dsp.add_data('inputs_map', getattr(mdl, 'inputs_map', {}))

{args=(), kwargs={'dmap': None, 'name': 'after_treatment_model', 'default_values': None, 'raises': False, 'description': 'Calculates the error of calibrated model of a reference.', 'executor': False}, deferred=[('add_data', {'data_id': 'inputs_map', 'filters': None, 'wait_inputs': False, 'wildcard': None, 'function': None, 'callback': None, 'initial_dist': 0.0, 'default_value': {}, 'description': None, 'await_result': None})]}

{args=(), kwargs={'dmap': None, 'name': 'after_treatment_model', 'default_values': None, 'raises': False, 'description': 'Calculates the error of calibrated model of a reference.', 'executor': False}, deferred=[]}

{args=(), kwargs={'dmap': None, 'name': 'after_treatment_model', 'default_values': None, 'raises': False, 'description': 'Calculates the error of calibrated model of a reference.', 'executor': False}, deferred=[('add_data', {'data_id': 'inputs_map', 'filters': None, 'wait_inputs': False, 'wildcard': None, 'function': None, 'callback': None, 'initial_dist': 0.0, 'default_value': {}, 'description': None, 'await_result': None}), ('add_function', {'function_id': 'select_inputs', 'inputs': ['inputs_map', 'data'], 'function': <function map_dict at 0x7ff54dadd5e0>, 'weight': None, 'input_domain': None, 'filters': None, 'await_result': None, 'await_domain': None, 'out_weight': None, 'description': None, 'outputs': ['inputs<0>'], 'inp_weight': None})]}

{args=(), kwargs={'dmap': None, 'name': 'after_treatment_model', 'default_values': None, 'raises': False, 'description': 'Calculates the error of calibrated model of a reference.', 'executor': False}, deferred=[('add_data', {'data_id': 'inputs_map', 'filters': None, 'wait_inputs': False, 'wildcard': None, 'function': None, 'callback': None, 'initial_dist': 0.0, 'default_value': {}, 'description': None, 'await_result': None}), ('add_function', {'function_id': 'select_inputs', 'inputs': ['inputs_map', 'data'], 'function': <function map_dict at 0x7ff54dadd5e0>, 'weight': None, 'input_domain': None, 'filters': None, 'await_result': None, 'await_domain': None, 'out_weight': None, 'description': None, 'outputs': ['inputs<0>'], 'inp_weight': None}), ('add_data', {'data_id': 'inputs', 'filters': None, 'wait_inputs': False, 'wildcard': None, 'function': None, 'callback': None, 'initial_dist': 0.0, 'default_value': ['times', 'on_engine', 'engine_speeds_out_hot', 'is_cycle_hot', 'on_idle', 'is_hybrid'], 'description': None, 'await_result': None})]}

{args=(), kwargs={'dmap': None, 'name': 'after_treatment_model', 'default_values': None, 'raises': False, 'description': 'Calculates the error of calibrated model of a reference.', 'executor': False}, deferred=[('add_data', {'data_id': 'inputs_map', 'filters': None, 'wait_inputs': False, 'wildcard': None, 'function': None, 'callback': None, 'initial_dist': 0.0, 'default_value': {}, 'description': None, 'await_result': None}), ('add_function', {'function_id': 'select_inputs', 'inputs': ['inputs_map', 'data'], 'function': <function map_dict at 0x7ff54dadd5e0>, 'weight': None, 'input_domain': None, 'filters': None, 'await_result': None, 'await_domain': None, 'out_weight': None, 'description': None, 'outputs': ['inputs<0>'], 'inp_weight': None}), ('add_data', {'data_id': 'inputs', 'filters': None, 'wait_inputs': False, 'wildcard': None, 'function': None, 'callback': None, 'initial_dist': 0.0, 'default_value': ['times', 'on_engine', 'engine_speeds_out_hot', 'is_cycle_hot', 'on_idle', 'is_hybrid'], 'description': None, 'await_result': None}), ('add_function', {'function_id': 'select_inputs', 'inputs': ['inputs', 'inputs<0>'], 'function': functools.partial(<function selector at 0x7ff54dadd700>, allow_miss=True), 'weight': None, 'input_domain': None, 'filters': None, 'await_result': None, 'await_domain': None, 'out_weight': None, 'description': None, 'outputs': ['inputs<1>'], 'inp_weight': None})]}

{args=(), kwargs={'dmap': None, 'name': 'after_treatment_model', 'default_values': None, 'raises': False, 'description': 'Calculates the error of calibrated model of a reference.', 'executor': False}, deferred=[('add_data', {'data_id': 'inputs_map', 'filters': None, 'wait_inputs': False, 'wildcard': None, 'function': None, 'callback': None, 'initial_dist': 0.0, 'default_value': {}, 'description': None, 'await_result': None}), ('add_function', {'function_id': 'select_inputs', 'inputs': ['inputs_map', 'data'], 'function': <function map_dict at 0x7ff54dadd5e0>, 'weight': None, 'input_domain': None, 'filters': None, 'await_result': None, 'await_domain': None, 'out_weight': None, 'description': None, 'outputs': ['inputs<0>'], 'inp_weight': None}), ('add_data', {'data_id': 'inputs', 'filters': None, 'wait_inputs': False, 'wildcard': None, 'function': None, 'callback': None, 'initial_dist': 0.0, 'default_value': ['times', 'on_engine', 'engine_speeds_out_hot', 'is_cycle_hot', 'on_idle', 'is_hybrid'], 'description': None, 'await_result': None}), ('add_function', {'function_id': 'select_inputs', 'inputs': ['inputs', 'inputs<0>'], 'function': functools.partial(<function selector at 0x7ff54dadd700>, allow_miss=True), 'weight': None, 'input_domain': None, 'filters': None, 'await_result': None, 'await_domain': None, 'out_weight': None, 'description': None, 'outputs': ['inputs<1>'], 'inp_weight': None}), ('add_function', {'function_id': None, 'inputs': ['calibrated_models', 'inputs<1>'], 'function': <function combine_dicts at 0x7ff54dadd0d0>, 'weight': None, 'input_domain': None, 'filters': None, 'await_result': None, 'await_domain': None, 'out_weight': None, 'description': None, 'outputs': ['prediction_inputs'], 'inp_weight': None})]}

{args=(), kwargs={'dmap': None, 'name': 'after_treatment_model', 'default_values': None, 'raises': False, 'description': 'Calculates the error of calibrated model of a reference.', 'executor': False}, deferred=[('add_data', {'data_id': 'inputs_map', 'filters': None, 'wait_inputs': False, 'wildcard': None, 'function': None, 'callback': None, 'initial_dist': 0.0, 'default_value': {}, 'description': None, 'await_result': None}), ('add_function', {'function_id': 'select_inputs', 'inputs': ['inputs_map', 'data'], 'function': <function map_dict at 0x7ff54dadd5e0>, 'weight': None, 'input_domain': None, 'filters': None, 'await_result': None, 'await_domain': None, 'out_weight': None, 'description': None, 'outputs': ['inputs<0>'], 'inp_weight': None}), ('add_data', {'data_id': 'inputs', 'filters': None, 'wait_inputs': False, 'wildcard': None, 'function': None, 'callback': None, 'initial_dist': 0.0, 'default_value': ['times', 'on_engine', 'engine_speeds_out_hot', 'is_cycle_hot', 'on_idle', 'is_hybrid'], 'description': None, 'await_result': None}), ('add_function', {'function_id': 'select_inputs', 'inputs': ['inputs', 'inputs<0>'], 'function': functools.partial(<function selector at 0x7ff54dadd700>, allow_miss=True), 'weight': None, 'input_domain': None, 'filters': None, 'await_result': None, 'await_domain': None, 'out_weight': None, 'description': None, 'outputs': ['inputs<1>'], 'inp_weight': None}), ('add_function', {'function_id': None, 'inputs': ['calibrated_models', 'inputs<1>'], 'function': <function combine_dicts at 0x7ff54dadd0d0>, 'weight': None, 'input_domain': None, 'filters': None, 'await_result': None, 'await_domain': None, 'out_weight': None, 'description': None, 'outputs': ['prediction_inputs'], 'inp_weight': None}), ('add_data', {'data_id': 'targets', 'filters': None, 'wait_inputs': False, 'wildcard': None, 'function': None, 'callback': None, 'initial_dist': 0.0, 'default_value': ['engine_speeds_base'], 'description': None, 'await_result': None})]}

