def model_generator(formula, data, volume_start_times, iterable=None,
slicetimes=None, model_type=OLSModel,
model_params = lambda x: ()):
"""
Generator for the models for a pass of fmristat analysis.
"""
for i, d in matrix_generator(fmri_generator(data, iterable=iterable)):
model_args = model_params(i) # model may depend on i
rmodel = model_type(formula.design(volume_start_times), *model_args)
yield i, d, rmodel
def model_generator(formula, data, volume_start_times, iterable=None,
slicetimes=None, model_type=OLSModel,
model_params = lambda x: ()):
"""
Generator for the models for a pass of fmristat analysis.
"""
volume_start_times = make_recarray(volume_start_times.astype(float), 't')
# Generator for slices of the data with time as first axis
axis0_gen = fmri_generator(data, iterable=iterable)
# Iterate over 2D slices of the data
for indexer, indexed_data in matrix_generator(axis0_gen):
model_args = model_params(indexer) # model may depend on i
# Get the design for these volume start times
design = formula.design(volume_start_times, return_float=True)
# Make the model from the design
rmodel = model_type(design, *model_args)
yield indexer, indexed_data, rmodel
def model_generator(formula, data, volume_start_times, iterable=None,
slicetimes=None, model_type=OLSModel,
model_params = lambda x: ()):
"""
Generator for the models for a pass of fmristat analysis.
"""
volume_start_times = make_recarray(volume_start_times.astype(float), 't')
# Generator for slices of the data with time as first axis
axis0_gen = axis0_generator(data, slicers=iterable)
# Iterate over 2D slices of the data
for indexer, indexed_data in matrix_generator(axis0_gen):
model_args = model_params(indexer) # model may depend on i
# Get the design for these volume start times
design = formula.design(volume_start_times, return_float=True)
# Make the model from the design
rmodel = model_type(design, *model_args)
yield indexer, indexed_data, rmodel
