = args.analyze[0]
model.plot_group = args.analyze[1:]
controller = controller_class(**controller_args)
return model, controller
label='Paradigm'
),
VGroup(
Include('dt_group'),
Include('speaker_group'),
label='Sound'
)
)
class Data(PositiveData, MLDataMixin, PumpDataMixin):
pass
class Experiment(AbstractPositiveExperiment, MLExperimentMixin):
data = Instance(Data, ())
paradigm = Instance(Paradigm, ())
node_name = 'PositiveDTCLExperiment'
if __name__ == '__main__':
import tables
from os.path import join
from cns import get_config
filename = join(get_config('TEMP_ROOT'), 'test_experiment.hd5')
file = tables.openFile(filename, 'w')
from experiments.trial_setting import add_parameters
add_parameters(['test'])
data = Data(store_node=file.root)
experiment = Experiment(data=data)
experiment.configure_traits()