{args=(), kwargs={'dmap': None, 'name': 'after_treatment_model', 'default_values': None, 'raises': False, 'description': 'Calculates the error of calibrated model of a reference.', 'executor': False}, deferred=[('add_data', {'data_id': 'inputs_map', 'filters': None, 'wait_inputs': False, 'wildcard': None, 'function': None, 'callback': None, 'initial_dist': 0.0, 'default_value': {}, 'description': None, 'await_result': None}), ('add_function', {'function_id': 'select_inputs', 'inputs': ['inputs_map', 'data'], 'function': <function map_dict at 0x7ff54dadd5e0>, 'weight': None, 'input_domain': None, 'filters': None, 'await_result': None, 'await_domain': None, 'out_weight': None, 'description': None, 'outputs': ['inputs<0>'], 'inp_weight': None}), ('add_data', {'data_id': 'inputs', 'filters': None, 'wait_inputs': False, 'wildcard': None, 'function': None, 'callback': None, 'initial_dist': 0.0, 'default_value': ['times', 'on_engine', 'engine_speeds_out_hot', 'is_cycle_hot', 'on_idle', 'is_hybrid'], 'description': None, 'await_result': None}), ('add_function', {'function_id': 'select_inputs', 'inputs': ['inputs', 'inputs<0>'], 'function': functools.partial(<function selector at 0x7ff54dadd700>, allow_miss=True), 'weight': None, 'input_domain': None, 'filters': None, 'await_result': None, 'await_domain': None, 'out_weight': None, 'description': None, 'outputs': ['inputs<1>'], 'inp_weight': None}), ('add_function', {'function_id': None, 'inputs': ['calibrated_models', 'inputs<1>'], 'function': <function combine_dicts at 0x7ff54dadd0d0>, 'weight': None, 'input_domain': None, 'filters': None, 'await_result': None, 'await_domain': None, 'out_weight': None, 'description': None, 'outputs': ['prediction_inputs'], 'inp_weight': None}), ('add_data', {'data_id': 'targets', 'filters': None, 'wait_inputs': False, 'wildcard': None, 'function': None, 'callback': None, 'initial_dist': 0.0, 'default_value': ['engine_speeds_base'], 'description': None, 'await_result': None}), ('add_function', {'function_id': 'select_targets', 'inputs': ['targets', 'data'], 'function': functools.partial(<function selector at 0x7ff54dadd700>, allow_miss=True), 'weight': None, 'input_domain': None, 'filters': None, 'await_result': None, 'await_domain': None, 'out_weight': None, 'description': None, 'outputs': ['references'], 'inp_weight': None})]}

{args=(), kwargs={'dmap': None, 'name': 'after_treatment_model', 'default_values': None, 'raises': False, 'description': 'Calculates the error of calibrated model of a reference.', 'executor': False}, deferred=[('add_data', {'data_id': 'inputs_map', 'filters': None, 'wait_inputs': False, 'wildcard': None, 'function': None, 'callback': None, 'initial_dist': 0.0, 'default_value': {}, 'description': None, 'await_result': None}), ('add_function', {'function_id': 'select_inputs', 'inputs': ['inputs_map', 'data'], 'function': <function map_dict at 0x7ff54dadd5e0>, 'weight': None, 'input_domain': None, 'filters': None, 'await_result': None, 'await_domain': None, 'out_weight': None, 'description': None, 'outputs': ['inputs<0>'], 'inp_weight': None}), ('add_data', {'data_id': 'inputs', 'filters': None, 'wait_inputs': False, 'wildcard': None, 'function': None, 'callback': None, 'initial_dist': 0.0, 'default_value': ['times', 'on_engine', 'engine_speeds_out_hot', 'is_cycle_hot', 'on_idle', 'is_hybrid'], 'description': None, 'await_result': None}), ('add_function', {'function_id': 'select_inputs', 'inputs': ['inputs', 'inputs<0>'], 'function': functools.partial(<function selector at 0x7ff54dadd700>, allow_miss=True), 'weight': None, 'input_domain': None, 'filters': None, 'await_result': None, 'await_domain': None, 'out_weight': None, 'description': None, 'outputs': ['inputs<1>'], 'inp_weight': None}), ('add_function', {'function_id': None, 'inputs': ['calibrated_models', 'inputs<1>'], 'function': <function combine_dicts at 0x7ff54dadd0d0>, 'weight': None, 'input_domain': None, 'filters': None, 'await_result': None, 'await_domain': None, 'out_weight': None, 'description': None, 'outputs': ['prediction_inputs'], 'inp_weight': None}), ('add_data', {'data_id': 'targets', 'filters': None, 'wait_inputs': False, 'wildcard': None, 'function': None, 'callback': None, 'initial_dist': 0.0, 'default_value': ['engine_speeds_base'], 'description': None, 'await_result': None}), ('add_function', {'function_id': 'select_targets', 'inputs': ['targets', 'data'], 'function': functools.partial(<function selector at 0x7ff54dadd700>, allow_miss=True), 'weight': None, 'input_domain': None, 'filters': None, 'await_result': None, 'await_domain': None, 'out_weight': None, 'description': None, 'outputs': ['references'], 'inp_weight': None}), ('add_function', {'function_id': None, 'inputs': ['prediction_inputs', 'calibrated_models'], 'function': <schedula.utils.blue.Blueprint object at 0x7ff54d25e970>, 'weight': None, 'input_domain': None, 'filters': None, 'await_result': None, 'await_domain': None, 'out_weight': None, 'description': None, 'outputs': ['results'], 'inp_weight': None})]}

{args=(), kwargs={'dmap': None, 'name': 'after_treatment_model', 'default_values': None, 'raises': False, 'description': 'Calculates the error of calibrated model of a reference.', 'executor': False}, deferred=[('add_data', {'data_id': 'inputs_map', 'filters': None, 'wait_inputs': False, 'wildcard': None, 'function': None, 'callback': None, 'initial_dist': 0.0, 'default_value': {}, 'description': None, 'await_result': None}), ('add_function', {'function_id': 'select_inputs', 'inputs': ['inputs_map', 'data'], 'function': <function map_dict at 0x7ff54dadd5e0>, 'weight': None, 'input_domain': None, 'filters': None, 'await_result': None, 'await_domain': None, 'out_weight': None, 'description': None, 'outputs': ['inputs<0>'], 'inp_weight': None}), ('add_data', {'data_id': 'inputs', 'filters': None, 'wait_inputs': False, 'wildcard': None, 'function': None, 'callback': None, 'initial_dist': 0.0, 'default_value': ['times', 'on_engine', 'engine_speeds_out_hot', 'is_cycle_hot', 'on_idle', 'is_hybrid'], 'description': None, 'await_result': None}), ('add_function', {'function_id': 'select_inputs', 'inputs': ['inputs', 'inputs<0>'], 'function': functools.partial(<function selector at 0x7ff54dadd700>, allow_miss=True), 'weight': None, 'input_domain': None, 'filters': None, 'await_result': None, 'await_domain': None, 'out_weight': None, 'description': None, 'outputs': ['inputs<1>'], 'inp_weight': None}), ('add_function', {'function_id': None, 'inputs': ['calibrated_models', 'inputs<1>'], 'function': <function combine_dicts at 0x7ff54dadd0d0>, 'weight': None, 'input_domain': None, 'filters': None, 'await_result': None, 'await_domain': None, 'out_weight': None, 'description': None, 'outputs': ['prediction_inputs'], 'inp_weight': None}), ('add_data', {'data_id': 'targets', 'filters': None, 'wait_inputs': False, 'wildcard': None, 'function': None, 'callback': None, 'initial_dist': 0.0, 'default_value': ['engine_speeds_base'], 'description': None, 'await_result': None}), ('add_function', {'function_id': 'select_targets', 'inputs': ['targets', 'data'], 'function': functools.partial(<function selector at 0x7ff54dadd700>, allow_miss=True), 'weight': None, 'input_domain': None, 'filters': None, 'await_result': None, 'await_domain': None, 'out_weight': None, 'description': None, 'outputs': ['references'], 'inp_weight': None}), ('add_function', {'function_id': None, 'inputs': ['prediction_inputs', 'calibrated_models'], 'function': <schedula.utils.blue.Blueprint object at 0x7ff54d25e970>, 'weight': None, 'input_domain': None, 'filters': None, 'await_result': None, 'await_domain': None, 'out_weight': None, 'description': None, 'outputs': ['results'], 'inp_weight': None}), ('add_data', {'data_id': 'outputs', 'filters': None, 'wait_inputs': False, 'wildcard': None, 'function': None, 'callback': None, 'initial_dist': 0.0, 'default_value': ['engine_speeds_base'], 'description': None, 'await_result': None})]}

