def __call__(self, **params):
p = ParamOverrides(self, params, allow_extra_keywords=True)
self._apply_cmd_overrides(p)
for fn in p.metadata_fns:
self.metadata = AttrDict(p.metadata, **fn(p.inputs, p.outputs))
output_names = self.metadata['outputs'].keys()
input_names = self.metadata.inputs.keys()
inputs = dict.fromkeys(input_names)
for k in inputs.keys():
inputs[k] = copy.deepcopy(p.pattern_generator)
responses = p.pattern_response_fn(inputs, output_names,
durations=p.durations)
results = self._collate_results(responses)
if p.measurement_storage_hook:
p.measurement_storage_hook(results)
return results
def __call__(self, **params):
p = ParamOverrides(self, params, allow_extra_keywords=True)
self._apply_cmd_overrides(p)
self.metadata = AttrDict(p.metadata)
for fn in p.metadata_fns:
self.metadata.update(fn(p.inputs, p.outputs))
output_names = self.metadata.outputs.keys()
input_names = self.metadata.inputs.keys()
inputs = dict.fromkeys(input_names)
if p.input_patterns:
for k, ip in p.input_patterns.items():
inputs[k] = ip
for name in [k for k, ip in inputs.items() if ip is None]:
self.warning("No pattern specified for input %s, defaulting"
"to blank Constant pattern." % name)
inputs[name] = imagen.Constant(scale=0)
else:
for k in inputs.keys():
inputs[k] = copy.deepcopy(p.pattern_generator)
for f in p.pre_presentation_hooks: f()
responses = p.pattern_response_fn(inputs, output_names,
durations=p.durations)
for f in p.post_presentation_hooks: f()
label = inputs.values()[0].__class__.__name__
results = self._collate_results(responses, label)
if p.measurement_storage_hook:
p.measurement_storage_hook(results)
return results
