def _validate_data(data, schema): # ensure enums match, convert ints/floats, apply scaling enum_info = template_common.parse_enums(schema['enum']) for k in data['models']: if k in schema['model']: template_common.validate_model(data['models'][k], schema['model'][k], enum_info)
def _validate_data(data, schema): # ensure enums match, convert ints/floats, apply scaling enum_info = template_common.validate_models(data, schema) for m in data.models.elements: template_common.validate_model( m, schema.model[LatticeUtil.model_name_for_data(m)], enum_info)
def _validate_data(data, schema): # ensure enums match, convert ints/floats, apply scaling enum_info = template_common.validate_models(data, schema) _correct_halo_gaussian_distribution_type(data['models']['bunch']) for model_type in ['elements', 'commands']: for m in data['models'][model_type]: template_common.validate_model(m, schema['model'][_model_name_for_data(m)], enum_info) _correct_halo_gaussian_distribution_type(m)
def _validate_data(data, schema): # ensure enums match, convert ints/floats, apply scaling enum_info = template_common.validate_models(data, schema) _correct_halo_gaussian_distribution_type(data['models']['bunch']) for model_type in ['elements', 'commands']: for m in data['models'][model_type]: template_common.validate_model( m, schema['model'][_model_name_for_data(m)], enum_info) _correct_halo_gaussian_distribution_type(m)
def _validate_data(data, schema): # ensure enums match, convert ints/floats, apply scaling enum_info = template_common.parse_enums(schema["enum"]) for k in data["models"]: if k in schema["model"]: template_common.validate_model(data["models"][k], schema["model"][k], enum_info) for model_type in ["elements", "commands"]: for m in data["models"][model_type]: template_common.validate_model(m, schema["model"][_model_name_for_data(m)], enum_info)
def _validate_data(data, schema): # ensure enums match, convert ints/floats, apply scaling enum_info = template_common.parse_enums(schema['enum']) for k in data['models']: if k in schema['model']: template_common.validate_model(data['models'][k], schema['model'][k], enum_info) for model_type in ['elements', 'commands']: for m in data['models'][model_type]: template_common.validate_model( m, schema['model'][_model_name_for_data(m)], enum_info)
def _validate_data(data, schema): # ensure enums match, convert ints/floats, apply scaling enum_info = template_common.parse_enums(schema['enum']) for k in data['models']: if k in schema['model']: template_common.validate_model(data['models'][k], schema['model'][k], enum_info) for m in data['models']['beamline']: template_common.validate_model(m, schema['model'][m['type']], enum_info) for item_id in data['models']['propagation']: _validate_propagation(data['models']['propagation'][item_id][0]) _validate_propagation(data['models']['propagation'][item_id][1]) _validate_propagation(data['models']['postPropagation'])
def _validate_data(self): def _fix(m): """the halo(gaussian) value will get validated/escaped to halogaussian, change it back""" if 'distribution_type' in m and 'halogaussian' in m.distribution_type: m.distribution_type = m.distribution_type.replace( 'halogaussian', 'halo(gaussian)') enum_info = template_common.validate_models(self.data, _SCHEMA) _fix(self.data.models.bunch) for t in ['elements', 'commands']: for m in self.data.models[t]: template_common.validate_model( m, _SCHEMA.model[LatticeUtil.model_name_for_data(m)], enum_info, ) _fix(m)
def _validate_data(data, schema): # ensure enums match, convert ints/floats, apply scaling enum_info = template_common.validate_models(data, schema) for m in data['models']['elements']: template_common.validate_model( m, schema['model'][_model_name_for_data(m)], enum_info)
def _validate_data(data, schema): # ensure enums match, convert ints/floats, apply scaling enum_info = template_common.validate_models(data, schema) for m in data['models']['elements']: template_common.validate_model(m, schema['model'][_model_name_for_data(m)], enum_info)