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{args=(), kwargs={'dmap': None, 'name': 'after_treatment_model', 'default_values': None, 'raises': False, 'description': 'Calculates the error of calibrated model of a reference.', 'executor': False}, deferred=[('add_data', {'data_id': 'inputs_map', 'filters': None, 'wait_inputs': False, 'wildcard': None, 'function': None, 'callback': None, 'initial_dist': 0.0, 'default_value': {}, 'description': None, 'await_result': None}), ('add_function', {'function_id': 'select_inputs', 'inputs': ['inputs_map', 'data'], 'function': <function map_dict at 0x7ff54dadd5e0>, 'weight': None, 'input_domain': None, 'filters': None, 'await_result': None, 'await_domain': None, 'out_weight': None, 'description': None, 'outputs': ['inputs<0>'], 'inp_weight': None}), ('add_data', {'data_id': 'inputs', 'filters': None, 'wait_inputs': False, 'wildcard': None, 'function': None, 'callback': None, 'initial_dist': 0.0, 'default_value': ['times', 'on_engine', 'engine_speeds_out_hot', 'is_cycle_hot', 'on_idle', 'is_hybrid'], 'description': None, 'await_result': None}), ('add_function', {'function_id': 'select_inputs', 'inputs': ['inputs', 'inputs<0>'], 'function': functools.partial(<function selector at 0x7ff54dadd700>, allow_miss=True), 'weight': None, 'input_domain': None, 'filters': None, 'await_result': None, 'await_domain': None, 'out_weight': None, 'description': None, 'outputs': ['inputs<1>'], 'inp_weight': None}), ('add_function', {'function_id': None, 'inputs': ['calibrated_models', 'inputs<1>'], 'function': <function combine_dicts at 0x7ff54dadd0d0>, 'weight': None, 'input_domain': None, 'filters': None, 'await_result': None, 'await_domain': None, 'out_weight': None, 'description': None, 'outputs': ['prediction_inputs'], 'inp_weight': None}), ('add_data', {'data_id': 'targets', 'filters': None, 'wait_inputs': False, 'wildcard': None, 'function': None, 'callback': None, 'initial_dist': 0.0, 'default_value': ['engine_speeds_base'], 'description': None, 'await_result': None}), ('add_function', {'function_id': 'select_targets', 'inputs': ['targets', 'data'], 'function': functools.partial(<function selector at 0x7ff54dadd700>, allow_miss=True), 'weight': None, 'input_domain': None, 'filters': None, 'await_result': None, 'await_domain': None, 'out_weight': None, 'description': None, 'outputs': ['references'], 'inp_weight': None}), ('add_function', {'function_id': None, 'inputs': ['prediction_inputs', 'calibrated_models'], 'function': <schedula.utils.blue.Blueprint object at 0x7ff54d25e970>, 'weight': None, 'input_domain': None, 'filters': None, 'await_result': None, 'await_domain': None, 'out_weight': None, 'description': None, 'outputs': ['results'], 'inp_weight': None}), ('add_data', {'data_id': 'outputs', 'filters': None, 'wait_inputs': False, 'wildcard': None, 'function': None, 'callback': None, 'initial_dist': 0.0, 'default_value': ['engine_speeds_base'], 'description': None, 'await_result': None}), ('add_func', {'function_id': None, 'inputs': None, 'function': <function select_predictions at 0x7ff54d29cd30>, 'weight': None, 'input_domain': None, 'filters': None, 'inputs_kwargs': False, 'inputs_defaults': False, 'await_result': None, 'await_domain': None, 'out_weight': None, 'description': None, 'outputs': ['predictions'], 'inp_weight': None}), ('add_data', {'data_id': 'metrics_inputs', 'filters': None, 'wait_inputs': False, 'wildcard': None, 'function': None, 'callback': None, 'initial_dist': 0.0, 'default_value': ['after_treatment_warm_up_phases'], 'description': None, 'await_result': None})]}

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{args=(), kwargs={'dmap': None, 'name': 'after_treatment_model', 'default_values': None, 'raises': False, 'description': 'Calculates the error of calibrated model of a reference.', 'executor': False}, deferred=[('add_data', {'data_id': 'inputs_map', 'filters': None, 'wait_inputs': False, 'wildcard': None, 'function': None, 'callback': None, 'initial_dist': 0.0, 'default_value': {}, 'description': None, 'await_result': None}), ('add_function', {'function_id': 'select_inputs', 'inputs': ['inputs_map', 'data'], 'function': <function map_dict at 0x7ff54dadd5e0>, 'weight': None, 'input_domain': None, 'filters': None, 'await_result': None, 'await_domain': None, 'out_weight': None, 'description': None, 'outputs': ['inputs<0>'], 'inp_weight': None}), ('add_data', {'data_id': 'inputs', 'filters': None, 'wait_inputs': False, 'wildcard': None, 'function': None, 'callback': None, 'initial_dist': 0.0, 'default_value': ['times', 'on_engine', 'engine_speeds_out_hot', 'is_cycle_hot', 'on_idle', 'is_hybrid'], 'description': None, 'await_result': None}), ('add_function', {'function_id': 'select_inputs', 'inputs': ['inputs', 'inputs<0>'], 'function': functools.partial(<function selector at 0x7ff54dadd700>, allow_miss=True), 'weight': None, 'input_domain': None, 'filters': None, 'await_result': None, 'await_domain': None, 'out_weight': None, 'description': None, 'outputs': ['inputs<1>'], 'inp_weight': None}), ('add_function', {'function_id': None, 'inputs': ['calibrated_models', 'inputs<1>'], 'function': <function combine_dicts at 0x7ff54dadd0d0>, 'weight': None, 'input_domain': None, 'filters': None, 'await_result': None, 'await_domain': None, 'out_weight': None, 'description': None, 'outputs': ['prediction_inputs'], 'inp_weight': None}), ('add_data', {'data_id': 'targets', 'filters': None, 'wait_inputs': False, 'wildcard': None, 'function': None, 'callback': None, 'initial_dist': 0.0, 'default_value': ['engine_speeds_base'], 'description': None, 'await_result': None}), ('add_function', {'function_id': 'select_targets', 'inputs': ['targets', 'data'], 'function': functools.partial(<function selector at 0x7ff54dadd700>, allow_miss=True), 'weight': None, 'input_domain': None, 'filters': None, 'await_result': None, 'await_domain': None, 'out_weight': None, 'description': None, 'outputs': ['references'], 'inp_weight': None}), ('add_function', {'function_id': None, 'inputs': ['prediction_inputs', 'calibrated_models'], 'function': <schedula.utils.blue.Blueprint object at 0x7ff54d25e970>, 'weight': None, 'input_domain': None, 'filters': None, 'await_result': None, 'await_domain': None, 'out_weight': None, 'description': None, 'outputs': ['results'], 'inp_weight': None}), ('add_data', {'data_id': 'outputs', 'filters': None, 'wait_inputs': False, 'wildcard': None, 'function': None, 'callback': None, 'initial_dist': 0.0, 'default_value': ['engine_speeds_base'], 'description': None, 'await_result': None}), ('add_func', {'function_id': None, 'inputs': None, 'function': <function select_predictions at 0x7ff54d29cd30>, 'weight': None, 'input_domain': None, 'filters': None, 'inputs_kwargs': False, 'inputs_defaults': False, 'await_result': None, 'await_domain': None, 'out_weight': None, 'description': None, 'outputs': ['predictions'], 'inp_weight': None}), ('add_data', {'data_id': 'metrics_inputs', 'filters': None, 'wait_inputs': False, 'wildcard': None, 'function': None, 'callback': None, 'initial_dist': 0.0, 'default_value': ['after_treatment_warm_up_phases'], 'description': None, 'await_result': None}), ('add_function', {'function_id': 'select_metrics_inputs', 'inputs': ['metrics_inputs', 'data'], 'function': functools.partial(<function selector at 0x7ff54dadd700>, allow_miss=True), 'weight': None, 'input_domain': None, 'filters': None, 'await_result': None, 'await_domain': None, 'out_weight': None, 'description': None, 'outputs': ['metrics_kwargs'], 'inp_weight': None}), ('add_data', {'data_id': 'metrics', 'filters': None, 'wait_inputs': False, 'wildcard': None, 'function': None, 'callback': None, 'initial_dist': 0.0, 'default_value': {'engine_speeds_base': <function metric_after_treatment_speed_model at 0x7ff54d26e790>}, 'description': None, 'await_result': None}), ('add_func', {'function_id': None, 'inputs': None, 'function': <function calculate_errors at 0x7ff54d29ce50>, 'weight': None, 'input_domain': None, 'filters': None, 'inputs_kwargs': False, 'inputs_defaults': False, 'await_result': None, 'await_domain': None, 'out_weight': None, 'description': None, 'outputs': ['errors'], 'inp_weight': None}), ('add_data', {'data_id': 'up_limit', 'filters': None, 'wait_inputs': False, 'wildcard': None, 'function': None, 'callback': None, 'initial_dist': 0.0, 'default_value': {'engine_speeds_base': 160}, 'description': None, 'await_result': None})]}

