def test_EstimateModel_inputs(): input_map = dict(ignore_exception=dict(nohash=True, usedefault=True, ), paths=dict(), spm_mat_file=dict(copyfile=True, mandatory=True, field='spmmat', ), use_v8struct=dict(min_ver='8', usedefault=True, ), use_mcr=dict(), estimation_method=dict(field='method', mandatory=True, ), flags=dict(), mfile=dict(usedefault=True, ), matlab_cmd=dict(), ) inputs = EstimateModel.input_spec() for key, metadata in input_map.items(): for metakey, value in metadata.items(): yield assert_equal, getattr(inputs.traits()[key], metakey), value
def test_EstimateModel_inputs(): input_map = dict( estimation_method=dict( field='method', mandatory=True, ), flags=dict(), ignore_exception=dict( nohash=True, usedefault=True, ), matlab_cmd=dict(), mfile=dict(usedefault=True, ), paths=dict(), spm_mat_file=dict( copyfile=True, field='spmmat', mandatory=True, ), use_mcr=dict(), use_v8struct=dict( min_ver='8', usedefault=True, ), ) inputs = EstimateModel.input_spec() for key, metadata in input_map.items(): for metakey, value in metadata.items(): yield assert_equal, getattr(inputs.traits()[key], metakey), value
def test_EstimateModel_outputs(): output_map = dict(residual_image=dict(), spm_mat_file=dict(), RPVimage=dict(), mask_image=dict(), beta_images=dict(), ) outputs = EstimateModel.output_spec() for key, metadata in output_map.items(): for metakey, value in metadata.items(): yield assert_equal, getattr(outputs.traits()[key], metakey), value
def test_EstimateModel_outputs(): output_map = dict( RPVimage=dict(), beta_images=dict(), mask_image=dict(), residual_image=dict(), spm_mat_file=dict(), ) outputs = EstimateModel.output_spec() for key, metadata in output_map.items(): for metakey, value in metadata.items(): yield assert_equal, getattr(outputs.traits()[key], metakey), value
# input: interscan interval / repetition time in secs (a float): l1design.inputs.interscan_interval = time_repetition # input: Model serial correlations AR(1), FAST or none: l1design.inputs.model_serial_correlations = 'AR(1)' # input: number of time-bins per scan in secs (an integer): l1design.inputs.microtime_resolution = 16 # input: the onset/time-bin in seconds for alignment (a float): l1design.inputs.microtime_onset = 1 # set expected thread and memory usage for the node: l1design.interface.num_threads = 1 l1design.interface.mem_gb = 2 # ====================================================================== # DEFINE NODE: ESTIMATE MODEL (ESTIMATE THE PARAMETERS OF THE MODEL) # ====================================================================== # function: use spm_spm to estimate the parameters of a model l1estimate = Node(EstimateModel(), name="l1estimate") # input: (a dictionary with keys which are 'Classical' or 'Bayesian2' # or 'Bayesian' and with values which are any value) l1estimate.inputs.estimation_method = {'Classical': 1} # set expected thread and memory usage for the node: l1estimate.interface.num_threads = 1 l1estimate.interface.mem_gb = 2 # ====================================================================== # DEFINE NODE: ESTIMATE CONTRASTS (ESTIMATES THE CONTRASTS) # ====================================================================== # function: use spm_contrasts to estimate contrasts of interest l1contrasts = Node(EstimateContrast(), name="l1contrasts") # input: list of contrasts with each contrast being a list of the form: # [('name', 'stat', [condition list], [weight list], [session list])]: # l1contrasts.inputs.contrasts = l1contrasts_list # node input: overwrite previous results:
# input: Model serial correlations AR(1), FAST or none: l1design.inputs.model_serial_correlations = 'AR(1)' # input: number of time-bins per scan in secs (an integer): l1design.inputs.microtime_resolution = 16 # input: the onset/time-bin in seconds for alignment (a float): l1design.inputs.microtime_onset = 1 # set expected thread and memory usage for the node: l1design.interface.num_threads = 1 l1design.interface.estimated_memory_gb = 2 # ====================================================================== # DEFINE NODE: ESTIMATE MODEL (ESTIMATE THE PARAMETERS OF THE MODEL) # ====================================================================== # function: use spm_spm to estimate the parameters of a model # EstimateModel - estimate the parameters of the model level1estimate = Node(EstimateModel(estimation_method={'Classical': 1}), name="level1estimate") l1estimate = Node(EstimateModel(), name="l1estimate") # input: (a dictionary with keys which are 'Classical' or 'Bayesian2' # or 'Bayesian' and with values which are any value) l1estimate.inputs.estimation_method = {'Classical': 1} # set expected thread and memory usage for the node: l1estimate.interface.num_threads = 1 l1estimate.interface.estimated_memory_gb = 2 # ====================================================================== # DEFINE NODE: ESTIMATE CONTRASTS (ESTIMATES THE CONTRASTS) # ====================================================================== # function: use spm_contrasts to estimate contrasts of interest l1contrasts = Node(EstimateContrast(), name="l1contrasts")