{args=(), kwargs={'dmap': None, 'name': 'after_treatment_model', 'default_values': None, 'raises': False, 'description': 'Calculates the error of calibrated model of a reference.', 'executor': False}, deferred=[('add_data', {'data_id': 'inputs_map', 'filters': None, 'wait_inputs': False, 'wildcard': None, 'function': None, 'callback': None, 'initial_dist': 0.0, 'default_value': {}, 'description': None, 'await_result': None}), ('add_function', {'function_id': 'select_inputs', 'inputs': ['inputs_map', 'data'], 'function': <function map_dict at 0x7ff54dadd5e0>, 'weight': None, 'input_domain': None, 'filters': None, 'await_result': None, 'await_domain': None, 'out_weight': None, 'description': None, 'outputs': ['inputs<0>'], 'inp_weight': None}), ('add_data', {'data_id': 'inputs', 'filters': None, 'wait_inputs': False, 'wildcard': None, 'function': None, 'callback': None, 'initial_dist': 0.0, 'default_value': ['times', 'on_engine', 'engine_speeds_out_hot', 'is_cycle_hot', 'on_idle', 'is_hybrid'], 'description': None, 'await_result': None}), ('add_function', {'function_id': 'select_inputs', 'inputs': ['inputs', 'inputs<0>'], 'function': functools.partial(<function selector at 0x7ff54dadd700>, allow_miss=True), 'weight': None, 'input_domain': None, 'filters': None, 'await_result': None, 'await_domain': None, 'out_weight': None, 'description': None, 'outputs': ['inputs<1>'], 'inp_weight': None}), ('add_function', {'function_id': None, 'inputs': ['calibrated_models', 'inputs<1>'], 'function': <function combine_dicts at 0x7ff54dadd0d0>, 'weight': None, 'input_domain': None, 'filters': None, 'await_result': None, 'await_domain': None, 'out_weight': None, 'description': None, 'outputs': ['prediction_inputs'], 'inp_weight': None}), ('add_data', {'data_id': 'targets', 'filters': None, 'wait_inputs': False, 'wildcard': None, 'function': None, 'callback': None, 'initial_dist': 0.0, 'default_value': ['engine_speeds_base'], 'description': None, 'await_result': None}), ('add_function', {'function_id': 'select_targets', 'inputs': ['targets', 'data'], 'function': functools.partial(<function selector at 0x7ff54dadd700>, allow_miss=True), 'weight': None, 'input_domain': None, 'filters': None, 'await_result': None, 'await_domain': None, 'out_weight': None, 'description': None, 'outputs': ['references'], 'inp_weight': None}), ('add_function', {'function_id': None, 'inputs': ['prediction_inputs', 'calibrated_models'], 'function': <schedula.utils.blue.Blueprint object at 0x7ff54d25e970>, 'weight': None, 'input_domain': None, 'filters': None, 'await_result': None, 'await_domain': None, 'out_weight': None, 'description': None, 'outputs': ['results'], 'inp_weight': None}), ('add_data', {'data_id': 'outputs', 'filters': None, 'wait_inputs': False, 'wildcard': None, 'function': None, 'callback': None, 'initial_dist': 0.0, 'default_value': ['engine_speeds_base'], 'description': None, 'await_result': None}), ('add_func', {'function_id': None, 'inputs': None, 'function': <function select_predictions at 0x7ff54d29cd30>, 'weight': None, 'input_domain': None, 'filters': None, 'inputs_kwargs': False, 'inputs_defaults': False, 'await_result': None, 'await_domain': None, 'out_weight': None, 'description': None, 'outputs': ['predictions'], 'inp_weight': None}), ('add_data', {'data_id': 'metrics_inputs', 'filters': None, 'wait_inputs': False, 'wildcard': None, 'function': None, 'callback': None, 'initial_dist': 0.0, 'default_value': ['after_treatment_warm_up_phases'], 'description': None, 'await_result': None}), ('add_function', {'function_id': 'select_metrics_inputs', 'inputs': ['metrics_inputs', 'data'], 'function': functools.partial(<function selector at 0x7ff54dadd700>, allow_miss=True), 'weight': None, 'input_domain': None, 'filters': None, 'await_result': None, 'await_domain': None, 'out_weight': None, 'description': None, 'outputs': ['metrics_kwargs'], 'inp_weight': None}), ('add_data', {'data_id': 'metrics', 'filters': None, 'wait_inputs': False, 'wildcard': None, 'function': None, 'callback': None, 'initial_dist': 0.0, 'default_value': {'engine_speeds_base': <function metric_after_treatment_speed_model at 0x7ff54d26e790>}, 'description': None, 'await_result': None}), ('add_func', {'function_id': None, 'inputs': None, 'function': <function calculate_errors at 0x7ff54d29ce50>, 'weight': None, 'input_domain': None, 'filters': None, 'inputs_kwargs': False, 'inputs_defaults': False, 'await_result': None, 'await_domain': None, 'out_weight': None, 'description': None, 'outputs': ['errors'], 'inp_weight': None}), ('add_data', {'data_id': 'up_limit', 'filters': None, 'wait_inputs': False, 'wildcard': None, 'function': None, 'callback': None, 'initial_dist': 0.0, 'default_value': {'engine_speeds_base': 160}, 'description': None, 'await_result': None}), ('add_data', {'data_id': 'dn_limit', 'filters': None, 'wait_inputs': False, 'wildcard': None, 'function': None, 'callback': None, 'initial_dist': 0.0, 'default_value': None, 'description': None, 'await_result': None})]}

{args=(), kwargs={'dmap': None, 'name': 'after_treatment_model', 'default_values': None, 'raises': False, 'description': 'Calculates the error of calibrated model of a reference.', 'executor': False}, deferred=[('add_data', {'data_id': 'inputs_map', 'filters': None, 'wait_inputs': False, 'wildcard': None, 'function': None, 'callback': None, 'initial_dist': 0.0, 'default_value': {}, 'description': None, 'await_result': None}), ('add_function', {'function_id': 'select_inputs', 'inputs': ['inputs_map', 'data'], 'function': <function map_dict at 0x7ff54dadd5e0>, 'weight': None, 'input_domain': None, 'filters': None, 'await_result': None, 'await_domain': None, 'out_weight': None, 'description': None, 'outputs': ['inputs<0>'], 'inp_weight': None}), ('add_data', {'data_id': 'inputs', 'filters': None, 'wait_inputs': False, 'wildcard': None, 'function': None, 'callback': None, 'initial_dist': 0.0, 'default_value': ['times', 'on_engine', 'engine_speeds_out_hot', 'is_cycle_hot', 'on_idle', 'is_hybrid'], 'description': None, 'await_result': None}), ('add_function', {'function_id': 'select_inputs', 'inputs': ['inputs', 'inputs<0>'], 'function': functools.partial(<function selector at 0x7ff54dadd700>, allow_miss=True), 'weight': None, 'input_domain': None, 'filters': None, 'await_result': None, 'await_domain': None, 'out_weight': None, 'description': None, 'outputs': ['inputs<1>'], 'inp_weight': None}), ('add_function', {'function_id': None, 'inputs': ['calibrated_models', 'inputs<1>'], 'function': <function combine_dicts at 0x7ff54dadd0d0>, 'weight': None, 'input_domain': None, 'filters': None, 'await_result': None, 'await_domain': None, 'out_weight': None, 'description': None, 'outputs': ['prediction_inputs'], 'inp_weight': None}), ('add_data', {'data_id': 'targets', 'filters': None, 'wait_inputs': False, 'wildcard': None, 'function': None, 'callback': None, 'initial_dist': 0.0, 'default_value': ['engine_speeds_base'], 'description': None, 'await_result': None}), ('add_function', {'function_id': 'select_targets', 'inputs': ['targets', 'data'], 'function': functools.partial(<function selector at 0x7ff54dadd700>, allow_miss=True), 'weight': None, 'input_domain': None, 'filters': None, 'await_result': None, 'await_domain': None, 'out_weight': None, 'description': None, 'outputs': ['references'], 'inp_weight': None}), ('add_function', {'function_id': None, 'inputs': ['prediction_inputs', 'calibrated_models'], 'function': <schedula.utils.blue.Blueprint object at 0x7ff54d25e970>, 'weight': None, 'input_domain': None, 'filters': None, 'await_result': None, 'await_domain': None, 'out_weight': None, 'description': None, 'outputs': ['results'], 'inp_weight': None}), ('add_data', {'data_id': 'outputs', 'filters': None, 'wait_inputs': False, 'wildcard': None, 'function': None, 'callback': None, 'initial_dist': 0.0, 'default_value': ['engine_speeds_base'], 'description': None, 'await_result': None}), ('add_func', {'function_id': None, 'inputs': None, 'function': <function select_predictions at 0x7ff54d29cd30>, 'weight': None, 'input_domain': None, 'filters': None, 'inputs_kwargs': False, 'inputs_defaults': False, 'await_result': None, 'await_domain': None, 'out_weight': None, 'description': None, 'outputs': ['predictions'], 'inp_weight': None}), ('add_data', {'data_id': 'metrics_inputs', 'filters': None, 'wait_inputs': False, 'wildcard': None, 'function': None, 'callback': None, 'initial_dist': 0.0, 'default_value': ['after_treatment_warm_up_phases'], 'description': None, 'await_result': None}), ('add_function', {'function_id': 'select_metrics_inputs', 'inputs': ['metrics_inputs', 'data'], 'function': functools.partial(<function selector at 0x7ff54dadd700>, allow_miss=True), 'weight': None, 'input_domain': None, 'filters': None, 'await_result': None, 'await_domain': None, 'out_weight': None, 'description': None, 'outputs': ['metrics_kwargs'], 'inp_weight': None}), ('add_data', {'data_id': 'metrics', 'filters': None, 'wait_inputs': False, 'wildcard': None, 'function': None, 'callback': None, 'initial_dist': 0.0, 'default_value': {'engine_speeds_base': <function metric_after_treatment_speed_model at 0x7ff54d26e790>}, 'description': None, 'await_result': None}), ('add_func', {'function_id': None, 'inputs': None, 'function': <function calculate_errors at 0x7ff54d29ce50>, 'weight': None, 'input_domain': None, 'filters': None, 'inputs_kwargs': False, 'inputs_defaults': False, 'await_result': None, 'await_domain': None, 'out_weight': None, 'description': None, 'outputs': ['errors'], 'inp_weight': None}), ('add_data', {'data_id': 'up_limit', 'filters': None, 'wait_inputs': False, 'wildcard': None, 'function': None, 'callback': None, 'initial_dist': 0.0, 'default_value': {'engine_speeds_base': 160}, 'description': None, 'await_result': None}), ('add_data', {'data_id': 'dn_limit', 'filters': None, 'wait_inputs': False, 'wildcard': None, 'function': None, 'callback': None, 'initial_dist': 0.0, 'default_value': None, 'description': None, 'await_result': None}), ('add_func', {'function_id': None, 'inputs': None, 'function': <function calculate_calibration_status at 0x7ff54d26e040>, 'weight': None, 'input_domain': None, 'filters': None, 'inputs_kwargs': True, 'inputs_defaults': False, 'await_result': None, 'await_domain': None, 'out_weight': None, 'description': None, 'outputs': ['status'], 'inp_weight': None})]}


def _mdl_errors(mdl, data_id, err_func):
    name = '%s-%s errors' % (mdl.name, data_id)
    dsp = sh.BlueDispatcher(
        name=name, description='Calculates the error of calibrated model.'
    )
    dsp.add_data('models', mdl.models)
    dsp.add_function(
        function_id='select_models',
        function=getattr(mdl, 'select_models', _select_data),
        inputs=['models', data_id],
        outputs=['calibrated_models']
    )
    dsp.add_data('data_in', data_id)
    for o in calibration_cycles:
        dsp.add_function(
            function=_map_list,
            inputs=['calibrated_models', o],
            outputs=['input/%s' % o]
        )
        dsp.add_function(
            function=err_func,
            inputs=['input/%s' % o],
            outputs=['error/%s' % o]
        )
    return dsp, name
_mdl_errors(mdl=<module 'co2mpas.core.model.selector.models.after_treatment_model' from '/local/rcs/XXX/code/pytrace-collector/logs/pypibugs/tried/JRCSTU+co2mpas-ta/JRCSTU+co2mpas-ta/co2mpas/core/model/selector/models/after_treatment_model.py'>, data_id='wltp_h', err_func={args=(<schedula.utils.blue.BlueDispatcher object at 0x7ff54d25e1f0>,), kwargs={'outputs': ['errors', 'status'], 'output_type': 'list'}, deferred=[]})

{args=(), kwargs={'dmap': None, 'name': 'after_treatment_model-wltp_h errors', 'default_values': None, 'raises': False, 'description': 'Calculates the error of calibrated model.', 'executor': False}, deferred=[]}

{args=(), kwargs={'dmap': None, 'name': 'after_treatment_model-wltp_h errors', 'default_values': None, 'raises': False, 'description': 'Calculates the error of calibrated model.', 'executor': False}, deferred=[('add_data', {'data_id': 'models', 'filters': None, 'wait_inputs': False, 'wildcard': None, 'function': None, 'callback': None, 'initial_dist': 0.0, 'default_value': ['after_treatment_speed_model', 'after_treatment_warm_up_duration', 'after_treatment_cooling_duration'], 'description': None, 'await_result': None})]}

{args=(), kwargs={'dmap': None, 'name': 'after_treatment_model-wltp_h errors', 'default_values': None, 'raises': False, 'description': 'Calculates the error of calibrated model.', 'executor': False}, deferred=[('add_data', {'data_id': 'models', 'filters': None, 'wait_inputs': False, 'wildcard': None, 'function': None, 'callback': None, 'initial_dist': 0.0, 'default_value': ['after_treatment_speed_model', 'after_treatment_warm_up_duration', 'after_treatment_cooling_duration'], 'description': None, 'await_result': None}), ('add_function', {'function_id': 'select_models', 'inputs': ['models', 'wltp_h'], 'function': functools.partial(<function selector at 0x7ff54dadd700>, allow_miss=True), 'weight': None, 'input_domain': None, 'filters': None, 'await_result': None, 'await_domain': None, 'out_weight': None, 'description': None, 'outputs': ['calibrated_models'], 'inp_weight': None})]}

{args=(), kwargs={'dmap': None, 'name': 'after_treatment_model-wltp_h errors', 'default_values': None, 'raises': False, 'description': 'Calculates the error of calibrated model.', 'executor': False}, deferred=[('add_data', {'data_id': 'models', 'filters': None, 'wait_inputs': False, 'wildcard': None, 'function': None, 'callback': None, 'initial_dist': 0.0, 'default_value': ['after_treatment_speed_model', 'after_treatment_warm_up_duration', 'after_treatment_cooling_duration'], 'description': None, 'await_result': None}), ('add_function', {'function_id': 'select_models', 'inputs': ['models', 'wltp_h'], 'function': functools.partial(<function selector at 0x7ff54dadd700>, allow_miss=True), 'weight': None, 'input_domain': None, 'filters': None, 'await_result': None, 'await_domain': None, 'out_weight': None, 'description': None, 'outputs': ['calibrated_models'], 'inp_weight': None}), ('add_data', {'data_id': 'data_in', 'filters': None, 'wait_inputs': False, 'wildcard': None, 'function': None, 'callback': None, 'initial_dist': 0.0, 'default_value': 'wltp_h', 'description': None, 'await_result': None})]}

{args=(), kwargs={'dmap': None, 'name': 'after_treatment_model-wltp_h errors', 'default_values': None, 'raises': False, 'description': 'Calculates the error of calibrated model.', 'executor': False}, deferred=[('add_data', {'data_id': 'models', 'filters': None, 'wait_inputs': False, 'wildcard': None, 'function': None, 'callback': None, 'initial_dist': 0.0, 'default_value': ['after_treatment_speed_model', 'after_treatment_warm_up_duration', 'after_treatment_cooling_duration'], 'description': None, 'await_result': None}), ('add_function', {'function_id': 'select_models', 'inputs': ['models', 'wltp_h'], 'function': functools.partial(<function selector at 0x7ff54dadd700>, allow_miss=True), 'weight': None, 'input_domain': None, 'filters': None, 'await_result': None, 'await_domain': None, 'out_weight': None, 'description': None, 'outputs': ['calibrated_models'], 'inp_weight': None}), ('add_data', {'data_id': 'data_in', 'filters': None, 'wait_inputs': False, 'wildcard': None, 'function': None, 'callback': None, 'initial_dist': 0.0, 'default_value': 'wltp_h', 'description': None, 'await_result': None}), ('add_function', {'function_id': None, 'inputs': ['calibrated_models', 'wltp_h'], 'function': functools.partial(<function map_list at 0x7ff54dadd670>, ['calibrated_models', 'data']), 'weight': None, 'input_domain': None, 'filters': None, 'await_result': None, 'await_domain': None, 'out_weight': None, 'description': None, 'outputs': ['input/wltp_h'], 'inp_weight': None})]}

{args=(), kwargs={'dmap': None, 'name': 'after_treatment_model-wltp_h errors', 'default_values': None, 'raises': False, 'description': 'Calculates the error of calibrated model.', 'executor': False}, deferred=[('add_data', {'data_id': 'models', 'filters': None, 'wait_inputs': False, 'wildcard': None, 'function': None, 'callback': None, 'initial_dist': 0.0, 'default_value': ['after_treatment_speed_model', 'after_treatment_warm_up_duration', 'after_treatment_cooling_duration'], 'description': None, 'await_result': None}), ('add_function', {'function_id': 'select_models', 'inputs': ['models', 'wltp_h'], 'function': functools.partial(<function selector at 0x7ff54dadd700>, allow_miss=True), 'weight': None, 'input_domain': None, 'filters': None, 'await_result': None, 'await_domain': None, 'out_weight': None, 'description': None, 'outputs': ['calibrated_models'], 'inp_weight': None}), ('add_data', {'data_id': 'data_in', 'filters': None, 'wait_inputs': False, 'wildcard': None, 'function': None, 'callback': None, 'initial_dist': 0.0, 'default_value': 'wltp_h', 'description': None, 'await_result': None}), ('add_function', {'function_id': None, 'inputs': ['calibrated_models', 'wltp_h'], 'function': functools.partial(<function map_list at 0x7ff54dadd670>, ['calibrated_models', 'data']), 'weight': None, 'input_domain': None, 'filters': None, 'await_result': None, 'await_domain': None, 'out_weight': None, 'description': None, 'outputs': ['input/wltp_h'], 'inp_weight': None}), ('add_function', {'function_id': None, 'inputs': ['input/wltp_h'], 'function': <schedula.utils.blue.Blueprint object at 0x7ff54d25e490>, 'weight': None, 'input_domain': None, 'filters': None, 'await_result': None, 'await_domain': None, 'out_weight': None, 'description': None, 'outputs': ['error/wltp_h'], 'inp_weight': None})]}

{args=(), kwargs={'dmap': None, 'name': 'after_treatment_model-wltp_h errors', 'default_values': None, 'raises': False, 'description': 'Calculates the error of calibrated model.', 'executor': False}, deferred=[('add_data', {'data_id': 'models', 'filters': None, 'wait_inputs': False, 'wildcard': None, 'function': None, 'callback': None, 'initial_dist': 0.0, 'default_value': ['after_treatment_speed_model', 'after_treatment_warm_up_duration', 'after_treatment_cooling_duration'], 'description': None, 'await_result': None}), ('add_function', {'function_id': 'select_models', 'inputs': ['models', 'wltp_h'], 'function': functools.partial(<function selector at 0x7ff54dadd700>, allow_miss=True), 'weight': None, 'input_domain': None, 'filters': None, 'await_result': None, 'await_domain': None, 'out_weight': None, 'description': None, 'outputs': ['calibrated_models'], 'inp_weight': None}), ('add_data', {'data_id': 'data_in', 'filters': None, 'wait_inputs': False, 'wildcard': None, 'function': None, 'callback': None, 'initial_dist': 0.0, 'default_value': 'wltp_h', 'description': None, 'await_result': None}), ('add_function', {'function_id': None, 'inputs': ['calibrated_models', 'wltp_h'], 'function': functools.partial(<function map_list at 0x7ff54dadd670>, ['calibrated_models', 'data']), 'weight': None, 'input_domain': None, 'filters': None, 'await_result': None, 'await_domain': None, 'out_weight': None, 'description': None, 'outputs': ['input/wltp_h'], 'inp_weight': None}), ('add_function', {'function_id': None, 'inputs': ['input/wltp_h'], 'function': <schedula.utils.blue.Blueprint object at 0x7ff54d25e490>, 'weight': None, 'input_domain': None, 'filters': None, 'await_result': None, 'await_domain': None, 'out_weight': None, 'description': None, 'outputs': ['error/wltp_h'], 'inp_weight': None}), ('add_function', {'function_id': None, 'inputs': ['calibrated_models', 'wltp_l'], 'function': functools.partial(<function map_list at 0x7ff54dadd670>, ['calibrated_models', 'data']), 'weight': None, 'input_domain': None, 'filters': None, 'await_result': None, 'await_domain': None, 'out_weight': None, 'description': None, 'outputs': ['input/wltp_l'], 'inp_weight': None})]}

{args=(), kwargs={'dmap': None, 'name': 'after_treatment_model-wltp_h errors', 'default_values': None, 'raises': False, 'description': 'Calculates the error of calibrated model.', 'executor': False}, deferred=[('add_data', {'data_id': 'models', 'filters': None, 'wait_inputs': False, 'wildcard': None, 'function': None, 'callback': None, 'initial_dist': 0.0, 'default_value': ['after_treatment_speed_model', 'after_treatment_warm_up_duration', 'after_treatment_cooling_duration'], 'description': None, 'await_result': None}), ('add_function', {'function_id': 'select_models', 'inputs': ['models', 'wltp_h'], 'function': functools.partial(<function selector at 0x7ff54dadd700>, allow_miss=True), 'weight': None, 'input_domain': None, 'filters': None, 'await_result': None, 'await_domain': None, 'out_weight': None, 'description': None, 'outputs': ['calibrated_models'], 'inp_weight': None}), ('add_data', {'data_id': 'data_in', 'filters': None, 'wait_inputs': False, 'wildcard': None, 'function': None, 'callback': None, 'initial_dist': 0.0, 'default_value': 'wltp_h', 'description': None, 'await_result': None}), ('add_function', {'function_id': None, 'inputs': ['calibrated_models', 'wltp_h'], 'function': functools.partial(<function map_list at 0x7ff54dadd670>, ['calibrated_models', 'data']), 'weight': None, 'input_domain': None, 'filters': None, 'await_result': None, 'await_domain': None, 'out_weight': None, 'description': None, 'outputs': ['input/wltp_h'], 'inp_weight': None}), ('add_function', {'function_id': None, 'inputs': ['input/wltp_h'], 'function': <schedula.utils.blue.Blueprint object at 0x7ff54d25e490>, 'weight': None, 'input_domain': None, 'filters': None, 'await_result': None, 'await_domain': None, 'out_weight': None, 'description': None, 'outputs': ['error/wltp_h'], 'inp_weight': None}), ('add_function', {'function_id': None, 'inputs': ['calibrated_models', 'wltp_l'], 'function': functools.partial(<function map_list at 0x7ff54dadd670>, ['calibrated_models', 'data']), 'weight': None, 'input_domain': None, 'filters': None, 'await_result': None, 'await_domain': None, 'out_weight': None, 'description': None, 'outputs': ['input/wltp_l'], 'inp_weight': None}), ('add_function', {'function_id': None, 'inputs': ['input/wltp_l'], 'function': <schedula.utils.blue.Blueprint object at 0x7ff54d25e490>, 'weight': None, 'input_domain': None, 'filters': None, 'await_result': None, 'await_domain': None, 'out_weight': None, 'description': None, 'outputs': ['error/wltp_l'], 'inp_weight': None})]}


{args=(), kwargs={'dmap': None, 'name': 'after_treatment_model-wltp_h errors', 'default_values': None, 'raises': False, 'description': 'Calculates the error of calibrated model.', 'executor': False}, deferred=[]}


{args=(), kwargs={'dmap': None, 'name': 'after_treatment_model-wltp_h errors', 'default_values': None, 'raises': False, 'description': 'Calculates the error of calibrated model.', 'executor': False}, deferred=[('add_data', {'data_id': 'models', 'filters': None, 'wait_inputs': False, 'wildcard': None, 'function': None, 'callback': None, 'initial_dist': 0.0, 'default_value': ['after_treatment_speed_model', 'after_treatment_warm_up_duration', 'after_treatment_cooling_duration'], 'description': None, 'await_result': None})]}


def setLevel(self, level):
        super(_Logger, self).setLevel(level)
        logging.basicConfig(level=level, **log_config)
        rlog = logging.getLogger()
        rlog.level = level
        logging.captureWarnings(True)
setLevel(self=<_Logger cli (NOTSET)>, level=20, __class__=<class 'co2mpas.cli._Logger'>, self._cache={}, self.disabled=False, self.filters=[], self.handlers=[<ClickHandler (NOTSET)>], self.level=0, self.name='cli', self.parent=None, self.propagate=False)


def test_1_demo(self, options):
        import glob
        from co2mpas.cli import demo
        kw = demo.make_context('demo', list(options)).params
        d_demo = osp.join(pdir, 'demos/*.xlsx')
        demos = {osp.basename(fp): fp for fp in glob.glob(d_demo)}
        with self.runner.isolated_filesystem():
            result = self.invoke(('demo',) + options)
            self.assertEqual(result.exit_code, 0)
            for fpath in glob.glob(osp.join(kw['output_folder'], '*.xlsx')):
                self.assertTrue(
                    filecmp.cmp(fpath, demos[osp.basename(fpath)]),
                    'Demo file (%s) is not as expected!' % fpath
                )
test_1_demo(self=<test_cli.CLI testMethod=test_1_demo_1___>, options=(), self._cleanups=[], self._outcome=<unittest.case._Outcome object at 0x7ff4aacf3e50>, self._subtest=None, self._testMethodDoc=None, self._testMethodName='test_1_demo_1___', self._type_equality_funcs={<class 'dict'>: 'assertDictEqual', <class 'list'>: 'assertListEqual', <class 'tuple'>: 'assertTupleEqual', <class 'set'>: 'assertSetEqual', <class 'frozenset'>: 'assertSetEqual', <class 'str'>: 'assertMultiLineEqual'}, self.invoke=functools.partial(<bound method CliRunner.invoke of <click.testing.CliRunner object at 0x7ff4aacf3520>>, <Group co2mpas>), self.runner=<click.testing.CliRunner object at 0x7ff4aacf3520>, self.test_1_demo_1___=<bound method CLI.test_1_demo of <test_cli.CLI testMethod=test_1_demo_1___>>)

<module 'glob' from '/local/rcs/XXX/miniforge3/envs/JRCSTU+co2mpas-ta/lib/python3.9/glob.py'>


kw = demo.make_context('demo', list(options)).params


result = self.invoke(('demo',) + options)


def save_demo_files(output_folder):
    import glob
    from shutil import copy2
    from pkg_resources import resource_filename
    os.makedirs(output_folder or '.', exist_ok=True)
    for src in glob.glob(resource_filename('co2mpas', 'demos/*.xlsx')):
        copy2(src, osp.join(output_folder, osp.basename(src)))
    log.info('CO2MPAS demos written into (%s).', output_folder)
save_demo_files(output_folder='./inputs')

<bound method ResourceManager.resource_filename of <pkg_resources.ResourceManager object at 0x7ff4aadb0c40>>


def _co2mpas_info2df(start_time, main_flags=None):
    import socket
    import datetime
    from co2mpas import __version__
    from ..load.schema import define_flags_schema
    time_elapsed = (datetime.datetime.today() - start_time).total_seconds()
    hostname = socket.gethostname()
    info = [
        ('CO2MPAS version', __version__),
        ('Simulation started', start_time.strftime('%Y/%m/%d-%H:%M:%S')),
        ('Time elapsed', '%.3f sec' % time_elapsed),
        ('Hostname', hostname),
    ]
    if main_flags:
        main_flags = define_flags_schema(read=False).validate(main_flags)
        info.extend(sorted(main_flags.items()))
    import pandas as pd
    df = pd.DataFrame(info, columns=['Parameter', 'Value'])
    df.set_index(['Parameter'], inplace=True)
    setattr(df, 'name', 'info')
    return df
_co2mpas_info2df(start_time=datetime.datetime(2024, 4, 3, 16, 28, 9, 62286), main_flags=None)

<module 'socket' from '/local/rcs/XXX/miniforge3/envs/JRCSTU+co2mpas-ta/lib/python3.9/socket.py'>

<module 'datetime' from '/local/rcs/XXX/miniforge3/envs/JRCSTU+co2mpas-ta/lib/python3.9/datetime.py'>

{__module__='co2mpas.core.load.schema', __name__='define_flags_schema', __qualname__='define_flags_schema', __doc__='\n    Define flag schema.\n\n    :param read:\n        Schema for reading?\n    :type read: bool\n\n    :return:\n        Flag schema.\n    :rtype: schema.Schema\n    ', __annotations__={}}

<module 'pandas' from '/local/rcs/XXX/miniforge3/envs/JRCSTU+co2mpas-ta/lib/python3.9/site-packages/pandas/__init__.py'>

Parameter                Value0     CO2MPAS version                4.1.61  Simulation started  2024/04/03-16:28:092        Time elapsed          103.530 sec3            Hostname                 thor

ValueParameter                              CO2MPAS version                   4.1.6Simulation started  2024/04/03-16:28:09Time elapsed                103.530 secHostname                           thor


<module 'datetime' from '/local/rcs/XXX/miniforge3/envs/JRCSTU+co2mpas-ta/lib/python3.9/datetime.py'>


from ..load.schema import define_flags_schema

{__module__='co2mpas.core.load.schema', __name__='define_flags_schema', __qualname__='define_flags_schema', __doc__='\n    Define flag schema.\n\n    :param read:\n        Schema for reading?\n    :type read: bool\n\n    :return:\n        Flag schema.\n    :rtype: schema.Schema\n    ', __annotations__={}}


time_elapsed = (datetime.datetime.today() - start_time).total_seconds()


def test_3_conf(self, options):
        import yaml
        import schedula as sh
        from co2mpas.defaults import dfl
        from co2mpas.cli import conf
        kw = conf.make_context('conf', list(options)).params
        t = {k for k, _ in sh.stack_nested_keys(dfl.to_dict())}
        with self.runner.isolated_filesystem():
            result = self.invoke(('conf',) + options)
            self.assertEqual(result.exit_code, 0)
            with open(kw['output_file'], 'rb') as f:
                r = dict(sh.stack_nested_keys(yaml.load(f)))
                self.assertSetEqual(set(r), t)
            if kw['model_conf']:
                with open(kw['model_conf'], 'rb') as f:
                    for k, v in sh.stack_nested_keys(yaml.load(f)):
                        self.assertEqual(r[k], v)
test_3_conf(self=<test_cli.CLI testMethod=test_3_conf_1___>, options=(), self._cleanups=[], self._outcome=<unittest.case._Outcome object at 0x7ff4a5632910>, self._subtest=None, self._testMethodDoc=None, self._testMethodName='test_3_conf_1___', self._type_equality_funcs={<class 'dict'>: 'assertDictEqual', <class 'list'>: 'assertListEqual', <class 'tuple'>: 'assertTupleEqual', <class 'set'>: 'assertSetEqual', <class 'frozenset'>: 'assertSetEqual', <class 'str'>: 'assertMultiLineEqual'}, self.invoke=functools.partial(<bound method CliRunner.invoke of <click.testing.CliRunner object at 0x7ff4a5582340>>, <Group co2mpas>), self.runner=<click.testing.CliRunner object at 0x7ff4a5582340>, self.test_3_conf_1___=<bound method CLI.test_3_conf of <test_cli.CLI testMethod=test_3_conf_1___>>)

<module 'yaml' from '/local/rcs/XXX/miniforge3/envs/JRCSTU+co2mpas-ta/lib/python3.9/site-packages/yaml/__init__.py'>

{'output_file': './conf.yaml', 'model_conf': None}

{('functions', '__constants__', 'calculate_raw_frontal_area_v1', '__constants__', 'formulas', 'A'), ('functions', '__constants__', 'calculate_rolling_resistance_coeff', '__constants__', 'coeff', 'C3', 'F'), ('functions', '__constants__', 'get_gear_box_efficiency_constants', '__constants__', 'PARAMS', True, 'gbp00', 'q', 'hot'), ('functions', '__constants__', '_filter_temperature_samples', '__constants__', 'max_abs_dt_cold'), ('functions', '__constants__', 'identify_idle_engine_speed_std', '__constants__', 'MAX_STD_PERC'), ('functions', '__constants__', 'get_gear_box_efficiency_constants', '__constants__', 'PARAMS', False, 'gbp10', 'q', 'hot'), ('functions', '__constants__', 'default_fuel_lower_heating_value', '__constants__', 'ENABLE'), ('functions', '__constants__', 'correct_constant_velocity', '__constants__', 'VEL_UP_WINDOW'), ('functions', '__constants__', '_rcb_correction', '__constants__', 'WILLANS', 'ethanol', 'positive natural aspiration'), ('functions', '__constants__', 'default_start_stop_activation_time', '__constants__', 'threshold'), ('functions', '__constants__', 'define_fuel_type_and_is_hybrid', '__constants__', 'fuel_type', 8), ('functions', '__constants__', 'calculate_engine_temperature_derivatives', '__constants__', 'tw'), ('values', '__constants__', 'has_energy_recuperation'), ('functions', '__constants__', 'default_fuel_carbon_content', '__constants__', 'CARBON_CONTENT', 'biodiesel'), ('functions', '__constants__', 'default_fuel_carbon_content', '__constants__', 'CARBON_CONTENT', 'NG'), ('functions', '__constants__', 'default_fuel_carbon_content', '__constants__', 'CARBON_CONTENT', 'gasoline'), ('functions', '__constants__', 'calculate_aerodynamic_drag_coefficient', '__constants__', 'cw', 'J'), ('functions', '__constants__', 'calculate_raw_frontal_area_v1', '__constants__', 'formulas', 'S'), ('functions', '__constants__', 'default_fuel_density', '__constants__', 'FUEL_DENSITY', 'NG'), ('functions', '__constants__', 'default_fuel_lower_heating_value', '__constants__', 'LHV', 'propane'), ('values', '__constants__', 'k1'), ('functions', '__constants__', 'calculate_frontal_area', '__constants__', 'projection_factor'), ('functions', '__constants__', 'calculate_engine_heat_capacity', '__constants__', 'PARAMS', 'heated_mass_percentage', 'coolant'), ('functions', '__constants__', 'default_planetary_ratio', '__constants__', 'ratio'), ('functions', '__constants__', 'calculate_rolling_resistance_coeff', '__constants__', 'coeff', 'C2', 'B'), ('functions', '__constants__', 'correct_constant_velocity', '__constants__', 'VEL_DN_WINDOW'), ('functions', '__constants__', 'select_default_n_dyno_axes', '__constants__', 'DYNO_AXES', 'WLTP', 4), ('values', '__constants__', 'engine_has_cylinder_deactivation'), ('functions', '__constants__', 'default_specific_gear_shifting', '__constants__', 'SPECIFIC_GEAR_SHIFTING'), ('functions', '__constants__', 'default_tc_normalized_m1000_curve', '__constants__', 'curve', 'y'), ('functions', '__constants__', '_tech_mult_factors', '__constants__', 'factors', 'lb', 1, 'b'), ('functions', '__constants__', 'default_start_stop_activation_time', '__constants__', 'ENABLE'), ('functions', '__constants__', 'calculate_cylinder_deactivation_valid_phases', '__constants__', 'LIMIT'), ('functions', '__constants__', '_rcb_correction', '__constants__', 'WILLANS', 'NG', 'positive turbo'), ('functions', '__constants__', 'default_fuel_carbon_content', '__constants__', 'CARBON_CONTENT', 'diesel'), ('functions', '__constants__', 'select_phases_integration_times', '__constants__', 'INTEGRATION_TIMES', 'NEDC'), ('functions', '__constants__', 'calculate_aerodynamic_drag_coefficient', '__constants__', 'cw', 'M'), ('functions', '__constants__', 'correct_constant_velocity', '__constants__', 'DV_DN_SHIFT'), ('functions', '__constants__', 'define_fuel_type_and_is_hybrid', '__constants__', 'is_hybrid', 16), ('functions', '__constants__', 'get_gear_box_efficiency_constants', '__constants__', 'PARAMS', False, 'gbp10', 'm'), ('functions', '__constants__', 'default_fuel_density', '__constants__', 'FUEL_DENSITY', 'dies...n'), ('functions', '__constants__', 'calculate_rolling_resistance_coeff', '__constants__', 'coeff', 'C3', 'G'), ('values', '__constants__', 'is_plugin'), ('functions', '__constants__', 'define_fuel_type_and_is_hybrid', '__constants__', 'fuel_type', 5), ('functions', '__constants__', 'define_fuel_type_and_is_hybrid', '__constants__', 'fuel_type', 14), ('functions', '__constants__', 'calculate_engine_heat_capacity', '__constants__', 'PARAMS', 'heated_mass_percentage', 'crankshaft'), ('functions', '__constants__', 'define_fuel_type_and_is_hybrid', '__constants__', 'fuel_type', 23), ('functions', '__constants__', 'define_fmep_model', '__constants__', 'lb_full_bmep_curve_percentage'), ('functions', '__constants__', '_tech_mult_factors', '__constants__', 'factors', 'vva', 1, 'a'), ('functions', '__constants__', 'MGS', '__constants__', 'MAX_VEL'), ('functions', '__constants__', 'FMEP_egr', '__constants__', 'egr_fact_map', ('compression', False)), ('functions', '__constants__', '_tech_mult_factors', '__constants__', 'factors', 'egr', 3, 'a2'), ('functions', '__constants__', 'calculate_aerodynamic_drag_coefficient_v1', '__constants__', 'cw', 'coupé'), ('values', '__constants__', 'enable_willans'), ('functions', '__constants__', 'get_gear_box_efficiency_constants', '__constants__', 'PARAMS', True, 'gbp00', 'm'), ('functions', '__constants__', 'default_fuel_carbon_content', '__constants__', 'ENABLE'), ('functions', '__constants__', 'default_tc_k_factor_curve', '__constants__', 'STAND_STILL_TORQUE_RATIO'), ('functions', '__constants__', 'calculate_maximum_velocity', '__constants__', 'DELTA_VEL'), ('functions', '__constants__', 'calculate_aerodynamic_drag_coefficient_v1', '__constants__', 'cw', 'hatchback'), ('functions', '__constants__', 'define_fuel_type_and_is_hybrid', '__constants__', 'fuel_type', 9), ('functions', '__constants__', 'correct_constant_velocity', '__constants__', 'CON_VEL_UP_SHIFT'), ('functions', '__constants__', 'define_fuel_type_and_is_hybrid', '__constants__', 'fuel_type', 18), ('functions', '__constants__', 'calculate_raw_frontal_area_v1', '__constants__', 'formulas', 'M'), ('functions', '__constants__', '_tech_mult_factors', '__constants__', 'factors', 'egr', 2, 'c'), ('functions', '__constants__', 'calculate_f1', '__constants__', 'qm'), ('functions', '__constants__', '_tech_mult_factors', '__constants__', 'factors', 'lb', 1, 'a2'), ('functions', '__constants__', 'identify_co2_emissions', '__constants__', 'enable_second_step'), ('functions', '__constants__', 'calculate_aerodynamic_drag_coefficient_v1', '__constants__', 'cw', 'sedan'), ('functions', '__constants__', 'calculate_aerodynamic_drag_coefficient_v1', '__constants__', 'cw', 'suv/crossover'), ('functions', '__constants__', 'calculate_rolling_resistance_coeff', '__constants__', 'coeff', 'C1', 'E'), ('functions', '__constants__', 'default_fuel_density', '__constants__', 'FUEL_DENSITY', 'propane'), ('functions', '__constants__', 'calculate_engine_max_torque', '__constants__', 'PARAMS', 'positive'), ('functions', '__constants__', 'default_static_friction', '__constants__', 'coeff', 'worm', 'puddles'), ('functions', '__constants__', 'calculate_aerodynamic_drag_coefficient', '__constants__', 'cw', 'D'), ('functions', '__constants__', 'default_initial_drive_battery_state_of_charge', '__constants__', 'initial_state_of_charge', 'mild'), ('values', '__constants__', 'has_periodically_regenerating_systems'), ('functions', '__constants__', '_rcb_correction', '__constants__', 'WILLANS', 'biodiesel', 'compression'), ('functions', '__constants__', 'define_fuel_type_and_is_hybrid', '__constants__', 'fuel_type', 4), ('functions', '__constants__', 'define_fuel_type_and_is_hybrid', '__constants__', 'fuel_type', 13), ('values', '__constants__', 'auxiliaries_torque_loss_factors'), ('functions', '__constants__', 'default_static_friction', '__constants__', 'coeff', 'new', 'wet'), ('functions', '__constants__', 'select_default_n_dyno_axes', '__constants__', 'DYNO_AXES', 'NEDC', 4), ('values', '__constants__', 'engine_n_cylinders'), ('values', '__constants__', 'max_time_NEDC')}


kw = conf.make_context('conf', list(options)).params

{'output_file': './conf.yaml', 'model_conf': None}


result = self.invoke(('conf',) + options)


def to_dict(self):
        import inspect
        s, pr = set(dir(self)) - set(dir(Constants)), {}
        for n in s.union(self.__class__.__dict__.keys()):
            if n.startswith('__'):
                continue
            v = getattr(self, n)
            if inspect.ismethod(v) or inspect.isbuiltin(v):
                continue
            if isinstance(v, Constants):
                pr[n] = {'__constants__': v.to_dict()}
            elif inspect.isclass(v) and issubclass(v, Constants):
                pr[n] = {'__constants__': v.to_dict(v)}
            else:
                pr[n] = v
        return pr
to_dict(self=<class 'co2mpas.defaults.Functions.calculate_frontal_area'>, self.__doc__=None, self.__module__='co2mpas.defaults', self.projection_factor=0.84)

<module 'inspect' from '/local/rcs/XXX/miniforge3/envs/JRCSTU+co2mpas-ta/lib/python3.9/inspect.py'>

<built-in method mro of type object at 0x55a90c5724e0>


s, pr = set(dir(self)) - set(dir(Constants)), {}


def dump(self, file, default_flow_style=False, **kw):
        import yaml
        kw['Dumper'] = kw.get('Dumper', yaml.CDumper)
        with open(file, 'w') as f:
            yaml.dump(
                self.to_dict(), f, default_flow_style=default_flow_style, **kw
            )
dump(self={}, file='./conf.yaml', default_flow_style=False, kw={})

<_io.TextIOWrapper name='./conf.yaml' mode='w' encoding='UTF-8'>


def try_to_compute_label(label_context: Context[str], wrapper: ArticleWrapper):
    assert wrapper.article, "Missing article for this step"
    with label_context.capture():
        label_context.push(wrapper.article.label)
try_to_compute_label(label_context=Context(history=None, error=None, data=None), wrapper=ArticleWrapper(article=<wostools.article.Article object at 0x7f1a971b1dc0>, label='L Robertson, 1999, Science'))

Context(history=[], error=None, data='L Robertson, 1999, Science')


def push(self, data: Optional[T], error: Optional[Exception] = None):
        if self.history is None:
            self.history = []
        if self.data:
            self.history.append(self.data)
        self.data = data
        self.error = error
push(self=Context(history=None, error=None, data=None), data='L Robertson, 1999, Science', error=None, self.data=None, self.error=None, self.history=None)

Context(history=[], error=None, data='L Robertson, 1999, Science')


def article_missing(field: str):
    article = Article(
        title=None, authors=["L, Robertson"], year=1999, journal="Science"
    )
    setattr(article, field, None)
    return ArticleWrapper(article=article)
article_missing(field='year')

{title=None, authors=['L, Robertson'], keywords=[], year=1999, journal='Science', volume=None, issue=None, page=None, doi=None, references=[], sources=set(), extra={}}

{title=None, authors=['L, Robertson'], keywords=[], year=None, journal='Science', volume=None, issue=None, page=None, doi=None, references=[], sources=set(), extra={}}


{title=None, authors=['L, Robertson'], keywords=[], year=None, journal='Science', volume=None, issue=None, page=None, doi=None, references=[], sources=set(), extra={}}


def from_isi_citation(cls, reference: str) -> "Article":
        match = ISI_CITATION_PATTERN.match(reference)
        if not match:
            raise InvalidReference(reference)
        data = {key: [value] for key, value in match.groupdict().items() if value}
        processed = parse_all(data)
        return cls(
            title=processed.get("title"),
            authors=processed.get("authors", []),
            year=processed.get("year"),
            journal=processed.get("source_abbreviation"),
            volume=processed.get("volume"),
            page=processed.get("beginning_page"),
            doi=processed.get("DOI"),
            extra=processed,
            sources={reference},
        )
from_isi_citation(cls=<class 'wostools.article.Article'>, reference='L Antuan, 2008, P IEEE, V69, P1810, DOI DOI 10.1109/JPROC.2008.2004315')

<re.Match object; span=(0, 70), match='L Antuan, 2008, P IEEE, V69, P1810, DOI DOI 10.11>


def _advance(self):
        self._current += 1
        return self.source[self._current - 1]
_advance(self=<pycalc.core.scanner.Scanner object at 0x7fe109cc5b50>, self._current=0, self._start=0, self.source='102%12%7', self.tokens=[])


def warning(self, msg, *args):
                _messages.append("W:" + msg % args)
warning(self=<test_api.TestUndefined.test_logging_undefined.<locals>.DebugLogger object at 0x7f3d58670fd0>, msg='Template variable warning: %s', args=("'missing' is undefined",), _messages=[])

W:Template variable warning: 'missing' is undefined


def test_includes(t, **ctx):
            ctx["foo"] = 42
            assert t.render(ctx) == "[42|23]"
test_includes(t=<Template memory:7f3d586442b0>, ctx={})